Full Anthology as BibTeX …with abstracts
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  • 2024
  • 2023
  • 2022
  • 2021
  • 2020
  • 2019
  • 2018
  • 2017
  • 2016
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  • University of Saskatchewan
  • University of Waterloo
  • Global Institute for Water Security
  • McMaster University
  • Environment and Climate Change Canada
  • Wilfrid Laurier University
  • University of Calgary
  • Université du Québec à Montréal
  • University of Guelph
  • Natural Resources Canada
  • University of Manitoba
  • University of Alberta
  • National Center for Atmospheric Research
  • Woodwell Climate Research Center
  • Northern Arizona University
  • Michigan State University
  • University of British Columbia
  • Universität Innsbruck
  • Jet Propulsion Laboratory
  • Finnish Meteorological Institute
  • University of Alaska Fairbanks
  • University of Toronto
  • Swedish University of Agricultural Sciences
  • Czech University of Life Sciences Prague
  • Agriculture and Agri-Food Canada
  • Lawrence Berkeley National Laboratory
  • University of Ottawa
  • University of Northern British Columbia
  • University of Arizona
  • University of California, Berkeley
  • University of Colorado Boulder
  • Max Planck Institute for Biogeochemistry
  • Imperial College London
  • Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences
  • ETH Zurich
  • French National Centre for Scientific Research
  • National Research Council
  • University of Helsinki
  • Wageningen University & Research
  • Pacific Institute for Climate Solutions
  • Aarhus University
  • San Diego State University
  • Swiss Federal Institute for Forest, Snow and Landscape Research
  • Pennsylvania State University
  • Dalhousie University
  • United States Geological Survey
  • Center for Northern Studies
  • Government of Northwest Territories
  • Université de Montréal
  • Xiamen University
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  • (eDNA) Next Generation Solutions to Ensure Healthy Water Resources for Future Generations
  • Agricultural Water Futures
  • Artificial Intelligence Applications for Rapid and Reliable Detection of Cryptosporidium oocysts and Giardia cysts
  • Boreal Water Futures: Canada’s Boreal Wildlands-Society-Water Nexus
  • Climate-Related Precipitation Extremes
  • Co-Creation of Indigenous Water Quality Tools
  • Collaborative Modelling Framework for Water Futures and Holistic Human Health Effects
  • Core Computer Science Team
  • Core Modelling & Forecasting Team
  • Core Technical Team
  • Crowdsourcing Water Science
  • Data Management
  • Developing ‘Omic’ and Chemical Fingerprinting Methodologies: Using Ultrahigh-Resolution Mass Spectrometry for Geochemistry and Healthy Waters
  • Diagnosing and Mitigating Hydrologic Model Uncertainty in High-Latitude Canadian Watersheds
  • Evaluation of Ice Models in Large Lakes: Using Three-Dimensional Coupled Hydrodynamic-Ice Models
  • FORMBLOOM: Forecasting Tools and Mitigation Options for Diverse Bloom-Affected Lakes
  • GLOBAL WATER CITIZENSHIP: INTEGRATING NETWORKED CITIZENS, SCIENTISTS AND LOCAL DECISION MAKERS
  • Geogenic contamination of groundwater resources in subarctic regions
  • Hydrological Processes in Frozen Soils
  • Integrated Modelling Program for Canada (IMPC)
  • Knowledge Mobilization Team
  • Lake Futures: Enhancing Adaptive Capacity and Resilience of Lakes and their Watersheds
  • Linking Multiple Stressors to Adverse Ecological Responses Across Watersheds
  • Linking Stream Network Process Models to Robust Data Management Systems for the Purpose of Land-Use Decision Support
  • Linking Water Governance in Canada to Global Economic, Social and Political Drivers
  • Managing Urban Eutrophication Risks under Climate Change: An Integrated Modelling and Decision Support Framework
  • Matawa Water Futures: Developing an Indigenous-Informed Framework for Watershed Monitoring and Stewardship
  • Mountain Water Futures
  • Northern Water Futures
  • Ohneganos – Indigenous ecological knowledge, training and co-creation of mixed method tools
  • Old Meets New: Subsurface Hydrogeological Connectivity and Groundwater Protection
  • Paradigm Shift in Downscaling Climate Model Projections: Building Models and Tools to Advance Climate Change Research in Cold Regions
  • Prairie Water: Enhancing resilience of Prairie communities through sustainable water management
  • Remotely Sensed Monitoring of Northern lake Ice Using RADARSAT Constellation Mission and Cloud Computing Processing
  • SAMMS: Sub-Arctic Metal Mobility Study
  • SPADE: Storms and Precipitation Across the Continental Divide Experiment
  • Saint John river Experiment on cold Season Storms (SaJESS)
  • Sensors and Sensing Systems for Water Quality Monitoring
  • Short‐Duration Extreme Precipitation in Future Climate
  • Significance of Groundwater Dynamics within Hydrologic Models
  • Southern Forests Water Futures
  • Transformative Sensor Technologies and Smart Watersheds for Canadian Water Futures
  • We need more than just water: Assessing sediment limitation in a large freshwater delta
  • What is Water Worth? Valuing Canada’s Water Resources and Aquatic Ecosystem Services
  • Winter Soil Processes in Transition
  • Others

Welcome to the GWF Publications Archive!

2024  |  2023  |  2022  |  2021  |  2020  |  2019  |  2018  |  2017  |  2016  | 

2024

The temperature sensitivity (Q10) of soil respiration is a critical parameter in modeling soil carbon dynamics; yet the regulating factors and the underlying mechanisms of Q10 in peat soils remain unclear. To address this gap, we conducted a comprehensive synthesis data analysis from 87 peatland sites (350 observations) spanning boreal, temperate, and tropical zones, and investigated the spatial distribution pattern of Q10 and its correlation with climate conditions, soil properties, and hydrology. Findings revealed distinct Q10 values across climate zones: boreal peatlands exhibited the highest Q10, trailed by temperate and then tropical peatlands. Latitude presented a positive correlation with Q10, while mean annual air temperature and precipitation revealed a negative correlation. The results from the structural equation model suggest that soil properties, such as carbon-to-nitrogen ratio (C/N) and peat type, were the primary drivers of the variance in Q10 of peat respiration. Peat C/N ratios negatively correlated with Q10 of peat respiration and the relationship between C/N and Q10 varied significantly between peat types. Our data analyses also revealed that Q10 was influenced by soil moisture levels, with significantly lower values observed for peat soils under wet than dry conditions. Essentially, boreal and temperate peatlands seem more vulnerable to global warming-induced soil organic carbon decomposition than tropical counterparts, with wet peatlands showing higher climate resilience.
The extensive use of road salts as deicers during winter months is causing the salinization of freshwater systems in cold climate regions worldwide. We analyzed 20 years (2001–2020) of data on lake water chemistry, land cover changes, and road salt applications for Lake Wilcox (LW) located in southern Ontario, Canada. The lake is situated within a rapidly urbanizing watershed in which, during the period of observation, on average 785 tons of road salt were applied annually. However, only about a quarter of this salt has reached the lake so far. That is, most salt has been retained in the watershed, likely through accumulation in soils and groundwater. Despite the high watershed salt retention, time series trend analyses for LW show significant increases in the dissolved concentrations of sodium (Na+) and chloride (Cl−), as well as those of sulfate (SO42−), calcium (Ca2+), and magnesium (Mg2+). The relative changes in the major ion concentrations indicate a shift of the lake water chemistry from the mixed SO42–Cl–Ca2+-Mg2+ type to the Na + -Cl- type. Salinization of LW has further been strengthening and lengthening the lake's summer stratification, which, in turn, has been enhancing hypoxia in the hypolimnion and increasing the internal loading of the limiting nutrient phosphorus. The theoretical salinity threshold at which fall overturn would become increasingly unlikely was estimated at around 1.23 g kg−1. A simple chloride mass balance model predicts that, under the current trend of impermeable land cover expansion, LW could reach this salinity threshold by mid-century. Our results also highlight the need for additional research on the accruing salt legacies in urbanizing watersheds because they represent potential long-term threats to water quality for receiving freshwater ecosystems and regional groundwater resources.
A sufficient supply of dissolved silicon (DSi) relative to dissolved phosphorus (DP) may decrease the likelihood of harmful algal blooms in eutrophic waters. Oxidative precipitation of Fe(II) at oxic-anoxic interfaces may contribute to the immobilization of DSi, thereby exerting control over the DSi availability in the overlying water. Nevertheless, the efficacy of DSi immobilization in this context remains to be precisely determined. To investigate the behavior of DSi during Fe(II) oxidation, anoxic solutions containing mixtures of aqueous Fe(II), DSi, and dissolved phosphorus (DP) were exposed to dissolved oxygen (DO) in the batch system. The experimental data, combined with kinetic reaction modeling, indicate that DSi removal during Fe(II) oxidation occurs via two pathways. At the beginning of the experiments, the oxidation of Fe(II)-DSi complexes induces the fast removal of DSi. Upon complete oxidation of Fe(II), further DSi removal is due to adsorption to surface sites of the Fe(III) oxyhydroxides. The presence of DP effectively competes with DSi via both of these pathways during the initial and later stages of the experiments, with as a result more limited removal of DSi during Fe(II) oxidation. Overall, we conclude that at near neutral pH the oxidation of Fe(II) has considerable capacity to immobilize DSi, where the rapid homogeneous oxidation of Fe(II)-DSi results in greater DSi removal compared to surface adsorption. Elevated DP concentration, however, effectively outcompetes DSi in co-precipitation interactions, potentially contributing to enhanced DSi availability within aquatic systems.

2023

Permafrost thaw/degradation in the Northern Hemisphere due to global warming is projected to accelerate in coming decades. Assessment of this trend requires improved understanding of the evolution and dynamics of permafrost areas. Land surface models (LSMs) are well-suited for this due to their physical basis and large-scale applicability. However, LSM application is challenging because (a) LSMs demand extensive and accurate meteorological forcing data, which are not readily available for historic conditions and only available with significant biases for future climate, (b) LSMs possess a large number of model parameters, and (c) observations of thermal/hydraulic regimes to constrain those parameters are severely limited. This study addresses these challenges by applying the MESH-CLASS modeling framework (Modélisation Environmenntale communautaire—Surface et Hydrology embedding the Canadian Land Surface Scheme) to three regions within the Mackenzie River Basin, Canada, under various meteorological forcing data sets, using the variogram analysis of response surfaces framework for sensitivity analysis and threshold-based identifiability analysis. The study shows that the modeler may face complex trade-offs when choosing a forcing data set; for current and future scenarios, forcing data require multi-variate bias correction, and some data sets enable the representation of some aspects of permafrost dynamics, but are inadequate for others. The results identify the most influential model parameters and show that permafrost simulation is most sensitive to parameters controlling surface insulation and runoff generation. But the identifiability analysis reveals that many of the most influential parameters are unidentifiable. These conclusions can inform future efforts for data collection and model parameterization.
Abstract. Wetland systems are among the largest stores of carbon on the planet, most biologically diverse of all ecosystems, and dominant controls of the hydrologic cycle. However, their representation in land surface models (LSMs), which are the terrestrial lower boundary of Earth system models (ESMs) that inform climate actions, is limited. Here, we explore different possible parametrizations to represent wetland-groundwater-upland interactions with varying levels of system and computational complexity. We perform a series of numerical experiments that are informed by field observations from wetlands in the well-instrumented White Gull Creek in Saskatchewan, in the boreal region of North America. We show that the typical representation of wetlands in LSMs, which ignores interactions with groundwater and uplands, can be inadequate. We show that the optimal level of model complexity depends on the land cover, soil type, and the ultimate modelling purpose, being nowcasting and prediction, scenario analysis, or diagnostic learning.
Abstract. This study evaluated the effects of climate perturbations on snowmelt, soil moisture and streamflow generation in small Canadian Prairie basins using a modeling approach based on classification of basin biophysical and hydraulic parameters. Seven basin classes that encompass the entirety of the Prairie ecozone in Canada were determined by cluster analysis of biophysical characteristics. Individual semi-distributed virtual basin (VB) models representing these classes were parameterized in the Cold Regions Hydrological Model (CRHM) platform which includes modules for snowmelt and sublimation, soil freezing and thawing, actual evapotranspiration (ET), soil moisture dynamics, groundwater recharge and depressional storage dynamics including fill and spill runoff generation and variable connected areas. Precipitation (P) and temperature (T) perturbation scenarios covering the range of climate model predictions for the 21st century were used to evaluate climate sensitivity of hydrological processes in individual land cover and basin types across the Prairie ecozone. Results indicated that snow accumulation in wetlands had a greater sensitivity to P and T than that in croplands and grasslands in all the basin types. Wetland soil moisture was also more sensitive to T than the cropland and grassland soil moisture. Jointly influenced by land cover distribution and local climate, basin-average snow accumulation was more sensitive to T in the drier and grassland-characterized basins than in the wetter basins dominated by cropland, whilst basin-average soil moisture was most sensitive to T and P perturbations in basins typified by pothole depressions and broad river valleys. Annual streamflow had the greatest sensitivities to T and P in the dry and poorly connected Interior Grassland basins but the smallest in the wet and well-connected Southern Manitoba basins. The ability of P to compensate for warming induced reductions in snow accumulation and streamflow was much higher in the wetter and cropland-dominated basins than in the drier and grassland-characterized basins, whilst decreases in cropland soil moisture induced by the maximum expected warming of 6 °C could be fully offset by P increase of 11 % in all the basins. These results can be used to 1) identify locations which had the largest hydrological sensitivities to changing climate; and 2) diagnose underlying processes responsible for hydrological responses to expected climate change. Variations of hydrological sensitivity in land cover and basin types suggest that different water management and adaptation methods are needed to address enhanced water stress due to expected climate change in different regions of the Prairie ecozone.
Abstract With global warming, the behavior of extreme precipitation shifts toward nonstationarity. Here, we analyze the annual maxima of daily precipitation (AMP) all over the globe using projections of the latest phase of the Coupled Model Intercomparison Project (CMIP6) under four shared socioeconomic pathways (SSPs). The projections were bias corrected using a semiparametric quantile mapping, a novel technique extended to extreme precipitation. This analysis 1) explores the variability of future AMP globally and 2) investigates the performance of stationary and nonstationary models in describing future AMP with trends. The results show that global warming potentially intensifies AMP. For the nonparametric analysis, the 33-yr precipitation levels are increasing up to 33.2 mm compared to the historical period. The parametric analysis shows that the return period of 100-yr historical events will decrease approximately to 50 and 70 years in the Northern and Southern Hemispheres, respectively. Under the highest emission scenario, the projected 100-yr levels are expected to increase by 7.5%–21% over the historical levels. Using stationary models to estimate the 100-yr return level for AMP projections with trends leads to an underestimation of 3.4% on average. Extensive Monte Carlo experiments are implemented to explain this underestimation.
The Canadian Prairies are a major grain production region, producing most of the wheat for export in Canada. Global warming and the associated changes in extreme precipitation and temperature events pose significant risks to agriculture on the Canadian Prairies. Compound hazards can cause higher crop failure than isolated events, especially in the main grain production regions in western Canada. To achieve informed climate risk management, it is critical to characterize the threats posed by compound hazards in current and future climates in western Canada. In this study, return periods of events were computed to assess the potential changes in the hotspots for agriculturally relevant compound events in western Canada using two convection-permitting climate simulations: current (CTL) climate and future climate under the RCP8.5 scenario based on a pseudo-global-warming (PGW) approach. Specifically, our study analyzed agricultural drought, low precipitation, heatwaves, and cool waves related to cool-season crops. The results showed the overall good performance of the CTL simulation in capturing spatial patterns of these compound events in western Canada. In the current climate, droughts and heatwaves co-occur mostly in southeastern parts of the prairies. Under the RCP8.5 scenario, they are likely to increase in frequency and expand to cover the major croplands of western Canada. This study provides information that policymakers in the fields of climate change adaptation and agricultural disaster management will find useful.
Humanity deals with several challenges in this century such as climate change, land use, and land use/cover change (LUCC). Determining the patterns, developments, and consequences of LUCC issues for the livelihoods of people, especially poor people, is very important. Therefore, this paper aims to investigate the interactions between LUCC and climate change over the period of 1966–2015 (50 years) as a complex system at the global level. CO2 emissions and surface temperature are considered as the main indicators of climate change (CC). The data were analyzed in time-oriented (time-based) and local or place-oriented (country-based) manners. The results showed that arable and rangeland use changes (LUC) affect CO2 emissions in both direct and indirect ways. However, the direct effect of rangeland use change is positive, and its indirect effect is negative. In addition, deforestation has increased CO2 emissions indirectly. LUCC can also change the ability of the ecosystem to deliver services to people, including biodiversity and other resources such as food, fiber, water, etc. Therefore, it is critical to determine the patterns, trends, and impacts of LUCC on CC. Thus, CC mitigation policies should be followed by considering both direct and indirect effects. Without a doubt, this will be realized when the decision and policymakers have a better understanding of the structure and interaction between CC, LUCC, and their components as a whole system.
Abstract Operational flood forecasting in Canada is a provincial responsibility that is carried out by several entities across the country. However, the increasing costs and impacts of floods require better and nationally coordinated flood prediction systems. A more coherent flood forecasting framework for Canada can enable implementing advanced prediction capabilities across the different entities with responsibility for flood forecasting. Recently, the Canadian meteorological and hydrological services were tasked to develop a national flow guidance system. Alongside this initiative, the Global Water Futures program has been advancing cold regions process understanding, hydrological modeling, and forecasting. A community of practice was established for industry, academia, and decision‐makers to share viewpoints on hydrological challenges. Taken together, these initiatives are paving the way towards a national flood forecasting framework. In this article, forecasting challenges are identified (with a focus on cold regions), and recommendations are made to promote the creation of this framework. These include the need for cooperation, well‐defined governance, and better knowledge mobilization. Opportunities and challenges posed by the increasing data availability globally are also highlighted. Advances in each of these areas are positioning Canada as a major contributor to the international operational flood forecasting landscape. This article highlights a route towards the deployment of capacities across large geographical domains.
Abstract The unprecedented progress in ensemble hydro‐meteorological modelling and forecasting on a range of temporal and spatial scales, raises a variety of new challenges which formed the theme of the Joint Virtual Workshop, ‘Connecting global to local hydrological modelling and forecasting: challenges and scientific advances’. Held from 29 June to 1 July 2021, this workshop was co‐organised by the European Centre for Medium‐Range Weather Forecasts (ECMWF), the Copernicus Emergency Management (CEMS) and Climate Change (C3S) Services, the Hydrological Ensemble Prediction EXperiment (HEPEX), and the Global Flood Partnership (GFP). This article aims to summarise the state‐of‐the‐art presented at the workshop and provide an early career perspective. Recent advances in hydrological modelling and forecasting, reflections on the use of forecasts for decision‐making across scales, and means to minimise new barriers to communication in the virtual format are also discussed. Thematic foci of the workshop included hydrological model development and skill assessment, uncertainty communication, forecasts for early action, co‐production of services and incorporation of local knowledge, Earth observation, and data assimilation. Connecting hydrological services to societal needs and local decision‐making through effective communication, capacity‐building and co‐production was identified as critical. Multidisciplinary collaborations emerged as crucial to effectively bring newly developed tools to practice.
Wastewater surveillance (WWS) is useful to better understand the spreading of coronavirus disease 2019 (COVID-19) in communities, which can help design and implement suitable mitigation measures. The main objective of this study was to develop the Wastewater Viral Load Risk Index (WWVLRI) for three Saskatchewan cities to offer a simple metric to interpret WWS. The index was developed by considering relationships between reproduction number, clinical data, daily per capita concentrations of virus particles in wastewater, and weekly viral load change rate. Trends of daily per capita concentrations of SARS-CoV-2 in wastewater for Saskatoon, Prince Albert, and North Battleford were similar during the pandemic, suggesting that per capita viral load can be useful to quantitatively compare wastewater signals among cities and develop an effective and comprehensible WWVLRI. The effective reproduction number (Rt) and the daily per capita efficiency adjusted viral load thresholds of 85 × 106 and 200 × 106 N2 gene counts (gc)/population day (pd) were determined. These values with rates of change were used to categorize the potential for COVID-19 outbreaks and subsequent declines. The weekly average was considered 'low risk' when the per capita viral load was 85 × 106 N2 gc/pd. A 'medium risk' occurs when the per capita copies were between 85 × 106 and 200 × 106 N2 gc/pd. with a rate of change <100 %. The start of an outbreak is indicated by a 'medium-high' risk classification when the week-over-week rate of change was >100 %, and the absolute magnitude of concentrations of viral particles was >85 × 106 N2 gc/pd. Lastly, a 'high risk' occurs when the viral load exceeds 200 × 106 N2 gc/pd. This methodology provides a valuable resource for decision-makers and health authorities, specifically given the limitation of COVID-19 surveillance based on clinical data.
Phosphorus (P) export from urban areas via stormwater runoff contributes to eutrophication of downstream aquatic ecosystems. Bioretention cells are a Low Impact Development (LID) technology promoted as a green solution to attenuate urban peak flow discharge, as well as the export of excess nutrients and other contaminants. Despite their rapidly growing implementation worldwide, a predictive understanding of the efficiency of bioretention cells in reducing urban P loadings remains limited. Here, we present a reaction-transport model to simulate the fate and transport of P in a bioretention cell facility in the greater Toronto metropolitan area. The model incorporates a representation of the biogeochemical reaction network that controls P cycling within the cell. We used the model as a diagnostic tool to determine the relative importance of processes immobilizing P in the bioretention cell. The model predictions were compared to multi-year observational data on 1) the outflow loads of total P (TP) and soluble reactive P (SRP) during the 2012-2017 period, 2) TP depth profiles collected at 4 time points during the 2012-2019 period, and 3) sequential chemical P extractions performed on core samples from the filter media layer obtained in 2019. Results indicate that exfiltration to underlying native soil was principally responsible for decreasing the surface water discharge from the bioretention cell (63 % runoff reduction). From 2012 to 2017, the cumulative outflow export loads of TP and SRP only accounted for 1 % and 2 % of the corresponding inflow loads, respectively, hence demonstrating the extremely high P reduction efficiency of this bioretention cell. Accumulation in the filter media layer was the predominant mechanism responsible for the reduction in P outflow loading (57 % retention of TP inflow load) followed by plant uptake (21 % TP retention). Of the P retained within the filter media layer, 48 % occurred in stable, 41 % in potentially mobilizable, and 11 % in easily mobilizable forms. There were no signs that the P retention capacity of the bioretention cell was approaching saturation after 7 years of operation. The reactive transport modeling approach developed here can in principle be transferred and adapted to fit other bioretention cell designs and hydrological regimes to estimate P surface loading reductions at a range of temporal scales, from a single precipitation event to long-term (i.e., multi-year) operation.
Abstract. Lake surface temperature (LST) is an important attribute that highlights regional weather and climate variability and trends. The spatial resolution and thermal sensors on Landsat platforms provide the capability of monitoring the temporal and spatial distribution of lake surface temperature on small- to medium-sized lakes. In this study, a retrieval algorithm was applied to the thermal bands of Landsat archives to generate a LST dataset (North Slave LST dataset) for 535 lakes in the North Slave Region (NSR) of the Northwest Territories (NWT), Canada, for the period of 1984 to 2021. North Slave LST was retrieved from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS); however, most of the dataset was created from the thermal bands of Landsat 5 (43 %) due to its longevity (1984–2013). Cloud masks were applied to Landsat images to eliminate cloud cover. In addition, a 100 m inward buffer was applied to lakes to prevent pixel mixing with shorelines. To evaluate the algorithm applied, retrieved LST was compared with in situ data and Moderate Resolution Imaging Spectroradiometer (MODIS) LST observations. A good agreement was observed between in situ observations and North Slave LST, with a mean bias of 0.12 ∘C and a root mean squared deviation (RMSD) of 1.7 ∘C. The North Slave LST dataset contains more available data for warmer months (May to September; 57.3 %) compared to colder months (October to April). The average number of images per year for each lake across the NSR ranged from 20 to 45. The North Slave LST dataset, available at https://doi.org/10.5683/SP3/J4GMC2 (Attiah et al., 2022), will provide communities, scientists, and stakeholders with spatial and temporal changing temperature trends on lakes for the past 38 years.
Abstract. Arctic soils store large amounts of organic carbon and other elements, such as amorphous silicon, silicon, calcium, iron, aluminum, and phosphorous. Global warming is projected to be most pronounced in the Arctic, leading to thawing permafrost which, in turn, changes the soil element availability. To project how biogeochemical cycling in Arctic ecosystems will be affected by climate change, there is a need for data on element availability. Here, we analyzed the amorphous silicon (ASi) content as a solid fraction of the soils as well as Mehlich III extractions for the bioavailability of silicon (Si), calcium (Ca), iron (Fe), phosphorus (P), and aluminum (Al) from 574 soil samples from the circumpolar Arctic region. We show large differences in the ASi fraction and in Si, Ca, Fe, Al, and P availability among different lithologies and Arctic regions. We summarize these data in pan-Arctic maps of the ASi fraction and available Si, Ca, Fe, P, and Al concentrations, focusing on the top 100 cm of Arctic soil. Furthermore, we provide element availability values for the organic and mineral layers of the seasonally thawing active layer as well as for the uppermost permafrost layer. Our spatially explicit data on differences in the availability of elements between the different lithological classes and regions now and in the future will improve Arctic Earth system models for estimating current and future carbon and nutrient feedbacks under climate change (https://doi.org/10.17617/3.8KGQUN, Schaller and Goeckede, 2022).
Nelson Churchill River Basin (NCRB), Canada, and USA. Soil temperature and moisture are essential variables that fluctuate based on soil depth, controlling several sub-surface hydrologic processes. The Hydrological Predictions for the Environment (HYPE) model’s soil profile depth can vary up to four meters, discretized into three soil layers. Here, we further discretized the HYPE subsurface domain to accommodate up to seven soil layers to improve the representation of subsurface thermodynamics and water transfer more accurately. Soil moisture data from different locations across NCRB are collected from 2013 to 2017 for model calibration. We use multi-objective optimization (MOO) to account for streamflow and soil moisture variability and improve the model fidelity at a continental scale. Our study demonstrates that MOO significantly improves soil moisture simulation from the median Kling Gupta Efficiency (KGE) of 0.21–0.66 without deteriorating the streamflow performance. Streamflow and soil moisture simulation performance improvements are statistically insignificant between the original three-layer and seven-layer discretization of HYPE. However, the finer discretization model shows improved simulation in sub-surface components such as the evapotranspiration when verified against reanalysis products, indicating a 12 % underestimation of evapotranspiration from the three-layer HYPE model. The improvement of the discretized HYPE model and simulating the soil temperature at finer vertical resolution makes it a prospective model for permafrost identification and climate change analysis.
Abstract Modelling is widely used in ecology and its utility continues to increase as scientists, managers and policy‐makers face pressure to effectively manage ecosystems and meet conservation goals with limited resources. As the urgency to forecast ecosystem responses to global change grows, so do the number and complexity of predictive ecological models and the value of iterative prediction, both of which demand validation and cross‐model comparisons. This challenges ecologists to provide predictive models that are reusable, interoperable, transparent and able to accommodate updates to both data and algorithms. We propose a practical solution to this challenge based on the PERFICT principles (frequent Predictions and Evaluations of Reusable, Freely accessible, Interoperable models, built within Continuous workflows that are routinely Tested), using a modular and integrated framework. We present its general implementation across seven common components of ecological model applications—(i) the modelling toolkit; (ii) data acquisition and treatment; (iii) model parameterisation and calibration; (iv) obtaining predictions; (v) model validation; (vi) analysing and presenting model outputs; and (vii) testing model code—and apply it to two approaches used to predict species distributions: (1) a static statistical model, and (2) a complex spatiotemporally dynamic model. Adopting a continuous workflow enabled us to reuse our models in new study areas, update predictions with new data, and re‐parameterise with different interoperable modules using freely accessible data sources, all with minimal user input. This allowed repeating predictions and automatically evaluating their quality, while centralised inputs, parameters and outputs, facilitated ensemble forecasting and tracking uncertainty. Importantly, the integrated model validation promotes a continuous evaluation of the quality of more‐ or less‐parsimonious models, which is valuable in predictive ecological modelling. By linking all stages of an ecological modelling exercise, it is possible to overcome common challenges faced by ecological modellers, such as changing study areas, choosing between different modelling approaches, and evaluating the appropriateness of the model. This ultimately creates a more equitable and robust playing field for both modellers and end users (e.g. managers), and contributes to position predictive ecology as a central contributor to global change forecasting.
Wildfire occurrence and severity is predicted to increase in the upcoming decades with severe negative impacts on human societies. The impacts of upwind wildfire activity on glacier melt, a critical source of freshwater for downstream environments, were investigated through analysis of field and remote sensing observations and modeling experiments for the 2015–2020 melt seasons at the well-instrumented Athabasca Glacier in the Canadian Rockies. Upwind wildfire activity influenced surface glacier melt through both a decrease in the surface albedo from deposition of soot on the glacier and through the impact of smoke on atmospheric conditions above the glacier. Athabasca Glacier on-ice weather station observations show days with dense smoke were warmer than clear, non-smoky days, and sustained a reduction in surface shortwave irradiance of 103 W m−2 during peak shortwave irradiance and an increase in longwave irradiance of 10 W m−2, producing an average 15 W m−2 decrease in net radiation. Albedo observed on-ice gradually decreased after the wildfires started, from a summer average of 0.29 in 2015 before the wildfires to as low as 0.16 in 2018 after extensive wildfires and remained low for two more melt seasons without substantial upwind wildfires. Reduced all-wave irradiance partly compensated for the increase in melt due to lowered albedo in those seasons when smoke was detected above Athabasca Glacier. In melt seasons without smoke, the suppressed albedo increased melt by slightly more than 10% compared to the simulations without fire-impacted albedo, increasing melt by 0.42 m. w.e. in 2019 and 0.37 m. w.e. in 2020.
Mountain glacierized headwaters are currently witnessing a transient shift in their hydrological and glaciological systems in response to rapid climate change. To characterize these changes, a robust understanding of the hydrological processes operating in the basin and their interactions is needed. Such an investigation was undertaken in the Peyto Glacier Research Basin, Canadian Rockies over 32 years (1988–2020). A distributed, physically based, uncalibrated glacier hydrology model was developed using the modular, object-oriented Cold Region Hydrological Modelling Platform to simulate both on and off-glacier high mountain processes and streamflow generation. The hydrological processes that generate streamflow from this alpine basin are characterized by substantial inter-annual variability over the 32 years. Snowmelt runoff always provided the largest fraction of annual streamflow (44% to 89%), with smaller fractional contributions occurring in higher streamflow years. Ice melt runoff provided 10% to 45% of annual streamflow volume, with higher fractions associated with higher flow years. Both rainfall and firn melt runoff contributed less than 13% of annual streamflow. Years with high streamflow were on average 1.43°C warmer than low streamflow years, and higher streamflow years had lower seasonal snow accumulation, earlier snowmelt and higher summer rainfall than years with lower streamflow. Greater ice exposure in warmer, low snowfall (high rainfall) years led to greater streamflow generation. The understanding gained here provides insight into how future climate and increased meteorological variability may impact glacier meltwater contributions to streamflow and downstream water availability as alpine glaciers continue to retreat.
Conifer forests historically have been resilient to wildfires in part due to thick organic soil layers that regulate combustion and post-fire moisture and vegetation change. However, recent shifts in fire activity in western North America may be overwhelming these resilience mechanisms with potential impacts for energy and carbon exchange. Here, we quantify the long-term recovery of the organic soil layer and its carbon pools across 511 forested plots. Our plots span ~ 140,000 km2 across two ecozones of the Northwest Territories, Canada, and allowed us to investigate the impacts of time-after-fire, site moisture class, and dominant canopy type on soil organic layer thickness and associated carbon stocks. Despite thinner soil organic layers in xeric plots immediately after fire, these drier stands supported faster post-fire recovery of the soil organic layer than in mesic plots. Unlike xeric or mesic stands, post-fire soil carbon accumulation rates in hydric plots were negligible despite wetter forested plots having greater soil organic carbon stocks immediately post-fire compared to other stands. While permafrost and high-water tables inhibit combustion and maintain thick organic soils immediately after fire, our results suggest that these wet stands are not recovering their pre-fire carbon stocks on a century timescale. We show that canopy conversion from black spruce to jack pine or deciduous dominance could reduce organic soil carbon stocks by 60–80% depending on stand age. Our two main findings—decreasing organic soil carbon storage with increasing deciduous cover and the lack of post-fire SOL recovery in hydric sites—have implications for the turnover time of carbon stocks in the western boreal forest region and also will impact energy fluxes by controlling albedo and surface soil moisture.
Variable retention harvest (VRH) is a silvicultural approach that retains differing proportions and patterns of canopy trees across a harvested area to emulate natural disturbance effects on stand structure and enhance the resilience of the regenerating stand to abiotic and biotic stresses. Four VRH treatments were applied to an 83-year-old red pine (Pinus resinosa Ait.) plantation forest in the Mixedwood Plains Ecozone of Canada that included 55% aggregate retention (55A), 55% dispersed retention (55D), 33% aggregate retention (33A), 33% dispersed retention (33D) and an unharvested control (CN). In the sixth growing season after harvest, tree stem sap flow and eddy covariance flux measurements were used to examine the impacts of VRH on the dominant components of total stand evapotranspiration (ET), i.e., canopy transpiration (TC) and water flux from the understory vegetation and soil (ETU) as well as understory and canopy water use efficiency (WUE). A positive relationship was found between harvest intensity and the growth of understory vegetation and ETU. The contribution of ETU to ET was higher in the dispersed compared to the aggregate VRH treatments. Canopy transpiration contributed 83% of ET in the CN plot and 58%, 55%, 30% and 23% in the 55D, 55A, 33A and 33D treatments, respectively. Overall, VRH treatments resulted in increased canopy WUE but little comparative effect on understory WUE. Our results suggest that the dispersed retention pattern led to higher ET and productivity than the aggregate pattern of the same retention level. Where carbon sequestration and climate change mitigation is the primary management objective, higher retention levels such as 55D might be used to favour stand level carbon storage while accepting slower rates of understory development. Our findings on the effects of VRH on productivity and WUE of the canopy and understory will help forest managers to better employ VRH as an option to meet multiple objectives and adapt forests to a warmer, more variable climate.
As fragments of SARS-CoV-2 RNA can be quantified and measured temporally in wastewater, surveillance of concentrations of SARS-CoV-2 in wastewater has become a vital resource for tracking the spread of COVID-19 in and among communities. However, the absence of standardized methods has affected the interpretation of data for public health efforts. In particular, analyzing either the liquid or solid fraction has implications for the interpretation of how viral RNA is quantified. Characterizing how SARS-CoV-2 or its RNA fragments partition in wastewater is a central part of understanding fate and behaviour in wastewater. In this study, partitioning of SARS-CoV-2 was investigated by use of centrifugation with varied durations of spin and centrifugal force, polyethylene glycol (PEG) precipitation followed by centrifugation, and ultrafiltration of wastewater. Partitioning of the endogenous pepper mild mottled virus (PMMoV), used to normalize the SARS-CoV-2 signal for fecal load in trend analysis, was also examined. Additionally, two surrogates for coronavirus, human coronavirus 229E and murine hepatitis virus, were analyzed as process controls. Even though SARS-CoV-2 has an affinity for solids, the total RNA copies of SARS-CoV-2 per wastewater sample, after centrifugation (12,000 g, 1.5 h, no brake), were partitioned evenly between the liquid and solid fractions. Centrifugation at greater speeds for longer durations resulted in a shift in partitioning for all viruses toward the solid fraction except for PMMoV, which remained mostly in the liquid fraction. The surrogates more closely reflected the partitioning of SARS-CoV-2 under high centrifugation speed and duration while PMMoV did not. Interestingly, ultrafiltration devices were inconsistent in estimating RNA copies in wastewater, which can influence the interpretation of partitioning. Developing a better understanding of the fate of SARS-CoV-2 in wastewater and creating a foundation of best practices is the key to supporting the current pandemic response and preparing for future potential infectious diseases.
Nutrient losses from agricultural fields are the largest sources of phosphorus (P) entering the Great Lakes in North America. Stacked conservation practices (CPs) may reduce P losses from individual fields. Simple low-cost, low disturbance, commercially available filters containing wood chips and phosphorus sorbing materials (PSM) were installed on two fields already using conservation practices in midwestern Ontario (ILD and LON) to quantify their ability to remove soluble reactive P (SRP), particulate P (PP), total P (TP) and total suspended sediments (TSS) from surface runoff. Laboratory tests on unused (new) and used (field) filter materials were also conducted to estimate P sorption and remobilization potentials. During the two-year study period, the filter retained 0.018 kg ha-1 of SRP, 0.38 kg ha-1 of PP, 0.4 kg ha-1 of TP and 8.75 kg ha-1 of TSS from surface runoff at the ILD site. In contrast, although the filter at LON removed 37 kg ha-1 of TSS and 0.07 kg ha-1 of PP, it released 0.22 kg ha-1 of SRP and 0.15 kg ha-1 TP. A reduction in filter efficacy was observed over time, particularly at the site with greater cumulative surface runoff and larger runoff events (LON). The majority of the SRP retained by the filter was held in a loosely bound form, thus, susceptible to P remobilization. The results of this study demonstrate that low-cost, simple PSMs have some potential to retain P from surface runoff, but their efficacy may decline over time and their P retention capability may differ with site hydrology (e.g., runoff volumes and velocity) and P supply.
The Hudson Bay basin is a large contributor of freshwater input in the Arctic Ocean and is also an area affected by destructive spring floods. In this study, the hydrological model MESH (Modelisation Environmentale Communautaire - Surface and hydrology) was set up for the Groundhog River watershed situated in the Hudson Bay basin, to simulate the future evolution of streamflow and annual maximum streamflow. MESH was forced by meteorological data from ERA5 reanalyses in the historical period (1979–2018) and 12 models of the Coupled model intercomparison Project Phase 5 (CMIP5) downscaled with the Canadian Regional Climate model version 5 (CRCM5) in historical (1979–2005) and scenario period (2006–2098). The projections consistently indicate an earlier spring flow and a reduction in the amount of annual maximum streamflow by the end of the 21st century. Under the RCP8.5 scenario, the annual maximum streamflow occurring in the spring is expected to be advanced by 2 weeks and reduced on average from 852 m3/s (±265) in the historical period (1979–2018) to 717m3/s (±250) by the end of the 21st century (2059–2098). Because the seasonal projection of streamflow was not investigated in previous studies, this work is an important first step to assess the seasonal change of streamflow in the Hudson Bay region under climate change.
Remote retrieval of near-surface chlorophyll-a (Chla) concentration in small inland waters is challenging due to substantial optical interferences of various water constituents and uncertainties in the atmospheric correction (AC) process. Although various algorithms have been developed to estimate Chla from moderate-resolution terrestrial missions (∼10–60 m), the production of both accurate distribution maps and time series of Chla has proven challenging, limiting the use of remote analyses for lake monitoring. Here, we develop a support vector regression (SVR) model, which uses satellite-derived remote-sensing reflectance spectra (Rrsδ) from Sentinel-2 and Landsat-8 images as input for Chla retrieval in a representative eutrophic prairie lake, Buffalo Pound Lake (BPL), Saskatchewan, Canada. Validated against in situ Chla from seven ice-free seasons (N ∼ 200; 2014–2020), the SVR model outperformed both locally tuned, Rrsδ-fed empirical models (Normalized Difference Chlorophyll Index, 2- and 3-band, and OC3) and Mixture Density Networks (MDNs) by 15–65%, while exhibiting comparable performance to a locally trained MDN, with an error of ∼35%. Comparison of Chla retrieval models, AC processors (iCOR, ACOLITE), and radiometric products (Rayleigh-corrected, surface, and top-of-atmosphere reflectance) showed that the best Chla maps and optimal time series (up to 100 mg m−3) were produced using a coupled SVR-iCOR system.
Abstract Photosynthesis of terrestrial ecosystems in the Arctic-Boreal region is a critical part of the global carbon cycle. Solar-induced chlorophyll Fluorescence (SIF), a promising proxy for photosynthesis with physiological insight, has been used to track gross primary production (GPP) at regional scales. Recent studies have constructed empirical relationships between SIF and eddy covariance-derived GPP as a first step to predicting global GPP. However, high latitudes pose two specific challenges: (a) Unique plant species and land cover types in the Arctic–Boreal region are not included in the generalized SIF-GPP relationship from lower latitudes, and (b) the complex terrain and sub-pixel land cover further complicate the interpretation of the SIF-GPP relationship. In this study, we focused on the Arctic-Boreal vulnerability experiment (ABoVE) domain and evaluated the empirical relationships between SIF for high latitudes from the TROPOspheric Monitoring Instrument (TROPOMI) and a state-of-the-art machine learning GPP product (FluxCom). For the first time, we report the regression slope, linear correlation coefficient, and the goodness of the fit of SIF-GPP relationships for Arctic-Boreal land cover types with extensive spatial coverage. We found several potential issues specific to the Arctic-Boreal region that should be considered: (a) unrealistically high FluxCom GPP due to the presence of snow and water at the subpixel scale; (b) changing biomass distribution and SIF-GPP relationship along elevational gradients, and (c) limited perspective and misrepresentation of heterogeneous land cover across spatial resolutions. Taken together, our results will help improve the estimation of GPP using SIF in terrestrial biosphere models and cope with model-data uncertainties in the Arctic-Boreal region.
MMEAD, or MS MARCO Entity Annotations and Disambiguations, is a resource for entity links for the MS MARCO datasets. We specify a format to store and share links for both document and passage collections of MS MARCO. Following this specification, we release entity links to Wikipedia for documents and passages in both MS MARCO collections (v1 and v2). Entity links have been produced by the REL and BLINK systems. MMEAD is an easy-to-install Python package, allowing users to load the link data and entity embeddings effortlessly. Using MMEAD takes only a few lines of code. Finally, we show how MMEAD can be used for IR research that uses entity information. We show how to improve recall@1000 and MRR@10 on more complex queries on the MS MARCO v1 passage dataset by using this resource. We also demonstrate how entity expansions can be used for interactive search applications.
This study is a meta-analysis of global articles on hydrological nutrient dynamics to determine trends and consensus on: (1) the effects of climate change-induced hydrological and temperature drivers on nutrient dynamics and how these effects vary along the catchment continuum from land to river to lake; (2) the convergence of climate change impacts with other anthropogenic pressures (agriculture, urbanization) in nutrient dynamics; and (3) regional variability in the effects of climate change on nutrient dynamics and water-quality impairment across different climate zones. An innovative web crawler tool was employed to help critically synthesize the information in the literature. The literature suggests that climate change will impact nutrient dynamics around the globe and exacerbate contemporary water-quality challenges. Nutrient leaching and overland flow transport are projected to increase globally, promoted by extreme precipitation. Seasonal variations in streamflow are expected to emulate changing precipitation patterns, but the specific local impacts of climate change on hydrology and nutrient dynamics will vary both seasonally and regionally. Plant activity may reduce some of this load in nonagricultural soils if the expected increase in plant uptake of nutrients prompted by increased temperatures can compensate for greater nitrogen (N) and phosphorus (P) mineralization, N deposition, and leaching rates. High-temperature forest and grass fires may help reduce mineralization and microbial turnover by altering N speciation via the pyrolysis of organic matter. In agricultural areas that are at higher risk of erosion, extreme precipitation will exacerbate existing water-quality issues, and greater plant nutrient uptake may lead to an increase in fertilizer use. Future urban expansion will amplify these effects. Higher ambient temperatures will promote harmful cyanobacterial blooms by enhancing thermal stratification, increasing nutrient load into streams and lakes from extreme precipitation events, decreasing summer flow and thus baseflow dilution capacity, and increasing water and nutrient residence times during increasingly frequent droughts. Land management decisions must consider the nuanced regional and seasonal changes identified in this review (realized and predicted). Such knowledge is critical to increasing international cooperation and accelerating action toward the United Nations’s global sustainability goals and the specific objectives of the Conference of Parties (COP) 26.
Fish live in continuous contact with various stressors and antigenic material present within their environments. The impact of stressors associated with wastewater-exposed environments on fish has become of particular interest in toxicology studies. The objectives of this study were to examine potential effects of wastewater treatment plant (WWTP) effluent-associated stressors on innate cytokine expression within the gills of darter species (Etheostoma spp.), using both field and laboratory approaches. Male and female darters (rainbow, greenside, fantail, and johnny darters) were collected upstream and downstream of the Waterloo WWTP in the Grand River, Ontario. Gill samples were collected from fish in the field and from a second subset of fish brought back to the laboratory. Laboratory fish were acutely exposed (96-h) to an environmentally relevant concentration of venlafaxine (1.0 μg/L), a commonly prescribed antidepressant. To assess the impacts of these stressors on the innate immunity of darters, the expression of key innate cytokines was examined. Minor significant effects on innate cytokine expression were observed between upstream and downstream fish. Moderate effects on cytokine expression were observed in venlafaxine-exposed fish compared to their control counterparts however, changes were not indicative of a biologically significant immune response occurring due to the exposure. Although the results of this study did not display extensive impacts of effluent and pharmaceutical exposure on innate cytokine expression within the gills, they provide a novel avenue of study, illustrating the importance of examining potential impacts that effluent-associated stressors can have on fundamental immune responses of native fish species.
Introduction Wastewater-based surveillance is at the forefront of monitoring for community prevalence of COVID-19, however, continued uncertainty exists regarding the use of fecal indicators for normalization of the SARS-CoV-2 virus in wastewater. Using three communities in Ontario, sampled from 2021–2023, the seasonality of a viral fecal indicator (pepper mild mottle virus, PMMoV) and the utility of normalization of data to improve correlations with clinical cases was examined. Methods Wastewater samples from Warden, the Humber Air Management Facility (AMF), and Kitchener were analyzed for SARS-CoV-2, PMMoV, and crAssphage. The seasonality of PMMoV and flow rates were examined and compared by Season-Trend-Loess decomposition analysis. The effects of normalization using PMMoV, crAssphage, and flow rates were analyzed by comparing the correlations to clinical cases by episode date (CBED) during 2021. Results Seasonal analysis demonstrated that PMMoV had similar trends at Humber AMF and Kitchener with peaks in January and April 2022 and low concentrations (troughs) in the summer months. Warden had similar trends but was more sporadic between the peaks and troughs for PMMoV concentrations. Flow demonstrated similar trends but was not correlated to PMMoV concentrations at Humber AMF and was very weak at Kitchener ( r = 0.12). Despite the differences among the sewersheds, unnormalized SARS-CoV-2 (raw N1–N2) concentration in wastewater ( n = 99–191) was strongly correlated to the CBED in the communities ( r = 0.620–0.854) during 2021. Additionally, normalization with PMMoV did not improve the correlations at Warden and significantly reduced the correlations at Humber AMF and Kitchener. Flow normalization ( n = 99–191) at Humber AMF and Kitchener and crAssphage normalization ( n = 29–57) correlations at all three sites were not significantly different from raw N1–N2 correlations with CBED. Discussion Differences in seasonal trends in viral biomarkers caused by differences in sewershed characteristics (flow, input, etc.) may play a role in determining how effective normalization may be for improving correlations (or not). This study highlights the importance of assessing the influence of viral fecal indicators on normalized SARS-CoV-2 or other viruses of concern. Fecal indicators used to normalize the target of interest may help or hinder establishing trends with clinical outcomes of interest in wastewater-based surveillance and needs to be considered carefully across seasons and sites.
We determined correlations between SARS-CoV-2 load in untreated water and COVID-19 cases and patient hospitalizations before the Omicron variant (September 2020-November 2021) at 2 wastewater treatment plants in the Regional Municipality of Peel, Ontario, Canada. Using pre-Omicron correlations, we estimated incident COVID-19 cases during Omicron outbreaks (November 2021-June 2022). The strongest correlation between wastewater SARS-CoV-2 load and COVID-19 cases occurred 1 day after sampling (r = 0.911). The strongest correlation between wastewater load and COVID-19 patient hospitalizations occurred 4 days after sampling (r = 0.819). At the peak of the Omicron BA.2 outbreak in April 2022, reported COVID-19 cases were underestimated 19-fold because of changes in clinical testing. Wastewater data provided information for local decision-making and are a useful component of COVID-19 surveillance systems.
Although the temporal transferability of input–output (IO) models has been examined before, no study has investigated the impacts of changing water availability conditions over time, e.g., due to climate change, on the predictive power of water-inclusive IO models. To address this gap, we investigate the performance of inter-regional supply-side input–output (ISIO) models that incorporate precipitation and water intake under varying climates over time in a transboundary water management context. Using the Saskatchewan River Basin in Western Canada as a case study, we develop four ISIO models based on available economic and hydrological data from years with different climatic conditions, i.e., two dry and two wet years. Accounting for price changes over these years, our findings indicate that the joint impact of changes in water availability and economic structural changes on economic output can be considerable. The results furthermore show that each model performs particularly well in predicting the economic output for similar climatic years. The models remain reliable in predicting economic outputs over several years as long as changes in water availability are within the range observed in the water-inclusive base year ISIO model.
Microbial communities are an important component of freshwater biodiversity that is threatened by anthropogenic impacts. Wastewater discharges pose a particular concern by being major sources of anthropogenic contaminants and microorganisms that may influence the composition of natural microbial communities. Nevertheless, the effects of wastewater treatment plant (WWTP) effluents on microbial communities remain largely unexplored. In this study, the effects of wastewater discharges on microbial communities from five different WWTPs in Southern Saskatchewan were investigated using rRNA gene metabarcoding. In parallel, nutrient levels and the presence of environmentally relevant organic pollutants were analyzed. Higher nutrient loads and pollutant concentrations resulted in significant changes in microbial community composition. The greatest changes were observed in Wascana Creek (Regina), which was found to be heavily polluted by wastewater discharges. Several taxa occurred in greater relative abundance in the wastewater-influenced stream segments, indicating anthropogenic pollution and eutrophication, especially taxa belonging to Proteobacteria, Bacteroidota, and Chlorophyta. Strong decreases were measured within the taxa Ciliphora, Diatomea, Dinoflagellata, Nematozoa, Ochrophyta, Protalveolata, and Rotifera. Across all sample types, a significant decline in sulfur bacteria was measured, implying changes in functional biodiversity. In addition, downstream of the Regina WWTP, an increase in cyanotoxins was detected which was correlated with a significant change in cyanobacterial community composition. Overall, these data suggest a causal relationship between anthropogenic pollution and changes in microbial communities, possibly reflecting an impairment of ecosystem health.
Wastewater-based surveillance has become an effective tool around the globe for indirect monitoring of COVID-19 in communities. Variants of Concern (VOCs) have been detected in wastewater by use of reverse transcription polymerase chain reaction (RT-PCR) or whole genome sequencing (WGS). Rapid, reliable RT-PCR assays continue to be needed to determine the relative frequencies of VOCs and sub-lineages in wastewater-based surveillance programs. The presence of multiple mutations in a single region of the N-gene allowed for the design of a single amplicon, multiple probe assay, that can distinguish among several VOCs in wastewater RNA extracts. This approach which multiplexes probes designed to target mutations associated with specific VOC's along with an intra-amplicon universal probe (non-mutated region) was validated in singleplex and multiplex. The prevalence of each mutation (i.e. VOC) is estimated by comparing the abundance of the targeted mutation with a non-mutated and highly conserved region within the same amplicon. This is advantageous for the accurate and rapid estimation of variant frequencies in wastewater. The N200 assay was applied to monitor frequencies of VOCs in wastewater extracts from several communities in Ontario, Canada in near real time from November 28, 2021 to January 4, 2022. This includes the period of the rapid replacement of the Delta variant with the introduction of the Omicron variant in these Ontario communities in early December 2021. The frequency estimates using this assay were highly reflective of clinical WGS estimates for the same communities. This style of qPCR assay, which simultaneously measures signal from a non-mutated comparator probe and multiple mutation-specific probes contained within a single qPCR amplicon, can be applied to future assay development for rapid and accurate estimations of variant frequencies.
Abstract. While conflict-and-cooperation phenomena in transboundary basins have been widely studied, much less work has been devoted to representing the process interactions in a quantitative way. This paper identifies the main factors in the riparian countries' willingness to cooperate in the Eastern Nile River basin, involving Ethiopia, Sudan, and Egypt, from 1983 to 2016. We propose a quantitative model of the willingness to cooperate at the national and river basin scales. Our results suggest that relative political stability and foreign direct investment can explain Ethiopia's decreasing willingness to cooperate between 2009 and 2016. Further, we show that the 2008 food crisis may account for Sudan recovering its willingness to cooperate with Ethiopia. Long-term lack of trust among the riparian countries may have reduced basin-wide cooperation. While the proposed model has some limitations regarding model assumptions and parameters, it does provide a quantitative representation of the evolution of cooperation pathways among the riparian countries, which can be used to explore the effects of changes in future dam operation and other management decisions on the emergence of conflict and cooperation in the basin.
Abstract Fish stranding is of global concern with increasing hydropower operations using hydropeaking to respond to fluctuating energy demand. Determining the effects hydropeaking has on fish communities is challenging because fish stranding is dependent on riverscape features, such as topography, bathymetry and substrate. By using a combination of physical habitat assessments, hydrodynamic modelling and empirical data on fish stranding, we estimated the number of fish stranding over a 5‐month period for three model years in a large Prairie river. More specifically, we modelled how many fish potentially stranded during the years 2019, 2020 and 2021 across a 16 km study reached downstream from E.B. Campbell Hydroelectric Station on the Saskatchewan River, Canada. Fish stranding densities calculated from data collected through remote photography and transect monitoring in 2021 were applied to the daily area subject to drying determined by the River2D hydrodynamic model. The cumulative area subject to change was 90.05, 53.02 and 80.74 km 2 for years 2019, 2020 and 2021, respectively, from June to October. The highest number of stranded fish was estimated for the year 2021, where estimates ranged from 89,800 to 1,638,000 individuals based on remote photography and transect monitoring fish stranding densities, respectively, 157 to 2,856 fish stranded per hectare. Our approach of estimating fish stranding on a large scale allows for a greater understanding of the impact hydropeaking has on fish communities and can be applied to other riverscapes threatened by hydropeaking.
With the continuous development of hydropower on a global scale, stranding of freshwater fishes is of growing concern, and an understanding of the mechanisms and variables affecting fish stranding in hydropeaking rivers is urgently needed. In particular, a methodology is required to identify the magnitude and timing at which fish stranding occurs in relation to environmental conditions. Here, we studied fish stranding in three reaches downstream of a hydropeaking generation station in the Saskatchewan River, Saskatchewan, Canada, using an innovative remote photography approach with 45 trail cameras and traditional transect monitoring, conducting 323 transects. We observed that juvenile sport and commercial fish species are stranding at a higher proportion than small bodied fish species. The remote photography approach provided more precise fish stranding timing and associated the environmental and physical conditions with a given stranding event, but captured fewer fish and only rarely allowed species identification. The comparison of the two methodologies resulted in similar stranded fish densities, but the remote photography allowed for continuous observations whereas the transect monitoring was limited by the observer availability in the field. Remote photography allowed for additional information on the scavenging of stranded fish, with scavenging occurring on average within 240 minutes of the fish being stranded. The probability of fish stranding increased significantly with increasing water temperature and substrate particle size resulted in greater stranding on finer substrates. Our findings have important implications for hydroelectric flow management by introducing an innovative, standardized method to study the effects of hydropeaking events on fish stranding that can be applied to increase our understanding of the impacts of hydropeaking on fish communities.
Freshwater has been shown to have a maximum density at about four degrees Celsius, and this leads to a phenomenon known as cabbeling. Cabbeling occurs when masses of water on different sides of the temperature of maximum density mix and create a denser mass. What happens when intruding and ambient temperatures in a gravity current are on opposite sides of the temperature of maximum density? How does cabbeling affect the evolution characteristics of gravity currents, and what sort of long term behavior arises?
Background Canadian fire management agencies track drought conditions using the Drought Code (DC) in the Canadian Forest Fire Danger Rating System. The DC represents deep organic layer moisture.Aims To determine if electronic soil moisture probes and land surface model estimates of soil moisture content can be used to supplement and/or improve our understanding of drought in fire danger rating.Methods We carried out field studies in the provinces of Alberta and Ontario. We installed in situ soil moisture probes at two different depths in seven forest plots, from the surface through the organic layers, and in some cases into the mineral soil.Results Our results indicated that the simple DC model predicted the moisture content of the deeper organic layers (10–18 cm depths) well, even compared with the more sophisticated land surface model.Conclusions Electronic moisture probes can be used to supplement the DC. Land surface model estimates of moisture content consistently underpredicted organic layer moisture content.Implications Calibration and validation of the land surface model to organic soils in addition to mineral soils is necessary for future use in fire danger prediction.
Crop–water interactions define productivity in water-limited dryland agricultural production systems in cold regions. Despite the agronomic and economic importance of this relationship there are challenges in quantifying crop water use efficiency (WUE). To understand dynamics driving crop water use and agricultural productivity in these environments, observations of evapotranspiration, carbon assimilation, meteorology, and crop growth were collected over 17 site-years at 5 agricultural sites in the sub-humid continental Canadian Prairies. Eddy-covariance (EC) derived water and carbon fluxes provided a means to comprehensively assess the WUE of current agricultural practices by both physiological (WUEP: g C kg−1 H2O) and agronomic (WUEY): kg yield mm H2O−1 hectare−1) approaches. Mean field scale WUEY for grain yields were 10.4 (Barley), 10.2 (Wheat), 6.0 (Canola), 19.3 (Peas), 12.2 (Lentils) and for silage/forage crops were 23.0 (Barley), 11.9 (Forage), and 20.7 (Corn) (kg yield mm H2O−1 hectare−1). An assessment of environmental factors and their covariance with WUE, utilising a conditional inference tree approach, demonstrated that WUE decreased when crops were under greater evapotranspiration demands. EC-based areal WUE approaches, measuring fluxes over footprints of hundreds of square metres, were compared with more commonly reported point-scale water balance residual approaches (WUEWB) and demonstrated consistently smaller magnitudes. WUEWB was greater than EC-estimated WUEY by an average of 52% and 65% for grain and forage/silage crops respectively. WUEWB also had greater variability than EC estimates, with standard deviations 188% and 128% greater than Barley and Wheat crops, respectively. This comparison highlights the scale dependency of WUE estimation methods, demonstrates considerable uncertainty in point scale water balance approaches due to spatial variability in crop–water interactions, and shows how this variability can be accounted for by EC observations. This improves the understanding of WUE and quantifies its variability in cold continental water-limited climates and provides a means to diagnose improved agricultural water management.
Abstract. Vegetation has a tremendous influence on snow processes and snowpack dynamics, yet remote sensing techniques to resolve the spatial variability of sub-canopy snow depth are not always available and are difficult from space-based platforms. Unmanned aerial vehicles (UAVs) have had recent widespread application to capture high-resolution information on snow processes and are herein applied to the sub-canopy snow depth challenge. Previous demonstrations of snow depth mapping with UAV structure from motion (SfM) and airborne lidar have focussed on non-vegetated surfaces or reported large errors in the presence of vegetation. In contrast, UAV-lidar systems have high-density point clouds and measure returns from a wide range of scan angles, increasing the likelihood of successfully sensing the sub-canopy snow depth. The effectiveness of UAV lidar and UAV SfM in mapping snow depth in both open and forested terrain was tested in a 2019 field campaign at the Canadian Rockies Hydrological Observatory, Alberta, and at Canadian prairie sites near Saskatoon, Saskatchewan, Canada. Only UAV lidar could successfully measure the sub-canopy snow surface with reliable sub-canopy point coverage and consistent error metrics (root mean square error (RMSE) <0.17 m and bias −0.03 to −0.13 m). Relative to UAV lidar, UAV SfM did not consistently sense the sub-canopy snow surface, the interpolation needed to account for point cloud gaps introduced interpolation artefacts, and error metrics demonstrated relatively large variability (RMSE<0.33 m and bias 0.08 to −0.14 m). With the demonstration of sub-canopy snow depth mapping capabilities, a number of early applications are presented to showcase the ability of UAV lidar to effectively quantify the many multiscale snow processes defining snowpack dynamics in mountain and prairie environments.
Abstract Groundwater discharge sustains the baseflow of alpine headwater streams, which is critical for water supply and aquatic environments in mountainous regions. Periglacial landforms typical of alpine headwaters (e.g., talus, moraine, rock glacier, alpine meadows) are important aquifers in alpine watersheds. This study examines the hydrological function of an alpine aquifer complex in a small headwater basin in the Canadian Rockies. The aquifer complex consisting of talus, alpine meadow underlain by a bedrock depression, and recessional moraine provided essentially all baseflow of a 6.5 km 2 watershed, even though the upper sub‐watershed containing the aquifer complex occupies only 14% of the watershed. Chemical and isotopic signatures indicated that the recessional moraine serves as a gatekeeper of the upper sub‐watershed, whereby it integrates groundwater components from multiple aquifers and controls the discharge from the outlet springs. Field observation of discharge and the water table in the moraine aquifer showed a nonlinear groundwater storage‐discharge relationship. Numerical groundwater flow models of the upper sub‐watershed showed that the transmissivity feedback resulting from a decrease in hydraulic conductivity with depth was essential for determining the nonlinear storage‐discharge relationship. A simple exponential function was proposed to represent the observed groundwater storage‐discharge relationship, which can be implemented within large‐scale hydrological models to simulate baseflow coming out of alpine headwater regions.
For over a decade, intersex has been observed in rainbow darter (RD) (Etheostoma caeruleum) populations living downstream wastewater treatment plants (WWTPs) in the Grand River, Ontario, Canada. To further our understanding of intersex development in adult male fish, the current study addressed three objectives: i) can intersex be induced in adult male fish, ii) is there a specific window of exposure when adult male fish are more susceptible to developing intersex, and iii) can pre-exposed adult male fish recover from intersex? To assess intersex induction in adult male fish, wild male RD were exposed in the laboratory for 22 weeks (during periods of spawning, gonadal regression, and gonadal recrudescence) to environmentally relevant concentrations of 17α-ethinylestradiol (EE2) including nominal 0, 1, and 10 ng/L. Intersex rates and severity at 10 ng/L EE2 were similar to those observed historically in adult male populations living downstream WWTPs in the Grand River and confirmed previous predictions that 1–10 ng/L EE2 would cause these adverse effects. To assess a window of sensitivity in developing intersex, male RD were exposed to nominal 0, 1 or 10 ng/L EE2 for 4 weeks during three different periods of gonadal development, including (i) spawning, (ii) early recrudescence and (iii) late recrudescence. These short-term exposures revealed that intersex incidence and severity were greater when RD were exposed while gonads were fully developed (during spawning) compared to periods of recrudescence. To assess if RD recover from intersex, wild fish were collected downstream WWTPs in the Grand River and assessed for intersex both before and after a 22-week recovery period in clean water that included gonadal regression and recrudescence. Results showed that fish did not recover from intersex, with intersex rates and severity similar to those both before and after the transition to clean water. This study further advances our knowledge on intersex manifestation in adult male fish including their sensitivity to endocrine active compounds during different periods of their annual reproductive cycle and their limited ability to recover from intersex after onset of the condition.
L-lactate is a key metabolite indicative of physiological states, glycolysis pathways, and various diseases such as sepsis, heart attack, lactate acidosis, and cancer. Detection of lactate has been relying on a few enzymes that need additional oxidants. In this work, DNA aptamers for L-lactate were obtained using a library-immobilization selection method and the highest affinity aptamer reached a Kd of 0.43 mM as determined using isothermal titration calorimetry. The aptamers showed up to 50-fold selectivity for L-lactate over D-lactate and had little responses to other closely related analogs such as pyruvate or 3-hydroxybutyrate. A fluorescent biosensor based on the strand displacement method showed a limit of detection of 0.55 mM L-lactate, and the sensor worked in 90 % serum. Simultaneous detection of L-lactate and D-glucose in the same solution was achieved. This work has broadened the scope of aptamers to simple metabolites and provided a useful probe for continuous and multiplexed monitoring.
Abstract As climate change intensifies, soil water flow, heat transfer, and solute transport in the active, unfrozen zones within permafrost and seasonally frozen ground exhibit progressively more complex interactions that are difficult to elucidate with measurements alone. For example, frozen conditions impede water flow and solute transport in soil, while heat and mass transfer are significantly affected by high thermal inertia generated from water‐ice phase change during the freeze‐thaw cycle. To assist in understanding these subsurface processes, the current study presents a coupled two‐dimensional model, which examines heat conduction‐convection with water‐ice phase change, soil water (liquid water and vapour) and groundwater flow, ad v ective‐dispersive solute transport with sorption, and soil deformation (frost heave and thaw settlement) in variably saturated soils subjected to freeze‐thaw actions. This coupled multiphysics problem is numerically solved using the finite element method. The model's performance is first verified by comparison to a well‐documented freezing test on unsaturated soil in a laboratory environment obtained from the literature. Then based on the proposed model, we quantify the impacts of freeze‐thaw cycles on the distribution of temperature, water content, displacement history, and solute concentration in three distinct soil types, including sand, silt and clay textures. The influence of fluctuations in the air temperature, groundwater level, hydraulic conductivity, and solute transport parameters was also comparatively studied.The results show that (i) there is a significant bidirectional exchange between groundwater in the saturated zone and soil water in the vadose zone during freeze‐thaw periods, and its magnitude increases with the combined influence of higher hydraulic conductivity and higher capillarity; (ii) the rapid dewatering ahead of the freezing front causes local volume shrinkage within the non‐frozen region when the freezing front propagates downward during the freezing stage and this volume shrinkage reduces the impact of frost heave due to ice formation. This gradually recovers when the thawed water replenishes the water loss zone during the thawing stage; and (iii) the profiles of soil moisture, temperature, displacement, and solute concentration during freeze‐thaw cycles are sensitive to the changes in amplitude and freeze‐thaw period of the sinusoidal varying air temperature near the ground surface, hydraulic conductivity of soil texture, and the initial groundwater levels. Our modelling framework and simulation results highlight the need to account for coupled thermal‐hydraulic‐mechanical‐chemical behaviours to better understand soil water and groundwater dynamics during freeze‐thaw cycles and further help explain the observed changes in water cycles and landscape evolution in cold regions. This article is protected by copyright. All rights reserved.
Abstract. A simple numerical solution procedure – namely the method of lines combined with an off-the-shelf ordinary differential equation (ODE) solver – was shown in previous work to provide efficient, mass-conservative solutions to the pressure-head form of Richards' equation. We implement such a solution in our model openRE. We developed a novel method to quantify the boundary fluxes that reduce water balance errors without negative impacts on model runtimes – the solver flux output method (SFOM). We compare this solution with alternatives, including the classic modified Picard iteration method and the Hydrus 1D model. We reproduce a set of benchmark solutions with all models. We find that Celia's solution has the best water balance, but it can incur significant truncation errors in the simulated boundary fluxes, depending on the time steps used. Our solution has comparable runtimes to Hydrus and better water balance performance (though both models have excellent water balance closure for all the problems we considered). Our solution can be implemented in an interpreted language, such as MATLAB or Python, making use of off-the-shelf ODE solvers. We evaluated alternative SciPy ODE solvers that are available in Python and make practical recommendations about the best way to implement them for Richards' equation. There are two advantages of our approach: (i) the code is concise, making it ideal for teaching purposes; and (ii) the method can be easily extended to represent alternative properties (e.g., novel ways to parameterize the K(ψ) relationship) and processes (e.g., it is straightforward to couple heat or solute transport), making it ideal for testing alternative hypotheses.
Abstract Climate change is increasing the frequency and extent of fires in the boreal biome of North America. These changes can alter the recovery of both canopy and understory vegetation. There is uncertainty about plant and lichen recovery patterns following fire, and how they are mediated by environmental conditions. Here, we aim to address these knowledge gaps by studying patterns of postfire vegetation recovery at the community and individual species level over the first 100+ years following fire. Data from vegetation surveys collected from 581 plots in the Northwest Territories, Canada, ranging from 1 to 275 years postfire, were used to assess the influence of time after fire and local environmental conditions on plant community composition and to model trends in the relative abundance of several common plant and lichen species. Time after fire significantly influenced vegetation community composition and interacted with local environmental conditions, particularly soil moisture. Soil moisture individually (in the absence of interactions) was the most commonly significant variable in plant and lichen recovery models. Patterns of postfire recovery varied greatly among species. Our results provide novel information on plant community recovery after fire and highlight the importance of soil moisture to local vegetation patterns. They will aid northern communities and land managers to anticipate the impacts of increased fire activity on both local vegetation and the wildlife that relies on it.
Abundant reserves of metals and oil have spurred large-scale mining developments across northwestern Canada during the past 80 years. Historically, the associated emissions footprint of hazardous metal(loid)s has been difficult to identify, in part, because monitoring records are too short and sparse to have characterized their natural concentrations before mining began. Stratigraphic analysis of lake sediment cores has been employed where concerns of pollution exist to determine pre-disturbance metal(loid) concentrations and quantify the degree of enrichment since mining began. Here, we synthesize the current state of knowledge via systematic re-analysis of temporal variation in sediment metal(loid) concentrations from 51 lakes across four key regions spanning 670 km from bitumen mining in the Alberta Oil Sands Region (AOSR) to gold mining (Giant and Con mines) at Yellowknife in central Northwest Territories. Our compilation includes upland and floodplain lakes at varying distances from the mines to evaluate dispersal of pollution-indicator metal(loid)s from bitumen (vanadium and nickel) and gold mining (arsenic and antimony) via atmospheric and fluvial pathways. Results demonstrate ‘severe’ enrichment of vanadium and nickel at near-field sites (≤20 km) within the AOSR and ‘severe’ (near-field; ≤ 40 km) to ‘considerable’ (far-field; 40–80 km) enrichment of arsenic and antimony due to gold mining at Yellowknife via atmospheric pathways, but no evidence of enrichment of vanadium or nickel via atmospheric or fluvial pathways at the Peace-Athabasca Delta and Slave River Delta. Findings can be used by decision makers to evaluate risks associated with contaminant dispersal by the large-scale mining activities. In addition, we reflect upon methodological approaches to be considered when evaluating paleolimnological data for evidence of anthropogenic contributions to metal(loid) deposition and advocate for proactive inclusion of paleolimnology in the early design stage of environmental contaminant monitoring programs.
The assessment and mapping of riverine flood hazards and risks is recognized by many countries as an important tool for characterizing floods and developing flood management plans. Often, however, these management plans give attention primarily to open-water floods, with ice-jam floods being mostly an afterthought once these plans have been drafted. In some Nordic regions, ice-jam floods can be more severe than open-water floods, with floodwater levels of ice-jam floods often exceeding levels of open-water floods for the same return periods. Hence, it is imperative that flooding due to river ice processes be considered in flood management plans. This also pertains to European member states who are required to submit renewed flood management plans every six years to the European governance authorities. On 19 and 20 October 2022, a workshop entitled “Assessing and mitigating ice-jam flood hazard and risk” was hosted in Poznań, Poland to explore the necessity of incorporating ice-jam flood hazard and risk assessments in the European Union’s Flood Directive. The presentations given at the workshop provided a good overview of flood risk assessments in Europe and how they may change due to the climate in the future. Perspectives from Norway, Sweden, Finland, Germany, and Poland were presented. Mitigation measures, particularly the artificial breakage of river ice covers and ice-jam flood forecasting, were shared. Advances in ice processes were also presented at the workshop, including state-of-the-art developments in tracking ice-floe velocities using particle tracking velocimetry, characterizing hanging dam ice, designing new ice-control structures, detecting, and monitoring river ice covers using composite imagery from both radar and optical satellite sensors, and calculating ice-jam flood hazards using a stochastic modelling approach.
Abstract In the spring of 2020, the town of Fort McMurray, which lies on the banks of the Athabasca River, experienced an ice-jam flood event that was the most severe in approximately 60 years. In order to capture the severity of the event, a stochastic modelling approach, previously developed by the author for ice-jam flood forecasting, has been refined for ice-jam flood hazard and risk assessments and ice-jam mitigation feasibility studies, which is the subject of this paper. Scenarios of artificial breakage demonstrate the applicability of the revised modelling framework.
Surface water quality modelling has become an important means of better understanding aquatic and riparian ecosystem processes at all scales, from the micro-scale (e [...]
A comprehensive review of experiences with water quality trading (WQT) programs worldwide is presented, spanning altogether more than 4 decades. A new WQT database is built, extracting data and information from existing review papers, complemented with gray and published literature about individual trading programs. Key aspects that affect trading volumes and program continuation are identified and categorized. No single success or fail factor emerges from this review, typically a mix of factors play a role. There is potential for WQT to evolve further and serve as a cost-effective pollution control instrument, but this requires nudging political will to regulate nonpoint source.
Stable Fe isotopes have only recently been measured in freshwater systems, mainly in meromictic lakes. Here we report the δ56Fe of dissolved, particulate, and sediment Fe in two small dimictic boreal shield headwater lakes: manipulated eutrophic Lake 227, with annual cyanobacterial blooms, and unmanipulated oligotrophic Lake 442. Within the lakes, the range in δ56Fe is large (ca. -0.9 to +1.8‰), spanning more than half the entire range of natural Earth surface samples. Two layers in the water column with distinctive δ56Fe of dissolved (dis) and particulate (spm) Fe were observed, despite differences in trophic states. In the epilimnia of both lakes, a large Δ56Fedis-spm fractionation of 0.4-1‰ between dissolved and particulate Fe was only observed during cyanobacterial blooms in Lake 227, possibly regulated by selective biological uptake of isotopically light Fe by cyanobacteria. In the anoxic layers in both lakes, upward flux from sediments dominates the dissolved Fe pool with an apparent Δ56Fedis-spm fractionation of -2.2 to -0.6‰. Large Δ56Fedis-spm and previously published metagenome sequence data suggest active Fe cycling processes in anoxic layers, such as microaerophilic Fe(II) oxidation or photoferrotrophy, could regulate biogeochemical cycling. Large fractionation of stable Fe isotopes in these lakes provides a potential tool to probe Fe cycling and the acquisition of Fe by cyanobacteria, with relevance for understanding biogeochemical cycling of Earth's early ferruginous oceans.
Biodiversity loss is caused by intensive human activities and threatens human well-being. However, less is known about how the combined effects of multiple stressors on the diversity of internal (alpha diversity) and multidimensional (beta diversity) communities. Here, we conducted a long-term experiment to quantify the contribution of environmental stressors (including water quality, land use, climate factors, and hydrological regimes) to macroinvertebrate communities alpha and beta diversity in the mainstream of the Songhua River, the third largest river in China, from 2012 to 2019. Our results demonstrated that the alpha and beta diversity indices showed a decline during the study period, with the dissimilarity in community composition between sites decreasing significantly, especially in the impacted river sections (upper and midstream). Despite overall improvement in water quality after management intervention, multiple human-caused stressors still have led to biotic homogenization of macroinvertebrate communities in terms of both taxonomic and functional diversities in the past decade. Our study revealed the increased human land use explained an important portion of the variation of diversities, further indirectly promoting biotic homogenization by changing the physical and chemical factors of water quality, ultimately altering assemblage ecological processes. Furthermore, the facets of diversity have distinct response mechanisms to stressors, providing complementary information from the perspective of taxonomy and function to better reflect the ecological changes of communities. Environmental filtering determined taxonomic beta diversity, and functional beta diversity was driven by the joint efforts of stressors and spatial processes. Finally, we proposed that traditional water quality monitoring alone cannot fully reveal the status of river ecological environment protection, and more importantly, we should explore the continuous changes in biodiversity over the long term. Meanwhile, our results also highlight timely control of nutrient input and unreasonable expansion of land use can better curb the ecological degradation of rivers and promote the healthy and sustainable development of floodplain ecosystems.
Perfluoroethylcyclohexane sulphonate (PFECHS) is an emerging, replacement perfluoroalkyl substance (PFAS) with little information available on the toxic effects or potencies with which to characterize its potential impacts on aquatic environments. This study aimed to characterize effects of PFECHS using in vitro systems, including rainbow trout liver cells (RTL-W1 cell line) and lymphocytes separated from whole blood. It was determined that exposure to PFECHS caused minor acute toxic effects for most endpoints and that little PFECHS was concentrated into cells with a mean in vitro bioconcentration factor of 81 ± 25 L/kg. However, PFECHS was observed to affect the mitochondrial membrane and key molecular receptors, such as the peroxisome proliferator receptor, cytochrome p450-dependent monooxygenases, and receptors involved in oxidative stress. Also, glutathione-S-transferase was significantly down-regulated at a near environmentally relevant exposure concentration of 400 ng/L. These results are the first to report bioconcentration of PFECHS, as well as its effects on the peroxisome proliferator and glutathione-S-transferase receptors, suggesting that even with little bioconcentration, PFECHS has potential to cause adverse effects.
Perfluoroethylcyclohexanesulfonate (PFECHS) is an emerging perfluoroalkyl substance (PFAS) that has been considered a potential replacement for perfluorooctanesulfonic acid (PFOS). However, there is little information characterizing the toxic potency of PFECHS to zebrafish embryos and its potential for effects in aquatic environments. This study assessed toxic potency of PFECHS in vivo during both acute (96-hour postfertilization) and chronic (21-day posthatch) exposures and tested concentrations of PFECHS from 500 ng/L to 2 mg/L. PFECHS was less likely to cause mortalities than PFOS for both the acute and chronic experiments based on previously published values for PFOS exposure, but exposure resulted in a similar incidence of deformities. Exposure to PFECHS also resulted in significantly increased abundance of transcripts of peroxisome proliferator activated receptor alpha (pparα), cytochrome p450 1a1 (cyp1a1), and apolipoprotein IV (apoaIV) at concentrations nearing those of environmental relevance. Overall, these results provide further insight into the safety of an emerging PFAS alternative in the aquatic environment and raise awareness that previously considered "safer" alternatives may show similar effects as legacy PFASs.
Freezing precipitation has major consequences for ground and air transportation, the health of citizens, and power networks. Previous studies using coarse resolution climate models have shown a northward migration of freezing rain in the future. Increased model resolution can better define local topography leading to improved representation of conditions that are favorable for freezing rain. The goal of this study is to examine the climatology and characteristics of future freezing rain events using very-high resolution climate simulations. Historical and pseudo-global warming simulations with a 4-km horizontal grid length were used and compared with available observations. Simulations revealed a northerly shift of freezing rain occurrence, and an increase in the winter. Freezing rain was still shown to occur in the Saint-Lawrence River Valley in a warmer climate, primarily due to stronger wind channeling. Up to 50% of the future freezing rain events also occurred in present day climate within 12 h of each other. In northern Maine, they are typically shorter than 6 h in current climate and longer than 6 h in warmer conditions due to the onset of precipitation during low-pressure systems occurrences. The occurrence of freezing rain also locally increases slightly north of Québec City in a warmer climate because of freezing rain that is produced by warm rain processes. Overall, the study shows that high-resolution regional climate simulations are needed to study freezing rain events in warmer climate conditions, because high horizontal resolutions better define small-scale topographic features and local physical mechanisms that have an influence on these events.
The exceedance probability of extreme daily precipitation is usually quantified assuming asymptotic behaviours. Non-asymptotic statistics, however, would allow us to describe extremes with reduced uncertainty and to establish relations between physical processes and emerging extremes. These approaches are still mistrusted by part of the community as they rely on assumptions on the tail behaviour of the daily precipitation distribution. This paper addresses this gap. We use global quality-controlled long rain gauge records to show that daily precipitation annual maxima are samples likely emerging from Weibull tails in most of the stations worldwide. These non-asymptotic tails can explain the statistics of observed extremes better than asymptotic approximations from extreme value theory. We call for a renewed consideration of non-asymptotic statistics for the description of extremes.
Abstract Space-based, global-extent digital elevation models (DEMs) are key inputs to many Earth sciences applications. However, many of these applications require the use of a ‘bare-Earth’ DEM versus a digital surface model (DSM), the latter of which may include systematic positive biases due to tree canopies in forested areas. Critical topographic features may be obscured by these biases. Vegetation-free datasets have been created by using statistical relationships and machine learning to train on local-scale datasets (e.g., lidar) to de-bias the global-extent datasets. Recent advances in satellite platforms coupled with increased availability of computational resources and lidar reference products has allowed for a new generation of vegetation- and urban-canopy removals. One of these is the Forest And Buildings removed Copernicus DEM (FABDEM), based on the most recent and most accurate global DSM Copernicus-30. Among the more challenging landscapes to quantify surface elevations are densely forested mountain catchments, where even airborne lidar applications struggle to capture surface returns. The increasing affordability and availability of UAV-based lidar platforms have resulted in new capacity to fly modest spatial extents with unrivalled point densities. These data allow an unprecedented ability to validate global sub-canopy DEMs against representative UAV-based lidar data. In this work, the FABDEM is validated against up-scaled lidar data in a steep and forested mountain catchment considering elevation, slope, and Terrain Position Index (TPI) metrics. Comparisons of FABDEM with SRTM, MERIT, and the Copernicus-30 dataset are made. It was found that the FABDEM had a 24% reduction in elevation RMSE and a 135% reduction in bias compared to the Copernicus-30 dataset. Overall, the FABDEM provides a clear improvement over existing deforested DEM products in complex mountain topography such as the MERIT DEM. This study supports the use of FABDEM in forested mountain catchments as the current best-in-class data product.
Estimates of near-surface wind speed and direction are key meteorological components for predicting many surface hydrometeorological processes that influence critical aspects of hydrological and biological systems. However, observations of near-surface wind are typically spatially sparse. The use of these sparse wind fields to force distributed models, such as hydrological models, is greatly complicated in complex terrain, such as mountain headwaters basins. In these regions, wind flows are heavily impacted by overlapping influences of terrain at different scales. This can have a great impact on calculations of evapotranspiration, snowmelt, and blowing snow transport and sublimation. The use of high-resolution atmospheric models allows for numerical weather prediction (NWP) model outputs to be dynamically downscaled. However, the computation burden for large spatial extents and long periods of time often precludes their use. Here, a wind-library approach is presented to aid in downscaling NWP outputs and terrain-correcting spatially interpolated observations. This approach preserves important spatial characteristics of the flow field at a fraction of the computational costs of even the simplest high-resolution atmospheric models. This approach improves on previous implementations by: scaling to large spatial extents O(1M km2); approximating lee-side effects; and fully automating the creation of the wind library. Overall, this approach was shown to have a third quartile RMSE of 1.8 and a third quartile RMSE of 58.2° versus a standalone diagnostic windflow model. The wind velocity estimates versus observations were better than existing empirical terrain-based estimates and computational savings were approximately 100-fold versus the diagnostic model.
Abstract Stochastic simulations of spatiotemporal patterns of hydroclimatic processes, such as precipitation, are needed to build alternative but equally plausible inputs for water‐related design and management, and to estimate uncertainty and assess risks. However, while existing stochastic simulation methods are mature enough to deal with relatively small domains and coarse spatiotemporal scales, additional work is required to develop simulation tools for large‐domain analyses, which are more and more common in an increasingly interconnected world. This study proposes a methodological advancement in the CoSMoS framework, which is a flexible simulation framework preserving arbitrary marginal distributions and correlations, to dramatically decrease the computational burden and make the algorithm fast enough to perform large‐domain simulations in short time. The proposed approach focuses on correlated processes with mixed (zero‐inflated) Uniform marginal distributions. These correlated processes act as intermediates between the target process to simulate (precipitation) and parent Gaussian processes that are the core of the simulation algorithm. Working in the mixed‐Uniform space enables a substantial simplification of the so‐called correlation transformation functions, which represent a computational bottle neck in the original CoSMoS formulation. As a proof of concept, we simulate 40 years of daily precipitation records from 1,000 gauging stations in the Mississippi River basin. Moreover, we extend CoSMoS incorporating parent non‐Gaussian processes with different degrees of tail dependence and suggest potential improvements including the separate simulation of occurrence and intensity processes, and the use of advection, anisotropy, and nonstationary spatiotemporal correlation functions.
Biomagnification of mercury (Hg) through lake food webs is understudied in rapidly changing northern regions, where wild-caught subsistence fish are critical to food security. We investigated estimates and among-lake variability of Hg biomagnification rates (BMR), relationships between Hg BMR and Hg levels in subsistence fish, and environmental drivers of Hg BMR in ten remote subarctic lakes in Northwest Territories, Canada. Lake-specific linear regressions between Hg concentrations (total Hg ([THg]) in fish and methyl Hg ([MeHg]) in primary consumers) and baseline-adjusted δ15N ratios were significant (p < 0.001, r2 = 0.58–0.88), indicating biomagnification of Hg through food webs of all studied lakes. Quantified using the slope of Hg-δ15N regressions, Hg BMR ranged from 0.16 to 0.25, with mean ± standard deviation of 0.20 ± 0.03). Using fish [MeHg] rather than [THg] lowered estimates of Hg BMR by ∼10%, suggesting that the use of [THg] as a proxy for [MeHg] in fish can influence estimates of Hg BMR. Among-lake variability of size-standardized [THg] in resident fish species from different trophic guilds, namely Lake Whitefish (Coregonus clupeaformis) and Northern Pike (Esox lucius), was not significantly explained by among-lake variability in Hg BMR. Stepwise multiple regressions indicated that among-lake variability of Hg BMR was best explained by a positive relationship with catchment forest cover (p = 0.009, r2 = 0.59), likely reflecting effects of forest cover on water chemistry of downstream lakes and ultimately, concentrations of biomagnifying MeHg (and percent MeHg of total Hg) in resident biota. These findings improve our understanding of Hg biomagnification in remote subarctic lakes.
The development of state-of-the-art convolutional neural networks (CNN) has allowed researchers to perform plant classification tasks previously thought impossible and rely on human judgment. Researchers often develop complex CNN models to achieve better performances, introducing over-parameterization and forcing the model to overfit on a training dataset. The most popular process for evaluating overfitting in a deep learning model is using accuracy and loss curves. Train and loss curves may help understand the performance of a model but do not provide guidance on how the model could be modified to attain better performance. In this article, we analyzed the relation between the features learned by a model and its capacity and showed that a model with higher representational capacity might learn many subtle features that may negatively affect its performance. Next, we showed that the shallow layers of a deep learning model learn more diverse features than the ones learned by the deeper layers. Finally, we propose SSIM cut curve, a new way to select the depth of a CNN model by using the pairwise similarity matrix between the visualization of the features learned at different depths by using Guided Backpropagation. We showed that our proposed method could potentially pave a new way to select a better CNN model.
Field-based assessment of transpiration phenology in boreal tree species is a significant challenge. Here we develop an objective approach that uses stem radius change and its correlation with sapwood temperature to determine the timing of phenological changes in transpiration in mixed evergreen species. We test the stem-temp approach using a five year stem-radius dataset from black spruce (Picea mariana) and jack pine (Pinus banksiana) trees in Saskatchewan (2016–2020). We further compare transpiration phenological transition dates from this approach with tower-based phenological assessment from green chromatic coordinate derived from phenocam images, eddy-covariance-derived evapotranspiration and carbon uptake, tower-based measurements of solar-induced chlorophyll fluorescence and snowmelt timing. The stem-temp approach identified the start and end of four key transpiration phenological phases: (i) the end of temperature-driven cycles indicating the start of biological activity, (ii) the onset of stem rehydration, (iii) the onset of transpiration, and (iv) the end of transpiration-driven cycles. The proposed method is thus useful for characterizing the timing of changes in transpiration phenology and provides information about distinct processes that cannot be assessed with canopy-level phenological measurements alone.
iWetland is a community science wetland water level monitoring platform developed by the McMaster Ecohydrology Lab and tested from 2016 to 2019 in wetlands located east of Georgian Bay, Ontario, Canada. The goal of iWetland is to engage community members in wetland science while collecting data to better understand the spatiotemporal variability in water level patterns of wetlands. We installed 24 iWetland water level monitoring stations in popular hiking and camping areas where visitors can text the water level of the wetland to an online database that automatically collates the data. Here, we share our approach for developing the iWetland community science platform and its importance for monitoring all types of wetland ecosystems. From 2016 through 2019, almost 2,000 individuals recorded more than 2,600 water table measurements. The iWetland platform successfully collected accurate water table data for 24 wetlands. We discuss the successes and shortcomings of the community science platform with respect to data collection, community engagement, and participation. We found that forming mutually beneficial partnerships with community groups paired with strong outreach presence were key to the success of this community science platform. Finally, we recommend that those interested in adopting the iWetland platform in their community partner with community groups, recognize participant contributions, identify accessible sites, and host outreach activities.
Wastewater monitoring and epidemiology have seen renewed interest during the recent COVID-19 pandemic. As a result, there is an increasing need to normalize wastewater-derived viral loads in local populations. Chemical tracers, both exogenous and endogenous compounds, have proven to be more stable and reliable for normalization than biological indicators. However, differing instrumentation and extraction methods can make it difficult to compare results. This review examines current extraction and quantification methods for ten common population indicators: creatinine, coprostanol, nicotine, cotinine, sucralose, acesulfame, androstenedione 5-hydroindoleacetic acid (5-HIAA), caffeine, and 1,7-dimethyluric acid. Some wastewater parameters such as ammonia, total nitrogen, total phosphorus, and daily flowrate were also evaluated. The analytical methods included direct injection, dilute and shoot, liquid/liquid, and solid phase extraction (SPE). Creatine, acesulfame, nicotine, 5-HIAA and androstenedione have been analysed by direct injection into LC-MS; however, most authors prefer to include SPE steps to avoid matrix effects. Both LC-MS and GC-MS have been successfully used to quantify coprostanol in wastewater, and the other selected indicators have been quantified successfully with LC-MS. Acidification to stabilize the sample before freezing to maintain the integrity of samples has been reported to be beneficial. However, there are arguments both for and against working at acidic pHs. Wastewater parameters mentioned earlier are quick and easy to quantify, but the data does not always represent the human population effectively. A preference for population indicators originating solely from humans is apparent. This review summarises methods employed for chemical indicators in wastewater, provides a basis for choosing an appropriate extraction and analysis method, and highlights the utility of accurate chemical tracer data for wastewater-based epidemiology.
Flow management has the potential to significantly affect ecosystem condition. Shallow lakes in arid regions are especially susceptible to flow management changes, which can have important implications for the formation of cyanobacterial blooms. Here, we reveal water quality shifts associated with changing source water inflow management. Using in situ monitoring data, we studied a seven-year time span during which inflows to a shallow, eutrophic drinking water reservoir transitioned from primarily natural landscape runoff (2014–2015) to managed flows from a larger upstream reservoir (Lake Diefenbaker; 2016–2020) and identified significant changes in cyanobacteria (as phycocyanin) using generalized additive models to classify cyanobacterial bloom formation. We then connected changes in water source with shifts in chemistry and the occurrence of cyanobacterial blooms using principal components analysis. Phycocyanin was greater in years with managed reservoir inflow from a mesotrophic upstream reservoir (2016–2020), but dissolved organic matter (DOM) and specific conductivity, important determinants of drinking water quality, were greatest in years when landscape runoff dominated lake water source (2014–2015). Most notably, despite changing rapidly, it took multiple years for lake water to return to a consistent and reduced level of DOM after managed inflows from the upstream reservoir were resumed, an observation that underscores how resilience may be hindered by weak resistance to change and slow recovery. Environmental flows for water quality are rarely defined, yet we show that trade-offs exist between poor water quality via elevated conductivity and DOM and higher bloom risk, depending on water source. Our work highlights the importance of source water quality, not just quantity, to water security, and our findings have important implications for water managers who must protect ecosystem services while adapting to projected hydroclimatic change.
Abstract Transpiration is a globally important component of evapotranspiration. Careful upscaling of transpiration from point measurements is thus crucial for quantifying water and energy fluxes. In spatially heterogeneous landscapes common across the boreal biome, upscaled transpiration estimates are difficult to determine due to variation in local environmental conditions (e.g., basal area, soil moisture, permafrost). Here, we sought to determine stand‐level attributes that influence transpiration scalars for a forested boreal peatland complex consisting of sparsely treed wetlands and densely treed permafrost plateaus as land cover types. The objectives were to quantify spatial and temporal variability in stand‐level transpiration, and to identify sources of uncertainty when scaling point measurements to the stand‐level. Using heat ratio method sap flow sensors, we determined sap velocity for black spruce and tamarack for 2‐week periods during peak growing season in 2013, 2017 and 2018. We found greater basal area, drier soils, and the presence of permafrost increased daily sap velocity in individual trees, suggesting that local environmental conditions are important in dictating sap velocity. When sap velocity was scaled to stand‐level transpiration using gridded 20 × 20 m resolution data across the ~10 ha Scotty Creek ForestGEO plot, we observed significant differences in daily plot transpiration among years (0.17–0.30 mm), and across land cover types. Daily transpiration was lowest in grid‐cells with sparsely treed wetlands compared to grid‐cells with well‐drained and densely treed permafrost plateaus, where daily transpiration reached 0.80 mm, or 30% of the daily evapotranspiration. When transpiration scalars (i.e., sap velocity) were not specific to the different land cover types (i.e., permafrost plateaus and wetlands), scaled stand‐level transpiration was overestimated by 42%. To quantify the relative contribution of tree transpiration to ecosystem evapotranspiration, we recommend that sampling designs stratify across local environmental conditions to accurately represent variation associated with land cover types, especially with different hydrological functioning as encountered in rapidly thawing boreal peatland complexes.
Mollisols support the most productive agroecosystems in the world. Despite their critical links to food quality and human health, the varying distributions of selenium (Se) species and factors governing Se mobility in the mollisol vadose zone remain elusive. This research reveals that, in northern mollisol agroecosystems, Se hotspots (≥0.32 mg/kg) prevail along the regional river systems draining the Lesser Khingan Mountains, where piedmont Se-rich oil shales are the most probable source of regional Se. While selenate and selenite dominate Se species in the water-soluble and absorbed pools, mollisol organic matter is the major host for Se. Poorly crystalline and crystalline Fe oxides are subordinate in Se retention, hosting inorganic and organic Se at levels comparable to those in the adsorbed pool. The depth-dependent distributions of mollisol Se species for the non-cropland and cropland sites imply a predominance of reduced forms of Se under the mildly acidic and reducing conditions that, in turn, are variably impacted by agricultural land use. These findings therefore highlight that fluvial deposition and land use change together are the main drivers of the spatial variability and speciation of mollisol Se.
The South Saskatchewan River (SSR) is one of the most important river systems in Saskatchewan and, arguably, in Canada. Most of the Saskatchewan residents, industries, and powerplants depend on the SSR for their water requirements. An established 1D modelling approach was chosen and coupled with the Hydrologic Engineering Center’s River Analysis System (HEC-RAS). The WASP (Water Quality Analysis Simulation Program) stream transport module, TOXI, is coupled with flow routing for free-flow streams, ponded segments, and backwater reaches and is capable of calculating the flow of water, sediment, and dissolved constituents across branched and ponded segments. Copper and nickel were chosen as two metals with predominantly anthropogenic (agriculture, mining, and municipal and industrial waste management) and geogenic (natural weathering and erosion) sources, respectively. Analysis was carried out at ten different sites along the South Saskatchewan River, both upstream and downstream of the City of Saskatoon, in the years 2020 and 2021. Model performance was evaluated by comparing model predictions with concentrations of copper and nickel measured in a previously published study. The model performed well in estimating the concentrations of copper and nickel in water samples and worked reasonably well for sediment samples. The model underestimated the concentration values at certain segments in both water and sediment samples. In order to calibrate the model more accurately, extra diffusive contaminant loads were added. While several default parameter values had to be used due to the unavailability of primary historical data, our study demonstrates the predictive power of combining WASP—TOXI and HEC-RAS models for the prediction of contaminant loading. Future studies, including those on the impacts of global climate change on water quality on the Canadian prairies, will benefit from this proof-of-concept study.
The seasonal dynamics of freshwater lake ice and its interactions with air and snow are studied in two small subarctic lakes with comparable surface areas but contrasting depths (4.3 versus 91 m). Two, 2.9 m long thermistor chain sensors (Snow and Ice Mass Balance Apparatuses), were used to remotely measure air, snow, ice, and water temperatures every 15-min between December 2021 and March 2022. Results showed that freeze-up occurred later in the deeper lake (Ryan Lake) and earlier in the shallow lake (Landing Lake). Ice growth was significantly faster in Ryan Lake than in Landing Lake, due to cold water temperatures (mean (Tw¯) =0.65 to 0.96°C) persisting beneath the ice. In Landing Lake, basal ice growth was hindered because of warm water temperatures (Tw¯=1.5 to 2.1°C) caused by heat released from lake sediments. Variability in air temperatures at both lakes had significant influences on the thermal regimes of ice and snow, particularly in Ryan Lake, where ice temperatures were more sensitive to rapid changes in air temperatures. This finding suggests that conductive heat transfer through the air-water continuum may be more sensitive to variability in air temperatures in deeper lakes with colder water temperatures than in shallow lakes with warmer water temperatures, if snow depths and densities are comparable. This study highlights the significance of lake morphology and rapid air temperature variability on influencing ice growth processes. Conclusions drawn aim to improve the representation of ice growth processes in regional and global climate models, and to improve ice safety for northern communities.
Bias correction methods are used to adjust simulations from global and regional climate models to use them in informed decision-making. Here we introduce a semi-parametric quantile mapping (SPQM) method to bias-correct daily precipitation. This method uses a parametric probability distribution to describe observations and an empirical distribution for simulations. Bias-correction techniques typically adjust the bias between observation and historical simulations to correct projections. The SPQM however corrects simulations based only on observations assuming the detrended simulations have the same distribution as the observations. Thus, the bias-corrected simulations preserve the climate change signal, including changes in the magnitude and probability dry, and guarantee a smooth transition from observations to future simulations. The results are compared with popular quantile mapping techniques, that is, the quantile delta mapping (QDM) and the statistical transformation of the CDF using splines (SSPLINE). The SPQM performed well in reproducing the observed statistics, marginal distribution, and wet and dry spells. Comparatively, it performed at least equally well as the QDM and SSPLINE, specifically in reproducing observed wet spells and extreme quantiles. The method is further tested in a basin-scale region. The spatial variability and statistics of the observed precipitation are reproduced well in the bias-corrected simulations. Overall, the SPQM is easy to apply, yet robust in bias-correcting daily precipitation simulations.
Cold regions are warming much faster than the global average, resulting in more frequent and intense freeze-thaw cycles (FTCs) in soils. In hydrocarbon-contaminated soils, FTCs modify the biogeochemical and physical processes controlling petroleum hydrocarbon (PHC) biodegradation and the associated generation of methane (CH4) and carbon dioxide (CO2). Thus, understanding the effects of FTCs on the biodegradation of PHCs is critical for environmental risk assessment and the design of remediation strategies for contaminated soils in cold regions. In this study, we developed a diffusion-reaction model that accounts for the effects of FTCs on toluene biodegradation, including methanogenic biodegradation. The model is verified against data generated in a 215 day-long batch experiment with soil collected from a PHC contaminated site in Ontario, Canada. The fully saturated soil incubations with six different treatments were exposed to successive 4-week FTCs, with temperatures oscillating between −10 °C and +15 °C, under anoxic conditions to stimulate methanogenic biodegradation. We measured the headspace concentrations and 13C isotope compositions of CH4 and CO2 and analyzed the porewater for pH, acetate, dissolved organic and inorganic carbon, and toluene. The numerical model represents solute diffusion, volatilization, sorption, as well as a reaction network of 13 biogeochemical processes. The model successfully simulates the soil porewater and headspace concentration time series data by representing the temperature dependencies of microbial reaction and gas diffusion rates during FTCs. According to the model results, the observed increases in the headspace concentrations of CH4 and CO2 by 87% and 136%, respectively, following toluene addition are explained by toluene fermentation and subsequent methanogenesis reactions. The experiment and the numerical simulation show that methanogenic degradation is the primary toluene attenuation mechanism under the electron acceptor-limited conditions experienced by the soil samples, representing 74% of the attenuation, with sorption contributing to 11%, and evaporation contributing to 15%. Also, the model-predicted contribution of acetate-based methanogenesis to total produced CH4 agrees with that derived from the 13C isotope data. The freezing-induced soil matrix organic carbon release is considered as an important process causing DOC increase following each freezing period according to the calculations of carbon balance and SUVA index. The simulation results of a no FTC scenario indicate that, in the absence of FTCs, CO2 and CH4 generation would decrease by 29% and 26%, respectively, and that toluene would be biodegraded 23% faster than in the FTC scenario. Because our modeling approach represents the dominant processes controlling PHC biodegradation and the associated CH4 and CO2 fluxes, it can be used to analyze the sensitivity of these processes to FTC frequency and duration driven by temperature fluctuations.
Despite being the largest freshwater lake system in the world, relatively little is known about the sestonic microbial community structure in the Laurentian Great Lakes. The goal of this research was to better understand this ecosystem using high-throughput sequencing of microbial communities as a function of water depth at six locations in the westernmost Great Lakes of Superior and Michigan. The water column was characterized by gradients in temperature, dissolved oxygen (DO), pH, and other physicochemical parameters with depth. Mean nitrate concentrations were 32 μmol/L, with only slight variation within and between the lakes, and with depth. Mean available phosphorus was 0.07 μmol/L, resulting in relatively large N:P ratios (97:1) indicative of P limitation. Abundances of the phyla Actinobacteria, Bacteroidetes, Cyanobacteria, Thaumarchaeota, and Verrucomicrobia differed significantly among the Lakes. Candidatus Nitrosopumilus was present in greater abundance in Lake Superior compared to Lake Michigan, suggesting the importance of ammonia-oxidating archaea in water column N cycling in Lake Superior. The Shannon diversity index was negatively correlated with pH, temperature, and salinity, and positively correlated with DO, latitude, and N2 saturation. Results of this study suggest that DO, pH, temperature, and salinity were major drivers shaping the community composition in the Great Lakes.
Glacier mass loss affects sea level rise, water resources, and natural hazards. We present global glacier projections, excluding the ice sheets, for shared socioeconomic pathways calibrated with data for each glacier. Glaciers are projected to lose 26 ± 6% (+1.5°C) to 41 ± 11% (+4°C) of their mass by 2100, relative to 2015, for global temperature change scenarios. This corresponds to 90 ± 26 to 154 ± 44 millimeters sea level equivalent and will cause 49 ± 9 to 83 ± 7% of glaciers to disappear. Mass loss is linearly related to temperature increase and thus reductions in temperature increase reduce mass loss. Based on climate pledges from the Conference of the Parties (COP26), global mean temperature is projected to increase by +2.7°C, which would lead to a sea level contribution of 115 ± 40 millimeters and cause widespread deglaciation in most mid-latitude regions by 2100.
The real-time polymerase chain reaction (PCR), commonly known as quantitative PCR (qPCR), is increasingly common in environmental microbiology applications. During the COVID-19 pandemic, qPCR combined with reverse transcription (RT-qPCR) has been used to detect and quantify SARS-CoV-2 in clinical diagnoses and wastewater monitoring of local trends. Estimation of concentrations using qPCR often features a log-linear standard curve model calibrating quantification cycle (Cq) values obtained from underlying fluorescence measurements to standard concentrations. This process works well at high concentrations within a linear dynamic range but has diminishing reliability at low concentrations because it cannot explain "non-standard" data such as Cq values reflecting increasing variability at low concentrations or non-detects that do not yield Cq values at all. Here, fundamental probabilistic modeling concepts from classical quantitative microbiology were integrated into standard curve modeling approaches by reflecting well-understood mechanisms for random error in microbial data. This work showed that data diverging from the log-linear regression model at low concentrations as well as non-detects can be seamlessly integrated into enhanced standard curve analysis. The newly developed model provides improved representation of standard curve data at low concentrations while converging asymptotically upon conventional log-linear regression at high concentrations and adding no fitting parameters. Such modeling facilitates exploration of the effects of various random error mechanisms in experiments generating standard curve data, enables quantification of uncertainty in standard curve parameters, and is an important step toward quantifying uncertainty in qPCR-based concentration estimates. Improving understanding of the random error in qPCR data and standard curve modeling is especially important when low concentrations are of particular interest and inappropriate analysis can unduly affect interpretation, conclusions regarding lab performance, reported concentration estimates, and associated decision-making.
This study proposes a reservoir operation optimization framework to maximize the regional agricultural profit under the constraints of downstream environmental flow requirements and climate change. Three climate change models—CanESM2, MIROC5, and NorESM1-M—and the soil and water assessment tool (SWAT) were used to simulate the reservoir inflow in future periods under uncertainty. Minimum and ideal environmental flow regimes were embedded in the structure of the reservoir operation model to optimize the environmental flow needs and water supply and assess their tradeoffs. Cropping pattern optimization was used to maximize farmer profit. Particle swarm optimization was applied in the optimization processes. The method was applied to a case study in the Tajan River basin, Iran, with the results showing the environmental flow regime considerably reduces irrigation supply and has significant impacts on farmer profits. The results showed that cropping pattern optimization was not an effective strategy to mitigate the economic impacts of climate change under environmental flow constraints, but this assessment may not be generalized to other areas. Uncertainties related to the climate change models are a notable weakness of the approach and should be considered in future studies.
The present study proposes and evaluates an integrated optimization framework for agricultural planning in which an environmental flow model, drought analysis, cropping pattern model, and deficit irrigation functions are linked. Fuzzy physical habitat simulation was used to assess the environmental flow regime. A regression model was applied to develop the deficit irrigation functions. Average river flow time series in three hydrological conditions (dry, normal, and wet) were obtained using drought analysis. The environmental flow model, cropping pattern model, deficit irrigation functions, and river flow time series were then used in the structure of the optimization model. The goal of the optimization model is to provide an agricultural plan, including optimal cropping patterns and irrigation supply that minimizes ecological impacts on the river ecosystem. A genetic algorithm was used in the optimization process. Based on case study results, the proposed model is able to minimize ecological impacts on the river ecosystem in all hydrological conditions and propose an optimal plan for cropping patterns and irrigation supply. The difference between average revenue in the optimal plan and current conditions in all simulated hydrological conditions is less than 10%, which means the optimization system provides a sustainable plan for agricultural and environmental management.
Monod growth kinetics predictions of competition outcomes between freshwater cyanobacteria and chlorophytes at low iron (Fe) was tested with dual-species competition experiments. Fe threshold concentrations (FeT) below which growth ceases and growth affinities (slope of Fe concentration vs growth rate near FeT) for three large-bodied cyanobacteria and two chlorophytes in batch cultures showed that cyanobacteria are more efficient at acquiring Fe and predicted that cyanobacteria will dominate chlorophytes at low Fe, similar to an earlier study where cyanobacteria were more efficient at acquiring phosphorus (P) at low P. The prediction of cyanobacteria dominance at low Fe was borne out in serial dilution competition experiments between a pico-cyanobacteria and a third chlorophyte. These results show that Monod kinetics can successfully predict competition outcomes between cyanobacteria and eukaryotic algae in a laboratory setting at low Fe. However, while nutrient acquisition and growth kinetics are clearly important, other factors also influence competition between pico-cyanobacteria, large-bodied cyanobacteria, and eukaryotic algae in natural systems. These factors include the effect of cell surface area/volume ratio on cellular nutrient supply rates, cyanobacteria dependence on membrane transport of Fe+2, Fe+2 supply from anaerobic sediments, buoyancy regulation, and intensive grazing of pico-cyanobacteria.
Microcystins (MCs) produced by some cyanobacteria can cause toxicity in animals and humans. In recent years, growing evidence suggests that MCs can act as endocrine disruptors. This research systematically investigated effects of microcystin-LR (MC-LR) on endocrine organs, biosynthesis of hormones and positive/negative feedback of the endocrine system in rats. Male, Sprague-Dawley rats were acutely administrated MC-LR by a single intraperitoneal injection at doses of 45, 67.5 or 90 μg MC-LR/kg body mass (bm), and then euthanized 24 h after exposure. In exposed rats, histological damage of hypothalamus, pituitary, adrenal, testis and thyroid were observed. Serum concentrations of corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH) and corticosterone (CORT), expressions of genes and proteins for biosynthesis of hormones were lesser, which indicated an overall suppression of the hypothalamus-pituitary-adrenal (HPA) axis. Along the hypothalamus-pituitary-gonadal (HPG) axis, lesser concentrations of gonadotropin-releasing hormone (GnRH) and testosterone (T), but greater concentrations of luteinizing hormone (LH), follicle-stimulating hormone (FSH) and estradiol (E2) were observed. Except for greater transcription of cyp19a1 in testes, transcriptions of genes and proteins for T and E2 biosynthesis along the HPG axis were lesser. As for the hypothalamus-pituitary-thyroid (HPT) axis, after MCs treatment, greater concentrations of thyroid-stimulating hormone (TSH), but lesser concentrations of free tri-iodothyronine (fT3) were observed in serum. Concentrations of free tetra-iodothyronine (fT4) were greater in rats dosed with 45 μg MCs/kg, bm, but lesser in rats dosed with 67.5 or 90 μg MCs/kg, bm. Transcripts of genes for biosynthesis of hormones and receptors along the HPT axis and expressions of proteins for biosynthesis of tetra-iodothyronine (T4) and tri-iodothyronine (T3) in thyroid were significantly altered. Cross-talk among the HPA, HPG and HPT axes probably occurred. It was concluded that MCs caused an imbalance of positive and negative feedback of hormonal regulatory axes, blocked biosynthesis of key hormones and exhibited endocrine-disrupting effects.
Abstract Research and shared interest in atmospheric rivers (ARs) have increased significantly in recent years, alongside technological improvements that allow better comprehension of these storms. The Nechako River Basin (NRB) in British Columbia, Canada, is significantly affected by ARs originating in the Pacific Ocean. This work analyses the frequency, intensity, duration and trends of ARs in two regions (South and North) near the NRB. Analyses are based on data provided by an updated AR catalogue. The AR catalogue is matched on a daily scale to an adaptation of the AR scale to compile so‐called AR‐Days (ARDs). In the South region, ARDs exhibit stronger associations with hydroclimatic variables total precipitation, rain, snow and snow depth water equivalent (SWE). The Mann–Kendall (MK) trend test was applied to 364 time series created by combining the two closest AR‐monitored regions to the NRB with the annual and seasonal scales of climate data and the adapted AR scale (ARD0‐ARD5). Results show higher AR frequency of mainly beneficial ARDs during fall and a significant reduction of ARD1‐ARD3 in both analysed regions. Rain and total precipitation related to ARD2‐ARD3 also present significant decreasing trends for most sub‐basins of the NRB. The MK test shows a shift in water contribution from total precipitation and rainfall linked to more potentially dangerous ARDs to short‐duration, beneficial ARDs (ARD0). Rain from non‐AR‐related meteorological systems presents an increasing trend for the Upper Nechako sub‐basin, where the Nechako Reservoir is located. Trends are mainly for AR‐related total precipitation and rainfall, and in the northern part of the NRB, results point to the increase of AR‐related SWE.
Differential impacts of policies or changes in environmental conditions on people is a growing area of interest to decision-makers, yet remains an often neglected area of study for the environmental valuation literature. Using data from a large national survey of over 24,000 people conducted in Canada, this paper implements a latent class Kuhn-Tucker recreation demand model to assess differences in preferences and values for nature-based activities. Preferences are disaggregated by self-reported Indigeneity, immigration status, and gender. We find that Indigenous people receive 63% greater benefits from participating in nature-based activities compared to non-Indigenous people living in Canada. Immigrants have the lowest participation in, and benefits associated with, nature-based activities. Similarly, women receive 21% lesser benefits associated with nature-based activities when compared to men. These results demonstrate that Indigenous peoples may be more vulnerable to adverse impacts on nature-based activities such as land-use changes, climate change, and government policies. The study also highlights the importance of disaggregated data and incorporating aspects of identity in the ecosystem service literature towards more equitable decision-making and reconciliation.
The community of Délı̨nę, located in the UNESCO Tsá Tué Biosphere Reserve, is experiencing the impacts of climate change on the lands surrounding Great Bear Lake, in Northwest Territories, Canada. These impacts are limiting the community's ability to access the land to support their food system, which depends on harvesting traditional foods. This article details a participatory action research approach, driven by the community, that used on-the-land activities, workshops, community meetings and interviews to develop a community food security action plan to deal with the uncertainties of a changing climate on the food system. Data was analyzed using the Community Capitals Framework (CCF) to describe the complex nature of the community's food system in terms of available or depleting capitals, as well as how the impacts of climate change affect these capitals, and the needs identified by the community to aid in adaptation. For Délı̨nę, the theme of self-sufficiency emerged out of concerns that climate change is negatively impacting supplies from the south and that building and maintaining both social and cultural capital are key to achieving food security in an uncertain future. Learning from the past and sharing Traditional Knowledge 1 was a key element of food security planning. However, other types of knowledge, such as research and monitoring of the health of the land, and building capacity of the community through training, were important aspects of adaptation planning in the community. This knowledge, in its many forms, may assist the community in determining its own direction for achieving food security, and offers a glimpse into food sovereignty in Northern regions.
Abstract Temperatures near 0°C represent a critical threshold for many environmental processes and socio‐economic activities. This study examines surface air temperatures ( T ) near 0°C (−2°C ≤ T ≤ 2°C) across much of southern Canada over a 13 year period (October 2000–September 2013). It utilized hourly data from 39 weather stations and from 4‐km resolution Weather Research and Forecasting model simulations that were both a retrospective simulation as well as a pseudo‐global warming simulation applicable near the end of the 21st century. Average annual occurrences of near‐0°C conditions increase by a relatively small amount of 5.1% from 985 hr in the current climate to 1,035 hr within the future one. Near‐0°C occurrences with precipitation vary from <5% to approximately 50% of these values. Near‐0°C occurrences are sometimes higher than values of neighboring temperatures. These near‐0°C peaks in temperature distributions can occur in both the current and future climate, in only one, or in neither. Only 4.3% of southern Canada is not associated with a near‐0°C peak and 65.8% is associated with a near‐0°C peak in both climates. It is inferred that latent heat exchanges from the melting and freezing of, for example, precipitation and the snowpack contribute significantly to some of these findings.
Solid precipitation falling near 0 °C, mainly snow, can adhere to surface features and produce major impacts. This study is concerned with characterizing this precipitation over the Canadian Prairie provinces of Manitoba and Saskatchewan in the current (2000–2013) and pseudo-global warming future climate, with an average 5.9 °C temperature increase, through the use of high resolution (4 km) model simulations. On average, simulations in the current climate suggest that this precipitation occurs within 11 events per year, lasting 33.6 h in total and producing 27.5 mm melted equivalent, but there are wide spatial variations that are partly due to enhancements arising from its relatively low terrain. Within the warmer climate, average values generally increase, and spatial patterns shift somewhat. This precipitation consists of four categories covering its occurrence just below and just above a wet-bulb temperature of 0 °C, and with or without liquid precipitation. It generally peaks in March or April, as well as in October, and these peaks move towards mid-winter by approximately one month within the warmer climate. Storms producing this precipitation generally produce winds with a northerly component during or shortly after the precipitation; these winds contribute to further damage. Overall, this study has determined the features of and expected changes to adhering precipitation across this region.
In mountains, the precipitation phase greatly varies in space and time and affects the evolution of the snow cover. Snowpack models usually rely on precipitation-phase partitioning methods (PPMs) that use near-surface variables. These PPMs ignore conditions above the surface thus limiting their ability to predict the precipitation phase at the surface. In this study, the impact on snowpack simulations of atmospheric-based PPMs, incorporating upper atmospheric information, is tested using the snowpack scheme Crocus. Crocus is run at 2.5-km grid spacing over the mountains of southwestern Canada and northwestern United States and is driven by meteorological fields from an atmospheric model at the same resolution. Two atmospheric-based PPMs were considered from the atmospheric model: the output from a detailed microphysics scheme and a post-processing algorithm determining the snow level and the associated precipitation phase. Two ground-based PPMs were also included as lower and upper benchmarks: a single air temperature threshold at 0°C and a PPM using wet-bulb temperature. Compared to the upper benchmark, the snow-level based PPM improved the estimation of snowfall occurrence by 5% and the simulation of snow water equivalent (SWE) by 9% during the snow melting season. In contrast, due to missing processes, the microphysics scheme decreased performances in phase estimate and SWE simulations compared to the upper benchmark. These results highlight the need for detailed evaluation of the precipitation phase from atmospheric models and the benefit for mountain snow hydrology of the post-processed snow level. The limitations to drive snowpack models at slope scale are also discussed.
Wetlands are the largest natural source of methane (CH4 ) to the atmosphere. The eddy covariance method provides robust measurements of net ecosystem exchange of CH4 , but interpreting its spatiotemporal variations is challenging due to the co-occurrence of CH4 production, oxidation, and transport dynamics. Here, we estimate these three processes using a data-model fusion approach across 25 wetlands in temperate, boreal, and Arctic regions. Our data-constrained model-iPEACE-reasonably reproduced CH4 emissions at 19 of the 25 sites with normalized root mean square error of 0.59, correlation coefficient of 0.82, and normalized standard deviation of 0.87. Among the three processes, CH4 production appeared to be the most important process, followed by oxidation in explaining inter-site variations in CH4 emissions. Based on a sensitivity analysis, CH4 emissions were generally more sensitive to decreased water table than to increased gross primary productivity or soil temperature. For periods with leaf area index (LAI) of ≥20% of its annual peak, plant-mediated transport appeared to be the major pathway for CH4 transport. Contributions from ebullition and diffusion were relatively high during low LAI (<20%) periods. The lag time between CH4 production and CH4 emissions tended to be short in fen sites (3 ± 2 days) and long in bog sites (13 ± 10 days). Based on a principal component analysis, we found that parameters for CH4 production, plant-mediated transport, and diffusion through water explained 77% of the variance in the parameters across the 19 sites, highlighting the importance of these parameters for predicting wetland CH4 emissions across biomes. These processes and associated parameters for CH4 emissions among and within the wetlands provide useful insights for interpreting observed net CH4 fluxes, estimating sensitivities to biophysical variables, and modeling global CH4 fluxes.
Abstract Arctic wetlands are known methane (CH 4 ) emitters but recent studies suggest that the Arctic CH 4 sink strength may be underestimated. Here we explore the capacity of well-drained Arctic soils to consume atmospheric CH 4 using >40,000 hourly flux observations and spatially distributed flux measurements from 4 sites and 14 surface types. While consumption of atmospheric CH 4 occurred at all sites at rates of 0.092 ± 0.011 mgCH 4 m −2 h −1 (mean ± s.e.), CH 4 uptake displayed distinct diel and seasonal patterns reflecting ecosystem respiration. Combining in situ flux data with laboratory investigations and a machine learning approach, we find biotic drivers to be highly important. Soil moisture outweighed temperature as an abiotic control and higher CH 4 uptake was linked to increased availability of labile carbon. Our findings imply that soil drying and enhanced nutrient supply will promote CH 4 uptake by Arctic soils, providing a negative feedback to global climate change.
Peatland microtopography contains hummocks (local high points) and hollows (local low points). Little is known about how the evaporation of peat (P), peat-bryophyte (BP), peat-litter (LP) and peat-bryophyte-litter (LBP) columns varies with peatland microforms. That is, whether there are fine-scale variations in peatland evaporation, and if they are critical when being upscaled to the entire peatland ecosystem is yet to be answered. This study found that evaporation was significantly affected by cover type (P, BP, LP or LBP) and the interaction effect of the cover type and microform, based on the field evaporation experiments in a montane peatland in the Canadian Rocky Mountains, during the growing season of 2021. Mean daily evaporation of P-Hummock and P-Hollow is 14.16 and 11.76 g day−1, respectively; BP-Hummock and BP-Hollow is 9.57 and 14.38 g day−1, respectively; LBP-Hummock and LBP-Hollow is 9.44 and 9.91 g day−1, respectively; and evaporation of LP-Hummock and LP-Hollow is 5.68 and 7.64 g day−1, respectively. Peatland microform indirectly affected evaporation through interactions with cover type, modifying the vertical profile of soil temperature and changing key environmental drivers of evaporation. Moreover, the ability of two widely used models in modelling the spatial variation of peatland evaporation was also tested. It was found that Penman–Monteith (P–M) model and the bryophyte layer model in the Atmosphere-Plant Exchange Simulator (APES) were able to yield satisfactory results based on field measurements of soil temperature and soil moisture. This study supports developing more practical evaluation tools on the hydrological state of peatland ecosystems.
Abstract. Northern peatlands cover approximately four million km2, and about half of these peatlands are estimated to contain permafrost and periglacial landforms, like palsas and peat plateaus. In northeastern Canada, peatland permafrost is predicted to be concentrated in the western interior of Labrador but is assumed to be largely absent along the Labrador Sea coastline. However, the paucity of observations of peatland permafrost in the interior, coupled with traditional and ongoing use of perennially frozen peatlands along the coast by Labrador Inuit and Innu, suggests a need for re-evaluation of the reliability of existing peatland permafrost distribution estimates for the region. In this study, we develop a multi-stage consensus-based point inventory of peatland permafrost complexes in coastal Labrador and adjacent parts of Quebec using high-resolution satellite imagery, and we validate it with extensive field visits and low-altitude aerial photography and videography. A subset of 2092 wetland complexes that potentially contained peatland permafrost were inventoried, of which 1119 were classified as likely containing peatland permafrost. Likely peatland permafrost complexes were mostly found in lowlands within 22 km of the coastline, where mean annual air temperatures often exceed +1 ∘C. A clear gradient in peatland permafrost distribution exists from the outer coasts, where peatland permafrost is more abundant, to inland peatlands, where permafrost is generally absent. This coastal gradient may be attributed to a combination of climatic and geomorphological influences which lead to lower insolation, thinner snowpacks, and poorly drained, frost-susceptible materials along the coast. The results of this study suggest that existing estimates of permafrost distribution for southeastern Labrador require adjustments to better reflect the abundance of peatland permafrost complexes to the south of the regional sporadic discontinuous permafrost limit. This study constitutes the first dedicated peatland permafrost inventory for Labrador and provides an important baseline for future mapping, modelling, and climate change adaptation strategy development in the region.
Arctic-boreal landscapes are experiencing profound warming, along with changes in ecosystem moisture status and disturbance from fire. This region is of global importance in terms of carbon feedbacks to climate, yet the sign (sink or source) and magnitude of the Arctic-boreal carbon budget within recent years remains highly uncertain. Here, we provide new estimates of recent (2003-2015) vegetation gross primary productivity (GPP), ecosystem respiration (Reco ), net ecosystem CO2 exchange (NEE; Reco - GPP), and terrestrial methane (CH4 ) emissions for the Arctic-boreal zone using a satellite data-driven process-model for northern ecosystems (TCFM-Arctic), calibrated and evaluated using measurements from >60 tower eddy covariance (EC) sites. We used TCFM-Arctic to obtain daily 1-km2 flux estimates and annual carbon budgets for the pan-Arctic-boreal region. Across the domain, the model indicated an overall average NEE sink of -850 Tg CO2 -C year-1 . Eurasian boreal zones, especially those in Siberia, contributed to a majority of the net sink. In contrast, the tundra biome was relatively carbon neutral (ranging from small sink to source). Regional CH4 emissions from tundra and boreal wetlands (not accounting for aquatic CH4 ) were estimated at 35 Tg CH4 -C year-1 . Accounting for additional emissions from open water aquatic bodies and from fire, using available estimates from the literature, reduced the total regional NEE sink by 21% and shifted many far northern tundra landscapes, and some boreal forests, to a net carbon source. This assessment, based on in situ observations and models, improves our understanding of the high-latitude carbon status and also indicates a continued need for integrated site-to-regional assessments to monitor the vulnerability of these ecosystems to climate change.
Abstract Depression focused recharge (DFR) may be a hydrologically important process that impacts the vulnerability of public supply wells, specifically related to pathogenic contaminants. The nature of DFR in glacial moraine environments, such as those located in northern latitudes within North America and Europe, is less well established than in other regions such as the Prairie Pothole Region (Northern United States, Western Canada) and the High Plains Aquifer (Central United States). The objectives of this study were to quantify seasonal infiltration flux beneath a topographically‐closed depression within 50 m of a public supply well and to interpret the impact of this DFR process on well vulnerability. Field instruments including groundwater monitoring wells, pressure transducers, soil moisture sensors and temperature sensors were installed in vertical clusters to capture the dynamics of infiltration, drainage and recharge within the depression feature. Continuous weather data were recorded by a meteorological station at the site. Transient infiltration was quantified during two contrasting hydrological events. The first event (~2 days) was an intense rainfall (>50 mm) on a melting snowpack during the fall season when the soils were unfrozen. The second was a longer (35 day) period during the spring freshet when the surficial soils were initially frozen and subject to diurnal freezing and thawing and occasional precipitation events. The water table fluctuation method augmented by Darcy flux contributions, in addition to numerical modelling using the HYDRUS‐1D model, were used to quantify recharge rates beneath the depression. Numerical DFR estimates and analytical results differed by ±8%. Results indicate that recharge rates on the order of the annual regional average can occur beneath localized features in response to extreme events associated with snowmelt and intense rainfall. Such events may represent a microbial threat to groundwater quality if public supply wells are located nearby.
The northern peatland carbon sink plays a vital role in climate regulation; however, the future of the carbon sink is uncertain, in part, due to the changing interactions of peatlands and wildfire. Here, we use empirical datasets from natural, degraded and restored peatlands in non-permafrost boreal and temperate regions to model net ecosystem exchange and methane fluxes, integrating peatland degradation status, wildfire combustion and post-fire dynamics. We find that wildfire processes reduced carbon uptake in pristine peatlands by 35% and further enhanced emissions from degraded peatlands by 10%. The current small net sink is vulnerable to the interactions of peatland degraded area, burn rate and peat burn severity. Climate change impacts accelerated carbon losses, where increased burn severity and burn rate reduced the carbon sink by 38% and 65%, respectively, by 2100. However, our study demonstrates the potential for active peatland restoration to buffer these impacts. Northern peatland carbon sink plays a vital role in climate regulation. Here, the authors show that wildfire reduced peatland carbon uptake and enhanced emissions from degraded peatlands; climate change impacts accelerated carbon losses where increased burn rate and severity reduced carbon sink.
Modeling of extreme events is important in many scientific fields, including environmental and civil engineering, and impacts and risk assessments. Among available methods, statistical models that allow estimating extremes’ frequency and intensity are regularly used in procedures to anticipate potential changes in extreme events. Extreme value theory provides a theoretical basis for statistical estimation of extreme events using frequency analysis. The challenge in modeling is knowing when to use the block maxima method or the peaks-over-threshold (POT) method. Each has its drawbacks. POT describes the main characteristics of the observed extreme series; the threshold selection is always challenging and might affect the accuracy of the simulated results and the credibility of changes in extreme values. To encompass this challenge, mixture models offer more flexibility to represent samples with nonhomogeneous data. This study presents the gamma generalized Pareto (GGP) mixture model for estimating risk occurrence of hydroclimatic extremes. The model was developed in its general form, whereas the observed hydrometeorological extreme events depend on multidimensional covariates. A maximum likelihood algorithm is proposed to estimate the parameters with a constraint on the shape parameter of the generalized Pareto (GP) distribution. A Monte Carlo (MC) simulation compared the proposed model with the classical POT approach, with a fixed threshold, and the annual maximum series of streamflow. The approach was applied using a daily hydrological data set from an observed station located in the Saint John River at Fort Kent (01AD002), New Brunswick, Canada. The results show a flexibility to model extremes for dependent or nonstationary time series and adequately describes the central part of the observed frequencies, as well as the tails.
Long-term atmospheric CO2 concentration records have suggested a reduction in the positive effect of warming on high-latitude carbon uptake since the 1990s. A variety of mechanisms have been proposed to explain the reduced net carbon sink of northern ecosystems with increased air temperature, including water stress on vegetation and increased respiration over recent decades. However, the lack of consistent long-term carbon flux and in situ soil moisture data has severely limited our ability to identify the mechanisms responsible for the recent reduced carbon sink strength. In this study, we used a record of nearly 100 site-years of eddy covariance data from 11 continuous permafrost tundra sites distributed across the circumpolar Arctic to test the temperature (expressed as growing degree days, GDD) responses of gross primary production (GPP), net ecosystem exchange (NEE), and ecosystem respiration (ER) at different periods of the summer (early, peak, and late summer) including dominant tundra vegetation classes (graminoids and mosses, and shrubs). We further tested GPP, NEE, and ER relationships with soil moisture and vapor pressure deficit to identify potential moisture limitations on plant productivity and net carbon exchange. Our results show a decrease in GPP with rising GDD during the peak summer (July) for both vegetation classes, and a significant relationship between the peak summer GPP and soil moisture after statistically controlling for GDD in a partial correlation analysis. These results suggest that tundra ecosystems might not benefit from increased temperature as much as suggested by several terrestrial biosphere models, if decreased soil moisture limits the peak summer plant productivity, reducing the ability of these ecosystems to sequester carbon during the summer.
Abstract. Hydrologic-land surface models (H-LSMs) provide physically-based understanding and predictions of the current and future states of the world’s vast high-latitude permafrost regions. Two major challenges, however, hamper their parametrization and validation when concurrently representing hydrology and permafrost. One is the high computational complexity, exacerbated by the need to include a deep soil profile to adequately capture the freeze/thaw cycles and heat storage. The other is that soil-temperature data are severely limited, and traditional model validation, based on streamflow, can show the right fit to these data for the wrong reasons. There are few observational sites for such vast, heterogeneous regions, and remote sensing provides only limited support. In light of these challenges, we develop 16 parametrizations of a Canadian H-LSM, MESH, for the sub-arctic Liard River Basin and validate them using three data sources: streamflows at multiple gauges, soil temperature profiles from few available boreholes, and multiple permafrost maps. The different parametrizations favor different sources of data and it is challenging to configure a model faithful to all three data sources, which are at times inconsistent with each other. Overall, the results show that: (1) surface insulation through snow cover primarily regulates permafrost dynamics after model initialization effects decay over, relatively long time and (2) different parametrizations yield different partitioning patterns of solid-vs-liquid soil-water and produce different low-flow but similar high-flow regimes. We conclude that, given data scarcity, an ensemble of model parametrizations is essential to provide a reliable picture of the current states and future spatio-temporal co-evolution of permafrost and hydrology.
Lead contamination in drinking water has become an increasingly serious global risk because a small concentration of lead can cause serious health problems, particularly for children. It is critical to frequently monitor lead concentration in drinking water, which can be challenging when using traditional centralized systems. In this study, we present an inexpensive, portable detection system for point-of-care (POC) monitoring of lead concentration in drinking water. The sensing mechanism is based on the interaction between the water sample flowing through a microchannel and a planar microwave resonator-based sensor that is integrated with the microfluidic chip. The microwave sensor has a double-T structure with a gap in between through which the microchannel is aligned and can be coated with gold nanoparticles to enhance its sensing performance. For proof-of-concept, the sample under test (SUT) was a small volume of deionized (DI) water or tap water spiked with lead ions at different concentrations. Results show that the gold nanoparticle-coated microwave sensor presents a much higher sensitivity than bare sensors with a detectable frequency shift of 5 MHz for a Pb2+ solution with a concentration of 1 ppb. The success of the system for testing lead ions in typical tap water which contains many different mineral ions confirms its real-world application. To highlight the potential for POC applications, a low-cost, portable vector network analyzer is used to capture the frequency shift of the sensor. The developed method offers a promising approach for POC monitoring of lead contamination in drinking water impactful for environmental and public health protection.
Abstract. The amount and phase of cold season precipitation accumulating in the upper Saint John River basin are critical factors in determining spring runoff, ice-jams, and flooding in downstream communities. To study the impact of winter and spring storms on the snowpack in the upper Saint John River (SJR) basin, the Saint John River Experiment on Cold Season Storms (SAJESS) utilized meteorological instrumentation, upper air soundings, human observations, and hydrometeor macrophotography during winter/spring 2020–21. Here, we provide an overview of the SAJESS study area, field campaign, and existing data networks surrounding the upper SJR basin. Initially, meteorological instrumentation was co-located with an Environment and Climate Change Canada station near Edmundston, New Brunswick, in early December 2020. This was followed by an intensive observation period that involved manual observations, upper-air soundings, a multi-angle snowflake camera, macrophotography of solid hydrometeors, and advanced automated instrumentation throughout March and April 2021. The resulting datasets include optical disdrometer size and velocity distributions of hydrometeors, micro rain radar output, near-surface meteorological observations, and wind speed, temperature, pressure and precipitation amounts from a K63 Hotplate precipitation gauge, the first one operating in Canada. These data are publicly available from the Federated Research Data Repository at https://doi.org/10.20383/103.0591 (Thompson et al., 2022). We also include a synopsis of the data management plan and data processing, and a brief assessment of the rewards and challenges of utilizing community volunteers for hydro-meteorological citizen science.
Abstract. Thermokarst lake water balances are becoming increasingly vulnerable to change in the Arctic as air temperature increases and precipitation patterns shift. In the tundra uplands east of the Mackenzie Delta in the Northwest Territories, Canada, previous research has found that lakes responded non-uniformly to changes in precipitation, suggesting that lake and watershed properties moderate the response of lakes to climate change. To investigate how lake and watershed properties and meteoro5 logical conditions influence the water balance of thermokarst lakes in this region, we sampled 25 lakes for isotope analysis five times in 2018, beginning before snowmelt on May 1 and ending on September 3. Water isotope data were used to calculate the ratio of evaporation-to-inflow (E/I) and the average isotope composition of lake source water (δI). We identified four distinct water balance phases as lakes responded to seasonal shifts in meteorological conditions and hydrological processes. During the freshet phase from May 1 to June 15, the median E/I ratio of lakes decreased from 0.20 to 0.13 in response to freshet runoff 10 and limited evaporation due to lake ice presence that persisted for the duration of this phase. During the following warm, dry, and ice-free period from June 15 to July 26, designated the evaporation phase, the median E/I ratio increased to 0.19. During the brief soil wetting phase, E/I ratios did not respond to rainfall between July 26 and August 2, likely because watershed soils absorbed most of the precipitation which resulted in minimal runoff to lakes. The median E/I ratio decreased to 0.11 after an unseasonably cool and rainy August, identified as the recharge phase. Throughout the sampling period, δI remained relatively 15 stable and most lakes contained a greater amount of rainfall-sourced water than snow-sourced water, even after the freshet phase due to snowmelt bypass. The range of average E/I ratios we observed at lakes (0.00–0.43) was relatively narrow and low compared to thermokarst lakes in other regions, likely owing to the large watershed area to lake area (WA/LA), efficient preferential flow pathways for runoff, and a shorter ice-free season. WA/LA strongly predicted average lake E/I ratio (R2 = 0.74), as lakes with smaller WA/LA tended to have higher E/I ratios because they received relatively less inflow. We used this 20 relationship to predict the average E/I ratio of 7340 lakes in the region, finding that lakes are not vulnerable to desiccation in this region, given that all predicted average E/I values were <0.33. If future permafrost thaw and warming cause less runoff to flow into lakes, we expect that lakes with smaller WA/LA will be more influenced by increasing evaporation, while lakes with larger WA/LA will be more resistant to lake-level drawdown. However under wetter conditions, lakes with larger WA/LA will likely experience greater increases in lake level and could be more susceptible to rapid drainage as a result.
Abstract. The US Northern Great Plains and the Canadian Prairies are known as the world’s breadbaskets for its large spring wheat production and exports to the world. It is essential to accurately represent spring wheat growing dynamics and final yield and improve our ability to predict food production under climate change. This study attempts to incorporate spring wheat growth dynamics into the Noah-MP crop model, for a long time period (13-year) and fine spatial scale (4-km). The study focuses on three aspects: (1) developing and calibrating the spring wheat model at point-scale, (2) applying a dynamic planting/harvest date to facilitate large-scale simulations, and (3) applying a temperature stress function to assess crop responses to heat stress amid extreme heat. Model results are evaluated using field observations, satellite leaf area index (LAI), and census data from Statistics Canada and the US Department of Agriculture (USDA). Results suggest that incorporating a dynamic planting/harvest threshold can better constrain the growing season, especially the peak timing and magnitude of wheat LAI, as well as obtain realistic yield compared to prescribing a static province/state-level map. Results also demonstrate an evident control of heat stress upon wheat yield in three Canadian Prairies Provinces, which are reasonably captured in the new temperature stress function. This study has important implications for estimating crop production, simulating the land-atmosphere interactions in croplands, and crop growth’s responses to the raising temperatures amid climate change.
Abstract Key message Black spruce ( Picea mariana (Mill.) B.S.P.) has historically self-replaced following wildfire, but recent evidence suggests that this is changing. One factor could be negative impacts of intensifying fire activity on black spruce seed rain. We investigated this by measuring black spruce seed rain and seedling establishment. Our results suggest that increases in fire activity could reduce seed rain meaning reductions in black spruce establishment. Context Black spruce is an important conifer in boreal North America that develops a semi-serotinous, aerial seedbank and releases a pulse of seeds after fire. Variation in postfire seed rain has important consequences for black spruce regeneration and stand composition. Aims We explore the possible effects of changes in fire regime on the abundance and viability of black spruce seeds following a very large wildfire season in the Northwest Territories, Canada (NWT). Methods We measured postfire seed rain over 2 years at 25 black spruce-dominated sites and evaluated drivers of stand characteristics and environmental conditions on total black spruce seed rain and viability. Results We found a positive relationship between black spruce basal area and total seed rain. However, at high basal areas, this increasing rate of seed rain was not maintained. Viable seed rain was greater in stands that were older, closer to unburned edges, and where canopy combustion was less severe. Finally, we demonstrated positive relationships between seed rain and seedling establishment, confirming our measures of seed rain were key drivers of postfire forest regeneration. Conclusion These results indicate that projected increases in fire activity will reduce levels of black spruce recruitment following fire.
Municipal wastewater treatment plant (WWTP) effluent is one of several point sources of contaminants (nutrients, pharmaceuticals, estrogens, etc.) which can lead to adverse responses in aquatic life. Studies of WWTP effluent impacts on rainbow darter (Etheostoma caeruleum) collected downstream of WWTPs in the Grand River, Ontario have reported disruption at multiple levels of biological organization, including altered vitellogenin gene expression, lower levels of in vitro steroid production, and high frequency of intersex. However, major upgrades have occurred at treatment plants in the central Grand River over the last decade. Treatment upgrades to the Waterloo WWTP were initiated in 2009 but due to construction delays, the upgrades came fully on-line in 2017/2018. Responses in rainbow darter have been followed at sites associated with the outfall consistently over this entire time period. The treatment plant upgrade resulted in nitrification of effluent, and once complete there was a major reduction in effluent ammonia, selected pharmaceuticals, and estrogenicity. This study compared several key responses in rainbow darter associated with the Waterloo WWTP outfall prior to and post upgrades. Stable isotopes signatures in fish were used to track exposure to effluent and changed dramatically over time, corresponding to the effluent quality. Disruptions in in vitro steroid production and intersex in the darters that had been identified prior to the upgrades were no longer statistically different from the upstream reference sites after the upgrades. Although annual variations in water temperature and flow can potentially mask or exacerbate the effects of the WWTP effluent, major capital investments in wastewater treatment targeted at improving effluent quality have corresponded with the reduction of adverse responses in fish in the receiving environment.
Abstract Nutrient and soil loss from agricultural areas impairs surface water quality globally. In the Great Lakes region, increases in the frequency and magnitude of harmful and nuisance algal blooms in freshwater lakes have been linked to elevated phosphorus (P) losses from agricultural fields, some of which are transported via tile drainage. This study examined whether concentrations and loads of P fractions, total suspended sediments (TSS), nitrate (NO 3 − ), and ammonium (NH 4 + ) in tile drainage in a clay soil differed between a continuous no‐till system combining cover crops and surface broadcast fertilizer (no‐till cover crop [NTCC]), and a more conventional tillage system with shallow tillage, fertilizer incorporation and limited use of cover crops (conventional conservation‐till, CT). Both sites had modest soil fertility levels. Year‐round, high‐frequency observations of tile drainage flow and chemistry are described over 4 full water years and related to management practices on the associated fields. There were similar water yields in tile drainage between the two systems; however, losses of TSS, particulate P (PP), and NO 3 − were consistently greater from the CT site, which received larger quantities of fertilizer. In contrast, dissolved reactive P (DRP) losses were considerably greater from the NTCC site, offsetting the lower PP losses, such that there was little difference in TP losses between sites. Approximately 60% of the DRP losses from the NTCC site over the 4 years were associated with incidental losses following surface application of fertilizer in fall. This study provides insight into trade‐offs in controlling losses of different nutrient fractions using different management systems.
Some systems of differential equations that model problems in science and engineering have natural splittings of the right-hand side into the sum of three parts, in particular, diffusion, reaction, and advection. Implicit-explicit (IMEX) methods treat these three terms with only two numerical methods, and this may not be desirable. Accordingly, this work gives a detailed study of 3-additive linear multi-step methods for the solution of diffusion-reaction-advection systems. Specifically, we construct new 3-additive linear multi-step methods that treat diffusion, reaction, and advection with separate methods. The stability of the new methods is investigated, and the order of convergence is tested numerically. A comparison of the new methods is made with some popular IMEX methods in terms of stability and performance. It is found that the new 3-additive methods have larger stability regions than the IMEX methods tested in some cases and generally outperform in terms of computational efficiency.
Food security and nutrient deficiencies are frequent issues for people living in northern remote regions of Canada.The objective of this study is to describe the nutrient intake of residents living in the Dene/Métis communities of the Dehcho and Sahtú regions of the Northwest Territories.A 24-h dietary recall survey was used to collect information from participants of a study completed in 9 communities during the winter seasons of January 2016 to March 2018. Intakes for food groups, vitamins, macroelements, and microelements were calculated. Nutrient intakes were compared with the available DRIs.In total, there were 197 participants. On average, 37% of their energy was consumed from fat, and fruit/vegetable consumption was low (2.8 servings). Some vitamin levels (i.e., folate and vitamins A, B-6, C, and D) indicated a risk of nutritional deficiency for at least half of the participants. Of the nutrients examined, the nutrients least likely to meet the DRIs, according to the age/sex category of respondents were vitamin D (6%-20%), fiber (0%-11%), and calcium (4%-30%). Males tended to have a higher rate of nutrient adequacy above the DRIs. Importantly, 52% of the childbearing age female participants appeared deficient in folate, 48% deficient in zinc, 41% deficient in B12, and 22% deficient in iron, which might affect pregnancy and children's development.A focus on supporting a higher intake of nutrient-dense foods would benefit the health of these communities. Nutrition and health promotion programs should be implemented to improve public health efforts in the region.
Fractional-step methods are a popular and powerful divide-and-conquer approach for the numerical solution of differential equations. When the integrators of the fractional steps are Runge--Kutta methods, such methods can be written as generalized additive Runge--Kutta (GARK) methods, and thus the representation and analysis of such methods can be done through the GARK framework. We show how the general Butcher tableau representation and linear stability of such methods are related to the coefficients of the splitting method, the individual sub-integrators, and the order in which they are applied. We use this framework to explain some observations in the literature about fractional-step methods such as the choice of sub-integrators, the order in which they are applied, and the role played by negative splitting coefficients in the stability of the method.
Lake Erie, the shallowest of the five North American Laurentian Great Lakes, exhibits degraded water quality associated with recurrent phytoplankton blooms. Optical remote sensing of these optically complex inland waters is challenging due to the uncertainties stemming from atmospheric correction (AC) procedures. In this study, the accuracy of remote sensing reflectance (Rrs) derived from three different AC algorithms applied to Ocean and Land Colour Instrument (OLCI) observations of western Lake Erie (WLE) is evaluated through comparison to a regional radiometric dataset. The effects of uncertainties in Rrs products on the retrieval of near-surface concentration of pigments, including chlorophyll-a (Chla) and phycocyanin (PC), from Mixture Density Networks (MDNs) are subsequently investigated. Results show that iCOR contained the fewest number of processed (unflagged) days per pixel, compared to ACOLITE and POLYMER, for parts of the lake. Limiting results to the matchup dataset in common between the three AC algorithms shows that iCOR and ACOLITE performed closely at 665 nm, while outperforming POLYMER, with the Median Symmetric Accuracy (MdSA) of ∼30 %, 28 %, and 53 %, respectively. MDN applied to iCOR- and ACOLITE-corrected data (MdSA < 37 %) outperformed MDN applied to POLYMER-corrected data in estimating Chla. Large uncertainties in satellite-derived Rrs propagated to uncertainties ∼100 % in PC estimates, although the model was able to recover concentrations along the 1:1 line. Despite the need for improvements in its cloud-masking scheme, we conclude that iCOR combined with MDNs produces adequate OLCI pigment products for studying and monitoring Chla across WLE.
Baseflow, the groundwater contribution to streamflow, sustains surface water between precipitation events and is an important indicator of groundwater availability. Although many site-specific studies have been completed, there are few studies of long-term Canada-wide baseflow trends. In this work, we detected monthly baseflow trends across Canada and related them to changes to climatic predictors (precipitation, temperature, and antecedent wetness) using streamflow data from 1275 hydrometer stations from 1989 to 2019. Lyne and Hollick’s one-parameter digital filter is used to obtain a baseflow time series and monotonic trends are identified using the Mann-Kendall Trend Test. Historical baseflow is related to climate parameters by means of Generalised Additive Models for Location, Scale and Shape (GAMLSS) statistical analysis. Results based on trend analysis detected no significant trends for most stations (85.7% of all stations and months). However, notable increasing trends were observed across most of southern Canada during the winter and spring months (October–April). Conversely, negative trends were detected from June to September in Alberta and British Columbia and in southern Northwest Territories. Model selection identified antecedent wetness most often as a climate predictor over the same period as trend analysis. Our results highlight that warmer temperatures and increased snow cover across much of Canada have contributed to increased baseflow likely from shifts in snow melt timing and volumes. During warmer months (June–August), results indicate that increases in temperature were related to decreased baseflow, likely through increased evapotranspiration. Many trends in baseflow were not related to any climate predictors. Furthermore, non-reference basins were twice as likely to have no climate predictor, indicating that anthropogenic activities may be driving changes in baseflow. The results of this work can inform water resources management to identify the direction of change in groundwater availability across Canada and regions where mitigation may be necessary.
Model calibration is the procedure of finding model settings such that simulated model outputs best match the observed data. Model calibration is necessary when the model parameters cannot directly be measured as is the case with a wide range of environmental models where parameters are conceptually describing upscaled and effective physical processes. Model calibration is therefore an important step of environmental modeling as the model might otherwise provide random outputs if never compared to a ground truth. Model calibration itself is often referred to be an art due to its plenitude of intertwined steps and necessary decisions along the way before a calibration can be carried out or can be regarded successful. This work provides a general guide specifying which steps a modeler needs to undertake, how to diagnose the success of each step, and how to identify the right action to revise steps that were not successful. The procedure is formalized into ten iterative steps generally appearing in calibration experiments. Each step of this “calibration life cycle” is either illustrated with an exemplary calibration experiment or providing an explicit checklist the modeler can follow. These ten strategies are: (1) using sensitivity information to guide the calibration, (2) handling of parameters with constraints, (3) handling of data ranging orders of magnitude, (4) choosing the data to base the calibration on, (5) presenting various methods to sample model parameters, (6) finding appropriate parameter ranges, (7) choosing objective functions, (8) selecting a calibration algorithm, (9) determining the success and quality of a multi-objective calibration, and (10) providing a checklist to diagnose calibration performance using ideas introduced in the previous steps. The formal definition of strategies through the calibration process is providing an overview while shedding a light on connections between these main ingredients to calibrate an environmental model and will therefore enable especially novice modelers to succeed.
Intercomparison studies play an important, but limited role in understanding the usefulness and limitations of currently available hydrological models. Comparison studies are often limited to well-behaved hydrological regimes, where rainfall-runoff processes dominate the hydrological response. These efforts have not covered western Canada due to the difficulty in simulating that region’s complex cold region hydrology with varying spatiotemporal contributing areas. This intercomparison study is the first of a series of studies under the intercomparison project of the international and interprovincial transboundary Nelson-Churchill River Basin (NCRB) in North America (Nelson-MIP), which encompasses different ecozones with major areas of the non-contributing Prairie potholes, forests, glaciers, mountains, and permafrost. The performance of eight hydrological and land surface models is compared at different unregulated watersheds within the NCRB. This is done to assess the models’ streamflow performance and overall fidelity without and with calibration, to capture the underlying physics of the region and to better understand why models struggle to accurately simulate its hydrology. Results show that some of the participating models have difficulties in simulating streamflow and/or internal hydrological variables (e.g., evapotranspiration) over Prairie watersheds but most models performed well elsewhere. This stems from model structural deficiencies, despite the various models being well calibrated to observed streamflow. Some model structural changes are identified for the participating models for future improvement. The outcomes of this study offer guidance for practitioners for the accurate prediction of NCRB streamflow, and for increasing confidence in future projections of water resources supply and management.
Shifts in hydroclimatic regimes associated with global climate change may impact freshwater availability and quality. In high latitudes of the northern hemisphere, where vast quantities of carbon are stored terrestrially, explaining landscape-scale carbon (C) budgets and associated pollutant transfer is necessary for understanding the impact of changing hydroclimatic regimes. We used a dynamic modelling approach to simulate streamflow, DOC concentration, and DOC export in a northern Canadian catchment that has undergone notable climate warming, and will continue to for the remainder of this century. The Integrated Catchment model for Carbon (INCA-C) was successfully calibrated to a multi-year period (2012–2016) that represents a range in hydrologic conditions. The model was subsequently run over 30-year periods representing baseline and two future climate scenarios. Average discharge is predicted to decrease under an elevated temperature scenario (22–27 % of baseline) but increase (116–175 % of baseline) under an elevated temperature and precipitation scenario. In the latter scenario the nival hydroclimatic regime is expected to shift to a combined nival and pluvial regime. Average DOC flux over 30 years is predicted to decrease (24–27 % of baseline) under the elevated temperature scenario, as higher DOC concentrations are offset by lower runoff. Under the elevated temperature and precipitation scenario, results suggest an increase in carbon export of 64–81 % above baseline. These increases are attributed to greater connectivity of the catchment. The largest increase in DOC export is expected to occur in early winter. These predicted changes in DOC export, particularly under a climate that is warmer and wetter could be part of larger ecosystem change and warrant additional monitoring efforts in the region.
Cyprosulfamide is a herbicide safener that works against the injurious effects of herbicides such as isoxaflutole, dicamba, nicosulfuron, tembotrione, thiencarbazone-methyl. However, its sorption behaviour in soils and toxicity to aquatic organisms are yet to be thoroughly examined. This study determined the octanol-water partition coefficient, sorption properties, acute and chronic toxic effects, and potency of cyprosulfamide to the cladoceran water flea (Daphnia magna). The influence of soil properties such as organic carbon content, cation exchange capacity, pH, and field capacity on adsorption and desorption properties were also examined. The Log Kow (0.55) of cyprosulfamide was less than that of some other safeners, such as benoxacor or furilazole, found in aquatic environments. The sorption of cyprosulfamide to the soil was driven by pH, so sorption decreased with an increase in pH. Other characteristics, such as cation exchange capacity (CEC), organic carbon content, and field capacity, do not directly correlate with the distribution coefficient. Cyprosulfamide generally has a low affinity for soil and is thus mobile and prone to transport to surrounding surface waters. No lethality was observed at the highest concentration (120 mg/L) tested for acute toxicity to D. magna; hence the LC50 will be >120 mg/L. During chronic exposures, cyprosulfamide caused adverse effects at a concentration of 120 mg/L on the number of neonates and brood size. The death rate for the chronic study was a function of concentration and increased with days of exposure. Cyprosulfamide is unlikely to cause lethality to D. magna at relevant environmental concentrations.
Abstract The term “blue justice” was coined in 2018 during the 3rd World Small-Scale Fisheries Congress. Since then, academic engagement with the concept has grown rapidly. This article reviews 5 years of blue justice scholarship and synthesizes some of the key perspectives, developments, and gaps. We then connect this literature to wider relevant debates by reviewing two key areas of research – first on blue injustices and second on grassroots resistance to these injustices. Much of the early scholarship on blue justice focused on injustices experienced by small-scale fishers in the context of the blue economy. In contrast, more recent writing and the empirical cases reviewed here suggest that intersecting forms of oppression render certain coastal individuals and groups vulnerable to blue injustices. These developments signal an expansion of the blue justice literature to a broader set of affected groups and underlying causes of injustice. Our review also suggests that while grassroots resistance efforts led by coastal communities have successfully stopped unfair exposure to environmental harms, preserved their livelihoods and ways of life, defended their culture and customary rights, renegotiated power distributions, and proposed alternative futures, these efforts have been underemphasized in the blue justice scholarship, and from marine and coastal literature more broadly. We conclude with some suggestions for understanding and supporting blue justice now and into the future.
Abstract Excess nutrient inputs from agricultural and urban sources have accelerated eutrophication and increased the incidence of algal blooms in the Great Lakes Basin (GLB). Lake basin management to address these threats relies on understanding the key drivers of pollution. Here, we use a random forest machine learning model to leverage information from 202 monitored streams in the GLB to predict seasonal and annual flow‐weighted concentrations of nitrogen and phosphorus, as well as nutrient ratios across the GLB. Land use (agricultural and urban land) and land management (tile drainage and wetland density) emerge as the two most important predictors for dissolved inorganic nitrogen (DIN; NO 3 − + NO 2 − ) and soluble reactive phosphorus (SRP; PO 4 3 ), while soil type and wetland density are more important for particulate P (PP). Partial dependence plots demonstrate increasing nutrient concentrations with increasing tile density and decreasing wetland density. In addition, increasing tile and livestock densities and decreasing forest cover correspond to higher SRP:Total Phosphorus (TP) ratios. Seasonally, the highest proportions of SRP occur in summer and fall. Higher livestock densities are also correlated with increasing N:P (DIN:TP) ratios. Livestock operations can contribute to the buildup of soil nutrients from excess manure application, while increasing subsurface drainage can provide transport pathways for dissolved nutrients. Given that both SRP:TP and the N:P ratios are strong predictors of harmful algal blooms, our study highlights the importance of livestock management, drainage management, and wetland restoration in efforts to address eutrophication in intensively managed landscapes.
Abstract Excess nitrogen from intensive agricultural production, atmospheric N deposition, and urban point sources elevates stream nitrate concentrations, leading to problems of eutrophication and ecosystem degradation in coastal waters. A major emphasis of current US‐scale analysis of water quality is to better our understanding of the relationship between changes in anthropogenic N inputs within watersheds and subsequent changes in riverine N loads. While most water quality modeling assumes a positive linear correlation between watershed N inputs and riverine N, many efforts to reduce riverine N through improved nutrient management practices result in little or no short‐term improvements in water quality. Here, we use nitrate concentration and load data from 478 US watersheds, along with developed N input trajectories for these watersheds, to quantify time‐varying relationships between N inputs and riverine N export. Our results show substantial variations in watershed N import‐export relationships over time, with quantifiable hysteresis effects. Our results show that more population‐dense urban watersheds in the northeastern U.S. more frequently show clockwise hysteresis relationships between N imports and riverine N export, with accelerated improvements in water quality being achieved through the implementation of point‐source controls. In contrast, counterclockwise hysteresis dynamics are more common in agricultural watersheds, where time lags occur between the implementation of nutrient management practices and water‐quality improvements. Finally, we find higher tile‐drainage densities to be associated with more linear relationships between N inputs and riverine N. The empirical analysis in this study is bolstered by modeled simulations to reproduce and further explain drivers behind the hysteretic relationships commonly observed in the monitored watersheds.
Abstract In this study, we manufacture an exact solution for a set of 2D thermochemical mantle convection problems. The derivation begins with the specification of a stream function corresponding to a non‐stationary velocity field. The method of characteristics is then applied to determine an expression for composition consistent with the velocity field. The stream function formulation of the Stokes equation is then applied to solve for temperature. The derivation concludes with the application of the advection‐diffusion equation for temperature to solve for the internal heating rate consistent with the velocity, composition, and temperature solutions. Due to the large number of terms, the internal heating rate is computed using Maple™, and code is also made available in Fortran and Python. Using the method of characteristics allows the compositional transport equation to be solved without the addition of diffusion or source terms. As a result, compositional interfaces remain sharp throughout time and space in the exact solution. The exact solution presented allows for precision testing of thermochemical convection codes for correctness and accuracy.
Abstract Peatlands are globally important long‐term sinks of atmospheric carbon dioxide (CO 2 ). However, there is concern that climate change‐mediated drying will reduce gross primary productivity (GPP) and increase ecosystem respiration (ER) making peatlands vulnerable to a weaker carbon sink function and potential net carbon loss. While large and deep peatlands are usually resilient to moderate summer drying, CO 2 exchange in shallow Boreal Shield peatlands is likely more sensitive to drying given the reduced groundwater connectivity and water storage potential. To better understand the carbon cycling responses of Boreal Shield peatlands to meteorological conditions, we examined ecohydrological controls on CO 2 fluxes using the eddy covariance technique at a shallow peatland during the summer season for 5 years, from 2016–2020. We found lower GPP in dry summer years. Mean summer water table depth (WTD) was found to be significantly correlated with summer total net ecosystem CO 2 exchange ( R 2 = 0.78; p value = 0.046) and GPP ( R 2 = 0.83; p value = 0.03), where wet summers with a WT close to the peat surface sequestered more than twice the amount of CO 2 than dry summers. Our findings suggest that shallow Boreal Shield peatland GPP may be sensitive to climate‐mediated drying as they may switch to a net CO 2 source in the summer season when WTDs exceed a critical ecohydrological threshold for a prolonged period of time.
Abstract Speleothem oxygen isotope records (δ 18 O) of tropical South American rainfall in the late Quaternary show a zonal “South American Precipitation Dipole” (SAPD). The dipole is characterized by opposing east‐west precipitation anomalies compared to the present—wetter in the east and drier in the west at the mid‐Holocene (∼7 ka), and drier in the east and wetter in the west at the Last Glacial Maximum (∼21 ka). However, the SAPD remains enigmatic because it is expressed differently in western versus eastern δ 18 O records and isotope‐enabled climate model simulations usually misrepresent the magnitude and/or spatial pattern of δ 18 O change. Here, we address the SAPD enigma in two parts. First, we re‐interpret the δ 18 O data to account for upwind rainout effects that are known to be pervasive in tropical South America, but are not always considered in Quaternary paleoclimate studies. Our revised interpretation reconciles the δ 18 O data with cave infiltration and other proxy records, and indicates that the centroid of tropical South American rainfall has migrated zonally over time. Second, using an energy balance model of tropical atmospheric circulation, we hypothesize that zonal migration of the precipitation centroid can be explained by regional energy budget shifts, such as changing Saharan albedo associated with the African Humid Period, that have not been modeled in previous SAPD studies. This hypothesis of a migrating precipitation centroid presents a new framework for interpreting δ 18 O records from tropical South America and may help explain the zonal rainfall anomalies that predate the late Quaternary.
Abstract. Ice thickness across lake ice is mainly influenced by the presence of snow and its distribution, which affects the rate of lake ice growth. The distribution of snow depth over lake ice varies due to wind redistribution and snowpack metamorphism, affecting the variability of lake ice thickness. Accurate and consistent snow depth data on lake ice are sparse and challenging to obtain. However, high spatial resolution lake snow depth observations are necessary for the next generation of thermodynamic lake ice models to improve the understanding of how the varying distribution of snow depth influences lake ice formation and growth. This study was conducted using ground-penetrating radar (GPR) acquisitions with ∼9 cm sampling resolution along transects totalling ∼44 km to map snow depth over four Canadian sub-arctic freshwater lakes. The lake snow depth derived from GPR two-way travel time (TWT) resulted in an average relative error of under 10 % when compared to 2430 in situ snow depth observations for the early and late winter season. The snow depth derived from GPR TWTs for the early winter season was estimated with a root mean square error (RMSE) of 1.6 cm and a mean bias error of 0.01 cm, while the accuracy for the late winter season on a deeper snowpack was estimated with a RMSE of 2.9 cm and a mean bias error of 0.4 cm. The GPR-derived snow depths were interpolated to create 1 m spatial resolution snow depth maps. The findings showed improved lake snow depth retrieval accuracy and introduced a fast and efficient method to obtain high spatial resolution snow depth information. The results suggest that GPR acquisitions can be used to derive lake snow depth, providing a viable alternative to manual snow depth monitoring methods. The findings can lead to an improved understanding of snow and lake ice interactions, which is essential for northern communities' safety and wellbeing and the scientific modelling community.
Process-based modelling offers interpretability and physical consistency in many domains of geosciences but struggles to leverage large datasets efficiently. Machine-learning methods, especially deep networks, have strong predictive skills yet are unable to answer specific scientific questions. In this Perspective, we explore differentiable modelling as a pathway to dissolve the perceived barrier between process-based modelling and machine learning in the geosciences and demonstrate its potential with examples from hydrological modelling. ‘Differentiable’ refers to accurately and efficiently calculating gradients with respect to model variables or parameters, enabling the discovery of high-dimensional unknown relationships. Differentiable modelling involves connecting (flexible amounts of) prior physical knowledge to neural networks, pushing the boundary of physics-informed machine learning. It offers better interpretability, generalizability, and extrapolation capabilities than purely data-driven machine learning, achieving a similar level of accuracy while requiring less training data. Additionally, the performance and efficiency of differentiable models scale well with increasing data volumes. Under data-scarce scenarios, differentiable models have outperformed machine-learning models in producing short-term dynamics and decadal-scale trends owing to the imposed physical constraints. Differentiable modelling approaches are primed to enable geoscientists to ask questions, test hypotheses, and discover unrecognized physical relationships. Future work should address computational challenges, reduce uncertainty, and verify the physical significance of outputs. Differentiable modelling is an approach that flexibly integrates the learning capability of machine learning with the interpretability of process-based models. This Perspective highlights the potential of differentiable modelling to improve the representation of processes, parameter estimation, and predictive accuracy in the geosciences.
Agricultural fields in the Red River Valley of the Northern Great Plains are located on flat clay soils, often drained by shallow, roadside ditches that are not graded and lacking relief. These conditions can result in flow reversals and subsequent flooding of adjacent fields during large runoff events, which can mobilize phosphorus (P). Surface runoff from two agricultural fields and their adjacent ditches was monitored from 2015 to 2017 in southern Manitoba, Canada. Overbank flooding of fields adjacent to ditches was observed in 5 of 21 hydrologic events, and such events dominated annual runoff and P budgets (>83% of losses over the 3-year study period). Flooding events were often dominated by soluble P fractions (57–67%) relative to events where flooding was not observed (39–63%). Concentrations of soluble reactive P in water standing on fields increased with time during flooding events, suggesting that P was mobilized during such events; however, the source of the soluble reactive P is not clear. This study has highlighted temporal differences in hydrologic and biogeochemical interactions between fields and ditches and demonstrated the need for an improved understanding of mechanisms of P mobilization in the landscape, which has direct implications for predicting P mobility in agricultural watersheds.
For cold regions, an ice cover reduces channel conveyance and hydroelectricity generation potential. Therefore, predicting the impact of ice cover on a river-reservoir system is of critical importance for hydro producers. Ice impact can be described using historical records, where typical conditions are characterized by a daily median ice factor (IF) curve. The daily median IF curve works well only for past years with typical climatic conditions. Moreover, the median curve would not respond to climate-induced changes in the ice cover. In this research, a novel statistical (ST) model, named ST-IF, is developed to simulate the impact of river ice on the conveyance of the Nelson River West Channel (NRWC) as a function of daily air temperature. ST-IF uses a series of statistically based functions, including regression and threshold functions to estimate different characteristics of IF, such as its initial and peak values, and its daily distribution during ice-on period. Model performance was evaluated against historical records and the daily median value of the ice cover impact. Results showed that ST-IF significantly improved the simulation of each year-specific IF curve in NRWC compared to the daily median curve. Moreover, the model was used to predict the impact of ice cover under future climate conditions using 19 climate simulations. Results showed that, due to the predicted warmer future, ice cover is expected to take longer to fully form. This leads to longer Ice Stabilization Program duration, higher program implementation cost, and potential additional downstream stakeholder impacts. In addition, earlier ice impact peak date, shorter ice impact duration, and lower ice impact magnitude leading to overall higher winter hydroelectricity generation potential for Manitoba Hydro are expected in the future. Such future alterations intensify from near to far future time periods.
The southern Canadian Rockies are prone to extreme precipitation that often leads to high streamflow, deep snowpacks, and avalanche risks. Many of these precipitation events are associated with rain–snow transitions, which are highly variable in time and space due to the complex topography. A warming climate will certainly affect these extremes and the associated rain–snow transitions. The goal of this study is to investigate the characteristics and variability of rain–snow transitions aloft and how they will change in the future. Weather Research and Forecasting (WRF) simulations were conducted from 2000 to 2013 and these were repeated in a warmer pseudo-global warming (PGW) future. Rain–snow transitions occurred aloft throughout the year over the southern Canadian Rockies, but their elevations and depths were highly variable, especially across the continental divide. In PGW conditions, with future air temperatures up to 4–5°C higher on average over the Canadian Rockies, rain–snow transitions are projected to occur more often throughout the year, except during summer. The near-0°C conditions associated with rain–snow transitions are expected to increase in elevation by more than 500 m, resulting in more rain reaching the surface. Overall, this study illustrates the variability of rain–snow transitions, which often impact the location of the snowline. This study also demonstrates the non-uniform changes under PGW conditions, due in part to differences in the types of weather patterns that generate rain–snow transitions across the region.
Abstract Wetlands protect downstream waters by filtering excess nitrogen (N) generated from agricultural and urban activities. Many small ephemeral wetlands, also known as geographically isolated wetlands (GIWs), are hotspots of N retention but have received fewer legal protections due to their apparent isolation from jurisdictional waters. Here, we hypothesize that the isolation of the GIWs make them more efficient N filters, especially when considering transient hydrologic dynamics. We use a reduced complexity model with 30 years of remotely sensed monthly wetland inundation levels in 3700 GIWs across eight wetlandscapes in the US to show how consideration of transient hydrologic dynamics can increase N retention estimates by up to 130%, with greater retention magnification for the smaller wetlands. This effect is more pronounced in semi-arid systems such as the prairies in North Dakota, where transient assumptions lead to 1.8 times more retention, compared to humid landscapes like the North Carolina Pocosins where transient assumptions only lead to 1.4 times more retention. Our results highlight how GIWs have an outsized role in retaining nutrients, and this service is enhanced due to their hydrologic disconnectivity which must be protected to maintain the integrity of downstream waters.
Abstract Arctic-boreal regions are experiencing major anthropogenic disturbances in addition to intensifying natural disturbance regimes as a consequence of climate change. Oil and natural gas (OG) activities are extensive in the Arctic-boreal region of western North America, a large portion of which is underlain by permafrost. The total number and distribution of OG wells and their potential fate remain unclear. Consequently, the collective impacts of OG wells on natural and cultural resources, human health and emissions of methane (CH 4 ), are poorly understood. Using public OG well databases, we analysed the distribution of OG wells drilled between 1984 and 2018 across the Core Domain of the NASA Arctic-Boreal Vulnerability Experiment (‘ABoVE domain’). We identified 242 007 OG wells drilled as of 2018 in the ABoVE domain, of which almost two thirds are now inactive or abandoned OG wells. We found that annual drilling has increased from 269 to 8599 OG wells from 1984 to 2014 with around 1000, 700 and 1800 OG wells drilled annually in evergreen forest, deciduous forest and herbaceous land cover types, respectively. 65 588 OG well sites were underlain by permafrost in 2012. Fugitive CH 4 emissions from active and abandoned OG wells drilled in the Canadian portion of the ABoVE domain accounted for approximately 13% of the total anthropogenic CH 4 emissions in Canada in 2018. Our analysis identified OG wells as an anthropogenic disturbance in the ABoVE domain with potentially non-negligible consequences to local populations, ecosystems, and the climate system.
Abstract Over the past several decades, various trends in vegetation productivity, from increases to decreases, have been observed throughout Arctic–Boreal ecosystems. While some of this variation can be explained by recent climate warming and increased disturbance, very little is known about the impacts of permafrost thaw on productivity across diverse vegetation communities. Active layer thickness data from 135 permafrost monitoring sites along a 10° latitudinal transect of the Northwest Territories, Canada, paired with a Landsat time series of normalized difference vegetation index from 1984 to 2019, were used to quantify the impacts of changing permafrost conditions on vegetation productivity. We found that active layer thickness contributed to the observed variation in vegetation productivity in recent decades in the northwestern Arctic–Boreal, with the highest rates of greening occurring at sites where the near‐surface permafrost recently had thawed. However, the greening associated with permafrost thaw was not sustained after prolonged periods of thaw and appeared to diminish after the thaw front extended outside the plants' rooting zone. Highest rates of greening were found at the mid‐transect sites, between 62.4° N and 65.2° N, suggesting that more southernly sites may have already surpassed the period of beneficial permafrost thaw, while more northern sites may have yet to reach a level of thaw that supports enhanced vegetation productivity. These results indicate that the response of vegetation productivity to permafrost thaw is highly dependent on the extent of active layer thickening and that increases in productivity may not continue in the coming decades.
This paper documents the first comprehensive inventory of thermokarst and thaw-sensitive terrain indicators for a 2 million km2 region of northwestern Canada. This is accomplished through the Thermokarst Mapping Collective (TMC), a research collaborative to systematically inventory indicators of permafrost thaw sensitivity by mapping and aerial assessments across the Northwest Territories (NT), Canada. The increase in NT-based permafrost capacity has fostered science leadership and collaboration with government, academic, and community researchers to enable project implementation. Ongoing communications and outreach have informed study design and strengthened Indigenous and stakeholder relationships. Documentation of theme-based methods supported mapper training, and flexible data infrastructure facilitated progress by Canada-wide researchers throughout the COVID-19 pandemic. The TMC inventory of thermokarst and thaw-sensitive landforms agree well with fine-scale empirical mapping (69% to 84% accuracy) and aerial inventory (74% to 96% accuracy) datasets. National- and circumpolar-scale modelling of sensitive permafrost terrain contrasts significantly with TMC outputs, highlighting their limitations and the value of empirically-based mapping approaches. We demonstrate that the multi-parameter TMC outputs support a holistic understanding and refined depictions of permafrost terrain sensitivity, provide novel opportunities for syntheses, and inform future modelling approaches, which are urgently required to comprehend better what permafrost thaw means for Canada’s North.
Lexical and semantic matching capture different successful approaches to text retrieval and the fusion of their results has proven to be more effective and robust than either alone. Prior work performs hybrid retrieval by conducting lexical and semantic matching using different systems (e.g., Lucene and Faiss, respectively) and then fusing their model outputs. In contrast, our work integrates lexical representations with dense semantic representations by densifying high-dimensional lexical representations into what we call low-dimensional dense lexical representations (DLRs). Our experiments show that DLRs can effectively approximate the original lexical representations, preserving effectiveness while improving query latency. Furthermore, we can combine dense lexical and semantic representations to generate dense hybrid representations (DHRs) that are more flexible and yield faster retrieval compared to existing hybrid techniques. In addition, we explore jointly training lexical and semantic representations in a single model and empirically show that the resulting DHRs are able to combine the advantages of the individual components. Our best DHR model is competitive with state-of-the-art single-vector and multi-vector dense retrievers in both in-domain and zero-shot evaluation settings. Furthermore, our model is both faster and requires smaller indexes, making our dense representation framework an attractive approach to text retrieval. Our code is available at https://github.com/castorini/dhr .
Abstract Understanding the roles of land surface conditions and atmospheric circulation on continental daily temperature variance is key to improving predictions of temperature extremes. Evaporative resistance ( r s , hereafter), a function of the land cover type, reflects the ease with which water can be evaporated or transpired and is a strong control on land–atmosphere interactions. This study explores the effects of r s perturbations on summer daily temperature variance using the Simple Land Interface Model (SLIM) by mimicking, for r s only, a global land cover conversion from forest to crop/grassland. Decreasing r s causes a global cooling. The cooling is larger in wetter areas and weaker in drier areas, and primarily results from perturbations in shortwave radiation (SW) and latent heat flux (LH). Decreasing r s enhances cloud cover due to greater land surface evaporation and thus reduces incoming SW over most land areas. When r s decreases, wetter areas experience strong evaporative cooling, while drier areas become more moisture-limited and thus experience less cooling. Thermal advection further shapes the temperature response by damping the combined impacts of SW and LH. Temperature variance increases in drier areas and decreases in wetter areas as r s decreases. The temperature variance changes can be largely explained from changes in the combined variance of SW and LH, including an important contribution of changes in the covariance of SW and LH. In contrast, the effects of changes in thermal advection variance mainly affect the Northern Hemisphere midlatitudes. Significance Statement This study aims to better understand processes governing daily near-surface air temperature variance over land. We use an idealized modeling framework to explore the effects of land surface evaporative resistance (a parameter that controls how hard it is to evaporate water from the surface) on summer daily temperature variance. We find that a uniform decrease of evaporative resistance across the global land surface causes changes in the temperature variance that can be predicted from changes in the combined variance of shortwave radiation and latent heat flux. The variance of horizontal advection is important in altering the temperature variance in the Northern Hemisphere midlatitudes. Our findings shed light on predicting the characteristics of temperature variability as a function of surface conditions.
Abstract El Niño–Southern Oscillation (ENSO) has a profound influence on the occurrence of extreme precipitation events at local and regional scales in the present-day climate, and thus it is important to understand how that influence may change under future global warming. We consider this question using the large-ensemble simulations of CESM2, which simulates ENSO well historically. CESM2 projects that the influence of ENSO on extreme precipitation will strengthen further under the SSP3–7.0 scenario in most regions whose extreme precipitation regimes are strongly affected by ENSO in the boreal cold season. Extreme precipitation in the boreal cold season that exceeds historical thresholds is projected to become more common throughout the ENSO cycle. The difference in the intensity of extreme precipitation events that occur under El Niño and La Niña conditions will increase, resulting in “more extreme and more variable hydroclimate extremes.” We also consider the processes that affect the future intensity of extreme precipitation and how it varies with the ENSO cycle by partitioning changes into thermodynamic and dynamic components. The thermodynamic component, which reflects increases in atmospheric moisture content, results in a relatively uniform intensification of ENSO-driven extreme precipitation variation. In contrast, the dynamic component, which reflects changes in vertical motion, produces a strong regional difference in the response to forcing. In some regions, this component amplifies the thermodynamic-induced changes, while in others, it offsets them or even results in reduction in extreme precipitation variation.
Public health communication about diet in Inuit communities must balance the benefits and risks associated with both country and store-bought food choices and processes to support Inuit well-being. An understanding of how dietary messages—public health communication addressing the health and safety of country and store-bought food—are developed and disseminated in the Arctic is currently lacking. As part of the Country Foods for Good Health study, this participatory research sought to characterize dietary messaging in the Inuvialuit Settlement Region (ISR), Northwest Territories (NWT), from the perspective of territorial, regional and local dietary message disseminators to further improve message communication in the region. We conducted an in-person interview (n=1) (February 2020), telephone interviews (n=13) (May-June 2020), and follow-up telephone interviews (n=5) (June 2021) with key informants about their involvement in developing and/or disseminating dietary messages about the health benefits and risks of country foods and/or store-bought foods in/for the ISR. Key informants interviewed included health professionals (n=5), government employees (n=6) and community nutrition or food program coordinators (n=3) located in Inuvik, Tuktoyaktuk, Paulatuk and Yellowknife, NWT. We conducted a thematic analysis on the 19 interviews. Our findings indicate that publicly disseminated dietary messages in the ISR are developed at all scales and communicated through a variety of methods. Dietary messages focus predominantly on encouraging healthy store-bought food choices and conveying nutritional advice about store-bought and country foods. As federal and territorial messaging is seldom tailored to the ISR, representation of the Inuvialuit food system and consideration of local food realities is generally lacking. There is a need to evaluate dietary messages and improve collaborations among Inuvialuit country food knowledge holders, researchers, and public health dietary message disseminators at all scales to develop more locally tailored and culturally relevant messaging in the ISR. We recommend utilizing a participatory, collaborative, culture-centered approach to dietary message development and dissemination in northern Indigenous contexts.
Abstract. The carbon cycle in Arctic-boreal regions (ABR) is an important component of the planetary carbon balance, with growing concerns about the consequences of ABR warming on the global climate system. The greatest uncertainty in annual carbon dioxide (CO2) budgets exists during the non-growing season, primarily due to challenges with data availability and limited spatial coverage in measurements. The goal of this study was to determine the main environmental controls of non-growing season CO2 fluxes in ABR over a latitudinal gradient (45° N to 69° N) featuring four different ecosystem types: closed-crown coniferous boreal forest, open-crown coniferous boreal forest, erect-shrub tundra, and prostrate-shrub tundra. CO2 fluxes calculated using a snowpack diffusion gradient method (n = 560) ranged from 0 to 1.05 gC m2 day-1. To assess the dominant environmental controls governing CO2 fluxes, a Random Forest machine learning approach was used. We identified that soil temperature as the main control of non-growing season CO2 fluxes with 68 % of relative model importance, except when soil liquid water occurred during zero degree Celsius curtain conditions (Tsoil ≈ 0 °C and liquid water coexists with ice in soil pores). Under zero-curtain conditions, liquid water content became the main control of CO2 fluxes with 87 % of relative model importance. We observed exponential regressions between CO2 fluxes and soil temperature (RMSE = 0.024 gC m-2 day-1) in frozen soils, as well as liquid water content (RMSE = 0.137 gC m-2 day-1) in zero-curtain conditions. This study is showing the role of several variables on the spatio-temporal variability of CO2 fluxes in ABR during the non-growing season and highlight that the complex vegetation-snow-soil interactions in northern environments must be considered when studying what drives the spatial variability of soil carbon emission during the non-growing season.
Abstract. Systematic tile drainage is used extensively in agricultural lands to remove excess water and improve crop growth; however, tiles can also transfer nutrients from farmlands to downstream surface water bodies, leading to water quality problems. There is a need to simulate the hydrological behaviour of tile drains to understand the impacts of climate or land management change on agricultural runoff. The Cold Regions Hydrological Model (CRHM) is a physically based, modular modelling system that enables the creation of comprehensive models appropriate for cold regions by including a full suite of winter, spring, and summer season processes and coupling these together via mass and energy balances. A new tile drainage module was developed for CRHM to account for this process in tile-drained landscapes that are increasingly common in cultivated basins of the Great Lakes and northern Prairies regions of North America. A robust multi-variable, multi-criteria model performance evaluation strategy was deployed to examine the ability of the module with CRHM to capture tile discharge under both winter and summer conditions. Results showed that soil moisture is largely regulated by tile flow and lateral flow from adjacent fields. The explicit representation of capillary rise for moisture interactions between the rooting zone and groundwater greatly improved model simulations, demonstrating its significance in the hydrology of tile drains in loam soils. Water level patterns revealed a bimodal behaviour that depended on the positioning of the capillary fringe relative to the tile. A novel aspect of this module is the use of field capacity and its corresponding pressure head to provide an estimate of drainable water and thickness of the capillary fringe, rather than a detailed soil retention curve that may not always be available. Understanding the bimodal nature of soil water levels provides better insight into the significance of dynamic water exchange between soil layers below drains to improve tile drainage representation in models.
Abstract. The US Northern Great Plains and the Canadian Prairies are known as the world's breadbaskets for their large spring wheat production and exports to the world. It is essential to accurately represent spring wheat growing dynamics and final yield and improve our ability to predict food production under climate change. This study attempts to incorporate spring wheat growth dynamics into the Noah-MP crop model for a long time period (13 years) and fine spatial scale (4 km). The study focuses on three aspects: (1) developing and calibrating the spring wheat model at a point scale, (2) applying a dynamic planting and harvest date to facilitate large-scale simulations, and (3) applying a temperature stress function to assess crop responses to heat stress amid extreme heat. Model results are evaluated using field observations, satellite leaf area index (LAI), and census data from Statistics Canada and the United States Department of Agriculture (USDA). Results suggest that incorporating a dynamic planting and harvest threshold can better constrain the growing season, especially the peak timing and magnitude of wheat LAI, as well as obtain realistic yield compared to prescribing a static province/state-level map. Results also demonstrate an evident control of heat stress upon wheat yield in three Canadian Prairies Provinces, which are reasonably captured in the new temperature stress function. This study has important implications in terms of estimating crop yields, modeling the land–atmosphere interactions in agricultural areas, and predicting crop growth responses to increasing temperatures amidst climate change.
Abstract. Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety of predictions from dynamical, physics-based models – such as numerical weather prediction, climate, land, hydrology, and Earth system models – into a final prediction product. They are recognized as a promising way of enhancing the prediction skill of meteorological and hydroclimatic variables and events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. Hybrid forecasting methods are now receiving growing attention due to advances in weather and climate prediction systems at subseasonal to decadal scales, a better appreciation of the strengths of AI, and expanding access to computational resources and methods. Such systems are attractive because they may avoid the need to run a computationally expensive offline land model, can minimize the effect of biases that exist within dynamical outputs, benefit from the strengths of machine learning, and can learn from large datasets, while combining different sources of predictability with varying time horizons. Here we review recent developments in hybrid hydroclimatic forecasting and outline key challenges and opportunities for further research. These include obtaining physically explainable results, assimilating human influences from novel data sources, integrating new ensemble techniques to improve predictive skill, creating seamless prediction schemes that merge short to long lead times, incorporating initial land surface and ocean/ice conditions, acknowledging spatial variability in landscape and atmospheric forcing, and increasing the operational uptake of hybrid prediction schemes.
Abstract. Thermokarst lake water balances are becoming increasingly vulnerable to change in the Arctic as air temperature increases and precipitation patterns shift. In the tundra uplands east of the Mackenzie Delta in the Northwest Territories, Canada, previous research has found that lakes responded non-uniformly to year-to-year changes in precipitation, suggesting that lake and watershed properties mediate the response of lakes to climate change. To investigate how lake and watershed properties and meteorological conditions influence the water balance of thermokarst lakes in this region, we sampled 25 lakes for isotope analysis five times in 2018, beginning before snowmelt on 1 May and sampling throughout the remainder of the ice-free season. Water isotope data were used to calculate the average isotope composition of lake source water (δI) and the ratio of evaporation to inflow (E/I). We identified four distinct water balance phases as lakes responded to seasonal shifts in meteorological conditions and hydrological processes. During the freshet phase from 1 May to 15 June, the median E/I ratio of lakes decreased from 0.20 to 0.13 in response to freshet runoff and limited evaporation due to lake ice presence that persisted for the duration of this phase. During the following warm, dry, and ice-free period from 15 June to 26 July, designated the evaporation phase, the median E/I ratio increased to 0.19. During the brief soil wetting phase, E/I ratios did not respond to rainfall between 26 July and 2 August, likely because watershed soils absorbed most of the precipitation which resulted in minimal runoff to lakes. The median E/I ratio decreased to 0.11 after a cool and rainy August, identified as the recharge phase. Throughout the sampling period, δI remained relatively stable and most lakes contained a greater amount of rainfall-sourced water than snow-sourced water, even after the freshet phase, due to snowmelt bypass. The range of average E/I ratios that we observed at lakes (0.00–0.43) was relatively narrow and low compared with thermokarst lakes in other regions, likely owing to the large ratio of watershed area to lake area (WA/LA), efficient preferential flow pathways for runoff, and a shorter ice-free season. Lakes with smaller WA/LA tended to have higher E/I ratios (R2 = 0.74). An empirical relationship between WA/LA and E/I was derived and used to predict the average E/I ratio of 7340 lakes in the region, which identified that these lakes are not vulnerable to desiccation, given that E/I ratios were < 0.33. If future permafrost thaw and warming cause less runoff to flow into lakes, we expect that lakes with a smaller WA/LA will be more influenced by increasing evaporation, while lakes with a larger WA/LA will be more resistant to lake-level drawdown. However under wetter conditions, lakes with a larger WA/LA will likely experience a greater increases in lake level and could be more susceptible to rapid drainage.
Abstract. This study evaluated the effects of climate perturbations on snowmelt, soil moisture, and streamflow generation in small Canadian Prairies basins using a modelling approach based on classification of basin biophysical characteristics. Seven basin classes that encompass the entirety of the Prairies Ecozone in Canada were determined by cluster analysis of these characteristics. Individual semi-distributed virtual basin (VB) models representing these classes were parameterized in the Cold Regions Hydrological Model (CRHM) platform, which includes modules for snowmelt and sublimation, soil freezing and thawing, actual evapotranspiration (ET), soil moisture dynamics, groundwater recharge, and depressional storage dynamics including fill and spill runoff generation and variable connected areas. Precipitation (P) and temperature (T) perturbation scenarios covering the range of climate model predictions for the 21st century were used to evaluate climate sensitivity of hydrological processes in individual land cover and basin types across the Prairies Ecozone. Results indicated that snow accumulation in wetlands had a greater sensitivity to P and T than that in croplands and grasslands in all basin types. Wetland soil moisture was also more sensitive to T than the cropland and grassland soil moisture. Jointly influenced by land cover distribution and local climate, basin-average snow accumulation was more sensitive to T in the drier and grassland-characterized basins than in the wetter basins dominated by cropland, whilst basin-average soil moisture was most sensitive to T and P perturbations in basins typified by pothole depressions and broad river valleys. Annual streamflow had the greatest sensitivities to T and P in the dry and poorly connected Interior Grasslands (See Fig. 1) basins but the smallest in the wet and well-connected Southern Manitoba basins. The ability of P to compensate for warming-induced reductions in snow accumulation and streamflow was much higher in the wetter and cropland-dominated basins than in the drier and grassland-characterized basins, whilst decreases in cropland soil moisture induced by the maximum expected warming of 6 ∘C could be fully offset by a P increase of 11 % in all basins. These results can be used to (1) identify locations which had the largest hydrological sensitivities to changing climate and (2) diagnose underlying processes responsible for hydrological responses to expected climate change. Variations of hydrological sensitivity in land cover and basin types suggest that different water management and adaptation methods are needed to address enhanced water stress due to expected climate change in different regions of the Prairies Ecozone.

2022

Improving understanding of how water use efficiency (WUE), evapotranspiration (ET), and gross primary productivity (GPP) (CO2 exchange) vary across agricultural systems can help farmers better prepare for an uncertain future due to climate change by assessing water requirements for a crop as a function of current environmental conditions. This study: (a) quantified field-scale plant–water–carbon dynamics for silage maize (Zea mays L.) and alfalfa (Medicago sativa L.) crops – two dominant forage crops in southern Ontario, Canada; and (b) identified differences in plant carbon–water dynamics between these two crops, relating these differences to vegetation-driven ecosystem controls. Climate and soil properties were similar between the two study sites, and water availability was not limiting, suggesting that the overall temporal differences in carbon–water relations were driven by vegetation differences, mainly crop choice and management practices. Alfalfa had greater seasonal GPP, ET, and WUE than maize, due to a longer growing season. Differences in daily WUE between maize and alfalfa were driven by differences in GPP rather than ET. Multiple harvests reduced leaf-aging effects and promoted periods of rapid growth in alfalfa. In contrast, late seedling emergence and self-shading reduced GPP in maize. Under a warmer future climate, crop selection (i.e., perennial vs. annual), harvest regimes, and changes in growing season length should be considered when trying to manage for increased WUE. However, longer duration studies to validate these results are required to better address the impacts of climatic variability—especially antecedent conditions—to better inform future crop choices within a climate change context.
Abstract In the Canadian prairies, eutrophication is a widespread issue, with agriculture representing a major anthropogenic nutrient source in many watersheds. However, efforts to mitigate agricultural nutrient export are challenged by the lack of coordinated monitoring programs and the unique hydrological characteristics of the prairies, notably, the dominance of snowmelt in both water flows and nutrient loads, variable runoff, variable contributing area and the issues of understanding how scale affects nutrient concentrations and prevalence of dissolved nutrient transport (over total nutrients). Efforts are being made to integrate these characteristics in process-based water quality models, but the models are often complex and are not yet ready for use by watershed managers for prioritizing implementation of beneficial management practices (BMPs). In this study, a screening and scoping approach based on nutrient export coefficient modeling was used to prioritize BMPs for the 55,700 km2 Qu’Appelle Watershed, Saskatchewan. By integrating land use information, in-stream monitoring data, stakeholder input and nutrient export coefficient modeling, the study assessed potential efficiencies of six BMPs involving fertilizer, manure, grazing, crop and wetland management in nutrient load reductions for nine tributaries of the watershed. Uncertainty around the effectiveness of the BMPs was assessed. Field-level export coefficients were adjusted with nutrient delivery ratios for estimating watershed-level exports. Of the BMPs examined, in general, wetland restoration had the greatest potential to reduce both nitrogen and phosphorus loads in most tributaries, followed by fertilizer management. The importance of wetland restoration was supported by positive, significant, linear correlations between nutrient delivery ratios and drainage intensity in the tributaries (nitrogen: R 2 = 0.67; phosphorus: R 2 = 0.82). Notably, the relative ranking of BMP efficiencies varied with tributaries, as a result of differing landscape characteristics, land uses and nutrient inputs. In conclusion, the approach developed here acknowledges uncertainty, but provides a means to guide management decisions within the context of an adaptive management approach, where BMP implementation is partnered with monitoring and assessment to revise ongoing plans and ensures selected practices are meeting goals for nutrient abatement.
Best Management Practices (BMPs) incentive programs have been introduced to protect agricultural land and reduce nutrient runoff in watersheds. However, their voluntary nature has not led to the expected high participation rates. We examine influencing factors and underlying drivers that are associated with BMP adoption and farmer preferences for specific BMPs. Data are collected through an online survey in Ontario, Canada in 2019. A binary logit model is estimated to explain current participation in BMP schemes and a multinomial logit model to predict preferences for future BMP uptake. Results show that a mix of farmer and farm characteristics and environmental attitudes explain both current participation in BMP schemes and the likelihood of adopting a future BMP. Farmers tend to endorse a BMP if they currently implement that BMP. The findings furthermore suggest that increasing farmers' environmental awareness and sharing positive BMP experiences with other farmers may help expand future BMP adoption in Ontario. • We examine underlying drivers of farmer BMP adoption and preferences in Canada. • We inspect both current participation and future choices using logit models. • Farmers fairly concerned about water pollution are more likely to adopt BMPs. • Farmers tend to endorse a BMP if they currently implement that BMP. • Demographic characteristics are not significant predictors of future adoption.
The wildfire regime in Canada’s boreal region is changing; extended fire seasons are characterized by more frequent large fires (≥200 ha) burning greater areas of land, whilst climate-mediated drying is increasing the vulnerability of peatlands to deep burning. Proactive management strategies, such as fuel modification treatments, are necessary to reduce fire danger at the wildland-human interface (WHI). Novel approaches to fuel management are especially needed in peatlands where deep smouldering combustion is a challenge to suppression efforts and releases harmful emissions. Here, we integrate surface compression within conventional stand treatments to examine the potential for reducing smouldering of near-surface moss and peat. A linear model (adj. R2=0.62, p=2.2e-16) revealed that ground cover (F(2,101)=60.97, p<0.001) and compression (F(1,101)=56.46, p<0.001) had the greatest effects on smouldering potential, while stand treatment did not have a significant effect (F(3,101)=0.44, p=0.727). On average, compressed Sphagnum and feather moss plots showed 57.1% and 58.7% lower smouldering potential, respectively, when compared to uncompressed analogs. While practical evaluation is warranted to better understand the evolving effectiveness of this strategy, these findings demonstrate that a compression treatment can be successfully incorporated within both managed and unmanaged peatlands to reduce fire danger at the WHI.
Indigenous households are 90 times more likely to be without running water than non-Indigenous households in Canada. Current primary indicators of water quality and security for Indigenous Peoples are based on federal boil water advisories, which do not disaggregate at household levels to identify who is most at risk within or between communities. A mixed methods approach was used to assess the level of water insecurity and perceptions of water access by gender and age for a sample of households in Six Nations of the Grand River First Nations in Ontario, Canada. A household survey captured water security using the Household Water InSecurity Experiences (HWISE) scale and Likert-type responses to perceptions of water access, contextualized using semi-structured individual and group interviews. From 2019 to 2020, 66 households participated in the survey, 18 individuals participated in semi-structured individual interviews, and 7 individuals participated in 3 semi-structured group interviews. The survey sample demonstrated high levels of household water insecurity (57.5%, n = 38). Interviews revealed that women were more dissatisfied with their drinking water situations due to quality, source, and cost, though they shared water sharing as a coping strategy. Women faced more physical and mental barriers accessing water for their households, due to their roles as caretakers of their family and knowledge protectors for their communities. Generational divides were found in interviews about what qualified as "good water," with older participants understanding it as relating to traditional water sourcing, and younger participants wanting clean, accessible tap water. Taken together, the participants demonstrated a frustration with the sub-standard drinking water on reserve.
In Canada, Indigenous populations have an increased prevalence of psychiatric disorders and distress. Mental health mobile applications can provide effective, easy-to-access, and low-cost support. Examining grey literature and academic sources, this review found three mobile apps that support mental health for Indigenous communities in Canada. Implications and future directions are discussed.
Advances in scientific domains are led to an increase in the complexity of the experiments. To address this growing complexity, scientists from different domains require to work collaboratively. Scientific Workflow Management Systems (SWfMSs) are popular tools for data-intensive experiments. To the best of our knowledge, very few of the existing SWfMSs support collaboration, and it is not efficient in many cases. Researchers share a single version of the workflow in existing collaborative data analysis systems, which increases the chance of interference as the number of collaborators grows. Moreover, for effective collaboration, contributors require a clear view of the project's status, the information that existing SWfMSs do not provide. Another significant problem is most scientists are not capable of adding collaborative tools to existing SWfMSs, and they need software engineers to take on this responsibility. Even for software engineers such tasks could be challenging and time consuming. In this paper, we attempted to address this crucial issue in scientific workflow composition and doing so in a collaborative setting. Hence, we propose a tool to facilitate collaborative workflow composition. This tool provides branching and versioning, which are standard version control system features to allow multiple researchers to contribute to the project asynchronously. We also suggest some visualizations and a variety of reports to increase group awareness and help the scientists to realize the project's status and issues. As a proof of concept, we developed an API to capture the provenance data and provide collaborative tools. This API is developed as an example for software engineers to help them understand how to integrate collaborative tools into any SWfMS. We collect provenance information during workflow composition and then employ it to track workflow versions using the proposed collaborative tool. Prior to implementing the visualizations, we surveyed to discover how much the proposed visualizations could contribute to group awareness. Moreover, in the survey we investigated to what extent the proposed version control system could help address shortcomings in collaborative experiments. The survey participants provided us with valuable feedback. In future, we will use the survey responses to enhance the proposed version control system and visualizations.
Reading through code, finding relevant methods, classes and files takes a significant portion of software development time. Having good tool support for this code browsing activity can reduce human effort and increase overall developer productivity. To help with program comprehension activities, building an abstract code summary of a software system from its call graph is an active research area. A call graph is a visual representation of the caller-callee relationships between different methods of a software system. Call graphs can be difficult to comprehend for a large code-base. Previous work by Gharibi et al. on abstract code summarizing suggested using the Agglomerative Hierarchical Clustering (AHC) tree for understanding the codebase. Each node in the tree is associated with the top five method names. When we replicated the previous approach, we observed that the number of nodes in the AHC tree is burdensome for developers to explore. We also noticed only five method names for each node is not sufficient to comprehend an abstract node. We propose a technique to transform the AHC tree using cluster flattening for natural grouping and reduced nodes. We also generate a natural text summary for each abstract node derived from method comments. In order to evaluate our proposed approach, we collected developers’ opinions about the abstract code summary tree based on their codebase. The evaluation results confirm that our approach can not only help developers get an overview of their codebases but also could assist them in doing specific software maintenance tasks.
Exploring the source code of a software system is a prevailing task that is frequently done by contributors to a system. Practitioners often use call graphs to aid in understanding the source code of an inadequately documented software system. Call graphs, when visualized, show caller and callee relationships between functions. A static call graph provides an overall structure of a software system and dynamic call graphs generated from dynamic execution logs can be used to trace program behaviour for a particular scenario. Unfortunately a call graph of an entire system can be very complicated and hard to understand. Hierarchically abstracting a call graph can be used to summarize an entire system’s structure and more easily comprehending function calls. In this work, we mine concepts from source code entities (functions) to generate a concept cluster tree with improved naming of cluster nodes to complement existing studies and facilitate more effective program comprehension for developers. We apply three different information retrieval techniques (TFIDF, LDA, and LSI) on function names and function name variants to label the nodes of a concept cluster tree generated by clustering execution paths. From our experiment in comparing automatic labelling with manual labeling by participants for 12 use cases, we found that among the techniques on average, TFIDF performs better with 64% matching. LDA and LSI had 37% and 23% matching respectively. In addition, using the words in function name variants performed at least 5% better in participant ratings for all three techniques on average for the use cases.
Testing software is considered to be one of the most crucial phases in software development life cycle. Software bug fixing requires a significant amount of time and effort. A rich body of recent research explored ways to predict bugs in software artifacts using machine learning based techniques. For a reliable and trustworthy prediction, it is crucial to also consider the explainability aspects of such machine learning models. In this paper, we show how the feature transformation techniques can significantly improve the prediction accuracy and build confidence in building bug prediction models. We propose a novel approach for improved bug prediction that first extracts the features, then finds a weighted transformation of these features using a genetic algorithm that best separates bugs from non-bugs when plotted in a low-dimensional space, and finally, trains the machine learning model using the transformed dataset. In our experiment with real-life bug datasets, the random forest and k-nearest neighbor classifier models that leveraged feature transformation showed 4.25% improvement in recall values on an average of over 8 software systems when compared to the models built on original data.
Software developers often submit questions to technical Q&A sites like Stack Overflow (SO) to resolve their code-level problems. Usually, they include example code segments with their questions to explain the programming issues. When users of SO attempt to answer the questions, they prefer to reproduce the issues reported in questions using the given code segments. However, such code segments could not always reproduce the issues due to several unmet challenges (e.g., too short code segment) that might prevent questions from receiving appropriate and prompt solutions. A previous study produced a catalog of potential challenges that hinder the reproducibility of issues reported at SO questions. However, it is unknown how the practitioners (i.e., developers) perceive the challenge catalog. Understanding the developers’ perspective is inevitable to introduce interactive tool support that promotes reproducibility. We thus attempt to understand developers’ perspectives by surveying 53 users of SO. In particular, we attempt to – (1) see developers’ viewpoints on the agreement to those challenges, (2) find the potential impact of those challenges, (3) see how developers address them, and (4) determine and prioritize tool support needs. Survey results show that about 90% of participants agree to the already exposed challenges. However, they report some additional challenges (e.g., error log missing) that might prevent reproducibility. According to the participants, too short code segment and absence of required Class/Interface/Method from code segments severely prevent reproducibility, followed by missing important part of code. To promote reproducibility, participants strongly recommend introducing tool support that interacts with question submitters with suggestions for improving the code segments if the given code segments fail to reproduce the issues.
Software bug prediction is one of the promising research areas in software engineering. Software developers must allocate a reasonable amount of time and resources to test and debug the developed software extensively to improve software quality. However, it is not always possible to test software thoroughly with limited time and resources to develop high quality software. Sometimes software companies release software products in a hurry to make profit in a competitive environment. As a result the released software might have software defects and can affect the reputation of those software companies. Ideally, any software application that is already in the market should not contain bugs. If it does, depending on its severity, it might cause a great cost. Although a significant amount of work has been done to automate different parts of testing to detect bugs, fixing a bug after it is discovered is still a costly task that developers need to do. Sometimes these bug fixing changes introduce new bugs in the system. Researchers estimated that 80% of the total cost of a software system is spent on fixing bugs [8]. They show that the software faults and failures costs the US economy $59.5 billion a year [9].
AbstractLet M be a two-dimensional table with each cell weighted by a nonzero positive number. A StreamTable visualization of M represents the columns as non-overlapping vertical streams and the rows as horizontal stripes such that the intersection between a stream and a stripe is a rectangle with area equal to the weight of the corresponding cell. To avoid large wiggle of the streams, it is desirable to keep the consecutive cells in a stream to be adjacent. Let B be the smallest axis-aligned bounding box containing the StreamTable. Then the difference between the area of B and the sum of the weights is referred to as the excess area. We attempt to optimize various StreamTable aesthetics (e.g., minimizing excess area, or maximizing cell adjacencies in streams). If the row permutation is fixed and the row heights are given, then we give an O(rc)-time algorithm to optimizes these aesthetics, where r and c are the number of rows and columns, respectively. If the row permutation is fixed but the row heights can be chosen, then we discuss a technique to compute an aesthetic (but not necessarily optimal) StreamTable by solving a quadratically-constrained quadratic program, followed by iterative improvements. If the row heights are restricted to be integers, then we prove the problem to be NP-hard. If the row permutations can be chosen, then we show that it is NP-hard to find a row permutation that optimizes the area or adjacency aesthetics. KeywordsGeometric AlgorithmsTable CartogramStreamgraphs
Software development is largely dependent on libraries to reuse existing functionalities instead of reinventing the wheel. Software developers often need to find analogical libraries (libraries similar to ones they are already familiar with) as an analogical library may offer improved or additional features. Developers also need to search for analogical libraries across programming languages when developing applications in different languages or for different platforms. However, manually searching for analogical libraries is a time-consuming and difficult task. This paper presents a technique, called XLibRec, that recommends analogical libraries across different programming languages. XLibRec collects Stack Overflow question titles containing library names, library usage information from Stack Overflow posts, and library descriptions from a third party website, Libraries.io. We generate word-vectors for each information and calculate a weight-based cosine similarity score from them to recommend analogical libraries. We performed an extensive evaluation using a large number of analogical libraries across four different programming languages. Results from our evaluation show that the proposed technique can recommend cross-language analogical libraries with great accuracy. The precision for the Top-3 recommendations ranges from 62-81% and has achieved 8-45% higher precision than the state-of-the-art technique.
A software release note is one of the essential documents in the software development life cycle. The software release contains a set of information, e.g., bug fixes and security fixes. Release notes are used in different phases, e.g., requirement engineering, software testing and release management. Different types of practitioners (e.g., project managers and clients) get benefited from the release notes to understand the overview of the latest release. As a result, several studies have been done about release notes production and usage in practice. However, two significant problems (e.g., duplication and inconsistency in release notes contents) exist in producing well-written & well-structured release notes and organizing appropriate information regarding different targeted users' needs. For that reason, practitioners face difficulties in writing and reading the release notes using existing tools. To mitigate these problems, we execute two different studies in our paper. First, we execute an exploratory study by analyzing 3,347 release notes of 21 GitHub repositories to understand the documented contents of the release notes. As a result, we find relevant key artifacts, e.g., issues (29%), pull-requests (32%), commits (19%), and common vulnerabilities and exposures (CVE) issues (6%) in the release note contents. Second, we conduct a survey study with 32 professionals to understand the key information that is included in release notes regarding users' roles. For example, project managers are more interested in learning about new features than less critical bug fixes. Our study can guide future research directions to help practitioners produce the release notes with relevant content and improve the documentation quality.
Multi-attribute dataset visualizations are often designed based on attribute types, i.e., whether the attributes are categorical or numerical. Parallel Sets and Parallel Coordinates are two well-known techniques to visualize categorical and numerical data, respectively. A common strategy to visualize mixed data is to use multiple information linked view, e.g., Parallel Coordinates are often augmented with maps to explore spatial data with numeric attributes. In this paper, we design visualizations for mixed data, where the dataset may include numerical, categorical, and spatial attributes. The proposed solution SET-STAT-MAP is a harmonious combination of three interactive components: Parallel Sets (visualizes sets determined by the combination of categories or numeric ranges), statistics columns (visualizes numerical summaries of the sets), and a geospatial map view (visualizes the spatial information). We augment these components with colors and textures to enhance users' capability of analyzing distributions of pairs of attribute combinations. To improve scalability, we merge the sets to limit the number of possible combinations to be rendered on the display. We demonstrate the use of Set-stat-map using two different types of datasets: a meteorological dataset and an online vacation rental dataset (Airbnb). To examine the potential of the system, we collaborated with the meteorologists, which revealed both challenges and opportunities for Set-stat-map to be used for real-life visual analytics.
The susceptible-infected-recovered (SIR) model is perhaps the most basic epidemiological model for the evolution of disease spread within a population. Because of its direct representation of fundamental physical quantities, a true solution to an SIR model possesses a number of qualitative properties, such as conservation of the total population or positivity or monotonicity of its constituent populations, that may only be guaranteed to hold numerically under step-size restrictions on the solver. Operator-splitting methods with order greater than two require backward sub-steps in each operator, and the effects of these backward sub-steps on the step-size restrictions for guarantees of qualitative correctness of numerical solutions are not well studied. In this study, we analyze the impact of backward steps on step-size restrictions for guaranteed qualitative properties by applying third- and fourth-order operator-splitting methods to the SIR epidemic model. We find that it is possible to provide step-size restrictions that guarantee qualitative property preservation of the numerical solution despite the negative sub-steps, but care must be taken in the choice of the method. Results such as this open the door for the design and application of high-order operator-splitting methods to other mathematical models in general for which qualitative property preservation is important.
This study was conducted at an oil sands operation in the Athabasca Oil Sands Region (AOSR), northeastern Alberta, Canada. The mine comprises open pit excavation of bituminous sands at two sites (Mildred Lake, ML, and Aurora North, AN), with a single hot-water extraction circuit connecting extraction plants at each mine. Water samples were collected and analyzed regularly over an eight-year period to establish inventories of site-wide water isotope signatures including seasonal and interannual changes in the recycle water circuit, and to permit future application of an isotope balance model to constrain poorly quantified processes such as evaporation losses, dewatering of tailings, and tailings pond connectivity of the recycle water circuit. Sampling of precipitation inputs over an 8-year period was used to constrain a local meteoric water line for the area. Differences in evaporative isotopic enrichment of tailings ponds at ML and AN are attributed to use of Athabasca River makeup water at the former site versus basal dewatering sources at the latter, with similar atmospheric controls at both. A conceptual model is developed summarizing temporal variations in water balance and isotopic signatures within the recycle water circuit, including accurate simulation of the unique isotopic enrichment of cooling tower blowdown. This study provides foundational evidence for application of stable isotope mass balance to monitor and improve industrial water use efficiency and management. • Detailed summary of stable isotope variations at oil sands mine sites. • New dataset for precipitation, makeup water, and mine circuits. • Updated regressions defining local meteoric water line for district. • Contrasts isotopic variations for nearby mine sites with distinct sources. • Previously unpublished effects of cooling tower blowdown.
The lower Athabasca River was used as a test case using total suspended sediment, chloride and vanadium as the model variables. Upstream model boundary conditions included water from the tributary Clearwater River (right stream tube) and the upper Athabasca River extending upstream of the tributary mouth (left stream tube). This model will be extended to include the Peace-Athabasca Delta (PAD), to determine the implications of mining outfall discharges on a large region of the Athabasca – PAD region. A novel, quasi-two-dimensional surface water-quality modelling approach is presented in which the model domain can be discretised in two dimensions, but a one-dimension solver can still be applied to capture water flow between the discretisation units (segments). The approach requires a river reach to be divided into two stream tubes, along the left and right river sides, with flows exchanging through the segments longitudinally and also laterally between adjacent segments along the two streams. The new method allows the transverse mixing of tributary and outfall water of different constituent concentrations to be simulated along the course of the river. Additional diffuse loading of dissolved vanadium could be determined from the model’s substance balance. A scenario was then simulated in which the transport and fate of vanadium in a floodplain lake and a secondary channel was determined. • Quasi-2D modelling approach proves to be viable for transverse mixing. • Quasi-2D approach allows secondary channels and side lakes to be modelled. • Quasi-2D approach is appropriate to scale up to entire lower Athabasca River reach. • The approach allowed a diffuse loading of dissolved vanadium to be quantified.
This paper synthesizes Canada's environmental valuation literature over the last six decades. Focusing on primary valuation benefit estimates, we link multiple research outputs from the same data collection effort to obtain an accurate measure of unique studies. We identify a total of 269 unique valuation studies conducted in Canada between 1964 and 2019. The number of valuation studies conducted per year has not increased since 1975 and the median data collection year is 1996. Stated preference (SP) methods are the most popular valuation approaches being used in more than 50% of studies and this share has increased to over 80% within the last decade. We discuss numerous gaps in our knowledge for certain environmental resources and regions, in particular Canada's three Northern territories. The paper provides information on the state of environmental valuation research in Canada and identifies future research needs.
A discrete choice experiment was conducted on the non-use value of avoiding climate impacts to coral reefs. • The Northwestern Hawaiian Islands coral reefs were utilized as a case study site. • Decreasing coral cover and fish numbers causes large welfare losses. • Declines to coral health and fish species diversity lead to moderate welfare losses. • Choice behaviour is compared between US mainland and Hawaiian residents. Global climate change is leading to rapid deteriorations of the health and productivity of coral reefs. There is limited research on the associated human welfare implications, particularly in terms of the non-use values that people hold for coral reefs. We examine climate related changes in non-use values of coral health, coral cover, water clarity, fish numbers, fish species diversity and presence of turtles. Using a discrete choice experiment conducted among 1,369 Hawaiian and US mainland residents, we find that climate change induced declines in coral cover and fish numbers result in large welfare losses; whereas, declines in coral health and fish species diversity lead to moderate welfare losses. Deterioration in water clarity results in large welfare losses for US mainland residents but relatively smaller losses for Hawaiian residents. On aggregate, differences in welfare estimates for the US mainland and Hawaii sample are minor. However, we find significant differences in the underlying determinants of willingness-to-pay for partial climate change mitigation including income and beliefs in the need to mitigate climate change. The paper concludes with some recommendations for policy on the basis of these findings.
DNA metabarcoding can provide a high-throughput and rapid method for characterizing responses of communities to environmental stressors. However, within bulk samples, DNA metabarcoding hardly distinguishes live from the dead organisms. Here, both DNA and RNA metabarcoding were applied and compared in experimental freshwater mesocosms conducted for assessment of ecotoxicological responses of zooplankton communities to remediation treatment until 38 days post oil-spill. Furthermore, a novel indicator of normalized vitality (NV), sequence counts of RNA metabarcoding normalized by that of DNA metabarcoding, was developed for assessment of ecological responses. DNA and RNA metabarcoding detected similar taxa richness and rank of relative abundances. Both DNA and RNA metabarcoding demonstrated slight shifts in measured α-diversities in response to treatments. NV presented relatively greater magnitudes of differential responses of community compositions to treatments compared to DNA or RNA metabarcoding. NV declined from the start of the experiment (3 days pre-spill) to the end (38 days post-spill). NV also differed between Rotifer and Arthropoda, possibly due to differential life histories and sizes of organisms. NV could be a useful indicator for characterizing ecological responses to anthropogenic influence; however, the biology of target organisms and subsequent RNA production need to be considered. • RNA normalized by DNA metabarcoding functions as normalized vitality. • Normalized vitality reflected temporal dynamics of zooplankton communities. • Normalized vitality revealed greater community differences between treatments. • Rotifer had greatest normalized vitality compared to Arthropoda.
Abstract Wastewater-based surveillance of SARS-CoV-2 RNA has been implemented at building, neighbourhood, and city levels throughout the world. Implementation strategies and analysis methods differ, but they all aim to provide rapid and reliable information about community COVID-19 health states. A viable and sustainable SARS-CoV-2 surveillance network must not only provide reliable and timely information about COVID-19 trends, but also provide for scalability as well as accurate detection of known or unknown emerging variants. Emergence of the SARS-CoV-2 variant of concern Omicron in late Fall 2021 presented an excellent opportunity to benchmark individual and aggregated data outputs of the Ontario Wastewater Surveillance Initiative in Canada; this public health-integrated surveillance network monitors wastewaters from over 10 million people across major population centres of the province. We demonstrate that this coordinated approach provides excellent situational awareness, comparing favourably with traditional clinical surveillance measures. Thus, aggregated datasets compiled from multiple wastewater-based surveillance nodes can provide sufficient sensitivity (i.e., early indication of increasing and decreasing incidence of SARS-CoV-2) and specificity (i.e., allele frequency estimation of emerging variants) with which to make informed public health decisions at regional- and state-levels.
Abstract Wastewater-based surveillance (WBS) has become an effective tool around the globe for indirect monitoring of COVID-19 in communities. Quantities of viral fragments of SARS-CoV-2 in wastewater are related to numbers of clinical cases of COVID-19 reported within the corresponding sewershed. Variants of Concern (VOCs) have been detected in wastewater by use of reverse transcription quantitative polymerase chain reaction (RT-qPCR) or sequencing. A multiplex RT-qPCR assay to detect and estimate the prevalence of multiple VOCs, including Omicron/Alpha, Beta, Gamma, and Delta, in wastewater RNA extracts was developed and validated. The probe-based multiplex assay, named “N200” focuses on amino acids 199-202, a region of the N gene that contains several mutations that are associated with variants of SARS- CoV-2 within a single amplicon. Each of the probes in the N200 assay are specific to the targeted mutations and worked equally well in single- and multi-plex modes. To estimate prevalence of each VOC, the abundance of the targeted mutation was compared with a non- mutated region within the same amplified region. The N200 assay was applied to monitor frequencies of VOCs in wastewater extracts from six sewersheds in Ontario, Canada collected between December 1, 2021, and January 4, 2022. Using the N200 assay, the replacement of the Delta variant along with the introduction and rapid dominance of the Omicron variant were monitored in near real-time, as they occurred nearly simultaneously at all six locations. The N200 assay is robust and efficient for wastewater surveillance can be adopted into VOC monitoring programs or replace more laborious assays currently being used to monitor SARS- CoV-2 and its VOCs.
Abstract Wastewater monitoring of SARS-CoV-2 allows for early detection and monitoring of COVID-19 burden in communities and can track specific variants of concern. Targeted assays enabled relative proportions of SARS-CoV-2 Omicron and Delta variants to be determined across 30 municipalities covering >75% of the province of Alberta (pop. 4.5M) in Canada, from November 2021 to January 2022. Larger cities like Calgary and Edmonton exhibited a more rapid emergence of Omicron relative to smaller and more remote municipalities. Notable exceptions were Banff, a small international resort town, and Fort McMurray, a more remote northern city with a large fly-in worker population. The integrated wastewater signal revealed that the Omicron variant represented close to 100% of SARS-CoV-2 burden prior to the observed increase in newly diagnosed clinical cases throughout Alberta, which peaked two weeks later. These findings demonstrate that wastewater monitoring offers early and reliable population-level results for establishing the extent and spread of emerging pathogens including SARS-CoV-2 variants.
Cyanobacterial blooms present challenges for water treatment, especially in regions like the Canadian prairies where poor water quality intensifies water treatment issues. Buoyant cyanobacteria that resist sedimentation present a challenge as water treatment operators attempt to balance pre-treatment and toxic disinfection by-products. Here, we used microscopy to identify and describe the succession of cyanobacterial species in Buffalo Pound Lake, a key drinking water supply. We used indicator species analysis to identify temporal grouping structures throughout two sampling seasons from May to October 2018 and 2019. Our findings highlight two key cyanobacterial bloom phases - a mid-summer diazotrophic bloom of Dolichospermum spp. and an autumn Planktothrix agardhii bloom. Dolichospermum crassa and Woronichinia compacta served as indicators of the mid-summer and autumn bloom phases, respectively. Different cyanobacterial metabolites were associated with the distinct bloom phases in both years: toxic microcystins were associated with the mid-summer Dolichospermum bloom and some newly monitored cyanopeptides (anabaenopeptin A and B) with the autumn Planktothrix bloom. Despite forming a significant proportion of the autumn phytoplankton biomass (>60%), the Planktothrix bloom had previously not been detected by sensor or laboratory-derived chlorophyll-a. Our results demonstrate the power of targeted taxonomic identification of key species as a tool for managers of bloom-prone systems. Moreover, we describe an autumn Planktothrix agardhii bloom that has the potential to disrupt water treatment due to its evasion of detection. Our findings highlight the importance of identifying this autumn bloom given the expectation that warmer temperatures and a longer ice-free season will become the norm.
Permafrost plays an important role in the hydrology of arctic/subarctic regions. However, permafrost thaw/degradation has been observed over recent decades in the Northern Hemisphere and is projected to accelerate. Hence, understanding the evolution of permafrost areas is urgently needed. Land surface models (LSMs) are well-suited for predicting permafrost dynamics due to their physical basis and large-scale applicability. However, LSM application is challenging because of the large number of model parameters and the complex memory of state variables. Significant interactions among the underlying processes and the paucity of observations of thermal/hydraulic regimes add further difficulty. This study addresses the challenges of LSM application by evaluating the uncertainty due to meteorological forcing, assessing the sensitivity of simulated permafrost dynamics to LSM parameters, and highlighting issues of parameter identifiability. Modelling experiments are implemented using the MESH-CLASS framework. The VARS sensitivity analysis and traditional threshold-based identifiability analysis are used to assess various aspects of permafrost dynamics for three regions within the Mackenzie River Basin. The study shows that the modeller may face significant trade-offs when choosing a forcing dataset as some datasets enable the representation of some aspects of permafrost dynamics, while being inadequate for others. The results also emphasize the high sensitivity of various aspects of permafrost simulation to parameters controlling surface insulation and soil texture; a detailed list of influential parameters is presented. Identifiability analysis reveals that many of the most influential parameters for permafrost simulation are unidentifiable. These conclusions will hopefully inform future efforts in data collection and model parametrization.
Abstract. Northern peatlands cover approximately four million km2, and about half of these peatlands are estimated to contain permafrost and periglacial landforms, like palsas and peat plateaux. In northeastern Canada, peatland permafrost is predicted to be spatially concentrated in the western interior of Labrador and largely absent along the Labrador Sea and Gulf of St. Lawrence coastline. However, the paucity of observations of peatland permafrost in the interior coupled with ongoing use of perennially frozen peatlands along the coast by Labrador Inuit and Innu cast doubt on the reliability of existing maps of peatland permafrost distribution in the region. In this study, we develop a multi-stage consensus-based inventory of peatland permafrost complexes in coastal Labrador and adjacent parts of Quebec using high-resolution satellite imagery and validate it with extensive field visits and low-altitude aerial photography and videography. A total of 1885 wetland complexes were inventoried, of which 1023 were interpreted as likely containing peatland permafrost. Likely peatland permafrost complexes were mostly found in lowlands within 40 km of the coastline where mean annual air temperatures of up to +1.2 °C are recorded. Evaluation of the geographic distribution of peatland permafrost complexes reveals a clear gradient from the outer coasts, where peatland permafrost is more abundant, to inland peatlands, where permafrost is generally absent. This coastal gradient may be attributed to a combination of climatic and geomorphological influences which lead to lower insolation, thinner snowpacks, and more frost-susceptible materials along the coast. The results of this study also suggest that existing maps of permafrost distribution for southeastern Labrador require adjustment to better reflect the abundance of peatland permafrost complexes which are located to the south of the regional sporadic discontinuous permafrost limit. This study constitutes the first dedicated peatland permafrost inventory for Labrador, and our results provide an important baseline for future mapping, modelling, and climate change adaptation strategy development in the region.
Abstract. Human-controlled reservoirs have a large influence on the global water cycle. While global hydrological models use generic parametrisations to model human dam operations, the representation of reservoir regulation is often still lacking in Earth System Models. Here we implement and evaluate a widely used reservoir parametrisation in the global river routing model mizuRoute, which operates on a vector-based river network resolving individual lakes and reservoirs, and which is currently being coupled to an Earth System Model. We develop an approach to determine the downstream area over which to aggregate irrigation water demand per reservoir. The implementation of managed reservoirs is evaluated by comparing to simulations ignoring inland waters, and simulations with reservoirs represented as natural lakes, using (i) local simulations for 26 individual reservoirs driven by observed inflows, and (ii) global-scale simulations driven by runoff from the Community Land Model. The local simulations show a clear added value of the reservoir parametrisation, especially for simulating storage for large reservoirs with a multi-year storage capacity. In the global-scale application, the implementation of reservoirs shows an improvement in outflow and storage compared to the no-reservoir simulation, but compared to the natural lake parametrisation, an overall similar performance is found. This lack of impact could be attributed to biases in simulated river discharge, mainly originating from biases in simulated runoff from the Community Land Model. Finally, the comparison of modelled monthly streamflow indices against observations highlights that the inclusion of dam operations improves the streamflow simulation compared to ignoring lakes and reservoirs. This study overall underlines the need to further develop and test water management parametrisations, as well as to improve runoff simulations for advancing the representation of anthropogenic interference with the terrestrial water cycle in Earth System Models.
Abstract. Fire is the dominant disturbance agent in Alaskan and Canadian boreal ecosystems and releases large amounts of carbon into the atmosphere. Burned area and carbon emissions have been increasing with climate change, which have the potential to alter the carbon balance and shift the region from a historic sink to a source. It is therefore critically important to track the spatiotemporal changes in burned area and fire carbon emissions over time. Here we developed a new burned area detection algorithm between 2001–2019 across Alaska and Canada at 500 meters (m) resolution that utilizes finer-scale 30 m Landsat imagery to account for land cover unsuitable for burning. This method strictly balances omission and commission errors at 500 m to derive accurate landscape- and regional-scale burned area estimates. Using this new burned area product, we developed statistical models to predict burn depth and carbon combustion for the same period within the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) core and extended domain. Statistical models were constrained using a database of field observations across the domain and were related to a variety of response variables including remotely-sensed indicators of fire severity, fire weather indices, local climate, soils, and topographic indicators. The burn depth and aboveground combustion models performed best, with poorer performance for belowground combustion. We estimate 2.37 million hectares (Mha) burned annually between 2001–2019 over the ABoVE domain (2.87 Mha across all of Alaska and Canada), emitting 79.3 +/- 27.96 (+/- 1 standard deviation) Teragrams of carbon (C) per year, with a mean combustion rate of 3.13 +/- 1.17 kilograms C m-2. Mean combustion and burn depth displayed a general gradient of higher severity in the northwestern portion of the domain to lower severity in the south and east. We also found larger fire years and later season burning were generally associated with greater mean combustion. Our estimates are generally consistent with previous efforts to quantify burned area, fire carbon emissions, and their drivers in regions within boreal North America; however, we generally estimate higher burned area and carbon emissions due to our use of Landsat imagery, greater availability of field observations, and improvements in modeling. The burned area and combustion data sets described here (the ABoVE Fire Emissions Database, or ABoVE-FED) can be used for local to continental-scale applications of boreal fire science.
Abstract. Ice thickness across lake ice is influenced mainly by the presence of snow and its distribution, as it directly impacts the rate of lake ice growth. The spatial distribution of snow depth over lake ice varies and is driven by wind redistribution and snowpack metamorphism, creating variability in the lake ice thickness. The accuracy and consistency of snow depth measurement data on lake ice are challenging and sparse to obtain. However, high spatial resolution lake snow depth observations are necessary for the next generation of thermodynamic lake ice models. Such information is required to improve the knowledge and understanding of snow depth distribution over lake ice. This study maps snow depth distribution over lake ice using ground-penetrating radar (GPR) two-way travel-time (TWT) with ~9 cm spatial resolution along transects totalling ~44 km over four freshwater lakes in Canada’s sub-arctic. The accuracy of the snow depth retrieval is assessed using in situ snow depth observations (n =2,430). On average, the snow depth derived from GPR TWTs for the early winter season is estimated with a root mean square error (RMSE) of 1.58 cm and a mean bias error of -0.01 cm. For the late winter season on a deeper snowpack, the accuracy is estimated with RMSE of 2.86 cm and a mean bias error of 0.41 cm. The GPR-derived snow depths are interpolated to create 1 m spatial resolution snow depth maps. Overall, this study improved lake snow depth retrieval accuracy and introduced a fast and efficient method to obtain high spatial resolution snow depth information, which is essential for the lake ice modelling community.
Due to their relatively large production and few restrictions on uses, novel substitutes for historically used per and poly-fluoroalkyl substances (PFAS) are being used and accumulating in the environment. However, due to a lack of information on their toxicological properties their hazards and risks are hard to estimate. Before fertilization, oocytes of two salmonid species, Arctic Char (Salvelinus alpinus) and Rainbow Trout (Oncorhynchus mykiss), were exposed to three PFAS substances used as substitutes for traditional PFAS, PFBA, PFBS or GenX or two archetypical, historically used, longer-chain PFAS, PFOA and PFOS. Exposed oocytes were subsequently fertilized, incubated and were sampled during several developmental stages, until swim-up. All five PFAS were accumulated into egg yolks with similar absorption rates, and their concentrations in egg yolks were less than respective concentrations in/on egg chorions. Rapid elimination of the five PFAS was observed during the first 3 days after fertilization. Thereafter, amounts of PFOS and PFOA were stable until swim-up, while PFBA, PFBS and GenX were further eliminated during development from one month after the fertilization to swim-up. In these two salmonid species, PFBA, PFBS and GenX were eliminated faster than were PFOS or PFOA.
The microbiome of the gut is vital for homeostasis of hosts with its ability to detoxify and activate toxicants, as well as signal to the immune and nervous systems. However, in the field of environmental toxicology, the gut microbiome has only recently been identified as a measurable indicator for exposure to environmental pollutants. Antidepressants found in effluents of wastewater treatment plants and surface waters have been shown to exhibit antibacterial-like properties in vitro, where some bacteria are known to express homologous proteins that bind antidepressants in vertebrates. Therefore, it has been hypothesized that exposure to antidepressant drugs might affect gut microbiota of aquatic organisms. In this study, the common antidepressant, fluoxetine, was investigated to determine whether it can modulate the gut microbiome of adult fathead minnows. A 28-day, sub-chronic, static renewal exposure was performed with nominal fluoxetine concentrations of 0.01, 10 or 100 μg/L. Using 16S rRNA amplicon sequencing, shifts among the gut-associated microbiota were observed in individuals exposed to the greatest concentration, with greater effects observed in females. These changes were associated with a decrease in relative proportions of commensal bacteria, which can be important for health of fish including bacteria essential for fatty acid oxidation, and an increase in relative proportions of pathogenic bacteria associated with inflammation. Results demonstrate, for the first time, how antidepressants found in some aquatic environments can influence gut microbiota of fishes.
There are no standardized protocols for quantifying severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater to date, especially for population normalization. Here, a pipeline was developed, applied, and assessed to quantify SARS-CoV-2 and key variants of concern (VOCs) RNA in wastewater at Saskatoon, Canada. Normalization approaches using recovery ratio and extraction efficiency, wastewater parameters, or population indicators were assessed by comparing to daily numbers of new cases. Viral load was positively correlated with daily new cases reported in the sewershed. Wastewater surveillance (WS) had a lead time of approximately 7 days, which indicated surges in the number of new cases. WS revealed the variant α and δ driving the third and fourth wave, respectively. The adjustment with the recovery ratio and extraction efficiency improved the correlation between viral load and daily new cases. Normalization of viral concentration to concentrations of the artificial sweetener acesulfame K improved the trend of viral load during the Christmas and New Year holidays when populations were dynamic and variable. Acesulfame K performed better than pepper mild mottle virus, creatinine, and ammonia for population normalization. Hence, quality controls to characterize recovery ratios and extraction efficiencies and population normalization with acesulfame are promising for precise WS programs supporting decision-making in public health.
The neurotoxic alkaloid β-N-methyl-amino-l-alanine (BMAA) and related isomers, including N-(2-aminoethyl glycine) (AEG), β-amino-N-methyl alanine (BAMA), and 2,4-diaminobutyric acid (DAB), have been reported previously in cyanobacterial samples. However, there are conflicting reports regarding their occurrence in surface waters. In this study, we evaluated the impact of amending lake water samples with trichloroacetic acid (0.1 M TCA) on the detection of BMAA isomers, compared with pre-existing protocols. A sensitive instrumental method was enlisted for the survey, with limits of detection in the range of 5-10 ng L-1. Higher detection rates and significantly greater levels (paired Wilcoxon's signed-rank tests, p < 0.001) of BMAA isomers were observed in TCA-amended samples (method B) compared to samples without TCA (method A). The overall range of B/A ratios was 0.67-8.25 for AEG (up to +725%) and 0.69-15.5 for DAB (up to +1450%), with absolute concentration increases in TCA-amended samples of up to +15,000 ng L-1 for AEG and +650 ng L-1 for DAB. We also documented the trends in the occurrence of BMAA isomers for a large breadth of field-collected lakes from Brazil, Canada, France, Mexico, and the United Kingdom. Data gathered during this overarching campaign (overall, n = 390 within 45 lake sampling sites) indicated frequent detections of AEG and DAB isomers, with detection rates of 30% and 43% and maximum levels of 19,000 ng L-1 and 1100 ng L-1, respectively. In contrast, BAMA was found in less than 8% of the water samples, and BMAA was not found in any sample. These results support the analyses of free-living cyanobacteria, wherein BMAA was often reported at concentrations of 2-4 orders of magnitude lower than AEG and DAB. Seasonal measurements conducted at two bloom-impacted lakes indicated limited correlations of BMAA isomers with total microcystins or chlorophyll-a, which deserves further investigation.
Instrumented buoys are used to monitor water quality, yet there remains a need to evaluate whether in vivo fluorometric measures of chlorophyll a (Chl a) produce accurate estimates of phytoplankton abundance. Here, 6 years (2014–2019) of in vitro measurements of Chl a by spectrophotometry were compared with coeval estimates from buoy-based fluorescence measurements in eutrophic Buffalo Pound Lake, Saskatchewan, Canada. Analysis revealed that fluorometric and in vitro estimates of Chl a differed both in terms of absolute concentration and patterns of relative change through time. Three models were developed to improve agreement between metrics of Chl a concentration, including two based on Chl a and phycocyanin (PC) fluorescence and one based on multiple linear regressions with measured environmental conditions. All models were examined in terms of two performance metrics; accuracy (lowest error) and reliability (% fit within confidence intervals). The model based on PC fluorescence was most accurate (error = 35%), whereas that using environmental factors was most reliable (89% within 3σ of mean). Models were also evaluated on their ability to produce spatial maps of Chl a using remotely sensed imagery. Here, newly developed models significantly improved system performance with a 30% decrease in Chl a errors and a twofold increase in the range of reconstructed Chl a values. Superiority of the PC model likely reflected high cyanobacterial abundance, as well as the excitation–emission wavelength configuration of fluorometers. Our findings suggest that a PC fluorometer, used alone or in combination with environmental measurements, performs better than a single-excitation-band Chl a fluorometer in estimating Chl a content in highly eutrophic waters.
Stable Fe isotopes have only recently been measured in freshwater systems, mainly in meromictic lakes. Here we report the δ56Fe of dissolved, particulate, and sediment Fe in two small dimictic boreal shield headwater lakes: manipulated eutrophic Lake 227, with annual cyanobacterial blooms, and unmanipulated oligotrophic Lake 442. Within the lakes, the range in δ56Fe is large (ca. -0.9 to +1.8‰), spanning more than half the entire range of natural Earth surface samples. Two layers in the water column with distinctive δ56Fe of dissolved (dis) and particulate (spm) Fe were observed, despite differences in trophic states. In the epilimnia of both lakes, a large Δ56Fedis-spm fractionation of 0.4-1‰ between dissolved and particulate Fe was only observed during cyanobacterial blooms in Lake 227, possibly regulated by selective biological uptake of isotopically light Fe by cyanobacteria. In the anoxic layers in both lakes, upward flux from sediments dominates the dissolved Fe pool with an apparent Δ56Fedis-spm fractionation of -2.2 to -0.6‰. Large Δ56Fedis-spm and previously published metagenome sequence data suggest active Fe cycling processes in anoxic layers, such as microaerophilic Fe(II) oxidation or photoferrotrophy, could regulate biogeochemical cycling. Large fractionation of stable Fe isotopes in these lakes provides a potential tool to probe Fe cycling and the acquisition of Fe by cyanobacteria, with relevance for understanding biogeochemical cycling of Earth's early ferruginous oceans.
With ongoing global warming and permafrost thawing, weathering processes will change on the Yukon River, with risks for water quality and ecosystem sustainability. Here, we explore the relationship between weathering processes and permafrost cover using elemental concentration and strontium and lithium isotopic data in the dissolved load of 102 samples collected during the summer across most major tributaries of the Yukon River. The Yukon River basin is dominated by silicate weathering with a high contribution from young volcanic rock units. In glaciated mountainous zones, we observe higher carbonate weathering contribution, low Li/Na ratios and low δ 7 Li values (<15‰). In these areas, the high denudation rate and high supply of fresh minerals associated with alpine glaciers favor congruent silicate weathering, and sulfide oxidation accelerates carbonate weathering. In floodplains covered by continuous permafrost, we observe a high carbonate weathering contribution, relatively high Li/Na ratios, and low δ 7 Li values (∼18‰). We argue that the minimal water–rock interactions in this setting inhibit silicate weathering and favor congruent weathering of easily weatherable minerals (i.e., carbonates). Conversely, in areas with discontinuous or sporadic permafrost, we observe a dominance of silicate weathering, with higher and more variable Li/Na ratios and high δ 7 Li values (11–33‰). In this setting, longer water–rock interactions combined with the high supply of fresh minerals from mountain zones favor more incongruent weathering. The unique history of Pleistocene glaciations on the Yukon River basin also influences weathering processes. Many areas of the basin were never glaciated during the Pleistocene, and rivers draining those regions have higher δ 7 Li values suggesting more incongruent weathering associated with deeper flow paths and longer water residence time in the regolith. Our work underlines that water–rock interactions, including active layer weathering and groundwater inputs, are highly dependent on climate conditions and glacial processes across the Yukon River basin, with key implications for future water quality in this warming basin.
In cold water (temperatures between water's freezing point and the temperature of maximum density), near-surface heating (from the sun) generates dense water which in turn induces vertical currents. If there is a near-surface current, the resulting convective instabilities efficiently move momentum from the current to regions lower in the water column. Then, there is an induced momentum flux across the plume boundary leading to a complicated series of three-dimensional interactions resulting in turbulence. How might this process be affected by factors such as water clarity and current speed?
Resource development and climate change are increasing concerns regarding safe water for Indigenous people in Canada. A research study was completed to characterize the consumption of water and beverages prepared with water and identify the perception of water consumption in Indigenous communities from the Northwest Territories and Yukon, Canada. As part of a larger research program, data for this study were available from a 24-hour recall dietary survey ( n = 162), a health messages survey ( n = 150), and an exposure factor survey ( n = 63). A focus group was conducted with Elders in an on-the-land camp setting. The consumption of water-based beverages in winter was 0.9 L/day on average, mainly consisting of tea and coffee. Of the 81% of respondents who reported consuming water-based beverages in the previous 24 hours of the survey, 33% drank more bottled water than tap water. About 2% of respondents consumed water from the land (during the winter season). Chlorine smell was the main limiting factor reported to the consumption of tap water. Results from the focus group indicated that Indigenous knowledge might impact both the perception and consumption of water. These findings aim to support public health efforts to enable people to make water their drink of choice.
Identification of integrated models is still hindered by submodels’ uncertainty propagation. In this article, a novel identifiability and identification framework is applied to screen and establish reasonable hypotheses of an integrated instream (WASP) and catchment water quality (VENSIM) model. Using the framework, the models were linked, and critical parameters and processes identified. First, an ensemble of catchment nutrient loads was simulated with randomized parameter settings of the catchment processes (e.g. nutrient decay rates). A second Monte Carlo analysis was then staged with randomized loadings and parameter values mimicking insteam processes (e.g. algae growth). The most significant parameters and their processes were identified. This coupling of models for a two-step global sensitivity analysis is a novel approach to integrated catchment-scale water quality model identification. Catchment processes were, overall, more significant to the river’s water quality than the instream processes of this Prairie river system investigated (Qu’Appelle River).
• Model benchmarking was performed using four different meteorological forcing data. • Calculation of water balance revealed the dominant hydrological processes. • Hydrological conditions under future climatic conditions were assessed. • Uncertainty in future flow projections were quantified. Climate change introduces substantial uncertainty in water resources planning and management. This is particularly the case for the river systems in the high latitudes of the Northern Hemisphere that are more vulnerable to global change. The situation becomes more challenging when there is a limited hydrological understanding of the basin. In this study, we assessed the impacts of future climate on the hydrology of the Saint John River Basin (SJRB), which is an important transboundary coastal river basin in northeastern North America. We also additionally performed model benchmarking for the SJRB using four different meteorological forcing datasets. Using the best performing forcing data and model parameters, we studied the water balance of the basin. Our results show that meteorological forcing data play a pivotal role in model performance and therefore can introduce a large degree of uncertainty in hydrological modelling. The analysis of the water balance highlights that runoff and evapotranspiration account for about 99% of the total basin precipitation, with each constituting approximately 50%. The simulation of future flows projects higher winter discharges, but summer flows are estimated to decrease in the 2041–2070 and 2071–2100 periods compared to the baseline period (1991–2020). However, the evaluation of model errors indicates higher confidence in the result that future winter flows will increase, but lower confidence in the results that future summer flows will decrease.
• Comprehensive and extended review on probabilistic methods for hydroclimatic extremes. • Synthesis of methods used in analyses of extremes in precipitation, streamflow and temperature. • Over 20 probability distribution estimation methods in 25 comparative studies reviewed. • Identification of most promising contemporary probabilistic methods. Here we review methods used for probabilistic analysis of extreme events in Hydroclimatology. We focus on streamflow, precipitation, and temperature extremes at regional and global scales. The review has four thematic sections: (1) probability distributions used to describe hydroclimatic extremes, (2) comparative studies of parameter estimation methods, (3) non-stationarity approaches, and (4) model selection tools. Synthesis of the literature shows that: (1) recent studies, in general, agree that precipitation and streamflow extremes should be described by heavy-tailed distributions, (2) the Method of Moments (MOM) is typically the first choice in estimating distribution parameters but it is outperformed by methods such as L-Moments (LM), Maximum Likelihood (ML), Least Squares (LS), and Bayesian Markov Chain Monte Carlo (BMCMC), (3) there are less popular parameter estimation techniques such as the Maximum Product of Spacings (MPS), the Elemental Percentile (EP), and the Minimum Density Power Divergence Estimator (MDPDE) that have shown competitive performance in fitting extreme value distributions, and (4) non-stationary analyses of extreme events are gaining popularity; the ML is the typically used method, yet literature suggests that the Generalized Maximum Likelihood (GML) and the Weighted Least Squares (WLS) may be better alternatives. The review offers a synthesis of past and contemporary methods used in the analysis of hydroclimatic extremes, aiming to highlight their strengths and weaknesses. Finally, the comparative studies summary helps the reader identify the most suitable modeling framework for their analyses, based on the extreme hydroclimatic variables, sample sizes, locations, and evaluation metrics reviewed.
The Grand River (GR) extends throughout the majority of Southern Ontario with its final outlet at Lake Erie and accommodates thirty wastewater treatment plants (WWTP) with varied filtration processes. Many WWTPs are unable to effectively eliminate several contaminants of concern (CECs) from final released effluent, leading to measurable concentrations in surface waters and ultimately chronically exposing aquatic species to mixed CECs. Exposures to CECs have reported impacts on oxidative stress, measurable through reactive oxygen species (ROS) and the antioxidant defense response. This research focuses on the effects of WWTP effluent on four Etheostoma (Darter) species endemic to the GR. Objectives of this study examined if any oxidative stress markers are present in darter brains downstream from the effluent release point compared to an upstream reference site relative to the Waterloo, ON WWTP across two separate years (Fall 2020 and 2021). This was assessed using transcriptional and enzyme analysis of antioxidant enzymes (SOD, GPX, CAT) and an enzyme involved in serotonin synthesis. In fall 2020, significant differences in transcript expression of markers were found between sites and sexes in greenside darters (GSD) with SOD and CAT showing increased expression downstream. Changes in transcript expression aligned with antioxidative enzyme activity where interactive effects with sex-related differences were observed in fish collected the Fall of 2020. In contrast, transcription markers measured in Fall 2021 were increased upstream compared to downstream species. Continued investigation on the impacts of pharmaceutical exposures in non-target organisms is crucial to further the knowledge of WWTP effluent impacts.
A strong atmospheric river made landfall in southwestern British Columbia, Canada on 14th November 2021, bringing two days of intense precipitation to the region. The resulting floods and landslides led to the loss of at least five lives, cut Vancouver off entirely from the rest of Canada by road and rail, and made this the costliest natural disaster in the province's history. Here we show that westerly atmospheric river events of this magnitude are approximately one in ten year events in the current climate of this region, and that such events have been made at least 60% more likely by the effects of human-induced climate change. Characterized in terms of the associated two-day precipitation, the event is approximately a one in 50-100 year event, and its probability has been increased by a best estimate of 50% by human-induced climate change. The effects of this precipitation on streamflow were exacerbated by already wet conditions preceding the event, and by rising temperatures during the event that led to significant snowmelt, which led to streamflow maxima exceeding estimated one in a hundred year events in several basins in the region. Based on a large ensemble of simulations with a hydrological model which integrates the effects of multiple climatic drivers, we find that the probability of such extreme streamflow events has been increased by human-induced climate change by a best estimate of 2 to 4. Together these results demonstrate the substantial human influence on this compound extreme event, and help motivate efforts to increase resiliency in the face of more frequent events of this kind in the future.
Extreme temperature is a major threat to urban populations; thus, it is crucial to understand future changes to plan adaptation and mitigation strategies. We assess historical and CMIP6 projected trends of minimum and maximum temperatures for the 18 most populated Canadian cities. Temperatures increase (on average 0.3°C/decade) in all cities during the historical period (1979–2014), with Prairie cities exhibiting lower rates (0.06°C/decade). Toronto (0.5°C/decade) and Montreal (0.7°C/decade) show high increasing trends in the observation period. Higher-elevation cities, among those with the same population, show slower increasing temperature rates compared to the coastal ones. Projections for cities in the Prairies show 12% more summer days compared to the other regions. The number of heat waves (HWs) increases for all cities, in both the historical and future periods; yet alarming increases are projected for Vancouver, Victoria, and Halifax from no HWs in the historical period to approximately 4 HWs/year on average, towards the end of 2100 for the SSP5–8.5. The cold waves reduce considerably for all cities in the historical period at a rate of 2 CWs/decade on average and are projected to further reduce by 50% compared to the observed period. • CMIP6 simulations for extreme temperature estimation of the largest Canadian cities. • Prairies' cities exhibit a lower rate of temperature increase compared to the cities in Great lakes in observation period. • Cities in Prairies are projected to have 12% more summer days than the rest of the cities. • The number of heat waves increases significantly, especially for Vancouver, Victoria, and Halifax. • Cold waves are expected to decrease by 50% in future.
• The probable impacts of future climate on ice-jam floods are discussed. • Practical suggestions for modelling ice-jam floods under both past and future climates are provided. • Research opportunities that could lead to further improvements in ice-jam flood modelling and prediction are presented. Ice-jam floods (IJFs) are a key concern in cold-region environments, where seasonal effects of river ice formation and break-up can have substantial impacts on flooding processes. Different statistical, machine learning, and process-based models have been developed to simulate IJF events in order to improve our understanding of river ice processes, to quantify potential flood magnitudes and backwater levels, and to undertake risk analysis under a changing climate. Assessment of IJF risks under future climate is limited due to constraints related to model input data. However, given the broad economic and environmental significance of IJFs and their sensitivity to a changing climate, robust modelling frameworks that can incorporate future climatic changes, and produce reliable scenarios of future IJF risks are needed. In this review paper, we discuss the probable impacts of future climate on IJFs and provide suggestions on modelling IJFs under both past and future climates. We also make recommendations around existing approaches and highlight some data and research opportunities, that could lead to further improvements in IJF modelling and prediction.
With the increasing availability of SAR imagery in recent years, more research is being conducted using deep learning (DL) for the classification of ice and open water; however, ice and open water classification using conventional DL methods such as convolutional neural networks (CNNs) is not yet accurate enough to replace manual analysis for operational ice chart mapping. Understanding the uncertainties associated with CNN model predictions can help to quantify errors and, therefore, guide efforts on potential enhancements using more–advanced DL models and/or synergistic approaches. This paper evaluates an approach for estimating the aleatoric uncertainty [a measure used to identify the noise inherent in data] of CNN probabilities to map ice and open water with a custom loss function applied to RADARSAT–2 HH and HV observations. The images were acquired during the 2014 ice season of Lake Erie and Lake Ontario, two of the five Laurentian Great Lakes of North America. Operational image analysis charts from the Canadian Ice Service (CIS), which are based on visual interpretation of SAR imagery, are used to provide training and testing labels for the CNN model and to evaluate the accuracy of the model predictions. Bathymetry, as a variable that has an impact on the ice regime of lakes, was also incorporated during model training in supplementary experiments. Adding aleatoric loss and bathymetry information improved the accuracy of mapping water and ice. Results are evaluated quantitatively (accuracy metrics) and qualitatively (visual comparisons). Ice and open water scores were improved in some sections of the lakes by using aleatoric loss and including bathymetry. In Lake Erie, the ice score was improved by ∼2 on average in the shallow near–shore zone as a result of better mapping of dark ice (low backscatter) in the western basin. As for Lake Ontario, the open water score was improved by ∼6 on average in the deepest profundal off–shore zone.
Abstract Gridded meteorological estimates are essential for many applications. Most existing meteorological datasets are deterministic and have limitations in representing the inherent uncertainties from both the data and methodology used to create gridded products. We develop the Ensemble Meteorological Dataset for Planet Earth (EM-Earth) for precipitation, mean daily temperature, daily temperature range, and dewpoint temperature at 0.1° spatial resolution over global land areas from 1950 to 2019. EM-Earth provides hourly/daily deterministic estimates, and daily probabilistic estimates (25 ensemble members), to meet the diverse requirements of hydrometeorological applications. To produce EM-Earth, we first developed a station-based Serially Complete Earth (SC-Earth) dataset, which removes the temporal discontinuities in raw station observations. Then, we optimally merged SC-Earth station data and ERA5 estimates to generate EM-Earth deterministic estimates and their uncertainties. The EM-Earth ensemble members are produced by sampling from parametric probability distributions using spatiotemporally correlated random fields. The EM-Earth dataset is evaluated by leave-one-out validation, using independent evaluation stations, and comparing it with many widely used datasets. The results show that EM-Earth is better in Europe, North America, and Oceania than in Africa, Asia, and South America, mainly due to differences in the available stations and differences in climate conditions. Probabilistic spatial meteorological datasets are particularly valuable in regions with large meteorological uncertainties, where almost all existing deterministic datasets face great challenges in obtaining accurate estimates.
Water quality models are an emerging tool in water management to understand and inform decisions related to eutrophication. This study tested flow scenario effects on the water quality of Buffalo Pound Lake—a eutrophic reservoir supplying water for approximately 25% of Saskatchewan’s population. The model CE-QUAL-W2 was applied to assess the impact of inter-basin water diversion after the impounded lake received high inflows from local runoff. Three water diversion scenarios were tested: continuous flow, immediate release after nutrient loading increased, and a timed release initiated when water levels returned to normal operating range. Each scenario was tested at three different transfer flow rates. The transfers had a dilution effect but did not affect the timing of the nutrient peaks in the upstream portion of the lake. In the lake’s downstream section, nutrients peaked at similar concentrations as the base model, but peaks arrived earlier in the season and attenuated rapidly. Results showed greater variation among scenarios in wet years compared to dry years. Dependent on the timing and quantity of water transferred, some but not all water quality parameters are predicted to improve along with the water diversion flows over the period tested. The results suggest that it is optimal to transfer water while local watershed runoff is minimal.
Abstract Surface meteorological analyses are an essential input (termed “forcing”) for hydrologic modeling. This study investigated the sensitivity of different hydrologic model configurations to temporal variations of seven forcing variables (precipitation rate, air temperature, longwave radiation, specific humidity, shortwave radiation, wind speed, and air pressure). Specifically, the effects of temporally aggregating hourly forcings to hourly daily average forcings were examined. The analysis was based on 14 hydrological outputs from the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model for the 671 Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) basins across the contiguous United States (CONUS). Results demonstrated that the hydrologic model sensitivity to temporally aggregating the forcing inputs varies across model output variables and model locations. We used Latin hypercube sampling to sample model parameters from eight combinations of three influential model physics choices (three model decisions with two options for each decision, i.e., eight model configurations). Results showed that the choice of model physics can change the relative influence of forcing on model outputs and the forcing importance may not be dependent on the parameter space. This allows for model output sensitivity to forcing aggregation to be tested prior to parameter calibration. More generally, this work provides a comprehensive analysis of the dependence of modeled outcomes on input forcing behavior, providing insight into the regional variability of forcing variable dominance on modeled outputs across CONUS.
Ice jams are impacted by several climatic factors that are likely to change under a future warming climate. Due to the complexity of river ice phenology, projection of future ice jams is challenging. However, it is important to be able to project future ice jam behavior. Additionally, ice jam research is limited by the shortage of long-term monitoring data. In this paper, a novel framework for projecting future ice jam behavior is developed and implemented for ice jams in a data-sparse region, the Slave River Delta, NWT, Canada, situated in the Mackenzie River Basin (MRB). This framework employs both historical records and future hydro-meteorological data, acquired from climate and hydrological models, to drive the river ice models and quantify climate-induced influences on ice jams. Ice jam behavior analysis is based on three outputs of the framework: potential of river ice jamming, ice jam initiation date, and the stage frequency distribution of backwater elevation induced by ice jams. Trends of later ice jam initiation and decreased possibility of ice jam formation are projected, but ice jamming events in the Slave River Delta are likely to be more severe and cause higher backwater levels.
Management strategies aimed at reducing nutrient enrichment of surface waters may be hampered by nutrient legacies that have accumulated in the landscape. Here, we apply the Net Anthropogenic Phosphorus Input (NAPI) model to reconstruct the historical phosphorus (P) input trajectories for the province of Ontario, which encompasses the Canadian portion of the drainage basin of the Laurentian Great Lakes (LGL). NAPI considers P inputs from detergent, human and livestock waste, fertilizer inputs, and P outputs by crop uptake. During the entire time period considered, from 1961 to 2016, Ontario experienced positive annual NAPI values. Despite a generally downward NAPI trend since the late 1970s, the lower LGL, especially Lake Erie, continue to be plagued by algal blooms. When comparing NAPI results and river monitoring data for the period 2003 to 2013, P discharged by Canadian rivers into Lake Erie only accounts for 12.5% of the NAPI supplied to the watersheds' agricultural areas. Thus, over 85% of the agricultural NAPI is retained in the watersheds where it contributes to a growing P legacy, primarily as soil P. The slow release of legacy P therefore represents a long-term risk to the recovery of the lake. To help mitigate this risk, we present a methodology to spatially map out the source areas with the greatest potential of erosional export of legacy soil P to surface waters. These areas should be prioritized in soil conservation efforts.
In the higher latitudes of the northern hemisphere, ice jam related flooding can result in millions of dollars of property damages, loss of human life and adverse impacts on ecology. Since ice-jam formation mechanism is stochastic and depends on numerous unpredictable hydraulic and river ice factors, ice-jam associated flood forecasting is a very challenging task. A stochastic modelling framework was developed to forecast real-time ice jam flood severity along the transborder (New Brunswick/Maine) Saint John River of North America during the spring breakup 2021. Modélisation environnementale communautaire—surface hydrology (MESH), a semi-distributed physically-based land-surface hydrological modelling system was used to acquire a 10-day flow forecast. A Monte-Carlo analysis (MOCA) framework was applied to simulate hundreds of possible ice-jam scenarios for the model domain from Fort Kent to Grand Falls using a hydrodynamic river ice model, RIVICE. First, a 10-day outlook was simulated to provide insight on the severity of ice jam flooding during spring breakup. Then, 3-day forecasts were modelled to provide longitudinal profiles of exceedance probabilities of ice jam flood staging along the river during the ice-cover breakup. Overall, results show that the stochastic approach performed well to estimate maximum probable ice-jam backwater level elevations for the spring 2021 breakup season.
Rapid shrub expansion has been observed across the Arctic, driving a need for regional-scale estimates of shrub biomass and shrub-mediated ecosystem processes such as rainfall interception. Synthetic-Aperture Radar (SAR) data have been shown sensitive to vegetation canopy characteristics across many ecosystems, thereby potentially providing an accurate and cost-effective tool to quantify shrub canopy cover. This study evaluated the sensitivity of L-band Advanced Land Observing Satellite 2 (ALOS-2) data to the aboveground biomass and Leaf Area Index (LAI) of dwarf birch and alder in the Trail Valley Creek watershed, Northwest Territories, Canada. The σ° VH /σ° VV ratio showed strong sensitivity to both LAI (R 2 = 0.72 with respect to in-situ measurements) and wet aboveground biomass (R 2 = 0.63) of dwarf birch. Our ALOS-2-derived maps revealed high variability of birch shrub LAI and biomass across spatial scales. The LAI map was fed into the sparse Gash model to estimate shrub rainfall interception, an important but under-studied component of the Arctic water balance. Results suggest that on average across the watershed, 17 ± 3% of incoming rainfall was intercepted by dwarf birch (during summer 2018), highlighting the importance of shrub rainfall interception for the regional water balance. These findings demonstrate the unexploited potential of L-band SAR observations from satellites for quantifying the impact of shrub expansion on Arctic ecosystem processes. • L-band SAR is a skillful predictor for tundra shrub biomass and leaf area index. • High spatial variation in tundra shrub cover captured by L-band SAR. • Distributed rainfall interception by shrub mapped across the watershed. • Amount of interception closely linked to shrub leaf area index.
• A comprehensive review and analysis of IMERG validation studies from 2016 to 2019. • There is robust representation of spatio-temporal patterns of precipitation. • Discrepancies can be found in extreme and light precipitation, and the winter season. • The 30-min scale has not yet been sufficiently evaluated. • Using IMERG in hydrological simulation results to high variance in their performance. Accurate, reliable, and high spatio-temporal resolution precipitation data are vital for many applications, including the study of extreme events, hydrological modeling, water resource management, and hydroclimatic research in general. In this study, we performed a systematic review of the available literature to assess the performance of the Integrated Multi-Satellite Retrievals for GPM (IMERG) products across different geographical locations and climatic conditions around the globe. Asia, and in particular China, are the subject of the largest number of IMERG evaluation studies on the continental and country level. When compared to ground observational records, IMERG is found to vary with seasons, as well as precipitation type, structure, and intensity. It is shown to appropriately estimate and detect regional precipitation patterns, and their spatial mean, while its performance can be improved over mountainous regions characterized by orographic precipitation, complex terrains, and for winter precipitation. Furthermore, despite IMERG's better performance compared to other satellite products in reproducing spatio-temporal patterns and variability of extreme precipitation, some limitations were found regarding the precipitation intensity. At the temporal scales, IMERG performs better at monthly and annual time steps than the daily and sub-daily ones. Finally, in terms of hydrological application, the use of IMERG has resulted in significant discrepancies in streamflow simulation. However, and most importantly, we find that each new version that replaces the previous one, shows substantial improvement in almost every spatiotemporal scale and climatic condition. Thus, despite its limitations, IMERG evolution reveals a promising path for current and future applications.
Abstract Foundation species have disproportionately large impacts on ecosystem structure and function. As a result, future changes to their distribution may be important determinants of ecosystem carbon (C) cycling in a warmer world. We assessed the role of a foundation tussock sedge ( Eriophorum vaginatum ) as a climatically vulnerable C stock using field data, a machine learning ecological niche model, and an ensemble of terrestrial biosphere models (TBMs). Field data indicated that tussock density has decreased by ~0.97 tussocks per m2 over the past ~38 years on Alaska’s North Slope from ~1981 to 2019. This declining trend is concerning because tussocks are a large Arctic C stock, which enhances soil organic layer C stocks by 6.9% on average and represents 745 Tg C across our study area. By 2100, we project that changes in tussock density may decrease the tussock C stock by 41% in regions where tussocks are currently abundant (e.g. -0.8 tussocks per m2 and -85 Tg C on the North Slope) and may increase the tussock C stock by 46% in regions where tussocks are currently scarce (e.g. +0.9 tussocks per m2 and +81 Tg C on Victoria Island). These climate-induced changes to the tussock C stock were comparable to, but sometimes opposite in sign, to vegetation C stock changes predicted by an ensemble of TBMs. Our results illustrate the important role of tussocks as a foundation species in determining future Arctic C stocks and highlights the need for better representation of this species in TBMs.
ABSTRACT Simple and robust hydrological modelling is critical for peat studies as water content (θ) and water table depth (d WT) are key controls on many biogeochemical processes. We show that near-surface θ can be a good predictor of θ at any depth and/or d WT in peat. This was achieved by further developing the formulae of an existing model and applying it for Mer Bleue bog (Ontario, Canada) and a permafrost peat plateau at Scotty Creek (Northwest Territories, Canada). Simulated θ dynamics at various depths in hummocks and hollows at both sites matched observations with R2 , Willmott’s index of agreement (d), and normalized Nash-Sutcliffe efficiency coefficient (NNSE), reaching 0.97, 0.95, and 0.86, respectively. Simulated bog WT dynamics matched observations with R2 , d, and NNSE reaching 0.67, 0.87, and 0.72. Our approach circumvents the difficulties of measuring subsurface hydrology and reveals a perspective for large spatial scale estimation of θ and d WT in peat.
Peatlands have acted as net CO2 sinks over millennia, exerting a global climate cooling effect. Rapid warming at northern latitudes, where peatlands are abundant, can disturb their CO2 sink function. Here we show that sensitivity of peatland net CO2 exchange to warming changes in sign and magnitude across seasons, resulting in complex net CO2 sink responses. We use multiannual net CO2 exchange observations from 20 northern peatlands to show that warmer early summers are linked to increased net CO2 uptake, while warmer late summers lead to decreased net CO2 uptake. Thus, net CO2 sinks of peatlands in regions experiencing early summer warming, such as central Siberia, are more likely to persist under warmer climate conditions than are those in other regions. Our results will be useful to improve the design of future warming experiments and to better interpret large-scale trends in peatland net CO2 uptake over the coming few decades.
ABSTRACT Accurate and frequent mapping of transient wetland inundation in the boreal region is critical for monitoring the ecological and societal functions of wetlands. Satellite Synthetic Aperture Radar (SAR) has long been used to map wetlands due to its sensitivity to surface inundation and ability to penetrate clouds, darkness, and certain vegetation canopies. Here, we track boreal wetland inundation by developing a two-step modified decision-tree algorithm implemented in Google Earth Engine using Sentinel-1 C-band SAR and Sentinel-2 Multispectral Instrument (MSI) time-series data as inputs. This approach incorporates temporal as well as spatial characteristics of SAR backscatter and is evaluated for the Peace-Athabasca Delta, Alberta (PAD), and Yukon Flats, Alaska (YF) from May 2017 to October 2019. Within these two boreal study areas, we map spatiotemporal patterns in wetland inundation classes of Open Water (OW), Floating Plants (FP), Emergent Plants (EP), and Flooded Vegetation (FV). Temporal variability, frequency, and maximum extents of transient wetland inundation are quantified. Retrieved inundation estimates are compared with in-situ field mapping obtained during the NASA Arctic-Boreal Vulnerability Experiment (ABoVE), and a multi-temporal Landsat-derived surface water map. Over the 2017–2019 study period, we find that fractional inundation area ranged from 18.0% to 19.0% in the PAD, and from 10.7% to 12.1% in the YF. Transient wetland inundation covered ~595 km2 of the PAD, comprising ~9.1% of its landscape, and ~102 km2 of the YF, comprising ~3.6%. The implications of these findings for wetland function monitoring, and estimating landscape-scale methane emissions are discussed, together with limitations and uncertainties of our approach. We conclude that time series of Sentinel-1 C-band SAR backscatter, screened with Sentinel-2 MSI optical imagery and validated by field measurements, offer a valuable tool for tracking transient boreal wetland inundation. GRAPHICAL ABSTRACT
Subsurface pipe failures in cold regions are generally believed to be exacerbated by differential strain in shallow soils induced by seasonal freeze and thaw cycles. The transient stress–strain fields resulting from soil water phase change may influence the occurrence of local buried pipe breaks including those related to urban water mains. This work proposes that freezing-induced frost loading results in uneven stress–strain distributions along the buried water mains placing them at risk of bending, breaking, and/or leaking. A coupled thermal-hydraulic-mechanical (THM) model was developed to illustrate the interactions among moisture, temperature, and stress–strain fields within variably saturated freezing soils. Several typical cases involving highly frost-susceptible and lower frost-susceptible soils underlying roadbeds were examined. Results show that the magnitude of frost-induced compressive stress and strain changes between different frost-susceptible soils can vary significantly. Such substantial differences in stress–strain fields would increase the breakage risk of water mains buried within different types of soils. Furthermore, even water mains buried within soils with low frost-susceptibility are at risk when additional sources of soil water exist and are available to migrate to the freezing front. To reduce the risk of damage to buried pipe-like infrastructure, such as municipal water mains, from soil freezing phenomena, the selected backfill material should have fairly consistent frost susceptibility or a broad zone of transition should be considered between materials with significantly different frost susceptibility. In addition, buried pipes should be kept as far away from external sources of subsurface water as possible considering the potential for the water source to exacerbate the level of risk to the pipe.
Wetlands are important ecosystems—they provide vital hydrological and ecological services such as regulating floods, storing carbon, and providing wildlife habitat. The ability to simulate their spatial extents and hydrological processes is important for valuing wetlands' function. The purpose of this study is to dynamically represent the spatial extents and hydrological processes of wetlands and investigate their feedback to regional climate in the Prairie Pothole Region (PPR) of North America, where a large number of wetlands exist. In this study, we incorporated a wetland scheme into the Noah-MP land surface model with two major modifications: (a) modifying the subgrid saturation fraction for spatial wetland extent and (b) incorporating a dynamic wetland storage to simulate hydrological processes. This scheme was evaluated at a fen site in central Saskatchewan, Canada and applied regionally in the PPR with 13-year climate forcing produced by a high-resolution convection-permitting model. The differences between wetland and no-wetland simulations are significant, with increasing latent heat and evapotranspiration while suppressing sensible heat and runoff in the wetland scheme. Finally, the dynamic wetland scheme was applied in the Weather Research and Forecasting (WRF) model. The wetlands scheme not only modifies the surface energy balance but also interacts with the lower atmosphere, shallowing the planetary boundary layer height and promoting cloud formation. A cooling effect of 1–3°C in summer temperature is evident where wetlands are abundant. In particular, the wetland simulation shows reduction in the number of hot days for >10 days over the summer of 2006, when a long-lasting heatwave occurred. This research has great implications for land surface/regional climate modeling and wetland conservation, especially in mitigating extreme heatwaves under climate change.
Model calibration and validation are critical in hydrological model robustness assessment. Unfortunately, the commonly used split‐sample test (SST) framework for data splitting requires modelers to make subjective decisions without clear guidelines. This large‐sample SST assessment study empirically assesses how different data splitting methods influence post‐validation model testing period performance, thereby identifying optimal data splitting methods under different conditions. This study investigates the performance of two lumped conceptual hydrological models calibrated and tested in 463 catchments across the United States using 50 different data splitting schemes. These schemes are established regarding the data availability, length and data recentness of continuous calibration sub‐periods (CSPs). A full‐period CSP is also included in the experiment, which skips model validation. The assessment approach is novel in multiple ways including how model building decisions are framed as a decision tree problem and viewing the model building process as a formal testing period classification problem, aiming to accurately predict model success/failure in the testing period. Results span different climate and catchment conditions across a 35‐year period with available data, making conclusions quite generalizable. Calibrating to older data and then validating models on newer data produces inferior model testing period performance in every single analysis conducted and should be avoided. Calibrating to the full available data and skipping model validation entirely is the most robust split‐sample decision. Experimental findings remain consistent no matter how model building factors (i.e., catchments, model types, data availability, and testing periods) are varied. Results strongly support revising the traditional split‐sample approach in hydrological modeling.
Access to and availability of food harvested from the land (called traditional food, country food, or wild food) are critical to food security and food sovereignty of Indigenous People. These foods can be particularly difficult to access for those living in urban environments. We ask: what policies are involved in the regulation of traditional/country foods and how do these policies affect access to traditional/country food for Indigenous Peoples living in urban centers? Which policies act as barriers? This paper provides a comparative policy analysis of wild food policies across Ontario, the Northwest Territories (NWT), and the Yukon Territory, Canada, by examining and making comparisons between various pieces of legislation, such as fish and wildlife acts, hunting regulations, food premises legislation, and meat inspection regulations. We provide examples of how some programs serving Indigenous Peoples have managed to provide wild foods, using creative ways to operate within the existing system. While there is overwhelming evidence that traditional/country food plays a critical role for the health and well-being of Indigenous Peoples within Canada, Indigenous food systems are often undermined by provincial and territorial wild food policies. Provinces like Ontario with more restrictive policies may be able to learn from the policies in the Territories. We found that on a system level, there are significant constraints on the accessibility of wild foods in urban spaces because the regulatory food environment is designed to manage a colonial market-based system that devalues Indigenous values of sharing and reciprocity and Indigenous food systems, particularly for traditional/country foods. Dismantling the barriers to traditional/country food access in that system can be an important way forward.
Abstract. Topography and vegetation play a major role in sub-pixel variability of Arctic snowpack properties but are not considered in current passive microwave (PMW) satellite SWE retrievals. Simulation of sub-pixel variability of snow properties is also problematic when downscaling snow and climate models. In this study, we simplified observed variability of snowpack properties (depth, density, microstructure) in a two-layer model with mean values and distributions of two multi-year tundra dataset so they could be incorporated in SWE retrieval schemes. Spatial variation of snow depth was parameterized by a log-normal distribution with mean (μsd) values and coefficients of variation (CVsd). Snow depth variability (CVsd) was found to increase as a function of the area measured by a remotely piloted aircraft system (RPAS). Distributions of snow specific surface area (SSA) and density were found for the wind slab (WS) and depth hoar (DH) layers. The mean depth hoar fraction (DHF) was found to be higher in Trail Valley Creek (TVC) than in Cambridge Bay (CB), where TVC is at a lower latitude with a subarctic shrub tundra compared to CB, which is a graminoid tundra. DHFs were fitted with a Gaussian process and predicted from snow depth. Simulations of brightness temperatures using the Snow Microwave Radiative Transfer (SMRT) model incorporating snow depth and DHF variation were evaluated with measurements from the Special Sensor Microwave/Imager and Sounder (SSMIS) sensor. Variation in snow depth (CVsd) is proposed as an effective parameter to account for sub-pixel variability in PMW emission, improving simulation by 8 K. SMRT simulations using a CVsd of 0.9 best matched CVsd observations from spatial datasets for areas > 3 km2, which is comparable to the 3.125 km pixel size of the Equal-Area Scalable Earth (EASE)-Grid 2.0 enhanced resolution at 37 GHz.
Synthetic aperture radar (SAR) is a widely used tool for Earth observation activities. It is particularly effective during times of persistent cloud cover, low light conditions, or where in situ measurements are challenging. The intensity measured by a polarimetric SAR has proven effective for characterizing Arctic tundra landscapes due to the unique backscattering signatures associated with different cover types. However, recently, there has been increased interest in exploiting novel interferometric SAR (InSAR) techniques that rely on both the amplitude and absolute phase of a pair of acquisitions to produce coherence measurements, although the simultaneous use of both intensity and interferometric coherence in Arctic tundra image classification has not been widely tested. In this study, a time series of dual-polarimetric (VV, VH) Sentinel-1 SAR/InSAR data collected over one growing season, in addition to a digital elevation model (DEM), was used to characterize an Arctic tundra study site spanning a hydrologically dynamic coastal delta, open tundra, and high topographic relief from mountainous terrain. SAR intensity and coherence patterns based on repeat-pass interferometry were analyzed in terms of ecological structure (i.e., graminoid, or woody) and hydrology (i.e., wet, or dry) using machine learning methods. Six hydro-ecological cover types were delineated using time-series statistical descriptors (i.e., mean, standard deviation, etc.) as model inputs. Model evaluations indicated SAR intensity to have better predictive power than coherence, especially for wet landcover classes due to temporal decorrelation. However, accuracies improved when both intensity and coherence were used, highlighting the complementarity of these two measures. Combining time-series SAR/InSAR data with terrain derivatives resulted in the highest per-class F1 score values, ranging from 0.682 to 0.955. The developed methodology is independent of atmospheric conditions (i.e., cloud cover or sunlight) as it does not rely on optical information, and thus can be regularly updated over forthcoming seasons or annually to support ecosystem monitoring.
Mercury concentrations ([Hg]) in fish reflect complex biogeochemical and ecological interactions that occur at a range of spatial and biological scales. Elucidating these interactions is crucial to understanding and predicting fish [Hg], particularly at northern latitudes, where environmental perturbations are having profound effects on land-water-animal interactions, and where fish are a critical subsistence food source. Using data from eleven subarctic lakes that span an area of ~60,000 km2 in the Dehcho Region of Northwest Territories (Canada), we investigated how trophic ecology and growth rates of fish, lake water chemistry, and catchment characteristics interact to affect [Hg] in Northern Pike (Esox lucius), a predatory fish of widespread subsistence and commercial importance. Results from linear regression and piecewise structural equation models showed that 83% of among-lake variability in Northern Pike [Hg] was explained by fish growth rates (negative) and concentrations of methyl Hg ([MeHg]) in benthic invertebrates (positive). These variables were in turn influenced by concentrations of dissolved organic carbon, MeHg (water), and total Hg (sediment) in lakes, which were ultimately driven by catchment characteristics. Lakes in relatively larger catchments and with more temperate/subpolar needleleaf and mixed forests had higher [Hg] in Northern Pike. Our results provide a plausible mechanistic understanding of how interacting processes at scales ranging from whole catchments to individual organisms influence fish [Hg], and give insight into factors that could be considered for prioritizing lakes for monitoring in subarctic regions.
Adapting to climate change as a consequence of increasing greenhouse gas (GHG) emissions is of paramount importance in the near future. Therefore, recognition of spatial and temporal variations of atmospheric carbon dioxide (CO 2 ) concentration both globally and regionally is critical. The goal of this study was to analyze spatio-temporal patterns of atmospheric CO 2 concentration (XCO 2 ) for Iran over the period from 2003 to 2020 to shed light on the role of various biotic and abiotic controls. First, by using atmospheric XCO 2 data obtained from the SCIAMACHY and GOSAT satellite instruments, a series of spatio-temporal XCO 2 distribution maps were developed. Second, to understand of the potential causes underlying the spatio-temporal distributions in XCO 2 , the correlations between monthly XCO 2 and vegetation abundance, air temperature, precipitation, and fossil fuel CO 2 emissions were examined. The spatio-temporal patterns in XCO 2 indicated an increasing gradient of XCO 2 from north to south and from west to east in Iran, with the highest XCO 2 in the central, southern and southeastern parts of the country. The findings revealed that XCO 2 was negatively correlated with vegetation abundance and precipitation, and positively correlated with air temperature in different months from 2003 to 2020. Among the different explanatory variables, vegetation abundance explained most of the spatial variation in XCO 2 . Furthermore, in spring (April and May), which has the highest amount of vegetation abundance and precipitation, biotic controls had a substantial impact on the diffusion and absorption of XCO 2 in the northern and northwestern parts of Iran. Our results suggest that CO 2 is moved from the center of Iran to the outer parts of the country in summer (July–September) and vice-versa in winter (January–March). Our findings provide policy- and decision makers with crucial information regarding the spatio-temporal dynamics in XCO 2 to reduce and, ultimately, halt its increase. • Over the spatial distribution of XCO 2 , biotic controls such as vegetation abundance were found to be the primary controlling factor especially in spring. • The results revealed a significant positive correlation between XCO 2 and CO 2 emissions only in temporal correlation but not in the spatial correlation. • The spatio-temporal distribution maps show the maximum XCO 2 in south and southeast of Iran, while the highest net increase of XCO 2 appeared in the west and north of Iran which are densely populated.
Studies of tree water source partitioning have primarily focused on the growing season. However, little is yet known about the source of transpiration before, during, and after snowmelt when trees rehydrate and recommence transpiration in the spring. This study investigates tree water use during spring snowmelt following tree's winter stem shrinkage. We document the source of transpiration of three boreal forest tree species—Pinus banksiana, Picea mariana, and Larix laricina—by combining observations of weekly isotopic signatures (δ18O and δ2H) of xylem, soil water, rainfall and snowmelt with measurements of soil moisture dynamics, snow depth and high-resolution temporal measurements of stem radius changes and sap flow. Our data shows that the onset of stem rehydration and transpiration overlaps with snowmelt for evergreens. During rehydration and transpiration onset, xylem water at the canopy reflected a constant pre-melt isotopic signature likely showing late fall conditions. As snowmelt infiltrates the soil and recharges the soil matrix, soil water shows a rapid isotopic shift to depleted-snowmelt water values. While there was an overlap between snowmelt and transpiration timing, xylem and soil water isotopic values did not overlap during transpiration onset. Our data showed 1–2-week delay in the shift in xylem water from pre-melt to clear snowmelt-depleted water signatures in evergreen species. This delay appears to be controlled by tree water transit time that was in the order of 9–18 days. Our study shows that snowmelt is a key source for stem rehydration and transpiration in the boreal forest during spring onset.
Concentrations of total mercury were measured in blood and hair samples collected as part of a human biomonitoring project conducted in First Nations communities of the Mackenzie Valley, Northwest Territories, Canada. Hair (n = 443) and blood (n = 276) samples were obtained from six communities in the Dehcho region and three communities in the Sahtú region of the Mackenzie Valley. The aim of this paper was to calculate hair to blood mercury ratios (for matched samples) and determine if: 1) ratios differed significantly between the two regions; 2) ratios differed from the 250:1 ratio proposed by the WHO; and, 3) point estimates of hair to blood mercury ratios could be used to estimate blood mercury concentrations. In addition, this paper aims to determine if there were seasonal patterns in hair mercury concentrations in these regions and if so, if patterns were related to among-season variability in fish consumption. The majority of mercury levels in hair and blood were below relevant health-based guidance values. The geometric mean hair (most recent segment) to blood mercury ratio (stratified by region) was 619:1 for the Dehcho region and 1220:1 for the Sahtú region. Mean log-transformed hair to blood mercury ratios were statistically significantly different between the two regions. Hair to blood ratios calculated in this study were far higher (2-5 times higher) than those typically reported in the literature and there was a large amount of inter-individual variation in calculated ratios (range: 114:1 to 4290:1). Using the 250:1 ratio derived by the World Health Organisation to estimate blood mercury concentrations from hair mercury concentrations would substantially over-estimate blood mercury concentrations in the studied regions. However, geometric mean site-specific hair to blood mercury ratios can provide estimates of measures of central tendency for blood mercury concentrations from hair mercury concentrations at a population level. Mercury concentrations were determined in segments of long hair samples to examine exposure of participants to mercury over the past year. Hair segments were assigned to six time periods and the highest hair mercury concentrations were generally observed in hair segments that aligned with September/October and November/December, whereas the lowest hair mercury concentrations were aligned with March/April and May/June. Mean log-transformed hair mercury concentrations were statistically significantly different between time periods. Between time periods (e.g., September/October vs. March/April), the geometric mean mercury concentration in hair differed by up to 0.22 μg/g, and the upper margins of mercury exposure (e.g., 95th percentile of hair mercury) varied by up to 0.86 μg/g. Results from self-reported fish consumption frequency questionnaires (subset of participants; n = 170) showed total fish intake peaked in late summer, decreased during the winter, and then increased during the spring. Visual assessment of results indicated that mean hair mercury concentrations followed this same seasonal pattern. Results from mixed effects models, however, indicated that variability in hair mercury concentrations among time periods was not best explained by total fish consumption frequency. Instead, seasonal trends in hair mercury concentrations may be more related to the consumption of specific fish species (rather than total wild-harvested fish in general). Future work should examine whether seasonal changes in the consumption of specific fish species are associated with seasonal changes in hair mercury concentrations.
A dietary transition away from traditional foods and toward a diet of the predominantly unhealthy market is a public health and sociocultural concern throughout Indigenous communities in Canada, including those in the sub-Arctic and remote regions of Dehcho and Sahtú of the Northwest Territories, Canada. The main aim of the present study is to describe dietary intakes for macronutrients and micronutrients in traditional and market food from the Mackenzie Valley study. We also show the trends of contributions and differences of dietary intakes over time from 1994 data collected and reported by the Centre for Indigenous People's Nutrition and Environment (CINE) in 1996. Based on 24-h dietary recall data, the study uses descriptive statistics to describe the observed dietary intake of the Dene First Nations communities in the Dehcho and Sahtú regions of the NWT. Indigenous people in Canada, like the sub-Arctic regions of Dehcho and Sahtú of the NWT, continue to consume traditional foods, although as a small percentage of their total dietary intake. The observed dietary intake calls for action to ensure that traditional food remains a staple as it is critical for the wellbeing of Dene in the Dehcho and Sahtú regions and across the territory.
At the edge of alpine and Arctic ecosystems all over the world, a transition zone exists beyond which it is either infeasible or unfavorable for trees to exist, colloquially identified as the treeline. We explore the possibility of a thermodynamic basis behind this demarcation in vegetation by considering ecosystems as open systems driven by thermodynamic advantage-defined by vegetation's ability to dissipate heat from the earth's surface to the air above the canopy. To deduce whether forests would be more thermodynamically advantageous than existing ecosystems beyond treelines, we construct and examine counterfactual scenarios in which trees exist beyond a treeline instead of the existing alpine meadow or Arctic tundra. Meteorological data from the Italian Alps, United States Rocky Mountains, and Western Canadian Taiga-Tundra are used as forcing for model computation of ecosystem work and temperature gradients at sites on both sides of each treeline with and without trees. Model results indicate that the alpine sites do not support trees beyond the treeline, as their presence would result in excessive CO[Formula: see text] loss and extended periods of snowpack due to temperature inversions (i.e., positive temperature gradient from the earth surface to the atmosphere). Further, both Arctic and alpine sites exhibit negative work resulting in positive feedback between vegetation heat dissipation and temperature gradient, thereby extending the duration of temperature inversions. These conditions demonstrate thermodynamic infeasibility associated with the counterfactual scenario of trees existing beyond a treeline. Thus, we conclude that, in addition to resource constraints, a treeline is an outcome of an ecosystem's ability to self-organize towards the most advantageous vegetation structure facilitated by thermodynamic feasibility.

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Earlier snowmelt may lead to late season declines in plant productivity and carbon sequestration in Arctic tundra ecosystems
Donatella Zona, Peter M. Lafleur, Koen Hufkens, Barbara Bailey, Beniamino Gioli, George Burba, Jordan P. Goodrich, A. K. Liljedahl, Eugénie Euskirchen, Jennifer D. Watts, Mary Farina, John S. Kimball, Martin Heimann, Mathias Göckede, Martijn Pallandt, Torben R. Christensen, Mikhail Mastepanov, Efrèn López‐Blanco, Marcin Jackowicz-Korczyński, Han Dolman, Luca Belelli Marchesini, R. Commane, Steven C. Wofsy, Charles E. Miller, David A. Lipson, Josh Hashemi, Kyle A. Arndt, Lars Kutzbach, David Holl, Julia Boike, Christian Wille, Torsten Sachs, Aram Kalhori, Xingyu Song, Xiaofeng Xu, Elyn Humphreys, C. Koven, Oliver Sonnentag, Gesa Meyer, Gabriel Gosselin, Philip Marsh, Walter C. Oechel
Scientific Reports, Volume 12, Issue 1

Arctic warming is affecting snow cover and soil hydrology, with consequences for carbon sequestration in tundra ecosystems. The scarcity of observations in the Arctic has limited our understanding of the impact of covarying environmental drivers on the carbon balance of tundra ecosystems. In this study, we address some of these uncertainties through a novel record of 119 site-years of summer data from eddy covariance towers representing dominant tundra vegetation types located on continuous permafrost in the Arctic. Here we found that earlier snowmelt was associated with more tundra net CO2 sequestration and higher gross primary productivity (GPP) only in June and July, but with lower net carbon sequestration and lower GPP in August. Although higher evapotranspiration (ET) can result in soil drying with the progression of the summer, we did not find significantly lower soil moisture with earlier snowmelt, nor evidence that water stress affected GPP in the late growing season. Our results suggest that the expected increased CO2 sequestration arising from Arctic warming and the associated increase in growing season length may not materialize if tundra ecosystems are not able to continue sequestering CO2 later in the season.

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The ABCflux database: Arctic–boreal CO<sub>2</sub> flux observations and ancillary information aggregated to monthly time steps across terrestrial ecosystems
Anna-Maria Virkkala, Susan M. Natali, Brendan M. Rogers, Jennifer D. Watts, K. E. Savage, Sara June Connon, Marguerite Mauritz, Edward A. G. Schuur, D. L. Peter, C. Minions, Julia Nojeim, R. Commane, Craig A. Emmerton, Mathias Goeckede, Manuel Helbig, David Holl, Hiroyasu Iwata, Hideki Kobayashi, Pasi Kolari, Efrèn López‐Blanco, Maija E. Marushchak, Mikhail Mastepanov, Lutz Merbold, Frans‐Jan W. Parmentier, Matthias Peichl, Torsten Sachs, Oliver Sonnentag, Masahito Ueyama, Carolina Voigt, Mika Aurela, Julia Boike, Gerardo Celis, Namyi Chae, Torben R. Christensen, M. Syndonia Bret‐Harte, Sigrid Dengel, Han Dolman, C. Edgar, Bo Elberling, Eugénie Euskirchen, Achim Grelle, Juha Hatakka, Elyn Humphreys, Järvi Järveoja, Ayumi Kotani, Lars Kutzbach, Tuomas Laurila, Annalea Lohila, Ivan Mammarella, Yukiko Matsuura, Gesa Meyer, Mats Nilsson, Steven F. Oberbauer, Sang Jong Park, Roman E. Petrov, А. С. Прокушкин, Christopher Schulze, Vincent L. St. Louis, Eeva‐Stiina Tuittila, Juha‐Pekka Tuovinen, William L. Quinton, Andrej Varlagin, Donatella Zona, Viacheslav I. Zyryanov
Earth System Science Data, Volume 14, Issue 1

Abstract. Past efforts to synthesize and quantify the magnitude and change in carbon dioxide (CO2) fluxes in terrestrial ecosystems across the rapidly warming Arctic–boreal zone (ABZ) have provided valuable information but were limited in their geographical and temporal coverage. Furthermore, these efforts have been based on data aggregated over varying time periods, often with only minimal site ancillary data, thus limiting their potential to be used in large-scale carbon budget assessments. To bridge these gaps, we developed a standardized monthly database of Arctic–boreal CO2 fluxes (ABCflux) that aggregates in situ measurements of terrestrial net ecosystem CO2 exchange and its derived partitioned component fluxes: gross primary productivity and ecosystem respiration. The data span from 1989 to 2020 with over 70 supporting variables that describe key site conditions (e.g., vegetation and disturbance type), micrometeorological and environmental measurements (e.g., air and soil temperatures), and flux measurement techniques. Here, we describe these variables, the spatial and temporal distribution of observations, the main strengths and limitations of the database, and the potential research opportunities it enables. In total, ABCflux includes 244 sites and 6309 monthly observations; 136 sites and 2217 monthly observations represent tundra, and 108 sites and 4092 observations represent the boreal biome. The database includes fluxes estimated with chamber (19 % of the monthly observations), snow diffusion (3 %) and eddy covariance (78 %) techniques. The largest number of observations were collected during the climatological summer (June–August; 32 %), and fewer observations were available for autumn (September–October; 25 %), winter (December–February; 18 %), and spring (March–May; 25 %). ABCflux can be used in a wide array of empirical, remote sensing and modeling studies to improve understanding of the regional and temporal variability in CO2 fluxes and to better estimate the terrestrial ABZ CO2 budget. ABCflux is openly and freely available online (Virkkala et al., 2021b, https://doi.org/10.3334/ORNLDAAC/1934).
Both unconventional and conventional oil and gas production have led to instances of brine contamination of near-surface environments from spills of saline produced waters. Strontium isotope ratios ( 87 Sr/ 86 Sr) have been used as a sensitive tracer of sources of brine contamination in surface waters and shallow aquifers in areas where oil and gas production are limited to only a few reservoirs and produced water sources are well-defined. Recent expansion of conventional and unconventional oil and gas production to additional tight formations within sedimentary basins has resulted in production of formation waters from multiple oil and gas reservoirs that may have similar chemical and isotopic ratios, including 87 Sr/ 86 Sr. This study evaluates the utility of 87 Sr/ 86 Sr, the most widely available tracer dataset beyond major ion chemistry and water stable isotopes, as a tracer of brine contamination related to conventional and unconventional oil and gas production in the Williston, Appalachian and Permian basins. Multiple stacked oil and gas reservoirs within each basin have overlapping formation water 87 Sr/ 86 Sr, based on a non-parametric statistical test. For example, in the Appalachian Basin, produced waters from unconventional gas production in the Middle Devonian Marcellus and Upper Ordovician Utica shales have overlapping 87 Sr/ 86 Sr. In the Permian Basin, produced waters from the unconventional Pennsylvanian-Permian Wolfcamp Shale and conventional and unconventional Pennsylvanian Cisco/Canyon/Strawn formations have similar 87 Sr/ 86 Sr. In the Williston Basin produced waters from Late Devonian to Early Mississippian Bakken Formation unconventional oil production have overlapping 87 Sr/ 86 Sr with produced waters associated with minor production of conventional oil from the Middle Devonian Winnipegosis. Improved spatial characterization of 87 Sr/ 86 Sr and other isotopic signatures of produced waters from various oil/gas reservoirs are needed to constrain geographic and depth variability of produced waters in hydrocarbon producing regions. This is particularly important, as unconventional oil and gas production expands in areas of existing conventional oil and gas production, where delineating sources of saline produced waters in cases of accidental surface spills or subsurface leakage will become a greater challenge. Sr isotopes alone may not be able to distinguish produced waters in areas with overlapping production from reservoirs with similar isotopic signatures. • Sr isotopes may not be effective tracers where stacked reservoirs are present. • More Sr isotope data required to understand spatial/depth variability. • Multiple tracers may be needed to identify sources of contamination.
• Eight rainfall models are compared as input for a simplified continuous hydrologic model. • The comparison is performed by investigating the simulated runoff properties. • Results suggest that all rainfall models lead to realistic runoff time series. • Four models will be further optimized to be adapted for data-scarce applications. Continuous hydrologic modelling is a natural evolution of the event-based design approach in modern hydrology. It improves the rainfall-runoff transformation and provides the practitioner with more effective hydrological output information for risk assessment. However, this approach is still not widely adopted, mainly because the choice of the most appropriate rainfall simulation model (which is the core of continuous frameworks) for the specific aim of risk analysis has not been sufficiently investigated. In this paper, we test eight rainfall models by evaluating the performances of the simulated rainfall time series when used as input for a simplified continuous rainfall-runoff model, the COSMO4SUB, which is particularly designed for small and ungauged basins. The comparison confirms the capability of all models to provide realistic flood events and allows identifying the models to be further improved and tailored for data-scarce hydrological risk applications. The suggested framework is transferable to any catchment while different hydrologic and rainfall models can be used.
• Rainfall erosivity for Yellow River basin increased significantly at both event and seasonal scale during 1971–2020. • Storms shifted towards longer durations and higher precipitation amounts. • Extreme precipitation within the basin occurred more frequently and intensely. • The increasing trend became more pronounced in the last two decades. Hourly precipitation data from 1971 to 2020, collected from 98 stations distributed across the Yellow River basin, were analyzed to detect changes in characteristics on rainfall and rainfall erosivity for all storms and storms with extreme erosivity (greater than 90 th percentile). Results showed that over the past 50 years, rainfall erosivity at both event and seasonal scales over the whole basin increased significantly ( p < 0.05) with rates of 5.46% and 6.86% decade -1 , respectively, compared to the 1981–2010 average values. Approximate 80% of 98 stations showed increasing trends and 20% of stations had statistically significant trends ( p < 0.1). The increase of rainfall erosivity resulted from the significant increasing trends of average storm precipitation ( p < 0.1), duration ( p < 0.1), rainfall energy ( p < 0.05) and maximum 1-h intensity ( p < 0.05). In addition, the total extreme erosivity showed significant upward trends at a relative rate of 6.05% decade -1 ( p < 0.05). Extreme erosivity storms occurred more frequently and with higher rainfall energy during the study period ( p < 0.05). Trends for seasonal total and extreme erosivity were also estimated based on daily rainfall data, and the changing magnitudes were similar to those based on hourly rainfall data, which suggested daily rainfall can be applied to detect interannual and long-term variations of rainfall erosivity in the absence of rainfall data with higher resolution. It was suggested that soil and water conservation strategies and vegetation projects conducted within the Yellow River basin should be continued and enhanced in the future.
• A first comprehensive and systematic review on the research of extreme precipitation in China. • Variation and regional characteristics of extreme precipitation under non-stationary conditions due to climate change and human activities. • Supports and basis for engineering application and further research on extreme precipitation and flood in China. Recent years have witnessed global massive property losses and casualties caused by extreme precipitation and its subsequent natural disasters, including floods and landslides. China is one of the countries deeply affected by these casualties. If the statistical characteristics and laws of extreme precipitation could be clearly grasped, then the negative impacts triggered by it may be minimized. China is a vast country and diverse in climate and terrain, hence different regions may be suitable for different analyses and research methods. Therefore, it is necessary to clarify the research progress, methods and current status of extreme precipitation across the country. This paper attempts to provide a comprehensive review of techniques and methods used in extreme precipitation research and engineering practice and their applications. The literature is reviewed focusing on seven aspects: (1) annual maxima method (AM), (2) peaks over threshold method (POT), (3) probable maximum precipitation (PMP), (4) non-stationary analysis of precipitation extremes, (5) intensity-duration-frequency curves (IDF), (6) uncertainty in extreme precipitation frequency analysis, and (7) spatial variability of extreme precipitation. Research on extreme precipitation in China is generally based or centered on the above seven aspects. The current study aims to provide ideas for further research on extreme precipitation frequency analysis and its response to climate change and human activities.
<abstract><p>This paper proposes a new very simply explicitly invertible function to approximate the standard normal cumulative distribution function (CDF). The new function was fit to the standard normal CDF using both MATLAB's Global Optimization Toolbox and the BARON software package. The results of three separate fits are presented in this paper. Each fit was performed across the range $ 0 \leq z \leq 7 $ and achieved a maximum absolute error (MAE) superior to the best MAE reported for previously published very simply explicitly invertible approximations of the standard normal CDF. The best MAE reported from this study is 2.73e–05, which is nearly a factor of five better than the best MAE reported for other published very simply explicitly invertible approximations.</p></abstract>
Globally, extreme temperatures have severe impacts on the economy, human health, food and water security, and ecosystems. Mortality rates have been increased due to heatwaves in several regions. Specifically, megacities have high impacts with the increasing temperature and ever-expanding urban areas; it is important to understand extreme temperature changes in terms of duration, magnitude, and frequency for future risk management and disaster mitigation. Here we framed a novel Semi-Parametric quantile mapping method to bias-correct the CMIP6 minimum and maximum temperature projections for 199 megacities worldwide. The changes in maximum and minimum temperature are quantified in terms of climate indices (ETCCDI and HDWI) for the four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Cities in northern Asia and northern North America (Kazan, Samara, Heihe, Montréal, Edmonton, and Moscow) are warming at a higher rate compared to the other regions. There is an increasing and decreasing trend for the warm and cold extremes respectively. Heatwaves increase exponentially in the future with the increase in warming, that is, from SSP1-2.6 to SSP5-8.5. Among the CMIP6 models, a huge variability is observed, and this further increases as the warming increases. All climate indices have steep slopes for the far future (2066–2100) compared to the near future (2031–2065). Yet the variability among CMIP6 models in near future is high compared to the far future for cold indices.
Molybdenum disulfide (MoS2) is a promising material for applications in sensors, energy storage, energy conversion devices, solar cells, and fuel cells. Because many of those applications require conductive materials, we recently developed a method for preparing a conductive form of MoS2 (c-MoS2) using dilute aqueous hydrogen peroxide in a simple and safe way. Here, we investigate modulating the chemical and mechanical surface properties of c-MoS2 thin films using diazonium chemistry. In addition to a direct passivation strategy of c-MoS2 with diazonium salts for electron-withdrawing groups, we also propose a novel in situ synthetic pathway for modification with electron-donating groups. The obtained results are examined by Raman spectroscopy and X-ray photoelectron spectroscopy. The degree of surface passivation of pristine and functionalized c-MoS2 films was tested by exposing them to aqueous solutions of different metal cations (Fe2+, Zn2+, Cu2+, and Co2+) and detecting the chemiresistive response. While pristine films were found to interact with several of the cations, modified films did not. We propose that a surface charge transfer mechanism is responsible for the chemiresistive response of the pristine films, while both modification routes succeeded at complete surface passivation. Functionalization was also found to lower the coefficient of friction for semiconducting 2H-MoS2, while all conductive materials (modified or not) also had lower coefficients of friction. This opens up a pathway to a palette of dry lubricant materials with improved chemical stability and tunable conductivity. Thus, both in situ and direct diazonium chemistries are powerful tools for tuning chemical and mechanical properties of conductive MoS2 for new devices and lubricants based on conductive MoS2.
Prevalence of high levels of metal ions in natural and drinking water is a growing problem to both ecosystems and human health. Several methods are broadly used for heavy metal monitoring in water resources, but most of them are laboratory-based. Here, we describe a method that simplifies the measurement process by enabling passive aliquoting and preconcentration of heavy metals. We use superabsorbent polymer beads that can take up hundreds of times their volume to aliquot the sample and preconcentrate the ionic species present in them by 2 orders of magnitude. We then use commercially available colorimetric dyes that are sensitive only at high concentrations to reveal a visible range change in the bead color that can be measured optically using a camera. Using this approach, we have detected the concentration of copper(II) ions in water as low as 5.4 ppb. We demonstrate that this method can also be used for drinking water and tap water samples to assess concentrations of copper and iron. This solid-state method significantly simplifies the analytical procedure and provides extremely low detection levels of heavy metals, eliminating the need for expensive equipment and hence could be useful in remote settings.
With the growing consumption of caffeine-containing beverages, detection of caffeine has become an important biomedical, bioanalytical, and environmental topic. We herein isolated four high-quality aptamers for caffeine with dissociation constants ranging from 2.2 to 14.6 μM as characterized using isothermal titration calorimetry. Different binding patterns were obtained for the three single demethylated analogues: theobromine, theophylline, and paraxanthine, highlighting the effect of the molecular symmetry of the arrangement of the three methyl groups in caffeine. A structure-switching fluorescent sensor was designed showing a detection limit of 1.2 μM caffeine, which reflected the labeled caffeine concentration within 6.1% difference for eight commercial beverages. In 20% human serum, a detection limit of 4.0 μM caffeine was achieved. With the four aptamer sensors forming an array, caffeine and the three analogues were well separated from nine other closely related molecules.
Abstract Monitoring the real-time status of food products using pH sensors is important to determine if pathogens are present and growing, which in turn affects food quality. A promising material for pH sensors is ruthenium dioxide (RuO2) due to its chemical stability and excellent performance. Furthermore, graphene oxide (GO) provides an electrode with large surface area and good electrical properties. Here, in situ sol-gel deposition of RuO2 nanoparticles on the surface of GO as a facile, cost-effective, and environmentally friendly approach is used for the fabrication of a flexible pH sensor. As-synthesized GO-RuO2 nanocomposites with a low volume were applied on the surface of screen printed carbon paste. The obtained GO-RuO2 nanocomposite pH sensor achieved high pH sensitivity (55.5 mV/pH) in the pH range of 4-10, up to 4 times higher compared to the unmodified carbon electrode. The increased sensitivity is due to the positive role of RuO2 nanoparticles densely anchored across the GO sheets. It also shows low drift (0.36 mV/hr) and low hysteretic width. Considering this novel method and material with the cost-effective green synthesis approach, as well as excellent pH sensing properties, GO-RuO2 can be considered as a promising material for production of high-performance electrochemical pH sensors.
Hydrogen peroxide (H2O2) is an intermediate molecule generated in numerous peroxidase assays used to measure concentrations of biomolecules such as glucose, galactose, and lactate. Here, we develop a solid-state reagent-free chemiresistive H2O2 sensor, which can measure H2O2 over a wide measuring range of 0.5–1000 ppm (0.015–29.4 mM). The sensor was fabricated using a network of functionalized single-walled carbon nanotubes (SWCNTs) as a sensitive layer and a xurographically patterned gold leaf as a contact electrode. The SWCNTs were functionalized with crystal violet to impart selective detection of H2O2. The crystal violet was self-assembled on the SWCNT film and subsequently polymerized via cyclic voltammetry to improve its retention on the sensing layer. The functionalized sensor exhibited good selectivity against common interferents such as uric acid, urea, glucose, and galactose. In addition, the sensor was used to measure in situ H2O2 generated during peroxidase assays performed using enzymes like glucose oxidase. The sensor was tested in standard buffer solutions for both enzymes. The glucose oxidase assay was also demonstrated in spiked pooled human plasma samples. The glucose oxidase-coated sensor exhibited a glucose detection range of 2–20 mM in standard buffer and blood plasma solutions, with a good recovery rate (∼95–107%) for glucose measurements in blood plasma.
Over the last three decades, numerous aptamer-based biosensors have been reported. The basis of these sensors is the selective binding of target analytes by aptamers. In the last few years, a number of papers have been published questioning the binding ability of some popular aptamers such as those documented for As(III), ampicillin, chloramphenicol, isocarbophos, phorate and dopamine. In this article, these papers are reviewed, and the binding assays are described, which may provide possible reasons for obtaining false positive aptamers. Additionally, relevant aptamer selection methods and typical characterization steps are described. It is found that for small molecular targets, using an immobilized library might result in better aptamers. Furthermore, the importance of carefully designed controls to ensure the quality of binding assays is discussed, especially in the case of mutated nonbinding aptamers. Only then, with fully validated aptamers, can subsequent biosensor design bring about meaningful results. • The first critical review of the literature on aptamers that were proven to be non-binding sequences. • Five different aptamers for various small molecules reviewed. • Possible reasons for the generation of such non-binding aptamer sequences proposed and methods to avoid them described.
Recent investigations using polarimetric decomposition and numerical models have helped to improve understanding of how radar signals interact with lake ice. However, further research is needed on how radar signals are impacted by varying lake ice properties. Radiative transfer models provide one method of improving this understanding. These are the first published experiments using the Snow Microwave Radiative Transfer (SMRT) model to investigate the response of different imaging SAR frequencies (L, C, and X-band) at HH and VV polarizations using various incidence angles (20°, 30°, and 40°) to changes in ice thickness, porosity, bubble radius, and ice-water interface roughness. This is also the first use of SMRT in combination with a thermodynamic lake ice model. Experiments were for a lake with tubular bubbles and one without tubular bubbles under difference scenarios. Analysis of the backscatter response to different properties indicate that increasing ice thickness and layer porosity have little impact on backscatter from lake ice. X-band backscatter shows increased response to surface ice layer bubble radius; however, this was limited for other frequencies except at shallower incidence angles (40°). All three frequencies display the largest response to increasing RMS height at the ice-water interface, which supports surface scattering at the ice-water interface as being the dominant scattering mechanism. These results demonstrate that SMRT is a valuable tool for understanding the response of SAR data to changes in freshwater lake ice properties and could be used in the development of inversion models.
Cyanobacterial harmful algal blooms are an increasing threat to coastal and inland waters. These blooms can be detected using optical radiometers due to the presence of phycocyanin (PC) pigments. The spectral resolution of best-available multispectral sensors limits their ability to diagnostically detect PC in the presence of other photosynthetic pigments. To assess the role of spectral resolution in the determination of PC, a large ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N =905$ </tex-math></inline-formula> ) database of colocated <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> radiometric spectra and PC are employed. We first examine the performance of selected widely used machine-learning (ML) models against that of benchmark algorithms for hyperspectral remote sensing reflectance ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{\mathrm {rs}}$ </tex-math></inline-formula> ) spectra resampled to the spectral configuration of the Hyperspectral Imager for the Coastal Ocean (HICO) with a full-width at half-maximum (FWHM) of < 6 nm. Results show that the multilayer perceptron (MLP) neural network applied to HICO spectral configurations (median errors < 65%) outperforms other ML models. This model is subsequently applied to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{\mathrm {rs}}$ </tex-math></inline-formula> spectra resampled to the band configuration of existing satellite instruments and of the one proposed for the next Landsat sensor. These results confirm that employing MLP models to estimate PC from hyperspectral data delivers tangible improvements compared with retrievals from multispectral data and benchmark algorithms (with median errors between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 73$ </tex-math></inline-formula> % and 126%) and shows promise for developing a globally applicable cyanobacteria measurement approach.
Estimating soil moisture (SM) over the circumpolar boreal forest would have numerous applications including wildfire risk detection, and weather prediction. Evaluation of satellite derived SM retrievals in boreal ecoregions is hindered by available in situ SM observation networks. To address this, an SM monitoring network was established in a boreal forest region in Saskatchewan, Canada. The network is unique as there are no other SM network of similar size in the boreal forest. The network consisted of 17 SM stations within a single Soil Moisture Active Passive (SMAP) satellite observation pixel ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$33\times 33$ </tex-math></inline-formula> km). We present an analysis of the sensitivity and accuracy of SMAP SM products in a boreal forest environment over a two-year period in 2018 and 2019. Results show current SMAP radiometer-based L2 SM products have higher correlation with the in situ lower mineral layer SM than with the top organic layer, although the overall correlation is low. Correlations between in situ mineral layer SM and SMAP brightness-temperature (TB) products are higher than those observed with the SMAP SM product, suggesting current SMAP SM retrieval from the TB using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\omega $ </tex-math></inline-formula> model introduces large uncertainties in the SM estimation, possibly from uncertain vegetation and surface parameters in the retrieval model. Results show SM can be retrieved using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\omega $ </tex-math></inline-formula> model with reasonable accuracy over the boreal forest provided the vegetation and soil parameters are optimized. The SM retrieval using a dual channel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\omega $ </tex-math></inline-formula> model, which utilize both horizontally and vertically polarized SMAP TB, performs better than that with a single channel algorithm (SCA), using optimized parameters.
Abstract Non-invasive contactless simultaneous sensing and heating of individual droplets would allow droplet microfluidics to empower a wide range of applications. However, it is challenging to realize simultaneous sensing and heating of individual droplets as the resonance frequency of the droplet fluid, which is decided by its permittivity, must be known so that energy is only supplied at this frequency for droplet heating with one resonator. To tailor the energy transfer in real-life heating applications, the droplet has to be sensed first to identify its corresponding resonance frequency, which is used to dynamically tune the frequency for supplying the required energy for heating this particular droplet. To achieve this goal, two resonators are needed, with one for sensing and one for heating. Integrating multiple resonators into one typical microfluidic device limits placement of the resonators to be as close as possible, which would raise the concern of crosstalk between them. The crosstalk would result in inaccurate sensing and heating. This study focuses on numerically and experimentally investigating the effect of influencing parameters on the crosstalk between two adjacent resonators with the ultimate goal of providing guidance for multiplexing the resonators in a typical microfluidic device. ANSYS HFSS is used to perform the electromagnetic analysis based on the finite element method. Experimental studies are conducted on a microfluidic chip integrated with two resonators to validate the numerical results. An optimal distance between two resonators is suggested, with the recommendation for the resonator size and heating power towards simultaneous sensing and heating of individual droplets.
Abstract. Data for small to mid-sized watersheds are seldom publicly available, but may be representative of diverse types of hydrological contexts when assessing patterns. These types of data may also prove valuable for informing numerical experimentation and practical modelling. This paper presents data collected in the Alder Creek watershed, located within the Grand River basin in Ontario, Canada. The Alder Creek watershed provides source water from the aquifers of the Waterloo Moraine for multiple well fields that supply the cities of Kitchener and Waterloo. Recharge rates and human impacts on streamflow are important topics for the watershed, and many numerical models of the area have been constructed. In order to support these types of analyses, field equipment was deployed within the watershed between 2013 and 2018 to monitor groundwater levels, stream stage, soil moisture, soil temperature, rainfall, and other weather parameters. The available data are described, complementary information is presented, and examples of possible analyses are cited and illustrated. The data presented and described in this paper are available at https://doi.org/10.20383/101.0178 (Wiebe et al., 2019).
• A novel analytical-numerical scheme for calculating temperature profiles in porous media with temperature-dependent thermal properties during the freezing process; • The hybrid analytical-numerical method can deal with different types of nonlinear soil freezing functions; • Neumann's two-layer solution underestimates the penetration rate and depth of the freezing front; • The profiles of temperature, equivalent thermal conductivity and diffusivity, conductive heat flux, and dynamics of the freezing front were significantly impacted by the shape of the unfrozen water content curve and the magnitude of soil grain thermal conductivity. The freeze-thaw cycle associated with climatic seasonality is a common phenomenon in cold regions affecting a wide range of subsurface processes. Due to the complex and highly nonlinear nature of the associated hydrologic processes, transient freeze-thaw dynamics are conventionally quantified in a numerical way. Here we present a hybrid analytical-numerical scheme for solving one-dimensional soil (or porous media) temperature profiles when the soil profile is subjected to unidirectional freezing (or thawing) conditions. This scheme divides the partially-frozen soil into multi-layers, each with constant thermal parameters and fixed-temperature boundaries. Temperature profiles within each layer were obtained by solving multiple moving-boundary problems. The proposed hybrid analytical-numerical scheme was tested into a freezing test of silty clay in a permafrost region on the Qinghai-Tibetan Plateau, and its solution was in good agreement with the finite element numerical solution. Results show that the proposed multi-layer method adapted well to the changes in unfrozen water content and thermal properties of soil over a wide range of subzero temperatures. By contrast, the freezing front's migration rate and penetration depth calculated by Neumann's classical solution, which only considers two zones (frozen and unfrozen), was found to be underestimated. As for our proposed multi-layer solution, by dividing the subsurface domain into many layers with smaller proportion ratios (thinner layers close to the freezing front), there was a slower penetration rate of the freezing front resulting in shallower penetration depth. The predicted profiles of temperature, thermal conductivity and diffusivity, heat flux, and dynamics of the freezing front were significantly impacted by the shape of the soil freezing curves and the magnitude of soil grain thermal conductivity, especially for the accuracy of long-term predictions.
Microbial activity persists in cold region agricultural soils during the fall, winter, and spring (i.e., non-growing season) and frozen condition, with peak activity during thaw events. Climate change is expected to change the frequency of freeze-thaw cycles (FTC) and extreme temperature events (i.e, altered timing, extreme heat/cold events) in temperate cold regions, which may hasten microbial consumption of fall-amended fertilizers, decreasing potency come the growing season. We conducted a high-resolution temporal examination of the impacts of freeze-thaw and nutrient stress on microbial communities in agricultural soils across both soil depth and time. Four soil columns were incubated under a climate model of a non-growing season including precipitation, temperature, and thermal gradient with depth over 60 days. Two columns were amended with fertilizer, and two incubated as unamended soil. The impacts of repeated FTC and nutrient stress on bacterial, archaeal, and fungal soil community members were determined, providing a deeply sampled longitudinal view of soil microbial response to non-growing season conditions. Geochemical changes from flow-through leachate and amplicon sequencing of 16S and ITS rRNA genes were used to assess community response. Despite nitrification observed in fertilized columns, there were no significant microbial diversity, core community, or nitrogen cycling population trends in response to nutrient stress. FTC impacts were observable as an increase in alpha diversity during FTC. Community compositions shifted across a longer time frame than individual FTC, with bulk changes to the community in each phase of the experiment. Our results demonstrate microbial community composition remains relatively stable for archaea, bacteria, and fungi through a non-growing season, independent of nutrient availability. This observation contrasts canonical thinking that FTC have significant and prolonged effects on microbial communities. In contrast to permafrost and other soils experiencing rare FTC, in temperate agricultural soils regularly experiencing such perturbations, the response to freeze-thaw and fertilizer stress may be muted by a more resilient community or be controlled at the level of gene expression rather than population turn-over. These results clarify the impacts of winter FTC on fertilizer consumption, with implications for agricultural best practices and modeling of biogeochemical cycling in agroecosystems.
Cold regions are warming faster than the rest of the planet, with the greatest warming occurring during the winter and shoulder seasons. Warmer winters are further predicted to result in more frequent soil freezing and thawing events. Freeze-thaw cycles affect biogeochemical soil processes and alter carbon and nutrient export from soils, hence impacting receiving ground and surface waters. Cold region agricultural management should therefore consider the possible effects on water quality of changing soil freeze-thaw dynamics under future climate conditions. In this study, soil column experiments were conducted to assess the leaching of fertilizer nitrogen (N) from an agricultural soil during the non-growing season. Identical time series temperature and precipitation were imposed to four parallel soil columns, two of which had received fertilizer amendments, the two others not. A 15-30-15 N-P-K fertilizer (5.8% ammonium and 9.2% urea) was used for fertilizer amendments. Leachates from the soil columns were collected and analyzed for major cations and anions. The results show that thawing following freezing caused significant export of chloride (Cl − ), sulfate (SO 4 2− ) and nitrate (NO 3 − ) from the fertilizer-amended soils. Simple plug flow reactor model calculations indicated that the high NO 3 − concentrations produced during the fertilized soil thawing events were due to nitrification of fertilizer N in the upper oxidized portion of the soil. The very low concentrations of NO 3 − and ammonium in the non-fertilized soils leachates implied that the freeze-thaw cycles had little impact on the mineralization of soil organic N. The findings, while preliminary, indicate that unwanted N enrichment of aquifers and rivers in agricultural areas caused by fall application of N fertilizers may be exacerbated by changing freeze-thaw activity.
Future warming of the Arctic not only threatens to destabilize the enormous pool of organic carbon accumulated in permafrost soils but may also mobilize elements such as calcium (Ca) or silicon (Si). While for Greenlandic soils, it was recently shown that both elements may have a strong effect on carbon dioxide (CO 2 ) production with Ca strongly decreasing and Si increasing CO 2 production, little is known about the effects of Si and Ca on carbon cycle processes in soils from Siberia, the Canadian Shield, or Alaska. In this study, we incubated five different soils (rich organic soil from the Canadian Shield and from Siberia (one from the top and one from the deeper soil layer) and one acidic and one non-acidic soil from Alaska) for 6 months under both drained and waterlogged conditions and at different Ca and amorphous Si (ASi) concentrations. Our results show a strong decrease in soil CO 2 production for all soils under both drained and waterlogged conditions with increasing Ca concentrations. The ASi effect was not clear across the different soils used, with soil CO 2 production increasing, decreasing, or not being significantly affected depending on the soil type and if the soils were initially drained or waterlogged. We found no methane production in any of the soils regardless of treatment. Taking into account the predicted change in Si and Ca availability under a future warmer Arctic climate, the associated fertilization effects would imply potentially lower greenhouse gas production from Siberia and slightly increased greenhouse gas emissions from the Canadian Shield. Including Ca as a controlling factor for Arctic soil CO 2 production rates may, therefore, reduces uncertainties in modeling future scenarios on how Arctic regions may respond to climate change.
The rapid growth in microplastic pollution research is influencing funding priorities, environmental policy, and public perceptions of risks to water quality and environmental and human health. Ensuring that environmental microplastics research data are findable, accessible, interoperable, and reusable (FAIR) is essential to inform policy and mitigation strategies. We present a bibliographic analysis of data sharing practices in the environmental microplastics research community, highlighting the state of openness of microplastics data. A stratified (by year) random subset of 785 of 6,608 microplastics articles indexed in Web of Science indicates that, since 2006, less than a third (28.5%) contained a data sharing statement. These statements further show that most often, the data were provided in the articles’ supplementary material (38.8%) and only 13.8% via a data repository. Of the 279 microplastics datasets found in online data repositories, 20.4% presented only metadata with access to the data requiring additional approval. Although increasing, the rate of microplastic data sharing still lags behind that of publication of peer-reviewed articles on environmental microplastics. About a quarter of the repository data originated from North America (12.8%) and Europe (13.4%). Marine and estuarine environments are the most frequently sampled systems (26.2%); sediments (18.8%) and water (15.3%) are the predominant media. Of the available datasets accessible, 15.4% and 18.2% do not have adequate metadata to determine the sampling location and media type, respectively. We discuss five recommendations to strengthen data sharing practices in the environmental microplastic research community.
Exploitation of bitumen-rich deposits in the Alberta Oil Sands Region (AOSR) by large-scale mining and processing activities has generated widespread concern about the potential for dispersal of harmful contaminants to aquatic ecosystems via fluvial and atmospheric pathways. The release of mercury has received attention because it is a potent neurotoxin for wildlife and humans. However, knowledge of baseline mercury concentration prior to disturbance is required to evaluate the extent to which oil sands development has contributed mercury to aquatic ecosystems. Here, we use stratigraphic analysis of total mercury concentration ([THg]) in radiometrically dated sediment cores from nine floodplain lakes in the AOSR and downstream Peace-Athabasca Delta (PAD) and two upland lakes in the PAD region to establish pre-1900 baseline [THg] and evaluate if [THg] has become enriched via fluvial and atmospheric pathways since oil sands mining and processing began in 1967. Concentrations of THg in sediment cores from the study lakes range from 0.022–0.096 mg/kg (dry wt.) and are below the Canadian interim sediment quality guidelines for freshwater (0.17 mg/kg). Results demonstrate no enrichment of [THg] above pre-1900 baseline via fluvial pathways at floodplain lakes in the AOSR or PAD. Enrichment of [THg] was detected via atmospheric pathways at upland lakes in the PAD region, but this occurred prior to oil sands development and aligns with long-range transport of emissions from coal combustion and other anthropogenic sources across the northern hemisphere recognized in many other lake sediment records. The inventory of anthropogenic [THg] in the upland lakes in the AOSR is less than at the Experimental Lakes Area of northwestern Ontario (Canada), widely regarded as a “pristine” area. The absence of enrichment of [THg] in lake sediment via fluvial pathways is a critical finding for stakeholders, and we recommend that monitoring at the floodplain lakes be used to inform stewardship as oil sands operators prepare to discharge treated oil sands process waters directly into the Athabasca River upstream of the PAD.
Much of the Arctic is experiencing rapid change in the productivity and recruitment of tall, deciduous shrubs. It is well established that shrub expansion can alter tundra ecosystem composition and function; however, less is known about the degree to which variability in the physical structure of shrub patches might mediate these changes. There is also limited information as to how different physical attributes of shrub patches may covary and how they differ with topography. Here, we address these knowledge gaps by measuring the physical structure, abiotic conditions, and understory plant community composition at sampling plots within undisturbed green alder patches at a taiga–tundra ecotone site in the Northwest Territories, Canada. We found surprisingly few associations between most structural variables and abiotic conditions at the plot scale, with the notable exceptions of canopy complexity and snow depth. Importantly, neither patch structure nor abiotic conditions were associated with the vegetation community at the plot scale when among-patch variation was accounted for. However, among-patch variation in plant community composition was significant and represented a gradient in the richness of tundra specialists and Sphagnum moss abundance. This gradient was strongly associated with mean patch snow depth, which was likely controlled at least in part by mean patch canopy complexity. Overall, natural variability in green alder patch structure had less of an association with abiotic conditions than expected, suggesting future changes in physical structure at undisturbed sites may have limited environmental impact at the plot scale. However, at the patch scale, increases in snow depth, likely related to canopy complexity, were negatively associated with tundra specialist richness, potentially due to phenological limitations associated with shortened growing seasons. In summary, our data suggest emergent properties exist at the patch scale that are not apparent at the plot scale such that plot-scale measurements do not represent variation in understory community composition across the landscape. The results presented here will inform future work addressing spatial variability in shrub impacts on ecosystem function and increase our understanding of understory community variation within alder patch habitats at the taiga–tundra ecotone.
As the global population increases, the expansion of road networks has led to the destruction and disturbance of terrestrial and aquatic habitats. Road-related stressors have significant effects on both lotic and lentic habitats. While there are several systematic reviews that evaluate the effects of roads on lotic environments, there are none that consider their effects on lentic habitats only. We conducted a literature review to achieve two objectives: (1) to summarize the effects of roads on the physical, chemical, and biological properties of lentic environments; and (2) to identify biases and gaps in our current knowledge of the effects of roads on lentic habitats, so that we could find promising areas for future research. Our review found 172 papers published between 1970 and 2020. The most frequently studied stressors associated with roads included road salt and heavy metal contamination (67 and 43 papers, respectively), habitat fragmentation (37 papers), and landscape change (14 papers). These stressors can lead to alterations in conductivity and chloride levels, changes in lake stratification patterns, increases in heavy metal concentrations in water and organisms, and significant mortality as amphibians disperse across roadways. We also identified a variety of other stressors that may be understudied based on their frequency of appearance in our search results, including polycyclic aromatic hydrocarbons, road dust, increased accessibility, hydrological changes, noise pollution, dust suppressants, sedimentation, invasive species introductions, and water withdrawal. Our review indicated that there are strong geographic biases in published studies, with 57.0% examining North American sites and 30.2% examining European sites. Furthermore, there were taxonomic biases in the published literature, with most studies focusing on amphibians (41.7%), fish (15.6%), and macroinvertebrates (14.6%), while few considered zooplankton (8.3%), diatoms (7.3%), amoebas (5.2%), water birds (3.1%), reptiles (2.1%), and macrophytes (1.0%). Based on our review, we have identified promising areas for future research for each of the major stressors related to roadways. However, we speculate that rectifying the geographic and taxonomic bias of our current knowledge could significantly advance our understanding of the impacts of roads on lentic environments, thereby better informing environmental management of these important habitats.
ABSTRACT Winter storms in eastern Canada can bring heavy precipitation, including large amounts of freezing rain. The resulting ice accumulation on structures such as trees and power lines can lead to widespread power outages and damage to infrastructure. The objective of this study is to provide a better understanding of the processes that led to extreme freezing rain events over New Brunswick (NB), Canada, during past events and how they may change in the future. To accomplish this, freezing rain events that affected the power network over NB were identified and analysed using high-resolution convection-permitting simulations. These simulations were produced from 2000 to 2013 climate data and using the pseudo global warming (WRF-PGW) approach, assuming warmer climate conditions. Our results show that through the process of cold air damming, the Appalachians enhance the development of strong temperature inversions, leading to an increase in the amount of freezing rain in central and southern NB. The occurrence of freezing rain events generally decreases by 40% in southern and eastern NB, while the occurrence of long-duration events (>6 h) increases slightly in northwestern NB in the WRF-PGW simulation. Overall, key local orographic effects that influence atmospheric conditions favorable for freezing precipitation were identified. This knowledge will enable us to better anticipate the impact of climate change on similar storms.
ABSTRACT A devastating storm struck southern Manitoba, Canada on 10–13 October 2019, producing a large region of mainly sticky and wet snow. Accumulations reached 75 cm, wind gusts exceeded 100 km h−1, and surface temperature (T) remained near 0°C (−1°C ≤ T ≤ 1°C) for up to 88 h. It produced the largest October snowfall and was the earliest to produce at least 20 cm since 1872 in Winnipeg. These factors led to unparalleled damage and power restoration challenges for Manitoba Hydro and, with leaves still largely on vegetation, the most damaging storm to Winnipeg’s trees ever recorded. The storm’s track was uncommon, and produced elevated convection related to buoyancy-driven instability and conditional symmetric instability (CSI), with a moist absolutely unstable layer (MAUL) near 500 hPa. Instabilities were released via lift through lower-tropospheric warm advection and frontogenesis, differential cyclonic vorticity advection, and jet streak dynamics. Precipitation bands, elevated convection, and lake effect snow bands enhanced local snowfall. Snow adhering to structures was not always wet but, when present, it sometimes occurred because of incomplete freezing of particles partially melted aloft in a near-surface (<100 m deep) inversion. Although other storms over the historical record have produced a similar combination of severe precipitation, temperature and wind conditions, none have done this for such a long period.
Abstract Freezing precipitation has major consequences for ground and air transportation, the health of citizens, and power networks. Previous studies using coarse resolution climate models have shown a northward migration of freezing rain in the future. Increased model resolution can better define local topography leading to improved representation of conditions that are favorable for freezing rain. The goal of this study is to examine the climatology and characteristics of future freezing rain events using very-high resolution climate simulations. Historical and pseudo-global warming simulations with a 4-km horizontal grid length were used and compared with available observations. Simulations revealed a northerly shift of freezing rain occurrence, and an increase in the winter. Freezing rain was still shown to occur in the Saint-Lawrence River Valley in a warmer climate, primarily due to stronger wind channeling. Up to 50% of the future freezing rain events also occurred in present day climate within 12 h of each other. In northern Maine, they are typically shorter than 6 h in current climate and longer than 6 h in warmer conditions due to the onset of precipitation during low-pressure systems occurrences. The occurrence of freezing rain also locally increases slightly north of Québec City in a warmer climate because of freezing rain that is produced by warm rain processes. Overall, the study shows that high-resolution regional climate simulations are needed to study freezing rain events in warmer climate conditions, because high horizontal resolutions better define small-scale topographic features and local physical mechanisms that have an influence on these events.
Abstract Winter precipitation is the source of many inconveniences in many regions of North America, for both infrastructure and the economy. The ice storm that hit the Canadian Maritime Provinces on 24–26 January 2017 remains one of the most expensive in history for the province of New Brunswick. Up to 50 mm of freezing rain caused power outages across the province, depriving up to one-third of New Brunswick residences of electricity, with some outages lasting 2 weeks. This study aims to use high-resolution atmospheric modeling to investigate the meteorological conditions during this severe storm and their contribution to major power outages. The persistence of a deep warm layer aloft, coupled with the slow movement of the associated low pressure system, contributed to widespread ice accumulation. When combined with the strong winds observed, extensive damage to electricity networks was inevitable. A 2-m temperature cold bias was identified between the simulation and the observations, in particular during periods of freezing rain. In the northern part of New Brunswick, cold-air advection helped keep temperatures below 0°C, while in southern regions, the 2-m temperature increased rapidly to slightly above 0°C because of radiational heating. The knowledge gained in this study on the processes associated with either maintaining or stopping freezing rain will enhance the ability to forecast and, in turn, to mitigate the hazards associated with those extreme events. Significance Statement A slow-moving low pressure system produced up to 50 mm of freezing rain for 31 h along the east coast of New Brunswick, Canada, on 24–26 January 2017, causing unprecedented power outages. Warm-air advection aloft, along with a combination of higher wind speeds and large amounts of ice accumulation, created ideal conditions for severe freezing rain. The storm began with freezing rain along the entire north–south cross section of eastern New Brunswick and changed to rain only in the south, when local temperatures increased to >0°C. Near-surface cold-air advection kept temperatures below 0°C in the north. Warming from the latent heat produced by freezing contributed to persistent near-0°C conditions during freezing rain.
Abstract Given their potentially severe impacts, understanding how freezing rain events may change as the climate changes is of great importance to stakeholders including electrical utility companies and local governments. Identification of freezing rain in climate models requires the use of precipitation-type algorithms, and differences between algorithms may lead to differences in the types of precipitation identified for a given thermodynamic profile. We explore the uncertainty associated with algorithm selection by applying four algorithms (Cantin and Bachand, Baldwin, Ramer, and Bourgouin) offline to an ensemble of simulations of the fifth-generation Canadian Regional Climate Model (CRCM5) at 0.22° grid spacing. First, we examine results for the CRCM5 driven by ERA-Interim reanalysis to analyze how well the algorithms reproduce the recent climatology of freezing rain and how results vary depending on algorithm parameters and the characteristics of available model output. We find that while the Ramer and Baldwin algorithms tend to be better correlated with observations than Cantin and Bachand or Bourgouin, their results are highly sensitive to algorithm parameters and to the number of pressure levels used. We also apply the algorithms to four CRCM5 simulations driven by different global climate models (GCMs) and find that the uncertainty associated with algorithm selection is generally similar to or greater than that associated with choice of driving GCM for the recent past climate. Our results provide guidance for future studies on freezing rain in climate simulations and demonstrate the importance of accounting for uncertainty between algorithms when identifying precipitation type from climate model output. Significance Statement Freezing rain events and ice storms can have major consequences, including power outages and dangerous road conditions. It is therefore important to understand how climate change might affect the frequency and severity of these events. One source of uncertainty in climate studies of these events is related to the choice of algorithm used to detect freezing rain in model output. We compare the frequency of freezing rain identified using four different algorithms and find sometimes large differences depending on the algorithm chosen over some regions. Our findings highlight the importance of taking this source of uncertainty into account and will provide researchers with guidance as to which algorithms are best suited for climate studies of freezing rain.
Permafrost thaw has been observed in recent decades in the Northern Hemisphere and is expected to accelerate with continued global warming. Predicting the future of permafrost requires proper representation of the interrelated surface/subsurface thermal and hydrologic regimes. Land surface models (LSMs) are well suited for such predictions, as they couple heat and water interactions across soil-vegetation-atmosphere interfaces and can be applied over large scales. LSMs, however, are challenged by the long-term thermal and hydraulic memories of permafrost and the paucity of historical records to represent permafrost dynamics under transient climate conditions. In this study, we aim to understand better how LSMs function under different spin-up states, which facilitates addressing the challenge of model initialization by characterizing the impact of initial climate conditions and initial soil frozen and liquid water contents on the simulation length required to reach equilibrium. Further, we quantify how the uncertainty in model initialization propagates to simulated permafrost dynamics. Modelling experiments are conducted with the Modélisation Environmentale Communautaire—Surface and Hydrology (MESH) framework and its embedded Canadian land surface scheme (CLASS). The study area is in the Liard River basin in the Northwest Territories of Canada with sporadic and discontinuous regions. Results show that uncertainty in model initialization controls various attributes of simulated permafrost, especially the active layer thickness, which could change by 0.5–1.5 m depending on the initial condition chosen. The least number of spin-up cycles is achieved with near field capacity condition, but the number of cycles varies depending on the spin-up year climate. We advise an extended spin-up of 200–1000 cycles to ensure proper model initialization under different climatic conditions and initial soil moisture contents.
A traditional engineering-based approach to hydro-economic modelling is to connect a partial equilibrium economic assessment, e.g., changes in sectoral production, to a detailed water resources system model. Since the 1990s, another approach emerged where water data are incorporated into a macro-economic model, e.g., a computable general equilibrium or input-output model, to estimate both direct and indirect economic impacts. This study builds on these different approaches and compares the outcomes from three models in the transboundary Saskatchewan River Basin in Canada. The economic impacts of drought and socioeconomic development are estimated using an engineering-based model, a macro-economic model, and a model that integrates a water resources model and a macro-economic model. Findings indicate that although the integrated model is more challenging to develop, its results seem most relevant for water allocation, owing to capturing both regional and sectoral economic interdependencies and key features of the water resources system in more detail. • We compare three hydro-economic modelling approaches in a transboundary river basin. • Their applicability is examined under drought and economic development scenarios. • Usefulness of integrating water management and macroeconomic models is demonstrated. • Ignoring linkages between basins and sectors affects the model simulation results. • This may mislead water allocation decision-making in transboundary river basins.
Lower Nelson River Basin, Manitoba, Canada Hydroelectricity makes up almost 97% of electricity generated in Manitoba, of which over 70% of its generation capacity is installed along the Lower Nelson River (LNR). In this study, 19 climate projections representing ~ 87% of climatic variability over Hudson Bay Drainage Basin are applied to coupled hydrologic-operations models to estimate water supply and hydropower generation potential changes under future climates. Future inflow to the forebay of the main hydropower generating stations along LNR is expected to increase in spring and summer but decrease in winter and fall. Consequently, hydropower generation potential is projected to increase for spring, the historical flood season, which may lead to reduced reservoir inflow retention efficiency. In extremely dry climatic simulations, winter seasons see a reduction in reservoir inflow and hydropower generation potential, up to 35% and 37% in 2021–2050 and 2041–2070, respectively. Projected changes in reservoir inflow and hydropower generation potential continue to diverge over time, with dry scenarios becoming drier and wet becoming wetter, yielding high basin climate sensitivity and uncertainty with system supply and generation potential. Despite the presence of statistically significant individual trends and changes, there is a low agreement within the climate ensemble. Analysis of system robustness shows adjustment of the operations along LNR should be considered over time to better leverage changing seasonal water supply. • Unique dynamic coupling of climate-hydrologic-operations models. • Projected reservoir inflow and hydropower generation potential for LNRB. • No significant change or trend in mean or median values due to uncertainty. • Wet seasons are getting wetter, dry seasons are getting drier. • Increase in uncertainty and extremes under future climates poses operational challenge.
Abstract. Model intercomparison studies are carried out to test and compare the simulated outputs of various model setups over the same study domain. The Great Lakes region is such a domain of high public interest as it not only resembles a challenging region to model with its trans-boundary location, strong lake effects, and regions of strong human impact but is also one of the most densely populated areas in the United States and Canada. This study brought together a wide range of researchers setting up their models of choice in a highly standardized experimental setup using the same geophysical datasets, forcings, common routing product, and locations of performance evaluation across the 1 million square kilometer study domain. The study comprises 13 models covering a wide range of model types from Machine Learning based, basin-wise, subbasin-based, and gridded models that are either locally or globally calibrated or calibrated for one of each of six predefined regions of the watershed. Unlike most hydrologically focused model intercomparisons, this study not only compares models regarding their capability to simulated streamflow (Q) but also evaluates the quality of simulated actual evapotranspiration (AET), surface soil moisture (SSM), and snow water equivalent (SWE). The latter three outputs are compared against gridded reference datasets. The comparisons are performed in two ways: either by aggregating model outputs and the reference to basin-level or by regridding all model outputs to the reference grid and comparing the model simulations at each grid-cell. The main results of this study are: (1) The comparison of models regarding streamflow reveals the superior quality of the Machine Learning based model in all experiments performance; even for the most challenging spatio-temporal validation the ML model outperforms any other physically based model. (2) While the locally calibrated models lead to good performance in calibration and temporal validation (even outperforming several regionally calibrated models), they lose performance when they are transferred to locations the model has not been calibrated on. This is likely to be improved with more advanced strategies to transfer these models in space. (3) The regionally calibrated models – while losing less performance in spatial and spatio-temporal validation than locally calibrated models – exhibit low performances in highly regulated and urban areas as well as agricultural regions in the US. (4) Comparisons of additional model outputs (AET, SSM, SWE) against gridded reference datasets show that aggregating model outputs and the reference dataset to basin scale can lead to different conclusions than a comparison at the native grid scale. This is especially true for variables with large spatial variability such as SWE. (5) A multi-objective-based analysis of the model performances across all variables (Q, AET, SSM, SWE) reveals overall excellent performing locally calibrated models (i.e., HYMOD2-lumped) as well as regionally calibrated models (i.e., MESH-SVS-Raven and GEM-Hydro-Watroute) due to varying reasons. The Machine Learning based model was not included here as is not setup to simulate AET, SSM, and SWE. (6) All basin-aggregated model outputs and observations for the model variables evaluated in this study are available on an interactive website that enables users to visualize results and download data and model outputs.
The Great Lakes (GL) in North America are among the largest freshwater resources on the planet facing serious eutrophication problems as a result of excessive nutrient loadings due to population and economic growth. More than a third of Canada's GDP is generated in and around the GL. Hence, the economic interests affected by pollution and pollution control are high. New policies to reduce pollution are often insufficiently informed due to the lack of integrated models and methods that provide decision-makers insight into the direct and indirect economic impacts of their policies. This study fills this knowledge gap and estimates the impacts of different total phosphorus (TP) restriction policy scenarios across the GL. A first of its kind multi-regional hydro-economic model is built for the Canadian GL, extended to include TP emissions from point and non-point sources. This optimization model is furthermore extended with a pollution abatement cost function that allows sectors to also take technical measures to meet the imposed pollution reduction targets. The latter is a promising new avenue for extending existing hydro-economic input-output modeling frameworks. The results show decision-makers the least cost-way to achieve different TP emission reduction targets. The estimated cost to reduce TP emissions by 40% in all GL amounts to a total annual cost of 3 billion Canadian dollars or 0.15% of Canada's GDP. The cost structure changes substantially as policy targets become more stringent, increasing the share of indirect costs and affecting not only the economic activities around the GL, but the economy of Canada as a whole due to the tightly interwoven economic structure.
Cold regions provide water resources for half the global population yet face rapid change. Their hydrology is dominated by snow, ice and frozen soils, and climate warming is having profound effects. Hydrological models have a key role in predicting changing water resources but are challenged in cold regions. Ground-based data to quantify meteorological forcing and constrain model parameterization are limited, while hydrological processes are complex, often controlled by phase change energetics. River flows are impacted by poorly quantified human activities. This paper discusses the scientific and technical challenges of the large-scale modelling of cold region systems and reports recent modelling developments, focussing on MESH, the Canadian community hydrological land surface scheme. New cold region process representations include improved blowing snow transport and sublimation, lateral land-surface flow, prairie pothole pond storage dynamics, frozen ground infiltration and thermodynamics, and improved glacier modelling. New algorithms to represent water management include multistage reservoir operation. Parameterization has been supported by field observations and remotely sensed data; new methods for parameter identification have been used to evaluate model uncertainty and support regionalization. Additionally, MESH has been linked to broader decision-support frameworks, including river ice simulation and hydrological forecasting. The paper also reports various applications to the Saskatchewan and Mackenzie River basins in western Canada (0.4 and 1.8 million km2). These basins arise in glaciated mountain headwaters, are partly underlain by permafrost, and include remote and incompletely understood forested, wetland, agricultural and tundra ecoregions. These illustrate the current capabilities and limitations of cold region modelling, and the extraordinary challenges to prediction, including the need to overcoming biases in forcing data sets, which can have disproportionate effects on the simulated hydrology.
Increasing incidences of eutrophication and groundwater quality impairment from agricultural nitrogen pollution are threatening humans and ecosystem health. Minimal improvements in water quality have been achieved despite billions of dollars invested in conservation measures worldwide. Such apparent failures can be attributed in part to legacy nitrogen that has accumulated over decades of agricultural intensification and that can lead to time lags in water quality improvement. Here, we identify the key knowledge gaps related to landscape nitrogen legacies and propose approaches to manage and improve water quality, given the presence of these legacies.
Intensification of agriculture and increased insecticide use have been implicated in global losses of farmland biodiversity and ecosystem services. We hypothesized that increased insecticide applications (proportion of area treated with insecticides) in Canada's expansive agricultural landscapes are due, in part, to shifts toward more simplified landscapes. To assess this relationship, we analyzed data from the Canadian Census of Agriculture spanning 20 years including five census periods (1996-2016) and across 225 census units within the four major agricultural regions of Pacific, Prairie, Central, and Atlantic Canada. Generalized mixed effects models were used to evaluate if changes in landscape simplification - defined as the proportion of farmland in crops (cereals, oilseeds, pulses and fruit/vegetables) - alongside other farming and climatic variables, influenced insecticide applications over time. Bayesian spatial-temporal models were further used to estimate the strength of the relationship with landscape simplification over time. We found that landscape simplification increased in 89% and insecticide applications increased in 70% of the Census Division spatial units during the 1996-2016 period. Nationally, significant increases in landscape simplification were observed in the two most agriculturally intensive regions of Prairie (from 55% to 63%) and Central (from 51% to 60%) Canada. For both regions, landscape simplification was a strong and significant predictor of higher insecticide applications, even after accounting for other factors such as climate, farm economics, farm size and land use practices (e.g., area in cash crops and tillage). If current trends continue, we estimated that insecticide applications will increase another 10%-20% by 2036 as a result of landscape simplification alone. To avoid increased reliance on toxic insecticides, agri-environmental policies need to consider that losing diverse natural habitat can increase insect pest pressure and resistance with negative environmental consequences extending beyond the field.
Abstract. Significant challenges from changes in climate and land use face sustainable water use in the Canadian Prairies ecozone. The region has experienced significant warming since the mid-20th century, and continued warming of an additional 2 ∘C by 2050 is expected. This paper aims to enhance understanding of climate controls on Prairie basin hydrology through numerical model experiments. It approaches this by developing a basin-classification-based virtual modelling framework for a portion of the Prairie region and applying the modelling framework to investigate the hydrological sensitivity of one Prairie basin class (High Elevation Grasslands) to changes in climate. High Elevation Grasslands dominate much of central and southern Alberta and parts of south-western Saskatchewan, with outliers in eastern Saskatchewan and western Manitoba. The experiments revealed that High Elevation Grassland snowpacks are highly sensitive to changes in climate but that this varies geographically. Spring maximum snow water equivalent in grasslands decreases 8 % ∘C−1 of warming. Climate scenario simulations indicated that a 2 ∘C increase in temperature requires at least an increase of 20 % in mean annual precipitation for there to be enough additional snowfall to compensate for enhanced melt losses. The sensitivity in runoff is less linear and varies substantially across the study domain: simulations using 6 ∘C of warming, and a 30 % increase in mean annual precipitation yields simulated decreases in annual runoff of 40 % in climates of the western Prairie but 55 % increases in climates of eastern portions. These results can be used to identify those areas of the region that are most sensitive to climate change and highlight focus areas for monitoring and adaptation. The results also demonstrate how a basin classification-based virtual modelling framework can be applied to evaluate regional-scale impacts of climate change with relatively high spatial resolution in a robust, effective and efficient manner.
Using data from five long-term field sites measuring soil moisture, we show the limitations of using soil moisture observations alone to constrain modelled hydrological fluxes. We test a land surface model, Modélisation Environnementale communautaire-Surface Hydrology/Canadian Land Surface Scheme, with two configurations: one where the soil hydraulic properties are determined using a pedotransfer function (the texture-based calibration) and one where they are assigned directly (the hydraulic properties-based calibration). The hydraulic properties-based calibration outperforms the texture-based calibration in terms of reproducing changes in soil moisture storage within a 1.6 m deep profile at each site, but both perform reasonably well, especially in the summer months. When the models are constrained using observations of changes in soil moisture, the predicted hydrological fluxes are subject to very large uncertainties associated with equifinality. The uncertainty is larger for the hydraulic properties-based calibration, even though the performance was better. We argue that since the pedotransfer functions constrain the model parameters in the texture-based calibrations in an unrealistic way, the texture-based calibration underestimates the uncertainty in the fluxes. We recommend that reproducing observed cumulative changes in soil moisture storage should be considered a necessary but insufficient criterion of model success. Additional sources of information are needed to reduce uncertainties, and these could include improved estimation of the soil hydraulic properties and direct observations of fluxes, particularly evapotranspiration.
Mountain regions are an important regulator in the global water cycle through their disproportionate water contribution. Often referred to as the “Water Towers of the World”, mountains contribute 40%–60% of the world's annual surface flow. Shade is a common feature in mountains, where complex terrain cycles land surfaces in and out of shadows over daily and seasonal scales, which can impact water use. This study investigated the turbulent water and carbon dioxide (CO2) fluxes during the snow‐free period in a subalpine wetland in the Canadian Rocky Mountains, from 7 June to 10 September 2018. Shading had a significant and substantial effect on water and CO2 fluxes at our site. When considering data from the entire study period, each hourly increase of shade per day reduced evapotranspiration (ET) and gross primary production (GPP) by 0.42 mm and 0.77 g C m−2, equivalent to 17% and 15% per day, respectively. However, the variability in shading changed throughout the study, it was stable to start and increased towards the end. Only during the peak growing season, the site experienced days with both stable and increasing shade. During this time, we found that shade, caused by the local complex terrain, reduced ET and potentially increased GPP, likely due to enhanced diffuse radiation. The overall result was greater water use efficiency during periods of increased shading in the peak growing season. These findings suggest that shaded subalpine wetlands can store large volumes of water for late season runoff and are productive through short growing seasons.
Flow regimes are critical for determining physical and biological processes in rivers, and their classification and regionalization traditionally seeks to link patterns of flow to physiographic, climate and other information. There are many approaches to, and rationales for, catchment classification, with those focused on streamflow often seeking to relate a particular response characteristic to a physical property or climatic driver. Rationales include such topics as Prediction in Ungauged Basins (PUB), and providing guidance for model selection in poorly understood hydrological systems. The Annual Daily Hydrograph (ADH) is a first-order easily visualized integrated expression of catchment function, and over many years the average ADH is a distinct hydrological signature that differentiate catchments from each other. In this study, we use t-SNE, a state-of-the-art technique of dimensionality reduction, to classify 17110 ADHs for 304 reference catchments in mountainous Western North America. t-SNE is chosen over other conventional methods of dimensionality reduction (e.g. PCA) as it presents greater separability of ADHs, which are projected on a 2D map where the similarities are evaluated according to their map distance. We then utilize a Deep Learning encoder to upgrade the non-parametric t-SNE to a parametric approach, enhancing its capability to address ’unseen’ samples. Results showed that t-SNE successfully clustered ADHs of similar flow regimes on the 2D map and allowed more accurate classification with KNN. In addition, many compact clusters on the 2D map in the coastal Pacific Northwest suggest information redundancy in the local streamflow network. The t-SNE map provides an intuitive way to visualize the similarity of high-dimensional data of ADHs, groups catchments with like characteristics, and avoids the reliance on subjective hydrometric indicators. This article is protected by copyright. All rights reserved.
Abstract The Canadian Rockies are a triple-continental divide, whose high mountains are drained by major snow-fed and rain-fed rivers flowing to the Pacific, Atlantic, and Arctic Oceans. The objective of the April–June 2019 Storms and Precipitation Across the continental Divide Experiment (SPADE) was to determine the atmospheric processes producing precipitation on the eastern and western sides of the Canadian Rockies during springtime, a period when upslope events of variable phase dominate precipitation on the eastern slopes. To do so, three observing sites across the divide were instrumented with advanced meteorological sensors. During the 13 observed events, the western side recorded only 25% of the eastern side’s precipitation accumulation, rainfall occurred rather than snowfall, and skies were mainly clear. Moisture sources and amounts varied markedly between events. An atmospheric river landfall in California led to moisture flowing persistently northward and producing the longest duration of precipitation on both sides of the divide. Moisture from the continental interior always produced precipitation on the eastern side but only in specific conditions on the western side. Mainly slow-falling ice crystals, sometimes rimed, formed at higher elevations on the eastern side (>3 km MSL), were lifted, and subsequently drifted westward over the divide during nonconvective storms to produce rain at the surface on the western side. Overall, precipitation generally crossed the divide in the Canadian Rockies during specific spring-storm atmospheric conditions although amounts at the surface varied with elevation, condensate type, and local and large-scale flow fields.
• The precipitation increase can offset the impact of warming on mountain snow hydrology. • The offsetting role of precipitation is effective at the high elevations and high latitudes. • The projected precipitation elasticity of annual runoff increases as latitude decreases. • The projected precipitation elasticity of peak snowpack increases as latitude increases. • Elasticities indicated that runoff changes are primarily attributed to precipitation change. Whether or not the impact of warming on mountain snow and runoff can be offset by precipitation increases has not been well examined, but it is crucially important for future downstream water supply. Using the physically based Cold Regions Hydrological Modelling Platform (CRHM), elasticity (percent change in runoff divided by change in a climate forcing) and the sensitivity of snow regimes to perturbations were investigated in three well-instrumented mountain research basins spanning the northern North American Cordillera. Hourly meteorological observations were perturbed using air temperature and precipitation changes and were then used to force hydrological models for each basin. In all three basins, lower temperature sensitivities of annual runoff volume ( ≤ 6% °C −1 ) and higher sensitivities of peak snowpack (−17% °C −1 ) showed that annual runoff was far less sensitive to temperature than the snow regime. Higher and lower precipitation elasticities of annual runoff (1.5 – 2.1) and peak snowpack (0.7 – 1.1) indicated that the runoff change is primarily attributed to precipitation change and, secondarily, to warming. A low discrepancy between observed and simulated precipitation elasticities showed that the model results are reliable, and one can conduct sensitivity analysis. The air temperature elasticities, however, must be interpreted with care as the projected warmings range beyond the observed temperatures and, hence, it is not possible to test their reliability. Simulations using multiple elevations showed that the timing of peak snowpack was most sensitive to temperature. For the range of warming expected from North American climate model simulations, the impacts of warming on annual runoff, but not on peak snowpack, can be offset by the size of precipitation increases projected for the near-future period 2041–2070. To offset the impact of 2 °C warming on annual runoff, precipitation would need to increase by less than 5% in all three basins. To offset the impact of 2 °C warming on peak snowpack, however, precipitation would need to increase by 12% in Wolf Creek in Yukon Territory, 18% in Marmot Creek in the Canadian Rockies, and an amount greater than the maximum projected at Reynolds Mountain in Idaho. The role of increased precipitation as a compensator for the impact of warming on snowpack is more effective at the highest elevations and higher latitudes. Increased precipitation leads to resilient and strongly coupled snow and runoff regimes, contrasting sharply with the sensitive and weakly coupled regimes at low elevations and in temperate climate zones.
• A novel physically based glacier hydrological model has been developed in CRHM. • The model considers processes such as blowing snow and sublimation, avalanches, firnification, glacier mass balance and energy-budget of snow/ice. • The model was driven with both in-situ and reanalysis data and evaluated with respect to albedo, mass balance, and runoff. • The hydrology of two partially glacierized catchments was simulated without any calibration of streamflow parameters. • The long term increases in discharge are due to increased glacier ice melt. A comprehensive glacier hydrology model was developed within the Cold Regions Hydrological Modelling platform (CRHM) to include modules representing wind flow over complex terrain, blowing snow redistribution and sublimation by wind, snow redistribution by avalanches, solar irradiance to sloping surfaces, surface sublimation, glacier mass balance and runoff, meltwater and streamflow routing. The physically based glacier hydrology model created from these modules in CRHM was applied to simulate the hydrology of the instrumented, glacierized and rapidly deglaciating Peyto and Athabasca glacier research basins in the Canadian Rockies without calibration of parameters from streamflow. It was tested against observed albedo, point and aggregated glacier mass balance, and streamflow and found to successfully simulate surface albedo, snow redistribution, snow and glacier accumulation and ablation, mass balance and streamflow discharge, both when driven by in-situ observations and reanalysis forcing data. Long term modelling results indicate that the increases in discharge from the 1960s to the present are due to increased glacier ice melt contributions, despite declining precipitation and snow melt.
The retreat of mountain glaciers affects mountain hazards and hydrology, and new methods are needed to rapidly map glacier retreat at planetary scales. We automatically map 14,329 glaciers (30,063 km 2 ) in British Columbia and Alberta, Canada, from 1984 to 2020 using satellite image archives from the Landsat 4, 5, 7 and 8 missions and reveal an acceleration in area loss that commenced in 2011. Glacier fragmentation, disappearance, and proglacial lake development also accelerated, as did the retreat of glaciers to higher elevations. Our annually-resolved method relies on the existence of previously published and manually validated glacier inventories from the mid-1980s and mid-2000's. Our methods performed well for clean ice glaciers, had occasional errors when proglacial lakes were present, and consistently underestimated the area of debris-covered glaciers. Clean ice glacier area loss accelerated sevenfold between the early [1984–2010] and late [2011−2020] epochs. This acceleration yielded rates of area shrinkage of −49 ± 7 km 2 a −1 [early] and − 340 ± 40 km 2 a −1 [late] with accelerated losses (32-fold increase) for small glaciers on Vancouver Island over the last decade. Glacier fragmentation accelerated from 26 ± 5.6 fragments a −1 to 88 ± 39 fragments a −1 . About 1141 glaciers fell below our 0.05 km 2 detection limit and so disappeared from our database, representing a loss of 8%. Proglacial lake area growth accelerated from 9.2 ± 1.1 km 2 a −1 to 49 ± 4.5 km 2 a −1 . We also observed an acceleration in the upwards migration of median glacier elevations for clean ice glaciers from 0.31 ± 0.08 m a −1 to 4.7 ± 0.7 m a −1 . Our workflow demonstrates the advantages of annual resolution glacier inventories and contributes towards the implementation of planetary mapping of glaciers and glacier attributes at annual resolution. • We produced annually-resolved [1984–2020], western Canadian glacier inventory. • Glaciers retreat, fragmentation and disappearance accelerated over last decade. • Our workflow could yield planetary-scale, annual glacier inventories.
Typical applications of process- or physically-based models aim to gain a better process understanding or provide the basis for a decision-making process. To adequately represent the physical system, models should include all essential processes. However, model errors can still occur. Other than large systematic observation errors, simplified, misrepresented, inadequately parametrised or missing processes are potential sources of errors. This study presents a set of methods and a proposed workflow for analysing errors of process-based models as a basis for relating them to process representations. The evaluated approach consists of three steps: (1) training a machine-learning (ML) error model using the input data of the process-based model and other available variables, (2) estimation of local explanations (i.e., contributions of each variable to an individual prediction) for each predicted model error using SHapley Additive exPlanations (SHAP) in combination with principal component analysis, (3) clustering of SHAP values of all predicted errors to derive groups with similar error generation characteristics. By analysing these groups of different error-variable association, hypotheses on error generation and corresponding processes can be formulated. That can ultimately lead to improvements in process understanding and prediction. The approach is applied to a process-based stream water temperature model HFLUX in a case study for modelling an alpine stream in the Canadian Rocky Mountains. By using available meteorological and hydrological variables as inputs, the applied ML model is able to predict model residuals. Clustering of SHAP values results in three distinct error groups that are mainly related to shading and vegetation-emitted long wave radiation. Model errors are rarely random and often contain valuable information. Assessing model error associations is ultimately a way of enhancing trust in implemented processes and of providing information on potential areas of improvement to the model.
Stream thermal regimes are critical to the stability of freshwater habitats. There is growing concern that climate change will result in stream warming due to rising air temperatures, decreased shading in forested areas due to wildfires, and changes in streamflow. Groundwater plays an important role in controlling stream temperatures in mountain headwaters, where it makes up a considerable portion of discharge. This study investigated the controls on the thermal regime of a headwater stream, and the surrounding groundwater processes, in a catchment on the eastern slopes of the Canadian Rocky Mountains. Groundwater discharge to the headwater spring is partially sourced by a seasonal lake. Spring, stream and lake temperature, water level, discharge and chemistry data were used to build a conceptual model of the system. Meteorological data was used to set up a stream temperature model. This study presents a unique example of an indirectly lake-headed stream, that is, a lake that only has transient subsurface hydrologic connections to the stream and no surface connections. The interaction of groundwater and lake water, and the subsurface connectivity between the lake and the headwater spring determine the resulting stream temperature. Radiation dominated the non-advective fluxes in the stream energy balance. Sensible and latent heat fluxes play a secondary role, but their effects generally cancel out. During snowfall events, the latent heat associated with melting of direct snowfall onto the water surface was responsible for rapid stream cooling. An increase in advective inputs from groundwater and hillslope pathways did not result in observed cooling of stream water during rainfall events. The results from this study will assist water resource and fisheries managers in adapting to stream temperature changes under a warming climate.
Subalpine regions of the Canadian Rocky Mountains are expected to experience continued changes in hydrometeorological processes due to anthropogenically mediated climate warming. As a result, fresh water supplies are at risk as snowmelt periods occur earlier in the year, and glaciers contribute less annual meltwater, resulting in longer growing seasons and greater reliance on rainfall to generate runoff. In such environments, wetlands are potentially important components that control runoff processes, but due to their location and harsh climates their hydrology is not well studied. We used stable water isotopes of hydrogen and oxygen (δ2H and δ18O), coupled with MixSIAR, a Bayesian mixing model, to understand relative source water contributions and mixing within Burstall Wetland, a subalpine wetland (1900 m a.s.l.), and the larger Burstall Valley. These results were combined with climate data from the Burstall Valley to understand hydrometeorological controls on Burstall Wetland source water dynamics over spatiotemporal timescales. Our results show that the seasonal isotopic patterns within Burstall Wetland reflect greater reliance on snowmelt in spring and rainfall in the peak and post-growing season periods. We found a substantial degree of mixing between precipitation (rain and snow) and stored waters in the landscape, especially during the pre-growing season. These findings suggest that longer growing seasons in subalpine snow-dominated landscapes put wetlands at risk of significant water loss and increased evaporation rates potentially leading to periods of reduced runoff during the peak- growing season and in extreme cases, wetland dry out.
Wetlands in Montane and Subalpine Subregions are increasingly recognized as important hydrologic features that support ecosystem function. However, it is currently not clear how climate trends will impact wetland hydrological processes (e.g., evaporative fluxes) across spatiotemporal scales. Therefore, identifying the factors that influence wetland hydrologic response to climate change is an important step in understanding the sensitivity of these ecosystems to environmental change. We used stable water isotopes of hydrogen and oxygen (δ2H and δ18O), coupled with climate data, to determine the spatiotemporal variability in isotopic signatures of wetland source waters and understand the influence of evaporative fluxes on wetlands in the Kananaskis Valley. Our results show that the primary runoff generation mechanism changes throughout the growing season resulting in considerable mixing in wetland surface waters. We found that evaporative fluxes increased with decreasing elevation and that isotopic values became further removed from meteoric water lines during the late peak- and into the post-growing seasons. These findings suggest that a change in the water balance in favor of enhanced evaporation (due to a warmer and longer summer season than present) will not only lead to greater water loss from the wetlands themselves but may also reduce the water inputs from their catchments.
Beavers are a keystone species known to strategically impound streamflow by building dams. Beaver colonization involves upstream ponding; after abandonment, the dams degrade, and the ponds slowly drain. This ponding-draining cycle likely modifies peatland nutrient availability, which is an important control on vegetation distribution and productivity. We compared soil mineral nutrient supply patterns in a beaver-dammed peatland in the Canadian Rocky Mountains over the growing and senescence study seasons during 2020. We used a nested design, comparing nutrient supply with ion-exchange probes among a full beaver pond (FBP with deep and shallow ponding), a drained beaver pond (DBP at its centre and margin) and unimpacted fen (UF at hummock and hollow hydrologic zones). Overall, FBP had lower soil total inorganic nitrogen (TIN) and nitrate (NO3), and higher ammonium (NH4) and phosphorus (PO4) supplies compared to UF. Interestingly, beaver pond drainage tended to restore the nutrient supply to its original status. The patterns we found in nutrient supply were consistent between the growing and senescence seasons. The key drivers of nutrient dynamics were water table level and soil temperature at 5 cm depth (TSoil); however, the controls affected each of the nutrients differently. Deepening of the water table level and higher TSoil non-linearly increased TIN/NO3 but decreased NH4 and PO4. We suggest that the variations in peatland nutrient availabilities in response to the beaver’s ponding-draining cycle may support downstream ecosystem heterogeneity and plant community composition diversity at a longer time scale.
Abstract. Lakes are key ecosystems within the global biogeosphere. However, the environmental controls on the biological productivity of lakes – including surface temperature, ice phenology, nutrient loads, and mixing regime – are increasingly altered by climate warming and land-use changes. To better characterize global trends in lake productivity, we assembled a dataset on chlorophyll-a concentrations as well as associated water quality parameters and surface solar radiation for temperate and cold-temperate lakes experiencing seasonal ice cover. We developed a method to identify periods of rapid net increase of in situ chlorophyll-a concentrations from time series data and applied it to data collected between 1964 and 2019 across 343 lakes located north of 40∘. The data show that the spring chlorophyll-a increase periods have been occurring earlier in the year, potentially extending the growing season and increasing the annual productivity of northern lakes. The dataset on chlorophyll-a increase rates and timing can be used to analyze trends and patterns in lake productivity across the northern hemisphere or at smaller, regional scales. We illustrate some trends extracted from the dataset and encourage other researchers to use the open dataset for their own research questions. The PCI dataset and additional data files can be openly accessed at the Federated Research Data Repository at https://doi.org/10.20383/102.0488 (Adams et al., 2021).
• Snow, glaciers, wetlands, frozen ground and permafrost needed in hydrological models. • Water quality export by coupling biochemical transformations to cold regions processes. • Hydrological sensitivity to land use depends on cold regions processes. • Strong cold regions hydrological sensitivity to climate warming. Cold regions involve hydrological processes that are not often addressed appropriately in hydrological models. The Cold Regions Hydrological Modelling platform (CRHM) was initially developed in 1998 to assemble and explore the hydrological understanding developed from a series of research basins spanning Canada and international cold regions. Hydrological processes and basin response in cold regions are simulated in a flexible, modular, object-oriented, multiphysics platform. The CRHM platform allows for multiple representations of forcing data interpolation and extrapolation, hydrological model spatial and physical process structures, and parameter values. It is well suited for model falsification, algorithm intercomparison and benchmarking, and has been deployed for basin hydrology diagnosis, prediction, land use change and water quality analysis, climate impact analysis and flood forecasting around the world. This paper describes CRHM’s capabilities, and the insights derived by applying the model in concert with process hydrology research and using the combined information and understanding from research basins to predict hydrological variables, diagnose hydrological change and determine the appropriateness of model structure and parameterisations.
Hydrological conditions in cold regions have been shown to be sensitive to climate change. However, a detailed understanding of how regional climate and basin landscape conditions independently influence the current hydrology and its climate sensitivity is currently lacking. This study, therefore, compares the climate sensitivity of the hydrology of two basins with contrasted landscape and meteorological characteristics typical of eastern Canada: a forested boreal climate basin (Montmorency) versus an agricultural hemiboreal climate basin (Acadie). The physically based Cold Regions Hydrological Modelling (CRHM) platform was used to simulate the current and future hydrological processes. Both basin landscape and regional climate drove differences in hydrological sensitivities to climate change. Projected peak SWE were highly sensitive to warming, particularly for milder baseline climate conditions and moderately influenced by differences in landscape conditions. Landscape conditions mediated a wide range of differing hydrological processes and streamflow responses to climate change. The effective precipitation was more sensitive to warming in the forested basin than in the agricultural one, due to reductions in forest canopy interception losses with warming. Under present climate, precipitation and discharge were found to be more synchronized in the greater relief and slopes of the forested basin, whereas under climate change, they are more synchronized in the agricultural basin due to reduced infiltration and storage capacities. Flow through and over agricultural soils translated the increase in water availability under a warmer and wetter climate into higher peak discharges, whereas the porous forest soils dampened the response of peak discharge to increased available water. These findings help diagnose the mechanisms controlling hydrological response to climate change in cold regions forested and agricultural basins.
• A real-time ice-jam risk assessment system was developed to better regulate reservoir discharges. • The modelling system improves ice-jam flood predictions considering the influence of reservoir regulation. • Machine learning with deterministic modelling provides more accurate ice-jam flood predictions of regulated rivers. • The modelling system was successfully verified for the Sanhuhekou bend reach regulated by the Sanshenggong reservoir. To effectively alleviate ice-jam flood disasters, it is necessary to carry out hazard assessments and predictions of ice-jam flooding influenced by the operational scheme of a reservoir. However, traditional hydrologic flood routing techniques cannot effectively address the huge uncertainties caused by the many factors that lead to ice-jam flooding. In this paper, a hazard assessment system for regulating flood discharge schemes is developed; it is composed of a machine learning (ML) model, Long Short-Term Memory (LSTM), and a river-ice dynamic model (RIVICE) within a probabilistic method. The modelling system is to aid in the challenge of predicting ice-jam flooding downstream of reservoirs. The LSTM model forecasts the downstream flow under the operational discharge scheme and, combined with the RIVICE model, the backwater level profile of ice jams can be forecasted. Furthermore, a set of backwater level profiles can be provided by probabilistic modelling, and the probability of ice-jam flood inundation can be calculated by comparing backwater levels with the elevation of the river bank; this information can be used to warn of the hazard induced by operational discharges to better aid in the preparedness and mitigation of ice-jam floods. This system was tested successfully for the ice-cover breakup period in the spring of 2008 and 2018 along the Sanhuhekou bend reach of the Yellow River in China.
Brominated disinfection by-products (Br-DBPs) can form during chlorination of drinking water in treatment plants (DWTP). Regulations exist for a small subset of Br-DBPs; However, hundreds of unregulated Br-DBPs have been detected and limited information exists on their occurrence, concentrations, and seasonal trends. Here, a data-independent precursor isolation and characteristic fragment (DIPIC-Frag) method was optimized to screen chlorinated waters for Br-DBPs. There were 553 Br-DBPs detected with m/z values ranging from 170.884 to 497.0278 and chromatographic retention times from 2.4 to 26.2 min. With MS 2 information, structures for 40 of the 54 most abundant Br-DBPs were predicted. The method was then applied to a year-long study in which raw, clear well, and finished water were analyzed monthly. The 54 most abundant unregulated Br-DBPs were subjected to trend analysis. Br-DBPs with higher oxygen-to-carbon (O/C) and bromine-to-carbon (Br/C) ratios increased as water moved from the clear well to the finished stage, which indicated the dynamic formation of Br-DBPs. Monthly trends of unregulated Br-DBPs were compared to raw water parameters such as natural organic matter, temperature, and total bromine, but no correlations were observed. It was found that total concentrations of bromine (TBr) in finished water (0.04–0.12 mg/L) were consistently and significantly greater than in raw water (0.013–0.038 mg/L, P < 0.001), suggesting the introduction of bromine during the disinfection process. Concentrations of TBr in treatment units, rather than raw water, were significantly correlated to 34 of the Br-DBPs at α = 0.05. This study provides the first evidence that monthly trends of unregulated Br-DBPs can be associated with the concentration of TBr in treated waters. - Ultrahigh resolution mass spectrometry was used to identify novel brominated disinfection byproducts in a Canadian water treatment facility. - Several hundred novel brominated compounds were identified of which 54 were assigned chemical structures. - Seasonal variation in the generated DBPs were assessed over 11 months of sampling. - Increases in total bromine in drinking water was noted with progress thru the treatment process.
• Maps of organic layer thickness and fuel load were developed using machine learning. • Tree species was the most important variable in the final random forest model. • Error in our final models was close to the natural variability we expected to find. • The resultant maps will help improve fuel consumption models. Forest organic layers are important soil carbon pools that can, in the absence of disturbance, accumulate to great depths, especially in lowland areas. Across the Canadian boreal forest, fire is the primary disturbance agent, often limiting organic layer accumulation through the direct consumption of these fuels. Organic layer thickness (OLT) and fuel load (OLFL) are common physical attributes used to characterize these layers, especially for wildland fire science. Understanding the drivers and spatial distribution of these attributes is important to improve predictions of fire behaviour, emissions and effects models. We developed maps of OLT and OLFL using machine learning approaches (weighted K-nearest neighbour and random forests) for the forested region of the province of Alberta, Canada (538, 058 km 2 ). The random forests approach was found to be the best approach to model the spatial distribution of these forest floor attributes. A databased of 3, 237 OLT and 594 OLFL plots were used to train the models. The error in our final model, particularly for OLT (5 cm), was relatively close to the variability we would expect to find naturally (3 cm). The dominant tree species was the most important covariate in the models. Age, solar radiation, spatial location, climate variables and surficial geology were also important drivers, although their level of importance varied between tree species and depended on the modelling method that was used.
Metal leaves are commercially available for decoration purposes and offers a low-cost alternative to sputtering thin metal films. Although thin metal leaves have been sparingly used in physical and chemical sensing and solar cells, their application has been limited primarily due to lack of a simple patterning methods and to form microscale features with them. Here, a low-cost, rapid and simple xurography based cutting method has been developed for direct pattering of metal leaves. The method was able to pattern features with line width of < 100 µm and it was also able to cut patterns with a pitch of < 100 µm. Conductive lines < 250 µm were also achieved which is a sufficient resolution for application in sensors and most biomedical devices. The versatile capability of this method to cut various geometric shapes like circle, rectangle, triangles and hexagons was also demonstrated. The method is robust and can be applied to pattern leaves made of several materials or which gold, silver, palladium, aluminum and copper were demonstrated. This patterning method was used to fabricate contact electrodes for chemiresistive sensors with low and high surface roughness. These sensors were evaluated using the resistance and noise characteristics. The peak-to-peak noise for gold contact electrodes (11.5 nA) for chemiresistive sensors was significantly lower than the copper tape contact electrodes (18.2 nA). The process was also used to fabricate gold interdigitated electrodes for biamperometric glucose sensing at low potential (~10 mV). Finally, the method was used to indirectly pattern gold leaf on a shrink film to fabricate high surface 3D electrodes costing around one-fifth (~20%) of a sputtered gold electrode.
Globally, maize ( Zea mays , a C4-plant) and alfalfa ( Medicago sativa , a C3-plant) are common and economically important crops. Predicting the response of their water use efficiency, WUE , to changing hydrologic and climatic conditions is vital in helping farmers adapt to a changing climate. In this study, we assessed the effective leaf area index ( eLAI - the leaf area most involved in CO 2 and H 2 O exchange) and stomatal conductance in canopy scale in maize and alfalfa fields. In the process we used a theoretically-based photosynthesis C3-C4 model (C3C4PM) and carbon and water vapour fluxes measured by Eddy Covariance towers at our study sites. We found that in our study sites the eLAI was in the range of 25–32% of the observed total LAI in these crops. WUE s were in range of 8–9 mmol/mol. C3C4PM can be used in predictions of stomatal conductance and eLAI responses in C3 and C4 agricultural crops to elevated CO 2 concentration and changes in precipitation and temperature under future climate scenarios. • ~25 (maize) & 32% (alfalfa) of the observed crop LAI was involved in photosynthesis. • Extinction coefficient for beam radiation was 1.08 (maize) and 0.84 (alfalfa). • Canopy stomatal conductance, SC , was ~0.13 (maize) and ~0.15 (alfalfa). • Effective LAI and canopy SC can be evaluated by Eddy Covariance records.
Abstract This study provides a comprehensive analysis of the human contribution to the observed intensification of precipitation extremes at different spatial scales. We consider the annual maxima of the logarithm of 1-day (Rx1day) and 5-day (Rx5day) precipitation amounts for 1950–2014 over the global land area, four continents, and several regions, and compare observed changes with expected responses to external forcings as simulated by CanESM2 in a large-ensemble experiment and by multiple models from phase 6 of the Coupled Model Intercomparison Project (CMIP6). We use a novel detection and attribution analysis method that is applied directly to station data in the areas considered without prior processing such as gridding, spatial or temporal dimension reduction or transformation to unitless indices and uses climate models only to obtain estimates of the space-time pattern of extreme precipitation response to external forcing. The influence of anthropogenic forcings on extreme precipitation is detected over the global land area, three continental regions (western Northern Hemisphere, western Eurasia and eastern Eurasia), and many smaller IPCC regions, including C. North-America, E. Asia, E.C. Asia, E. Europe, E. North-America, N. Europe, and W. Siberia for Rx1day, and C. North-America, E. Europe, E. North-America, N. Europe, Russian-Arctic, and W. Siberia for Rx5day. Consistent results are obtained using forcing response estimates from either CanESM2 or CMIP6. Anthropogenic influence is estimated to have substantially decreased the approximate waiting time between extreme annual maximum events in regions where anthropogenic influence has been detected, which has important implications for infrastructure design and climate change adaptation policy.
Abstract Globally, the extent of inland wetlands has declined by approximately 70% since the start of the twentieth century, resulting in the loss of important wetland-associated ecosystem services. We evaluate the drivers of wetland values in agricultural landscapes to increase the effectiveness and reliability of benefit transfer tools to assign values to local wetland services. We reviewed 668 studies that analyzed wetland ecosystem services within agricultural environments and identified 45 studies across 22 countries that provided sufficient economic information to be included in a quantitative meta-analysis. We developed meta-regression models to represent provisioning and regulating wetland ecosystem services and identify the main drivers of these ecosystem service categories. Provisioning wetland ecosystem service values were best explained (direction of effects in parenthesis) by high-income variable (+), peer-reviewed journal publications (+), agricultural total factor productivity index (−) and population density (+), while agricultural total factor productivity index (−), income level ( +) and wetland area (−) had significant effects on regulating wetland ecosystem service values. Our models can help estimate wetland values more reliably across similar regions because they have significantly lower transfer errors (66 and 185% absolute percentage error for the provisioning and regulating models, respectively) than the errors from unit value transfers. Model predicted wetland values ($/Ha/Year) range from $0.62 to $11,216 for regulating services and $0.95 to $2,122 for provisioning services and vary based on the differences in the levels of the variables (in the wetland locations) that best explained the estimated models.
Reclamation of wetlands, including peatlands, is legally required in the Athabasca oil sands region following bitumen extraction via surface mining which leaves large open pits that are backfilled with saline tailings waste. Six years of hydrochemical data (2013 – 2018) from the Sandhill Fen Watershed (SFW), a 52-ha upland-peatland catchment that was built upon highly saline soft tailings, were used to evaluate salinity and ion patterns and provide insight on its trajectory. In general, electrical conductivity (EC) increased throughout SFW from 1) reduced inflow and outflow, 2) changes in water table positions and 3) increased mixing of site-wide waters. Salinity has increased site-wide over time as EC increased by an average of 1585 and 2313 µS/cm in the wetland and margins, respectively from 2013 to 2018. The uplands were the only region where EC declined by 1747 µS/cm over the six years. There is also evidence of mixing with underlying tailings waste (Na-Cl dominant) as the chemical composition of SFW waters shifted from largely Ca-dominant in 2013 (> 90%) to Na-dominant by 2018 (> 70%). Based on its current conditions, SFW cannot support freshwater peat-forming bryophytes and is most chemically similar to naturally occurring saline fens. A shift in design strategies is recommended (replicating saline instead of freshwater peatlands) to increase the success of these systems. Changes in site-wide average annual electrical conductivity (µS/cm) from 2013 to 2018. • Electrical conductivity (EC) and Na+ increased site-wide from 2013 to 2018. • Decreased inflow and outflow increased EC and ion accumulation. • Higher water tables diluted wetland EC and increased margin EC (ion mobilization). • Increased mixing with tailings waste shifted waters from Ca +2 to Na + dominant. • Waters have become similar to brackish marshes and saline fens.
A simple algorithm is provided for randomly sampling a set of N +1 weights such that their sum is constrained to be equal to one, analogous to randomly subdividing a pie into N +1 slices where the probability distribution of slice volumes are identically distributed. The cumulative density and probability density functions of the random weights are provided. The algorithmic implementation for the random number sampling are made available. This algorithm has potential applications in calibration, uncertainty analysis, and sensitivity analysis of environmental models. Three example applications are provided to demonstrate the efficiency and superiority of the proposed method compared to alternative sampling methods. • Present unbiased method to sample weights that sum up to 1. • Examples demonstrating the benefit of unbiased sampling. • Code made available in multiple languages.
The surface properties of electrically conductive membranes (ECMs) govern their advanced abilities. During operation, these properties may differ considerably from their initially measured properties. Depending on their operating conditions, ECMs may undergo various degrees of passivation. ECM passivation can detrimentally impact their real time performance, causing large deviations from expected behaviour based on their initially measured properties. Quantifying these changes will enable consistent performance comparisons across the active and electrically conductive membrane research field. As such, consistent methods must be established to quantify ECM membrane properties. In this work, we proposed three standardized methods to assess the electrochemical, chemical, and physical stability of such membrane coatings: 1) electrochemical oxidation, 2) surface scratch testing, and 3) pressurized leaching. ECMs were synthesized by the most common approach - coating support ultrafiltration (UF) and/or microfiltration (MF) polyethersulfone (PES) membranes with carbon nanotubes (CNT) cross-linked with polyvinyl alcohol (PVA) and two types of cross-linkers (either succinic acid (SA) or glutaraldehyde (GA)). We then evaluated these ECMs based on the three standardized methods: 1) We evaluated electrochemical stability as a function of electro-oxidation induced by applying anodic potentials. 2) We measured the scratch resistance to quantify the surface mechanical stability. 3) We measured physical stability by quantifying the leaching of PVA during separation of a model foulant (polyethylene oxide (PEO)). Our methods can be extended to all types of electrically conductive membranes including MF, UF, nanofiltration (NF), and reverse osmosis (RO) ECMs. We propose that these fundamental measurements are critical to assessing the viability of ECMs for industrial MF, UF, NF, and RO applications.•Anodic-oxidation was used to check the electrochemical stability of ECMs•Depth of penetration resulted from scratch test is an indicator of the electrically conductive membrane coating's mechanical stability•The leaching of the main components forming the nanolayer was quantified to assess the membranes' physical stability.
Activities of gut microbiomes are often overlooked in assessments of ecotoxicological effects of environmental contaminants. Effects of the polycyclic aromatic hydrocarbon, benzo[a]pyrene (BaP) on active gut microbiomes of juvenile fathead minnows (Pimephales promelas) were investigated. Fish were exposed for two weeks, to concentrations of 0, 1, 10, 100, or 1000 μg BaP g-1 in the diet. The active gut microbiome was characterized using 16S rRNA metabarcoding to determine its response to dietary exposure of BaP. BaP reduced alpha-diversity at the greatest exposure concentrations. Additionally, exposure to BaP altered community composition of active microbiome and resulted in differential proportion of taxa associated with hydrocarbon degradation and fish health. Neighborhood selection networks of active microbiomes were not reduced with greater concentrations of BaP, which suggests ecological resistance and/or resilience of gut microbiota. The active gut microbiome had a similar overall biodiversity as that of the genomic gut microbiota, but had a distinct composition from that of the 16S rDNA profile. Responses of alpha- and beta-diversities of the active microbiome to BaP exposure were consistent with that of genomic microbiomes. Normalized activity of microbiome via the ratio of rRNA to rDNA abundance revealed rare taxa that became active or dormant due to exposure to BaP. These differences highlight the need to assess both 16S rDNA and rRNA metabarcoding to fully derive bacterial compositional changes resulting from exposure to contaminants.
A strong atmospheric river made landfall in southwestern British Columbia, Canada on November 14th, 2021, bringing two days of intense precipitation to the region. The resulting floods and landslides led to the loss of at least five lives, cut Vancouver off entirely from the rest of Canada by road and rail, and made this the costliest natural disaster in the province's history. Here we show that when characterised in terms of storm-averaged water vapour transport, the variable typically used to characterise the intensity of atmospheric rivers, westerly atmospheric river events of this magnitude are approximately one in ten year events in the current climate of this region, and that such events have been made at least 60% more likely by the effects of human-induced climate change. Characterised in terms of the associated two-day precipitation, the event is substantially more extreme, approximately a one in fifty to one in a hundred year event, and the probability of events at least this large has been increased by a best estimate of 45% by human-induced climate change. The effects of this precipitation on streamflow were exacerbated by already wet conditions preceding the event, and by rising temperatures during the event that led to significant snowmelt, which led to streamflow maxima exceeding estimated one in a hundred year events in several basins in the region. Based on a large ensemble of simulations with a hydrological model which integrates the effects of multiple climatic drivers, we find that the probability of such extreme streamflow events in October to December has been increased by human-induced climate change by a best estimate of 120–330%. Together these results demonstrate the substantial human influence on this compound extreme event, and help motivate efforts to increase resiliency in the face of more frequent events of this kind in the future.
Abstract Climate change is a threat to the 500 Gt carbon stored in northern peatlands. As the region warms, the rise in mean temperature is more pronounced during the non-growing season (NGS, i.e., winter and parts of the shoulder seasons) when net ecosystem loss of carbon dioxide (CO 2 ) occurs. Many studies have investigated the impacts of climate warming on NGS CO 2 emissions, yet there is a lack of consistency amongst researchers in how the NGS period is defined. This complicates the interpretation of NGS CO 2 emissions and hinders our understanding of seasonal drivers of important terrestrial carbon exchange processes. Here, we analyze the impact of alternative definitions of the NGS for a peatland site with multiple years of CO 2 flux records. Three climatic parameters were considered to define the NGS: air temperature, soil temperature, and snow cover. Our findings reveal positive correlations between estimates of the cumulative non-growing season net ecosystem CO 2 exchange (NGS-NEE) and the length of the NGS for each alternative definition, with the greatest proportion of variability explained using snow cover ( R 2 = 0.89, p < 0.001), followed by air temperature ( R 2 = 0.79, p < 0.001) and soil temperature ( R 2 = 0.54, p = 0.006). Using these correlations, we estimate average daily NGS CO 2 emitted between 1.42 and 1.90 gCO 2 m −2 , depending on which NGS definition is used. Our results highlight the need to explicitly define the NGS based on available climatic parameters to account for regional climate and ecosystem variability.
Abstract. Increasing hydrological variability, accelerating population growth and urbanisation, and the resurgence of water resources development projects have all indicated increasing tension among the riparian countries of transboundary rivers. While a wide range of disciplines develop their understandings of conflict and cooperation in transboundary river basins, few process-based interdisciplinary approaches are available for investigating the mechanism of conflict and cooperation. This article aims to develop a meta-theoretical socio-hydrological framework that brings the slow and less visible societal processes into existing hydrological–economic models and enables observations of the change in the cooperation process and the societal processes underlying this change, thereby contributing to revealing the mechanism that drives conflict and cooperation. This framework can act as a “middle ground”, providing a system of constituent disciplinary theories and models for developing formal models according to a specific problem or system under investigation. Its potential applicability is demonstrated in the Nile, Lancang–Mekong, and Columbia rivers.
In mountains, the precipitation phase greatly varies in space and time and affects the evolution of the snow cover. Snowpack models usually rely on precipitation-phase partitioning methods (PPMs) that use near-surface variables. These PPMs ignore conditions above the surface thus limiting their ability to predict the precipitation phase at the surface. In this study, the impact on snowpack simulations of atmospheric-based PPMs, incorporating upper atmospheric information, is tested using the snowpack scheme Crocus. Crocus is run at 2.5-km grid spacing over the mountains of southwestern Canada and northwestern United States and is driven by meteorological fields from an atmospheric model at the same resolution. Two atmospheric-based PPMs were considered from the atmospheric model: the output from a detailed microphysics scheme and a post-processing algorithm determining the snow level and the associated precipitation phase. Two ground-based PPMs were also included as lower and upper benchmarks: a single air temperature threshold at 0°C and a PPM using wet-bulb temperature. Compared to the upper benchmark, the snow-level based PPM improved the estimation of snowfall occurrence by 5% and the simulation of snow water equivalent (SWE) by 9% during the snow melting season. In contrast, due to missing processes, the microphysics scheme decreased performances in phase estimate and SWE simulations compared to the upper benchmark. These results highlight the need for detailed evaluation of the precipitation phase from atmospheric models and the benefit for mountain snow hydrology of the post-processed snow level. The limitations to drive snowpack models at slope scale are also discussed.
Fire plays a major role in the structuring and functioning of boreal ecosystems. As peatlands are important components of boreal forests, the impact of fire upon these wetter ecosystems is increasingly studied, but with the focus on treed peatlands and Sphagnum-dominated bogs so far. Important fires occurring more frequently in the past decade in southern Northwest Territories (Canada) provide the opportunity to assess early post-fire vegetation regeneration in open rich fens (one, two, and five years post-fire) and to better understand early recovery succession. We aimed to (i) evaluate whether and how open rich fens are affected by fire, and (ii) describe short-term vegetation regeneration for both bryophytes and vascular species. A shift was observed between pioneer bryophytes and brown mosses between the second and fifth year post-fire. Vascular plants, especially slow-growing species and the ones reproducing mainly by seeds, recovered partially. The first bryophyte species recovering were pioneer species adapted to colonize burned environments such as Marchantia polymorpha L. or Ceratodon purpureus (Hedw.) Brid. For vascular plant species, the ones previously present and able to regrow rapidly from unburned plant structures (base of tussocks, rhizomes, roots) were represented by species like Betula glandulosa Michx. or Carex aquatilis Wahlenb. The wetter conditions and lower fuel availability of fen depressional biotopes were important factors controlling the resistance and regeneration of species associated with them.
Heavy metal pollution on earth has evolved into a global issue causing serious risks to human health and other living entities and having an impact on sustainability. Accurate identification of metal contamination is often carried out in centralized facilities involving sampling, transportation, and the need for highly trained personnel, which becomes expensive, often causes delays in response to potential tragedies, and is prone to sample properties changes. Rapid, affordable methods for point-of-care (POC) detection of heavy metals with reasonable accuracy are ideal to address these challenges enabling diligent monitoring of metal pollution. There have been many POC systems reported, however, the systems that could work with real samples in which heavy metals are present in a complex form at a low concentration are limited. Sample preparation is often needed for the accurate identification of metal ions. Microfluidics offers tremendous potential for sample preparation and integration with various detection methods such as optical and electrochemical methods for POC detection of heavy metals. This review is limited to reviewing the reported microfluidic-based POC devices for heavy metal sensing and providing a brief perspective on the integration of microwave sensing methods with microfluidic devices for heavy metal detection. This review starts with introducing microfluidic-based heavy metal sensing using optical and electrochemical methods and then focuses on briefly discussing the development and potential of integrating microwave sensing with microfluidic devices for heavy metal sensing. The principle of each method and the limit of detection are briefly discussed.
Cyanobacterial blooms present challenges for water treatment, especially in regions like the Canadian prairies where poor water quality intensifies water treatment issues. Buoyant cyanobacteria that resist sedimentation present a challenge as water treatment operators attempt to balance pre-treatment and toxic disinfection by-products. Here, we used microscopy to identify and describe the succession of cyanobacterial species in Buffalo Pound Lake, a key drinking water supply. We used indicator species analysis to identify temporal grouping structures throughout two sampling seasons from May to October 2018 and 2019. Our findings highlight two key cyanobacterial bloom phases - a mid-summer diazotrophic bloom of Dolichospermum spp. and an autumn Planktothrix agardhii bloom. Dolichospermum crassa and Woronichinia compacta served as indicators of the mid-summer and autumn bloom phases, respectively. Different cyanobacterial metabolites were associated with the distinct bloom phases in both years: toxic microcystins were associated with the mid-summer Dolichospermum bloom and some newly monitored cyanopeptides (anabaenopeptin A and B) with the autumn Planktothrix bloom. Despite forming a significant proportion of the autumn phytoplankton biomass (>60%), the Planktothrix bloom had previously not been detected by sensor or laboratory-derived chlorophyll-a. Our results demonstrate the power of targeted taxonomic identification of key species as a tool for managers of bloom-prone systems. Moreover, we describe an autumn Planktothrix agardhii bloom that has the potential to disrupt water treatment due to its evasion of detection. Our findings highlight the importance of identifying this autumn bloom given the expectation that warmer temperatures and a longer ice-free season will become the norm.
Baseflow originating primarily from groundwater is a critical streamflow component, although its accurate estimation is fraught with significant difficulties. This study estimates baseflow through existing graphical and digital filter methods, using actual streamflow data from a gauging station at the Alder Creek Watershed (ACW) and synthetic streamflow data at ten study locations within the same watershed simulated with HydroGeoSphere (HGS) (Aquanty Inc., 2018). There are four widely used graphical (Institute for Hydrology, 1980; Sloto and Crouse, 1996; Aksoy et al., 2008) and six digital filtering (Lyne and Hollick, 1979; Chapman and Maxwell, 1996; Furey and Gupta, 2001; Eckhardt, 2005; Tularam and Ilahee, 2008; Aksoy et al., 2009) baseflow separation approaches compared in this study. To determine the most optimal approach, baseflow estimates from real data are assessed based on the subjective concept of hydrologic plausibility, while baseflow estimates obtained from a HGS streamflow record with graphical and digital filtering methods are compared to those computed directly by HGS. Overall, results from this study indicate that baseflow hydrographs reveal a seasonal pattern at the ACW. During wintertime, streamflow is composed almost entirely of baseflow, whereas during summertime, baseflow only consists approximately 20% to 60% of streamflow. After comparing baseflow estimates with those computed by HGS, the most optimal approaches at the ten study locations are assessed. Results show that the best approach at six study locations is the FUKIH (Aksoy et al., 2009) approach, while at three locations, the Chapman and Maxwell (1996) approach and for one location, the Eckhardt (2005) approach performed the best. In conclusion, it is inferred that the most optimal approach within the ACW varies spatially.
Data-driven hydrological modeling has seen rapid development in recent years owing to its flexibility to approximate the complex relationships between driving forces and hydrological fluxes. However, traditional data-driven models typically cannot simultaneously capture the processes that pose both chronic and acute impacts on streamflow, thus impeding further inference. Therefore, this study presents a baseflow-filtered hydrological inference model to gain insights into hydrological processes in irrigated watersheds. The proposed model starts with separating the streamflow process into two sub-processes using a process-based baseflow separation method. Each sub-process is simulated through a new interpretable data-driven model. The resulting hydrological inferences facilitate the identification of the dominant factors influencing flows in saturated and unsaturated zones. The proposed model is applied to three irrigated watersheds, and the evaluation metrics show that the proposed model outperforms two conventional data-driven models. Our findings reveal that predictors associated with air temperature and long-term (i.e., monthly) irrigation are mainly responsible for characterizing baseflow dynamics, while precipitation and short-term (i.e., semi-weekly or weekly) irrigation are primarily responsible for describing overland flow and interflow dynamics. The fidelity of the derived hydrological inference is further demonstrated through sensitivity analysis. The results show that the relative importance of predictors not only reflects their significance on model performance, but also influence the changes on streamflow.
Biofouling detection enables the adoption of effective cleaning strategies for biofouling prevention. This work investigates the use of electrical impedance spectroscopy (EIS) to monitor the biofilm development and the use of electric fields to mitigate biofouling on the surface of gold-coated membranes. The multi-bacterial suspension was injected into a two-electrode crossflow filtration system where the permeate flux and impedance spectra were recorded to monitor the biofilm growth. Permeate flux declined over time while the impedance at low frequency regions (<10 Hz) rapidly decreased with fouling at the early stages of fouling, and then gradually decreased as biofilm matured. The normalized diffusion-related impedance (Rd), an EIS-derived parameter, was extracted to determine the sensitivity of EIS detection. We observed that impedance-based detection was more sensitive to changes as compared to the decline of permeate flux during the early stage of biofouling. With early detection of fouling, fouling mitigation strategies could be applied more effectively. Further, under the same conditions as fouling detection, either applying an intermittent cathodic potential (−1.5 V) or cross-flow flushing delayed the biofilm growth on the electrically conductive membranes (ECMs). EIS sensitivity was repeatably recovered across four cycles of mechanical fouling removal. Hence ECMs were demonstrated to play a dual function: EIS-enabled detection of biofouling evolution and surface biofouling mitigation.
Abstract Wastewater monitoring of SARS-CoV-2 enables early detection and monitoring of the COVID-19 disease burden in communities and can track specific variants of concern. We determined proportions of the Omicron and Delta variants across 30 municipalities covering >75% of the province of Alberta (population 4.5 million), Canada, during November 2021–January 2022. Larger cities Calgary and Edmonton exhibited more rapid emergence of Omicron than did smaller and more remote municipalities. Notable exceptions were Banff, a small international resort town, and Fort McMurray, a medium-sized northern community that has many workers who fly in and out regularly. The integrated wastewater signal revealed that the Omicron variant represented close to 100% of SARS-CoV-2 burden by late December, before the peak in newly diagnosed clinical cases throughout Alberta in mid-January. These findings demonstrate that wastewater monitoring offers early and reliable population-level results for establishing the extent and spread of SARS-CoV-2 variants.
The availability of dissolved silicon (DSi) exerts an important control on phytoplankton communities in freshwater environments: DSi limitation can shift species dominance to non-siliceous algae and increase the likelihood of harmful algal blooms. The availability of DSi in the water column in turn depends on the dissolution kinetics of amorphous silica (ASi), including diatoms frustules and phytoliths. Here, batch dissolution experiments conducted with diatom frustules from three diatom species and synthetic Aerosil OX 50 confirmed the previously reported non-linear dependence of ASi dissolution rate on the degree of undersaturation of the aqueous solution. At least two first-order dissolution rate constants are therefore required to describe the dissolution kinetics at high (typically, ≥0.55) and low (typically, <0.55) degrees of undersaturation. Our results further showed aqueous ferrous ion (Fe2+), which is ubiquitous in anoxic waters, strongly inhibited ASi dissolution. The inhibition is attributed to the preferential binding of Fe2+ to Q2 groups (i.e., surface silicate groups bonded to the silica lattice via two bridging oxygen) which stabilizes the silica surface. However, further increasing the aqueous Fe2+ concentration likely catalyzes the detachment of Q3 groups (i.e., silicate groups bonded to the silica lattice via three bridging oxygen) from the surface. Overall, our study illustrates the manyfold effects the aqueous solution composition, notably the inhibition effect of Fe2+ under anoxic conditions, has on ASi dissolution. The results help to explain the controversial redox dependence of DSi internal loading from sediments, which is vital to quantitatively understanding silicon (Si) cycling in freshwater systems.
Most pharmaceuticals are found at trace concentrations in aquatic systems, but their continuous release and potential accumulation can lead to adverse health effects in exposed organisms. Concentrations can vary temporally, driven by variations in discharges of receiving waters, sorption to sediments, and other biotic and abiotic exchange processes. The principal aim of this research was to better understand the occurrence, trends, and dynamics of pharmaceuticals in a cold-climate, riverine environment. To this end, a suite of seven representative antipsychotic pharmaceuticals was measured upstream and downstream of two wastewater treatment plants (WWTPs) in Saskatchewan, Canada, located in the South Saskatchewan River and Wascana Creek, respectively, across three seasons. Concentrations of analytes were in the ng/L range and generally greater downstream of both WWTPs compared to upstream. Some compounds, including the tricyclic antidepressant amitriptyline, which was the most abundant analyte in water and sediment from both sites and across seasons, reached low μg/L concentrations. Data collected from this research effort indicate contamination with antipsychotic pharmaceuticals, with the potential to adversely impact exposed organisms.
Conservation practices that reduce nutrient and soil loss from agricultural lands to water are fundamental to watershed management programs. Avoiding trade-offs of conservation practices is essential to the successful mitigation of watershed phosphorus (P) losses. We review documented trade-offs associated with conservation practices, particularly those practices that are intended to control and trap P from agricultural sources. A regular theme is the trade-off between controlling P loss linked to sediment while increasing dissolved P losses (no-till, cover crops, vegetated buffers, constructed wetlands, sediment control basins). A variety of factors influence the degree to which these trade-offs occur, complicated by their interaction and uncertainties associated with climate change. However, acknowledging these trade-offs and anticipating their contribution to watershed outcomes are essential to the sustainability of conservation systems.
Abstract The stable isotopes of hydrogen and oxygen in xylem water are often used to investigate tree water sources. But this traditional approach does not acknowledge the contribution of water stored in the phloem to transpiration and how this may affect xylem water and source water interpretations. Additionally, there is a prevailing assumption that there is no isotope fractionation during tree water transport. Here, we systematically sampled xylem and phloem water at daily and subdaily resolutions in a large lysimeter planted with Salix viminalis . Stem diurnal change in phloem water storage and transpiration rates were also measured. Our results show that phloem water is significantly less enriched in heavy isotopes than xylem water. At subdaily resolution, we observed a larger isotopic difference between xylem and phloem during phloem water refilling and under periods of tree water deficit. These findings contrast with the expectation of heavy‐isotope enriched water in phloem due to downward transport of enriched leaf water isotopic signatures. Because of previous evidence of aquaporin mediated phloem and xylem water transport and higher osmotic permeability of lighter hydrogen isotopologues across aquaporins, we propose that radial water transport across the xylem–phloem boundary may drive the relative depletion of heavy isotopes in phloem and their relative enrichment in xylem.
Abstract Machine learning (ML) applications in Earth and environmental sciences (EES) have gained incredible momentum in recent years. However, these ML applications have largely evolved in ‘isolation’ from the mechanistic, process‐based modelling (PBM) paradigms, which have historically been the cornerstone of scientific discovery and policy support. In this perspective, we assert that the cultural barriers between the ML and PBM communities limit the potential of ML, and even its ‘hybridization’ with PBM, for EES applications. Fundamental, but often ignored, differences between ML and PBM are discussed as well as their strengths and weaknesses in light of three overarching modelling objectives in EES, (1) nowcasting and prediction, (2) scenario analysis, and (3) diagnostic learning. The paper ponders over a ‘coevolutionary’ approach to model building, shifting away from a borrowing to a co‐creation culture, to develop a generation of models that leverage the unique strengths of ML such as scalability to big data and high‐dimensional mapping, while remaining faithful to process‐based knowledge base and principles of model explainability and interpretability, and therefore, falsifiability.
Abstract Ecohydrological investigations commonly use the stable isotopes of water (hydrogen and oxygen) as conservative ecosystem tracers. This approach requires accessing and analysing water from plant and soil matrices. Generally, there are six steps involved to retrieve hydrogen and oxygen isotope values from these matrices: (1) sampling, (2) sample storage and transport, (3) extraction, (4) pre‐analysis processing, (5) isotopic analysis, and (6) post‐processing and correction. At each step, cumulative errors can be introduced which sum to non‐trivial magnitudes. These can impact subsequent interpretations about water cycling and partitioning through the soil–plant‐atmosphere continuum. At each of these steps, there are multiple possible options to select from resulting in tens of thousands of possible combinations used by researchers to go from plant and soil samples to isotopic data. In a newly emerging field, so many options can create interpretive confusion and major issues with data comparability. This points to the need for development of shared standardized approaches. Here we critically examine the state of the process chain, reflecting on the issues associated with each step, and provide suggestions to move our community towards standardization. Assessing this shared ‘process chain’ will help us see the problem in its entirety and facilitate community action towards agreed upon standardized approaches.
Delineating the relative solubility of soil phosphorus (P) in agricultural landscapes is essential to predicting potential P mobilization in the landscape and can improve nutrient management strategies. This study describes spatial patterns of soil extractable P (easily, moderately, and poorly soluble P) in agricultural landscapes of the Red River basin and the southern Great Lakes region. Surface soils (0-30 cm) and select deeper cores (0-90 cm) were collected from 10 cropped fields ranging in terrain (near-level to hummocky), soil texture (clay to loam), composition (calcareous to noncalcareous), and climate across these differing glacial landscapes. Poorly soluble P dominated (up to 91%) total extractable P in the surface soils at eight sites. No differences in the relative solubilities of soil extractable P with microtopography were apparent in landscapes without defined surface depressions. In contrast, in landscapes with pronounced surface depressions, increased easily soluble P (Sol-P), and decreased soil P sorption capacity were found in soil in wetter, low-slope zones relative to drier upslope locations. The Sol-P pool was most important to soil P retention (up to 28%) within the surface depressions of the Red River basin and at sites with low-carbonate soils in the southern Lake Erie watershed (up to 28%), representing areas at elevated risk of soil P remobilization. This study demonstrates interrelationships among soil extractable P pools, soil development, and soil moisture regimes in agricultural glacial landscapes and provides insight into identifying potential areas for soil P remobilization and associated P availability to crops and runoff.
Abstract Software engineering (SE) methodologies are widely used in both academia and industry to manage the software development life cycle. A number of studies of SE methodologies involve interviewing stakeholders to explore the real‐world practice. Although these interview‐based studies provide us with a user's perspective of an organization's practice, they do not describe the concrete summary of releases in open‐source social coding platforms. In particular, no existing studies investigated how releases are evolved in open‐source coding platforms, which assist release planners to a large extent. This study explores software development patterns followed in open‐source projects to see the overall management's reflection on software release decisions rather than concentrating on a particular methodology. Our experiments on 51 software origins (with 1777k revisions and 12k releases) from the Software Heritage Graph Dataset (SWHGD) and their GitHub project boards (with 23k cards) reveal reasonably active project management with phase simplicity can release software versions more frequently and can follow the small release conventions of Extreme Programming. Additionally, the study also reveals that a combination of development and management activities can be applied to predict the possible number of software releases in a month ( ).
Resilience of plant communities to disturbance is supported by multiple mechanisms, including ecological legacies affecting propagule availability, species' environmental tolerances, and biotic interactions. Understanding the relative importance of these mechanisms for plant community resilience supports predictions of where and how resilience will be altered with disturbance. We tested mechanisms underlying resilience of forests dominated by black spruce (Picea mariana) to fire disturbance across a heterogeneous forest landscape in the Northwest Territories, Canada. We combined surveys of naturally regenerating seedlings at 219 burned plots with experimental manipulations of ecological legacies via seed addition of four tree species and vertebrate exclosures to limit granivory and herbivory at 30 plots varying in moisture and fire severity. Black spruce recovery was greatest where it dominated pre-fire, at wet sites with deep residual soil organic layers, and fire conditions of low soil or canopy combustion and longer return intervals. Experimental addition of seed indicated all species were seed-limited, emphasizing the importance of propagule legacies. Black spruce and birch (Betula papyrifera) recruitment were enhanced with vertebrate exclusion. Our combination of observational and experimental studies demonstrates black spruce is vulnerable to effects of increased fire activity that erode ecological legacies. Moreover, black spruce relies on wet areas with deep soil organic layers where other species are less competitive. However, other species can colonize these areas if enough seed is available or soil moisture is altered by climate change. Testing mechanisms underlying species' resilience to disturbance aids predictions of where vegetation will transform with effects of climate change.The online version contains supplementary material available at 10.1007/s10021-022-00772-7.
Abstract Warming temperatures in the circumpolar north have led to new discussions around climate-driven frontiers for agriculture. In this paper, we situate northern food systems in Canada within the corporate food regime and settler colonialism, and contend that an expansion of the conventional, industrial agriculture paradigm into the Canadian North would have significant socio-cultural and ecological consequences. We propose agroecology as an alternative framework uniquely accordant with northern contexts. In particular, we suggest that there are elements of agroecology that are already being practiced in northern Indigenous communities as part of traditional hunter-gatherer food systems. We present a framework for agroecology in the North and discuss its components of environmental stewardship, economies, knowledge, social dimensions and governance using examples from the Dehcho region, Northwest Territories, Canada. Finally, we discuss several challenges and cautions in creating policy around agroecology in the North and encourage community-based research in developing and testing this framework moving forward.
Amplified climate warming in high latitudes is expected to affect growing season timing of the vast boreal biome. It is unclear whether the presence of permafrost (perennially frozen ground) might have an influence on changes in growing season timing. This study examined how different environmental variables explained, either directly or indirectly, the variation in growing season timing of boreal forest stands with and without permafrost. We expected that environmental variables explaining the variation in growing season timing differed or had different explanatory power depending on permafrost presence or absence. The growing season was delineated from daily gross primary productivity (GPP) time series derived from 40 site-year data of net ecosystem carbon dioxide exchange measured with eddy covariance techniques over five black spruce (Picea mariana [Mill.])-dominated boreal forest stands in North America. In permafrost-free forest stands, a combination of start in canopy ‘green-up’ in spring and the timing of air and soil temperature increasing above freezing explained the start-of-season (SOSGPP). Results from commonality analysis and structural equation modeling suggest that canopy ‘green-up’ and air temperature directly affected SOSGPP in permafrost-free forest stands. In addition, soil temperature acted as mediator for an indirect effect of air temperature on SOSGPP. In contrast, none of the environmental variables, or their combination, explained the variation in SOSGPP in forest stands with permafrost. The explanatory power of environmental variables was more consistent regarding the end-of-season (EOSGPP). In both, forest stands with and without permafrost, EOSGPP was directly explained by mean soil water content in the fall and the first day of continuous snowpack formation. A better understanding how environmental variables control SOSGPP and EOSGPP in forest stands with and without permafrost will help to refine parameterizations of the boreal biome in Earth system models.
Wetland CH4 emissions are among the most uncertain components of the global CH4 budget. The complex nature of wetland CH4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH4 emissions. In this study, we used the flux measurements of CH4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH4 emissions at sub-seasonal scale. We found that soil temperature is the dominant factor for CH4 emissions in all studied wetland types. Ecosystem respiration (CO2) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH4 emissions differed by up to a factor of 4 under a +1°C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH4 emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH4 emissions within earth system land models.
In many practical geochemical systems that are at the center of providing indispensable energy, resources and service to our society, (bio)geochemical reactions are coupled with other physical processes, such as multiphase flow, fracturing and deformation. Predictive understanding of these processes in hosting and evolving porous media is the key to design reliable and sustainable practices. In this article, we provide a brief review of recent developments and applications of reactive transport modeling to study geochemically driven processes and alteration in porous media. We also provide a perspective on opportunities and challenges for continuously developing and expanding the role of this valuable methodology to advance fundamental understanding and transferable knowledge of various dynamic geochemical systems.
This paper describes the occurrences and conditions before Mesoscale Convective Systems (MCSs) are initiated over the Canadian Prairies, using 10 years of observations and convection-permitting climate model simulations. The features of MCSs occurring in summer were analyzed using the Regional Deterministic Reforecast System (RDRS, hourly and 10-km grid spacing) and ECMWF Reanalysis v5 (ERA5)-forced Weather Research and Forecasting (WRF) model simulations (FSCT, 4-km grid spacing). MCSs were defined and tracked using the Method for Object-Based Diagnostic Evaluation-Time Domain (MTD). MTD-identified MCSs were divided into short−/long-lived and daytime/nighttime, considering the longevities and initiation times. FCST showed the skills to simulate MCSs but overestimated MCS features compared to RDRS. Fifteen meteorological parameters were calculated using sounding data from FCST to determine pre-conventional conditions of MCSs (at init. -9, −6, −3, and − 1 h). The distributions of parameters were tested to determine the significance of differences between short- and long-lived MCSs. The key findings are as follows: 1) long-lived daytime MCSs (LLM12) showed favorable thermodynamic processes and 2) long-lived nighttime MCSs (LLM00) were initiated based on dynamic processes. We also found that the most appropriate parameters (i.e., those that were statistically different in short- and long-lived MCSs) to determine the longevities of MCSs were 1) most unstable convective available potential energy and 2) vertical wind shear of 0–3 km.
Many mathematical models of natural phenomena are described by partial differential equations (PDEs) that consist of additive contributions from different physical processes. Classical methods for the numerical solution to such equations are monolithic in that all processes are treated with a single method. Additive numerical methods, in contrast, apply distinct methods to each additive term. There are, however, different ways mathematically to specify the additive terms, and it is not always clear which ways (if any) offer advantages over monolithic methods. This study compares the performance of two different additive splitting techniques (physics-based splitting and dynamic linearization) on a suite of eight test problems that involve advection, reaction, and diffusion with various 2-additive Runge–Kutta methods and (monolithic) Runge–Kutta–Chebyshev (RKC) methods. Results show that dynamic linearization generally outperforms physics-based splitting and so should be preferred as the splitting technique when splitting is required or otherwise desirable. RKC methods are the best performers on three of the eight problems, especially at coarse tolerances, but they can also be prone to severe underperformance.
The Grand River watershed is the largest in southern Ontario and assimilates thirty wastewater treatment plants (WWTP) with varied degrees of treatment. Many WWTPs are unable to effectively eliminate several contaminants of emerging concern (CECs) from final effluent, leading to measurable concentrations in surface waters. Exposures to CECs have reported impacts on oxidative stress measured through antioxidative enzymes (SOD, CAT, GPX). This study focuses on the effects of WWTP effluent on four Etheostoma (Darter) species endemic to the Grand River, by investigating if increased antioxidative response markers are present in darter brains downstream from the effluent outfall compared to an upstream reference site relative to the Waterloo, ON WWTP across two separate years (Oct 2020 and Oct 2021). This was assessed using transcriptional and enzyme analysis of antioxidant enzymes and an enzyme involved in serotonin synthesis, tryptophan hydroxylase (tph). In fall 2020, significant differences in transcript markers were found between sites and sexes in GSD with SOD and CAT showing increased expression downstream, in JD with both sexes showing increased SOD downstream, and an interactive effect for tph in RBD. Changes in transcripts aligned with enzyme activity where interactive effects with sex-related differences were observed in fish collected fall 2020. In contrast, transcripts measured in fall 2021 were increased upstream compared to downstream species in RBD and GSD. This study additionally displayed yearly, species and sex differences in antioxidant responses. Continued investigation on the impacts of CECs in effluent in non-target species is required to better understand WWTP effluent impacts.
In the United States, high-resolution, century-long, hydroclimate projection datasets have been developed for water resources planning, focusing on the contiguous United States (CONUS) domain. However, there are few statewide hydroclimate projection datasets available for Alaska and Hawaiʻi. The limited information on hydroclimatic change motivates developing hydrologic scenarios from 1950 to 2099 using climate-hydrology impact modeling chains consisting of multiple statistically downscaled climate projections as input to hydrologic model simulations for both states. We adopt an approach similar to the previous CONUS hydrologic assessments where: 1) we select the outputs from ten global climate models (GCM) from the Coupled Model Intercomparison Project Phase 5 with Representative Concentration Pathways 4.5 and 8.5; 2) we perform statistical downscaling to generate climate input data for hydrologic models (12-km grid-spacing for Alaska and 1-km for Hawaiʻi); and 3) we perform process-based hydrologic model simulations. For Alaska, we have advanced the hydrologic model configuration from CONUS by using the full water-energy balance computation, frozen soils and a simple glacier model. The simulations show that robust warming and increases in precipitation produce runoff increases for most of Alaska, with runoff reductions in the currently glacierized areas in Southeast Alaska. For Hawaiʻi, we produce the projections at high resolution (1 km) which highlight high spatial variability of climate variables across the state, and a large spread of runoff across the GCMs is driven by a large precipitation spread across the GCMs. Our new ensemble datasets assist with state-wide climate adaptation and other water planning.
Widespread application of poly- and per-fluoroalkyl substances (PFAS) has resulted in some substances being ubiquitous in environmental matrices. That and their resistance to degradation have allowed them to accumulate in wildlife and humans with potential for toxic effects. While specific substances of concern have been phased-out or banned, other PFAS that are emerging as alternative substances are still produced and are being released into the environment. This review focuses on describing three emerging, replacement PFAS: perfluoroethylcyclohexane sulphonate (PFECHS), 6:2 chlorinated polyfluoroalkyl ether sulfonate (6:2 Cl-PFAES), and hexafluoropropylene oxide dimer acid (HFPO-DA). By summarizing their physicochemical properties, environmental fate and transport, and toxic potencies in comparison to other PFAS compounds, this review offers insight into the viabilities of these chemicals as replacement substances. Using the chemical scoring and ranking assessment model, the relative hazards, uncertainties, and data gaps for each chemical were quantified and related to perfluorooctane sulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) based on their chemical and uncertainty scores. The substances were ranked PFOS > 6:2 Cl-PFAES > PFOA > HFPO-DA > PFECHS according to their potential toxicity and PFECHS > HFPO-DA > 6:2 Cl-PFAES > PFOS > PFOA according to their need for future research. Since future uses of PFAS remain uncertain in the face of governmental regulations and production bans, replacement PFAS will continue to emerge on the world market and in the environment, raising concerns about their general lack of information on mechanisms and toxic potencies.
Selenium (Se) is an environmental contaminant of global concern that can cause adverse effects in fish at elevated levels. Fish gut microbiome play essential roles in gastrointestinal function and host health and can be perturbed by environmental contaminants, including metals and metalloids. Here, an in-situ Se exposure of female finescale dace (Phoxinus neogaeus) using mesocosms was conducted to determine the impacts of Se accumulation on the gut microbiome and morphometric endpoints. Prior to this study, the gut microbiome of finescale dace, a widespread Cyprinid throughout North America, had not been characterized. Exposure to Se caused a hormetic response of alpha diversity of the gut microbiome, with greater diversity at the lesser concentration of 1.6 μg Se/L, relative to that of fish exposed to the greater concentration of 5.6 μg Se/L. Select gut microbiome taxa of fish were differentially abundant between aqueous exposure concentrations and significantly correlated with liver-somatic index (LSI). The potential effects of gut microbiome dysbiosis on condition of wild fish might be a consideration when assessing adverse effects of Se in aquatic environments. More research regarding effects of Se on field-collected fish gut microbiome and the potential adverse effects or benefits on the host is warranted.
Seeking available and economical carbon sources for denitrification process is an intractable issue for wastewater treatment. However, no study compared different types of waste sludge as carbon source from denitrification mechanism, organics utilization and microbial community aspects. In this study, primary and secondary sludge were pretreated by thermophilic bacteria (TB), and its hydrolysis or acidogenic liquid were prepared as carbon sources for denitrification. At C/N of 8-3, the variations of NO3--N and NO2--N were profiled in typical cycles and denitrification kinetics was analyzed. Primary sludge achieved a competitive NOX-N removal efficiency with less dosage than secondary sludge. Fourier transform infrared (FTIR) spectroscopy was introduced to analyze organic composition from functional-group perspective and the utilization of organic matters in different sludge carbon sources was investigated. To further analyze the microbial community shift in different denitrification systems, high-throughput sequencing technology was applied. Results showed that denitrifier Thauera, belonging to Proteobacteria, was predominant, and primary sludge acidogenic liquid enriched Thauera most intensively with relative abundance of 47.3%.
Flavins and siderophores secreted by various plants, fungi and bacteria under iron (Fe) deficient conditions play important roles in the biogeochemical cycling of Fe in the environment. Although the mechanisms of flavin and siderophore mediated Fe(III) reduction and dissolution under anoxic conditions have been widely studied, the influence of these compounds on Fe(II) oxidation under oxic conditions is still unclear. In this study, we investigated the kinetics of aqueous Fe(II) (17.8 μM) oxidation by O 2 at pH 5‒7 in the presence of riboflavin (oxidized (RBF) and reduced (RBFH 2 )) and desferrioxamine B (DFOB) as representative flavins and siderophores, respectively. Results showed that the addition of RBF/RBFH 2 or DFOB markedly accelerates the oxidation of aqueous Fe(II) by O 2 . For instance, at pH 6, the rate of Fe(II) oxidation was enhanced 20‒70 times when 10 μM RBFH 2 was added. The mechanisms responsible for the accelerated Fe(II) oxidation are related to the redox reactivity and complexation ability of RBFH 2 , RBF and DFOB. While RBFH 2 does not readily complex Fe(II)/Fe(III), it can activate O 2 and generate reactive oxygen species, which then rapidly oxidize Fe(II). In contrast, both RBF and DFOB do not reduce O 2 but react with Fe(II) to form RBF/DFOB-complexed Fe(II), which in turn accelerates Fe(II) oxidation. Furthermore, the lower standard reduction potential of the Fe(II)-DFOB complex, compared to the Fe(II)-RBF complex, correlates with a higher oxidation rate constant for the Fe(II)-DFOB complex. Our study reveals an overlooked catalytic role of flavins and siderophores that may contribute to Fe(II)/Fe(III) cycling at oxic-anoxic interfaces.
There is increasing interest in the cost-effectiveness and economic benefits of replacing traditional engineering-based ‘grey’ infrastructure with nature-based ‘green’ infrastructure in the water sector. This study builds on the emerging literature in this field and sets itself apart in several ways. New in this study is the focus on the interrelationship between green infrastructure, water treatment costs proxied by drinking water rates, and drinking water safety. The latter refers to adverse treated water quality incidents (AWQI's) such as unsatisfactory bacteriological test results that may lead to drinking water advisories when sufficiently severe. An integrated modelling framework is furthermore developed, accounting simultaneously for possible spatial spill-over effects due to watershed land cover and potential endogeneity embedded in the relationship between water treatment costs, drinking water billing, and the occurrence of AWQI's. Data from the water- and forest-abundant and densely populated Canadian province of Ontario were used and significant negative correlations between forested land area and both drinking water rates and AWQI's are observed. While causality underlying these relationships needs further investigation, these results indicate support for the use of techno-ecological nature-based solutions in drinking water risk management.
Floating solar photovoltaic (FPV) deployments are increasing globally as the switch to renewable energy intensifies, representing a considerable water surface transformation. FPV installations can potentially impact aquatic ecosystem function, either positively or negatively. However, these impacts are poorly resolved given the challenges of collecting empirical data for field or modelling experiments. In particular, there is limited evidence on the response of phytoplankton to changes in water body thermal dynamics and light climate with FPV. Given the importance of understanding phytoplankton biomass and species composition for managing ecosystem services, we use an uncertainty estimation approach to simulate the effect of FPV coverage and array siting location on a UK reservoir. FPV coverage was modified in 10% increments from a baseline with 0% coverage to 100% coverage for three different FPV array siting locations based on reservoir circulation patterns. Results showed that FPV coverage significantly impacted thermal properties, resulting in highly variable impacts on phytoplankton biomass and species composition. The impacts on phytoplankton were often dependent on array siting location as well as surface coverage. Changes to phytoplankton species composition were offset by the decrease in phytoplankton biomass associated with increasing FPV coverage. We identified that similar phytoplankton biomass reductions could be achieved with less FPV coverage by deploying the FPV array on the water body's faster-flowing area than the central or slower flowing areas. The difference in response dependent on siting location could be used to tailor phytoplankton management in water bodies. Simulation of water body-FPV interactions efficiently using an uncertainty approach is an essential tool to rapidly develop understanding and ultimately inform FPV developers and water body managers looking to minimise negative impacts and maximise co-benefits.
The eutrophication of freshwater systems is a pervasive issue in North America and elsewhere, which has been linked to elevated phosphorus (P) loading from watersheds. Most excess P is thought to originate from non-point agricultural sources, and less attention has been given to small rural point sources, such as bunker silos on livestock farms. Sophisticated management practices are rarely used to attenuate nutrients from bunker silo effluent, leaving simple vegetated buffer strips or riparian zones to protect surface water; however, the efficacy of these systems or larger constructed treatment systems is unclear. This study compared two systems receiving bunker silo effluent, one a natural riparian system with a vegetated buffer strip that is the most common practice and the other a constructed treatment system with a forebay, slag filter, and swale. The study quantified P retention within various subsections of each system and characterized the forms of stored P to infer the potential for remobilization. Results indicate that soils receiving bunker silo effluent represent considerable stores of legacy P in the landscape (750 and 3400 kg ha−1), the majority of which is stored in labile forms that may be vulnerable to remobilization under the waterlogged conditions that often occur in management practices and riparian zones. Some areas of the systems were able to store considerably more P than others, with the slag filter showing the greatest treatment efficacy. Spatial variability in stored P was apparent, where sections of the systems that directly received effluent retained more P than sections located farther away from bunker silos (indirect inputs). Results indicate that passive treatment systems become P saturated over time, limiting their longterm P removal efficacy. The efficacy of these systems may be improved with the inclusion of sorptive materials as a slag filter within the constructed treatment system significantly increased the life expectancy of that system. Greater understanding of both quantity and forms of P retained in systems and soils receiving bunker silo effluent will help develop management strategies that are more effective and longer-lasting for reducing excess P losses to surface water bodies.
Several studies have shown that large, experimental additions of nitrate (NO3) to eutrophic systems can mitigate large populations of nuisance cyanobacteria and that high NO3 concentrations can oxidize anoxic sediments. These studies are consistent with observations from numerous aquatic systems across a broad trophic range showing development of reduced surficial sediments precedes the formation of large cyanobacteria populations. We use 50+ years of data to explore whether high NO3 concentrations may have been instrumental both in the absence of large populations of cyanobacteria in eutrophic Hamilton Harbour, Lake Ontario in the 1970s when total phosphorus (TP) and total nitrogen (TN) concentrations were high, and in delaying large populations until August and September in recent decades despite much lower TP and TN. Our results indicate that large cyanobacteria population events do not occur at the central station in July-September when epilimnetic NO3 > 2.2 mg N L−1. The results further suggest that remedial improvements to wastewater treatment plant oxidation capacity may have been inadvertently responsible for high NO3 concentrations > 2.2 mg N L−1 and thus for mitigating large cyanobacteria populations. This also implies that large cyanobacteria populations may form earlier in the summer if NO3 concentrations are lowered.
Extensive efforts are underway to reduce phosphorus (P) export from the Lake Erie watershed. On the Canadian side, the Thames River is the largest tributary source of P to Lake Erie’s western basin. However, the role of dams in retaining and modifying riverine P loading to the lake has not been comprehensively evaluated. We assessed whether Fanshawe Reservoir, the largest dam reservoir on the Thames River, acts as a source or sink of P, using year-round discharge and water chemistry data collected in 2018 and 2019. We also determined how in-reservoir processes alter P speciation by comparing the dissolved reactive P to total P ratio (DRP:TP) in upstream and downstream loads. Annually, Fanshawe Reservoir was a net sink for P, retaining 25% (36 tonnes) and 47% (91 tonnes) of TP in 2018 and 2019, respectively. Seasonally, the reservoir oscillated between a source and sink of P. Net P release occurred during the spring of 2018 and the summers of 2018 and 2019, driven by internal P loading and hypolimnetic discharge from the dam. The reservoir did not exert a strong influence on DRP:TP annually, but ratio increases occurred during both summers, concurrent with water column stratification. Our analysis demonstrates that Fanshawe Reservoir is not only an important P sink on the Thames River, but also modulates the timing and speciation of P loads. We therefore propose that the potential of using existing dam reservoirs to attenuate downstream P loads should be more thoroughly explored alongside source based P mitigation strategies.
The Köppen-Geiger (KG) climate classification has been widely used to determine the climate at global and regional scales using precipitation and temperature data. KG maps are typically developed using a single product; however, uncertainties in KG climate types resulting from different precipitation and temperature datasets have not been explored in detail. Here, we assess seven global datasets to show uncertainties in KG classification from 1980 to 2017. Using a pairwise comparison at global and zonal scales, we quantify the similarity among the seven KG maps. Gauge- and reanalysis-based KG maps have a notable difference. Spatially, the highest and lowest similarity is observed for the North and South Temperate zones, respectively. Notably, 17% of grids among the seven maps show variations even in the major KG climate types, while 35% of grids are described by more than one KG climate subtype. Strong uncertainty is observed in south Asia, central and south Africa, western America, and northeastern Australia. We created two KG master maps (0.5° resolution) by merging the climate maps directly and by combining the precipitation and temperature data from the seven datasets. These master maps are more robust than the individual ones showing coherent spatial patterns. This study reveals the large uncertainty in climate classification and offers two robust KG maps that may help to better evaluate historical climate and quantify future climate shifts.
Soot deposition from wildfires decreases snow and ice albedo and increases the absorption of shortwave radiation, which advances and accelerates melt. Soot deposition also induces algal growth, which further decreases snow and ice albedo. In recent years, increasingly severe and widespread wildfire activity has occurred in western Canada in association with climate change. In the summers of 2017 and 2018, westerly winds transported smoke from extensive record-breaking wildfires in British Columbia eastward to the Canadian Rockies, where substantial amounts of soot were deposited on high mountain glaciers, snowfields, and icefields. Several studies have addressed the problem of soot deposition on snow and ice, but the spatiotemporal resolution applied has not been compatible with studying mountain icefields that are extensive but contain substantial internal variability and have dynamical albedos. This study evaluates spatial patterns in the albedo decrease and net shortwave radiation (K*) increase caused by soot from intense wildfires in Western Canada deposited on the Columbia Icefield (151 km2), Canadian Rockies, during 2017 and 2018. Twelve Sentinel-2 images were used to generate high spatial resolution albedo retrievals during four summers (2017 to 2020) using a MODIS bidirectional reflectance distribution function (BRDF) model, which was employed to model the snow and ice reflectance anisotropy. Remote sensing estimates were evaluated using site-measured albedo on the icefield's Athabasca Glacier tongue, resulting in a R2, mean bias, and root mean square error (RMSE) of 0.68, 0.019, and 0.026, respectively. The biggest inter-annual spatially averaged soot-induced albedo declines were of 0.148 and 0.050 (2018 to 2020) for southeast-facing glaciers and the snow plateau, respectively. The highest inter-annual spatially-averaged soot-induced shortwave radiative forcing was 203 W/m2 for southeast-facing glaciers (2018 to 2020) and 106 W/m2 for the snow plateau (2017 to 2020). These findings indicate that snow albedo responded rapidly to and recovered rapidly from soot deposition. However, ice albedo remained low the year after fire, and this was likely related to a bio-albedo feedback involving microorganisms. Snow and ice K* were highest during low albedo years, especially for south-facing glaciers. These large-scale effects accelerated melt of the Columbia Icefield. The findings highlight the importance of using large-area high spatial resolution albedo estimates to analyze the effect of wildfire soot deposition on snow and ice albedo and K* on icefields, which is not possible using other approaches.
The availability and quality of quad-pol synthetic aperture radar (SAR) datasets has increased substantially since the early 2000s, allowing for polarimetrically complete investigations of freshwater ice. These investigations have lead to improved ice classification methods, new understanding of microwave-ice scattering processes, and the potential for new methods to extract ice observables. Such analyses are predicated on the decomposition of the target’s polarimetric properties along mathematical or physical lines. This paper comprehensively reviews the underlying theory and contemporary application of radar polarimetric decomposition as it applies to freshwater ice systems. Modelling and investigation of lake ice, river ice, and glacial systems are discussed. We conclude with recommendations for further research, discussing the value of further development of freshwater-ice models, their use in characterization of the scattering process, and the potential for new methods to extract environmental observables.
Arctic Indigenous Peoples are among the most exposed humans when it comes to foodborne mercury (Hg). In response, Hg monitoring and research have been on-going in the circumpolar Arctic since about 1991; this work has been mainly possible through the involvement of Arctic Indigenous Peoples. The present overview was initially conducted in the context of a broader assessment of Hg research organized by the Arctic Monitoring and Assessment Programme. This article provides examples of Indigenous Peoples' contributions to Hg monitoring and research in the Arctic, and discusses approaches that could be used, and improved upon, when carrying out future activities. Over 40 mercury projects conducted with/by Indigenous Peoples are identified for different circumpolar regions including the U.S., Canada, Greenland, Sweden, Finland, and Russia as well as instances where Indigenous Knowledge contributed to the understanding of Hg contamination in the Arctic. Perspectives and visions of future Hg research as well as recommendations are presented. The establishment of collaborative processes and partnership/co-production approaches with scientists and Indigenous Peoples, using good communication practices and transparency in research activities, are key to the success of research and monitoring activities in the Arctic. Sustainable funding for community-driven monitoring and research programs in Arctic countries would be beneficial and assist in developing more research/monitoring capacity and would promote a more holistic approach to understanding Hg in the Arctic. These activities should be well connected to circumpolar/international initiatives to ensure broader availability of the information and uptake in policy development.
Monitoring the communal incidence of COVID-19 is important for both government and residents of an area to make informed decisions. However, continuous reliance on one means of monitoring might not be accurate because of biases introduced by government policies or behaviours of residents. Wastewater surveillance was employed to monitor concentrations of SARS-CoV-2 RNA in raw influent wastewater from wastewater treatment plants serving three Canadian Prairie cities with different population sizes. Data obtained from wastewater are not directly influenced by government regulations or behaviours of individuals. The means of three weekly samples collected using 24 h composite auto-samplers were determined. Viral loads were determined by RT-qPCR, and whole-genome sequencing was used to charaterize variants of concern (VOC). The dominant VOCs in the three cities were the same but with different proportions of sub-lineages. Sub-lineages of Delta were AY.12, AY.25, AY.27 and AY.93 in 2021, while the major sub-lineage of Omicron was BA.1 in January 2022, and BA.2 subsequently became a trace-level sub-variant then the predominant VOC. When each VOC was first detected varied among cities; However, Saskatoon, with the largest population, was always the first to present new VOCs. Viral loads varied among cities, but there was no direct correlation with population size, possibly because of differences in flow regimes. Population is one of the factors that affects trends in onset and development of local outbreaks during the pandemic. This might be due to demography or the fact that larger populations had greater potential for inter- and intra-country migration. Hence, wastewater surveillance data from larger cities can typically be used to indicate what to expect in smaller communities.
Rapid climate warming across northern high latitudes is leading to permafrost thaw and ecosystem carbon release while simultaneously impacting other biogeochemical cycles including nitrogen. We used a two-year laboratory incubation study to quantify concomitant changes in carbon and nitrogen pool quantity and quality as drivers of potential CO2 production in thawed permafrost soils from eight soil cores collected across the southern Northwest Territories (NWT), Canada. These data were contextualized via in situ annual thaw depth measurements from 2015 to 2019 at 40 study sites that varied in burn history. We found with increasing time since experimental thaw the dissolved carbon and nitrogen pool quality significantly declined, indicating sustained microbial processing and selective immobilization across both pools. Piecewise structural equation modeling revealed CO2 trends were predominantly predicted by initial soil carbon content with minimal influence of dissolved phase carbon. Using these results, we provide a first-order estimate of potential near-surface permafrost soil losses of up to 80 g C m−2 over one year in southern NWT, exceeding regional historic mean primary productivity rates in some areas. Taken together, this research provides mechanistic knowledge needed to further constrain the permafrost‑carbon feedback and parameterize Earth system models, while building on empirical evidence that permafrost soils are at high risk of becoming weaker carbon sinks or even significant carbon sources under a changing climate.
Lake Wilcox (LW), a shallow kettle lake located in southern Ontario, has experienced multiple phases of land use change associated with human settlement and residential development in its watershed since the early 1900s. Urban growth has coincided with water quality deterioration, including the occurrence of algal blooms and depletion of dissolved oxygen (DO) in the water column. We analyzed 22 years of water chemistry, land use, and climate data (1996-2018) using principal component analysis (PCA) and multiple linear regression (MLR) to identify the contributions of climate, urbanization, and nutrient loading to the changes in water chemistry. Variations in water column stratification, phosphorus (P) speciation, and chl-a (as a proxy for algal abundance) explain 76 % of the observed temporal trends of the four main PCA components derived from water chemistry data. MLR results further imply that the intensity of stratification, quantified by the Brunt-Väisälä frequency, is a major predictor of the changes in water quality. Other important factors explaining the variations in nitrogen (N) and P speciation, and the DO concentrations, are watershed imperviousness and lake chloride concentrations that, in turn, are closely correlated. We conclude that the observed in-lake water quality trends over the past two decades are linked to urbanization via increased salinization associated with expanding impervious land cover, rather than increasing external P loading. The rising salinity promotes water column stratification, which reduces the oxygenation of the hypolimnion and enhances internal P loading to the water column. Thus, stricter controls on the application and runoff of de-icing salt should be considered as part of managing eutrophication symptoms in lakes of cold climate regions.
Clinical testing has been the cornerstone of public health monitoring and infection control efforts in communities throughout the COVID-19 pandemic. With the anticipated reduction of clinical testing as the disease moves into an endemic state, SARS-CoV-2 wastewater surveillance (WWS) will have greater value as an important diagnostic tool. An in-depth analysis and understanding of the metrics derived from WWS is required to interpret and utilize WWS-acquired data effectively (McClary-Gutierrez et al., 2021; O'Keeffe, 2021). In this study, the SARS-CoV-2 wastewater signal to clinical cases (WC) ratio was investigated across seven cities in Canada over periods ranging from 8 to 21 months. This work demonstrates that significant increases in the WC ratio occurred when clinical testing eligibility was modified to appointment-only testing, identifying a period of insufficient clinical testing (resulting in a reduction to testing access and a reduction in the number of daily tests) in these communities, despite increases in the wastewater signal. Furthermore, the WC ratio decreased significantly in 6 of the 7 studied locations, serving as a potential signal of the emergence of the Alpha variant of concern (VOC) in a relatively non-immunized community (40-60 % allelic proportion), while a more muted decrease in the WC ratio signaled the emergence of the Delta VOC in a relatively well-immunized community (40-60 % allelic proportion). Finally, a significant decrease in the WC ratio signaled the emergence of the Omicron VOC, likely because of the variant's greater effectiveness at evading immunity, leading to a significant number of new reported clinical cases, even when community immunity was high. The WC ratio, used as an additional monitoring metric, could complement clinical case counts and wastewater signals as individual metrics in its potential ability to identify important epidemiological occurrences, adding value to WWS as a diagnostic technology during the COVID-19 pandemic and likely for future pandemics.
Various functional DNA molecules have been used for the detection of environmental contaminants in water, but their practical applications have been limited. To address this gap, this review highlights the efforts to develop field-deployable water quality biosensors. The biosensor devices include microfluidic, lateral flow and paper-based devices, and other novel ideas such as the conversion of glucometers for the detection of environmental analytes. In addition, we also review DNA-functionalized hydrogels and their use in diffusive gradients in thin films (DGT) devices. We classify the sensors into one-step and two-step assays and discuss their practical implications. While the review is focused on works reported in the last five years, some classic early works are cited as well. Overall, most of the existing work only tested spiked water samples. Future work needs to shift to real environmental samples and the comparison of DNA-based sensors to standard analytical methods. • Recent development in field-deployable functional DNA based biosensors for environmental water monitoring reviewed. • Articulated the concept of one-step and two-step assays. • Microfluidic device, lateral flow device, paper, hydrogel, and glucose meter based examples reviewed.
Abstract Debris-covered glaciers are an important component of the mountain cryosphere and influence the hydrological contribution of glacierized basins to downstream rivers. This study examines the potential to make estimates of debris thickness, a critical variable to calculate the sub-debris melt, using ground-based thermal infrared radiometry (TIR) images. Over four days in August 2019, a ground-based, time-lapse TIR digital imaging radiometer recorded sequential thermal imagery of a debris-covered region of Peyto Glacier, Canadian Rockies, in conjunction with 44 manual excavations of debris thickness ranging from 10 to 110 cm, and concurrent meteorological observations. Inferring the correlation between measured debris thickness and TIR surface temperature as a base, the effectiveness of linear and exponential regression models for debris thickness estimation from surface temperature was explored. Optimal model performance ( R 2 of 0.7, RMSE of 10.3 cm) was obtained with a linear model applied to measurements taken on clear nights just before sunrise, but strong model performances were also obtained under complete cloud cover during daytime or nighttime with an exponential model. This work presents insights into the use of surface temperature and TIR observations to estimate debris thickness and gain knowledge of the state of debris-covered glacial ice and its potential hydrological contribution.
Abstract Reliable, long-term records of glacier mass change are invaluable to the glaciological and climate-change communities and used to assess the importance of glacier wastage on streamflow. Here we evaluate the in-situ observations of glacier mass change for Place (1982–2020) and Peyto glaciers (1983–2020) in western Canada. We use geodetic mass balance to calibrate a physically-based mass-balance model coupled with an ice dynamics routine. We find large discrepancies between the glaciological and geodetic records for the periods 1987–1993 (Place) and 2001–2006 (Peyto). Over the period of observations, the exclusion of ice dynamics in the model increased simulated cumulative mass change by ~10.6 (24%) and 7.1 (21%) m w.e. for Place and Peyto glacier, respectively. Cumulative mass loss using geodetic, modelled and glaciological approaches are respectively − 30.5 ± 4.5, − 32.0 ± 3.6, − 29.7 ± 3.6 m w.e. for Peyto Glacier (1982–2017) and − 45.9 ± 5.2, − 43.1 ± 3.1, − 38.4 ± 5.1 m w.e. for Place Glacier (1981–2019). Based on discrepancies noted in the mass-balance records for certain decades (e.g. 1990s), we caution the community if these data are to be used for hydrological model development.
Health risks of chronic exposure to microcystins (MCs), a family of aquatic contaminants produced mainly by cyanobacteria, are critical yet unsolved problems. Despite a few epidemiological studies, the metabolic profiles of humans exposed to MCs remain unknown, hindering the deep understanding of the molecular toxicity mechanisms. Here, sensitive nuclear magnetic resonance (NMR)- and liquid chromatography-mass spectrometry (LC-MS)-based metabolomics were applied to investigate the serum metabolic profiles of humans living near Lake Chao, where toxic cyanobacterial blooms occur annually. MCs were positively detected in 92 of 144 sera by ultra-high-pressure liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) with a median concentration of 0.016 μg/L. The estimated daily intake (0.15-0.27 μg MC-LReq/day) was less than the tolerable daily intake (TDI, 2.4 μg MC-LR for 60 kg adults) recommended by the World Health Organization (WHO). Obvious disruptions of the amino acid metabolism were confirmed and played important roles in renal impairments associated with serum MC burdens. Chronic oral exposure of mice to 30 μg MC-LR/kg body mass, which is less than the no observed adverse effect level, also led to obvious renal lesions and metabolic dysfunction. These observations provide the first evidence of metabolic disturbance of humans exposed to MCs and indicate that the WHO's TDI value determined traditionally should be lessened to protect human health effectively.
In situ bioremediation is a common remediation strategy for many groundwater contaminants. It was traditionally believed that (in the absence of mixing-limitations) a better in situ bioremediation is obtained in a more homogeneous medium where the even distribution of both substrate and bacteria facilitates the access of a larger portion of the bacterial community to a higher amount of substrate. Such conclusions were driven with the typical assumption of disregarding substrate inhibitory effects on the metabolic activity of enzymes at high concentration levels. To investigate the influence of pore matrix heterogeneities on substrate inhibition, we use a numerical approach to solve reactive transport processes in the presence of pore-scale heterogeneities. To this end, a rigorous reactive pore network model is developed and used to model the reactive transport of a self-inhibiting substrate under both transient and steady-state conditions through media with various, spatially correlated, pore-size distributions. For the first time, we explore on the basis of a pore-scale model approach the link between pore-size heterogeneities and substrate inhibition. Our results show that for a self-inhibiting substrate, (1) pore-scale heterogeneities can consistently promote degradation rates at toxic levels, (2) the effect reverses when the concentrations fall to levels essential for microbial growth, and (3) an engineered combination of homogeneous and heterogeneous media can increase the overall efficiency of bioremediation.
Endocrine-disrupting potential was evaluated during the sewage treatment process using in vitro bioassays. Aryl hydrocarbon receptor (AhR)-, androgen receptor (AR)-, glucocorticoid receptor (GR)-, and estrogen receptor (ER)-mediated activities were assessed over five steps of the treatment process. Bioassays of organic extracts showed that AhR, AR, and GR potencies tended to decrease through the sewage treatment process, whereas ER potencies did not significantly decrease. Bioassays on reverse-phase high-performance liquid chromatography fractions showed that F5 (log KOW 2.5-3.0) had great ER potencies. Full-scan screening of these fractions detected two novel ER agonists, arenobufagin and loratadine, which are used pharmaceuticals. These compounds accounted for 3.3-25% of the total ER potencies and 4% of the ER potencies in the final effluent. The well-known ER agonists, estrone and 17β-estradiol, accounted for 60 and 17% of the ER potencies in F5 of the influent and primary treatment, respectively. Fourier transform ion cyclotron resonance mass spectrometry analysis showed that various molecules were generated during the treatment process, especially CHO and CHOS (C: carbon, H: hydrogen, O: oxygen, and S: sulfur). This study documented that widely used pharmaceuticals are introduced into the aquatic environments without being removed during the sewage treatment process.
In vitro biotransformation assays with primary trout hepatocytes (RT-HEP) or liver subcellular fractions (RT-S9) have been proposed as valuable tools to help scientists and regulators better understand the toxicokinetics of chemicals. While both assays have been applied successfully to a diversity of neutral organic chemicals, only the RT-S9 assay has been applied to a large number of ionizable organic chemicals. Here, a combination of an in vitro biotransformation assay with RT-HEP with an active transport assay based on the permanent rainbow trout liver cell line RTL-W1 was used to qualitatively predict the potential hepatic clearance of nine psychotropic drugs with various degrees of ionization. Predictions were compared with rates of clearance measured in isolated perfused rainbow trout livers, and the importance of active transport was verified in the presence of the active transport inhibitor cyclosporin A. For the first time, it was demonstrated that a combination of biotransformation and active transport assays is powerful for the prediction of rates of hepatic clearance of ionizable chemicals. Ultimately, it is expected that this approach will allow for use of fewer animals while at the same time improving our confidence in the use of data from in vitro assays in chemical risk assessment.
Since the report of the RNA aptamer for theophylline, theophylline has become a key molecule in chemical biology for designing RNA switches and riboswitches. In addition, theophylline is an important drug for treating airway diseases including asthma. The classic RNA aptamer with excellent selectivity for theophylline has been used to design biosensors, although DNA aptamers are more desirable for stability and cost considerations. In this work, we selected DNA aptamers for theophylline, and all the top sequences shared the same binding motifs. Binding was confirmed using isothermal titration calorimetry and a nuclease digestion assay, showing a dissociation constant (Kd) around 0.5 μM theophylline. The Theo2201 aptamer can be truncated down to 23-mer while still has a Kd of 9.8 μM. The selectivity for theophylline over caffeine is around 250,000-fold based on a strand-displacement assay, which was more than 20-fold higher compared to the classic RNA aptamer. For other tested analogs, the DNA aptamer also showed better selectivity. Using the structure-switching aptamer sensor design method, a detection limit of 17 nM theophylline was achieved in the selection buffer, and a detection limit of 31 nM was obtained in 10% serum.
Abstract Machine learning (ML) models have been widely used for hydrological simulation. Previous studies have reported that conventional ML models fail to accurately simulate extreme flows which are crucial for design flood estimation and associated risk analysis in the context of climate change. Therefore, this study proposes a joint probabilistic rainfall‐runoff model (JPRR) for improving high‐to‐extreme flow projection. With the aid of paired copula constructions, bootstrap aggregation, and multi‐model ensemble approaches, the proposed model is able to effectively characterize the dependence relationships of predictors (i.e., precipitation time series with different moving sums) with various probability distributions. JPRR has been applied to four pristine basins in China, representing different climate zones and landscapes. The results reveal that JPRR significantly outperforms three well‐known ML models (i.e., random forest, artificial neural networks, and long short‐term memory) in high‐to‐extreme flow simulations. In JPRR, the copulas exhibiting the right tail dependence play a more important role in streamflow simulations at mountainous basins. Moreover, a significant difference in streamflow projections (from 2030 to 2099) derived from JPRR and benchmark models imply that flood risks from conventional ML models may be underestimated under changing climatic conditions.
Abstract What elements should a parsimonious model reproduce at a single scale to precisely simulate rainfall at many scales? We posit these elements are: (a) the probability of dry and linear correlation structure of the wet/dry sequence as a proxy reproducing the distribution of wet/dry spells, and (b) the marginal distribution of nonzero rainfall and its correlation structure. We build a two‐state rainfall model, the CoSMoS‐2s, that explicitly reproduces these elements and is easily applicable at any timescale. Additionally, the paper: (a) introduces the Generalized Exponential ( ) distribution system comprising six flexible distributions with desired properties to describe nonzero rainfall and facilitate time series generation; (b) extends the CoSMoS framework to allow simulations with negative correlations; (c) simplifies the generation of binary sequences with any correlation structure by analytical approximations; (d) introduces the rank‐based CoSMoS‐2s that preserves Spearman's correlations, has an analytical formulation, and is also applicable for infinite variance time series, (e) introduces the copula‐based CoSMoS‐2s enabling intermittent times series generation with nonzero values having the dependence structure of any desired copula, and (f) offers conceptual generalizations for rainfall modeling and beyond, with specific ideas for future improvements and extensions. The CoSMoS‐2s is tested using four long hourly rainfall records; the simulations reproduce rainfall properties at multiple scales including the wet/dry spells, probability of dry, characteristics of nonzero rainfall, and the behavior of extremes.
Despite the proliferation of computer-based research on hydrology and water resources, such research is typically poorly reproducible. Published studies have low reproducibility due to incomplete availability of data and computer code, and a lack of documentation of workflow processes. This leads to a lack of transparency and efficiency because existing code can neither be quality controlled nor reused. Given the commonalities between existing process-based hydrologic models in terms of their required input data and preprocessing steps, open sharing of code can lead to large efficiency gains for the modeling community. Here, we present a model configuration workflow that provides full reproducibility of the resulting model instantiations in a way that separates the model-agnostic preprocessing of specific data sets from the model-specific requirements that models impose on their input files. We use this workflow to create large-domain (global and continental) and local configurations of the Structure for Unifying Multiple Modeling Alternatives (SUMMA) hydrologic model connected to the mizuRoute routing model. These examples show how a relatively complex model setup over a large domain can be organized in a reproducible and structured way that has the potential to accelerate advances in hydrologic modeling for the community as a whole. We provide a tentative blueprint of how community modeling initiatives can be built on top of workflows such as this. We term our workflow the “Community Workflows to Advance Reproducibility in Hydrologic Modeling” (CWARHM; pronounced “swarm”).
Abstract Hydrologic sciences depend on data monitoring, analyses, and simulations of hydrologic processes to ensure safe, sufficient, and equal water distribution. These hydrologic data come from but are not limited to primary (lab, plot, and field experiments) and secondary sources (remote sensing, UAVs, hydrologic models) that typically follow FAIR Principles (Findable, Accessible, Interoperable, and Reusable: ( go-fair.org )). Easy availability of FAIR data has become possible because the hydrology‐oriented organizations have pushed the community to increase coordination of the protocols for generating data and sharing model platforms. In addition, networking at all levels has emerged with an invigorated effort to activate community science efforts that complement conventional data collection methods. However, it has become difficult to decipher various complex hydrologic processes with increasing data. Machine learning, a branch of artificial intelligence, provide more accurate and faster alternatives to better understand different hydrological processes. The Integrated, Coordinated, Open, Networked (ICON) framework provides a pathway for water users to include and respect diversity, equity, and inclusivity. In addition, ICONs support the integration of peoples with historically marginalized identities into this professional discipline of water sciences. This article comprises three independent commentaries about the state of ICON principles in hydrology and discusses the opportunities and challenges of adopting them.
Abstract Wildfire occurrence and severity is predicted to increase in the upcoming decades with severe negative impacts on human societies. The impacts of upwind wildfire activity on glacier melt, a critical source of freshwater for downstream environments, were investigated through analysis of field and remote sensing observations and modeling experiments for the 2015–2020 melt seasons at the well‐instrumented Athabasca Glacier in the Canadian Rockies. Upwind wildfire activity influenced surface glacier melt through both a decrease in the surface albedo from deposition of soot on the glacier and through the impact of smoke on atmospheric conditions above the glacier. Athabasca Glacier on‐ice weather station observations show days with dense smoke were warmer than clear, non‐smoky days, and sustained a reduction in surface shortwave irradiance of 103 W m −2 during peak shortwave irradiance and an increase in longwave irradiance of 10 W m −2 , producing an average 15 W m −2 decrease in net radiation. Albedo observed on‐ice gradually decreased after the wildfires started, from a summer average of 0.29 in 2015 before the wildfires to as low as 0.16 in 2018 after extensive wildfires and remained low for two more melt seasons without substantial upwind wildfires. Reduced all‐wave irradiance partly compensated for the increase in melt due to lowered albedo in those seasons when smoke was detected above Athabasca Glacier. In melt seasons without smoke, the suppressed albedo increased melt by slightly more than 10% compared to the simulations without fire‐impacted albedo, increasing melt by 0.42 m. w.e. in 2019 and 0.37 m. w.e. in 2020.
Abstract Earth System Models’ complex land components simulate a patchwork of increases and decreases in surface water availability when driven by projected future climate changes. Yet, commonly‐used simple theories for surface water availability, such as the Aridity Index (P/E0) and Palmer Drought Severity Index (PDSI), obtain severe, globally dominant drying when driven by those same climate changes, leading to disagreement among published studies. In this work, we use a common modeling framework to show that Earth System Model (ESM) simulated runoff‐ratio and soil‐moisture responses become much more consistent with the P/E0 and PDSI responses when several previously known factors that the latter do not account for are cut out of the simulations. This reconciles the disagreement and makes the full ESM responses more understandable. For ESM runoff ratio, the most important factor causing the more positive global response compared to P/E0 is the concentration of precipitation in time with greenhouse warming. For ESM soil moisture, the most important factor causing the more positive global response compared to PDSI is the effect of increasing carbon dioxide on plant physiology, which also drives most of the spatial variation in the runoff ratio enhancement. The effect of increasing vapor‐pressure deficit on plant physiology is a key secondary factor for both. Future work will assess the utility of both the ESMs and the simple indices for understanding observed, historical trends.
Abstract Intensifying permafrost thaw alters carbon cycling by mobilizing large amounts of terrestrial substrate into aquatic ecosystems. Yet, few studies have measured aquatic carbon fluxes and constrained drivers of ecosystem carbon balance across heterogeneous Arctic landscapes. Here, we characterized hydrochemical and landscape controls on fluvial carbon cycling, quantified fluvial carbon fluxes, and estimated fluvial contributions to ecosystem carbon balance across 33 watersheds in four ecoregions in the continuous permafrost zone of the western Canadian Arctic: unglaciated uplands, ice‐rich moraine, and organic‐rich lowlands and till plains. Major ions, stable isotopes, and carbon speciation and fluxes revealed patterns in carbon cycling across ecoregions defined by terrain relief and accumulation of organics. In previously unglaciated mountainous watersheds, bicarbonate dominated carbon export (70% of total) due to chemical weathering of bedrock. In lowland watersheds, where soil organic carbon stores were largest, lateral transport of dissolved organic carbon (50%) and efflux of biotic CO 2 (25%) dominated. In watersheds affected by thaw‐induced mass wasting, erosion of ice‐rich tills enhanced chemical weathering and increased particulate carbon fluxes by two orders of magnitude. From an ecosystem carbon balance perspective, fluvial carbon export in watersheds not affected by thaw‐induced wasting was, on average, equivalent to 6%–16% of estimated net ecosystem exchange (NEE). In watersheds affected by thaw‐induced wasting, fluvial carbon export approached 60% of NEE. Because future intensification of thermokarst activity will amplify fluvial carbon export, determining the fate of carbon across diverse northern landscapes is a priority for constraining trajectories of permafrost region ecosystem carbon balance.
Long-term groundwater droughts are known to persist over timescales from multiple years up to decades. The mechanisms leading to drought persistence are, however, only partly understood. Applying a unique terrestrial system modeling platform in a probabilistic simulation framework over Europe, we discovered an important positive feedback mechanism from groundwater into the atmosphere that may increase drought persistence at interannual time scales over large continental regions. In the feedback loop, groundwater drought systematically increases net solar radiation via a cloud feedback, which, in turn, increases the drying of the land. In commonly applied climate and Earth system models, this feedback cannot be simulated due to a lack of groundwater memory effects in the representation of terrestrial hydrology. Thus, drought persistence and compound events may be underestimated in current climate projections.
Abstract Freezing rain events have caused severe socioeconomic and ecosystem impacts. An understanding of how these events may evolve as the Earth warms is necessary to adequately adapt infrastructure to these changes. We present an analysis of projected changes to freezing rain events over North America relative to the 1980–2009 recent past climate for the periods during which +2, +3, and +4°C of global warming is attained. We diagnose freezing rain using four precipitation‐type algorithms (Cantin and Bachand, Bourgouin, Ramer, and Baldwin) applied to four simulations of the fifth‐generation Canadian Regional Climate Model (CRCM5) driven by four global climate models (GCMs). We find that the choice of driving GCM strongly influences the spatial pattern of projected change. The choice of algorithm has a comparatively smaller impact, and primarily affects the magnitude but not the sign of projected change. We identify several regions where all simulations and algorithms agree on the sign of change, with increases projected over portions of western Canada and decreases over the central, eastern, and southern United States. However, we also find large regions of disagreement on the sign of change depending on driving GCM and even ensemble member of the same GCM, highlighting the importance of examining freezing rain events in a multi‐member ensemble of simulations driven by multiple GCMs to sufficiently account for uncertainty in projections of these hazardous events.

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Vegetation type is an important predictor of the arctic summer land surface energy budget
Jacqueline Oehri, Gabriela Schaepman‐Strub, Jin‐Soo Kim, Raleigh Grysko, Heather Kropp, Inge Grünberg, Vitalii Zemlianskii, Oliver Sonnentag, Eugénie Euskirchen, Merin Reji Chacko, Giovanni Muscari, Peter D. Blanken, Joshua Dean, Alcide di Sarra, R. J. Harding, Ireneusz Sobota, Lars Kutzbach, Elena Plekhanova, Aku Riihelä, Julia Boike, Nathaniel B. Miller, Jason Beringer, Efrèn López‐Blanco, Paul C. Stoy, Ryan C. Sullivan, Marek Kejna, Frans‐Jan W. Parmentier, John A. Gamon, Mikhail Mastepanov, Christian Wille, Marcin Jackowicz-Korczyński, Dirk Nikolaus Karger, William L. Quinton, Jaakko Putkonen, Dirk van As, Torben R. Christensen, Maria Z. Hakuba, Robert S. Stone, Stefan Metzger, Baptiste Vandecrux, G. V. Frost, Martin Wild, Birger Ulf Hansen, Daniela Meloni, Florent Dominé, Mariska te Beest, Torsten Sachs, Aram Kalhori, A. V. Rocha, Scott Williamson, Sara Morris, A. L. Atchley, Richard Essery, Benjamin R. K. Runkle, David Holl, Laura Riihimaki, Hiroyasu Iwata, Edward A. G. Schuur, Christopher Cox, Andrey A. Grachev, J. P. McFadden, Robert S. Fausto, Mathias Goeckede, Masahito Ueyama, Norbert Pirk, Gijs de Boer, M. Syndonia Bret‐Harte, Matti Leppäranta, Konrad Steffen, Thomas Friborg, Atsumu Ohmura, C. Edgar, Johan Olofsson, Scott D. Chambers
Nature Communications, Volume 13, Issue 1

Abstract Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm −2 ) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.

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The challenge of unprecedented floods and droughts in risk management
Heidi Kreibich, Anne F. Van Loon, Kai Schröter, Philip J. Ward, Maurizio Mazzoleni, N. Sairam, Guta Wakbulcho Abeshu, Svetlana Agafonova, Amir AghaKouchak, Hafzullah Aksoy, Camila Álvarez-Garretón, Blanca Aznar, Laila Balkhi, Marlies Barendrecht, Sylvain Biancamaria, Liduin Bos-Burgering, Chris Bradley, Yus Budiyono, Wouter Buytaert, Lucinda Capewell, Hayley Carlson, Yonca Cavus, Anaïs Couasnon, Gemma Coxon, Ioannis Ν. Daliakopoulos, Marleen de Ruiter, Claire Delus, Mathilde Erfurt, Giuseppe Esposito, François Dagognet, Frédéric Frappart, Jim Freer, Natalia Frolova, Animesh K. Gain, Manolis Grillakis, Jordi Oriol Grima, Diego Alejandro Guzmán Arias, Laurie S. Huning, Monica Ionita, М. А. Харламов, Đào Nguyên Khôi, Natalie Kieboom, Maria Kireeva, Aristeidis Koutroulis, Waldo Lavado‐Casimiro, Hong Yi Li, M. C. Llasat, David Macdonald, Johanna Mård, Hannah Mathew-Richards, Andrew McKenzie, Alfonso Mejía, Eduardo Mário Mendiondo, Marjolein Mens, Shifteh Mobini, Guilherme Samprogna Mohor, Viorica Nagavciuc, Thanh Ngo‐Duc, Thi Thao Nguyen Huynh, Pham Thi Thao Nhi, Olga Petrucci, Hồng Quân Nguyễn, Pere Quintana-Seguí, Saman Razavi, Elena Ridolfi, Jannik Riegel, Md. Shibly Sadik, Elisa Savelli, А. А. Сазонов, Sanjib Sharma, Johanna Sörensen, Felipe Augusto Arguello Souza, Kerstin Stahl, Max Steinhausen, Michael Stoelzle, Wiwiana Szalińska, Qiuhong Tang, Fuqiang Tian, Tamara Tokarczyk, Carolina Tovar, Thi Van Thu Tran, M.H.J. van Huijgevoort, Michelle T. H. van Vliet, Sergiy Vorogushyn, Thorsten Wagener, Yueling Wang, Doris Wendt, Elliot Wickham, Long Yang, Mauricio Zambrano‐Bigiarini, Günter Blöschl, Giuliano Di Baldassarre
Nature, Volume 608, Issue 7921

Abstract Risk management has reduced vulnerability to floods and droughts globally 1,2 , yet their impacts are still increasing 3 . An improved understanding of the causes of changing impacts is therefore needed, but has been hampered by a lack of empirical data 4,5 . On the basis of a global dataset of 45 pairs of events that occurred within the same area, we show that risk management generally reduces the impacts of floods and droughts but faces difficulties in reducing the impacts of unprecedented events of a magnitude not previously experienced. If the second event was much more hazardous than the first, its impact was almost always higher. This is because management was not designed to deal with such extreme events: for example, they exceeded the design levels of levees and reservoirs. In two success stories, the impact of the second, more hazardous, event was lower, as a result of improved risk management governance and high investment in integrated management. The observed difficulty of managing unprecedented events is alarming, given that more extreme hydrological events are projected owing to climate change 3 .
Sustained eutrophication of the aquatic environment by the remobilization of legacy phosphorus (P) stored in soils and sediments is a prevailing issue worldwide. Fluxes of P from the sediments to the water column, referred to as internal P loading, often delays the recovery of water quality following a reduction in external P loads. Here, we report on the vertical distribution and geochemistry of P, lanthanum (La), iron (Fe) and carbon (C) in the culturally eutrophied Lake Bromont. This lake underwent remediation treatment using La modified bentonite (LMB) commercially available as Phoslock™. We investigated the effectiveness of LMB in decreasing soluble reactive phosphorus (SRP) availability in sediments and in reducing dissolved fluxes of P across the sediment-water interface. Sediment cores were retrieved before and after LMB treatment at three sites representing bottom sediment, sediment influenced by lakeside housing and finally littoral sediment influenced by the lake inflow. Sequential extractions were used to assess changes in P speciation. Depth profiles of dissolved porewater concentrations were obtained after LMB treatment at each site. Results indicate that SRP extracted from the sediments decreased at all sites, while total extracted P (PTOT) bound to redox-sensitive metal oxides increased. 31P NMR data on P extract reveals that 20-43% of total solid-phase P is in the form of organic P (Porg) susceptible to be released via microbial degradation. Geochemical modelling of porewater data provides evidence that LaPO4(s) mineral phases, such as rhabdophane and/or monazite, are likely forming. However, results also suggest that La3+ binding by dissolved organic carbon (DOC) hinders La-phosphate precipitation. We rely on thermodynamic modelling to suggest that high Fe2+ would bind to DOC instead of La3+, therefore promoting P sequestrations by LMB under anoxic conditions.
Evaluations of analytical performance through interlaboratory comparisons and proficiency tests are underway globally for biomolecular-based methods [e.g., reverse-transcription quantitative polymerase chain reaction (RT-qPCR)] used in the surveillance of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater. These evaluations often rely on sharing a common reference wastewater sample that is split among participating laboratories. A known quantity of recovery surrogates can be introduced to the wastewater matrix by the coordinating laboratory as an exogenous control in a spike-and-recovery approach; however, split-sample comparisons are increasingly performed to evaluate in situ quantities of SARS-CoV-2 genetic signal native to the sample due to the lack of a universally accepted recovery surrogate of SARS-CoV-2. A reproducible procedure that minimizes the variability of SARS-CoV-2 genetic signal among split wastewater aliquots is therefore necessary to facilitate the method comparisons, especially when a large number of aliquots are required. Emerging literature has suggested that SARS-CoV-2 genetic signal in wastewater is linked to the solids fraction. Accordingly, a protocol that allows for equal distribution of solids content evenly among wastewater aliquots was also likely to facilitate even distribution of the SARS-CoV-2 genetic signal. Based on this premise, we reviewed existing sample splitting apparatus and approaches used for solids-based parameters in environmental samples. A portable batch reactor was designed, comprised of readily accessible materials and equipment. This design was validated through splitting of real wastewater samples collected from a municipal wastewater treatment facility serving a population with reported cases of COVID-19. This work applies well-established solid-liquid mixing theory and concepts that are likely unfamiliar to molecular microbiologists and laboratory analysts, providing (1) a prototype adaptable for a range of sample quantities, aliquot sizes, microbial targets, and water matrices; and (2) a pragmatic demonstration of critical considerations for design and validation of a reproducible and effective sample splitting protocol.
Sustainable groundwater management is founded on the sound understanding of the effects of water extraction on the aquifer water level and the springs and streams receiving groundwater discharge. Pumping test data are commonly used in extraction licence applications to evaluate aquifer properties and assess the magnitude of storage depletion resulting from pumping. However, a short duration (eg 48 hours) pumping test can fail to detect the presence of aquifer boundaries, as the cone of depression is not large enough to reach the boundaries. This may cause an underestimation of long-term drawdown and an overestimation of permissible extraction rate (ie safe yield). In the rural town of Irricana in Alberta, groundwater extraction licences for municipal water supply wells were issued in the early 1980s based on the analysis of 48-hour pumping tests. Actual water extraction rates were substantially below the licensed rates, but the unanticipated and excessive drawdown in the aquifer forced the town to discontinue pumping and switch to surface water supply after 25 years. To examine the cause of overallocation, a new 48-hour pumping test was conducted in the same aquifer, which included an extended drawdown analysis using 26 days of recovery data. Geological formation logs for existing wells in the area surrounding Irricana were used to infer the extent of sandstone aquifer units within the heterogeneous bedrock formation. The new data analysis showed that the aquifer is semi-closed, contrary to the infinite-aquifer assumption used in the original pumping test, which caused additional drawdown due to the aquifer boundary effects. This study suggests an improved procedure for estimation of storage depletion using standard hydrogeological methods and readily available data. The new procedure provides a useful tool as part of adaptive groundwater management, in which water levels and other relevant variables are monitored and licensed extraction rates are adjusted accordingly.
Surface roughness plays an important role in microwave remote sensing. In the agricultural domain, surface roughness is crucial for soil moisture retrieval methods that use electromagnetic surface scattering or microwave radiative transfer models. Therefore, improved characterization of Soil Surface Roughness (SSR) is of considerable importance. In this study, three approaches, including a standard pin profiler, a LiDAR point cloud generated from an iPhone 12 Pro, and a Structure from Motion (SfM) photogrammetric point cloud, were applied over 24 surface profiles with different roughness variations to measure surface roughness. The objective of this study was to evaluate the capability of smartphone-based LiDAR technology to measure surface roughness parameters and compare the results of this technique with the more common approaches. Results showed that the iPhone LiDAR technology, when point cloud data is captured in a fine-resolution mode, has a significant correlation with SfM photogrammetry (R2 = 0.70) and a relatively close agreement with pin profiler (R2 = 0.60). However, this accuracy tends to be greater for random surfaces and rough profiles with row structure orientations. The results of this study confirm that smartphone-based LiDAR can be used as a cost-effective, fast, and time-efficient alternative tool for measuring surface roughness, especially for rough, wide, and inaccessible areas.
Abstract Historic land alterations and agricultural intensification have resulted in legacy phosphorus (P) accumulations within lakes and reservoirs. Internal loading from such legacy stores can be a major driver of future water quality degradation. Yet, little is known about the magnitude and spatial patterns of legacy P accumulation in lentic systems, and how watershed disturbance trajectories drive these patterns. Here, we used a meta-analysis of 113 paleolimnological studies across 124 lakes and four reservoirs (referred here on as lakes) in 20 countries to quantify the linkages between the 100 year trajectories of P concentrations in lake sediments, watershed inputs, and lake morphology. We find five distinct clusters for lake sediment P trajectories, with lakes in the developing and developed world showing distinctly different patterns. Lakes in the developed world (Europe and North America) with early agricultural intensification had the highest sediment P concentrations (1176–1628 mg kg −1 ), with a peak between the 1970–1980s and a decline since then, while lakes in the developing world, specifically China, documented monotonically increasing sediment P concentrations (857–1603 mg kg −1 ). Sediment P trajectories reflected watershed disturbance patterns and were driven by a combination of anthropogenic drivers (fertilizer input and population density) and lake morphology (watershed to lake area ratio). Specifically, we found the largest legacy accumulation rates to occur in shallow lakes experiencing long-term land-use disturbances. These links between land-use change and P accumulation in lentic systems can provide insights about inland water quality response and help to develop robust predictive models useful for resource managers and decision-makers.
Abstract Oceans are well-known to be directly altered by global climate forcings such as greenhouse gas changes, but how oceans are indirectly influenced by land and its response to such forcings remains less explored. Here, we assess the present-day and projected future state of a little-explored feature of the climate system—a ‘land wake’ in relative humidity downwind of the east coast of North America, consisting of low-humidity continental air extending roughly 1000 km over the Atlantic ocean. The wake exists throughout the year, but is supported by high continental temperatures in summer and low continental moisture in winter. The wake is well represented in an ensemble of global climate models (GCMs), qualitatively matching reanalysis data. Under increasing atmospheric CO 2 , the land wake intensifies in GCM simulations through two pathways: the radiative effects of CO 2 on surface temperatures, and the biogeochemical effect of CO 2 on terrestrial vegetation. Vegetation responses to increased CO 2 alter the summer wake from Florida to Newfoundland, and both the radiative and biogeochemical effects of CO 2 drive reductions in coastal cloud cover. These changes illustrate the potential of rapidly changing terrestrial climate to influence coastal regions and the ocean environment downwind of continents through both light conditions and the energy balance of the surface ocean.
Abstract Ecosystems in the North American Arctic-Boreal Zone (ABZ) experience a diverse set of disturbances associated with wildfire, permafrost dynamics, geomorphic processes, insect outbreaks and pathogens, extreme weather events, and human activity. Climate warming in the ABZ is occurring at over twice the rate of the global average, and as a result the extent, frequency, and severity of these disturbances are increasing rapidly. Disturbances in the ABZ span a wide gradient of spatiotemporal scales and have varying impacts on ecosystem properties and function. However, many ABZ disturbances are relatively understudied and have different sensitivities to climate and trajectories of recovery, resulting in considerable uncertainty in the impacts of climate warming and human land use on ABZ vegetation dynamics and in the interactions between disturbance types. Here we review the current knowledge of ABZ disturbances and their precursors, ecosystem impacts, temporal frequencies, spatial extents, and severity. We also summarize current knowledge of interactions and feedbacks among ABZ disturbances and characterize typical trajectories of vegetation loss and recovery in response to ecosystem disturbance using satellite time-series. We conclude with a summary of critical data and knowledge gaps and identify priorities for future study.
Despite major improvements in weather and climate modelling and substantial increases in remotely sensed observations, drought prediction remains a major challenge. After a review of the existing methods, we discuss major research gaps and opportunities to improve drought prediction. We argue that current approaches are top-down, assuming that the process(es) and/or driver(s) are known—i.e. starting with a model and then imposing it on the observed events (reality). With the help of an experiment, we show that there are opportunities to develop bottom-up drought prediction models—i.e. starting from the reality (here, observed events) and searching for model(s) and driver(s) that work. Recent advances in artificial intelligence and machine learning provide significant opportunities for developing bottom-up drought forecasting models. Regardless of the type of drought forecasting model (e.g. machine learning, dynamical simulations, analogue based), we need to shift our attention to robustness of theories and outputs rather than event-based verification. A shift in our focus towards quantifying the stability of uncertainty in drought prediction models, rather than the goodness of fit or reproducing the past, could be the first step towards this goal. Finally, we highlight the advantages of hybrid dynamical and statistical models for improving current drought prediction models. This article is part of the Royal Society Science+ meeting issue ‘Drought risk in the Anthropocene’.
In this paper, non-contact microplastic concentration monitoring is enabled using a sensitivity-enhanced planar microwave sensor. The sensing platform is a tag-reader structure that consists of a dual-resonator tag and a passive microwave reader. The dual-tag structure is combined with a silicon reservoir as a sample container and is energized through the electromagnetic (EM) coupling with the reader. The extremely sensitive area of the tag resonators is exposed to the sample under the test; it creates an exceptional microplastic deposition monitoring with the concentration of the microplastic in the liquid under the test as low as 400K particles/L. The sensor monitors the deposition of microplastic particles in the mixed liquid in a real-time manner and reflects it as the frequency resonance variation in the transmission response.
Abstract Modelling is widely used in ecology and its utility continues to increase as scientists, managers and policy‐makers face pressure to effectively manage ecosystems and meet conservation goals with limited resources. As the urgency to forecast ecosystem responses to global change grows, so do the number and complexity of predictive ecological models and the value of iterative prediction, both of which demand validation and cross‐model comparisons. This challenges ecologists to provide predictive models that are reusable, interoperable, transparent and able to accommodate updates to both data and algorithms. We propose a practical solution to this challenge based on the PERFICT principles (frequent Predictions and Evaluations of Reusable, Freely accessible, Interoperable models, built within Continuous workflows that are routinely Tested), using a modular and integrated framework. We present its general implementation across seven common components of ecological model applications—(i) the modelling toolkit; (ii) data acquisition and treatment; (iii) model parameterisation and calibration; (iv) obtaining predictions; (v) model validation; (vi) analysing and presenting model outputs; and (vii) testing model code—and apply it to two approaches used to predict species distributions: (1) a static statistical model, and (2) a complex spatiotemporally dynamic model. Adopting a continuous workflow enabled us to reuse our models in new study areas, update predictions with new data, and re‐parameterise with different interoperable modules using freely accessible data sources, all with minimal user input. This allowed repeating predictions and automatically evaluating their quality, while centralised inputs, parameters and outputs, facilitated ensemble forecasting and tracking uncertainty. Importantly, the integrated model validation promotes a continuous evaluation of the quality of more‐ or less‐parsimonious models, which is valuable in predictive ecological modelling. By linking all stages of an ecological modelling exercise, it is possible to overcome common challenges faced by ecological modellers, such as changing study areas, choosing between different modelling approaches, and evaluating the appropriateness of the model. This ultimately creates a more equitable and robust playing field for both modellers and end users (e.g. managers), and contributes to position predictive ecology as a central contributor to global change forecasting.
Abstract As groundwater levels steadily decline in India, authorities are concerned about reducing extraction for irrigation purposes without jeopardizing food security. Very low or zero prices for electricity and water in agriculture is partly responsible for overextraction, but charging higher prices is politically not feasible. In this study, we describe the results of a pilot scheme implemented in Punjab, India, where farmers who enrolled were allocated a monthly entitlement of electricity units and compensated for unused electricity. Eight hours of uninterrupted daytime electricity supply were also provided under the scheme instead of the usual mix of daytime and night‐time supply. Analyzing data from a cross‐sectional farm household survey and instrumenting for enrollment, we find that self‐reported hours of irrigation for enrolled farmers were significantly lower than for non‐enrolled ones, with no impact on rice yields. We also find a reduction in monthly electricity consumption at electricity‐feeder level due to the pilot scheme using the synthetic control method. Our results suggest that the combination of daytime electricity provision and cash incentives for unused electricity has the potential to incentivize farmers to reduce electricity consumption and irrigation hours by at least 7.5% and up to 30% without impacting paddy yields.
Groundwater Monitoring & RemediationVolume 42, Issue 3 p. 131-132 In My Experience In My Experience: The Nature of Groundwater Discharge David Rudolph, Corresponding Author David Rudolph [email protected] Search for more papers by this author David Rudolph, Corresponding Author David Rudolph [email protected] Search for more papers by this author First published: 05 July 2022 https://doi.org/10.1111/gwmr.12540Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume42, Issue3Special Issue with Focus on Data ManagementSummer 2022Pages 131-132 RelatedInformation
Abstract The Yukon River Basin (YRB) is one of the most important river networks shared between Canada and The United States, and is one of the largest river basins in the subarctic region of North America. The Canadian part of the YRB is characterized by steeply sloped, partly glaciated mountain headwaters that generate considerable runoff during melt of glaciers and seasonal snowcover. Snow redistribution, snowmelt, glacier melt and freezing–thawing soil processes in winter and spring along with summertime rainfall‐runoff and evapotranspiration processes are thus key components of streamflow generation in the basin, making conceptual rainfall‐runoff models unsuitable for this cold region. Due to the remote high latitudes and high altitudes of the basin, there is a paucity of observational data, making heavily calibrated conceptual modeling approaches infeasible. At the request of the Yukon Government, this project developed and operationalized a streamflow forecasting system for the Yukon River and several of its tributary rivers using a distributed land surface modeling approach developed for large‐scale implementation in cold regions. This represents a substantial advance in bringing operational hydrological forecasting to the Canadian subarctic for the first time. This experience will inform future research to operation improvements as Canada develops a nationally coordinated flood forecast system.
Abstract The Global Environmental Multiscale Model (GEM) is currently in operational use for data assimilation and forecasting at 25–15 km scales; regional 10 km scales over North America; and 2.5 km scales over Canada. To evaluate the GEM model for forecasting applications in Iran, global daily temperature and precipitation outputs of GEM at a 25 km scale were compared to data sets from hydrometeorological stations and the De Martonne climate classification method was used to demarcate climate zones for comparisons. GEM model outputs were compared to observations in each of these zones. The results show good agreement between GEM outputs and measured daily temperatures with Kling‐Gupta efficiencies of 0.76 for the arid, 0.71 for the semiarid, and 0.78 for the humid regions. There is also an agreement between GEM outputs and measured annual precipitation with differences of 50% for the arid, 36% for the semiarid, and 15% for the humid region. There is a ~13% systematic difference between the elevation of stations and the average elevation of corresponding GEM grid cells; differences in elevation associated with forcing data sets can be potentially corrected using environmental lapse rates. Compared with hydrometeorological data sets, the GEM model precipitation outputs are less accurate than temperature outputs, and this may influence the accuracy of potential Iranian forecasting operations utilizing GEM. The results of this study provide an understanding of the operation and limitations of the GEM model for climate change and hydro‐climatological studies.
We report metagenomic sequencing analyses of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in composite wastewater influent from 10 regions in Ontario, Canada, during the transition between Delta and Omicron variants of concern. The Delta and Omicron BA.1/BA.1.1 and BA.2-defining mutations occurring in various frequencies were reported in the consensus and subconsensus sequences of the composite samples.
Good comments are invaluable assets to software projects, as they help developers understand and maintain projects. However, due to some poor commenting practices, comments are often missing or inconsistent with the source code. Software engineering practitioners often spend a significant amount of time and effort reading and understanding programs without or with poor comments. To counter this, researchers have proposed various techniques to automatically generate code comments in recent years, which can not only save developers time writing comments but also help them better understand existing software projects. However, it is unclear whether these techniques can alleviate comment issues and whether practitioners appreciate this line of research. To fill this gap, we performed an empirical study by interviewing and surveying practitioners about their expectations of research in code comment generation. We then compared what practitioners need and the current state-of-the-art research by performing a literature review of papers on code comment generation techniques published in the premier publication venues from 2010 to 2020. From this comparison, we highlighted the directions where researchers need to put effort to develop comment generation techniques that matter to practitioners.
We present a hybrid text and geospatial search application for hydrographic datasets built on the open-source Lucene search library. Our goal is to demonstrate that it is possible to build custom GIS applications by integrating existing open-source components and data sources, which contrasts with existing approaches based on monolithic platforms such as ArcGIS and QGIS. Lucene provides rich index structures and search capabilities for free text and geometries; the former has already been integrated and exposed via our group's Anserini and Pyserini IR toolkits. In this work, we extend these toolkits to include geospatial capabilities. Combining knowledge extracted from Wikidata with the HydroSHEDS dataset, our application enables text and geospatial search of rivers worldwide.
Nigella sativa (NS) is a plant that has long been utilized in traditional medicine as a treatment for certain diseases. The aim of this work was to valorize the essential oil (EO) of this species by phytochemical analysis and antimicrobial and antioxidant evaluation. EO was extracted by hydrodistillation from the seeds of Nigella sativa (EO-NS). Phytochemical content of EO-NS was evaluated by use of gas chromatography coupled to mass spectrometry (GC-MS/MS). Antioxidant ability was in vitro determined by use of three assays: 2.2-diphenyl-1-picrylhydrazyl (DPPH), ferric reducing power (FRAP), and total antioxidant capacity (TAC) relative to two synthetic antioxidants: BHT and quercetin. Antimicrobial effect was evaluated against four clinically important bacterial strains (Staphylococcus aureus, ATCC 6633; Escherichia coli, K12; Bacillus subtilis, DSM 6333; and Proteus mirabilis, ATCC 29906) and against four fungal strains (Candida albicans, ATCC 10231; Aspergillus niger, MTCC 282; Aspergillus flavus, MTCC 9606; and Fusarium oxysporum, MTCC 9913). Fifteen constituents that accounted for the majority of the mass of the EO-NS were identified and quantified by use of GC-MSMS. The main component was O-cymene (37.82%), followed by carvacrol (17.68%), α-pinene (10.09%), trans-sabinene hydrate (9.90%), and 4-terpineol (7.15%). EO-NS exhibited significant antioxidant activity with IC50, EC50, and total antioxidant capacity (TAC) of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>0.017</mn> <mo>±</mo> <mn>0.0002</mn> </math> , <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mn>0.1196</mn> <mo>±</mo> <mn>0.012</mn> </math> , and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mn>114.059</mn> <mo>±</mo> <mn>0.97</mn> </math> mg EAA/g, respectively. Additionally, EO-NS exhibited promising antibacterial activity on all strains under investigation, especially on E. coli K12 resulting in inhibition diameter of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mn>38.67</mn> <mo>±</mo> <mn>0.58</mn> </math> mm and a minimum inhibitory concentration (MIC) of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mn>1.34</mn> <mo>±</mo> <mn>0.00</mn> </math> μg/mL. Also, EO-NS had significant antifungal efficacy, with a percentage of inhibition of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mn>67.45</mn> <mo>±</mo> <mn>2.31</mn> </math> % and MIC of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7"> <mn>2.69</mn> <mo>±</mo> <mn>0.00</mn> </math> μg/mL against F. oxysporum, MTCC 9913 and with a diameter of inhibition <math xmlns="http://www.w3.org/1998/Math/MathML" id="M8"> <mn>42</mn> <mo>±</mo> <mn>0.00</mn> </math> mm and MIC of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M9"> <mn>0.67</mn> <mo>±</mo> <mn>0.00</mn> </math> μg/mL against C. albicans. To minimize development of antibiotic-resistant bacteria, EO-NS can be utilized as a natural, alternative to synthetic antibiotics and antioxidants to treat free radicals implicated in microbial infection-related inflammatory reactions.
Abstract Statistical processing of numerical model output has been a part of both weather forecasting and climate applications for decades. Statistical techniques are used to correct systematic biases in atmospheric model outputs and to represent local effects that are unresolved by the model, referred to as downscaling. Many downscaling techniques have been developed, and it has been difficult to systematically explore the implications of the individual decisions made in the development of downscaling methods. Here we describe a unified framework that enables the user to evaluate multiple decisions made in the methods used to statistically postprocess output from weather and climate models. The Ensemble Generalized Analog Regression Downscaling (En-GARD) method enables the user to select any number of input variables, predictors, mathematical transformations, and combinations for use in parametric or nonparametric downscaling approaches. En-GARD enables explicitly predicting both the probability of event occurrence and the event magnitude. Outputs from En-GARD include errors in model fit, enabling the production of an ensemble of projections through sampling of the probability distributions of each climate variable. We apply En-GARD to regional climate model simulations to evaluate the relative importance of different downscaling method choices on simulations of the current and future climate. We show that choice of predictor variables is the most important decision affecting downscaled future climate outputs, while having little impact on the fidelity of downscaled outcomes for current climate. We also show that weak statistical relationships prevent such approaches from predicting large changes in extreme events on a daily time scale.
Abstract Surface meteorological analyses serve a wide range of research and applications, including forcing inputs for hydrological and ecological models, climate analysis, and resource and emergency management. Quantifying uncertainty in such analyses would extend their utility for probabilistic hydrologic prediction and climate risk applications. With this motivation, we enhance and evaluate an approach for generating ensemble analyses of precipitation and temperature through the fusion of station observations, terrain information, and numerical weather prediction simulations of surface climate fields. In particular, we expand a spatial regression in which static terrain attributes serve as predictors for spatially distributed 1/16th degree daily surface precipitation and temperature by including forecast outputs from the High-Resolution Rapid Refresh (HRRR) numerical weather prediction model as additional predictors. We demonstrate the approach for a case study domain of California, focusing on the meteorological conditions leading to the 2017 flood and spillway failure event at Lake Oroville. The approach extends the spatial regression capability of the Gridded Meteorological Ensemble Tool (GMET) and also adds cross-validation to the uncertainty estimation component, enabling the use of predictive rather than calibration uncertainty. In evaluation against out-of-sample station observations, the HRRR-based predictors alone are found to be skillful for the study setting, leading to overall improvements in the enhanced GMET meteorological analyses. The methodology and associated tool represent a promising method for generating meteorological surface analyses for both research-oriented and operational applications, as well as a general strategy for merging in situ and gridded observations.
Abstract In this work, we characterized the occurrences and conditions before the initiations of mesoscale convective systems (MCSs) in the central United States, using 15 years of observations and convection-permitting climate model simulations. The variabilities of MCSs in summer were obtained using high-resolution (4 km) observation data [Stage-IV (stIV)] and ECMWF Re-Analysis v5 (ERA5)-forced Weather Research and Forecasting (WRF) Model simulations (E5RUN). MCSs were identified using the object tracking algorithm MODE-time domain (MTD). MTD-determined MCSs were divided into daytime short-lived MCSs (SLM12), daytime long-lived MCSs (LLM12), nighttime short-lived MCSs (SLM00), and nighttime long-lived MCSs (LLM00). E5RUN showed skill to simulate MCSs by obtaining similar statistics in occurrences, areal coverages, and propagation speeds compared to those of stIV. We calculated the 15 parameters using sounding data from E5RUN before an MCS was initiated (−1, −3, −6, and −9 h) at each location of an MCS. The parameters were tested to figure out the significance of predicting the longevities of MCSs. The key findings are 1) LLM12 showed favorable thermodynamic variables compared to that of SLM12 and 2) LLM00 showed significant conditions of vertically rotating winds and sheared environments that affect the longevity of MCSs. Moreover, storm-relative helicity of 0–3 km, precipitable water, and vertical wind shear of 0–6 km are the most significant parameters to determine the longevities of MCSs (both daytime and nighttime MCSs). Significance Statement The purpose of this study is to understand the features of mesoscale convective systems (MCSs) in observational data and convection-permitting climate model simulations. We tested long-term simulations using new forcing data (ERA5) to see the benefits and limitations. We designed a novel approach to obtain the distributions of meteorological parameters (instead of obtaining one value for one event of MCS) before initiations of MCSs to understand preconvective conditions (times from −9 to −1 h from initiation). We also divided MCSs into daytime/nighttime and short-/long-lived MCSs to help predict MCSs longevity considering the initiation times. Our results provide hints for the forecasters to predict MCS longevity based on preconvective conditions from parameters discussed in this work.
Most North American temperate forests are plantation or regrowth forests, which are actively managed. These forests are in different stages of their growth cycles and their ability to sequester atmospheric carbon is affected by extreme weather events. In this study, the impact of heat and drought events on carbon sequestration in an age-sequence (80, 45, and 17 years as of 2019) of eastern white pine (Pinus strobus L.) forests in southern Ontario, Canada was examined using eddy covariance flux measurements from 2003 to 2019.Over the 17-year study period, the mean annual values of net ecosystem productivity (NEP) were 180 ± 96, 538 ± 177 and 64 ± 165 g C m-2 yr-1 in the 80-, 45- and 17-year-old stands, respectively, with the highest annual carbon sequestration rate observed in the 45-year-old stand. We found that air temperature (Ta) was the dominant control on NEP in all three different-aged stands and drought, which was a limiting factor for both gross ecosystem productivity (GEP) and ecosystems respiration (RE), had a smaller impact on NEP. However, the simultaneous occurrence of heat and drought events during the early growing seasons or over the consecutive years had a significant negative impact on annual NEP in all three forests. We observed a similar trend of NEP decline in all three stands over three consecutive years that experienced extreme weather events, with 2016 being a hot and dry, 2017 being a dry, and 2018 being a hot year. The youngest stand became a net source of carbon for all three of these years and the oldest stand became a small source of carbon for the first time in 2018 since observations started in 2003. However, in 2019, all three stands reverted to annual net carbon sinks.Our study results indicate that the timing, frequency and concurrent or consecutive occurrence of extreme weather events may have significant implications for carbon sequestration in temperate conifer forests in Eastern North America. This study is one of few globally available to provide long-term observational data on carbon exchanges in different-aged temperate plantation forests. It highlights interannual variability in carbon fluxes and enhances our understanding of the responses of these forest ecosystems to extreme weather events. Study results will help in developing climate resilient and sustainable forestry practices to offset atmospheric greenhouse gas emissions and improving simulation of carbon exchange processes in terrestrial ecosystem models.
Abstract Background Variable Retention Harvesting (VRH) is a forest management practice applied to enhance forest growth, improve biodiversity, preserve ecosystem function and provide economic revenue from harvested timber. There are many different forms and compositions in which VRH is applied in forest ecosystems. In this study, the impacts of four different VRH treatments on transpiration were evaluated in an 83-year-old red pine (Pinus Pinus resinosa ) plantation forest in the Great Lakes region in Canada. These VRH treatments included 55% aggregated crown retention (55A), 55% dispersed crown retention (55D), 33% aggregated crown retention (33A), 33% dispersed crown retention (33D) and unharvested control (CN) plot. These VRH treatments were implemented in 1-ha plots in the winter of 2014, while sap flow measurements were conducted from 2018 to 2020. Results Study results showed that tree-level transpiration was highest among trees in the 55D treatment, followed by 33D, 55A, 33A and CN plots. We found that photosynthetically active radiation (PAR) and vapor pressure deficit (VPD) were major controls or drivers of transpiration in all VRH treatments. Our study suggests that dispersed or distributed retention of 55% basal area (55D) is the ideal forest management technique to enhance transpiration and forest growth. Conclusions This study will help researchers, forest managers and decision-makers to improve their understanding of water cycling in forest ecosystem and adopt the best forest management regimes to enhance forest growth, health and resiliency to climate change.
The main objective of this study is to assess the economic value of the Brazilian Amazon’s ecosystem services accruing to Brazilians based on a meta-analysis of the Brazilian valuation literature. Insight in these local values provides an important benchmark to demonstrate the importance of preserving the Brazilian Amazon forest. The review covers almost 30 years of Brazilian valuation research on the Amazon, published predominantly in Portuguese, highlighting a high degree of study and data heterogeneity. The estimated mean value of the provision of habitat for species, carbon sequestration, water regulation, recreation and ecotourism to local populations is about 410 USD/ha/year. The standard deviation is however high, reflecting a wide dispersion in the distribution of values. Between 50 and 70 percent of the variation in these values can be explained with the help of the estimated meta-regression models, resulting in considerable prediction errors when applying a within-sample resampling procedure. These findings demonstrate the need for a more robust, common ecosystem services accounting and valuation framework before these values can be scaled up and aggregated across the entire Brazilian Amazon.
Abstract Thermal pollution is an environmental impact of large dams altering the natural temperature regime of downstream river ecosystems. The present study proposes a simulation–optimization framework to reduce thermal pollution downstream from reservoirs and tests it on a real-world case study. This framework attempts to simultaneously minimize the environmental impacts as well as losses to reservoir objectives for water supply. A hybrid machine-learning model is applied to simulate water temperature downstream of the reservoir under various operation scenarios. This model is shown to be robust and achieves acceptable predictive accuracy. The results of simulation–optimization indicate that the reservoir could be operated in such a way that the natural temperature regime is reasonably preserved to protect downstream habitats. Doing so, however, would result in significant trade-offs for reservoir storage and water supply objectives. Such trade-offs can undermine the benefits of reservoirs and need to be carefully considered in reservoir design and operation.
In boreal North America, much of the landscape is covered by fire-adapted forests dominated by serotinous conifers. For these forests, reductions in fire return interval could limit reproductive success, owing to insufficient time for stands to reach reproductive maturity i.e., to initiate cone production. Improved understanding of the drivers of reproductive maturity can provide important information about the capacity of these forests to self-replace following fire. Here, we assessed the drivers of reproductive maturity in two dominant and widespread conifers, semi-serotinous black spruce and serotinous jack pine. Presence or absence of female cones were recorded in approximately 15,000 individuals within old and recently burned stands in two distinct ecozones of the Northwest Territories (NWT), Canada. Our results show that reproductive maturity was triggered by a minimum tree size threshold rather than an age threshold, with trees reaching reproductive maturity at smaller sizes where environmental conditions were more stressful. The number of reproductive trees per plot increased with stem density, basal area, and at higher latitudes (colder locations). The harsh climatic conditions present at these higher latitudes, however, limited the recruitment of jack pine at the treeline ecotone. The number of reproductive black spruce trees increased with deeper soils, whereas the number of reproductive jack pine trees increased where soils were shallower. We examined the reproductive efficiency i.e., the number of seedlings recruited per reproductive tree, linking pre-fire reproductive maturity of recently burned stands and post-fire seedling recruitment (recorded up to 4 years after the fires) and found that a reproductive jack pine can recruit on average three times more seedlings than a reproductive black spruce. We suggest that the higher reproductive efficiency of jack pine can explain the greater resilience of this species to wildfire compared with black spruce. Overall, these results help link life history characteristics, such as reproductive maturity, to variation in post-fire recruitment of dominant serotinous conifers.
Northern Indigenous communities require collaborative approaches to health communication about food that are grounded in Indigenous knowledges and cultures; however, preferences and best methods for this process remain understudied. This participatory study discusses how Inuvialuit (Inuit from the Western Arctic) knowledge and the perspectives of territorial, regional, and local dietary message stakeholders can inform the co-development of culture-centered dietary messaging to support healthy, safe, and culturally appropriate diets in Tuktoyaktuk, NWT. A community researcher in Tuktoyaktuk conducted storytelling interviews with country food knowledge holders (n = 7) and community members (n = 3), and a talking circle with local public health dietary message disseminators (n = 2) in June-July 2021. The lead author conducted key informant telephone and videoconference interviews with territorial and regional dietary message disseminators (n = 5) in June 2021. Interviews were coded and analyzed thematically. Our findings indicate that participants at all levels support increased inclusion of cultural and community perspectives about food to develop regionally and locally tailored dietary messaging. While most dietary message stakeholders wish to be involved in co-development processes, some country food knowledge holders in Tuktoyaktuk expressed a desire to lead local communications about country foods. Informed by participants' experiences and needs, we provide recommendations for future community-led approaches to further (co-)develop and communicate effective, culturally meaningful dietary messaging that promotes Inuvialuit food sovereignty.
Source code repositories allow developers to manage multiple versions (or branches) of a software system. Pull-requests are used to modify a branch, and backporting is a regular activity used to port changes from a current development branch to other versions. In open-source software, backports are common and often need to be adapted by hand, which motivates us to explore backports and backporting challenges and strategies. In our exploration of 68,424 backports from 10 GitHub projects, we found that bug, test, document, and feature changes are commonly backported. We identified a number of backporting challenges, including that backports were inconsistently linked to their original pull-request (49%), that backports had incompatible code (13%), that backports failed to be accepted (10%), and that there were backporting delays (16 days to create, 5 days to merge). We identified some general strategies for addressing backporting issues. We also noted that backporting strategies depend on the project type and that further investigation is needed to determine their suitability. Furthermore, we created the first-ever backports dataset that can be used by other researchers and practitioners for investigating backports and backporting.
Abstract. Human-controlled reservoirs have a large influence on the global water cycle. While global hydrological models use generic parameterizations to model dam operations, the representation of reservoir regulation is still lacking in many Earth system models. Here we implement and evaluate a widely used reservoir parametrization in the global river-routing model mizuRoute, which operates on a vector-based river network resolving individual lakes and reservoirs and is currently being coupled to an Earth system model. We develop an approach to determine the downstream area over which to aggregate irrigation water demand per reservoir. The implementation of managed reservoirs is evaluated by comparing them to simulations ignoring inland waters and simulations with reservoirs represented as natural lakes using (i) local simulations for 26 individual reservoirs driven by observed inflows and (ii) global-domain simulations driven by runoff from the Community Land Model. The local simulations show the clear added value of the reservoir parametrization, especially for simulating storage for large reservoirs with a multi-year storage capacity. In the global-domain application, the implementation of reservoirs shows an improvement in outflow and storage compared to the no-reservoir simulation, but a similar performance is found compared to the natural lake parametrization. The limited impact of reservoirs on skill statistics could be attributed to biases in simulated river discharge, mainly originating from biases in simulated runoff from the Community Land Model. Finally, the comparison of modelled monthly streamflow indices against observations highlights that including dam operations improves the streamflow simulation compared to ignoring lakes and reservoirs. This study overall underlines the need to further develop and test runoff simulations and water management parameterizations in order to improve the representation of anthropogenic interference of the terrestrial water cycle in Earth system models.
Abstract. The Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) is a flexible modelling framework reproducing the behaviour of 47 established hydrological models. This toolbox can be used to calibrate and run models in a user-friendly and consistent way and is designed to facilitate the sharing of model code for reproducibility and to support intercomparison between hydrological models. Additionally, it allows users to create or modify models using components of existing ones. We present a new MARRMoT release (v2.1) designed for improved speed and ease of use. While improved computational efficiency was the main driver for this redevelopment, MARRMoT v2.1 also succeeds in drastically reducing the verbosity and repetitiveness of the code, which improves readability and facilitates debugging. The process to create new models or modify existing ones within the toolbox is also simplified in this version, making MARRMoT v2.1 accessible for researchers and practitioners at all levels of expertise. These improvements were achieved by implementing an object-oriented structure and aggregating all common model operations into a single class definition from which all models inherit. The new modelling framework maintains and improves on several good practices built into the original MARRMoT and includes a number of new features such as the possibility of retrieving more output in different formats that simplifies troubleshooting, and a new functionality that simplifies the calibration process. We compare outputs of 36 of the models in the framework to an earlier published analysis and demonstrate that MARRMoT v2.1 is highly consistent with the previous version of MARRMoT (v1.4), while achieving a 3.6-fold improvement in runtime on average. The new version of the toolbox and user manual, including several workflow examples for common application, are available from GitHub (https://github.com/wknoben/MARRMoT, last access: 12 May 2022; https://doi.org/10.5281/zenodo.6484372, Trotter and Knoben, 2022b).
Abstract. Mountain snow and ice greatly influence the hydrological cycle of alpine regions by regulating both the quantity of and seasonal variations in water availability downstream. This study considers the combined impacts of climate and glacier changes due to recession on the hydrology and water balance of two high-elevation basins in the Canadian Rockies. A distributed, physically based, uncalibrated glacier hydrology model developed in the Cold Regions Hydrological Modelling platform (CRHM) was used to simulate the glacier mass balance and basin hydrology of the Peyto and Athabasca glacier basins in Alberta, Canada. Bias-corrected reanalysis data were used to drive the model. The model calculates the water balance of glacierized basins, influenced by the surface energy and mass balance, and considers the redistribution of snow by wind and avalanches. It was set up using hydrological response units based on elevation bands, surface slope, and aspect, as well as changing land cover. Aerial photos, satellite images and digital elevation models (DEMs) were assimilated to represent the changing configurations of glacier area and the exposure of ice and firn. Observations of glacier mass balance, snow, and glacier ice surface elevation changes at glacier and alpine tundra meteorological stations and streamflow discharge at the glacier outlets were used to evaluate the model performance. Basin hydrology was simulated over two periods, 1965–1975 and 2008–2018, using the observed glacier configurations for those time periods. Both basins have undergone continuous glacier loss over the last 3 to 5 decades, leading to a 6 %–31 % reduction in glacierized area, a 78 %–109 % increase in ice exposure, and changes to the elevation and slope of the glacier surfaces. Air temperatures are increasing, mainly due to increasing winter maximum and summer minimum daily temperatures. Annual precipitation has increased by less than 11 %, but rainfall ratios have increased by 29 %–44 %. The results show that changes in both climate and glacier configuration have influenced the melt rates and runoff and a shift of peak flows in the Peyto Glacier basin from August to July. Glacier melt contributions increased/decreased from 27 %–61 % to 43 %–59 % of the annual discharges. Recent discharges were 3 %–19 % higher than in the 1960s and 1970s. The results suggest that increased exposure of glacier ice and lower surface elevation due to glacier thinning were less influential than climate warming in increasing streamflow. Streamflow from these glaciers continues to increase.
Abstract. Model intercomparison studies are carried out to test and compare the simulated outputs of various model setups over the same study domain. The Great Lakes region is such a domain of high public interest as it not only resembles a challenging region to model with its transboundary location, strong lake effects, and regions of strong human impact but is also one of the most densely populated areas in the USA and Canada. This study brought together a wide range of researchers setting up their models of choice in a highly standardized experimental setup using the same geophysical datasets, forcings, common routing product, and locations of performance evaluation across the 1×106 km2 study domain. The study comprises 13 models covering a wide range of model types from machine-learning-based, basin-wise, subbasin-based, and gridded models that are either locally or globally calibrated or calibrated for one of each of the six predefined regions of the watershed. Unlike most hydrologically focused model intercomparisons, this study not only compares models regarding their capability to simulate streamflow (Q) but also evaluates the quality of simulated actual evapotranspiration (AET), surface soil moisture (SSM), and snow water equivalent (SWE). The latter three outputs are compared against gridded reference datasets. The comparisons are performed in two ways – either by aggregating model outputs and the reference to basin level or by regridding all model outputs to the reference grid and comparing the model simulations at each grid-cell. The main results of this study are as follows: The comparison of models regarding streamflow reveals the superior quality of the machine-learning-based model in the performance of all experiments; even for the most challenging spatiotemporal validation, the machine learning (ML) model outperforms any other physically based model. While the locally calibrated models lead to good performance in calibration and temporal validation (even outperforming several regionally calibrated models), they lose performance when they are transferred to locations that the model has not been calibrated on. This is likely to be improved with more advanced strategies to transfer these models in space. The regionally calibrated models – while losing less performance in spatial and spatiotemporal validation than locally calibrated models – exhibit low performances in highly regulated and urban areas and agricultural regions in the USA. Comparisons of additional model outputs (AET, SSM, and SWE) against gridded reference datasets show that aggregating model outputs and the reference dataset to the basin scale can lead to different conclusions than a comparison at the native grid scale. The latter is deemed preferable, especially for variables with large spatial variability such as SWE. A multi-objective-based analysis of the model performances across all variables (Q, AET, SSM, and SWE) reveals overall well-performing locally calibrated models (i.e., HYMOD2-lumped) and regionally calibrated models (i.e., MESH-SVS-Raven and GEM-Hydro-Watroute) due to varying reasons. The machine-learning-based model was not included here as it is not set up to simulate AET, SSM, and SWE. All basin-aggregated model outputs and observations for the model variables evaluated in this study are available on an interactive website that enables users to visualize results and download the data and model outputs.
Abstract. Wetland drainage has been pervasive in the North American Prairie Pothole Region. There is strong evidence that this drainage increases the hydrological connectivity of previously isolated wetlands and, in turn, runoff response to snowmelt and rainfall. It can be hard to disentangle the role of climate from the influence of wetland drainage in observed records. In this study, a basin-classification-based virtual modelling approach is described that can isolate these effects on runoff regimes. The basin class which was examined, entitled Pothole Till, extends throughout much of Canada's portion of the Prairie Pothole Region. Three knowledge gaps were addressed. First, it was determined that the spatial pattern in which wetlands are drained has little influence on how much the runoff regime was altered. Second, no threshold could be identified below which wetland drainage has no effect on the runoff regime, with drainage thresholds as low as 10 % in the area being evaluated. Third, wetter regions were less sensitive to drainage as they tend to be better hydrologically connected, even in the absence of drainage. Low flows were the least affected by drainage. Conversely, during extremely wet years, runoff depths could double as the result of complete wetland removal. Simulated median annual runoff depths were the most responsive, potentially tripling under typical conditions with high degrees of wetland drainage. As storage capacity is removed from the landscape through wetland drainage, the size of the storage deficit of median years begins to decrease and to converge on those of the extreme wet years. Model simulations of flood frequency suggest that, because of these changes in antecedent conditions, precipitation that once could generate a median event with wetland drainage can generate what would have been a maximum event without wetland drainage. The advantage of the basin-classification-based virtual modelling approach employed here is that it simulated a long period that included a wide variety of precipitation and antecedent storage conditions across a diversity of wetland complexes. This has allowed seemingly disparate results of past research to be put into context and finds that conflicting results are often only because of differences in spatial scale and temporal scope of investigation. A conceptual framework is provided that shows, in general, how annual runoff in different climatic and drainage situations will likely respond to wetland drainage in the Prairie Pothole Region.
Abstract. The Red River is one of the largest contributing sources of discharge and nutrients to the world's 10th largest freshwater lake, Lake Winnipeg. Conversion of large areas of annual cropland to perennial forage has been proposed as a strategy to reduce both flooding and nutrient export to Lake Winnipeg. Such reductions could occur either via a reduction in the concentration of nutrients in runoff or through changes in the basin-scale hydrology, resulting in a lower water yield and the concomitant export of nutrients. This study assessed the latter mechanism by using the physically based Cold Regions Hydrological Modelling platform to examine the hydrological impacts of land use conversion from annual crops to perennial forage in a subbasin of the La Salle River basin in Canada. This basin is a typical agricultural subbasin in the Red River Valley, characterised by flat topography, clay soils, and a cold subhumid, continental climate. Long-term simulations (1992–2013) of the major components of water balance were compared between canola and smooth bromegrass, representing a conversion from annual cropping systems to perennial forage. An uncertainty framework was used to represent a range of fall soil saturation status (0 % to 70 %), which governs the infiltration to frozen soil in the subsequent spring. The model simulations indicated that, on average, there was a 36.5 ± 6.6 % (36.5 ± 7.2 mm) reduction in annual cumulative discharge and a 29.9 ± 16.3 % (2.6 ± 1.6 m3 s−1) reduction in annual peak discharge due to forage conversion over the assessed period. These reductions were driven by reduced overland flow 52.9 ± 12.8 % (28.8 ± 10.1 mm), increased peak snowpack (8.1 ± 1.5 %, 7.8 ± 1.6 mm), and enhanced infiltration to frozen soils (66.7 ± 7.7 %, 141.5 ± 15.2 mm). Higher cumulative evapotranspiration (ET) from perennial forage (34.5 ± 0.9 %, 94.1 ± 2.5 mm) was also predicted by the simulations. Overall, daily soil moisture under perennial forage was 18.0 % (57.2 ± 1.2 mm) higher than that of crop simulation, likely due to the higher snow water equivalent (SWE) and enhanced infiltration. However, the impact of forage conversion on daily soil moisture varied interannually. Soil moisture under perennial forage stands could be either higher or lower than that of annual crops, depending on antecedent spring snowmelt infiltration volumes.
Abstract. Snow represents the largest potential source of water for thermokarst lakes, but the runoff generated by snowmelt (freshet) can flow beneath lake ice and via the outlet without mixing with and replacing pre-snowmelt lake water. Although this phenomenon, called “snowmelt bypass”, is common in ice-covered lakes, it is unknown which lake and watershed properties cause variation in snowmelt bypass among lakes. Understanding the variability of snowmelt bypass is important because the amount of freshet that is mixed into a lake affects the hydrological and biogeochemical properties of the lake. To explore lake and watershed attributes that influence snowmelt bypass, we sampled 17 open-drainage thermokarst lakes for isotope analysis before and after snowmelt. Isotope data were used to estimate the amount of lake water replaced by freshet and to observe how the water sources of lakes changed in response to the freshet. Among the lakes, a median of 25.2 % of lake water was replaced by freshet, with values ranging widely from 5.2 % to 52.8 %. For every metre that lake depth increased, the portion of lake water replaced by freshet decreased by an average of 13 %, regardless of the size of the lake's watershed. The thickness of the freshet layer was not proportional to maximum lake depth, so that a relatively larger portion of pre-snowmelt lake water remained isolated in deeper lakes. We expect that a similar relationship between increasing lake depth and greater snowmelt bypass could be present at all ice-covered open-drainage lakes that are partially mixed during the freshet. The water source of freshet that was mixed into lakes was not exclusively snowmelt but a combination of snowmelt mixed with rain-sourced water that was released as the soil thawed after snowmelt. As climate warming increases rainfall and shrubification causes earlier snowmelt timing relative to lake ice melt, snowmelt bypass may become more prevalent, with the water remaining in thermokarst lakes post-freshet becoming increasingly rainfall sourced. However, if climate change causes lake levels to fall below the outlet level (i.e., lakes become closed-drainage), more freshet may be retained by thermokarst lakes as snowmelt bypass will not be able to occur until lakes reach their outlet level.
Abstract. Snowpack microstructure controls the transfer of heat to, as well as the temperature of, the underlying soils. In situ measurements of snow and soil properties from four field campaigns during two winters (March and November 2018, January and March 2019) were compared to an ensemble of CLM5.0 (Community Land Model) simulations, at Trail Valley Creek, Northwest Territories, Canada. Snow micropenetrometer profiles allowed for snowpack density and thermal conductivity to be derived at higher vertical resolution (1.25 mm) and a larger sample size (n=1050) compared to traditional snowpit observations (3 cm vertical resolution; n=115). Comparing measurements with simulations shows CLM overestimated snow thermal conductivity by a factor of 3, leading to a cold bias in wintertime soil temperatures (RMSE=5.8 ∘C). Two different approaches were taken to reduce this bias: alternative parameterisations of snow thermal conductivity and the application of a correction factor. All the evaluated parameterisations of snow thermal conductivity improved simulations of wintertime soil temperatures, with that of Sturm et al. (1997) having the greatest impact (RMSE=2.5 ∘C). The required correction factor is strongly related to snow depth (R2=0.77,RMSE=0.066) and thus differs between the two snow seasons, limiting the applicability of such an approach. Improving simulated snow properties and the corresponding heat flux is important, as wintertime soil temperatures are an important control on subnivean soil respiration and hence impact Arctic winter carbon fluxes and budgets.
Objective: To explore literature regarding youth with Adverse childhood experiences (ACEs), their potential reactivity to research, and research trauma mitigation protocols. Methods: A systematic scoping review was conducted in APA PsychInfo, CINAHL, Embase, and OVID Medline. 2 reviewers screened each article for 12 eligible studies. Quantitative and qualitative studies measuring maltreatment and trauma research responses were eligible. Youth were defined as individuals aged 10-19. Results: No study utilized the ACEs questionnaire with research-related stress measures. Among those that included research reactivity measures, various forms of childhood and youth victimization were considered. The majority of participants did not report feeling upset, with many reporting benefits to participation. Information on protocols for managing distress was available for 11 studies, the most common being the provision of a resource helpsheet and/or referral system. Implications: There is no indication of distress following ACEs-related research, with few studies measuring across the research experience. One study measured follow-up for distress and further action. Additional research may be indicated to assess the effectiveness of these protocols in this population with a follow-up assessment.
Agricultural trade poses dilemmas for adaptive water governance as farmers and irrigation systems become integrated into global food value chains and are affected by their ongoing dynamics. The benefits and risks of agricultural trade and agrarian transitions are unevenly distributed, giving rise to complex interdependencies and externalities. Despite these growing linkages, the understanding of agricultural markets and their influence on water conflict and cooperation remains limited and dependent on context, which can lead to seemingly contradictory evidence. Progress has been hampered by boundary problems, disputed concepts, measurement issues, and divergent normative perspectives. Addressing these challenges will require that water governance scholars account more explicitly for agricultural trade when diagnosing collective action problems and assessing different modes of adaptive water governance. Drawing on the common-pool resource governance literature, we distinguish three separate, but interrelated, conceptual perspectives examining agricultural trade as an external factor in water governance: (1) market integration as a disturbance, (2) market integration as an opportunity, and (3) agricultural trade as a form of telecoupling with nested externalities. We compare these perspectives in terms of the externalities involved, their major claims about the relationship between market integration and collective action in the context of irrigation governance, and the broader implications for adaptive water governance. The comparison demonstrates the prevalence of institutional misfits and the common struggle of boundary shifting, i.e., matching water governance to the expanding problem-shed associated with agricultural markets. Institutional fit offers one important lens through which to consider the shifting boundaries (and actors) relevant for water governance, the scope and limits for strengthening fit through social learning, and the importance of nested governance to address nested externalities. These insights point the way for an agenda of research that examines the evolution of agricultural trade and adaptive water governance and pays explicit attention to the politics and power relations that shape who wins and loses and the different levers and entry points to improve management of the associated transitions and trade-offs. We conclude by arguing that future research should identify and examine pathways of adaptive water governance that strengthen processes of social learning and institutional nesting to address the external pressures and opportunities created by global food value chains.

2021

Water use efficiency (WUE) can be calculated using a range of methods differing in carbon uptake and water use variable selection. Consequently, inconsistencies arise between WUE calculations due to complex physical and physiological interactions. The purpose of this study was to quantify and compare WUE estimates (harvest or flux-based) for alfalfa (C3 plant) and maize (C4 plant) and determine effects of input variables, plant physiology and farming practices on estimates. Four WUE calculations were investigated: two “harvest-based” methods, using above ground carbon content and either precipitation or evapotranspiration (ET), and two “flux-based” methods, using gross primary productivity (GPP) and either ET or transpiration. WUE estimates differed based on method used at both half-hourly and seasonal scales. Input variables used in calculations affected WUE estimates, and plant physiology led to different responses in carbon assimilation and water use variables. WUE estimates were also impacted by different plant physiological responses and processing methods, even when the same carbon assimilation and water use variables were considered. This study highlights a need to develop a metric of measuring cropland carbon-water coupling that accounts for all water use components, plant carbon responses, and biomass production.
Agricultural tile drainage is expanding in the northern Great Plains of North America. Given ongoing environmental and political concerns related to the eutrophication of Lake Winnipeg in Canada and the potential for tile drains to transport significant quantities of nutrients from agricultural fields, an improved understanding of nutrient dynamics in tile drains in this region is needed. This study characterized seasonal patterns in tile flow and chemistry under variable hydroclimatic conditions and related this variance to temporal variability in soil hydraulic properties in a farm in southern Manitoba, Canada, from 2015 to 2017. Tile flow, soil hydraulic properties, and groundwater table position all varied seasonally, as did the chemistry of tile drain effluent. The majority of annual tile discharge, which occurred in late spring, appears to have been contributed by shallow groundwater, primarily through soil matrix pathways. At these greater tile flow rates, concentrations of soluble reactive phosphorus (SRP) and total phosphorus (TP) were low (<0.03 mg L<sup>–1</sup> SRP, <0.04 mg L<sup>–1</sup> TP), but concentrations of nitrate (NO<sub>3</sub>-N) were high (20 to 25 mg L<sup>–1</sup> NO<sub>3</sub>-N). In contrast, tile flows outside of this peak period appeared to be primarily attributed to preferential flow pathways through frozen (snowmelt) and dry soil cracks (summer). Phosphorus (P) concentrations were greater during snowmelt and summer (~0.05 mg L<sup>–1</sup> SRP, ~0.1 mg L<sup>–1</sup> TP) but did not produce significant nutrient loads due to the minimal tile discharge rates (<1 mm d<sup>–1</sup>). This work suggests that the expansion of tile drainage may not exacerbate water quality issues involving P in the northern Great Plains but may increase nitrogen (N) loads in local water bodies.
Phosphorus (P) runoff from agricultural land plays a critical role in downstream water quality. This article summarizes P and sediment runoff data for both snowmelt and rainfall runoff from 30 arable fields in the Canadian provinces of Saskatchewan, Manitoba and Ontario. The data were collected from 216 site-years of field experiments, with climates ranging from semi-arid to humid and a wide range of field management practices. In the article, mean annual and seasonal (in terms of snowmelt and rain) precipitation inputs, runoff depths, and P and sediment concentrations and loads are presented, along with ranges of yearly values. In addition, information of field management and soil characteristics (e.g. soil type and soil Olsen P) is also presented for each field. The data have potential to be reused for national and international cross-region comparisons of P and sediment losses, constructing and validating decision-support models and tools for assessing and managing P losses in both snowmelt and rainfall runoff, and informing beneficial management practices to improve agricultural water quality. Interpretation of the data is found in “Phosphorus runoff from Canadian agricultural land: A cross-region synthesis of edge-of-field results” [1] .
Algal blooms fueled by phosphorus (P) enrichment are threatening surface water quality around the world. Although P loss from arable land is a critical contributor to P loads in many agricultural watersheds , there has been a lack of understanding of P loss patterns and drivers across regions. Here, we synthesized edge-of-field P and sediment runoff data for 30 arable fields in the Canadian provinces of Saskatchewan, Manitoba and Ontario (a total of 216 site-years) to elucidate spatial and temporal differences in runoff and P mobilization in snowmelt and rainfall runoff, and discuss climatic, soil and management drivers for these patterns. Across all regions, precipitation inputs were positively correlated with runoff amounts and consequently P loads. Runoff and P losses were dominated by snowmelt across all sites, however, regional differences in runoff amounts, and P concentrations, loads and speciation were apparent. Proportions of total P in the dissolved form were greater in the prairie region (55–94% in Manitoba) than in the Great Lakes region (26–35% in Ontario). In Manitoba, dissolved P concentrations in both snowmelt and rainfall runoff were strongly positively correlated to soil Olsen P concentrations in the 0–5 cm soil depth; however, this relationship was not found for Ontario fields, where tile drainage dominated hydrologic losses. Although precipitation amounts and runoff volumes were greater in Ontario than Manitoba, some of the greatest P loads were observed from Manitoba fields, driven by management practices. This synthesis highlights the differences across the Canadian agricultural regions in P runoff patterns and drivers, and suggests the need of co-ordinated and standardized monitoring programs to better understand regional differences and inform management. Phosphorus runoff patterns vary with climatic regions across Canada. †The dissolved P was measured as total dissolved P in MB and dissolved reactive P in SK and ON. ‡Total P was not measured in SK. • Phosphorus runoff patterns and drivers vary with climatic regions across Canada. • Co-ordinated and standardized monitoring programs are key to clarify regional differences. • Snowmelt dominates runoff volume and phosphorus loss across Canada. • The predominant form of P in runoff differs between the Prairie region and the Great Lakes region. • Reducing phosphorus sources is important for mitigating phosphorus runoff.
Agricultural phosphorus (P) losses to surface water bodies remain a global eutrophication concern, despite the application of conservation practices on farm fields. Although it is generally agreed upon that the use of multiple conservation practices (“stacking”) will lead to greater improvements to water quality, this may not be cost effective to farmers, reducing the likelihood of adoption. At present, wholesale recommendations of conservation practices are given; however, the application of specific conservation practices in certain environments (e.g., no-till with surface application, cover crops) may not be effective and can even lead to unintended consequences. In this paper, we present the Lake Erie watershed as a case study. The Lake Erie watershed contains regions with unique physical geographies that include differences in climate, soil, topography, and land use, which have implications for both P transport from agricultural fields and the efficacy of conservation practices in mitigating P losses. We define major regions within the Lake Erie watershed where common strategies for conservation practice implementation are appropriate, and we propose a five-step plan for bringing regionally tailored, adaptive, and cost-conscious conservation practice into watershed planning. Although this paper is specific to the Lake Erie watershed, our framework can be transferred across broader geographic regions to provide guidance for watershed planning.
Nutrient losses from agricultural operations are a major contributor to the eutrophication of freshwaters. Although many studies have quantified diffuse nutrient losses, less is known about agricultural point-source contributions, such as bunker silos, to watershed phosphorus (P) loads. This study examined the contributions of a dairy farm bunker silo effluent to watershed soluble reactive P (SRP) and total P (TP) losses. The bunker silo effluent discharged to an adjacent stream via a riparian soakaway for ca. 15 years. Prior to the annual refilling of the bunker silo, flow weighted mean concentrations of SRP (TP) were similar between stream locations up and downstream of the farm. After the bunker silo was refilled, flow-weighted SRP (TP) concentrations in the stream increased by factors of 1.5(2.2) during events and 3.1(2.3) during baseflow. Higher P concentrations occurred in the riparian soils receiving bunker silo effluent (525–3125 mg/kg TP, and 0.1–9.9 mg/kg water extractable P (WEP), compared with 525–939 mg/kg TP, and 0.11–1.43 mg/kg WEP on the opposite side of the stream with no bunker silo effluent. Riparian soils impacted by the bunker silo were near P-saturation, and the riparian zone did little to reduce P transfer in shallow groundwater. The net contributions of bunker silo effluent to annual watershed P losses were 32% (SRP) and 22% (TP). This study highlights the importance of agricultural point sources, and the need to quantify their contributions to watershed P budgets to target P remediation effectively.
To assess the potential change in agroclimatic indices in western Canada, this study used a convection‐permitting Weather Research Forecasting (WRF) model to conduct simulations for the current climate (CTL, 2000–2015) and future climate under the RCP8.5 scenario based on a pseudo‐global‐warming (PGW) approach. Both CTL and PGW simulations were bias‐corrected to the GEM‐CaPA dataset using a multivariate quantile mapping method. An evaluation of the CTL simulation of daily maximum and minimum temperatures and precipitation during the growing season against the gridded observations has been performed, indicating good agreements in the spatial patterns of air temperature and precipitation in western Canada. The PGW − CTL differences in several selected agroclimatic indices were then examined. Due to rising temperatures, substantial increases in growing degree‐days (GDD) by 800–1,200° days and reductions in frost days by 10 to 20 days, favouring regional crop production, are found in southern Alberta and Saskatchewan. However, global warming also poses great risks to Canadian agriculture by modifying heat accumulations and water availability during the growing season. Plant heat stress will substantially increase by ∼50° days in southern Alberta and Saskatchewan, offsetting the positive effects caused by the reduction in frost days and increase in GDD. The southern Canadian Prairies will experience statistically significant increases in the number of dry days and precipitation deficit, suggesting an exacerbation of water stress on the Canadian Prairies by global warming.
• Greater restored moss cover decreased peat burn severity. • Deep vs shallow harvesting depth drove divergent post-fire soil water conditions. • Shallow harvest increased suitable conditions for Sphagnum establishment. • Deep harvest lowers the risk of subsequent peat ignition. • Deep harvest likely to promote longer-term carbon sequestration due to fewer fires. Peatland disturbances can disrupt the ecohydrological functions that sustain net carbon sequestration in peatlands. Anthropogenic disturbances, such as peatland drainage and harvesting, are often followed by peatland restoration that aims to return the carbon sink function. This is typically achieved by raising the water table and re-establishing keystone Sphagnum moss species. However, with an increasingly uncertain climate and intensifying land-use changes, the potential for multiple disturbances (such as co-occurring wildfires, drainage, and harvesting) to disrupt the ecohydrological feedbacks that support peatland function is increasing. Yet, few studies investigate the ecohydrological trade-offs induced by multiple disturbances in peatlands. To elucidate the complexities of multiple disturbances and restoration on Sphagnum re-establishment and wildfire potential, we studied a Deep and Shallow harvested area in a drained and restored peatland in southern Ontario, Canada that experienced a wildfire in 2012. Harvesting depth did not significantly increase the bulk density of the upper 32 cm of exposed peat, but the shallower harvest depth did significantly increase the depth of burn (DOB) due to the more varied remnant topography. The difference in topography of the shallower harvested area increased peat carbon losses (16.5 kg C m −2 ) from the wildfire relative to the deeper harvest area (15.1 kg C m −2 ). The difference in post-fire peat hydrophysical properties of the Deep and Shallow harvest area drove divergent soil water conditions. In the post-burn peat, the establishment of suitable conditions for the regeneration of Sphagnum mosses was more prevalent at the Shallow harvest areas but the higher soil water retention capabilities of the Deep harvest peat lowered the risk of subsequent peat ignition. This study highlights the complex interactions multiple disturbances have on peatland ecohydrology and that we urgently need to understand these interactions to better manage our shared peatland resources in an increasingly uncertain future.
• The CRHM-created Boreal Hydrology Model performed quite well on simultaneously simulating runoff, snow water equivalent, soil liquid water content and evapotranspiration (ET) with minor parameter calibration. • The basin hydrological variables showed quite different sensitivities to perturbations of precipitation (P) and temperature (T). Annual runoff was more sensitive to rising P than warming T, but annual ET was more sensitive to warming T. • Perturbed P and T had distinctively different influences on the streamflow regime. Increased P enhanced the intra- and inter-annual variabilities of basin runoff, whilst rising T resulted in the inverse changes. • Effects of warming on annual runoff and snow processes could be compensated for to varying degrees by the effects of increases in P. Hydrological processes over and through frozen and unfrozen ground were simulated in the well instrumented boreal forest basin of White Gull Creek, Saskatchewan, Canada using a model created using the flexible Cold Regions Hydrological Modelling (CRHM) platform. The CRHM-created Boreal Hydrology Model was structured and initially parameterized using decades of process hydrology research in the southern boreal forest with minor parameter calibration, and generally produced quite good performance on simultaneously reproducing the measurements of runoff, snow water equivalent (SWE), soil liquid water content and eddy correlation flux tower observations of evapotranspiration (ET) over two decades. To examine the sensitivity of basin hydrology to perturbed climate inputs, air temperature (T) inputs were set up by linear increments in the reference observation of up to +6 ℃, and precipitation (P) inputs were generated by multiplying the reference observed P from 70% to 130%. The model results showed that the basin hydrological variables showed quite different sensitivities to perturbations of P and T. The volume of annual runoff and the annual runoff coefficient increased more rapidly with rising P, at rates of 31% and 16% per 10% increase in P, but decreased by only 3.8% and 4.7% per 1 ℃ of warming. Annual ET increased rapidly with temperature, by 7% per 1 ℃ of warming and therefore drove the streamflow volumetric changes with warming, but increased only 1% per 10% increase in P. Perturbations of P and T had distinctively different influences on the streamflow regime. Increased P enhanced the intra- and inter-annual variabilities of basin runoff, reduced the relative contribution of winter runoff to annual runoff and increased the relative contribution of summer runoff; whilst rising T resulted in the inverse changes in the streamflow regime. Effects of warming on some hydrological processes could be compensated for to varying degrees by the effects of increases in P. Reductions in the annual runoff volume and runoff coefficient caused by warming up to 6 ℃ could be compensated for by increases of <20% in P. However, the maximum increase in P (+30%) examined could only compensate for the changes in snow processes caused by warming of less than 4 ℃ and snow-cover duration decreases with 1 ℃ warming could not be compensated for by any precipitation increase considered. These results inform the vulnerability of boreal forest hydrology to the first-order changes in P and T and provide guidance for further climate impact assessments for hydrology in the southern boreal forest in Canada.
2020 is the year of wildfire records. California experienced its three largest fires early in its fire season. The Pantanal, the largest wetland on the planet, burned over 20% of its surface. More than 18 million hectares of forest and bushland burned during the 2019–2020 fire season in Australia, killing 33 people, destroying nearly 2500 homes, and endangering many endemic species. The direct cost of damages is being counted in dozens of billion dollars, but the indirect costs on water-related ecosystem services and benefits could be equally expensive, with impacts lasting for decades. In Australia, the extreme precipitation (“200 mm day −1 in several location”) that interrupted the catastrophic wildfire season triggered a series of watershed effects from headwaters to areas downstream. The increased runoff and erosion from burned areas disrupted water supplies in several locations. These post-fire watershed hazards via source water contamination, flash floods, and mudslides can represent substantial, systemic long-term risks to drinking water production, aquatic life, and socio-economic activity. Scenarios similar to the recent event in Australia are now predicted to unfold in the Western USA. This is a new reality that societies will have to live with as uncharted fire activity, water crises, and widespread human footprint collide all-around of the world. Therefore, we advocate for a more proactive approach to wildfire-watershed risk governance in an effort to advance and protect water security. We also argue that there is no easy solution to reducing this risk and that investments in both green (i.e., natural) and grey (i.e., built) infrastructure will be necessary. Further, we propose strategies to combine modern data analytics with existing tools for use by water and land managers worldwide to leverage several decades worth of data and knowledge on post-fire hydrology.
Hydrological processes in mountain headwater basins are changing as climate and vegetation change. Interactions between hydrological processes and subalpine forest ecological function are important to mountain water supplies due to their control on evapotranspiration (ET). Improved understanding of the sensitivity of these interactions to seasonal and interannual changes in snowmelt and summer rainfall is needed as these interactions can impact forest growth, succession, health, and susceptibility to wildfire. To better understand this sensitivity, this research examined ET for a sub-alpine forest in the Canadian Rockies over two contrasting growing seasons and quantified the contribution of transpiration (T) from the younger tree population to overall stand ET. The younger population was focused on to permit examination of trees that have grown under the effect of recent climate change and will contribute to treeline migration, and subalpine forest densification and succession. Research sites were located at Fortress Mountain Research Basin, Kananaskis, Alberta, where the subalpine forest examined is composed of Abies lasiocarpa (Subalpine fir) and Picea engelmannii (Engelmann spruce). Seasonal changes in water availability from snowmelt, precipitation, soil moisture reserves yielded stark differences in T and ET between 2016 and 2017. ET was higher in the drier year (2017), which had late snowmelt and lower summer rainfall than in the wetter year (2016) that had lower snowmelt and a rainy summer, highlighting the importance of spring snowmelt recharge of soil moisture. However, stand T of the younger trees (73% of forest population) was greater (64 mm) in 2016 (275 mm summer rainfall) than 2017 (39 mm T, 147 mm summer rainfall), and appears to be sensitive to soil moisture decreases in fall, which are largely a function of summer period rainfall. Relationships between subalpine forest water use and different growing season and antecedent (snowmelt period) hydrological conditions clarify the interactions between forest water use and alpine hydrology, which can lead to better anticipation of the hydrological response of subalpine forest-dominated basins to climate variability and change.
Global Institute for Water Security, University of Saskatchewan, Saskatoon, Saskatchewan, Canada School of Geosciences, University of Birmingham, Birmingham, UK Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia, Vancouver, British Columbia, Canada Landcare Research, Lincoln, New Zealand Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland Centre for Hydrology, University of Saskatchewan, Canmore, Alberta, Canada School of Geographical Sciences, University of Bristol, Bristol, UK Cabot Institute, University of Bristol, Bristol, UK Hetch Hetchy Power, San Francisco, California, USA Division of Earth and Ocean Sciences, Nicolas School of the Environment, Duke University, Durham, North Carolina, USA GNS Science, Lower Hutt, New Zealand Department of Civil Engineering, Univeristy of Calgary, Calgary, Alberta, Canada Disaster Prevention Research Institute, Kyoto University, Kyoto, Japan Department of Geography, University of Zurich, Zurich, Switzerland Dept of Biological and Ecological Engineering, Oregon State University, Corvallis, Oregon, USA Faculty of Environment & Natural Resources, University of Freiburg, Freiburg, Germany Faculty of Engineering, University of Bristol, Bristol, UK
Treed peatlands exhibit both crown and smouldering fire potential; however, neither are included in Canadian wildfire management models and, as such, they are not formally represented in management decision-making. The lack of smouldering fire risk assessment is a critical research gap as these fires can represent heavy resource draws and are predominant sources of smoke, air pollutants and atmospheric carbon. Here, for the first time, we combine existing knowledge of the controls on smouldering peat fire with expert opinion-based weightings through a multi-criteria decision analysis, to map the smouldering fire potential (i.e. hazard) of treed peatlands in the Boreal Plains, Alberta, Canada. We find that smouldering potential varies considerably between treed peatlands and that areas of sparser peatland coverage may contain high smouldering-potential peatlands. Further, we find that treed peatlands are a common feature in the wildland–human interface and that proportionally, the area of high smouldering potential is greater closer to roads compared with farther away. Our approach enables a quantitative measure of smouldering fire potential and evidences the need to incorporate peatland–wildfire interactions into wildfire management operations. We suggest that similar frameworks could be used in other peatland dominated regions as part of smouldering fire risk assessments.
In Canada, Indigenous youth have remained resilient despite being confronted with a wide range of structural and systemic risks, such as long-lasting boil water advisories, over-representation in the child welfare system, and injustices related to land treaties. As people of the land, all disruptions to ecological health are a disruption to personal and community holistic health. Land-based activities and cultural continuity strengthen pathways of perseverance for Indigenous youth (Toombs et al., 2016). For youth, cultural self-expression and personal agency are enhanced with digital platforms, which are well-suited to Indigenous people’s strengths in art, music, and oral forms of passing on knowledge. The field of mental health has turned to e-supports such as mobile applications (apps) that can provide easy-to-access intervention, when needed. To date, resilience interventions have received comparatively less attention than the study of resilience factors and processes. It is timely to review the extant literature on mental health apps with Indigenous youth as, currently, Indigenous apps are in early research stages. Critically reviewing work to date, it is argued that an inclusive and expansive concept of resilience, coherent with Indigenous holistic health views, is well-positioned as a foundation for collaborative resilience app development. To date, few mental health apps have been researched with Indigenous youth, and fewer have been co-constructed with Indigenous youth and their community members. The current literature points to feasibility in terms of readiness or potential usage, and functionality for promoting an integrated cultural and holistic health lens. As this effort may be specific to a particular Indigenous nation’s values, stories, and practices, we highlight the Haudenosaunee conceptual wellness model as one example to guide Indigenous and non-Indigenous science integration, with a current project underway with the JoyPopTM mHealth app for promoting positive mental health and resilience.
Changes in spatiotemporal data may often go unnoticed due to their inherent noise and low variability (e.g., geological processes over years). Commonly used approaches such as side-by-side contour plots and spaghetti plots do not provide a clear idea about the temporal changes in such data. We propose ContourDiff, a vector-based visualization over contour plots to visualize the trends of change across spatial regions and temporal domain. Our approach first aggregates for each location, its value differences from the neighboring points over the temporal domain, and then creates a vector field representing the prominent changes. Finally, it overlays the vectors along the contour paths, revealing differential trends that the contour lines experienced over time. We evaluated our visualization using real-life datasets, consisting of millions of data points, where the visualizations were generated in less than a minute in a single-threaded execution. Our experimental results reveal that ContourDiff can reliably visualize the differential trends, and provide a new way to explore the change pattern in spatiotemporal data.
Water science data are a valuable asset that both underpins the original research project and bolsters new research questions, particularly in view of the increasingly complex water issues facing Canada and the world. Whilst there is general support for making data more broadly accessible, and a number of water science journals and funding agencies have adopted policies that require researchers to share data in accordance with the FAIR (Findable, Accessible, Interoperable, Reusable) principles, there are still questions about effective management of data to protect their usefulness over time. Incorporating data management practices and standards at the outset of a water science research project will enable researchers to efficiently locate, analyze and use data throughout the project lifecycle, and will ensure the data maintain their value after the project has ended. Here, some common misconceptions about data management are highlighted, along with insights and practical advice to assist established and early career water science researchers as they integrate data management best practices and tools into their research. Freely available tools and training opportunities made available in Canada through Global Water Futures, the Portage Network, Gordon Foundation's DataStream, Compute Canada, and university libraries, among others are compiled. These include webinars, training videos, and individual support for the water science community that together enable researchers to protect their data assets and meet the expectations of journals and funders. The perspectives shared here have been developed as part of the Global Water Futures programme's efforts to improve data management and promote the use of common data practices and standards in the context of water science in Canada. Ten best practices are proposed that may be broadly applicable to other disciplines in the natural sciences and can be adopted and adapted globally. This article is protected by copyright. All rights reserved.
As continental to global scale high-resolution meteorological datasets continue to be developed, there are sufficient meteorological datasets available now for modellers to construct a historical forcing ensemble. The forcing ensemble can be a collection of multiple deterministic meteorological datasets or come from an ensemble meteorological dataset. In hydrological model calibration, the forcing ensemble can be used to represent forcing data uncertainty. This study examines the potential of using the forcing ensemble to identify more robust parameters through model calibration. Specifically, we compare an ensemble forcing-based calibration with two deterministic forcing-based calibrations and investigate their flow simulation and parameter estimation properties and the ability to resist poor-quality forcings. The comparison experiment is conducted with a six-parameter hydrological model for 30 synthetic studies and 20 real data studies to provide a better assessment of the average performance of the deterministic and ensemble forcing-based calibrations. Results show that the ensemble forcing-based calibration generates parameter estimates that are less biased and have higher frequency of covering the true parameter values than the deterministic forcing-based calibration does. Using a forcing ensemble in model calibration reduces the risk of inaccurate flow simulation caused by poor-quality meteorological inputs, and improves the reliability and overall simulation skill of ensemble simulation results. The poor-quality meteorological inputs can be effectively filtered out via our ensemble forcing-based calibration methodology and thus discarded in any post-calibration model applications. The proposed ensemble forcing-based calibration method can be considered as a more generalized framework to include parameter and forcing uncertainties in model calibration.
To process a large amount of data sequentially and systematically, proper management of workflow components (i.e., modules, data, configurations, associations among ports and links) in a Scientific Workflow Management System (SWfMS) is inevitable. Managing data with provenance in a SWfMS to support reusability of workflows, modules, and data is not a simple task. Handling such components is even more burdensome for frequently assembled and executed complex workflows for investigating large datasets with different technologies (i.e., various learning algorithms or models). However, a great many studies propose various techniques and technologies for managing and recommending services in a SWfMS, but only a very few studies consider the management of data in a SWfMS for efficient storing and facilitating workflow executions. Furthermore, there is no study to inquire about the effectiveness and efficiency of such data management in a SWfMS from a user perspective. In this paper, we present and evaluate a GUI version of such a novel approach of intermediate data management with two use cases (Plant Phenotyping and Bioinformatics). The technique we call GUI-RISPTS (Recommending Intermediate States from Pipelines Considering Tool-States) can facilitate executions of workflows with processed data (i.e., intermediate outcomes of modules in a workflow) and can thus reduce the computational time of some modules in a SWfMS. We integrated GUI-RISPTS with an existing workflow management system called SciWorCS. In SciWorCS, we present an interface that users use for selecting the recommendation of intermediate states (i.e., modules' outcomes). We investigated GUI-RISPTS's effectiveness from users' perspectives along with measuring its overhead in terms of storage and efficiency in workflow execution.
Given an m×n table T of positive weights, and a rectangle R with an area equal to the sum of the weights, a table cartogram computes a partition of R into m×n convex quadrilateral faces such that each face has the same adjacencies as its corresponding cell in T, and has an area equal to the cell’s weight. In this paper, we examine constraint optimization-based and physics-inspired cartographic transformation approaches to produce cartograms for large tables with thousands of cells. We show that large table cartograms may provide diagrammatic representations in various real-life scenarios, e.g., for analyzing correlations between geospatial variables and creating visual effects in images. Our experiments with real-life datasets provide insights into how one approach may outperform the other in various application contexts.
Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations. It aims to provide effective, reproducible, and easy-to-use first-stage retrieval in a multi-stage ranking architecture. Our toolkit is self-contained as a standard Python package and comes with queries, relevance judgments, pre-built indexes, and evaluation scripts for many commonly used IR test collections. We aim to support, out of the box, the entire research lifecycle of efforts aimed at improving ranking with modern neural approaches. In particular, Pyserini supports sparse retrieval (e.g., BM25 scoring using bag-of-words representations), dense retrieval (e.g., nearest-neighbor search on transformer-encoded representations), as well as hybrid retrieval that integrates both approaches. This paper provides an overview of toolkit features and presents empirical results that illustrate its effectiveness on two popular ranking tasks. Around this toolkit, our group has built a culture of reproducibility through shared norms and tools that enable rigorous automated testing.
Many organizations use legacy systems as these systems contain their valuable business rules. However, these legacy systems answer the past requirements but are difficult to maintain and evolve due to old technology use. In this situation, stockholders decide to renovate the system with a minimum amount of cost and risk. Although the renovation process is a more affordable choice over redevelopment, it comes with its risks such as performance loss and failure to obtain quality goals. A proper test process can minimize risks incorporated with the renovation process. This work introduces a testing model tailored for the migration and re-engineering process and employs test automation, which results in early bug detection. Moreover, the automated tests ensure functional sameness between the old and the new system. This process enhances reliability, accuracy, and speed of testing.
Distributed hydrological models predict the spatial variability in processes that govern observed mass and energy fluxes. A challenge associated with the use of these models is the computational burden associated with representing the Earth's (sub)surface via millions of computational elements. This burden is exacerbated as more complex process representations are included because their parameterizations involve computationally intensive mathematical functions. Lookup tables (LUTs) approximate a mathematical function by interpolating precomputed values of the function. Highly accurate approximations are possible for substantially reduced computational costs. In this work, a general methodology using the C++ LUT library FunC is applied to identify and replace computationally intensive mathematical function evaluations in the Canadian Hydrological Model (CHM). The use of LUTs introduces a pointwise relative error below 10 − 8 and provides a reduction in run time of almost 20%. This work shows how LUTs can be implemented with relatively little pain and yield significant computational savings for distributed hydrological models. • The Canadian Hydrological Model (CHM) is profiled and expensive mathematical functions identified. • FunC was used to replace the expensive mathematical functions in CHM with lookup tables. • The run-time performance of CHM was improved by approximately 20% on two realistic simulations. • A general methodology for using FunC to replace expensive mathematical functions with lookup tables is given.
Accurate streamflow prediction largely relies on historical meteorological records and streamflow measurements. For many regions, however, such data are only scarcely available. Facing this problem, many studies simply trained their machine learning models on the region's available data, leaving possible repercussions of this strategy unclear. In this study, we evaluate the sensitivity of tree- and LSTM-based models to limited training data, both in terms of geographic diversity and different time spans. We feed the models meteorological observations disseminated with the CAMELS dataset, and individually restrict the training period length, number of training basins, and input sequence length. We quantify how additional training data improve predictions and how many previous days of forcings we should feed the models to obtain best predictions for each training set size. Further, our findings show that tree- and LSTM-based models provide similarly accurate predictions on small datasets, while LSTMs are superior given more training data.
Causes of software architectural change are classified as perfective, preventive, corrective, and adaptive. Change classification is used to promote common approaches for addressing similar changes, produce appropriate design documentation for a release, construct a developer’s profile, form a balanced team, support code review, etc. However, automated architectural change classification techniques are in their infancy, perhaps due to the lack of a benchmark dataset and the need for extensive human involvement. To address these shortcomings, we present a benchmark dataset and a text classifier for determining the architectural change rationale from commit descriptions. First, we explored source code properties for change classification independent of project activity descriptions and found poor outcomes. Next, through extensive analysis, we identified the challenges of classifying architectural change from text and proposed a new classifier that uses concept tokens derived from the concept analysis of change samples. We also studied the sensitivity of change classification of various types of tokens present in commit messages. The experimental outcomes employing 10-fold and cross-project validation techniques with five popular open-source systems show that the F1 score of our proposed classifier is around 70%. The precision and recall are mostly consistent among all categories of change and more promising than competing methods for text classification
Release notes are admitted as an essential document by practitioners. They contain the summary of the source code changes for the software releases, such as issue fixes, added new features, and performance improvements. Manually producing release notes is a time-consuming and challenging task. For that reason, sometimes developers neglect to write release notes. For example, we collect data from GitHub with over 1,900 releases, among them 37% of the release notes are empty. We propose an automatic generate release notes approach based on the commit messages and merge pull-request (PR) titles to mitigate this problem. We implement one of the popular extractive text summarization techniques, i.e., the TextRank algorithm. However, accurate keyword extraction is a vital issue in text processing. The keyword matching and topic extraction process of the TextRank algorithm ignores the semantic similarity among texts. To improve the keyword extraction method, we integrate the GloVe word embedding technique with TextRank. We develop a dataset with 1,213 release notes (after null filtering) and evaluate the generated release notes through the ROUGE metric and human evaluation. We also compare the performance of our technique with another popular extractive algorithm, latent semantic analysis (LSA). Our evaluation results show that the improved TextRank method outperforms LSA.
Software architectural changes involve more than one module or component and are complex to analyze compared to local code changes. Development teams aiming to review architectural aspects (design) of a change commit consider many essential scenarios such as access rules and restrictions on usage of program entities across modules. Moreover, design review is essential when proper architectural formulations are paramount for developing and deploying a system. Untangling architectural changes, recovering semantic design, and producing design notes are the crucial tasks of the design review process. To support these tasks, we construct a lightweight tool [4] that can detect and decompose semantic slices of a commit containing architectural instances. A semantic slice consists of a description of relational information of involved modules, their classes, methods and connected modules in a change instance, which is easy to understand to a reviewer. We extract various directory and naming structures (DANS) properties from the source code for developing our tool. Utilizing the DANS properties, our tool first detects architectural change instances based on our defined metric and then decomposes the slices (based on string processing). Our preliminary investigation with ten open-source projects (developed in Java and Kotlin) reveals that the DANS properties produce highly reliable precision and recall (93-100%) for detecting and generating architectural slices. Our proposed tool will serve as the preliminary approach for the semantic design recovery and design summary generation for the project releases.
Evolutionary coupling is a well investigated phenomenon in software maintenance research and practice. Association rules and two related measures, support and confidence, have been used to identify evolutionary coupling among program entities. However, these measures only emphasize the co-change (i.e., changing together) frequency of entities and cannot determine whether the entities co-evolved by experiencing related changes. Consequently, the approach reports false positives and fails to detect evolutionary coupling among infrequently co-changed entities. We propose a new measure, identifier correspondence (id-correspondence), that quantifies the extent to which changes that occurred to the co-changed entities are related based on identifier similarity. Identifiers are the names given to different program entities such as variables, methods, classes, packages, interfaces, structures, unions etc. We use Dice-Sørensen co-efficient for measuring lexical similarity between the identifiers involved in the changed lines of the co-changed entities. Our investigation on thousands of revisions from nine subject systems covering three programming languages shows that id-correspondence can considerably improve the detection accuracy of evolutionary coupling. It outperforms the existing state-of-the-art evolutionary coupling based techniques with significantly higher recall and F-score in predicting future co-change candidates.
When a programmer changes a particular code fragment, the other similar code fragments in the code-base may also need to be changed together (i.e., co-changed) consistently to ensure that the software system remains consistent. Existing studies and tools apply clone detectors to identify these similar co-change candidates for a target code fragment. However, clone detectors suffer from a confounding configuration choice problem and it affects their accuracy in retrieving co-change candidates.In our research, we propose and empirically evaluate a lightweight co-change suggestion technique that can automatically suggest fragment level similar co-change candidates for a target code fragment using WA-DiSC (Weighted Average Dice-Sørensen Co-efficient) through a context-sensitive mining of the entire code-base. We apply our technique, FLeCCS (Fragment Level Co-change Candidate Suggester), on six subject systems written in three different programming languages (Java, C, and C#) and compare its performance with the existing state-of-the-art techniques. According to our experiment, our technique outperforms not only the existing code clone based techniques but also the association rule mining based techniques in detecting co-change candidates with a significantly higher accuracy (precision and recall). We also find that File Proximity Ranking performs significantly better than Similarity Extent Ranking when ranking the co-change candidates suggested by our proposed technique.
Release notes are admitted as an essential technical document in software maintenance. They summarize the main changes, e.g. bug fixes and new features, that have happened in the software since the previous release. Manually producing release notes is a time-consuming and challenging task. For that reason, sometimes developers neglect to write release notes. For example, we collect data from GitHub with over 1900 releases, and among them, 37% of the release notes are empty. To mitigate this problem, we propose an automatic release notes generation approach by applying the text summarization techniques, i.e. TextRank. To improve the keyword extraction method of traditional TextRank, we integrate the GloVe word embedding technique with TextRank. After generating release notes automatically, we apply machine learning algorithms to classify the release note contents (or sentences). We classify the contents into six categories, e.g. bug fixes and performance improvements, to represent the release notes better for users. We use the evaluation metric, e.g. ROUGE, to evaluate the automatically generated release notes. We also compare the performance of our technique with two popular extractive algorithms, e.g. Luhn’s and latent semantic analysis (LSA). Our evaluation results show that the improved TextRank method outperforms the two algorithms.
The acceleration of climate change and its impact highlight the need for long-term reliable climate data at high spatiotemporal resolution to answer key science questions in cold regions hydrology. Prior to the digital age, climate records were archived on paper. For example, from the 1950s to the 1990s, solar radiation data from recording stations worldwide were published in booklets by the former Union of Soviet Socialist Republics (USSR) Hydrometeorological Service. As a result, the data are not easily accessible by most researchers. The overarching aim of this research is to develop techniques to convert paper-based climate records into a machine-readable format to support environmental research in cold regions. This study compares the performance of a proprietary optical character recognition (OCR) service with an open-source OCR tool for digitizing hydrometeorological data. We built a digitization pipeline combining different image preprocessing techniques, semantic segmentation, and an open-source OCR engine for extracting data and metadata recorded in the scanned documents. Each page contains blocks of text with station names and tables containing the climate data. The process begins with image preprocessing to reduce noise and to improve quality before the page content is segmented to detect tables and finally run through an OCR engine for text extraction. We outline the digitization process and report on initial results, including different segmentation approaches, preprocessing image algorithms, and OCR techniques to ensure accurate extraction and organization of relevant metadata from thousands of scanned climate records. We evaluated the performance of Tesseract OCR and ABBYY FineReader on text extraction. We find that although ABBY FineReader has better accuracy on the sample data, our custom extraction pipeline using Tesseract is efficient and scalable because it is flexible and allows for more customization.
Applications of image registration tasks are computation-intensive, memory-intensive, and communication-intensive. Robust efforts are required on error recovery and re-usability of both the data and the operations, along with performance optimization. Considering these, we explore various programming models aiming to minimize the folding operations (such as join and reduce) which are the primary candidates of data shuffling, concurrency bugs and expensive communication in a distributed cluster. Particularly, we analyze modular MapReduce execution of an image registration pipeline (IRP) with the external and internal data (data-tunneling) flow mechanism and compare them with the compact model. Experimental analyzes with the ComputeCanada cluster and a crop field data-sets containing 1000 images show that these design options are valuable for large-scale IRPs executed with a MapReduce cluster. Additionally, we present an effectiveness measurement metric to analyze the impact of a design model for the Big IRP, accumulating the error-recovery and re-usability metrics along with the data size and execution time. Our explored design models and their performance analysis can serve as a benchmark for the researchers and application developers who deploy large-scale image registration and other image processing tasks.
We describe a method for analyzing the structure of a system of nonlinear integro-differential–algebraic equations (IDAEs) that generalizes the Σ -method for the structural analysis of differential–algebraic equations. The method is based on the sparsity pattern of the IDAE and the ν -smoothing property of a Volterra integral operator. It determines which equations and how many times they need to be differentiated to determine the index, and it reveals the hidden constraints and compatibility conditions in order to prove the existence of a solution. The success of the Σ -method is indicated by the non-singularity of a certain Jacobian matrix. Although it is likely the Σ -method can be directly applied with success to many problems of practical interest, it can fail on some solvable IDAEs. Accordingly, we also present two techniques for addressing these failures.
Abstract The intent of this paper is to encourage improved numerical implementation of land models. Our contributions in this paper are two-fold. First, we present a unified framework to formulate and implement land model equations. We separate the representation of physical processes from their numerical solution, enabling the use of established robust numerical methods to solve the model equations. Second, we introduce a set of synthetic test cases (the laugh tests) to evaluate the numerical implementation of land models. The test cases include storage and transmission of water in soils, lateral sub-surface flow, coupled hydrological and thermodynamic processes in snow, and cryosuction processes in soil. We consider synthetic test cases as “laugh tests” for land models because they provide the most rudimentary test of model capabilities. The laugh tests presented in this paper are all solved with the Structure for Unifying Multiple Modeling Alternatives model (SUMMA) implemented using the SUite of Nonlinear and DIfferential/Algebraic equation Solvers (SUNDIALS). The numerical simulations from SUMMA/SUNDIALS are compared against (1) solutions to the synthetic test cases from other models documented in the peer-reviewed literature; (2) analytical solutions; and (3) observations made in laboratory experiments. In all cases, the numerical simulations are similar to the benchmarks, building confidence in the numerical model implementation. We posit that some land models may have difficulty in solving these benchmark problems. Dedicating more effort to solving synthetic test cases is critical in order to build confidence in the numerical implementation of land models.
Abstract Stations are an important source of meteorological data, but often suffer from missing values and short observation periods. Gap filling is widely used to generate serially complete datasets (SCDs), which are subsequently used to produce gridded meteorological estimates. However, the value of SCDs in spatial interpolation is scarcely studied. Based on our recent efforts to develop a SCD over North America (SCDNA), we explore the extent to which gap filling improves gridded precipitation and temperature estimates. We address two specific questions: (1) Can SCDNA improve the statistical accuracy of gridded estimates in North America? (2) Can SCDNA improve estimates of trends on gridded data? In addressing these questions, we also evaluate the extent to which results depend on the spatial density of the station network and the spatial interpolation methods used. Results show that the improvement in statistical interpolation due to gap filling is more obvious for precipitation, followed by minimum temperature and maximum temperature. The improvement is larger when the station network is sparse and when simpler interpolation methods are used. SCDs can also notably reduce the uncertainties in spatial interpolation. Our evaluation across North America from 1979 to 2018 demonstrates that SCDs improve the accuracy of interpolated estimates for most stations and days. SCDNA-based interpolation also obtains better trend estimation than observation-based interpolation. This occurs because stations used for interpolation could change during a specific period, causing changepoints in interpolated temperature estimates and affect the long-term trends of observation-based interpolation, which can be avoided using SCDNA. Overall, SCDs improve the performance of gridded precipitation and temperature estimates.
Abstract In this study, two sets of precipitation estimates based on the regional Weather Research and Forecasting model (WRF) –the high Asia refined analysis (HAR) and outputs with a 9 km resolution from WRF (WRF-9km) are evaluated at both basin and point scales, and their potential hydrological utilities are investigated by driving the Variable Infiltration Capacity (VIC) large-scale land surface hydrological model in seven Third Pole (TP) basins. The regional climate model (RCM) tends to overestimate the gauge-based estimates by 20–95% in annual means among the selected basins. Relative to the gauge observations, the RCM precipitation estimates can accurately detect daily precipitation events of varying intensities (with absolute bias < 3 mm). The WRF-9km exhibits a high potential for hydrological application in the monsoon-dominated basins in the southeastern TP (with NSE of 0.7–0.9 and bias of -11% to 3%), while the HAR performs well in the upper Indus (UI) and upper Brahmaputra (UB) basins (with NSE of 0.6 and bias of -15% to -9%). Both the RCM precipitation estimates can accurately capture the magnitudes of low and moderate daily streamflow, but show limited capabilities in flood prediction in most of the TP basins. This study provides a comprehensive evaluation of the strength and limitation of RCMs precipitation in hydrological modeling in the TP with complex terrains and sparse gauge observations.
Abstract Obtaining reliable water balance estimates remains a major challenge in Canada for large regions with scarce in situ measurements. Various remote sensing products can be used to complement observation-based datasets and provide an estimate of the water balance at river basin or regional scales. This study provides an assessment of the water balance using combinations of various remote sensing and data assimilation-based products and quantifies the non-closure errors for river basins across Canada, ranging from 90,900 to 1,679,100 km 2 , for the period from 2002 to 2015. A water balance equation combines the following to estimate the monthly water balance closure: multiple sources of data for each water budget component, including two precipitation products - the global product WATCH Forcing Data ERA-Interim (WFDEI), and the Canadian Precipitation Analysis (CaPA); two evapotranspiration products - MODIS, and Global Land-surface Evaporation: the Amsterdam Methodology (GLEAM); one source of water storage data - GRACE from three different centers; and observed discharge data from hydrometric stations (HYDAT). The non-closure error is attributed to the different data products using a constrained Kalman filter. Results show that the combination of CaPA, GLEAM, and the JPL mascon GRACE product tended to outperform other combinations across Canadian river basins. Overall, the error attributions of precipitation, evapotranspiration, water storage change, and runoff were 36.7, 33.2, 17.8, and 12.2 percent, which corresponded to 8.1, 7.9, 4.2, and 1.4 mm month -1 , respectively. In particular, non-closure error from precipitation dominated in Western Canada, whereas that from evapotranspiration contributed most in the Mackenzie River basin.
Plant transpiration is the dominant water flux in the global terrestrial water balance and a key process in the hydrological sciences. Stable isotopes have contributed greatly to this understanding...
Source water apportionment studies using the dual isotopes of oxygen and hydrogen have revolutionized our understanding of ecohydrology. But despite these developments—mostly over the past decade—many technical problems still exist in terms of linking xylem water to its soil water and groundwater sources. This is mainly due to sampling issues and possible fractionation of xylem water. Here we explore whether or not leaf water alone can be used to quantify the blend of rainfall event inputs from which the leaf water originates. Leaf water has historically been avoided in plant water uptake studies due to the extreme fractionation processes at the leaf surface. In our proof of concept work we embrace those processes and use the well-known Craig and Gordon model to map leaf water back to its individual precipitation event water sources. We also employ a Bayesian uncertainty estimation approach to quantify source apportionment uncertainties. We show this using a controlled, vegetated lysimeter experiment where we were able to use leaf water to correctly identify the mean seasonal rainfall that was taken up by the plant, with an uncertainty typically within ±1‰ for δ18O. While not appropriate for all source water studies, this work shows that leaf water isotope composition may provide a new, relatively un-intrusive method for addressing questions about the plant water source.
Closure of the soil water balance is fundamental to ecohydrology. But closing the soil water balance with hydrometric information offers no insight into the age distribution of water transiting the soil column via deep drainage or the combination of soil evaporation and transpiration. This is a major challenge in our discipline currently; tracing the water balance is the needed next step. Here we report results from a controlled tracer experiment aimed at both closing the soil water balance and tracing its individual components. This was carried out on a 2.5 m3 lysimeter planted with a willow tree. We applied 25 mm of isotopically enriched water on top of the lysimeter and tracked it for 43 days through the soil water, the bottom drainage, and the plant xylem. We then destructively sampled the system to quantify the remaining isotope mass. More than 900 water samples were collected for stable isotope analysis to trace the labeled irrigation. We then used these data to quantify when and where the labeled irrigation became the source of plant uptake or deep percolation. Evapotranspiration dominated the water balance outflow (88%). Tracing the transpiration flux showed further that transpiration was soil water that had fallen as precipitation 1–2 months prior. The tracer breakthrough in transpiration was complex and different from the breakthrough curves observed within the soil or in the bottom drainage. Given the lack of direct experimental data on travel time to transpiration, these results provide a first balance closure where all the relevant outflows are traced.
This commentary explores the challenges and opportunities associated with a possible transition of Water Resources Research to a publication model where all articles are freely available upon publication (“Gold” open access). It provides a review of the status of open access publishing models, a summary of community input, and a path forward for AGU leadership. The decision to convert to open access is framed by a mix of finances and values. On the one hand, the challenge is to define who pays, and how, and what we can do to improve the affordability of publishing. On the other hand, the challenge is to increase the extent to which science is open and accessible. The next steps for the community include an incisive analysis of the financial feasibility of different cost models, and weighing the financial burden for open access against the desire to further advance open science.
Projections of change in high-flow extremes with global warming vary widely among, and within, large midlatitude river basins. The spatial variability of these changes is attributable to multiple causes. One possible and little-studied cause of changes in high-flow extremes is a change in the synchrony of mainstem and tributary streamflow during high-flow extremes at the mainstem-tributary confluence. We examined reconstructed and simulated naturalized daily streamflow at confluences on the Columbia River in western North America, quantifying changes in synchrony in future streamflow projections and estimating the impact of these changes on high-flow extremes. In the Columbia River basin, projected flow regimes across colder tributaries initially diverge with warming as they respond to climate change at different rates, leading to a general decrease in synchrony, and lower high-flow extremes, relative to a scenario with no changes in synchrony. Where future warming is sufficiently large to cause most subbasins upstream from a confluence to transition toward a rain-dominated, warm regime, the decreasing trend in synchrony reverses itself. At one confluence with a major tributary (the Willamette River), where the mainstem and tributary flow regimes are initially very different, warming increases synchrony and, therefore, high-flow magnitudes. These results may be generalizable to the class of large rivers with large contributions to flood risk from the snow (i.e., cold) regime, but that also receive considerable discharge from tributaries that drain warmer basins.
Hydrometeorological flood generating processes (excess rain, short rain, long rain, snowmelt, and rain-on-snow) underpin our understanding of flood behavior. Knowledge about flood generating processes improves hydrological models, flood frequency analysis, estimation of climate change impact on floods, etc. Yet, not much is known about how climate and catchment attributes influence the spatial distribution of flood generating processes. This study aims to offer a comprehensive and structured approach to close this knowledge gap. We employ a large sample approach (671 catchments across the contiguous United States) and evaluate how catchment attributes and climate attributes influence the distribution of flood processes. We use two complementary approaches: A statistics-based approach which compares attribute frequency distributions of different flood processes; and a random forest model in combination with an interpretable machine learning approach (accumulated local effects [ALE]). The ALE method has not been used often in hydrology, and it overcomes a significant obstacle in many statistical methods, the confounding effect of correlated catchment attributes. As expected, we find climate attributes (fraction of snow, aridity, precipitation seasonality, and mean precipitation) to be most influential on flood process distribution. However, the influence of catchment attributes varies both with flood generating process and climate type. We also find flood processes can be predicted for ungauged catchments with relatively high accuracy (R2 between 0.45 and 0.9). The implication of these findings is flood processes should be considered for future climate change impact studies, as the effect of changes in climate on flood characteristics varies between flood processes.
Rationale Hydrogen and oxygen stable isotope ratios (δ2H, δ17O, and δ18O values) are commonly used tracers of water. These ratios can be measured by isotope ratio infrared spectroscopy (IRIS). However, IRIS approaches are prone to errors induced by organic compounds present in plant, soil, and natural water samples. A novel approach using 17O-excess values has shown promise for flagging spectrally contaminated plant samples during IRIS analysis. A systematic assessment of this flagging system is needed to prove it useful. Methods Errors induced by methanol and ethanol water mixtures on measured IRIS and isotope ratio mass spectrometry (IRMS) results were evaluated. For IRIS analyses both liquid- and vapour-mode (via direct vapour equilibration) methods are used. The δ2H, δ17O, and δ18O values were measured and compared with known reference values to determine the errors induced by methanol and ethanol contamination. In addition, the 17O-excess contamination detection approach was tested. This is a post-processing detection tool for both liquid and vapour IRIS triple-isotope analyses, utilizing calculated 17O-excess values to flag contaminated samples. Results Organic contamination induced significant errors in IRIS results, not seen in IRMS results. Methanol caused larger errors than ethanol. Results from vapour-IRIS analyses had larger errors than those from liquid-IRIS analyses. The 17O-excess approach identified methanol driven error in liquid- and vapour-mode IRIS samples at levels where isotope results became unacceptably erroneous. For ethanol contaminated samples, a mix of erroneous and correct flagging occurred with the 17O-excess method. Our results indicate that methanol is the more problematic contaminant for data corruption. The 17O-excess method was therefore useful for data quality control. Conclusions Organic contamination caused significant errors in IRIS stable isotope results. These errors were larger during vapour analyses than during liquid IRIS analyses, and larger for methanol than ethanol contamination. The 17O-excess method is highly sensitive for detecting narrowband (methanol) contamination error in vapour and liquid analysis modes in IRIS.
• Natural tracers reveal runoff sources in UK natural flood management catchment. • Water already stored in the catchments dominated runoff in high flow events. • Plantation forest cover reduced the fraction of rapid rainfall runoff. • Soils and geology dominated forest cover as control on rapid rainfall runoff fraction. • Differences in sources were greater between events than between catchments. United Kingdom (UK). Natural flood management (NFM) schemes are increasingly prominent in the UK. Studies of NFM have not yet used natural tracers at catchment scale to investigate how interventions influence partitioning during storms between surface rainfall runoff and water already stored in catchments. Here we investigate how catchment properties, particularly plantation forestry, influence surface storm rainfall runoff. We used hydrograph separation based on hydrogen and oxygen isotopes ( 2 H, 18 O) and acid neutralising capacity from high flow events to compare three headwater catchments (2.4-3.1 km 2 ) with differences in plantation forest cover ( Picea sitchensis: 94%, 41%, 1%) within a major UK NFM pilot, typical of the UK uplands. Plantation forest cover reduced the total storm rainfall runoff fraction during all events (by up to 11%) when comparing two paired catchments with similar soils, geology and topography but ∼50% difference in forest cover. However, comparison with the third catchment, with negligible forest cover but different characteristics, suggests that soils and geology were dominant controls on storm rainfall runoff fraction. Furthermore, differences between events were greater than differences between catchments. These findings suggest that while plantation forest cover may influence storm rainfall runoff fractions, it is not a dominant control in temperate upland UK catchments, especially for larger events. Soils and geology may exert greater influence, with implications for planning NFM.
• Streamflow was satisfactorily simulated by MESH model in Hudson Bay lowlands. • Higher precipitation and streamflow observed in the western watersheds in 1995–2008. • The wet period in 1995–2008 was due to a shift in regional atmospheric circulation. • PDO and EP-NP also influenced this wet period. • Dryer period but sustained streamflow in 2009–2019 due to permafrost thaw. Hudson Bay Lowlands watersheds, Ontario, Canada. The rivers in the Hudson Bay Lowlands are a major source of freshwater entering the Arctic Ocean and they also cause major floods. In recent decades, this region has been affected by major changes in hydroclimatic processes attributed to climate change and natural climate variability. In this study, we used ERA5 reanalysis data, hydrometric observations, and the hydrological model MESH, to investigate the impact of atmospheric circulation on the inter-decadal variability of streamflow between 1979 and 2018 in the Hudson Bay Lowlands. The natural climate variability was assessed using a weather regimes approach based on the discretization of daily geopotential height anomalies (Z500) from ERA5 reanalysis, as well as large scale oceanic and atmospheric variability modes. The results showed an anomalous convergence of atmospheric moisture flux between 1995–2008 that enhanced precipitation and increased streamflow in the western part of the region. This moisture convergence was likely driven by the combination of (i) low pressure anomalies in the East Coast of North America and (ii) low pressure anomalies in western regions of Canada, associated with the cold phase of the pacific decadal oscillation (PDO). Since 2009, streamflow remains high, likely due to more groundwater discharge associated with the degradation of permafrost.
• Stable isotope tracing of plant water use can illuminate plant water sources. • Xylem water isotope values showed strong sorting and niche segregation. • The majority of the observed species relied on 0.0–0.2 m depth soil water. • Tropical forest water uptake depth is largely driven by tree functional traits. Stable isotope tracing of plant water use can illuminate plant water sources. But to date, the number of species tested at any given site has been minimal. Here, we sample 46 tropical hardwood tree species in a 0.32 ha plot with uniform soils. Soil water was characterized at 6 depths at 0.2 m intervals down to 1 m and showed simple and predictable depth patterns of δ 2 H and δ 18 O, and simple and spatially uniform isotope composition at each depth. Nevertheless, tree xylem water δ 2 H and δ 18 O showed remarkable variation covering the full range of soil composition, suggesting strong sorting and niche segregation across the small plot. Wood density, tree size and mean basal area increment together explained approximately 55% of the variance of xylem water isotope composition through principal component analysis. A Bayesian mixing model was applied to the data and showed that sampled trees were either sourcing their water from very shallow or deep soil layers, with very little contribution from the middle portion of the soil profile. The majority of the observed species relied on 0.0–0.2 m depth soil water. This layer contributed approximately 75% of the xylem water which was significantly higher than the contributions from all other depths. The contribution from shallow soil was highest for trees with high wood density, slow-growing trees and small-sized trees. Our work suggests that stable isotope tracers may aid a better understanding of tropical forest water uptake depths and their relation to tree functional traits and potential hydrological niche segregation among co-occurring tropical species.
Studies of plant water sources generally assume that xylem water integrates the isotopic composition (δ 2 H and δ 18 O) of water sources and does not fractionate during uptake or transport along the transpiration pathway. However, woody xerophytes, halophytes, and trees in mesic environments can show isotopic fractionation from source waters. Isotopic fractionation and variation in isotope composition can affect the interpretation of tree water sources, but most studies to date have been greenhouse experiments. Here we present a field-based forensic analysis of xylem water isotope composition for 12 Eucalyptus tetrodonta and Corymbia nesophila trees . We used a 25-tonne excavator to access materials from the trees' maximum rooting depth of 3 m to their highest canopies at 38 m. Substantial within-tree variation occurred in δ 2 H (−91.1‰ to −35.7‰ E. tetrodonta ; −88.8‰ to −24.5‰ C. nesophila ) and δ 18 O (−12.3‰ to −5.0‰ E. tetrodonta ; −10.9‰ to −0.3‰ C. nesophila ), with different root-to-branch isotope patterns in each species. Soil water δ 2 H and δ 18 O dual isotope slopes (7.26 E. tetrodonta , 6.66 C. nesophila ) were closest to the Local Meteoric Water Line (8.4). The dual isotope slopes of the trees decreased progressively from roots (6.45 E. tetrodonta , 6.07 C. nesophila ), to stems (4.61 E. tetrodonta , 5.97 C. nesophila ) and branches (4.68 E. tetrodonta , 5.67 C. nesophila ), indicative of fractionation along the xylem stream. Roots of both species were more enriched in 2 H and 18 O than soil water at all sampled depths. Bayesian mixing model analysis showed that estimated proportions of water sourced from different depths reflected the contrasting root systems of these species. Our study adds evidence of isotopic fractionation from water uptake and along the transpiration stream in mature trees in monsoonal environments, affecting the interpretation of water sources. We discuss the findings with view of interpreting aboveground xylem water isotopic composition, incorporating knowledge of root systems. • Isotopic fractionation of xylem water may affect plant water source identification. • We analysed xylem δ 2 H and δ 18 O from roots to branches in mature trees in a savanna. • Fractionation increased from below- to aboveground xylem in the dual isotope space • Root structure assessment helped clarify aboveground interpretation of water use. • Future studies should consider xylem water fractionation and include plant traits.
Rivers are under enormous threat worldwide and large amounts of money are invested in river restoration. Contrary to the costs, the benefits of river restoration are much harder to quantify. In this study, the benefits of restoring different sections of the Yongding River in Beijing, China, are estimated through a discrete choice experiment (DCE). Place attachment is measured by sampling residents upstream and downstream and using the river sections as labelled alternatives in the DCE. As expected, the improvement of water quality is valued highly by all river basin residents, and place attachment and spatial preference heterogeneity play a significant role in public willingness to pay (WTP) for river restoration. Although respondents are willing to give up only a small share of their disposable income, public WTP for improved river water quality is a factor 2 to 4 higher than the current household water bill. These findings provide important guidance for the recovery of the investment costs associated with river restoration projects. • The benefits of urban river restoration in Beijing are estimated • Residents living up and downstream of the Yongding River are interviewed • Public WTP for water quality improvements is several times higher than the current water bill • Spatial preferences and place attachment play a key role in public preferences • The study provides important info to guide investment decisions in river restoration
Three decades of non-market water quality valuation (NMWQV) studies in Canada are analyzed to generate a generic benefits transfer function. Contrary to the large valuation literature focusing on water and wilderness-based recreation in Canada, the number of studies related to water quality is limited. NMWQV studies lack a common design, including consistent adherence to a Canada-specific water quality ladder (WQL). Despite the high degree of data heterogeneity, values extracted from the literature show an increasing step function when relating them to the Resources for the Future WQL. Meta-regression models (MRMs) explain a large share of the variation in value estimates based on the type of water resources, population and methodological characteristics. Baseline water quality and the size of the water quality change are significant determinants of the estimated non-market values. With a relative mean prediction error of no more than 20 percent, the predictive power of the estimated MRMs is high. As such, they are an important step forward in the development of a policy-relevant water quality valuation model. However, there is a clear need for the development of more coherent non-market valuation guidelines in the Canadian water context.
Extensive wetland drainage has occurred across the Canadian Prairies, and drainage activities are ongoing in many areas (Dahl 1990; Watmough and Schmoll 2007; Bartzen et al. 2010; Dahl 2014; Prairi...
Abstract Regulators require adequate information to select best practices with less ecosystem impacts for remediation of freshwater ecosystems after oil spills. Zooplankton are valuable indicators of aquatic ecosystem health as they play pivotal roles in biochemical cycles while stabilizing food webs. Compared with morphological identification, metabarcoding holds promise for cost-effective, high-throughput, and benchmarkable biomonitoring of zooplankton communities. The objective of this study was to apply DNA and RNA metabarcoding of zooplankton for ecotoxicological assessment and compare it with traditional morphological identification in experimental shoreline enclosures in a boreal lake. These identification methods were also applied in context of assessing response of the zooplankton community exposed to simulated spills of diluted bitumen (dilbit), with experimental remediation practices (enhanced monitored natural recovery and shoreline cleaner application). Metabarcoding detected boreal zooplankton taxa up to the genus level, with a total of 24 shared genera, and while metabarcoding-based relative abundance served as an acceptable proxy for biomass inferred by morphological identification (ρ ≥ 0.52). Morphological identification determined zooplankton community composition changes due to treatments at 11 days post-spill (PERMANOVA, p = 0.0143) while metabarcoding methods indicated changes in zooplankton richness and communities at 38 days post-spill (T-test, p
Due to commercial uses and environmental degradation of aryl phosphate esters, diphenyl phosphate (DPhP) is frequently detected in environmental matrices and is thus of growing concern worldwide. However, information on potential adverse effects of chronic exposure to DPhP at environmentally realistic concentrations was lacking. Here, we investigated the effects of life cycle exposure to DPhP on zebrafish at environmentally relevant concentrations of 0.8, 3.9, or 35.6 μg/L and employed a dual-omics approach (metabolomics and transcriptomics) to characterize potential modes of action. Exposure to DPhP at 35.6 μg/L for 120 days resulted in significant reductions in body mass and length of male zebrafish, but did not cause those same effects to females. Predominant toxicological mechanisms, including inhibition of oxidative phosphorylation, down-regulation of fatty acid oxidation, and up-regulation of phosphatidylcholine degradation, were revealed by integrated dual-omics analysis and successfully linked to adverse outcomes. Activity of succinate dehydrogenase and protein content of carnitine O-palmitoyltransferase 1 were significantly decreased in livers of male fish exposed to DPhP, which further confirmed the proposed toxicological mechanisms. This study is the first to demonstrate that chronic, low-level exposure to DPhP can retard growth via inhibiting energy output in male zebrafish.
Detection of SARS-CoV-2 RNA in wastewater is a promising tool for informing public health decisions during the COVID-19 pandemic. However, approaches for its analysis by use of reverse transcription quantitative polymerase chain reaction (RT-qPCR) are still far from standardized globally. To characterize inter- and intra-laboratory variability among results when using various methods deployed across Canada, aliquots from a real wastewater sample were spiked with surrogates of SARS-CoV-2 (gamma-radiation inactivated SARS-CoV-2 and human coronavirus strain 229E [HCoV-229E]) at low and high levels then provided "blind" to eight laboratories. Concentration estimates reported by individual laboratories were consistently within a 1.0-log10 range for aliquots of the same spiked condition. All laboratories distinguished between low- and high-spikes for both surrogates. As expected, greater variability was observed in the results amongst laboratories than within individual laboratories, but SARS-CoV-2 RNA concentration estimates for each spiked condition remained mostly within 1.0-log10 ranges. The no-spike wastewater aliquots provided yielded non-detects or trace levels (<20 gene copies/mL) of SARS-CoV-2 RNA. Detections appear linked to methods that included or focused on the solids fraction of the wastewater matrix and might represent in-situ SARS-CoV-2 to the wastewater sample. HCoV-229E RNA was not detected in the no-spike aliquots. Overall, all methods yielded comparable results at the conditions tested. Partitioning behavior of SARS-CoV-2 and spiked surrogates in wastewater should be considered to evaluate method effectiveness. A consistent method and laboratory to explore wastewater SARS-CoV-2 temporal trends for a given system, with appropriate quality control protocols and documented in adequate detail should succeed.
The microbiome has been described as an additional host “organ” with well-established beneficial roles. However, the effects of exposures to chemicals on both structure and function of the gut microbiome of fishes are understudied. To determine effects of benzo[ a ]pyrene (BaP), a model persistent organic pollutant, on structural shifts of gut microbiome in juvenile fathead minnows ( Pimephales promelas ), fish were exposed ad libitum in the diet to concentrations of 1, 10, 100, or 1000 μg BaP g −1 food, in addition to a vehicle control, for two weeks. To determine the link between exposure to BaP and changes in the microbial community, concentrations of metabolites of BaP were measured in fish bile and 16S rRNA amplicon sequencing was used to evaluate the microbiome. Exposure to BaP only reduced alpha-diversity at the greatest exposure concentrations. However, it did alter community composition assessed as differential abundance of taxa and reduced network complexity of the microbial community in all exposure groups. Results presented here illustrate that environmentally-relevant concentrations of BaP can alter the diversity of the gut microbiome and community network connectivity. Highlights • Dominant phyla of gut microbiome are consistent with those of other freshwater fishes. • BaP metabolites and exposure doses were consistent with those found in contaminated sites. • Dietary BaP exposure has significant, dose-dependent effects on the fish gut microbiome. • Dietary BaP exposure altered association networks of gut microbiome. Environmentally-relevant concentrations of BaP can alter the diversity of the gut microbiome and community network connectivity via dietary exposure route.
Like many amphibians, wood frog (Lithobates sylvaticus) populations have likely declined or experienced local extirpations as a result of habitat alterations. Despite this, wood frogs are still present and breeding in altered landscapes, like the agricultural Prairie Pothole Region of central Canada, and are exposed to a variety of anthropogenic impacts. As tadpoles, water contamination can have negative effects on growth, development, and immune systems. To investigate the potential effects of agricultural land use on tadpole growth and immune system stress, we used boosted regression trees to model body mass, body condition, and neutrophil to lymphocyte ratios, a measure of immune stress, against 32 variables including water quality, wetland habitat, and landscape-level measures. Developmental stage strongly influenced all 3 endpoints, and body mass was negatively influenced by higher levels of total dissolved solids (>600-700 mg/L) and at the first sign of pesticide detection (>0.01 proportion pesticides detected of those screened). While correlative, these data suggest that tadpoles developing in agricultural environments may experience survival and reproductive disadvantages if they metamorphose at smaller body sizes. Given the potential impacts this can have on adult frogs and frog populations, these results provide an impetus for further field-based investigation into the effects that pesticides, and especially total dissolved solids, may have on tadpoles. Environ Toxicol Chem 2021;40:2269-2281. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
Arctic warming associated with global climate change poses a significant threat to populations of wildlife in the Arctic. Since lipids play a vital role in adaptation of organisms to variations in temperature, high-resolution mass-spectrometry-based lipidomics can provide insights into adaptive responses of organisms to a warmer environment in the Arctic and help to illustrate potential novel roles of lipids in the process of thermal adaption. In this study, we studied an ecologically and economically important species-Arctic char (Salvelinus alpinus)-with a detailed multi-tissue analysis of the lipidome in response to chronic shifts in temperature using a validated lipidomics workflow. In addition, dynamic alterations in the hepatic lipidome during the time course of shifts in temperature were also characterized. Our results showed that early life stages of Arctic char were more susceptible to variations in temperature. One-year-old Arctic char responded to chronic increases in temperature with coordinated regulation of lipids, including headgroup-specific remodeling of acyl chains in glycerophospholipids (GP) and extensive alterations in composition of lipids in membranes, such as less lyso-GPs, and more ether-GPs and sphingomyelin. Glycerolipids (e.g., triacylglycerol, TG) also participated in adaptive responses of the lipidome of Arctic char. Eight-week-old Arctic char exhibited rapid adaptive alterations of the hepatic lipidome to stepwise decreases in temperature while showing blunted responses to gradual increases in temperature, implying an inability to adapt rapidly to warmer environments. Three common phosphatidylethanolamines (PEs) (PE 36:6|PE 16:1_20:5, PE 38:7|PE 16:1_22:6, and PE 40:7|PE 18:1_22:6) were finally identified as candidate lipid biomarkers for temperature shifts via machine learning approach. Overall, this work provides additional information to a better understanding of underlying regulatory mechanisms of the lipidome of Arctic organisms in the face of near-future warming.
High‐throughput pipelines supported by eDNA metabarcoding have been applied in various freshwater ecosystems. Both eDNA in ethanol (EtOH) samples (ES‐eDNA) and in water samples (WS‐eDNA) can provide comprehensive classification lists with good taxonomic resolution and coverage for determining freshwater biodiversity and biomonitoring. But, the advantages of ES‐eDNA metabarcoding over WS‐eDNA metabarcoding remain unclear for routine assessments of diversity of benthic macroinvertebrates in streams.
Chlorophyll-a concentration (chla) is a useful indicator of harmful algal blooms in early warning systems that use remote sensing data as input. However, its retrieval is challenging in small waterbodies due to the lack of high spatial-resolution water-color sensors and the substantial optical interference of other water constituents. Here, we demonstrate the potential of support vector machines and Sentinel-2 images to retrieve chla in a shallow eutrophic lake (Buffalo Pound Lake, Saskatchewan, Canada). Following validation against in-situ chla measurements over three open water seasons (2017–2019), our proposed method based on Support Vector Regression (SVR) outperforms the most common semi-empirical models, i.e., locally-tuned indices (normalized difference chlorophyll index, 2band, and 3band), as well as the state-of-the-art global empirical model (Mixture Density Network). The superiority of SVR is shown in terms of overall and stratified accuracy, as well as spatial validity. We argue that for small waterbodies where numerous matched pairs of in-situ chla and reflectance are not available, SVR might retrieve chla more accurately than locally-tuned indices and global empirical models.
Haig HA, Chegoonian AM, Davies J-M, Bateson D, Leavitt PR. 2021. Marked blue discoloration of late winter ice and water due to autumn blooms of cyanobacteria. Lake Reserv Manage. XX:XXX–XXX.Continu...
Molot LA, Schiff SL, Venkiteswaran JJ, Baulch HM, Higgins SN, Zastepa A, Verschoor MJ, Walters D. 2021. Low sediment redox promotes cyanobacteria blooms across a trophic range: implications for man...
Abstract Two small, oligotrophic lakes at the IISD-Experimental Lakes Area in northwestern Ontario, Canada were fertilized weekly with only phosphorus (P) in the summer and early fall of 2019. The P fertilization rates were high enough (13.3 µ g l −1 added weekly) to produce dense, month-long blooms of N 2 -fixing Dolichospermum species in both lakes within 9–12 weeks after fertilization began, turning them visibly green without the addition of nitrogen. P-only fertilization increased average seasonal chlorophyll a concentrations and cyanobacteria biomass well above the pre-fertilization levels of 2017 and 2018. Nitrogen (N) content in the epilimnion of thermally stratified Lake 304 and the water column of shallow Lake 303 doubled and P storage in the water column temporarily increased during the blooms. These whole-lake fertilization experiments demonstrate that large cyanobacteria blooms can develop rapidly under high P loading without anthropogenic N inputs, suggesting that aggressive N control programs are unlikely to prevent bloom formation and that P controls should remain the cornerstone for cyanobacteria management.
Phytoplankton blooms are a global water quality issue, and successful management depends on understanding their responses to multiple and interacting drivers, including nutrient loading and climate change. Here, we examine a long-term dataset from Lake 227, a site subject to a fertilization experiment (1969–present) with changing nitrogen:phosphorus (N:P) ratios. We applied a process-oriented model, MyLake, and updated the model structure with nutrient uptake kinetics that incorporated shifting N:P and competition among phytoplankton functional groups. We also tested different temperature and P-loading scenarios to examine the interacting effects of climate change and nutrient loading on phytoplankton blooms. The model successfully reproduced lake physics over 48 yr and the timing, overall magnitude, and shifting community structure (diazotrophs vs. non-diazotrophs) of phytoplankton blooms. Intra- and interannual variability was captured more accurately for the P-only fertilization period than for the high N:P and low N:P fertilization periods, highlighting the difficulty of modeling complex blooms even in well-studied systems. A model scenario was also run which removed climate-driven temperature trends, allowing us to disentangle concurrent drivers of blooms. Results showed that increases in water temperature in the spring led to earlier and larger phytoplankton blooms under climate change than under the effects of nutrient fertilization alone. These findings suggest that successful lake management efforts should incorporate the effects of climate change in addition to nutrient reductions, including intensifying and/or expanding monitoring periods and incorporating climate change into uncertainty estimates around future conditions.
Communities in Canada’s Northwest Territories (NWT) are at the forefront of the global climate emergency. Yet, they are not passive victims; local-level programs are being implemented across the region to maintain livelihoods and promote adaptation. At the same time, there is a recent call within global governance literature to pay attention to how global policy is implemented and affecting people on the ground. Thinking about these two processes, we ask the question: (how) can global governance assist northern Indigenous communities in Canada in reaching their goals of adapting their food systems to climate change? To answer this question, we argue for a “community needs” approach when engaging in global governance literature and practice, which puts community priorities and decision-making first. As part of a collaborative research partnership, we highlight the experiences of Ka’a’gee Tu First Nation, located in Kakisa, NWT, Canada. We include their successes of engaging in global network building and the systemic roadblock of lack of formal land tenure. Moreover, we analyze potential opportunities for this community to engage with global governance instruments and continue connecting to global networks that further their goals related to climate change adaptation and food sovereignty.
This paper reports the findings of an ethnographic study that involved working with local organizations, food advocates, and communities to develop strategies for expanding the nascent Northwest Territories (NWT), Canada agri-food industry. The NWT represents a unique case study in that the fledging agri-food industry has been recognized for its promise in contributing to the core goals of the transitioning NWT food system. The study is guided by two research questions: (1) How is the promise of the emerging NWT agri-food industry framed within the context of the broader food system? (2) Given this framing of the NWT agri-food industry, how can it contribute to the sustainability of the NWT food system and to the goals of food security, poverty reduction, nutrition, and economic development? Grounded in a food systems approach, we used a correlative, evolutionary SWOT analysis to profile the nascent NWT agri-food industry within the context of the existing NWT food system. Through further thematic analysis, we identify and describe two dominant narratives (agri-food industry business case narrative and agri-food industry implications narrative) and key themes within the narratives based on an adapted food systems framework. The agri-food business case narrative highlights discourse articulating the business or commercial viability for a local agri-food value chain to function, evolve, and expand. The agri-food industry implications narrative envisions the ways in which the emerging NWT agri-food industry may interact within the existing NWT food system, highlighting potential environmental, social, cultural, and political implications of an expanding commercial-based agri-food value chain. Within the two narratives, certain subcomponents of the NWT agri-food system appear to be more prevalent, including climate, soil, and ecosystems, policy/regulations/governance, socio-cultural norms, knowledge, inputs, finance, production, and consumption. We make policy and practice recommendations for co-designing an agri-food industry that serves the multiple goals of the NWT food system. As an exploratory, descriptive-structural analysis the study provides a critical empirical basis for future in-depth, fully integrated synthesis of the complex social, cultural, economic, political, and ecological dynamics shaping Northern food systems in transition.
Climate models are crucial for assessing climate variability and change. A reliable model for future climate should reasonably simulate the historical climate. Here, we assess the performance of CMIP6 models in reproducing statistical properties of observed annual maxima of daily precipitation. We go beyond the commonly used methods and assess CMIP6 simulations on three scales by performing: (a) univariate comparison based on L-moments and relative difference measures; (b) bivariate comparison using Kernel densities of mean and L-variation, and of L-skewness and L-kurtosis, and (c) comparison of the entire distribution function using the Generalized Extreme Value () distribution coupled with a novel application of the Anderson-Darling Goodness-of-fit test. The results reveal that the statistical shape properties (related to the frequency and magnitude of extremes) of CMIP6 simulations match well with the observational datasets. The simulated mean and variation differ among the models with 70% of simulations having a difference within 10% from the observations. Biases are observed in the bivariate investigation of mean and variation. Several models perform well with the HadGEM3-GC31-MM model performing well in all three scales when compared to the ground-based Global Precipitation Climatology Centre data. Finally, the study highlights biases of CMIP6 models in simulating extreme precipitation in the Arctic, Tropics, arid and semi-arid regions.
As droughts have widespread social and ecological impacts, it is critical to develop long-term adaptation and mitigation strategies to reduce drought vulnerability. Climate models are important in quantifying drought changes. Here, we assess the ability of 285 CMIP6 historical simulations, from 17 models, to reproduce drought duration and severity in three observational data sets using the Standardized Precipitation Index (SPI). We used summary statistics beyond the mean and standard deviation, and devised a novel probabilistic framework, based on the Hellinger distance, to quantify the difference between observed and simulated drought characteristics. Results show that many simulations have less than ±10% error in reproducing the observed drought summary statistics. The hypothesis that simulations and observations are described by the same distribution cannot be rejected for more than 80% of the grids based on our H distance framework. No single model stood out as demonstrating consistently better performance over large regions of the globe. The variance in drought statistics among the simulations is higher in the tropics compared to other latitudinal zones. Though the models capture the characteristics of dry spells well, there is considerable bias in low precipitation values. Good model performance in terms of SPI does not imply good performance in simulating low precipitation. Our study emphasizes the need to probabilistically evaluate climate model simulations in order to both pinpoint model weaknesses and identify a subset of best-performing models that are useful for impact assessments.
Abstract. Floods cause extensive damage, especially if they affect large regions. Assessments of current, local, and regional flood hazards and their future changes often involve the use of hydrologic models. A reliable hydrologic model ideally reproduces both local flood characteristics and spatial aspects of flooding under current and future climate conditions. However, uncertainties in simulated floods can be considerable and yield unreliable hazard and climate change impact assessments. This study evaluates the extent to which models calibrated according to standard model calibration metrics such as the widely used Kling–Gupta efficiency are able to capture flood spatial coherence and triggering mechanisms. To highlight challenges related to flood simulations, we investigate how flood timing, magnitude, and spatial variability are represented by an ensemble of hydrological models when calibrated on streamflow using the Kling–Gupta efficiency metric, an increasingly common metric of hydrologic model performance also in flood-related studies. Specifically, we compare how four well-known models (the Sacramento Soil Moisture Accounting model, SAC; the Hydrologiska Byråns Vattenbalansavdelning model, HBV; the variable infiltration capacity model, VIC; and the mesoscale hydrologic model, mHM) represent (1) flood characteristics and their spatial patterns and (2) how they translate changes in meteorologic variables that trigger floods into changes in flood magnitudes. Our results show that both the modeling of local and spatial flood characteristics are challenging as models underestimate flood magnitude, and flood timing is not necessarily well captured. They further show that changes in precipitation and temperature are not always well translated to changes in flood flow, which makes local and regional flood hazard assessments even more difficult for future conditions. From a large sample of catchments and with multiple models, we conclude that calibration on the integrated Kling–Gupta metric alone is likely to yield models that have limited reliability in flood hazard assessments, undermining their utility for regional and future change assessments. We underscore that such assessments can be improved by developing flood-focused, multi-objective, and spatial calibration metrics, by improving flood generating process representation through model structure comparisons and by considering uncertainty in precipitation input.
Predictions of floods, droughts, and fast drought‐flood transitions are required at different time scales to develop management strategies targeted at minimizing negative societal and economic impacts. Forecasts at daily and seasonal scale are vital for early warning, estimation of event frequency for hydraulic design, and long‐term projections for developing adaptation strategies to future conditions. All three types of predictions—forecasts, frequency estimates, and projections—typically treat droughts and floods independently, even though both types of extremes can be studied using related approaches and have similar challenges. In this review, we (a) identify challenges common to drought and flood prediction and their joint assessment and (b) discuss tractable approaches to tackle these challenges. We group challenges related to flood and drought prediction into four interrelated categories: data, process understanding, modeling and prediction, and human–water interactions. Data‐related challenges include data availability and event definition. Process‐related challenges include the multivariate and spatial characteristics of extremes, non‐stationarities, and future changes in extremes. Modeling challenges arise in frequency analysis, stochastic, hydrological, earth system, and hydraulic modeling. Challenges with respect to human–water interactions lie in establishing links to impacts, representing human–water interactions, and science communication. We discuss potential ways of tackling these challenges including exploiting new data sources, studying droughts and floods in a joint framework, studying societal influences and compounding drivers, developing continuous stochastic models or non‐stationary models, and obtaining stakeholder feedback. Tackling one or several of these challenges will improve flood and drought predictions and help to minimize the negative impacts of extreme events.
The goal of this commentary is to critically evaluate the use of popular performance metrics in hydrologic modeling. We focus on the Nash-Sutcliffe Efficiency (NSE) and the Kling-Gupta Efficiency (KGE) metrics, which are both widely used in hydrologic research and practice around the world. Our specific objectives are: (a) to provide tools that quantify the sampling uncertainty in popular performance metrics; (b) to quantify sampling uncertainty in popular performance metrics across a large sample of catchments; and (c) to prescribe the further research that is, needed to improve the estimation, interpretation, and use of popular performance metrics in hydrologic modeling. Our large-sample analysis demonstrates that there is substantial sampling uncertainty in the NSE and KGE estimators. This occurs because the probability distribution of squared errors between model simulations and observations has heavy tails, meaning that performance metrics can be heavily influenced by just a few data points. Our results highlight obvious (yet ignored) abuses of performance metrics that contaminate the conclusions of many hydrologic modeling studies: It is essential to quantify the sampling uncertainty in performance metrics when justifying the use of a model for a specific purpose and when comparing the performance of competing models.
Ice-jam floods pose a serious threat to many riverside communities in cold regions. Ice-jam-related flooding can cause loss of human life, millions of dollars in property damage, and adverse impacts on ecology. An effective flood management strategy is necessary to reduce the overall risk in flood-prone areas. Most of these strategies require a detailed risk-based management study to assess their effectiveness in reducing flood risk. Zoning regulation is a sustainable measure to reduce overall flood risk for a flood-prone area. Zoning regulation is a specified area in a floodplain where certain restrictions apply to different land uses (e.g., development or business). A stochastic framework was introduced to evaluate the effectiveness of a potential zoning regulation. A stochastic framework encompasses the impacts of all the possible expected floods instead of a more traditional approach where a single design flood is incorporated. The downtown area of Fort McMurray along the Athabasca River was selected to explore the impact of zoning regulation on reducing expected annual damages (EAD) from ice-jam flooding. The results show that a hypothetical zoning regulation for a certain area in the town of Fort McMurray (TFM) can be effective in substantially reducing the level of EAD. A global sensitivity analysis was also applied to understand the impacts of model inputs on ice-jam flood risk using a regional sensitivity method. The results show that model boundary conditions such as river discharge, the inflowing volume of ice and ice-jam toe locations are highly sensitive to ice-jam flood risk.
River ice is an important hydraulic and hydrological component of many rivers in the high northern latitudes of the world. It controls the hydraulic characteristics of streamflow, affects the geomorphology of channels, and can cause flooding due to ice-jam formation during ice-cover freeze-up and breakup periods. In recent decades, climate change has considerably altered ice regimes, affecting the severity of ice-jam flooding. Although many approaches have been developed to model river ice regimes and the severity of ice-jam flooding, appropriate methods that account for the impacts of future climate on ice-jam flooding have not been well established. Therefore, the main goals of this study are to review current knowledge regarding climate change impacts on river ice processes and to assess current modelling capabilities to determine the severity of ice jams under future climatic conditions. Finally, a conceptual river ice-jam modelling approach is presented for incorporating climate change impacts on ice jams.
• Most conceptual bucket models have an upper limit on simulated soil moisture deficit. • Problems arise when the bucket “empties” because ET drops to unrealistic (low) levels. • Alternatives include bottomless buckets or deficit-based soil moisture accounting. • Here, we switch to a deficit-based scheme while keeping everything else constant. • Tested over historic drought, model performance and realism are enhanced. Rainfall-runoff models based on conceptual “buckets” are frequently used in climate change impact studies to provide runoff projections. When these buckets approach empty, the simulated evapotranspiration approaches zero, which places an implicit limit on the soil moisture deficit that can accrue within the model. Such models may cease to properly track the moisture deficit accumulating in reality as dry conditions continue, leading to overestimation of subsequent runoff and possible long-term bias under drying climate. Here, we suggest that model realism may be improved through alternatives which remove the upper limit on simulated soil moisture deficit, such as “bottomless” buckets or deficit-based soil moisture accounting. While some existing models incorporate such measures, no study until now has systematically assessed their impact on model realism under drying climate. Here, we alter a common bucket model by changing the soil moisture storage to a deficit accounting system in such a way as to remove the upper limit on simulated soil moisture deficit. Tested on 38 Australian catchments, the altered model is better able to track the decline in soil moisture at the end of seasonal dry periods, which leads to superior performance over varied historic climate, including the 13-year “Millennium” drought. However, groundwater and GRACE data reveal long-term trends that are not matched in simulations, indicating that further changes may be required. Nonetheless, the results suggest that a broader adoption of bottomless buckets and/or deficit accounting within conceptual rainfall runoff models may improve the realism of runoff projections under drying climate.
Models that mimic an original model might have a different model structure than the original model, that affects model output. This study assesses model structure differences and their impact on output by comparing 7 model implementations that carry the name HBV. We explain and quantify output differences with individual model structure components at both the numerical (e.g., explicit/implicit scheme) and mathematical level (e.g., lineair/power outflow). It was found that none of the numerical and mathematical formulations of the mimicking models were (originally) the same as the benchmark, HBV-light. This led to small but distinct output differences in simulated streamflow for different numerical implementations (KGE difference up to 0.15), and major output differences due to mathematical differences (KGE median loss of 0.27). These differences decreased after calibrating the individual models to the simulated streamflow of the benchmark model. We argue that the lack of systematic model naming has led to a diverging concept of the HBV-model, diminishing the concept of model mimicry. Development of a systematic model naming framework, open accessible model code and more elaborate model descriptions are suggested to enhance model mimicry and model development.
Descriptions of runoff generation processes continue to grow, helping to reveal complexities and hydrologic behavior across a wide range of environments and scales. But to date, there has been little grouping of these process facts. Here, we discuss how the “fill‐and‐spill” concept can provide a framework to group event‐based runoff generation processes. The fill‐and‐spill concept describes where vertical and lateral additions of water to a landscape unit are placed into storage (the fill)—and only when this storage reaches a critical level (the spill), and other storages are filled and become connected, does a previously infeasible (but subsequently important) outflow pathway become activated. We show that fill‐and‐spill can be observed at a range of scales and propose that future fieldwork should first define the scale of interest and then evaluate what is filling‐and‐spilling at that scale. Such an approach may be helpful for those instrumenting and modeling new hillslopes or catchments because it provides a structured way to develop perceptual models for runoff generation and to group behaviors at different sites and scales.
Abstract. Hydrological models are usually systems of nonlinear differential equations for which no analytical solutions exist and thus rely on approximate numerical solutions. While some studies have investigated the relationship between numerical method choice and model error, the extent to which extreme precipitation like that observed during hurricanes Harvey and Katrina impacts numerical error of hydrological models is still unknown. This knowledge is relevant in light of climate change, where many regions will likely experience more intense precipitation events. In this experiment, a large number of hydrographs is generated with the modular modeling framework FUSE, using eight numerical techniques across a variety of forcing datasets. Multiple model structures, parameter sets, and initial conditions are incorporated for generality. The computational expense and numerical error associated with each hydrograph were recorded. It was found that numerical error (root mean square error) usually increases with precipitation intensity and decreases with event duration. Some numerical methods constrain errors much more effectively than others, sometimes by many orders of magnitude. Of the tested numerical methods, a second-order adaptive explicit method is found to be the most efficient because it has both low numerical error and low computational cost. A basic literature review indicates that many popular modeling codes use numerical techniques that were suggested by this experiment to be sub-optimal. We conclude that relatively large numerical errors might be common in current models, and because these will likely become larger as the climate changes, we advocate for the use of low cost, low error numerical methods.
Cold regions provide water resources for half the global population yet face rapid change. Their hydrology is dominated by snow, ice and frozen soils, and climate warming is having profound effects. Hydrological models have a key role in predicting changing water resources, but are challenged in cold regions. Ground-based data to quantify meteorological forcing and constrain model parameterization are limited, while hydrological processes are complex, often controlled by phase change energetics. River flows are impacted by poorly quantified human activities. This paper reports scientific developments over the past decade of MESH, the Canadian community hydrological land surface scheme. New cold region process representation includes improved blowing snow transport and sublimation, lateral land-surface flow, prairie pothole storage dynamics, frozen ground infiltration and thermodynamics, and improved glacier modelling. New algorithms to represent water management include multi-stage reservoir operation. Parameterization has been supported by field observations and remotely sensed data; new methods for parameter identification have been used to evaluate model uncertainty and support regionalization. Additionally, MESH has been linked to broader decision-support frameworks, including river ice simulation and hydrological forecasting. The paper also reports various applications to the Saskatchewan and Mackenzie River basins in western Canada (0.4 and 1.8 million km). These basins arise in glaciated mountain headwaters, are partly underlain by permafrost, and include remote and incompletely understood forested, wetland, agricultural and tundra ecoregions. This imposes extraordinary challenges to prediction, including the need to overcoming biases in forcing data sets, which can have disproportionate effects on the simulated hydrology.
Abstract. Snowpack microstructure controls the transfer of heat to, and the temperature of, the underlying soils. In situ measurements of snow and soil properties from four field campaigns during two different winters (March and November 2018, January and March 2019) were compared to an ensemble of CLM5.0 (Community Land Model) simulations, at Trail Valley Creek, Northwest Territories, Canada. Snow MicroPenetrometer profiles allowed snowpack density and thermal conductivity to be derived at higher vertical resolution (1.25 mm) and a larger sample size (n = 1050) compared to traditional snowpit observations (3 cm vertical resolution; n = 115). Comparing measurements with simulations shows CLM overestimated snow thermal conductivity by a factor of 3, leading to a cold bias in wintertime soil temperatures (RMSE = 5.8 °C). Bias-correction of the simulated thermal conductivity (relative to field measurements) improved simulated soil temperatures (RMSE = 2.1 °C). Multiple linear regression shows the required correction factor is strongly related to snow depth (R2 = 0.77, RMSE = 0.066) particularly early in the winter. Furthermore, CLM simulations did not adequately represent the observed high proportions of depth hoar. Addressing uncertainty in simulated snow properties and the corresponding heat flux is important, as wintertime soil temperatures act as a control on subnivean soil respiration, and hence impact Arctic winter carbon fluxes and budgets.
Abstract. Methane emissions from boreal and arctic wetlands, lakes, and rivers are expected to increase in response to warming and associated permafrost thaw. However, the lack of appropriate land cover datasets for scaling field-measured methane emissions to circumpolar scales has contributed to a large uncertainty for our understanding of present-day and future methane emissions. Here we present the Boreal-Arctic Wetland and Lake Dataset (BAWLD), a land cover dataset based on an expert assessment, extrapolated using random forest modelling from available spatial datasets of climate, topography, soils, permafrost conditions, vegetation, wetlands, and surface water extents and dynamics. In BAWLD, we estimate the fractional coverage of five wetland, seven lake, and three river classes within 0.5 × 0.5° grid cells that cover the northern boreal and tundra biomes (17 % of the global land surface). Land cover classes were defined using criteria that ensured distinct methane emissions among classes, as indicated by a co-developed comprehensive dataset of methane flux observations. In BAWLD, wetlands occupied 3.2 × 106 km2 (14 % of domain) with a 95 % confidence interval between 2.8 and 3.8 × 106 km2. Bog, fen, and permafrost bog were the most abundant wetland classes, covering ~28 % each of the total wetland area, while the highest methane emitting marsh and tundra wetland classes occupied 5 and 12 %, respectively. Lakes, defined to include all lentic open-water ecosystems regardless of size, covered 1.4 × 106 km2 (6 % of domain). Low methane-emitting large lakes (> 10 km2) and glacial lakes jointly represented 78 % of the total lake area, while high-emitting peatland and yedoma lakes covered 18 and 4 %, respectively. Small (< 0.1 km2) glacial, peatland, and yedoma lakes combined covered 17 % of the total lake area, but contributed disproportionally to the overall spatial uncertainty of lake area with a 95 % confidence interval between 0.15 and 0.38 × 106 km2. Rivers and streams were estimated to cover 0.12 × 106 km2 (0.5 % of domain) of which 8 % was associated with high-methane emitting headwaters that drain organic-rich landscapes. Distinct combinations of spatially co-occurring wetland and lake classes were identified across the BAWLD domain, allowing for the mapping of “wetscapes” that will have characteristic methane emission magnitudes and sensitivities to climate change at regional scales. With BAWLD, we provide a dataset which avoids double-accounting of wetland, lake and river extents, and which includes confidence intervals for each land cover class. As such, BAWLD will be suitable for many hydrological and biogeochemical modelling and upscaling efforts for the northern Boreal and Arctic region, in particular those aimed at improving assessments of current and future methane emissions. Data is freely available at https://doi.org/10.18739/A2C824F9X (Olefeldt et al., 2021).
Abstract. Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 million square km of Earth's surface (31 % of the land area) each year, and is thus an important expression of and driver of the Earth’s climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (~ −13 %/decade) as Arctic summer sea ice. More than one-sixth of the world’s population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth’s cold regions' ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of snow stored on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations will not be able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high socio-economic value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-Band Synthetic Aperture Radar (SAR) for global monitoring of SWE. We describe radar interactions with snow-covered landscapes, characterization of snowpack properties using radar measurements, and refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimetre-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modelling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, densities, and layering. We describe radar interactions with snow-covered landscapes, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and applications communities on progress made in recent decades, and sets the stage for a new era in SWE remote-sensing from SAR measurements.
High-latitude cold regions are warming more than twice as fast as the rest of the planet, with the greatest warming occurring during the winter. Warmer winters are associated with shorter periods of snow cover, resulting in more frequent and extensive soil freezing and thawing. Freeze-thaw cycles influence soil chemical, biological, and physical properties and any changes to winter soil processes may impact carbon and nutrients export from affected soils, possibly altering soil health and nearby water quality. These impacts are relevant for agricultural soils and practices in cold regions as they are critical in governing water flows and quality within agroecosystems. In this study, a soil column experiment was conducted to assess the leaching of nutrients from fertilized agricultural soil during the non-growing season. Four soil columns were exposed to a non-growing season temperature and precipitation model and fertilizer amendments were made to two of the columns to determine the efficacy of fall-applied fertilizers and compared to other two unfertilized control columns. Leachates from the soil columns were collected and analyzed for cations and anions. The experiment results showed that a transition from a freeze period to a thaw period resulted in significant loss of chloride (Cl-), sulfate (SO42-) and nitrate (NO3-). Even with low NO3- concentrations in the applied artificial rainwater and fertilizer, high NO3- concentrations (~150 mg l-1) were observed in fertilized column leachates. Simple plug flow reactor model results indicate the high NO3- leachates are found to be due to active nitrification occurring in the upper oxidized portion of the soil columns mimicking overwinter NO3- losses via nitrification in agricultural fields. The low NO3- leachates in unfertilized columns suggest that freeze-thaw cycling had little effect on N mineralization in soil. Findings from this study will ultimately be used to bolster winter soil biogeochemical models by elucidating nutrient fluxes over changing winter conditions to refine best management practices for fertilizer application.
Recent breakthroughs in artificial intelligence (AI), and particularly in deep learning (DL), have created tremendous excitement and opportunities in the earth and environmental sciences communities. To leverage these new ‘data-driven’ technologies, however, one needs to understand the fundamental concepts that give rise to DL and how they differ from ‘process-based’, mechanistic modelling. This paper revisits those fundamentals and addresses 10 questions often posed by earth and environmental scientists with the aid of a real-world modelling experiment. The overarching objective is to contribute to a future of AI-assisted earth and environmental sciences where DL models can (1) embrace the typically ignored knowledge base available, (2) function credibly in ‘true’ out-of-sample prediction, and (3) handle non-stationarity in earth and environmental systems. Comparing and contrasting earth and environmental problems with prominent AI applications, such as playing chess and trading in stock markets, provides critical insights for better directing future research in this field.
Abstract. Increasing hydrologic variability, accelerating population growth, and resurgence of water resources development projects have all indicated increasing tensions among the riparian countries of transboundary rivers. This article aims to review the existing knowledge on conflict and cooperation in transboundary rivers from a multidisciplinary perspective and propose a socio-hydrological framework that integrates the slow and less visible societal processes with existing hydrological-economic models, revealing the hidden feedbacks between changes in societal processes and hydrological changes. This framework contributes to understanding the mechanism that drives conflict and cooperation in transboundary river management.
Stream thermal regimes are critical to the stability of freshwater habitats. There is growing concern that climate change will result in stream warming due to rising air temperatures, decreased shading in forested areas due to wildfires, and changes in streamflow. Groundwater plays an important role in controlling stream temperatures in mountain headwaters, where it makes up a considerable portion of discharge. This study investigated the controls on the thermal regime of a headwater stream, and the surrounding groundwater processes, in a catchment on the eastern slopes of the Canadian Rocky Mountains. Groundwater discharge to the headwater spring is partially sourced by a seasonal lake. Spring, stream, and lake temperature, water level, discharge and chemistry data were used to build a conceptual model of the system. Meteorological data was used to set up a stream temperature model. A tracer test was carried out to estimate hyporheic exchange along the study reach. This study presents a unique example of an indirectly lake-headed stream i.e., where the interaction of groundwater and lake water, and the hydraulic gradient determine the resulting stream temperature. Energy balance of the stream is mainly controlled by radiation. Sensible and latent heat fluxes play a secondary role, but their effects generally cancel out. Hyporheic exchange is present but plays only a minor role in the energy balance. During snowfall events, the latent heat associated with melting of direct snowfall onto the water surface was responsible for rapid stream cooling. An increase in advective inputs from groundwater and hillslope pathways did not result in observed cooling of stream water during rainfall events. The results from this study will assist water resource and fisheries managers in adapting to stream temperature changes under a warming climate.
Abstract The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has claimed millions of lives to date. Antigenic drift has resulted in viral variants with putatively greater transmissibility, virulence, or both. Early and near real-time detection of these variants of concern (VOC) and the ability to accurately follow their incidence and prevalence in communities is wanting. Wastewater-based epidemiology (WBE), which uses nucleic acid amplification tests to detect viral fragments, is a faithful proxy of COVID-19 incidence and prevalence, and thus offers the potential to monitor VOC viral load in a given population. Here, we describe and validate a primer extension PCR strategy targeting a signature mutation in the N gene of SARS-CoV-2. This allows quantification of the proportional expression of B.1.1.7 versus non-B.1.1.7 alleles in wastewater without the need to employ quantitative RT-PCR standard curves. We show that the wastewater B.1.1.7 profile correlates with its clinical counterpart and benefits from a near real-time and facile data collection and reporting pipeline. This assay can be quickly implemented within a current SARS-CoV-2 WBE framework with minimal cost; allowing early and contemporaneous estimates of B.1.1.7 community transmission prior to, or in lieu of, clinical screening and identification. Our study demonstrates that this strategy can provide public health units with an additional and much needed tool to rapidly triangulate VOC incidence/prevalence with high sensitivity and lineage specificity.
Abstract. According to the living data process in ESSD, this publication presents extensions of a comprehensive hydrometeorological and glaciological data set for several research sites in the Rofental (1891–3772 m a.s.l., Ötztal Alps, Austria). Whereas the original dataset has been published in a first original version in 2018 (https://doi.org/10.5194/essd-10-151-2018), the new time series presented here originate from meteorological and snow-hydrological recordings that have been collected from 2017 to 2020. Some data sets represent continuations of time series at existing locations, others come from new installations complementing the scientific monitoring infrastructure in the research catchment. Main extensions are a fully equipped automatic weather and snow monitoring station, as well as extensive additional installations to enable continuous observation of snow cover properties. Installed at three high Alpine locations in the catchment, these include automatic measurements of snow depth, snow water equivalent, volumetric solid and liquid water content, snow density, layered snow temperature profiles, and snow surface temperature. One station is extended by a particular arrangement of two snow depth and water equivalent recording devices to observe and quantify wind-driven snow redistribution. They are installed at nearby wind-exposed and sheltered locations and are complemented by an acoustic-based snow drift sensor. The data sets represent a unique time series of high-altitude mountain snow and meteorology observations. We present three years of data for temperature, precipitation, humidity, wind speed, and radiation fluxes from three meteorological stations. The continuous snow measurements are explored by combined analyses of meteorological and snow data to show typical seasonal snow cover characteristics. The potential of the snow drift observations are demonstrated with examples of measured wind speeds, snow drift rates and redistributed snow amounts in December 2019 when a tragic avalanche accident occurred in the vicinity of the station. All new data sets are provided to the scientific community according to the Creative Commons Attribution License by means of the PANGAEA repository (https://www.pangaea.de/?q=%40ref104365).
Abstract. Climate warming may cause mountain snowpacks to melt earlier, reducing summer streamflow and threatening water supplies and ecosystems. Few observations allow separating rain and snowmelt contributions to streamflow, so physically based models are needed for hydrological predictions and analyses. We develop an observational technique for detecting streamflow responses to snowmelt using incoming solar radiation and diel (daily) cycles of streamflow. We measure the 20th percentile of snowmelt days (DOS20), across 31 watersheds in the western US, as a proxy for the beginning of snowmelt-initiated streamflow. Historic DOS20 varies from mid-January to late May, with warmer sites having earlier and more intermittent snowmelt-mediated streamflow. Mean annual DOS20 strongly correlates with the dates of 25 % and 50 % annual streamflow volume (DOQ25 and DOQ50, both R2 = 0.85), suggesting that a one-day earlier DOS20 corresponds with a one-day earlier DOQ25 and 0.7-day earlier DOQ50. Empirical projections of future DOS20 (RCP8.5, late 21st century), using space-for-time substitution, show that DOS20 will occur 11 ± 4 days earlier per 1 °C of warming, and that colder places (mean November–February air temperature, TNDJF <−8 °C) are 70 % more sensitive to climate change on average than warmer places (TNDJF > 0 °C). Moreover, empirical space-for-time based projections of DOQ25 and DOQ50 are about four and two times more sensitive to earlier streamflow than those from NoahMP-WRF. Given the importance of changing streamflow timing for headwater resources, snowmelt detection methods such as DOS20 based on diel streamflow cycles may constrain hydrological models and improve hydrological predictions.
Abstract. The Arctic is warming at two to three times the rate of the global average, significantly impacting snow accumulation and melt. Unfortunately, conventional methods to measure snow water equivalent (SWE), a key aspect of the Arctic snow cover, have numerous limitations that hinder our ability to document annual cycles, the impact of climate change, or to test predictive models. As a result, there is an urgent need for improved methods that measure Arctic SWE; allow for continuous, unmanned measurements over the entire winter; and allow measurements that are representative of spatially variable, Arctic snow covers. In-situ, or invasive, cosmic ray neutron sensors (CRNSs) may fill this observational gap, but few studies have tested these types of sensors or considered their applicability at remote sites in the Arctic. During the winters of 2016/17 and 2017/18 we tested an in-situ CRNS system at two locations in Canada; a cold, low- to high-SWE environment in the Canadian Arctic and at a warm, low-SWE landscape in Southern Ontario that allowed easier access for validation purposes. CRNS moderated neutron counts were compared to manual snow survey SWE values obtained during both winter seasons. Pearson correlation coefficients ranged from −0.89 to −0.98, while regression analyses provided R2 values from 0.79 to 0.96. RMSE of the CRNS-measured SWE averaged 2 mm at the southern Ontario site and ranged from 28 to 40 mm at the Arctic site. These data show that in-situ CRNS instruments are able to continuously measure SWE with sufficient accuracy, and have important applications for measuring SWE in a variety of environments, including remote Arctic locations. These sensors can provide important SWE data for testing snow and hydrological models, water resource management applications, and the validation of remote-sensing applications.
Abstract Ice jam floods (IJF) are a major concern for many riverine communities, government and non-government authorities and companies in the higher latitudes of the northern hemisphere. Ice jam related flooding can result in millions of dollars of property damages, loss of human life and adverse impacts on ecology. Ice jam flood forecasting is challenging as its formation mechanism is chaotic and depends on numerous unpredictable hydraulic and river ice factors. In this study, Modélisation environnementale communautaire – surface hydrology (MESH), a semi-distributed physically-based land-surface hydrological modelling system was used to acquire a 10-day flow forecast, an important boundary condition for any modelling of river ice-jam flood forecasting. A stochastic modelling approach was then applied to simulate hundreds of possible ice-jam scenarios using the hydrodynamic river ice model RIVICE within a Monte-Carlo Analysis (MOCA) framework for the Saint John River from Fort Kent to Grand Falls. First, a 10-day outlook was simulated to provide insight on the severity of ice jam flooding during spring breakup. Then, 3-day forecasts were modelled to provide longitudinal profiles of exceedance probabilities of ice jam flood staging along the river during the ice-cover breakup. Overall, results show that the stochastic approach performed well to estimate maximum probable ice-jam backwater level elevations for the spring 2021 breakup season.
Abstract Changes in land surface albedo and land surface evaporation modulate the atmospheric energy budget by changing temperatures, water vapor, clouds, snow and ice cover, and the partitioning of surface energy fluxes. Here idealized perturbations to land surface properties are imposed in a global model to understand how such forcings drive shifts in zonal mean atmospheric energy transport and zonal mean tropical precipitation. For a uniform decrease in global land albedo, the albedo forcing and a positive water vapor feedback contribute roughly equally to increased energy absorption at the top of the atmosphere (TOA), while radiative changes due to the temperature and cloud cover response provide a negative feedback and energy loss at TOA. Decreasing land albedo causes a northwards shift in the zonal mean intertropical convergence zone (ITCZ). The combined effects on ITCZ location of all atmospheric feedbacks roughly cancel for the albedo forcing; the total ITCZ shift is comparable to that predicted for the albedo forcing alone. For an imposed increase in evaporative resistance that reduces land evaporation, low cloud cover decreases in the northern mid-latitudes and more energy is absorbed at TOA there; longwave loss due to warming provides a negative feedback on the TOA energy balance and ITCZ shift. Imposed changes in land albedo and evaporative resistance modulate fundamentally different aspects of the surface energy budget. However, the pattern of TOA radiation changes due to the water vapor and air temperature responses are highly correlated for these two forcings because both forcings lead to near-surface warming.
Abstract Motivated by the hemispheric asymmetry of land distribution on Earth, we explore the climate of Northland, a highly idealized planet with a Northern Hemisphere continent and a Southern Hemisphere ocean. The climate of Northland can be separated into four distinct regions: the Southern Hemisphere ocean, the seasonally wet tropics, the midlatitude desert, and the Great Northern Swamp. We evaluate how modifying land surface properties on Northland drives changes in temperatures, precipitation patterns, the global energy budget, and atmospheric dynamics. We observe a surprising response to changes in land surface evaporation, where suppressing terrestrial evaporation in Northland cools both land and ocean. In previous studies, suppressing terrestrial evaporation has been found to lead to local warming by reducing latent cooling of the land surface. However, reduced evaporation can also decrease atmospheric water vapor, reducing the strength of the greenhouse effect and leading to large-scale cooling. We use a set of idealized climate model simulations to show that suppressing terrestrial evaporation over Northern Hemisphere continents of varying size can lead to either warming or cooling of the land surface, depending on which of these competing effects dominates. We find that a combination of total land area and contiguous continent size controls the balance between local warming from reduced latent heat flux and large-scale cooling from reduced atmospheric water vapor. Finally, we demonstrate how terrestrial heat capacity, albedo, and evaporation all modulate the location of the ITCZ both over the continent and over the ocean.
Drylines are atmospheric boundaries separating dry from moist air that can initiate convection. Potential changes in the location, frequency, and characteristics of drylines in future climates are unknown. This study applies a multi-parametric algorithm to objectively identify and characterize the dryline in North America using convection-permitting regional climate model simulations with 4-km horizontal grid spacing for 13-years under a historical and a pseudo-global warming climate projection by the end of the century. The dryline identification is successfully achieved with a set of standardized algorithm parameters across the lee side of the Rocky Mountains from the Canadian Rockies to the Sierra Madres in Mexico. The dryline is present 27% of the days at 00 UTC between April and September in the current climate, with a mean humidity gradient magnitude of 0.16 g−1 kg−1 km−1. The seasonal cycle of drylines peak around April and May in the southern Plains, and in June and July in the northern Plains. In the future climate, the magnitude and frequency of drylines increase 5% and 13%, correspondingly, with a stronger intensification southward. Future drylines strengthen during their peak intensity in the afternoon in the Southern U.S. and Northeast Mexico. Drylines also show increasing intensities in the morning with future magnitudes that are comparable to peak intensities found in the afternoon in the historical climate. Furthermore, an extension of the seasonality of intense drylines could produce end-of-summer drylines that are as strong as mid-summer drylines in the current climate. This might affect the seasonality and the diurnal cycle of convective activity in future climates, challenging weather forecasting and agricultural planning.
A vector‐river network explicitly uses realistic geometries of river reaches and catchments for spatial discretization in a river model. This enables improving the accuracy of the physical properties of the modeled river system, compared to a gridded river network that has been used in Earth System Models. With a finer‐scale river network, resolving smaller‐scale river reaches, there is a need for efficient methods to route streamflow and its constituents throughout the river network. The purpose of this study is twofold: (1) develop a new method to decompose river networks into hydrologically independent tributary domains, where routing computations can be performed in parallel; and (2) perform global river routing simulations with two global river networks, with different scales, to examine the computational efficiency and the differences in discharge simulations at various temporal scales. The new parallelization method uses a hierarchical decomposition strategy, where each decomposed tributary is further decomposed into many sub‐tributary domains, enabling hybrid parallel computing. This parallelization scheme has excellent computational scaling for the global domain where it is straightforward to distribute computations across many independent river basins. However, parallel computing for a single large basin remains challenging. The global routing experiments show that the scale of the vector‐river network has less impact on the discharge simulations than the runoff input that is generated by the combination of land surface model and meteorological forcing. The scale of vector‐river networks needs to consider the scale of local hydrologic features such as lakes that are to be resolved in the network.
Abstract. This study employs a stochastic hydrologic modeling framework to evaluate the sensitivity of flood frequency analyses to different components of the hydrologic modeling chain. The major components of the stochastic hydrologic modeling chain, including model structure, model parameter estimation, initial conditions, and precipitation inputs were examined across return periods from 2 to 100 000 years at two watersheds representing different hydroclimates across the western USA. A total of 10 hydrologic model structures were configured, calibrated, and run within the Framework for Understanding Structural Errors (FUSE) modular modeling framework for each of the two watersheds. Model parameters and initial conditions were derived from long-term calibrated simulations using a 100 member historical meteorology ensemble. A stochastic event-based hydrologic modeling workflow was developed using the calibrated models in which millions of flood event simulations were performed for each basin. The analysis of variance method was then used to quantify the relative contributions of model structure, model parameters, initial conditions, and precipitation inputs to flood magnitudes for different return periods. Results demonstrate that different components of the modeling chain have different sensitivities for different return periods. Precipitation inputs contribute most to the variance of rare floods, while initial conditions are most influential for more frequent events. However, the hydrological model structure and structure–parameter interactions together play an equally important role in specific cases, depending on the basin characteristics and type of flood metric of interest. This study highlights the importance of critically assessing model underpinnings, understanding flood generation processes, and selecting appropriate hydrological models that are consistent with our understanding of flood generation processes.
Abstract. Hydrological models are usually systems of nonlinear differential equations for which no analytical solutions exist and thus rely on numerical solutions. While some studies have investigated the relationship between numerical method choice and model error, the extent to which extreme precipitation such as that observed during hurricanes Harvey and Katrina impacts numerical error of hydrological models is still unknown. This knowledge is relevant in light of climate change, where many regions will likely experience more intense precipitation. In this experiment, a large number of hydrographs are generated with the modular modeling framework FUSE (Framework for Understanding Structural Errors), using eight numerical techniques across a variety of forcing data sets. All constructed models are conceptual and lumped. Multiple model structures, parameter sets, and initial conditions are incorporated for generality. The computational cost and numerical error associated with each hydrograph were recorded. Numerical error is assessed via root mean square error and normalized root mean square error. It was found that the root mean square error usually increases with precipitation intensity and decreases with event duration. Some numerical methods constrain errors much more effectively than others, sometimes by many orders of magnitude. Of the tested numerical methods, a second-order adaptive explicit method is found to be the most efficient because it has both a small numerical error and a low computational cost. A small literature review indicates that many popular modeling codes use numerical techniques that were suggested by this experiment to be suboptimal. We conclude that relatively large numerical errors may be common in current models, highlighting the need for robust numerical techniques, in particular in the face of increasing precipitation extremes.
ABSTRACT One of the most prominent sources of error and uncertainty in water quality modelling results is the input data. In this study, data from three meteorological databases were used to test the performance of a water temperature model of Lake Diefenbaker: the data from Environment and Climate Change Canada (ECCC) had long-term quality control history (>20 years); the data from the AccuWeather had short-term quality control history (<10 years), and the data from the MeteoBlue database were modelled values. The CE-QUAL-W2 hydrodynamic and water quality model was used for this study. The model was calibrated by adjusting model coefficients controlling the amounts of measured solar radiation and wind that reach the surface of the water. The sensitivity results showed very similar performances, with slightly better performances (root mean square root difference of ± 0.1) with the ECCC data followed by the MeteoBlue data and thereafter by the AccuWeather data.
Global sensitivity analysis (GSA) has long been recognized as an indispensable tool for model analysis. GSA has been extensively used for model simplification, identifiability analysis, and diagnostic tests. Nevertheless, computationally efficient methodologies are needed for GSA, not only to reduce the computational overhead, but also to improve the quality and robustness of the results. This is especially the case for process-based hydrologic models, as their simulation time typically exceeds the computational resources available for a comprehensive GSA. To overcome this computational barrier, we propose a data-driven method called VISCOUS, variance-based sensitivity analysis using copulas. VISCOUS uses Gaussian mixture copulas to approximate the joint probability density function of a given set of input-output pairs for estimating the variance-based sensitivity indices. Our method identifies dominant hydrologic factors by recycling existing input-output data, and thus can deal with arbitrary sample sets drawn from the input-output space. We used two hydrologic models of increasing complexity (HBV and VIC) to assess the performance of VISCOUS. Our results confirm that VISCOUS and the conventional variance-based method can detect similar important and unimportant factors. Furthermore, the VISCOUS method can substantially reduce the computational cost required for sensitivity analysis. Our proposed method is particularly useful for process-based models with many uncertain parameters, large domain size, and high spatial and temporal resolution.
Numerous wetlands in the prairies of Canada provide important ecosystem services, yet are threatened by climate and land-use changes. Understanding the impacts of climate change on prairie wetlands is critical to effective conservation planning. In this study, we construct a wetland model with surface water balance and ecoregions to project future distribution of wetlands. The climatic conditions downscaled from the Weather Research and Forecasting model were used to drive the Noah-MP land surface model to obtain surface water balance. The climate change perturbation is derived from an ensemble of general circulation models using the pseudo global warming method, under the RCP8.5 emission scenario by the end of 21st century. The results show that climate change impacts on wetland extent are spatiotemporally heterogenous. Future wetter climate in the western Prairies will favor increased wetland abundance in both spring and summer. In the eastern Prairies, particularly in the mixed grassland and mid-boreal upland, wetland areas will increase in spring but experience enhanced declines in summer due to strong evapotranspiration. When these effects of climate change are considered in light of historical drainage, they suggest a need for diverse conservation and restoration strategies. For the mixed grassland in the western Canadian Prairies, wetland restoration will be favorable, while the highly drained eastern Prairies will be challenged by the intensified hydrological cycle. The outcomes of this study will be useful to conservation agencies to ensure that current investments will continue to provide good conservation returns in the future.
Abstract Meteorological data from ground stations suffer from temporal discontinuities caused by missing values and short measurement periods. Gap-filling and reconstruction techniques have proven to be effective in producing serially complete station datasets (SCDs) that are used for a myriad of meteorological applications (e.g., developing gridded meteorological datasets and validating models). To our knowledge, all SCDs are developed at regional scales. In this study, we developed the serially complete Earth (SC-Earth) dataset, which provides daily precipitation, mean temperature, temperature range, dewpoint temperature, and wind speed data from 1950 to 2019. SC-Earth utilizes raw station data from the Global Historical Climatology Network–Daily (GHCN-D) and the Global Surface Summary of the Day (GSOD). A unified station repository is generated based on GHCN-D and GSOD after station merging and strict quality control. ERA5 is optimally matched with station data considering the time shift issue and then used to assist the global gap filling. SC-Earth is generated by merging estimates from 15 strategies based on quantile mapping, spatial interpolation, machine learning, and multistrategy merging. The final estimates are bias corrected using a combination of quantile mapping and quantile delta mapping. Comprehensive validation demonstrates that SC-Earth has high accuracy around the globe, with degraded quality in the tropics and oceanic islands due to sparse station networks, strong spatial precipitation gradients, and degraded ERA5 estimates. Meanwhile, SC-Earth inherits potential limitations such as inhomogeneity and precipitation undercatch from raw station data, which may affect its application in some cases. Overall, the high-quality and high-density SC-Earth dataset will benefit research in fields of hydrology, ecology, meteorology, and climate. The dataset is available at https://zenodo.org/record/4762586 .
Abstract. Probabilistic methods are useful to estimate the uncertainty in spatial meteorological fields (e.g., the uncertainty in spatial patterns of precipitation and temperature across large domains). In ensemble probabilistic methods, “equally plausible” ensemble members are used to approximate the probability distribution, hence the uncertainty, of a spatially distributed meteorological variable conditioned to the available information. The ensemble members can be used to evaluate the impact of uncertainties in spatial meteorological fields for a myriad of applications. This study develops the Ensemble Meteorological Dataset for North America (EMDNA). EMDNA has 100 ensemble members with daily precipitation amount, mean daily temperature, and daily temperature range at 0.1∘ spatial resolution (approx. 10 km grids) from 1979 to 2018, derived from a fusion of station observations and reanalysis model outputs. The station data used in EMDNA are from a serially complete dataset for North America (SCDNA) that fills gaps in precipitation and temperature measurements using multiple strategies. Outputs from three reanalysis products are regridded, corrected, and merged using Bayesian model averaging. Optimal interpolation (OI) is used to merge station- and reanalysis-based estimates. EMDNA estimates are generated using spatiotemporally correlated random fields to sample from the OI estimates. Evaluation results show that (1) the merged reanalysis estimates outperform raw reanalysis estimates, particularly in high latitudes and mountainous regions; (2) the OI estimates are more accurate than the reanalysis and station-based regression estimates, with the most notable improvements for precipitation evident in sparsely gauged regions; and (3) EMDNA estimates exhibit good performance according to the diagrams and metrics used for probabilistic evaluation. We discuss the limitations of the current framework and highlight that further research is needed to improve ensemble meteorological datasets. Overall, EMDNA is expected to be useful for hydrological and meteorological applications in North America. The entire dataset and a teaser dataset (a small subset of EMDNA for easy download and preview) are available at https://doi.org/10.20383/101.0275 (Tang et al., 2020a).

DOI bib
FLUXNET-CH<sub>4</sub>: a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
Kyle Delwiche, Sara Knox, Avni Malhotra, Etienne Fluet‐Chouinard, Gavin McNicol, Sarah Féron, Zutao Ouyang, Dario Papale, Carlo Trotta, Eleonora Canfora, You Wei Cheah, Danielle Christianson, Ma. Carmelita R. Alberto, Pavel Alekseychik, Mika Aurela, Dennis Baldocchi, Sheel Bansal, David P. Billesbach, Gil Bohrer, Rosvel Bracho, Nina Buchmann, David I. Campbell, Gerardo Celis, Jiquan Chen, Weinan Chen, Housen Chu, Higo J. Dalmagro, Sigrid Dengel, Ankur R. Desai, Matteo Detto, Han Dolman, Elke Eichelmann, Eugénie Euskirchen, D. Famulari, Kathrin Fuchs, Mathias Goeckede, Sébastien Gogo, Mangaliso J. Gondwe, Jordan P. Goodrich, Pia Gottschalk, Scott L. Graham, Martin Heimann, Manuel Helbig, Carole Helfter, Kyle S. Hemes, Takashi Hirano, David Y. Hollinger, Lukas Hörtnagl, Hiroyasu Iwata, Adrien Jacotot, Gerald Jurasinski, Minseok Kang, Kuno Kasak, John S. King, Janina Klatt, Franziska Koebsch, Ken W. Krauss, Derrick Y.F. Lai, Annalea Lohila, Ivan Mammarella, Luca Belelli Marchesini, Giovanni Manca, Jaclyn Hatala Matthes, Trofim C. Maximov, Lutz Merbold, Bhaskar Mitra, Timothy H. Morin, Eiko Nemitz, Mats Nilsson, Shuli Niu, Walter C. Oechel, Patricia Y. Oikawa, Kaori Ono, Matthias Peichl, Olli Peltola, Michele L. Reba, Andrew D. Richardson, William J. Riley, Benjamin R. K. Runkle, Youngryel Ryu, Torsten Sachs, Ayaka Sakabe, Camilo Rey‐Sánchez, Edward A. G. Schuur, Karina V. R. Schäfer, Oliver Sonnentag, Jed P. Sparks, Ellen Stuart-Haëntjens, Cove Sturtevant, Ryan C. Sullivan, Daphne Szutu, Jonathan E. Thom, M. S. Torn, Eeva‐Stiina Tuittila, J. Turner, Masahito Ueyama, Alex Valach, Rodrigo Vargas, Andrej Varlagin, Alma Vázquez‐Lule, Joseph Verfaillie, Timo Vesala, George L. Vourlitis, Eric J. Ward, Christian Wille, Georg Wohlfahrt, Guan Xhuan Wong, Zhen Zhang, Donatella Zona, Lisamarie Windham‐Myers, Benjamin Poulter, Robert B. Jackson
Earth System Science Data, Volume 13, Issue 7

Abstract. Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20∘ S to 20∘ N) the spring onset of elevated CH4 emissions starts 3 d earlier, and the CH4 emission season lasts 4 d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.
Reservoir expansion over the last century has largely affected downstream flow characteristics. Yet very little is known about the impacts of reservoir expansion on the climate. Here, we implement reservoir construction in the Community Land Model by enabling dynamical lake area changes, while conserving mass and energy. Transient global lake and reservoir extent are prescribed from the HydroLAKES and Global Reservoir and Dam databases. Land-only simulations covering the 20th century with reservoir expansion enabled, highlight increases in terrestrial water storage and decreases in albedo matching the increase in open water area. The comparison of coupled simulations including and excluding reservoirs shows only limited influence of reservoirs on global temperatures and the surface energy balance, but demonstrates substantial responses locally, in particular where reservoirs make up a large fraction of the grid cell. In those locations, reservoirs dampen the diurnal temperature range by up to −1.5 K (for reservoirs covering >15% of the grid cell), reduce temperature extremes, and moderate the seasonal temperature cycle. This study provides a first step towards a coupled representation of reservoirs in Earth System Models.
Abstract. In situ measurements of water equivalent of snow cover (SWE) – the vertical depth of water that would be obtained if all the snow cover melted completely – are used in many applications including water management, flood forecasting, climate monitoring, and evaluation of hydrological and land surface models. The Canadian historical SWE dataset (CanSWE) combines manual and automated pan-Canadian SWE observations collected by national, provincial and territorial agencies as well as hydropower companies. Snow depth (SD) and bulk snow density (defined as the ratio of SWE to SD) are also included when available. This new dataset supersedes the previous Canadian Historical Snow Survey (CHSSD) dataset published by Brown et al. (2019), and this paper describes the efforts made to correct metadata, remove duplicate observations and quality control records. The CanSWE dataset was compiled from 15 different sources and includes SWE information for all provinces and territories that measure SWE. Data were updated to July 2020, and new historical data from the Government of Northwest Territories, Government of Newfoundland and Labrador, Saskatchewan Water Security Agency, and Hydro-Québec were included. CanSWE includes over 1 million SWE measurements from 2607 different locations across Canada over the period 1928–2020. It is publicly available at https://doi.org/10.5281/zenodo.4734371 (Vionnet et al., 2021).
Abstract Gridded precipitation datasets are used in many applications such as the analysis of climate variability/change and hydrological modelling. Regridding precipitation datasets is common for model coupling (e.g., coupling atmospheric and hydrological models) or comparing different models and datasets. However, regridding can considerably alter precipitation statistics. In this global analysis, the effects of regridding a precipitation dataset are emphasized using three regridding methods (first order conservative, bilinear, and distance weighted averaging). The differences between the original and regridded dataset are substantial and greatest at high quantiles. Differences of 46 mm and 0.13 mm are noted in high (0.95) and low (0.05) quantiles respectively. The impacts of regridding vary spatially for land and oceanic regions; there are substantial differences at high quantiles in tropical land regions, and at low quantiles in polar regions. These impacts are approximately the same for different regridding methods. The differences increase with the size of the grid at higher quantiles and vice versa for low quantiles. As the grid resolution increases, the difference between original and regridded data declines, yet the shift size dominates for high quantiles for which the differences are higher. Whilst regridding is often necessary to use gridded precipitation datasets, it should be used with great caution for fine resolutions (e.g., daily and sub-daily), as it can severely alter the statistical properties of precipitation, specifically at high and low quantiles.
• Application of popular catchment nutrient models is problematic in cold regions. • New nutrient modules have been developed for the Cold Regions Hydrological Model. • The model was applied to a sub-basin of the increasingly eutrophic Lake Winnipeg, Canada. • Simulated SWE, discharge, NO3, NH4, SRP and partP were compared against observations. • Typical ∼9 day-freshet accounted for 16–31% of the total annual nutrient load. Excess nutrients in aquatic ecosystems is a major water quality problem globally. Worsening eutrophication issues are notable in cold temperate areas, with pervasive problems in many agriculturally dominated catchments. Predicting nutrient export to rivers and lakes is particularly difficult in cold agricultural environments because of challenges in modelling snow, soil, frozen ground, climate, and anthropogenic controls. Previous research has shown that the use of many popular small basin nutrient models can be problematic in cold regions due to poor representation of cold region hydrology. In this study, the Cold Regions Hydrological Modelling Platform (CRHM), a modular modelling system, which has been widely deployed across Canada and cold regions worldwide, was used to address this problem. CRHM was extended to simulate biogeochemical and transport processes for nitrogen and phosphorus through a complex of new process-based modules that represent physicochemical processes in snow, soil and freshwater. Agricultural practices such as tillage and fertilizer application, which strongly impact the availability and release of soil nutrients, can be explicitly represented in the model. A test case in an agricultural basin draining towards Lake Winnipeg shows that the model can capture the extreme hydrology and nutrient load variability of small agricultural basins at hourly time steps. It was demonstrated that fine temporal resolutions are an essential modelling requisite to capture strong concentration changes in agricultural tributaries in cold agricultural environments. Within these ephemeral and intermittent streams, on average, 30%, 31%, 20%, and 16% of the total annual load of nitrate (NO 3 ), ammonium (NH 4 ), soluble reactive phosphorus (SRP), and particulate phosphorous (partP)NO 3 , NH 4 , SRP and partP occurred during the episodic snowmelt freshet ( ∼ 9 days, accounting for 21% of the annual flow), but shows extreme temporal variation. The new nutrient modules are critical tools for predicting nutrient export from small agricultural drainage basins in cold climates via better representation of key hydrological processes, and a temporal resolution more suited to capture dynamics of ephemeral and intermittent streams.
• A methodological framework to combine multiple precipitation products is proposed. • Hybrid datasets based on hydrological evaluation improve hydrological modelling. • Considering seasonal characteristics of the river basin enhance model performance. Hydrologic-Land Surface Models (H-LSMs) are subject to input uncertainties arising from climate forcing data, especially precipitation. For better streamflow simulations and predictions, the generation of a hybrid dataset by combining existing precipitation products has attracted considerable interest in recent years. To assess the accuracy of the hybrid dataset, in-situ precipitation-gauge stations are used as a reference point. However, the robustness of the hybrid dataset in representing spatial details can be problematic when the evaluation uses only a sparse network of in-situ observations at regional or basin scales. This study aims to develop a methodological framework to generate hybrid precipitation datasets based on the model performance of streamflow simulations that are spatially representative across large river basins. The framework is illustrated using a Canadian H-LSM known as MESH (Modélisation Environmentale communautaire – Surface Hydrology) in the Saskatchewan River basin, Canada, for the period 2002–2010. Five regional and global precipitation products (Global Meteorological Forcing Dataset at Princeton University (Princeton); the WATCH Forcing Data methodology applied to the ERA-Interim (WFDEI) augmented by Climatic Research Unit (WFDEI [CRU]) and Global Precipitation Climatology Centre (WFDEI [GPCC]); North American Regional Reanalysis (NARR); and Canadian Precipitation Analysis (CaPA)) were included as candidates in this study. Results indicate that the generation of a hybrid dataset based on hydrological evaluation was useful for improving H-LSM modelling skills. Hybrid datasets showed a similar or better model performance compared to that of the best basin-wide precipitation product in the headwaters and gradually performed better downstream and at the basin outlet. When multiple products are combined model performance can be further enhanced by considering seasonality with respect to the hydrological regime of the river basin. This study demonstrates the usefulness of hybrid datasets in a large-scale river basin with low climate station network density.
Tree rings provide an invaluable long-term record for understanding how climate and other drivers shape tree growth and forest productivity. However, conventional tree-ring analysis methods were not designed to simultaneously test effects of climate, tree size, and other drivers on individual growth. This has limited the potential to test ecologically relevant hypotheses on tree growth sensitivity to environmental drivers and their interactions with tree size. Here, we develop and apply a new method to simultaneously model nonlinear effects of primary climate drivers, reconstructed tree diameter at breast height (DBH), and calendar year in generalized least squares models that account for the temporal autocorrelation inherent to each individual tree's growth. We analyze data from 3811 trees representing 40 species at 10 globally distributed sites, showing that precipitation, temperature, DBH, and calendar year have additively, and often interactively, influenced annual growth over the past 120 years. Growth responses were predominantly positive to precipitation (usually over ≥3-month seasonal windows) and negative to temperature (usually maximum temperature, over ≤3-month seasonal windows), with concave-down responses in 63% of relationships. Climate sensitivity commonly varied with DBH (45% of cases tested), with larger trees usually more sensitive. Trends in ring width at small DBH were linked to the light environment under which trees established, but basal area or biomass increments consistently reached maxima at intermediate DBH. Accounting for climate and DBH, growth rate declined over time for 92% of species in secondary or disturbed stands, whereas growth trends were mixed in older forests. These trends were largely attributable to stand dynamics as cohorts and stands age, which remain challenging to disentangle from global change drivers. By providing a parsimonious approach for characterizing multiple interacting drivers of tree growth, our method reveals a more complete picture of the factors influencing growth than has previously been possible.
Dissolved organic matter (DOM) represents a mixture of organic molecules that vary due to different source materials and degree of processing. Characterizing how DOM composition evolves along the aquatic continuum can be difficult. Using a size‐exclusion chromatography technique (liquid chromatography‐organic carbon detection [LC‐OCD]), we assessed the variability in DOM composition from both surface and groundwaters across a number of Canadian ecozones (mean annual temperature spanning −10°C to +6°C). A wide range in DOM concentration was found from 0.2 to 120 mg C L−1. Proportions of different size‐based groupings across ecozones were variable, yet similarities between specific waterbody types, regardless of location, suggest commonality in the processes dictating DOM composition. A principal component analysis identified 70% of the variation in LC‐OCD derived DOM compositions could be explained by the waterbody type. We find that DOM composition within a specific waterbody type is similar regardless of the differences in climate or surrounding vegetation where the sample originated from.
Intensifying wildfire activity and climate change can drive rapid forest compositional shifts. In boreal North America, black spruce shapes forest flammability and depends on fire for regeneration. This relationship has helped black spruce maintain its dominance through much of the Holocene. However, with climate change and more frequent and severe fires, shifts away from black spruce dominance to broadleaf or pine species are emerging, with implications for ecosystem functions including carbon sequestration, water and energy fluxes, and wildlife habitat. Here, we predict that such reductions in black spruce after fire may already be widespread given current trends in climate and fire. To test this, we synthesize data from 1,538 field sites across boreal North America to evaluate compositional changes in tree species following 58 recent fires (1989 to 2014). While black spruce was resilient following most fires (62%), loss of resilience was common, and spruce regeneration failed completely in 18% of 1,140 black spruce sites. In contrast, postfire regeneration never failed in forests dominated by jack pine, which also possesses an aerial seed bank, or broad-leaved trees. More complete combustion of the soil organic layer, which often occurs in better-drained landscape positions and in dryer duff, promoted compositional changes throughout boreal North America. Forests in western North America, however, were more vulnerable to change due to greater long-term climate moisture deficits. While we find considerable remaining resilience in black spruce forests, predicted increases in climate moisture deficits and fire activity will erode this resilience, pushing the system toward a tipping point that has not been crossed in several thousand years.
Climate warming is driving tundra shrub expansion with implications for ecosystem function and regional climate. Understanding associations between shrub ecophysiological function, distribution and environment is necessary for predicting consequences of expansion. We evaluated the role of topographic gradients on upland shrub productivity to understand potential constraints on shrub expansion. At a low arctic tundra site near Inuvik, Northwest Territories, Canada, we measured sap flow, stem water potential and productivity-related functional traits in green alder, and environmental predictors (water and nutrient availability and seasonal thaw depth) across a toposequence in alder patches. Seasonal thaw reduced stem sap flow whereas topographic position predicted stem water potential and productivity-related functional traits. Upslope shrubs were more water-limited than those downslope. Shrubs in drainage channels had traits associated with greater productivity than those on the tops of slopes. The effect of thaw depth on sap flow has implications for seasonal water-use patterns and warming impacts on tundra ecohydrology. Topographic variation in functional traits corresponds with observed spatial patterns of tundra shrub expansion along floodplains and concave hillslopes rather than in upland areas. Green alder is expanding rapidly across the low arctic tundra in northwestern North America; thus, anticipating the implications of its expansion is essential for predicting tundra function.

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Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions
Kuang‐Yu Chang, William J. Riley, Sara Knox, Robert B. Jackson, Gavin McNicol, Benjamin Poulter, Mika Aurela, Dennis Baldocchi, Sheel Bansal, Gil Bohrer, David I. Campbell, Alessandro Cescatti, Housen Chu, Kyle Delwiche, Ankur R. Desai, Eugénie Euskirchen, Thomas Friborg, Mathias Goeckede, Manuel Helbig, Kyle S. Hemes, Takashi Hirano, Hiroyasu Iwata, Minseok Kang, Trevor F. Keenan, Ken W. Krauss, Annalea Lohila, Ivan Mammarella, Bhaskar Mitra, Akira Miyata, Mats Nilsson, Asko Noormets, Walter C. Oechel, Dario Papale, Matthias Peichl, Michele L. Reba, Janne Rinne, Benjamin R. K. Runkle, Youngryel Ryu, Torsten Sachs, Karina V. R. Schäfer, Hans Peter Schmid, Narasinha Shurpali, Oliver Sonnentag, Angela C. I. Tang, M. S. Torn, Carlo Trotta, Eeva‐Stiina Tuittila, Masahito Ueyama, Rodrigo Vargas, Timo Vesala, Lisamarie Windham‐Myers, Zhen Zhang, Donatella Zona
Nature Communications, Volume 12, Issue 1

Abstract Wetland methane (CH 4 ) emissions ( $${F}_{{{CH}}_{4}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>F</mml:mi> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>C</mml:mi> <mml:mi>H</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:msub> </mml:math> ) are important in global carbon budgets and climate change assessments. Currently, $${F}_{{{CH}}_{4}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>F</mml:mi> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>C</mml:mi> <mml:mi>H</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:msub> </mml:math> projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent $${F}_{{{CH}}_{4}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>F</mml:mi> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>C</mml:mi> <mml:mi>H</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:msub> </mml:math> temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that $${F}_{{{CH}}_{4}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>F</mml:mi> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>C</mml:mi> <mml:mi>H</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:msub> </mml:math> are often controlled by factors beyond temperature. Here, we evaluate the relationship between $${F}_{{{CH}}_{4}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>F</mml:mi> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>C</mml:mi> <mml:mi>H</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:msub> </mml:math> and temperature using observations from the FLUXNET-CH 4 database. Measurements collected across the globe show substantial seasonal hysteresis between $${F}_{{{CH}}_{4}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>F</mml:mi> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>C</mml:mi> <mml:mi>H</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:msub> </mml:math> and temperature, suggesting larger $${F}_{{{CH}}_{4}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>F</mml:mi> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>C</mml:mi> <mml:mi>H</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:msub> </mml:math> sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH 4 production are thus needed to improve global CH 4 budget assessments.
In the discontinuous permafrost zone of the Northwest Territories (NWT), Canada, snow covers the ground surface for half the year. Snowmelt constitutes a primary source of moisture supply for the short growing season and strongly influences stream hydrographs. Permafrost thaw has changed the landscape by increasing the proportional coverage of permafrost-free wetlands at the expense of permafrost-cored peat plateau forests. The biophysical characteristics of each feature affect snow water equivalent (SWE) accumulation and melt rates. In headwater streams in the southern Dehcho region of the NWT, snowmelt runoff has significantly increased over the past 50 years, despite no significant change in annual SWE. At the Fort Simpson A climate station, we found that SWE measurements made by Environment and Climate Change Canada using a Nipher precipitation gauge were more accurate than the Adjusted and Homogenized Canadian Climate Dataset which was derived from snow depth measurements. Here, we: (a) provide 13 years of snow survey data to demonstrate differences in end-of-season SWE between wetlands and plateau forests; (b) provide ablation stake and radiation measurements to document differences in snow melt patterns among wetlands, plateau forests, and upland forests; and (c) evaluate the potential impact of permafrost-thaw induced wetland expansion on SWE accumulation, melt, and runoff. We found that plateaus retain significantly (p < 0.01) more SWE than wetlands. However, the differences are too small (123 mm and 111 mm, respectively) to cause any substantial change in basin SWE. During the snowmelt period in 2015, wetlands were the first feature to become snow-free in mid-April, followed by plateau forests (7 days after wetlands) and upland forests (18 days after wetlands). A transition to a higher percentage cover of wetlands may lead to more rapid snowmelt and provide a more hydrologically-connected landscape, a plausible mechanism driving the observed increase in spring freshet runoff.
Climate change is predicted to have dramatic effects on Arctic freshwater ecosystems through changes to the abiotic template that are expected to influence biodiversity. Changes are already ongoing in Arctic systems, but there is a lack of coordinated monitoring of Arctic freshwaters that hinders our ability to assess changes in biodiversity. To address the need for coordinated monitoring on a circumpolar scale, the Arctic Council working group, Conservation of Arctic Flora and Fauna, established the Circumpolar Biodiversity Monitoring Program, which is an adaptive monitoring program for the Arctic centred around four ecosystem themes (i.e., Freshwater, Terrestrial, Coastal, Marine). The freshwater theme developed a monitoring plan for Arctic freshwater biodiversity and recently completed the first assessment of status and trends in Arctic freshwater biodiversity. Circumpolar Biodiversity Monitoring Program–Freshwater has compiled and analysed a database of Arctic freshwater monitoring data to form the first report of the state of circumpolar Arctic freshwater biodiversity. This special issue presents the scientific analyses that underlie the Circumpolar Biodiversity Monitoring Program–Freshwater report and provides analyses of spatial and temporal diversity patterns and the multiple-stressor scenarios that act on the biological assemblages and biogeochemistry of Arctic lakes and rivers. This special issue includes regional patterns for selected groups of organisms in Arctic rivers and lakes of northern Europe, Russia, and North America. Circumpolar assessments for benthic diatoms, macrophytes, plankton, benthic macroinvertebrates, and fish demonstrate how climate change and associated environmental drivers affect freshwater biodiversity. Also included are papers on spatial and temporal trends in water chemistry across the circumpolar region, and a systematic review of documented Indigenous Knowledge that demonstrates its potential to support assessment and conservation of Arctic freshwaters. This special issue includes the first circumpolar assessment of trends in Arctic freshwater biodiversity and provides important baseline information for future assessments and studies. It represents the largest compilation and assessment of Arctic freshwater biodiversity data to date and strives to provide a holistic view of ongoing change in these ecosystems to support future monitoring efforts. By identifying gaps in monitoring data across the circumpolar region, as well as identifying best practices for monitoring and assessment, this special issue presents an important resource for researchers, policy makers, and Indigenous and local communities that can support future assessments of ecosystem change.
Time series of vegetation indices derived from satellite imagery are useful in measuring vegetation response to climate warming in remote northern regions. These indices show that productivity is generally declining in the boreal forest, but it is unclear which components of boreal vegetation are driving these trends. We aimed to compare trends in the normalized difference vegetation index (NDVI) to forest growth and demographic data taken from a 10 ha mapped plot located in a spruce-dominated boreal peatland. We used microcores to quantify recent growth trends and tree census data to characterize mortality and recruitment rates of the three dominant tree species. We then compared spatial patterns in growth and demography to patterns in Landsat-derived maximum NDVI trends (1984-2019) in 78 pixels that fell within the plot. We found that NDVI trends were predominantly positive (i.e., greening) in spite of the ongoing loss of black spruce (the dominant species; 80% of stems) from the plot. The magnitude of these trends correlated positively with black spruce growth trends, but was also governed to a large extent by tree mortality and recruitment. Greening trends were weaker (lower slope) in areas with high larch mortality, and high turnover of spruce and birch, but stronger (higher slope) in areas with high larch recruitment. Larch dominance is currently low (~11% of stems), but it is increasing in abundance as permafrost thaw progresses and will likely have a substantial influence on future NDVI trends. Our results emphasize that NDVI trends in boreal peatlands can be positive even when the forest as a whole is in decline, and that the magnitude of trends can be strongly influenced by the demographics of uncommon species.
Several large-scale human biomonitoring projects have been conducted in Canada, including the Canadian Health Measures Survey (CHMS) and the First Nations Biomonitoring Initiative (FNBI). However, neither of these studies included participants living in the Yukon. To address this data gap, a human biomonitoring project was implemented in Old Crow, a fly-in Gwich'in community in the northern Yukon. The results of this project provide baseline levels of contaminant and nutrient biomarkers from Old Crow in 2019. Samples of hair, blood, and/or urine were collected from approximately 44% of community residents (77 of 175 adults). These samples were analyzed for contaminants (including heavy metals and persistent organic pollutants (POPs)), and nutrients (including trace elements and omega-3 fatty acids). Levels of these analytes were compared to health-based guidance values, when available, and results from other human biomonitoring projects in Canada. Levels of lead (GM 0.64 μg/g creatinine in urine/24 μg/L blood), cadmium (GM 0.32 μg/g creatinine in urine/0.85 μg/L blood), and mercury (GM < LOD in urine/0.76 μg/L blood/0.31 μg/g hair) were below select health-based guidance values for more than 95% of participants. However, compared to the general Canadian population, elevated levels of some contaminants, including lead (approximately 2× higher), cobalt (approximately 1.5× higher), manganese (approximately 1.3× higher), and hexachlorobenzene (approximately 1.5× higher) were observed. In contrast, levels of other POPs, including insecticides such as dichlorodiphenyltrichloroethane (DDT), its metabolite, dichlorodiphenyldichloroethylene (DDE), and polychlorinated biphenyls (PCBs) were similar to, or lower than, those reported in the general Canadian population. This study can be used along with future biomonitoring programs to evaluate the effectiveness of international initiatives designed to reduce the contaminant burden in the Arctic, including the Stockholm Convention and the Minamata Convention. Regionally, this project complements environmental monitoring being conducted in the region, informing local and regional traditional food consumption advisories.
Polyfluoroalkyl substances and perfluoroalkyl substances (PFAS) are a family of anthropogenic chemicals that are used in food packaging, waterproof clothing, and firefighting foams for their water and oil resistant properties. Though levels of some PFAS appear to be decreasing in Canada's south, environmental levels have been increasing in the Arctic due to long-range transport. However, the implications of this on human exposures in sub-Arctic and Arctic populations in Canada have yet to be established. To address this data gap, human biomonitoring research was completed in Old Crow, Yukon, and the Dehcho region, Northwest Territories. Blood samples were collected from adults residing in seven northern First Nations and were analyzed by liquid chromatography mass spectrometry. A total of nine PFAS were quantified: perfluorooctanoic acid (PFOA), perfluorooctane sulphonic acid (PFOS), perfluorohexane sulphonic acid (PFHxS), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), and perfluoroundecanoic acid (PFUdA), perfluorobutanoic acid (PFBA), perfluorohexanoic acid (PFHxA), and perfluorobutane sulphonic acid (PFBS). In the Dehcho (n = 124), five PFAS had a detection rate greater than 50% including PFOS, PFOA, PFHxS, PFNA, and PFDA. In addition to these PFAS, PFUdA was also detected in at least half of the samples collected in Old Crow (n = 54). Generally, male participants had higher concentrations of PFAS compared to female participants, and PFAS concentrations tended to increase with age. For most PFAS, Old Crow and Dehcho levels were similar or lower to those measured in the general Canadian population (as measured through the Canadian Health Measures Survey or CHMS) and other First Nations populations in Canada (as measured through the First Nations Biomonitoring Initiative or FNBI). The key exception to this was for PFNA which, relative to the CHMS (0.51 μg/L), was approximately 1.8 times higher in Old Crow (0.94 μg/L) and 2.8 times higher in Dehcho (1.42 μg/L) than observed in the general Canadian population. This project provides baseline PFAS levels for participating communities, improving understanding of human exposures to PFAS in Canada. Future research should investigate site-specific PFNA exposure sources and monitor temporal trends in these regions.
Abstract Developing spatially explicit permafrost datasets and climate assessments at scales relevant to northern communities is increasingly important as land users and decision makers incorporate changing permafrost conditions in community and adaptation planning. This need is particularly strong within the discontinuous permafrost zone of the Northwest Territories (NWT) Canada where permafrost peatlands are undergoing rapid thaw due to a warming climate. Current data products for predicting landscapes at risk of thaw are generally built at circumpolar scales and do not lend themselves well to fine-scale regional interpretations. Here, we present a new permafrost vulnerability dataset that assesses the degree of permafrost thaw within peatlands across a 750 km latitudinal gradient in the NWT. This updated dataset provides spatially explicit estimates of where peatland thermokarst potential exists, thus making it much more suitable for local, regional or community usage. Within southern peatland complexes, we show that permafrost thaw affects up to 70% of the peatland area and that thaw is strongly mediated by both latitude and elevation, with widespread thaw occuring particularly at low elevations. At the northern end of our latitudinal gradient, peatland permafrost remains climate-protected with relatively little thaw. Collectively these results demonstrate the importance of scale in permafrost analyses and mapping if research is to support northern communities and decision makers in a changing climate. This study offers a more scale-appropriate approach to support community adaptative planning under scenarios of continued warming and widespread permafrost thaw.
Increased fire activity due to climate change may impact the successional dynamics of boreal forests, with important consequences for caribou habitat. Early successional forests have been shown to support lower quantities of caribou forage lichens, but geographic variation in, and controls on, the rates of lichen recovery has been largely unexplored. In this study, we sampled across a broad region in northwestern Canada to compare lichen biomass accumulation in ecoprovinces, including the Saskatchewan Boreal Shield, the Northwest Territories Taiga Shield, and Northwest Territories Taiga Plains, divided into North and South. We focused on the most valuable Cladonia species for boreal and barren-ground caribou: Cladonia mitis and C. arbuscula, C. rangiferina and C. stygia, and C. stellaris and C. uncialis. We developed new allometric equations to estimate lichen biomass from field measurements of lichen cover and height; allometries were consistent among ecoprovinces, suggesting generalizability. We then used estimates of lichen biomass to quantify patterns of lichen recovery in different stand types, ecoprovinces, and with time following stand-replacing fire. We used a hurdle model to account both for the heterogeneous nature of lichen presence (zero inflation) and for the range of abundance in stands where lichen was present. The first component of the hurdle model, a generalized linear model, identified stand age, stand type, and ecoprovince as significant predictors of lichen presence. With a logistic growth model, a measure of lichen recovery (time to 50% asymptotic value) varied from 28 to 73 yr, dependent on stand type and ecoprovince. The combined predictions of the hurdle model suggest the most rapid recovery of lichen biomass across our study region occurred in jack pine in the Boreal Shield (30 yr), while stands located in the Taiga Plains (North and South) required a longer recovery period (approximately 75 yr). These results provide a basis for estimating future caribou habitat that encompasses some of the large variation in fire effects on lichen abundance and vegetation types across the range of boreal and barren-ground caribou in North America.
Abstract. Vegetation optical depth (VOD) retrieved from microwave radiometry correlates with the total amount of water in vegetation, based on theoretical and empirical evidence. Because the total amount of water in vegetation varies with relative water content (as well as with biomass), this correlation further suggests a possible relationship between VOD and plant water potential, a quantity that drives plant hydraulic behavior. Previous studies have found evidence for that relationship on the scale of satellite pixels tens of kilometers across, but these comparisons suffer from significant scaling error. Here we used small-scale remote sensing to test the link between remotely sensed VOD and plant water potential. We placed an L-band radiometer on a tower above the canopy looking down at red oak forest stand during the 2019 growing season in central Massachusetts, United States. We measured stem xylem and leaf water potentials of trees within the stand and retrieved VOD with a single-channel algorithm based on continuous radiometer measurements and measured soil moisture. VOD exhibited a diurnal cycle similar to that of leaf and stem water potential, with a peak at approximately 05:00 eastern daylight time (UTC−4). VOD was also positively correlated with both the measured dielectric constant and water potentials of stem xylem over the growing season. The presence of moisture on the leaves did not affect the observed relationship between VOD and stem water potential. We used our observed VOD–water-potential relationship to estimate stand-level values for a radiative transfer parameter and a plant hydraulic parameter, which compared well with the published literature. Our findings support the use of VOD for plant hydraulic studies in temperate forests.
• Coupled modelling of water flow, heat transfer, water-ice phase change and ice lens formation in deformable, variably saturated freezing soils. • Moisture, vapour, temperature and stress-strain fields significantly interact with each other and should be fully accounted for within the modeling platform. • The large increases in effective stress ahead of the freezing front causes substantial compaction in the unfrozen zone. Although many frost heave and freezing soil models have been developed in the past decades, saturated conditions are commonly assumed and/or the behavior of pore ice rather than ice lenses are conventionally predicted. This study presents a fully coupled thermal-hydraulic-mechanical (THM) model for variably saturated freezing soil, which examines a number of processes. These include heat conduction and convection, phase change, water (moisture) movement through cryosuction, and the development of independent ice lenses. Instead of directly solving for the pore pressure distributions, the void ratio is considered as a dependent variable related to the degree of water saturation. Both the stress-deformation and ice lens segregation are inextricably linked to the evolution of the void ratio as well. The coupled mechanism and performance of the model is first verified by comparison with laboratory freezing experiment observations obtained from literature and then is further evaluated by a series of parametric analyses. The results show that the calculated profiles of temperature, water content and frost heave are in good agreement with literature experimental data, demonstrating that the proposed THM coupling model appropriately represents the mechanisms of heat-moisture-deformation in variably saturated freezing soil. In addition, the sensitivity analysis illustrates that in the test cases considered, thermally-induced cryosuction due to phase change is the main driving force for water migrating towards the freezing front. Also, ahead of the freezing front, a significant increase in effective stress developed due to the elevated negative pore pressure and expansion of ice lenses causing substantial consolidation and reduction in porosity in the unfrozen zone. As the freezing front penetrated with time, the temperature, moisture, vapour and stress-strain fields interact with each other. The distribution of water vapour was mainly controlled by the temperature gradient and location of the freezing front. Both the initial degree of saturation and hydraulic conductivity affected the distribution of pore pressure and displacements. Higher compression moduli and lower overburden load led to greater frost heave but exerted little influence on the temperature field. Finally, the two-sided freezing scenario for soils underlain by permafrost made the middle ice-poor zone highly compacted with ice lenses accumulating near both freezing boundaries.

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Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
Jeremy Irvin, Sharon Zhou, Gavin McNicol, Fred Lu, Vincent Liu, Etienne Fluet‐Chouinard, Zutao Ouyang, Sara Knox, Antje Lucas-Moffat, Carlo Trotta, Dario Papale, Domenico Vitale, Ivan Mammarella, Pavel Alekseychik, Mika Aurela, Anand Avati, Dennis Baldocchi, Sheel Bansal, Gil Bohrer, David I. Campbell, Jiquan Chen, Housen Chu, Higo J. Dalmagro, Kyle Delwiche, Ankur R. Desai, Eugénie Euskirchen, Sarah Féron, Mathias Goeckede, Martin Heimann, Manuel Helbig, Carole Helfter, Kyle S. Hemes, Takashi Hirano, Hiroyasu Iwata, Gerald Jurasinski, Aram Kalhori, Andrew Kondrich, Derrick Y.F. Lai, Annalea Lohila, Avni Malhotra, Lutz Merbold, Bhaskar Mitra, Andrew Y. Ng, Mats Nilsson, Asko Noormets, Matthias Peichl, Camilo Rey‐Sánchez, Andrew D. Richardson, Benjamin R. K. Runkle, Karina V. R. Schäfer, Oliver Sonnentag, Ellen Stuart-Haëntjens, Cove Sturtevant, Masahito Ueyama, Alex Valach, Rodrigo Vargas, George L. Vourlitis, Eric J. Ward, Guan Xhuan Wong, Donatella Zona, Ma. Carmelita R. Alberto, David P. Billesbach, Gerardo Celis, Han Dolman, Thomas Friborg, Kathrin Fuchs, Sébastien Gogo, Mangaliso J. Gondwe, Jordan P. Goodrich, Pia Gottschalk, Lukas Hörtnagl, Adrien Jacotot, Franziska Koebsch, Kuno Kasak, Regine Maier, Timothy H. Morin, Eiko Nemitz, Walter C. Oechel, Patricia Y. Oikawa, Kaori Ono, Torsten Sachs, Ayaka Sakabe, Edward A. G. Schuur, Robert Shortt, Ryan C. Sullivan, Daphne Szutu, Eeva‐Stiina Tuittila, Andrej Varlagin, Joeseph G. Verfaillie, Christian Wille, Lisamarie Windham‐Myers, Benjamin Poulter, Robert B. Jackson
Agricultural and Forest Meteorology, Volume 308-309

• We evaluate methane flux gap-filling methods across 17 boreal-to-tropical wetlands • New methods for generating realistic artificial gaps and uncertainties are proposed • Decision tree algorithms perform slightly better than neural networks on average • Soil temperature and generic seasonality are the most important predictors • Open-source code is released for gap-filling steps and uncertainty evaluation Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).
Abstract. Grounded in situ, or invasive, cosmic ray neutron sensors (CRNSs) may allow for continuous, unattended measurements of snow water equivalent (SWE) over complete winter seasons and allow for measurements that are representative of spatially variable Arctic snow covers, but few studies have tested these types of sensors or considered their applicability at remote sites in the Arctic. During the winters of 2016/2017 and 2017/2018 we tested a grounded in situ CRNS system at two locations in Canada: a cold, low- to high-SWE environment in the Canadian Arctic and at a warm, low-SWE landscape in southern Ontario that allowed easier access for validation purposes. Five CRNS units were applied in a transect to obtain continuous data for a single significant snow feature; CRNS-moderated neutron counts were compared to manual snow survey SWE values obtained during both winter seasons. The data indicate that grounded in situ CRNS instruments appear able to continuously measure SWE with sufficient accuracy utilizing both a linear regression and nonlinear formulation. These sensors can provide important SWE data for testing snow and hydrological models, water resource management applications, and the validation of remote sensing applications.

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Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales
Sara Knox, Sheel Bansal, Gavin McNicol, Karina V. R. Schäfer, Cove Sturtevant, Masahito Ueyama, Alex Valach, Dennis Baldocchi, Kyle Delwiche, Ankur R. Desai, Eugénie Euskirchen, Jinxun Liu, Annalea Lohila, Avni Malhotra, Lulie Melling, William J. Riley, Benjamin R. K. Runkle, J. Turner, Rodrigo Vargas, Qing Zhu, Tuula Alto, Etienne Fluet‐Chouinard, Mathias Goeckede, Joe R. Melton, Oliver Sonnentag, Timo Vesala, Eric J. Ward, Zhen Zhang, Sarah Féron, Zutao Ouyang, Pavel Alekseychik, Mika Aurela, Gil Bohrer, David I. Campbell, Jiquan Chen, Housen Chu, Higo J. Dalmagro, Jordan P. Goodrich, Pia Gottschalk, Takashi Hirano, Hiroyasu Iwata, Gerald Jurasinski, Minseok Kang, Franziska Koebsch, Ivan Mammarella, Mats Nilsson, Kaori Ono, Matthias Peichl, Olli Peltola, Youngryel Ryu, Torsten Sachs, Ayaka Sakabe, Jed P. Sparks, Eeva‐Stiina Tuittila, George L. Vourlitis, Guan Xhuan Wong, Lisamarie Windham‐Myers, B. Poulter, Robert B. Jackson
Global Change Biology, Volume 27, Issue 15

While wetlands are the largest natural source of methane (CH4) to the atmosphere, they represent a large source of uncertainty in the global CH4 budget due to the complex biogeochemical controls on CH4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. We used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by ~17 ± 11 days, and lagged air and soil temperature by median values of 8 ± 16 and 5 ± 15 days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH4. At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH4 emissions.
Abstract. As permafrost thaws in the Arctic, new subsurface pathways open for the transport of groundwater, energy, and solutes. We identify different ways that these subsurface changes are driving observed surface consequences, including the potential for increased contaminant transport, modification to water resources, and enhanced rates of infrastructure (e.g. buildings and roads) damage. Further, as permafrost thaws it allows groundwater to transport carbon, nutrients, and other dissolved constituents from terrestrial to aquatic environments via progressively deeper subsurface flow paths. Cryohydrogeology, the study of groundwater in cold regions, should be included in northern research initiatives to account for this hidden catalyst of environmental and societal change.
A comprehensive understanding of how interactions between catchments and downstream lakes affect fish growth rate is lacking for many species and systems, yet is necessary for predicting impacts of environmental change on productivity of freshwater fish populations. We investigated among-lake variability in growth rate of Northern Pike (Esox lucius), a fish species of widespread subsistence and commercial importance. Northern Pike were captured from 11 subarctic lakes that span 60,000 km2 and four ecoregions in the Dehcho Region of the Northwest Territories, Canada. Growth rates were related to stable isotope ratios and to lake and catchment physicochemistry. Growth, modelled using increment widths (n = 2953) measured on cleithra (n = 432), was significantly slower (p < 0.001, adj. r2 = 0.78) in lakes subject to greater inferred catchment influence, which was quantified using a combination of lake and catchment characteristics. While Northern Pike growth rate was not related to δ15N, it was positively related to δ13C (p < 0.001, adj. r2 = 0.75). Further analyses revealed that benthic invertebrates in lakes subject to greater inferred catchment influence had more depleted δ13C ratios, and we posit that Northern Pike growth is slower in these lakes because terrestrially derived organic matter has relatively lower nutritional value and bioaccessibility, but further research is necessary. By linking current among-lake variability in Northern Pike growth to trophic ecology and to both lake and catchment physicochemical data, results inform predictions of how future changes to subarctic lakes and catchments may affect fish growth and productivity.
The Peace-Athabasca Delta, a Ramsar Wetland of International Importance in northeastern Alberta, is protected within Wood Buffalo National Park and contributes to its UNESCO World Heritage status yet is threatened by climate change and upstream energy projects. Recent drawdown of the delta’s abundant shallow lakes and rivers has deteriorated vital habitat for wildlife and impaired navigation routes. Here, we report continuous measurements at ~50 lakes during open-water seasons of 2018 and 2019 to improve understanding of hydrological processes causing lake-level variation. Analyses reveal four patterns of lake-level variation attributable to influential hydrological processes, which provide the basis for a new lake classification scheme: 1) ‘Drawdown’ (≥15 cm decline) by evaporation and/or outflow after ice-jam floods, 2) ‘Stable’ lake levels (<15 cm change) sustained by rainfall, 3) ‘Gradual Rise’ by inundation from the open-drainage network, and 4) ‘Rapid Rise’ by input of river floodwater. River flooding during the open-water season is an under-recognized recharge mechanism yet occurred extensively in the Athabasca sector and appears to be a common occurrence based on the Athabasca River hydrometric record. Lake-level loggers show strong ability to track shifts in hydrological processes, and can be integrated with other methods to decipher their causes and ecological consequences across water-rich landscapes. • Concerns over lake drying in the Peace-Athabasca Delta motivated this study. • Depth loggers captured lake-level responses to flooding, rainfall and evaporation. • Four patterns comprise a new classification scheme for lakes in the PAD. • Timing, magnitude and extent of open-water flooding was quantified. • Open-water season river flooding identified as an important recharge mechanism.
Peace-Athabasca Delta (PAD), northeastern Alberta. Potential for downstream delivery of contaminants via Athabasca River floodwaters to lakes of the PAD has raised local to international concern. Here, we quantify enrichment of eight metals (Be, Cd, Cr, Cu, Ni, Pb, V, Zn) in aquatic biota, relative to sediment-based pre-industrial baselines, via analysis of biofilm-sediment mixtures accrued on artificial substrate samplers deployed during summers of 2017 and 2018 in > 40 lakes. Widespread flooding in the southern portion of the delta in spring 2018 allows for assessment of metal enrichment by Athabasca River floodwaters. River floodwaters are not implicated as a pathway of metal enrichment to biofilm-sediment mixtures in PAD lakes from upstream sources. MANOVA tests revealed no significant difference in residual concentrations of all eight metals in lakes that did not flood versus lakes that flooded during one or both study years. Also, no enrichment was detected for concentrations of biologically inert metals (Be, Cr, Pb) and those related to oil-sands development (Ni, V). Enrichment of Cd, Cu, and Zn at non-flooded lakes, however, suggests uptake of biologically active metals complicates comparisons of organic-rich biofilm-sediment mixtures to sediment-derived baselines for these metals. Results demonstrate that this novel approach could be adopted for lake monitoring within the federal Action Plan. • Oil sands monitoring of lakes in the Peace-Athabasca Delta needs pre-disturbance data. • Study compares [metals] in biofilm-sediment to [metals] in pre-1920 lake sediment. • Athabasca River floodwaters not implicated as pathway for metal enrichment. • Monitoring framework contributes to Wood Buffalo National Park Action Plan.
The boreal forest is a major contributor to the global climate system, therefore, reducing uncertainties in how the forest will respond to a changing climate is critical. One source of uncertainty is the timing and drivers of the spring transition. Remote sensing can provide important information on this transition, but persistent foliage greenness, seasonal snow cover, and a high prevalence of mixed forest stands (both deciduous and evergreen species) complicate interpretation of these signals. We collected tower-based remotely sensed data (reflectance-based vegetation indices and Solar-Induced Chlorophyll Fluorescence [SIF]), stem radius measurements, gross primary productivity, and environmental conditions in a boreal mixed forest stand. Evaluation of this data set shows a two-phased spring transition. The first phase is the reactivation of photosynthesis and transpiration in evergreens, marked by an increase in relative SIF, and is triggered by thawed stems, warm air temperatures, and increased available soil moisture. The second phase is a reduction in bulk photoprotective pigments in evergreens, marked by an increase in the Chlorophyll-Carotenoid Index. Deciduous leaf-out occurs during this phase, marked by an increase in all remotely sensed metrics. The second phase is controlled by soil thaw. Our results demonstrate that remote sensing metrics can be used to detect specific physiological changes in boreal tree species during the spring transition. The two-phased transition explains inconsistencies in remote sensing estimates of the timing and drivers of spring recovery. Our results imply that satellite-based observations will improve by using a combination of vegetation indices and SIF, along with species distribution information.
Log-transforming the dependent variable of a regression model, though convenient and frequently used, is accompanied by an under-prediction problem. We found that this underprediction can reach up to 20%, which is significant in studies that aim to estimate annual budgets. The fundamental reason for this problem is simply that the log-function is concave, and it has nothing to do with whether the dependent variable has a log-normal distribution or not. Using field-observed data of soil CO2 emission, soil temperature and soil moisture in a saturated-specification of a regression model for predicting emissions, we revealed that the under-predictions of the log-transformed approach were pervasive and systematically biased. The key determinant of the problem's severity was the coefficient of variation in the dependent variable that differed among different combinations of the values of the explanatory factors. By applying a parsimonious (Gaussian-Gamma) specification of the regression model to data from four different ecosystems, we found that this under-prediction problem was serious to various extents, and that for a relatively weak explanatory factor, the log-transformed approach is prone to yield a physically nonsensical estimated coefficient. Finally, we showed and concluded that the problem can be avoided by switching to the nonlinear approach, which does not require the assumption of homoscedasticity for the error term in computing the standard errors of the estimated coefficients.
The ongoing deforestation process in Amazonia has led to intensified forest fires in the region, particularly in Brazil, after more than a decade of effective forest conservation policy. This study aims to investigate the recovery of two mature sub‐montane ombrophile Amazonian forests affected by fire in terms of energy, water and carbon fluxes utilizing remote sensing (MODIS) and climate reanalysis data (GLDAS). These two forest plots, mainly composed of Manilkara spp. (Maçaranduba), Protium spp. (Breu) (∼30 m), Bertholletia excelsa (Castanheira) and Dinizia excelsa Ducke (Angelim‐Pedra) (∼50 m), occupy areas of 100.5 and 122.1 km2 and were subject to fire on the same day, on September 12, 2010. The fire significantly increased land surface temperature (0.8°C) and air temperature (1.2°C) in the forests over a 3 years interval. However, the forests showed an ability to recover their original states in terms of coupling between the carbon and water cycles comparing the 3‐year periods before and after the fires. Results from a wavelet analysis showed an intensification in annual and seasonal fluctuations, and in some cases (e.g., daily net radiation and evapotrasnspiration) sub‐annual fluctuation. We interpreted these changes to be consistent with overall intensification of the coupling of energy balance components and drivers imposed by climate and solar cycle seasonality, as well as faster time scale changes, consistent with a shift toward greater forest openness and consequent reduction in the interception of incoming solar radiation by the canopy.
Abstract Background Through their support of local agriculture, relationships, and healthy diets, farmers markets can contribute to a sustainable food system. Markets like the Yellowknife Farmers Market (YKFM) are social spaces that support local food, yet the COVID-19 pandemic has forced changes to their current model. We explore the potential of online marketplaces to contribute to a resilient, sustainable food system through a case study of the YKFM. Methods In 2019, a collaborative mixed-method evaluation was initiated by the YKFM and university partners in the Northwest Territories (NWT), Canada. The evaluation included an in-person Rapid Market Assessment dot survey and questionnaire of market patrons from two YKFM dates prior to the pandemic. Due to COVID-19, a vendor survey and interviews were deferred. Data collected from the two patron surveys, alongside researcher observations, available literature, public announcements, and informal email and phone discussions, inform the discussion. Results For the patron surveys, 59 dot survey and 31 questionnaire participants were recruited. The top motivators for attendance were eating dinner, atmosphere, and supporting local businesses, and most patrons attended as couples and spent over half of their time talking to others. The YKFM did not move online; instead, they proposed and implemented a “Shop, don’t stop” market. Informal conversations suggested the small scale of the market and technology challenges were perceived barriers to moving online. The physically-distanced market was well-attended and featured in local media. Conclusions NWT food strategies rely on farmers markets to nurture a local food system. Data suggest a potential incongruence between an online model and important market characteristics such as the event-like atmosphere. Available literature suggests online markets can support local food by facilitating purchasing and knowledge-sharing, yet they do not replicate the open-air or social experience. The decision not to move online for the YKFM reflects market patron characteristics and current food context in Yellowknife and the NWT. While online adaptation does not fit into the YKFM plan today, online markets may prove useful as a complementary strategy for future emerging stressors to enhance the resiliency of local systems.
Abstract Objective: Game bird consumption is an important part of the diet of Indigenous populations in Canada and, as part of country food consumption, is associated with improved nutritional status. The objective of this project was to document the consumption of game birds for Dene First Nations in the Northwest Territories (NWT), Canada. Design: Participants were invited to complete a FFQ using an iPad to document the types of country foods consumed, as well as consumption frequency and preparation methods, including thirteen types of game birds. Setting: The project was implemented in nine communities in the Dehcho and Sahtú regions of the NWT, Canada. Participants: A total of 237 children and adult participants from Dene First Nations in the Mackenzie Valley region of the NWT took part in the current study. Results: FFQ findings indicated that game birds were frequently consumed in both Dehcho and Sahtú communities. Canada goose and mallard were found to be consumed by the largest number of participants. Five different species (including Canada goose and mallard) were found to be consumed by at least 25 % of participants over the last year. When consuming game birds, most participants reported consuming the meat as well as most, if not all, other parts of the bird. Conclusions: Differences were observed since the last country food assessment in the 1990s in the same regions. These findings increase knowledge of the current Dene diet patterns and support the understanding of diet transition.
Boreal peatlands are critical ecosystems globally because they house 30%–40% of terrestrial carbon (C), much of which is stored in permafrost soil vulnerable to climate warming-induced thaw. Permafrost thaw leads to thickening of the active (seasonally thawed) layer and alters nutrient and light availability. These physical changes may influence community-level plant functional traits through intraspecific trait variation and/or species turnover. As permafrost thaw is expected to cause an efflux of carbon dioxide (CO2) and methane (CH4) from the soil to the atmosphere, it is important to understand thaw-induced changes in plant community productivity to evaluate whether these changes may offset some of the anticipated increases in C emissions. To this end, we collected vascular plant community composition and foliar functional trait data along gradients in aboveground tree biomass and active layer thickness (ALT) in a rapidly thawing boreal peatland, with the expectation that changes in above- and belowground conditions are indicative of altered resource availability. We aimed to determine whether community-level traits vary across these gradients, and whether these changes are dominated by intraspecific trait variation, species turnover, or both. Our results highlight that variability in community-level traits was largely attributable to species turnover and that both community composition and traits were predominantly driven by ALT. Specifically, thicker active layers associated with permafrost-free peatlands (i.e., bogs and fens) shifted community composition from slower-growing evergreen shrubs to faster-growing graminoids and forbs with a corresponding shift toward more productive trait values. The results from this rapidly thawing peatland suggest that continued warming-induced permafrost thaw and thermokarst development alter plant community composition and community-level traits and thus ecosystem productivity. Increased productivity may help to mitigate anticipated CO2 efflux from thawing permafrost, at least in the short term, though this response may be swamped by increase CH4 release.

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Statistical upscaling of ecosystem CO <sub>2</sub> fluxes across the terrestrial tundra and boreal domain: Regional patterns and uncertainties
Anna‐Maria Virkkala, Juha Aalto, Brendan M. Rogers, Torbern Tagesson, Claire C. Treat, Susan M. Natali, Jennifer D. Watts, Stefano Potter, Aleksi Lehtonen, Marguerite Mauritz, Edward A. G. Schuur, John Kochendorfer, Donatella Zona, Walter C. Oechel, Hideki Kobayashi, Elyn Humphreys, Mathias Goeckede, Hiroyasu Iwata, Peter M. Lafleur, Eugénie Euskirchen, Stef Bokhorst, Maija E. Marushchak, Pertti J. Martikainen, Bo Elberling, Carolina Voigt, Christina Biasi, Oliver Sonnentag, Frans‐Jan W. Parmentier, Masahito Ueyama, Gerardo Celis, Vincent L. St. Louis, Craig A. Emmerton, Matthias Peichl, Jinshu Chi, Järvi Järveoja, Mats Nilsson, Steven F. Oberbauer, M. S. Torn, Sang Jong Park, Han Dolman, Ivan Mammarella, Namyi Chae, Rafael Poyatos, Efrèn López‐Blanco, Torben R. Christensen, Mi Hye Kwon, Torsten Sachs, David Holl, Miska Luoto
Global Change Biology, Volume 27, Issue 17

The regional variability in tundra and boreal carbon dioxide (CO2) fluxes can be high, complicating efforts to quantify sink-source patterns across the entire region. Statistical models are increasingly used to predict (i.e., upscale) CO2 fluxes across large spatial domains, but the reliability of different modeling techniques, each with different specifications and assumptions, has not been assessed in detail. Here, we compile eddy covariance and chamber measurements of annual and growing season CO2 fluxes of gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem exchange (NEE) during 1990–2015 from 148 terrestrial high-latitude (i.e., tundra and boreal) sites to analyze the spatial patterns and drivers of CO2 fluxes and test the accuracy and uncertainty of different statistical models. CO2 fluxes were upscaled at relatively high spatial resolution (1 km2) across the high-latitude region using five commonly used statistical models and their ensemble, that is, the median of all five models, using climatic, vegetation, and soil predictors. We found the performance of machine learning and ensemble predictions to outperform traditional regression methods. We also found the predictive performance of NEE-focused models to be low, relative to models predicting GPP and ER. Our data compilation and ensemble predictions showed that CO2 sink strength was larger in the boreal biome (observed and predicted average annual NEE −46 and −29 g C m−2 yr−1, respectively) compared to tundra (average annual NEE +10 and −2 g C m−2 yr−1). This pattern was associated with large spatial variability, reflecting local heterogeneity in soil organic carbon stocks, climate, and vegetation productivity. The terrestrial ecosystem CO2 budget, estimated using the annual NEE ensemble prediction, suggests the high-latitude region was on average an annual CO2 sink during 1990–2015, although uncertainty remains high.
Abstract Soil respiration (i.e. from soils and roots) provides one of the largest global fluxes of carbon dioxide (CO 2 ) to the atmosphere and is likely to increase with warming, yet the magnitude of soil respiration from rapidly thawing Arctic-boreal regions is not well understood. To address this knowledge gap, we first compiled a new CO 2 flux database for permafrost-affected tundra and boreal ecosystems in Alaska and Northwest Canada. We then used the CO 2 database, multi-sensor satellite imagery, and random forest models to assess the regional magnitude of soil respiration. The flux database includes a new Soil Respiration Station network of chamber-based fluxes, and fluxes from eddy covariance towers. Our site-level data, spanning September 2016 to August 2017, revealed that the largest soil respiration emissions occurred during the summer (June–August) and that summer fluxes were higher in boreal sites (1.87 ± 0.67 g CO 2 –C m −2 d −1 ) relative to tundra (0.94 ± 0.4 g CO 2 –C m −2 d −1 ). We also observed considerable emissions (boreal: 0.24 ± 0.2 g CO 2 –C m −2 d −1 ; tundra: 0.18 ± 0.16 g CO 2 –C m −2 d −1 ) from soils during the winter (November–March) despite frozen surface conditions. Our model estimates indicated an annual region-wide loss from soil respiration of 591 ± 120 Tg CO 2 –C during the 2016–2017 period. Summer months contributed to 58% of the regional soil respiration, winter months contributed to 15%, and the shoulder months contributed to 27%. In total, soil respiration offset 54% of annual gross primary productivity (GPP) across the study domain. We also found that in tundra environments, transitional tundra/boreal ecotones, and in landscapes recently affected by fire, soil respiration often exceeded GPP, resulting in a net annual source of CO 2 to the atmosphere. As this region continues to warm, soil respiration may increasingly offset GPP, further amplifying global climate change.
The high northern latitudes (>50°) experienced a pronounced surface stilling (i.e., decline in winds) with climate change. As a drying factor, the influences of changes in winds on the date of autumn foliar senescence (DFS) remain largely unknown and are potentially important as a mechanism explaining the interannual variability of autumn phenology. Using 183,448 phenological observations at 2,405 sites, long-term site-scale water vapor and carbon dioxide flux measurements, and 34 y of satellite greenness data, here we show that the decline in winds is significantly associated with extended DFS and could have a relative importance comparable with temperature and precipitation effects in contributing to the DFS trends. We further demonstrate that decline in winds reduces evapotranspiration, which results in less soil water losses and consequently more favorable growth conditions in late autumn. In addition, declining winds also lead to less leaf abscission damage which could delay leaf senescence and to a decreased cooling effect and therefore less frost damage. Our results are potentially useful for carbon flux modeling because an improved algorithm based on these findings projected overall widespread earlier DFS than currently expected by the end of this century, contributing potentially to a positive feedback to climate.
Since we first conceived of this Special Issue, “Levering Sustainable Food Systems to Address Climate Change—Possible Transformations”, COVID-19 has turned the world upside down [...]
The transport of helium from the crystalline continental basement and overlying Phanerozoic sedimentary formations to the near surface can be controlled by both diffusive and advective processes. The relative role of each is vital to helium resource prediction, and important in quantifying the residence times of fluids relevant to groundwater resources, hydrocarbon systems, geologic repositories for nuclear waste and carbon sequestration. The Williston Basin, North America, is a prominent sedimentary basin, providing an excellent natural laboratory to assess these processes. Here, we report noble gas isotopic and composition data for 28 gas samples from natural gas wells that sample different stratigraphic horizons down to the basement (Cretaceous to the Cambrian). Helium isotope ratios show a resolvable mantle 3 He component (up to 4.7%) in most samples. Neon isotopic compositions of the Cambrian samples are consistent with a crystalline basement gas contribution. Both helium and neon isotopic observations provide evidence for the contribution of conservative noble gases from the crystalline basement or deeper into the overlying sedimentary basin. 4 He groundwater concentrations in the sedimentary formations, calculated from 4 He/ 20 Ne values in gas samples, are in excess of in situ U+Th 4 He production in some shallow units and depleted in others, providing further evidence of cross formation gas contributions. The highest 4 He groundwater concentrations can be compared with the results obtained from a fully-coupled vertical scale transport model characterising diffusive-dominated transport through a static groundwater column. The model includes the 4 He flux into the basin from the Precambrian basement and quantifies the apparent basement 4 He flux to be between 0.8 - 1.6 × 10 − 6 mol 4 He/m 2 yr, comparable to the steady-state flux estimated for the average continental crust (1.47 × 10 − 6 mol 4 He/m 2 yr) ( Torgersen, 2010 ). The lithologies in which 4 He concentrations are significantly lower than the reference model predictions are consistent with a history of water flooding and produced water disposal in those formations over decades of hydrocarbon production. While an advective component cannot be ruled out, this work demonstrates the importance of both diffusion and the basin architecture development in controlling 4 He flux into and out of different lithologies. The assumption of negligible 4 He loss from the top surface of a lithology is often made when determining the 4 He age of its groundwater. In the Williston Basin, this study shows that deeper lithologies may reach steady state at different stages of basin development, with shallower lithologies sometimes also showing significant 4 He loss from their top surface. In the Williston Basin, 4 He diffusive loss from the target lithology must be considered to accurately interpret 4 He groundwater residence times and accumulation potential. • Noble gases were measured in gas wells from sedimentary units of the Williston Basin. • A He and Ne flux from the crystalline basement is consistent with their isotopes. • Numerical model consistent with diffusive transport of He through sedimentary units. • Numerical model shows multiple periods of steady state He flux. • Steady state He flux critical in He dating applications and He exploration.
Disappearing groundwater requires action to prevent widespread water scarcity
Global groundwater volumes in the upper 2 km of the Earth's continental crust—critical for water security—are well estimated. Beyond these depths, a vast body of largely saline and non-potable groundwater exists down to at least 10 km—a volume that has not yet been quantified reliably at the global scale. Here, we estimate the amount of groundwater present in the upper 10 km of the Earth's continental crust by examining the distribution of sedimentary and crystalline rocks with depth and applying porosity-depth relationships. We demonstrate that groundwater in the 2–10 km zone (what we call “deep groundwater”) has a volume comparable to that of groundwater in the upper 2 km of the Earth's crust. These new estimates make groundwater the largest continental reservoir of water, ahead of ice sheets, provide a basis to quantify geochemical cycles, and constrain the potential for large-scale isolation of waste fluids.
Large volumes of saline formation water are both produced from and injected into sedimentary basins as a by-product of oil and gas production. Despite this, the location of production and injection wells has not been studied in detail at the regional scale and the effects on deep groundwater flow patterns (i.e., below the base of groundwater protection) possibly driving fluid flow toward shallow aquifers remain uncertain. Even where injection and production volumes are equal at the basin scale, local changes in hydraulic head can occur due to the distribution of production and injection wells. In the Canadian portion of the Williston Basin, over 4.6 × 109 m3 of water has been co-produced with 5.4 × 108 m3 of oil, and over 5.5 × 109 m3 of water has been injected into the subsurface for salt water disposal or enhanced oil recovery. Despite approximately equal values of produced and injected fluids at the sedimentary basin scale over the history of development, cumulative fluid deficits and surpluses per unit area in excess of a few 100 mm are present at scales of a few 100 km2 . Fluid fluxes associated with oil and gas activities since 1950 likely exceed background groundwater fluxes in these areas. Modeled pressures capable of creating upward hydraulic gradients are predicted for the Midale Member and Mannville Group, two of the strata with the highest amounts of injection in the study area. This could lead to upward leakage of fluids if permeable pathways, such as leaky wells, are present.
Deep meteoric waters comprise a key component of the hydrologic cycle, transferring water, energy, and life between the earth’s surface and deeper crustal environments, yet little is known about th...
The impacts of Pleistocene glaciation on groundwater flow systems in sedimentary basins are widely recognized, but the timing and distribution of subglacial recharge events remain poorly constrained. We investigate the spatial and temporal variability of recharge events from glaciations over the last 2 million years in the Williston Basin, Canada. Integration of fluid chemistry, stable isotope data, and transport modeling indicate that meltwater arrived at depths of ∼600–1000 m in the northcentral region of the Williston Basin at two separate time periods, 75–150 and 300 ka, which we attribute to permeability differences between stacked aquifer systems. Our findings indicate that meltwater recharge extended along the northern margin of the Williston Basin as well as previously identified recharge areas to the east. Given the distance of measurements from recharge areas, evidence of recharge from the early to mid-Pleistocene appears to be preserved in the Williston Basin.
The interactions between old abandoned wellbores of suspect well integrity with hydraulic fracturing (HF), enhanced oil recovery (EOR), or salt water disposal (SWD) operations can result in upward leakage of deep aqueous liquids into overlying aquifers. This potential for upward fluid migration is largely unquantified as monitoring abandoned wells is rarely done, and leakage may go unnoticed especially when in deeper aquifers. This study performs a proximity analysis between old abandoned wells and HF, EOR, and SWD wells, and identifies commingled old abandoned wellbores, which are those wells where groundwater may flow from one aquifer to one or more other aquifers, to identify the locations with the greatest potential for upward aqueous fluid migration at three study sites in the Western Canadian Sedimentary Basin. Our analysis indicates that at all three study sites there are several locations where HF, EOR, or SWD operations are located in close proximity to a given old abandoned well. Much of this overlap occurs in formations above typically produced hydrocarbon reservoirs but below exploited potable aquifers, otherwise known as the intermediate zone, which is often connected between abandonment plugs in old abandoned wells. Information on the intermediate zone is often lacking, and this study suggests that unanticipated alterations to groundwater flow systems within the intermediate zone may be occurring. Results indicate the need for more field-based research on the intermediate zone.
• Analysis shows the G E V distribution can underestimate precipitation extremes. • G E V + and B r X I I describe more consistently extreme precipitation than the G E V . • Maps of rainfall depths for different return periods are provided for Italy. Italy. Knowing magnitude and frequency of extreme precipitation is necessary to reduce their impact on vulnerable areas. Here we investigate the performance of the Generalized Extreme Value ( G E V ) distribution, using a fine-resolution satellite-based gridded product, to analyze 13,247 daily rainfall annual maxima samples. A non-extreme value distribution with a power-type behavior, that is, the Burr Type XII ( B r X I I ), is also evaluated and used to test the reliability of the G E V in describing extreme rainfall. (1) in 44.9 % of the analyzed samples the G E V predicts an upper rainfall limit; we deem this is an artifact due to sample variations; (2) we suggest the G E V + distribution, that is, the G E V with shape parameters restricted only to positive values as a more consistent model complying with the nature of extreme precipitation; (3) G E V , G E V + , and B r X I I performed equally well in describing the observed annual precipitation, yet all distributions underestimate the observed sample maximum; (4) the B r X I I , for large return periods, predicts larger rainfall amounts compared to G E V indicating that G E V estimates could underestimate the risk of extremes; and (5) the correlation between the predicted rainfall and the elevation is investigated. Based on the results of this study, we suggest instead of using the classical G E V to use the G E V + and non-extreme value distributions such as the B r X I I to describe precipitation extremes.
Realistic stochastic simulation of hydro-environmental fluxes in space and time, such as rainfall, is challenging yet of paramount importance to inform environmental risk analysis and decision making under uncertainty. Here, we advance random fields simulation by introducing the concepts of general velocity fields and general anisotropy transformations. This expands the capabilities of the so-called Complete Stochastic Modeling Solution (CoSMoS) framework enabling the simulation of random fields (RF's) preserving: (a) any non-Gaussian marginal distribution, (b) any spatiotemporal correlation structure (STCS), (c) general advection expressed by velocity fields with locally varying speed and direction, and (d) locally varying anisotropy. We also introduce new copula-based STCS's and provide conditions guaranteeing their positive definiteness. To illustrate the potential of CoSMoS, we simulate RF's with complex patterns and motion mimicking rainfall storms moving across an area, spiraling fields resembling weather cyclones, fields converging to (or diverging from) a point, and colliding air masses. The proposed methodology is implemented in the freely available CoSMoS R package.
The phenomenon of freezing point depression in frozen soils results in the co-existence of ice and liquid water in soil pores at temperatures below 273.15 K (0°C), and is thought to have two causes: (a) capillary and adsorption effects, where the phase transition relationship is modified due to soil-air-water-ice interactions, and (b) solute effects, where the presence of salts lowers the freezing temperature. The soil freezing characteristic curve (SFC) characterizes the relationship between liquid water content and temperature in frozen soils. Most hydrological models represent the SFC using only capillary and adsorption effects with a relationship known as the Generalized Clapeyron Equation (GCE). In this study, we develop and test a salt exclusion model for characterizing the SFC, comparing this with the GCE-based model and a combined salt-GCE effect model. We test these models against measured SFCs in laboratory and field experiments with diverse soil textures and salinities. We consistently found that the GCE-based models under-predicted freezing-point depression. We were able to match the observations with the salt exclusion model and the combined model, suggesting that salinity is a dominant control on the SFC in real soils that always contain solutes. In modeling applications where the salinity is unknown, the soil bulk solute concentration can be treated as a single fitting parameter. Improved characterization of the SFC may result in improvements in coupled mass-heat transport models for simulating hydrological processes in cold regions, particularly the hydraulic properties of frozen soils and the hydraulic head in frozen soils that drives cryosuction.
Like civilization and technology, our understanding of the global water cycle has been continuously evolving, and we have adapted our quantification methods to better exploit new technological resources. The accurate quantification of global water fluxes and storages is crucial in studying the global water cycle. These fluxes and storages physically interact with each other, are related through the water budget, and are constrained by it. First attempts to quantify them date back to the early 1900s, and during the past few decades, they have received an increasing research interest, which is reflected in the vast amount of data sources available nowadays. However, these data have not been comprehensive enough due to the high spatiotemporal variability of the global water cycle. Herein, we provide a comprehensive review of the chronological evolution of global water cycle quantification, the distinct data sources and methods used, and a critical assessment of their contribution to improving the spatiotemporal monitoring of the global water cycle. The chronology of global water cycle components shows that the uncertainty of flux estimates over oceans remains higher than that over land. Comparing the standard deviation and the interquartile range of the estimates from the 2000s onward with those from all the estimates (1905-2019), we can affirm that statistical variability has diminished in recent years. Moreover, the variability of ocean precipitation and evaporation estimates from the 2000 onward was reduced by more than 70% compared with earlier studies. These findings advocate that the consistency of global water cycle quantification has been improved.
• An algorithm for incorporating climate indices in streamflow generation is proposed • The algorithm is based on vine copulas, merged with a formal input selector • The algorithm enables representing dynamic impacts of climate indices on streamflow • The algorithm shows a better prediction skill, particularly in high flow seasons • The algorithm captures modes of streamflow variability better than existing schemes • The algorithm is generic and can be applied in single and multisite modes Despite the existence of several stochastic streamflow generators, not much attention has been given to representing the impacts of large-scale climate indices on seasonal to interannual streamflow variability. By merging a formal predictor selection scheme with vine copulas, we propose a generic approach to explicitly incorporate large-scale climate indices in ensemble streamflow generation at single and multiple sites and in both short-term prediction and long-term projection modes. The proposed framework is applied at three headwater streams in the Oldman River Basin in southern Alberta, Canada. The results demonstrate higher skills than existing models both in terms of representing intra- and inter-annual variability, as well as accuracy and predictability of streamflow, particularly during high flow seasons. The proposed algorithm presents a globally relevant scheme for the stochastic streamflow generation, where the impacts of large-scale climate indices on streamflow variability across time and space are significant.
Multi-parameter water quality monitoring is crucial in resource-limited areas to provide persistent water safety. Conventional water monitoring techniques are time-consuming, require skilled personnel, are not user-friendly and are incompatible with operating on-site. Here, we develop a multi-parameter water quality monitoring system (MWQMS) that includes an array of low-cost, easy-to-use, high-sensitivity electrochemical sensors, as well as custom-designed sensor readout circuitry and smartphone application with wireless connectivity. The system overcomes the need of costly laboratory-based testing methods and the requirement of skilled workers. The proposed MWQMS system can simultaneously monitor pH, free chlorine, and temperature with sensitivities of 57.5 mV/pH, 186 nA/ppm and 16.9 mV/°C, respectively, as well as sensing of BPA with <10 nM limit of detection. The system also provides seamless interconnection between transduction of the sensors’ signal, signal processing, wireless data transfer and smartphone app-based operation. This interconnection was accomplished by fabricating nanomaterial and carbon nanotube-based sensors on a common substrate, integrating these sensors to a readout circuit and transmitting the sensor data to an Android application. The MWQMS system provides a general platform technology where an array of other water monitoring sensors can also be easily integrated and programmed. Such a system can offer tremendous opportunity for a broad range of environmental monitoring applications.
Smart packaging of fresh produce is an emerging technology toward reduction of waste and preservation of consumer health and safety. Smart packaging systems also help to prolong the shelf life of perishable foods during transport and mass storage, which are difficult to regulate otherwise. The use of these ever-progressing technologies in the packaging of fruits has the potential to result in many positive consequences, including improved fruit quality, reduced waste, and associated improved public health. In this review, we examine the role of smart packaging in fruit packaging, current-state-of-the-art, challenges, and prospects. First, we discuss the motivation behind fruit quality monitoring and maintenance, followed by the background on the development process of fruits, factors used in determining fruit quality, and the classification of smart packaging technologies. Then, we discuss conventional freshness sensors for packaged fruits including direct and indirect freshness indicators. After that, we provide examples of possible smart packaging systems and sensors that can be used in monitoring fruits quality, followed by several strategies to mitigate premature fruit decay, and active packaging technologies. Finally, we discuss the prospects of smart packaging application for fruit quality monitoring along with the associated challenges and prospects.
The rapid detection and quantification of infectious pathogens is an essential component to the control of potentially lethal outbreaks among human populations worldwide. Several of these highly infectious pathogens, such as Middle East respiratory syndrome (MERS) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), have been cemented in human history as causing epidemics or pandemics due to their lethality and contagiousness. SARS-CoV-2 is an example of these highly infectious pathogens that have recently become one of the leading causes of globally reported deaths, creating one of the worst economic downturns and health crises in the last century. As a result, the necessity for highly accurate and increasingly rapid on-site diagnostic platforms for highly infectious pathogens, such as SARS-CoV-2, has grown dramatically over the last two years. Current conventional non-microfluidic diagnostic techniques have limitations in their effectiveness as on-site devices due to their large turnaround times, operational costs and the need for laboratory equipment. In this review, we first present criteria, both novel and previously determined, as a foundation for the development of effective and viable on-site microfluidic diagnostic platforms for several notable pathogens, including SARS-CoV-2. This list of criteria includes standards that were set out by the WHO, as well as our own “seven pillars” for effective microfluidic integration. We then evaluate the use of microfluidic integration to improve upon currently, and previously, existing platforms for the detection of infectious pathogens. Finally, we discuss a stage-wise means to translate our findings into a fundamental framework towards the development of more effective on-site SARS-CoV-2 microfluidic-integrated platforms that may facilitate future pandemic diagnostic and research endeavors. Through microfluidic integration, many limitations in currently existing infectious pathogen diagnostic platforms can be eliminated or improved upon.
Detection of arsenic is a long-standing challenge in environmental analytical chemistry. In recent years, using biomolecules and nanomaterials for sensing arsenic has been growingly reported. In this article, this field is critically reviewed based on some recent fundamental understandings including interactions between arsenic and gold, thiol, and DNA aptamers. First, taking advantage of the adsorption of As(III) on noble metal surfaces such as silver and gold, sensors were developed based on surface enhanced Raman spectroscopy, electrochemistry and colorimetry. In addition, by functionalizing metal nanoparticles with thiol containing molecules, As(III) induced aggregation of the particles based on As(III)/thiol interactions. As(V) interacts with metal oxides strongly and competitive sensors were developed by displacing pre-adsorbed DNA oligonucleotides. A DNA aptamer was selected for As(III) and many sensors were reported based on this aptamer, although careful binding measurements indicated that the sequence has no affinity towards As(III). Overall, bio/nano systems are promising for the detection of arsenic. Future work on fundamental studies, searching for more specific arsenic binding materials and aptamers, incorporation of sensors into portable devices, and more systematic test of sensors in real samples could be interesting and useful research topics.
Stability of electrically conductive membranes (ECM) is critical for expanding their application in separation-based technologies. In this work, ECMs were synthesized by coating polyethersulfone membranes with carbon nanotubes (CNT) crosslinked to polyvinyl alcohol (PVA) using two types of crosslinkers (succinic acid or glutaraldehyde). ECMs demonstrated a 21% reduction in flux over 4 h under cathodic potential (2 V) in comparison to a 69% reduction in flux for control experiments when filtering a realistic bacterial suspension. Subsequently, the electrochemical, physical, and mechanical stability of the ECMs were explored using chronoamperometry and cyclic voltammetry, an evaluation of polymer leaching from membranes, and micro mechanical scratch testing, respectively. ECMs were shown to be unstable under anodic potentials (2–4 V) with the glutaraldehyde crosslinking demonstrating the highest electrochemical stability. PVA was shown to be a physically unstable crosslinking agent for CNTs under concentration polarization conditions. Instability was moderated by extending CP layers through thicker and less dense nanolayers. ECMs showed higher mechanical stability and resistance to surface damage, in particular when coated with glutaraldehyde. We quantified the relationship between ECM surface instability and their physical and electrochemical properties. In so doing, we provide guidance for making practical and scalable electrically conductive membranes. • Applied potential impedes the development of membrane biofouling. • Electrically conductive membranes are unstable under operating conditions. • Physical, mechanical, and electrochemical stability of membranes were investigated. • PVA and GA protect CNT from anodic electro-oxidation.
Electrically conductive membranes have shown significant promise in combining conventional separations with in situ contaminant oxidation, but little has been done to consider chlorine removal. This study demonstrates the simultaneous chlorine removal and oxidation of organic compounds during filtration using an electrochemically assisted electrically conductive carbon nanotube (CNT) membrane. As much as 80% of chlorine was removed in the feed by CNT membranes at the initial phase of continuous filtration. The efficacy of these CNT membranes toward chlorine removal was dependent on the mass of CNTs within the membranes and the applied pressure to the membranes, indicating the central role of available CNT active sites and sufficient reaction time. Furthermore, the removal mechanism of chlorine by CNTs was revealed by studying the degradation of benzoic acid and cyclic voltammetry on the membrane surface. Reactive oxidants were generated by the reductive decomposition of chlorine through the catalytic interaction with CNTs. Subsequently, electrical potentials were applied to the CNT membrane surfaces during the filtration of chlorinated feed waters. The simultaneous decomposition of chlorine and oxidation of benzoic acid were significantly enhanced by applying a cathodic current to CNT membranes enabling continuous dechlorination. The cathodic current applied to CNT membranes is believed to regenerate CNT membranes by providing electrons for the reductive decomposition of chlorine. In situ chemical-free dechlorination coupled with membrane filtration offers great opportunity to reducing the environmental impact of desalination, while maximizing the lifetime of reverse osmosis membranes and demonstrating greener approaches available to industrial water treatment.
Multifunctional and low-cost electrode materials are desirable for the next-generation sensors and energy storage applications. This paper reports the use of pencil graphite as an electrode for dual applications that include the detection of free residual chlorine using electro-oxidation process and as an electrochemical energy storage cathode. The pencil graphite is transferred to cellulose paper by drawing ten times and applied for the detection of free residual chlorine, which shows a sensitivity of 27 μA mM-1 cm-2 with a limit of detection of 88.9 μM and linearity up to 7 mM. The sample matrix effect study for the commonly interfering ions such as NO3-, SO42-, CO32-, Cl-, HCO3- shows minimal impact on free residual chlorine detection. Pencil graphite then used after cyclic voltammogram treatment as a cathode in the aqueous Zn/Al-ion battery, showing an average discharge potential plateau of ~1.1 V, with a specific cathode capacity of ~54.1 mAh g-1 at a current of 55 mA g-1. It maintains ~95.8% of its initial efficiency after 100 cycles. Results obtained from the density functional theory calculation is consistent with the electro-oxidation process involved in the detection of free residual chlorine, as well as intercalation and de-intercalation behavior of Al3+ into the graphite layers of Zn/Al-ion battery. Therefore, pencil graphite due to its excellent electro-oxidation and conducting properties, can be successfully implemented as low cost, disposable and green material for both sensor and energy-storage applications.
A rapid, sensitive and simple microflow cytometry-based agglutination immunoassay (MCIA) was developed for point-of-care (POC) quantitative detection of SARS-CoV-2 IgM and IgG antibodies. The antibody concentration was determined by using the transit time of beads aggregates. A linear relationship was established between the average transit time and the concentration of SARS-CoV-2 IgM and IgG, respectively. The limit of detection (LOD) of SARS-CoV-2 IgM and IgG by the MCIA measurement are 0.06 mg/L and 0.10 mg/L, respectively. The 10 µL sample consumption, 30 min assay time and the compact setup make this technique suitable for POC quantitative detection of SARS-CoV-2 antibodies.
A self-cleaving ribozyme was obtained from <italic>in vitro</italic> selection, displaying site-specific cleavage activity under various denaturing conditions, such as high temperatures, 20 M formamide, and low salt concentrations.
Abstract Copper (Cu) is a bio-essential trace element that is of concerns due to its potential toxicity at concentrations commonly encountered in coastal waters. Here, we revisit the applicability of Cu(II) ion selective electrode (Cu-ISE) based on a jalpaite membrane for the measurement of Cufree in seawater. At high total Cu concentration (>0.1 mM), (near)Nernstian slope was obtained and determination of Cufree down to fM levels was possible. However, this slope decreases with decreasing total Cu concentration (e.g. 7 mV/decade at 15 nM total Cu) making the use of a common single calibration approach unreliable. To solve this problem, we carried out several calibrations at different levels of total Cu (15 nM - 1 mM) and ethylenediamine (EN: 5 μM - 15 mM) and fitted the calibration parameters (slope and intercept) as a function of total Cu using the Gompertz function (a meta-calibration approach). The derived empirical equations allowed the determination of Cufree at any total Cu concentration above 20 nM (determination of Cufree at lower total Cu levels is prevented by the dissolution of the electrode). We successfully tested this meta-calibration approach in UV digested seawater in presence of a synthetic ligand (EN), isolated natural organic matter (humic acid, HA) and in a natural estuarine sample. In each case, our meta-calibration approach provided a good agreement with modeled speciation data (Visual MINTEQ), while standard single approach failed. We provide here a new method for the direct determination of the free Cu ion concentration in seawater at levels relevant for coastal waters.
• Fundamentals of phosphorothioate nucleic acids reviewed from synthesis to metal binding. • Applications of phosphorothioate nucleic acids in developing biosensors and chemical biology reviewed. • Applications of phosphorothioate DNA in assembly and the directed growth of nanomaterials reviewed. Phosphorothioate (PS) modification replaces one of the non-bridging oxygen atoms by sulfur in the phosphate backbone of nucleic acids. While PS DNAs have been traditionally used as nuclease-resistant antisense agents and PS RNA as probe of metal binding in ribozymes, multiple new applications have emerged in recent years. In this review, we start by briefly introducing the structure and synthesis of PS nucleic acids followed by their fundamental chemical and biochemical properties. Further, their recently emerged surface science applications are discussed, such as attachment of DNA to various surfaces and nanomaterials containing thiophilic metals such as gold, silver and cadmium, and templating the growth of these materials. Their role in conferring structural effects in the presence of certain metal ions and in fishing out novel aptamers are also discussed. Covalent chemistry can be performed on the sulfur atom for further grafting functional groups to the backbone of DNA. For PS RNA, we discuss their role as probes for metal binding in ribozymes and DNAzymes, which leads to applications in detection of thiophilic metal ions. Since each PS modification site produces a chiral phosphorus center, the synthesis and purification of diastereomers and their applications are emphasized throughout this review. In the end, a few future research directions are discussed.
Phosphate is an important analyte to monitor in various water bodies. Cobalt based sensors are attractive for this application as they are solid-state, have a quick response time, are easy to fabricate and can perform reagent-less measurements. However, these sensors have lower sensitivity, limited dynamic range and require a chemical conditioning in a standard solution before measurement. In this study, an in situ anodic current pretreatment method in sample solution itself is used to enhance the sensitivity of the sensor and alleviate the need of chemical conditioning before measurement. With electrical pretreatment, the sensor exhibited a linear range from 10 −6 M to 10 −3 M with a sensitivity of −91.4 mV/decade of change in dihydrogen phosphate concentration. No significant interference was detected with common interfering anions that are typically present in field water samples such as nitrate, sulfate and chloride. Finally, the sensor was also responsive when tested real water samples such as tap water, lake water and creek water spiked with phosphate. • A new in situ electrical pretreatment method is used to enhance the sensitivity of cobalt based phosphate sensors. • The in situ electrical pretreatment method eliminates the need of the tedious chemical pretreatment in standard solution. • The rapid pretreatment protocol can even extend the range of measurements to much lower concentrations (10-8 M). • Use of electrical pretreatment makes this a practical format for field use as standard solutions are not needed.
The RNA-cleaving 17E DNAzyme exhibits different levels of cleavage activity in the presence of various divalent metal ions, with Pb2+ giving the fastest cleavage. In this study, the metal-phosphate interaction is probed to understand the trend of activity with different metal ions. For the first-row transition metals, the lowest activity shown by Ni2+ correlates with the inhibition by the inorganic phosphate and its water ligand exchange rate, suggesting inner-sphere metal coordination. Cleavage activity with the two stereoisomers of the phosphorothioate-modified substrates, Rp and Sp, indicated that Mg2+, Mn2+, Fe2+, and Co2+ had the highest Sp:Rp activity ratio of >900. Comparatively, the activity was much less affected using the thiophilic metals, including Pb2+, suggesting inner-sphere coordination. The pH-rate profiles showed that Pb2+ was different than the rest of the metal ions in having a smaller slope and a similar fitted apparent pKa and the pKa of metal-bound water. Combining previous reports and our current results, we propose that Pb2+ most likely plays the role of a general acid while the other metal ions are Lewis acid catalysts interacting with the scissile phosphate.
Surface roughness is an important factor in many soil moisture retrieval models. Therefore, any mischaracterization of surface roughness parameters (root mean square height, RMSH, and correlation length, ʅ) may result in unreliable predictions and soil moisture estimations. In many environments, but particularly in agricultural settings, surface roughness parameters may show different behaviours with respect to the orientation or azimuth. Consequently, the relationship between SAR polarimetric variables and surface roughness parameters may vary depending on measurement orientation. Generally, roughness obtained for many SAR-based studies is estimated using pin profilers that may, or may not, be collected with careful attention to orientation to the satellite look angle. In this study, we characterized surface roughness parameters in multi-azimuth mode using a terrestrial laser scanner (TLS). We characterized the surface roughness parameters in different orientations and then examined the sensitivity between polarimetric variables and surface roughness parameters; further, we compared these results to roughness profiles obtained using traditional pin profilers. The results showed that the polarimetric variables were more sensitive to the surface roughness parameters at higher incidence angles (θ). Moreover, when surface roughness measurements were conducted at the look angle of RADARSAT-2, more significant correlations were observed between polarimetric variables and surface roughness parameters. Our results also indicated that TLS can represent more reliable results than pin profiler in the measurement of the surface roughness parameters.
The ability to correct for the influence of forest cover is crucial for retrieval of surface geophysical parameters such as snow cover and soil properties from microwave remote sensing. Existing co...
Soil freeze-thaw events have important implications for water resources, flood risk, land productivity, and climate change. A property of these phenomena is the relationship between unfrozen water content and sub-freezing temperature, known as the soil freezing characteristic curve (SFC). It is documented that this relationship exhibits hysteretic behaviour when frozen soil thaws, leading to the definition of the soil thawing characteristic curve (STC). Although explanations have been given for SFC/STC hysteresis, the effect that “scale”—particularly “measurement scale”—may have on these curves has received little attention. The most commonly used measurement scale metric is the “grain” or “support,” which is the spatial (or temporal) unit within which the measured variable is integrated—in this case, the soil volume sampled. We show (1) measurement support can influence the range and shape of the SFC and (2) hysteresis can be, at least partially, attributed to the support and location of the measurements comprising the SFC/STC. We simulated lab measured temperature, volumetric water content (VWC), and permittivity from soil samples undergoing freeze-thaw transitions using Hydrus-1D and a modified Dobson permittivity model. To assess the effect of measurement support and location on SFC/STC, we masked the simulated temperature and VWC/permittivity extent to match the instrument’s grain and location. By creating a detailed simulation of the intra- and inter-grain variability associated with the penetration of a freezing front, we demonstrate how measurement support and location can influence the temperature range over which water freezing events are captured. We show it is possible to simulate hysteresis in homogenous media with purely geometric considerations, suggesting that SFC/STC hysteresis may be more of an apparent phenomenon than mechanistically real. Lastly, we develop an understanding of how the location and support of soil temperature and VWC/permittivity measurements influence the temperature range over which water freezing events are captured.
Estimating snow water equivalent (SWE) in the northern high latitudesis important from climate, ecological and human perspectives since it enables us to track changes in spatiotemporal distribution...
Mid-latitude snow is understudied compared to snow in the northern high latitudes despite its importance as a source of freshwater to this economically significant region. The mid-latitudes provide...
Dynamic recharge events related to extreme rainfall or snowmelt are becoming more common due to climate change. The vulnerability of public supply wells to water quality degradation may temporarily increase during these types of events. The Walkerton, ON, Canada, tragedy (2000) highlighted the threat to human health associated with the rapid transport of microbial pathogens to public supply wells during dynamic recharge events. Field research at the Thornton (Woodstock, ON, Canada) and Mannheim West (Kitchener, ON, Canada) well fields, situated in glacial overburden aquifers, identified a potential increase in vulnerability due to event-based recharge phenomena. Ephemeral surface water flow and local ponding containing microbial pathogen indicator species were observed and monitored within the capture zones of public supply wells following heavy rain and/or snowmelt. Elevated recharge rates beneath these temporary surface water features were estimated to range between 40 and 710 mm over two-week periods using analytical and numerical modelling based on the water level, soil moisture, and temperature data. Modelling also suggested that such events could reduce contaminant travel times to a supply well, increasing vulnerability to water quality degradation. These studies suggest that event-based recharge processes occurring close to public supply wells may enhance the vulnerability of the wells to surface-sourced contaminants.
Northwestern Canada’s boreal forest has experienced rapid warming, drying, and changes to permafrost, yet the growth responses and mechanisms driving productivity have been under-studied at broad scales. Forest responses are largely driven by black spruce (Picea mariana (Mill.) B.S.P.) — the region’s most widespread and dominant tree. We collected tree ring samples from four black spruce-dominated sites across 15° of latitude, spanning gradients in climate and permafrost. We investigated (i) differences in growth patterns, (ii) variations in climatic drivers of growth, and (iii) trends in water use efficiency (WUE) through 13 C isotope analysis from 1945 to 2006. We found positive growth trends at all sites except those at mid-latitude, where rapid permafrost thaw drove declines. Annual growth was lowest at the tree limit site and highest at the tree line. Climatic drivers of these growth patterns varied; positive growth responses at the northerly sites were associated with warmer winters, whereas Δ 13 C trends and climate-growth responses at mid-latitude sites indicated that growth was limited by moisture availability. Δ 13 C signatures indicated increased WUE at the southernmost site, with no significant trends at northern sites. These results suggest that warming will increase the growth of trees at the northern extent of black spruce, but southerly areas may face drought stress if precipitation does not balance evapotranspiration.
The uniform risk engineering practices that are increasingly being adopted for structural design require estimates of the extreme wind loads with very low annual probabilities of exceedance, corresponding to return periods of up to 3000-years in some cases. These estimates are necessarily based on observational wind data that typically spans only a few decades. The estimates are therefore affected by both large sampling uncertainty and, potentially, non-negligible biases. Design practices that aim to meet mandated structural reliability criteria take the sampling uncertainty of long period wind speed or wind pressure estimates into account, but reliability could be compromised if estimates are also biased. In many circumstances, estimates are obtained by fitting an extreme value distribution to annual maximum wind speed observed over a few decades. A key assumption implicit in doing so is that wind speed annual maxima are max-stable. Departures from max-stability can exacerbate the uncertainty of long-period return level estimates by inducing systematic estimation bias as well. Observational records, however, are generally too short to assess max-stability. We therefore use wind speed data from a large (50-member) ensemble of CanRCM4 historical simulations over North America to assess whether wind speed annual maxima are max-stable. While results are generally reassuring at the continental scale, disquieting evidence of a lack of max-stability is often found in the central and southern parts of the continent. Results show that when annual maximum wind speeds are not max-stable, long period return level extreme wind speeds tend to be underestimated, which would compromise reliability if used to design infrastructure such as tall buildings and towers.
Simultaneous concurrence of extreme values across multiple climate variables can result in large societal and environmental impacts. Therefore, there is growing interest in understanding these concurrent extremes. In many applications, not only the frequency but also the magnitude of concurrent extremes are of interest. One way to approach this problem is to study the distribution of one climate variable given that another is extreme. In this work we develop a statistical framework for estimating bivariate concurrent extremes via a conditional approach, where univariate extreme value modeling is combined with dependence modeling of the conditional tail distribution using techniques from quantile regression and extreme value analysis to quantify concurrent extremes. We focus on the distribution of daily wind speed conditioned on daily precipitation taking its seasonal maximum. The Canadian Regional Climate Model large ensemble is used to assess the performance of the proposed framework both via a simulation study with specified dependence structure and via an analysis of the climate model-simulated dependence structure.
Impacts of anthropogenic aerosols in China on autumn precipitation over Southwest China were investigated using version 5.1 of the Community Atmosphere Model. Simulations with and without anthropogenic aerosol emissions were compared to examine the effects of anthropogenic aerosols on surface air temperature and precipitation in East Asia. Our results show that the aerosol increase induces strong cooling over East Asia by aerosols' direct effect on radiation and indirect effect on clouds. Substantial reductions in precipitation are found across eastern China, but the largest decrease is in Southwest China. Anthropogenic aerosols cause a considerable increase in the cloud condensation nuclei number concentration and a decline in the cloud droplet effective radius in East Asia. The reduced cloud droplet sizes suppress the formation of precipitation and increase cloud depth and liquid water path. Consequently, aerosols' direct radiative effect as well as indirect effect on cloud depth and albedo significantly reduce the shortwave radiation for all sky between 20°and 40°N in China. More absorbing aerosols in the lower troposphere increase shortwave radiative heating, which possibly burns off low-level convective clouds and could cause significant reductions in condensational heating in the lower troposphere. The patterns of the shortwave heating increase and condensational heating reduction are generally consistent with significant reductions in the convective precipitation over China. We further investigated other factors governing precipitation and found moderate stability enhancement and moisture transport reductions in most of China, both of which partially contribute to a decrease in the convective precipitation in Southwest China. Aerosols' direct and indirect effects reduce the amount of solar radiation reaching the surface and cool the surface and lower troposphere between 20° and 40°N, causing anomalous subsidence and reductions in the large-scale precipitation over central and eastern China. Both convective and large-scale precipitation are suppressed over Southwest China, leading to a significant decrease in total precipitation over this area. • Aerosols induce strong cooling by their direct and indirect effects. • The largest decrease in autumn precipitation is found over Southwest China. • Absorbing aerosols increase shortwave heating, reducing low-level convective clouds. • Aerosols' cooling causes subsidence and large-scale precipitation reductions.
ABSTRACT Recent studies have identified stronger warming in the latest generation of climate model simulations globally, and the same is true for projected changes in Canada. This study examines differences for Canada and six sub-regions between simulations from the latest Sixth Coupled Model Intercomparison Project (CMIP6) and its predecessor CMIP5. Ensembles from both experiments are assessed using a set of derived indices calculated from daily precipitation and temperature, with projections compared at fixed future time intervals and fixed levels of global temperature change. For changes calculated at fixed time intervals most temperature indices display higher projected changes in CMIP6 than CMIP5 for most sub-regions, while greater precipitation changes in CMIP6 occur mainly in extreme precipitation indices. When future projections are calculated at fixed levels of global average temperature increase, the size and spread of differences for future projected changes between CMIP6 and CMIP5 are substantially reduced for most indices. Temperature scaling behaviour, or the regional response to increasing global temperatures, is similar in both ensembles, with annual temperature anomalies for Canada and its sub-regions increasing at between 1.5 and 2.5 times the rate of increase globally, depending on the region. The CMIP6 ensemble projections exhibit modestly stronger scaling behaviour for temperature anomalies in northern Canada, as well as for certain indices of moderate and extreme events. Such temperature scaling differences persist even if anomalously warm CMIP6 global climate models are omitted. Comparing the mean and variance of future projections for Canada in CMIP5 and CMIP6 simulations from the same modelling centre suggests CMIP6 models are significantly warmer in Canada than CMIP5 models at the same level of forcing, with some evidence that internal temperature variability in CMIP6 is reduced compared with CMIP5.
Freezing precipitation, in the form of freezing rain, freezing drizzle, and/or wet snow, can damage transportation networks, infrastructure, and vegetation. Ten events with freezing precipitation (including freezing rain and wet snow) over the province of Manitoba, Canada were examined using surface observational datasets, reanalysis products and 4-km resolution Weather Research and Forecasting (WRF) products that were both a retrospective control (CTRL) simulation as well as a pseudo-global warming (PGW) simulation. All events tracked to the south and/or east of Manitoba and most (8 of 10) events were associated with a consistent large scale pattern of extratropical cyclone with 500 hPa trough, low surface pressure center nearby, and an atmospheric river. Local factors, such as the 400 m elevated terrain of Riding Mountain, influenced 2 events mainly by altering surface temperature to be favorable for freezing precipitation. These events in the PGW simulation occurred 40–120 km farther north on average, with freezing rain generally being enhanced and wet snow generally being reduced, although wet snow was introduced into events which originally only had freezing rain. This study further showed that power lines aligned west to east, perpendicular to the strongest winds, are most susceptible to the consequences of icing and accretion within the current climate as well as the thermodynamically forced future one. • Freezing rain, wet snow and their mixtures lead to major impacts in Manitoba, Canada. • Ten historic freezing precipitation events over this area are examined in detail. • Freezing precipitation is dramatically altered by the area's low topographic features. • Pseudo-global warming is assumed to examine future freezing precipitation. • With this warming, freezing precipitation types and their locations are altered.
Field significance tests have been widely used to detect climate change. In most cases, a local test is used to identify significant changes at individual locations, which is then followed by a field significance test that considers the number of locations in a region with locally significant changes. The choice of local test can affect the result, potentially leading to conflicting assessments of the impact of climate change on a region. We demonstrate that when considering changes in the annual extremes of daily precipitation, the simple Mann‐Kendall trend test is preferred as the local test over more complex likelihood ratio tests that compare the fits of stationary and nonstationary generalized extreme value distributions. This lesson allows us to report, with enhanced confidence, that the intensification of annual extremes of daily precipitation in China since 1961 became field significant much earlier than previously reported.
Phytoplankton monitoring is essential for better understanding and mitigation of phytoplankton bloom formation. We present a microfluidic cytometer with two imaging modalities for onsite detection and identification of phytoplankton: a lensless imaging mode for morphological features, and a fluorescence imaging mode for autofluorescence signal of phytoplankton. Both imaging modes are integrated in a microfluidic device with a field of view (FoV) of 3.7 mm × 2.4 mm and a depth of field (DoF) of 0.8 mm. The particles in the water flow channel can be detected and classified with automated image processing algorithms and machine learning models using their morphology and fluorescence features. The performance of the device was demonstrated by measuring Chlamydomonas, Euglena, and non-fluorescent beads in both separate and mixed flow samples. The recall rates for Chlamydomonas and Euglena ware 93.6% and 94.4%. The dual-modality imaging approach enabled observing both morphology and fluorescence features with a large DoF and FoV which contribute to high-throughput analysis. Moreover, this imaging flow cytometer platform is portable, low-cost, and shows potential in the onsite phytoplankton monitoring.
• Water security risks in a watershed are modelled using InVEST’s water yield model. • The impacts of future climate and land use changes on water stress are analyzed. • Water yield is negatively affected by climate change and positively by land use change. • Future water supply is less than the operating flow of a newly constructed dam. • Spatially differentiated conservation efforts are identified to ensure water security. Water security, a key policy objective for sustainable development, is under stress as a result of land use and climate change, especially in (semi-)arid areas like Iran. Land use change alters surface runoff and affects basin-wide hydrological processes and water consumption, while climate change modifies precipitation and temperature patterns and consequently evapotranspiration and water supply. In this study, water yield, supply and consumption are simulated in a watershed draining into the Caspian Sea in northern Iran, using the water yield model in the Integrated Valuation of Environmental Service and Tradeoffs (InVEST) tool. The novelty of this study is found in the combined modelling of the impacts of climate and land use change scenarios on water security, translating these results into a water stress indicator, and estimating the associated economic costs of reduced future water supply. The results show substantial spatial variation of the negative impacts of water supply and future water security across the watershed, further increasing the pressure on its inhabitants, their economic activities and ecological values. The estimation of the economic costs of increased water insecurity allows us to inform policy and decision-makers about future investments in climate adaptation and mitigation.
• Basins in the Canadian Prairies have varying contributing fractions of their areas. • Caused by the variable storage of water in depressions. • The effects of the spatial and frequency distributions of depressions are quantified. • Will lead to the development of improved hydrological models for the region. Runoff in many locations within the Canadian Prairies is dominated by intermittent fill-and-spill between depressions. As a result, many basins have varying fractions of their areas connected to their outlets, due to changing depressional storage. The objective of this research is to determine the causes of the relationships between water storage and the connected fraction of depression-dominated Prairie basins. It is hypothesized that the shapes of the relationship curves are influenced by both the spatial and frequency distributions of depressional storage. Three sets of numerical experiments are presented to test the hypothesis. The first set of experiments demonstrates that where the number of depressions is small, their size and spatial distributions are important in controlling the relationship between the volume of depressional storage and the connected fraction of a basin. As the number of depressions is increased, the areal fractions of the largest depressions decrease, which reduces the importance of the spatial distribution of depressions. The second set of experiments demonstrates that the curve enveloping the connected fraction of a basin can be derived from the frequency distribution of depression areas, and scaling relationships between the area, volume and catchment area of the depressions, when the area of the largest depression is no greater than approximately 5% of the total. The third set of experiments demonstrates that the presence of a single large depression can strongly influence the relationship between the depressional storage and the connected fraction of a basin, depending on the relative size of the large depression, and its location within the basin. A single depression containing 30% of the total depressional area located near the outlet was shown to cause a basin to be nearly endorheic. A similar depression near the top of a basin was demonstrated not to fill and was therefore unable to contribute flows. The implications of the findings for developing hydrological models of large Prairie drainage basins are discussed.
In this study, we develop a hydro-economic modelling framework for river-basin scales by integrating a water resources system model and an economic model. This framework allows for the representation of both local-scale features, such as reservoirs, diversions, and water licenses and priorities, and regional- and provincial-scale features, such as cross-sectoral and inter-regional connectedness and trade flows. This framework is able to: (a) represent nonlinearities and interactions that cannot be represented by either of typical water resources or economic models; (b) analyze the sensitivity of macro-scale economy to different local water management decisions (called 'decision levers' herein); and (c) identify water allocation strategies that are economically sound across sectors and regions. This integrated model is applied to the multi-jurisdictional Saskatchewan River Basin in Western Canada. Our findings reveal that an economically optimal water allocation strategy can mitigate the economic losses of water stress up to 80% compared to the existing water allocation strategy. We draw lessons from our analysis and discuss how integrated inter-regional hydro-economic modelling can benefit vulnerability assessment and robust decision making.
The farmers in the Bow River Basin (BRB), Canada, have adopted water conservation strategies to reduce water needs. This reduction, however, encouraged irrigation expansion, which may rebound agric...
• Time-varying GSA offers a good understanding of the coupled human-natural systems. • Economy is the most influential factor in the rebound phenomenon of the BRB. • Social interaction had a high total-effect on the rebound phenomenon of the BRB. • Raising farmers’ awareness by formal channels could avoid the rebound phenomenon. • Switching to crops needing less water could prevent the rebound phenomenon. Modernizing traditional irrigation systems has long been recognized as a means to reduce water losses. However, empirical evidence shows that this practice may not necessarily reduce water use in the long run; in fact, in many cases, the converse is true—a concept known as the rebound phenomenon. This phenomenon is at the heart of a fundamental research gap in the explicit evaluation of co-evolutionary dynamics and interactions among socio-economic and hydrologic factors in agricultural systems. This gap calls for the application of systems-based methods to evaluate such dynamics. To address this gap, we use a previously developed Agent-Based Agricultural Water Demand (ABAD) model, applied to the Bow River Basin (BRB) in Canada. We perform a time-varying variance-based global sensitivity analysis (GSA) on the ABAD model to examine the individual effect of factors, as well as their joint effect, that may give rise to the rebound phenomenon in the BRB. Our results show that economic factors dominantly control possible rebounds. Although social interaction among farmers is found to be less influential than the irrigation expansion factor, its interaction effect with other factors becomes more important, indicating the highly interactive nature of the underlying socio-hydrological system. Based on the insights gained via GSA, we discuss several strategies, including community participation and water restrictions, that can be adopted to avoid the rebound phenomenon in irrigation systems. This study demonstrates that a time-varying variance-based GSA can provide a better understanding of the co-evolutionary dynamics of the socio-hydrological systems and can pave the way for better management of water resources.
• Organic layer dry thermal conductivity dominates ground thaw uncertainty. • Significant snowpack and active layer changes are expected under climate warming. • Data poor regions would benefit from pursuing physically based approaches to reduce uncertainty. To predict future hydrological cycling in permafrost-dominated regions requires consideration of complex hydrological interactions that involve cryospheric states and fluxes, and hence thermodynamics. This challenges many hydrological models, particularly those applied in the Arctic. This study presents the implementation and validation of set of algorithms representing permafrost and frozen ground dynamics, coupled into a physically based, modular, cold regions hydrological model at two tundra sites in northern Yukon Territory, Canada. Hydrological processes represented in the model include evapotranspiration, soil moisture dynamics, flow through organic and mineral terrain, ground freeze–thaw, infiltration to frozen and unfrozen soils, snowpack energy balance, and the accumulation, wind redistribution, sublimation, and canopy interception of snow. The model was able to successfully represent observed ground surface temperature, ground thaw and snow accumulation at the two sites without calibration. A sensitivity analysis of simulated ground thaw revealed that the soil properties of the upper organic layer dominated the model response; however, its performance was robust for a range of realistic physical parameters. Different modelling decisions were assessed by removing the physically based algorithms for snowpack dynamics and ground surface temperature and replacing them with empirical approaches. Results demonstrate that more physically based approaches should be pursued to reduce uncertainties in poorly monitored environments. Finally, the model was driven by three climate warming scenarios to assess the sensitivity of snow redistribution and ablation processes and ground thaw to warming temperatures. This showed great sensitivity of snow regime and soil thaw to warming, even in the cold continental climate of the northwestern Canadian Arctic. The results are pertinent to transportation infrastructure and water management in this remote, cold, sparsely gauged region where traditional approaches to hydrological prediction are not possible.
• Boreal forests evaporate considerably more than higher elevation shrub ecosystems. • Forests exist in a growing season water deficit relying on snowmelt recharge. • ET variability declines with increased vegetation cover. • Majority of growing season water for streamflow is generated at higher elevations. • We propose treeline advance will result in drying of northern catchments. As a result of altitude and latitude amplified climate change, widespread changes in vegetation composition, density and distribution have been observed across northern regions. Despite wide documentation of shrub proliferation and treeline advance, few field-based studies have evaluated the hydrological implications of these changes. Quantification of total evapotranspiration (ET) across a range of vegetation gradients is essential for predicting water yield, yet challenging in cold alpine catchments due to heterogeneous land cover, including both boreal forest and shrub taiga ecosystems. Here, we present six years of surface energy balance components and ET dynamics at three sites along an elevational gradient in a subarctic, alpine catchment near Whitehorse, Yukon Territory, Canada. These sites span a gradient of thermal and vegetation regimes, providing a space-for-time comparison for future ecosystem shifts: 1) a low-elevation boreal white spruce forest (~12–20 m), 2) a mid-elevation subalpine taiga comprised of tall, dense willow ( Salix ) and birch ( Betula ) shrubs (~1–3 m) and 3) a high-elevation subalpine taiga with short, sparse shrub cover (<0.75 m) and moss, lichen, and bare rock. Eddy covariance instrumentation ran year-round at the forest and during the growing season at the two shrub sites. Total ET decreased and interannual variability increased with elevation, with mean May to September ET totals of 349 (±3) mm at the forest, 249 (±10) mm at the tall, dense shrub site, and 240 (±26) mm at the short, sparse shrub site. Comparatively, over the same period, ET:R ratios were the highest and most variable at the forest (2.19 ± 0.37) and similar at the tall, dense shrub (1.22 ± 0.09) and short, sparse shrub (1.14 ± 0.05) sites. Our results suggest that advances in treeline will increase overall ET and lower interannual variability; however, the large growing season water deficit at the forest indicates strong reliance on soil moisture from late fall and snowmelt recharge. In contrast, ET was considerably less at the cooler higher elevation shrub sites , which exhibited similar ET losses over 6 years despite differences in shrub height and abundance. ET rates between the two shrub sites were similar throughout the year, except during the peak growing season. Greater interannual variability in ET at the short, sparse shrub site indicates the reduced influence of vegetation controls on ET. Results suggest that predicted changes in vegetation type and structure in northern regions will have a considerable impact on water partitioning and will vary in a complex way in response to changing precipitation timing, phase and magnitude, growing season length, and vegetation snow and rain interactions.
Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling. The tremendous potential benefits of SA are, however, yet to be fully realized, both for advancing mechanistic and data-driven modeling of human and natural systems, and in support of decision making. In this perspective paper, a multidisciplinary group of researchers and practitioners revisit the current status of SA, and outline research challenges in regard to both theoretical frameworks and their applications to solve real-world problems. Six areas are discussed that warrant further attention, including (1) structuring and standardizing SA as a discipline, (2) realizing the untapped potential of SA for systems modeling, (3) addressing the computational burden of SA, (4) progressing SA in the context of machine learning, (5) clarifying the relationship and role of SA to uncertainty quantification, and (6) evolving the use of SA in support of decision making. An outlook for the future of SA is provided that underlines how SA must underpin a wide variety of activities to better serve science and society. • Sensitivity analysis (SA) should be promoted as an independent discipline. • Several grand challenges hinder full realization of the benefits of SA. • The potential of SA for systems modeling & machine learning is untapped. • New prospects exist for SA to support uncertainty quantification & decision making. • Coordination rather than consensus is key to cross-fertilize new ideas.
Sensitivity analysis (SA) as a ‘formal’ and ‘standard’ component of scientific development and policy support is relatively young. Many researchers and practitioners from a wide range of disciplines have contributed to SA over the last three decades, and the SAMO (sensitivity analysis of model output) conferences, since 1995, have been the primary driver of breeding a community culture in this heterogeneous population. Now, SA is evolving into a mature and independent field of science, indeed a discipline with emerging applications extending well into new areas such as data science and machine learning. At this growth stage, the present editorial leads a special issue consisting of one Position Paper on “ The future of sensitivity analysis ” and 11 research papers on “ Sensitivity analysis for environmental modelling ” published in Environmental Modelling & Software in 2020–21. • Advances of science and policy has deep but informal roots in sensitivity analysis. • Modern sensitivity analysis is now evolving into a formal and independent discipline. • New areas such data science and machine learning benefit from sensitivity analysis. • Challenges, methodological progress, and outlook are outlined in this special issue.
An integrated framework was employed to develop probabilistic floodplain maps, taking into account hydrologic and hydraulic uncertainties under climate change impacts. To develop the maps, several scenarios representing the individual and compounding effects of the models’ input and parameters uncertainty were defined. Hydrologic model calibration and validation were performed using a Dynamically Dimensioned Search algorithm. A generalized likelihood uncertainty estimation method was used for quantifying uncertainty. To draw on the potential benefits of the proposed methodology, a flash-flood-prone urban watershed in the Greater Toronto Area, Canada, was selected. The developed floodplain maps were updated considering climate change impacts on the input uncertainty with rainfall Intensity–Duration–Frequency (IDF) projections of RCP8.5. The results indicated that the hydrologic model input poses the most uncertainty to floodplain delineation. Incorporating climate change impacts resulted in the expansion of the potential flood area and an increase in water depth. Comparison between stationary and non-stationary IDFs showed that the flood probability is higher when a non-stationary approach is used. The large inevitable uncertainty associated with floodplain mapping and increased future flood risk under climate change imply a great need for enhanced flood modeling techniques and tools. The probabilistic floodplain maps are beneficial for implementing risk management strategies and land-use planning.
Abstract SAR data provide the high-resolution images useful for monitoring environment, and natural resources. Nevertheless, it has been a great challenge to retrieve soil moisture over vegetated sites from SAR backscatter coefficients, as it is almost impossible to parameterize spatially heterogeneous and time-varying roughness, the effect of rainfall or canopy volume scattering with implicit equations. We suggest a Monte Carlo Method (MCM) as a strategy to mitigate non-linear errors in retrievals arising from rainfall, and vegetation growth. The Advanced Integral Equation Model (AIEM) is repeatedly run in a forward mode for establishing the Gaussian-distributed soil roughness and backscatter coefficients. The mean value of soil moisture ensembles inverted from those was taken as an optimal estimate. Local validations show that Root Mean Square Errors (RMSEs) were 0.05 ∼ 0.07 m3/m3 at the stations in Saskatchewan, Canada. Biases were 0.01 m3/m3. Spatial distribution illustrates that the retrieval biases were mitigated, resolving AIEM inversion errors.
A quasi-two-dimensional (quasi-2D) modelling approach is introduced to mimic transverse mixing of an inflow into a river from one of its banks, either an industrial outfall or a tributary. The concentrations of determinands in the inflow vary greatly from those in the river, leading to very long mixing lengths in the river downstream of the inflow location. Ideally, a two-dimensional (2D) model would be used on a small scale to capture the mixing of the two flow streams. However, for large-scale applications of several hundreds of kilometres of river length, such an approach demands too many computational resources and too much computational time, especially if the application will at some point require ensemble input from climate-change scenario data. However, a one-dimensional (1D) model with variables varying in the longitudinal flow direction but averaged across the cross-sections is too simple of an approach to capture the lateral mixing between different flow streams within the river. Hence, a quasi-2D method is proposed in which a simplified 1D solver is still applied but the discretisation of the model setup can be carried out in such a way as to enable a 2D representation of the model domain. The quasi-2D model setup also allows secondary channels and side lakes in floodplains to be incorporated into the discretisation. To show proof-of-concept, the approach has been tested on a stretch of the lower Athabasca River in Canada flowing through the oil sands region between Fort McMurray and Fort MacKay. A dye tracer and suspended sediments are the constituents modelled in this test case.
The hydrography of the Canadian Prairies and adjacent northern US Great Plains is unusual in that the landscape is flat and recently formed due to the effects of pleistocene glaciation and a semi-arid climate since holocene deglaciation. Therefore, there has not been sufficient energy, time, or runoff water to carve typical dendritic surface water drainage networks in many locations. In these regions, runoff is often detented and sometimes stored by the millions of depressions (known locally as “potholes” or “sloughs”) that cover the landscape.
Abstract. East of the Continental Divide in the cold interior of Western Canada, the Mackenzie and Nelson River basins have some of the world's most extreme and variable climates, and the warming climate is changing the landscape, vegetation, cryosphere, and hydrology. Available data consist of streamflow records from a large number (395) of natural (unmanaged) gauged basins, where flow may be perennial or temporary, collected either year-round or during only the warm season, for a different series of years between 1910 and 2012. An annual warm-season time window where observations were available across all stations was used to classify (1) streamflow regime and (2) seasonal trend patterns. Streamflow trends were compared to changes in satellite Normalized Difference Indices. Clustering using dynamic time warping, which overcomes differences in streamflow timing due to latitude or elevation, identified 12 regime types. Streamflow regime types exhibit a strong connection to location; there is a strong distinction between mountains and plains and associated with ecozones. Clustering of seasonal trends resulted in six trend patterns that also follow a distinct spatial organization. The trend patterns include one with decreasing streamflow, four with different patterns of increasing streamflow, and one without structure. The spatial patterns of trends in mean, minimum, and maximum of Normalized Difference Indices of water and snow (NDWI and NDSI) were similar to each other but different from Normalized Difference Index of vegetation (NDVI) trends. Regime types, trend patterns, and satellite indices trends each showed spatially coherent patterns separating the Canadian Rockies and other mountain ranges in the west from the poorly defined drainage basins in the east and north. Three specific areas of change were identified: (i) in the mountains and cold taiga-covered subarctic, streamflow and greenness were increasing while wetness and snowcover were decreasing, (ii) in the forested Boreal Plains, particularly in the mountainous west, streamflows and greenness were decreasing but wetness and snowcover were not changing, and (iii) in the semi-arid to sub-humid agricultural Prairies, three patterns of increasing streamflow and an increase in the wetness index were observed. The largest changes in streamflow occurred in the eastern Canadian Prairies.
Abstract. For many ungauged mountain regions, global datasets of different meteorological and land surface parameters are the only data sources available. However, their applicability in modelling high-alpine regions has been insufficiently investigated so far. Therefore, we tested a suite of globally available datasets by applying the physically based Cold Regions Hydrological Model (CRHM) for a 10-year (September 2000–August 2010) period in the gauged high-alpine Research Catchment Zugspitze (RCZ), which is 12 km2 and located in the European Alps. Besides meteorological data, snow depth is measured at two stations. We ran CRHM with a reference run with in situ-measured meteorological data and a 2.5 m high-resolution digital elevation model (DEM) for the parameterization of the surface characteristics. Regarding different meteorological setups, we used 10 different globally available datasets (including versions of ERA, GLDAS, CFSR, CHIRPS) and additionally one transferred dataset from a similar station in the vicinity. Regarding the different DEMs, we used ALOS (Advanced Land Observing Satellite) and SRTM (Shuttle Radar Topography Mission) (both 30 m) as well as GTOPO30 (1 km). The following two main goals were investigated: (a) the reliability of simulations of snow depth, specific snow hydrological parameters and runoff with global meteorological products and (b) the influence of different global DEMs on snow hydrological simulations in such a topographically complex terrain. The range between all setups in mean decadal temperature is high at 3.5 ∘C and for the mean decadal precipitation sum at 1510 mm, which subsequently leads to large offsets in the snow hydrological results. Only three meteorological setups, the reference, the transferred in situ dataset and the CHIRPS dataset, substituting precipitation only, showed agreeable results when comparing modelled to measured snow depth. Nevertheless, those setups showed obvious differences in the catchment's runoff regime and in snow depth, snow cover, ablation period, the date, and quantity of maximum snow water equivalent in the entire catchment and in specific parts. All other globally available meteorological datasets performed worse. In contrast, all globally available DEM setups reproduced snow depth, the snow hydrological parameters and runoff quite well. Differences occurred mainly due to differences in radiation model input due to different spatial realizations. Even though SRTM and ALOS have the same spatial resolution, they showed considerable differences due to their different product origins. Despite the fact that the very coarse GTOPO30 DEM performed relatively well on the catchment mean, we advise against using this product in such heterogeneous high-alpine terrain since small-scale topographic characteristics cannot be captured. While global meteorological data are not suitable for sound snow hydrological modelling in the RCZ, the choice of the DEM with resolutions in the decametre level is less critical. Nevertheless, global meteorological data can be a valuable source to substitute single missing variables. For the future, however, we expect an increasing role of global data in modelling ungauged high-alpine basins due to further product improvements, spatial refinements and further steps regarding assimilation with remote sensing data.
Scaling sap flux measurements to whole-tree water use or stand-level transpiration is often done using measurements conducted at a single point in the sapwood of the tree and has the potential to cause significant errors. Previous studies have shown that much of this uncertainty is related to (i) measurement of sapwood area and (ii) variations in sap flow at different depths within the tree sapwood.This study measured sap flux density at three depth intervals in the sapwood of 88-year-old red pine (Pinus resinosa) trees to more accurately estimate water-use at the tree- and stand-level in a plantation forest near Lake Erie in Southern Ontario, Canada. Results showed that most of the water transport (65%) occurred in the outermost sapwood, while only 26% and 9% of water was transported in the middle and innermost depths of sapwood, respectively.These results suggest that failing to consider radial variations in sap flux density within trees can lead to an overestimation of transpiration by as much as 81%, which may cause large uncertainties in water budgets at the ecosystem and catchment scale. This study will help to improve our understanding of water use dynamics and reduce uncertainties in sap flow measurements in the temperate pine forest ecosystems in the Great Lakes region and help in protecting these forests in the face of climate change.
In terrestrial ecosystems, leaves are aggregated into different spatial structures and their spatial distribution is non-random. Clumping index (CI) is a key canopy structural parameter, characterizing the extent to which leaf deviates from the random distribution. To assess leaf clumping effects on global terrestrial ET, we used a global leaf area index (LAI) map and the latest version of global CI product derived from MODIS BRDF data as well as the Boreal Ecosystem Productivity Simulator (BEPS) to estimate global terrestrial ET. The results show that global terrestrial ET in 2015 was 511.9 ± 70.1 mm yr−1 for Case I, where the true LAI and CI are used. Compared to this baseline case, (1) global terrestrial ET is overestimated by 4.7% for Case II where true LAI is used ignoring clumping; (2) global terrestrial ET is underestimated by 13.0% for Case III where effective LAI is used ignoring clumping. Among all plant functional types (PFTs), evergreen needleleaf forests were most affected by foliage clumping for ET estimation in Case II, because they are most clumped with the lowest CI. Deciduous broadleaf forests are affected by leaf clumping most in Case III because they have both high LAI and low CI compared to other PFTs. The leaf clumping effects on ET estimation in both Case II and Case III is robust to the errors in major input parameters. Thus, it is necessary to consider clumping effects in the simulation of global terrestrial ET, which has considerable implications for global water cycle research.
• A new PRI based LUE estimation method was proposed. • This method separated the half hourly observed PRI into PRI0 and ΔPRI. • PRI0 indicated daily maximal light use efficiency (LUE max ). • The ΔPRI linked PRI to different diurnal meteorological stress conditions. • This new method significantly improves LUE accuracy (R 2 from 0.1 to 0.7). Photosynthetic light use efficiency (LUE) determines the ability of a plant to assimilate atmospheric carbon dioxide to biomass and is known to be controlled by environmental conditions, light regimes and forest age. The photochemical reflectance index (PRI), derived from leaf or canopy remotely sensed spectra, has been shown to be an effective and accurate estimator of LUE. In this study, we propose a new LUE estimation method that separates the PRI into daily maximal PRI (PRI0) for indicating daily maximal light use efficiency (LUE max ) and ΔPRI, defined as the difference between PRI0 and instantaneous PRI, for estimating the diurnal physiological stress ( fstress) . We develop and apply the method across three temperate pine stands and a deciduous stand of different ages, in Southern Ontario, Canada. Half hourly canopy level spectra were acquired from a tower-based spectro-radiometer system (AMSPEC-III) over the growing season at the four stands. Results show that the PRI0 predicted well LUE max (R 2 > 0.6, p < 0.05) in both coniferous and deciduous stands and was able to track seasonal changes in pigment pools sizes. The ΔPRI was sensitive to short-term meteorological conditions, specifically temperature, vapor pressure deficit (VPD), and light variations resulting in strong correlations ( p < 0.05 ) with fstress and half hourly LUE. This new method significantly improves the estimation accuracy (R 2 increases from 0.1 to around 0.7) for PRI-based LUE estimation across all four stands of varying age and species composition and suggests that PRI-based LUE estimation has the ability to inform on both the effects of seasonal and diurnal change in photosynthetic efficiency under different meteorological conditions.
• 514 sites-years of flux data were used to analyze the potential of physiological and phenological metrics in explaining the variability of forest NEP; • Summer physiological metrics performed better than phenological metrics in explaining IAV of NEP; • Ecosystem respiration played an important role in controlling the variability of NEP in forest ecosystem; • MCUI exhibited a great potential in explaining both IAV and SV of NEP. Understanding the feedback of ecosystem carbon uptake on climate change at temporal and spatial scales is crucial for developing ecosystem models. Previous studies have focused on the role of spring and autumn phenology in regulating carbon sequestration in forest stands, but few on the impact of physiological status in summer. However, plant accumulated the most carbon in summer compared with spring and autumn, therefore, it is of great significance to explore the role of summer phenological metrics on the variability of carbon sequestration. Using 514 site-years of flux data obtained at 40 FLUXNET sites including three forest ecosystems (i.e. evergreen needleleaf forest (ENF), deciduous broadleaf forest (DBF) and mixed forest (MF)) in Europe and North America, we compared the potential of physiological and phenological metrics of Gross Primary Production (GPP) and Ecosystem Respiration (RECO) in explaining the interannual and spatial variability (IAV and SV) of forest net ecosystem production (NEP). In view of the better performance of physiological metrics, we developed the maximum carbon uptake index (MCUI), which integrated the physiology metrics of photosynthesis and respiration in summer, and further explored its ability in explaining the IAV and SV of NEP. The results suggest that the MCUI had a better ability than respiration-growth length ratio (RGR) in predicting NEP for all three forest types. The interpretation of MCUI based on meteorological variables illustrated that the controlling meteorological factors of MCUI differed substantially among ecosystems. The summer shortwave radiation had the greatest influence on MCUI at DBF sites, while the soil water content played an important but opposite role at ENF and DBF sites, and no significant meteorological driver was found at MF sites. The higher potential of MCUI in explaining IAV and SV of NEP highlights the importance of summer physiology in controlling the forest carbon sequestration, and further confirms the significant role of peak plant growth in regulating carbon cycle of forest ecosystems. Understanding the drivers of peak plant growth is therefore of a great significance for further improving the precious of ecosystem model in the future.

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Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data
Gilberto Pastorello, Carlo Trotta, Eleonora Canfora, Housen Chu, Danielle Christianson, You-Wei Cheah, C. Poindexter, Jiquan Chen, Abdelrahman Elbashandy, Marty Humphrey, Peter Isaac, Diego Polidori, Markus Reichstein, Alessio Ribeca, Catharine van Ingen, Nicolas Vuichard, Leiming Zhang, B.D. Amiro, Christof Ammann, M. Altaf Arain, Jonas Ardö, Timothy J. Arkebauer, Stefan K. Arndt, Nicola Arriga, Marc Aubinet, Mika Aurela, Dennis Baldocchi, Alan Barr, Eric Beamesderfer, Luca Belelli Marchesini, Onil Bergeron, Jason Beringer, Christian Bernhofer, Daniel Berveiller, D. P. Billesbach, T. Andrew Black, Peter D. Blanken, Gil Bohrer, Julia Boike, Paul V. Bolstad, Damien Bonal, Jean-Marc Bonnefond, David R. Bowling, Rosvel Bracho, Jason Brodeur, Christian Brümmer, Nina Buchmann, Benoît Burban, Sean P. Burns, Pauline Buysse, Peter Cale, M. Cavagna, Pierre Cellier, Shiping Chen, Isaac Chini, Torben R. Christensen, James Cleverly, Alessio Collalti, Claudia Consalvo, Bruce D. Cook, David Cook, Carole Coursolle, Edoardo Cremonese, Peter S. Curtis, Ettore D’Andrea, Humberto da Rocha, Xiaoqin Dai, Kenneth J. Davis, Bruno De Cinti, A. de Grandcourt, Anne De Ligne, Raimundo Cosme de Oliveira, Nicolas Delpierre, Ankur R. Desai, Carlos Marcelo Di Bella, Paul Di Tommasi, Han Dolman, Francisco Domingo, Gang Dong, Sabina Dore, Pierpaolo Duce, Éric Dufrêne, Allison L. Dunn, J.T. Dusek, Derek Eamus, Uwe Eichelmann, Hatim Abdalla M. ElKhidir, Werner Eugster, Cäcilia Ewenz, B. E. Ewers, D. Famulari, Silvano Fares, Iris Feigenwinter, Andrew Feitz, Rasmus Fensholt, Gianluca Filippa, M. L. Fischer, J. M. Frank, Marta Galvagno, Mana Gharun, Damiano Gianelle, Bert Gielen, Beniamino Gioli, Anatoly A. Gitelson, Ignacio Goded, Mathias Goeckede, Allen H. Goldstein, Christopher M. Gough, Michael L. Goulden, Alexander Graf, Anne Griebel, Carsten Gruening, Thomas Grünwald, Albin Hammerle, Shijie Han, Xingguo Han, Birger Ulf Hansen, Chad Hanson, Juha Hatakka, Yongtao He, Markus Hehn, Bernard Heinesch, Nina Hinko‐Najera, Lukas Hörtnagl, Lindsay B. Hutley, Andreas Ibrom, Hiroki Ikawa, Marcin Jackowicz-Korczyński, Dalibor Janouš, W.W.P. Jans, Rachhpal S. Jassal, Shicheng Jiang, Tomomichi Kato, Myroslava Khomik, Janina Klatt, Alexander Knohl, Sara Knox, Hideki Kobayashi, Georgia R. Koerber, Olaf Kolle, Yukio Kosugi, Ayumi Kotani, Andrew S. Kowalski, Bart Kruijt, Juliya Kurbatova, Werner L. Kutsch, Hyojung Kwon, Samuli Launiainen, Tuomas Laurila, B. E. Law, R. Leuning, Yingnian Li, Michael J. Liddell, Jean‐Marc Limousin, Marryanna Lion, Adam Liska, Annalea Lohila, Ana López‐Ballesteros, Efrèn López‐Blanco, Benjamin Loubet, Denis Loustau, Antje Lucas-Moffat, Johannes Lüers, Siyan Ma, Craig Macfarlane, Vincenzo Magliulo, Regine Maier, Ivan Mammarella, Giovanni Manca, Barbara Marcolla, Hank A. Margolis, Serena Marras, W. J. Massman, Mikhail Mastepanov, Roser Matamala, Jaclyn Hatala Matthes, Francesco Mazzenga, Harry McCaughey, Ian McHugh, Andrew M. S. McMillan, Lutz Merbold, Wayne S. Meyer, Tilden P. Meyers, S. D. Miller, Stefano Minerbi, Uta Moderow, Russell K. Monson, Leonardo Montagnani, Caitlin E. Moore, Eddy Moors, Virginie Moreaux, Christine Moureaux, J. William Munger, T. Nakai, Johan Neirynck, Zoran Nesic, Giacomo Nicolini, Asko Noormets, Matthew Northwood, Marcelo D. Nosetto, Yann Nouvellon, Kimberly A. Novick, W. C. Oechel, Jørgen E. Olesen, Jean‐Marc Ourcival, S. A. Papuga, Frans‐Jan W. Parmentier, Eugénie Paul‐Limoges, Marián Pavelka, Matthias Peichl, Elise Pendall, Richard P. Phillips, Kim Pilegaard, Norbert Pirk, Gabriela Posse, Thomas L. Powell, Heiko Prasse, Suzanne M. Prober, Serge Rambal, Üllar Rannik, Naama Raz‐Yaseef, Corinna Rebmann, David E. Reed, Víctor Resco de Dios, Natalia Restrepo‐Coupe, Borja R. Reverter, Marilyn Roland, Simone Sabbatini, Torsten Sachs, S. R. Saleska, Enrique P. Sánchez-Cañete, Zulia Mayari Sánchez-Mejía, Hans Peter Schmid, Marius Schmidt, Karl Schneider, Frederik Schrader, Ivan Schroder, Russell L. Scott, Pavel Sedlák, Penélope Serrano-Ortíz, Changliang Shao, Peili Shi, Ivan Shironya, Lukas Siebicke, Ladislav Šigut, Richard Silberstein, Costantino Sirca, Donatella Spano, R. Steinbrecher, Robert M. Stevens, Cove Sturtevant, Andy Suyker, Torbern Tagesson, Satoru Takanashi, Yanhong Tang, Nigel Tapper, Jonathan E. Thom, Michele Tomassucci, Juha‐Pekka Tuovinen, S. P. Urbanski, Р. Валентини, M. K. van der Molen, Eva van Gorsel, J. van Huissteden, Andrej Varlagin, Joe Verfaillie, Timo Vesala, Caroline Vincke, Domenico Vitale, N. N. Vygodskaya, Jeffrey P. Walker, Elizabeth A. Walter‐Shea, Huimin Wang, R. J. Weber, Sebastian Westermann, Christian Wille, Steven C. Wofsy, Georg Wohlfahrt, Sebastian Wolf, William Woodgate, Yuelin Li, Roberto Zampedri, Junhui Zhang, Guoyi Zhou, Donatella Zona, D. Agarwal, Sébastien Biraud, M. S. Torn, Dario Papale
Scientific Data, Volume 8, Issue 1

A Correction to this paper has been published: https://doi.org/10.1038/s41597-021-00851-9.

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Global transpiration data from sap flow measurements: the SAPFLUXNET database
Rafael Poyatos, Víctor Granda, Víctor Flo, Mark A. Adams, Balázs Adorján, David Aguadé, Marcos Pereira Marinho Aidar, Scott T. Allen, M. Susana Alvarado-Barrientos, Kristina J. Anderson‐Teixeira, L. M. T. Aparecido, M. Altaf Arain, Ismael Aranda, Heidi Asbjornsen, Robert C. Baxter, Eric Beamesderfer, Z. Carter Berry, Daniel Berveiller, B. Blakely, Johnny L. Boggs, Gil Bohrer, Paul V. Bolstad, Damien Bonal, Rosvel Bracho, Patricia Brito, Jason Brodeur, Fernando Casanoves, Jérôme Chave, Hui Chen, César Cisneros Vaca, Kenneth L. Clark, Edoardo Cremonese, Jorge S. David, Teresa S. David, Nicolas Delpierre, Ankur R. Desai, Frédéric Chauvaud, Michal Dohnal, Jean‐Christophe Domec, Sebinasi Dzikiti, C. Edgar, Rebekka Eichstaedt, Tarek S. El‐Madany, J.A. Elbers, Cleiton B. Eller, Eugénie Euskirchen, B. E. Ewers, Patrick Fonti, Alicia Forner, David I. Forrester, Helber C. Freitas, Marta Galvagno, Omar García-Tejera, Chandra Prasad Ghimire, Teresa E. Gimeno, J. P. Grace, André Granier, Anne Griebel, Guangyu Yang, Mark B Gush, P. J. Hanson, Niles J. Hasselquist, Ingo Heinrich, Virginia Hernández‐Santana, Valentine Herrmann, Teemu Hölttä, F. Holwerda, Hongzhong Dang, J. E. Irvine, Supat Isarangkool Na Ayutthaya, P. G. Jarvis, Hubert Jochheim, Carlos A. Joly, Julia Kaplick, Hyun‐Seok Kim, Leif Klemedtsson, Heather Kropp, Fredrik Lagergren, Patrick Lane, Petra Lang, Andrei Lapenas, Víctor Lechuga, Minsu Lee, Christoph Leuschner, Jean‐Marc Limousin, Juan Carlos Linares, Maj-Lena Linderson, A. Lindroth, Pilar Llorens, Álvaro López-Bernal, M. M. Loranty, Dietmar Lüttschwager, Cate Macinnis‐Ng, Isabelle Maréchaux, Timothy A. Martin, Ashley M. Matheny, Nate G. McDowell, Sean M. McMahon, Patrick Meir, Ilona Mészáros, Mirco Migliavacca, Patrick J. Mitchell, Meelis Mölder, Leonardo Montagnani, Georgianne W. Moore, Ryogo Nakada, Furong Niu, Rachael H. Nolan, R. J. Norby, Kimberly A. Novick, Walter Oberhuber, Nikolaus Obojes, Christopher A. Oishi, Rafael S. Oliveira, Ram Oren, Jean‐Marc Ourcival, Teemu Paljakka, Óscar Pérez-Priego, Pablo Luís Peri, Richard L. Peters, Sebastian Pfautsch, William T. Pockman, Yakir Preisler, Katherine G. Rascher, George R. Robinson, Humberto Ribeiro da Rocha, Alain Rocheteau, Alexander Röll, Bruno H. P. Rosado, Lucy Rowland, Alexey V. Rubtsov, Santiago Sabaté, Yann Salmon, Roberto L. Salomón, Elisenda Sánchez-Costa, Karina V. R. Schäfer, Bernhard Schuldt, A. V. Shashkin, Clément Stahl, Marko Stojanović, Juan Carlos Suárez, Ge Sun, Justyna Szatniewska, Fyodor Tatarinov, Miroslav Tesař, Frank M. Thomas, Pantana Tor‐ngern, Josef Urban, Fernando Valladares, Christiaan van der Tol, Ilja van Meerveld, Andrej Varlagin, Holm Voigt, Jeffrey M. Warren, Christiane Werner, Willy Werner, Gerhard Wieser, Lisa Wingate, Stan D. Wullschleger, K. Yi, Roman Zweifel, Kathy Steppe, Maurizio Mencuccini, Jordi Martínez‐Vilalta
Earth System Science Data, Volume 13, Issue 6

Abstract. Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land–atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80 % of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50 % of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56 % of the datasets. Many datasets contain data for species that make up 90 % or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely available from the Zenodo repository (https://doi.org/10.5281/zenodo.3971689; Poyatos et al., 2020a). The “sapfluxnetr” R package – designed to access, visualize, and process SAPFLUXNET data – is available from CRAN.
Abstract. This paper presents hydrometeorological, glaciological and geospatial data from the Peyto Glacier Research Basin (PGRB) in the Canadian Rockies. Peyto Glacier has been of interest to glaciological and hydrological researchers since the 1960s, when it was chosen as one of five glacier basins in Canada for the study of mass and water balance during the International Hydrological Decade (IHD, 1965–1974). Intensive studies of the glacier and observations of the glacier mass balance continued after the IHD, when the initial seasonal meteorological stations were discontinued, then restarted as continuous stations in the late 1980s. The corresponding hydrometric observations were discontinued in 1977 and restarted in 2013. Datasets presented in this paper include high-resolution, co-registered digital elevation models (DEMs) derived from original air photos and lidar surveys; hourly off-glacier meteorological data recorded from 1987 to the present; precipitation data from the nearby Bow Summit weather station; and long-term hydrological and glaciological model forcing datasets derived from bias-corrected reanalysis products. These data are crucial for studying climate change and variability in the basin and understanding the hydrological responses of the basin to both glacier and climate change. The comprehensive dataset for the PGRB is a valuable and exceptionally long-standing testament to the impacts of climate change on the cryosphere in the high-mountain environment. The dataset is publicly available from Federated Research Data Repository at https://doi.org/10.20383/101.0259 (Pradhananga et al., 2020).
Abstract Climate change and land use management were competing explanations for vegetation dynamics in cold and semi-arid region of north-eastern Inner Mongolia, China. In order to reveal the role of human disturbance and clarify the regional climate-vegetation relationship, long-term (1982–2013) datasets of climate variables and vegetation dynamics in a forest-steppe transition zone of north-eastern Inner Mongolia, China were collected. Partial correlation analyses, principal components regression (PCR), and residual analyses were conducted to reveal the vegetation sensitivities to different climate variables and the impact of anthropogenic activities on climate-vegetation relationship. The results showed that. (1) Annual mean air temperature (TMP) significantly increased at a linear slope of 0.08 °C per decade, annual precipitation (PRE) had an insignificantly linear slope of −16.42 mm per decade (p = 0.15). The average Normalized Difference Vegetation Index (NDVI) had a significantly negative trend over the past decades. A change point around the year 1998, coincided with the occurrence of an intense global El Nino event was also identified. (2) Regional climate change can be represented by changes in temperature, humidity and radiation. NDVI in the steppes display high sensitivity to moisture availability. Whereas, forests was influenced by the warmth index (WMI), accumulation of monthly temperature above a threshold of 5 °C. Partial correlation analyses showed that pixels of positive correlation with PRE (controlling TMP) overlap with the pixels of high partial correlation with minimum temperature (controlling maximum temperature), which suggests a hidden link between minimum temperature and PRE in this region. (3) The spatial distribution of significantly decreased NDVI overlap with cropland expansion, as well as the low residual square (R2) from PCR analysis. The NDVI decline in these expanded croplands suggests human disturbance on vegetation dynamics. Following climate warming, NDVI of forested land displayed positive trend. Whereas, most of steppe displayed negative trend, possibly resulting from combined effects of climate drying and human disturbance. We conclude that the regional climate change can be characterized as warming and drying. Steppe areas were sensitive to humidity changes while forested land was mostly influenced by growing season warmth. Overall, the regional NDVI displayed significantly negative trend over the past decades. Beyond climate drying, cropland expansion in the transition area between grassland and forested land is also an important driver for decreased NDVI. Further studies on the ecological and hydrological consequences of crop land expansion is necessary.
• Phenological controls over aerodynamic resistance ( R ah ) were investigated. • R ah exhibits significant seasonal variability across a wide range of sites. • These shifts in R ah were caused by phenology in some ecosystems. • Accounting for variation in kB −1 is important for improving predictions of H . Surface roughness – a key control on land-atmosphere exchanges of heat and momentum – differs between dormant and growing seasons. However, how surface roughness shifts seasonally at fine time scales (e.g., days) in response to changing canopy conditions is not well understood. This study: (1) explores how aerodynamic resistance changes seasonally; (2) investigates what drives these seasonal shifts, including the role of vegetation phenology; and (3) quantifies the importance of including seasonal changes of aerodynamic resistance in “big leaf” models of sensible heat flux ( H ). We evaluated aerodynamic resistance and surface roughness lengths for momentum ( z 0m ) and heat ( z 0h ) using the kB −1 parameter (ln( z 0m / z 0h )). We used AmeriFlux data to obtain surface-roughness estimates, and PhenoCam greenness data for phenology. This analysis included 23 sites and ∼190 site years from deciduous broadleaf, evergreen needleleaf, woody savanna, cropland, grassland, and shrubland plant-functional types (PFTs). Results indicated clear seasonal patterns in aerodynamic resistance to sensible heat transfer ( R ah ). This seasonality tracked PhenoCam-derived start-of-season green-up transitions in PFTs displaying the most significant seasonal changes in canopy structure, with R ah decreasing near green-up transitions. Conversely, in woody savanna sites and evergreen needleleaf forests, patterns in R ah were not linked to green-up. Our findings highlight that decreases in kB −1 are an important control over R ah , explaining > 50% of seasonal variation in R ah across most sites. Decreases in kB −1 during green-up are likely caused by increasing z 0h in response to higher leaf area index. Accounting for seasonal variation in kB −1 is key for predicting H as well; assuming kB −1 to be constant resulted in significant biases that also exhibited strong seasonal patterns. Overall, we found that aerodynamic resistance can be sensitive to phenology in ecosystems having strong seasonality in leaf area, and this linkage is critical for understanding land-atmosphere interactions at seasonal time scales.
As atmospheric carbon dioxide concentrations continue to rise and global temperatures increase, there is growing concern about the sustainability, health, and carbon sequestration potential of forest ecosystems. Variable retention harvesting (VRH) has been suggested to be a potential method to increase forest biodiversity, growth, and carbon (C) sequestration. A field trial was established in an 88-year-old red pine ( Pinus resinosa Ait.) plantation in southern Ontario, Canada, using a completely randomized design to examine the response of tree productivity and other forest values to five harvesting treatments: 33% aggregate retention (33A), 55% aggregate retention (55A), 33% dispersed retention (33D), and 55% dispersed retention (55D) in comparison to an unharvested control (CN). In this study, we explored the impacts of VRH on aboveground stem radial growth and annual C increment. Standard dendrochronological methods and allometric equations were used to quantify tree- and stand-level treatment effects during a five-year pre-harvest (2009–2013) and post-harvest (2014–2018) period. Tree-level growth and C increment were increased by the dispersed retention pattern regardless of retention level. At the stand level, the total C increment was highest at greater retention levels and did not vary with retention pattern. These results suggest that the choice of retention level and pattern can have a large influence on management objectives as they relate to timber production, climate change adaptation, and/or climate change mitigation.
Hydrometeorological events have been the predominant type of natural hazards to affect communities across Canada. While climate change is a concern to all Canadians, Indigenous communities in Canada have been disproportionately more affected by these extreme climate events than non-Indigenous communities. As the impacts of climate change intensify, it becomes increasingly important that high-resolution climate services are made available to Indigenous decision makers for the development of climate change adaptation plans. This paper examined extreme climate trends in the Six Nations of the Grand River reserve, the most populated Indigenous community in Canada. A set of 12 indices were used to evaluate changes in extreme climate events from 1951 to 2013, and 2006 to 2099 under Representative Concentration Pathways (RCP) 4.5 and 8.5. Results indicated that from 1951 to 2013, Six Nations became warmer and wetter with an average temperature increase of 0.7 °C and precipitation increase of 42 mm. Over this period, the frequency and duration of extreme heat and extreme precipitation events also increased, while extreme cold events decreased. In the future (2006 to 2099), temperature is expected to increase by 3 to 6 °C, while seasonal precipitation is expected to increase in winter, early spring, and fall. Projected rate of increase of heatwaves is 0.4 to 1.5 days per year and extreme annual rainfall events is 0.2 to 0.5 mm per year under both RCP scenarios. The climate information and data provide by this study will help Six Nations’ decision makers in planning for climate change impacts.

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Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites
Housen Chu, Xiangzhong Luo, Zutao Ouyang, Wai-Yin Stephen Chan, Sigrid Dengel, Sébastien Biraud, M. S. Torn, Stefan Metzger, Jitendra Kumar, M. Altaf Arain, T. J. Arkebauer, Dennis Baldocchi, Carl J. Bernacchi, D. P. Billesbach, T. Andrew Black, Peter D. Blanken, Gil Bohrer, Rosvel Bracho, Scott Brown, Nathaniel A. Brunsell, Jiquan Chen, Xingyuan Chen, Kenneth L. Clark, Ankur R. Desai, Tomer Duman, David Durden, Silvano Fares, Inke Forbrich, John A. Gamon, Christopher M. Gough, Timothy J. Griffis, Manuel Helbig, David Y. Hollinger, Elyn Humphreys, Hiroki Ikawa, Hiroyasu Iwata, Yang Ju, John F. Knowles, Sara Knox, Hideki Kobayashi, Thomas E. Kolb, Beverly E. Law, Xuhui Lee, M. E. Litvak, Heping Li, J. William Munger, Asko Noormets, Kim Novick, Steven F. Oberbauer, Walter C. Oechel, Patricia Y. Oikawa, S. A. Papuga, Elise Pendall, Prajaya Prajapati, John H. Prueger, William L. Quinton, Andrew D. Richardson, Eric S. Russell, Russell L. Scott, Gregory Starr, R. M. Staebler, Paul C. Stoy, Ellen Stuart-Haëntjens, Oliver Sonnentag, Ryan C. Sullivan, Andy Suyker, Masahito Ueyama, Rodrigo Vargas, J. D. Wood, Donatella Zona
Agricultural and Forest Meteorology, Volume 301-302

• Large-scale eddy-covariance flux datasets need to be used with footprint-awareness • Using a fixed-extent target area across sites can bias model-data integration • Most sites do not represent the dominant land-cover type at a larger spatial extent • A representativeness index provides general guidance for site selection and data use Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? We evaluate flux footprints—the temporally dynamic source areas that contribute to measured fluxes—and the representativeness of these footprints for target areas (e.g., within 250–3000 m radii around flux towers) that are often used in flux-data synthesis and modeling studies. We examine the land-cover composition and vegetation characteristics, represented here by the Enhanced Vegetation Index (EVI), in the flux footprints and target areas across 214 AmeriFlux sites, and evaluate potential biases as a consequence of the footprint-to-target-area mismatch. Monthly 80% footprint climatologies vary across sites and through time ranging four orders of magnitude from 10 3 to 10 7 m 2 due to the measurement heights, underlying vegetation- and ground-surface characteristics, wind directions, and turbulent state of the atmosphere. Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that use a fixed-extent target area across sites introduce biases on the order of 4%–20% for EVI and 6%–20% for the dominant land cover percentage. These biases are site-specific functions of measurement heights, target area extents, and land-surface characteristics. We advocate that flux datasets need to be used with footprint awareness, especially in research and applications that benchmark against models and data products with explicit spatial information. We propose a simple representativeness index based on our evaluations that can be used as a guide to identify site-periods suitable for specific applications and to provide general guidance for data use.
Globally, harmful algal blooms (HABs) are on the rise, as is evidence of their toxicity. The impacts associated with blooms, however, vary across Nation states, as do the strategies and protocols to assess, monitor, and manage their occurrence. In Canada, water quality guidelines are standardized nationally, but the management strategies for HABs are not. Here, we explore current strategies to understand how to better communicate risks associated with HABs to the public. Our team conducted an environmental scan on provincial and territorial government agency protocols around HABs. Results suggest that there are variations in the monitoring, managing, and communicating of risk to the public: British Columbia, Manitoba, New Brunswick, and Quebec have well-established inter-agency protocols, and most provinces report following federal guidelines for water quality. Notably, 3 northern territories have no HABs monitoring or management protocols in place. More populous provinces use a variety of information venues (websites, social media, on site postings, and radio) to communicate risks associated with HABs, whereas others’ communications are limited. To induce more collaboration on HABs monitoring and management and reduce the associated risks, creating a coherent system with consistent messaging and inter-agency communication is suggested.
Glaciers provide cold, turbid runoff to many mountain streams in the late summer and buffer against years with low snowfall. The input of glacial meltwater to streams maintains unique habitats and support a diversity of stream flora and fauna. In western Canada, glaciers are anticipated to retreat by 60–80% by the end of the century, and this retreat will invoke widespread changes in mountain ecosystems. We used a space-for-time substitution along a gradient of glacierization in western Canada to develop insights into changes that may occur in glaciated regions over the coming decades. Here we report on observed changes in physical (temperature, turbidity), and chemical (dissolved and total nutrients) characteristics of mountain streams and the associated shifts in their diatom communities during de-glacierization. Shifts in habitat characteristics across gradients include changes in nutrient concentrations, light penetration, temperatures, and flow, all of which have led to distinct changes in diatom community composition. Importantly, glacial-fed rivers were 3–5 °C cooler than rivers without glacial contributions. Declines in glacial meltwater contribution to streams resulted in shifts in the timing of nutrient fluxes and lower concentrations of total phosphorus (TP), soluble reactive phosphorus (SRP), and higher dissolved inorganic nitrogen (DIN) and light penetration. The above set of conditions were linked to the overgrowth of the benthic diatom Didymosphenia geminata . These changes in stream condition and D. geminata colony development primarily occurred in streams with marginal (2–5%) to no glacier cover. Our data support a hypothesis that climate-induced changes in river hydrochemistry and physical condition lead to a phenological mismatch that favors D. geminata bloom development. • We use a space-for-time substitution to examine glacier recession impacts on rivers. • Temperature changes through time and by season were greatest in glacierized systems. • Peaks in turbidity and nutrients decreased and shifted to earlier in the year. • These shifts cause a phenological mismatch that favors D. geminata colony formation.
Mountains are global biodiversity hotspots where cold environments and their associated ecological communities are threatened by climate warming. Considerable research attention has been devoted to understanding the ecological effects of alpine glacier and snowfield recession. However, much less attention has been given to identifying climate refugia in mountain ecosystems where present-day environmental conditions will be maintained, at least in the near-term, as other habitats change. Around the world, montane communities of microbes, animals, and plants live on, adjacent to, and downstream of rock glaciers and related cold rocky landforms (CRL). These geomorphological features have been overlooked in the ecological literature despite being extremely common in mountain ranges worldwide with a propensity to support cold and stable habitats for aquatic and terrestrial biodiversity. CRLs are less responsive to atmospheric warming than alpine glaciers and snowfields due to the insulating nature and thermal inertia of their debris cover paired with their internal ventilation patterns. Thus, CRLs are likely to remain on the landscape after adjacent glaciers and snowfields have melted, thereby providing longer-term cold habitat for biodiversity living on and downstream of them. Here, we show that CRLs will likely act as key climate refugia for terrestrial and aquatic biodiversity in mountain ecosystems, offer guidelines for incorporating CRLs into conservation practices, and identify areas for future research.
Atmospheric boundary layer (ABL) dynamics over glaciers mediate the response of glacier mass balance to large‐scale climate forcing. Despite this, very few ABL observations are available over mountain glaciers in complex terrain. An intensive field campaign was conducted in June 2015 at the Athabasca Glacier outlet of Columbia Icefield in the Canadian Rockies. Observations of wind and temperature profiles with novel kite and radio‐acoustic sounding systems showed a well‐defined mesoscale circulation developed between the glacier and snow‐free valley in fair weather. The typical vertical ABL structure above the glacier differed from that expected for “glacier winds”; strong daytime down‐glacier winds extended through the lowest 200 m with no up‐valley return flow aloft. This structure suggests external forcing at mesoscale scales or greater and is provisionally termed an “icefield breeze.” A wind speed maximum near the surface, characteristic of a “glacier wind,” was only observed during night‐time and one afternoon. Lapse rates of air temperature down the glacier centerline show the interaction of down‐glacier cooling driven by sensible heat loss into the ice, entrainment and periodic disruption and warming. Down‐glacier cooling was weaker in “icefield breeze” conditions, while in “glacier wind” conditions, stronger down‐glacier cooling enabled large increases in near‐surface temperature on the lower glacier during periods of surface boundary layer (SBL) disruption. These results raise several questions, including the impact of Columbia Icefield on the ABL and melt of Athabasca Glacier. Future work should use these observations as a testbed for modeling spatio‐temporal variations in the ABL and SBL within complex glaciated terrain.
Surface energy budgets are important to the ecohydrology of complex terrain, where land surfaces cycle in and out of shadows creating distinct microclimates. Shading in such environments can help regulate downstream flow over the course of a growing season, but our knowledge on how shadows impact the energy budget and consequently ecohydrology in montane ecosystems is very limited. We investigated the influence of horizon shade on the surface energy fluxes of a subalpine headwater wetland in the Canadian Rocky Mountains during the growing season. During the study, surface insolation decreased by 60% (32% due to evolving horizon shade and 28% from seasonality). The influence of shade on the energy budget varied between two distinct periods: (1) Stable Shade, when horizon shade was constant and reduced sunlight by 2 h per day; and (2) Dynamic Shade, when shade increased and reduced sunlight by 0.18 h more each day, equivalent to a 13% reduction in incoming shortwave radiation and 16% in net radiation. Latent heat flux, the dominant energy flux at our site, varied temporally because of changes in incoming radiation, atmospheric demand, soil moisture and shade. Horizon shade controlled soil moisture at our site by prolonging snowmelt and reducing evapotranspiration in the late growing season, resulting in increased water storage capacity compared to other mountain wetlands. With the mounting risk of climate-change-driven severe spring flooding and late season droughts downstream of mountain headwaters, shaded subalpine wetlands provide important ecohydrological and mitigation services that are worthy of further study and mapping. This will help us better understand and protect mountain and prairie water resources.
Glaciers distinct from the Greenland and Antarctic ice sheets are shrinking rapidly, altering regional hydrology1, raising global sea level2 and elevating natural hazards3. Yet, owing to the scarcity of constrained mass loss observations, glacier evolution during the satellite era is known only partially, as a geographic and temporal patchwork4,5. Here we reveal the accelerated, albeit contrasting, patterns of glacier mass loss during the early twenty-first century. Using largely untapped satellite archives, we chart surface elevation changes at a high spatiotemporal resolution over all of Earth's glaciers. We extensively validate our estimates against independent, high-precision measurements and present a globally complete and consistent estimate of glacier mass change. We show that during 2000-2019, glaciers lost a mass of 267 ± 16 gigatonnes per year, equivalent to 21 ± 3 per cent of the observed sea-level rise6. We identify a mass loss acceleration of 48 ± 16 gigatonnes per year per decade, explaining 6 to 19 per cent of the observed acceleration of sea-level rise. Particularly, thinning rates of glaciers outside ice sheet peripheries doubled over the past two decades. Glaciers currently lose more mass, and at similar or larger acceleration rates, than the Greenland or Antarctic ice sheets taken separately7-9. By uncovering the patterns of mass change in many regions, we find contrasting glacier fluctuations that agree with the decadal variability in precipitation and temperature. These include a North Atlantic anomaly of decelerated mass loss, a strongly accelerated loss from northwestern American glaciers, and the apparent end of the Karakoram anomaly of mass gain10. We anticipate our highly resolved estimates to advance the understanding of drivers that govern the distribution of glacier change, and to extend our capabilities of predicting these changes at all scales. Predictions robustly benchmarked against observations are critically needed to design adaptive policies for the local- and regional-scale management of water resources and cryospheric risks, as well as for the global-scale mitigation of sea-level rise.
Abstract As a consequence of increasing temperatures, a rapid increase in shrub vegetation has occurred throughout the circumpolar North and is expected to continue. Rates of shrub expansion are highly variable, both at the regional scale and within local study areas. This study uses repeat airborne LiDAR and field surveys to measure changes in shrub vegetation cover along with landscape-scale variations in a well-studied subarctic headwater catchment in Yukon Territory, Canada. Airborne LiDAR surveys were conducted in August 2007 and 2018, whereas vegetation surveys were conducted in summer 2019. Machine learning classification algorithms were used to predict shrub presence/absence in 2018 based on rasterized LiDAR metrics, with the best-performing model applied to the 2007 LiDAR to create binary shrub cover layers to compare between survey years. Results show a 63.3% total increase in detectable shrub cover >= 0.45 m in height between 2007 and 2018, with an average yearly expansion of 5.8%. These changes were compared across terrain derivatives to quantify the influence of topography on shrub expansion. Terrain comparisons show that shrubs are located in and are preferentially expanding into lower and flatter areas near stream networks, at lower slope positions and with a higher potential for topographic wetness. Overall, the findings from this research reinforce the documented increase in pan-Arctic shrub vegetation in recent years, quantify the variation in shrub expansion over terrain derivatives at the landscape scale, and demonstrate the feasibility of using LiDAR to compare changes in shrub properties over time.
Abstract The collection efficiency of a typical precipitation gauge-shield configuration decreases with increasing wind speed, with a high scatter for a given wind speed. The high scatter in the collection efficiency for a given wind speed arises in part from the variability in the characteristics of falling snow and atmospheric turbulence. This study uses weighing gauge data collected at the Marshall Field Site near Boulder, Colorado, during the WMO Solid Precipitation Intercomparison Experiment (SPICE). Particle diameter and fall speed data from a laser disdrometer were used to show that the scatter in the collection efficiency can be reduced by considering the fall speed of solid precipitation particles. The collection efficiency was divided into two classes depending on the measured mean-event particle fall speed during precipitation events. Slower-falling particles were associated with a lower collection efficiency. A new transfer function (i.e., the relationship between collection efficiency and other meteorological variables, such as wind speed or air temperature) that includes the fall speed of the hydrometeors was developed. The root-mean-square error of the adjusted precipitation with the new transfer function with respect to a weighing gauge placed in a double fence intercomparison reference was lower than using previously developed transfer functions that only consider wind speed and air temperature. This shows that the measured fall speed of solid precipitation with a laser disdrometer accounts for a large amount of the observed scatter in weighing gauge collection efficiency.
Abstract Accurate snowfall measurement is challenging because it depends on the precipitation gauge used, meteorological conditions, and the precipitation microphysics. Upstream of weighing gauges, the flow field is disturbed by the gauge and any shielding used usually creates an updraft, which deflects solid precipitation from falling in the gauge, resulting in significant undercatch. Wind shields are often used with weighing gauges to reduce this updraft, and transfer functions are required to adjust the snowfall measurements to consider gauge undercatch. Using these functions reduces the bias in precipitation measurement but not the root-mean-square error (RMSE). In this study, the accuracy of the Hotplate precipitation gauge was compared to standard unshielded and shielded weighing gauges collected during the WMO Solid Precipitation Intercomparison Experiment program. The analysis performed in this study shows that the Hotplate precipitation gauge bias after wind correction is near zero and similar to wind corrected weighing gauges. The RMSE of the Hotplate precipitation gauge measurements is lower than weighing gauges (with or without an Alter shield) for wind speeds up to 5 m s −1 , the wind speed limit at which sufficient data were available. This study shows that the Hotplate precipitation gauge measurement has a low bias and RMSE due to its aerodynamic shape, making its performance mostly independent of the type of solid precipitation.
Beaver dam analogues (BDAs) are becoming an increasingly popular stream restoration technique. One ecological function BDAs might help restore is suitable habitat conditions for fish in streams where loss of beaver dams and channel incision has led to their decline. A critical physical characteristic for fish is stream temperature. We examined the thermal regime of a spring-fed Canadian Rocky Mountain stream in relation to different numbers of BDAs installed in series over three study periods (April–October; 2017–2019). While all BDA configurations significantly influenced stream and pond temperatures, single- and double-configuration BDAs incrementally increased stream temperatures. Single and double configuration BDAs warmed the downstream waters of mean maxima of 9.9, 9.3 °C by respective mean maxima of 0.9 and 1.0 °C. Higher pond and stream temperatures occurred when ponding and discharge decreased, and vice versa. In 2019, variation in stream temperature below double-configuration BDAs was lower than the single-configuration BDA. The triple-configuration BDA, in contrast, cooled the stream, although the mean maximum stream temperature was the highest below these structures. Ponding upstream of BDAs increased discharge and resulted in cooling of the stream. Rainfall events sharply and transiently reduced stream temperatures, leading to a three-way interaction between BDA configuration, rainfall and stream discharge as factors co-influencing the stream temperature regime. Our results have implications for optimal growth of regionally important and threatened bull and cutthroat trout fish species.
The mass-balance—elevation relation for a given glacier is required for many numerical models of ice flow. Direct measurements of this relation using remotely-sensed methods are complicated by ice dynamics, so observations are currently limited to glaciers where surface mass-balance measurements are routinely made. We test the viability of using the continuity equation to estimate annual surface mass balance between flux-gates in the absence of in situ measurements, on five glaciers in the Columbia Mountains of British Columbia, Canada. Repeat airborne laser scanning surveys of glacier surface elevation, ice penetrating radar surveys and publicly available maps of ice thickness are used to estimate changes in surface elevation and ice flux. We evaluate this approach by comparing modeled to observed mass balance. Modeled mass-balance gradients well-approximate those obtained from direct measurement of surface mass balance, with a mean difference of +6.6 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mo>±</mml:mo></mml:math> 4%. Regressing modeled mass balance, equilibrium line altitudes are on average 15 m higher than satellite-observations of the transient snow line. Estimates of mass balance over flux bins compare less favorably than the gradients. Average mean error (+0.03 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><mml:mo>±</mml:mo></mml:math> 0.07 m w.e.) between observed and modeled mass balance over flux bins is relatively small, yet mean absolute error (0.55 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m3"><mml:mo>±</mml:mo></mml:math> 0.18 m w.e.) and average modeled mass-balance uncertainty (0.57 m w.e.) are large. Mass conservation, assessed with glaciological data, is respected (when estimates are within 1σ uncertainties) for 84% of flux bins representing 86% of total glacier area. Uncertainty on ice velocity, especially for areas where surface velocity is low (<10 m a −1 ) contributes the greatest error in estimating ice flux. We find that using modeled ice thicknesses yields comparable modeled mass-balance gradients relative to using observations of ice thickness, but we caution against over-interpreting individual flux-bin mass balances due to large mass-balance residuals. Given the performance of modeled ice thickness and the increasing availability of ice velocity and surface topography data, we suggest that similar efforts to produce mass-balance gradients using modern high-resolution datasets are feasible on larger scales.
Beaver ( Castor canadensis and Castor fiber ) are regarded widely as ecosystem engineers and the dams they create are well-known for their ability to drastically alter the hydrology of rivers. As a result, beaver are increasingly being included in green infrastructure practices to combat the effects of climate change and enhance ecosystem resilience. Both drought and flood mitigation capabilities have been observed in watersheds with beaver dam structures; however, how dams possess contrasting mitigation abilities is not fully understood since most studies neglect to acknowledge variation in beaver dam structures. In this study, an extensive cross-site survey of the physical and hydrologic properties of beaver dams was conducted in the Canadian Rocky Mountains in Alberta. This research aimed to improve the understanding of the hydrology of beaver dams by categorizing dams using their intrinsic properties and landscape settings to identify fundamental patterns that may be applicable across landscape types. The dam flow type classification from Woo and Waddington (1990) was evaluated in this new context and adapted to include two new flow types. The survey of intrinsic beaver dam properties revealed significant differences in dam structure across different sites. Physical differences in dam structure altered the dynamics and variance of pond storage and certain dam attributes related to the landscape setting. For instance, dam material influenced dam height and water source influenced dam length. However, a closer analysis of large rain events showed that the physical structure of dams alters seasonal dynamics of pond storage but not the response to rain events. Overall, this research shows that beaver dams can be both structurally and hydrologically very different from each other. Establishing broadly applicable classifications is vital to understanding the ecosystem resilience and mitigation services beaver dams provide. • Beaver dams in Canadian Rockies are highly diverse structurally and hydrologically. • Beaver dams can be classified by their flow state. • Dam flow state relates to dam physical structure and landscape setting. • Dam hydrological effectiveness depends on flow state. • Important implications for nature-based solutions to climate change.
Snowmelt contributions to streamflow in mid-latitude mountain basins typically dominate other runoff sources on annual and seasonal timescales. Future increases in temperature and changes in precipitation will affect both snow accumulation and seasonal runoff timing and magnitude, but the underlying and fundamental roles of mountain basin geometry and hypsometry on snowmelt sensitivity have received little attention. To investigate the role of basin geometry in snowmelt sensitivity, a linear snow accumulation model and the Cold Regions Hydrological Modeling (CRHM) platform driven are used to estimate how hypsometry affects basin-wide snow volumes and snowmelt runoff. Area-elevation distributions for fifty basins in western Canada were extracted, normalized according to their elevation statistics, and classified into three clusters that represent top-heavy, middle, and bottom-heavy basins. Prescribed changes in air temperature alter both the snow accumulation gradient and the total snowmelt energy, leading to snowpack volume reductions (10–40%), earlier melt onsets (1–4 weeks) and end of melt season (3 weeks), increases in early spring melt rates and reductions in seasonal areal melt rates (up to 50%). Basin hypsometry controls the magnitude of the basin response. The most sensitive basins are bottom-heavy, and have a greater proportion of their area at low elevations. The least sensitive basins are top-heavy, and have a greater proportion of their area at high elevations. Basins with similar proportional areas at high and low elevations fall in between the others in terms of sensitivity and other metrics. This work provides context for anticipating the impacts of ongoing hydrological change due to climate change, and provides guidance for both monitoring networks and distributed modeling efforts.
We compared stable isotopes of water in plant stem (xylem) water and soil collected over a complete growing season from five well-known long-term study sites in northern/cold regions. These spanned a decreasing temperature gradient from Bruntland Burn (Scotland), Dorset (Canadian Shield), Dry Creek (USA), Krycklan (Sweden), to Wolf Creek (northern Canada). Xylem water was isotopically depleted compared to soil waters, most notably for deuterium. The degree to which potential soil water sources could explain the isotopic composition of xylem water was assessed quantitatively using overlapping polygons to enclose respective data sets when plotted in dual isotope space. At most sites isotopes in xylem water from angiosperms showed a strong overlap with soil water; this was not the case for gymnosperms. In most cases, xylem water composition on a given sampling day could be better explained if soil water composition was considered over longer antecedent periods spanning many months. Xylem water at most sites was usually most dissimilar to soil water in drier summer months, although sites differed in the sequence of change. Open questions remain on why a significant proportion of isotopically depleted water in plant xylem cannot be explained by soil water sources, particularly for gymnosperms. It is recommended that future research focuses on the potential for fractionation to affect water uptake at the soil-root interface, both through effects of exchange between the vapour and liquid phases of soil water and the effects of mycorrhizal interactions. Additionally, in cold regions, evaporation and diffusion of xylem water in winter may be an important process.
Abstract. The continental divide along the spine of the Canadian Rockies in southwestern Canada is a critical headwater region for hydrological drainages to the Pacific, Arctic, and Atlantic oceans. Major flooding events are typically attributed to heavy precipitation on its eastern side due to upslope (easterly) flows. Precipitation can also occur on the western side of the divide when moisture originating from the Pacific Ocean encounters the west-facing slopes of the Canadian Rockies. Often, storms propagating across the divide result in significant precipitation on both sides. Meteorological data over this critical region are sparse, with few stations located at high elevations. Given the importance of all these types of events, the Storms and Precipitation Across the continental Divide Experiment (SPADE) was initiated to enhance our knowledge of the atmospheric processes leading to storms and precipitation on either side of the continental divide. This was accomplished by installing specialized meteorological instrumentation on both sides of the continental divide and carrying out manual observations during an intensive field campaign from 24 April–26 June 2019. On the eastern side, there were two field sites: (i) at Fortress Mountain Powerline (2076 m a.s.l.) and (ii) at Fortress Junction Service, located in a high-elevation valley (1580 m a.s.l.). On the western side, Nipika Mountain Resort, also located in a valley (1087 m a.s.l.), was chosen as a field site. Various meteorological instruments were deployed including two Doppler light detection and ranging instruments (lidars), three vertically pointing micro rain radars, and three optical disdrometers. The three main sites were nearly identically instrumented, and observers were on site at Fortress Mountain Powerline and Nipika Mountain Resort during precipitation events to take manual observations of precipitation type and microphotographs of solid particles. The objective of the field campaign was to gather high-temporal-frequency meteorological data and to compare the different conditions on either side of the divide to study the precipitation processes that can lead to catastrophic flooding in the region. Details on field sites, instrumentation used, and collection methods are discussed. Data from the study are publicly accessible from the Federated Research Data Repository at https://doi.org/10.20383/101.0221 (Thériault et al., 2020). This dataset will be used to study atmospheric conditions associated with precipitation events documented simultaneously on either side of a continental divide. This paper also provides a sample of the data gathered during a precipitation event.
Abstract. The main drawback of the reconstruction of high-resolution distributed global radiation (Rg) time series in mountainous semiarid environments is the common lack of station-based solar radiation registers. This work presents 19 years (2000–2018) of high-spatial-resolution (30 m) daily, monthly, and annual global radiation maps derived using the GIS-based model proposed by Aguilar et al. (2010) in a mountainous area in southern Europe: Sierra Nevada (SN) mountain range (Spain). The model was driven by in situ daily global radiation measurements, from 16 weather stations with historical records in the area; a 30 m digital elevation model; and 240 cloud-free Landsat images. The applicability of the modeling scheme was validated against daily global radiation records at the weather stations. Mean RMSE values of 2.63 MJ m−2 d−1 and best estimations on clear-sky days were obtained. Daily Rg at weather stations revealed greater variations in the maximum values but no clear trends with altitude in any of the statistics. However, at the monthly and annual scales, there is an increase in the high extreme statistics with the altitude of the weather station, especially above 1500 m a.s.l. Monthly Rg maps showed significant spatial differences of up to 200 MJ m−2 per month that clearly followed the terrain configuration. July and December were clearly the months with the highest and lowest values of Rg received, and the highest scatter in the monthly Rg values was found in the spring and fall months. The monthly Rg distribution was highly variable along the study period (2000–2018). Such variability, especially in the wet season (October–May), determined the interannual differences of up to 800 MJ m−2 yr−1 in the incoming global radiation in SN. The time series of the surface global radiation datasets here provided can be used to analyze interannual and seasonal variation characteristics of the global radiation received in SN with high spatial detail (30 m). They can also be used as cross-validation reference data for other global radiation distributed datasets generated in SN with different spatiotemporal interpolation techniques. Daily, monthly, and annual datasets in this study are available at https://doi.org/10.1594/PANGAEA.921012 (Aguilar et al., 2021).
We report on the characteristics of precipitation associated with three types of landfalling atmospheric rivers (ARs) over western North America in the winter season from 1980 to 2004. The ARs are classified according to three landfalling regions as southern, middle and northern types. Two main centers of precipitation are associated with the contributions by the ARs: one over Baja California linked to the southern type of the ARs, and the other over Washington State correlated with the northern and middle types of the ARs. ARs are seen to play a dominant role in the occurrences of extreme precipitation events, with a proportionately greater impact on more extreme events. Moisture flux convergence makes the dominant contribution to precipitation when ARs and extreme precipitation occur simultaneously in the studied areas. Moisture flux convergence in these cases is, in turn, dominated by the mean and transient moisture transported by the transient wind, with greater contribution from the latter, which is mainly concentrated in certain areas. The magnitude and direction of vertically integrated vapor transport (IVT) also play a role in determining the amount of precipitation received in the three regions considered. Larger IVT magnitude corresponds to more precipitation, while an IVT direction of about 220° (0° indicating east wind) is most favorable for high precipitation amount, which is especially obvious for the northern type of the ARs.
Abstract. The interior of western Canada, like many similar cold mid- to high-latitude regions worldwide, is undergoing extensive and rapid climate and environmental change, which may accelerate in the coming decades. Understanding and predicting changes in coupled climate–land–hydrological systems are crucial to society yet limited by lack of understanding of changes in cold-region process responses and interactions, along with their representation in most current-generation land-surface and hydrological models. It is essential to consider the underlying processes and base predictive models on the proper physics, especially under conditions of non-stationarity where the past is no longer a reliable guide to the future and system trajectories can be unexpected. These challenges were forefront in the recently completed Changing Cold Regions Network (CCRN), which assembled and focused a wide range of multi-disciplinary expertise to improve the understanding, diagnosis, and prediction of change over the cold interior of western Canada. CCRN advanced knowledge of fundamental cold-region ecological and hydrological processes through observation and experimentation across a network of highly instrumented research basins and other sites. Significant efforts were made to improve the functionality and process representation, based on this improved understanding, within the fine-scale Cold Regions Hydrological Modelling (CRHM) platform and the large-scale Modélisation Environmentale Communautaire (MEC) – Surface and Hydrology (MESH) model. These models were, and continue to be, applied under past and projected future climates and under current and expected future land and vegetation cover configurations to diagnose historical change and predict possible future hydrological responses. This second of two articles synthesizes the nature and understanding of cold-region processes and Earth system responses to future climate, as advanced by CCRN. These include changing precipitation and moisture feedbacks to the atmosphere; altered snow regimes, changing balance of snowfall and rainfall, and glacier loss; vegetation responses to climate and the loss of ecosystem resilience to wildfire and disturbance; thawing permafrost and its influence on landscapes and hydrology; groundwater storage and cycling and its connections to surface water; and stream and river discharge as influenced by the various drivers of hydrological change. Collective insights, expert elicitation, and model application are used to provide a synthesis of this change over the CCRN region for the late 21st century.
Abstract. Long Short-Term Memory (LSTM) networks have been applied to daily discharge prediction with remarkable success. Many practical applications, however, require predictions at more granular timescales. For instance, accurate prediction of short but extreme flood peaks can make a lifesaving difference, yet such peaks may escape the coarse temporal resolution of daily predictions. Naively training an LSTM on hourly data, however, entails very long input sequences that make learning difficult and computationally expensive. In this study, we propose two multi-timescale LSTM (MTS-LSTM) architectures that jointly predict multiple timescales within one model, as they process long-past inputs at a different temporal resolution than more recent inputs. In a benchmark on 516 basins across the continental United States, these models achieved significantly higher Nash–Sutcliffe efficiency (NSE) values than the US National Water Model. Compared to naive prediction with distinct LSTMs per timescale, the multi-timescale architectures are computationally more efficient with no loss in accuracy. Beyond prediction quality, the multi-timescale LSTM can process different input variables at different timescales, which is especially relevant to operational applications where the lead time of meteorological forcings depends on their temporal resolution.
Abstract Rain-on-snow (ROS) events can trigger severe floods in mountain regions. There is high uncertainty about how the frequency of ROS events (ROS) and associated floods will change as climate warms. Previous research has found considerable spatial variability in ROS responses to climate change. Detailed global assessments have not been conducted. Here, atmospheric reanalysis data was used to drive a physically based snow hydrology model to simulate the snowpack and the streamflow response to climate warming of a 5.25 km 2 virtual basin (VB) applied to different high mountain climates around the world. Results confirm that the sensitivity of ROS to climate warming is highly variable among sites, and also with different elevations, aspects and slopes in each basin. The hydrological model predicts a decrease in the frequency of ROS with warming in 30 out 40 of the VBs analyzed; the rest have increasing ROS. The dominant phase of precipitation, duration of snow cover and average temperature of each basin are the main factors that explain this variation in the sensitivity of ROS to climate warming. Within each basin, the largest decreases in ROS were predicted to be at lower elevations and on slopes with sunward aspects. Although the overall frequency of ROS drops, the hydrological importance of ROS is not expected to decline. Peak streamflows due to ROS are predicted to increase due to more rapid melting from enhanced energy inputs, and warmer snowpacks during future ROS.
Currently, post-mining landscape plans in the Athabasca Oil Sand Region include large watersheds terminating in pit lakes. In 2012, Base Mine Lake (BML), was constructed with the aim of demonstrating technologies associated with lake reclamation in the region. This paper examines the first 6.5 years of lake-atmosphere energy and carbon exchange. Energetically, BML behaved similar to other northern lakes, storing large quantities of heat in the spring and releasing it in the fall as sensible and latent heat fluxes. At various times a hydrocarbon sheen formed on the lake, which may have suppressed evaporation. However, simple linear relationships failed to statistically quantify the impacts and more comprehensive modelling of the variability may be required. At daily scales, variability in evaporation was well explained by the product of vapour pressure deficit and wind speed as well as the available energy (R2 = 0.74), while sensible heat was explained by the product of wind speed and the difference in air and surface temperature as well as available energy (R2 = 0.85). Spring CH4 fluxes were high, particularly around ice melt, with a maximum flux of 3.3 g m-2 day-1. Otherwise fluxes were low, except during irregular periods. The peak flux of these periods occurred following ~58 h of continuously falling pressure, relating cyclone activity to these large periods of methane emissions. Annually, CO2 and CH4 fluxes were initially high, with median fluxes of 231 mg CO2 m-2 h-1 and 23 mg CH4 m-2 h-1 in 2014. However, the median fluxes reduced quickly and over the least three years of the study (2017 through 2019) the median fluxes declined to 36 mg CO2 m-2 h-1 and 10 mg CH4 m-2 h-1. Overall, BML behaves similar to other boreal lake ecosystems with above average carbon fluxes compared to other constructed reservoirs.
Streamflow prediction from meteorological observations provides the basic information for the management of water resources systems. The amount and magnitude of streamflow has long term consequences on human lives and the environment. Various approaches have been proposed to simulate streamflow records, ranging from physically based models to conceptual and fully data-driven models. However, the quality of streamflow prediction can be more reliable with intelligence-based data-driven models that handle nonlinear hydrological processes. We propose FlowDyn, a dynamic web based streamflow prediction pipeline to intelligently simulate and forecast global streamflow time series data. In this research a number of data driven deep learning (DL) models including multi-layer perception (MLP), long short-term memory (LSTM) and a hybrid network of convolutional neural network and LSTM (CNN-LSTM) have been implemented as a pipeline to analyze and forecast sequential streamflow values that are embedded within a web-based application for visualization. To improve the quality of streamflow forecast a set of climate and flow influencing factors were obtained from weather stations belonging to different countries across the globe. This study has focused on the effects of input data characteristics on model performance; therefore, the amount of input data and data correlation have been considered. Datasets are gathered from different databases including CAMELS [1], [2], NCDC and GRDC. To impute the missing values and pre-process the available data, several methods have been used and a reliable dataset was generated for models in the pipeline to run prediction tasks on. The developed prediction models were validated and tested using NSE (Nash–Sutcliffe efficiency), KGE (Kling-Gupta efficiency), Normalized Mutual Information (NMI), and Root Mean Squared Error (RMSE) indices. A JavaScript (JS) based web application was provided with the pipeline for users to view flow prediction and variabilities over any station at any watershed system and region. Through the findings of this paper, we advocate the merit of applying data-driven neural models in the field of rainfall-runoff prediction and prove it with global hydrological and weather data.
Landsat 4–5 Thematic Mapper, Landsat 8 Operational Land Imager, and RapidEye-3 data sets were used to identify potential groundwater discharge zones, via icings, in the Central Mackenzie Valley (CMV) of the Northwest Territories. Given that this area is undergoing active shale oil exploration and climatic changes, identification of groundwater discharge zones is of great importance both for pinpointing potential contaminant transport pathways and for characterizing the hydrologic system. Following the work of Morse and Wolfe (2015), a series of image algorithms were applied to imagery for the entire CMV and for the Bogg Creek watershed (a sub watershed of the CMV) for selected years between 2004 and 2017. Icings were statistically examined for all of the selected years to determine whether a significant difference in their spatial occurrence existed. It was concluded that there was a significant difference in the spatial distribution of icings from year to year (α = 0.05), but that there were several places where icings were recurring. During the summer of 2018, these recurrent icings, which are expected to be spring sourced, were verified using a thermal camera aboard a helicopter, as well as in situ measurements of hydraulic gradient, groundwater geochemistry, and electroconductivity. Strong agreement was found between the mapped icings and summer field data, making them ideal field monitoring locations. Furthermore, identifying these discharge points remotely is expected to have drastically reduced the field efforts that would have been required to find them in situ. This work demonstrates the value of remote sensing methods for hydrogeological applications, particularly in remote northern locations.
Abstract This paper provides an updated analysis of observed changes in extreme precipitation using high-quality station data up to 2018. We examine changes in extreme precipitation represented by annual maxima of 1-day (Rx1day) and 5-day (Rx5day) precipitation accumulations at different spatial scales and attempt to address whether the signal in extreme precipitation has strengthened with several years of additional observations. Extreme precipitation has increased at about two-thirds of stations and the percentage of stations with significantly increasing trends is significantly larger than that can be expected by chance for the globe, continents including Asia, Europe, and North America, and regions including central North America, eastern North America, northern Central America, northern Europe, the Russian Far East, eastern central Asia, and East Asia. The percentage of stations with significantly decreasing trends is not different from that expected by chance. Fitting extreme precipitation to generalized extreme value distributions with global mean surface temperature (GMST) as a covariate reaffirms the statistically significant connections between extreme precipitation and temperature. The global median sensitivity, percentage change in extreme precipitation per 1 K increase in GMST is 6.6% (5.1% to 8.2%; 5%–95% confidence interval) for Rx1day and is slightly smaller at 5.7% (5.0% to 8.0%) for Rx5day. The comparison of results based on observations ending in 2018 with those from data ending in 2000–09 shows a consistent median rate of increase, but a larger percentage of stations with statistically significant increasing trends, indicating an increase in the detectability of extreme precipitation intensification, likely due to the use of longer records.
An abundant number of clone detection tools have been proposed in the literature due to the many applications and benefits of clone detection. However, there has been difficulty in the performance evaluation and comparison of these clone detectors. This is due to a lack of reliable benchmarks, and the manual efforts required to validate a large number of candidate clones. In particular, there has been a lack of a synthetic benchmark that can precisely and comprehensively measure clone-detection recall. In this paper, we present a mutation-analysis based benchmarking framework that can be used not only to evaluate the recall of clone detection tools for different types of clones but also for specific kinds of clone edits and without any manual efforts. The framework uses an editing taxonomy of clone synthesis for generating thousands of artificial clones, injects into code bases and automatically evaluates the subject clone detection tools following the mutation analysis approach. Additionally, the framework has features where custom clone pairs could also be used in the framework for evaluating the subject tools. This gives the opportunity of evaluating specialized tools for specialized contexts such as evaluating a tool’s capability for the detection of complex Type-4 clones or real world clones without writing complex mutation operators for them. We demonstrate this framework by evaluating the performance of ten modern clone detection tools across two clone granularities (function and block) and three programming languages (Java, C and C#). Furthermore, we provide a variant of the framework that can be used to evaluate specialized tools such as for large gaped clone detection. Our experiments demonstrate confidence in the accuracy of our Mutation and Injection Framework when comparing against the expected results of the corresponding tools, and widely used real-world benchmarks such as Bellon’s benchmark and BigCloneBench. We provide features so that most clone detection tools that report clones in the form of clone pairs (either in filename/line numbers or filename/tokens) could be evaluated using the framework.
Over the past decade, western North America glaciers experienced strong mass loss. Regional mass loss during the ablation season is influenced by air temperature, but the importance of other factors such as changes in surface albedo remains uncertain. We examine changes in surface albedo for 17 glaciated regions of western North America as documented in a 20-year record (2000 to 2019) of MODIS daily snow albedo (MOD10A1). Trend analysis reveals that albedo declined for 4% to 81% of the albedo grid cells, and the largest negative trends were situated south of 60°N and in the provinces of British Columbia and Alberta. Sen's slope estimates indicate that 15 of 17 regions showed a decline of which the majority of the largest declines occurred within 100 m of glacier median elevation, suggesting that these declines are driven by a rise of the transient snowline. For most regions, albedo correlates strongly to temperature, and albedo trend in the Chugach region of Alaska, the South Coast, Southern Interior and Central and Southern Rockies of Canada show a significant relationship to aerosols optical depth. Temperature is approximately 2–6 times more predictive of the variation in albedo than AOD for the majority of regions, except the Southern Interior and Southern Rockies where albedo shows a greater dependence on AOD. Investigation of broadband albedo (MCD43A3) for snow grid cells above glacier median elevation in the Central and Southern Rockies shows that declines in the visible and near infrared portions of the spectrum are linked to the presence of forest fire generated aerosols. The results of this study indicate that glacier surface mass balance experiences a regional dependence on forest fire generated light absorbing particles. • End of melt season glacier albedo is declining across in western North America. • Albedo decline is largest at glacier median elevation. • Albedo decline strongly correlates to temperature increase. • Albedo decline is correlated with forest fire generated aerosols regionally.
Electrically conductive membranes (ECMs) self-induce antifouling mechanisms at their surface under certain applied electrical currents. Quantifying these mechanisms is critical to enhancing ECMs’ self-cleaning performance. Local pH change and H2O2 production are among the most important self-cleaning mechanisms previously hypothesized for ECMs. However, the impacts of these mechanisms have not previously been isolated and comprehensively studied. In this study, we quantified the individual impact of electrochemically induced acidic conditions, alkaline conditions, and H2O2 concentration on model bacteria, Escherichia coli. To this end, we first quantified the electrochemical potential of carbon nanotube-based ECMs to generate stressors, such as protons, hydroxyl ions, and H2O2, under a range of applied electrical currents (±0–150 mA, 0–2.7 V). Next, these chemical stressors with similar magnitude to that generated at the ECM surfaces were imposed on E. coli cells and biofilms. In the flow-through ECM systems, biofilm viability using LIVE/DEAD staining indicated biofilm viabilities of 39 ± 9.9%, 38 ± 4.7%, 45 ± 5.0%, 34 ± 3.1%, and 75 ± 4.9% after separate exposure to pH 3.5, anodic potential (2 V), pH 11, cathodic potential (2 V), and H2O2 concentration (188 μM). Electrical current-induced pH change at the membrane surface was shown to be more effective in reducing bacterial viability than H2O2 generation and more efficient than bulk pH changes. This study identified antibiofouling mechanisms of ECMs and provides guidance for determining the current patterns that maximize their antifouling effects.
Allometric equations for calculation of tree above-ground biomass (AGB) form the basis for estimates of forest carbon storage and exchange with the atmosphere. While standard models exist to calculate forest biomass across the tropics, we lack a standardized tool for computing AGB across boreal and temperate regions that comprise the global extratropics. Here we present an integrated R package, allodb, containing systematically selected published allometric equations and proposed functions to compute AGB. The data component of the package is based on 701 woody species identified at 24 large Forest Global Earth Observatory (ForestGEO) forest dynamics plots representing a wide diversity of extratropical forests. A total of 570 parsed allometric equations to estimate individual tree biomass were retrieved, checked and combined using a weighting function designed to ensure optimal equation selection over the full tree size range with smooth transitions across equations. The equation dataset can be customized with built-in functions that subset the original dataset and add new equations. Although equations were curated based on a limited set of forest communities and number of species, this resource is appropriate for large portions of the global extratropics and can easily be expanded to cover novel forest types.
Canadians spend approximately 2.2% of the country's gross domestic product on outdoor recreation, but we do not yet know the economic benefits people receive from participating in these activities. I provide the first ever comprehensive assessment of the economic benefits of outdoor recreation in Canada. I use a nationally representative survey of recreational behaviour on over 24,000 Canadians to estimate a Kuhn–Tucker demand model that accounts for substitution between activities and satiation in demand. The results demonstrate that participation in outdoor recreation provides Canadians with $98 billion in annual economic benefits, which is well over twice as large as reported expenditures. I also reveal substantial heterogeneity in recreation benefits across activities and regions in Canada.
Landscape evolution models (LEMs) have the capability to characterize key aspects of geomorphological and hydrological processes. However, their usefulness is hindered by model equifinality and paucity of available calibration data. Estimating uncertainty in the parameter space and resultant model predictions is rarely achieved as this is computationally intensive and the uncertainties inherent in the observed data are large. Therefore, a limits-of-acceptability (LoA) uncertainty analysis approach was adopted in this study to assess the value of uncertain hydrological and geomorphic data. These were used to constrain simulations of catchment responses and to explore the parameter uncertainty in model predictions. We applied this approach to the River Derwent and Cocker catchments in the UK using a LEM CAESAR-Lisflood. Results show that the model was generally able to produce behavioural simulations within the uncertainty limits of the streamflow. Reliability metrics ranged from 24.4% to 41.2% and captured the high-magnitude low-frequency sediment events. Since different sets of behavioural simulations were found across different parts of the catchment, evaluating LEM performance, in quantifying and assessing both at-a-point behaviour and spatial catchment response, remains a challenge. Our results show that evaluating LEMs within uncertainty analyses framework while taking into account the varying quality of different observations constrains behavioural simulations and parameter distributions and is a step towards a full-ensemble uncertainty evaluation of such models. We believe that this approach will have benefits for reflecting uncertainties in flooding events where channel morphological changes are occurring and various diverse (and yet often sparse) data have been collected over such events.
Aqueous film–forming foams (AFFFs) are used in firefighting and are sources of per- and polyfluoroalkyl substances (PFAS) to the environment through surface runoff and groundwater contamination at defense and transportation sites. Little is known regarding the toxicity and bioaccumulation of newer AFFF formulations containing novel PFAS. To mimic maternal transfer of PFAS, prefertilization rainbow trout eggs were exposed to three PFAS using novel methodologies. Batches of unfertilized oocytes were exposed for 3 h to 0, 0.01, 0.1, 1, or 10 µg/ml separately to perfluorooctanoic acid, perfluorohexanoic acid, or perfluorooctanesulfonic acid in either coelomic fluid or Cortland's solution. After exposure, the gametes were fertilized and rinsed with dechlorinated water. Egg yolk was aspirated from a subset of fertilized eggs for PFAS quantification. Each PFAS was detected in yolks of eggs exposed to the respective PFAS, and yolk concentrations were directly proportional to concentrations in aqueous media to which they were exposed. Exposure in coelomic fluid or Cortland's solution resulted in similar concentrations of PFAS in egg yolks. Ratios of PFAS concentrations in oocytes to concentrations in exposure media (oocyte fluid ratios) were <0.99 when exposed from 0.01 to 10 µg/ml and <0.45 when exposed from 0.1 to 10 µg/ml for both media and all three PFAS, demonstrating that the water solubility of the chemicals was relatively great. Prefertilization exposure of eggs effectively introduced PFAS into unfertilized egg yolk. This method provided a means of mimicking maternal transfer to evaluate toxicity to developing embryos from an early stage. This method is more rapid and efficient than injection of individual fertilized eggs and avoids trauma from inserting needles into eggs. Environ Toxicol Chem 2021;40:3159–3165. © 2021 SETAC
Baker Creek drains water from subarctic Canadian Shield terrain comprised of a mix of exposed Precambrian bedrock, lakes, open black spruce forest and peat filled depressions. Research in the catchment has focused on hydrological processes at the hillslope and catchment scales. Streamflow is gauged from several diverse sub-catchments ranging in size from 9 to 155 km2. The period of record (2003–2019) of streamflow from these sub-catchments extends from 12 to 17 years, and these data are the focus of this note. Such data are unique in this remote region. 2003–2019 was a period that included both historic wet and dry conditions. Observations during such a diversity of conditions are helping to improve understanding of how stream networks that drain this landscape expand and contract in response to short and long hydroclimatic cycles. These data from a distinctly cold and dry region of low relief, thin soils, exposed bedrock and permafrost are a valuable contribution to the global diversity of research catchment data.
The Panola Mountain Research Watershed (PMRW) is a 41-hectare forested catchment within the Piedmont Province of the Southeastern United States. Observations, experimentation, and numerical modelling have been conducted at Panola over the past 35 years. But to date, these studies have not been fully incorporated into a more comprehensive synthesis. Here we describe the evolving perceptual understanding of streamflow generation mechanisms at the PMRW. We show how the long-term study has enabled insights that were initially unforeseen but are also unachievable in short-term studies. In particular, we discuss how the accumulation of field evidence, detailed site characterization, and modelling enabled a priori hypotheses to be formed, later rejected, and then further refined through repeated field campaigns. The extensive characterization of the soil and bedrock provided robust process insights not otherwise achievable from hydrometric measurements and numerical modelling alone. We focus on two major aspects of streamflow generation: the role of hillslopes (and their connection to the riparian zone) and the role of catchment storage in controlling fluxes and transit times of water in the catchment. Finally, we present location-independent hypotheses based on our findings at PMRW and suggest ways to assess the representativeness of PMRW in the broader context of headwater watersheds.
Measuring the stable isotope compositions of atmospheric CO2 is common in earth and atmospheric sciences, and various analytical methods have been developed utilizing continuous-flow (CF) or dual-inlet (DI) isotope ratio mass spectrometry (IRMS). Air is typically collected via passive, manual, or automated collection methods and the volume of the air sample ranges from 10 to 300 mL for CF-IRMS to >1 L for DI-IRMS to yield a measurable amount of atmospheric CO2 gas. It has been determined that the integrity of vials and flasks for air sample storage can be compromised after 3 days of air collection for δ13C values and within 10 hours for δ18O values. Air samples must be purified after collection to remove constituents of air, such as Ar, O2, N2, N2O, and water vapor, to avoid isobaric interferences during mass spectrometric measurement. Purification is generally undertaken by utilizing commercial or custom-made preconcentration devices, the blanking method for CF-IRMS, or an offline/online cryogenic separation using a vacuum line for DI-IRMS. Ambient N2O is a component of air that may affect analytical results and thus must either be corrected for or be removed using a gas chromatographic column. In some cases, water is removed during air collection by using a common chemical desiccant, magnesium perchlorate (Mg(ClO4)2), or by a dry ice/alcohol mixture (−78°C). Lastly, a linearity issue for IRMS due to the low amount of purified CO2 from a typical ambient air sample must be considered. In general, analytical precisions of 0.02–0.21‰ and 0.04–0.34‰ for CF-IRMS and 0.01–0.02‰ and 0.01–0.02‰ for DI-IRMS are expected for δ13C and δ18O measurements, respectively.
Food wastage is a critical and world-wide issue resulting from an excess of food supply, poor food storage, poor marketing, and unstable markets. Since food quality depends on consumer standards, it becomes necessary to monitor the quality to ensure it meets those standards. Embedding sensors with active nanomaterials in food packaging enables customers to monitor the quality of their food in real-time. Though there are many different sensors that can monitor food quality and safety, pH sensors and time-temperature indicators (TTIs) are the most critical metrics in indicating quality. This review showcases some of the recent progress, their importance, preconditions, and the various future needs of pH sensors and TTIs in food packaging for smart sensors in food packaging applications. In discussing these topics, this review includes the materials used to make these sensors, which vary from polymers, metals, metal-oxides, carbon-based materials; and their modes of fabrication, ranging from thin or thick film deposition methods, solution-based chemistry, and electrodeposition. By discussing the use of these materials, novel fabrication process, and problems for the two sensors, this review offers solutions to a brighter future for the use of nanomaterials for pH indicator and TTIs in food packaging applications.
The infiltrability of frozen soils modulates the partitioning of snowmelt between infiltration and runoff in cold regions. Preferential flow in macropores may enhance infiltration, but flow dynamics in frozen soil are complicated by soil heat transfer processes. We developed a dual-permeability model that considers the interacting effects of freeze–thaw and preferential flow on infiltration and runoff generation in structured soils. This formulation was incorporated into the fully integrated groundwater–surface water model HydroGeoSphere, to represent water–ice phase change in macropores such that porewater freezing is governed by macropore–matrix heat exchange. Model performance was evaluated against laboratory experiments and synthetic test cases designed to examine the effects of preferential flow on snowmelt partitioning between infiltration, runoff, and drainage. Simulations were able to reproduce experimental observations of rapid infiltration and drainage behavior due to macropores very well, and approximated soil thaw to an acceptable degree. Simulation of measured data highlighted the importance of macropore hydraulic conductivity, as well as macropore–matrix heat and water transfer, on controlling preferential flow dynamics. Test cases replicated a range of snowmelt partitioning behavior commonly observed in frozen soils, including subsurface conditions that produce rapid infiltration and deeper drainage, the contrast between limited vs. unlimited infiltration responses to snowmelt, and the temporal evolution of runoff generation. This study demonstrates the important influence that water freezing along preferential flowpaths can have on infiltrability and runoff characteristics in frozen soils and provides a physically based description of this mechanism that links infiltration behavior to hydraulic and thermal properties of structured soils.
Abstract In 2015, a new automatic weather station (AWS) was installed in a high elevation site in Gredos mountains (Central System, Spain). Since then, a surprisingly high number of heavy precipitation events have been recorded (55 days with precipitation over 50 mm, and a maximum daily precipitation of 446.9 mm), making this site a hotspot in Spain in terms of annual precipitation (2177 mm year) and extreme precipitation events. The neighboring stations available in the region with longer data series, including the closest ones, already informed of wet conditions in the area, but not comparable with such anomaly behavior detected in the new station (51% higher). In this study, we present the temporal variability of detected heavy precipitation events in this mountain area, and its narrow relation with atmospheric patterns over the Iberian Peninsula. Results revealed that 65% of the events occurred during advections from West, Southwest, South and cyclonic situations. A regression analysis showed that the precipitation anomaly is mostly explained by the location windward to the Atlantic wet air masses and the elevation. However, the variance explained by the models is rather low (average R 2 for all events > 50 mm is 0.21). The regression models underestimate on average a 60% intensity of rainfall events. Oppositely, the high-resolution weather forecast model AROME at 0.025° was able to point out the extraordinary character of precipitation at this site, and the underestimation of observed precipitation in the AWS was about 26%. This result strongly suggests the usefulness of weather models to improve the knowledge of climatic extremes over large areas, and to improve the design of currently available observational networks.
The Arctic has been warming faster than the global average during recent decades, and trends are projected to continue through the twenty-first century. Analysis of climate change impacts across the Arctic using dynamical models has almost exclusively been limited to outputs from global climate models or coarser regional climate models. Coarse resolution simulations limit the representation of physical processes, particularly in areas of complex topography and high land-surface heterogeneity. Here, current climate reference and future regional climate model simulations based on the RCP8.5 scenario over Alaska at 4 km grid spacing are compared to identify changes in snowfall and snowpack. In general, results show increases in total precipitation, large decreases in snowfall fractional contribution over 30% in some areas, decreases in snowpack season length by 50–100 days in lower elevations and along the southern Alaskan coastline, and decreases in snow water equivalent. However, increases in snowfall and snowpack of sometimes greater than 20% are evident for some colder northern areas and at the highest elevations in southern Alaska. The most significant changes in snow cover and snowfall fractional contributions occur during the spring and fall seasons. Finally, the spatial pattern of winter temperatures above freezing has small-scale spatial features tied to the topography. Such areas would not be resolved with coarser resolution regional or global climate model simulations.
Lead (Pb) is a highly toxic heavy metal of great environmental and health concerns, and interestingly Pb 2+ has played important roles in nucleic acids chemistry. Since 2000, using DNA for selective detection of Pb 2+ has become a rapidly growing topic in the analytical community. Pb 2+ can serve as the most active cofactor for RNA-cleaving DNAzymes including the GR5, 17E and 8–17 DNAzymes. Recently, Pb 2+ was found to promote a porphyrin metalation DNAzyme named T30695. In addition, Pb 2+ can tightly bind to various G-quadruplex sequences inducing their unique folding and binding to other molecules such as dyes and hemin. The peroxidase-like activity of G-quadruplex/hemin complexes was also used for Pb 2+ sensing. In this article, these Pb 2+ recognition mechanisms are reviewed from fundamental chemistry to the design of fluorescent, colorimetric, and electrochemical biosensors. In addition, various signal amplification mechanisms such as rolling circle amplification, hairpin hybridization chain reaction and nuclease-assisted methods are coupled to these sensing methods to drive up sensitivity. We mainly cover recent examples published since 2015. In the end, some practical aspects of these sensors and future research opportunities are discussed. • Fundamentals of Pb 2+ recognition by DNA including DNAzymes and G-quadruplex DNA reviewed • Literature in the last five years summarized • Practical aspect of Pb 2+ detection using DNA discussed
• We present four methods to calculate LAI on a daily basis from PAR. • Each method shows high linear correlation to MODIS and LAI-2000 datasets. • All methods provide a precise indication of start and end of the growing season. • PAR based LAI has broad potential to reveal phenological response to global change. Leaf area index (LAI) is a critical biophysical indicator that describes foliage abundance in ecosystems. An accurate and continuous estimation of LAI is therefore desirable to quantify ecosystem status and function (e.g. carbon and water exchange between the land surface and the atmosphere). However, deriving accurate LAI measurements at regular temporal intervals remains challenging, requiring either destructive sampling or manual collection of canopy gap fraction measurements at discrete time intervals. In this study, we present four methods to obtain continuous LAI data, simply derived from above and below canopy measurements of photosynthetically active radiation (PAR) at the Borden Forest Research Station from 1999 to 2018. We compared LAI derived using the four PAR-based methods to independent measurements of LAI from optical methods and the MODIS satellite LAI product. LAI derived from all four PAR-based methods captured the seasonal changes in observed and remotely sensed LAI and showed a close linear correspondence with one another (R 2 of 0.55 to 0.76 compared to MODIS LAI, and R 2 of 0.78 to 0.84 compared to LAI-2000 measurements). A PAR-based method using Miller's Integral theorem showed the strongest linear relationship with LAI-2000 measurements (R 2 =0.84, p<0.001, SE=0.40). In many years MODIS LAI indicated an earlier start of season and earlier end of season than the daily PAR-based LAI datasets showing systematic biases in the MODIS assessment of growing season. The four PAR-based LAI methods outlined in this study provide an LAI dataset of unprecedented temporal resolution. These methods will allow precise determination of phenological events, improve leaf to canopy scaling in process-based models, and provide valuable insight into dynamic vegetation responses to global climate change.
Gold mining operations near Yellowknife (Northwest Territories, Canada) released vast quantities of arsenic trioxide during the 1950s, which dispersed across the landscape. Contemporary measurements of arsenic concentrations in lake water and surficial sediment identify enrichment within a 30 km radius. However, paleolimnological studies have identified possible evidence of mining influence during the 1950s at a lake beyond this distance, suggesting a more expansive legacy footprint may exist. Here, we analyze spatiotemporal patterns of arsenic, antimony, and lead deposition from sediment cores at lakes located 10–40 km (near-field) and 50–80 km (far-field) from the mines along the prevailing northwesterly wind direction (NW) and 20–40 km to the northeast (NE) of the mines to improve characterization of the legacy footprint of emissions. We build upon previous findings to determine if: 1) there is evidence of mine-related pollutants beyond the well-established 30 km radius and 2) enrichment is greatest in the prevailing wind direction, as expected for aerial dispersion from a point source of emissions. Results demonstrate enrichment since the 1950s for arsenic and antimony at least as far as 80 km to the NW and 40 km to the NE, thus legacy deposition extended beyond the currently defined 30 km radius ‘zone of immediate influence’. Concentrations, enrichment factors, and total excess inventories of arsenic and antimony decline with distance from the mines and are greater along the prevailing (NW) than orthogonal (NE) wind direction. Peak concentrations in uppermost sediment strata at near-field lakes in the prevailing wind direction suggest supply of arsenic and antimony remains high from legacy stores in the catchment and lake sediment profiles >60 years after emissions were released. Such lasting influence of legacy emissions likely is not limited to mines in the Yellowknife region, and paleolimnological approaches can effectively delineate zones of past and ongoing pollution from legacy sources elsewhere. • We analyze metals in sediment cores to track dispersal of legacy mine emissions. • Enrichment of As and Sb evident beyond known 30-km radius pollution zone. • Distance from source and wind direction influenced contaminant dispersal. • Enriched surface sediments within 30 km suggest ongoing delivery of legacy metals.
Brominated flame retardants (BFRs) can enter aquatic environments where they can have adverse effects on organisms. The BFR, 1,2,5,6-Tetrabromocyclooctane (TBCO), has been introduced as a potential replacement for the major use BRF, Hexabromocyclododecane (HBCD). However, little is known about effects of TBCO on aquatic organisms. Using zebrafish (Danio rerio) as a model species, objectives of this study were to determine whether TBCO has adverse effects on early life-stages and to investigate the molecular and biochemical mechanisms of any effects on development. Exposure to TBCO caused a concentration dependant increase in mortality, decrease in heart rate, and increase in incidences of spinal curvature and uninflated swim bladders. Neither peroxidation of lipids or mRNA abundances of genes important for the response to oxidative stress were greater in embryos exposed to TBCO suggesting effects were not caused by oxidative stress. The mRNA abundance of cytochrome p4501a was not greater in embryos exposed to TBCO suggesting that effects were not caused by activation of the aryl hydrocarbon receptor. Finally, mRNA abundances of genes important for development and inflation of the swim bladder were not affected by TBCO. Overall, TBCO causes adverse effects on early life-stages of zebrafish, but mechanisms of effects require further investigation.
Abstract Model parameters and boundary conditions characterizing flood domains in riverine flood modelling play an important role in the delineation of flood hazard along rivers. Since the digital elevation model (DEM) is an integral part of the delineation of flood hazard, it is necessary to determine the relative sensitivity of the DEM alongside the hydraulic model parameters and boundary conditions. This study provides a novel framework to examine the relative sensitivity of a river ice hydraulic model and various DEMs on ice-jam flood delineation. The Athabasca River at Fort McMurray in Canada is presented as a test site. The study found that ice-jam flood delineation is highly sensitive to DEMs. While flood hazard delineation is low to moderate sensitive to all the model parameters, it is highly sensitive to almost all the boundary conditions.
Oxygen-18 and deuterium were measured in streamflow samples collected from 331 gauging stations across Canada during 2013 to 2019. This dataset includes 9206 isotopic analyses made on 4603 individual water samples, and an additional 1259 analysis repeats for quality assurance/quality control. We also include arithmetic and flow-weighted averages, and other basic statistics for stations where adequate data were available. Station data are provided including station code, name, province, latitude, longitude and drainage area. Flow data were extracted from the historical database of the Water Survey of Canada. Details on the preliminary application of these data are provided in “ 18 O and 2 H in streamflow across Canada” [1] . Overall, these data are expected to be useful when combined with precipitation datasets and analytical or numerical models for water resource management and planning, including tracing streamflow source, water balance, evapotranspiration partitioning, residence time analysis, and early detection of climate and land use changes in Canada.
System-of-systems approaches for integrated assessments have become prevalent in recent years. Such approaches integrate a variety of models from different disciplines and modeling paradigms to represent a socio-environmental (or social-ecological) system aiming to holistically inform policy and decision-making processes. Central to the system-of-systems approaches is the representation of systems in a multi-tier framework with nested scales. Current modeling paradigms, however, have disciplinary-specific lineage, leading to inconsistencies in the conceptualization and integration of socio-environmental systems. In this paper, a multidisciplinary team of researchers, from engineering, natural and social sciences, have come together to detail socio-technical practices and challenges that arise in the consideration of scale throughout the socio-environmental modeling process. We identify key paths forward, focused on explicit consideration of scale and uncertainty, strengthening interdisciplinary communication, and improvement of the documentation process. We call for a grand vision (and commensurate funding) for holistic system-of-systems research that engages researchers, stakeholders, and policy makers in a multi-tiered process for the co-creation of knowledge and solutions to major socio-environmental problems.
Cyberinfrastructure needs to be advanced to enable open and reproducible environmental modeling research. Recent efforts toward this goal have focused on advancing online repositories for data and model sharing, online computational environments along with containerization technology and notebooks for capturing reproducible computational studies, and Application Programming Interfaces (APIs) for simulation models to foster intuitive programmatic control. The objective of this research is to show how these efforts can be integrated to support reproducible environmental modeling. We present first the high-level concept and general approach for integrating these three components. We then present one possible implementation that integrates HydroShare (an online repository), CUAHSI JupyterHub and CyberGIS-Jupyter for Water (computational environments), and pySUMMA (a model API) to support open and reproducible hydrologic modeling. We apply the example implementation for a hydrologic modeling use case to demonstrate how the approach can advance reproducible environmental modeling through the seamless integration of cyberinfrastructure services. • New approaches are needed to support open and reproducible environmental modeling. • Efforts should focus on integrating existing cyberinfrastructure to build new systems. • Our focus is on integrating repositories, computational environments, and model APIs. • An example implementation is shown using HydroShare, JupyterHub, and pySUMMA. • We demonstrate how the approach fosters reproducibility using a modeling case study.
Abstract Integration of Earth system data from various sources is a challenging task. Except for their qualitative heterogeneity, different data records exist for describing similar Earth system processes at different spatiotemporal scales. Data inter-comparison and validation are usually performed at a single spatial or temporal scale, which could hamper the identification of potential discrepancies in other scales. Here, we propose a simple, yet efficient, graphical method for synthesizing and comparing observed and modelled data across a range of spatiotemporal scales. Instead of focusing at specific scales, such as annual means or original grid resolution, we examine how their statistical properties change across spatiotemporal continuum. The proposed cross-scale framework for integrating multi-source data in Earth system sciences is already developed as a stand-alone R package that is freely available to download.
Abstract This study develops a novel reservoir regulation routine, incorporated into a continental-scale hydrologic model in the Nelson, Churchill, Yenisey, Ob, and Lena basins. This regulation routine is integrated into the Hydrological Predictions for the Environment (HYPE) hydrologic model, used for continental-scale applications. Applying this daily timestep regulation routine at 19 reservoirs in the Arctic Ocean watershed, performance is shown to improve upon the reservoir regulation currently available in the HYPE model when testing outflow and storage Nash Sutcliffe Efficiencies (NSEs). Improvements stem from intra-annually variable storage rule curves and a variety of stage-dependent outflow functions, improving simulation skill (median NSE increases of 0.18 over 21 reservoir outflow records and 0.49 over 19 reservoir storage records). This new, reservoir regulation routine is suitable for continental-scale modelling by deriving varying, rather than fixed, threshold water surface levels and associated outflow rules in a programmatic way for multiple reservoirs.
Though mitigation measures and research have increased over the last few decades, ice jams and associated flooding continue to be one of the most underestimated disasters in many northern countries. Operational ice jam flood forecasting systems are becoming one of the more prominent tools used in mitigating ice-related flood risk within Canada. Several forecasting systems have been adopted across the country and forecasters are constantly looking to improve the accuracy and consistency of their systems. The Lower Red River in Manitoba has been the subject in discussion of many ice jam related studies, and a data-driven ice-jam hazard forecasting system is currently in use at this site. This system differs from hydrologic model driven forecasting systems used for other ice jam prone rivers across Canada. This study focuses on identifying the methodology of the data driven ice jam flood forecasting system, along with the methodology of the forecasting procedures. Furthermore, the effectiveness of the data driven forecasting system is measured and assessed for the Lower Red River's 2020 breakup season.
Activated carbons have been widely used for water treatment due to their large surface area and structural stability. Their high cost has motivated the development of sustainable bio-based sorbents. However, their industrial acceptance within the water industry is limited by lower surface areas and poorer adsorptive capacities as compared with commercial sorbents. We herein report a green, high performance porous carbon produced from boreal peats for organic micropollutant removal. Boreal peatlands are increasingly damaged due to climate change-induced wildfires and droughts, which lead to increased run-off and impeded forest regrowth. Fire-impacted peatland soils therefore were excavated and converted into value-added porous carbons through ZnCl 2 activation at low temperature (400 – 600 °C). These products have significantly higher surface areas (> 1377 m 2 /g) than commercial activated carbon Norit GSX (965 m 2 /g). Adsorption of p -nitrophenol, a micropollutant, onto the porous carbons is efficient, and superior to that of Norit GSX and most sorbents reported in the literature. Adsorption mainly occurred through multi-layer chemisorption and was impacted by the electron donor-acceptor complexes mechanism, π-π interactions and steric effects. Because of the massive environmental and economic benefits, peat porous carbons are strong candidates for use in large-scale water treatment facilities. • Simple and rapid synthesis of highly porous carbons from damaged peatland soils. • Peat porous carbons exhibit extraordinary removal for p -nitrophenol (> 530 mg/g). • Maximum adsorption capacity substantially greater than literature values. • Boreal peat porous carbons are eco-friendly high-performance bio-based sorbents for market use.
Climate mediated warming water temperature, drought and extreme flooding are projected to shift the phenology of nutrients in receiving lakes and reservoirs further intensifying eutrophication and algal blooms, especially in temperate reservoirs. An emerging issue in reservoir management is the prediction of climate change impacts, a necessity for sound decision making and sustainable management. Lake Diefenbaker is a large multipurpose reservoir in the Canadian Prairies. In this study, the impact of climate change on nutrient speciation in Lake Diefenbaker is examined using loosely linked SpAtially Referenced Regression On Watershed attributes (SPARROW) and CE-QUAL-W2 models. Two climate mediated scenarios, RCP 8.5 representing the most extreme climate change, and climate induced streamflow were modelled. Nutrient levels are anticipated to double under the climate change and streamflow scenarios. Winter and spring were identified as hot moments for nitrogen pollution with a plausible saturation of nitrous oxides in the future. Of concern is a plausible recycling of nitrate through dissimilatory nitrate reduction to ammonium. Summer and fall on the other hand represent the period for phosphorus enrichment and internal loading with a probable succession of cyanobacteria in the summer. • Nutrient cycling in a large reservoir is investigated under two climate mediated scenarios. • Two loosely coupled models are forced with projected climate and streamflow changes. • Nitrogen pollution is projected to worsen during winter and spring during the 2040 decade. • Reservoir internal loading is anticipated to accelerate during the intermediate decade.
Abstract Nutrient export from agricultural areas is among the main contributors to water pollution in various watersheds. Agricultural Beneficial Management Practices (BMPs) are commonly used to reduce excessive nutrient runoff and improve water quality. The successful uptake of BMPs not only depends on their effectiveness but also on their costs of implementation. This study conducts a set of cost-effectiveness analyses to help stakeholders identify their preferred combinations of BMPs in the Qu’Appelle River Basin, a typical watershed in the Canadian Prairies. The considered BMPs are related to cattle and cropping farms and are initially selected by agricultural producers in this region. The analyses use a water quality model to estimate the impact of implementing BMPs on nutrient export, and the cost estimation model to approximate the cost of implementing BMPs at tributary and watershed scales. Our results show that BMPs' effectiveness, total costs of implementation and costs per kilogram of nutrient abatement vary between tributaries. However, wetland conservation is among the optimal practices to improve water quality across the watershed. It is also found that the rates of BMP adoption by stakeholders can influence the effectiveness of practices in a large watershed scale, which highlights the importance of stakeholder engagement in water quality management. This type of analyses can help stakeholders choose single or a combination of BMPs according to their available budget and acceptable levels of reduction in nutrients.
• The choice of energy-balance or temperature-index snowmelt modules is often ad-hoc. • Two snowmelt modules under two snow density functions are examined in SWAT model. • Cascade of uncertainty for future projections varies across spatiotemporal scales. • Snow density approach is a major control of snow depth simulation and projection. • Unlike mountains, in plain, snowmelt module uncertainties are scanty but vary in time. Snowmelt is a major driver of the hydrological cycle in cold regions, as such, its accurate representation in hydrological models is key to both regional snow depth and streamflow prediction. The choice of a proper method for snowmelt representation is often improvised; however, a thorough characterization of uncertainty in such process representations particularly in the context of climate change has remained essential. To fill this gap, this study revisits and characterizes performance and uncertainty around the two general approaches to snowmelt representation, namely Energy-Balance Modules (EBMs) and Temperature-Index Modules (TIMs). To account for snow depth simulation and projection, two common Snow Density formulations (SNDs) are implemented that map snow water equivalent (SWE) to snow depth. The major research questions we address are two-fold. First, we examine the dominant controls of uncertainty in snow depth and streamflow simulations across scales and in different climates. Second, we evaluate the cascade of uncertainty of snow depth projections resulting from impact model parameters, greenhouse gas emission scenarios, climate models and their internal variability, and downscaling processes. We enable the Soil and Water Assessment Tool (SWAT) by coupling EBM, TIM, and two SND modules for examination of different snowmelt representation methods, and Analysis of Variance (ANOVA) for uncertainty decomposition and attribution. These analyses are implemented in mountainous, foothill, and plain regions in a large snow-dominated watershed in western Canada. Results show, rather counter-intuitively, that the choice of SND is a major control of performance and uncertainty of snow depth simulation rather than the choice between TIMs and EBMs and of their uncertain parameters. Also, analysis of streamflow simulations suggest that EBMs generally overestimate streamflow on main tributaries. Finally, uncertainty decompositions show that parameter uncertainty related to snowmelt modules dominantly controls uncertainty in future snow depth projections under climate change, particularly in mountainous regions. However, in plain regions, the uncertainty contribution of model parameters becomes more variable with time and less dominant compared with the other sources of uncertainty. Overall, it is shown that the hydro-climatic and topographic conditions of different regions, as well as input data availability, have considerable effect on reproduction of snow depth, snowmelt and resulting streamflow, and on the share of different uncertainty sources when projecting regional snow depth.
• Simulation-optimization techniques are essential but computationally cumbersome. • Classic surrogates that globally emulate response surfaces can be of limited help. • Local surrogate models are proposed using automatic clustering for simulation. • The proposed method is shown to be efficient and robust in groundwater remediation. Simulation-optimization techniques in support of groundwater management are computationally expensive. To tackle such computational burden, a variety of surrogate modeling-frameworks have been proposed, where a cheaper-to-run model referred to as a surrogate is used in lieu of a computationally intensive model. These frameworks are generally based on what referred herein to as ‘global surrogate modelling’ where a single surrogate approximates the underlying response surface of a model. Such classic frameworks, however, are sub-optimal when the response surface is complex and/or high-dimensional. This paper proposes a novel ‘local surrogate modelling’ framework that simultaneously builds and evolves multiple local surrogates, guided by an automatic clustering method. Unlike traditional clustering methods that select the number of clusters a priori, the proposed automatic clustering method concurrently determines the optimum number of clusters and the clustering scheme itself. To serve as the surrogate, Artificial Neural Networks (ANNs) are used. The proposed framework is applied to solve a computationally intensive groundwater remediation optimization problem. This study shows that the proposed automatic clustering-based local surrogate modeling is effective and reliable while reducing at least 60 percent of the computational burden.
• Time-variant variogram analysis reveals significant event scale parameter importance variability. • The type of modeling objectives used influences parameter importance. • Input rainfall intensity and hyetograph shape affects parameters importance. • VARS is an effective and robust approach for identifying key modeling parameters. Runoff and sediment yield predictions using rainfall-runoff modeling systems play a significant role in developing sustainable rangeland and water resource management strategies. To characterize the behavior and predictive uncertainty of the KINEROS2 physically-based distributed hydrologic model, we assessed model parameters importance at the event-scale for small nested semi-arid subwatersheds in southeastern Arizona using the Variogram Analysis of Response Surfaces (VARS) methodology. A two-pronged approach using time-aggregate and time-variant parameter importance analysis was adopted to improve understanding of the control and behavior of models. The time-aggregate analysis looks at several signature responses, including runoff volume, sediment yield, peak runoff, runoff duration, time to peak, lag time, and recession duration, to investigate the influence of parameter and input on the model predictions. The time-variant analysis looks at the dynamical influence of parameters on the simulation of flow and sediment rates at every simulation time step using the different forcing inputs. This investigation was able to address Simpson’s paradox-type issues where the analysis across the different objective functions and full data set vs. its subsets (i.e., different events and/or time steps) could yield inconsistent and potentially misleading results. The results indicated the uncertainties in the flow responses are primarily due to the saturated hydraulic conductivity, the Manning’s coefficient, the soil capillary coefficient, and the cohesion in sediment and flow-related responses. The level of influence of K2 parameters depends on the type of the model response surface, the rainfall, and the watershed size.
• Development of the ensemble-based data assimilation framework is examined. • GRACE assimilation improves the simulation of snow estimates at the basin and grid scales. • Data assimilation can effectively constrain the amplitude of modeled water storage dynamics. • GRACE data assimilation improves the simulation of high flows during snowmelt season. Accurate estimation of snow mass or snow water equivalent (SWE) over space and time is required for global and regional predictions of the effects of climate change. This work investigates whether integration of remotely sensed terrestrial water storage (TWS) information, which is derived from the Gravity Recovery and Climate Experiment (GRACE), can improve SWE and streamflow simulations within a semi-distributed hydrology land surface model. A data assimilation (DA) framework was developed to combine TWS observations with the MESH (Modélisation Environnementale Communautaire – Surface Hydrology) model using an ensemble Kalman smoother (EnKS). The snow-dominated Liard Basin was selected as a case study. The proposed assimilation methodology reduced bias of monthly SWE simulations at the basin scale by 17.5% and improved unbiased root-mean-square difference (ubRMSD) by 23%. At the grid scale, the DA method improved ubRMSD values and correlation coefficients for 85% and 97% of the grid cells, respectively. Effects of GRACE DA on streamflow simulations were evaluated against observations from three river gauges, where it effectively improved the simulation of high flows during snowmelt season from April to June. The influence of GRACE DA on the total flow volume and low flows was found to be variable. In general, the use of GRACE observations in the assimilation framework not only improved the simulation of SWE, but also effectively influenced streamflow simulations.
The topic of satellite remote sensing of lake ice has gained considerable attention in recent years. Optical satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) allow for the monitoring of lake ice cover (an Essential Climate Variable or ECV), and dates associated with ice phenology (freeze-up, break-up, and ice cover duration) over large areas in an era where ground-based observational networks have nearly vanished in many northern countries. Ice phenology dates as well as dates of maximum and minimum ice cover extent (for lakes that do not form a complete ice cover in winter or do not totally lose their ice cover in summer) are useful for assessing long-term trends and variability in climate, particularly due to their sensitivity to changes in near-surface air temperature. Existing knowledge-driven (threshold-based) retrieval algorithms for lake ice cover mapping that use top-of-atmosphere (TOA) reflectance products do not perform well under lower solar illumination conditions (i.e. large solar zenith angles), resulting in low TOA reflectance. This research assessed the capability of four machine learning classifiers (i.e. multinomial logistic regression, MLR; support vector machine, SVM; random forest, RF; gradient boosting trees, GBT) for mapping lake ice cover, water and cloud cover during both break-up and freeze-up periods using the MODIS/Terra L1B TOA (MOD02) product. The classifiers were trained and validated using samples collected from 17 large lakes across the Northern Hemisphere (Europe and North America); lakes that represent different characteristics with regards to area, latitude, freezing frequency, and ice duration. Following an accuracy assessment using random k-fold cross-validation (k = 100), all machine learning classifiers using a 7-band combination (visible, near-infrared and shortwave-infrared) were found to be able to produce overall classification accuracies above 94%. Both RF and GBT provided overall and class-specific accuracies above 98% and a more visually accurate depiction of lake ice, water and cloud cover. The two tree-based classifiers offered the most robust spatial transferability over the 17 lakes and performed consistently well across ice seasons. However, only RF was relatively insensitive to the choice of the hyperparameters compared to the other three classifiers. The results demonstrate the potential of RF for mapping lake ice cover globally from MODIS TOA reflectance data. • This study assessed the capability of ML classifiers for lake ice mapping from MOIDS. • RF and GBT produced the best performance in terms of classification accuracies. • RF and GBT offered the most robust spatial and temporal transferability. • RF was insensitive to the choice of the hyperparameters compared to other classifiers. • The results show the potential of RF for mapping lake ice cover globally from MODIS.
Abstract The Windermere Humic Aqueous Model (WHAM) is often used for risk assessment of metals; WHAM can be used to estimate the potential bioavailability of dissolved metals, where metals complexed to dissolved organic matter (DOM) are expected to be less toxic than ionic forms. Silver is a potential metal of concern but WHAM has not been rigorously tested against experimental measurements. This study compares WHAM predictions to measured ionic silver during fixed pH (4, 8 or 10) argentometric titrations of DOM from diverse origins. There were almost two orders of magnitude variation in free silver between sources but, within model uncertainty, WHAM captured this variability. This agreement, between measurements and models, suggests that WHAM is an appropriate tool for silver risk assessment in surface receiving waters when DOM is predominantly in the form of humic/fulvic acids. In sewage samples WHAM dramatically underestimated silver binding by approximately 3 orders of magnitude. Simulations with additional specific strong silver binding sulphide-like binding sites could explain Ag binding at low loadings, but not at higher loadings. This suggests the presence of additional intermediate strength binding sites. These additional ligands would represent components of the raw sewage largely absent in natural waters unimpacted by sewage effluents. A revised empirical model was proposed to account for these sewage-specific binding sites. Further, it is suspected that as sewage organic matter is degraded, either by natural attenuation or by engineered treatment, that sewage organic matter will degrade to a form more readily modelled by WHAM; i.e., humic-like substances. These ageing experiments were performed starting from raw sewage, and the material did in fact become more humic-like, but even after 30 days of aerobic incubation still showed greater Ag+ binding than WHAM predictions. In these incubation experiments it was found that silver (up to 1000 μg/L) had minimal impact on ammonia oxidation kinetics.
Hydroclimatic time series analysis focuses on a few feature types (e.g., autocorrelations, trends, extremes), which describe a small portion of the entire information content of the observations. Aiming to exploit a larger part of the available information and, thus, to deliver more reliable results (e.g., in hydroclimatic time series clustering contexts), here we approach hydroclimatic time series analysis differently, i.e., by performing massive feature extraction. In this respect, we develop a big data framework for hydroclimatic variable behaviour characterization. This framework relies on approximately 60 diverse features and is completely automatic (in the sense that it does not depend on the hydroclimatic process at hand). We apply the new framework to characterize mean monthly temperature, total monthly precipitation and mean monthly river flow. The applications are conducted at the global scale by exploiting 40-year-long time series originating from over 13 000 stations. We extract interpretable knowledge on seasonality, trends, autocorrelation, long-range dependence and entropy, and on feature types that are met less frequently. We further compare the examined hydroclimatic variable types in terms of this knowledge and, identify patterns related to the spatial variability of the features. For this latter purpose, we also propose and exploit a hydroclimatic time series clustering methodology. This new methodology is based on Breiman's random forests. The descriptive and exploratory insights gained by the global-scale applications prove the usefulness of the adopted feature compilation in hydroclimatic contexts. Moreover, the spatially coherent patterns characterizing the clusters delivered by the new methodology build confidence in its future exploitation...
Increased fluxes of reactive nitrogen (N r ), often associated with N fertilizer use in agriculture, have resulted in negative environmental consequences, including eutrophication, which cost billions of dollars per year globally. To address this, best management practices (BMPs) to reduce N r loading to the environment have been introduced in many locations. However, improvements in water quality associated with BMP implementation have not always been realised over expected timescales. There is a now a significant body of scientific evidence showing that the dynamics of legacy N r storage and associated time lags invalidate the assumptions of many models used by policymakers for decision making regarding N r BMPs. Building on this evidence, we believe that the concepts of legacy N r storage dynamics and time lags need to be included in these models. We believe the biogeochemical research community could play a more proactive role in advocating for this change through both awareness raising and direct collaboration with policymakers to develop improved datasets and models. We anticipate that this will result in more realistic expectations of timescales for water quality improvements associated with BMPs. Given the need for multi-nutrient policy responses to tackle challenges such as eutrophication, integration of N stores will have the further benefit of aligning both researchers and policymakers in the N community with the phosphorus and carbon communities, where estimation of stores is more widespread. Ultimately, we anticipate that integrating legacy N r storage dynamics and time lags into policy frameworks will better meet the needs of human and environmental health. • Nitrogen (N) pollution from agriculture has negative environmental impacts. • Environmental benefits of initiatives to reduce N loads not always detectable. • N storage dynamics and time lag invalidate steady state models often used in policy. • Researchers should advocate for integrating N stores and time lags into policy. • Quantifying N storage aligns with phosphorus and carbon cycling research.
• Chemiresistive sensors can be fabricated from percolation networks of few-layer graphene (FLG) flakes. • Functionalization with suitable ligands can achieve selective sensor response to Ag + ions in the 3 ppb to 1 ppm range. • Sensors are robust and reusable, can be reset at pH3 due to a shift in the complexation equilibrium. • The sensor response was tested in an environmental sample (river water) and found to correlate well with ICP-MS data. Silver is used as a water disinfectant in hospital settings as well as in purifiers for potable water. Although there are no strict regulations on the concentration of silver in water, adverse effects such as argyria and respiratory tract irritation have been correlated to excess silver consumption. Based on this, the levels of silver in water are recommended to be maintained below 100 ppb to ensure safety for human consumption. In this work, we present a silver sensor for use in aqueous media that utilizes bathocuproine, a silver selective chromophore, adsorbed onto few-layer graphene (FLG) flake networks for the chemiresistive detection of silver. Complexation of silver to bathocuproine modulates the conductivity of the FLG film, which can be probed by applying a small voltage bias. The decrease in resistance of the film correlates with the concentration of silver in solution between 3 ppb and 1 ppm. Exposing the sensor to a lower pH resets the sensor, allowing it to be reused and reset multiple times. This sensor demonstrates a new pathway to chemiresistive cation sensing using known selective complexing agents adsorbed onto graphitic thin films. This concept can be expanded to the detection of other relevant analytes in domestic, industrial and environmental water sources.
• RT-ddPCR is more sensitive to inhibitors than RT-qPCR for primary clarified sludge. • Primary clarified sludge has elevated frequency of SARS-CoV-2 RNA detection. • Primary clarified sludge allows detection of RNA during low COVID-19 incidence. • PMMoV normalization of RNA data reduces noise and increases precision. • PMMoV normalization of RNA shows strongest correlation to epidemiological metrics. In the absence of an effective vaccine to prevent COVID-19 it is important to be able to track community infections to inform public health interventions aimed at reducing the spread and therefore reduce pressures on health-care, improve health outcomes and reduce economic uncertainty. Wastewater surveillance has rapidly emerged as a potential tool to effectively monitor community infections through measuring trends of RNA signal in wastewater systems. In this study SARS-CoV-2 viral RNA N1 and N2 gene regions are quantified in solids collected from influent post grit solids (PGS) and primary clarified sludge (PCS) in two water resource recovery facilities (WRRF) serving Canada's national capital region, i.e., the City of Ottawa, ON (pop. ≈ 1.1M) and the City of Gatineau, QC (pop. ≈ 280K). PCS samples show signal inhibition using RT-ddPCR compared to RT-qPCR, with PGS samples showing similar quantifiable concentrations of RNA using both assays. RT-qPCR shows higher frequency of detection of N1 and N2 gene regions in PCS (92.7, 90.6%, n = 6) as compared to PGS samples (79.2, 82.3%, n = 5). Sampling of PCS may therefore be an effective approach for SARS-CoV-2 viral quantification, especially during periods of declining and low COVID-19 incidence in the community. The pepper mild mottle virus (PMMoV) is determined to have a less variable RNA signal in PCS over a three month period for two WRRFs, regardless of environmental conditions, compared to Bacteroides 16S rRNA or human 18S rRNA, making PMMoV a potentially useful biomarker for normalization of SARS-CoV-2 signal. PMMoV-normalized PCS RNA signal from WRRFs of two cities correlated with the regional public health epidemiological metrics, identifying PCS normalized to a fecal indicator (PMMoV) as a potentially effective tool for monitoring trends during decreasing and low-incidence of infection of SARS-Cov-2 in communities.
Abstract Recent progress has been made in quantifying snowmelt in the Himalaya. Although the conditions are favorable for refreezing, little is known about the spatial variability of meltwater refreezing, hindering a complete understanding of seasonal snowmelt dynamics. This study aims to improve our understanding about how refreezing varies in space and time. We simulated refreezing with the seNorge (v2.0) snow model for the Langtang catchment, Nepalese Himalaya, covering a 5-year period. Meteorological forcing data were derived from a unique elaborate network of meteorological stations and high-resolution meteorological simulations. The results show that the annual catchment average refreezing amounts to 122 mm w.e. (21% of the melt), and varies strongly in space depending on elevation and aspect. In addition, there is a seasonal altitudinal variability related to air temperature and snow depth, with most refreezing during the early melt season. Substantial intra-annual variability resulted from fluctuations in snowfall. Daily refreezing simulations decreased by 84% (annual catchment average of 19 mm w.e.) compared to hourly simulations, emphasizing the importance of using sub-daily time steps to capture melt–refreeze cycles. Climate sensitivity experiments revealed that refreezing is highly sensitive to changes in air temperature as a 2°C increase leads to a refreezing decrease of 35%.
The current study represents a comprehensive investigation of the occurrence and fates of trenbolone acetate (TBA) and metabolites 17α-trenbolone (17α-TBOH), 17β-TBOH, and trendione (TBO); melengesterol acetate (MGA); and the less commonly studied β-andrenergic agonist ractopamine (RAC) in two 8 month cattle feeding trials and simulated rainfall runoff experiments. Cattle were administered TBA, MGA, or RAC, and their residues were measured in fresh feces, pen floor material, and simulated rainfall runoff from pen floor surfaces and manure-amended pasture. Concentrations of RAC ranged from 3600 ng g–1, dry weight (dw), in pen floor to 58 000 ng g–1 in fresh feces and were, on average, observed at 3–4 orders of magnitude greater than those of TBA and MGA. RAC persisted in pen floors (manure t1/2 = 18–49 days), and contamination of adjacent sites was observed, likely via transport of windblown particulates. Concentrations in runoff water from pen floors extrapolated to larger-scale commercial feedlots revealed that a single rainfall event could result in mobilization of gram quantities of RAC. This is the first report of RAC occurrence and fate in cattle feedlot environments, and will help understand the risks posed by this chemical and inform appropriate manure-management practices.
Precipitation extremes are expected to intensify under climate change with consequent impacts in flooding and ecosystem functioning. Here we use station data and high-resolution simulations from the WRF convection permitting climate model (∼4 km, 1 h) over the US to assess future changes in hourly precipitation extremes. It is demonstrated that hourly precipitation extremes and storm depths are expected to intensify under climate change and what is now a 20-year rainfall will become a 7-year rainfall on average for ∼ 75% of gridpoints over the US. This intensification is mostly expressed as an increase in rainfall tail heaviness. Statistically significant changes in the seasonality and duration of rainfall extremes are also exhibited over ∼ 95% of the domain. Our results suggest more non-linear future precipitation extremes with shorter spell duration that are distributed more uniformly throughout the year.
In the future, the intensity, phases, and frequency of precipitation are expected to change due to global warming, in particular during colder seasons when temperatures are near 0°C. To investigate the impacts of warmer atmospheric conditions on the microphysical processes that lead to several precipitation types, the extreme 1998 Ice Storm was simulated using the Weather Research and Forecasting (WRF) model, with and without a pseudo-global warming. The pseudo-global warming approach simulates similar large-scale conditions but in warmer conditions, which allows for the assessment of thermodynamic feedback from cloud and precipitation microphysics. For both simulations, WRF was coupled with the Predicted Particle Properties (P3) bulk microphysics scheme that predicts the liquid fraction of mixed-phase particles. Results of the pseudo-global warming simulation show an increase of ∼828 m in the upper 0°C level and a northeastward migration (∼60 km) of the rain-snow transition region. The results also show a 20% decrease in domain-averaged freezing rain amounts, but with an increased maximum amount of 50%. The horizontal distance associated with a melting aloft and a refreezing layer near the surface is 105 km longer in southern Quebec due to the combined effects of the pseudo-warming and the presence of the Appalachian Mountains. The microphysical processes that lead to precipitation are impacted as well; the increased ice mass and riming conditions aloft in warmer temperatures result in higher liquid precipitation rates. This study contributes to our understanding of the changes in the fine-scale processes of an extreme storm, simulated with pseudo-global warming conditions.
Land Surface Albedo (LSA) of forested environments continues to be a source of uncertainty in land surface modeling, especially across seasonally snow covered domains. Assessment and improvement of global scale model performance has been hampered by the contrasting spatial scales of model resolution and in‐situ LSA measurements. In this study, point‐scale simulations of the Community Land Model 5.0 (CLM5) were evaluated across a large range of forest structures and solar angles at two climatically different locations. LSA measurements, using an uncrewed aerial vehicle with up and down‐looking shortwave radiation sensors, showed canopy structural shading of the snow surface exerted a primary control on LSA. Diurnal patterns of measured LSA revealed strong effects of both azimuth and zenith angles, neither of which were adequately represented in simulations. In sparse forest environments, LSA were overestimated by up to 66%. Further analysis revealed a lack of correlation between Plant Area Index (PAI), the primary canopy descriptor in CLM5, and measured LSA. Instead, measured LSA showed considerable correlation with the fraction of snow visible in the sensor's field of view, a correlation which increased further when only considering the sunlit fraction of visible snow. The use of effective PAI values as a simple first‐order correction for the discrepancy between measured and simulated LSA in sparse forest environments substantially improved model results (64%–76% RMSE reduction). However, the large biases suggest the need for a more generic solution, for example, by introducing a canopy metric that represents canopy gap fraction rather than assuming a spatially homogeneous canopy.
The successful Soil Moisture Active Passive (SMAP) mission provides operational soil moisture products of high quality; yet its impacts on global carbon and water cycle estimation are yet to be further investigated. Here we assimilated the SMAP enhanced Level-2 soil moisture product at 9 km resolution into a land surface scheme in order to study the soil moisture control on the functioning of terrestrial ecosystems. We found that SMAP significantly improves soil moisture simulations, especially in the spring. Extensive wetting signals were revealed over croplands in arid and semi-arid regions and could not be explained using reanalysis meteorological data, indicating an additional water input, for example, irrigation. Stronger impacts on gross primary production and evapotranspiration simulations are found in wetting adjustments than in drying adjustments after data assimilation. This study suggests that the performance of the land surface scheme benefits greatly from assimilating the SMAP soil moisture product.
Peat accumulation in high latitude wetlands represents a natural long-term carbon sink, resulting from the cumulative excess of growing season net ecosystem production over non-growing season (NGS) net mineralization in soils. With high latitudes experiencing warming at a faster pace than the global average, especially during the NGS, a major concern is that enhanced mineralization of soil organic carbon will steadily increase CO2 emissions from northern peatlands. In this study, we conducted laboratory incubations with soils from boreal and temperate peatlands across Canada. Peat soils were pretreated for different soil moisture levels, and CO2 production rates were measured at 12 sequential temperatures, covering a range from - 10 to + 35 °C including one freeze-thaw event. On average, the CO2 production rates in the boreal peat samples increased more sharply with temperature than in the temperate peat samples. For same temperature, optimum soil moisture levels for CO2 production were higher in the peat samples from more flooded sites. However, standard reaction kinetics (e.g., Q10 temperature coefficient and Arrhenius equation) failed to account for the apparent lack of temperature dependence of CO2 production rates measured below 0 °C, and a sudden increase after a freezing event. Thus, we caution against using the simple kinetic expressions to represent the CO2 emissions from northern peatlands, especially regarding the long NGS period with multiple soil freeze and thaw events.
Abstract Peatlands are important ecosystems that store approximately one third of terrestrial organic carbon. Non-growing season carbon fluxes significantly contribute to annual carbon budgets in peatlands, yet their response to climate change is poorly understood. Here, we investigate the governing environmental variables of non-growing season carbon emissions in a northern peatland. We develop a support-vector regression model using a continuous 13-year dataset of eddy covariance flux measurements from the Mer Blue Bog, Canada. We determine that only seven variables were needed to reproduce carbon fluxes, which were most sensitive to net radiation above the canopy, soil temperature, wind speed and soil moisture. We find that changes in soil temperature and photosynthesis drove changes in net carbon flux. Assessing net ecosystem carbon exchange under three representative concentration pathways, we project a 103% increase in peatland carbon loss by 2100 under a high emissions scenario. We suggest that peatland carbon losses constitute a strong positive climate feedback loop.
Microbial degradation of organic matter is a key driver of subsurface biogeochemistry. Here, we present a bioenergetics-informed kinetic model for the anaerobic degradation of macromolecular organi...
Vernal pools are small, temporary, forested wetlands of ecological importance with a high sensitivity to changing climate and land-use patterns. These ecosystems are under considerable development ...
Despite several high-profile data breaches and business models that commercialize user data, participation in social media networks continues to require users to trust corporations to safeguard the...
The Internet of Things (IoT) has become an integral part of future solutions, ranging from industrial to everyday human life applications. Adding a new level of intelligence to objects and automating decisions make this new technology appealing to everyone. However, applications that involve data are more vulnerable to various types of attacks. As a result, researchers are constantly exploring secure connections between IoT edge nodes. On one hand, suitable IoT nodes should be cheap and require low power, which means lower computational performance. On the other hand, a secure connection layer is power hungry and requires powerful hardware resources. Lightweight cryptography (LWC) algorithms are a promising solution to reduce computation complexity while maintaining a desired level of security. In the presented work, we attempt to address the issue of adding security to the IoT network layer by comparing the performance of 32 LWC algorithms with currently well-known algorithms on multiple IoT platforms (Raspberry Pi 3, Raspberry Pi Zero W, and iMX233). These 32 authenticated encryption with associated data algorithms have been selected from the second round of the LWC standardization process conducted by the National Institute of Standards and Technology. Power consumption, random access memory usage, and execution time are measured for these algorithms using the targeted embedded platforms that are used as IoT sensor nodes. The results of this study will assist researchers in choosing a suitable platform and optimal LWC algorithm for IoT applications.
Ice‐jam flood risk management requires new approaches to reduce flood damages. Although many structural and non‐structural measures are implemented to reduce the impacts of ice‐jam flooding, there are still many challenges in identifying appropriate strategies to reduce the ice‐jam flood risk along northern rivers. The main purpose of this study is to provide a novel methodological framework to assess the feasibility of various ice‐jam flood mitigation measures based on risk analysis. A total of three ice‐jam flood mitigation measures (artificial breakup, sediment dredging and dike installation) were examined using a stochastic modelling framework for the potential to reduce the ice‐jam flood risk along the Athabasca River at Fort McMurray. An ensemble of hundreds of backwater level profiles was used to construct ice‐jam flood hazard maps to estimate expected annual damages, using depth‐damage curves for structural and content damages, within the downtown area of Fort McMurray. The results show that, while sediment dredging may be able to reduce a certain level of expected annual damages in the town, and artificial breakup and a dike with a crest elevation of 250 m a.s.l. can be the most effective measures to reduce the amount of expected annual damages.
Glacier melt is an important fresh water source. Seasonal changes can have impacting consequences on downstream water resources management. Today’s glacier monitoring lacks an observation-based tool for regional, sub-seasonal observation of glacier mass balance and a quantification of associated meltwater release at high temporal resolution. The snowline on a glacier marks the transition between the ice and snow surface, and is, at the end of the summer, a proxy for the annual glacier mass balance. It was shown that glacier mass balance model simulations closely tied to sub-seasonal snowline observations on optical satellite sensors are robust for the observation date. Recent advances in remote sensing permit efficient and extensive snowline mapping. Different methods automatically discriminate snow over ice on high- to medium-resolution optical satellite images. Other studies rely on lower ground resolution optical imagery to retrieve snow cover fraction at pixel level and produce regional maps of snow cover extent. However, state-of-the-art methods using optical sensors still have important shortcomings, such as cloud-cover related issues. Images acquired by Synthetic Aperture Radar (SAR), which are almost insensitive to cloud coverage, have proofed suitable for transient snowline delineation. The combination of SAR and optical data in a complementary way carries a unique potential for a better monitoring of snow depletion on high temporal and spatial resolution. The aim of this work is to map snow cover over glaciers by combining Sentinel-1 SAR, Sentinel-2 multispectral and lower resolution MODIS images. Consecutively, we developed an approach that can automatically handle classification of multi-source and multi-resolution satellite image stacks. This provides a unique solution for continuous snowline mapping since the beginning of the century. With the provided close-to-daily transient snow cover fractions on glacier level, we provide the basis for a new strategy to directly integrate multi-source satellite image classification into glacier mass balance monitoring.
Arctic tundra environments are characterized by a spatially heterogeneous end-of-winter snow depth resulting from wind transport and deposition. Traditional methods for measuring snow depth do not accurately capture such heterogeneity at catchment scales. In this study we address the use of high-resolution, spatially distributed, snow depth data for Arctic environments through the application of unmanned aerial systems (UASs). We apply Structure-from-Motion photogrammetry to images collected using a fixed-wing UAS to produce a 1 m resolution snow depth product across seven areas of interest (AOIs) within the Trail Valley Creek Research Watershed, Northwest Territories, Canada. We evaluated these snow depth products with in situ measurements of both the snow surface elevation (n = 8434) and snow depth (n = 7191). When all AOIs were averaged, the RMSE of the snow surface elevation models was 0.16 m (<0.01 m bias), similar to the snow depth product (UAS SD ) RMSE of 0.15 m (+0.04 m bias). The distribution of snow depth between in situ measurements and UAS SD was similar along the transects where in situ snow depth was collected, although similarity varies by AOI. Finally, we provide a discussion of factors that may influence the accuracy of the snow depth products including vegetation, environmental conditions, and study design.
Fires are a natural phenomenon in the boreal forest, but their frequency is expected to increase over the coming century. Fires may affect water quality and invertebrates in lakes, but there have been few studies in the northern boreal forest to describe these impacts. We collected data on water quality, macrophytes, and invertebrates from 20 lakes in the Sahtú Settlement Area of the Northwest Territories. Nine lakes were affected by fires in their catchments 4–5 years before data collection, while eleven were not. Our results showed that few water quality variables were associated with fires. However, remote sensing and field observations suggested that macrophyte biomass was higher in lakes affected by burns, and this variable was a significant predictor of invertebrate composition. Burn history was an important predictor of the richness and abundance of invertebrates, but natural variability in lake properties was more important for explaining differences among lakes. Our results suggest that a better understanding of the effects of wildfires might be gained by examining how postfire changes in macrophytes affect other trophic levels.
The COVID-19 pandemic and anthropogenic climate change are global crises. We show how strongly these crises are connected, including the underlying societal inequities and problems of poverty, substandard housing, and infrastructure including clean water supplies. The origins of all these crises are related to modern consumptive industrialisation, including burning of fossil fuels, increasing human population density, and replacement of natural with human dominated ecosystems. Because business as usual is unsustainable on all three fronts, transformative responses are needed. We review the literature on risk management interventions, implications for COVID-19, for climate change risk and for equity associated with biodiversity, water and WaSH, health systems, food systems, urbanization and governance. This paper details the considerable evidence base of observed synergies between actions to reduce pandemic and climate change risks while enhancing social justice and biodiversity conservation. It also highlights constraints imposed by governance that can impede deployment of synergistic solutions. In contrast to the response to the COVID-19 pandemic, governance systems have procrastinated on addressing climate change and biodiversity loss as these are interconnected chronic crises. It is now time to address all three to avoid a multiplication of future crises across health, food, water, nature, and climate systems.
Global climate warming disproportionately affects high-latitude and mountainous terrestrial ecosystems. Warming is accompanied by permafrost thaw, shorter winters, earlier snowmelt, more intense soil freeze-thaw cycles, drier summers, and longer fire seasons. These environmental changes in turn impact surface water and groundwater flow regimes, water quality, greenhouse gas emissions, soil stability, vegetation cover, and soil (micro)biological communities. Warming also facilitates agricultural expansion, urban growth, and natural resource development, adding growing anthropogenic pressures to cold regions’ landscapes, soil health, and biodiversity. Further advances in the predictive understanding of how cold regions’ critical zone processes, functions, and ecosystem services will continue to respond to climate warming and land use changes require multiscale monitoring technologies coupled with integrated observational and modeling tools. We highlight some of the major challenges, knowledge gaps, and opportunities in cold region critical zone research, with an emphasis on subsurface processes and responses in both natural and agricultural ecosystems.
Abstract Twenty-seven models participated in the Earth System Model–Snow Model Intercomparison Project (ESM-SnowMIP), the most data-rich MIP dedicated to snow modeling. Our findings do not support the hypothesis advanced by previous snow MIPs: evaluating models against more variables and providing evaluation datasets extended temporally and spatially does not facilitate identification of key new processes requiring improvement to model snow mass and energy budgets, even at point scales. In fact, the same modeling issues identified by previous snow MIPs arose: albedo is a major source of uncertainty, surface exchange parameterizations are problematic, and individual model performance is inconsistent. This lack of progress is attributed partly to the large number of human errors that led to anomalous model behavior and to numerous resubmissions. It is unclear how widespread such errors are in our field and others; dedicated time and resources will be needed to tackle this issue to prevent highly sophisticated models and their research outputs from being vulnerable because of avoidable human mistakes. The design of and the data available to successive snow MIPs were also questioned. Evaluation of models against bulk snow properties was found to be sufficient for some but inappropriate for more complex snow models whose skills at simulating internal snow properties remained untested. Discussions between the authors of this paper on the purpose of MIPs revealed varied, and sometimes contradictory, motivations behind their participation. These findings started a collaborative effort to adapt future snow MIPs to respond to the diverse needs of the community.
Abstract This study presents an analysis of daily temperature and precipitation extremes with return periods ranging from 2 to 50 years in phase 6 of the Coupled Model Intercomparison Project (CMIP6) multimodel ensemble of simulations. Judged by similarity with reanalyses, the new-generation models simulate the present-day temperature and precipitation extremes reasonably well. In line with previous CMIP simulations, the new simulations continue to project a large-scale picture of more frequent and more intense hot temperature extremes and precipitation extremes and vanishing cold extremes under continued global warming. Changes in temperature extremes outpace changes in global annual mean surface air temperature (GSAT) over most landmasses, while changes in precipitation extremes follow changes in GSAT globally at roughly the Clausius–Clapeyron rate of ~7% °C −1 . Changes in temperature and precipitation extremes normalized with respect to GSAT do not depend strongly on the choice of forcing scenario or model climate sensitivity, and do not vary strongly over time, but with notable regional variations. Over the majority of land regions, the projected intensity increases and relative frequency increases tend to be larger for more extreme hot temperature and precipitation events than for weaker events. To obtain robust estimates of these changes at local scales, large initial-condition ensemble simulations are needed. Appropriate spatial pooling of data from neighboring grid cells within individual simulations can, to some extent, reduce the needed ensemble size.
Abstract The diurnal cycle of solar radiation represents the strongest energetic forcing and dominates the exchange of heat and mass of the land surface with the atmosphere. This diurnal heat redistribution represents a core of land–atmosphere coupling that should be accurately represented in land surface models (LSMs), which are critical parts of weather and climate models. We employ a diagnostic model evaluation approach using a signature-based metric that describes the diurnal variation of heat fluxes. The metric is obtained by decomposing the diurnal variation of surface heat fluxes into their direct response and the phase lag to incoming solar radiation. We employ the output of 13 different LSMs driven with meteorological forcing of 20 FLUXNET sites (PLUMBER dataset). All LSMs show a poor representation of the evaporative fraction and thus the diurnal magnitude of the sensible and latent heat flux under cloud-free conditions. In addition, we find that the diurnal phase of both heat fluxes is poorly represented. The best performing model only reproduces 33% of the evaluated evaporative conditions across the sites. The poor performance of the diurnal cycle of turbulent heat exchange appears to be linked to how models solve for the surface energy balance and redistribute heat into the subsurface. We conclude that a systematic evaluation of diurnal signatures is likely to help to improve the simulated diurnal cycle, better represent land–atmosphere interactions, and therefore improve simulations of the near-surface climate.
The professional practice of planning and the state-controlled mechanisms under which western-science planning operate offer little to improve the lives of Indigenous people and their communities. Arguably, western-science planning along with its many legal tools, collectively reproduce existing colonial relations in the interest of state domination over, and suppression of, Indigenous people. In this paper, we describe a different planning model, one that Viswanathan (2019) refers to as “parallel planning”, wherein Indigenous planning principles are practiced in parallel to western-science planning, with each approach informing, and complementing, the other. Our case example is from the Saskatchewan River Delta wherein Indigenous values nested in traditional knowledge in the land and water are the centrepiece of a planning process supported by the western-science planning framework. Challenges facing this approach will be discussed alongside suggestions on how these challenges may be overcome.
Industrial wastewaters and urban discharges contain complex mixtures of chemicals capable of impacting reproductive performance in freshwater fish, called endocrine-disrupting compounds (EDCs). In Chile, the issue was highlighted by our group beginning over 15 years ago, by analyzing the impacts of pulp and paper mill effluents (PPME) in the Biobio, Itata, and Cruces River basins. All of the rivers studied are important freshwater ecosystems located in the Mediterranean region of Central Chile, each with a unique fish biodiversity. Sequentially, we developed a strategy based on laboratory assays, semicontrolled-field experiments (e.g., caging) and wild fish population assessments to explore the issue of reproductive impacts on both introduced and native fish in Chile. The integration of watershed, field, and laboratory studies was effective at understanding the endocrine responses in Chilean freshwater systems. The studies demonstrated that regardless of the type of treatment, pulp mill effluents can contain compounds capable of impacting endocrine systems. Urban wastewater treatment plant effluents (WWTP) were also investigated using the same integrated strategy. Although not directly compared, PPME and WWTP effluent seem to cause similar estrogenic effects in fish after waterborne exposure, with differing intensities. This body of work underscores the urgent need for further studies on the basic biology of Chilean native fish species, and an improved understanding on reproductive development and variability across Chilean ecosystems. The lack of knowledge of the ontogeny of Chilean fish, especially maturation and sexual development, with an emphasis on associated habitats and landscapes, are impediment factors for their conservation and protection against the threat of EDCs. The assessment of effects on native species in the receiving environment is critical for supporting and designing protective regulations and remediation strategies, and for conserving the unique Chilean fish biodiversity.
Citizen science initiatives span a wide range of topics, designs, and research needs. Despite this heterogeneity, there are several common barriers to the uptake and sustainability of citizen science projects and the information they generate. One key barrier often cited in the citizen science literature is data quality. Open-source tools for the analysis, visualization, and reporting of citizen science data hold promise for addressing the challenge of data quality, while providing other benefits such as technical capacity-building, increased user engagement, and reinforcing data sovereignty. We developed an operational citizen science tool called the Community Water Data Analysis Tool (CWDAT)—a R/Shiny-based web application designed for community-based water quality monitoring. Surveys and facilitated user-engagement were conducted among stakeholders during the development of CWDAT. Targeted recruitment was used to gather feedback on the initial CWDAT prototype’s interface, features, and potential to support capacity building in the context of community-based water quality monitoring. Fourteen of thirty-two invited individuals (response rate 44%) contributed feedback via a survey or through facilitated interaction with CWDAT, with eight individuals interacting directly with CWDAT. Overall, CWDAT was received favourably. Participants requested updates and modifications such as water quality thresholds and indices that reflected well-known barriers to citizen science initiatives related to data quality assurance and the generation of actionable information. Our findings support calls to engage end-users directly in citizen science tool design and highlight how design can contribute to users’ understanding of data quality. Enhanced citizen participation in water resource stewardship facilitated by tools such as CWDAT may provide greater community engagement and acceptance of water resource management and policy-making.
Hydrological signatures, i.e., statistical features of streamflow time series, are used to characterize the hydrology of a region. A relevant problem is the prediction of hydrological signatures in ungauged regions using the attributes obtained from remote sensing measurements at ungauged and gauged regions together with estimated hydrological signatures from gauged regions. The relevant framework is formulated as a regression problem, where the attributes are the predictor variables and the hydrological signatures are the dependent variables. Here we aim to provide probabilistic predictions of hydrological signatures using statistical boosting in a regression setting. We predict 12 hydrological signatures using 28 attributes in 667 basins in the contiguous US. We provide formal assessment of probabilistic predictions using quantile scores. We also exploit the statistical boosting properties with respect to the interpretability of derived models. It is shown that probabilistic predictions at quantile levels 2.5% and 97.5% using linear models as base learners exhibit better performance compared to more flexible boosting models that use both linear models and stumps (i.e., one-level decision trees). On the contrary, boosting models that use both linear models and stumps perform better than boosting with linear models when used for point predictions. Moreover, it is shown that climatic indices and topographic characteristics are the most important attributes for predicting hydrological signatures.
We evaluate the potential of using a process-based ecosystem model (BEPS) for crop biomass mapping at 20 m resolution over the research site in Manitoba, western Canada driven by spatially explicit leaf area index (LAI) retrieved from Sentinel-2 spectral reflectance throughout the entire growing season. We find that overall, the BEPS-simulated crop gross primary production (GPP), net primary production (NPP), and LAI time-series can explain 82%, 83%, and 85%, respectively, of the variation in the above-ground biomass (AGB) for six selected annual crops, while an application of individual crop LAI explains only 50% of the variation in AGB. The linear relationships between the AGB and these three indicators (GPP, NPP and LAI time-series) are rather high for the six crops, while the slopes of the regression models vary for individual crop type, indicating the need for calibration of key photosynthetic parameters and carbon allocation coefficients. This study demonstrates that accumulated GPP and NPP derived from an ecosystem model, driven by Sentinel-2 LAI data and abiotic data, can be effectively used for crop AGB mapping; the temporal information from LAI is also effective in AGB mapping for some crop types.
Abstract. Methane emissions from boreal and arctic wetlands, lakes, and rivers are expected to increase in response to warming and associated permafrost thaw. However, the lack of appropriate land cover datasets for scaling field-measured methane emissions to circumpolar scales has contributed to a large uncertainty for our understanding of present-day and future methane emissions. Here we present the Boreal–Arctic Wetland and Lake Dataset (BAWLD), a land cover dataset based on an expert assessment, extrapolated using random forest modelling from available spatial datasets of climate, topography, soils, permafrost conditions, vegetation, wetlands, and surface water extents and dynamics. In BAWLD, we estimate the fractional coverage of five wetland, seven lake, and three river classes within 0.5 × 0.5∘ grid cells that cover the northern boreal and tundra biomes (17 % of the global land surface). Land cover classes were defined using criteria that ensured distinct methane emissions among classes, as indicated by a co-developed comprehensive dataset of methane flux observations. In BAWLD, wetlands occupied 3.2 × 106 km2 (14 % of domain) with a 95 % confidence interval between 2.8 and 3.8 × 106 km2. Bog, fen, and permafrost bog were the most abundant wetland classes, covering ∼ 28 % each of the total wetland area, while the highest-methane-emitting marsh and tundra wetland classes occupied 5 % and 12 %, respectively. Lakes, defined to include all lentic open-water ecosystems regardless of size, covered 1.4 × 106 km2 (6 % of domain). Low-methane-emitting large lakes (>10 km2) and glacial lakes jointly represented 78 % of the total lake area, while high-emitting peatland and yedoma lakes covered 18 % and 4 %, respectively. Small (<0.1 km2) glacial, peatland, and yedoma lakes combined covered 17 % of the total lake area but contributed disproportionally to the overall spatial uncertainty in lake area with a 95 % confidence interval between 0.15 and 0.38 × 106 km2. Rivers and streams were estimated to cover 0.12 × 106 km2 (0.5 % of domain), of which 8 % was associated with high-methane-emitting headwaters that drain organic-rich landscapes. Distinct combinations of spatially co-occurring wetland and lake classes were identified across the BAWLD domain, allowing for the mapping of “wetscapes” that have characteristic methane emission magnitudes and sensitivities to climate change at regional scales. With BAWLD, we provide a dataset which avoids double-accounting of wetland, lake, and river extents and which includes confidence intervals for each land cover class. As such, BAWLD will be suitable for many hydrological and biogeochemical modelling and upscaling efforts for the northern boreal and arctic region, in particular those aimed at improving assessments of current and future methane emissions. Data are freely available at https://doi.org/10.18739/A2C824F9X (Olefeldt et al., 2021).
Abstract. The evaluation of models in general is a nontrivial task and can, due to epistemological and practical reasons, never be considered complete. Due to this incompleteness, a model may yield correct results for the wrong reasons, i.e., via a different chain of processes than found in observations. While guidelines and strategies exist in the atmospheric sciences to maximize the chances that models are correct for the right reasons, these are mostly applicable to full physics models, such as numerical weather prediction models. The Intermediate Complexity Atmospheric Research (ICAR) model is an atmospheric model employing linear mountain wave theory to represent the wind field. In this wind field, atmospheric quantities such as temperature and moisture are advected and a microphysics scheme is applied to represent the formation of clouds and precipitation. This study conducts an in-depth process-based evaluation of ICAR, employing idealized simulations to increase the understanding of the model and develop recommendations to maximize the probability that its results are correct for the right reasons. To contrast the obtained results from the linear-theory-based ICAR model to a full physics model, idealized simulations with the Weather Research and Forecasting (WRF) model are conducted. The impact of the developed recommendations is then demonstrated with a case study for the South Island of New Zealand. The results of this investigation suggest three modifications to improve different aspects of ICAR simulations. The representation of the wind field within the domain improves when the dry and the moist Brunt–Väisälä frequencies are calculated in accordance with linear mountain wave theory from the unperturbed base state rather than from the time-dependent perturbed atmosphere. Imposing boundary conditions at the upper boundary that are different to the standard zero-gradient boundary condition is shown to reduce errors in the potential temperature and water vapor fields. Furthermore, the results show that there is a lowest possible model top elevation that should not be undercut to avoid influences of the model top on cloud and precipitation processes within the domain. The method to determine the lowest model top elevation is applied to both the idealized simulations and the real terrain case study. Notable differences between the ICAR and WRF simulations are observed across all investigated quantities such as the wind field, water vapor and hydrometeor distributions, and the distribution of precipitation. The case study indicates that the precipitation maximum calculated by the ICAR simulation employing the developed recommendations is spatially shifted upwind in comparison to an unmodified version of ICAR. The cause for the shift is found in influences of the model top on cloud formation and precipitation processes in the ICAR simulations. Furthermore, the results show that when model skill is evaluated from statistical metrics based on comparisons to surface observations only, such an analysis may not reflect the skill of the model in capturing atmospheric processes like gravity waves and cloud formation.
Abstract. Despite the high historical losses attributed to flood events, Canadian flood mitigation efforts have been hindered by a dearth of current, accessible flood extent/risk models and maps. Such resources often entail large datasets and high computational requirements. This study presents a novel, computationally efficient flood inundation modeling framework (“InundatEd”) using the height above nearest drainage (HAND)-based solution for Manning's equation, implemented in a big-data discrete global grid system (DGGS)-based architecture with a web-GIS (Geographic Information Systems) platform. Specifically, this study aimed to develop, present, and validate InundatEd through binary classification comparisons to recently observed flood events. The framework is divided into multiple swappable modules including GIS pre-processing; regional regression; inundation models; and web-GIS visualization. Extent testing and processing speed results indicate the value of a DGGS-based architecture alongside a simple conceptual inundation model and a dynamic user interface.
Abstract. Soil microwave permittivity is a crucial parameter in passive microwave retrieval algorithms but remains a challenging variable to measure. To validate and improve satellite microwave data products, precise and reliable estimations of the relative permittivity (εr=ε/ε0=ε′-jε′′; unitless) of soils are required, particularly for frozen soils. In this study, permittivity measurements were acquired using two different instruments: the newly designed open-ended coaxial probe (OECP) and the conventional Stevens HydraProbe. Both instruments were used to characterize the permittivity of soil samples undergoing several freeze–thaw cycles in a laboratory environment. The measurements were compared to soil permittivity models. The OECP measured frozen (εfrozen′=[3.5; 6.0], εfrozen′′=[0.46; 1.2]) and thawed (εthawed′=[6.5; 22.8], εthawed′′=[1.43; 5.7]) soil microwave permittivity. We also demonstrate that cheaper and widespread soil permittivity probes operating at lower frequencies (i.e., Stevens HydraProbe) can be used to estimate microwave permittivity given proper calibration relative to an L-band (1–2 GHz) probe. This study also highlighted the need to improve dielectric soil models, particularly during freeze–thaw transitions. There are still important discrepancies between in situ and modeled estimates and no current model accounts for the hysteresis effect shown between freezing and thawing processes, which could have a significant impact on freeze–thaw detection from satellites.
Abstract. The snowpack over the Mediterranean mountains constitutes a key water resource for the downstream populations. However, its dynamics have not been studied in detail yet in many areas, mostly because of the scarcity of snowpack observations. In this work, we present a characterization of the snowpack over the two mountain ranges of Lebanon. To obtain the necessary snowpack information, we have developed a 1 km regional-scale snow reanalysis (ICAR_assim) covering the period 2010–2017. ICAR_assim was developed by means of an ensemble-based data assimilation of Moderate Resolution Imaging Spectroradiometer (MODIS) fractional snow-covered area (fSCA) through an energy and mass snow balance model, the Flexible Snow Model (FSM2), using the particle batch smoother (PBS). The meteorological forcing data were obtained by a regional atmospheric simulation from the Intermediate Complexity Atmospheric Research model (ICAR) nested inside a coarser regional simulation from the Weather Research and Forecasting model (WRF). The boundary and initial conditions of WRF were provided by the ERA5 atmospheric reanalysis. ICAR_assim showed very good agreement with MODIS gap-filled snow products, with a spatial correlation of R=0.98 in the snow probability (P(snow)) and a temporal correlation of R=0.88 on the day of peak snow water equivalent (SWE). Similarly, ICAR_assim has shown a correlation with the seasonal mean SWE of R=0.75 compared with in situ observations from automatic weather stations (AWSs). The results highlight the high temporal variability in the snowpack in the Lebanese mountain ranges, with the differences between Mount Lebanon and the Anti-Lebanon Mountains that cannot only be explained by hypsography as the Anti-Lebanon Mountains are in the rain shadow of Mount Lebanon. The maximum fresh water stored in the snowpack is in the middle elevations, approximately between 2200 and 2500 m a.s.l. (above sea level). Thus, the resilience to further warming is low for the snow water resources of Lebanon due to the proximity of the snowpack to the zero isotherm.
Abstract. The theory that forms the basis of TOPMODEL (a topography-based hydrological model) was first outlined by Mike Kirkby some 45 years ago. This paper recalls some of the early developments, the rejection of the first journal paper, the early days of digital terrain analysis, model calibration and validation, the various criticisms of the simplifying assumptions, and the relaxation of those assumptions in the dynamic forms of TOPMODEL. A final section addresses the question of what might be done now in seeking a simple, parametrically parsimonious model of hillslope and small catchment processes if we were starting again.
Abstract. The European Alps stretch over a range of climate zones which affect the spatial distribution of snow. Previous analyses of station observations of snow were confined to regional analyses. Here, we present an Alpine-wide analysis of snow depth from six Alpine countries – Austria, France, Germany, Italy, Slovenia, and Switzerland – including altogether more than 2000 stations of which more than 800 were used for the trend assessment. Using a principal component analysis and k-means clustering, we identified five main modes of variability and five regions which match the climatic forcing zones: north and high Alpine, north-east, north-west, south-east, and south and high Alpine. Linear trends of monthly mean snow depth between 1971 and 2019 showed decreases in snow depth for most stations from November to May. The average trend among all stations for seasonal (November to May) mean snow depth was −8.4 % per decade, for seasonal maximum snow depth −5.6 % per decade, and for seasonal snow cover duration −5.6 % per decade. Stronger and more significant trends were observed for periods and elevations where the transition from snow to snow-free occurs, which is consistent with an enhanced albedo feedback. Additionally, regional trends differed substantially at the same elevation, which challenges the notion of generalizing results from one region to another or to the whole Alps. This study presents an analysis of station snow depth series with the most comprehensive spatial coverage in the European Alps to date.
Abstract. The evaluation of snowpack models capable of accounting for snow management in ski resorts is a major step towards acceptance of such models in supporting the daily decision-making process of snow production managers. In the framework of the EU Horizon 2020 (H2020) project PROSNOW, a service to enable real-time optimization of grooming and snow-making in ski resorts was developed. We applied snow management strategies integrated in the snowpack simulations of AMUNDSEN, Crocus, and SNOWPACK–Alpine3D for nine PROSNOW ski resorts located in the European Alps. We assessed the performance of the snow simulations for five winter seasons (2015–2020) using both ground-based data (GNSS-measured snow depth) and spaceborne snow maps (Copernicus Sentinel-2). Particular attention has been devoted to characterizing the spatial performance of the simulated piste snow management at a resolution of 10 m. The simulated results showed a high overall accuracy of more than 80 % for snow-covered areas compared to the Sentinel-2 data. Moreover, the correlation to the ground observation data was high. Potential sources for local differences in the snow depth between the simulations and the measurements are mainly the impact of snow redistribution by skiers; compensation of uneven terrain when grooming; or spontaneous local adaptions of the snow management, which were not reflected in the simulations. Subdividing each individual ski resort into differently sized ski resort reference units (SRUs) based on topography showed a slight decrease in mean deviation. Although this work shows plausible and robust results on the ski slope scale by all three snowpack models, the accuracy of the results is mainly dependent on the detailed representation of the real-world snow management practices in the models. As snow management assessment and prediction systems get integrated into the workflow of resort managers, the formulation of snow management can be refined in the future.
Abstract. The interaction of mountain terrain with meteorological processes causes substantial temporal and spatial variability in snow accumulation and ablation. Processes impacted by complex terrain include large-scale orographic enhancement of snowfall, small-scale processes such as gravitational and wind-induced transport of snow, and variability in the radiative balance such as through terrain shadowing. In this study, a multi-scale modelling approach is proposed to simulate the temporal and spatial evolution of high-mountain snowpacks. The multi-scale approach combines atmospheric data from a numerical weather prediction system at the kilometre scale with process-based downscaling techniques to drive the Canadian Hydrological Model (CHM) at spatial resolutions allowing for explicit snow redistribution modelling. CHM permits a variable spatial resolution by using the efficient terrain representation by unstructured triangular meshes. The model simulates processes such as radiation shadowing and irradiance to slopes, blowing-snow transport (saltation and suspension) and sublimation, avalanching, forest canopy interception and sublimation, and snowpack melt. Short-term, kilometre-scale atmospheric forecasts from Environment and Climate Change Canada's Global Environmental Multiscale Model through its High Resolution Deterministic Prediction System (HRDPS) drive CHM and are downscaled to the unstructured mesh scale. In particular, a new wind-downscaling strategy uses pre-computed wind fields from a mass-conserving wind model at 50 m resolution to perturb the mesoscale HRDPS wind and to account for the influence of topographic features on wind direction and speed. HRDPS-CHM was applied to simulate snow conditions down to 50 m resolution during winter 2017/2018 in a domain around the Kananaskis Valley (∼1000 km2) in the Canadian Rockies. Simulations were evaluated using high-resolution airborne light detection and ranging (lidar) snow depth data and snow persistence indexes derived from remotely sensed imagery. Results included model falsifications and showed that both wind-induced and gravitational snow redistribution need to be simulated to capture the snowpack variability and the evolution of snow depth and persistence with elevation across the region. Accumulation of windblown snow on leeward slopes and associated snow cover persistence were underestimated in a CHM simulation driven by wind fields that did not capture lee-side flow recirculation and associated wind speed decreases. A terrain-based metric helped to identify these lee-side areas and improved the wind field and the associated snow redistribution. An overestimation of snow redistribution from windward to leeward slopes and subsequent avalanching was still found. The results of this study highlight the need for further improvements of snowdrift-permitting models for large-scale applications, in particular the representation of subgrid topographic effects on snow transport.
Urbanization typically leads to erosion and instability in rivers, and many management and restoration strategies have been developed to dampen the worst impacts. Stream power, defined as the rate of energy expenditure in a river, is a promising metric for analyzing cumulative effects. In this paper we describe a spatial decision support system called the Stream Power Index for Networks (SPIN) toolbox that can be used to assess urban river stability at a watershed scale. The objectives of the paper are to: a) describe the toolbox algorithms and procedures and b) demonstrate the utility of the approach. SPIN is written in Python and packaged as an ArcGIS toolbox. The toolbox combines existing landscape analysis algorithms with new algorithms to model river confluences, channel sinuosity, and threshold sediment particle sizes. Data can also be ingested from a standard hydraulic model. Two case studies demonstrate use of the toolbox to: i) anticipate current morphology; ii) predict urban morphologic change; and iii) analyze the benefits for stormwater management and channel restoration scenarios on channel stability.
In partially-alluvial channels, an understanding of cover formation over a non-alluvial substrate is necessary for effective river management or restoration. Urban rivers, for example, are often sediment starved such that the underlying substrate may be exposed. Few experiments have investigated cover development in meandering channels, particularly where width and meander geometry are irregular as is often the case for partially alluvial channels. The purpose of this work is to support the development of sediment augmentation strategies to mitigate channel degradation and restore alluvial cover. The experiments also provide new insight into the impact of sediment supply rates on alluvial cover dynamics in variable-width channels. Under constant flow discharge and a continuous supply of sediment, sediment disperses downstream of the feed location and cover develops in a fragmented fashion. Cover initiation occurs downstream of bend apexes and develops as a series of discrete fixed bars whose morphology differs as a function of bend geometry and channel width. Cover expands and bars merge with time under steady sediment supply and discharge, eventually thickening to an equilibrium state in which sediment supply and output are approximately balanced. Higher sediment supply rates result in more extensive and thicker cover at equilibrium, including cover expanding into the cross-overs between the main bars. Coarse and fine fractions of the sediment supply are preferentially retained in the cover sediment because of fine particle deposition on bar tops and burial of initial coarse deposits. Models of areal cover with feed rate and cover thickness are proposed and compared with other studies. More experimentation is needed, but augmentation of alluvial gravel cover is a feasible approach to maintaining a sediment balance in partially-alluvial channels and for developing mobile alluvial cover in engineered channels.
Compositions of microbial communities associated with blooms of algae in a storage reservoir in Macau, China were investigated between 2013 and 2016. Algae were enumerated by visible light microscopy. Profiles of organisms in water were examined by 16S rRNA sequences and viral metagenomics, based on next generation sequencing. Results of 16S rRNA sequencing indicated that majority of the identified organisms were bacteria closely related to Proteobacteria, Cyanobacteria, Verrucomicrobia, Bacteroidetes, and Actinobacteria. Metagenomics sequences demonstrated that the dominant virus was Phycodnavirus, accounting for 70% of the total population. Patterns of relative numbers of bacteria in the microbial community and their temporal changes were determined through alpha diversity indices, principal coordinates analysis (PCoA), relative abundance, and visualized by Venn diagrams. Ways in which the bacterial and viral communities are influenced by various water-related variables were elucidated based on redundancy analysis (RDA). Relationships of the relative numbers of bacteria with trophic status in a reservoir used for drinking water in Macau, provided insight into associations of Phycodnavirus and Proteobacteria with changes in blooms of algae.
Dissolved organic matter (DOM) is a ubiquitous component of aquatic systems, impacting aquatic health and drinking water quality. These impacts depend on the mixture of organic molecules that comprise DOM. Changing climates are altering both the amount and character of DOM being transported from the terrestrial system into adjacent surface waters, yet DOM composition is not monitored as often as overall concentration. Many DOM characterization methods exist, confounding comparison of DOM composition across different studies. The objective of this research is to determine which parameters in a suite of relatively simple and common DOM characterization techniques explain the most variability in DOM composition from surface and groundwater sites. Further, we create a simple visualization tool to easily compare compositional differences in DOM. A large number of water samples (n = 250) was analyzed from six Canadian ecozones for DOM concentration, ultraviolet-visible light absorbance, molecular size, and elemental ratios. Principal component analyses was used to identify quasi-independent DOM compositional parameters that explained the highest variability in the dataset: spectral slope, specific-UV absorbance at 255nm, humic substances fraction, and dissolved organic carbon to dissolved organic nitrogen ratio. A ‘Composition Wheel’ was created by plotting these four parameters as a polygon. Our results find similarities in DOM composition irrespective of site differences in vegetation and climate. Further, two main end-member Composition Wheel shapes were revealed that correspond to DOM in organic-rich groundwaters and DOM influenced by photodegradation. The Composition Wheel approach uses easily visualized differences in polygon shape to quantify how DOM evolves by natural processes along the aquatic continuum and to track sources and degradation of DOM.
Curtailing the Spring 2020 COVID-19 surge required sweeping and stringent interventions by governments across the world. Wastewater-based COVID-19 epidemiology programs have been initiated in many countries to provide public health agencies with a complementary disease tracking metric and non-discriminating surveillance tool. However, their efficacy in prospectively capturing resurgences following a period of low prevalence is unclear. In this study, the SARS-CoV-2 viral signal was measured in primary clarified sludge harvested every two days at the City of Ottawa's water resource recovery facility during the summer of 2020, when clinical testing recorded daily percent positivity below 1%. In late July, increases of >400% in normalized SARS-CoV-2 RNA signal in wastewater were identified 48 h prior to reported >300% increases in positive cases that were retrospectively attributed to community-acquired infections. During this resurgence period, SARS-CoV-2 RNA signal in wastewater preceded the reported >160% increase in community hospitalizations by approximately 96 h. This study supports wastewater-based COVID-19 surveillance of populations in augmenting the efficacy of diagnostic testing, which can suffer from sampling biases or timely reporting as in the case of hospitalization census.
Here, we explore how people entangled in natural resource conflicts employ and discuss data. We draw on ethnographic research with two cases of conflict: salmon fisheries in Alaska, USA, and agricultural water management in Saskatchewan, Canada. Both cases illustrate how data, through the scientization of environmental governance, can become a means of empowerment and disempowerment: empowering those with access and influence over data and disempowering those without such access. In both locales, people find it necessary to perform their expertise, justify the veracity of their data, and discount the data held by others if they wish to achieve or maintain standing. We call this “datamentality” and draw lessons from these cases for how we can structure environmental governance such that it benefits from robust data and science while meeting the needs of individuals, avoiding or managing power struggles, and protecting the rights of all involved.
Circulating plasma microRNAs (miRNAs) are well established as biomarkers of several diseases in humans and have recently been used as indicators of environmental exposures in fish. However, the role of plasma miRNAs in regulating acute stress responses in fish is largely unknown. Tissue and plasma miRNAs have recently been associated with excreted miRNAs; however, external miRNAs have never been measured in fish. The objective of this study was to identify the altered plasma miRNAs in response to acute stress in rainbow trout ( Oncorhynchus mykiss ), as well as altered miRNAs in fish epidermal mucus and the surrounding ambient water. Small RNA was extracted and sequenced from plasma, mucus, and water collected from rainbow trout pre- and 1 h-post a 3-min air stressor. Following small RNA-Seq and pathway analysis, we identified differentially expressed plasma miRNAs that targeted biosynthetic, degradation, and metabolic pathways. We successfully isolated miRNA from trout mucus and the surrounding water and detected differences in miRNA expression 1-h post air stress. The expressed miRNA profiles in mucus and water were different from the altered plasma miRNA profile, which indicated that the plasma miRNA response was not associated with or immediately reflected in external samples, which was further validated through qPCR. This research expands understanding of the role of plasma miRNA in the acute stress response of fish and is the first report of successful isolation and profiling of miRNA from fish mucus or samples of ambient water. Measurements of miRNA from plasma, mucus, or water can be further studied and have potential to be applied as non-lethal indicators of acute stress in fish.
Release of sorbed phosphate from ferric iron oxyhydroxides can contribute to excessive algal growth in surface water bodies. Dissolved silicate has been hypothesized to facilitate phosphate desorption by competing for mineral surface sites. Here, we conducted phosphate and silicate adsorption experiments with goethite under a wide pH range (3–11), both individually (P or Si) and simultaneously (P plus Si). The entire experimental data set was successfully reproduced by the charge distribution multisite surface complexation (CD-MUSIC) model. Phosphate adsorption was highest under acidic conditions and gradually decreased from near-neutral to alkaline pH conditions. Maximum silicate adsorption, in contrast, occurred under alkaline conditions, peaking around pH 10. The competitive effect of silicate on phosphate adsorption was negligible under acidic conditions, becoming more pronounced under alkaline conditions and elevated molar Si:P ratios (>4). In a subsequent experiment, desorption of phosphate with increasing pH was monitored, in the presence or absence of dissolved silicate. While, as expected, desorption of phosphate was observed during the transition from acidic to alkaline conditions, a fraction of phosphate remained irreversibly bound to goethite. Even at high Si:P ratios and alkaline pH, dissolved silicate did not affect phosphate desorption, implying that kinetic factors prevented silicate from displacing phosphate from goethite binding sites.
Stated preference methods remain the only means capable of estimating non-use values yet can suffer from many types of well-known biases. We construct an approach to identify the role of social desirability bias, relative to other potential survey biases, using a stated preference survey for improving the status of species at risk. The survey respondents were asked how they would vote, how they think their fellow survey participants would vote, as well as how they think people in their region would vote in an actual referendum. We find that willingness-to-pay estimates for public good (passive use) values differ across these vote question types. Our results demonstrate how stated preference practitioners can use multiple referent groups to help disentangle social desirability bias from other survey biases.

2020

The detrimental impacts of agricultural subsurface tile flows and their associated pollutants on water quality is a major environmental issue in the Great Lakes region and many other places globally. A strong understanding of water quality indicators along with the contribution of tile-drained agriculture to water contamination is necessary to assess and reduce a significant source of non-point source pollution. In this study, DRAINMOD, a field-scale hydrology and water quality model, was applied to assess the impact of future climatic change on depth to water table, tile flow and associated nitrate loss from an 8.66 ha agricultural field near Londesborough, in Southwestern Ontario, Canada. The closest available climate data from a weather station approximately 10 km from the field site was used by the Ontario Ministry of Natural Resources and Forestry (MNRF) to generate future predictions of daily precipitation and maximum and minimum air temperatures required to create the weather files for DRAINMOD. Of the 28 models applied by MNRF, three models (CGCM3T47-Run5, GFDLCM2.0, and MIROC3.2hires) were selected based on the frequency of the models recommended for use in Ontario with SRA1B emission scenario. Results suggested that simulated tile flows and evapotranspiration (ET) in the 2071–2100 period are expected to increase by 7% and 14% compared to 1960–1990 period. Results also suggest that under future climates, significant increases in nitrate losses (about 50%) will occur along with the elevated tile flows. This work suggests that climate change will have a significant effect on field hydrology and water quality in tile-drained agricultural regions.
Representing climate-crop interactions is critical to earth system modeling. Despite recent progress in modeling dynamic crop growth and irrigation in land surface models (LSMs), transitioning thes...
Abstract. Long Short-Term Memory Networks (LSTMs) have been applied to daily discharge prediction with remarkable success. Many practical scenarios, however, require predictions at more granular timescales. For instance, accurate prediction of short but extreme flood peaks can make a life-saving difference, yet such peaks may escape the coarse temporal resolution of daily predictions. Naively training an LSTM on hourly data, however, entails very long input sequences that make learning hard and computationally expensive. In this study, we propose two Multi-Timescale LSTM (MTS-LSTM) architectures that jointly predict multiple timescales within one model, as they process long-past inputs at a single temporal resolution and branch out into each individual timescale for more recent input steps. We test these models on 516 basins across the continental United States and benchmark against the US National Water Model. Compared to naive prediction with a distinct LSTM per timescale, the multi-timescale architectures are computationally more efficient with no loss in accuracy. Beyond prediction quality, the multi-timescale LSTM can process different input variables at different timescales, which is especially relevant to operational applications where the lead time of meteorological forcings depends on their temporal resolution.
Dissolved Organic Matter (DOM) represents a mixture of organic molecules that vary due to different source materials and degree of processing. Characterizing how DOM composition evolves along the aquatic continuum can be difficult. Using a size-exclusion chromatography technique (LC-OCD), we assessed the variability in DOM composition from both surface and groundwaters across a number of Canadian ecozones (mean annual temperature spanning -10 to +6 C). A wide range in DOM concentration was found from 0.2 to 120 mg C/L. Proportions of different size-based groupings across ecozones were variable, yet similarities between specific water-body types, regardless of location, suggest commonality in the processes dictating the evolution of DOM composition. A principal-component analysis identified 70% of the variation in LC-OCD derived DOM compositions could be explained by the water-body type. We find that water-body type has a greater influence on DOM composition than differences in climate or surrounding vegetation.
Abstract. This paper presents hydrometeorological, glaciological and geospatial data of the Peyto Glacier Research Basin (PGRB) in the Canadian Rockies. Peyto Glacier has been of interest to glaciological and hydrological researchers since the 1960s, when it was chosen as one of five glacier basins in Canada for the study of mass and water balance during the International Hydrological Decade (IHD, 1965–1974). Intensive studies of the glacier and observations of the glacier mass balance continued after the IHD, when the initial seasonal meteorological stations were discontinued, then restarted as continuous stations in the late 1980s. The corresponding hydrometric observations were discontinued in 1977 and restarted in 2013. Data sets presented in this paper include: high resolution, co-registered DEMs derived from original air photos and LiDAR surveys; hourly off-glacier meteorological data recorded from 1987 to present; precipitation data from nearby Bow Summit; and long-term hydrological and glaciological model forcing datasets derived from bias-corrected reanalysis products. These data are crucial for studying climate change and variability in the basin, and to understanding the hydrological responses of the basin to both glacier and climate change. The comprehensive data set for the PGRB is a valuable and exceptionally long-standing testament to the impacts of climate change on the cryosphere in the high mountain environment. The dataset is publicly available from Federated Research Data Repository at https://doi.org/10.20383/101.0259 (Pradhananga et al., 2020).
Abstract. Thirty-year simulations of seasonal snow cover in 22 physically based models driven with bias-corrected meteorological reanalyses are examined at four sites with long records of snow observations. Annual snow cover durations differ widely between models but interannual variations are strongly correlated because of the common driving data. No significant trends are observed in starting dates for seasonal snow cover, but there are significant trends towards snow cover ending earlier at two of the sites in observations and most of the models. A simplified model with just two parameters controlling solar radiation and sensible heat contributions to snowmelt spans the ranges of snow cover durations and trends. This model predicts that sites where snow persists beyond annual peaks in solar radiation and air temperature will experience rapid decreases in snow cover duration with warming as snow begins to melt earlier and at times of year with more energy available for melting.
Abstract. The interaction of mountain terrain with meteorological processes causes substantial temporal and spatial variability in snow accumulation and ablation. Processes impacted by complex terrain include large-scale orographic enhancement of snowfall, small-scale processes such as gravitational and wind-induced transport of snow, and variability in the radiative balance such as through terrain shadowing. In this study, a multi-scale modeling approach is proposed to simulate the temporal and spatial evolution of high mountain snowpacks using the Canadian Hydrological Model (CHM), a multi-scale, spatially distributed modelling framework. CHM permits a variable spatial resolution by using the efficient terrain representation by unstructured triangular meshes. The model simulates processes such as radiation shadowing and irradiance to slopes, blowing snow redistribution and sublimation, avalanching, forest canopy interception and sublimation and snowpack melt. Short-term, km-scale atmospheric forecasts from Environment and Climate Change Canada's Global Environmental Multiscale Model through its High Resolution Deterministic Prediction System (HRDPS) drive CHM, and were downscaled to the unstructured mesh scale using process-based procedures. In particular, a new wind downscaling strategy combines meso-scale HRDPS outputs and micro-scale pre-computed wind fields to allow for blowing snow calculations. HRDPS-CHM was applied to simulate snow conditions down to 50-m resolution during winter 2017/2018 in a domain around the Kananaskis Valley (~1000 km2) in the Canadian Rockies. Simulations were evaluated using high-resolution airborne Light Detection and Ranging (LiDAR) snow depth data and snow persistence indexes derived from remotely sensed imagery. Results included model falsifications and showed that both blowing snow and gravitational snow redistribution need to be simulated to capture the snowpack variability and the evolution of snow depth and persistence with elevation across the region. Accumulation of wind-blown snow on leeward slopes and associated snow-cover persistence were underestimated in a CHM simulation driven by wind fields that did not capture leeside flow recirculation and associated wind speed decreases. A terrain-based metric helped to identify these lee-side areas and improved the wind field and the associated snow redistribution. An overestimation of snow redistribution from windward to leeward slopes and subsequent avalanching was still found. The results of this study highlight the need for further improvements of snowdrift-permitting models for large-scale applications, in particular the representation of subgrid topographic effects on snow transport.
Abstract. Floods cause large damages, especially if they affect large regions. Assessments of current, local and regional flood hazards and their future changes often involve the use of hydrologic models. However, uncertainties in simulated floods can be considerable and yield unreliable hazard and climate change impact assessments. A reliable hydrologic model ideally reproduces both local flood characteristics and spatial aspects of flooding, which is, however, not guaranteed especially when using standard model calibration metrics. In this paper we investigate how flood timing, magnitude and spatial variability are represented by an ensemble of hydrological models when calibrated on streamflow using the Kling–Gupta efficiency metric, an increasingly common metric of hydrologic model performance. We compare how four well-known models (SAC, HBV, VIC, and mHM) represent (1) flood characteristics and their spatial patterns; and (2) how they translate changes in meteorologic variables that trigger floods into changes in flood magnitudes. Our results show that both the modeling of local and spatial flood characteristics is challenging. They further show that changes in precipitation and temperature are not necessarily well translated to changes in flood flow, which makes local and regional flood hazard assessments even more difficult for future conditions. We conclude that models calibrated on integrated metrics such as the Kling–Gupta efficiency alone have limited reliability in flood hazard assessments, in particular in regional and future assessments, and suggest the development of alternative process-based and spatial evaluation metrics.
Abstract. Land models are increasingly used in terrestrial hydrology due to their process-oriented representation of water and energy fluxes. Land models can be set up at a range of spatial configurations, often ranging from grid sizes of 0.02 to 2 degrees (approximately 2 to 200 km) and applied at sub-daily temporal resolutions for simulation of energy fluxes. A priori specification of the grid size of the land models typically is derived from forcing resolutions, modeling objectives, available geo-spatial data and computational resources. Typically, the choice of model configuration and grid size is based on modeling convenience and is rarely examined for adequate physical representation in the context of modeling. The variability of the inputs and parameters, forcings, soil types, and vegetation covers, are masked or aggregated based on the a priori chosen grid size. In this study, we propose an alternative to directly set up a land model based on the concept of Group Response Unit (GRU). Each GRU is a unique combination of land cover, soil type, and other desired geographical features that has hydrological significance, such as elevation zone, slope, and aspect. Computational units are defined as GRUs that are forced at a specific forcing resolution; therefore, each computational unit has a unique combination of specific geo-spatial data and forcings. We set up the Variable Infiltration Capacity (VIC) model, based on the GRU concept (VIC-GRU). Utilizing this model setup and its advantages we try to answer the following questions: (1) how well a model configuration simulates an output variable, such as streamflow, for range of computational units, (2) how well a model configuration with fewer computational units, coarser forcing resolution and less geo-spatial information, reproduces a model set up with more computational units, finer forcing resolution and more geo-spatial information, and finally (3) how uncertain the model structure and parameters are for the land model. Our results, although case dependent, show that the models may similarly reproduce output with a lower number of computational units in the context of modeling (streamflow for example). Our results also show that a model configuration with a lower number of computational units may reproduce the simulations from a model configuration with more computational units. Similarly, this can assist faster parameter identification and model diagnostic suites, such as sensitivity and uncertainty, on a less computationally expensive model setup. Finally, we encourage the land model community to adopt flexible approaches that will provide a better understanding of accuracy-performance tradeoff in land models.
Abstract. Northwestern Alaska has been highly affected by changing climatic patterns with new temperature and precipitation maxima over the recent years. In particular, the Baldwin and northern Seward peninsulas are characterized by an abundance of thermokarst lakes that are highly dynamic and prone to lake drainage, like many other regions at the southern margins of continuous permafrost. We used Sentinel-1 synthetic aperture radar (SAR) and Planet CubeSat optical remote sensing data to analyze recently observed widespread lake drainage. We then used synoptic weather data, climate model outputs and lake-ice growth simulations to analyze potential drivers and future pathways of lake drainage in this region. Following the warmest and wettest winter on record in 2017/2018, 192 lakes were identified to have completely or partially drained in early summer 2018, which exceeded the average drainage rate by a factor of ~ 10 and doubled the rates of the previous extreme lake drainage years of 2005 and 2006. The combination of abundant rain- and snowfall and extremely warm mean annual air temperatures (MAAT), close to 0 °C, may have led to the destabilization of permafrost around the lake margins. Rapid snow melt and high amounts of excess meltwater further promoted rapid lateral breaching at lake shores and consequently sudden drainage of some of the largest lakes of the study region that likely persisted for millenia. We hypothesize that permafrost destabilization and lake drainage will accelerate and become the dominant drivers of landscape change in this region. Recent MAAT are already within the range of predictions by UAF SNAP ensemble climate predictions in scenario RCP6.0 for 2100. With MAAT in 2019 exceeding 0 °C at the nearby Kotzebue, Alaska climate station for the first time since continuous recording started in 1949, permafrost aggradation in drained lake basins will become less likely after drainage, strongly decreasing the potential for freeze-locking carbon sequestered in lake sediments, signifying a prominent regime shift in ice-rich permafrost lowland regions.
Abstract Background Through their support of local agriculture, relationships, and healthy diets, farmers’ markets can contribute to a sustainable food system. Markets like the Yellowknife Farmers Market (YKFM) are social spaces that support local food, yet the COVID-19 pandemic has forced changes to their current model. This paper explores the potential of online marketplaces to contribute to a resilient, sustainable food system and the barriers to making this transition for the YKFM. Methods In 2019, a collaborative mixed-method evaluation was initiated by the YKFM and university partners in the Northwest Territories (NWT), Canada. The co-created evaluation plan included two patron surveys, a vendor survey and vendor interviews. The evaluation began with an in-person Rapid Market Assessment dot survey and questionnaire of market patrons from two YKFM dates prior to the pandemic. Due to COVID-19, we determined it was not a good time to conduct the vendor survey and interviews. Ongoing engagement with the market facilitated an assessment of the COVID-19 response. Results For the patron surveys, 59 dot survey and 31 questionnaire participants were recruited. The top motivators for attendance were eating dinner, atmosphere, and supporting local businesses, and most patrons attended as couples and spent over half of their time talking to others. The YKFM did not move online, citing concerns about meeting produce demand, incongruence between the online model and market strengths, low dependency on the YKFM by vendors, and potential challenges for patrons using new technology. Conclusions NWT food strategies rely on farmers’ markets to nurture a local food system. Online markets can support local food by facilitating purchases and knowledge-sharing, yet they do not replicate the open-air or social experience. Challenges to the online transition reflect the survey findings and current food context in the NWT. While online adaptation does not fit into the YKFM plan today, online markets may prove useful as a complementary strategy for future emerging stressors to enhance the resiliency of local systems.
Hydrologic-Land Surface Models (H-LSMs) have been progressively developed to a stage where they represent the dominant hydrological processes for a variety of hydrological regimes and include a range of water management practices, and are increasingly used to simulate water storages and fluxes of large basins under changing environmental conditions across the globe. However, efforts for comprehensive evaluation of the utility of H-LSMs in large, regulated watersheds have been limited. In this study, we evaluated the capability of a Canadian H-LSM, called MESH, in the highly regulated Saskatchewan River Basin (SaskRB), Canada, under the constraint of significant precipitation uncertainty. A comprehensive analysis of the MESH model performance was carried out in two steps. First, the reliability of multiple precipitation products was evaluated against climate station observations and based on their performance in simulating streamflow across the basin when forcing the MESH model with a default parameterization. Second, a state-of-the-art multi-criteria calibration approach was applied, using various observational information including streamflow, storage and fluxes for calibration and validation. The first analysis shows that the quality of precipitation products had a direct and immediate impact on simulation performance for the basin headwaters but effects were dampened when going downstream. The subsequent analyses show that the MESH model was able to capture observed responses of multiple fluxes and storage across the basin using a global multi-station calibration method. Despite poorer performance in some basins, the global parameterization generally achieved better model performance than a default model parameterization. Validation using storage anomaly and evapotranspiration generally showed strong correlation with observations, but revealed potential deficiencies in the simulation of storage anomaly over open water areas. Keywords: Precipitation Uncertainty, Hydrologic-Land Surface Models, multi-criteria calibration, storage and fluxes validation, Saskatchewan River Basin, Canada
Wet alpine meadow ecosystems generally act as a significant carbon sink due to their higher rate of photosynthesis than the rate of decomposition. However, it remains unclear whether the low decomposition rate is determined by low temperatures or by nearly-saturated soil conditions. Using five years of measurements from two sites on the Tibetan Plateau with significantly different soil water conditions, we showed that compared to the dry site (which had a deep water table), the much larger carbon sink at the site with a shallow groundwater was mainly caused by the inhibiting effects of the nearly-saturated soil condition on soil respiration rather than by the low temperature. The findings suggested that thawing of frozen soil may partially slow down soil carbon decomposition through increasing soil water. We highlights that a warming-induced shrinking cryosphere may largely affect the carbon dynamics of wet and cold ecosystems through changes in soil hydrology.
Abstract. Probabilistic methods are very useful to estimate the spatial variability in meteorological conditions (e.g., spatial patterns of precipitation and temperature across large domains). In ensemble probabilistic methods, equally plausible ensemble members are used to approximate the probability distribution, hence uncertainty, of a spatially distributed meteorological variable conditioned on the available information. The ensemble can be used to evaluate the impact of the uncertainties in a myriad of applications. This study develops the Ensemble Meteorological Dataset for North America (EMDNA). EMDNA has 100 members with daily precipitation amount, mean daily temperature, and daily temperature range at 0.1° spatial resolution from 1979 to 2018, derived from a fusion of station observations and reanalysis model outputs. The station data used in EMDNA are from a serially complete dataset for North America (SCDNA) that fills gaps in precipitation and temperature measurements using multiple strategies. Outputs from three reanalysis products are regridded, corrected, and merged using the Bayesian Model Averaging. Optimal Interpolation (OI) is used to merge station- and reanalysis-based estimates. EMDNA estimates are generated based on OI estimates and spatiotemporally correlated random fields. Evaluation results show that (1) the merged reanalysis estimates outperform raw reanalysis estimates, particularly in high latitudes and mountainous regions; (2) the OI estimates are more accurate than the reanalysis and station-based regression estimates, with the most notable improvement for precipitation occurring in sparsely gauged regions; and (3) EMDNA estimates exhibit good performance according to the diagrams and metrics used for probabilistic evaluation. We also discuss the limitations of the current framework and highlight that persistent efforts are needed to further develop probabilistic methods and ensemble datasets. Overall, EMDNA is expected to be useful for hydrological and meteorological applications in North America. The whole dataset and a teaser dataset (a small subset of EMDNA for easy download and preview) are available at https://doi.org/10.20383/101.0275 (Tang et al., 2020a).
Abstract. The verification of models in general is a non-trivial task and can, due to epistemological and practical reasons, never be considered as complete. As a consequence, a model may yield correct results for the wrong reasons, i.e. by a different chain of processes than found in observations. While in the atmospheric sciences guidelines and strategies exist to maximize the chances that models are correct for the right reasons, these are mostly applicable to full-physics models, such as numerical weather prediction models. The Intermediate Complexity Atmospheric Research (ICAR) model is an atmospheric model employing linear mountain wave theory to represent the wind field. In this wind field atmospheric quantities, such as temperature and moisture are advected and a microphysics scheme is applied to represent the formation of clouds and precipitation. This study conducts an in-depth process-based evaluation of ICAR, employing idealized simulations to increase the understanding of the model and develop recommendations to maximize the probability that its results are correct for the right reasons. To contrast the obtained results from the linear-theory-based ICAR model to a full-physics model, idealized simulations with the Weather Research and Forecasting (WRF) model are conducted. The impact of the developed recommendations is then demonstrated with a case study for the South Island of New Zealand. The results of this investigation suggest three modifications to improve different aspects of ICAR simulations. The representation of the wind field within the domain improves when the dry and the moist Brunt-Väisälä frequencies are calculated in accordance to linear mountain wave theory from the unperturbed base state rather than from the time-dependent perturbed atmosphere. Imposing boundary conditions at the upper boundary different to the standard zero gradient boundary condition is shown to reduce errors in the potential temperature and water vapor fields. Furthermore, the results show that there is a lowest possible model top elevation that should not be undercut to avoid influences of the model top on cloud and precipitation processes within the domain. The method to determine the lowest model top elevation is applied to both the idealized simulations as well as the real terrain case study. Notable differences between the ICAR and WRF simulations are observed across all investigated quantities such as the wind field, water vapor and hydrometeor distributions, and the distribution of precipitation. The case study indicates a large shift in the precipitation maximum for the ICAR simulation employing the developed recommendations in contrast to an unmodified version of ICAR. The cause for the shift is found in influences of the model top on cloud formation and precipitation processes in the ICAR simulations. Furthermore, the results show that when model skill is evaluated from statistical metrics based on comparisons to surface observations only, such analysis may not reflect the skill of the model in capturing atmospheric processes such as gravity waves and cloud formation.
Abstract. The snowpack over the Mediterranean mountains constitutes a key water resource for the downstream populations. However, its dynamics have not been studied in detail yet in many areas, mostly because of the scarcity of snowpack observations. In this work, we present a characterization of the snowpack over the two mountain ranges of Lebanon. To obtain the necessary snowpack information, we have developed a 1 km regional scale snow reanalysis (ICAR_assim) covering the period 2010–2017. ICAR_assim was developed by means of ensemble-based data assimilation of MODIS fractional snow-covered area (fSCA) through the energy and mass balance model the Flexible Snow Model (FSM2), using the Particle Batch Smoother (PBS). The meteorological forcing data was obtained by a regional atmospheric simulation developed through the Intermediate Complexity Atmospheric Research model (ICAR) nested inside a coarser regional simulation developed by the Weather Research and Forecasting model (WRF). The boundary and initial conditions of WRF were provided by the ERA5 atmospheric reanalysis. ICAR_assim showed very good agreement with MODIS gap-filled snow products, with a spatial correlation of R = 0.98 in the snow probability (P(snow)), and a temporal correlation of R = 0.88 in the day of peak snow water equivalent (SWE)Similarly, ICAR_assim has shown a correlation with the seasonal mean SWE of R = 0.75 compared with in-situ observations from Automatic Weather Stations (AWS). The results highlight the high temporal variability of the snowpack in the Lebanon ranges, with differences between Mount Lebanon and Anti-Lebanon that cannot be only explained by its hypsography been Anti-Lebanon in the rain shadow of Mount Lebanon. The maximum fresh water stored in the snowpack is in the middle elevations approximately between 2200 and 2500 m. a.s.l. Thus, the resilience to further warming is low for the snow water resources of Lebanon due to the proximity of the snowpack to the zero isotherm.
Abstract. The Snow Ensemble Uncertainty Project (SEUP) is an effort to establish a baseline characterization of snow water equivalent (SWE) uncertainty across North America with the goal of informing global snow observational needs. An ensemble-based modeling approach, encompassing a suite of current operational models, is used to assess the uncertainty in SWE and total snow storage (SWS) estimation over North America during the 2009&ndashl2017 period. The highest modeled SWE uncertainty is observed in mountainous regions, likely due to the relatively deep snow, forcing uncertainties, and variability between the different models in resolving the snow processes over complex terrain. This highlights a need for high-resolution observations in mountains to capture the high spatial SWE variability. The greatest SWS is found in Tundra regions where even though the spatiotemporal variability in modeled SWE is low, there is considerable uncertainty in the SWS estimates due to the large areal extent over which those estimates are spread. This highlights the need for high accuracy in snow estimations across the Tundra. In mid-latitude boreal forests, large uncertainties in both SWE and SWS indicate that vegetation-snow impacts are a critical area where focused improvements to modeled snow estimation efforts need to be made. Finally, the SEUP results indicate that SWE uncertainty is driving runoff uncertainty and measurements may be beneficial in reducing uncertainty in SWE and runoff, during the melt season at high latitudes (e.g., Tundra and Taiga regions) and in the Western mountain regions, whereas observations at (or near) peak SWE accumulation are more helpful over the mid-latitudes.
Abstract. The European Alps stretch over a range of climate zones, which affect the spatial distribution of snow. Previous analyses of station observations of snow were confined to regional analyses. Here, we present an Alpine wide analysis of snow depth from six Alpine countries: Austria, France, Germany, Italy, Slovenia, and Switzerland; including altogether more than 2000 stations. Using a principal component analysis and k-means clustering, we identified five main modes of variability and five regions, which match the climatic forcing zones: north and high Alpine, northeast, northwest, southeast and southwest. Linear trends of mean monthly snow depth between 1971 to 2019 showed decreases in snow depth for 87 % of the stations. December to February trends were on average −1.1 cm decade−1 (min, max: −10.8, 4.4; elevation range 0–1000 m), −2.5 (−25.1, 4.4; 1000–2000 m) and −0.1 (−23.3, 9.9; 2000–3000 m), with stronger trends in March to May: −0.6 (−10.9, 1.0; 0–1000 m), −4.6 (−28.1, 4.1; 1000–2000 m) and −7.6 (−28.3, 10.5; 2000–3000 m). However, regional trends differed substantially, which challenges the notion of generalizing results from one Alpine region to another or to the whole Alps. This study presents an analysis of station snow depth series with the most comprehensive spatial coverage in the European Alps to date.
&lt;p&gt;Soil heterotrophic respiration has been considered as a key source of CO&lt;sub&gt;2&lt;/sub&gt; flux into the atmosphere and thus plays an important role in global warming. Although the relationship between soil heterotrophic respiration and soil water content has been frequently studied both theoretically and experimentally, model development has thus far been empirically based. Empirical models are often limited to the specific condition of their case studies and cannot be used as a general platform for modeling. Moreover, it is difficult to extend the empirical models by theoretically defined affinities to any desired degree of accuracy. As a result, it is of high priority to develop process-based models that are able to describe the mechanisms behind this phenomenon with more deterministic terms.&lt;/p&gt;&lt;p&gt;Here we present a mechanistic, mathematically-driven model that is based on the common geometry of a pore in porous media. Assuming that the aerobic respiration of bacteria requires oxygen as an electron acceptor and dissolved organic carbon (DOC) as a substrate, the CO&lt;sub&gt;2&lt;/sub&gt; fluxes are considered a function of the bioavailable fraction of both DOC and oxygen. In this modeling approach, the availability of oxygen is controlled by its penetration into the aquatic phase through the interface between air and water. DOC on the other hand is only available to a section of the soil that is in contact with water. As the water saturation in the pore changes, it dynamically and kinematically impacts these interfaces through which the mass transfer of nutrients occurs, and therefore the CO&lt;sub&gt;2&lt;/sub&gt; fluxes are directly controlled by water content. We showcased the model applicability on several case studies and illustrated the model capability in simulating the observed microbial respiration rates versus the soil water contents. Furthermore, we showed the model potential to accept additional physically-motivated parameters in order to explain respiration rates in frozen soils or at different temperatures.&lt;/p&gt;
Abstract. Despite the high historical losses attributed to flood events, Canadian flood mitigation efforts have been hindered by a dearth of current, accessible flood extent/risk models and maps. Such resources often entail large datasets and high computational requirements. This study presents a novel, computationally efficient flood inundation modeling framework (InundatEd) using the height above the nearest drainage-based solution for Manning's equation, implemented in a big-data discrete global grid systems-based architecture with a web-GIS platform. Specifically, this study aimed to develop, present, and validate InundatEd through binary classification comparisons to known flood extents. The framework is divided into multiple swappable modules including GIS pre-processing; regional regression; inundation model; and web-GIS visualization. Extent testing and processing speed results indicate the value of a DGGS-based architecture alongside a simple conceptual inundation model and a dynamic user interface.
The stable isotopes of hydrogen and oxygen (δ2H and δ18O, respectively) have been widely used to investigate tree water source partitioning. These tracers have shed new light on patterns of tree water use in time and space. However, there are several limiting factors to this methodology (e.g., the difficult assessment of isotope fractionation in trees, and the labor‐intensity associated with the collection of significant sample sizes) and the use of isotopes alone has not been enough to provide a mechanistic understanding of source water partitioning. Here, we combine isotope data in xylem and soil water with measurements of tree's physiological information including tree water deficit (TWD), fine root distribution, and soil matric potential, to investigate the mechanism driving tree water source partitioning. We used a 2 m3 lysimeter with willow trees (Salix viminalis) planted within, to conduct a high spatial–temporal resolution experiment. TWD provided an integrated response of plant water status to water supply and demand. The combined isotopic and TWD measurement showed that short‐term variation (within days) in source water partitioning is determined mainly by plant hydraulic response to changes in soil matric potential. We observed changes in the relationship between soil matric potential and TWD that are matched by shifts in source water partitioning. Our results show that tree water use is a dynamic process on the time scale of days. These findings demonstrate tree's plasticity to water supply over days can be identified with high‐resolution measurements of plant water status. Our results further support that root distribution alone is not an indicator of water uptake dynamics. Overall, we show that combining physiological measurements with traditional isotope tracing can reveal mechanistic insights into plant responses to changing environmental conditions.
Aquatic environments with high levels of dissolved ferrous iron and low levels of sulfate serve as an important systems for exploring biogeochemical processes relevant to the early Earth. Boreal Shield lakes, which number in the tens of millions globally, commonly develop seasonally anoxic waters that become iron rich and sulfate poor, yet the iron-sulfur microbiology of these systems has been poorly examined. Here we use genome-resolved metagenomics and enrichment cultivation to explore the metabolic diversity and ecology of anoxygenic photosynthesis and iron/sulfur cycling in the anoxic water columns of three Boreal Shield lakes. We recovered four high-completeness and low-contamination draft genome bins assigned to the class Chlorobia (formerly phylum Chlorobi) from environmental metagenome data and enriched two novel sulfide-oxidizing species, also from the Chlorobia. The sequenced genomes of both enriched species, including the novel "Candidatus Chlorobium canadense", encoded the cyc2 gene that is associated with photoferrotrophy among cultured Chlorobia members, along with genes for phototrophic sulfide oxidation. One environmental genome bin also encoded cyc2. Despite the presence of cyc2 in the corresponding draft genome, we were unable to induce photoferrotrophy in "Ca. Chlorobium canadense". Genomic potential for phototrophic sulfide oxidation was more commonly detected than cyc2 among environmental genome bins of Chlorobia, and metagenome and cultivation data suggested the potential for cryptic sulfur cycling to fuel sulfide-based growth. Overall, our results provide an important basis for further probing the functional role of cyc2 and indicate that anoxygenic photoautotrophs in Boreal Shield lakes could have underexplored photophysiology pertinent to understanding Earth's early microbial communities.
Boreal peatlands are frequently underlain by permafrost, which is thawing rapidly. A common ecological response to thaw is the conversion of raised forested plateaus to treeless wetlands, but unexplained spatial variation in responses, combined with a lack of stand‐level data, make it difficult to predict future trajectories of boreal forest composition and structure. We sought to characterize patterns and identify drivers of forest structure, composition, mortality and recruitment in a boreal peatland experiencing permafrost thaw. To do this, we established a large (10 ha) permanent forest plot (completed in 2014), located in the Northwest Territories, Canada, that includes 40,584 mapped and measured trees. In 2018, we conducted a comprehensive mortality and recruitment recensus. We also measured frost table depth, soil moisture, soil humification and organic layer thickness within the plot between 2012 and 2018, and used habitat association tests to link these variables to forest characteristics and dynamics. Forest composition and structure varied markedly throughout the plot and were strongly governed by patterns in permafrost presence and organic layer thickness. Overall, there was a net loss of trees from the plot at a rate of 0.7% year−1. Mortality of black spruce, the dominant tree species, was more than double that of recruitment and was strongly associated with permafrost thaw. In contrast, recruitment of larch was over four times greater than mortality, and occurred primarily in low‐lying, permafrost‐free wetlands with mineral soil near the surface. Synthesis. The trends in tree demography and underlying drivers suggest that spruce‐dominated permafrost plateaus will be converted into larch‐dominated wetlands as permafrost thaw progresses in boreal peatlands, particularly in areas where mineral soil is near the surface. In the longer term, thaw could increase the hydrologic connectivity of the landscape, resulting in widespread drainage and re‐vegetation by spruce, but we did not find evidence that this is occurring yet. Given the increasing rates of permafrost thaw, and positive feedbacks between thaw and forest change, we predict that larch abundance will continue to increase in boreal peatlands over the coming decades, leading to shifts in ecosystem function, wildlife habitat, albedo and snow dynamics.

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Shallow soils are warmer under trees and tall shrubs across Arctic and Boreal ecosystems
Heather Kropp, M. M. Loranty, Susan M. Natali, Alexander Kholodov, A. V. Rocha, Isla H. Myers‐Smith, Benjamin W Abbot, Jakob Abermann, Elena Blanc‐Betes, Daan Blok, Gesche Blume‐Werry, Julia Boike, A. L. Breen, Sean M. P. Cahoon, Casper T. Christiansen, Thomas A. Douglas, Howard E. Epstein, G. V. Frost, Mathias Goeckede, Toke T. Høye, Steven D. Mamet, J. A. O’Donnell, David Olefeldt, Gareth K. Phoenix, V. G. Salmon, A. Britta K. Sannel, Sharon L. Smith, Oliver Sonnentag, Lydia Smith Vaughn, Mathew Williams, Bo Elberling, Laura Gough, Jan Hjort, Peter M. Lafleur, Eugénie Euskirchen, Monique M. P. D. Heijmans, Elyn Humphreys, Hiroyasu Iwata, Benjamin Jones, M. Torre Jorgenson, Inge Grünberg, Yongwon Kim, James A. Laundre, Marguerite Mauritz, Anders Michelsen, Gabriela Schaepman‐Strub, Ken D. Tape, Masahito Ueyama, Bang‐Yong Lee, Kirsty Langley, Magnus Lund
Environmental Research Letters, Volume 16, Issue 1

Abstract Soils are warming as air temperatures rise across the Arctic and Boreal region concurrent with the expansion of tall-statured shrubs and trees in the tundra. Changes in vegetation structure and function are expected to alter soil thermal regimes, thereby modifying climate feedbacks related to permafrost thaw and carbon cycling. However, current understanding of vegetation impacts on soil temperature is limited to local or regional scales and lacks the generality necessary to predict soil warming and permafrost stability on a pan-Arctic scale. Here we synthesize shallow soil and air temperature observations with broad spatial and temporal coverage collected across 106 sites representing nine different vegetation types in the permafrost region. We showed ecosystems with tall-statured shrubs and trees (>40 cm) have warmer shallow soils than those with short-statured tundra vegetation when normalized to a constant air temperature. In tree and tall shrub vegetation types, cooler temperatures in the warm season do not lead to cooler mean annual soil temperature indicating that ground thermal regimes in the cold-season rather than the warm-season are most critical for predicting soil warming in ecosystems underlain by permafrost. Our results suggest that the expansion of tall shrubs and trees into tundra regions can amplify shallow soil warming, and could increase the potential for increased seasonal thaw depth and increase soil carbon cycling rates and lead to increased carbon dioxide loss and further permafrost thaw.
The current study undertook a systematic scoping review on the drivers and implications of dietary changes among Inuit in the Canadian Arctic.A keyword search of peer-reviewed articles was performed using PubMed, Web of Science, CINAHL, Academic Search Premier, Circumpolar Health Bibliographic Database and High North Research Documents. Eligibility criteria included all full-text articles of any design reporting on research on food consumption, nutrient intake, dietary adequacy, dietary change, food security, nutrition-related chronic diseases or traditional food harvesting and consumption among Inuit populations residing in Canada. Articles reporting on in vivo and in vitro experiments or on health impacts of environmental contaminants were excluded.A total of 162 studies were included. Studies indicated declining country food (CF) consumption in favour of market food (MF). Drivers of this transition include colonial processes, poverty and socio-economic factors, changing food preferences and knowledge, and climate change. Health implications of the dietary transition are complex. Micro-nutrient deficiencies and dietary inadequacy are serious concerns and likely exacerbated by increased consumption of non-nutrient dense MF. Food insecurity, overweight, obesity and related cardiometabolic health outcomes are growing public health concerns. Meanwhile, declining CF consumption is entangled with shifting culture and traditional knowledge, with potential implications for psychological, spiritual, social and cultural health and well-being.By exploring and synthesising published literature, this review provides insight into the complex factors influencing Inuit diet and health. Findings may be informative for future research, decision-making and intersectoral actions around risk assessment, food policy and innovative community programmes.

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Expert assessment of future vulnerability of the global peatland carbon sink
Julie Loisel, Angela Gallego‐Sala, Matthew J. Amesbury, Gabriel Magnan, Gusti Z. Anshari, David W. Beilman, Juan C. Benavides, Jerome Blewett, Philip Camill, Dan J. Charman, Sakonvan Chawchai, Alexandra Hedgpeth, Thomas Kleinen, Atte Korhola, David J. Large, Claudia A Mansilla, Jurek Müller, Simon van Bellen, Jason B. West, Zicheng Yu, Jill L. Bubier, Michelle Garneau, Tim R. Moore, A. Britta K. Sannel, Susan Page, Minna Väliranta, Michel Bechtold, Victor Brovkin, Lydia Cole, Jeffrey P. Chanton, Torben R. Christensen, Marissa A. Davies, François De Vleeschouwer, Sarah A. Finkelstein, Steve Frolking, Mariusz Gałka, Laure Gandois, Nicholas T. Girkin, Lorna I. Harris, Andreas Heinemeyer, Alison M. Hoyt, Miriam C. Jones, Fortunat Joos, Sari Juutinen, Karl Kaiser, Terri Lacourse, Mariusz Lamentowicz, Tuula Larmola, Jens Leifeld, Annalea Lohila, Alice M. Milner, Kari Minkkinen, Patrick Moss, B. David A. Naafs, J. E. Nichols, J. A. O’Donnell, Richard J. Payne, Michael Philben, Sanna Piilo, Anne Quillet, Amila Sandaruwan Ratnayake, Thomas P. Roland, Sofie Sjögersten, Oliver Sonnentag, Graeme T. Swindles, Ward Swinnen, Julie Talbot, Claire C. Treat, Alex Valach, Jiequn Wu
Nature Climate Change, Volume 11, Issue 1

The carbon balance of peatlands is predicted to shift from a sink to a source this century. However, peatland ecosystems are still omitted from the main Earth system models that are used for future climate change projections, and they are not considered in integrated assessment models that are used in impact and mitigation studies. By using evidence synthesized from the literature and an expert elicitation, we define and quantify the leading drivers of change that have impacted peatland carbon stocks during the Holocene and predict their effect during this century and in the far future. We also identify uncertainties and knowledge gaps in the scientific community and provide insight towards better integration of peatlands into modelling frameworks. Given the importance of the contribution by peatlands to the global carbon cycle, this study shows that peatland science is a critical research area and that we still have a long way to go to fully understand the peatland–carbon–climate nexus. Peatlands are impacted by climate and land-use changes, with feedback to warming by acting as either sources or sinks of carbon. Expert elicitation combined with literature review reveals key drivers of change that alter peatland carbon dynamics, with implications for improving models.
Abstract. Vegetation has a tremendous influence on snow processes and snowpack dynamics, yet remote sensing techniques to resolve the spatial variability of sub-canopy snow depth are not always available and are difficult from space-based platforms. Unmanned aerial vehicles (UAVs) have had recent widespread application to capture high-resolution information on snow processes and are herein applied to the sub-canopy snow depth challenge. Previous demonstrations of snow depth mapping with UAV structure from motion (SfM) and airborne lidar have focussed on non-vegetated surfaces or reported large errors in the presence of vegetation. In contrast, UAV-lidar systems have high-density point clouds and measure returns from a wide range of scan angles, increasing the likelihood of successfully sensing the sub-canopy snow depth. The effectiveness of UAV lidar and UAV SfM in mapping snow depth in both open and forested terrain was tested in a 2019 field campaign at the Canadian Rockies Hydrological Observatory, Alberta, and at Canadian prairie sites near Saskatoon, Saskatchewan, Canada. Only UAV lidar could successfully measure the sub-canopy snow surface with reliable sub-canopy point coverage and consistent error metrics (root mean square error (RMSE) <0.17 m and bias −0.03 to −0.13 m). Relative to UAV lidar, UAV SfM did not consistently sense the sub-canopy snow surface, the interpolation needed to account for point cloud gaps introduced interpolation artefacts, and error metrics demonstrated relatively large variability (RMSE<0.33 m and bias 0.08 to −0.14 m). With the demonstration of sub-canopy snow depth mapping capabilities, a number of early applications are presented to showcase the ability of UAV lidar to effectively quantify the many multiscale snow processes defining snowpack dynamics in mountain and prairie environments.
It is commonly thought that old groundwater cannot be pumped sustainably, and that recently recharged groundwater is inherently sustainable. We argue that both old and young groundwaters can be used in physically sustainable or unsustainable ways.
Conductive forms of MoS 2 are important emerging 2D materials due to their unique combination of properties such as high electrical conductivity, availability of active sites in edge and basal planes for catalytic activity and expanded interlayer distances. Consequently, there has been a drive to find synthetic routes toward conductive forms of MoS 2 . Naturally occurring or synthetically grown semiconducting 2H-MoS 2 can either be converted into metallic 1T-MoS 2 , or various dopants may be introduced to modulate the electronic band gap of the 2H-MoS 2 phase and increase its conductivity. Chemical and electrochemical intercalation methods, hydrothermal and solvothermal processes, and chemical vapor deposition have all been developed to synthesize conductive MoS 2 . Conductive MoS 2 finds applications in energy storage devices, electrocatalytic reactions, and sensors. Here, we summarize a detailed understanding of the atomic structure and electronic properties of conductive MoS 2 which is crucial for its applications. We also discuss various fabrication methods that have been previously reported along with their advantages and disadvantages. Finally, we will give an overview of current trends in different applications in energy storage and electrocatalytic reactions in order to help researchers to further explore the applications of conductive MoS 2 .
Applications of molybdenum disulfide (MoS2) in energy storage devices, solar cells, electrocatalysts, and sensors require good electrical conductivity. However, neither of the current ways to prepa...
IoT (Internet of Things)-based remote monitoring and controlling applications are increasing in dimensions and domains day by day. Sensor-based remote monitoring using a Wireless Sensor Network (WSN) becomes challenging for applications when both temporal and spatial data from widely spread sources are acquired in real time. In applications such as environmental, agricultural, and water quality monitoring, the data sources are geographically distributed, and have little or no cellular connectivity. These applications require long-distance wireless or satellite connections for IoT connectivity. Present WSNs are better suited for densely populated applications and require a large number of sensor nodes and base stations for wider coverage but at the cost of added complexity in routing and network organization. As a result, real time data acquisition using an IoT connected WSN is a challenge in terms of coverage, network lifetime, and wireless connectivity. This paper proposes a lightweight, dynamic, and auto-reconfigurable communication protocol (LDAP) for Wide-Area Remote Monitoring (WARM) applications. It has a mobile data sink for wider WSN coverage, and auto-reconfiguration capability to cope with the dynamic network topology required for device mobility. The WSN coverage and lifetime are further improved by using a Long-Range (LoRa) wireless interface. We evaluated the performance of the proposed LDAP in the field in terms of the data delivery rate, Received Signal Strength (RSS), and Signal to Noise Ratio (SNR). All experiments were conducted in a field trial for a water quality monitoring application as a case study. We have used both static and mobile data sinks with static sensor nodes in an IoT-connected environment. The experimental results show a significant reduction (up to 80%) of the number of data sinks while using the proposed LDAP. We also evaluated the energy consumption to determine the lifetime of the WSN using the LDAP algorithm.
Hydrogen peroxide (H2O2) is a key molecule in numerous physiological, industrial, and environmental processes. H2O2 is monitored using various methods like colorimetry, luminescence, fluorescence, and electrochemical methods. Here, we aim to provide a comprehensive review of solid state sensors to monitor H2O2. The review covers three categories of sensors: chemiresistive, conductometric, and field effect transistors. A brief description of the sensing mechanisms of these sensors has been provided. All three sensor types are evaluated based on the sensing parameters like sensitivity, limit of detection, measuring range and response time. We highlight those sensors which have advanced the field by using innovative materials or sensor fabrication techniques. Finally, we discuss the limitations of current solid state sensors and the future directions for research and development in this exciting area.
The more the merrier: Better selectivity for Zn2+ ions is shown for DNAzymes that bind more metal ions. This selectivity is exemplified by using a series of in vitro selected DNAzymes that contain a single metal ligand modification at the cleavage junction.
Abstract Nickel is a highly important metal, and the detection of Ni2+ using biosensors is a long-stand analytical challenge. DNA has been widely used for metal detection, although no DNA-based sensors were reported for Ni2+. DNAzymes are DNA-based catalysts, and they recruit metal ions for catalysis. In this work, in vitro selection of RNA-cleaving DNAzymes was carried out using a library containing a region of 50 random nucleotides in the presence of Ni2+. To increase Ni2+ binding, a glycyl–histidine-functionalized tertiary amine moiety was inserted at the cleavage junction. A representative DNAzyme named Ni03 showed a high cleavage yield with Ni2+ and it was further studied. After truncation, the optimal sequence of Ni03l could bind one Ni2+ or two Co2+ for catalysis, while other metal ions were inactive. Its cleavage rates for 100 μM Ni2+ reached 0.63 h−1 at pH 8.0. A catalytic beacon biosensor was designed by labeling a fluorophore and a quencher on the Ni03l DNAzyme. Fluorescence enhancement was observed in the presence of Ni2+ with a detection limit of 12.9 μM. The sensor was also tested in spiked Lake Ontario water achieving a similar sensitivity. This is another example of using single-site modified DNAzyme for sensing transition metal ions.
Since 1994, deoxyribozymes or DNAzymes have been in vitro selected to catalyze various types of reactions. Metal ions play a critical role in DNAzyme catalysis, and Zn2+ is a very important one among them. Zn2+ has good biocompatibility and can be used for intracellular applications. Chemically, Zn2+ is a Lewis acid and it can bind to both the phosphate backbone and the nucleobases of DNA. Zn2+ undergoes hydrolysis even at neutral pH, and the partially hydrolyzed polynuclear complexes can affect the interactions with DNA. These features have made Zn2+ a unique cofactor for DNAzyme reactions. This review summarizes Zn2+ -dependent DNAzymes with an emphasis on RNA-/DNA-cleaving reactions. A key feature is the sharp Zn2+ concentration and pH-dependent activity for many of the DNAzymes. The applications of these DNAzymes as biosensors for Zn2+ , as therapeutic agents to cleave intracellular RNA, and as chemical biology tools to manipulate DNA are discussed. Future studies can focus on the selection of new DNAzymes with improved performance and detailed biochemical characterizations to understand the role of Zn2+ , which can facilitate practical applications of Zn2+ -dependent DNAzymes.
Microflow cytometers and many other miniaturized microfluidic devices have shown great potential in many fields, such as, particle detection, cell sorting and classification. A reliable signal analysis method is required to improve the measurement accuracy of the emerging microfluidic devices. In this paper, a novel method is presented to analyze the signal from microspheres with different diameters based on transit time and amplitude. Experimental results show that transit time threshold plays a more important role at lower flow rate for particle differentiation and can be used to improve the performance of a microflow cytometer.
Continuous measurement of dissolved oxygen (DO) variation is important in water monitoring and biomedical applications, which require low-cost and low-maintenance sensors capable of automated operation. A frequency-domain optofluidic DO sensor with total internal reflection (TIR) design has been developed based on fluorescence quenching of Ruthenium complex (Ru(dpp) <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> Cl <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ). To minimize artifacts causing drift in fluorescence measurements such as background autofluorescence, photobleaching, optical alignment variation, a low-cost frequency-domain approach is implemented in an optofluidic platform to measure the phase shift between the excitation and emission light. We show that the frequency domain optofluidic DO sensor provides absolute DO concentrations in repeated measurements. TIR design can enhance fluorescence signal in the integrated device and minimize background autofluorescence in the sample, which can subsequently improve overall sensitivity. Furthermore, photobleaching in the samples would be mitigated as the incident light does not enter the microfluidic channel. Our results demonstrate a measurement resolution of 0.2 ppm and response times of less than one minute. In accelerated photobleaching conditions, the long-term drift is shown to be less than ±0.4 ppm. These results suggest the potential of this optofluidic DO sensor as an in situ platform for water monitoring and biomedical applications.
• Analysis shows the G E V distribution might not be the best choice for flood frequency analysis. • Burr type III and XII are consistent and robust models to describe annual flood peaks. • Pan-Canadian investigation of annual streamflow peaks. Safe and cost-effective design of infrastructures, such as dams, bridges, highways, often requires knowing the magnitude and frequency of peak floods. The Generalized Extreme Value distribution ( G E V ) prevailed in flood frequency analysis along with distributions comprising location, scale, and shape parameters. Here we explore alternative models and propose power-type models, having one scale and two shape parameters. The Burr type III ( Ɓr III) and XII ( Ɓ rXII) distributions are compared against the G E V in 1088 streamflow records of annual peaks across Canada. A generic L-moment algorithm is devised to fit the distributions, also applicable to distributions without analytical L-moment expressions. The analysis shows: (1) the models perform equally well when describing the observed annual peaks; (2) the right tail appears heavier in the Ɓr III and Ɓr XII models leading to larger streamflow predictions when compared to those of G E V ; (3) the G E V predicts upper streamflow limits in 39.1% of the records—these limits have realistic exceedance probabilities based on the other two models; (4) the tail heaviness estimation seems not robust in the G E V case when compared to the Ɓr III and Ɓr XII models and this could challenge G E V ’s reliability in predicting streamflow at large return periods; and, (5) regional variation is observed in the behaviour of flood peaks across different climatic regions of Canada. The findings of this study reveal potential limitations in using the G E V for flood frequency analysis and suggest the Ɓr III and Ɓr XII as consistent alternatives worth exploring.
Forests play a major role in the global carbon cycle. Understanding the dynamics of the forest carbon cycle and its driving factors is challenging. This study utilized dendrochronology and long-term (2003–2017) eddy covariance (EC) carbon flux data to investigate the relationships between tree growth and gross and net ecosystem productivities (GEPEC and NEPEC) in different-age (15-, 42- and 78-year old) pine plantation forests in the Great Lakes region in eastern North America. Tree growth in these different-age pine forests was significantly (p < 0.05) correlated with observed annual GEPEC values, while coherence between tree growth and NEPEC was relatively poor. Current-year and 1-year lagged ring-width chronologies and climate variables, including spring (April–May) temperature (TSPR) and Standardized Potential Evapotranspiration Index (SPEISUM) over the summer months (June–August) were used to test ten different linear regression models to simulate tree-ring-based GEP (GEPTR) values at all three sites. This analysis showed that current-year growth was the best predictor of GEPTR at all three sites, when compared to observed GEPEC, except during drought years, when GEPTR was underestimated. Current-year tree growth models were then used to reconstruct GEPTR over the life span of each stand. These reconstructions showed low GEPTR values from 1978 to 1988 and from 2002 to 2007. Low GEPTR in late 1970s occurred in response to below average temperatures when there were no major drought periods, while low GEPTR in early 2000s occurred following drought-like conditions in 2002. However, in recent years relatively higher GEPTR was observed at all three different-age forest sites. This interdisciplinary study will help to improve our understanding of carbon exchanges and the key environmental controls and associated uncertainties on tree growth in these different-age plantation stands in eastern North America. It will also help to determine how these forests may respond to climate change.
Evergreen conifer forests are the most prevalent land cover type in North America. Seasonal changes in the color of evergreen forest canopies have been documented with near-surface remote sensing, but the physiological mechanisms underlying these changes, and the implications for photosynthetic uptake, have not been fully elucidated. Here, we integrate on-the-ground phenological observations, leaf-level physiological measurements, near surface hyperspectral remote sensing and digital camera imagery, tower-based CO2 flux measurements, and a predictive model to simulate seasonal canopy color dynamics. We show that seasonal changes in canopy color occur independently of new leaf production, but track changes in chlorophyll fluorescence, the photochemical reflectance index, and leaf pigmentation. We demonstrate that at winter-dormant sites, seasonal changes in canopy color can be used to predict the onset of canopy-level photosynthesis in spring, and its cessation in autumn. Finally, we parameterize a simple temperature-based model to predict the seasonal cycle of canopy greenness, and we show that the model successfully simulates interannual variation in the timing of changes in canopy color. These results provide mechanistic insight into the factors driving seasonal changes in evergreen canopy color and provide opportunities to monitor and model seasonal variation in photosynthetic activity using color-based vegetation indices.
Agriculture is practiced on 3–4 million acres of First Nations reserve lands in the Saskatchewan Prairies—predominantly by non-Indigenous farmers. A confluence of factors including an increase in agricultural land holdings on reserve and greater autonomy in land management have renewed conversations on how First Nations can realize the full economic benefits and exert greater control over agricultural activities that affect the reserve land base. We hosted a Forum on Indigenous Agriculture to share current knowledge on the contemporary status of Indigenous agriculture and to co-formulate research, capacity building, and policy priorities. First Nations’ roles in agriculture are diverse and were categorized in three broad contexts: as farmers, relying on traditional Indigenous or western practice, or a synergy of both; as landlords negotiating lease agreements; and as agribusiness entrepreneurs. Five themes emerged from the forum: centring Indigenous knowledge and traditional relationships to the land, capacity building, building respectful partnerships and relationships, financing farming and equitable economies, and translating research to policy and legislation. The forum provided foundational data to inform research and capacity building to meet community-defined goals in agriculture on reserve lands and by First Nations people.
Cowichan Lake lamprey ( Entosphenus macrostomus) is a threatened species resident to Mesachie Lake, Cowichan Lake, and adjoining Bear Lake and their major tributaries in British Columbia. Decreases in trapping success have created concerns that the population is declining. Some potential threats include water use, climate change, and management actions. Owing to the absence of long-term data on population trends, little information is available to estimate habitat quality and factors that influence it. We sought to fill this gap by examining associations between habitat area and variables representing suspected key drivers of habitat availability. Critical habitat areas were imaged using an unmanned aerial vehicle over a period of three years at three sites at Cowichan Lake and a subsequent habitat area was classified. Meteorological and anthropogenic controls on habitat area were investigated through automatic relevance detection regression models. The major driver of habitat area during the critical spawning period was water level during the storage season, which also depends on the meteorological variables and anthropogenic control. It is recommended that regulation of the weir should aim to ensure that the water level remains above the 1 m mark, which roughly equates to the 67% coverage of water on the habitat area used for spawning.
The increasing prevalence of cyanobacteria-dominated harmful algal blooms is strongly associated with nutrient loading and changing climatic patterns. Changes to precipitation frequency and intensity, as predicted by current climate models, are likely to affect bloom development and composition through changes in nutrient fluxes and water column mixing. However, few studies have directly documented the effects of extreme precipitation events on cyanobacterial composition, biomass, and toxin production. We tracked changes in a eutrophic reservoir following an extreme precipitation event, describing an atypically early toxin-producing cyanobacterial bloom and successional progression of the phytoplankton community, toxins, and geochemistry. An increase in bioavailable phosphorus by more than 27-fold in surface waters preceded notable increases in Aphanizomenon flos-aquae throughout the reservoir approximately 2 weeks postevent and ∼5 weeks before blooms typically occur. Anabaenopeptin-A and three microcystin congeners (microcystin-LR, -YR, and -RR) were detected at varying levels across sites during the bloom period, which lasted between 3 and 5 weeks. These findings suggest extreme rainfall can trigger early cyanobacterial bloom initiation, effectively elongating the bloom season period of potential toxicity. However, effects will vary depending on factors including the timing of rainfall and reservoir physical structure.
Agricultural drainage is a complicated and often conflict-ridden natural resource management issue, impacting contested ecosystem services related to the retention of wetlands as well as the productivity of farmland. This research identifies opportunities to transform the conflict over agricultural drainage in Saskatchewan, Canada, towards collaboration. We report on ethnographic research informed by a conservation conflict-transformation framework to evaluate the nature of the conflict and whether drivers of the conflict operate principally at the level of disputes over discrete ecosystem services or if they reach deeper into local social circumstances and build on larger unresolved conflict(s) among groups in the region. In addition to the conflict-transformation framework, we apply the Social–Ecological Systems Framework to elicit details regarding the substantive, relational, and material dimensions of this conflict. Our research suggests that processes for governing natural resources, such as those in place for governing drainage in Saskatchewan, need to have mechanisms to facilitate relationship building and shared understandings, need to be adaptable to people’s changing needs and concerns, and should focus on inclusivity and empowerment of actors to address conflict.
This essay reviews challenges posed to community-engaged scholars regarding tenure/promotion processes in Canadian universities, with a note to characteristics of community-engaged scholarship that were developed by Catherine Jordan (2007) to address gaps in academic assessment of engaged scholarship. These characteristics are: clear goals, adequate preparation, appropriate methods: scientific rigor and community engagement, significant results/impact, effective presentation/dissemination, reflective critique, leadership and personal contribution, and consistently ethical behavior. These are then applied to a non-peer reviewed work that describes the cumulative effects of environmental change for people in the Slave River Delta Region of the North West Territories, Canada. The reader is asked to view Delta Ways Remembered, a 13-minute video employing an enhanced e-storytelling technique to share and disseminate traditional knowledge about the delta from a compendium of people as a single-voiced narrative. The purpose is to highlight the scholarship underlying non-traditional academic expositions not readily assessed under current paradigms of academic evaluation. This essay strives to illustrate how Jordan’s characteristics can be applied to evaluate non-peer reviewed scholarly work, and also to share rewards and challenges associated with the harmonious blending of Indigenous and western knowledge addressing societal/environmental issues identified by the Indigenous community.
This study complements the existing literature on disparities associated with Indigenous and non-Indigenous small drinking water systems. The team took a quantitative approach and assessed relationships between seasonality, location, and type of community against the number of drinking water advisories in Saskatchewan for a 4-year period from 2012 to 2016. Generalised estimating equations were used to determine significant factors contributing to the likelihood of drinking water advisories comparing Indigenous to non-Indigenous communities of similar sizes. Results indicated that the season and the interaction between community type and region (north vs. south) were significant in the model for counts of advisories. Reserve communities in the north had a drinking water advisory count that was 5.19 times greater than those of reserves in the south, 2.63 times greater than counts for towns in the south and 4.94 times greater than those of villages in the south. Additional comparisons indicated that reserves in the north had 2.43 times as many advisories as villages in the north, but towns situated in the south part of the province had 1.98 times as many advisories as reserves in the south, and 1.88 times as many advisories as villages in the south. The work confirms heightened risk among northern Indigenous communities and suggests that increased attention to, and investment in, securing water resources is necessary in rural Saskatchewan and globally.
The development of hydrological models that produce practically useful and physically defensible results is an ongoing challenge in hydrology. This challenge is further compounded in large, spatial...
Abstract Groundwater storage in alpine regions is essential for maintaining baseflows in mountain streams. Recent studies have shown that common alpine landforms (e.g., talus and moraine) have substantial groundwater storage capacity, but the hydrogeological connectivity between individual landforms has not been understood. This study characterizes the hydrogeology of an alpine cirque basin in the Canadian Rocky Mountains that contains typical alpine landforms (talus, meadow, moraines) and hydrological features (tarn, streams, and springs). Geological, hydrological, and hydrochemical observations were used to understand the overall hydrogeological setting of the study basin, and three different geophysical methods (electrical resistivity tomography, seismic refraction tomography, and ground penetrating radar) were used to characterize the subsurface structure and connectivity, and to develop a hydrogeological conceptual model. Geophysical imaging shows that the talus is typically 20–40 m thick and highly heterogeneous. The meadow sediments are only up to 11 m thick but are part of a 30–40-m-thick accumulation of unconsolidated material that fills a bedrock overdeepening (i.e. a closed, subglacial basin). A minor, shallow groundwater system feeds springs on the talus and streams on the meadow, whereas a deep system in the moraine supplies most of the water to the basin outlet springs, thereby serving as a ‘gate keeper’ of the basin. Although the hydrologic functions of the talus in this study are substantially different from other locations, primarily due to differences in bedrock lithology and geomorphic processes, the general conceptual framework developed in this study is expected to be applicable to other alpine regions.
Pleistocene-aged glacial sediments are found in many parts the Northern Hemisphere and are often composed of clay-rich tills which form aquitards that can control drainage and influence groundwater movement and contaminant transport. Site-scale investigations have characterized the hydraulic properties of till aquitards; however, the hydraulic conductivity of these units has not been quantitatively described at a regional scale of tens of kilometers. This study constrains regionally representative hydraulic conductivity estimates and characterizes the hydrogeological properties of Pleistocene-aged till aquitards from data collected at 15 sites compiled from 21 studies. The data quantify the scale dependence of hydraulic conductivity measurements in till aquitards and further define the relationship between hydraulic conductivity and depth. Data from centimeter-scale laboratory tests remained generally constant with depth, with a geometric mean hydraulic conductivity of 7.0 × 10−11 m/s and a standard deviation of 0.4 orders of magnitude, while the meter-scale in-situ tests had a geometric mean of 4.9 × 10−9 m/s and a standard deviation of 1.0 orders of magnitude at depths less than 10 m, and 3.7 × 10−11 m/s and 0.2 order of magnitude at depths greater than 23 m. The results support the existence of a shallow fractured zone of higher hydraulic conductivity and a deeper zone characterized by matrix permeability. The observed data variability occurred primarily at the site scale, while the central tendency and variability of the data were consistent between sites separated by hundreds of kilometers suggesting that statistically derived, depth-defined regional hydraulic conductivity estimates can be meaningful.
Alpine headwaters in subarctic regions are particularly sensitive to climate change, yet there is little information on stream thermal regimes in these areas and how they might respond to global warming. In this paper, we characterize and compare the hydrological and thermal regimes of two subarctic headwater alpine streams within an empirical framework. The streams investigated are located within two adjacent catchments with similar geology, size, elevation and landscape, Granger Creek (GC) and Buckbrush Creek (BB), which are part of the Wolf Creek Research Basin in the Yukon Territory, Canada. Hydrometeorological and high‐resolution stream temperature data were collected throughout summer 2016. Both sites exhibited a flow regime typical of cold alpine headwater catchments influenced by frozen ground and permafrost. Comparatively, GC was characterized by a flashier response with more extreme flows, than BB. In both sites, stream temperature was highly variable and very responsive to short‐term changes in climatic conditions. On average, stream temperature in BB was slightly higher than in GC (respectively 5.8 and 5.7°C), but less variable (average difference between 75th and 25th quantiles of 1.6 and 2.0°C). Regression analysis between mean daily air and stream temperature suggested that a greater relative (to stream flow) groundwater contribution in BB could more effectively buffer atmospheric fluctuations. Heat fluxes were derived and utilized to assess their relative contribution to the energy balance. Overall, non‐advective fluxes followed a daily pattern highly correlated to short‐wave radiation. G1enerally, solar radiation and latent heat were respectively the most important heat source and sink, while air–water interface processes were major factors driving nighttime stream temperature fluctuations.
Subalpine forests are hydrologically important to the function and health of mountain basins. Identifying the specific water sources and the proportions used by subalpine forests is necessary to understand potential impacts to these forests under a changing climate. The recent “Two Water Worlds” hypothesis suggests that trees can favour tightly bound soil water instead of readily available free-flowing soil water. Little is known about the specific sources of water used by subalpine trees Abies lasiocarpa (Subalpine fir) and Picea engelmannii (Engelmann spruce) in the Canadian Rocky Mountains. In this study, stable water isotope (δ18O and δ2H) samples were obtained from S. fir and Engelmann spruce trees at three points of the growing season in combination with water sources available at time of sampling (snow, vadose zone water, saturated zone water, precipitation). Using the Bayesian Mixing Model, MixSIAR, relative source water proportions were calculated. In the drought summer examined, there was a net loss of water via evapotranspiration from the system. Results highlighted the importance of tightly vadose zone, or bound soil water, to subalpine forests, providing insights of future health under sustained years of drought and net loss in summer growing seasons. This work builds upon concepts from the “Two Water Worlds” hypothesis, showing that subalpine trees can draw from different water sources depending on season and availability. In our case, water use was largely driven by a tension gradient within the soil allowing trees to utilize vadose zone water and saturated zone water at differing points of the growing season.
Water resources in semi‐arid regions like the Mediterranean Basin are highly vulnerable because of the high variability of weather systems. Additionally, climate change is altering the timing and pattern of water availability in a region where growing populations are placing extra demands on water supplies. Importantly, how reservoirs and dams have an influence on the amount of water resources available is poorly quantified. Therefore, we examine the impact of reservoirs on water resources together with the impact of climate change in a semi‐arid Mediterranean catchment. We simulated the Susurluk basin (23.779‐km2) using the Soil and Water Assessment Tool (SWAT) model. We generate results for with (RSV) and without reservoirs (WRSV) scenarios. We run simulations for current and future conditions using dynamically downscaled outputs of the MPI‐ESM‐MR general circulation model under two greenhouse gas relative concentration pathways (RCPs) in order to reveal the coupled effect of reservoir and climate impacts. Water resources were then converted to their usages – blue water (water in aquifers and rivers), green water storage (water in the soil) and green water flow (water losses by evaporation and transpiration). The results demonstrate that all water resources except green water flow are projected to decrease under all RCPs compared to the reference period, both long‐term and at seasonal scales. However, while water scarcity is expected in the future, reservoir storage is shown to be adequate to overcome this problem. Nevertheless, reservoirs reduce the availability of water, particularly in soil moisture stores, which increases the potential for drought by reducing streamflow. Furthermore, reservoirs cause water losses through evaporation from their open surfaces. We conclude that pressures to protect society from economic damage by building reservoirs have a strong impact on the fluxes of watersheds. This is additional to the effect of climate change on water resources.
Abstract The freeze–thaw changes of seasonally frozen ground (SFG) are an important indicator of climate change. Based on observed daily freeze depth of SFG from meteorological stations on the Tibetan Plateau (TP) from 1960 to 2014, the spatial–temporal characteristics and trends in SFG were analyzed, and the relationships between them and climatic and geographical factors were explored. Freeze–thaw changes of SFG on a regional scale were assessed by multiple regression functions. Results showed multiyear mean maximum freeze depth, freeze–thaw duration, freeze start date, and thaw end date that demonstrate obvious distribution characteristics of climatic zones. A decreasing trend in maximum freeze depth and freeze–thaw duration occurred on the TP from 1960 to 2014. The freeze start date has been later, and the thaw end date has been significantly earlier. The freeze–thaw changes of SFG significantly affected by soil hydrothermal conditions on the TP could be assessed by elevation and latitude or by air temperature and precipitation, due to their high correlations. The regional average of maximum freeze depth and freeze–thaw duration caused by climatic and geographical factors were larger than those averaged using meteorological station data because most stations are located at lower altitudes. Maximum freeze depth and freeze–thaw duration have decreased sharply since 2000 on the entire TP. Warming and wetting conditions of the soil resulted in a significant decrease in maximum freeze depth and freeze–thaw duration in the most area of the TP, while drying soil results in a slight increase of them in the southeast of the TP.
Abstract Long-term changes in extreme daily and subdaily precipitation simulated by climate models are often compared with corresponding temperature changes to estimate the sensitivity of extreme precipitation to warming. Such “trend scaling” rates are difficult to estimate from observations, however, because of limited data availability and high background variability. Intra-annual temperature scaling (here called binning scaling), which relates extreme precipitation to temperature at or near the time of occurrence, has been suggested as a possible substitute for trend scaling. We use a large ensemble simulation of the Canadian regional climate model (CanRCM4) to assess this possibility, considering both daily near-surface air temperature and daily dewpoint temperature as scaling variables. We find that binning curves that are based on precipitation data for the whole year generally look like the composite of binning curves for winter and summer, with the lower temperature portion similar to winter and the higher temperature portion similar to summer, indicating that binning curves reflect seasonal changes in the relationship between temperature and extreme precipitation. The magnitude and spatial pattern of binning and trend scaling rates are also quantitatively different, with little spatial correlation between them, regardless of precipitation duration or choice of temperature variable. The evidence therefore suggests that binning scaling with temperature is not a reliable predictor for future changes in precipitation extremes in the climate simulated by CanRCM4. Nevertheless, external forcing does have a discernable influence on binning curves, which are seen to shift upward and to the right in some regions, consistent with a general increase in extreme precipitation.
Beaver dam analogues (BDAs) are intended to simulate natural beaver dam ecohydrological functions including modifying stream hydrology and enhancing stream‐riparian hydrological connectivity. River restoration practitioners are proactively deploying BDAs in thousands of degraded streams. How various BDAs or their configurations impact stream hydrology and the riparian water table remains poorly understood. We investigated three types of BDA configurations (single, double and triple) in a spring‐fed Canadian Rocky Mountain stream over three study seasons (April–October; 2017–2019). All three BDA configurations significantly elevated the upstream stage. The deepest pools occurred upstream of the triple‐configuration BDAs (0.46 m) and the shallowest pools occurred upstream of the single‐configuration (0.36 m). Further, the single‐BDA configuration lowered stream stage and flow peaks below it but raised low flows. The double‐BDA configuration modulated flow peaks but had little influence on low flows. Unexpectedly, higher flow peaks and low flows were recorded below the triple‐BDA configuration, owing to groundwater seep. Similar to the natural beaver dam function, we observed an immediate water table rise in the riparian area after installation of the BDAs. The water table rise was greatest 2 m from the stream (0.14 m) and diminished with increasing lateral distance from the stream. Also noted was a reversal in the direction of flow between the stream and riparian area after BDA installation. Future research should further explore the dynamics of stream‐riparian hydrological connections under various BDA configurations and spacings, with the goal of identifying best practices for simulating the ecohydrological functions of natural beaver dams.
In mountain lakes, water transparency is regulated primarily by materials loaded from the surrounding catchment. Consequently, transparency within a lake can vary over time due to meteorological co...
Abstract Water storage dynamics modulate fluxes within catchments, control the rainfall-runoff response and regulate the velocity of water particles through mixing associated processes. Tracer-aided models are useful tools for tracking the interactions between catchment storage and fluxes, as they can capture both the celerity of the runoff response and the velocity of water particles revealed by tracer dynamics. The phase-space reconstruction of modelled systems can help in this regard; it traces the evolution of a dynamic system from a known initial state as phase trajectories in response to inputs. In this study, we compared the modelled storage-flux dynamics obtained from the application of a spatially distributed tracer-aided hydrological model (STARR) in five contrasting long-term research catchments with varying degrees of snow influence. The models were calibrated using a consistent multivariate methodology based on discharge, isotope composition and snowpack water equivalent. Analysis of extracted modelled storage dynamics gave insights into the system functioning. Large volumes of total stored water needed to be invoked at most sites to reconcile celerity and travel times to match observe discharge and isotope responses. This is because changes in dynamic storage from water balance considerations are small when compared to volume of storage necessary for observed tracer dampening. In the phase-space diagrams, the rates of storage change gave insights into the relative storage volume and seasonal catchment functioning. The storage increase was dominated by hydroclimatic inputs; thus, it presented a stochastic response. Furthermore, depending on the dominance of snow or rainfall inputs, catchments had different seasonal responses in storage dynamics. Decreases in storage were more predictable and reflected the efficiency of catchment drainage, yet at lower storages the influence of ET was also evident. Activation of flow paths due to overland and near-surface flows resulted in non-linearity of catchment functioning largely at high storage states. The storage-discharge relationships generally showed a non-linear distribution, with more scattered states during wettest condition. In turn, all the catchments exhibited an inverse storage effect, with modelled water ages decreasing with increasing storage as lateral flow paths were activated. Insights from this inter-comparison of storage-flux-age dynamics show the benefits of tracer-aided hydrological models in exploring their interactions at well-instrumented sites to better understand hydrological functioning of contrasting catchments.
Abstract Performance in simulating atmospheric rivers (ARs) over western North America based on AR frequency and landfall latitude is evaluated for 10 models from phase 5 of the Coupled Model Intercomparison Project among which the CanESM2 model performs well. ARs are classified into southern, northern, and middle types using self-organizing maps in the ERA-Interim reanalysis and CanESM2. The southern type is associated with the development and eastward movement of anomalous lower pressure over the subtropical eastern Pacific, while the northern type is linked with the eastward movement of anomalous cyclonic circulation stimulated by warm sea surface temperatures over the subtropical western Pacific. The middle type is connected with the negative phase of North Pacific Oscillation–west Pacific teleconnection pattern. CanESM2 is further used to investigate projected AR changes at the end of the twenty-first century under the representative concentration pathway 8.5 scenario. AR definitions usually reference fixed integrated water vapor or integrated water vapor transport thresholds. AR changes under such definitions reflect both thermodynamic and dynamic influences. We therefore also use a modified AR definition that isolates change from dynamic influences only. The total AR frequency doubles compared to the historical period, with the middle AR type contributing the largest increases along the coasts of Vancouver Island and California. Atmospheric circulation (dynamic) changes decrease northern AR type frequency while increasing middle AR type frequency, indicating that future changes of circulation patterns modify the direct effect of warming on AR frequency, which would increase ARs (relative to fixed thresholds) almost everywhere along the North American coastline.
It is becoming increasingly popular to reintroduce beaver to streams with the hopes of restoring riparian ecosystem function or reducing some of the hydrological impacts of climate change. One of the risks of relying on beaver to enhance ecosystem water storage is that their dams are reportedly more apt to fail during floods which can exacerbate flood severity. Missing are observations of beaver dam persistence and water storage capacity during floods, information needed to evaluate the risk of relying on beaver as a nature-based flood solution. A June rainstorm in 2013 triggered the largest recorded flood in the Canadian Rocky Mountains west of Calgary, Alberta. We opportunistically recorded hydrometric data during the rainfall event at a beaver-occupied peatland that has been studied for more than a decade. We supplemented these observations with a post-event regional analysis of beaver dam persistence. Results do not support two long-held hypotheses—that beaver ponds have limited flood attenuation capacity and commonly fail during large flood events. Instead we found that 68% of the beaver dam cascade systems across the region were intact or partially intact after the event. Pond fullness, in addition to the magnitude of the water-sediment surge, emerged as important factors in determining the structural fate of dam cascade sequences. Beaver ponds at the instrumented site quickly filled in the first few hours of the rain event and levels were dynamic during the event. Water storage offered by the beaver ponds, even ones that failed, delayed downstream floodwater transmission. Study findings have important implications for reintroducing beaver as part of nature-based restoration and climate change adaptation strategies.
In cold agricultural regions, seasonal snowmelt over frozen soils provides the primary source of runoff and transports large nutrient loads downstream. The postglacial landscape of the Canadian Prairies and Northern Plains of the United States creates challenges for hydrological and water quality modeling. Here, the application of conventional hydrological models is problematic because of cold regions hydrological and chemical processes, the lack of fluvially eroded drainage systems, large noncontributing areas to streamflow and level topography. A new hydrodynamic model was developed to diagnose overland flow from snowmelt in this situation. The model was used to calculate the effect of variable contributing areas on (1) hydrological connectivity and the development of (2) tipping points in streamflow generation and (3) predominant chemical transport pathways. The agricultural Steppler Basin in Manitoba, Canada, was used to evaluate the model and diagnose snowmelt runoff. Relationships were established between contributing area and (1) snowmelt runoff intensity, (2) seasonal snowmelt volumes and duration, and (3) inundated, active and connected areas. Variations in the contributing area depended on terrain and snowmelt characteristics including wind redistribution of snow. Predictors of hydrological response and the size of the contributing area were developed which can be used in larger scale hydrological models of similar regions
Climate extremes threaten human health, economic stability, and the well-being of natural and built environments (e.g., 2003 European heat wave). As the world continues to warm, climate hazards are expected to increase in frequency and intensity. The impacts of extreme events will also be more severe due to the increased exposure (growing population and development) and vulnerability (aging infrastructure) of human settlements. Climate models attribute part of the projected increases in the intensity and frequency of natural disasters to anthropogenic emissions and changes in land use and land cover. Here, we review the impacts, historical and projected changes,and theoretical research gaps of key extreme events (heat waves, droughts, wildfires, precipitation, and flooding). We also highlight the need to improve our understanding of the dependence between individual and interrelated climate extremes because anthropogenic-induced warming increases the risk of not only individual climate extremes but also compound (co-occurring) and cascading hazards. ▪ Climate hazards are expected to increase in frequency and intensity in a warming world. ▪ Anthropogenic-induced warming increases the risk of compound and cascading hazards. ▪ We need to improve our understanding of causes and drivers of compound and cascading hazards.
Groundwater is a crucial resource for current and future generations, but it is not being sustainably used in many parts of the world. The objective of this review is to provide a clear portrait of global-scale groundwater sustainability, systems, and resources in the Anthropocene to inspire a pivot toward more sustainable pathways of groundwater use. We examine groundwater from three different but related perspectives of sustainability science, natural resource governance and management, and Earth System science. An Earth System approach highlights the connections between groundwater and the other parts of the system and how these connections are impacting, or are impacted by, groundwater pumping. Groundwater is the largest store of unfrozen freshwater on Earth and is heterogeneously connected to many Earth System processes on different timescales. We propose a definition of groundwater sustainability that has a direct link with observable data, governance, and management as well as the crucial functions and services of groundwater. ▪ Groundwater is depleted or contaminated in some regions; it is ubiquitously distributed, which, importantly, makes it broadly accessible but also slow and invisible and therefore challenging to govern and manage. ▪ Regional differences in priorities, hydrology, politics, culture, and economic contexts mean that different governance and management tools are important, but a global perspective can support higher level international policies in an increasingly globalized world that require broader analysis of interconnections and knowledge transfer between regions. ▪ A coherent, overarching framework of groundwater sustainability is more important for groundwater governance and management than the concepts of safe yield, renewability, depletion, or stress.
Bisphenol A, an endocrine disrupting compound, is widely used in food and beverage packaging, and it then leaches in food and source water cycles, and thus must be monitored. Here, we report a simple, low-cost and sensitive electrochemical sensor using graphene oxide and β-cyclodextrin functionalized multiwalled carbon nanotubes for the detection of BPA in water. This sensor electrode system combines the high surface area of graphene oxide and carbon nanotubes, and the superior host-guest interaction capability of β-cyclodextrin. A diffusion-controlled oxidation reaction involving equal numbers of protons and electrons facilitated the electrochemical sensing of BPA. The sensor showed a two-step linear response from 0.05 to 5 μM and 5-30 μM with a limit of detection of 6 nM. The sensors also exhibited a reproducible and stable response over one month with negligible interference from common inorganic and organic species, and an excellent recovery with real water samples. The proposed electrochemical sensor can be promising for the development of simple low-cost water quality monitoring system for monitoring of BPA in water.
Rapid, accurate and inexpensive monitoring of water quality parameters is indispensable for continued water safety, especially in resource-limited areas. Most conventional sensing systems either can only monitor one parameter at a time or lack user-friendly on-site monitoring capabilities. A fully integrated electrochemical sensor array is an excellent solution to this barrier. Electrochemical sensing methods involve transduction of water quality parameters where chemical interactions are converted to electrical signals. The challenge remains in designing low-cost, easy-to-use, and highly sensitive sensor array that can continuously monitor major water quality parameters such as pH, free chlorine, temperature along with emerging pharmaceutical contaminants, and heavy metal without the use of expensive laboratory-based techniques and trained personnel. Here, we overcame this challenge through realizing a fully integrated electrochemical sensing system that offers simultaneous monitoring of pH (57.5 mV/pH), free chlorine (186 nA/ppm), and temperature (16.9 mV/°C) and on-demand monitoring of acetaminophen and 17β-estradiol (<10 nM) and heavy metal (<10 ppb), bridging the technological gap between signal transduction, processing, wireless transmission, and smartphone interfacing. This was achieved by merging nanomaterials and carbon nanotube-based sensors fabricated on microscopic glass slides controlled by a custom-designed readout circuit, a potentiostat, and an Android app. The sensing system can be easily modified and programmed to integrate other sensors, a capability that can be exploited to monitor a range of water quality parameters. We demonstrate the integrated system for monitoring tap, swimming pool, and lake water. This system opens the possibility for a wide range of low-cost and ubiquitous environmental monitoring applications.
The oil sands industry in Canada uses soil–vegetation–atmosphere-transfer (SVAT) water balance models, calibrated against short-term (<ã10 years) field monitoring data, to evaluate long-term (ã60 years) reclamation cover design performance. These evaluations use long-term historical climate data; however, the effects of climate change should also be incorporated in these analyses. Although statistical downscaling of global climate change projections is commonly used to obtain local, site-specific climate, high resolution dynamical downscaling can also be used. The value of this latter approach to obtain local site-specific projections for mine reclamation covers has not been evaluated previously. This study explored the differences in key water balance components of three reclamation covers and three natural sites in northern Alberta, Canada, under future, site-specific, statistical, and dynamical climate change projections. Historical meteorological records were used to establish baseline periods. Temperature datasets were used to calculate potential evapotranspiration (PET) using the Hargreaves–Samani method. Statistical downscaling uses the Long Ashton Research Station Weather Generator (LARS-WG) and global circulation model (GCM) projections of temperature and precipitation. Dynamical climate change projections were generated on a 4 km grid using the weather research and forecasting (WRF) model. These climate projections were applied to a physically-based water balance model (i.e. Hydrus-1D) to simulate actual evapotranspiration (AET) and net percolation (NP) for the baseline and future periods. The key findings were: (a) LARS-WG outperformed WRF in simulating baseline temperatures and precipitation; (b) both downscaling methods showed similar directional shifts in the future temperatures and precipitation; (c) this, in turn, created similar directional shifts in future growing season median AET and NP, although the increase in future NP for LARS-WG was higher than that for WRF. The relative increases in future NP were much higher than the relative increases in future AET, particularly for the reclamation covers.
Not only do newly proposed code clone detection techniques, but existing techniques and tools also need to be evaluated and compared. This evaluation process could be done by assessing the reported clones manually or by using benchmarks. The main limitations of available benchmarks include: they are restricted to one programming language; they have a limited number of clone pairs that are confined within the selected system(s); they require manual validation; they do not support all types of code clones. To overcome these limitations, we proposed a methodology to generate a wide range of semantic clone benchmark(s) for different programming languages with minimal human validation. Our technique is based on the knowledge provided by developers who participate in the crowd-sourced information website, Stack Overflow. We applied automatic filtering, selection and validation to the source code in Stack Overflow answers. Finally, we build a semantic code clone benchmark of 4000 clones pairs for the languages Java, C, C# and Python.
A code clone is defined as a pair of similar code fragments within a software system. While code clones are not always harmful, they can have a detrimental effect on the overall quality of a software system due to the propagation of bugs and other maintenance implications. Because of this, software developers need to analyse the code clones that exist in a software system. However, despite the availability of several clone detection systems, the adoption of such tools outside of the clone community remains low. A possible reason for this is the difficulty and complexity involved in setting up and using these tools. In this paper, we present Clone Swarm, a code clone analytics tool that identifies clones in a project and presents the information in an easily accessible manner. Clone Swarm is publicly available and can mine any open-sourced GIT repository. Clone Swarm internally uses NiCad, a popular clone detection tool in the cloud and lets users interactively explore code clones using a web-based interface at multiple granularity levels (Function and Block level). Clone results are visualized in multiple overviews, all the way from a high-level plot down to an individual line by line comparison view of cloned fragments. Also, to facilitate future research in the area of clone detection and analysis, users can directly download the clone detection results for their projects. Clone Swarm is available online at clone-swarm.usask.ca. The source code for Clone Swarm is freely available under the MIT license on GitHub.
Code reuse by copying and pasting from one place to another place in a codebase is a very common scenario in software development which is also one of the most typical reasons for introducing code clones. There is a huge availability of tools to detect such cloned fragments and a lot of studies have already been done for efficient clone detection. There are also several studies for evaluating those tools considering their clone detection effectiveness. Unfortunately, we find no study which compares different clone detection tools in the perspective of detecting cloned co-change candidates during software evolution. Detecting cloned co-change candidates is essential for clone tracking. In this study, we wanted to explore this dimension of code clone research. We used six promising clone detection tools to identify cloned and non-cloned co-change candidates from six $C$ and Java-based subject systems and evaluated the performance of those clone detection tools in detecting the cloned co-change fragments. Our findings show that a good clone detector may not perform well in detecting cloned co-change candidates. The amount of unique lines covered by a clone detector and the number of detected clone fragments plays an important role in its performance. The findings of this study can enrich a new dimension of code clone research.
Abstract. Cold region hydrology is very sensitive to the impacts of climate warming. Impacts of warming over recent decades in western Canada include glacier retreat, permafrost thaw, and changing patterns of precipitation, with an increased proportion of winter precipitation falling as rainfall and shorter durations of snow cover, as well as consequent changes in flow regimes. Future warming is expected to continue along these lines. Physically realistic and sophisticated hydrological models driven by reliable climate forcing can provide the capability to assess hydrological responses to climate change. However, the provision of reliable forcing data remains problematic, particularly in data-sparse regions. Hydrological processes in cold regions involve complex phase changes and so are very sensitive to small biases in the driving meteorology, particularly in temperature and precipitation, including precipitation phase. Cold regions often have sparse surface observations, particularly at high elevations that generate a large amount of runoff. This paper aims to provide an improved set of forcing data for large-scale hydrological models for climate change impact assessment. The best available gridded data in Canada are from the high-resolution forecasts of the Global Environmental Multiscale (GEM) atmospheric model and outputs of the Canadian Precipitation Analysis (CaPA), but these datasets have a short historical record. The EU WATCH ERA-Interim reanalysis (WFDEI) has a longer historical record but has often been found to be biased relative to observations over Canada. The aim of this study, therefore, is to blend the strengths of both datasets (GEM-CaPA and WFDEI) to produce a less-biased long-record product (WFDEI-GEM-CaPA) for hydrological modelling and climate change impact assessment over the Mackenzie River Basin. First, a multivariate generalization of the quantile mapping technique was implemented to bias-correct WFDEI against GEM-CaPA at 3 h ×0.125∘ resolution during the 2005–2016 overlap period, followed by a hindcast of WFDEI-GEM-CaPA from 1979. The derived WFDEI-GEM-CaPA data are validated against station observations as a preliminary step to assess their added value. This product is then used to bias-correct climate projections from the Canadian Centre for Climate Modelling and Analysis Canadian Regional Climate Model (CanRCM4) between 1950 and 2100 under RCP8.5, and an analysis of the datasets shows that the biases in the original WFDEI product have been removed and the climate change signals in CanRCM4 are preserved. The resulting bias-corrected datasets are a consistent set of historical and climate projection data suitable for large-scale modelling and future climate scenario analysis. The final historical product (WFDEI-GEM-CaPA, 1979–2016) is freely available at the Federated Research Data Repository at https://doi.org/10.20383/101.0111 (Asong et al., 2018), while the original and corrected CanRCM4 data are available at https://doi.org/10.20383/101.0162 (Asong et al., 2019).
Abstract. The assumption of stationarity in water resources no longer holds, particularly within the context of future climate change. Plausible scenarios of flows that fluctuate outside the envelope of variability of the gauging data are required to assess the robustness of water resource systems to future conditions. This study presents a novel method of generating weekly time step flows based on tree-ring chronology data. Specifically, this method addresses two long-standing challenges with paleo-reconstruction: (i) the typically limited predictive power of tree-ring data at the annual and sub-annual scale and (ii) the inflated short-term persistence in tree-ring time series and improper use of pre-whitening. Unlike the conventional approach, this method establishes relationships between tree-ring chronologies and naturalized flow at a biennial scale to preserve persistence properties and variability of hydrological time series. Biennial flow reconstructions are further disaggregated to weekly flow reconstructions, according to the weekly flow distribution of reference 2-year instrumental periods, identified as periods with broadly similar tree-ring properties to those of every 2-year paleo-period. The Saskatchewan River basin (SaskRB) in Western Canada is selected as a study area, and weekly flows in its four major tributaries are extended back to the year 1600. The study shows that the reconstructed flows properly preserve the statistical properties of the reference flows, particularly in terms of short- to long-term persistence and the structure of variability across timescales. An ensemble approach is presented to represent the uncertainty inherent in the statistical relationships and disaggregation method. The ensemble of reconstructed weekly flows are publicly available for download from https://doi.org/10.20383/101.0139 (Slaughter and Razavi, 2019).
A new flow for Canadian young hydrologists: Key scientific challenges addressed by research cultural shiftsCaroline Aubry-Wake1, Lauren D. Somers2,3, Hayley Alcock4, Aspen M. Anderson5, Amin Azarkhish6, Samuel Bansah7, Nicole M. Bell8, Kelly Biagi9, Mariana Castaneda-Gonzalez10, Olivier Champagne9, Anna Chesnokova10, Devin Coone6, Tasha-Leigh J. Gauthier11, Uttam Ghimire6, Nathan Glas6, Dylan M. Hrach11, Oi Yin Lai14, Pierrick Lamontagne-Halle3, Nicolas R. Leroux1, Laura Lyon3, Sohom Mandal12, Bouchra R. Nasri13, Natasa Popovic11, Tracy. E. Rankin14, Kabir Rasouli15, Alexis Robinson16, Palash Sanyal17, Nadine J. Shatilla9, 18, Brandon Van Huizen11, Sophie Wilkinson9, Jessica Williamson11, Majid Zaremehrjardy191 Centre for Hydrology, University of Saskatchewan, Saskatoon, SK, Canada2 Civil and Environmental Engineering, Massachusetts Institute of Technology, MA, USA3 Department of Earth and Planetary Sciences, McGill University, Montreal QC4 Department of Natural Resource Science, McGill University, Montreal, QC, Canada5 Department of Earth Sciences, Simon Fraser University, Burnaby, BC, Canada6 School of Engineering, University of Guelph, Ontario, ON, Canada7 Department of Geological Sciences, University of Manitoba, Winnipeg, Canada8 Centre for Water Resources Studies, Department of Civil & Resource Engineering, Dalhousie University, Halifax, NS, Canada9 School of Geography and Earth Sciences, McMaster University, Hamilton, ON, Canada.10 Department of Construction Engineering, Ecole de technologie superieure, Montreal, QC, Canada11 Department of Geography & Environmental Management, University of Waterloo, Waterloo, ON, Canada12 Department of Geography and Environmental Studies, Ryerson University, Toronto, ON, Canada13 Department of Mathematics and Statistics, McGill University, Montreal, Qc, Canada14 Geography Department, McGill University, Montreal, QC, Canada15 Meteorological Service of Canada, Environment and Climate Change Canada, Dorval, QC, Canada16 Department of Geography and Planning, University of Toronto, Toronto, ON17 Global Institute for Water Security, University of Saskatchewan.18 Lorax Environmental Services Ltd, Vancouver, BC, Canada.19 Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, AB, Canada
Global Institute for Water Security, School of Environment and Sustainability, Department of Civil, Geological, and Environmental Engineering, University of Saskatchewan, Saskatoon, Saskatchewan, Canada School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, Arizona School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, Australia Water Institute and Department of Economics, University of Waterloo, Waterloo, Ontario, Canada Institute for Environmental Studies, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Department of Civil and Environmental Engineering, Imperial College London, London, UK
The complex terrain, seasonality, and cold region hydrology of the Nelson Churchill River Basin (NCRB) presents a formidable challenge for hydrological modeling, which complicates the calibration of model parameters. Seasonality leads to different hydrological processes dominating at different times of the year, which translates to time variant sensitivity in model parameters. In this study, Hydrological Predictions for the Environment model (HYPE) is set up in the NCRB to analyze the time variant sensitivity analysis (TVSA) of model parameters using a Global Sensitivity Analysis technique known as Variogram Analysis of Response Surfaces (VARS). TVSA can identify parameters that are highly influential in a short period but relatively uninfluential over the whole simulation period. TVSA is generally effective in identifying model’s sensitivity to event-based parameters related to cold region processes such as snowmelt and frozen soil. This can guide event-based calibration, useful for operational flood forecasting. In contrast to residual based metrics, flow signatures, specifically the slope of the mid-segment of the flow duration curve, allows VARS to detect the influential parameters throughout the timescale of analysis. The results are beneficial for the calibration process in complex and multi-dimensional models by targeting the informative parameters, which are associated with the cold region hydrological processes.
Resource extraction in Canada's boreal ecozone increases the risk of contaminant release into the area's extensive bog and fen peatlands. Lateral spreading, then upwards transport of solutes into the vadose zone of these moss‐dominated ecosystems, could be toxic to vegetation. To evaluate the rate and character of contaminant rise in a subarctic bog, vadose zone‐specific conductance and water content were measured in four hummocks ∼5 m downslope of a 45‐d 300‐mg L−1 NaCl release. Four 30‐cm‐deep hummock peat mesocosms were extracted adjacent to the release site for an unsaturated evaporation‐driven NaCl breakthrough experiment and subsequent parameterization. The field rate of solute accumulation was slower in near‐surface (0–5 cm) peat, where low water contents limited pore connectivity. Solute accumulation was reduced by downward flushing by rain, though this was lesser in near surface moss where solute remained held in small disconnected pores. In the laboratory, Cl− rise reached the 15‐cm depth in all mesocosms by Day 65. Sodium rise was 2.2 times slower, likely due to adsorption to the peat matrix. Rates of upwards solute movement were highly variable; the highest rates occurred in the mesocosm with small but hydrologically conductive pores near the surface, and the lowest occurred where vascular roots disrupted the physical structure of the peat. This research demonstrates that solute spilled into a bog peatland is likely to rise and be retained in the vadose zone. However, hydraulic and solute transport behaviors are sensitive to the vertical structure of peat, underscoring the need for extensive sampling and parameter characterization.
Tile drainage of agricultural fields is a conduit for nutrient losses. Preferential flow in the soil can more directly connect surface runoff with tile drainage compared with matrix flow, which may also increase P losses. In this study, water temperature was monitored in surface runoff and tile drainage and electrical conductivity (EC) was measured in tile drainage at two sites in southern Ontario with different soil types (i.e., clay and loam). These data were used to estimate the percentage of preferential flow in tile drainage based on end member mixing. Estimates using temperature were compared with estimates using EC, and both were evaluated across seasons and hydrographs and compared with P concentration and load data. There was strong correlation (r = .83) between estimates of preferential flow using the two methods, but due to variability in surface temperatures, EC provided a less flashy estimate for preferential flow (Durbin–Watson statistics of 0.34 for temperature and 0.09 for EC). Preferential flow accounted for a higher percentage of tile drainage flow in clay soil than loam, but percentages were not significantly different between seasons or timing within events. Phosphorus concentrations and loads were weakly correlated with preferential flow, suggesting that P transport was influenced by other factors as well. Although further work is necessary to calibrate these methods for estimating preferential flow from continuously monitored temperature and EC, this technique can be applied to already collected data to model and test posited explanations of observed phenomena in P, other nutrients, and water transport from tile‐drained agricultural land.
Globally, ecosystem respiration of carbon dioxide (CO2) is the second largest terrestrial carbon (C) flux after photosynthesis (Mahecha et al., 2010). Soil respiration is the main contributor to ecosystem respiration (e.g. c. 70% in temperate forests; reviewed in Ryan & Law, 2005). Plants shunt tremendous quantities of newly photosynthesized C belowground for storage in their roots but also to support rootmetabolism, root exudate production, and resource trading with root symbionts, most notably mycorrhizas (Raich & Nadelhoffer, 1989). These latter C end-points result in newly-fixed C being respired by roots or their symbionts or becoming substrate for use by free-living soil microorganisms. The respiration of this new photosynthetic C can occur within a few days to a month or two after fixation and can contribute to > 50% of the soil respiration (H€ogberg et al., 2001). Plants allocate photosynthetic C differentially aboveground and belowground depending on resource limitation and the demands of the mutualists with whom they collaborate, suggesting that this contribution to soil respiration may vary. As such, both belowground and aboveground vegetation composition, structure, function, and mutualistic partnerships are quite important for determining soil and thus ecosystem respiration. A new paper by Parker et al. (2020; pp. 1818–1830), in this issue of New Phytologist advances our understanding of the contributions of canopy-forming species to soil respiration at the boreal forest–tundra ecotone (FTE), the world’s largest vegetation transition zone spanning rapidly warming high-latitude regions.
Abstract There is an urgent need to include northern peatland hydrology in global Earth system models to better understand land-atmosphere interactions and sensitivities of peatland functions to climate change, and, ultimately, to improve climate change predictions. In this study, we introduced for the first time peatland-specific model physics into an assimilation scheme for L-band brightness temperature (Tb) data from the Soil Moisture Ocean Salinity (SMOS) mission to improve groundwater table estimates. We conducted two sets of model-only and data assimilation experiments using the Catchment Land Surface Model (CLSM), applying (over peatlands only) in one of them a peatland-specific adaptation (PEATCLSM). The evaluation against in-situ measurements of peatland groundwater table depth indicates the superiority of PEATCLSM model physics and additionally improved performance after assimilating SMOS Tb observations. The better performance of PEATCLSM over nearly all Northern Hemisphere peatlands is further supported by the better agreement between SMOS Tb observations and Tb estimates from the model-only and data assimilation runs. Within the data assimilation scheme, PEATCLSM reduces Tb observation-minus-forecast residuals and leads to reduced data assimilation updates of water storage components and, thus, reduced water budget imbalances in the assimilation system.
Abstract Soil Moisture (SM) is a direct measure of agricultural drought. While there are several global SM indices, none of them directly use SM observations in a near-real-time capacity and as an operational tool. This paper presents a near-real-time global SM index monitor based on integrated SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) remote sensing data. We make use of the short period (2015–2018) of SMAP datasets in combination with two approaches—Cumulative Distribution Function Mapping (CDFM) and Bayesian conditional process—and integrate them with SMOS data in a way that SMOS data is consistent with SMAP. The integrated SMOS and SMAP (SMOS/SMAP) has an increased global revisit frequency and a period of record from 2010 to the present. A four-parameter Beta distribution was fitted to the SMOS/SMAP dataset for each calendar month of each grid cell at ~36 km resolution for the period from 2010 to 2018. We used an asymptotic method that guarantees the values of the bounding parameters of the Beta distribution will envelop both the smallest and largest observed values. The Kolmogorov-Smirnov (KS) test showed that more grids globally will pass if the integrated dataset is from the Bayesian conditional approach. A daily global SM index map is generated and posted online based on translating each grid's integrated SM value for that day to a corresponding probability percentile relevant to the particular calendar month from 2010 to 2018. For validation, we use the Canadian Prairies Ecozone (CPE). We compare the integrated SM with the SMAP core validation and RISMA sites from ISMN, compare our indices with other models (VIC, ESA's CCI SM v04.4 integrated satellite data, and SPI-1), and make a two-by-two comparison of candidate indices using heat maps and summary CDF statistics. Furthermore, we visually compare our global SM-based index maps with those produced by other organizations. Our Global SM Index Monitor (GSMIM) performed, in many tests, similarly to the CCI's product SM index but with the advantage of being a near-real-time tool, which has applications for identifying evolving drought for food security conditions, insurance, policymaking, and crop planning especially for the remote parts of the globe.
Abstract Climate warming and changing precipitation patterns have thermally (active layer deepening) and physically (permafrost-thaw related mass movements) disturbed permafrost-underlain watersheds across much of the Arctic, increasing the transfer of dissolved and particulate material from terrestrial to aquatic ecosystems. We examined the multiyear (2006–2017) impact of thermal and physical permafrost disturbances on all of the major components of fluvial flux. Thermal disturbances increased the flux of dissolved organic carbon (DOC), but localized physical disturbances decreased multiyear DOC flux. Physical disturbances increased major ion and suspended sediment flux, which remained elevated a decade after disturbance, and changed carbon export from a DOC to a particulate organic carbon (POC) dominated system. As the magnitude and frequency of physical permafrost disturbance intensifies in response to Arctic climate change, disturbances will become an increasingly important mechanism to deliver POC from terrestrial to aquatic ecosystems. Although nival runoff remained the primary hydrological driver, the importance of pluvial runoff as driver of fluvial flux increased following both thermal and physical permafrost disturbance. We conclude the transition from a nival-dominated fluvial regime to a regime where rainfall runoff is proportionately more important will be a likely tipping point to accelerated High Arctic change.
Abstract The recurring devastation caused by extreme events underscores the need for reliable estimates of their intensity and frequency. Operational frequency and intensity estimates are very often obtained from generalized extreme value (GEV) distributions fitted to samples of annual maxima. GEV distributed random variables are “max-stable,” meaning that the maximum of a sample of several values drawn from a given GEV distribution is again GEV distributed with the same shape parameter. Long-period return value estimation relies on this property of the distribution. The data to which the models are fitted may not, however, be max-stable. Observational records are generally too short to assess whether max-stability holds in the upper tail of the observations. Large ensemble climate simulations, from which we can obtain very large samples of annual extremes, provide an opportunity to assess whether max-stability holds in a model-simulated climate and to quantify the impact of the lack of max-stability on very long period return-level estimates. We use a recent large ensemble simulation of the North American climate for this purpose. We find that the annual maxima of short-duration precipitation extremes tend not to be max-stable in the simulated climate, as indicated by systematic variation in the estimated shape parameter as block length is increased from 1 to 20 years. We explore how the lack of max-stability affects the estimation of very long period return levels and discuss reasons why short-duration precipitation extremes may not be max-stable.
Abstract The freeze/thaw state of permafrost landscapes is an essential variable for monitoring ecological, hydrological and climate processes. Ground surface state can be obtained from satellite data through time series analysis of C-band backscatter from scatterometer and Synthetic Aperture Radar (SAR) observations. Scatterometer data has been used in a variety of studies concerning freeze/thaw retrieval of the land surface. Coarse spatial resolution scatterometer data has great potential for application in this field due to its high temporal resolution (approx. daily observations). In this study, we investigate the influence of sub-grid cell (12.5 km) surface water (ice free and ice covered) on freeze/thaw retrieval based on ASCAT data using a threshold algorithm. We found discrepancies related to the surface water fraction in the detected timing of thawing and freezing of up to 2 days earlier thawing for spring and 3.5 days earlier freezing for autumn for open water fractions of 40% resulting in an overestimation of the frozen season. Results of this study led to the creation of a method for correction of water fraction impact on freeze/thaw data. Additionally, this study demonstrates the applicability of a new approach to freeze/thaw retrieval which has not so far been tested for SAR, specifically Sentinel-1.
Abstract River ice monitoring is important for hydrological research and water resource management of the Tibetan Plateau but limited by the serious shortage of field observations, and remote sensing can be used as an effective supplementary means for monitoring river ice. However, remote sensing high-altitude river ice is scarce and a basin-scale understanding of river ice is lacking on the Tibetan Plateau. To ascertain the spatial and temporal distribution characteristics of high-altitude river ice at the basin scale, we selected the Babao River basin as the study area, which is a typical river basin located in the northeastern Tibetan Plateau. Utilizing 447 available Landsat images during the river ice period from 1999 to 2018 and the classical normalized difference snow index (NDSI) algorithm, we monitored the river ice in a long time series at the Babao River basin. The average Khat of accuracy validation reached 0.973. The average area of river ice in the river ice period of this basin showed a weak decreasing trend and was negatively correlated with air temperature. We also found that gentle slopes and high elevations are beneficial for the development of river ice. The melting of river ice supplements river discharge in spring. This study is the first to reveal the distribution characteristics and changing trend of river ice at the basin scale on the Tibetan Plateau, and the results provide a reference for river ice research in this region.
The fork-based development mechanism provides the flexibility and the unified processes for software teams to collaborate easily in a distributed setting without too much coordination overhead.Currently, multiple social coding platforms support fork-based development, such as GitHub, GitLab, and Bitbucket. Although these different platforms virtually share the same features, they have different emphasis. As GitHub is the most popular platform and the corresponding data is publicly available, most of the current studies are focusing on GitHub hosted projects. However, we observed anecdote evidences that people are confused about choosing among these platforms, and some projects are migrating from one platform to another, and the reasons behind these activities remain unknown.With the advances of Software Heritage Graph Dataset (SWHGD),we have the opportunity to investigate the forking activities across platforms. In this paper, we conduct an exploratory study on 10popular open-source projects to identify cross-platform forks and investigate the motivation behind. Preliminary result shows that cross-platform forks do exist. For the 10 subject systems in this study, we found 81,357 forks in total among which 179 forks are on GitLab. Based on our qualitative analysis, we found that most of the cross-platform forks that we identified are mirrors of the repositories on another platform, but we still find cases that were created due to preference of using certain functionalities (e.g. Continuous Integration (CI)) supported by different platforms. This study lays the foundation of future research directions, such as understanding the differences between platforms and supporting cross-platform collaboration.
Mine reclamation in the Athabasca oil sands region Canada, is required by law where companies must reconstruct disturbed landscapes into functioning ecosystems such as forests, wetlands and lakes that existed in the Boreal landscape prior to mining. Winter is a major hydrological factor in this region as snow covers the landscape for 5 to 6 months and is ~25% of the annual precipitation, yet few studies have explored the influence of winter processes on the hydrology of constructed watersheds. One year (2017-2018) of intensive snow hydrology measurements are supplemented with six years (2013-2018) of meteorological measurements from the constructed Sandhill Fen Watershed to: 1) understand snow accumulation and redistribution, snowmelt timing, rate and partitioning, 2) apply a physically-based model for simulating winter processes on hillslopes and 3) evaluate the impact of soil prescriptions and climate change projections on winter processes in reclaimed systems. The 2017-2018 snow season was between November and April and SWE ranged between 40-140 mm. Snow distribution was primarily influenced by topography with little influence of snow trapping from developing vegetation. Snow accumulation was most variable on hillslopes and redistribution was driven by slope position, with SWE greatest at the base of slopes and decreased towards crests. Snowmelt on hillslopes was controlled by slope aspect, as snow declined rapidly on west and south-facing slopes, compared to east and north-facing slopes. Unlike results previously reported on constructed uplands, snowmelt runoff from uplands was much less (~30%), highlighting the influence of different construction materials. Model simulations indicate that antecedent soil moisture and soil temperature have a large influence on partitioning snowmelt over a range of observed conditions. Under a warmer and wetter climate, average annual peak SWE and snow season duration could decline up to 52 % and up to 61 days, respectively while snowmelt runoff ceases completely under the warmest scenarios. Results suggest considerable future variability in snowmelt runoff from hillslopes, yet soil properties can be used to enhance vertical or lateral flows.
Abstract Melting seasonal ground ice (SGI) in western Boreal Plains (WBP) peatlands can reduce the available energy at the surface by reducing potential evapotranspiration (PET). PET often exceeds annual precipitation in the WBP. Including this effect in hydrological models may be important in assessing water deficits. However, SGI melt and the timing of ice‐free conditions vary spatially, which suggests PET spatial variability could be influenced by SGI. Understanding this potential linkage can help improve site scale PET in peatland hydrological models. The objectives of this paper were (a) to quantify the effect of ice thickness and melt rate on peatland PET; (b) quantify the spatial variability of SGI thickness and melt rate across spatial scales; and (c) assess how/if spatial variability in SGI thickness/melt rate affects site scale PET. Results from the sensitivity analysis indicated that SGI thickness had a bigger impact on reducing PET compared with the melt rate. Two SGI thickness values were used that were observed on site: 0.32 m, which was measured in a more treed area, and 0.18 m, which was in a more open area. The 0.32 m had an average PET reduction of 14 mm (±0.7), over the month of May, compared with 9 mm (±1 mm) when there was 0.18 m of SGI, which are 13.7 and 8.8% reductions, respectively. SGI thickness and melt rate, both exhibited large‐ and small‐scale spatial variability. At the large scale, spatial patterns in SGI thickness appeared to be influenced by extensive shading from the adjacent hillslopes. Small scale, SGI thickness may be a function of tree proximity and the snowpack. Finally, net radiation, rather than SGI, appeared to be the main driver behind PET spatial variability. This work enhances our conceptual understanding of the role of SGI in WBP peatlands. Future work can use the findings to better inform peatland hydrological models, allowing for better representation of peatlands in regional‐scale models.
The continuous growth of available geospatial data requires new methods for its integration, analysis, and visualization to be explored and implemented in software available to the geospatial community. Discrete Global Grid Systems (DGGS) are an emerging method for spatial data handling in the digital earth framework. DGGS are hierarchical data structures for discretizing the Earth’s surface that have seen considerable theoretical development over the last two decades. In this paper, four software implementations are reviewed, dggridR, H3, OpenEAGGR, and S2, to explore their potential applications in data modelling and GIS, as well as their performance. These software implementations were also evaluated against the recently published Open Geospatial Consortium (OGC) abstract specification. The results indicate great potential and versatility for utilizing such systems in geospatial analysis, if basic methods for converting and handling spatial features are further developed. The performance of these systems is shown to be highly scalable and operational with datasets of various sizes. Yet, it is demonstrated that the current software implementations generally fall short of fulfilling all of the OGC requirements or it was not possible to confirm their compliance. The assessment here identified that further enhancements, endorsement of OGC criteria, and their explicit acknowledgment within official documentation remain key research needs for the evaluated software packages. Further work developing operational DGGS that solve real world problems may promote greater community adoption and integration of DGGS data structures into commonly used geospatial platforms.
Hypothetical bias is tested based on inter- and intra-respondent comparisons of choice behavior, applying a hypothetical and real choice experiment. The inter-respondent comparison commonly applied in the environmental and agricultural economics literature consists of a control group of buyers who are asked to hypothetically choose between conventional and organic beans and an experimental group of buyers who are endowed to purchase the same beans using an identical experimental design. Hypothetical bias is tested by comparing inter- and intra-respondents’ (i) hypothetical and real choices, (ii) preference parameters of the estimated choice models related to hypothetical and real choices, and (iii) hypothetical and real willingness to pay (WTP). Choices in the experimental group are highly consistent when switching from hypothetical to real choices for this study's homegrown goods. However, after being endowed, the price sensitivity of lower income households drops, suggesting a house money effect. WTP derived from actual purchases is higher than WTP based on hypothetical choices, indicating a negative hypothetical bias, but differences are only significant in the case of the inter-respondent comparison. Actual prices paid by respondents in the field experiment appear to be considerably lower than the estimated WTP values and yield a mixed picture of hypothetical bias.
Abstract In the Oil Sands Regions of Alberta, Canada, Indigenous reassertion of rights and responsibilities has lead to a renewed leadership in monitoring the effects of industries on various environment receptors. This study, conducted with Cold Lake First Nations, Alberta (CLFN), sought to explore local concerns regarding fish consumption safety and population health in response to multiple anthropogenic stressors focusing upon oil extraction. We undertook this work using a novel research design comprised of two distinct approaches including a participatory fish health and toxicology study and a cultural consensus survey of CLFN members. The cultural consensus study assessed similarities and differences in knowledge and perceptions of CLFN members. The fish toxicology and health research involved implementing a co-designed protocol to collect and sample fish for toxicants and overall population health using scientific indicators. We discuss the results of each study as well as the tangible application of our work in achieving a Multiple Evidence Base approach. Our work highlights that complementarities between our studies as part of a negotiated research process can form a single cohesive narrative to better inform fisheries management while respecting community knowledge, culture and rights to access land, water and country foods.
Abstract Dynamic contributing areas, various fill-and-spill mechanisms and cold-region processes make the hydrological modelling of the Prairies very challenging. Several models (from simple conceptual to advanced process-based) are available, but the focus has been largely in reproducing streamflow. Few studies have assimilated soil moisture and other hydrological fluxes for improved simulation, but the emphasis has been predominately on simulating contributing areas. However, previous research has shown that the contributing areas are dynamic, and can vary from one year to the next, depending on hydro-meteorological conditions. Therefore, the areas deemed non-contributing can also occasionally contribute to streamflow. In this study, we introduce a progressive two-stage calibration strategy to constrain soil moisture in non-contributing areas. We demonstrate that constraining soil moisture in non-contributing areas can result in improved hydrological simulations and more realistic process representations. The Nash–Sutcliffe efficiency (NSE) values for simulated soil moisture in contributing areas increased by 68% at 20 cm and 25% at 50 cm soil depths during validation when non-contributing areas were constrained. This further led to increases in NSE values in streamflow simulation during calibration (6%) and validation (12%). Our findings suggest that soil moisture in non-contributing areas should be properly constrained for improved modelling of Prairie catchments.
Traditionally, hydrological models are only calibrated to reproduce streamflow regime without considering other hydrological state variables, such as soil moisture and evapotranspiration. Limited s...
Radio‐frequency identification (RFID) transponders are now widely used to track sediment in a variety of environments. A recent innovation placed the transponder inside of a rotating inner mechanism that is designed to minimize missed detections due to burial and shielding or ‘signal collision’ effects between tracers, while also allowing a rapid measurement of the burial depth of the particle. Here we test a developed protocol for burial depth measurement and deploy the ‘Wobblestone’ tracers in the field for the first time. Results show that new tracers can be reliably positioned in the horizontal plane (median error ± 0.03 m) and that the burial depth can be accurately measured (~0.02 m maximum error). The field study was characterized by high mobility and travel lengths, and ~20% of the tracers were buried at depths up to 0.15 m. A comparison of exponential distributions for travel length of surface deposited and buried tracers indicate that the buried tracers on average traveled farther and earlier in the flood event. Tracers that did not move were also buried at one site as a result of sediment transport from upstream. Overall the technique has great potential for characterizing vertical mixing and understanding this rarely considered control on sediment transport. © 2020 John Wiley & Sons, Ltd.
Dam operations are known to have significant impacts on reservoir hydrodynamics and solute transport processes. The Gardiner Dam, one of the structures that forms the Lake Diefenbaker reservoir located in the Canadian Prairies, is managed for hydropower generation and agricultural irrigation and is known to have widely altering temperature regimes and nutrient circulations. This study applies the hydrodynamic and nutrient CE-QUAL-W2 model to explore how various withdrawal depths (5, 15, 25, 35, 45, and 55 m) influence the concentrations and distribution of nutrients, temperature, and dissolved oxygen (DO) within the Lake Diefenbaker reservoir. As expected, the highest dissolved nutrient (phosphate, PO43--P and nitrate, NO3--N ) concentrations were associated with hypoxic depth horizons in both studied years. During summer high flow period spillway operations impact the distribution of nutrients, water temperatures, and DO as increased epilimnion flow velocities route the incoming water through the surface of the reservoir and reduce mixing and surface warming. This reduces reservoir concentrations but can lead to increased outflow nitrogen (N) and phosphorus (P) concentrations. Lower withdrawal elevations pull warmer surface water deeper within the reservoir and decrease reservoir DO during summer stratification. During fall turnover low outflow elevations increase water column mixing and draws warmer water deeper, leading to slightly higher temperatures and nutrient concentrations than shallow withdrawal elevations. The 15 m depth (540 m above sea level) outflow generally provided the best compromise for overall reservoir and outflow nutrient reduction.
Abstract A prognostic equation for the liquid fraction of mixed-phase particles has been recently added to the Predicted Particle Properties (P3) bulk microphysics scheme. Mixed-phase particles are necessary to simulate key microphysical processes leading to various winter precipitation types, such as ice pellets and freezing rain. To illustrate the impacts of predicting the bulk liquid fraction, the 1998 North American Ice Storm is simulated using the Weather Research and Forecasting (WRF) Model with the modified P3 scheme. It is found that simulating partial melting by predicting the bulk liquid fraction produces higher mass and number mixing ratios of rain. This leads to smaller rain sizes reaching the refreezing layer as well as a decrease in the freezing rain accumulation at the surface by up to 30% in some locations compared to when no liquid fraction is predicted. The increase in fall speed and density and decrease of particle diameter during partial melting combined with an improved representation of the refreezing process in the modified P3 leads to generally higher total solid surface precipitation rates than using the original P3 scheme. There is also an increase of solid precipitation in regions of ice pellet accumulation. Overall, the simulation of mixed-phase particles notably impacts the vertical and spatial distributions of precipitation properties.
Because compounds accumulate through dry periods and enter aquatic systems in just a few seasonal events such as snowmelt and summer storms, surface waters in semi-arid, cold regions, such as the Canadian Prairies, are particularly vulnerable to loading of contaminant from runoff events from surfaces. This study assessed concentrations of metals and selected trace organics entering a river via surface runoff from an urban region and how these semi-arid regions with large seasonal variations in temperature might differ from more temperate regions. Selected potentially harmful elements (PHEs) including, Mn with Cr, Cu, Zn, Ba and U all exceeded guideline discharge values set by the Canadian Council of the Ministers of the Environment (CCME) by as much as 16-fold. Variation among discharges during spring, summer and winter was observed. For example, across the whole city, an estimated 6 kg of zinc was discharged in a spring storm, 36 kg in a summer storm and 17 tonnes in snowmelt. The mass of Zn discharged is similar to the annual loading estimated for Stockholm, Sweden, but in Saskatoon, Saskatchewan, Canada, the bulk of runoff was during snowmelt. The mean sum of poly- and per-fluoroalkyl substances (PFAS) in stormwater was 9.0 ng L−1, which is consistent with concentrations observed in other Canadian cities (6.5–16 ng L−1). These concentrations of PFAS are likely due to dispersed sources and orders of magnitude less than thresholds for toxicity to fish and aquatic invertebrates.
Watersheds have served as one of our most basic units of organization in hydrology for over 300 years (Dooge, 1988, https://doi.org/10.1080/02626668809491223; McDonnell, 2017, https://doi.org/10.1038/ngeo2964; Perrault, 1674, https://www.abebooks.com/first‐edition/lorigine‐fontaines‐Perrault‐Pierre‐Petit‐Imprimeur/21599664536/bd). With growing interest in groundwater‐surface water interactions and subsurface flow paths, hydrologists are increasingly looking deeper. But the dialog between surface water hydrologists and groundwater hydrologists is still embryonic, and many basic questions are yet to be posed, let alone answered. One key question is: where is the bottom of a watershed? Knowing where to draw the bottom boundary has not yet been fully addressed in the literature, and how to define the watershed “bottom” is a fraught question. There is large variability across physical and conceptual models regarding how to implement a watershed bottom, and what counts as “deep” varies markedly in different communities. In this commentary, we seek to initiate a dialog on existing approaches to defining the bottom of the watershed. We briefly review the current literature describing how different communities typically frame the answer of just how deep we should look and identify situations where deep flow paths are key to developing realistic conceptual models of watershed systems. We then review the common conceptual approaches used to delineate the watershed lower boundary. Finally, we highlight opportunities to trigger this potential research area at the interface of catchment hydrology and hydrogeology.
Global sensitivity analysis (GSA) provides essential insights into the behavior of Earth and environmental systems models and identifies dominant controls of output uncertainty. Previous work on GSA, however, has typically been under the assumption that the controlling factors such as model inputs and parameters are independent, whereas, in many cases, they are correlated and their joint distribution follows a variety of forms. Although this assumption can limit the credibility of GSA and its results, very few studies in the field of water and environmental modeling address this issue. In this paper, we first discuss the significance of correlation effects in GSA and then propose a new GSA framework for properly accounting for correlations in input/parameter spaces. To this end, we extend the “variogram‐based” theory of GSA, called variogram analysis of response surfaces (VARS), and develop a new generalized star sampling technique (called gSTAR) to accommodate correlated multivariate distributions. We test the new gSTAR‐VARS method on two test functions, against a state‐of‐the‐art GSA method that handles correlation effects. We then apply gSTAR‐VARS to the HBV‐SASK model, calibrated via a Bayesian, Markov chain Monte Carlo approach, for design flood estimation in the Oldman River Basin in Canada. Results demonstrate that accounting for correlation effects can be critically important in GSA, especially in the presence of nonlinearity and interaction effects in the underlying response surfaces. The proposed method can efficiently handle correlations and different distribution types and simultaneously generate a range of sensitivity indices, such as total‐variogram effects, variance‐based total‐order effects, and derivative‐based elementary effects.
Quantifying the degradation of micropollutants in streams is important for river‐water quality management. While biodegradation is believed to be enhanced in transient‐storage zones of rivers, it can also occur in the main channel. Photodegradation is restricted to the main channel and surface transient‐storage zones. In this study, we propose a transient‐storage model framework to address the transport and fate of micropollutants in different domains of a river. We fitted the model to nighttime and daytime measurements of a tracer and four pharmaceuticals in River Steinlach, Germany. We could separate the surface and subsurface fractions of the total transient‐storage zone by fitting fluorescein photodegradation at daytime versus conservative nighttime transport. In reactive transport, we tested two model variants, allowing biodegradation in the main channel or restricting it to the transient‐storage zones, obtaining similar model performances but different degradation rate coefficients. Carbamazepine is relatively conservative; photodegradation of metoprolol and venlafaxine can be quantitatively attributed to the main channel and surface transient‐storage zone; metoprolol, venlafaxine, and sulfamethoxazole undergo biodegradation. We projected a decrease of overall pollutant removal under higher flow conditions, regardless of attributing biodegradation to specific river compartments. Our study indicates that model‐based analysis of daytime and nighttime field experiments allows (1) distinguishing photodegradation and biodegradation, (2) reducing equifinality of surface and subsurface transient‐storage, and (3) estimating biodegradation in different domains under different assumptions. However, entirely reducing the equifinality of attributing biodegradation to different compartments is hardly possible in lowland rivers with only limited transient storage.
Abstract The hydrology of cold regions has been studied for decades with substantial progress in process understanding and prediction. Simultaneously, work on nutrient yields from agricultural land in cold regions has shown much slower progress. Advancement of nutrient modelling is constrained by well-documented issues of spatial heterogeneity, climate dependency, data limitations and over-parameterization of models, as well as challenges specific to cold regions due to the complex (and often unknown) behaviour of hydro-biogeochemical processes at temperatures close to and below freezing where a phase change occurs. This review is a critical discussion of these issues by taking a close look at the conceptual models and methods behind used catchment nutrient models. The impact of differences in model structure and the methods used for the prediction of hydrological processes, erosion and biogeochemical cycles are examined. The appropriateness of scale, scope, and complexity of models are discussed to propose future research directions.
The preferential elution of ions from melting snowpacks is a complex problem that has been linked to temporary acidification of water bodies. However, the understanding of these processes in snowpacks around the world, including the polar regions that are experiencing unprecedented warming and melting, remains limited despite being instrumental in supporting climate change adaptation. In this study, data collected from a snowmelt lysimeter and snowpits at meadow and forest-gap sites in a high elevation watershed in Colorado were combined with the PULSE multi-phase snowpack chemistry model to investigate the controls of meltwater chemistry and preferential elution. The snowdepth at the meadow site was 64% of that at the forest-gap site, and the snowmelt rate was greater there (meadow snowpit) due to higher solar irradiance. Cations such as Ca2+ and NH4+ were deposited mostly within the upper layers of both the meadow and forest-gap snowpacks, and acid anions such as NO3- and SO42- were more evenly distributed. The snow ion concentrations were generally greater at the forest-gap snowpit, except for NH4+, which indicates that wind erosion of wet and dry deposited ions from the meadow may have reduced concentrations of residual snow. Furthermore, at the forest-gap site, snow interception and scavenging processes such as sublimation, ventilation, and throughfall led to particular ion enrichment of Ca2+, Mg2+, K+, Cl-, SO42- and NO3-. Model simulations and observations highlight that preferential elution is enhanced by low snowmelt rates, with the model indicating that this is due to lower dilution rates and increased contact time and area between the percolating meltwater and the snow. Results suggest that low snowmelt rates can cause multiple early meltwater ionic pulses for ions subject to lower ion exclusion. Ion exclusion rates at the grain-size level have been estimated for the first time.
AbstractIn cold-region environments, ice-jam floods (IJFs) can result in high water levels in rivers to overtop levees, leading to devastating floods. Since climatic conditions play an important ro...
Fungi play key roles in carbon (C) dynamics of ecosystems: saprotrophs decompose organic material and return C in the nutrient cycle, and mycorrhizal species support plants that accumulate C through photosynthesis. The identities and functions of extremophile fungi present after fire can influence C dynamics, particularly because plant-fungal relationships are often species-specific. However, little is known about the function and distribution of fungi that survive fires. We aim to assess the distribution of heat-resistant soil fungi across burned stands of boreal forest in the Northwest Territories, Canada, and understand their functions in relation to decomposition and tree seedling growth. We cultured and identified fungi from heat-treated soils and linked sequences from known taxa with high throughput sequencing fungal data (Illumina MiSeq, ITS1) from soils collected in 47 plots. We assessed functions under controlled conditions by inoculating litter and seedlings with heat-resistant fungi to assess decomposition and effects on seedling growth, respectively, for black spruce ( Picea mariana ), birch ( Betula papyrifera ), and jack pine ( Pinus banksiana ). We also measured litter decomposition rates and seedling densities in the field without inoculation. We isolated seven taxa of heat-resistant fungi and found their relative abundances were not associated with environmental or fire characteristics. Under controlled conditions, Fayodia gracilipes and Penicillium arenicola decomposed birch, but no taxa decomposed black spruce litter significantly more than the control treatment. Seedlings showed reduced biomass and/or mortality when inoculated with at least one of the fungal taxa. Penicillium turbatum reduced growth and/or caused mortality of all three species of seedlings. In the field, birch litter decomposed faster in stands with greater pre-fire proportion of black spruce, while black spruce litter decomposed faster in stands experiencing longer fire-free intervals. Densities of seedlings that had germinated since fire were positively associated with ectomycorrhizal richness while there were fewer conifer seedlings with greater heat-resistant fungal abundance. Overall, our study suggests that extremophile fungi present after fires have multiple functions and may have unexpected negative effects on forest functioning and regeneration. In particular, heat-resistant fungi after fires may promote shifts away from conifer dominance that are observed in these boreal forests.
Increases in fire frequency, extent, and severity are expected to strongly impact the structure and function of boreal forest ecosystems. An important function of the boreal forest is its ability to sequester and store carbon (C). Increasing disturbance from wildfires, emitting large amounts of C to the atmosphere, may create a positive feedback to climate warming. Variation in ecosystem structure and function throughout the boreal forest are important for predicting the effects of climate warming and changing fire regimes on C dynamics. In this study, we compiled data on soil characteristics, stand structure, pre-fire C pools, C loss from fire, and the potential drivers of these C metrics from 527 sites distributed across six ecoregions of North America’s western boreal forests. We assessed structural and functional differences between these fire-prone ecoregions using data from 417 recently burned sites (2004-2015) and estimated ecoregion-specific relationships between soil characteristics and depth from 167 of these sites plus an additional 110 sites (27 burned, 83 unburned). We found that northern boreal ecoregions were generally older, stored and emitted proportionally more belowground than aboveground C and exhibited lower rates of C accumulation over time than southern ecoregions. We present ecoregion specific estimates of depth-wise soil characteristics that are important for predicting C combustion from fire. As climate continues to warm and disturbance from wildfires increases, the C dynamics of these fire-prone ecoregions are likely to change with significant implications for the global C cycle and its feedbacks to climate change.
© 2020 The Authors. Ecography published by John Wiley & Sons Ltd on behalf of Nordic Society Oikos Climate change is altering disturbance regimes outside historical norms, which can impact biodiversity by selecting for plants with particular traits. The relative impact of disturbance characteristics on plant traits and community structure may be mediated by environmental gradients. We aimed to understand how wildfire impacted understory plant communities and plant regeneration strategies along gradients of environmental conditions and wildfire characteristics in boreal forests. We established 207 plots (60 m2) in recently burned stands and 133 plots in mature stands with no recent fire history in comparable gradients of stand type, site moisture (drainage) and soil organic layer (SOL) depth in two ecozones in Canada's Northwest Territories. At each plot, we recorded all vascular plant taxa in the understory and measured the regeneration strategy (seeder, resprouter, survivor) in burned plots, along with seedbed conditions (mineral soil and bryophyte cover). Dispersal, longevity and growth form traits were determined for each taxon. Fire characteristics measured included proportion of pre-fire SOL combusted (fire severity), date of burn (fire seasonality) and pre-fire stand age (time following fire). Results showed understory community composition was altered by fire. However, burned and mature stands had similar plant communities in wet sites with deep SOL. In the burned plots, regeneration strategies were determined by fire severity, drainage and pre- and post-fire SOL depth. Resprouters were more common in wet sites with deeper SOL and lower fire severity, while seeders were associated with drier sites with thinner SOL and greater fire severity. This led to drier burned stands being compositionally different from their mature counterparts and seedbed conditions were important. Our study highlights the importance of environment–wildfire interactions in shaping plant regeneration strategies and patterns of understory plant community structure across landscapes, and the overriding importance of SOL depth and site drainage in mediating fire severity, plant regeneration and community structure.
Across the Boreal, there is an expansive wildland–society interface (WSI), where communities, infrastructure, and industry border natural ecosystems, exposing them to the impacts of natural disturbances, such as wildfire. Treed peatlands have previously received little attention with regard to wildfire management; however, their role in fire spread, and the contribution of peat smouldering to dangerous air pollution, have recently been highlighted. To help develop effective wildfire management techniques in treed peatlands, we use seismic line disturbance as an analog for peatland fuel modification treatments. To delineate below-ground hydrocarbon resources using seismic waves, seismic lines are created by removing above-ground (canopy) fuels using heavy machinery, forming linear disturbances through some treed peatlands. We found significant differences in moisture content and peat bulk density with depth between seismic line and undisturbed plots, where smouldering combustion potential was lower in seismic lines. Sphagnum mosses dominated seismic lines and canopy fuel load was reduced for up to 55 years compared to undisturbed peatlands. Sphagnum mosses had significantly lower smouldering potential than feather mosses (that dominate mature, undisturbed peatlands) in a laboratory drying experiment, suggesting that fuel modification treatments following a strategy based on seismic line analogs would be effective at reducing smouldering potential at the WSI, especially under increasing fire weather.
In addition to aiding in digestion of food and uptake of nutrients, microbiota in guts of vertebrates are responsible for regulating several beneficial functions, including development of an organism and maintaining homeostasis. However, little is known about effects of exposures to chemicals on structure and function of gut microbiota of fishes. To assess effects of exposure to polycyclic aromatic hydrocarbons (PAHs) on gut microbiota, male and female fathead minnows ( Pimephales promelas ) were exposed to environmentally-relevant concentrations of the legacy PAH benzo[ a ]pyrene (BaP) in water. Measured concentrations of BaP ranged from 2.3 × 10 −3 to 1.3 μg L −1 . The community of microbiota in the gut were assessed by use of 16S rRNA metagenetics. Exposure to environmentally-relevant aqueous concentrations of BaP did not alter expression levels of mRNA for cyp1a1 , a “classic” biomarker of exposure to BaP, but resulted in shifts in relative compositions of gut microbiota in females rather than males. Results presented here illustrate that in addition to effects on more well-studied molecular endpoints, relative compositions of the microbiota in guts of fish can also quickly respond to exposure to chemicals, which can provide additional mechanisms for adverse effects on individuals. • Female and male fathead minnows exhibited significantly different gut microbiota. • Exposure to BaP altered structures in female gut microbiota, but not in males. • Exposure to BaP altered predicted functions in gut microbiota of fathead minnow. • Gut microbiome was more sensitive to a low dose BaP than host’s ahr1 and cyp1a1.
The reproductive status of walleye (Sander vitreus) and lake whitefish (Coregonus clupeaformis) is largely unstudied in the northern extent of their ranges. Tathlina Lake and Kakisa Lake are large, shallow lakes in the Northwest Territories, Canada, supporting important commercial and subsistence fisheries for these species while being threatened by climate change. Fish were sampled in both lakes across multiple years in the spring and autumn to assess differences in reproductive status in the pre- and post-spawning periods for both species. Condition factor (K), gonadosomatic index (GSI), liversomatic index (LSI), and fecundity were calculated, and plasma samples were also taken from each fish to determine levels of reproductive hormones, specifically 17β-estradiol in females, and 11-ketotestosterone in males. Significant temporal (intra- and interannual) and spatial (between lakes) variation was found for both species and both sexes for all metrics. Expected differences in hormones and indices of reproductive success between pre- and post- spawning periods were demonstrated. When compared with previously published data, a latitudinal gradient for LSI, GSI and fecundity was evident for walleye, but not for lake whitefish. The differences in the reproductive biology of lake whitefish and walleye in these two neighbouring lakes highlights limitations in the use of a reference lake approach in biomonitoring studies. The data in this study can be used and expanded upon to provide information for the sustainable management of these fish stocks for the future.
Finding sustainable pathways to efficiently allocate limited available water resources among increasingly competing water uses has become crucial due to climate-change-induced water shortages and increasing water demand. This has led to an urgent need for the inclusion of economic principles, models, and methods in water resources management. Although several studies have developed macro-economic models to evaluate the economic impacts of alternative water allocation strategies, many if not most ignore the hydrological boundaries of transboundary river basins. Furthermore, of those using input-output (IO) models, only a handful have applied supply-side IO models. In this paper, we present one of the first attempts to develop an inter-regional, supply-side IO modelling framework for a multi-jurisdictional, transboundary river basin to assess the direct and indirect economic impacts of water supply restrictions due to climate and policy change. Applying this framework to the Saskatchewan River Basin in Canada encompassing three provinces, we investigate the economic impacts of two different water supply restriction scenarios on the entire river basin and its sub-basins individually. We find that in the face of climate-change-induced water shortage, economic losses can be reduced by almost 50% by adopting appropriate management practices, including prioritization of water allocation, using alternative water sources, and water re-use technologies. • Sectoral water use is incorporated into a supply-side input-output model. • The model is spatially disaggregated into 6 sub-basins across 3 Canadian provinces. • The model evaluates the economic impacts of alternative water allocation policies. • The model results assist policymakers prepare efficient water management plans. • Adopting proper policies, potential economic drought losses can be reduced by 50%.
Abstract. Permafrost is an important feature of cold-region hydrology, particularly in river basins such as the Mackenzie River basin (MRB), and it needs to be properly represented in hydrological and land surface models (H-LSMs) built into existing Earth system models (ESMs), especially under the unprecedented climate warming trends that have been observed. Higher rates of warming have been reported in high latitudes compared to the global average, resulting in permafrost thaw with wide-ranging implications for hydrology and feedbacks to climate. The current generation of H-LSMs is being improved to simulate permafrost dynamics by allowing deep soil profiles and incorporating organic soils explicitly. Deeper soil profiles have larger hydraulic and thermal memories that require more effort to initialize. This study aims to devise a robust, yet computationally efficient, initialization and parameterization approach applicable to regions where data are scarce and simulations typically require large computational resources. The study further demonstrates an upscaling approach to inform large-scale ESM simulations based on the insights gained by modelling at small scales. We used permafrost observations from three sites along the Mackenzie River valley spanning different permafrost classes to test the validity of the approach. Results show generally good performance in reproducing present-climate permafrost properties at the three sites. The results also emphasize the sensitivity of the simulations to the soil layering scheme used, the depth to bedrock, and the organic soil properties.
Abstract. The spatial distribution of snow water equivalent (SWE) and melt are important for estimating areal melt rates and snow-cover depletion (SCD) dynamics but are rarely measured in detail during the late ablation period. This study contributes results from high-resolution observations made using large numbers of sequential aerial photographs taken from an unmanned aerial vehicle on an alpine ridge in the Fortress Mountain Snow Laboratory in the Canadian Rocky Mountains from May to July in 2015. Using structure-from-motion and thresholding techniques, spatial maps of snow depth, snow cover and differences in snow depth (dHS) during ablation were generated in very high resolution as proxies for spatial SWE, spatial ablation rates and SCD. The results indicate that the initial distribution of snow depth was highly variable due to overwinter snow redistribution; thus, the subsequent distribution of dHS was also variable due to albedo, slope/aspect and other unaccountable differences. However, the initial distribution of snow depth was 5 times more variable than that of the subsequent dHS values, which varied by a factor of 2 between the north and south aspects. dHS patterns were somewhat spatially persistent over time but had an insubstantial impact on SCD curves, which were overwhelmingly governed by the initial distribution of snow depth. The reason for this is that only a weak spatial correlation developed between the initial snow depth and dHS. Previous research has shown that spatial correlations between SWE and ablation rates can strongly influence SCD curves. Reasons for the lack of a correlation in this study area were analysed and a generalisation to other regions was discussed. The following questions were posed: what is needed for a large spatial correlation between initial snow depth and dHS? When should snow depth and dHS be taken into account to correctly model SCD? The findings of this study suggest that hydrological and atmospheric models need to incorporate realistic distributions of SWE, melt energy and cold content; therefore, they must account for realistic correlations (i.e. not too large or too small) between SWE and melt in order to accurately model SCD.
Abstract The grand challenge of producing hydrometeorological estimates every time and everywhere has motivated the fusion of sparse observations with dense numerical models, with a particular interest on discharge in river modeling. Ensemble methods are largely preferred as they enable the estimation of error properties, but at the expense of computational load and generally with underestimations. These imperfect stochastic estimates motivate the use of correction methods, that is, error localization and inflation, although the physical justifications for their optimality are limited. The purpose of this study is to use one of the simplest forms of data assimilation when applied to river modeling and reveal the underlying mechanisms impacting its performance. Our framework based on assimilating daily averaged in situ discharge measurements to correct daily averaged runoff was tested over a 4-yr case study of two rivers in Texas. Results show that under optimal conditions of inflation and localization, discharge simulations are consistently improved such that the mean values of Nash–Sutcliffe efficiency are enhanced from −11.32 to 0.55 at observed gauges and from −12.24 to −1.10 at validation gauges. Yet, parameters controlling the inflation and the localization have a large impact on the performance. Further investigations of these sensitivities showed that optimal inflation occurs when compensating exactly for discrepancies in the magnitude of errors while optimal localization matches the distance traveled during one assimilation window. These results may be applicable to more advanced data assimilation methods as well as for larger applications motivated by upcoming river-observing satellite missions, such as NASA’s Surface Water and Ocean Topography mission.
As urban droughts make headlines across the globe, it is increasingly relevant to critically evaluate the long‐term sustainability of both water supply and demand in the world's cities. This is the case even in water‐rich regions, where upward swings in water demands during periods of hot, dry weather can aggravate already strained water supplies and increase cities' vulnerability to water shortage. Summer spikes in water demand have motivated several cities to impose permanent restrictions on outdoor water uses; however, little is yet known about their effectiveness. This paper examines daily water production data from 15 Canadian cities to (1) quantify how overall and seasonal demands are evolving over time across humid and semiarid settings and (2) determine whether permanent water use restrictions have been effective in curbing summer water demands both seasonally and during specific hot and dry periods. Results show that while per‐capita water demand is declining in all cities studied, the seasonal distribution of that demand has remained largely stable in all but a few cases. While average demands in the summer months remain largely unaffected by the imposition of permanent restrictions, cities that enforce stringent limits on outdoor water use have seen a reduction in the variability of daily demands and a decline in peak demands following their implementation. During short‐term periods of exceptionally hot and dry weather when vulnerability to water shortage is most acute, cities with strict restrictions also see smaller surges in demand than those with weaker or no restrictions in place.
Evidence suggests that catchment state variables such as groundwater can exhibit multiyear trends. This means that their state may reflect not only recent climatic conditions but also climatic conditions in past years or even decades. Here we demonstrate that five commonly used conceptual “bucket” rainfall‐runoff models are unable to replicate multiyear trends exhibited by natural systems during the “Millennium Drought” in south‐east Australia. This causes an inability to extrapolate to different climatic conditions, leading to poor performance in split sample tests. Simulations are examined from five models applied in 38 catchments, then compared with groundwater data from 19 bores and Gravity Recovery and Climate Experiment data for two geographic regions. Whereas the groundwater and Gravity Recovery and Climate Experiment data decrease from high to low values gradually over the duration of the 13‐year drought, the model storages go from high to low values in a typical seasonal cycle. This is particularly the case in the drier, flatter catchments. Once the drought begins, there is little room for decline in the simulated storage, because the model “buckets” are already “emptying” on a seasonal basis. Since the effects of sustained dry conditions cannot accumulate within these models, we argue that they should not be used for runoff projections in a drying climate. Further research is required to (a) improve conceptual rainfall‐runoff models, (b) better understand circumstances in which multiyear trends in state variables occur, and (c) investigate links between these multiyear trends and changes in rainfall‐runoff relationships in the context of a changing climate.
We present a method to characterize soil moisture freeze‐thaw events and freezing/melting point depression using permittivity and temperature measurements, readily available from in situ sources. In cold regions soil freeze‐thaw processes play a critical role in the surface energy and water balance, with implications ranging from agricultural yields to natural disasters. Although monitoring of the soil moisture phase state is of critical importance, there is an inability to interpret soil moisture instrumentation in frozen conditions. To address this gap, we investigated the freeze‐thaw response of a widely used soil moisture probe, the HydraProbe, in the laboratory. Soil freezing curves (SFCs) and soil thawing curves (STCs) were identified using the relationship between soil permittivity and temperature. The permittivity SFC/STC was fit using a logistic growth model to estimate the freezing/melting point depression (Tf/m) and its spread (s). Laboratory results showed that the fitting routine requires permittivity changes greater than 3.8 to provide robust estimates and suggested that a temperature bias is inherent in horizontally placed HydraProbes. We tested the method using field measurements collected over the last 7 years from the Environment and Climate Change Canada and the University of Guelph's Kenaston Soil Moisture Network in Saskatchewan, Canada. By dividing the time series into freeze‐thaw events and then into individual transitions, the permittivity SFC/STC was identified. The freezing and melting point depression for the network was estimated as Tf/m = − 0.35 ± 0.2,with Tf = − 0.41 ± 0.22 °C and Tm = − 0.29 ± 0.16 °C, respectively.
This paper presents a water-restricted multi-regional input–output model to evaluate the economic impacts of water supply reductions in the Canadian Great Lakes Basin (GLB), one of the largest fres...
Data-intensive research and decision-making continue to gain adoption across diverse organizations. As researchers and practitioners increasingly rely on analyzing large data products to both answer scientific questions and for operational needs, data acquisition and pre-processing become critical tasks. For environmental science, the Canadian Surface Prediction Archive (CaSPAr) facilitates easy access to custom subsets of numerical weather predictions. We demonstrate a new open-source interface for CaSPAr that provides easy-to-use map-based querying capabilities and automates data ingestion into the CaSPAr batch processing server.
Lake ice thickness is a sensitive indicator of climate change largely through its dependency on near-surface air temperature and on-ice snow mass (depth and density). Monitoring of the seasonal variations and trends in ice thickness is also important for the operation of winter ice roads that northern communities rely on for the movement of goods as well as for cultural and leisure activities (e.g., snowmobiling). Therefore, consistent measurements of ice thickness over lakes is important; however, field measurements tend to be sparse in both space and time in many northern countries. Here, we present an application of L-band frequency Global Navigation Satellite System (GNSS) Interferometric Reflectometry (GNSS-IR) for the estimation of lake ice thickness. The proof of concept is demonstrated through the analysis of Signal-to-Noise Ratio (SNR) time series extracted from Global Positioning System (GPS) constellation L1 band raw data acquired between 8 and 22 March (2017 and 2019) at 14 lake ice sites located in the Northwest Territories, Canada. Dominant frequencies are extracted using Least Squares Harmonic Estimation (LS-HE) for the retrieval of ice thickness. Estimates compare favorably with in-situ measurements (mean absolute error = 0.05 m, mean bias error = −0.01 m, and root mean square error = 0.07 m). These results point to the potential of GPS/GNSS-IR as a complementary tool to traditional field measurements for obtaining consistent ice thickness estimates at many lake locations, given the relatively low cost of GNSS antennas/receivers.
Fresh water – the bloodstream of the biosphere – is at the centre of the planetary drama of the Anthropocene. Water fluxes and stores regulate the Earth’s climate and are essential for thriving aquatic and terrestrial ecosystems, as well as water, food and energy security. But the water cycle is also being modified by humans at an unprecedented scale and rate. A holistic understanding of freshwater’s role for Earth System resilience and the detection and monitoring of anthropogenic water cycle modifications across scales is urgent, yet existing methods and frameworks are not well suited for this. In this paper we highlight four core Earth System functions of water (hydroclimatic regulation, hydroecological regulation, storage, and transport) and key related processes. Building on systems and resilience theory, we review the evidence of regional-scale regime shifts and disruptions of the Earth System functions of water. We then propose a framework for detecting, monitoring, and establishing safe limits to water cycle modifications, and identify four possible spatially explicit methods for their quantification. In sum, this paper presents an ambitious scientific and policy Grand Challenge that could substantially improve our understanding of the role of water in the Earth System and cross-scale management of water cycle modifications that would be a complementary approach to existing water management tools.
The planetary boundaries framework proposes quantified guardrails to human modification of global environmental processes that regulate the stability of the planet and has been considered in sustainability science, governance, and corporate management. However, the planetary boundary for human freshwater use has been critiqued as a singular measure that does not reflect all types of human interference with the complex global water cycle and Earth System. We suggest that the water planetary boundary will be more scientifically robust and more useful in decision-making frameworks if it is redesigned to consider more specifically how climate and living ecosystems respond to changes in the different forms of water on Earth: atmospheric water, frozen water, groundwater, soil moisture, and surface water. This paper provides an ambitious scientific road map to define a new water planetary boundary consisting of sub-boundaries that account for a variety of changes to the water cycle.
Peaking hydroelectric facilities release water from dams to match energy production with demand, often on a daily cycle. These fluctuating flows downstream can exert several potential stressors on organisms that may inhibit their growth, indirectly causing higher contaminant concentrations through reduced growth dilution. We collected spottail shiner (Notropis hudsonius) at two sites upstream and two sites downstream of a peaking hydroelectric dam in east‐central Saskatchewan, Canada, and compared their body condition, triglyceride concentrations, and mercury concentrations. Condition decline was observed in one of two downstream sites from August to September, and the lowest triglyceride concentrations were consistently found downstream of the dam where hydropeaking had the most perceptible effects on the shoreline. Mercury concentrations were significantly greater at both downstream sites relative to upstream. Despite these results, inconsistencies in response parameters across sites and time limited our ability to isolate the effects of hydropeaking as a causative agent and suggest indirect effects such as shifts in algal and macroinvertebrate communities may be responsible for our observations. These results suggest that hydroelectric power generation may indirectly increase mercury concentrations in downstream fish, but more research will be required to determine the specific mechanisms by which this occurs. The results and data also provide useful insights into the physiology of wild spottail shiner populations, which can help to inform the development of these fish as a North American sentinel species.
Although there is increasing consensus that river restoration should focus on restoring processes rather than form, proven techniques to design and monitor projects for sediment transport processes are lacking. This study monitors bedload transport and channel morphology in a rural, an urban unrestored, and an urban restored reach. Objectives are to compare bedload transport regimes, assess the stability and self‐maintenance of constructed riffle‐pool sequences, and evaluate the impact of the project on coarse sediment continuity in the creek. Sediment tracking is done using radio frequency identification tracers and morphologic change is assessed from repeated cross‐section surveys. Mean annual velocity is used to quantify the average downstream velocity of tracers, defined as the mean overall tracer travel length divided by the total study duration. The channel reconstruction slows down the downstream velocity of particles in the D75 and D90 size classes, but does not significantly change the velocity of particles in the D50 size class or smaller. Surveys show that riffle features remain stable and that pool depths are maintained or deepened, while tracer paths match with what has been observed in natural riffle‐pools. However, the slowdown of coarse sediment and increase in channel slope may lead to future failures related to over‐steepening of the banks and a disruption in the continuity of sediment transport in the creek. This study demonstrates how bedload tracking and morphological surveys can be used to assess river restoration projects, and highlights the importance of incorporating coarse sediment connectivity into restoration design and monitoring.
Emanation graphs of grade k, introduced by Hamedmohseni, Rahmati, and Mondal, are plane spanners made by shooting \(2^{k+1}\) rays from each given point, where the shorter rays stop the longer ones upon collision. The collision points are the Steiner points of the spanner.
Lakes and reservoirs have critical impacts on hydrological, biogeochemical, and ecological processes, and they should be an essential component of regional-scale hydrological and eco-hydrological m...
A good water quality model needs sufficient data to characterise the waterbody, yet monitoring resources are often limited. Inadequate boundary data often contribute to model uncertainty and error....
Abstract Water quality issues, including harmful and nuisance algal blooms (HNABs), related to nitrogen (N) and phosphorus (P) exported from agricultural lands persist in the Great Lakes region. Previous work examining N and P loss from agricultural fields in portions of the United States (US) and Canada (CA) that drain into Lake Erie, consistently indicate significant nutrient loss from fields in Indiana and Ohio, US compared with those in southwestern Ontario, CA. The primary objective of this study was to examine variation in environmental and management characteristics from 30 sites (US: n = 28, CA: n = 2) located within the Lake Erie Basin and subsequently determine the influence of among-site variation on edge-of-field N and P losses. Using principal component analyses (PCA), we found that among-site variation was predominantly controlled by broad-scale patterns in fertilizer management practices and soil properties; however, N and P loss metrics were largely unexplained by these gradients. As such, fine-scale variability and the interaction of environmental and management characteristics at individual sites more strongly influenced N and P loss. Ultimately, these results further emphasize the importance of site- and nutrient-specific management plans that are needed to mitigate N and P losses from agricultural fields.
In 1985, remedial action plan development was initiated to restore impaired beneficial uses in 42 Great Lakes Areas of Concern (AOCs). A 43rd AOC was designated in 1991. AOC restoration has not been easy as it requires networks focused on gathering stakeholders, coordinating efforts, and ensuring use restoration. As of 2019, seven AOCs were delisted, two were designated as Areas of Concern in Recovery, and 79 of 137 known use impairments in Canadian AOCs and 90 of 255 known use impairments in U.S. AOCs were eliminated. Between 1985 and 2019, a total of $22.78 billion U.S. was spent on restoring all AOCs. Pollution prevention investments should be viewed as spending to avoid future cleanups, and AOC restoration investments should be viewed as spending to help revitalize communities that has over a 3 to 1 return on investment. The pace of U.S. AOC restoration has accelerated under the Great Lakes Legacy Act (GLLA) and Great Lakes Restoration Initiative (GLRI). Sustained funding through U.S. programs like GLRI and GLLA and Canadian programs such as Canada-Ontario Agreement Respecting Great Lakes Water Quality and Ecosystem Health and the Great Lakes Protection Initiative is needed to restore all AOCs. Other major AOC program achievements include use of locally-designed ecosystem approaches, contaminated sediment remediation, habitat rehabilitation, controlling eutrophication, and advancing science. Key lessons learned include: ensure meaningful public participation; engage local leaders; establish a compelling vision; establish measurable targets; practice adaptive management; build partnerships; pursue collaborative financing; build a record of success; quantify benefits; and focus on life after delisting.
Abstract Conservation conflicts are pressing social and environmental sustainability issues, and the complex underlying causes and escalating factors of such conflicts can often be difficult to understand. Appropriate tools are needed for breaking down complex conservation conflicts into their varied, heterogenous parts so their nature and the complex relationships between them may be better understood and addressed using appropriate interventions. Importantly, these tools must transcend disciplinary silos so as to be applicable across social science disciplines as well as within and outside of the academic context. This article synthesizes a breadth of conservation conflict literature to lay out a transdisciplinary framework for diagnosing complex conservation conflicts composed of six key aspects: complexity, emergence, and stages; conflict status; basis of contention and cognitive framing; state of knowledge; state of values; and interventions. This framework is based in systems thinking and differs from other key conservation conflict frameworks by using conflict emergence as a starting point. To complement this approach, our diagnostic tool encourages users to harness thinking based in storytelling and consider how a conservation conflict represents a larger ongoing narrative with depth, meaning, and containing complex, interrelated storylines. As poorly understood stakeholder disputes can seriously undermine conservation efforts, this framework pushes forward practical understandings of conservation conflict interventions by offering a novel, transdisciplinary diagnostic tool for better understanding their complex, multifaceted variables.
Eye tracking systems can provide people with severe motor impairments a way to communicate through gaze-based interactions. Such systems transform a user's gaze input into mouse pointer coordinates that can trigger keystrokes on an on-screen keyboard. However, typing using this approach requires large back-and-forth eye movements, and the required effort depends both on the length of the text and the keyboard layout. Motivated by the idea of sketch-based image search, we explore a gaze-based approach where users draw a shape on a sketchpad using gaze input, and the shape is used to search for similar letters, words, and other predefined controls. The sketch-based approach is area efficient (compared to an on-screen keyboard), allows users to create custom commands, and creates opportunities for gaze-based authentication. Since variation in the drawn shapes makes the search difficult, the system can show a guide (e.g., a 14-segment digital display) on the sketchpad so that users can trace their desired shape. In this paper, we take a first step that investigates the feasibility of the sketch-based approach, by examining how well users can trace a given shape using gaze input. We designed an interface where participants traced a set of given shapes. We then compared the similarity of the drawn and traced shapes. Our study results show the potential of the sketch-based approach: users were able to trace shapes reasonably well using gaze input, even for complex shapes involving three letters; shape tracing accuracy for gaze was better than `free-form' hand drawing. We also report on how different shape complexities influence the time and accuracy of the shape tracing tasks.

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Increasing contribution of peatlands to boreal evapotranspiration in a warming climate
Manuel Helbig, J. M. Waddington, Pavel Alekseychik, B.D. Amiro, Mika Aurela, Alan G. Barr, T. Andrew Black, Peter D. Blanken, Sean K. Carey, Jiquan Chen, Jinshu Chi, Ankur R. Desai, Allison L. Dunn, Eugénie Euskirchen, Lawrence B. Flanagan, Inke Forbrich, Thomas Friborg, Achim Grelle, Silvie Harder, Michal Heliasz, Elyn Humphreys, Hiroki Ikawa, Pierre‐Érik Isabelle, Hiroyasu Iwata, Rachhpal S. Jassal, Mika Korkiakoski, Juliya Kurbatova, Lars Kutzbach, Anders Lindroth, Mikaell Ottosson Löfvenius, Annalea Lohila, Ivan Mammarella, Philip Marsh, Trofim C. Maximov, Joe R. Melton, Paul A. Moore, Daniel F. Nadeau, Erin M. Nicholls, Mats Nilsson, Takeshi Ohta, Matthias Peichl, Richard M. Petrone, Roman E. Petrov, Anatoly Prokushkin, William L. Quinton, David E. Reed, Nigel T. Roulet, Benjamin R. K. Runkle, Oliver Sonnentag, I. B. Strachan, Pierre Taillardat, Eeva‐Stiina Tuittila, Juha‐Pekka Tuovinen, J. Turner, Masahito Ueyama, Andrej Varlagin, Martin Wilmking, Steven C. Wofsy, Vyacheslav Zyrianov
Nature Climate Change, Volume 10, Issue 6

The response of evapotranspiration (ET) to warming is of critical importance to the water and carbon cycle of the boreal biome, a mosaic of land cover types dominated by forests and peatlands. The effect of warming-induced vapour pressure deficit (VPD) increases on boreal ET remains poorly understood because peatlands are not specifically represented as plant functional types in Earth system models. Here we show that peatland ET increases more than forest ET with increasing VPD using observations from 95 eddy covariance tower sites. At high VPD of more than 2 kPa, peatland ET exceeds forest ET by up to 30%. Future (2091–2100) mid-growing season peatland ET is estimated to exceed forest ET by over 20% in about one-third of the boreal biome for RCP4.5 and about two-thirds for RCP8.5. Peatland-specific ET responses to VPD should therefore be included in Earth system models to avoid biases in water and carbon cycle projections.
Changes to ice cover on lakes throughout the northern landscape has been established as an indicator of climate change and variability, expected to have implications for both human and environmental systems. Monitoring lake ice cover is also required to enable more reliable weather forecasting across lake-rich northern latitudes. Currently, the Canadian Ice Service (CIS) monitors lakes using synthetic aperture radar (SAR) and optical imagery through visual interpretation, with total lake ice cover reported weekly as a fraction out of ten. An automated method of classification would allow for more detailed records to be delivered operationally. In this research, we present an automatic ice-mapping approach which integrates unsupervised segmentation from the Iterative Region Growing using Semantics (IRGS) algorithm with supervised random forest (RF) labeling. IRGS first locally segments homogeneous regions in an image, then merges similar regions into classes across the entire scene. Recently, these output regions were manually labeled by the user to generate ice maps, or were labeled using a Support Vector Machine (SVM) classifier. Here, three labeling methods (Manual, SVM, and RF) are applied after IRGS segmentation to perform ice-water classification on 36 RADARSAT-2 scenes of Great Bear Lake (Canada). SVM and RF classifiers are also tested without integration with IRGS. An accuracy assessment has been performed on the results, comparing outcomes with author-generated reference data, as well as the reported ice fraction from CIS. The IRGS-RF average classification accuracy for this dataset is 95.8%, demonstrating the potential of this automated method to provide detailed and reliable lake ice cover information operationally.
Changes in the frequency and extent of wildfires are expected to lead to substantial and irreversible alterations to permafrost landscapes under a warming climate. Here we review recent publications (2010–2019) that advance our understanding of the effects of wildfire on surface and ground temperatures, on active layer thickness and, where permafrost is ice‐rich, on ground subsidence and the development of thermokarst features. These thermal and geomorphic changes are initiated immediately following wildfire and alter the hydrology and biogeochemistry of permafrost landscapes, including the release of previously frozen carbon. In many locations, permafrost has been resilient, with key characteristics such as active layer thickness returning to pre‐fire conditions after several decades. However, permafrost near its southern limit is losing this resiliency as a result of ongoing climate warming and increasingly common vegetation state changes. Shifts in fire return intervals, severity and extent are expected to alter the trajectories of wildfire impacts on permafrost, and to enlarge spatial impacts to more regularly include the burning of tundra areas. Modeling indicates some lowland boreal forest and tundra environments will remain resilient while uplands and areas with thin organic layers and dry soils will experience rapid and irreversible permafrost degradation. More work is needed to relate modeling to empirical studies, particularly incorporating dynamic variables such as soil moisture, snow and thermokarst development, and to identify post‐fire permafrost responses for different landscape types and regions. Future progress requires further collaboration among geocryologists, ecologists, hydrologists, biogeochemists, modelers and remote sensing specialists.
Aufeis, also known as an icing or naled, is an accumulation of ice that forms primarily during winter when water is expelled onto frozen ground or ice surfaces and freezes in layers. Process‐oriented aufeis research initially expanded in the 20th century, but recent interest in changing hydrological conditions in permafrost regions has rejuvenated this field. Despite its societal relevance, the controls on aufeis distribution and dynamics are not well defined and this impedes projections of variation in aufeis size and distribution expected to accompany climate change. This paper reviews the physical controls on aufeis development, current broad‐scale aufeis distribution and anticipated change, and approaches to aufeis investigation. We propose an adjustment to terminology to better distinguish between the formation process and resulting ice bodies, a clarification of the aufeis classification approach based on source water, and a size threshold for broad‐scale aufeis inventory to facilitate collaborative research. We identify additional objectives for future research including advancing process knowledge at fine spatial scales, describing broad‐scale distribution using current remote sensing capabilities, and improving our understanding and predictive capacity over the interactions between aufeis and landscape‐scale permafrost, hydrogeological, geotectonic, and climate conditions.
Scientific workflow management systems such as Galaxy, Taverna and Workspace, have been developed to automate scientific workflow management and are increasingly being used to accelerate the specification, execution, visualization, and monitoring of data-intensive tasks. For example, the popular bioinformatics platform Galaxy is installed on over 168 servers around the world and the social networking space myExperiment shares almost 4,000 Galaxy scientific workflows among its 10,665 members. Most of these systems offer graphical interfaces for composing workflows. However, while graphical languages are considered easier to use, graphical workflow models are more difficult to comprehend and maintain as they become larger and more complex. Text-based languages are considered harder to use but have the potential to provide a clean and concise expression of workflow even for large and complex workflows. A recent study showed that some scientists prefer script/text-based environments to perform complex scientific analysis with workflows. Unfortunately, such environments are unable to meet the needs of scientists who prefer graphical workflows. In order to address the needs of both types of scientists and at the same time to have script-based workflow models because of their underlying benefits, we propose a visually guided workflow modeling framework that combines interactive graphical user interface elements in an integrated development environment with the power of a domain-specific language to compose independently developed and loosely coupled services into workflows. Our domain-specific language provides scientists with a clean, concise, and abstract view of workflow to better support workflow modeling. As a proof of concept, we developed VizSciFlow, a generalized scientific workflow management system that can be customized for use in a variety of scientific domains. As a first use case, we configured and customized VizSciFlow for the bioinformatics domain. We conducted three user studies to assess its usability, expressiveness, efficiency, and flexibility. Results are promising, and in particular, our user studies show that VizSciFlow is more desirable for users to use than either Python or Galaxy for solving complex scientific problems.

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Scientists’ Warning to Humanity: Rapid degradation of the world’s large lakes
Jean‐Philippe Jenny, Orlane Anneville, Fabien Arnaud, Yoann Baulaz, Damien Bouffard, Isabelle Domaizon, Serghei A. Bocaniov, Nathalie Chèvre, Maria Dittrich, Jean Marcel Dorioz, Erin S. Dunlop, Gaël Dur, Jean Guillard, Thibault Guinaldo, Stéphan Jacquet, Aurélien Jamoneau, Zobia Jawed, Erik Jeppesen, Gail Krantzberg, John D. Lenters, Barbara Leoni, Michel Meybeck, Veronica Nava, Tiina Nõges, Peeter Nõges, M Patelli, Victoria Pebbles, Marie Elodie Perga, Séréna Rasconi, Carl R. Ruetz, Lars G. Rudstam, Nico Salmaso, Sapna Sharma, Dietmar Straile, Olga Tammeorg, Michael R. Twiss, Donald G Uzarski, Anne Mari Ventelä, Warwick F. Vincent, Steven W. Wilhelm, Sten Åke Wängberg, Gesa A. Weyhenmeyer
Journal of Great Lakes Research, Volume 46, Issue 4

Abstract Large lakes of the world are habitats for diverse species, including endemic taxa, and are valuable resources that provide humanity with many ecosystem services. They are also sentinels of global and local change, and recent studies in limnology and paleolimnology have demonstrated disturbing evidence of their collective degradation in terms of depletion of resources (water and food), rapid warming and loss of ice, destruction of habitats and ecosystems, loss of species, and accelerating pollution. Large lakes are particularly exposed to anthropogenic and climatic stressors. The Second Warning to Humanity provides a framework to assess the dangers now threatening the world’s large lake ecosystems and to evaluate pathways of sustainable development that are more respectful of their ongoing provision of services. Here we review current and emerging threats to the large lakes of the world, including iconic examples of lake management failures and successes, from which we identify priorities and approaches for future conservation efforts. The review underscores the extent of lake resource degradation, which is a result of cumulative perturbation through time by long-term human impacts combined with other emerging stressors. Decades of degradation of large lakes have resulted in major challenges for restoration and management and a legacy of ecological and economic costs for future generations. Large lakes will require more intense conservation efforts in a warmer, increasingly populated world to achieve sustainable, high-quality waters. This Warning to Humanity is also an opportunity to highlight the value of a long-term lake observatory network to monitor and report on environmental changes in large lake ecosystems.
• Lack of pre-industrial baseline data hampers assessment of oil sands river pollution. • We analyzed metals concentrations in cores of Athabasca Delta floodplain lakes. • No enrichment was detected for metals associated with oil sands development. • Results inform decision on World Heritage status of Wood Buffalo National Park. • A framework has been established for ongoing aquatic ecosystem monitoring. Sediment quality monitoring is widely used to quantify extent of river pollution, but requires knowledge of pre-disturbance conditions in the potentially altered landscape. This has long been identified as a critical aspect to develop for addressing concerns of river pollution in the Alberta Oil Sands Region. Here, we use analyses of sediment cores from eight floodplain lakes spanning a 67 river-km transect across the Athabasca Delta to define pre-1920 (pre-industrial) baseline concentrations for vanadium and five primary pollutants. We then evaluate if sediment metals concentrations have become enriched above baseline since onset of oil sands development and other industrial activities. Results demonstrate no enrichment of metals concentrations (except zinc at one lake) and absence of consistent temporal increases above pre-industrial baselines. Thus, natural processes continue to dominate metal deposition in floodplain lakes of the Athabasca Delta -- an important finding to inform stewardship decisions. The pre-1920 metals concentrations baselines offer a useful tool for ongoing sediment monitoring in aquatic ecosystems of the Athabasca Delta.
Although the majority of coastal sediments consist of sandy material, in some areas marine ingression caused the submergence of terrestrial carbon‐rich peat soils. This affects the coastal carbon balance, as peat represents a potential carbon source. We performed a column experiment to better understand the coupled flow and biogeochemical processes governing carbon transformations in submerged peat under coastal fresh groundwater (GW) discharge and brackish water intrusion. The columns contained naturally layered sediments with and without peat (organic carbon content in peat 39 ± 14 wt%), alternately supplied with oxygen‐rich brackish water from above and oxygen‐poor, low‐saline GW from below. The low‐saline GW discharge through the peat significantly increased the release and ascent of dissolved organic carbon (DOC) from the peat (δ13CDOC − 26.9‰ to − 27.7‰), which was accompanied by the production of dissolved inorganic carbon (DIC) and emission of carbon dioxide (CO2), implying DOC mineralization. Oxygen respiration, sulfate ( SO42− ) reduction, and methane (CH4) formation were differently pronounced in the sediments and were accompanied with higher microbial abundances in peat compared to sand with SO42− ‐reducing bacteria clearly dominating methanogens. With decreasing salinity and SO42− concentrations, CH4 emission rates increased from 16.5 to 77.3 μmol m−2 d−1 during a 14‐day, low‐saline GW discharge phase. In contrast, oxygenated brackish water intrusion resulted in lower DOC and DIC pore water concentrations and significantly lower CH4 and CO2 emissions. Our study illustrates the strong dependence of carbon cycling in shallow coastal areas with submerged peat deposits on the flow and mixing dynamics within the subterranean estuary.
Abstract. Water resources in cold regions in western Canada face severe risks posed by anthropogenic global warming as evapotranspiration increases and precipitation regimes shift. Although understanding the water cycle is key for addressing climate change issues, it is difficult to obtain high spatial- and temporal-resolution observations of hydroclimatic processes, especially in remote regions. Climate models are useful tools for dissecting and diagnosing these processes, especially the convection-permitting (CP) high-resolution regional climate simulation, which provides advantages over lower-resolution models by explicitly representing convection. In addition to better representing convective systems, higher spatial resolution also better represents topography, mountain meteorology, and highly heterogeneous geophysical features. However, there is little work with convection-permitting regional climate models conducted over western Canada. Focusing on the Mackenzie River and Saskatchewan River basins, this study investigated the surface water budget and atmospheric moisture balance in historical and representative concentration pathway (RCP8.5) projections using 4 km CP Weather Research and Forecasting (WRF). We compared the high-resolution 4 km CP WRF and three common reanalysis datasets, namely the North American Regional Reanalysis (NARR), the Japanese 55-year Reanalysis (JRA-55), and European Centre for Medium-Range Weather Forecasts reanalysis interim dataset (ERA-Interim). High-resolution WRF outperforms the reanalyses in balancing the surface water budget in both river basins with much lower residual terms. For the pseudo-global-warming scenario at the end of the 21st century with representative concentration pathway (RCP8.5) radiative forcing, both the Mackenzie River and Saskatchewan River basins show increases in the amplitude for precipitation and evapotranspiration and a decrease in runoff. The Saskatchewan River basin (SRB) shows a moderate increase in precipitation in the west and a small decrease in the east. Combined with a significant increase in evapotranspiration in a warmer climate, the Saskatchewan River basin would have a larger deficit of water resources than in the current climate based on the pseudo-global-warming (PGW) simulation. The high-resolution simulation also shows that the difference of atmospheric water vapour balance in the two river basins is due to flow orientation and topography differences at the western boundaries of the two basins. The sensitivity of water vapour balance to fine-scale topography and atmospheric processes shown in this study demonstrates that high-resolution dynamical downscaling is important for large-scale water balance and hydrological cycles.
During the past few decades, contamination of sediments by persistent toxic substances (PTSs) has been observed in estuarine and coastal areas on the west coast of South Korea. The contaminants are suspected to cause toxicities in aquatic biota, but little is known about their ecological effects, particularly on benthic microbial communities. In this study, an eDNA-based assessment was applied along with classic assessments of exposure, such as chemistry and in vitro bioassays, to evaluate condition of benthic bacterial communities subjected to PTSs. Two strategies were adopted for the study. One was to conduct a comprehensive assessment in space (by comparing seawater and freshwater sites at five coastal regions) and in time (by following change over a 5-y period). Although we found that bacterial composition varied among and within years, some phyla, such as Proteobacteria (28.7%), Actinobacteria (13.1%), Firmicutes (12.7%), and Chloroflexi (12.5%) were consistently dominated across the study regions. Certain bacterial groups, such as Firmicutes and Verrucomicrobia have been linked to contamination at some sites in the study area and at specific points in time. Bacterial communities were not significantly correlated with salinity or AhR- and ER-mediated potencies, whereas concentrations of PAHs, APs, and certain metals (Cd and Hg) exhibited significant associations to the structure of bacterial communities at the phylum level. In fact, the relative abundance of microbes in the phylum Planctomycetes was significantly and negatively correlated with concentrations of PAHs and metals. Thus, the relative abundance of Planctomycetes could be used as an indicator of sedimentary contamination by PAHs and/or metals. Based on our correlation analyses, Cd and ER-mediated potencies were associated more with bacterial abundances at the class taxonomic level than were other PTSs and metals. Overall, the eDNA-based assessment was useful by augmenting more traditional measures of exposure and responses in a sediment triad approach and has potential as a more rapid screening tool.
Abstract The combination of Cu(II) with peroxymonosulfate (PMS) (i.e., the Cu(II)/PMS system) synergistically inactivated P. aeruginosa cells in the planktonic state, and in biofilms grown on RO membranes. The enhanced bacterial inactivation by the Cu(II)/PMS system appears to be due to the reactive oxidants produced by the catalytic reactions of the Cu(II)/Cu(I) redox couple with PMS. In the presence of chloride ion (Cl−), the Cu(II)/PMS system showed increased microbicidal effects on the planktonic P. aeruginosa cells, which was explained by the role of hypochlorous acid (HOCl) produced by the reaction of chloride with PMS. In addition, the combination of Cu(II) with HOCl showed synergistic microbicidal effects on the planktonic cells. Compared to planktonic cells, biofilm cells were more resistant to the Cu(II)/PMS treatment. Cl− did not significantly affect the inactivation of biofilm cells by the Cu(II)/PMS system. It is believed that the extracellular polymeric substances of biofilms play a role as oxidant sinks (particularly HOCl), protecting the cells inside the biofilm matrix. The HOCl-generating systems, such as PMS/Cl− and Cu(II)/PMS/Cl−, greatly degraded proteins and polysaccharides in biofilms. Experiments on the cross-flow filtration of NaCl solution showed that the Cu(II)/PMS treatment of fouled RO membranes resulted in partial recovery of permeate flux.
Clone detection on large code repository is necessary for many big code analysis tasks. The goal is to provide rich information on identical and similar code across projects. Detecting near-miss code clones on big code is challenging since it requires intensive computing and memory resources as the scale of the source code increases. In this work, we propose SAGA, an efficient suffix-array based code clone detection tool designed with sophisticated GPU optimization. SAGA not only detects Type-l and Type-2 clones but also does so for cross-project large repositories and for the most computationally expensive Type-3 clones. Meanwhile, it also works at segment granularity, which is even more challenging. It detects code clones in 100 million lines of code within 11 minutes (with recall and precision comparable to state-of-the-art approaches), which is more than 10 times faster than state-of-the-art tools. It is the only tool that efficiently detects Type-3 near-miss clones at segment granularity in large code repository (e.g., within 11 hours on 1 billion lines of code). We conduct a preliminary case study on 85,202 GitHub Java projects with 1 billion lines of code and exhibit the distribution of clones across projects. We find about 1.23 million Type-3 clone groups, containing 28 million lines of code at arbitrary segment granularity, which are only detectable with SAGA. We believe SAGA is useful in many software engineering applications such as code provenance analysis, code completion, change impact analysis, and many more.
When a programmer makes changes to a target program entity (files, classes, methods), it is important to identify which other entities might also get impacted. These entities constitute the impact set for the target entity. Association rules have been widely used for discovering the impact sets. However, such rules only depend on the previous co-change history of the program entities ignoring the fact that similar entities might often need to be updated together consistently even if they did not co-change before. Considering this fact, we investigate whether cloning relationships among program entities can be associated with association rules to help us better identify the impact sets. In our research, we particularly investigate whether the impact set detection capability of a clone detector can be utilized to enhance the capability of the state-of-the-art association rule mining technique, Tarmaq, in discovering impact sets. We use the well known clone detector called NiCad in our investigation and consider both regular and micro-clones. Our evolutionary analysis on thousands of commit operations of eight diverse subject systems reveals that consideration of code clones can enhance the impact set detection accuracy of Tarmaq with a significantly higher precision and recall. Micro-clones of 3LOC and 4LOC and regular code clones of 5LOC to 20LOC contribute the most towards enhancing the detection accuracy.
Evolutionary coupling is a well investigated phenomenon during software evolution and maintenance. If two or more program entities co-change (i.e., change together) frequently during evolution, it is expected that the entities are coupled. This type of coupling is called evolutionary coupling or change coupling in the literature. Evolutionary coupling is realized using association rules and two measures: support and confidence. Association rules have been extensively used for predicting co-change candidates for a target program entity (i.e., an entity that a programmer attempts to change). However, association rules often predict a large number of co-change candidates with many false positives. Thus, it is important to rank the predicted co-change candidates so that the true positives get higher priorities. The predicted co-change candidates have always been ranked using the support and confidence measures of the association rules. In our research, we investigate five different ranking mechanisms on thousands of commits of ten diverse subject systems. On the basis of our findings, we propose a history-based ranking approach, HistoRank (History-based Ranking), that analyzes the previous ranking history to dynamically select the most appropriate one from those five ranking mechanisms for ranking co-change candidates of a target program entity. According to our experiment result, HistoRank outperforms each individual ranking mechanism with a significantly better MAP (mean average precision). We investigate different variants of HistoRank and realize that the variant that emphasizes the ranking in the most recent occurrence of co-change in the history performs the best.
Developers often prefer dynamically typed programming languages, such as JavaScript, because such languages do not require explicit type declarations. However, such a feature hinders software engineering tasks, such as code completion, type related bug fixes and so on. Deep learning-based techniques are proposed in the literature to infer the types of code elements in JavaScript snippets. These techniques are computationally expensive. While several type inference techniques have been developed to detect types in code snippets written in statically typed languages, it is not clear how effective those techniques are for inferring types in dynamically typed languages, such as JavaScript. In this paper, we investigate the type inference techniques of JavaScript to understand the above two issues further. While doing that we propose a new technique that considers the locally specific code tokens as the context to infer the types of code elements. The evaluation result shows that the proposed technique is 20-47% more accurate than the statically typed language-based techniques and 5–14 times faster than the deep learning techniques without sacrificing accuracy. Our analysis of sensitivity, overlapping of predicted types and the number of training examples justify the importance of our technique.
The relative effect of climate change and El Niño–Southern Oscillation (ENSO) is essential not only for understanding the hydrological mechanism over Jiangxi province in China but also for local water resources management as well as flood control. This study quantitatively researched in-depth information on climate change in Jiangxi using the up-to-date “ground truth” precipitation and temperature data, the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE, 1961–2015, 0.25°) data; analyzed the connections between ENSO and climate factors (including precipitation and temperature); and discussed the relationships between the ENSO and climate change. The main findings of this study were (1) during the period of 1961–2015, annual precipitation and temperature generally increased at a rate of 2.68 mm/year and 0.16 °C/10a, respectively; (2) the precipitation temporal trends have significant spatial differences. For example, the high precipitation increasing rates occurred in northern Jiangxi province in summer, while the large decreasing rates happened in most regions of Jiangxi province in spring; (3) an abrupt temperature change was detected around 1984, with general decreasing trends and increasing trends in 1961–1984 and 1984–2015, respectively; (4) ENSO had significant impacts on precipitation changes over Jiangxi province, for example; the El Niño events, beginning in April and May, were likely to enlarge the amounts of precipitation in the following summer, and the El Niño events beginning in October were likely to enlarge the precipitation amounts in the following spring and summer; and (5) the El Niño events, starting in the second half of the year, were likely to raise the temperature in the winter and the following spring. These findings would provide valuable information for better understanding the climate change issues over Jiangxi province.
Quantifying uncertainties of precipitation estimation, especially in extreme events, could benefit early warning of water-related hazards like flash floods and landslides. Rain gauges, weather radars, and satellites are three mainstream data sources used in measuring precipitation but have their own inherent advantages and deficiencies. With a focus on extremes, the overarching goal of this study is to cross-examine the similarities and differences of three state-of-the-art independent products (Muti-Radar Muti-Sensor Quantitative Precipitation Estimates, MRMS; National Center for Environmental Prediction gridded gauge-only hourly precipitation product, NCEP; Integrated Multi-satellitE Retrievals for GPM, IMERG), with both traditional metrics and the Multiplicative Triple Collection (MTC) method during Hurricane Harvey and multiple Tropical Cyclones. The results reveal that: (a) the consistency of cross-examination results against traditional metrics approves the applicability of MTC in extreme events; (b) the consistency of cross-events of MTC evaluation results also suggests its robustness across individual storms; (c) all products demonstrate their capacity of capturing the spatial and temporal variability of the storm structures while also magnifying respective inherent deficiencies; (d) NCEP and IMERG likely underestimate while MRMS overestimates the storm total accumulation, especially for the 500-year return Hurricane Harvey; (e) both NCEP and IMERG underestimate extreme rainrates (>= 90 mm/h) likely due to device insensitivity or saturation while MRMS maintains robust across the rainrate range; (g) all three show inherent deficiencies in capturing the storm core of Harvey possibly due to device malfunctions with the NCEP gauges, relative low spatiotemporal resolution of IMERG, and the unusual “hot” MRMS radar signals. Given the unknown ground reference assumption of MTC, this study suggests that MRMS has the best overall performance. The similarities, differences, advantages, and deficiencies revealed in this study could guide the users for emergency response and motivate the community not only to improve the respective sensor/algorithm but also innovate multidata merging methods for one best possible product, specifically suitable for extreme storm events.
Travel cost models using the wage rate to value time make the implicit assumption that the value of time is equalized throughout the year. We develop a seasonal travel cost model that allows the value of time to vary by season. We estimate the model using data from a survey of recreational anglers in the Gulf of Mexico. We find that people’s value of time is 55% larger on average in the summer compared to other times of year and find substantial differences in derived welfare estimates if a time-constant value of time measure is used instead.
Lake ice is a dominant component of Canada’s landscape and can act as an indicator for how freshwater aquatic ecosystems are changing with warming climates. While lake ice monitoring through government networks has decreased in the last three decades, the increased availability of remote sensing images can help to provide consistent spatial and temporal coverage for areas with annual ice cover. Synthetic aperture radar (SAR) data are commonly used for lake ice monitoring, due to the acquisition of images in any condition (time of day or weather). Using Sentinel-1 A/B images, a high-density time series of SAR images was developed for Lake Hazen in Nunavut, Canada, from 2015–2018. These images were used to test two different methods of monitoring lake ice phenology: one method using the first difference between SAR images and another that applies the Otsu segmentation method. Ice phenology dates determined from the two methods were compared with visual interpretation of the Sentinel-1 images. Mean errors for the pixel comparison of the first difference method ranged 3–10 days for ice-on and ice-off, while average error values for the Otsu method ranged 2–10 days. Mean errors for comparisons of different sections of the lake ranged 0–15 days for the first difference method and 2–17 days for the Otsu method. This research demonstrates the value of temporally consistent image acquisition for improving the accuracy of lake ice monitoring.
Two vertex-labelled polygons are compatible if they have the same clockwise cyclic ordering of vertices. The definition extends to polygonal regions (polygons with holes) and to triangulations—for every face, the clockwise cyclic order of vertices on the boundary must be the same. It is known that every pair of compatible n -vertex polygonal regions can be extended to compatible triangulations by adding O ( n 2 ) Steiner points. Furthermore, Ω ( n 2 ) Steiner points are sometimes necessary, even for a pair of polygons. Compatible triangulations provide piecewise linear homeomorphisms and are also a crucial first step in morphing planar graph drawings, aka “2D shape animation.” An intriguing open question, first posed by Aronov, Seidel, and Souvaine in 1993, is to decide if two compatible polygons have compatible triangulations with at most k Steiner points. In this paper we prove the problem to be NP-hard for polygons with holes. The question remains open for simple polygons.
Snow interception is a crucial hydrological process in cold regions needleleaf forests, but is rarely measured directly. Indirect estimates of snow interception can be made by measuring the difference in the increase in snow accumulation between the forest floor and a nearby clearing over the course of a storm. Pairs of automatic weather stations with acoustic snow depth sensors provide an opportunity to estimate this, if snow density can be estimated reliably. Three approaches for estimating fresh snow density were investigated: weighted post‐storm density increments from the physically based Snobal model, fresh snow density estimated empirically from air temperature (Hedstrom, N. R., et al. [1998]. Hydrological Processes, 12, 1611–1625), and fresh snow density estimated empirically from air temperature and wind speed (Jordan, R. E., et al. [1999]. Journal of Geophysical Research, 104, 7785–7806). Automated snow depth observations from adjacent forest and clearing sites and estimated snow densities were used to determine snowstorm snow interception in a subalpine forest in the Canadian Rockies, Alberta, Canada. Then the estimated snow interception and measured interception information from a weighed, suspended tree and a time‐lapse camera were assimilated into a model, which was created using the Cold Regions Hydrological Modelling platform (CRHM), using Ensemble Kalman Filter or a simple rule‐based direct insertion method. Interception determined using density estimates from the Hedstrom‐Pomeroy fresh snow density equation agreed best with observations. Assimilating snow interception information from automatic snow depth measurements improved modelled snow interception timing by 7% and magnitude by 13%, compared to an open loop simulation driven by a numerical weather model; its accuracy was close to that simulated using locally observed meteorological data. Assimilation of tree‐measured snow interception improved the snow interception simulation timing and magnitude by 18 and 19%, respectively. Time‐lapse camera snow interception information assimilation improved the snow interception simulation timing by 32% and magnitude by 7%. The benefits of assimilation were greatly influenced by assimilation frequency and quality of the forcing data.
Agricultural pest control products are a major cause of degradation of water quality and biodiversity loss worldwide. In the Canadian Prairie Pothole Region, the landscape is characterized by millions of ecologically important wetlands, but also large farm sizes and high agrochemical use. Despite the region's agricultural intensity, the spatial extent of pesticide use and likelihood of pesticides contaminating surface water has been poorly studied. Here, we estimated the pesticide use patterns for three main groups (herbicides, fungicides and insecticides) using the most recent (2015) pesticide use survey data and digital crop maps. Furthermore, we developed a Wetland Pesticide Occurrence Index (WPOI; 1 km2 resolution), to robustly estimate potential wetland exposure using spatially explicit data on pesticide use density, wetland density, precipitation and pesticide-specific physicochemical properties. In total, 39,236 metric tonnes of pesticides consisting of 94 active ingredients were applied to the Prairies in 2015. Herbicides had the highest density of use (24-183 kg/km2), followed by fungicides (0.4-23.8 kg/km2) and insecticides (0.4-3.6 kg/km2). Pesticide use differed by province; however, the major pesticides applied (e.g., glyphosate, prothioconazole, and thiamethoxam) were consistent across the region and were largely associated with wheat and canola crops. Although insecticides and fungicides had lower mass applied than herbicides, they had slightly higher overall WPOI scores. The predicted pesticide occurrence for insecticides and fungicides in wetlands was higher in the wetter central and eastern part of the Prairie region (WPOI = 0.6-1) compared to the drier western and southwestern part (WPOI = 0.1-0.6), suggesting that wetlands in much of Saskatchewan and southern Manitoba may be more vulnerable to higher and frequent contamination. Identifying crops, chemicals and landscapes with the greatest likelihood of pesticide contamination to wetlands will help prioritize future environmental monitoring programs and aid in assessing the ecological risk of specific pest control products in Canada's most agriculturally intensive region.
This study conducted linear and change-point analyses of historical trends since 1942 in the length and number of days suitable for skating on backyard rinks in the “Original Six” National Hockey League cities of Boston, Chicago, Detroit, Montreal, New York, and Toronto. Analysis is based on the relationship between ambient air temperatures and the probability of skating, using thresholds identified through the RinkWatch citizen science project. In all cities, coefficient estimates suggest the number of high-probability skating days per winter is declining, with easternmost cities displaying notable declines and growing inter-annual variability in skating days in recent decades. Linear analysis shows a statistically significant decline in Toronto, with a step-change emerging in 1980, after which there is on average one-third fewer skating days compared with preceding decades. The outdoor skating season trends towards later start dates in Boston, Montreal, New York, and Toronto. Future monitoring of outdoor rinks provides an opportunity for engaging the public in identification of winter warming trends that might otherwise be imperceptible, and for raising awareness of the impacts of climate change.
The Upper Mississippi River Basin is the largest source of reactive nitrogen (N) to the Gulf of Mexico. Concentration‐discharge (C‐Q) relationships offer a means to understand both the terrestrial sources that generate this reactive N and the in‐stream processes that transform it. Progress has been made on identifying land use controls on C‐Q dynamics. However, the impact of basin size and river network structure on C‐Q relationships is not well characterized. Here, we show, using high‐resolution nitrate concentration data, that tile drainage is a dominant control on C‐Q dynamics, with increasing drainage density contributing to more chemostatic C‐Q behavior. We further find that concentration variability increases, relative to discharge variability, with increasing basin size across six orders of magnitude, and this pattern is attributed to different spatial correlation structures for C and Q. Our results show how land use and river network structure jointly control riverine N export.
Blowing snow is ubiquitous in cold, windswept environments. In some regions, blowing snow sublimation losses can ablate a notable fraction of the seasonal snowfall. It is advantageous to predict alpine snow regimes at the spatial scale of snowdrifts (≈1 to 100 m) because of the role of snow redistribution in governing the duration and volume of snowmelt. However, blowing snow processes are often neglected due to computational costs. Here, a three‐dimensional blowing snow model is presented that is spatially discretized using a variable resolution unstructured mesh. This represents the heterogeneity of the surface explicitly yet, for the case study reported, gained a 62% reduction in computational elements versus a fixed‐resolution mesh and resulted in a 44% reduction in total runtime. The model was evaluated for a subarctic mountain basin using transects of measured snow water equivalent (SWE) in a tundra valley. Including blowing snow processes improved the prediction of SWE by capturing inner‐annual snowdrift formation, more than halved the total mean bias error, and increased the coefficient of variation of SWE from 0.04 to 0.31 better matching the observed CV (0.41). The use of a variable resolution mesh did not dramatically degrade the model performance. Comparison with a constant resolution mesh showed a similar CV and RMSE as the variable resolution mesh. The constant resolution mesh had a smaller mean bias error. A sensitivity analysis showed that snowdrift locations and immediate up‐wind sources of blowing snow are the most sensitive areas of the landscape to wind speed variations.
Nature manifests itself in space and time. The spatiotemporal complexity of processes such as precipitation, temperature, and wind, does not allow purely deterministic modeling. Spatiotemporal random fields have a long history in modeling such processes, and yet a single unified framework offering the flexibility to simulate processes that may differ profoundly does not exist. Here we introduce a blueprint to efficiently simulate spatiotemporal random fields that preserve any marginal distribution, any valid spatiotemporal correlation structure, and intermittency. We suggest a set of parsimonious yet flexible marginal distributions and provide a rule of thumb for their selection. We propose a new and unified approach to construct flexible spatiotemporal correlation structures by combining copulas and survival functions. The versatility of our framework is demonstrated by simulating conceptual cases of intermittent precipitation, double‐bounded relative humidity, and temperature maxima fields. As a real‐word case we simulate daily precipitation fields. In all cases, we reproduce the desired properties. In an era characterized by advances in remote sensing and increasing availability of spatiotemporal data, we deem that this unified approach offers a valuable and easy‐to‐apply tool for modeling complex spatiotemporal processes.
Abstract. Despite debate in the rainfall–runoff hydrology literature about the merits of physics-based and spatially distributed models, substantial work in cold-region hydrology has shown improved predictive capacity by including physics-based process representations, relatively high-resolution semi-distributed and fully distributed discretizations, and the use of physically identifiable parameters that require limited calibration. While there is increasing motivation for modelling at hyper-resolution (< 1 km) and snowdrift-resolving scales (≈ 1 to 100 m), the capabilities of existing cold-region hydrological models are computationally limited at these scales. Here, a new distributed model, the Canadian Hydrological Model (CHM), is presented. Although designed to be applied generally, it has a focus for application where cold-region processes play a role in hydrology. Key features include the ability to do the following: capture spatial heterogeneity in the surface discretization in an efficient manner via variable-resolution unstructured meshes; include multiple process representations; change, remove, and decouple hydrological process algorithms; work at both a point and spatially distributed scale; scale to multiple spatial extents and scales; and utilize a variety of forcing fields (boundary and initial conditions). This paper focuses on the overall model philosophy and design, and it provides a number of cold-region-specific features and examples.
Peatlands are wetlands that provide important ecosystem services including carbon sequestration and water storage that respond to hydrological, biological, and biogeochemical processes. These processes are strongly influenced by the complex pore structure of peat soils. We explore the literature on peat pore structure and the implications for hydrological, biogeochemical, and microbial processes in peat, highlighting the gaps in our current knowledge and a path to move forward. Peat is an elastic and multi-porous structured organic soil. Surficial (near-surface) peats are typically dominated by large interconnected macropores that rapidly transmit water and solutes when saturated, but these large pores drain rapidly with a reduction in pore-water pressure, and disproportionally decrease the bulk effective hydraulic conductivity, thus water fluxes that drive ecohydrological functions. The more advanced state of decomposition of older (deeper) peat, with a greater abundance of small pores, restricts the loss of moisture at similar soil water pressures and is associated with higher unsaturated hydraulic conductivities. As evaporation and precipitation occur, peat soils shrink and swell, respectively, changing the hydrological connectivity that maintain physiological processes at the peat surface. Due to the disproportionate change in pore structure and associated hydraulic properties with state of decomposition, transport processes are limited at depth, creating a zone of enhanced transport in the less decomposed peat near the surface. At the micro-scale, rapid equilibration of solutes and water occurs between the mobile and immobile pores due to diffusion, resulting in pore regions with similar chemical concentrations that are not affected by advective fluxes. These immobile regions may be the primary sites for microbial biogeochemical processes in peat. Mass transfer limitations may therefore largely regulate belowground microbial turnover and, hence, biogeochemical cycling. For peat, the development of a comprehensive theory that links the hydrological, biological, and biogeochemical processes will require a concerted interdisciplinary effort. To that end, we have highlighted four primary areas to focus our collective research: 1) understanding the combined and interrelated effects of parent material, decomposition, and nutrient status on peat pore connectivity, macropore development and collapse, and solute transport, 2) determining the influence of changing pore structure due to freeze-thaw or dewatering on the hydrology and biogeochemistry, 3) better elucidating the non-equilibrium transport processes in peat, and 4) exploring the implications of peat’s pore structure on microbiological and biogeochemical processes.
Abstract Structure-from-motion (SfM) and multi-view stereo (MVS) algorithms coupled with the use of unmanned aerial vehicles (UAVs) have become a popular tool in the geosciences for modelling complex landscapes on-demand allowing for high-resolution topographic change-detection studies to be conducted at minimal cost. To identify the effects of UAV image orientation on the accuracy of SfM-MVS 3D surface models, we tested four different UAV image acquisition schemes that incorporated both nadir and oblique imagery of an agricultural field. The coupling of nadir and oblique imaging angles led to the highest surface model accuracy in the absence of ground control points (GCPs; vertical RMSE: 0.047 m, horizontal RMSE: 0.019 m), while with a normative distribution of GCPs the nadir-only image sets had similar accuracy metrics (vertical RMSE 0.028 m, horizontal RMSE 0.017 m) to surface models generated with nadir and oblique imaging angles (vertical RMSE 0.028 m, horizontal RMSE 0.013 m). Homologous keypoint matching between nadir and oblique imagery was poor when the survey conditions were bright and the surface texture of the field was homogeneous, leading to broad-scale vertical noise in the generated surface models. Results indicate that a nadir-only image set accompanied with a dense deployment of GCPs is the most ideal for SfM-MVS agricultural 3D surface reconstructions. The diachronic analysis of surface models generated from nadir-only image sets were able to detect surface-change >0.040 m in depth (i.e., rill and gully erosion, depositional zones) and were comparable to results obtained from a terrestrial laser scanner. Stable GCPs should be used where possible to ensure precise co-registration between subsequent UAV surveys.
Abstract. The 0 ∘C temperature threshold is critical for many meteorological and hydrological processes driven by melting and freezing in the atmosphere, surface, and sub-surface and by the associated precipitation varying between rain, freezing rain, wet snow, and snow. This threshold is especially important in cold regions such as Canada, because it is linked with freeze–thaw, snowmelt, and permafrost. This study develops a Canada-wide perspective on near-0 ∘C conditions using hourly surface temperature and precipitation type observations from 92 climate stations for the period from 1981 to 2011. In addition, nine stations from various climatic regions are selected for further analysis. Near-0 ∘C conditions are defined as periods when the surface temperature is between −2 and 2 ∘C. Near-0 ∘C conditions occur often across all regions of the country, although the annual number of days and hours and the duration of these events varies dramatically. Various types of precipitation (e.g., rain, freezing rain, wet snow, and ice pellets) sometimes occur with these temperatures. Near-0 ∘C conditions and the reported precipitation type occurrences tend to be higher in Atlantic Canada, although high values also occur in other regions. Trends of most temperature-based and precipitation-based indicators show little or no change despite a systematic warming in annual surface temperatures over Canada. Over the annual cycle, near-0 ∘C temperatures and precipitation often exhibit a pattern: short durations occur around summer, driven by the diurnal cycle, and a tendency toward longer durations around winter, associated with storms. There is also a tendency for near-0 ∘C surface temperatures to occur more often than expected relative to other temperature windows at some stations due, at least in part, to diabatic cooling and heating that take place with melting and freezing, respectively, in the atmosphere and at the surface.
Abstract. In mountainous terrain, the snowpack is strongly affected by incoming shortwave and longwave radiation. In this study, a thorough evaluation of the solar and longwave downwelling irradiance products (DSSF and DSLF) derived from the Meteosat Second Generation satellite was undertaken in the French Alps and the Pyrenees. The satellite-derived products were compared with forecast fields from the meteorological model AROME and with analysis fields from the SAFRAN system. A new satellite-derived product (DSLFnew) was developed by combining satellite observations and AROME forecasts. An evaluation against in situ measurements showed lower errors for DSSF than AROME and SAFRAN in terms of solar irradiances. For longwave irradiances, we were not able to select the best product due to contrasted results falling in the range of uncertainty of the sensors. Spatial comparisons of the different datasets over the Alpine and Pyrenean domains highlighted a better representation of the spatial variability of solar fluxes by DSSF and AROME than SAFRAN. We also showed that the altitudinal gradient of longwave irradiance is too strong for DSLFnew and too weak for SAFRAN. These datasets were then used as radiative forcing together with AROME near-surface forecasts to drive distributed snowpack simulations by the model Crocus in the French Alps and the Pyrenees. An evaluation against in situ snow depth measurements showed higher biases when using satellite-derived products, despite their quality. This effect is attributed to some error compensations in the atmospheric forcing and the snowpack model. However, satellite-derived irradiance products are judged beneficial for snowpack modelling in mountains, when the error compensations are solved.
Abstract. From 19 to 22 June 2013, intense rainfall and concurrent snowmelt led to devastating floods in the Canadian Rockies, foothills and downstream areas of southern Alberta and southeastern British Columbia, Canada. Such an event is typical of late-spring floods in cold-region mountain headwater, combining intense precipitation with rapid melting of late-lying snowpack, and represents a challenge for hydrological forecasting systems. This study investigated the factors governing the ability to predict such an event. Three sources of uncertainty, other than the hydrological model processes and parameters, were considered: (i) the resolution of the atmospheric forcings, (ii) the snow and soil moisture initial conditions (ICs) and (iii) the representation of the soil texture. The Global Environmental Multiscale hydrological modeling platform (GEM-Hydro), running at a 1 km grid spacing, was used to simulate hydrometeorological conditions in the main headwater basins of southern Alberta during this event. The GEM atmospheric model and the Canadian Precipitation Analysis (CaPA) system were combined to generate atmospheric forcing at 10, 2.5 and 1 km over southern Alberta. Gridded estimates of snow water equivalent (SWE) from the Snow Data Assimilation System (SNODAS) were used to replace the model SWE at peak snow accumulation and generate alternative snow and soil moisture ICs before the event. Two global soil texture datasets were also used. Overall 12 simulations of the flooding event were carried out. Results show that the resolution of the atmospheric forcing affected primarily the flood volume and peak flow in all river basins due to a more accurate estimation of intensity and total amount of precipitation during the flooding event provided by CaPA analysis at convection-permitting scales (2.5 and 1 km). Basin-averaged snowmelt also changed with the resolution due to changes in near-surface wind and resulting turbulent fluxes contributing to snowmelt. Snow ICs were the main sources of uncertainty for half of the headwater basins. Finally, the soil texture had less impact and only affected peak flow magnitude and timing for some stations. These results highlight the need to combine atmospheric forcing at convection-permitting scales with high-quality snow ICs to provide accurate streamflow predictions during late-spring floods in cold-region mountain river basins. The predictive improvement by inclusion of high-elevation weather stations in the precipitation analysis and the need for accurate mountain snow information suggest the necessity of integrated observation and prediction systems for forecasting extreme events in mountain river basins.
Code clones are the same or nearly similar code fragments in a software system's code-base. While the existing studies have extensively studied regular code clones in software systems, micro-clones have been mostly ignored. Although an existing study investigated consistent changes in exact micro-clones, near-miss micro-clones have never been investigated. In our study, we investigate the importance of near-miss micro-clones in software evolution and maintenance by automatically detecting and analyzing the consistent updates that they experienced during the whole period of evolution of our subject systems. We compare the consistent co-change tendency of near-miss micro-clones with that of exact micro-clones and regular code clones. According to our investigation on thousands of revisions of six open-source subject systems written in two different programming languages, near-miss micro-clones have a significantly higher tendency of experiencing consistent updates compared to exact micro-clones and regular (both exact and near-miss) code clones. Consistent updates in near-miss micro-clones have a high tendency of being related with bug-fixes. Moreover, the percentage of commit operations where near-miss micro-clones experience consistent updates is considerably higher than that of regular clones and exact micro-clones. We finally observe that near-miss micro-clones staying in close proximity to each other have a high tendency of experiencing consistent updates. Our research implies that near-miss micro-clones should be considered equally important as of regular clones and exact micro-clones when making clone management decisions.
Abstract Code clones, identical or nearly similar code fragments in a software system’s code-base, have mixed impacts on software evolution and maintenance. Focusing on the issues of clones researchers suggest managing them through refactoring, and tracking. In this paper we present a survey on the state-of-the-art of clone refactoring and tracking techniques, and identify future research possibilities in these areas. We define the quality assessment features for the clone refactoring and tracking tools, and make a comparison among these tools considering these features. To the best of our knowledge, our survey is the first comprehensive study on clone refactoring and tracking. According to our survey on clone refactoring we realize that automatic refactoring cannot eradicate the necessity of manual effort regarding finding refactoring opportunities, and post refactoring testing of system behaviour. Post refactoring testing can require a significant amount of time and effort from the quality assurance engineers. There is a marked lack of research on the effect of clone refactoring on system performance. Future investigations in this direction will add much value to clone refactoring research. We also feel the necessity of future research towards real-time detection, and tracking of code clones in a big-data environment.
Detection of heavy metal contamination in the environment is an on-going analytical challenge. In effort of developing portable biosensors, deoxyribonucleic acid (DNA)-based designs have gained much attention for their high affinity and specificity to metals, stability, cost-efficiency, ease of modification, and batch-to-batch reproducibility. Specific sequences of DNA aptamers and DNAzymes provide grounds for rational designs of fluorescent, colorimetric, and electrochemical detection methods. Aptamers exert only a binding function, while DNAzymes can use heavy metals to catalyze specific chemical and biological transformations. This article starts with a brief introduction of heavy metals and their interactions with DNA. Then DNA aptamers and DNAzymes are respectively reviewed from their in vitro selection, representative DNA sequences, and design of biosensors. For signal transduction, various fluorescent, colorimetric, and electrochemical examples are described. Finally, future perspectives are discussed.
Abstract While there are novel approaches for detecting and categorizing similar software applications, previous research focused on detecting similarity in applications written in the same programming language and not on detecting similarity in applications written in different programming languages. Cross-language software similarity detection is inherently more challenging due to variations in language, application structures, support libraries used, and naming conventions. In this paper we propose a novel model, CroLSim, to detect similar software applications across different programming languages. We define a semantic relationship among cross-language libraries and API methods (both local and third party) using functional descriptions and a word-vector learning model. Our experiments show that CroLSim can successfully detect cross-language similar software applications, which outperforms all existing approaches (mean average precision rate of 0.65, confidence rate of 3.6, and 75% highly rated successful queries). Furthermore, we applied CroLSim to a source code repository to see whether our model can recommend cross-language source code fragments if queried directly with source code. From our experiments we found that CroLSim can recommend cross-language functional similar source code when source code is directly used as a query (average precision=0.28, recall=0.85, and F-Measure=0.40).
In vitro bioassays have been used as a bioanalytical means of detecting dioxin-like compounds (DLCs) in environmental matrices and have been suggested as a tool for quantifying DLCs in sediments. The present study evaluated the relationship between bioanalytical results from the micro-7-ethoxyresorufin-O-deethylase (EROD) bioassay and chemical analytical results in 25 sediment samples collected from rivers across Germany. Sediments were collected, polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) and dioxin-like polychlorinated biphenyls (DL-PCBs) were extracted from the sediments, biological toxicity equivalent quotients (BEQs) were determined by micro-EROD assay and toxicity equivalent quotients (TEQs) were calculated from chemical analysis. Correlations between BEQs and TEQs were evaluated, and linear regression modeling was performed, excluding 6 samples as validation data, to derive equations for predicting TEQs from BEQs. Validation data was tested to evaluate predictive capabilities of the models. Correlations were observed between BEQ and TEQ for PCDD/Fs (r=0.987), PCBs (r=0.623), measured sum of PCDD/F and PCBs (r = 0.975) and calculated sum of PCDD/F and PCBs (r = 0.971). The modeling equations provided low variances as evaluated by mean absolute error (MAE) (≤10.3 pg/g) and root mean square error (RMSE) (≤15.8 pg/g) indicating that expected TEQs could be reasonably well calculated from BEQs. Predicted TEQs from validation data fell within the 95% probability intervals of the test data and had low variances (MAE≤6.5 pg/g) and (RMSE≤10.7 pg/g). Our results indicate that the micro-EROD bioassay can be used as a screening tool for DLCs in sediment and has the capability to be used as an alternate method to chemical analysis for quantifying dioxin-like potential of sediments.
Abstract Well-designed monitoring approaches are needed to assess effects of industrial development on downstream aquatic environments and guide environmental stewardship. Here, we develop and apply a monitoring approach to detect potential enrichment of metals concentrations in surficial lake sediments of the Peace-Athabasca Delta (PAD), northern Alberta, Canada. Since the ecological integrity of the PAD is strongly tied to river floodwaters that replenish lakes in the delta, and the PAD is located downstream of the Alberta oil sands, concerns have been raised over the potential transport of industry-supplied metals to the PAD via the Athabasca River. Surface sediment samples were collected in September 2017 from 61 lakes across the delta, and again in July 2018 from 20 of the same lakes that had received river floodwaters 2 months earlier, to provide snapshots of metals concentrations (Be, Cd, Cr, Cu, Ni, Pb, V, and Zn) that have recently accumulated in these lakes. To assess for anthropogenic enrichment, surficial sediment metals concentrations were normalized to aluminum and compared to pre-industrial baseline (i.e., reference) metal-aluminum linear relations for the Athabasca and Peace sectors of the PAD developed from pre-1920 measurements in lake sediment cores. Numerical analysis demonstrates no marked enrichment of these metals concentrations above pre-1920 baselines despite strong ability (> 99% power) to detect enrichment of 10%. Measurements of river sediment collected by the Regional Aquatics- and Oil Sands-Monitoring Programs (RAMP/OSM) also did not exceed pre-1920 concentrations. Thus, results presented here show no evidence of substantial oil sands-derived metals enrichment of sediment supplied by the Athabasca River to lakes in the PAD and demonstrate the usefulness of these methods as a monitoring framework.
This study conducts a detection and attribution analysis of the observed changes in extreme precipitation during 1951–2015. Observed and CMIP6 multimodel simulated changes in annual maximum daily and consecutive 5-day precipitation are compared using an optimal fingerprinting technique for different spatial scales from global land, Northern Hemisphere extratropics, tropics, three continental regions (North America and western and eastern Eurasia), and global “dry” and “wet” land areas (as defined by their average extreme precipitation intensities). Results indicate that anthropogenic greenhouse gas influence is robustly detected in the observed intensification of extreme precipitation over the global land and most of the subregions considered, all with clear separation from natural and anthropogenic aerosol forcings. Also, the human-induced greenhouse gas increases are found to be a dominant contributor to the observed increase in extreme precipitation intensity, which largely follows the increased moisture availability under global warming.
• We reconstructed time series of boreal tree growth with a biometric approach. • Aboveground tree growth was a minor and decoupled fraction of carbon input. • Partitioned estimates of tree carbon sink are valuable observational constraints. • Such observational constraints can be used for model validation and policy making. The boreal biome accounts for approximately one third of the terrestrial carbon (C) sink. However, estimates of its individual C pools remain uncertain. Here, focusing on the southern boreal forest, we quantified the magnitude and temporal dynamics of C allocation to aboveground tree growth at a mature black spruce ( Picea mariana )-dominated forest stand in Saskatchewan, Canada. We reconstructed aboveground tree biomass increments (AGBi) using a biometric approach, i.e., species-specific allometry combined with forest stand characteristics and tree ring widths collected with a C-oriented sampling design. We explored the links between boreal tree growth and ecosystem C input by comparing AGBi with eddy-covariance-derived ecosystem C fluxes from 1999 to 2015 and we synthesized our findings with a refined meta-analysis of published values of boreal forest C use efficiency (CUE). Mean AGBi at the study site was decoupled from ecosystem C input and equal to 71 ± 7 g C m –2 (1999–2015), which is only a minor fraction of gross ecosystem production (GEP; i.e., AGBi / GEP ≈ 9 %). Moreover, C allocation to AGBi remained stable over time (AGBi / GEP; –0.0001 yr –1 ; p -value=0.775), contrary to significant trends in GEP (+5.72 g C m –2 yr –2 ; p -value=0.02) and CUE (–0.0041 yr –1 , p -value=0.007). CUE was estimated as 0.50 ± 0.03 at the study area and 0.41 ± 0.12 across the reviewed boreal forests. These findings highlight the importance of belowground tree C investments, together with the substantial contribution of understory, ground cover and soil to the boreal forest C balance. Our quantitative insights into the dynamics of aboveground boreal tree C allocation offer additional observational constraints for terrestrial ecosystem models that are often biased in converting C input to biomass, and can guide forest-management strategies for mitigating carbon dioxide emissions.
The acceleration of permafrost thaw due to warming, wetting, and disturbance is altering circumpolar landscapes. The effect of thaw is largely determined by ground ice content in near‐surface permafrost, making the characterization and prediction of ground ice content critical. Here we evaluate the spatial and stratigraphic variation of near‐surface ground ice characteristics in the dominant forest types in the North Slave region near Yellowknife, Northwest Territories, Canada. Physical variation in the permafrost was assessed through cryostructure, soil properties, and volumetric ice content, and relationships between these parameters were determined. Near‐surface ground ice characteristics were contrasted between forest types. In black spruce forests the top of the permafrost was ice‐rich and characterized by lenticular and ataxitic cryostructures, indicating the presence of an intermediate layer. Most white spruce/birch forests showed similar patterns; however, an increase in the active layer thickness and permafrost thaw at some sites have eradicated the transition zone, and the large ice lenses encountered at depth reflect segregated ground ice developed during initial downward aggradation of permafrost. Our findings indicate that white spruce/birch terrain will be less sensitive than black spruce forests to near‐surface permafrost thaw. However, if permafrost thaws completely, white spruce/birch terrain will probably be transformed into wetland–thaw lake complexes due to high ground ice content at depth.
Abstract Mercury (Hg) is a toxic metal posing major health risks to human beings and wildlife. The characterization of Hg fate and transport in aquatic environments is hindered by a lack of sensitive, selective and easily field-deployable analytical techniques. Here we assess the reliability and performance of a Hg2+ sensor based on the selective binding of Hg2+ to a thymine-rich DNA under environmentally-relevant conditions. Experimental results indicate that the interactions between the DNA and SYBR Green I, which produce the detection fluorescence signal, are significantly impacted by pH, metal ligands and natural dissolved organic matter (NDOM). These interferences are largely eliminated by immobilizing the DNA in a polyacrylamide hydrogel, although high concentrations of NDOM, such as fulvic acids, still affect the sensor’s performance due to competitive binding of Hg2+. The binding of Hg2+ to NDOM, however, can be accounted for via equilibrium speciation calculations, which also yield the complexation constant for Hg2+ binding to the DNA in the hydrogel. The equilibrium calculations reproduce the results for the entire set of experimental conditions, from simple electrolyte solutions to complex aqueous compositions mimicking natural lake waters, and across large ranges of pH (3-10) and temperature (5-50 °C).
A human biomonitoring project investigating environmental exposures to metals from hair, blood and urine samples was implemented in the Northwest Territories, Canada, between January 2016 and March 2018. This study reports the metal biomarker levels from nine Dene communities located in the Dehcho and Sahtú regions to identify contaminants of interest. Levels of metals in the urine (n = 198), blood (n = 276) and hair (n = 443) samples were generally similar to those seen in other biomonitoring studies in Canada, but lead levels in blood (GM = 16 μg/L; 95th percentile = 71 μg/L) and urine (GM = 0.59 μg/L, 0.69 μg/g of creatinine; 95th percentile = 4.2 μg/L, 4.0 μg/g of creatinine) were higher than those observed in the Canadian Health Measure Survey (CHMS, cycles 2 and 5). Hair mercury (but not blood mercury) appeared higher than observed in participants from the CHMS cycle 5. The vast majority of participants had biomarker levels below the biomonitoring guidance values established for mercury and lead. Based on a comparative analysis of biomarker statistics relative to a nationally-representative survey, metals and essential trace elements of particular interest for follow-up research include: lead, manganese, mercury, and selenium. This project provided baseline biomarker levels in participating regions, which is essential to track changes in the future, and identify the contaminants to prioritize for further investigation of exposure determinants. • A biomonitoring project was implemented in nine Dene communities in 2016–2018. • Urine, blood and hair samples were collected from the Dehcho and Sahtú regions. • Most metals were at similar levels to those in national studies. • Blood lead levels appeared particularly high compared to national levels. • This biomonitoring baseline data will inform environmental monitoring initiatives.
Food Frequency Questionnaires (FFQ) can be used to document food consumption and to estimate the intake of contaminants for Indigenous populations. The objective of this project was to refine and i...
Abstract Sustainable approaches capable of tracking status, trends and drivers of lake water balances in complex, remote landscapes are needed to inform ecosystem stewardship and water-security actions. At the Peace-Athabasca Delta (Alberta, Canada), a globally recognized freshwater floodplain landscape, concerns about water-level drawdown and multiple potential stressors have prompted need to improve knowledge of lake water balances and establish a lake monitoring program. Yet, the delta’s remoteness and dynamic nature present challenges to these goals. Here we use over 1000 measurements of water isotope composition at ∼60 lakes and 9 river sites during the spring, summer and fall of five consecutive years (2015–2019) to elucidate patterns in lake water balance over time and space, the influential roles of evaporation and river floodwaters, and relations with meteorological conditions and river water levels. Calculation of evaporation-to-inflow ratios using a coupled-isotope tracer approach, displayed via generalized additive models and geospatial ‘isoscapes’, reveal strongly varying lake water balances. Results identify distinct areas vulnerable to lake-level drawdown, given the likelihood of continued decline in ice-jam flood frequency, longer ice-free season duration and reduced snowmelt runoff. Results also demarcate areas of the delta where lakes are more resilient to factors that cause drawdown. The former defines the Peace sector, which is influenced by floodwaters from the Peace River during episodic ice-jam flood events, whereas the latter describes portions of the active floodplain environment of the Athabasca sector which receives more frequent contributions of Athabasca River floodwaters during both spring ice-jam and open-water seasons. Efficiency of water isotope tracers to capture the marked temporal and spatial heterogeneity in lake water balances during this 5 year time span, and their diagnostic responses to key hydrological processes, serves as a foundation for ongoing lake monitoring, an approach readily transferable to other remote and dynamic lake-rich landscapes.
Abstract Peatlands typically act as carbon sinks, however, increasing wildfire severity and annual area burned may challenge this carbon sink status. Whilst most peat resistance to wildfire and drought research is based on deep peatlands that rarely lose their water table below the peat profile, shallow peatlands and peat deposits may be most vulnerable to high peat burn severity and extensive carbon loss. To examine the role of pre-fire peat depth on peat burn severity, we measured the depth of burn (DOB) in peat of varying depths (0.1–1.6 m) within a rock barrens landscape. We found that DOB (0–0.4 m) decreased with increasing pre-fire peat depth, and that there was a strong correlation between the percent of the profile that burned and pre-fire peat depth. Breakpoint analysis indicates a threshold depth of 0.66 m where deeper peat deposits experienced little impact of wildfire, whereas shallower peat typically experienced high peat burn severity (median percent burned = 2.2 and 65.1, respectively). This threshold also corresponded to the loss of the water table in some nearby unburned peatlands, where water table drawdown rates were greater in shallower peat. We suggest that peat depth may control peat burn severity through feedbacks that regulate water table drawdown. As such, we argue that the identification of a critical peat depth threshold could have important implications for wildfire management and peatland restoration aiming to protect vulnerable carbon stores.

DOI bib
The biophysical climate mitigation potential of boreal peatlands during the growing season
Manuel Helbig, J. M. Waddington, Pavel Alekseychik, B.D. Amiro, Mika Aurela, Alan G. Barr, T. Andrew Black, Sean K. Carey, Jiquan Chen, Jinshu Chi, Ankur R. Desai, Allison L. Dunn, Eugénie Euskirchen, Lawrence B. Flanagan, Thomas Friborg, Michelle Garneau, Achim Grelle, Silvie Harder, Michal Heliasz, Elyn Humphreys, Hiroki Ikawa, Pierre‐Érik Isabelle, Hiroyasu Iwata, Rachhpal S. Jassal, Mika Korkiakoski, Juliya Kurbatova, Lars Kutzbach, Е. Д. Лапшина, Anders Lindroth, Mikaell Ottosson Löfvenius, Annalea Lohila, Ivan Mammarella, Philip Marsh, Paul A. Moore, Trofim C. Maximov, Daniel F. Nadeau, Erin M. Nicholls, Mats Nilsson, Takeshi Ohta, Matthias Peichl, Richard M. Petrone, Anatoly Prokushkin, William L. Quinton, Nigel T. Roulet, Benjamin R. K. Runkle, Oliver Sonnentag, I. B. Strachan, Pierre Taillardat, Eeva‐Stiina Tuittila, Juha‐Pekka Tuovinen, J. Turner, Masahito Ueyama, Andrej Varlagin, Timo Vesala, Martin Wilmking, Vyacheslav Zyrianov, Christopher Schulze
Environmental Research Letters, Volume 15, Issue 10

Peatlands and forests cover large areas of the boreal biome and are critical for global climate regulation. They also regulate regional climate through heat and water vapour exchange with the atmosphere. Understanding how land-atmosphere interactions in peatlands differ from forests may therefore be crucial for modelling boreal climate system dynamics and for assessing climate benefits of peatland conservation and restoration. To assess the biophysical impacts of peatlands and forests on peak growing season air temperature and humidity, we analysed surface energy fluxes and albedo from 35 peatlands and 37 evergreen needleleaf forests - the dominant boreal forest type - and simulated air temperature and vapour pressure deficit (VPD) over hypothetical homogeneous peatland and forest landscapes. We ran an evapotranspiration model using land surface parameters derived from energy flux observations and coupled an analytical solution for the surface energy balance to an atmospheric boundary layer (ABL) model. We found that peatlands, compared to forests, are characterized by higher growing season albedo, lower aerodynamic conductance, and higher surface conductance for an equivalent VPD. This combination of peatland surface properties results in a ∼20% decrease in afternoon ABL height, a cooling (from 1.7 to 2.5 °C) in afternoon air temperatures, and a decrease in afternoon VPD (from 0.4 to 0.7 kPa) for peatland landscapes compared to forest landscapes. These biophysical climate impacts of peatlands are most pronounced at lower latitudes (∼45°N) and decrease toward the northern limit of the boreal biome (∼70°N). Thus, boreal peatlands have the potential to mitigate the effect of regional climate warming during the growing season. The biophysical climate mitigation potential of peatlands needs to be accounted for when projecting the future climate of the boreal biome, when assessing the climate benefits of conserving pristine boreal peatlands, and when restoring peatlands that have experienced peatland drainage and mining. © 2020 The Author(s). Published by IOP Publishing Ltd. (Less)
In an effort to feed a growing world population, agriculture has rapidly intensified over the last six decades, relying heavily on agrochemicals (fertilizers, insecticides, fungicides, and herbicides) to increase and maintain desired crop yields. Despite environmental concerns in Canada’s agricultural regions, long-term patterns of crop change and the associated trends in the proportion of cropland treated with agrochemicals are poorly documented. Using the Canadian Census of Agriculture, we compiled historical data over 35 years (8 census periods: 1981-2016) on agrochemical applications measured as the proportion of cropland treated with pesticides and fertilizers and the associated crop classes to identify and interpret spatial and temporal trends in Canada’s agricultural practices across 260 census units. Due to differences in agricultural practices, soil and climatic conditions across the country, the Pacific (British Columbia), Prairie (Alberta, Saskatchewan, Manitoba), Central (Ontario, Quebec), and Atlantic (Nova Scotia, New Brunswick, Newfoundland/Labrador, Prince Edward Island) regions were analyzed separately. Most of the agrochemicals in Canada were applied in the Prairie and Central regions, which combined comprise 97% of the total cropland. Fertilizers were the dominant agrochemicals across Canada applied on 48% (Pacific) to 78% (Prairie) of the total cropland area, followed by herbicides, which were applied on 30% (Pacific) to 81% (Prairie) of the total cropland area in 2016. Notably, we observed significant changes between 1996 and 2016 in area treated with fungicides and insecticides, which increased by 412% and 50% in the Prairie region and by 291% and 149% in the Central region, respectively. The proportion and distribution of crops shifted in favour of more oilseeds and soybeans in the most intensive Prairie and Central regions, whereas cereals decreased over the same time period. Our analysis of past and current trends of agrochemicals and cropping patterns within Canada indicates a rapid and systemic increase in chemical use, and policies that promote a shift toward lower chemical reliance through sustainable agricultural practices are urgently needed.
Abstract Discrete global grid systems (DGGS) have been proposed as a data model for a digital earth framework. We introduce a new data model and analytics system called IDEAS – integrated discrete environmental analysis system to create an operational DGGS-based GIS which is suitable for large scale environmental modelling and analysis. Our analysis demonstrates that DGGS-based GIS is feasible within a relational database environment incorporating common data analytics tools. Common GIS operations implemented in our DGGS data model outperformed the same operations computed using traditional geospatial data types. A case study into wildfire modelling demonstrates the capability for data integration and supporting big data geospatial analytics. These results indicate that DGGS data models have significant capability to solve some of the key outstanding problems related to geospatial data analytics, providing a common representation upon which fast and scalable algorithms can be built.
Abstract High-latitude forests of North America are characterized by their natural dependence on large and severe wildfires. However, these wildfires also pose a range of social, economic, and environmental risks, with growing concern regarding persistent effects on stream flow volume, seasonal timing of flow, water quality, aquatic ecosystem health, and downstream community drinking water treatment. Here, we present the outcomes of a comprehensive scoping review of post-fire hydrologic studies in high-latitude forests of North America (Canada and Alaska). Our objectives were to (1) create an inventory of studies on post-fire hydrologic effects on surface water; (2) analyze those studies in terms of watershed characteristics and the type and duration of hydrologic effects; (3) identify and evaluate the link between upstream hydrologic effects with hydrologic ecosystem services; and (4) propose a research agenda addressing the link between wildfire science and hydrologic ecosystem services. We screened 2935 peer-reviewed articles and selected 82 studies to include based on their relevance according to a systematic, multi-step selection process. Next, we classified the papers into five themes: (a) runoff volume and flow regimes, (b) erosion and sediment transport, (c) water chemistry, (d) hydromorphology, and (e) aquatic food webs. For each study, we documented location, fire regime, watershed characteristics, and ecosystem services. The annual number of published studies on post-fire hydrology in high-latitude forests and, in particular, those addressing hydrologic ecosystem services, has increased steadily in recent years. Descriptions of wildfire characteristics, watershed characteristics, and effects on hydrologic ecosystem services were highly variable across studies, hindering cross-study comparisons. Moreover, there were limited efforts to extend study results to implications for forest or water management decisions regarding ecosystem services from source watersheds. Most studies focused on fire impacts on aquatic habitats and water chemistry while services of direct concern to communities, such as drinking water, were rarely addressed. We contend that study standardization, further use of geospatial technologies, and more studies directly addressing ecosystem services will help mitigate the increasing risks to water resources in northern forests.
Abstract Forecasting river ice breakup is critical for supporting emergency responses to river ice-related flooding along rivers in the northern hemisphere. However, due to complex river ice processes, forecasting river ice breakup is more challenging than predicting open-water flood conditions. Although considerable progress has been made in understanding the mechanisms and characteristics of breakup processes and in forecasting breakup timing using empirical methods at the local scale, fewer advances have been made in understanding and forecasting breakup using physically-based models, particularly at the catchment scale. In this study, we present a physically-based coupled hydrological and water temperature modelling framework for breakup prediction in cold region catchments in real time. The modelling framework was applied for operational forecasting of the 2019 breakup event along the Athabasca River at Fort McMurray in Alberta. Further model validation was performed by hindcasting the 2016, 2017 and 2018 breakup events. The model shows promising results for predicting the ice cover breakup with an average error of about 5 days, demonstrating its usefulness in real-time operational forecasting. Importantly, the model generates breakup progression at the catchment scale, providing an advantage over existing site specific breakup prediction methods.
Anthropogenic and climatic‐induced changes to flow regimes pose significant risks to river systems. Northern rivers and their deltas are particularly vulnerable due to the disproportionate warming of the Northern Hemisphere compared with the Southern Hemisphere. Of special interest is the Peace–Athabasca Delta (PAD) in western Canada, a productive deltaic lake and wetland ecosystem, which has been recognized as a Ramsar site. Both climate‐ and regulation‐induced changes to the hydrological regime of the Peace River have raised concerns over the delta's ecological health. With the damming of the headwaters, the role of downstream unregulated tributaries has become more important in maintaining, to a certain degree, a natural flow regime, particularly during open‐water conditions. However, their flow contributions to the mainstem river under future climatic conditions remain largely uncertain. In this study, we first evaluated the ability of a land‐surface hydrological model to simulate hydro‐ecological relevant indicators, highlighting the model's strengths and weaknesses. Then, we investigated the streamflow conditions in the Smoky River, the largest unregulated tributary of the Peace River, in the 2071–2100 versus the 1981–2010 periods. Our modelling results revealed significant changes in the hydrological regime of the Smoky River, such as increased discharge in winter (+190%) and spring (+130%) but reduced summer flows (−33%) in the 2071–2100 period compared with the baseline period, which will have implications for the sustainability of the downstream PAD. In particular, the projected reductions in 30‐day and 90‐day maximum flows in the Smoky River will affect open‐water flooding, which is important in maintaining lake levels and connectivity to perimeter delta wetlands in the Peace sector of the PAD. The evaluation of breakup and freeze‐up flows for the 2071–2100 period showed mixed implications for the ice‐jam flooding, which is essential for recharging high‐elevation deltaic basins. Thus, despite projected increase in annual and spring runoff in the 2071–2100 period from the Smoky sub‐basin, the sustainability of the PAD still remains uncertain.
Abstract The extent, timing and duration of seasonal freeze/thaw (FT) state exerts dominant control on boreal forest carbon, water and energy cycle processes. Recent and on-going L-Band (≈1.4 GHz) spaceborne missions have the potential to provide enhanced information on FT state over large geographic regions with rapid revisit time. However, the low spatial resolution of these spaceborne observations (≈45 km) makes it difficult to isolate the primary contributions (soil, vegetation, snow) to the FT signal in boreal forest. To better quantify these controls, two L-Band radiometers were deployed (September 2016 to July 2017) at a black spruce (Picea mariana) dominated boreal forest site; one unit above and one unit on the ground surface below the canopy to disentangle the microwave contributions of overstory canopy, and the ground surface on the FT brightness temperature (TB) signal. Bi-weekly multi-angular measurements from both units were combined in order to estimate effective scattering albedo (ω) and the microwave vegetative optical depth (τ), using the τ-ω microwave vegetation radiative transfer model. Soil moisture probes were inserted in the trunk of two black spruce and one larch (Larix laricina) trunks located in the footprint of the above-canopy radiometer to measure tree trunk relative dielectric constant (RDCtree). Results showed a strong relationship between RDCtree and tree skin temperature (Ttree) under freezing temperature conditions, which led to a gradual decrease of τ in winter. During the spring thawing period in April and May, τ remained relatively stable. In contrast, it increased substantially in June, most likely in relation to the growing season onset. Overall, τ was related to the seasonal RDCtree cycle (r = 0.76). Regarding ω, a value of 0.086 (±0.029) was obtained, but no dependency on Ttree or RDCtree was observed. Despite the observed impact of FT on vegetation L-Band signals, results from continuous TB observations spanning from 14 September 2016 to 25 May 2017, indicated that the main contribution to the observed L-Band TB freeze-up signal in the fall originated from the ground surface. The above-canopy unit showed some sensitivity to overstory canopy FT, yet the sensitivity was lower compared to the signal induced by the ground FT. In April and May, L-Band radiometer FT retrieval agreed closely to the melt onset detection using RDCtree but it was likely related to the coincident presence of liquid water in the snow. Our findings have important applications to L-Band spaceborne FT algorithm development and validation across the boreal forest. More specifically, our findings allow better quantification of the potential effect of frozen ground on various biogeophysical and biogeochemical processes in boreal forests.
Evapotranspiration (ET) from the land surface is an important component of the terrestrial hydrological cycle. Since the advent of Earth observation by satellites, various models have been developed to use thermal and shortwave remote sensing data for ET estimation. In this review, we provide a brief account of the key milestones in the history of remote sensing ET model development in two categories: temperature-based and conductance-based models. Temperature-based ET models utilize land surface temperature (LST) observed through thermal remote sensing to calculate the sensible heat flux from which ET is estimated as a residual of the surface energy balance or to estimate the evaporative fraction from which ET is derived from the available energy. Models of various complexities have been developed to estimate ET from surfaces of different vegetation fractions. One-source models combine soil and vegetation into a composite surface for ET estimation, while two-source models estimate ET of soil and vegetation components separately. Image contexture-based triangular and trapezoid models are simple and effective temperature-based ET models based on spatial and/or temporal variation patterns of LST. Several effective temporal scaling schemes are available for extending instantaneous temperature-based ET estimation to daily or longer time periods. Conductance-based ET models usually use the Penman-Monteith (P-M) equation to estimate ET with shortwave remote sensing data. A key put to these models is canopy conductance to water vapor, which depends on canopy structure and leaf stomatal conductance. Shortwave remote sensing data are used to determine canopy structural parameters, and stomatal conductance can be estimated in different ways. Based on the principle of the coupling between carbon and water cycles, stomatal conductance can be reliably derived from the plant photosynthesis rate. Three types of photosynthesis models are available for deriving stomatal or canopy conductance: (1) big-leaf models for the total canopy conductance, (2) two-big-leaf models for canopy conductances for sunlit and shaded leaf groups, and (3) two-leaf models for stomatal conductances for the average sunlit and shaded leaves separately. Correspondingly, there are also big-leaf, two-big-leaf and two-leaf ET models based on these conductances. The main difference among them is the level of aggregation of conductances before the P-M equation is used for ET estimation, with big-leaf models having the highest aggregation. Since the relationship between ET and conductance is nonlinear, this aggregation causes negative bias errors, with the big-leaf models having the largest bias. It is apparent from the existing literature that two-leaf conductance-based ET models have the least bias in comparison with flux measurements. Based on this review, we make the following recommendations for future work: (1) improving key remote sensing products needed for ET mapping purposes, including soil moisture, foliage clumping index, and leaf carboxylation rate, (2) combining temperature-based and conductance-based models for regional ET estimation, (3) refining methodologies for tight coupling between carbon and water cycles, (4) fully utilizing vegetation structural and biochemical parameters that can now be reliably retrieved from shortwave remote sensing, and (5) to improve regional and global ET monitoring capacity.
In the fish embryo toxicity (FET) test with zebrafish (Danio rerio) embryos, 3,4-dichloroaniline (3,4-DCA) is often employed as a positive control substance. Previous studies have characterized bioconcentration and transformation of 3,4-DCA in this test under flow-through conditions. However, the dynamic changes of chemical concentrations in exposure media and embryos were not studied systematically under the commonly used semi-static exposure conditions in multiwell plates. To overcome these limitations, we conducted semi-static exposures experiments where embryolarval zebrafish were exposed to 0.5, 2.0, and 4.0 mg L−1 of 3,4-DCA for up to 120 hpf, with 24-h renewal intervals. During each renewal interval, concentrations of 3,4-DCA were quantified in water samples at 0, 6, 18, and 24 h using high-performance liquid chromatography with diode array detection. Levels of 3,4-DCA in larvae were measured after 120 h exposure. Concentrations of 3,4-DCA in the test vessels decreased rapidly during exposure. Taking these dynamics into account, bioconcentration factors in the present study ranged from 12.9 to 29.8 L kg−1, depending on exposure concentration. In summary, this study contributed to our knowledge of chemical dynamics in the FET test with embryolarval zebrafish, which will aid in defining suitable exposure conditions for future studies.
High-resolution lake ice/water observations retrieved from satellite imagery through efficient, automated methods can provide critical information to lake ice forecasting systems. Synthetic aperture radar (SAR) data is well-suited to this purpose due to its high spatial resolution (approximately 50 m). With recent increases in the volume of SAR data available, the development of automated retrieval methods for these data is a priority for operational centres. However, automated retrieval of ice/water data from SAR imagery is difficult, due to ambiguity in ice and open water signatures, both in terms of image tone and in terms of parameterized texture features extracted from these images. Convolutional neural networks (CNNs) can learn features from imagery in an automated manner, and have been found effective in previous studies on sea ice concentration estimation from SAR. In this study the use of CNNs to retrieve ice/water observations from dual-polarized SAR imagery of two of the Laurentian Great Lakes, Lake Erie and Lake Ontario, is investigated. For data assimilation, it is crucial that the retrieved observations are of high quality. To this end, quality control measures based on the uncertainty of the CNN output to eliminate incorrect retrievals are discussed and demonstrated. The quality control measures are found to be effective in both dual-polarized and single-polarized retrievals. The ability of the CNN to downscale the coarse resolution training labels is demonstrated qualitatively.
Lying in the frontline of the prevailing midlatitude westerlies, British Columbia and southeastern Alaska (BCSAK) often receive copious amounts of precipitation through atmospheric rivers (ARs). This study quantifies the contribution of ARs to annual, seasonal, and extreme precipitation across BCSAK from 1979 to 2012 using a recently developed high‐resolution gridded precipitation data set, a regional AR catalog, and integrated vapor transport fields calculated from a reanalysis data set. On average, ARs contribute 13% of total annual precipitation with the higher contribution along the coastal regions (up to 33%), parts of which are one of the wettest locations on Earth, followed by the Columbia and Rocky Mountains (~9%–15%). The highest contributions occur during September (up to 57%) and October (up to 49%). The contribution of ARs to extreme precipitation attains >90% along the western arc of the Coast Mountains and the coastal regions of BCSAK. ARs act as the main synoptic‐scale mechanism that brings rainfall to the Rocky Mountains in winter. The probability of observing AR‐related precipitation increases over the study period; however, no change occurs in the average AR‐related precipitation amount for most of BCSAK during 1979–2012. This work provides insights on the critical role ARs play on the water resources of northwestern North America and has broader implications on community water supply and management, hydropower operations, and flood risk assessment and mitigation.
Abstract This study quantifies the contribution of atmospheric rivers (ARs) to annual and extreme river runoff and evaluates the relationships between watershed characteristics and AR-related maximum river runoff across British Columbia and southeastern Alaska (BCSAK). Datasets used include gauged runoff from 168 unregulated watersheds, topographic characteristics of those watersheds, a regional AR catalog, and integrated vapor transport fields for water years (WYs) 1979–2016. ARs contribute ~22% of annual river runoff along the Coast and Insular Mountains watersheds, which decreases inland to ~11% in the watersheds of the Interior Mountains and Plateau. Average association between ARs and annual maximum river runoff attains >80%, >50%, and <50% along the watersheds of the western flanks of the Coast Mountains, the Interior Mountains, and Interior Plateau, respectively. There is no significant change in AR-related extreme annual maximum runoff across BCSAK during 1979–2016. AR conditions occur during 25 out of 32 of the flood-related natural disasters in British Columbia during WYs 1979–2016. AR-related annual maximum runoff magnitude is significantly higher than non-AR-related annual maximum runoff for 30% of the watersheds studied. Smaller and steeper watersheds closer to the coast are more susceptible to AR-related annual maximum runoff than their inland counterparts. These results illustrate the importance of AR activity as a major control for the distribution of peak runoff in BCSAK. This work provides insights on the hydrological response of watersheds of northwestern North America to landfalling ARs that may improve flood risk assessment and disaster management in this region.
Abstract Gridded precipitation data are very important for hydrological and meteorological studies. However, gridded precipitation can exhibit significant statistical bias that needs to be corrected before application, especially in regions where high wind speeds, frequent snowfall, and sparse observation networks can induce significant uncertainties in the final gridded datasets. In this paper, we present a method for the production of gridded precipitation on the Tibetan Plateau (TP). This method reduces the statistical distribution error by correcting for wind-induced undercatch and optimizing the interpolation method. A gridded precipitation product constructed by this method was compared with previous products on the TP. The results show that undercatch correction is necessary for station data, which can reduce the distributional error by 30% at most. A thin-plate splines interpolation algorithm considering altitude as a covariate is helpful to reduce the statistical distributional error in general. Our method effectively inhibits the smoothing effect in gridded precipitation, and compared to previous products, results in a higher mean value, larger 98th percentile, and greater temporal variance. This study can help to improve the quality of gridded precipitation over the TP.
Most contaminants of emerging concern are polar and/or ionizable organic compounds, whose removal from engineered and environmental systems is difficult. Carbonaceous sorbents include activated carbon, biochar, fullerenes, and carbon nanotubes, with applications such as drinking water filtration, wastewater treatment, and contaminant remediation. Tools for predicting sorption of many emerging contaminants to these sorbents are lacking because existing models were developed for neutral compounds. A method to select the appropriate sorbent for a given contaminant based on the ability to predict sorption is required by researchers and practitioners alike. Here, we present a widely applicable deep learning neural network approach that excellently predicted the conventionally used Freundlich isotherm fitting parameters log KF and n (R2 > 0.98 for log KF, and R2 > 0.91 for n). The neural network models are based on parameters generally available for carbonaceous sorbents and/or parameters freely available from online databases. A freely accessible graphical user interface is provided.
H latitude regions around the world are experiencing particularly rapid climate change. These regions include the 625 million ha North American boreal region, which contains 16% of the world’s forests and plays a major role in the global carbon cycle (Brandt et al. 2013). Boreal ecosystems are particularly susceptible to rapid climatedriven vegetation change initiated by standreplacing natural disturbances (notably fires), which have increased in number, extent, and frequency (Kasischke and Turetsky 2006; Hanes et al. 2018) and are expected to continue under future climate change (Boulanger et al. 2014). Such disturbances will increasingly complicate species persistence, and it will therefore be critical to identify locations of possible climatechange refugia (areas “relatively buffered from contemporary climate change”) (Morelli et al. 2016). These “slow lanes” for biodiversity will be especially important for conservation and management of boreal species and ecosystems (Morelli et al. 2020). Practically speaking, the refugia concept can translate into specific sites or regions that are expected to be more resistant to the influence of climate change than other areas (“in situ refugia”; Ashcroft 2010). Refugia may also encompass sites or regions to which species may more readily retreat as climate conditions change (“ex situ refugia”; Ashcroft 2010; Keppel et al. 2012), as well as temporary “stepping stones” (Hannah et al. 2014) linking current and future habitats. In addition to areas that are climatically buffered, fire refugia – “places that are disturbed less frequently or less severely by wildfire” (Krawchuk et al. 2016) – may also play key roles in promoting ecosystem persistence under changing conditions (Meddens et al. 2018). Previous examinations of climatechange refugia have primarily emphasized external, terrainmediated mechanisms. Factors such as topographic shading and temperature inverClimatechange refugia in boreal North America: what, where, and for how long?
Abstract. Station-based serially complete datasets (SCDs) of precipitation and temperature observations are important for hydrometeorological studies. Motivated by the lack of serially complete station observations for North America, this study seeks to develop an SCD from 1979 to 2018 from station data. The new SCD for North America (SCDNA) includes daily precipitation, minimum temperature (Tmin⁡), and maximum temperature (Tmax⁡) data for 27 276 stations. Raw meteorological station data were obtained from the Global Historical Climate Network Daily (GHCN-D), the Global Surface Summary of the Day (GSOD), Environment and Climate Change Canada (ECCC), and a compiled station database in Mexico. Stations with at least 8-year-long records were selected, which underwent location correction and were subjected to strict quality control. Outputs from three reanalysis products (ERA5, JRA-55, and MERRA-2) provided auxiliary information to estimate station records. Infilling during the observation period and reconstruction beyond the observation period were accomplished by combining estimates from 16 strategies (variants of quantile mapping, spatial interpolation, and machine learning). A sensitivity experiment was conducted by assuming that 30 % of observations from stations were missing – this enabled independent validation and provided a reference for reconstruction. Quantile mapping and mean value corrections were applied to the final estimates. The median Kling–Gupta efficiency (KGE′) values of the final SCDNA for all stations are 0.90, 0.98, and 0.99 for precipitation, Tmin⁡, and Tmax⁡, respectively. The SCDNA is closer to station observations than the four benchmark gridded products and can be used in applications that require either quality-controlled meteorological station observations or reconstructed long-term estimates for analysis and modeling. The dataset is available at https://doi.org/10.5281/zenodo.3735533 (Tang et al., 2020).
Abstract The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) produces the latest generation of satellite precipitation estimates and has been widely used since its release in 2014. IMERG V06 provides global rainfall and snowfall data beginning from 2000. This study comprehensively analyzes the quality of the IMERG product at daily and hourly scales in China from 2000 to 2018 with special attention paid to snowfall estimates. The performance of IMERG is compared with nine satellite and reanalysis products (TRMM 3B42, CMORPH, PERSIANN-CDR, GSMaP, CHIRPS, SM2RAIN, ERA5, ERA-Interim, and MERRA2). Results show that the IMERG product outperforms other datasets, except the Global Satellite Mapping of Precipitation (GSMaP), which uses daily-scale station data to adjust satellite precipitation estimates. The monthly-scale station data adjustment used by IMERG naturally has a limited impact on estimates of precipitation occurrence and intensity at the daily and hourly time scales. The quality of IMERG has improved over time, attributed to the increasing number of passive microwave samples. SM2RAIN, ERA5, and MERRA2 also exhibit increasing accuracy with time that may cause variable performance in climatological studies. Even relying on monthly station data adjustments, IMERG shows good performance in both accuracy metrics at hourly time scales and the representation of diurnal cycles. In contrast, although ERA5 is acceptable at the daily scale, it degrades at the hourly scale due to the limitation in reproducing the peak time, magnitude and variation of diurnal cycles. IMERG underestimates snowfall compared with gauge and reanalysis data. The triple collocation analysis suggests that IMERG snowfall is worse than reanalysis and gauge data, which partly results in the degraded quality of IMERG in cold climates. This study demonstrates new findings on the uncertainties of various precipitation products and identifies potential directions for algorithm improvement. The results of this study will be useful for both developers and users of satellite rainfall products.
Abstract Context APIs play a central role in software development. The seminal research of Carroll et al. [15] on minimal manual and subsequent studies by Shull et al. [79] showed that developers prefer task-based API documentation instead of traditional hierarchical official documentation (e.g., Javadoc). The Q&A format in Stack Overflow offers developers an interface to ask and answer questions related to their development tasks. Objective With a view to produce API documentation, we study automated techniques to mine API usage scenarios from Stack Overflow. Method We propose a framework to mine API usage scenarios from Stack Overflow. Each task consists of a code example, the task description, and the reactions of developers towards the code example. First, we present an algorithm to automatically link a code example in a forum post to an API mentioned in the textual contents of the forum post. Second, we generate a natural language description of the task by summarizing the discussions around the code example. Third, we automatically associate developers reactions (i.e., positive and negative opinions) towards the code example to offer information about code quality. Results We evaluate the algorithms using three benchmarks. We compared the algorithms against seven baselines. Our algorithms outperformed each baseline. We developed an online tool by automatically mining API usage scenarios from Stack Overflow. A user study of 31 software developers shows that the participants preferred the mined usage scenarios in Opiner over API official documentation. The tool is available online at: http://opiner.polymtl.ca/ . Conclusion With a view to produce API documentation, we propose a framework to automatically mine API usage scenarios from Stack Overflow, supported by three novel algorithms. We evaluated the algorithms against a total of eight state of the art baselines. We implement and deploy the framework in our proof-of-concept online tool, Opiner.
Soils are sources of the potent greenhouse gas nitrous oxide (N2O) globally, but emissions from permafrost-affected soils have been considered negligible owing to nitrogen (N) limitation. Recent measurements of N2O emissions have challenged this view, showing that vegetated soils in permafrost regions are often small but evident sources of N2O during the growing season (~30 μg N2O–N m−2 day−1). Moreover, barren or sparsely vegetated soils, common in harsh climates, can serve as substantial sources of N2O (~455 μg N2O–N m−2 day−1), demonstrating the importance of permafrost-affected soils in Earth’s N2O budget. In this Review, we discuss N2O fluxes from subarctic, Arctic, Antarctic and alpine permafrost regions, including areas that likely serve as sources (such as peatlands) and as sinks (wetlands, dry upland soils), and estimate global permafrost-affected soil N2O emissions from previously published fluxes. We outline the below-ground N cycle in permafrost regions and examine the environmental conditions influencing N2O dynamics. Climate-change-related impacts on permafrost ecosystems and how these impacts could alter N2O fluxes are reviewed, and an outlook on the major questions and research needs to better constrain the global impact of permafrost N2O emissions is provided.
Successful management of natural and engineered channels with discontinuous alluvial cover requires knowledge of how the cover develops and evolves. We report on physical model experiments designed to compare alluvial cover dynamics in straight and sinuous fixed‐bed channels at a range of gravel‐bed material supply rates and constant discharge conditions. Experiments investigated the formation of alluvial cover from a bare bed, relationships between equilibrium cover characteristics and sediment supply rate, and the evolution of an initial uniform cover of varying thickness. A stable partially‐alluviated state is achieved in both the straight and sinuous channels for a range of sediment supply rates. The areal extent and stored mass of the cover increase progressively with supply rate, and the rate of increase is higher in the straight channel. While alluvial cover develops from isolated patches in the straight channel, cover in the sinuous channel develops as well‐defined bars, with deposition on the inside of bends and expanding outwards along the channel as cover area increases. Artificially emplaced cover quickly adjusts to a cover extent within 4–20% of that formed from a bare bed at the same feed rate, with initial cover thickness only influencing the final cover in the sinuous channel. Neither the sinuous nor the straight channel can sustain an alluvial cover in the absence of upstream sediment supply. This study can inform the management of semi‐alluvial channels because it highlights the primary roles of sediment supply and planform geometry in maintaining an alluvial cover in natural and engineered channels.
Abstract Changes in Canadian Prairie streamflow, particularly trends over time, have not been well studied but are particularly relevant for food and water security in this vast agricultural region. Streamflow records for this region are often unsuitable for conventional trend analysis; streams are often intermittent and have only a few days per year with flow, and stations operate only during the warm season, because of a lack of flow during the very cold Prairie winter. This study takes an alternative approach; streamflow data for the period from March to October for individual years between 1910 and 2015 from 169 hydrometric stations from the Prairie and adjacent areas in Canada were converted to annual cumulative runoff series. These 5895 individual station-years were then clustered based upon their shape, using dynamic time warping. Three clusters of cumulative annual runoff were found; the first and most common type has infrequent days with flow and low total annual runoff [0–50 mm], the second has more days with flow and slightly greater runoff [48–175 mm], and the least common third type has the fewest days without flow, includes perennial streams, and has much greater annual runoff [>173 mm]. For each hydrometric station a time series of annual cluster memberships was created. Trends in the fractions of cluster types were determined using logistic regression, with spatial groupings of these time series over five-year periods. Trends in the fractions of types within an ecoregion indicate spatially consistent and organized changes in the pattern of runoff over the region. In the western Canadian Prairies, particularly in the Mixed Grassland and Cypress Upland ecoregions, drying is occurring, as indicated by the increased frequency of the dry type. In the northern and eastern Canadian Prairies, conditions are shifting to greater runoffs, particularly in the Aspen Parkland, where the wet types are increasing in frequency.
Abstract Rainfall is often the largest component of the water budget and even a small uncertainty percentage may lead to challenges for accurately estimating groundwater recharge as a calculated residual within a water budget approach. Watersheds are a common scale for water budget assessment, and rainfall monitoring networks typically have widely spaced gauges that are frequently outside the watershed of interest. The effects of rainfall spatial variability and uncertainty on groundwater recharge estimates have received little attention and may influence water budget-derived recharge estimations. In the present study, the influence of spatial density in rainfall measurement on the numerical estimation of groundwater recharge was investigated through a series of modelling scenarios utilizing field data obtained from progressively denser rain gauge networks associated with a typical watershed in southern Ontario. The uncertainty of the recharge component of the water budget was used as a metric to aid interpretation of results. The scenarios employed networks composed of: 1) one nearby national weather station (within 3 km), 2) a regional network of six stations (within 30 km), and 3) a local network of six stations, five of which were within the selected watershed. A coupled and fully distributed hydrologic model (MIKE SHE) was used in the scenario analysis and applied to the Alder Creek watershed on the Waterloo Moraine near Kitchener-Waterloo, Ontario. Rainfall showed poor spatial correlation, even at the daily time scale. Average annual results over a three-year period showed that recharge rates varied up to 140 mm per year (~40% of previously estimated annual recharge) among scenarios, with differences between scenarios greater than the water budget uncertainty during one of the years. These findings suggest that the availability of local rainfall measurements has the potential to influence the calibration of transient watershed hydrogeological models.
Opposing interpretations of Lower Peace River ice-jam flood frequency data sets are at the centre of identifying causes of reduced freshwater availability in the Peace-Athabasca Delta (northern Alberta), a Ramsar Wetland of International Importance and a major contributor to Wood Buffalo National Park’s listing as a UNESCO World Heritage Site. Recently, conclusions drawn from statistical inference of traditional knowledge and historical observation sources suggested that flood frequency was accelerating during 1880–1967 and then declined coincident with hydroelectric regulation of Peace River flow since 1968 that altered the river’s hydrograph. In contrast, prior paleolimnological measurements of laminated sediments from oxbow lakes proximal to the Peace River have, along with alternate presentation of the traditional knowledge and historical observation sources, identified flood frequency was in decline for decades preceding river regulation due to climate change since the Little Ice Age. Here we revisit these data sets and, specifically, review their inherent uncertainties to assess their value and limitations. The notion of increasing versus decreasing flood frequency in the decades preceding river regulation (1880–1967) is tested using previously published paleohydrological records from perched lakes in the delta. Those records from lakes most proximal and sensitive to changes in the flow regime of the Peace River show increasing influence of lake evaporation during 1880–1967, consistent with long-term decline in flood frequency. Reconciling uncertainties of multiple lines-of-evidence and their findings should inform decisions by UNESCO on the World Heritage status of Wood Buffalo National Park and execution of the park’s federally funded Action Plan. New paleolimnological studies that have recently been launched will continue to probe the hydrological history of the Peace-Athabasca Delta to serve as a foundation for effective stewardship.
To detect large-variance code clones (i.e. clones with relatively more differences) in large-scale code repositories is difficult because most current tools can only detect almost identical or very similar clones. It will make promotion and changes to some software applications such as bug detection, code completion, software analysis, etc. Recently, CCAligner made an attempt to detect clones with relatively concentrated modifications called large-gap clones. Our contribution is to develop a novel and effective detection approach of large-variance clones to more general cases for not only the concentrated code modifications but also the scattered code modifications. A detector named LVMapper is proposed, borrowing and changing the approach of sequencing alignment in bioinformatics which can find two similar sequences with more differences. The ability of LVMapper was tested on both self-synthetic datasets and real cases, and the results show substantial improvement in detecting large-variance clones compared with other state-of-the-art tools including CCAligner. Furthermore, our new tool also presents good recall and precision for general Type-1, Type-2 and Type-3 clones on the widely used benchmarking dataset, BigCloneBench.
Abstract Groundwater provides critical freshwater supply, particularly in dry regions where surface water availability is limited. Climate change impacts on GWS (groundwater storage) could affect the sustainability of freshwater resources. Here, we used a fully-coupled climate model to investigate GWS changes over seven critical aquifers identified as significantly distressed by satellite observations. We assessed the potential climate-driven impacts on GWS changes throughout the 21 st century under the business-as-usual scenario (RCP8.5). Results show that the climate-driven impacts on GWS changes do not necessarily reflect the long-term trend in precipitation; instead, the trend may result from enhancement of evapotranspiration, and reduction in snowmelt, which collectively lead to divergent responses of GWS changes across different aquifers. Finally, we compare the climate-driven and anthropogenic pumping impacts. The reduction in GWS is mainly due to the combined impacts of over-pumping and climate effects; however, the contribution of pumping could easily far exceed the natural replenishment.
Boreal peatlands provide critical global and regional ecosystem functions including climate regulation and nutrient and water retention. Wildfire represents the largest disturbance to these ecosystems. Peatland resilience depends greatly on the extent of post-fire peat soil hydrophobicity. Climate change is altering wildfire intensity and severity and consequently impacting post-fire peat soil chemistry and structure. However, research on fire-impacted peatlands has rarely considered the influence of peat soil chemistry and structure on peatland resilience. Here we characterized the geochemical and physical properties of natural peat soils under laboratory heating conditions. The general trend observed is that hydrophilic peat soils become hydrophobic under moderate heating and then become hydrophilic again after heating for longer, or at higher, temperatures. The loss of peat soil hydrophilicity initially occurs due to evaporative water loss (250 °C and 300 °C for <5 min). Gently but thoroughly dried peat soils (105 °C for 24 h) also show mass losses after heating, indicating the loss of organic compounds through thermal degradation. Gas chromatography-mass spectrometry (GC-MS) and Fourier transform infrared (FTIR) spectroscopy were used to characterize the chemistry of unburned and 300 °C burned peat soils, and various fatty acids, polycyclic compounds, saccharides, aromatic acids, short-chain molecules, lignin and carbohydrates were identified. We determined that the heat-induced degradation of polycyclic compounds and aliphatic hydrocarbons, especially fatty acids, caused dried, hydrophobic peat soils to become hydrophilic after only 20 min of heating at 300 °C. Furthermore, peat soils became hydrophilic more quickly (20 min vs 6 h) with an increase in heat from 250 °C to 300 °C. Minimal structural changes occurred, as characterized by BET and SEM analyses, confirming that surface chemistry, in particular fatty acid content, rather than structure govern changes in peat soil hydrophobicity.
Microbial fuel cells (MFCs) based sensors had been studied in measuring biochemical oxygen demand (BOD) or the equivalent chemical oxygen demand (COD) recently. Limited attention has been paid to the effect of the microbial communities in wastewater on the responses of these sensors. This study systematically evaluated, for the first time, the effect of wastewater samples from a variety of sources on the electrical response of a micro-fabricated double-chamber MFC device. It was found that the response of the MFC is positively correlated with the bacterial composition, in particular electroactive bacteria. The presence of aerobic bacteria in the sample reduces the current generation. These findings indicated that the bacterial content of the water sample could be a significant interference source and must be considered in the use of µMFC-based sensors. Filtering samples may be effective in improving the reliability of these microsensors.
• Plasma increased hydrophilicity, encouraging bacterial growth and diversity. • CNT changed anode surface morphology, encouraging electroactive bacteria growth. • Both plasma and CNT treatment do not increase the sensitivity of the biosensor. • The conditions optimal for power generation may not be optimal for MFC sensors. The anode surface is known to play an important role in the microbial growth and in mediating electron transfer between electroactive bacteria and the electrodes in power generating microbial fuel cells (MFCs). However, the effect of the anode surface and its modification on MFC-based biosensor performance has not been studied previously. In this study, our results show that the surface modification influences certain aspect of the biosensor performance. Plasma treatment makes the carbon cloth electrode hydrophilic with contact angle of 82 ± 5° from that of 139 ± 3° without treatment which consequently increases the amount of biofilm and produces higher current generation. Carbon nanotube (CNT) treatment doesn’t increase the amount of biofilm but significantly changes its electroactive microorganism composition from 2.3% to 17.3% that improves current generation. Interestingly, the sensitivity of the MFC sensor was not improved by either of these treatments. These findings would be important for the optimized design and manufacturing of biosensing MFCs.
Abstract The rapid quantification of biological oxygen demand (BOD) plays an important role in environmental management, for instance, wastewater treatment. This study used xurographic fabrication technology to rapidly fabricate a low cost miniaturized microbial fuel cell (MFC) and demonstrated its suitability to measure BOD. The miniaturized sensor could be fabricated in 10 min with low cost of $0.5 U.S. per device. The reaction volume was designed to be 1.8 μL to obtain faster response time. The sensor was tested using sodium acetate (NaAc) as a model BOD analyte. It could response to a wide range of BOD concentration between 20 and 490 mg/L which would cover the majority range of wastewater BOD concentration in a wastewater treatment plant. The response time of this microsensor was 1.1 min which was significantly shorter than other conventional methods for BOD measurements (5 days). This study demonstrated that the use of xurographic methods to fabricate MFCs could enable rapid fabrication of microsensors to measure BOD in a rapid manner. This study also identified the potential of the sensor for application in wastewater treatment plants to monitor BOD and provide guidance for controlling treatment processes.
Abstract Detecting the onset of membrane fouling is critical for effectively removing membrane foulants during microfiltration (MF) separation. This work investigates the use of electrical impedance spectroscopy (EIS) on the surface of electrically conductive membranes (ECMs) to measure early development of membrane surface fouling. An electrochemical cell was developed in which an ECM acted as a working electrode and a graphite electrode acted as the counter electrode. Conductive membranes were fabricated by coating single-walled/double-walled carbon nanotubes (f-SW/DWCNT) on microfiltration polyethersulfone (PES) supporting membranes. Membrane fouling was simulated by pressure depositing different amounts of latex beads onto the surface of the membrane in a dead-end filtration cell. Changes in membrane water permeability were correlated to the degree of membrane fouling. Clean membranes had water permeability of 392 ± 28 LMH/bar. Reduction of membrane water permeability of 13.8 ± 3.3%, 15.8 ± 4.7%, 17.8 ± 0.5% and 27.1 ± 4.6% were observed for membranes covered with 0.028 mg/m2, 0.28 mg/m2, 1.40 mg/m2 and 2.80 mg/m2 on the membranes, respectively. These small differences in fouling degree were statistically resolvable in measured Nyquist plots. It was observed that the diameter of the higher frequency charge transfer region (104–106 Hz) of the Nyquist plot semicircles increased with greater fouling. These observations were hypothesized to correspond to decreasing surface conductivities of the membranes by the incorporation of insulating materials (latex beads) within the porous conductive coating. This proposed hypothesis was supported by measured EIS results modeled with a theoretical equivalent circuit. Fouled membrane surface conductivity, surface hydrophilicity, and pore size were measured by SEM, four-point probe conductivity, contact angle, and MWCO experiments, respectively, to compare conventional characterization techniques with non-destructive EIS measurements.
Abstract. Shallow groundwater in the Prairie Pothole Region (PPR) is predominantly recharged by snowmelt in the spring and supplies water for evapotranspiration through the summer and fall. This two-way exchange is underrepresented in current land surface models. Furthermore, the impacts of climate change on the groundwater recharge rates are uncertain. In this paper, we use a coupled land–groundwater model to investigate the hydrological cycle of shallow groundwater in the PPR and study its response to climate change at the end of the 21st century. The results show that the model does a reasonably good job of simulating the timing of recharge. The mean water table depth (WTD) is well simulated, except for the fact that the model predicts a deep WTD in northwestern Alberta. The most significant change under future climate conditions occurs in the winter, when warmer temperatures change the rain/snow partitioning, delaying the time for snow accumulation/soil freezing while advancing early melting/thawing. Such changes lead to an earlier start to a longer recharge season but with lower recharge rates. Different signals are shown in the eastern and western PPR in the future summer, with reduced precipitation and drier soils in the east but little change in the west. The annual recharge increased by 25 % and 50 % in the eastern and western PPR, respectively. Additionally, we found that the mean and seasonal variation of the simulated WTD are sensitive to soil properties; thus, fine-scale soil information is needed to improve groundwater simulation on the regional scale.
This paper investigates different methods for quantifying thaw subsidence using terrestrial laser scanning (TLS) point clouds. Thaw subsidence is a slow (millimetre to centimetre per year) vertical displacement of the ground surface common in ice‐rich permafrost‐underlain landscapes. It is difficult to quantify thaw subsidence in tundra areas as they often lack stable reference frames. Also, there is no solid ground surface to serve as a basis for elevation measurements, due to a continuous moss–lichen cover. We investigate how an expert‐driven method improves the accuracy of benchmark measurements at discrete locations within two sites using multitemporal TLS data of a 1‐year period. Our method aggregates multiple experts’ determination of the ground surface in 3D point clouds, collected in a web‐based tool. We then compare this to the performance of a fully automated ground surface determination method. Lastly, we quantify ground surface displacement by directly computing multitemporal point cloud distances, thereby extending thaw subsidence observation to an area‐based assessment. Using the expert‐driven quantification as reference, we validate the other methods, including in‐situ benchmark measurements from a conventional field survey. This study demonstrates that quantifying the ground surface using 3D point clouds is more accurate than the field survey method. The expert‐driven method achieves an accuracy of 0.1 ± 0.1 cm. Compared to this, in‐situ benchmark measurements by single surveyors yield an accuracy of 0.4 ± 1.5 cm. This difference between the two methods is important, considering an observed displacement of 1.4 cm at the sites. Thaw subsidence quantification with the fully automatic benchmark‐based method achieves an accuracy of 0.2 ± 0.5 cm and direct point cloud distance computation an accuracy of 0.2 ± 0.9 cm. The range in accuracy is largely influenced by properties of vegetation structure at locations within the sites. The developed methods enable a link of automated quantification and expert judgement for transparent long‐term monitoring of permafrost subsidence.
Thaw slumps in ice‐rich permafrost can retreat tens of metres per summer, driven by the melt of subaerially exposed ground ice. However, some slumps retain an ice‐veneering debris cover as they retreat. A quantitative understanding of the thermal regime and geomorphic evolution of debris‐covered slumps in a warming climate is largely lacking. To characterize the thermal regime, we instrumented four debris‐covered slumps in the Canadian Low Arctic and developed a numerical conduction‐based model. The observed surface temperatures 20°C and steep thermal gradients indicate that debris insulates the ice by shifting the energy balance towards radiative and turbulent losses. After the model was calibrated and validated with field observations, it predicted sub‐debris ice melt to decrease four‐fold from 1.9 to 0.5 m as the thickness of the fine‐grained debris quadruples from 0.1 to 0.4 m. With warming temperatures, melt is predicted to increase most rapidly, in relative terms, for thick (~0.5‐1.0 m) debris covers. The morphology and evolution of the debris‐covered slumps were characterized using field and remote sensing observations, which revealed differences in association with morphology and debris composition. Two low‐angle slumps retreated continually despite their persistent fine‐grained debris covers. The observed elevation losses decreased from ~1.0 m/yr where debris thickness ~.2 m to 0.1 m/yr where thickness ~1.0 m. Conversely, a steep slump with a coarse‐grained debris veneer underwent short‐lived bursts of retreat, hinting at a complex interplay of positive and negative feedback processes. The insulative protection and behaviour of debris vary significantly with factors such as thickness, grain size and climate: debris thus exerts a fundamental, spatially variable influence on slump trajectories in a warming climate.
There is growing concern about possible effects of exploitation of the Alberta Oil Sands on the ambient environment, including possible effects on populations of fishes in the Athabasca River and farther downstream in Lake Athabasca and the Slave River. In the present study, concentrations of metals in dorsal muscle tissue of 5 fish species-goldeye, northern pike, walleye, whitefish, and burbot-from the Slave, Peace, and Athabasca Rivers were quantified. A suite of 25 metals including As, Hg, Se, Tl, and V was analyzed. Most metals exhibited no significant variations in concentration among locations. Concentrations of 5 metals, As, Hg, Se, Tl, and V, revealed significant variations among locations and were of sufficient magnitude to be of interest. Concentrations of Hg did not vary significantly among locations; however, because it was detected at concentrations of concern and the use of the selected fishes was a local source of food for humans and pets, it was of interest. Concentrations of As, Se, Tl, and V in dorsal muscle of certain fishes in the farthest downstream sites on the Slave River were greater than those in the same tissues and species in the farther upstream sites on the Peace and Athabasca Rivers. This phenomenon was most prevalent with Tl and to a lesser extent with As and Se. Nevertheless, concentrations were not of concern for the health of human consumers. Although metals did not appear to be increased in fish in the Alberta Oil Sands region in the present study, further research is needed to understand the potential impacts. Environ Toxicol Chem 2020;39:2180-2195. © 2020 SETAC.
Understanding the role of forest fires on water budgets of subarctic Precambrian Shield catchments is important because of growing evidence that fire activity is increasing. Most research has focused on assessing impacts on individual landscape units, so it is unclear how changes manifest at the catchment scale enough to alter water budgets. The objective of this study was to determine the water budget impact of a forest fire that partially burned a ~450 km2 subarctic Precambrian Shield basin. Water budget components were measured in a pair of catchments: one burnt and another unburnt. Burnt and unburnt areas had comparable net radiation, but thaw was deeper in burned areas. There were deeper snow packs in burns. Differences in streamflow between the catchments were within measurement uncertainty. Enhanced winter streamflow from the burned watershed was evident by icing growth at the streamflow gauge location, which was not observed in the unburned catchment. Wintertime water chemistry was also clearly elevated in dissolved organics, and organic‐associated nutrients. Application of a framework to assess hydrological resilience of watersheds to wildfire reveal that watersheds with both high bedrock and open water fractions are more resilient to hydrological change after fire in the subarctic shield, and resilience decreases with increasingly climatically wet conditions. This suggests significant changes in runoff magnitude, timing and water chemistry of many Shield catchments following wildfire depend on pre‐fire land cover distribution, the extent of the wildfire and climatic conditions that follow the fire.
The dynamics and processes of nutrient cycling and release were examined for a lowland wetland-pond system, draining woodland in southern England. Hydrochemical and meteorological data were analyzed from 1997 to 2017, along with high-resolution in situ sensor measurements from 2016 to 2017. The results showed that even a relatively pristine wetland can become a source of highly bioavailable phosphorus (P), nitrogen (N), and silicon (Si) during low-flow periods of high ecological sensitivity. The drivers of nutrient release were primary production and accumulation of biomass, which provided a carbon (C) source for microbial respiration and, via mineralization, a source of bioavailable nutrients for P and N co-limited microorganisms. During high-intensity nutrient release events, the dominant N-cycling process switched from denitrification to nitrate ammonification, and a positive feedback cycle of P and N release was sustained over several months during summer and fall. Temperature controls on microbial activity were the primary drivers of short-term (day-to-day) variability in P release, with subdaily (diurnal) fluctuations in P concentrations driven by water body metabolism. Interannual relationships between nutrient release and climate variables indicated “memory” effects of antecedent climate drivers through accumulated legacy organic matter from the previous year's biomass production. Natural flood management initiatives promote the use of wetlands as “nature-based solutions” in climate change adaptation, flood management, and soil and water conservation. This study highlights potential water quality trade-offs and shows how the convergence of climate and biogeochemical drivers of wetland nutrient release can amplify background nutrient signals by mobilizing legacy nutrients, causing water quality impairment and accelerating eutrophication risk.
This study investigated the temporal variability and changes in snow cover duration and the average snow depth from December to April in the Pyrenees at 1,500 and 2,100 m a.s.l. for the period 1958–2017. This is the first such analysis for the entire mountain range using SAFRAN‐Crocus simulations run for this specific purpose. The SAFRAN‐Crocus simulations were evaluated for the period 1980–2016 using 28 in situ snow depth data time series, and for the period 2000–2017 using MODIS observations of the snow cover duration. Following confirmation that the simulated snow series satisfactorily reproduced the observed evolution of the snowpack, the Mann–Kendall test showed that snow cover duration and average depth decreased during the full study period, but this was only statistically significant at 2,100 m a.s.l. The temporal evolution in the snow series indicated marked differences among massifs, elevations, and snow variables. In general, the most western massifs of the French Pyrenees underwent a greater decrease in the snowpack, while in some eastern massifs the snowpack did not decrease, and in some cases increased at 1,500 m a.s.l. The results suggest that the trends were consistent over time, as they were little affected by the start and end year of the study period, except if trends are computed only starting after 1980, when no significant trends were apparent. Most of the observed negative trends were not correlated with changes in the atmospheric circulation patterns during the snow season. This suggests that the continuous warming in the Pyrenees since the beginning of the industrial period, and particularly the sharp increase since 1955, is a major driver explaining the snow cover decline in the Pyrenees.
The growing concerns over urbanization and climate change have resulted in an exponential growth in publications on urban climatology in recent decades. However, an advanced synthesis that characterizes the existing studies is lacking. In this review, we used citation network analysis and a text mining approach to identify research trends and extract common research topics and the emerging domains in urban climatology. Based on the clustered networks, we found that aerosols and ozone, and urban heat island are the most popular topics. Together with other clusters, four emerging topical fields were identified: secondary organic aerosols, urban precipitation, flood risk and adaptation, and greenhouse gas emissions. The city case studies' geographical information was analyzed to explore the spatial–temporal patterns, especially in the emerging topical fields. Interdisciplinary research grew in recent years as the field of urban climatology expanded to interact with urban hydrology, health, energy issues, and social sciences. A few knowledge gaps were proposed: the lack of long‐term high‐temporal‐resolution observational data of organic aerosols for model validation and improvements, the need for predictions of urban effects on precipitation and extreme flooding events under climate change, and the lack of a framework for cooperation between physical sciences and social sciences under urban settings. To fill these gaps, we call for more observational data with high spatial and temporal resolution, using high‐resolution models that adequately represent urban processes to conduct scenario analyses for urban planning, and the development of intellectual frameworks for better integration of urban climatology and social‐economical systems in cities. This article is categorized under: Climate, History, Society, Culture > Disciplinary Perspectives
Délįnę, a community of approximately 600 people, is located in Canada’s Northwest Territories. This small Indigenous community is the only settlement on Great Bear Lake, one of the largest and most pristine freshwater lakes in the world. The lake and the surrounding landscape play an important role in the lives of the Sahtúot’ine Dene, or Bear Lake People. It is a source of spiritual well-being and the basis of the community’s food system and livelihood. Délįnę̨ depends on traditional food from activities like hunting, fishing, trapping and gathering from the surrounding boreal forest ecosystem. The alternative is expensive food from the local stores which is often unhealthy, over-packaged and not fresh. Now the impacts of climate change are also having negative impacts on the food system creating yet another barrier to accessing the land for food. The community is actively implementing self-government, which took effect in 2016, has been implementing numerous programs aimed at benefiting the community, creating economic opportunities and employment, and adapting to the impacts of climate change. Délįnę̨ is not immune to the impacts of COVID-19, with the territorial government closing borders to all travelers and implementing social distancing measures including working from home. The Canadian Federal government announced support for Indigenous communities being out on the land during this time. So those who have the means to, meaning access to tools and transportation, left the community and are social distancing at cabins or camps around the lake. For many, it is an ideal way to pass the time, being on the land and reconnecting to cultural and traditional practices. But for others, who don’t have access to transportation (a snowmobile, or boat if the weather was warmer) or a cabin or camp on the lake, find themselves stranded in town. With jobs and activities closed or winding down, many community members are finding it difficult to cope with the new realities that this pandemic has brought. While COVID-19 has put a pause on activity in the community, it offers time to reflect on community projects and priorities. For many, those with jobs, the pressures of employment and participating in the formal economy have been decreased and they are now able to spend more time immersed in traditional activities and cultural practices. But for others, it shows the barriers to accessing traditional food sources. These barriers are the result of decades of colonialism and trauma resulting from forced assimilation and policies that have eroded and interrupted ancient old intergenerational lines of knowledge transfer and flow. This loss of connection to rich histories, knowledge and, most importantly, language and spirituality, has been detrimental to the community’s overall health and makes many people in Délı̨nę vulnerable to COVID-19. This pandemic has created a state of nostalgia and deja-vu in the community. As all the false senses of security in the settlement begin to fall away, many in the community are realizing what is truly important. It is not a coincidence that the first response to the declaration of a global state of emergency was to go back to the Land. It was instinct. The traditional way of life provides the ability to both survive and thrive on the land. The complex and rich knowledge system holds all the information and skills needed to take care of the community including the physical, mental, emotional, and spiritual aspects. However, resources and programming are needed to support the most vulnerable in the community, and particularly the youth who are the future leaders of Délįnę. More opportunities to learn traditional skills and cultural practices, better access to training and tools, and mentorship and support to learn the language and spiritual connections to the land are key pillars of the community working together to address these challenges. In other words, the way forward is to strengthen and preserve the way of being as Dene and remember again how to walk in the footsteps of grandfathers and grandmothers. This is the message from the community’s Prophets, Elders, and Ancestors and the very message that has echoed through eternity since the beginning of time: Hold on to your way of life and hold on to the Land and you will have a good life in the future for yourselves and for your children.
Object-based algorithm provides additional spatiotemporal information of precipitation, besides traditional aspects such as amount and intensity. Using the Method for Object-based Diagnostic Evaluation with Time Dimension (MODE-TD, or MTD), precipitation features in western Canada have been analyzed comprehensively based on the Canadian Precipitation Analysis, North American Regional Reanalysis, Multi-Source Weighted-Ensemble Precipitation, and a convection-permitting climate model. We found light precipitation occurs frequently in the interior valleys of western Canada while moderate to heavy precipitation is rare there. The size of maritime precipitation system near the coast is similar to the continental precipitation system on the Prairies for moderate to heavy precipitation while light precipitation on the Prairies is larger in size than that occurs near the coast. For temporal features, moderate to heavy precipitation lasts longer than light precipitation over the Pacific coast, and precipitation systems on the Prairies generally move faster than the coastal precipitation. For annual cycle, the west coast has more precipitation events in cold seasons while more precipitation events are identified in warm seasons on the Prairies due to vigorous convection activities. Using two control experiments, the way how the spatiotemporal resolution of source data influences the MTD results has been examined. Overall, the spatial resolution of source data has little influence on MTD results. However, MTD driven by dataset with coarse temporal resolution tend to identify precipitation systems with relatively large size and slow propagation speed. This kind of precipitation systems normally have short track length and relatively long lifetime. For a typical precipitation system (0.7 ∼ 2 × 104 km2 in size) in western Canada, the maximum propagation speed that can be identified by 6-h data is approximately 25 km h−1, 33 km h−1 for 3-h, and 100 km h−1 for hourly dataset. Since the propagation speed of precipitation systems in North America is basically between 0 and 80 km h−1, we argue that precipitation features can be identified properly by MTD only when dataset with hourly or higher temporal resolution is used.
Abstract Indicators are widely used in climate variability and climate change assessments to simplify the tracking of complex processes and phenomena in the state of the environment. Apart from the climatic criteria, the snow indicators in ski tourism have been increasingly extended with elements that relate to the technical, operational, and commercial aspects of ski tourism. These non-natural influencing factors have gained in importance in comparison with the natural environmental conditions but are more difficult to comprehend in time and space, resulting in limited explanatory power of the related indicators when applied for larger/longer scale assessments. We review the existing indicator approaches to derive quantitative measures for the snow conditions in ski areas, to formulate the criteria that the indicators should fulfill, and to provide a list of indicators with their technical specifications which can be used in snow condition assessments for ski tourism. For the use of these indicators, a three-step procedure consisting of definition, application, and interpretation is suggested. We also provide recommendations for the design of indicator-based assessments of climate change effects on ski tourism. Thereby, we highlight the importance of extensive stakeholder involvement to allow for real-world relevance of the achieved results.
This review identifies strengths and weaknesses of water monitoring programs selected by Canadian water managers. We used 22 criteria, guided by outcomes of an exploratory study and supported by 21 semi-structured key informant interviews. The highest-scoring programs include the Slave Watershed Environmental Effects Program (Canada), the Government of Canada’s Environmental Effects Monitoring Program, and Healthy Land and Water (Australia). We describe five recommendations for improving future freshwater monitoring frameworks: (1) recognize different knowledge approaches (especially Indigenous), (2) use multiple reporting formats, (3) clarify monitoring and management roles, (4) apply a whole-watershed approach, and (5) link monitoring to management and decision-making.
Developers often search for relevant code examples on the web for their programming tasks. Unfortunately, they face three major problems. First, they frequently need to read and analyse multiple results from the search engines to obtain a satisfactory solution. Second, the search is impaired due to a lexical gap between the query (task description) and the information associated with the solution (e.g., code example). Third, the retrieved solution may not be comprehensible, i.e., the code segment might miss a succinct explanation. To address these three problems, we propose CROKAGE (CrowdKnowledge Answer Generator), a tool that takes the description of a programming task (the query) as input and delivers a comprehensible solution for the task. Our solutions contain not only relevant code examples but also their succinct explanations written by human developers. The search for code examples is modeled as an Information Retrieval (IR) problem. We first leverage the crowd knowledge stored in Stack Overflow to retrieve the candidate answers against a programming task. For this, we use a fine-tuned IR technique, chosen after comparing 11 IR techniques in terms of performance. Then we use a multi-factor relevance mechanism to mitigate the lexical gap problem, and select the top quality answers related to the task. Finally, we perform natural language processing on the top quality answers and deliver the comprehensible solutions containing both code examples and code explanations unlike earlier studies. We evaluate and compare our approach against ten baselines, including the state-of-art. We show that CROKAGE outperforms the ten baselines in suggesting relevant solutions for 902 programming tasks (i.e., queries) of three popular programming languages: Java, Python and PHP. Furthermore, we use 24 programming tasks (queries) to evaluate our solutions with 29 developers and confirm that CROKAGE outperforms the state-of-art tool in terms of relevance of the suggested code examples, benefit of the code explanations and the overall solution quality (code + explanation).
The late John Glew contributed valuable equipment to the paleolimnology community for successful collection and processing of cores of sediment from aquatic ecosystems. Unfortunately, tubes that fit his hammer-gravity corer design are no longer conveniently available for purchase and, with his sudden passing, Glew gravity and coring equipment is difficult or impossible to access. In some field-sampling situations, other commercially available equipment present limitations. Here, we provide an updated design of the Glew gravity corer which accommodates a hammer-percussion instrument and overcomes limitations we have encountered when coring lakes in remote locations from floats of a helicopter or small, inflatable watercraft. Our approach integrates the ‘best of both worlds’ provided by the Glew and commercially available Uwitec designs, using readily available components. We updated the Glew corer tube collar to be compatible with standard, commercially available 90-mm external diameter (86-mm internal diameter) PVC tubing that fits Uwitec components (e.g., Uwitec rubber ‘piston’ and ‘stoppers’; using terminology of the Uwitec catalogue), and designed a spring-loaded gasket-style plunger that achieves greater suction than the standard Glew designs. We also updated the Glew vertical sectioner to be compatible with 90-mm-diameter core tubes typically ranging from 60–120 cm long. An outcome is consolidation of the Uwitec and Glew gravity coring systems, which has allowed for interchangeability and choice among use of original and hammer-driven Glew, Uwitec, and the new hybrid ‘Uwi-Glew-ee’ gravity corer and sectioner configurations, depending on logistical constraints of fieldwork and anticipated lake sediment composition. The parts and systems are available from University of Waterloo’s Science Technical Services (https://uwaterloo.ca/science-technical-services/).
Flooding is one of the most frequent and most costly natural disasters that occur throughout Canada, and although there is ongoing work to update and improve flood hazard assessments and mapping of high flood risk rivers throughout the country, most studies only delve into open water flooding. However, many rivers in Canada experience higher peak water levels due to ice jamming, resulting in severe flooding of surrounding areas. Hence, there is an urgency to expand current flood hazard assessments to include ice jam flooding for better flood management practices. One area that is often plagued with ice jam flooding is the lowest reach of Manitoba’s Red River. The Lower Red River is a low-lying river with a terminus inland delta where water levels are governed by Lake Winnipeg. Ice jam floods often divert water into the lower Red River’s floodplain that is continually being encroached by development. RIVICE, Environment Canada’s one-dimensional ice hydraulic model, was set up within a Monte Carlo framework to simulate an envelope of backwater level profiles that result from ice jams within the study site. Non-exceedance probability profiles were created from the envelope of backwater level profiles to assess ice jam flood hazard.
Describe the state of knowledge on how the retail food environment contributes to diet-related health and obesity among Indigenous populations, and assess how the literature incorporates Indigenous perspectives, methodologies and engagement throughout the research process. Outcomes included dietary behaviour (purchasing, intakes and diet quality) and diet-related health outcomes (weight-related outcomes, non-communicable diseases and holistic health or definitions of health as defined by Indigenous populations involved in the study).
Glaciers and ice sheets are masses of ice and snow that persist over many years formed by the accumulation and compaction of snow. They cover a significant amount of the Earth’s land surface and store most of the world’s fresh water. Glaciers flow under their own weight, carving out landscapes and transporting sediment and rocks as they move, and they advance and retreat in response to changes in the mass balance, or difference between annual accumulation and ablation. Glaciers and glacierized river basins have unique hydrological characteristics. They serve as an important store of freshwater and influence the characteristics of annual and seasonal runoff downstream. Glaciers and ice sheets also represent an important biome with a rich diversity of life, from microbial communities to microscopic organisms and macroinvertebrates, and they influence ecosystem functioning well beyond their margins and termini. In recent decades, most glaciers worldwide have been losing mass and retreating in response to climatic variations, now primarily driven by human activity. The Greenland and Antarctic Ice Sheets have also begun to lose significant amount of mass and have exhibited an accelerating pattern of loss. This is expected to continue for many decades or more under current and expected future climate conditions, with the loss of much of the world’s mountain glaciers, and significant changes in polar ice caps and ice sheets. The loss of glaciers and ice sheets poses many problems and challenges, including sea level rise implications, regional changes in water availability, impacts on glacial and downstream ecosystems, release of legacy contaminants stored on and within glaciers, glacier-related hazards, feedback effects on regional and global climate, and many others that affect the wellbeing of people and communities. There is a need for more observations, better understanding and prediction of glacier dynamics, coordinated adaptation and mitigation strategies across multiple levels from local to international, and a coupled systems approach that integrates physical dimensions of changing ice environments with the human systems that engage with or depend upon them.
Abstract The seasonal dynamic of gross primary productivity (GPP) has influences on the annual GPP (AGPP) of the terrestrial ecosystem. However, the spatiotemporal variation of the seasonal dynamic of GPP and its effects on spatial and temporal variations of AGPP are still poorly addressed. In this study, we developed a parameter, αGPP, defined as the ratio of mean daily GPP (GPPmean) to the maximum daily GPP (GPPmax) during the growing season, to analyze the seasonal dynamic of GPP based on Weibull function. The αGPP was a comprehensive parameter characterizing the shape, scale, and location of the seasonal dynamic curve of GPP. We calculated αGPP based on the data of GPP for 942 site-years from 115 flux sites in the Northern Hemisphere, and analyzed the spatiotemporal variation and influencing factors of the αGPP. We found that the αGPP of terrestrial ecosystems in the Northern Hemisphere ranged from 0.47 to 0.85, with an average of 0.62 ± 0.06. The αGPP varied significantly both among different climatic zones and different ecosystem types. The αGPP was stable on the interannual scale, while decreased as latitude increased, which was consistent across different ecosystem types. The spatial pattern of the seasonal dynamic of astronomical radiation was the dominating factor of the spatial pattern of αGPP, that was, the spatial pattern of the seasonal dynamic of astronomical radiation determined that of the seasonal dynamic of GPP by controlling that of seasonal dynamics of total radiation and temperature. In addition, we assessed the spatial variation of AGPP preliminarily based on αGPP and other seasonal dynamic parameters of GPP, indicating that the understanding of the spatiotemporal variation of αGPP could provide a new approach for studying the spatial and temporal variations of AGPP and estimating AGPP based on the seasonal dynamic of GPP.
• PhenoCam data at 13 sites were used to analyze its potential of phenology modeling. • GCC and RCC performed well in capturing GPP-based SOS and EOS at DBF sites. • RCC showed unrecognized importance than GCC for phenology modeling at ENF sites. Vegetation phenology has received increasing attention in climate change research. Near-surface sensing using digital repeat photography has proven to be useful for ecosystem-scale monitoring of vegetation phenology. However, our understanding of the link between phenological metrics derived from digital repeat photography and the phenology of forest canopy photosynthesis is still incomplete, especially for evergreen plant species. Using 49 site-years of digital images from the PhenoCam Network from eight evergreen needleleaf forest (ENF) and six deciduous broadleaf forest (DBF) sites in North America, we explored the potential of the green chromatic (GCC) and red chromatic coordinates (RCC) in tracking forest canopy photosynthesis by comparing camera-derived start- and end-of-growing season (SOS and EOS, respectively) with corresponding estimates derived from eddy covariance-derived daily gross primary productivity (GPP). We found that for DBF sites, both GCC and RCC performed comparable in capturing SOS and EOS. However, similar to earlier studies, GCC had limited potential in capturing GPP-based SOS or EOS for ENF sites. In contrast, we found RCC was a better predictor of both GPP-based SOS and EOS for ENF sites. Environmental and ecological explanations were both provided that phenological transitions derived from RCC were highly correlated with spring and autumn meteorological conditions, as well as having overall higher correlations with phenology based on LAI, a critical variable for describing canopy development. Our results demonstrate that RCC is an underappreciated metric for tracking vegetation phenology, especially for ENF sites where GCC failed to provide reliable estimates for GPP-based SOS or EOS. Our results improve confidence in using digital repeat photography to characterize the phenology of canopy photosynthesis across forest types.
• A concentration dependent increase of B[ a ]P metabolites was observed • No induction of phase I or II activity was observed with increasing B[ a ]P exposure • Biotransformation of B[ a ]P was successfully implemented into in silico models • The models accurately predicted life stage-specific abundances of B[ a ]P metabolites Understanding internal dose metrics is integral to adequately assess effects environmental contaminants might have on aquatic wildlife, including fish. In silico toxicokinetic (TK) models are a leading approach for quantifying internal exposure metrics for fishes; however, they often do not adequately consider chemicals that are actively biotransformed and have not been validated against early-life stages (ELS) that are often considered the most sensitive to the exposure to contaminants. To address these uncertainties, TK models were parameterized for the rapidly biotransformed chemical benzo[ a ]pyrene (B[ a ]P) in embryo-larval and adult life stages of fathead minnows. Biotransformation of B[ a ]P was determined through measurements of in vitro clearance. Using in vitro-in vivo extrapolation, in vitro clearance was integrated into a multi-compartment TK model for adult fish and a one-compartment model for ELS. Model predictions were validated using measurements of B[ a ]P metabolites from in vivo flow-through exposures to graded concentrations of water-borne B[ a ]P. Significantly greater amounts of B[ a ]P metabolites were observed with exposure to greater concentrations of parent compound in both life stages. However, when assessing biotransformation capacity, no differences in phase I or phase II biotransformation were observed with greater exposures to B[ a ]P. Results of modelling suggested that biotransformation of B[ a ]P can be successfully implemented into in silico models to accurately predict life stage-specific abundances of B[ a ]P metabolites in either whole-body larvae or the bile of adult fish. Models developed increase the scope of applications in which TK models can be used to support environmental risk assessments.
• Concentrations of PAHs in muscle suggests continued exposure to the residual spilled oil. • Identity of the host species was the dominant driver in shaping the gut microbiome of fish. • Structures of gut microbiomes were correlated with concentrations of PAHs in muscle in walleye. In July 2016, a Husky Energy pipeline spilled 225,000 L of diluted heavy crude oil, with a portion of the oil entering the North Saskatchewan River near Maidstone, SK, Canada. This event provided a unique opportunity to assess potential effects of a crude oil constituent (namely polycyclic aromatic hydrocarbons, PAHs) on a possible sensitive indicator of freshwater ecosystem health, the gut microbiota of native fishes. In summer 2017, goldeye ( Hiodon alosoides ), walleye ( Sander vitreus ), northern pike ( Esox lucius ), and shorthead redhorse ( Moxostoma macrolepidotum ) were collected at six locations upstream and downstream of the spill. Muscle and bile were collected from individual fish for quantification of PAHs and intestinal contents were collected for characterization of the microbial community of the gut. Results suggested that host species is a significant determinant of gut microbiota, with significant differences among the species across sites. Concentrations of PAHs in dorsal muscle were significantly correlated with gut community compositions of walleye, but not of the other fishes. Concentrations of PAHs in muscle were also correlated with abundances of several families of bacteria among fishes. This study represents one of the first to investigate the response of the gut microbiome of wild fishes to chemical stressors.
Abstract In this study we investigated the sensitivity of the snowpack to increased temperature and short-wave radiation, and precipitation change along an elevation gradient (1500–2500 m a.s.l.) over the main mountain ranges of the Iberian Peninsula (Cantabrian Range, Central Range, Iberian Range, Pyrenees, and the Sierra Nevada). The output of a meso-atmospheric model (WRF) was used as forcing data in a physically-based energy and mass balance snowpack model (FSM2). A cluster analyses was applied to the input data of the FSM2 model to identify a total of 12 cells that summarized the climatic variability of the mountain ranges. The WRF output was then rescaled to various elevation bands using an array of psychrometric and radiative formulae and air temperature lapse rates. A factorial experiment was performed to generate synthetic meteorological series involving gradual alteration of the temperature (0–4 °C increases), short-wave radiation (0–40 Wm-2 increases), and precipitation (variations of ±20%) to force the FSM2. We found differing sensitivities across the various mountainous areas as a consequence of differences in their energy and mass balances. The results showed a generally negative impact of climate warming on the magnitude, duration, and melt rates of the snowpack over all elevation bands, even under scenarios of greater precipitation. The average effect of warming on the duration of the snowpack ranged from −23% per °C at 1500 m a.s.l. to −13% per °C at 2500 m a.s.l., on the peak snow water equivalent ranged from −20% per °C at 1500 m a.s.l. to −15% per °C at 2500 m a.s.l., and on melt rates ranged from −9% to −6% per °C. The effect of increasing short-wave radiation on the snowpack ranged from approximately −2% per 10 Wm−2 at 1500 m a.s.l. to −1% per 10 Wm−2 at 2500 m a.s.l. for both the snowpack duration and peak SWE indices. The effect on the snowpack caused by precipitation changes reduced gradually with increasing elevation, especially in the colder areas. The response of the melt rates to warming was negative in most of the areas at all elevations, suggesting less intense but longer melt seasons.
Abstract Uncertainties of snowpack models and of their meteorological forcings limit their use by avalanche hazard forecasters, or for glaciological and hydrological studies. The spatialized simulations currently available for avalanche hazard forecasting are only assimilating sparse meteorological observations. As suggested by recent studies, their forecasting skills could be significantly improved by assimilating satellite data such as snow reflectances from satellites in the visible and the near-infrared spectra. Indeed, these data can help constrain the microstructural properties of surface snow and light absorbing impurities content, which in turn affect the surface energy and mass budgets. This paper investigates the prerequisites of satellite data assimilation into a detailed snowpack model. An ensemble version of Meteo-France operational snowpack forecasting system (named S2M) was built for this study. This operational system runs on topographic classes instead of grid points, so-called ‘semi-distributed’ approach. Each class corresponds to one of the 23 mountain massifs of the French Alps (about 1000 km2 each), an altitudinal range (by step of 300 m) and aspect (by step of 45°). We assess the feasability of satellite data assimilation in such a semi-distributed geometry. Ensemble simulations are compared with satellite observations from MODIS and Sentinel-2, and with in-situ reflectance observations. The study focuses on the 2013–2014 and 2016–2017 winters in the Grandes-Rousses massif. Substantial Pearson R2 correlations (0.75–0.90) of MODIS observations with simulations are found over the domain. This suggests that assimilating it could have an impact on the spatialized snowpack forecasting system. However, observations contain significant biases (0.1–0.2 in reflectance) which prevent their direct assimilation. MODIS spectral band ratios seem to be much less biased. This may open the way to an operational assimilation of MODIS reflectances into the Meteo-France snowpack modelling system.
• Few guidelines on sample size requirements for water quality improvement in streams. • Sample sizes for acceptable statistical power were estimated for common indicators. • 20% reductions of pollutant indicators required decades to centuries of data. • 40% reductions of pollutant indicators varied significantly by site. • 80% reductions required 5 years or less of data. Many water quality managers seek to demonstrate reductions in pollutants after a remedial program or policy change of some sort is implemented, but there is little information in the literature to help guide the extent of water quality sampling that is required to be confident that a change has occurred. Statistical power refers to the likelihood of avoiding a Type II error in hypothesis testing. It is critical to examine statistical power levels to ensure results are not unduly influenced by insufficient quantity of data. This study presents the first published record, to the best of our knowledge, on sample size requirements to achieve acceptable levels of statistical power in hypothesis testing of annual water quality (nutrients) in streams. We examined 13 temperate agricultural watersheds spanning a gradient of size from 11 to 16,000 km 2 using data synthesized from long-term flow and water quality records. We found that achieving commonly accepted levels of statistical power (0.8) after reductions of 20% in load or flow-weighted mean concentration (FWMC) required an inordinate quantity of data (50–250 years for load, 10–120 years for FWMC), while achieving statistical power of 0.8 after reductions of 80% of load or FWMC required very little data (2–4 years for FWMC, 2–7 years for load). Load reductions of 40% required a range of 8–50 years of data depending on analyte, while FWMC reductions of 40% required 3–10 years of total phosphorus (TP) data, 5–25 years for soluble reactive phosphorus (SRP), and 2–6 years for nitrate (NO 3 ). We examined relationships among times to achieve statistical power and a number of common landscape descriptors (discharge, baseflow index, basin size, concentration-discharge slope) and found no discernable relationships for either TP or SRP, whereas catchments with higher baseflow indices were found to have lower data requirements for achieving statistical power of 0.8 for NO 3 . We also show through subsampling experiments that higher frequency sampling tended to reduce data requirements to achieve acceptable statistical power, though these gains diminish as the sample frequency increases. The information presented will help those tasked with watershed monitoring to design appropriate sampling regimes to ensure adequate data are obtained to detect change.
Funding and in-kind support for analytical costs and logistics was provided by Environment and Climate Change Canada via a Grants and Contributions Agreement and by InnoTech Alberta via an Internal Investment Grant.
Traditional food consumption for Indigenous peoples is associated with improved nutrition and health but can also pose potential risks via exposure to contaminants. Polycyclic aromatic hydrocarbons (PAHs) are compounds of interest due to their widespread presence (e.g., their metabolites are detected in up to 100% of the Canadian population) and their toxicological potential. To better understand the range of exposures faced by Indigenous populations in northern Canada and to address a contaminant of emerging concern identified by the Arctic Monitoring and Assessment Programme, a multi-year biomonitoring study investigated levels of PAH exposure in subarctic First Nations communities of the Northwest Territories, Canada. Secondary data analysis of banked samples from a subset of the cross-sectional study was done. PAHs and cotinine markers in the urine samples (n = 97) of participants from two regions from the Mackenzie Valley (Dehcho and Sahtú) was completed by liquid and gas chromatography coupled with mass spectrometry. Also, participants completed a 24-hr recall food survey. When compared according to age/sex categories, the GM of several biomarkers (1-hydroxypyrene, 1-naphthol, 2-hydroxyfluorene, 2-hydroxyphenanthrene, 2-naphthol, 3-hydroxyfluorene, 3-hydroxyphenanthrene, 4-hydroxyphenanthrene, 9-hydroxyfluorene, 9-hydroxyphenanthrene) appeared higher than observed for the general Canadian population. The PAHs levels observed were, however, below clinical levels associated with adverse health outcomes. Altogether, these elevated biomarkers are metabolites of pyrene, naphthalene, fluorene and phenanthrene. Statistically significant non-parametric associations were observed between several biomarkers and i) the consumption of cooked meat in the last 24 h; and, ii) smoking status (self-reported status and adjusted on urine cotinine level). This work is the first to report PAH levels in a northern Canadian population and provides local baseline data for monitoring the effects of changes to climate and lifestyle over time. These findings will support regional and territorial decision makers in identifying environmental health priorities.
Abstract Freshwater ecosystems, particularly those in agricultural areas, remain at risk of eutrophication due to anthropogenic inputs of nutrients. While community-based monitoring has helped improve awareness and spur action to mitigate nutrient loads, monitoring is challenging due to the reliance on expensive laboratory technology, poor data management, time lags between measurement and availability of results, and risk of sample degradation during transport or storage. In this study, an easy-to-use smartphone-based application (The Nutrient App) was developed to estimate NO 3 and PO 4 concentrations through the image-processing of on-site qualitative colorimetric-based results obtained via cheap commercially-available instantaneous test kits. The app was tested in rivers, wetlands, and lakes across Canada and relative errors between 30% (filtered samples) and 70% (unfiltered samples) were obtained for both NO 3 and PO 4 . The app can be used to identify sources and hotspots of contamination, which can empower communities to take immediate remedial action to reduce nutrient pollution.
Abstract Soil erosion from agricultural lands continues to be a global societal problem. The movement of soils is often accompanied by nitrogen and phosphorus that are crucial to crop growth, but their redistribution from farm fields to waterways can reduce crop yields and degrade water quality. While within-field sediment and nutrient movement has been quantified using small plots and edge-of-field monitoring, these approaches fail to capture their spatial distribution. The pairing of soil sampling with unmanned aerial vehicle (UAV) data offers a novel and low-cost approach to map the spatial distribution of soil characteristics and nutrient concentrations within a farm field. UAV data are used to generate a digital terrain model and subsequently map within-field topographic variation and erosional flow pathways. Topographic variation is discretized into landform elements (flat, shoulder, backslope, footslope) that capture within-field heterogeneity and have potential for scaling out soil sampling to larger spatial extents. Our results show the controlling factor of water content and organic matter on crop yield, as represented by normalized difference vegetation index (NDVI). Significant differences in water content and organic matter were found across landform elements with increases in both parameters downslope. Upslope landform elements contained more sand content (9–20%) and had lower NDVI values than downslope elements. Complementing these findings, significant differences in organic matter, soluble nitrogen, and soluble reactive phosphorus occurred along erosional flow pathways. Our within-field results have implications for farmers, as our analysis of soil characteristics indicated that NDVI was positively correlated with water content (0.05), organic matter (0.15), silt (0.36), and clay (0.17) content and negatively correlated with soluble nitrogen (−0.47) and phosphorus (−0.30) concentrations. In addition to discussing the challenges and opportunities for expanding upon the presented research, we use a simple proof-of-concept hydrological model to demonstrate the potential role of hydrological connectivity and variable source area as a driver of within-field nutrient movement. The combination of our empirical results showing water content and organic matter as controlling factors on agricultural yield, the role of hydrological connectivity, and climate predictions of increased storm intensity suggest that additional research into the generation of novel time-series soil sampling and UAV-generated erosion and flow data could advance our understanding of the variations in soil characteristics and nutrient concentrations within individual farm fields.
Abstract A code clone is a pair of code fragments, within or between software systems that are similar. Since code clones often negatively impact the maintainability of a software system, several code clone detection techniques and tools have been proposed and studied over the last decade. However, the clone detection tools are not always perfect and their clone detection reports often contain a number of false positives or irrelevant clones from specific project management or user perspective. To detect all possible similar source code patterns in general, the clone detection tools work on the syntax level while lacking user-specific preferences. This often means the clones must be manually inspected before analysis in order to remove those false positives from consideration. This manual clone validation effort is very time-consuming and often error-prone, in particular for large-scale clone detection. In this paper, we propose a machine learning approach for automating the validation process. First, a training dataset is built by taking code clones from several clone detection tools for different subject systems and then manually validating those clones. Second, several features are extracted from those clones to train the machine learning model by the proposed approach. The trained algorithm is then used to automatically validate clones without human inspection. Thus the proposed approach can be used to remove the false positive clones from the detection results, automatically evaluate the precision of any clone detectors for any given set of datasets, evaluate existing clone benchmark datasets, or even be used to build new clone benchmarks and datasets with minimum effort. In an experiment with clones detected by several clone detectors in several different software systems, we found our approach has an accuracy of up to 87.4% when compared against the manual validation by multiple expert judges. The proposed method also shows better results in several comparative studies with the existing related approaches for clone classification.
Abstract In the context of the growing climate emergency and the negative social and environmental impacts of the industrial food system, significant attention is focused on the question of how we will feed ourselves sustainably. Small-scale fisheries are receiving more attention and communities are increasingly resisting a resourcist perspective that treats fish as a commodity by engaging in efforts to (re)envision fisheries as part of food systems. This paper presents four case studies from freshwater and marine fisheries across Canada to demonstrate ways of using food systems as an organizing concept to protect small-scale fisheries, build sustainable communities, and influence fisheries governance and policy. Insights are shared from the lobster fishery in Shelburne County, Nova Scotia; fish and country foods harvesting in Kakisa, Northwest Territories; traditional fisheries of Batchewana First Nation on Lake Superior, Ontario; and the national sustainable seafood partnership program, SeaChoice. We conclude by providing our collective ideas for how governance and policy may better support sustainability at the nexus of fisheries and food systems, emphasizing a need for structures and policies that are better adapted to the contexts of small-scale fisheries and that empower community participation in decision-making.
M.A. Ben Alaya was supported by the Climate Related Precipitation Extremes project of the Global Water Futures program.
Uptake and effects of ionizable organic chemicals (IOCs) that are weak acids in aqueous solution by fish can differ as a function of pH. While the pH-dependent behavior of select IOCs is well-understood, complex mixtures of IOCs, e.g., from oil sands process-affected water (OSPW), have not yet been studied systematically. Here, we established an in vitro screening method using the rainbow trout gill cell line, RTgill-W1, to investigate pH-dependent cytotoxicity and permeation of IOCs across cultured epithelia using ultra-high-performance liquid chromatography with high-resolution mass spectrometry (UPLC-HRMS). The assay was benchmarked using model chemicals and technical mixtures, and then used to characterize fractions and reconstituted extracts of field-collected OSPW. Significant pH-dependent cytotoxicity of individual IOCs, acidic fractions, and reconstituted extracts of OSPW was observed. In vitro data were in good agreement with data from a 96 h in vivo exposure experiment with juvenile rainbow trout. Permeation of some IOCs from OSPW was mediated by active transport, as revealed by studies in which inhibitors of these active transport mechanisms were applied. We conclude that the RTgill-W1 in vitro assay is useful for the screening of pH-dependent uptake of IOCs in fish, and has applications for in vitro-in vivo extrapolation, and prioritization of chemicals in nontarget screenings.
A potential risk from human uptake of microplastics is the release of plastics-associated xenobiotics, but the key physicochemical properties of microplastics controlling this process are elusive. Here, we show that the gastrointestinal bioaccessibility, assessed using an in vitro digestive model, of two model xenobiotics (pyrene, at 391-624 mg/kg, and 4-nonylphenol, at 3054-8117 mg/kg) bound to 18 microplastics (including pristine polystyrene, polyvinyl chloride, polyethylene terephthalate, polypropylene, thermoplastic polyurethane, and polyethylene, and two artificially aged samples of each polymer) covered wide ranges: 16.1-77.4% and 26.4-83.8%, respectively. Sorption/desorption experiments conducted in simulated gastric fluid indicated that structural rigidity of polymers was an important factor controlling bioaccessibility of the nonpolar, nonionic pyrene, likely by inducing physical entrapment of pyrene in porous domains, whereas polarity of microplastics controlled bioaccessibility of 4-nonylphenol, by regulating polar interactions. The changes of bioaccessibility induced by microplastics aging corroborated the important roles of polymeric structures and surface polarity in dictating sorption affinity and degree of desorption hysteresis, and consequently, gastrointestinal bioaccessibility. Variance-based global sensitivity analysis using a deep learning neural network approach further revealed that micropore volume was the most important microplastics property controlling bioaccessibility of pyrene, whereas the O/C ratio played a key role in dictating the bioaccessibility of 4-nonylphenol in the gastric tract.
Z et al. published a paper on machine learning based predictions of organic contaminant sorption onto carbonaceous materials and resins. The authors provide a novel approach to predict concentration-dependent sorption distribution coefficients (KD) to these materials, without the need to link it to any specific isotherm model. This study is a valuable contribution to the field that can stimulate the scientific discussion in the adsorption-modeling community regarding (i) mechanistic assumptions prior to model building, (ii) the parametrization of the model based on these assumptions, (iii) the grouping of data to train the algorithm, and (iv) data filtering strategies. We recently published a paper on a similar topic and are confident that this discussion is valuable to improve the future applicability of machine learning techniques to sorption phenomena.
Brominated azo dyes (BADs) have been identified as predominant indoor brominated pollutants in daycare dust; thus, their potential health risk to children is of concern. However, the toxicities of BADs remain elusive. In this study, the toxicokinetics of two predominant BADs, Disperse Blue 373 (DB373) and Disperse Violet 93 (DV93), and their suspect metabolite 2-bromo-4,6-dinitroaniline (BDNA) was investigated in embryos of zebrafish (Danio rerio). The bioconcentration factor of DV93 at 120 hpf is 6.2-fold lower than that of DB373. The nontarget analysis revealed distinct metabolism routes between DB373 and DV93 by reducing nitro groups to nitroso (DB373) or amine (DV93), despite their similar structures. NAD(P)H quinone oxidoreductase 1 (NQO1) and pyruvate dehydrogenase were predicted as the enzymes responsible for the reduction of DB373 and DV93 by correlating time courses of the metabolites and enzyme development. Further in vitro recombinant enzyme and in vivo inhibition results validated NQO1 as the enzyme specifically reducing DB373, but not DV93. Global proteome profiling revealed that the expression levels of proteins from the "apoptosis-induced DNA fragmentation" pathway were significantly upregulated by all three BADs, supporting the bioactivation of BADs to mutagenic aromatic amines. This study discovered the bioactivation of BADs via distinct eukaryotic enzymes, implying their potential health risks.
In eastern North America, many deciduous forest ecosystems grow at the northernmost extent of their geographical ranges, where climate change could aid or impede their growth. This region experiences frequent extreme weather conditions, allowing us to study the response of these forests to environmental conditions, reflective of future climates. Here we determined the impact of seasonal and annual climate variations and extreme weather events on the carbon (C) uptake capacity of an oak-dominated forest in southern Ontario, Canada, from 2012 to 2016. We found that changes in meteorology during late May to mid-July were key in determining the C sink strength of the forest, impacting the seasonal and annual variability of net ecosystem productivity (NEP). Overall, higher temperatures and dry conditions reduced ecosystem respiration (RE) much more than gross ecosystem productivity (GEP), leading to higher NEP. Variability in NEP was primarily driven by changes in RE, rather than GEP. The mean annual GEP, RE, and NEP values at our site during the study were 1,343 ± 85, 1,171 ± 139, and 206 ± 92 g C m-2 yr-1, respectively. The forest was a C sink even in years that experienced heat and water stresses. Mean annual NEP at our site was within the range of NEP (69-459 g C m-2 yr-1) observed in similar North American forests from 2012 to 2016. The growth and C sequestration capabilities of our oak-dominated forest were not adversely impacted by changes in environmental conditions and extreme weather events experienced over the study period.
The choice of hydrological model structure, that is, a model's selection of states and fluxes and the equations used to describe them, strongly controls model performance and realism. This work investigates differences in performance of 36 lumped conceptual model structures calibrated to and evaluated on daily streamflow data in 559 catchments across the United States. Model performance is compared against a benchmark that accounts for the seasonality of flows in each catchment. We find that our model ensemble struggles to beat the benchmark in snow-dominated catchments. In most other catchments model structure equifinality (i.e., cases where different models achieve similar high efficiency scores) can be very high. We find no relation between the number of model parameters and performance during either calibration or evaluation periods nor evidence of increased risk of overfitting for models with more parameters. Instead, the choice of model parametrization (i.e., which equations are used and how parameters are used within them) dictates the model's strengths and weaknesses. Results suggest that certain model structures are inherently better suited for certain objective functions and thus for certain study purposes. We find no clear relationships between the catchments where any model performs well and descriptors of those catchments' geology, topography, soil, and vegetation characteristics. Instead, model suitability seems to relate strongest to the streamflow regime each catchment generates, and we have formulated several tentative hypotheses that relate commonalities in model structure to similarities in model performance. Modeling results are made publicly available for further investigation.
Accurate representation of flow sources in process‐based hydrologic models remains challenging for remote, data‐scarce regions. This study applies stable isotope tracers (18O and 2H) in water as auxiliary data for the calibration of the isoWATFLOOD™ model. The most efficient method of those evaluated for introducing isotope data into model calibration was the PA‐DDS multiobjective search algorithm. The compromise solutions incorporating isotope data performed slightly inferior in terms of streamflow simulation compared to the calibrated solution using streamflow data only. However, the former solution outperformed the latter one in terms of isotope simulation. Approximation of the model parameter uncertainty into internal flow path partitioning was explored. Inclusion of isotope error facilitated a broader examination of the total parameter space, resulting in significant differences in internal storage and flow paths, most significantly for soil storage and evapotranspiration loss. Isotope‐optimized calibration reduced evaporation rates and increased soil moisture content within the model, impacting soil water velocity but not streamflow celerity. Flow‐only calibration resulted in artificially narrow model prediction bounds, significantly underestimating the propagation of parameter uncertainty, while isotope‐informed calibrations yielded more reliable and robust bound on model predictions. Our findings demonstrate that the accuracy of a complex, spatially distributed, and process‐based model cannot be judged from one summative flow‐based model performance evaluation metric alone.
Recent studies of water flow through dry porous media have shown progress in simulating preferential flow propagation. However, current methods applied to snowpacks have neglected the dynamic nature of the capillary pressure, such as conditions for capillary pressure overshoot, resulting in a rather limited representation of the water flow patterns through snowpacks observed in laboratory and field experiments. Indeed, previous snowmelt models using a water entry pressure to simulate preferential flow paths do not work for natural snowpack conditions where snow densities are less than 380 kg m−3. Because preferential flow in snowpacks greatly alters the flow velocity and the timing of delivery of meltwater to the base of a snowpack early in the melt season, a better understanding of this process would aid hydrological predictions. This study presents a 2‐D water flow through snow model that solves the non‐equilibrium Richards equation. This model, coupled with random perturbations of snow properties, can represent realistic preferential flow patterns. Using 1‐D laboratory data, two model parameters were linked to snow properties and model boundary conditions. Parameterizations of these model parameters were evaluated against 2‐D snowpack observations from a laboratory experiment, and the resulting model sensitivity to varying inputs and boundary conditions was calculated. The model advances both the physical understanding of and ability to simulate water flow through snowpacks and can be used in the future to parameterize 1‐D snowmelt models to incorporate flow variations due to preferential flow path formation.
Multi-model climate experiments carried out as part of different phases of the Coupled Model Intercomparison Project (CMIP) are crucial to evaluate past and future climate change. The reliability of models' simulations is often gauged by their ability to reproduce the historical climate across many time scales. This study compares the global mean surface air temperature from 29 CMIP6 models with observations from three datasets. We examine (1) warming and cooling rates in five subperiods from 1880 to 2014, (2) autocorrelation and long-term persistence, (3) models' performance based on probabilistic and entropy metrics, and (4) the distributional shape of temperature. All models simulate the observed long-term warming trend from 1880 to 2014. The late twentieth century warming (1975–2014) and the hiatus (1942–1975) are replicated by most models. The post-1998 warming is overestimated in 90% of the simulations. Only six out of 29 models reproduce the observed long-term persistence. All models show differences in distributional shape when compared with observations. Varying performance across metrics reveals the challenge to determine the "best" model. Thus, we argue that models should be selected, based on case-specific metrics, depending on the intended use. Metrics proposed here facilitate a comprehensive assessment for various applications.
Reactive nitrogen (N) fluxes have increased tenfold over the last century, driven by increases in population, shifting diets, and increased use of commercial N fertilizers. Runoff of excess N from intensively managed landscapes threatens drinking water quality and disrupts aquatic ecosystems. Excess N is also a major source of greenhouse gas emissions from agricultural soils. While N emissions from agricultural landscapes are known to originate from not only current‐year N input but also legacy N accumulation in soils and groundwater, there has been limited access to fine‐scale, long‐term data regarding N inputs and outputs over decades of intensive agricultural land use. In the present work, we synthesize population, agricultural, and atmospheric deposition data to develop a comprehensive, 88‐year (1930–2017) data set of county‐scale components of the N mass balance across the contiguous United States (Trajectories Nutrient Dataset for nitrogen [TREND‐nitrogen]). Using a machine‐learning algorithm, we also develop spatially explicit typologies for components of the N mass balance. Our results indicate a large range of N trajectory behaviors across the United States due to differences in land use and management and particularly due to the very different drivers of N dynamics in densely populated urban areas compared with intensively managed agricultural zones. Our analysis of N trajectories also demonstrates a widespread functional homogenization of agricultural landscapes. This newly developed typology of N trajectories improves our understanding of long‐term N dynamics, and the underlying data set provides a powerful tool for modeling the impacts of legacy N on past, present, and future water quality.
Floods often affect large regions and cause adverse societal impacts. Regional flood hazard and risk assessments therefore require a realistic representation of spatial flood dependencies to avoid the overestimation or underestimation of risk. However, it is not yet well understood how spatial flood dependence, that is, the degree of co-occurrence of floods at different locations, varies in space and time and which processes influence the strength of this dependence. We identify regions in the United States with seasonally similar flood behavior and analyze processes governing spatial dependence. We find that spatial flood dependence varies regionally and seasonally and is generally strongest in winter and spring and weakest in summer and fall. Moreover, we find that land-surface processes are crucial in shaping the spatiotemporal characteristics of flood events. We conclude that the regional and seasonal variations in spatial flood dependencies must be considered when conducting current and future flood risk assessments.
River damming alters nutrient fluxes along the land‐ocean aquatic continuum as a result of biogeochemical processes in reservoirs. Both the changes in riverine nutrient fluxes and nutrient ratios impact ecosystem functioning of receiving water bodies. We utilize spatially distributed mechanistic models of nitrogen (N), phosphorus (P), and silicon (Si) cycling in reservoirs to quantify changes in nutrient stoichiometry of river discharge to coastal waters. The results demonstrate that the growing number of dams decouples the riverine fluxes of N, P, and Si. Worldwide, preferential removal of P over N in reservoirs increases N:P ratios delivered to the ocean, raising the potential for P limitation of coastal productivity. By midcentury, more than half of the rivers discharging to the coastal zone will experience a higher removal of reactive Si relative to reactive P and total N, in response to the rapid pace at which new hydroelectric dams are being built.
Sensitivity analysis in Earth and environmental systems modeling typically demands an onerous computational cost. This issue coexists with the reliance of these algorithms on ad hoc designs of experiments, which hampers making the most out of the existing data sets. We tackle this problem by introducing a method for sensitivity analysis, based on the theory of variogram analysis of response surfaces (VARS), that works on any sample of input-output data or pre-computed model evaluations. Called data-driven VARS (D-VARS), this method characterizes the relationship strength between inputs and outputs by investigating their covariograms. We also propose a method to assess “robustness” of the results against sampling variability and numerical methods' imperfectness. Using two hydrologic modeling case studies, we show that D-VARS is highly efficient and statistically robust, even when the sample size is small. Therefore, D-VARS can provide unique opportunities to investigate geophysical systems whose models are computationally expensive or available data is scarce.
It is challenging to develop observationally based spatial estimates of meteorology in Alaska and the Yukon. Complex topography, frozen precipitation undercatch, and extremely sparse in situ observations all limit our capability to produce accurate spatial estimates of meteorological conditions. In this Arctic environment, it is necessary to develop probabilistic estimates of precipitation and temperature that explicitly incorporate spatiotemporally varying uncertainty and bias corrections. In this paper we exploit the recently developed ensemble Climatologically Aided Interpolation (eCAI) system to produce daily historical estimates of precipitation and temperature across Alaska and the Yukon Territory at a 2 km grid spacing for the time period 1980–2013. We extend the previous eCAI method to address precipitation gauge undercatch and wetting loss, which is of high importance for this high-latitude region where much of the precipitation falls as snow. Leave-one-out cross-validation shows our ensemble has little bias in daily precipitation and mean temperature at the station locations, with an overestimate in the daily standard deviation of precipitation. The ensemble is statistically reliable compared to climatology and can discriminate precipitation events across different precipitation thresholds. Long-term mean loss adjusted precipitation is up to 36% greater than the unadjusted estimate in windy areas that receive a large fraction of frozen precipitation, primarily due to wind induced undercatch. Comparing the ensemble mean climatology of precipitation and temperature to PRISM and Daymet v3 shows large interproduct differences, particularly in precipitation across the complex terrain of southeast and northern Alaska.
Lake 227 of the Experimental Lakes Area (ELA) in Ontario, Canada, has been fertilized with phosphorus (P) since 1969, which resulted in a rapid transition from oligotrophic to eutrophic conditions. Sediment cores collected from the oxygenated epilimnion, and the mostly anoxic hypolimnion of this unique lake contain a historical record of the changes in sediment P speciation and burial rates across the trophic transition. To elucidate these changes, results of chemical extractions were combined with 210Pb sediment dating, and with 31P NMR, Mossbauer, and XANES spectroscopies. Prior to 1969, organic P (POrg) was the major sedimentary P sink in Lake 227. Eutrophication of the lake coincided with marked increases in the burial rate of total P (TP), as well as in the relative contribution of the NaHCO3-extractable P pool (humic-bound P, PHum). Together, PHum and POrg account for ≥70% of total P burial in the sediments deposited since artificial fertilization started. The PHum fraction likely comprises phosphate complexes with humic substances. The strong linear correlation between P and iron (Fe) extracted by NaHCO3 implies a close association of the two elements in the humic fraction. Mossbauer and XANES spectra further indicate that most Fe in the post-1969 sediments remained in the Fe (III) oxidation state, which is attributed to the stabilization of reducible Fe by organic matter, in part via the formation of phosphate-Fe (III)-humic complexes. Importantly, our results show that the eutrophication experimentation of Lake 227 caused the accumulation of a large reservoir of reactive sediment P, which may continue to fuel internal P loading to the water column once artificial fertilization is terminated.
Remote sensing is a key method for advancing our understanding of global photosynthesis and is thus critical to understanding terrestrial carbon uptake and climate change. Increasingly sophisticated spectral indices including solar-induced florescence (SIF) and the photochemical reflectance index (PRI) are considered good proxies of canopy structure, biochemistry, and physiology. However, the relative influences of illumination intensity and angle on these measures are difficult to unravel, particularly at the scale of whole forest canopies. We exploit the solar dimming during the 2017 North American solar eclipse as well as a clear day before and cloudy day after the day of the eclipse. This novel approach allows us to assess changes in spectral vegetation indices due to illumination intensity independent of changes in illumination angle. Physiologically relevant spectral indices were most affected by dimming, with illumination level explaining 97% of variability in SIF and 99% of variability in PRI during the eclipse. The spectral change in reflectance through the eclipse period revealed changes in PRI were driven by reflectance differences at the 570 nm reference band rather than at the 531 nm signal band associated with xanthophyll pigment interconversions. This study refines our interpretation of vegetation properties from space with implications for our interpretation of signals related to terrestrial photosynthesis derived from sensors spanning a range of illumination conditions and angles.
Widespread flooding can cause major damages and substantial recovery costs. Still, estimates of how susceptible a region is to widespread flooding are largely missing mainly because of the sparseness of widespread flood events in records. The aim of this study is to assess the seasonal susceptibility of regions in the United States to widespread flooding using a stochastic streamflow generator, which enables simulating a large number of spatially consistent flood events. Furthermore, we ask which factors influence the strength of regional flood susceptibilities. We show that susceptibilities to widespread flooding vary regionally and seasonally. They are highest in regions where catchments show regimes with a strong seasonality, that is, the Pacific Northwest, the Rocky Mountains, and the Northeast. In contrast, they are low in regions where catchments are characterized by a weak seasonality and intermittent regimes such as the Great Plains. Furthermore, susceptibility is found to be the highest in winter and spring when spatial flood dependencies are strongest because of snowmelt contributions and high soil moisture availability. We conclude that regional flood susceptibilities emerge in river basins with catchments sharing similar streamflow and climatic regimes.
Carbon (C) emissions from wildfires are a key terrestrial–atmosphere interaction that influences global atmospheric composition and climate. Positive feedbacks between climate warming and boreal wildfires are predicted based on top-down controls of fire weather and climate, but C emissions from boreal fires may also depend on bottom-up controls of fuel availability related to edaphic controls and overstory tree composition. Here we synthesized data from 417 field sites spanning six ecoregions in the northwestern North American boreal forest and assessed the network of interactions among potential bottom-up and top-down drivers of C emissions. Our results indicate that C emissions are more strongly driven by fuel availability than by fire weather, highlighting the importance of fine-scale drainage conditions, overstory tree species composition and fuel accumulation rates for predicting total C emissions. By implication, climate change-induced modification of fuels needs to be considered for accurately predicting future C emissions from boreal wildfires.
Growing populations and agricultural intensification have led to raised riverine nitrogen (N) loads, widespread oxygen depletion in coastal zones (coastal hypoxia)1 and increases in the incidence of algal blooms.Although recent work has suggested that individual wetlands have the potential to improve water quality2,3,4,5,6,7,8,9, little is known about the current magnitude of wetland N removal at the landscape scale. Here we use National Wetland Inventory data and 5-kilometre grid-scale estimates of N inputs and outputs to demonstrate that current N removal by US wetlands (about 860 ± 160 kilotonnes of nitrogen per year) is limited by a spatial disconnect between high-density wetland areas and N hotspots. Our model simulations suggest that a spatially targeted increase in US wetland area by 10 per cent (5.1 million hectares) would double wetland N removal. This increase would provide an estimated 54 per cent decrease in N loading in nitrate-affected watersheds such as the Mississippi River Basin. The costs of this increase in area would be approximately 3.3 billion US dollars annually across the USA—nearly twice the cost of wetland restoration on non-agricultural, undeveloped land—but would provide approximately 40 times more N removal. These results suggest that water quality improvements, as well as other types of ecosystem services such as flood control and fish and wildlife habitat, should be considered when creating policy regarding wetland restoration and protection.

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The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data
Gilberto Pastorello, Carlo Trotta, Eleonora Canfora, Housen Chu, Danielle Christianson, You-Wei Cheah, C. Poindexter, Jiquan Chen, Abdelrahman Elbashandy, Marty Humphrey, Peter Isaac, Diego Polidori, Markus Reichstein, Alessio Ribeca, Catharine van Ingen, Nicolas Vuichard, Leiming Zhang, B.D. Amiro, Christof Ammann, M. Altaf Arain, Jonas Ardö, Timothy J. Arkebauer, Stefan K. Arndt, Nicola Arriga, Marc Aubinet, Mika Aurela, Dennis Baldocchi, Alan Barr, Eric Beamesderfer, Luca Belelli Marchesini, Onil Bergeron, Jason Beringer, Christian Bernhofer, Daniel Berveiller, D. P. Billesbach, T. Andrew Black, Peter D. Blanken, Gil Bohrer, Julia Boike, Paul V. Bolstad, Damien Bonal, Jean-Marc Bonnefond, David R. Bowling, Rosvel Bracho, Jason Brodeur, Christian Brümmer, Nina Buchmann, Benoît Burban, Sean P. Burns, Pauline Buysse, Peter Cale, M. Cavagna, Pierre Cellier, Shiping Chen, Isaac Chini, Torben R. Christensen, James Cleverly, Alessio Collalti, Claudia Consalvo, Bruce D. Cook, David Cook, Carole Coursolle, Edoardo Cremonese, Peter S. Curtis, Ettore D’Andrea, Humberto da Rocha, Xiaoqin Dai, Kenneth J. Davis, Bruno De Cinti, A. de Grandcourt, Anne De Ligne, Raimundo Cosme de Oliveira, Nicolas Delpierre, Ankur R. Desai, Carlos Marcelo Di Bella, Paul Di Tommasi, Han Dolman, Francisco Domingo, Gang Dong, Sabina Dore, Pierpaolo Duce, Éric Dufrêne, Allison L. Dunn, J.T. Dusek, Derek Eamus, Uwe Eichelmann, Hatim Abdalla M. ElKhidir, Werner Eugster, Cäcilia Ewenz, B. E. Ewers, D. Famulari, Silvano Fares, Iris Feigenwinter, Andrew Feitz, Rasmus Fensholt, Gianluca Filippa, M. L. Fischer, J. M. Frank, Marta Galvagno, Mana Gharun, Damiano Gianelle, Bert Gielen, Beniamino Gioli, Anatoly A. Gitelson, Ignacio Goded, Mathias Goeckede, Allen H. Goldstein, Christopher M. Gough, Michael L. Goulden, Alexander Graf, Anne Griebel, Carsten Gruening, Thomas Grünwald, Albin Hammerle, Shijie Han, Xingguo Han, Birger Ulf Hansen, Chad Hanson, Juha Hatakka, Yongtao He, Markus Hehn, Bernard Heinesch, Nina Hinko‐Najera, Lukas Hörtnagl, Lindsay B. Hutley, Andreas Ibrom, Hiroki Ikawa, Marcin Jackowicz-Korczyński, Dalibor Janouš, W.W.P. Jans, Rachhpal S. Jassal, Shicheng Jiang, Tomomichi Kato, Myroslava Khomik, Janina Klatt, Alexander Knohl, Sara Knox, Hideki Kobayashi, Georgia R. Koerber, Olaf Kolle, Yukio Kosugi, Ayumi Kotani, Andrew S. Kowalski, Bart Kruijt, Juliya Kurbatova, Werner L. Kutsch, Hyojung Kwon, Samuli Launiainen, Tuomas Laurila, B. E. Law, R. Leuning, Yingnian Li, Michael J. Liddell, Jean‐Marc Limousin, Marryanna Lion, Adam Liska, Annalea Lohila, Ana López‐Ballesteros, Efrèn López‐Blanco, Benjamin Loubet, Denis Loustau, Antje Maria Moffat, Johannes Lüers, Siyan Ma, Craig Macfarlane, Vincenzo Magliulo, Regine Maier, Ivan Mammarella, Giovanni Manca, Barbara Marcolla, Hank A. Margolis, Serena Marras, W. J. Massman, Mikhail Mastepanov, Roser Matamala, Jaclyn Hatala Matthes, Francesco Mazzenga, Harry McCaughey, Ian McHugh, Andrew M. S. McMillan, Lutz Merbold, Wayne S. Meyer, Tilden P. Meyers, S. D. Miller, Stefano Minerbi, Uta Moderow, Russell K. Monson, Leonardo Montagnani, Caitlin E. Moore, Eddy Moors, Virginie Moreaux, Christine Moureaux, J. William Munger, T. Nakai, Johan Neirynck, Zoran Nesic, Giacomo Nicolini, Asko Noormets, Matthew Northwood, Marcelo D. Nosetto, Yann Nouvellon, Kimberly A. Novick, W. C. Oechel, Jørgen E. Olesen, Jean‐Marc Ourcival, S. A. Papuga, Frans‐Jan W. Parmentier, Eugénie Paul‐Limoges, Marián Pavelka, Matthias Peichl, Elise Pendall, Richard P. Phillips, Kim Pilegaard, Norbert Pirk, Gabriela Posse, Thomas L. Powell, Heiko Prasse, Suzanne M. Prober, Serge Rambal, Üllar Rannik, Naama Raz‐Yaseef, Corinna Rebmann, David E. Reed, Víctor Resco de Dios, Natalia Restrepo‐Coupe, Borja R. Reverter, Marilyn Roland, Simone Sabbatini, Torsten Sachs, S. R. Saleska, Enrique P. Sánchez-Cañete, Zulia Mayari Sánchez-Mejía, Hans Peter Schmid, Marius Schmidt, Karl Schneider, Frederik Schrader, Ivan Schroder, Russell L. Scott, Pavel Sedlák, Penélope Serrano-Ortíz, Changliang Shao, Peili Shi, Ivan Shironya, Lukas Siebicke, Ladislav Šigut, Richard Silberstein, Costantino Sirca, Donatella Spano, R. Steinbrecher, Robert M. Stevens, Cove Sturtevant, Andy Suyker, Torbern Tagesson, Satoru Takanashi, Yanhong Tang, Nigel Tapper, Jonathan E. Thom, Michele Tomassucci, Juha‐Pekka Tuovinen, S. P. Urbanski, Р. Валентини, M. K. van der Molen, Eva van Gorsel, J. van Huissteden, Andrej Varlagin, Joe Verfaillie, Timo Vesala, Caroline Vincke, Domenico Vitale, N. N. Vygodskaya, Jeffrey P. Walker, Elizabeth A. Walter‐Shea, Huimin Wang, R. J. Weber, Sebastian Westermann, Christian Wille, Steven C. Wofsy, Georg Wohlfahrt, Sebastian Wolf, William Woodgate, Yuelin Li, Roberto Zampedri, Junhui Zhang, Guoyi Zhou, Donatella Zona, D. Agarwal, Sébastien Biraud, M. S. Torn, Dario Papale
Scientific Data, Volume 7, Issue 1

Abstract The FLUXNET2015 dataset provides ecosystem-scale data on CO 2 , water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.
A state-of-the-art review on the probable maximum precipitation (PMP) as it relates to climate variability and change is presented. The review consists of an examination of the current practice and the various developments published in the literature. The focus is on relevant research where the effect of climate dynamics on the PMP are discussed, as well as statistical methods developed for estimating very large extreme precipitation including the PMP. The review includes interpretation of extreme events arising from the climate system, their physical mechanisms, and statistical properties, together with the effect of the uncertainty of several factors determining them, such as atmospheric moisture, its transport into storms and wind, and their future changes. These issues are examined as well as the underlying historical and proxy data. In addition, the procedures and guidelines established by some countries, states, and organizations for estimating the PMP are summarized. In doing so, attention was paid to whether the current guidelines and research published literature take into consideration the effects of the variability and change of climatic processes and the underlying uncertainties.
Calibration of hydrological models is challenging in high-latitude regions where hydrometric data are minimal. Process-based models are needed to predict future changes in water supply, yet often w...
Abstract Terrestrial evapotranspiration (ET) is thermodynamically expected to increase with increasing atmospheric temperature; however, the actual constraints on the intensification of ET remain uncertain due to a lack of direct observations. Based on the FLUXNET2015 Dataset, we found that relative humidity (RH) is a more important driver of ET than temperature. While actual ET decrease at reduced RH, potential ET increases, consistently with the complementary relationship (CR) framework stating that the fraction of energy not used for actual ET is dissipated as increased sensible heat flux that in turn increases potential ET. In this study, we proposed an improved CR formulation requiring no parameter calibration and assessed its reliability in estimating ET both at site-level with the FLUXNET2015 Dataset and at basin-level. Using the ERA-Interim meteorological dataset for 1979–2017 to calculate ET, we found that the global terrestrial ET showed an increasing trend until 1998, while the trend started to decline afterwards. Such decline was largely associated with a reduced RH, inducing water stress conditions that triggered stomatal closure to conserve water. For the first time, this study quantified the global-scale implications of changes in RH on terrestrial ET, indicating that the temperature-driven acceleration of the terrestrial water cycle will be likely constrained by terrestrial vegetation feedbacks.
Abstract Climate warming will reduce the duration of mountain snowpacks and spring runoff, impacting the timing, volume, reliability, and sources of water supplies to mountain headwaters of rivers that support a large proportion of humanity. It is often assumed that snow hydrology will change in proportion to climate warming, but this oversimplifies the complex non-linear physical processes that drive precipitation phases and snowmelt. In this study, snow hydrology predictions made using a physical process snow hydrology model for 44 mountains areas worldwide enabled analysis of how snow and hydrological regimes will respond and interact under climate warming. The results show a generalized decoupling of mountain river hydrology from headwater snowpack regimes. Consequently, most river hydrological regimes shifted from reflecting the seasonal snowmelt freshet to responding rapidly to winter and spring precipitation. Similar to that already observed in particular regions, this study confirms that the worldwide decline in snow accumulation and snow cover duration with climate warming is substantial and spatially variable, yet highly predictable from air temperature and humidity data. Hydrological regimes showed less sensitivity, and less variability in their sensitivity to warming than did snowpack regimes. The sensitivity of the snowpack to warming provides crucial information for estimating shifts in the timing and contribution of snowmelt to runoff. However, no link was found between the magnitude of changes in the snowpack and changes in annual runoff.
Chlorophyll-A concentration is one of the most commonly measured water quality parameters. It is an indicator of algal biomass and provides insight into stressors such as eutrophication and bloom risk. It is also a widely used metric in terrestrial ecosystems as an indicator of photosynthetic activity and nutrient limitation. Laboratory-based methods for measuring chlorophyll-A require expensive instrumentation. In this paper, we proposed a smart, low-cost, and portable smart sensor system to measure the concentration of chlorophyll-A in an extracted solution using two consumer-grade spectral sensors that read the reflectance at 12 discrete wavelengths in visible and near-infrared spectra. The system was tuned for an optimal distance from the sensors to the solution and an enclosure was printed to maintain the distance, as well as to avoid natural light interference. Extracted chlorophyll solutions of 51 different concentrations were prepared, and at least 100 readings per sample were taken using our smart sensor system. The ground truth values of the samples were measured in the laboratory using Thermo Nano 2000C. After cleaning the anomalous data, different machine learning models were trained to determine the significant wavelengths that contribute most towards chlorophyll-A measurement. Finally, a decision tree model with 5 important features was chosen based on the lowest Root Mean Square and Mean Absolute Error when it was tested on the validation set. Our final model resulted in a mean error of ±0.9μg/L when applied on our test set. The total cost was around $150.

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COSORE: A community database for continuous soil respiration and other soil‐atmosphere greenhouse gas flux data
Ben Bond‐Lamberty, Danielle Christianson, Avni Malhotra, Stephanie Pennington, Debjani Sihi, Amir AghaKouchak, Hassan Anjileli, M. Altaf Arain, Juan J. Armestó, Samaneh Ashraf, Mioko Ataka, Dennis Baldocchi, T. Andrew Black, Nina Buchmann, Mariah S. Carbone, Shih Chieh Chang, Patrick Crill, Peter S. Curtis, Eric A. Davidson, Ankur R. Desai, John E. Drake, Tarek S. El‐Madany, Michael Gavazzi, Carolyn-Monika Görres, Christopher M. Gough, Michael L. Goulden, Jillian W. Gregg, O. Gutiérrez del Arroyo, Jin Sheng He, Takashi Hirano, Anya M. Hopple, Holly Hughes, Järvi Järveoja, Rachhpal S. Jassal, Jinshi Jian, Haiming Kan, Jason P. Kaye, Yuji Kominami, Naishen Liang, David A. Lipson, Catriona A. Macdonald, Kadmiel Maseyk, Kayla Mathes, Marguerite Mauritz, Melanie A. Mayes, Steven G. McNulty, Guofang Miao, Mirco Migliavacca, S. D. Miller, Chelcy Ford Miniat, Jennifer Goedhart Nietz, Mats Nilsson, Asko Noormets, Hamidreza Norouzi, Christine O’Connell, Bruce Osborne, Cecilio Oyonarte, Zhuo Pang, Matthias Peichl, Elise Pendall, Jorge F. Perez‐Quezada, Claire L. Phillips, Richard P. Phillips, James W. Raich, Alexandre A. Renchon, Nadine K. Ruehr, Enrique P. Sánchez‐Cañete, Matthew Saunders, K. E. Savage, Marion Schrumpf, Russell L. Scott, Ulli Seibt, Whendee L. Silver, Wu Sun, Daphne Szutu, Kentaro Takagi, Masahiro Takagi, Masaaki Teramoto, Mark G. Tjoelker, Susan E. Trumbore, Masahito Ueyama, Rodrigo Vargas, R. K. Varner, Joseph Verfaillie, Christoph S. Vogel, Jinsong Wang, G. Winston, Tana E. Wood, Juying Wu, Thomas Wutzler, Jiye Zeng, Tianshan Zha, Quan Zhang, Junliang Zou
Global Change Biology, Volume 26, Issue 12

Globally, soils store two to three times as much carbon as currently resides in the atmosphere, and it is critical to understand how soil greenhouse gas (GHG) emissions and uptake will respond to ongoing climate change. In particular, the soil-to-atmosphere CO2 flux, commonly though imprecisely termed soil respiration (RS ), is one of the largest carbon fluxes in the Earth system. An increasing number of high-frequency RS measurements (typically, from an automated system with hourly sampling) have been made over the last two decades; an increasing number of methane measurements are being made with such systems as well. Such high frequency data are an invaluable resource for understanding GHG fluxes, but lack a central database or repository. Here we describe the lightweight, open-source COSORE (COntinuous SOil REspiration) database and software, that focuses on automated, continuous and long-term GHG flux datasets, and is intended to serve as a community resource for earth sciences, climate change syntheses and model evaluation. Contributed datasets are mapped to a single, consistent standard, with metadata on contributors, geographic location, measurement conditions and ancillary data. The design emphasizes the importance of reproducibility, scientific transparency and open access to data. While being oriented towards continuously measured RS , the database design accommodates other soil-atmosphere measurements (e.g. ecosystem respiration, chamber-measured net ecosystem exchange, methane fluxes) as well as experimental treatments (heterotrophic only, etc.). We give brief examples of the types of analyses possible using this new community resource and describe its accompanying R software package.
Photosynthetic phenology is an important indicator of annual gross primary productivity (GPP). Assessing photosynthetic phenology remotely is difficult for evergreen conifers as they remain green year-round. Carotenoid-based vegetation indices such as the photochemical reflectance index (PRI) and chlorophyll/carotenoid index (CCI) are promising tools to remotely track the invisible phenology of photosynthesis by assessing carotenoid pigment dynamics. PRI, CCI and the near-infrared reflectance of vegetation (NIRV ) index may act as proxies of photosynthetic efficiency (ɛ), an important parameter in light-use efficiency models, or direct proxies of photosynthesis. To understand the physiological mechanisms reflected by PRI and CCI and the ability of vegetation indices to act as proxies of photosynthetic activity for estimating GPP, we measured leaf pigment composition, PRI, CCI, NIRV and photosynthetic activity at the leaf and canopy scales over 2 years in an evergreen and mixed deciduous forest. PRI and CCI captured the large seasonal carotenoid/chlorophyll ratio changes and good relationships were observed between PRI-ɛ and CCI-photosynthesis and NIRV -photosynthesis. PRI-, CCI- and NIRV -based models effectively tracked observed seasonal GPP. We propose that carotenoid-based and near-infrared reflectance vegetation indices may provide useful proxies of photosynthetic activity and can improve remote sensing-based models of GPP in evergreen and deciduous forests.
The combination of photocatalysis and biodegradation was investigated for the removal of nine selected pharmaceuticals as a means to reduce loadings into the environment. The combined process, consisting of a resource-efficient mild photocatalysis and a subsequent biological treatment, was compared to single processes of intensive photocatalysis and biological treatment. The UV-TiO2 based photocatalysis effectively removed atorvastatin, atenolol and fluoxetine (>80%). Biological treatment after mild photocatalytic pretreatment removed diclofenac effectively (>99%), while it persisted during the single biological treatment (
Our reference lake evaporation dataset for high-elevation Tibetan lakes reduces evaporation-induced bias in water budget analysis.
[Extract] The Brazilian Amazon—the largest tropical rainforest in the world—has reached its highest level of deforestation since 2008 (Display footnote number:1). In 2019, 10,897 km2 of land were deforested, a 50.7% jump over the previous year (Display footnote number:1). A combination of threats, including tens of thousands of forest fires (Display footnote number:2), expanding road networks (Display footnote number:3, 4), weakened environmental laws (Display footnote number:5, 6), and a failure to enforce environmental laws and regulations (Display footnote number:6), is responsible. Given the staunchly pro-development policies of Brazil’s current government, a coalition of key actors in the financial sector is needed to help protect the embattled Amazon rainforest.
In many northern rivers, ice-jam flooding can be more severe than open-water flooding, leading to human casualties, damages to property and infrastructure, and adverse impacts on the ecology. Consequently, ice jam related flooding is a major concern for many riverside communities, water authorities, insurance companies, and government agencies. Ice-jam flood hazard delineation and risk analysis are important measures for flood preparation, mitigation, and management strategies. Although methodologies and techniques for open-water flood hazard and risk assessment are well established, methodologies and techniques for ice-jam flood hazard and risk assessment are often unavailable or less developed. In addition to this, a considerable number of studies have been conducted in the context of flood management, but a very limited number of studies have been carried out in real-time flood risk analysis during operational flood forecasting. In this paper, the current status of ice-jam flood hazard delineation and risk analysis is discussed. A framework for real-time risk analysis for operational flood forecasting is also discussed. Finally, current limitations and future requirements for developing effective ice-jam flood hazard delineation and risk analysis methodologies are provided.
Abstract Global gridded precipitation products have proven essential for many applications ranging from hydrological modeling and climate model validation to natural hazard risk assessment. They provide a global picture of how precipitation varies across time and space, specifically in regions where ground-based observations are scarce. While the application of global precipitation products has become widespread, there is limited knowledge on how well these products represent the magnitude and frequency of extreme precipitation—the key features in triggering flood hazards. Here, five global precipitation datasets (MSWEP, CFSR, CPC, PERSIANN-CDR, and WFDEI) are compared to each other and to surface observations. The spatial variability of relatively high precipitation events (tail heaviness) and the resulting discrepancy among datasets in the predicted precipitation return levels were evaluated for the time period 1979–2017. The analysis shows that 1) these products do not provide a consistent representation of the behavior of extremes as quantified by the tail heaviness, 2) there is strong spatial variability in the tail index, 3) the spatial patterns of the tail heaviness generally match the Köppen–Geiger climate classification, and 4) the predicted return levels for 100 and 1000 years differ significantly among the gridded products. More generally, our findings reveal shortcomings of global precipitation products in representing extremes and highlight that there is no single global product that performs best for all regions and climates.
Peat cores from boreal bog and fen sites in the Hudson Bay Lowlands of Northern Ontario, Canada, were analysed to calculate Holocene carbon accumulation rates, and to show how testate amoeba taxonomic assemblages, inferred depths to water table, and four morpho-traits that may be linked to function (mixotrophy, aperture size, aperture position, and biovolume) changed since peatland initiation. Carbon accumulation rates were on average higher for the Holocene in the fen record (19.4 g C m −2 yr −1 ) in comparison with the bog record (15.7 g C m −2 yr −1 ), which underwent a fen-to-bog transition around 6900 cal yr BP. Changes in rates of carbon accumulation were most strongly driven by changes in rates of peat vertical accretion, with more rapid rates in the fen record. Carbon accumulation rates were highest following peatland initiation when reconstructed water tables were highest, and in the late Holocene, when water table positions were variable. Taxa with larger biovolumes and apertures were generally more abundant when reconstructed water tables were higher, most notably following peatland initiation. Mixotrophic taxa were more prevalent in drier conditions and in the bog record. Changing frequencies of morpho-traits suggest that testate amoebae may occupy a higher trophic position in the microbial food web during wetter periods, signaling the possibility of internal feedbacks between peatland ecohydrology and critical ecosystem functions including long-term carbon accumulation.
This paper explores how Canadian federal policy and frameworks can better support community-based initiatives to reduce food insecurity and build sustainable food systems in the North. Through an examination of the current state of food systems infrastructure, transportation, harvest, and production in the Yukon, Northwest Territories, Nunavut, Nunavik, and Nunatsiavut, we argue in favour of a multi-sector approach that supports diversified food systems, including traditional/country food production and distribution, in a way that values and prioritizes community-led initiatives and Indigenous peoples’ self-determination and self-governance. The challenge of developing sustainable, northern food systems requires made-in-the-North solutions that are attuned to cultural, geographic, environmental, and political contexts. Recent policy developments suggest some progress in this direction, however much more work is needed. Ultimately, sustainable northern food systems must be defined by and for Northerners at community, local, and regional levels, with particular attention paid to treaty rights and the right to self-determination of First Nations and other Indigenous communities.
This article explores how Indigenous Knowledge and medical anthropology can co-construct community health knowledge through boundary work and the use of boundary objects. It will highlight how community-based participatory research (CBPR) in medical anthropology can help co-develop methods and strategies with Indigenous research partners to assess the human health impact of the First Nations water crisis. We draw on a case study of our community-based approach to health research with Six Nations of the Grand River First Nation community stakeholders and McMaster University researchers. We highlight how framing a co-constructed health survey as a boundary object can create dialogical space for Indigenous and western academic pedagogies and priorities. We also explore how this CBPR anthropology approach, informed by Indigenous Knowledge, allows for deeper foundations of culturally centered health to guide our work in identifying current and future community health needs concerning these ongoing water contamination and access issues. Through three health survey versions, priorities and research questions shifted and expanded to suit growing community health priorities. This led to collaborative action to communicate specific messages around water contamination and access across governance, community, and institutional boundaries. We demonstrate how our co-constructed approach and boundary work allows for the respectful and reciprocal development of these long-term research partnerships and works in solidarity with the Two-Row Wampum (Kaswentha) treaty established by the Haudenosaunee Nation and European settler nations.
To advance monitoring of surface water resources, new remote sensing technologies including the forthcoming Surface Water and Ocean Topography (SWOT) satellite (expected launch 2022) and its experimental airborne prototype AirSWOT are being developed to repeatedly map water surface elevation (WSE) and slope (WSS) of the world’s rivers, lakes, and reservoirs. However, the vertical accuracies of these novel technologies are largely unverified; thus, standard and repeatable field procedures to validate remotely sensed WSE and WSS are needed. To that end, we designed, engineered, and operationalized a Water Surface Profiler (WaSP) system that efficiently and accurately surveys WSE and WSS in a variety of surface water environments using Global Navigation Satellite Systems (GNSS) time-averaged measurements with Precise Point Positioning corrections. Here, we present WaSP construction, deployment, and a data processing workflow. We demonstrate WaSP data collections from repeat field deployments in the North Saskatchewan River and three prairie pothole lakes near Saskatoon, Saskatchewan, Canada. We find that WaSP reproducibly measures WSE and WSS with vertical accuracies similar to standard field survey methods [WSE root mean squared difference (RMSD) ∼8 cm, WSS RMSD ∼1.3 cm/km] and that repeat WaSP deployments accurately quantify water level changes (RMSD ∼3 cm). Collectively, these results suggest that WaSP is an easily deployed, self-contained system with sufficient accuracy for validating the decimeter-level expected accuracies of SWOT and AirSWOT. We conclude by discussing the utility of WaSP for validating airborne and spaceborne WSE mappings, present 63 WaSP in situ lake WSE measurements collected in support of NASA’s Arctic-Boreal and Vulnerability Experiment, highlight routine deployment in support of the Lake Observation by Citizen Scientists and Satellites project, and explore WaSP utility for validating a novel GNSS interferometric reflectometry LArge Wave Warning System.
The conversion from primary forest to agriculture drives widespread changes that have the potential to modify the hydroclimatology of the Xingu River Basin. Moreover, climate impacts over eastern Amazonia have been strongly related to pasture and soybean expansion. This study carries out a remote-sensing, spatial-temporal approach to analyze inter- and intra-annual patterns in evapotranspiration (ET) and precipitation (PPT) over pasture and soybean areas in the Xingu River Basin during a 13-year period. We used ET estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) and PPT estimates from the Tropical Rainfall Measurement Mission (TRMM) satellite. Our results showed that the annual average ET in the pasture was ~20% lower than the annual average in soybean areas. We show that PPT is notably higher in the northern part of the Xingu River Basin than the drier southern part. ET, on the other hand, appears to be strongly linked to land-use and land-cover (LULC) patterns in the Xingu River Basin. Lower annual ET averages occur in southern areas where dominant LULC is savanna, pasture, and soybean, while more intense ET is observed over primary forests (northern portion of the basin). The primary finding of our study is related to the fact that the seasonality patterns of ET can be strongly linked to LULC in the Xingu River Basin. Further studies should focus on the relationship between ET, gross primary productivity, and water-use efficiency in order to better understand the coupling between water and carbon cycling over this expanding Amazonian agricultural frontier.
Smart packaging is an emerging technology that has a great potential in solving conventional food packaging problems and in meeting the evolving packaged vegetables market needs. The advantages of using such a system lies in extending the shelf life of products, ensuring the safety and the compliance of these packages while reducing the food waste; hence, lessening the negative environmental impacts. Many new concepts were developed to serve this purpose, especially in the meat and fish industry with less focus on fruits and vegetables. However, making use of these evolving technologies in packaging of vegetables will yield in many positive outcomes. In this review, we discuss the new technologies and approaches used, or have the potential to be used, in smart packaging of vegetables. We describe the technical aspects and the commercial applications of the techniques used to monitor the quality and the freshness of vegetables. Factors affecting the freshness and the spoilage of vegetables are summarized. Then, some of the technologies used in smart packaging such as sensors, indicators, and data carriers that are integrated with sensors, to monitor and provide a dynamic output about the quality and safety of the packaged produce are discussed. Comparison between various intelligent systems is provided followed by a brief review of active packaging systems. Finally, challenges, legal aspects, and limitations facing this smart packaging industry are discussed together with outlook and future improvements.
The effluent from municipal wastewater treatment plants is a major point source of contamination in Canadian waterways. The improvement of effluent quality to reduce contaminants, such as pharmaceuticals and personal care products, before being released into the environment is necessary to reduce the impacts on organisms that live in the river downstream. Here, we aimed to characterize the metabolic and gill physiological responses of rainbow (Etheostoma caeruleum), fantail (Etheostoma flabellare), and greenside (Etheostoma blennioides) darters to the effluent in the Grand River from the recently upgraded Waterloo municipal wastewater treatment plant. The routine metabolism of darters was not affected by effluent exposure, but some species had increased maximum metabolic rates, leading to an increased aerobic scope. The rainbow darter aerobic scope increased by 2.2 times and the fantail darter aerobic scope increased by 2.7 times compared to the reference site. Gill samples from effluent-exposed rainbow darters and greenside darters showed evidence of more pathologies and variations in morphology. These results suggest that darters can metabolically adjust to effluent-contaminated water and may also be adapting to the urban and agricultural inputs. The modification and damage to the gills provide a useful water quality indicator but does not necessarily reflect how well acclimated the species is to the environment due to a lack of evidence of poor fish health.
The aim of this work is to understand aerosol transfers to the snowpack in the Spanish Pyrenees (Southern Europe) by determining their episodic mass-loading and composition, and to retrieve their regional impacts regarding optical properties and modification of snow melting. Regular aerosol monitoring has been performed during three consecutive years. Complementarily, short campaigns have been carried out to collect dust-rich snow samples. Atmospheric samples have been chemically characterized in terms of elemental composition and, in some cases, regarding their mineralogy. Snow albedo has been determined in different seasons along the campaign, and temporal variations of snow-depth from different observatories have been related to concentration of impurities in the snow surface. Our results noticed that aerosol flux in the Central Pyrenees during cold seasons (from November to May, up to 12–13 g m−2 of insoluble particles overall accumulated) is much higher than the observed during the warm period (from June to October, typically around 2.1–3.3 g m−2). Such high values observed during cold seasons were driven by the impact of severe African dust episodes. In absence of such extreme episodes, aerosol loadings in cold and warm season appeared comparable. Our study reveals that mineral dust particles from North Africa are a major driver of the aerosol loading in the snowpack in the southern side of the Central Pyrenees. Field data revealed that the heterogeneous spatial distribution of impurities on the snow surface led to differences close to 0.2 on the measured snow albedo within very short distances. Such impacts have clear implications for modelling distributed energy balance of snow and predicting snow melting from mountain headwaters.
In forest ecosystems, soil CO2 efflux is an important component of ecosystem respiration (RE), which is generally driven by variability in soil temperature and soil moisture. Tree harvesting in forests can alter the soil variables and, consequently, impact soil CO2 efflux. This study investigated the response of total soil CO2 efflux, and its components, to a shelterwood harvesting event of a mature temperate white pine (Pinus strobus L.) forest located in Southern Ontario, Canada. The objective was to explore the response of soil CO2 effluxes to changes in the forest microclimate, such as soil temperature and soil moisture, after shelterwood harvesting removed approximately one-third of the overstory canopy. No significant differences were found in both soil temperature and soil moisture between the pre-harvesting (2008–2011) and post-harvesting (2012–2014) periods. Despite similar soil microclimates, total soil CO2 effluxes were significantly reduced by up to 37%. Soil CO2 effluxes from heterotrophic sources were significantly reduced post-harvesting by approximately 27%, while no significant difference in the mineral-soil horizon sources were measured. An analysis of RE, measured with an eddy covariance tower over the study area, showed an increase post-harvesting. However, the overall net ecosystem carbon exchange showed no significant difference between pre- and post-harvesting. This was due to an increase in the gross ecosystem productivity post-harvesting, compensating for the increased losses (i.e., increased RE). This study highlights the complexities of soil CO2 efflux after a disturbance, such as a harvest. The knowledge gained from this study adds to our understanding of how shelterwood harvesting may influence ecosystem carbon exchange and will be useful for forest managers focused on carbon sequestration and forest conservation.
Deforestation in the Brazilian Amazon is related to the use of fire to remove natural vegetation and install crop cultures or pastures. In this study, we evaluated the relation between deforestation, land-use and land-cover (LULC) drivers and fire emissions in the Apyterewa Indigenous Land, Eastern Brazilian Amazon. In addition to the official Brazilian deforestation data, we used a geographic object-based image analysis (GEOBIA) approach to perform the LULC mapping in the Apyterewa Indigenous Land, and the Brazilian biomass burning emission model with fire radiative power (3BEM_FRP) to estimate emitted particulate matter with a diameter less than 2.5 µm (PM2.5), a primary human health risk. The GEOBIA approach showed a remarkable advancement of deforestation, agreeing with the official deforestation data, and, consequently, the conversion of primary forests to agriculture within the Apyterewa Indigenous Land in the past three years (200 km2), which is clearly associated with an increase in the PM2.5 emissions from fire. Between 2004 and 2016 the annual average emission of PM2.5 was estimated to be 3594 ton year−1, while the most recent interval of 2017–2019 had an average of 6258 ton year−1. This represented an increase of 58% in the annual average of PM2.5 associated with fires for the study period, contributing to respiratory health risks and the air quality crisis in Brazil in late 2019. These results expose an ongoing critical situation of intensifying forest degradation and potential forest collapse, including those due to a savannization forest-climate feedback, within “protected areas” in the Brazilian Amazon. To reverse this scenario, the implementation of sustainable agricultural practices and development of conservation policies to promote forest regrowth in degraded preserves are essential.
Organic fractions and extracts of willow (Salix safsaf) leaves, produced by sequential solvent extraction as well as infusion and decoction, exhibited anticancer potencies in four cancerous cell lines, including breast (MCF-7), colorectal (HCT-116), cervical (HeLa) and liver (HepG2). Results of the MTT assay revealed that chloroform (CHCl3) and ethyl acetate (EtOAc)-soluble fractions exhibited specific anticancer activities as marginal toxicities were observed against two non-cancerous control cell lines (BJ-1 and MCF-12). Ultra-high-resolution mass spectrometry Q-Exactive™ HF Hybrid Quadrupole-Orbitrap™ coupled with liquid chromatography (UHPLC) indicated that both extracts are enriched in features belonging to major phenolic and purine derivatives. Fluorescence-activated cell sorter analysis (FACS), employing annexin V-FITC/PI double staining indicated that the observed cytotoxic potency was mediated via apoptosis. FACS analysis, monitoring the increase in fluorescence signal, associated with oxidation of DCFH to DCF, indicated that the mechanism of apoptosis is independent of reactive oxygen species (ROS). Results of immunoblotting and RT-qPCR assays showed that treatment with organic fractions under investigation resulted in significant up-regulation of pro-apoptotic protein and mRNA markers for Caspase-3, p53 and Bax, whereas it resulted in a significant reduction in amounts of both protein and mRNA of the anti-apoptotic marker Bcl-2. FACS analysis also indicated that pre-treatment and co-treatment of human amniotic epithelial (WISH) cells exposed to the ROS H2O2 with EtOAc fraction provide a cytoprotective and antioxidant capacity against generated oxidative stress. In conclusion, our findings highlight the importance of natural phenolic and flavonoid compounds with unparalleled and unique antioxidant and anticancer properties.
Wildfires are a concerning issue in Canada due to their immediate impact on people’s lives, local economy, climate, and environment. Studies have shown that the number of wildfires and affected areas in Canada has increased during recent decades and is a result of a warming and drying climate. Therefore, identifying potential wildfire risk areas is increasingly an important aspect of wildfire management. The purpose of this study is to investigate if remotely sensed soil moisture products from the Soil Moisture and Ocean Salinity (SMOS) satellite can be used to identify potential wildfire risk areas for better wildfire management. We used the National Fire Database (NFDB) fire points and polygons to group the wildfires according to ecozone classifications, as well as to analyze the SMOS soil moisture data over the wildfire areas, between 2010–2017, across fourteen ecozones in Canada. Timeseries of 3-day, 5-day, and 7-day soil moisture anomalies prior to the onset of each wildfire occurrence were examined over the ecozones individually. Overall, the results suggest, despite the coarse-resolution, SMOS soil moisture products are potentially useful in identifying soil moisture anomalies where wildfire hot-spots may occur.
Sentinel-2 provides the opportunity to map the snow cover at unprecedented spatial and temporal resolutions on a global scale. Here we calibrate and evaluate a simple empirical function to estimate the fractional snow cover (FSC) in open terrains using the normalized difference snow index (NDSI) from 20 m resolution Sentinel-2 images. The NDSI is computed from flat surface reflectance after masking cloud and snow-free areas. The NDSI–FSC function is calibrated using Pléiades very high-resolution images and evaluated using independent datasets including SPOT 6/7 satellite images, time lapse camera photographs, terrestrial lidar scans and crowd-sourced in situ measurements. The calibration results show that the FSC can be represented with a sigmoid-shaped function 0.5 × tanh(a × NDSI + b) + 0.5, where a = 2.65 and b = −1.42, yielding a root mean square error (RMSE) of 25%. Similar RMSE are obtained with different evaluation datasets with a high topographic variability. With this function, we estimate that the confidence interval on the FSC retrievals is 38% at the 95% confidence level.
Snow surface spectral reflectance is very important in the Earth’s climate system. Traditional land surface models with parameterized schemes can simulate broadband snow surface albedo but cannot accurately simulate snow surface spectral reflectance with continuous and fine spectral wavebands, which constitute the major observations of current satellite sensors; consequently, there is an obvious gap between land surface model simulations and remote sensing observations. Here, we suggest a new integrated scheme that couples a radiative transfer model with a land surface model to simulate high spectral resolution snow surface reflectance information specifically targeting multisource satellite remote sensing observations. Our results indicate that the new integrated model can accurately simulate snow surface reflectance information over a large spatial scale and continuous time series. The integrated model extends the range of snow spectral reflectance simulation to the whole shortwave band and can predict snow spectral reflectance changes in the solar spectrum region based on meteorological element data. The kappa coefficients (K) of both the narrowband snow albedo targeting Moderate Resolution Imaging Spectroradiometer (MODIS) data simulated by the new integrated model and the retrieved snow albedo based on MODIS reflectance data are 0.5, and both exhibit good spatial consistency. Our proposed narrowband snow albedo simulation scheme targeting satellite remote sensing observations is consistent with remote sensing satellite observations in time series and can predict narrowband snow albedo even during periods of missing remote sensing observations. This new integrated model is a significant improvement over traditional land surface models for the direct spectral observations of satellite remote sensing. The proposed model could contribute to the effective combination of snow surface reflectance information from multisource remote sensing observations with land surface models.
Despite numerous studies in statistical downscaling methodologies, there remains a lack of methods that can downscale from precipitation modeled in global climate models to regional level high resolution gridded precipitation. This paper reports a novel downscaling method using a Generative Adversarial Network (GAN), CliGAN, which can downscale large-scale annual maximum precipitation given by simulation of multiple atmosphere-ocean global climate models (AOGCM) from Coupled Model Inter-comparison Project 6 (CMIP6) to regional-level gridded annual maximum precipitation data. This framework utilizes a convolution encoder-dense decoder network to create a generative network and a similar network to create a critic network. The model is trained using an adversarial training approach. The critic uses the Wasserstein distance loss function and the generator is trained using a combination of adversarial loss Wasserstein distance, structural loss with the multi-scale structural similarity index (MSSIM), and content loss with the Nash-Sutcliff Model Efficiency (NS). The MSSIM index allowed us to gain insight into the model’s regional characteristics and shows that relying exclusively on point-based error functions, widely used in statistical downscaling, may not be enough to reliably simulate regional precipitation characteristics. Further use of structural loss functions within CNN-based downscaling methods may lead to higher quality downscaled climate model products.
Large-area, long-duration droughts are among Canada’s costliest natural disasters. A particularly vulnerable region includes the Canadian Prairies where droughts have, and are projected to continue to have, major impacts. However, individual droughts often differ in their stages such as onset, growth, persistence, retreat, and duration. Using the Standardized Precipitation Evapotranspiration Index, this study assesses historical and projected future changes to the stages and other characteristics of severe drought occurrence across the agricultural region of the Canadian Prairies. Ten severe droughts occurred during the 1900–2014 period with each having unique temporal and spatial characteristics. Projected changes from 29 global climate models (GCMs) with three representative concentration pathways reveal an increase in severe drought occurrence, particularly toward the end of this century with a high emissions scenario. For the most part, the overall duration and intensity of future severe drought conditions is projected to increase mainly due to longer persistence stages, while growth and retreat stages are generally shorter. Considerable variability exists among individual GCM projections, including their ability to simulate observed severe drought characteristics. This study has increased understanding in potential future changes to a little studied aspect of droughts, namely, their stages and associated characteristics. This knowledge can aid in developing future adaptation strategies.
Abstract. The annual carbon and water dynamics of two eastern North American temperate forests were compared over a 6-year period from 2012 to 2017. The geographic location, forest age, soil, and climate were similar between the two stands; however, stand composition varied in terms of tree leaf-retention and shape strategy: one stand was a deciduous broadleaf forest, while the other was an evergreen needleleaf forest. The 6-year mean annual net ecosystem productivity (NEP) of the coniferous forest was slightly higher and more variable (218±109 g C m−2 yr−1) compared to that of the deciduous forest NEP (200±83 g C m−2 yr−1). Similarly, the 6-year mean annual evapotranspiration (ET) of the coniferous forest was higher (442±33 mm yr−1) than that of the deciduous forest (388±34 mm yr−1), but with similar interannual variability. Summer meteorology greatly impacted the carbon and water fluxes in both stands; however, the degree of response varied among the two stands. In general, warm temperatures caused higher ecosystem respiration (RE), resulting in reduced annual NEP values – an impact that was more pronounced at the deciduous broadleaf forest compared to the evergreen needleleaf forest. However, during warm and dry years, the evergreen forest had largely reduced annual NEP values compared to the deciduous forest. Variability in annual ET at both forests was related most to the variability in annual air temperature (Ta), with the largest annual ET observed in the warmest years in the deciduous forest. Additionally, ET was sensitive to prolonged dry periods that reduced ET at both stands, although the reduction at the coniferous forest was relatively larger than that of the deciduous forest. If prolonged periods (weeks to months) of increased Ta and reduced precipitation are to be expected under future climates during summer months in the study region, our findings suggest that the deciduous broadleaf forest will likely remain an annual carbon sink, while the carbon sink–source status of the coniferous forest remains uncertain.
Abstract. The large stocks of soil organic carbon (SOC) in soils and deposits of the northern permafrost region are sensitive to global warming and permafrost thawing. The potential release of this carbon (C) as greenhouse gases to the atmosphere does not only depend on the total quantity of soil organic matter (SOM) affected by warming and thawing, but it also depends on its lability (i.e., the rate at which it will decay). In this study we develop a simple and robust classification scheme of SOM lability for the main types of soils and deposits in the northern permafrost region. The classification is based on widely available soil geochemical parameters and landscape unit classes, which makes it useful for upscaling to the entire northern permafrost region. We have analyzed the relationship between C content and C-CO2 production rates of soil samples in two different types of laboratory incubation experiments. In one experiment, ca. 240 soil samples from four study areas were incubated using the same protocol (at 5 ∘C, aerobically) over a period of 1 year. Here we present C release rates measured on day 343 of incubation. These long-term results are compared to those obtained from short-term incubations of ca. 1000 samples (at 12 ∘C, aerobically) from an additional three study areas. In these experiments, C-CO2 production rates were measured over the first 4 d of incubation. We have focused our analyses on the relationship between C-CO2 production per gram dry weight per day (µgC-CO2 gdw−1 d−1) and C content (%C of dry weight) in the samples, but we show that relationships are consistent when using C ∕ N ratios or different production units such as µgC per gram soil C per day (µgC-CO2 gC−1 d−1) or per cm3 of soil per day (µgC-CO2 cm−3 d−1). C content of the samples is positively correlated to C-CO2 production rates but explains less than 50 % of the observed variability when the full datasets are considered. A partitioning of the data into landscape units greatly reduces variance and provides consistent results between incubation experiments. These results indicate that relative SOM lability decreases in the order of Late Holocene eolian deposits to alluvial deposits and mineral soils (including peaty wetlands) to Pleistocene yedoma deposits to C-enriched pockets in cryoturbated soils to peat deposits. Thus, three of the most important SOC storage classes in the northern permafrost region (yedoma, cryoturbated soils and peatlands) show low relative SOM lability. Previous research has suggested that SOM in these pools is relatively undecomposed, and the reasons for the observed low rates of decomposition in our experiments need urgent attention if we want to better constrain the magnitude of the thawing permafrost carbon feedback on global warming.
Abstract. Connections between vegetation and soil thermal dynamics are critical for estimating the vulnerability of permafrost to thaw with continued climate warming and vegetation changes. The interplay of complex biophysical processes results in a highly heterogeneous soil temperature distribution on small spatial scales. Moreover, the link between topsoil temperature and active layer thickness remains poorly constrained. Sixty-eight temperature loggers were installed at 1–3 cm depth to record the distribution of topsoil temperatures at the Trail Valley Creek study site in the northwestern Canadian Arctic. The measurements were distributed across six different vegetation types characteristic for this landscape. Two years of topsoil temperature data were analysed statistically to identify temporal and spatial characteristics and their relationship to vegetation, snow cover, and active layer thickness. The mean annual topsoil temperature varied between −3.7 and 0.1 ∘C within 0.5 km2. The observed variation can, to a large degree, be explained by variation in snow cover. Differences in snow depth are strongly related with vegetation type and show complex associations with late-summer thaw depth. While cold winter soil temperature is associated with deep active layers in the following summer for lichen and dwarf shrub tundra, we observed the opposite beneath tall shrubs and tussocks. In contrast to winter observations, summer topsoil temperature is similar below all vegetation types with an average summer topsoil temperature difference of less than 1 ∘C. Moreover, there is no significant relationship between summer soil temperature or cumulative positive degree days and active layer thickness. Altogether, our results demonstrate the high spatial variability of topsoil temperature and active layer thickness even within specific vegetation types. Given that vegetation type defines the direction of the relationship between topsoil temperature and active layer thickness in winter and summer, estimates of permafrost vulnerability based on remote sensing or model results will need to incorporate complex local feedback mechanisms of vegetation change and permafrost thaw.
Abstract. Extreme events are widely studied across the world because of their major implications for many aspects of society and especially floods. These events are generally studied in terms of precipitation or temperature extreme indices that are often not adapted for regions affected by floods caused by snowmelt. The rain on snow index has been widely used, but it neglects rain-only events which are expected to be more frequent in the future. In this study, we identified a new winter compound index and assessed how large-scale atmospheric circulation controls the past and future evolution of these events in the Great Lakes region. The future evolution of this index was projected using temperature and precipitation from the Canadian Regional Climate Model large ensemble (CRCM5-LE). These climate data were used as input in Precipitation Runoff Modelling System (PRMS) hydrological model to simulate the future evolution of high flows in three watersheds in southern Ontario. We also used five recurrent large-scale atmospheric circulation patterns in north-eastern North America and identified how they control the past and future variability of the newly created index and high flows. The results show that daily precipitation higher than 10 mm and temperature higher than 5 ∘C were necessary historical conditions to produce high flows in these three watersheds. In the historical period, the occurrences of these heavy rain and warm events as well as high flows were associated with two main patterns characterized by high Z500 anomalies centred on eastern Great Lakes (HP regime) and the Atlantic Ocean (South regime). These hydrometeorological extreme events will still be associated with the same atmospheric patterns in the near future. The future evolution of the index will be modulated by the internal variability of the climate system, as higher Z500 on the east coast will amplify the increase in the number of events, especially the warm events. The relationship between the extreme weather index and high flows will be modified in the future as the snowpack reduces and rain becomes the main component of high-flow generation. This study shows the value of the CRCM5-LE dataset in simulating hydrometeorological extreme events in eastern Canada and better understanding the uncertainties associated with internal variability of climate.
Abstract. Glacier mass balance (MB) data are crucial to understanding and quantifying the regional effects of climate on glaciers and the high-mountain water cycle, yet observations cover only a small fraction of glaciers in the world. We present a dataset of annual glacier-wide mass balance of all the glaciers in the French Alps for the 1967–2015 period. This dataset has been reconstructed using deep learning (i.e. a deep artificial neural network) based on direct MB observations and remote-sensing annual estimates, meteorological reanalyses and topographical data from glacier inventories. The method's validity was assessed previously through an extensive cross-validation against a dataset of 32 glaciers, with an estimated average error (RMSE) of 0.55 mw.e.a-1, an explained variance (r2) of 75 % and an average bias of −0.021 mw.e.a-1. We estimate an average regional area-weighted glacier-wide MB of −0.69±0.21 (1σ) mw.e.a-1 for the 1967–2015 period with negative mass balances in the 1970s (−0.44 mw.e.a-1), moderately negative in the 1980s (−0.16 mw.e.a-1) and an increasing negative trend from the 1990s onwards, up to −1.26 mw.e.a-1 in the 2010s. Following a topographical and regional analysis, we estimate that the massifs with the highest mass losses for the 1967–2015 period are the Chablais (−0.93 mw.e.a-1), Champsaur (−0.86 mw.e.a-1), and Haute-Maurienne and Ubaye ranges (−0.84 mw.e.a-1 each), and the ones presenting the lowest mass losses are the Mont-Blanc (−0.68 mw.e.a-1), Oisans and Haute-Tarentaise ranges (−0.75 mw.e.a-1 each). This dataset – available at https://doi.org/10.5281/zenodo.3925378 (Bolibar et al., 2020a) – provides relevant and timely data for studies in the fields of glaciology, hydrology and ecology in the French Alps in need of regional or glacier-specific annual net glacier mass changes in glacierized catchments.
Abstract. A sensor comprised of an electronic circuit and a hybrid single and dual heat pulse probe was constructed and tested along with a novel signal processing procedure to determine changes in the effective dual-probe spacing radius over the time of measurement. The circuit utilized a proportional–integral–derivative (PID) controller to control heat inputs into the soil medium in lieu of a variable resistor. The system was designed for onboard signal processing and implemented USB, RS-232, and SDI-12 interfaces for machine-to-machine (M2M) exchange of data, thereby enabling heat inputs to be adjusted to soil conditions and data availability shortly after the time of experiment. Signal processing was introduced to provide a simplified single-probe model to determine thermal conductivity instead of reliance on late-time logarithmic curve fitting. Homomorphic and derivative filters were used with a dual-probe model to detect changes in the effective probe spacing radius over the time of experiment to compensate for physical changes in radius as well as model and experimental error. Theoretical constraints were developed for an efficient inverse of the exponential integral on an embedded system. Application of the signal processing to experiments on sand and peat improved the estimates of soil water content and bulk density compared to methods of curve fitting nominally used for heat pulse probe experiments. Applications of the technology may be especially useful for soil and environmental conditions under which effective changes in probe spacing radius need to be detected and compensated for over the time of experiment.
Abstract. This paper introduces the Topographically InformEd Regression (TIER) model, which uses terrain attributes in a regression framework to distribute in situ observations of precipitation and temperature to a grid. The framework enables our understanding of complex atmospheric processes (e.g., orographic precipitation) to be encoded into a statistical model in an easy-to-understand manner. TIER is developed in a modular fashion with key model parameters exposed to the user. This enables the user community to easily explore the impacts of our methodological choices made to distribute sparse, irregularly spaced observations to a grid in a systematic fashion. The modular design allows incorporating new capabilities in TIER. Intermediate processing variables are also output to provide a more complete understanding of the algorithm and any algorithmic changes. The framework also provides uncertainty estimates. This paper presents a brief model evaluation and demonstrates that the TIER algorithm is functioning as expected. Several variations in model parameters and changes in the distributed variables are described. A key conclusion is that seemingly small changes in a model parameter result in large changes to the final distributed fields and their associated uncertainty estimates.
Abstract. Aerosol- and cloud-induced changes in diffuse light have important impacts on the global land carbon cycle, as they alter light distribution and photosynthesis in vegetation canopies. However, this effect remains poorly represented or evaluated in current land surface models. Here, we add a light partitioning module and a new canopy light transmission module to the ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems) land surface model (trunk version, v5453) and use the revised model, ORCHIDEE_DF, to estimate the fraction of diffuse light and its effect on gross primary production (GPP) in a multilayer canopy. We evaluate the new parameterizations using flux observations from 159 eddy covariance sites over the globe. Our results show that, compared with the original model, ORCHIDEE_DF improves the GPP simulation under sunny conditions and captures the observed higher photosynthesis under cloudier conditions in most plant functional types (PFTs). Our results also indicate that the larger GPP under cloudy conditions compared with sunny conditions is mainly driven by increased diffuse light in the morning and in the afternoon as well as by a decreased vapor pressure deficit (VPD) and decreased air temperature at midday. The observations show that the strongest positive effects of diffuse light on photosynthesis are found in the range from 5 to 20 ∘C and at a VPD < 1 kPa. This effect is found to decrease when the VPD becomes too large or the temperature falls outside of the abovementioned range, which is likely due to the increasing stomatal resistance to leaf CO2 uptake. ORCHIDEE_DF underestimates the diffuse light effect at low temperature in all PFTs and overestimates this effect at high temperature and at a high VPD in grasslands and croplands. The new model has the potential to better investigate the impact of large-scale aerosol changes and long-term changes in cloudiness on the terrestrial carbon budget, both in the historical period and in the context of future air quality policies and/or climate engineering.
Abstract. Climate change is anticipated to impact the hydrology of the Saskatchewan River, which originates in the Canadian Rockies mountain range. To better understand the climate change impacts in the mountain headwaters of this basin, a physically based hydrological model was developed for this basin using the Cold Regions Hydrological Modelling platform (CRHM) for Marmot Creek Research Basin (∼9.4 km2), located in the Front Ranges of the Canadian Rockies. Marmot Creek is composed of ecozones ranging from montane forests to alpine tundra and alpine exposed rock and includes both large and small clearcuts. The model included blowing and intercepted snow redistribution, sublimation, energy-balance snowmelt, slope and canopy effects on melt, Penman–Monteith evapotranspiration, infiltration to frozen and unfrozen soils, hillslope hydrology, streamflow routing, and groundwater components and was parameterised without calibration from streamflow. Near-surface outputs from the 4 km Weather Research and Forecasting (WRF) model were bias-corrected using the quantile delta mapping method with respect to meteorological data from five stations located from low-elevation montane forests to alpine ridgetops and running over October 2005–September 2013. The bias-corrected WRF outputs during a current period (2005–2013) and a future pseudo global warming period (PGW, 2091–2099) were used to drive model simulations to assess changes in Marmot Creek's hydrology. Under a “business-as-usual” forcing scenario, Representative Concentration Pathway 8.5 (RCP8.5) in PGW, the basin will warm up by 4.7 ∘C and receive 16 % more precipitation, which will lead to a 40 mm decline in seasonal peak snowpack, 84 mm decrease in snowmelt volume, 0.2 mm d−1 slower melt rate, and 49 d shorter snow-cover duration. The alpine snow season will be shortened by almost 1.5 months, but at some lower elevations there will be large decreases in peak snowpack (∼45 %) in addition to a shorter snow season. Declines in the peak snowpack will be much greater in clearcuts than under mature forest canopies. In alpine and treeline ecozones, blowing snow transport and sublimation will be suppressed by higher-threshold wind speeds for transport, in forest ecozones, sublimation losses from intercepted snow will decrease due to faster unloading and drip, and throughout the basin, evapotranspiration will increase due to a longer snow-free season and more rainfall. Runoff will begin earlier in all ecozones, but, as a result of variability in surface and subsurface hydrology, forested and alpine ecozones will generate the greatest runoff volumetric increases, ranging from 12 % to 25 %, whereas the treeline ecozone will have a small (2 %) decrease in runoff volume due to decreased melt volumes from smaller snowdrifts. The shift in timing in streamflow will be notable, with 236 % higher flows in spring months and 12 % lower flows in summer and 13 % higher flows in early fall. Overall, Marmot Creek's annual streamflow discharge will increase by 18 % with PGW, without a change in its streamflow generation efficiency, despite its basin shifting from primarily snowmelt runoff towards rainfall-dominated runoff generation.
Abstract. Fluvial systems in southern Ontario are regularly affected by widespread early-spring flood events primarily caused by rain-on-snow events. Recent studies have shown an increase in winter floods in this region due to increasing winter temperature and precipitation. Streamflow simulations are associated with uncertainties mainly due to the different scenarios of greenhouse gas emissions, global climate models (GCMs) or the choice of the hydrological model. The internal variability of climate, defined as the chaotic variability of atmospheric circulation due to natural internal processes within the climate system, is also a source of uncertainties to consider. Uncertainties of internal variability can be assessed using hydrological models fed by downscaled data of a global climate model large ensemble (GCM-LE), but GCM outputs have too coarse of a scale to be used in hydrological modeling. The Canadian Regional Climate Model Large Ensemble (CRCM5-LE), a 50-member ensemble downscaled from the Canadian Earth System Model version 2 Large Ensemble (CanESM2-LE), was developed to simulate local climate variability over northeastern North America under different future climate scenarios. In this study, CRCM5-LE temperature and precipitation projections under an RCP8.5 scenario were used as input in the Precipitation Runoff Modeling System (PRMS) to simulate streamflow at a near-future horizon (2026–2055) for four watersheds in southern Ontario. To investigate the role of the internal variability of climate in the modulation of streamflow, the 50 members were first grouped in classes of similar projected change in January–February streamflow and temperature and precipitation between 1961–1990 and 2026–2055. Then, the regional change in geopotential height (Z500) from CanESM2-LE was calculated for each class. Model simulations showed an average January–February increase in streamflow of 18 % (±8.7) in Big Creek, 30.5 % (±10.8) in Grand River, 29.8 % (±10.4) in Thames River and 31.2 % (±13.3) in Credit River. A total of 14 % of all ensemble members projected positive Z500 anomalies in North America's eastern coast enhancing rain, snowmelt and streamflow volume in January–February. For these members the increase of streamflow is expected to be as high as 31.6 % (±8.1) in Big Creek, 48.3 % (±11.1) in Grand River, 47 % (±9.6) in Thames River and 53.7 % (±15) in Credit River. Conversely, 14 % of the ensemble projected negative Z500 anomalies in North America's eastern coast and were associated with a much lower increase in streamflow: 8.3 % (±7.8) in Big Creek, 18.8 % (±5.8) in Grand River, 17.8 % (±6.4) in Thames River and 18.6 % (±6.5) in Credit River. These results provide important information to researchers, managers, policymakers and society about the expected ranges of increase in winter streamflow in a highly populated region of Canada, and they will help to explain how the internal variability of climate is expected to modulate the future streamflow in this region.
Abstract. Blowing snow transport has considerable impact on the hydrological cycle in alpine regions both through the redistribution of the seasonal snowpack and through sublimation back into the atmosphere. Alpine energy and mass balances are typically modeled with time-averaged approximations of sensible and latent heat fluxes. This oversimplifies nonstationary turbulent mixing in complex terrain and may overlook important exchange processes for hydrometeorological prediction. To determine if specific turbulent motions are responsible for warm- and dry-air advection during blowing snow events, quadrant analysis and variable interval time averaging was used to investigate turbulent time series from the Fortress Mountain Snow Laboratory alpine study site in the Canadian Rockies, Alberta, Canada, during the winter of 2015–2016. By analyzing wind velocity and sonic temperature time series with concurrent blowing snow, such turbulent motions were found to supply substantial sensible heat to near-surface wind flows. These motions were responsible for temperature fluctuations of up to 1 ∘C, a considerable change for energy balance estimation. A simple scaling relationship was derived that related the frequency of dominant downdraft and updraft events to their duration and local variance. This allows for the first parameterization of entrained or advected energy for time-averaged representations of blowing snow sublimation and suggests that advection can strongly reduce thermodynamic feedbacks between blowing snow sublimation and the near-surface atmosphere. The downdraft and updraft scaling relationship described herein provides a significant step towards a more physically based blowing snow sublimation model with more realistic mixing of atmospheric heat. Additionally, calculations of return frequencies and event durations provide a field-measurement context for recent findings of nonstationarity impacts on sublimation rates.
Abstract. Accurate knowledge of snow depth distributions in mountain catchments is critical for applications in hydrology and ecology. Recently, a method was proposed to map snow depth at meter-scale resolution from very-high-resolution stereo satellite imagery (e.g., Pléiades) with an accuracy close to 0.5 m. However, the validation was limited to probe measurements and unmanned aircraft vehicle (UAV) photogrammetry, which sampled a limited fraction of the topographic and snow depth variability. We improve upon this evaluation using accurate maps of the snow depth derived from Airborne Snow Observatory laser-scanning measurements in the Tuolumne river basin, USA. We find a good agreement between both datasets over a snow-covered area of 138 km2 on a 3 m grid, with a positive bias for a Pléiades snow depth of 0.08 m, a root mean square error of 0.80 m and a normalized median absolute deviation (NMAD) of 0.69 m. Satellite data capture the relationship between snow depth and elevation at the catchment scale and also small-scale features like snow drifts and avalanche deposits at a typical scale of tens of meters. The random error at the pixel level is lower in snow-free areas than in snow-covered areas, but it is reduced by a factor of 2 (NMAD of approximately 0.40 m for snow depth) when averaged to a 36 m grid. We conclude that satellite photogrammetry stands out as a convenient method to estimate the spatial distribution of snow depth in high mountain catchments.
Abstract. Northwestern Alaska has been highly affected by changing climatic patterns with new temperature and precipitation maxima over the recent years. In particular, the Baldwin and northern Seward peninsulas are characterized by an abundance of thermokarst lakes that are highly dynamic and prone to lake drainage like many other regions at the southern margins of continuous permafrost. We used Sentinel-1 synthetic aperture radar (SAR) and Planet CubeSat optical remote sensing data to analyze recently observed widespread lake drainage. We then used synoptic weather data, climate model outputs and lake ice growth simulations to analyze potential drivers and future pathways of lake drainage in this region. Following the warmest and wettest winter on record in 2017/2018, 192 lakes were identified as having completely or partially drained by early summer 2018, which exceeded the average drainage rate by a factor of ∼ 10 and doubled the rates of the previous extreme lake drainage years of 2005 and 2006. The combination of abundant rain- and snowfall and extremely warm mean annual air temperatures (MAATs), close to 0 ∘C, may have led to the destabilization of permafrost around the lake margins. Rapid snow melt and high amounts of excess meltwater further promoted rapid lateral breaching at lake shores and consequently sudden drainage of some of the largest lakes of the study region that have likely persisted for millennia. We hypothesize that permafrost destabilization and lake drainage will accelerate and become the dominant drivers of landscape change in this region. Recent MAATs are already within the range of the predictions by the University of Alaska Fairbanks' Scenarios Network for Alaska and Arctic Planning (UAF SNAP) ensemble climate predictions in scenario RCP6.0 for 2100. With MAAT in 2019 just below 0 ∘C at the nearby Kotzebue, Alaska, climate station, permafrost aggradation in drained lake basins will become less likely after drainage, strongly decreasing the potential for freeze-locking carbon sequestered in lake sediments, signifying a prominent regime shift in ice-rich permafrost lowland regions.
Abstract. The 30-year simulations of seasonal snow cover in 22 physically based models driven with bias-corrected meteorological reanalyses are examined at four sites with long records of snow observations. Annual snow cover durations differ widely between models, but interannual variations are strongly correlated because of the common driving data. No significant trends are observed in starting dates for seasonal snow cover, but there are significant trends towards snow cover ending earlier at two of the sites in observations and most of the models. A simplified model with just two parameters controlling solar radiation and sensible heat contributions to snowmelt spans the ranges of snow cover durations and trends. This model predicts that sites where snow persists beyond annual peaks in solar radiation and air temperature will experience rapid decreases in snow cover duration with warming as snow begins to melt earlier and at times of year with more energy available for melting.
Abstract. We present a novel approach to simulate and reconstruct annual glacier-wide surface mass balance (SMB) series based on a deep artificial neural network (ANN; i.e. deep learning). This method has been included as the SMB component of an open-source regional glacier evolution model. While most glacier models tend to incorporate more and more physical processes, here we take an alternative approach by creating a parameterized model based on data science. Annual glacier-wide SMBs can be simulated from topo-climatic predictors using either deep learning or Lasso (least absolute shrinkage and selection operator; regularized multilinear regression), whereas the glacier geometry is updated using a glacier-specific parameterization. We compare and cross-validate our nonlinear deep learning SMB model against other standard linear statistical methods on a dataset of 32 French Alpine glaciers. Deep learning is found to outperform linear methods, with improved explained variance (up to +64 % in space and +108 % in time) and accuracy (up to +47 % in space and +58 % in time), resulting in an estimated r2 of 0.77 and a root-mean-square error (RMSE) of 0.51 m w.e. Substantial nonlinear structures are captured by deep learning, with around 35 % of nonlinear behaviour in the temporal dimension. For the glacier geometry evolution, the main uncertainties come from the ice thickness data used to initialize the model. These results should encourage the use of deep learning in glacier modelling as a powerful nonlinear tool, capable of capturing the nonlinearities of the climate and glacier systems, that can serve to reconstruct or simulate SMB time series for individual glaciers in a whole region for past and future climates.
Resource-access road crossings are expected to alter peatland hydrological properties by obstructing surface and sub-surface water flows. We conducted a multi-year study at two boreal peatlands – a forested bog and a shrubby rich fen near Peace River, Alberta – to study the impacts of resource access roads on the hydrology of adjacent peatland. Field measurements (bi-weekly depth to water table and hydraulic head, one-time hydraulic conductivity) during the growing seasons (May-August) of 2016 and 2017 were taken from sampling plots representing: 1) sides of the road (upstream and downstream); 2) distance from the road (obstruction); and 3) distance from culverts. Compared to the growing season average precipitation for the region of 1.8 mm d−1, the study period had very wet conditions in 2016 (3.7 mm d−1) and dry conditions in 2017 (1.1 mm d−1). In contrast to our assumptions, resource access road disturbed the surface and sub-surface water flow at the bog, but the effect was minimal at the fen as the road orientation was nearly parallel to the flow direction at the latter. At the bog, the shallowest depth to water table position was observed at upstream areas closer to the road, when culverts were located >20 m distance from transects. In contrast, when culverts were present <2 m from the transects, variation in hydrological conditions between upstream and downstream areas were greatly reduced. Our work shows road effects on peatland hydrology could be minimized by aligning roads parallel to the water flow direction when possible. If water flow is perpendicular to the road, adequate spacing and installation of culverts could help to reduce flow obstruction.
Abstract Water quality of freshwater lakes within the Laurentian Great Lakes region is vulnerable to degradation owing to multiple environmental stressors including climate change, alterations in land use, and the introduction of invasive species. Our research questions were two‐fold: (1) What are the temporal patterns and trends in water quality? (2) Are climate, invasive species and lake morphology associated with changes in water quality? Our study incorporated timeseries data for at least 20 years from 36 lakes in Ontario and Wisconsin sampled between 1976 and 2016. We evaluated patterns in water quality (total phosphorus [TP], total nitrogen, dissolved organic carbon [DOC], and chlorophyll a [Chl a ]) using segmented regression analysis which identified significant breakpoints in Chl a and TP in the 1900s to mid‐2000s after which Chl a and TP began to increase, whereas breakpoints in DOC exhibited increasing trends prior to the year 2000 with levels declining afterward. Next, we examined linear trends in water quality and climate (temperature and precipitation) using Sen slope analysis where, generally, over the past 40 years, lake TP and Chl a have significantly declined, whereas DOC has increased. Lastly, we conducted a redundancy analysis (RDA) to identify how climate, lake morphology, and the presence of invasive dreissenid mussels contributed to changes in water quality. The RDA revealed that precipitation, air temperature, and morphology explained 73.1% of the variation in water quality trends for the Great Lakes whereas precipitation, temperature, morphology, and occurrence of mussels explained 45.6% of the variation for smaller inland lakes.
A key ecophysiological measurement is the flow of water (or sap) along the tree's water-transport system, which is an essential process for maintaining the hydraulic connection within the soil–plant–atmosphere continuum. The thermal dissipation method (TDM) is widespread in the scientific community for measuring sap flow and has provided novel insights into water use and its environmental sensitivity, from the tree- to the forest-stand level. Yet, methodological approaches to determine sap flux density (SFD) from raw TDM measurements remain case-specific, introducing uncertainties and hampering data syntheses and meta-analyses. Here, we introduce the r package TREX (TRee sap flow EXtractor), incorporating a wide range of sap flow data-processing procedures to quantify SFD from raw TDM measurements. TREX provides functions for (a) importing and assimilating raw measurements, (b) data quality control and filtering and (c) calculating standardized SFD outputs and their associated uncertainties according to different data-processing methods. A case study using a Norway spruce tree illustrates TREX's functionalities, featuring interactive data curation and generating outputs in a reproducible and transparent way. The calculations of SFD in TREX can, for instance, use the original TDM calibration coefficients, user-supplied calibration parameters or calibration data from a recently compiled database of 22 studies and 37 species. Moreover, the package includes an automatic procedure for quantifying the sensitivity and uncertainty of the obtained results to user-defined assumptions and parameter values, by means of a state-of-the-art global sensitivity analysis. Time series of plant ecophysiological measurements are becoming increasingly available and enhance our understanding of climate change impacts on tree functioning. TREX allows for establishing a baseline for data processing of TDM measurements and supports comparability between case studies, facilitating robust, transparent and reproducible large-scale syntheses of sap flow patterns. Moreover, TREX facilitates the simultaneous application of multiple common data-processing approaches to convert raw data to physiological relevant quantities. This allows for robust quantification of the impact (i.e. sensitivity and uncertainty) of user-specific choices and methodological assumptions, which is necessary for process understanding and policy making.
The Three Gorges Reservoir (TGR), China, is the largest man-made reservoir in the world. Harmful algal blooms (HABs) have become common since the reservoir’s impoundment in 2003. To investigate the mechanisms of HAB formation in the reservoir and to determine possible mitigation measures, we conducted surveys over a range of spatial scales and temporal resolutions over a 2-y period (March 2013–December 2014). The large-scale survey (the portion of the reservoir on the main stem of the Yangtze River and 22 tributaries) revealed that cyanobacteria blooms were restricted to the upper reaches of the tributaries. The medium-scale survey (1 tributary: Pengxi River) showed that cyanobacteria blooms were confined to the early-spring period with the initiation of thermal stratification in the deep-water column. The small-scale survey (a local, backwater lake in the Pengxi River), which was of higher-temporal resolution than the other 2 surveys, demonstrated that the bloom occurred at the same time as the formation of a surface-density layer unique to the geomorphology and water-control management of the reservoir. The vertical distributions of the bloom and surface-density layer appeared to be related, although the density layer persisted beyond the duration of the HABs. We hypothesized that limited nutrient diffusion into these density layers could result in nutrient limitation despite the hyper-eutrophic conditions that generally characterize the TGR basin. In the main stem of the Yangtze River and lower reaches of the tributaries in the TGR, algal blooms were not observed because of continuous, deep mixing throughout the year. We conclude that the hydrological stability and geomorphological characteristics of the TGR play critical roles in regulating the temporal and spatial patterns of algal blooms and that artificial mixing of the water column is currently the best option to limit HAB formation, especially in upper tributaries.
Abstract Climate change poses a significant threat to Arctic freshwater biodiversity, but impacts depend upon the strength of organism response to climate‐related drivers. Currently, there is insufficient knowledge about Arctic freshwater biodiversity patterns to guide assessment, prediction, and management of biodiversity change. As part of the Circumpolar Biodiversity Monitoring Program's first freshwater assessment, we evaluated diversity of diatoms, benthic macroinvertebrates, and fish in North American Arctic rivers. Alpha diversity was assessed in relation to temperature, water chemistry, bedrock geology, and glaciation history to identify important environmental correlates. Biotic composition was compared among groups to evaluate response to environmental gradients. Macroinvertebrate α‐diversity declined strongly with increasing latitude from 48°N to 82°N, whereas diatom and fish diversity peaked around 70°N without a clear latitudinal decline. Macroinvertebrate diversity was significantly positively related to air temperature. Diatom diversity was related to bedrock geology and temperature, whereas fish diversity was related to glaciation history. Fish and macroinvertebrate assemblages differed between sites in western Canada, where invertebrate composition was more variable, and Alaska, where fish composition was more variable. In sites with both diatom and macroinvertebrate data, diatom composition was distinct in Alaska, where richness was highest in former glacial refugia. Macroinvertebrate composition was distinct in lowest‐latitude eastern and high‐latitude western Canadian sites where temperature was highest. Temperature, precipitation, geology, calcium, and substrate size were important environmental correlates for diatoms and macroinvertebrates, although the relative importance of each correlate differed. Diatom taxa were most strongly associated with water chemistry, whereas benthic invertebrate composition related most strongly to precipitation and temperature. This large‐scale study provides the most substantial integration and analysis of river diatom, macroinvertebrate, and fish data from the North American Arctic to date. Findings suggest that macroinvertebrates will show the strongest response to climate‐related shifts in temperature, whereas diatoms and fish are more likely to respond to climate‐induced shifts in nutrients and hydraulic connectivity. However, significant gaps in data coverage limited our ability to reliably evaluate spatial patterns and detect change. These gaps could be reduced by improving collaborative efforts between the U.S.A. and Canada to harmonise future monitoring.

2019

The impacts of unconventional oil and gas production via high-volume hydraulic fracturing (HVHF) on water resources, such as water use, groundwater and surface water contamination, and disposal of produced waters, have received a great deal of attention over the past decade. Conventional oil and gas production (e.g., enhanced oil recovery [EOR]), which has been occurring for more than a century in some areas of North America, shares the same environmental concerns, but has received comparatively little attention. Here, we compare the amount of produced water versus saltwater disposal (SWD) and injection for EOR in several prolific hydrocarbon producing regions in the United States and Canada. The total volume of saline and fresh to brackish water injected into depleted oil fields and nonproductive formations is greater than the total volume of produced waters in most regions. The addition of fresh to brackish "makeup" water for EOR may account for the net gain of subsurface water. The total amount of water injected and produced for conventional oil and gas production is greater than that associated with HVHF and unconventional oil and gas production by well over a factor of 10. Reservoir pressure increases from EOR and SWD wells are low compared to injection of fluids for HVHF, however, the longer duration of injections could allow for greater solute transport distances and potential for contamination. Attention should be refocused from the subsurface environmental impacts of HVHF to the oil and gas industry as a whole.
There is growing interest to develop processes for creating user-informed watershed scale models of hydrology and water quality and to assist in decision-making for balanced policies for managing watersheds. Watershed models can be enhanced with the incorporation of social dimensions of watershed management as brought forward by participants such as the perspectives, values, and norms of people that depend on the land, water, and ecosystems for sustenance, economies, and overall wellbeing. In this work, we explore the value of combining both qualitative and quantitative methods and social science data to enhance salience and legitimacy of watershed models so that end-users are more engaged. We discuss pilot testing and engagement workshops for building and testing a systems dynamics model of the Qu'Appelle Valley to gather insights from local farmers and understand their perceptions of Beneficial Management Practices (BMPs). Mixed-method workshops with agricultural producers in the Qu'Appelle Watershed gathered feedback on the developing model and the incorporation of social determinants affecting decision-making. Analysis of focus groups and factor analysis of Q-sorts were used to identify the desired components of the model, and whether it supported farmers' understanding of the potential effects of BMPs on water quality. We explored farmers' engagement with models testing BMPs and the potential of incorporating their decision processes within the model itself. Finally, we discuss the reception of the process and the practicality of the approach in providing legitimate and credible decision support tools for a community of farmers.
Rural communities dependent on unregulated drinking water are potentially at increased health risk from exposure to contaminants. Perception of drinking water safety influences water consumption, exposure, and health risk. A community-based participatory approach and probabilistic Bayesian methods were applied to integrate risk perception in a holistic human health risk assessment. Tap water arsenic concentrations and risk perception data were collected from two Saskatchewan communities. Drinking water health standards were exceeded in 67% (51/76) of households in Rural Municipality #184 (RM184) and 56% (25/45) in Beardy's and Okemasis First Nation (BOFN). There was no association between the presence of a health exceedance and risk perception. Households in RM184 or with an annual income >$50,000 were most likely to have in-house water treatment. The probability of consuming tap water perceived as safe (92%) or not safe (0%) suggested that households in RM184 were unlikely to drink water perceived as not safe. The probability of drinking tap water perceived as safe (77%) or as not safe (11%) suggested households in BOFN contradicted their perception and consumed water perceived as unsafe. Integration of risk perception lowered the adult incremental lifetime cancer risk by 3% to 1.3 × 10-5 (95% CI 8.4 × 10-8 to 9.0 × 10-5 ) for RM184 and by 8.9 × 10-6 (95% CI 2.2 × 10-7 to 5.9 × 10-5 ) for BOFN. Probability of exposure to arsenic concentrations >1:100,000, negligible cancer risk, was 23% for RM184 and 22% for BOFN.
Provision of safe water on reserves is an ongoing problem in Canada that can be addressed by mobilizing water knowledge across diverse platforms to a variety of audiences. A participatory artistic animation video on the lived experiences of Elderswith water in Yellow Quill First Nation, Treaty Four territory, was created to mobilize knowledge beyond conventional peer-review channels. Research findings from interviews with 22 Elders were translated through a collaborative process into a video with a storytelling format that harmonized narratives, visual arts, music, and meaningful symbols. Three themes emerged which centered on the spirituality of water, the survival need for water, and standoffs in water management. The translation process, engagement and video output were evaluated using an autoethnographic approach with two members of the research team. We demonstrate how the collaborative research process and co-created video enhance community-based participatory knowledge translation and sharing. We also express how the video augments First Nations community ownership, control, access and possession (OCAP) of research information that aligns with their storytelling traditions and does so in a youth-friendly, e-compatible form. Through the evaluative process we share lessons learned about the value and effectiveness of the video as a tool for fostering partnerships, and reconciliation. The benefits and positive impacts of the video for the Yellow Quill community and for community members are discussed.
Access to drinkable water is essential to human life. The consequence of unsafe drinking water can be damaging to communities and catastrophic to human health. Today, one in five First Nation communities in Canada is on a boil water advisory, with some advisories lasting over 10 years. Factors contributing to this problem stretch back to colonial structures and institutional arrangement that reproduce woefully inadequate community drinking water systems. Notwithstanding these challenges, First Nation communities remain diligent, adaptive, and innovative in their efforts to provide drinkable water to their community members. One example is through the practice of source water protection planning. Source water is untreated water from groundwater or surface water that supplies drinking water for human consumption. Source water protection is operationalized through land and water planning activities aimed at reducing the risk of contamination from entering a public drinking water supply. Here, we introduce a source water protection planning process at Muskowekwan First Nation, Treaty 4, Saskatchewan. The planning process followed a community-based participatory approach guided by trust, respect, and reciprocity between community members and university researchers. Community members identified threats to the drinking water source followed by restorative land management actions to reduce those threats. The result of this process produced much more than a planning document but engaged multiple community members in a process of empowerment and self-determination. The process of plan-making produced many unintended results including human–land connectivity, reconnection with the water spirit, as well as the reclaiming of indigenous planning. Source water protection planning may not correct all the current water system inadequacies that exist on many First Nations, but it will empower communities to take action to protect their drinking water sources for future generations as a pathway to local water security.
Extensive land use changes and uncertainties arising from climate change in recent years have contributed to increased flood magnitudes in the Canadian Prairies and threatened the vulnerabilities of many small and indigenous communities. There is, thus, a need to create modernized flood risk management tools to support small and rural communities’ preparations for future extreme events. In this study, we developed spatial flood information for an indigenous community in Central Saskatchewan using LiDAR based DEM and a spatial modeling tool, the wetland DEM ponding model (WDPM). A crucial element of flood mapping in this study was community engagement in data collection, scenario description for WDPM, and flood map validation. Community feedback was also used to evaluate the utility of the modelled flood outputs. The results showed the accuracy of WDPM outputs could be improved not only with the quality of DEM but also with additional community-held information on contributing areas (watershed information). Based on community feedback, this accessible, spatially-focused modeling approach can provide relevant information for community spatial planning and developing risk management strategies. Our study found community engagement to be valuable in flood modeling and mapping by: providing necessary data, validating input data through lived experiences, and providing alternate scenarios to be used in future work. This research demonstrates the suitability and utility of LiDAR and WDPM complemented by community participation for improving flood mapping in the Prairie Pothole Region (PPR). The approach used in the study also serves as an important guide for applying transdisciplinary tools and methods for establishing good practice in research and helping build resilient communities in the Prairies.
Groundwater discharge in alpine headwaters sustains baseflow in rivers originating in mountain ranges of the world, which is critically important for aquatic habitats, run-of-river hydropower generation, and downstream water supply. Groundwater storage in alpine watersheds was long considered negligible, but recent field-based studies have shown that aquifers are ubiquitous in the alpine zone with no soil and vegetation. Talus, moraine, and rock glacier aquifers are common in many alpine regions of the world, although bedrock aquifers occur in some geological settings. Alpine aquifers consisting of coarse sediments have a fast recession of discharge after the recharge season (e.g., snowmelt) or rainfall events, followed by a slow recession that sustains discharge over a long period. The two-phase recession is likely controlled by the internal structure of the aquifers. Spatial extent and distribution of individual aquifers determine the groundwater storage-discharge characteristics in first- and second-order watersheds in the alpine zone, which in turn govern baseflow characteristics in major rivers. Similar alpine landforms appear to have similar hydrogeological characteristics in many mountain ranges across the world, suggesting that a common conceptual framework can be used to understand alpine aquifers based on geological and geomorphological settings. Such a framework will be useful for parameterizing storage-discharge characteristics in large river hydrological models.
River management based solely on physical science has proven to be unsustainable and unsuccessful, evidenced by the fact that the problems this approach intended to solve (e.g., flood hazards, water scarcity, and channel instability) have not been solved and long‐term deterioration in river environments has reduced the capacity of rivers to continue meeting the needs of society. In response, there has been a paradigm shift in management over the past few decades, towards river restoration. But the ecological, morphological, and societal benefits of river restoration have, on the whole, been disappointing. We believe that this stems from the fact that restoration overrelies on the same physical analyses and approaches, with flowing water still regarded as the universally predominant driver of channel form and structural intervention seen as essential to influencing fluvial processes. We argue that if river restoration is to reverse long‐standing declines in river functions, it is necessary to recognize the influence of biology on river forms and processes and re‐envisage what it means to restore a river. This entails shifting the focus of river restoration from designing and constructing stable channels that mimic natural forms to reconnecting streams within balanced and healthy biomes, and so levering the power of biology to influence river processes. We define this new approach as biomic river restoration.
Abstract. Permafrost strongly controls hydrological processes in cold regions, and our understanding of how changes in seasonal and perennial frozen ground disposition and linked storage dynamics affects runoff generation processes remains limited. Storage dynamics and water redistribution are influenced by the seasonal variability and spatial heterogeneity of frozen ground, snow accumulation and melt. Stable isotopes provide a potentially useful technique to quantify the dynamics of water sources, flow paths and ages; yet few studies have employed isotope data in permafrost-influenced catchments. Here, we applied the conceptual model STARR (Spatially distributed Tracer-Aided Rainfall-Runoff model), which facilitates fully distributed simulations of hydrological storage dynamics and runoff processes, isotopic composition and water ages. We adapted this model to a subarctic catchment in Yukon Territory, Canada, with a time-variable implementation of field capacity to include the influence of thaw dynamics. A multi-criteria calibration based on stream flow, snow water equivalent and isotopes was applied to three years of data. The integration of isotope data in the spatially distributed model provided the basis to quantify spatio-temporal dynamics of water storage and ages, emphasizing the importance of thaw layer dynamics in mixing and damping the melt signal. By using the model conceptualisation of spatially and temporally variant storage, this study demonstrates the ability of tracer-aided modelling to capture thaw layer dynamics that cause mixing and damping of the isotopic melt signal.
Wildland fires in the contiguous United States (CONUS) have increased in size and severity, but much remains unclear about the impact of fire size and burn severity on water supplies used for drinking, irrigation, industry, and hydropower. While some have investigated large-scale fire patterns, long-term effects on runoff, and the simultaneous effect of fire and climate trends on surface water yield, no studies account for all these factors and their interactions at the same time. In this report, we present critical new information for the National Cohesive Wildland Fire Management Strategy—a first-time CONUS-wide assessment of observed and potential wildland fire impacts on surface water yield. First, we analyzed data from 168 fire-affected locations, collected between 1984 and 2013, with machine learning and used climate elasticity models to correct for the local climate baseline impact. Stream gage data show that annual river flow increased most in the Lower Mississippi and Lower and Upper Colorado water resource regions, however they do not show which portion of this increase is caused by fire and which portion results from local climate trends. Our machine learning model identified local climate trends as the main driver of water yield change and determined wildland fires must affect at least 19 percent of a watershed >10 km2 to change its annual water yield. A closer look at 32 locations with fires covering at least 19 percent of a watershed >10 km2 revealed that wildfire generally enhanced annual river flow. Fires increased river flow relatively the most in the Lower Colorado, Pacific Northwest, and California regions. In the Lower Colorado and Pacific Northwest regions, flow increased despite post-fire drought conditions. In southern California, post-fire drought effects masked the flow enhancement attributed to wildfire, meaning that annual water yield declined but not as much as expected based on the decline in precipitation. Prescribed burns in the Southeastern United States did not produce a widespread effect on river flow, because the area affected was typically too small and characterized by only low burn severity. In the second stage of the assessment, we performed full-coverage simulations of the CONUS with the Water Supply Stress Index (WaSSI) hydrologic model (88,000 HUC-12-level watersheds) for the period between 2001 and 2010. This enables us to fill in the gaps of areas with scarce data and to identify regions with large potential increases in post-fire annual water yield (+10 to +50 percent): midto high-elevation forests in northeastern Washington, northwestern Montana, central Minnesota, southern Utah, Colorado, and South Dakota, and coastal forests in Georgia and northern Florida. A hypothetical 20-percent forest burn impact scenario for the CONUS suggests that surface yield can increase up to +10 percent in most watersheds, and even more in some watersheds depending on climate, soils, and vegetation. The insights gained from this quantitative analysis have major implications for flood mitigation and watershed restoration, and are vital to forest management policies aimed at reducing fire impact risk and improving water supply under a changing climate.
Abstract Cyanobacterial blooms are causing increasing issues across the globe. Bloom forecasting can facilitate adaptation to blooms. Most bloom forecasting models depend on weekly or fortnightly sampling, but these sparse measurements can miss important dynamics. Here we develop forecasting models from five years of high frequency summer monitoring in a shallow lake (which serves as an important regional water supply). A suite of models were calibrated to predict cyanobacterial fluorescence (a biomass proxy) using measurements of: cyanobacterial fluorescence, water temperature, light, and wind speed. High temporal autocorrelation contributed to relatively strong predictive power over 1, 4 and 7 day intervals. Higher order derivatives of water temperature helped improve forecasting accuracy. While traditional monitoring and modelling have supported forecasting on longer timescales, we show high frequency monitoring combined with telemetry allows forecasting over timescales of 1 day to 1 week, supporting early warning, enhanced monitoring, and adaptation of water treatment processes.
Abstract The increasing prevalence of cyanobacteria-dominated harmful algal blooms is strongly associated with nutrient loading and changing climatic patterns. Changes to precipitation frequency and intensity, as predicted by current climate models, are likely to affect bloom development and composition through changes in nutrient fluxes and water column mixing. However, few studies have directly documented the effects of extreme precipitation events on cyanobacterial composition, biomass, and toxin production. We tracked changes in a eutrophic reservoir following an extreme precipitation event, describing an atypically early toxin-producing cyanobacterial bloom, successional progression of the phytoplankton community, toxins, and geochemistry. An increase in bioavailable phosphorus by more than 27-fold in surface waters preceded notable increases in Aphanizomenon flos-aquae throughout the reservoir approximately 2 weeks post flood and ~5 weeks before blooms typically occur. Anabaenopeptin-A and three microcystin congeners (microcystin-LR, -YR, and -RR) were detected at varying levels across sites during the bloom period, which lasted between 3 and 5 weeks. Synthesis and applications: These findings suggest extreme rainfall can trigger early cyanobacterial bloom initiation, effectively elongating the bloom season period of potential toxicity. However, effects will vary depending on factors including the timing of rainfall and reservoir physical structure. In contrast to the effects of early season extreme rainfall, a mid-summer runoff event appeared to help mitigate the bloom in some areas of the reservoir by increasing flushing.
Abstract. To assess the hydroclimatic risks posed by climate change in western Canada, this study conducted a retrospective simulation (CTL) and a pseudo-global warming (PGW) dynamical downscaling of future warming projection under RCP8.5 from an ensemble of CMIP5 climate model projections using a convection-permitting 4-km Weather Research Forecasting (WRF) model. The convection-permitting resolution of the model avoids the error-prone convection parameterization by explicitly resolving cumulus plumes. The evaluation of surface air temperature by the retrospective simulation WRF-CTL against a gridded observation ANUSPLIN shows that WRF simulation of daily mean temperature agrees well with ANUSPLIN temperature in terms of the geographical distribution of cold biases east of the Canadian Rockies, especially in spring. Compared with the observed precipitation from ANUSPLIN and CaPA, the WRF-CTL simulation captures the main pattern of distribution, but with a wet bias seen in higher precipitation near the British Columbia coast in winter and over the immediate region on the lee side of the Canadian Rockies. The PGW simulation shows more warming than CTL, especially over the polar region in the northeast, during the cold season, and in daily minimum temperature. Precipitation changes in PGW over CTL vary with the seasons: In spring and late fall for both basins, precipitation is shown to increase, whereas in summer in the Saskatchewan River Basin, it either shows no increase or decreases, with less summer precipitation shown in PGW than in CTL for some parts of the Prairies. This seasonal difference in precipitation change suggests that in summer the Canadian Prairies and the southern Boreal Forest biomes will likely see a slight decline in precipitation minus evapotranspiration, which might impact soil moisture for farming and forest fires. With almost no increase in summer precipitation and much more evapotranspiration in PGW than in CTL, the water availability during the growing season will be challenging for the Canadian Prairies. WRF-PGW shows an increase of high-intensity precipitation events and shifts the distribution of precipitation events toward more extremely intensive events in all seasons, as current moderate events become extreme events with more vapor loading, especially in summer. Due to this shift in precipitation intensity to the higher end in the PGW simulation, the seemingly moderate increase in the total amount of precipitation in summer for both the Mackenzie and Saskatchewan river basins may not reflect the real change in flooding risk and water availability for agriculture. The high-resolution downscaled climate simulations provide abundant opportunities both for investigating local-scale atmospheric dynamics and for studying climate impacts in hydrology, agriculture, and ecosystems. The change in the probability distribution of precipitation intensity also calls for innovative bias-correction methods to be developed for the application of the dataset when bias-correction is required.
Abstract. Hydrological processes are widely understood to be sensitive to changes in climate, but the effects of changes in vegetation and soils have seldom been considered. The response of mountain hydrology to future climate and vegetation/soil changes is modelled in three snowmelt dominated mountain basins in the North American Cordillera. A Cold Regions Hydrological Model developed for each basin was driven with perturbed observed meteorological time series. Monthly perturbations were developed from differences in eleven regional climate model outputs between the present and future scenarios. Future climate change in these basins results in decreased modelled peak snow water equivalent (SWE) but increased evapotranspiration in all basins. All three watersheds became more rainfall-dominated. In Wolf Creek in the Yukon Territory, an insignificant increasing effect of vegetation change on peak SWE was found to be important enough to offset the significant climate change effect on alpine snow. In Marmot Creek in the Canadian Rockies, a combined effect of soil and climate changes on increasing annual runoff becomes significant while their individual effects are not statistically significant. In the relatively warmer Reynolds Mountain East catchment in Idaho, USA, only vegetation change decreases annual runoff volume and changes in soil, climate, or combination of them do not affect runoff. At high elevations in Wolf and Marmot Creeks, modelled vegetation/soil changes moderated the impact of climate change on peak SWE, the timing of peak SWE, evapotranspiration, and annual runoff volume. At medium elevations, these changes intensified the impact of climate change, decreasing peak SWE, and sublimation. The modelled hydrological impacts of changes in climate, vegetation, and soil in mountain environments are similar in magnitude but not consistently in the direction in all biomes; in some combinations, this results in enhanced impacts at lower elevations and latitudes and offsetting effects at higher elevations and latitudes.
Abstract. Hydrologic-Land Surface Models (H-LSMs) have been progressively developed to a stage where they represent the dominant hydrological processes for a variety of hydrological regimes and include a range of water management practices, and are increasingly used to simulate water storages and fluxes of large basins under changing environmental conditions across the globe. However, efforts for comprehensive evaluation of the utility of H-LSMs in large, regulated watersheds have been limited. In this study, we evaluated the capability of a Canadian H-LSM, called MESH, in the highly regulated Saskatchewan River Basin (SaskRB), Canada, under the constraint of significant precipitation uncertainty. The SaskRB is a complex system characterized by hydrologically-distinct regions that include the Rocky Mountains, Boreal Forest, and the Prairies. This basin is highly vulnerable to potential climate change and extreme events. A comprehensive analysis of the MESH model performance was carried out in two steps. First, the reliability of multiple precipitation products was evaluated against climate station observations and based on their performance in simulating streamflow across the basin when forcing the MESH model with a default parameterization. Second, a state-of-the-art multi-criteria calibration approach was applied, using various observational information including streamflow, storage and fluxes for calibration and validation. The first analysis shows that the quality of precipitation products had a direct and immediate impact on simulation performance for the basin headwaters but effects were dampened when going downstream. In particular, the Canadian Precipitation Analysis (CaPA) performed the best among the precipitation products in capturing timings and minimizing the magnitude of error against observation, despite a general underestimation of precipitation amount. The subsequent analyses show that the MESH model was able to capture observed responses of multiple fluxes and storage across the basin using a global multi-station calibration method. Despite poorer performance in some basins, the global parameterization generally achieved better model performance than a default model parameterization. Validation using storage anomaly and evapotranspiration generally showed strong correlation with observations, but revealed potential deficiencies in the simulation of storage anomaly over open water areas.
Abstract. High latitude environments store approximately half of the global organic carbon pool in peatlands, organic soils and permafrost while large arctic rivers convey an estimated 18–50 Tg C a−1 to the Arctic Ocean. Warming trends associated with climate change affect dissolved organic carbon (DOC) export from terrestrial to riverine environments. However, there is limited consensus as to whether exports will increase or decrease due to complex interactions between climate, soils, vegetation, and associated production, mobilization and transport processes. A large body of research has focused on large river system DOC and DOM lability and observed trends conserved across years, whereas investigation at smaller watershed scales show that thermokarst and fire have a transient impact on hydrologically-mediated solute transport. This study, located in the Wolf Creek Research Basin situated ~ 20 km south of Whitehorse, YT, Canada, utilises a nested design to assess seasonal and annual patterns of DOC and DOM composition across diverse landscape types (headwater, wetland, lake) and watershed scales. Peak DOC concentration and export occurred during freshet per most northern watersheds, however, peaks were lower than a decade ago at the headwater site Granger Creek. DOM composition was most variable during freshet with high A254, SUVA254 and low FI and BIX. DOM composition was relatively insensitive to flow variation during summer and fall. The influence of increasing watershed scale and downstream mixing of landscape contributions was an overall dampening of DOC concentrations and optical indices with increasing groundwater contribution. Forecasted vegetation shifts, permafrost thaw and other changes due to climate change may alter DOM sources from predominantly organic soils to decomposing vegetation, and facilitate transport through deeper flow pathways with an enhanced groundwater role.
Abstract. Complex, software-intensive, technically advanced, and computationally demanding models, presumably with ever-growing realism and fidelity, have been widely used to simulate and predict the dynamics of the Earth and environmental systems. The parameter-induced simulation crash (failure) problem is typical across most of these models, despite considerable efforts that modellers have directed at model development and implementation over the last few decades. A simulation failure mainly occurs due to the violation of the numerical stability conditions, non-robust numerical implementations, or errors in programming. However, the existing sampling-based analysis techniques such as global sensitivity analysis (GSA) methods, which require running these models under many configurations of parameter values, are ill-equipped to effectively deal with model failures. To tackle this problem, we propose a novel approach that allows users to cope with failed designs (samples) during the GSA, without knowing where they took place and without re-running the entire experiment. This approach deems model crashes as missing data and uses strategies such as median substitution, single nearest neighbour, or response surface modelling to fill in for model crashes. We test the proposed approach on a 10-paramter HBV-SASK rainfall-runoff model and a 111-parameter MESH land surface-hydrology model. Our results show that response surface modelling is a superior strategy, out of the data filling strategies tested, and can scale well to the dimensionality of the model, sample size, and the ratio of number of failures to the sample size. Further, we conduct a "failure analysis" and discuss some possible causes of the MESH model failure.
Abstract. The Interior of Western Canada, up to and including the Arctic, has experienced rapid change in its climate, hydrology, cryosphere and ecosystems and this is expected to continue. Although there is general consensus that warming will occur in the future, many critical issues remain. In this first of two articles, attention is placed on atmospheric-related issues that range from large scales down to individual precipitation events. Each of these is considered in terms of expected change organized by season and utilizing climate scenario information as well as thermodynamically-driven future climatic forcing simulations. Large scale atmospheric circulations affecting this region are generally projected to become stronger in each season and, coupled with warming temperatures, lead to enhancements of numerous water-related and temperature-related extremes. These include winter snowstorms, freezing rain, drought as well as atmospheric forcing of spring floods although not necessarily summer convection. Collective insights of these atmospheric findings are summarized in a consistent, connected physical framework.
Global fire regimes are changing, with increases in wildfire frequency and severity expected for many North American forests over the next 100 years. Fires can result in dramatic changes to C stocks and can restructure plant and microbial communities, which can have long-lasting effects on ecosystem functions. We investigated wildfire effects on soil microbial communities (bacteria and fungi) in an extreme fire season in the northwestern Canadian boreal forest, using field surveys, remote sensing, and high-throughput amplicon sequencing. We found that fire occurrence, along with vegetation community, moisture regime, pH, total carbon, and soil texture are all significant predictors of soil microbial community composition. Communities become increasingly dissimilar with increasingly severe burns, and the burn severity index (an index of the fractional area of consumed organic soils and exposed mineral soils) best predicted total bacterial community composition, while burned/unburned was the best predictor for fungi. Globally abundant taxa were identified as significant positive fire responders, including the bacteria Massilia sp. (64x more abundant with fire) and Arthrobacter sp. (35x), and the fungi Penicillium sp. (22x) and Fusicladium sp. (12x) Bacterial and fungal co-occurrence network modules were characterized by fire responsiveness as well as pH and moisture regime. Building on the efforts of previous studies, our results identify specific fire-responsive microbial taxa and suggest that accounting for burn severity improves our understanding of their response to fires, with potentially important implications for ecosystem functions.
Abstract. Classification and clustering approaches provide a means to group watersheds according to similar attributes, functions, or behaviours, and can aid in managing natural resources within these regions. While widely used, approaches based on hydrological response parameters restrict analyses to regions where well-developed hydrological records exist, and overlook factors contributing to other management concerns, including biogeochemistry and ecology. In the Canadian Prairie, hydrometric gauging is sparse and often seasonal, large areas are endorheic and the landscape is highly modified by human activity, complicating classification based solely on hydrological parameters. We compiled climate, geological, topographical, and land cover data from the Prairie and conducted a classification of watersheds using a hierarchical clustering of principal components. Seven classes were identified based on the clustering of watersheds, including those distinguishing southern Manitoba, the pothole region, river valleys, and grasslands. Important defining variables were climate, elevation, surficial geology, wetland distribution, and land cover. In particular, three classes occur almost exclusively within regions that tend not to contribute to major river systems, and collectively encompass the majority of the study area. The gross difference in key characteristics across the classes suggests that future water management and climate change may carry with them heterogeneous sets of implications for water security across the Prairies. This emphasizes the importance of developing management strategies that target sub-regions expected to behave coherently as current human-induced changes to the landscape will affect how watersheds react to change. This study provides the first classification of watersheds within the Prairie based on climatic and biophysical attributes, and our findings provide a foundation for addressing questions related to hydrological, biogeochemical, and ecological behaviours at a regional level.
Abstract. Reservoirs significantly affect flow regimes in watershed systems by changing the magnitude and timing of streamflows. Failure to represent these effects limits the performance of hydrological and land surface models (H-LSMs) in the many highly regulated basins across the globe and limits the applicability of such models to investigate the futures of watershed systems through scenario analysis (e.g., scenarios of climate, land use, or reservoir regulation changes). An adequate representation of reservoirs and their operation in an H-LSM is therefore essential for a realistic representation of the downstream flow regime. In this paper, we present a general parametric reservoir operation model based on piecewise linear relationships between reservoir storage, inflow, and release, to approximate actual reservoir operations. For the identification of the model parameters, we propose two strategies: (a) a generalized parameterization that requires a relatively limited amount of data; and (b) direct calibration via multi-objective optimization when more data on historical storage and release are available. We use data from 37 reservoir case studies located in several regions across the globe for developing and testing the model. We further build this reservoir operation model into the MESH modelling system, which is a large-scale H-LSM. Our results across the case studies show that the proposed reservoir model with both of the parameter identification strategies leads to improved simulation accuracy compared with the other widely used approaches for reservoir operation simulation. We further show the significance of enabling MESH with this reservoir model and discuss the interdependent effects of the simulation accuracy of natural processes and that of reservoir operation on the overall model performance. The reservoir operation model is generic and can be integrated into any H-LSM.
Abstract. The 0 °C temperature threshold is critical to many meteorological and hydrological processes driven by melting and freezing in the atmosphere, surface and sub-surface and by the associated precipitation varying between rain, freezing rain, wet snow and snow. This threshold, linked with freeze-thaw, is especially important in cold regions such as Canada. This study develops a Canada-wide perspective on near 0 °C conditions with a particular focus on the occurrence of its associated precipitation. Since this analysis requires hourly values of surface temperature and precipitation type observations, it was limited to 92 stations over the 1981–2011 period. In addition, nine stations representative of various climatic regions are selected for further analysis. Near 0 °C conditions are defined as periods when the surface temperature is between −2 °C and 2 °C. Near 0 °C conditions occur often across all regions of the country although the annual number of days and hours and the duration of these events varies dramatically. Various forms of precipitation (including rain, freezing rain, wet snow and ice pellets) are sometimes linked with these temperatures with highest fractions tending to occur in Atlantic Canada. Trends of most temperature-based and precipitation-based indicators show little or no change despite a systematic warming in annual temperatures. Over the annual cycle, near 0 °C temperatures and precipitation often exhibit a pattern with short durations near summer driven by the diurnal cycle, while longer durations tend to occur more towards winter associated with storms. There is also a tendency for near 0 °C temperatures to occur more often than expected relative to other temperature windows; due at least in part to diabatic cooling and heating occurring with melting and freezing, respectively, in the atmosphere and at the surface.
Abstract. Water resources in cold regions in western Canada face severe risks posed by anthropogenic global warming as evapotranspiration increases and precipitation regimes shift. Although understanding the water cycle is key in addressing climate change issues, it is difficult to obtain high spatial and temporal resolution observations of hydroclimatic processes, especially in remote regions. Climate models are useful tools for dissecting and diagnosing these processes, especially, convection-permitting (CP) high-resolution regional climate simulation provides advantages over lower-resolution models by explicitly representing convection. In addition to better representing convective systems, higher spatial resolution also better represents topography and mountain meteorology, and highly heterogeneous geophysical features. However, there is little work with convection-permitting regional climate models conducted over western Canada. Focusing on the Mackenzie and Saskatchewan river basins, this study investigated the surface water budget and atmospheric moisture balance in historical and RCP8.5 projections using 4-km CP Weather Research and Forecast (WRF). We compared the high-resolution 4-km CP WRF and three common reanalysis datasets: NARR, JRA-55, and ERA-Interim. High-resolution WRF out-performs the reanalyses in balancing the surface water budget in both river basins with much lower residual terms. For the pseudo-global warming scenario at the end of the 21st century with RCP8.5 radiative forcing, both the Mackenzie and Saskatchewan river basins show increases in the amplitude for precipitation and evapotranspiration and a decrease in runoff. The Saskatchewan river basin shows a moderate increase of precipitation in the west and a small decrease in the east. Combined with a significant increase of evapotranspiration in a warmer climate, the Saskatchewan river basin would have a larger deficit of water resources than in the current climate based on the PGW simulation. The high-resolution simulation also shows the difference of atmospheric water vapour balance in the two river basins is due to flow orientation and topography differences at the western boundaries of the two basins. The sensitivity of water vapour balance to fine-scale topography and atmospheric processes shown in this study demonstrates that high-resolution dynamical downscaling is important for large-scale water balance and hydrological cycles.
Abstract. A set of hydrometeorological data is presented in this paper, which can be used to characterize the hydrometeorology and climate of a subarctic mountain basin and has proven particularly useful for forcing hydrological models and assessing their performance in capturing hydrological processes in subarctic alpine environments. The forcing dataset includes daily precipitation, hourly air temperature, humidity, wind, solar and net radiation, soil temperature, and geographical information system data. The model performance assessment data include snow depth and snow water equivalent, streamflow, soil moisture, and water level in a groundwater well. This dataset was recorded at different elevation bands in Wolf Creek Research Basin, near Whitehorse, Yukon Territory, Canada, representing forest, shrub tundra, and alpine tundra biomes from 1993 through 2014. Measurements continue through 2018 and are planned for the future at this basin and will be updated to the data website. The database presented and described in this article is available for download at https://doi.org/10.20383/101.0113.
Abstract. The Lake O'Hara watershed in the Canadian Rockies has been the site of several hydrological investigations. It has been instrumented to a degree uncommon for many alpine study watersheds. Air temperature, relative humidity, wind, precipitation, radiation, and snow depth are measured at two meteorological stations near Lake O'Hara and in the higher elevation Opabin Plateau. Water levels at Lake O'Hara, Opabin Lake, and several stream gauging stations are recorded using pressure transducers and validated against manual measurements. Stage–discharge rating curves were determined at gauging stations and used to calculate discharge from stream stage. The database includes additional data such as water chemistry (temperature, electrical conductivity, and stable isotope abundance) and snow survey (snow depth and density) for select years, as well as geospatial data (elevation and land cover). This dataset will be useful for the future study of alpine regions, where substantial and long-term hydrological datasets are scarce due to difficult field conditions. The dataset can be accessed at https://doi.org/10.20383/101.035.
Abstract. High-latitude environments store approximately half of the global organic carbon pool in peatlands, organic soils and permafrost, while large Arctic rivers convey an estimated 18–50 Tg C a−1 to the Arctic Ocean. Warming trends associated with climate change affect dissolved organic carbon (DOC) export from terrestrial to riverine environments. However, there is limited consensus as to whether exports will increase or decrease due to complex interactions between climate, soils, vegetation, and associated production, mobilization and transport processes. A large body of research has focused on large river system DOC and dissolved organic matter (DOM) lability and observed trends conserved across years, whereas investigation at smaller watershed scales show that thermokarst and fire have a transient impact on hydrologically mediated solute transport. This study, located in the Wolf Creek Research Basin situated ∼20 km south of Whitehorse, YT, Canada, utilizes a nested design to assess seasonal and annual patterns of DOC and DOM composition across diverse landscape types (headwater, wetland and lake) and watershed scales. Peak DOC concentration and export occurred during freshet, as is the case in most northern watersheds; however, peaks were lower than a decade ago at the headwater site Granger Creek. DOM composition was most variable during freshet with high A254 and SUVA254 and low FI and BIX. DOM composition was relatively insensitive to flow variation during summer and fall. The influence of increasing watershed scale and downstream mixing of landscape contributions was an overall dampening of DOC concentrations and optical indices with increasing groundwater contribution. Forecasted vegetation shifts, enhanced permafrost and seasonal thaw, earlier snowmelt, increased rainfall and other projected climate-driven changes will alter DOM sources and transport pathways. The results from this study support a projected shift from predominantly organic soils (high aromaticity and less fresh) to decomposing vegetation (more fresh and lower aromaticity). These changes may also facilitate flow and transport via deeper flow pathways and enhance groundwater contributions to runoff.
Abstract. The occurrence of various types of winter precipitation is an important issue over the southern Canadian Cordillera. This issue is examined from January to April of 2010 by exploiting the high-resolution Weather Research and Forecasting (WRF) model Version 3.4.1 dataset that was used to simulate both a historical reanalysis-driven (control – CTRL) and a pseudo-global-warming (PGW) experiment (Liu et al., 2016). Transition regions, consisting of both liquid and solid precipitation or liquid precipitation below 0 ∘C, occurred on 93 % and 94 % of the days in the present and PGW future, respectively. This led to accumulated precipitation within the transition region increasing by 27 % and was associated with a rise in its average elevation by 374 m over the Coast Mountains and Insular Mountains and by 240 m over the Rocky Mountains and consequently to an eastward shift towards the higher terrain of the Rocky Mountains. Transition regions comprised of only rain and snow were most common under both the CTRL and PGW simulations, although all seven transition region categories occurred. Transition region changes would enhance some of the factors leading to avalanches and would also impact ski resort operations.
Abstract. Classification and clustering approaches provide a means to group watersheds according to similar attributes, functions, or behaviours, and can aid in managing natural resources. Although they are widely used, approaches based on hydrological response parameters restrict analyses to regions where well-developed hydrological records exist, and overlook factors contributing to other management concerns, including biogeochemistry and ecology. In the Canadian Prairie, hydrometric gauging is sparse and often seasonal. Moreover, large areas are endorheic and the landscape is highly modified by human activity, complicating classification based solely on hydrological parameters. We compiled climate, geological, topographical, and land-cover data from the Prairie and conducted a classification of watersheds using a hierarchical clustering of principal components. Seven classes were identified based on the clustering of watersheds, including those distinguishing southern Manitoba, the pothole region, river valleys, and grasslands. Important defining variables were climate, elevation, surficial geology, wetland distribution, and land cover. In particular, three classes occur almost exclusively within regions that tend not to contribute to major river systems, and collectively encompass the majority of the study area. The gross difference in key characteristics across the classes suggests that future water management and climate change may carry with them heterogeneous sets of implications for water security across the Prairie. This emphasizes the importance of developing management strategies that target sub-regions expected to behave coherently as current human-induced changes to the landscape will affect how watersheds react to change. The study provides the first classification of watersheds within the Prairie based on climatic and biophysical attributes, with the framework used being applicable to other regions where hydrometric data are sparse. Our findings provide a foundation for addressing questions related to hydrological, biogeochemical, and ecological behaviours at a regional level, enhancing the capacity to address issues of water security.
Abstract. Reservoirs significantly affect flow regimes in watershed systems by changing the magnitude and timing of streamflows. Failure to represent these effects limits the performance of hydrological and land-surface models (H-LSMs) in the many highly regulated basins across the globe and limits the applicability of such models to investigate the futures of watershed systems through scenario analysis (e.g., scenarios of climate, land use, or reservoir regulation changes). An adequate representation of reservoirs and their operation in an H-LSM is therefore essential for a realistic representation of the downstream flow regime. In this paper, we present a general parametric reservoir operation model based on piecewise-linear relationships between reservoir storage, inflow, and release to approximate actual reservoir operations. For the identification of the model parameters, we propose two strategies: (a) a “generalized” parameterization that requires a relatively limited amount of data and (b) direct calibration via multi-objective optimization when more data on historical storage and release are available. We use data from 37 reservoir case studies located in several regions across the globe for developing and testing the model. We further build this reservoir operation model into the MESH (Modélisation Environmentale-Surface et Hydrologie) modeling system, which is a large-scale H-LSM. Our results across the case studies show that the proposed reservoir model with both parameter-identification strategies leads to improved simulation accuracy compared with the other widely used approaches for reservoir operation simulation. We further show the significance of enabling MESH with this reservoir model and discuss the interdependent effects of the simulation accuracy of natural processes and that of reservoir operations on the overall model performance. The reservoir operation model is generic and can be integrated into any H-LSM.
Mountain fens are limited in their spatial extent but are vital ecosystems for biodiversity, habitat, and carbon and water cycling. Studies of fen hydrological function in northern regions indicate the timing and magnitude of runoff is variable, with atmospheric and environmental conditions playing key roles in runoff production. How the complex ecohydrological processes of mountain fens that govern water storage and release as well as peat accumulation will respond to a warmer and less snowy future climate is unclear. To provide insight, we studied the hydrological processes and function of Sibbald fen, located at the low end of the known elevation range in the Canadian Rocky Mountains, over a dry period. We added an evapotranspiration function to the Spence hydrological function method to better account for storage loss. When frozen in spring and early summer, the fen primarily transmits water. When thawed, the fen's hydrological function switches from water transmission to water release, leading to a summertime water table decline of nearly 1 m. Rainfall events larger than 5 mm can transiently switch fen hydrological function to storage, followed by contribution, depending on antecedent conditions. The evapotranspiration function was dominant only for a brief period in late June and early July when rainfall was low and the ground was still partially frozen, even though evapotranspiration accounted for the largest loss of storage from the system. This research highlights the mechanisms by which mountain peatlands supply baseflow during drought conditions, and the importance of frozen ground and rainfall in regulating their hydrological function. The study has important implications for the sustainability of low elevation mountain fens under climate change.
The role of hummocky terrain in governing runoff routing and focussing groundwater recharge in the Northern Prairies of North America is widely recognised. However, most hydrological studies in the region have not effectively utilised information on the surficial geology and associated landforms in large‐scale hydrological characterization. The present study uses an automated digital elevation model (DEM) analysis of a 6500‐km² area in the Northern Prairies to quantify hydrologically relevant terrain parameters for the common types of terrains in the prairies with different surficial deposits widespread in the prairies, namely, moraines and glaciolacustrine deposits. Runoff retention (and storage) capacity within depressions varies greatly between different surficial deposits and is comparable in magnitude with a typical amount of seasonal snowmelt runoff generation. The terrain constraint on potential runoff retention varies from a few millimetres in areas classified as moraine to tens of millimetres in areas classified as stagnant ice moraine deposits. Fluted moraine and glaciolacustrine deposits have intermediate storage capacity values. The study also identified the probability density function describing a number of immediate upstream neighbours for each depression in a fill‐and‐spill network. A relationship between depression parameters and surficial deposits, as well as identified depression network structure, allows parametrisation of hydrologic models outside of the high‐resolution DEM coverage, which can still account for terrain variation in the Prairies.
Hydrological monitoring in complex, dynamic northern floodplain landscapes is challenging, but increasingly important as a consequence of multiple stressors. The Peace‐Athabasca Delta in northern Alberta, Canada, is a Ramsar Wetland of International Importance reliant on episodic river ice‐jam flood events to recharge abundant perched lakes and wetlands. Improved and systematic monitoring of landscape‐scale hydrological connectivity among freshwater ecosystems (rivers, channels, wetlands, and lakes) is needed to guide stewardship decisions in the face of climate change and upstream industrial development. Here, we use water isotope compositions, supplemented by measurements of specific conductivity and field observations, from 68 lakes and 9 river sites in May 2018 to delineate the extent and magnitude of spring ice‐jam induced flooding along the Peace and Athabasca rivers. Lake‐specific estimates of input water isotope composition (δI) were modelled after accounting for influence of evaporative isotopic enrichment. Then, using the distinct isotopic signature of input water sources, we develop a set of binary mixing models and estimate the proportion of input to flooded lakes attributable to river floodwater and precipitation (snow or rain). This approach allowed identification of areas and magnitude of flooding that were not captured by other methods, including direct observations from flyovers, and to demarcate flow pathways in the delta. We demonstrate water isotope tracers as an efficient and effective monitoring tool for delineating spatial extent and magnitude of an important hydrological process and elucidating connectivity in the Peace‐Athabasca Delta, an approach that can be readily adopted at other floodplain landscapes.
We assessed the hydrological implications of climate effects on vegetation phenology in northern environments by fusion of data from remote-sensing and local catchment monitoring. Studies using satellite data have shown earlier and later dates for the start (SOS) and end of growing seasons (EOS), respectively, in the Northern Hemisphere over the last 3 decades. However, estimates of the change greatly depend on the satellite data utilized. Validation with experimental data on climate-vegetation-hydrology interactions requires long-term observations of multiple variables which are rare and usually restricted to small catchments. In this study, we used two NDVI (normalized difference vegetation index) products (at ~25 & 0.5 km spatial resolutions) to infer SOS and EOS for six northern catchments, and then investigated the likely climate impacts on phenology change and consequent effects on catchment water yield, using both assimilated data (GLDAS: global land data assimilation system) and direct catchment observations. The major findings are: (1) The assimilated air temperature compared well with catchment observations (regression slopes and R2 close to 1), whereas underestimations of summer rainstorms resulted in overall underestimations of precipitation (regression slopes of 0.3-0.7, R2 ≥ 0.46). (2) The two NDVI products inferred different vegetation phenology characteristics. (3) Increased mean pre-season temperature significantly influenced the advance of SOS and delay of EOS. The precipitation influence was weaker, but delayed SOS corresponding to increased pre-season precipitation at most sites can be related to later snow melting. (4) Decreased catchment streamflow over the last 15 years could be related to the advance in SOS and extension of growing seasons. Greater streamflow reductions in the cold sites than the warm ones imply stronger climate warming impacts on vegetation and hydrology in colder northerly environments. The methods used in this study have potential for better understanding interactions between vegetation, climate and hydrology in observation-scarce regions.
Although researchers now recognize that Indigenous knowledge can strengthen environmental planning and assessment, little research has empirically demonstrated how to bring together Indigenous know...
The design and maintenance of APIs (Application Programming Interfaces) are complex tasks due to the constantly changing requirements of their users. Despite the efforts of their designers, APIs may suffer from a number of issues (such as incomplete or erroneous documentation, poor performance, and backward incompatibility). To maintain a healthy client base, API designers must learn these issues to fix them. Question answering sites, such as Stack Overflow (SO), have become a popular place for discussing API issues. These posts about API issues are invaluable to API designers, not only because they can help to learn more about the problem but also because they can facilitate learning the requirements of API users. However, the unstructured nature of posts and the abundance of non-issue posts make the task of detecting SO posts concerning API issues difficult and challenging. In this paper, we first develop a supervised learning approach using a Conditional Random Field (CRF), a statistical modeling method, to identify API issue-related sentences. We use the above information together with different features collected from posts, the experience of users, readability metrics and centrality measures of collaboration network to build a technique, called CAPS, that can classify SO posts concerning API issues. In total, we consider 34 features along eight different dimensions. Evaluation of CAPS using carefully curated SO posts on three popular API types reveals that the technique outperforms all three baseline approaches we consider in this study. We then conduct studies to find important features and also evaluate the performance of the CRF-based technique for classifying issue sentences. Comparison with two other baseline approaches shows that the technique has high potential. We also test the generalizability of CAPS results, evaluate the effectiveness of different classifiers, and identify the impact of different feature sets.
Abstract The development of highly sensitive sensors and power generators that could function efficiently in extreme temperatures and contact with fire can be lifesaving but challenging to accomplish. Herein, we report, for the first time, a fire-retardant and self-extinguishing triboelectric nanogenerator (FRTENG), which can be utilized as a motion sensor and/or power generator in occupations such as oil drilling, firefighting or working in extreme temperature environments with flammable and combustible materials. The device takes advantage of the excellent thermal properties of carbon derived from resorcinol-formaldehyde aerogel whose electrical, mechanical and triboelectric properties have been improved via the introduction of Polyacrylonitrile nanofibers and graphene oxide nanosheets. This FRTENG is not flammable even after 90 s of trying, whereas conventional triboelectric materials were entirely consumed by fire under the same conditions. The developed device shows exceptional charge transfer characteristics, leading to a potential difference up to 80 V and a current density up to 25 µA/m2. When integrated into firefighter's shoes, the FRTENG is able to discern the movements of a firefighter in hazardous situations, while providing the high thermal stability missing in conventional TENGs. The fire-retardant and self-extinguishing characteristics offered by the FRTENG makes it a path-breaking device for lifesaving wearable applications.
Since their debut in 2012, triboelectric nanogenerators (TENGs) have attained high performance in terms of both energy density and instantaneous conversion, reaching up to 500 W m-2 and 85%, respectively, synchronous with multiple energy sources and hybridized designs. Here, a comprehensive review of the design guidelines of TENGs, their performance, and their designs in the context of Internet of Things (IoT) applications is presented. The development stages of TENGs in large-scale self-powered systems and technological applications enabled by harvesting energy from water waves or wind energy sources are also reviewed. This self-powered capability is essential considering that IoT applications should be capable of operation anywhere and anytime, supported by a network of energy harvesting systems in arbitrary environments. In addition, this review paper investigates the development of self-charging power units (SCPUs), which can be realized by pairing TENGs with energy storage devices, such as batteries and capacitors. Consequently, different designs of power management circuits, supercapacitors, and batteries that can be integrated with TENG devices are also reviewed. Finally, the significant factors that need to be addressed when designing and optimizing TENG-based systems for energy harvesting and self-powered sensing applications are discussed.
The development of power generators that can function in harsh snowy environments and in contact with snow can be beneficial but challenging to accomplish. Herein, we introduce the first snow-based triboelectric nanogenerator (snow-TENG) that can be used as an energy harvester and a multifunctional sensor based on the principle of snow-triboelectrification. In this work, we used a 3D printing technique for the precise design and deposition of the electrode and triboelectric layer, leading to flexible, stretchable and metal-free triboelectric generators. Based on the single electrode mode, the device can generate an instantaneous output power density as high as 0.2 mW/m2, an open circuit voltage up to 8 V, and a current density of 40 μA/m2. In addition, the snow-TENG can function as a miniaturized weather station to monitor the weather in real time to provide accurate information about the snowfall rate, snow accumulation depth, wind direction, and speed in snowy and/or icy environments. In addition, the snow-TENG can be used as a wearable power source and biomechanical sensor to detect human body motions, which may prove useful for snow-related sports. Unlike conventional sensor platforms, our design works without the need for batteries or image processing systems. We envision these devices could potentially be integrated into solar panels to ensure continuous power supply during snowy weather conditions.
This code, developed in MATLAB R2018a, is a process based mass balance modelfor simulating the biogeochemical cycling of nitrogen in dam reservoirs.
Heavy metal pollution is a severe environmental problem affecting many water resources. The non-biodegradable nature of the heavy metals such as lead (Pb) causes severe human health issues, so their cost-effective, sensitive and rapid detection is needed. In this work, we describe a simple, facile and low cost modifications of multiwalled carbon nanotubes (MWCNT) and \b{eta}-cyclodextrin (\b{eta}CD) through non-covalent/physical (Phys) and a covalent Steglich esterification (SE) approaches. The Phys modification approach resulted Pb detection with a limit-of-detection (LoD) of 0.9 ppb, while the SE approach showed an LoD of 2.3 ppb, both of which are well below the WHO Pb concentration guideline of 10 ppb. The MWCNT-\b{eta}CD (Phys) based electrodes show negligible interference with other common heavy metal ions such as Cd2+ and Zn2+. The MWCNT-\b{eta}CD based electrodes were of low-cost owing to their simple synthesis approaches, exhibited good selectivity and reusability. The proposed MWCNT-\b{eta}CD based electrodes is a promising technology in developing a highly affordable and sensitive electrochemical sensing system of Pb in drinking water.
Plant phenology—the timing of cyclic or recurrent biological events in plants—offers insight into the ecology, evolution, and seasonality of plant-mediated ecosystem processes. Traditionally studied phenologies are readily apparent, such as flowering events, germination timing, and season-initiating budbreak. However, a broad range of phenologies that are fundamental to the ecology and evolution of plants, and to global biogeochemical cycles and climate change predictions, have been neglected because they are “cryptic”—that is, hidden from view (e.g., root production) or difficult to distinguish and interpret based on common measurements at typical scales of examination (e.g., leaf turnover in evergreen forests). We illustrate how capturing cryptic phenology can advance scientific understanding with two case studies: wood phenology in a deciduous forest of the northeastern USA and leaf phenology in tropical evergreen forests of Amazonia. Drawing on these case studies and other literature, we argue that conceptualizing and characterizing cryptic plant phenology is needed for understanding and accurate prediction at many scales from organisms to ecosystems. We recommend avenues of empirical and modeling research to accelerate discovery of cryptic phenological patterns, to understand their causes and consequences, and to represent these processes in terrestrial biosphere models.
Lebanon is facing an increasing water supply deficit due to the increasing demand for freshwater, decreasing surface and groundwater resources and malfunctioning water governance structures. Technological and policy changes are needed to alleviate the impact of water scarcity and secure water in the future. This paper investigates farmers' preferences and willingness to pay (WTP) in a choice experiment for a series of water saving measures at plot and irrigation district level, including more timely information of water delivery. These measures are expected to strengthen water security and use water more efficiently. Farmers are willing to pay higher water prices of $0.32/m3 and $0.22/m3 to support the implementation of water saving measures at plot level and the installation of water metering devices across the irrigation district, respectively. They are not willing to pay extra for obtaining information related to their water delivery earlier in time if this means that they will also have to pay earlier in the year for the water. Farmers with higher income and education levels who decide on their cropping pattern based on expected rainfall data are more interested in taking action than farmers whose cropping decisions are primarily based on last year's sales prices. The study shows that when aiming to design more effective sustainable water management strategies, accounting for farmers' needs and preferences, their age also has to be considered: younger farmers (<40 years) are on average more interested in and willing to pay more for new water saving measures than older farmers (>40 years).
Recent advances in mass spectrometry have facilitated chemical characterization and profiling of complex environmental mixtures such as oil sand process-affected water (OSPW) and identification of previously unresolved chemicals. However, because OSPW is a complex mixture of salts, metals, suspended particulate matter, and dissolved organics, extraction techniques are required to reduce the effects of signal suppression/enhancement. In this work, Orbitrap, ultrahigh resolution mass spectrometry was used to perform a comprehensive comparison of solid phase extraction (SPE) and liquid–liquid extraction (LLE) techniques on profiling of dissolved organic chemicals in OSPW. When operated in negative ion mode, extraction of naphthenic acid (NAs–O2) was dependent on acidification of OSPW samples for C18 and LLE techniques. However, when applying a hydrophilic lipophilic balance (HLB) sorbent (ABN) SPE technique, the extractability of NAs was independent of pH. When operated in positive ion mode, for all extracti...
Abstract The sustainable development goals (SDGs) and the Paris agreement target a global cleaner energy transition with wider adaptation, poverty reduction and climate resilience benefits. Hydropower development in the transboundary Koshi river basin in the Himalayan region presents an intervention that can support the SDGs whilst meeting the regional commitments to the Paris agreement. This study aims to quantify the benefits of proposed water resource development projects in the transboundary basin (4 storage and 7 run-of-the-river hydropower dams) in terms of hydroelectric power generation, crop production and flood damage reduction. A hydro-economic model is constructed by soft coupling hydrological and crop growth simulation models to an economic optimization model. The model assesses the potential of the interventions to break the vicious cycle of poverty and water, food, and energy insecurity. Unlike previous studies, the model (a) incorporates the possibility of using hydropower to pump groundwater for irrigation as well as flood regulation and (b) quantifies the resilience of the estimated benefits under future climate scenarios from downscaled general circulation models affecting both river flows and crop growth. The results show significant potential economic benefits generated from electricity production, increased agricultural production, and flood damage control at the transboundary basin scale. The estimated annual benefits are around USD 2.3 billion under the baseline scenario and USD 2.4 billion under a future (RCP 4.5) climate scenario, compared to an estimated annual investment cost of USD 0.7 billion. The robustness of the estimated benefits illustrates the climate resilience of the water resource development projects. Contrary to the commonly held view that the benefits of these proposed projects are limited to hydropower, the irrigation and flood regulation benefits account for 40 percent of the total benefits. The simulated scenarios also show substantial irrigation gains from the construction of the ROR schemes, provided the generated power is also used for groundwater irrigation. The integrated modelling framework and results provide useful policy insights for evidence-based decision-making in transboundary river basins around the globe facing the challenges posed by the water-food-energy nexus.
River flows connect people, places, and other forms of life, inspiring and sustaining diverse cultural beliefs, values, and ways of life. The concept of environmental flows provides a framework for improving understanding of relationships between river flows and people, and for supporting those that are mutually beneficial. Nevertheless, most approaches to determining environmental flows remain grounded in the biophysical sciences. The newly revised Brisbane Declaration and Global Action Agenda on Environmental Flows (2018) represents a new phase in environmental flow science and an opportunity to better consider the co-constitution of river flows, ecosystems, and society, and to more explicitly incorporate these relationships into river management. We synthesize understanding of relationships between people and rivers as conceived under the renewed definition of environmental flows. We present case studies from Honduras, India, Canada, New Zealand, and Australia that illustrate multidisciplinary, collaborative efforts where recognizing and meeting diverse flow needs of human populations was central to establishing environmental flow recommendations. We also review a small body of literature to highlight examples of the diversity and interdependencies of human-flow relationships-such as the linkages between river flow and human well-being, spiritual needs, cultural identity, and sense of place-that are typically overlooked when environmental flows are assessed and negotiated. Finally, we call for scientists and water managers to recognize the diversity of ways of knowing, relating to, and utilizing rivers, and to place this recognition at the center of future environmental flow assessments. This article is categorized under: Water and Life > Conservation, Management, and Awareness Human Water > Water Governance Human Water > Water as Imagined and Represented.
Abstract. Land surface evaporation has considerable spatial variability that is not captured by point-scale estimates calculated from meteorological data alone. Knowing how evaporation varies spatially remains an important issue for improving parameterisations of land surface schemes and hydrological models and various land management practices. Satellite-based and aerial remote sensing has been crucial for capturing moderate- to larger-scale surface variables to indirectly estimate evaporative fluxes. However, more recent advances for field research via unmanned aerial vehicles (UAVs) now allow for the acquisition of more highly detailed surface data. Integrating models that can estimate “actual” evaporation from higher-resolution imagery and surface reference data would be valuable to better examine potential impacts of local variations in evaporation on upscaled estimates. This study introduces a novel approach for computing a normalised ratiometric index from surface variables that can be used to obtain more-realistic distributed estimates of actual evaporation. For demonstration purposes the Granger–Gray evaporation model (Granger and Gray, 1989) was applied at a rolling prairie agricultural site in central Saskatchewan, Canada. Visible and thermal images and meteorological reference data required to parameterise the model were obtained at midday. Ratiometric indexes were computed for the key surface variables albedo and net radiation at midday. This allowed point observations of albedo and mean daily net radiation to be scaled across high-resolution images over a large study region. Albedo and net radiation estimates were within 5 %–10 % of measured values. A daily evaporation estimate for a grassed surface was 0.5 mm (23 %) larger than eddy covariance measurements. Spatial variations in key factors driving evaporation and their impacts on upscaled evaporation estimates are also discussed. The methods applied have two key advantages for estimating evaporation over previous remote-sensing approaches: (1) detailed daily estimates of actual evaporation can be directly obtained using a physically based evaporation model, and (2) analysis of more-detailed and more-reliable evaporation estimates may lead to improved methods for upscaling evaporative fluxes to larger areas.
Abstract. Hydrological processes are widely understood to be sensitive to changes in climate, but the effects of concomitant changes in vegetation and soils have seldom been considered in snow-dominated mountain basins. The response of mountain hydrology to vegetation/soil changes in the present and a future climate was modeled in three snowmelt-dominated mountain basins in the North American Cordillera. The models developed for each basin using the Cold Regions Hydrological Modeling platform employed current and expected changes to vegetation and soil parameters and were driven with recent and perturbed high-altitude meteorological observations. Monthly perturbations were calculated using the differences in outputs between the present- and a future-climate scenario from 11 regional climate models. In the three basins, future climate change alone decreased the modeled peak snow water equivalent (SWE) by 11 %–47 % and increased the modeled evapotranspiration by 14 %–20 %. However, including future changes in vegetation and soil for each basin changed or reversed these climate change outcomes. In Wolf Creek in the Yukon Territory, Canada, a statistically insignificant increase in SWE due to vegetation increase in the alpine zone was found to offset the statistically significant decrease in SWE due to climate change. In Marmot Creek in the Canadian Rockies, the increase in annual runoff due to the combined effect of soil and climate change was statistically significant, whereas their individual effects were not. In the relatively warmer Reynolds Mountain in Idaho, USA, vegetation change alone decreased the annual runoff volume by 8 %, but changes in soil, climate, or both did not affect runoff. At high elevations in Wolf and Marmot creeks, the model results indicated that vegetation/soil changes moderated the impact of climate change on peak SWE, the timing of peak SWE, evapotranspiration, and the annual runoff volume. However, at medium elevations, these changes intensified the impact of climate change, further decreasing peak SWE and sublimation. The hydrological impacts of changes in climate, vegetation, and soil in mountain environments were similar in magnitude but not consistent in direction for all biomes; in some combinations, this resulted in enhanced impacts at lower elevations and latitudes and moderated impacts at higher elevations and latitudes.
• Potential and realized values of a bundle of six ecosystem services are estimated for Southern Ontario, Canada. • The realized value of the ecosystem services averages 51% of the potential value. • Within the Greenbelt, a protected area surrounding the Toronto conurbation, 61% of the potential eco-services are realized. • The spatial distribution of realized ecosystem services helps inform environmental policy-making. The full production of a given ecosystem service is called the potential ecosystem service; the fraction of the potential ecosystem service that is actually used by society is referred to as the realized ecosystem service. Because they are directly contributing to human well-being, the realized ecosystem services are of particular socio-economic importance. A key challenge faced by the economic valuation of ecosystem services is how to differentiate between realized and potential ecosystem services. Here, we address this challenge for Southern Ontario, which is the most densely populated region of Canada. We apply the Co$ting Nature model to generate the combined spatial distribution and use intensity of a bundle of six ecosystem services: water provisioning and supply, water quality, carbon sequestration, carbon storage, flood regulation, and nature-based tourism. The relative distribution of the potential ecosystem services is then combined with region-specific unit values for the land covers supplying the ecosystem services. The unit values are expressed in 2017 Canadian dollars per hectare and per year. Our analysis yields a total potential value of the bundled ecosystem services of $19 billion per year for Southern Ontario. To estimate the value of the realized ecosystem services, the potential values are scaled by the corresponding relative use indices. The resulting value of the realized ecosystem services is $9.7 billion per year, that is, about 50% of the value of the potential ecosystem services. The importance of accounting for the use intensity of ecosystem services is illustrated for the Greenbelt, a protected area of about 7600 km 2 surrounding the Greater Toronto-Hamilton conurbation, which is home to more than nine million people. Within the Greenbelt, 61% of the value of potential ecosystem services is realized, significantly higher than the regional average. Of particular importance is flood regulation by the Greenbelt, given the growing threat of urban flooding in the Toronto area.
The Northern Great Plains is a key region to global food production. It is also a region of water stress that includes poor water quality associated with high concentrations of nutrients. Agricultural nitrogen and phosphorus loads to surface waters need to be reduced, yet the unique characteristics of this environment create challenges. The biophysical reality of the Northern Great Plains is one where snowmelt is the major period of nutrient transport, and where nutrients are exported predominantly in dissolved form. This limits the efficacy of many beneficial management practices (BMPs) commonly used in other regions and necessitates place-based solutions. We discuss soil and water management BMPs through a regional lens—first understanding key aspects of hydrology and hydrochemistry affecting BMP efficacy, then discussing the merits of different BMPs for nutrient control. We recommend continued efforts to “keep water on the land” via wetlands and reservoirs. Adoption and expansion of reduced tillage and perennial forage may have contributed to current nutrient problems, but both practices have other environmental and agronomic benefits. The expansion of tile and surface drainage in the Northern Great Plains raises urgent questions about effects on nutrient export and options to mitigate drainage effects. Riparian vegetation is unlikely to significantly aid in nutrient retention, but when viewed against an alternative of extending cultivation and fertilization to the waters’ edge, the continued support of buffer strip management and refinement of best practices (e.g., harvesting vegetation) is merited. While the hydrology of the Northern Great Plains creates many challenges for mitigating nutrient losses, it also creates unique opportunities. For example, relocating winter bale-grazing to areas with low hydrologic connectivity should reduce loadings. Managing nutrient applications must be at the center of efforts to mitigate eutrophication. In this region, ensuring nutrients are not applied during hydrologically sensitive periods such as late autumn, on snow, or when soils are frozen will yield benefits. Working to ensure nutrient inputs are balanced with crop demands is crucial in all landscapes. Ultimately, a targeted approach to BMP implementation is required, and this must consider the agronomic and economic context but also the biophysical reality.
Abstract Recently dam managers have begun to use data produced by regional climate models to estimate how probable maximum precipitation (PMP) might evolve in the future. Before accomplishing such a task, it is essential to assess PMP estimates derived from regional climate models (RCMs). In the current study PMP over North America estimated from two Canadian RCMs, CanRCM4 and CRCM5, is compared with estimates derived from three reanalysis products: ERA-Interim, NARR, and CFSR. An additional hybrid dataset (MSWEP-ERA) produced by combining precipitation from the Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset and precipitable water (PW) from ERA-Interim is also considered to derive PMP estimates that can serve as a reference. A recently developed approach using a statistical bivariate extreme values distribution is used to provide a probabilistic description of the PMP estimates using the moisture maximization method. Such a probabilistic description naturally allows an assessment of PMP estimates that includes quantification of their uncertainty. While PMP estimates based on the two RCMs exhibit spatial patterns similar to those of MSWEP-ERA and the three sets of reanalyses on the continental scale over North America, CanRCM4 has a tendency for overestimation while CRCM5 has a tendency for modest underestimation. Generally, CRCM5 shows good agreement with ERA-Interim, while CanRCM4 is more comparable to CFSR. Overall, the good ability of the two RCMs to reproduce the major characteristics of the different components involved in the estimation of PMP suggests that they may be useful tools for PMP estimation that could serve as a basis for flood studies at the basin scale.
In the context of climate change and projected increase in global temperature, the atmosphere’s water holding capacity is expected to increase at the Clausius-Clapeyron (C-C) rate by about 7% per 1 °C warming. Such an increase may lead to more intense extreme precipitation events and thus directly affect the probable maximum precipitation (PMP), a parameter that is often used for dam safety and civil engineering purposes. We therefore use a statistically motivated approach that quantifies uncertainty and accounts for nonstationarity, which allows us to determine the rate of change of PMP per 1 °C warming. This approach, which is based on a bivariate extreme value model of precipitable water (PW) and precipitation efficiency (PE), provides interpretation of how PW and PE may evolve in a warming climate. Nonstationarity is accounted for in this approach by including temperature as a covariate in the bivariate extreme value model. The approach is demonstrated by evaluating and comparing projected changes to 6-hourly PMP from two Canadian regional climate models (RCMs), CanRCM4 and CRCM5, over North America. The main results suggest that, on the continental scale, PMP increases in these models at a rate of approximately 4% per 1 °C warming, which is somewhat lower than the C-C rate. At the continental scale, PW extremes increase on average at the rate of 5% per 1 °C near surface warming for both RCMs. Most of the PMP increase is caused by the increase in PW extremes with only a minor contribution from changes in PE extremes. Nevertheless, substantial deviations from the average rate of change in PMP rates occur in some areas, and these are mostly caused by sensitivity of PE extremes to near surface warming in these regions.
Quantifying the behavior and performance of hydrologic models is an important aspect of understanding the underlying hydrologic systems. We argue that classical error measures do not offer a complete picture for building this understanding. This study demonstrates how the information theoretic measure known as transfer entropy can be used to quantify the active transfer of information between hydrologic processes at various timescales and facilitate further understanding of the behavior of these systems. To build a better understanding of the differences in dynamics, we compare model instances of the Structure for Unifying Multiple Modeling Alternatives (SUMMA), the Variable Infiltration Capacity (VIC) model, and the Precipitation Runoff Modeling System (PRMS) across a variety of hydrologic regimes in the Columbia River Basin in the Pacific Northwest of North America. Our results show differences in the runoff of the SUMMA instance compared to the other two models in several of our study locations. In the Snake River region, SUMMA runoff was primarily snowmelt driven, while VIC and PRMS runoff was primarily influenced by precipitation and evapotranspiration. In the Olympic mountains, evapotranspiration interacted with the other water balance variables much differently in PRMS than in VIC and SUMMA. In the Willamette River, all three models had similar process networks at the daily time scale but showed differences in information transfer at the monthly timescale. Additionally, we find that all three models have similar connectivity between evapotranspiration and soil moisture. Analyzing information transfers to runoff at daily and monthly time steps shows how processes can operate on different timescales. By comparing information transfer with correlations, we show how transfer entropy provides a complementary picture of model behavior.
Forests play a crucial role in the global carbon (C) cycle by storing and sequestering a substantial amount of C in the terrestrial biosphere. Due to temporal dynamics in climate and vegetation activity, there are significant regional variations in carbon dioxide (CO2) fluxes between the biosphere and atmosphere in forests that are affecting the global C cycle. Current forest CO2 flux dynamics are controlled by instantaneous climate, soil, and vegetation conditions, which carry legacy effects from disturbances and extreme climate events. Our level of understanding from the legacies of these processes on net CO2 fluxes is still limited due to their complexities and their long-term effects. Here, we combined remote sensing, climate, and eddy-covariance flux data to study net ecosystem CO2 exchange (NEE) at 185 forest sites globally. Instead of commonly used non-dynamic statistical methods, we employed a type of recurrent neural network (RNN), called Long Short-Term Memory network (LSTM) that captures information from the vegetation and climate's temporal dynamics. The resulting data-driven model integrates interannual and seasonal variations of climate and vegetation by using Landsat and climate data at each site. The presented LSTM algorithm was able to effectively describe the overall seasonal variability (Nash-Sutcliffe efficiency, NSE = 0.66) and across-site (NSE = 0.42) variations in NEE, while it had less success in predicting specific seasonal and interannual anomalies (NSE = 0.07). This analysis demonstrated that an LSTM approach with embedded climate and vegetation memory effects outperformed a non-dynamic statistical model (i.e. Random Forest) for estimating NEE. Additionally, it is shown that the vegetation mean seasonal cycle embeds most of the information content to realistically explain the spatial and seasonal variations in NEE. These findings show the relevance of capturing memory effects from both climate and vegetation in quantifying spatio-temporal variations in forest NEE.

DOI bib
Twenty-three unsolved problems in hydrology (UPH) – a community perspective
Günter Blöschl, M. F. Bierkens, António Chambel, Christophe Cudennec, Georgia Destouni, Aldo Fiori, J. W. Kirchner, Jeffrey J. McDonnell, H. H. G. Savenije, Murugesu Sivapalan, Christine Stumpp, Elena Toth, Elena Volpi, Gemma Carr, Claire Lupton, José Luis Salinas, Borbála Széles, Alberto Viglione, Hafzullah Aksoy, Scott T. Allen, Anam Amin, Vazken Andréassian, Berit Arheimer, Santosh Aryal, Victor R. Baker, Earl Bardsley, Marlies Barendrecht, Alena Bartošová, Okke Batelaan, Wouter Berghuijs, Keith Beven, Theresa Blume, Thom Bogaard, Pablo Borges de Amorim, Michael E. Böttcher, Gilles Boulet, Korbinian Breinl, Mitja Brilly, Luca Brocca, Wouter Buytaert, Attilio Castellarin, Andrea Castelletti, Xiaohong Chen, Yangbo Chen, Yuanfang Chen, Peter Chifflard, Pierluigi Claps, Martyn P. Clark, Adrian L. Collins, Barry Croke, Annette Dathe, Paula Cunha David, Felipe P. J. de Barros, Gerrit de Rooij, Giuliano Di Baldassarre, Jessica M. Driscoll, Doris Duethmann, Ravindra Dwivedi, Ebru Eriş, William Farmer, James Feiccabrino, Grant Ferguson, Ennio Ferrari, Stefano Ferraris, Benjamin Fersch, David C. Finger, Laura Foglia, Keirnan Fowler, Б. И. Гарцман, Simon Gascoin, Éric Gaumé, Alexander Gelfan, Josie Geris, Shervan Gharari, Tom Gleeson, Miriam Glendell, Alena Gonzalez Bevacqua, M. P. González‐Dugo, Salvatore Grimaldi, A.B. Gupta, Björn Guse, Dawei Han, David M. Hannah, A. A. Harpold, Stefan Haun, Kate Heal, Kay Helfricht, Mathew Herrnegger, Matthew R. Hipsey, Hana Hlaváčiková, Clara Hohmann, Ladislav Holko, C. Hopkinson, Markus Hrachowitz, Tissa H. Illangasekare, Azhar Inam, Camyla Innocente, Erkan Istanbulluoglu, Ben Jarihani, Zahra Kalantari, Andis Kalvāns, Sonu Khanal, Sina Khatami, Jens Kiesel, M. J. Kirkby, Wouter Knoben, Krzysztof Kochanek, Silvia Kohnová, Alla Kolechkina, Stefan Krause, David K. Kreamer, Heidi Kreibich, Harald Kunstmann, Holger Lange, Margarida L. R. Liberato, Eric Lindquist, Timothy E. Link, Junguo Liu, Daniel P. Loucks, Charles H. Luce, Gil Mahé, Olga Makarieva, Julien Malard, Shamshagul Mashtayeva, Shreedhar Maskey, Josep Mas‐Pla, Maria Mavrova-Guirguinova, Maurizio Mazzoleni, Sebastian H. Mernild, Bruce Misstear, Alberto Montanari, Hannes Müller-Thomy, Alireza Nabizadeh, Fernando Nardi, Christopher M. U. Neale, Nataliia Nesterova, Bakhram Nurtaev, V.O. Odongo, Subhabrata Panda, Saket Pande, Zhonghe Pang, Georgia Papacharalampous, Charles Perrin, Laurent Pfister, Rafael Pimentel, María José Polo, David Post, Cristina Prieto, Maria‐Helena Ramos, Maik Renner, José Eduardo Reynolds, Elena Ridolfi, Riccardo Rigon, Mònica Riva, David Robertson, Renzo Rosso, Tirthankar Roy, João Henrique Macedo Sá, Gianfausto Salvadori, Melody Sandells, Bettina Schaefli, Andreas Schumann, Anna Scolobig, Jan Seibert, Éric Servat, Mojtaba Shafiei, Ashish Sharma, Moussa Sidibé, Roy C. Sidle, Thomas Skaugen, Hugh G. Smith, Sabine M. Spiessl, Lina Stein, Ingelin Steinsland, Ulrich Strasser, Bob Su, Ján Szolgay, David G. Tarboton, Flavia Tauro, Guillaume Thirel, Fuqiang Tian, Rui Tong, Kamshat Tussupova, Hristos Tyralis, R. Uijlenhoet, Rens van Beek, Ruud van der Ent, Martine van der Ploeg, Anne F. Van Loon, Ilja van Meerveld, Ronald van Nooijen, Pieter van Oel, Jean‐Philippe Vidal, Jana von Freyberg, Sergiy Vorogushyn, Przemysław Wachniew, Andrew J. Wade, Philip J. Ward, Ida Westerberg, Christopher White, Eric F. Wood, Ross Woods, Zongxue Xu, Koray K. Yılmaz, Yongqiang Zhang
Hydrological Sciences Journal, Volume 64, Issue 10

This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come.
It is often assumed that large shallow water bodies are net sediment nondepositional annually and that if they have nutrient loads from multiple sources, those loads are quickly homogenized before exiting the water bodies. Where this is not the case, it impacts understanding and predicting consequences of nutrient load reductions, both for the water body and for those downstream of it. We applied a three‐dimensional ecological model to a large shallow lake, Lake St. Clair (US/Canada), to quantify the total and dissolved reactive phosphorus (TP and DRP) transport and retention, and construct tributary‐specific relationships between phosphorus load to the lake and the amount of phosphorus that leaves the lake for the three major tributaries. Lake St. Clair is situated between the St. Clair and Detroit rivers, the latter enters Lake Erie. Efforts to reduce Lake Erie's re‐eutrophication requires an understanding of nutrient transport and retention in each of its subwatersheds including those that feed indirectly via Lake St. Clair. We found that over the simulation period, the lake retained a significant portion of TP (17%) and DRP (35%) load and that TP and DRP retention was spatially variable and largely controlled by a combination of lake depth, resuspension, and plankton uptake. Compared to the Clinton and Sydenham rivers, the Thames River contributed a larger proportion of its load to the lake's outflow. However, because the lake's load is dominated by the St. Clair River, 40% reductions of nutrients from those subwatersheds will result in less than a 5% reduction in the load to Lake Erie.
Snow water equivalent (SWE) is one of the most hydrologically important physical properties of a snowpack. The U.S. National Weather Service's Snow Data Assimilation System (SNODAS) provides snow products at high spatial (~1 km2) and temporal (daily) resolution for the contiguous United States and southern Canada. This study evaluated the SNODAS SWE product in the boreal forest, prairie, and Canadian Rockies of western Canada against extensive snow survey measurements. SNODAS was found to work well in sheltered environments, to overestimate SWE under needle‐leaf forests, and to be unable to capture the spatial variation of SWE in windswept prairie and alpine environments. Results indicate that SNODAS SWE accuracy is strongly influenced by the missing blowing snow redistribution and canopy energetics and snow interception and sublimation processes in the mass balance calculations of the SNODAS model and by erroneous precipitation data forcing the model. To demonstrate how errors caused by missing processes can be corrected in areas with low assimilation frequency, SNODAS data were assimilated into a physically based hydrological model created using the modular Cold Region Hydrological Modelling (CRHM) platform that includes blowing and intercepted snow redistribution and subcanopy melt energetic processes. This approach decreased the overestimation of SWE compared to SNODAS from 135 to 79% in the study area and suggests that snow assimilation modeled SWE quality can be improved if snow redistribution, sublimation, and subcanopy melt processes are incorporated.
Watershed urbanization and stormwater management (SWM) alter the hydrologic processes of rivers. Although differences have been documented in channel morphology and sediment yield pre‐ and posturbanization, little is known about how the modified hydrology affects grain‐scale bedload transport dynamics. This study aims to characterize the bedload sediment transport regime of three rivers with different hydrologic settings: rural, urban with no SWM, and urban with peak‐shaving SWM. The rivers are “semi‐alluvial,” characterized by an alluvial layer over a cohesive till. Bedload transport was monitored using tracer stones over 3 years. Hydrograph characteristics of the streams fit with what is expected in urban and SWM systems, and the rural stream has an episodic transport regime typical of gravel‐bed rivers. Entrainment thresholds are not detectably impacted by the semi‐alluvial bed cover, but travel lengths of grains relative to their size are longer than in alluvial gravel‐bed streams. Downstream displacement rates of particles up to the D90 are accelerated in the urban river due to more frequent mobilization rather than increased event‐based travel lengths and may explain channel enlargement. SWM decreases the mobility and travel lengths of particles below those in the rural system, which is combined with channel narrowing, and the loss of bed forms suggests a shift toward a competence‐limited transport regime. This new regime is a result of reduced shear stresses that are insufficient to transport coarse material. This study presents empirical evidence of the effects of watershed urbanization and SWM on bedload transport and provides recommendations for process‐based river management strategies.
The Canadian Forest Fire Weather Index System is the primary measurement of wildfire danger in Canada. Interpolating daily precipitation, one of the inputs for the Fire Weather Index System is a key challenge in areas without sufficient weather stations. This work evaluates the performance of gridded precipitation from the Canadian Precipitation Analysis (CaPA) System and six interpolation methods to achieve the best fire danger rating in Alberta, Canada. Results show that the CaPA System has only average performance due to limited radar coverage (10%) in the forested region; however, using the CaPA System as a covariate with regression kriging generates significantly better precipitation estimates. Ordinary kriging, regression kriging with elevation as a covariate, and the thin‐plate smoothed spline are methods with similar performance. Fuel moisture codes of the Fire Weather Index System respond differently to precipitation amounts due to differences in their time constants for drying. Fine fuels with a short drying time (Fine Fuel Moisture Code) are best estimated by the CaPA System because of its enhanced skill in estimating small precipitation events. Compacted organic fuels with longer drying times (Duff Moisture Code and Drought Code) are best estimated by regression kriging with CaPA because it better predicts significant precipitation events. The dense fire weather station network in our study area (~3.0 stations/10,000 km2) allows us to perform a sensitivity analysis, and we find that a threshold of >0.5 stations/10,000 km2 is needed for regression kriging with CaPA to become appreciably better than the CaPA System.
Controls on nutrient transport in cold, low-relief agricultural regions vary dramatically among seasons. The spring snowmelt is often the dominant runoff and nutrient loading event of the year. However, climate change may increase the proportion of runoff occurring with rainfall, and there is an urgent need to understand seasonal controls on nutrient transport to understand how patterns may change in the future. In this study, we assess patterns and drivers of total P (TP) dynamics in eight streams draining agriculturally dominated watersheds, located in southern Manitoba, Canada. Data from three years of monitoring revealed highly coherent patterns of TP concentrations in streams, with pronounced peaks in the spring and midsummer across the region. This coherent pattern was in spite of considerable interannual variability in the magnitude and timing of discharge; in particular, a major storm event occurred in summer 2014, which resulted in more discharge than the preceding spring melt. Concentration-discharge model fits were generally poor or not significant, suggesting that runoff generation is not the primary driver of TP dynamics in the majority of streams. Seasonal patterns of conductivity and stream temperature suggest that mechanisms controlling TP vary by season; a spring TP concentration maximum may be related to surface runoff over frozen soils, whereas the summer TP maximum may be related to temperature-driven biogeochemical processes, which are not well represented in current conceptual or predictive models. These findings suggest that controls on stream TP concentrations are dynamic through the year, and responses to increases in dormant and nondormant season temperatures may depend on seasonally variable processes.
Reducing eutrophication in surface water is a major environmental challenge in many countries around the world. In cold Canadian prairie agricultural regions, part of the eutrophication challenge arises during spring snowmelt when a significant portion of the total annual nutrient export occurs, and plant residues can act as a nutrient source instead of a sink. Although the total mass of nutrients released from various crop residues has been studied before, little research has been conducted to capture fine-timescale temporal dynamics of nutrient leaching from plant residues, and the processes have not been represented in water quality models. In this study, we measured the dynamics of P and N release from a cold-hardy perennial plant species, alfalfa ( L.), to meltwater after freeze-thaw through a controlled snowmelt experiment. Various winter conditions were simulated by exposing alfalfa residues to different numbers of freeze-thaw cycles (FTCs) of uniform magnitude prior to snowmelt. The monitored P and N dynamics showed that most nutrients were released during the initial stages of snowmelt (first 5 h) and that the magnitude of nutrient release was affected by the number of FTCs. A threshold of five FTCs was identified for a greater nutrient release, with plant residue contributing between 0.29 (NO) and 9 (PO) times more nutrients than snow. The monitored temporal dynamics of nutrient release were used to develop the first process-based predictive model controlled by three potentially measurable parameters that can be integrated into catchment water quality models to improve nutrient transport simulations during snowmelt.
This study quantified the contributions of overland and tile flow to total runoff (sum of overland and tile flow) and nutrient losses in a Vertisolic soil in the Red River valley (Manitoba, Canada), a region with a cold climate where tile drainage is rapidly expanding. Most annual runoff occurred as overland flow (72-89%), during spring snowmelt and large spring and summer storms. Tile drains did not flow in early spring due to frozen ground. Although tiles flowed in late spring and summer (33-100% of event flow), this represented a small volume of annual runoff (10-25%), which is in stark contrast with what has been observed in other tile-drained landscapes. Median daily flow-weighted mean concentrations of soluble reactive P (SRP) and total P (TP) were significantly greater in overland flow than in tile flow ( < 0.001), but the reverse pattern was observed for NO-N ( < 0.001). Overland flow was the primary export pathway for both P and NO-N, accounting for >95% of annual SRP and TP and 50 to 60% of annual NO-N losses. Data suggest that tile drains do not exacerbate P export from Vertisols in the Red River valley because they are decoupled from the surface by soil-ice during snowmelt, which is the primary time for P loss. However, NO-N loading to downstream water bodies may be exacerbated by tiles, particularly during spring and summer storms after fertilizer application.
Cold agricultural regions are important sites of global food production. This has contributed to widespread water quality degradation influenced by processes and hydrologic pathways that differ from warm region analogues. In cold regions, snowmelt is often a dominant period of nutrient loss. Freeze-thaw processes contribute to nutrient mobilization. Frozen ground can limit infiltration and interaction with soils, and minimal nutrient uptake during the nongrowing season may govern nutrient export from agricultural catchments. This paper reviews agronomic, biogeochemical, and hydrological characteristics of cold agricultural regions and synthesizes findings of 23 studies that are published in this special section, which provide new insights into nutrient cycling and hydrochemical processes, model developments, and the efficacy of different potentially beneficial management practices (BMPs) across varied cold regions. Growing evidence suggests the need to redefine optimum soil phosphorus levels and input regimes in cold regions to allow achievement of water quality targets while still supporting strong agricultural productivity. Practices should be considered through a regional and site-specific lens, due to potential interactions between climate, hydrology, vegetation, and soils, which influence the efficacy of nutrient, crop, water, and riparian buffer management. This leads to differing suitability of BMPs across varied cold agricultural regions. We propose a systematic approach (""), to achieve water quality objectives in variable and changing climates, which combines nutrient transport process onceptualization, nderstanding BMP functions, redicting effects of variability and change, onsideration of producer input and agronomic and environmental tradeoffs, practice daptation, nowledge mobilization, and valuation of water quality improvement.
Managing P export from agricultural land is critical to address freshwater eutrophication. However, soil P management, and options to draw down soil P have received little attention in snowmelt-dominated regions because of limited interaction between soil and snowmelt. Here, we assessed the impacts of soil P drawdown (reducing fertilizer P inputs combined with harvest removal) on soil Olsen P dynamics, runoff P concentrations, and crop yields from 1997 to 2014 in paired fields in Manitoba, Canada. We observed that Olsen P concentrations in the 0- to 5-cm soil layer were negatively correlated with the cumulative P depletion and declined rapidly at the onset of the drawdown practice (3.1 to 5.4 mg kg yr during 2007-2010). In both snowmelt runoff and rainfall runoff, concentrations of total dissolved P (TDP) were positively correlated with the concentrations of soil Olsen P. Soil P drawdown to low to moderate fertility levels significantly decreased mean annual flow-weighted TDP concentrations in snowmelt runoff from 0.60 to 0.30 mg L in the field with high initial soil P and from 1.17 to 0.42 mg L in the field with very high initial soil P. Declines in TDP concentration in rainfall runoff were greater. Critically, yields of wheat ( spp.) and canola ( L.) were not affected by soil P depletion. In conclusion, we demonstrate that relatively rapid reductions in P loads are achievable at the field scale via managing P inputs and soil P pools, highlighting a management opportunity that can maintain food security while improving water security in cold regions.
The use of cover crops and crop residues is a common strategy to mitigate sediment and nutrient losses from land to water. In cold climates, elevated dissolved P losses can occur associated with freeze-thaw of plant materials. Here, we review the impacts of cover crops and crop residues on dissolved P and total P loss in cold climates across ∼41 studies, exploring linkages between water-extractable P (WEP) in plant materials and P loss in surface runoff and subsurface drainage. Water-extractable P concentrations are influenced by plant type and freezing regimes. For example, WEP was greater in brassica cover crops than in non-brassicas, and increased with repeated freeze-thaw cycles. However, total P losses in surface runoff and subsurface drainage from cropped fields under cold climates were much lower than plant WEP, owing to retention of 45 to >99% of released P by soil. In cold climatic regions, cover crops and crop residues generally prevented soil erosion and loss of particle-bound P during nongrowing seasons in erodible landscapes but tended to elevate dissolved P loss in nonerodible soils. Their impact on total P loss was inconsistent across studies and complicated by soil, climate, and management factors. More research is needed to understand interactions between these factors and plant type that influence P loss, and to improve the assessment of crop contributions to P loss in field settings in cold climates. Further, tradeoffs between P loss and the control of sediment loss and N leaching by plants should be acknowledged.
Agricultural P losses are a global economic and water quality concern. Much of the current understanding of P dynamics in agricultural systems has been obtained from rainfall-driven runoff, and less is known about cold-season processes. An improved understanding of the magnitude, form, and transport flow paths of P losses from agricultural croplands year round, and the climatic drivers of these processes, is needed to prioritize and evaluate appropriate best management practices (BMPs) to protect soil-water quality in cold regions. This study examines multiyear, year-round, high-frequency edge-of-field P losses (soluble reactive P and total P [TP]) in overland flow and tile drainage from three croplands in southern Ontario, Canada. Annual and seasonal budgets for water, P, and estimates of field P budgets (including fertilizer inputs, crop uptake, and runoff) were calculated for each site. Annual edge-of-field TP loads ranged from 0.18 to 1.93 kg ha yr (mean = 0.59 kg ha yr) across the region, including years with fertilizer application. Tile drainage dominated runoff across sites, whereas the contribution of tiles and overland flow to P loss differed regionally, likely related to site-specific topography, soil type, and microclimate. The nongrowing season was the dominant period for runoff and P loss across sites, where TP loss during this period was often associated with overland flow during snowmelt. These results indicate that emphasis should be placed on BMPs that are effective during both the growing and nongrowing season in cold regions, but that the suitability of various BMPs may vary for different sites.
Measurement of the retention of dissolved nutrients in riparian areas with snowmelt runoff are much less common than for rainfall runoff, but low rates of uptake or the release of nutrients with snowmelt have been attributed to frozen soils, lower biotic uptake, and release of nutrients from senesced vegetation. In the research presented here, we evaluate whether the potential for uptake of dissolved reactive phosphorus (DRP) and NO differ significantly between snowmelt and summer seasons with flow through 13 riparian buffers downstream of cropland in Manitoba, Canada. Flow-through buffers in small channels are typical in this landscape, and pulsed releases of a conservative tracer and dissolved nutrients were used to measure uptake rates. Although mean uptake rates of NO were higher in summer than for snowmelt, responses varied widely. Aerial uptake rate of DRP showed a significant negative relationships with soil Olsen-P ( = 0.54, < 0.001) and a P saturation index ( = 0.48, < 0.001) across both seasons. Biological processes may be of greater importance for NO retention, but DRP retention appears to be driven by adsorption-desorption regardless of season. Olsen-P is identified as a good indicator of potential for release or retention of DRP in riparian buffers with fine-textured calcareous soils, for both snowmelt and summer seasons. Soil testing may be a good tool to aid in the siting of new buffers and to track the effectiveness of management interventions to remove P from riparian areas, such as harvest of vegetation.
Snowmelt runoff often comprises the majority of annual runoff in the Canadian Prairies and a significant proportion of total nutrient loss from agricultural land to surface water. Our objective was to determine the effect of agroecosystem management on snowmelt runoff and nutrient losses from a long-term field experiment at Swift Current, SK. Runoff quantity, nutrient concentrations, and loads were estimated after a change in management from conventionally tilled wheat ( L.)-fallow (Conv W-F) to no-till wheat-fallow and subsequently no-till wheat-pulse (NT W-F/LP) and to an organic system with a wheat-green manure rotation (Org W-GM). The conversion from conventional tillage practices to no-till increased snowmelt runoff likely due to snow trapping by standing stubble after summer fallow. Relatedly, runoff after no-till summer fallow had higher dissolved P losses (0.07 kg P ha). Replacing summer fallow with a pulse crop in the no-till rotation decreased snowmelt runoff losses and nutrient concentrations. The Org W-GM treatment had the lowest P loss after stubble (0.02 kg P ha) but had high dissolved P concentrations in snowmelt following the green manure (0.55 mg P L), suggesting a contribution from incorporated crop residues. In this semiarid climate with little runoff, dissolved reactive P and NO-N loads in snowmelt runoff were smaller than those reported elsewhere on the prairies (averaging <0.05 kg P ha yr, and <0.2 kg NO-N ha yr); however, the nutrient concentrations we observed, in particular for P, even without P fertilizer addition for organic production, question the practicality of agricultural management systems in this region meeting water quality guidelines.
In the northern Great Plains, most runoff transport of N, and P to surface waters has historically occurred with snowmelt. In recent years, significant rainfall runoff events have become more frequent and intense in the region. Here, we examine the influence of landscape characteristics on hydrology and nutrient export in nine tributary watersheds of the Assiniboine River in Manitoba, Canada, during snowmelt runoff and with an early summer extreme rainfall runoff event (ERRE). All watersheds included in the study have land use that is primarily agricultural, but with differing proportions of land remaining as wetlands, grassland, and that has been artificially drained. Those watersheds with greater capacity for storage of water in surface depressions (noneffective contributing areas) exhibited lower rates of runoff and nutrient export with snowmelt. During the ERRE, higher export of total P (TP), but not total N, was observed from those watersheds with larger amounts of contributing area that had been added through artificial surface drainage, and this was associated primarily with higher TP concentrations. Increasing or restoring the storage of water on the landscape is likely to reduce nutrient export; however, the importance of antecedent conditions was evident during the ERRE, when small surface depressions were at or near capacity from snowmelt. Total P concentrations observed during the summer ERRE were as high as those observed with snowmelt, and N/P ratios were significantly lower. If the frequency of summer ERREs increases with climate change, this is likely to result in negative water quality outcomes.
In northern regions, a high proportion of annual runoff and phosphorus (P) export from cropland occurs with snowmelt. In this study, we analyze 57 site-years of field-scale snowmelt runoff data from 16 small watersheds draining fine-textured soils (clay or clay loam) in Manitoba, Canada. These fields were selected across gradients of soil P (2.4 to 26.7 mg kg, 0- to 15-cm Olsen P), tillage intensity (high frequency to long-term no-till), and fertilizer input. The strongest predictor of flow-weighted mean concentrations of total dissolved P (TDP) in snowmelt runoff was Olsen P in the top 5 cm of soil ( = 0.45, < 0.01). Residual variation in this relationship related positively to volumetric soil moisture and negatively to water yield. Although Olsen P levels were relatively consistent from year to year, suggesting control by long-term fertilization and tillage history, Olsen P stratification (ratio of 0-5/0-15 cm) increased with rates of fertilizer application. Particulate P (PP) comprised <34% of total P on average, and concentrations were not well predicted by soil or management characteristics. Loads of PP and TDP exported during snowmelt were primarily a function of water yield and size of accumulated snowpack; however, residual variation in the TDP relationship correlated positively with both soil moisture and Olsen P. Retention of runoff water on the landscape could reduce loads, but careful management of near-surface soil P is required to prevent snowmelt runoff losses of P at the source and to reduce the potential for the eutrophication of downstream aquatic ecosystems.
Abstract Flooding is a major concern for Canadian society as it is the costliest natural disaster type in Canada. Southern Ontario, which houses one-third of the Canadian population, is located in an area of high vulnerability for floods. The most significant floods in the region have historically occurred during the months of March and April due to snowmelt coupled with extreme rain events. However, during the last three decades, there has been a shift of flooding events to earlier months. The aim of this study was to understand the impacts of atmospheric circulation on the temporal shift of streamflow and high flow events observed in southern Ontario over 1957–2013 period. Predominant weather regimes over North America, corresponding to recurrent meteorological situations, were identified using a discretization of daily geopotential height at 500HpA level (Z500). A regime-normalized hypothetical temperature and precipitation dataset was constructed to quantify the contribution of atmospheric circulation on streamflow response. The hypothetical dataset was used as input in the Precipitation Runoff Modeling System (PRMS), a rainfall-runoff semi-distributed hydrological model, and applied to four watersheds in southern Ontario. The results showed an increase in the temporal frequency of the regime identified here as High Pressure (HP) close to eight occurrences per decade. Regime HP, characterized by a northern position of the polar vortex, is correlated with a positive phase of the NAO and is associated with warm and wet conditions over southern Ontario during winter. The temporal increase in HP contributed more than 40% of the increase in streamflow in winter and 30–45% decrease in streamflow in April. This atmospheric situation also contributed to increase the number of high flows by 25–50% in January. These results are important to improve the seasonal forecasting of high flows and to assess the uncertainty in the temporal evolution of streamflow in the Great Lakes region.
Emergence of global mean sea level (GMSL) from a ‘hiatus’ following a persistent La Niña highlights the need to understand the causes of interannual variability in GMSL. Several studies link interannual variability in GMSL to anomalous transport of water mass between land and ocean—and subsequent changes in water storage in these reservoirs—primarily driven by El Niño/Southern Oscillation (ENSO). Despite this, asymmetries in teleconnections between ENSO mode and land water storage have received less attention. We use historical simulations of natural climate variability to characterize asymmetries in the hydrological response to ENSO based on phase and duration. Findings indicate pronounced phase-specific and duration-specific asymmetries covering up to 93 and 50 million km2 land area, respectively. The asymmetries are seasonally dependent, and based on the mean regional climate are capable of influencing inherently bounded storage by pushing the storage-precipitation relationship towards nonlinearity. The nonlinearities are more pronounced in dry regions in the dry season, wet regions in the wet season, and during Year 2 of persistent ENSO events, limiting the magnitude of associated anomalies under persistent ENSO influence. The findings have implications for a range of stakeholders, including sea level researchers and water managers.
Methodological choices can have strong effects on projections of climate change impacts on hydrology. In this study, we investigate the ways in which four different steps in the modeling chain influence the spread in projected changes of different aspects of hydrology. To form the basis of these analyses, we constructed an ensemble of 160 simulations from permutations of two Representative Concentration Pathways, 10 global climate models, two downscaling methods, and four hydrologic model implementations. The study is situated in the Pacific Northwest of North America, which has relevance to a diverse, multinational cast of stakeholders. We analyze the effects of each modeling decision on changes in gridded hydrologic variables of snow water equivalent and runoff, as well as streamflow at point locations. Results show that the choice of representative concentration pathway or global climate model is the driving contributor to the spread in annual streamflow volume and timing. On the other hand, hydrologic model implementation explains most of the spread in changes in low flows. Finally, by grouping the results by climate region the results have the potential to be generalized beyond the Pacific Northwest. Future hydrologic impact assessments can use these results to better tailor their modeling efforts.
Uncertainties in representing land–atmosphere interactions can substantially influence regional climate simulations. Among these uncertainties, the surface exchange coefficient Ch is a critical parameter, controlling the total energy transported from the land surface to the atmosphere. Although it directly impacts the coupling strength between the surface and atmosphere, it has not been properly evaluated for regional climate models. This study assesses the representation of surface coupling strength in a stand-alone Noah-MP land surface model and in coupled 4-km Weather Research and Forecasting (WRF) model simulations. The data collected at eight FLUXNET sites of the Canadian Carbon Program and seven AMRIFLUX sites are used to evaluate the offline Noah-MP simulations. Nine of these FLUXNET sites are used for the evaluation of the coupled WRF simulations. These sites are categorized into three land use types: grassland, cropland, and forest. The surface exchange coefficients derived using three formulations in Noah-MP simulations are compared to those calculated from observations. Then, the default Czil  = 0 and new canopy-height dependent Czil are used in coupled WRF simulations over the spring and summer in 2006 to compare their effects on surface heat flux, temperature, and precipitation. When the new canopy-height dependent Czil scheme is used, the simulated Ch exchange coefficient agrees better with observation and improves the daily maximum air temperature and heat flux simulation over grassland and cropland in the US Great Plains. Over grassland, the modeled Ch shows a different diurnal cycle than that for observed Ch, which makes WRF lag behind the observed diurnal cycle of sensible heat flux and temperature. The difference in precipitation between the two schemes is not as clear as the temperature difference because the impact of changing Ch is not local.
Abstract Bulk microphysics parameterizations that are used to represent clouds and precipitation usually allow only solid and liquid hydrometeors. Predicting the bulk liquid fraction on ice allows an explicit representation of mixed-phase particles and various precipitation types, such as wet snow and ice pellets. In this paper, an approach for the representation of the bulk liquid fraction into the predicted particle properties (P3) microphysics scheme is proposed and described. Solid-phase microphysical processes, such as melting and sublimation, have been modified to account for the liquid component. New processes, such as refreezing and condensation of the liquid portion of mixed-phase particles, have been added to the parameterization. Idealized simulations using a one-dimensional framework illustrate the overall behavior of the modified scheme. The proposed approach compares well to a Lagrangian benchmark model. Temperatures required for populations of ice crystals to melt completely also agree well with previous studies. The new processes of refreezing and condensation impact both the surface precipitation type and feedback between the temperature and the phase changes. Overall, prediction of the bulk liquid fraction allows an explicit description of new precipitation types, such as wet snow and ice pellets, and improves the representation of hydrometeor properties when the temperature is near 0°C.
Abstract Northern peatlands contain up to 20% of the ∼3000 Pg of global soil organic carbon. Carbon-rich peatlands cover upwards of 65% of the landscape in northern Canada where resource extraction activities disturb both the carbon pools and the future carbon sequestration capacity of the landscape. Previous estimates of the carbon losses from this disturbance predict a complete loss of the region’s peatland carbon pool. Mining industries operating in these sensitive environments have recently begun constructing closure landscapes which are intended to develop carbon cycle processes similar to undisturbed northern peatlands. This study investigates eddy covariance fluxes of carbon dioxide (CO2) at one of Canada’s first fully constructed boreal plains watersheds, the Sandhill Fen Watershed. During the first three years since inception, only the lowland region had an annual net ecosystem exchange of CO2 (NEE) indicative of increasing CO2 sink potential. The lowland region was characterized by saturated salvaged peat soils, standing water, thriving communities of Typha and Carex spp. and was a net CO2 sink of 77 g C m−2 in the third year. At the same time the upland and the midland regions characterized by moist salvaged peat soils and a mix of herbaceous, shrub and planted Picea glauca and Pinus banksiana remained net sources of CO2. Despite similar rates of gross primary production, ecosystem and plot-level respiration rates in the lowland were significantly lower than in the midland region, likely due to very low reduction potentials within the lowland’s saturated soils. With no other significant outflows of carbon, the lowland of the Sandhill Fen Watershed may be in the early stages of organic matter accumulation. Due to limited oxidation of the salvaged peat substrate in the lowland region, wetland reclamation employing these techniques may reduce the disturbance loss of the carbon pool in the boreal plains.
Recently, the World Scientists’ Warning to Humanity: a Second Notice was issued in response to ongoing and largely unabated environmental degradation due to anthropogenic activities. In the warning, humanity is urged to practice more environmentally sustainable alternatives to business as usual to avoid potentially catastrophic outcomes. Following the success of their warning, the Alliance of World Scientists called for discipline-specific follow-up papers. This paper is an answer to that call for the topic of wildland fire. Across much of Canada and the world, wildfires are anticipated to increase in severity and frequency in response to anthropogenic activities. The world scientists’ second warning provides the opportunity for wildland fire researchers to raise the profile of the potential impacts that anthropogenic activities are likely to have on future fire regimes and, in return, what impacts future fire regimes may have on humanity. We discuss how wildfire is related to several issues of concern raised in the world scientists’ second warning, including climate change, human population growth, biodiversity and forests, and freshwater availability. Furthermore, we touch on the potential future health impacts and challenges to wildfire suppression and management in Canada. In essence, our wildfire scientists’ warning to humanity is that we, as a society, will have to learn to live with more fire on the landscape. We provide some recommendations on how we might move forward to prepare for and adapt to future wildfire regimes in Canada. Although this paper is primarily Canadian in focus, the concepts and information herein also draw from international examples and are of relevance globally.
Seasonal snowcovers release nutrients accumulated over the winter during spring snowmelt and this can be an important part of the annual biogeochemical cycling of chemicals and their loading to soils and water bodies. The characteristics of this load are controlled by snowmelt dynamics and the physical and chemical properties of the snowpack, which are affected by overwinter and snowmelt metamorphism, refreezing of meltwater, and ion exclusion from snow crystals. Rain-on-snow (ROS) events can accelerate and modify the snowpack discharge process. The interplay of these processes can cause microscale flow heterogeneity and preferential flow pathways (PFP). Previous experimental work has examined PFP and ion elution processes in snowpacks, but their combined effect on the spatial and temporal characteristics of snowmelt ion elution remains uncertain. In this research, two controlled laboratory experiments were performed to investigate the role of PFP and ROS in controlling snow ion release to runoff. These involved the high frequency monitoring of flow and meltwater concentrations during snowmelt induced by radiation-convection (RC) processes and rain-on-snow (ROS). Results showed that when ROS was included, PFP was responsible for the transport of 68% and 73% of the total NO3 and PO4 load discharged during the early snowmelt phase recorded by the experiment. However, this initial load increased to 95% and 75% when ROS was removed, causing the release of more than 20% of the total snowpack NO3 and PO4 during the first 1.5% of melt. Small intensity ROS may refreeze in the snowpack, which may affect the ability of lateral flow to deliver snow ions located beyond the leading edge of PFP.
There is great interest in modelling the export of nitrogen (N) and phosphorus (P) from agricultural fields because of ongoing challenges of eutrophication. However, the use of existing hydrochemistry models can be problematic in cold regions because models frequently employ incomplete or conceptually incorrect representations of the dominant cold regions hydrological processes and are overparameterized, often with insufficient data for validation. Here, a process‐based N model, WINTRA, which is coupled to a physically based cold regions hydrological model, was expanded to simulate P and account for overwinter soil nutrient biochemical cycling. An inverse modelling approach, using this model with consideration of parameter equifinality, was applied to an intensively monitored agricultural basin in Manitoba, Canada, to help identify the main climate, soil, and anthropogenic controls on nutrient export. Consistent with observations, the model results suggest that snow water equivalent, melt rate, snow cover depletion rate, and contributing area for run‐off generation determine the opportunity time and surface area for run‐off–soil interaction. These physical controls have not been addressed in existing models. Results also show that the time lag between the start of snowmelt and the arrival of peak nutrient concentration in run‐off increased with decreasing antecedent soil moisture content, highlighting potential implications of frozen soils on run‐off processes and hydrochemistry. The simulations showed TDP concentration peaks generally arriving earlier than NO₃ but also decreasing faster afterwards, which suggests a significant contribution of plant residue Total dissolved Phosphorus (TDP) to early snowmelt run‐off. Antecedent fall tillage and fertilizer application increased TDP concentrations in spring snowmelt run‐off but did not consistently affect NO₃ run‐off. In this case, the antecedent soil moisture content seemed to have had a dominant effect on overwinter soil N biogeochemical processes such as mineralization, which are often ignored in models. This work demonstrates both the need for better representation of cold regions processes in hydrochemical models and the model improvements that are possible if these are included.
Global and regional projections of climate change by Earth system models are limited by their uncertain estimates of terrestrial ecosystem productivity. At the middle to low latitudes, the East Asian monsoon region has higher productivity than forests in Europe‐Africa and North America, but its estimate by current generation of terrestrial biosphere models (TBMs) has seldom been systematically evaluated. Here, we developed a traceability framework to evaluate the simulated gross primary productivity (GPP) by 15 TBMs in the East Asian monsoon region. The framework links GPP to net primary productivity, biomass, leaf area and back to GPP via incorporating multiple vegetation functional properties of carbon‐use efficiency (CUE), vegetation C turnover time (τveg), leaf C fraction (Fleaf), specific leaf area (SLA), and leaf area index (LAI)‐level photosynthesis (PLAI), respectively. We then applied a relative importance algorithm to attribute intermodel variation at each node. The results showed that large intermodel variation in GPP over 1901–2010 were mainly propagated from their different representation of vegetation functional properties. For example, SLA explained 77% of the intermodel difference in leaf area, which contributed 90% to the simulated GPP differences. In addition, the models simulated higher CUE (18.1 ± 21.3%), τveg (18.2 ± 26.9%), and SLA (27.4±36.5%) than observations, leading to the overestimation of simulated GPP across the East Asian monsoon region. These results suggest the large uncertainty of current TBMs in simulating GPP is largely propagated from their poor representation of the vegetation functional properties and call for a better understanding of the covariations between plant functional properties in terrestrial ecosystems.
Snow‐dominated watersheds are bellwethers of climate change. Hydroclimate projections in such basins often find reductions in annual peak runoff due to decreased snowpack under global warming. British Columbia's Fraser River Basin (FRB) is a large, nival basin with exposure to moisture‐laden atmospheric rivers originating in the Pacific Ocean. Landfalling atmospheric rivers over the region in winter are projected to increase in both strength and frequency in Coupled Model Intercomparison Project Phase 5 climate models. We investigate future changes in hydrology and annual peak daily streamflow in the FRB using a hydrologic model driven by a bias‐corrected Coupled Model Intercomparison Project Phase 5 ensemble. Under Representative Concentration Pathway (8.5), the FRB evolves toward a nival‐pluvial regime featuring an increasing association of extreme rainfall with annual peak daily flow, a doubling in cold season peak discharge, and a decrease in the return period of the largest historical flow, from a 1‐in‐200‐year to 1‐in‐50‐year event by the late 21st century.
The transport of freshwater from continents to oceans through rivers has traditionally been estimated by routing runoff from land surface models within river models to obtain discharge. This paradigm imposes that errors are transferred from runoff to discharge, yet the analytical propagation of uncertainty from runoff to discharge has never been derived. Here we apply statistics to the continuity equation within a river network to derive two equations that propagate the mean and variance/covariance of runoff errors independently. We validate these equations in a case study of the rivers in the western United States and, for the first time, invert observed discharge errors for spatially distributed runoff errors. Our results suggest that the largest discharge error source is the joint variability of runoff errors across space, not the mean or amplitude of individual errors. Our findings significantly advance the science of error quantification in model‐based estimates of river discharge.
Wildfire is the dominant disturbance in boreal forests and fire activity is increasing in these regions. Soil fungal communities are important for plant growth and nutrient cycling postfire but there is little understanding of how fires impact fungal communities across landscapes, fire severity gradients, and stand types in boreal forests. Understanding relationships between fungal community composition, particularly mycorrhizas, and understory plant composition is therefore important in predicting how future fire regimes may affect vegetation. We used an extreme wildfire event in boreal forests of Canada's Northwest Territories to test drivers of fungal communities and assess relationships with plant communities. We sampled soils from 39 plots 1 year after fire and 8 unburned plots. High-throughput sequencing (MiSeq, ITS) revealed 2,034 fungal operational taxonomic units. We found soil pH and fire severity (proportion soil organic layer combusted), and interactions between these drivers were important for fungal community structure (composition, richness, diversity, functional groups). Where fire severity was low, samples with low pH had higher total fungal, mycorrhizal, and saprotroph richness compared to where severity was high. Increased fire severity caused declines in richness of total fungi, mycorrhizas, and saprotrophs, and declines in diversity of total fungi and mycorrhizas. The importance of stand age (a surrogate for fire return interval) for fungal composition suggests we could detect long-term successional patterns even after fire. Mycorrhizal and plant community composition, richness, and diversity were weakly but significantly correlated. These weak relationships and the distribution of fungi across plots suggest that the underlying driver of fungal community structure is pH, which is modified by fire severity. This study shows the importance of edaphic factors in determining fungal community structure at large scales, but suggests these patterns are mediated by interactions between fire and forest stand composition.
Dust samples were collected from four indoor environments, including childcare facilities, houses, hair salons, and a research facility from the USA and were analyzed for brominated compounds using full scan liquid chromatography high-resolution mass spectrometry. A total of 240 brominated compounds were detected in these dust samples, and elemental formulas were predicted for 120 more abundant ions. In addition to commonly detected brominated flame retardants (BFRs), nitrogen-containing brominated azo dyes (BADs) were among the most frequently detected and abundant. Specifically, greater abundances of BADs were detected in indoor dusts from daycares and salons compared to houses and the research facility. Using authentic standards, a quantitative method was established for two BADs (DB373: Disperse Blue 373 and DV93: Disperse Violet 93) and 2-bromo-4,6-dinitroaniline, a commonly used precursor in azo dye production, in indoor dust. Generally, greater concentrations of DB373 (≤3850 ng/g) and DV93 (≤1190 ng/g) were observed in indoor dust from daycares highlighting children as a susceptible population to potential health risk from exposure to BADs. These data are important because, to date, targeted analysis of brominated compounds in indoor environments has focused mainly on BFRs and appears to underestimate the total amount of brominated compounds.
Bioretention cells are a popular control strategy for stormwater volume and quality, but their efficiency for water infiltration and nutrient removal under cold climate conditions has been poorly studied. In this work, soil cores were collected from an active bioretention cell containing engineered soil material amended with a phosphate sorbent medium. The cores were used in laboratory column experiments conducted to obtain a detailed characterization of the soil's bioretention performance during six consecutive freeze-thaw cycles (FTCs, from -10 to +10 °C). At the start of each FTC, the experimental column undergoing the FTCs and a control column kept at room temperature were supplied with a solution containing 25 mg/L of bromide, nitrate and phosphate. Water saturated conditions were established to mimic the presence of an internal water storage zone to support anaerobic nitrate removal. At the end of each FTC, the pore solution was allowed to drain from the columns. The results indicate that the FTCs enhanced the infiltration efficiency of the soil: with each successive cycle the drainage rate increased in the experimental column. Freezing and thawing also increased the saturated hydraulic conductivity of the bioretention soil. X-ray tomography imaging identified a key role of macro-pore formation in maintaining high infiltration rates. Both aqueous nitrate and phosphate supplied to the columns were nearly completely removed from solution. Sufficiently long retention times and the presence of the internal water storage zone promoted anaerobic nitrate elimination despite the low temperatures. Dissolved phosphate was efficiently trapped at all depths in the soil columns, with ≤2% of the added stormwater phosphate recovered in the drainage effluent. These findings imply that, when designed properly, bioretention cells can support high infiltration rates and mitigate nutrient pollution in cold climates.
The United Nations (UN) has identified 17 Sustainable Development Goals (SDGs) to tackle major barriers to sustainable development by 2030. Achieving these goals will rely on the contribution of all nations and require balancing trade-offs among different sectors. Water and food insecurity have long been the two major challenges facing China. To address these challenges and achieve the SDGs, China needs to safeguard its agricultural irrigation and water conservancy projects. Although China is making efforts to transition its agricultural development to a sustainable trajectory by promoting water-saving irrigation, a number of issues are emerging, both with policy reforms and technological innovations. Through synthesizing the historical development of agriculture and its relationship with policy and political regimes, this paper identifies four major issues that are challenging the sustainability transformation of China’s agricultural irrigation system and water conservancy projects: (1) problems with financial policy coordination between central and local governments; (2) the lack of incentives for farmers to construct and maintain irrigation infrastructure; (3) conflicts between decentralized operation of land and benefits from shared irrigation infrastructure; and (4) deterioration of small-scale irrigation infrastructure calls for action. In addressing these challenges, policy changes are required: government financial accountability at all levels needs to be clarified; subsidies need to be raised for the construction and management of small-scale irrigation and water conservancy projects; local non-profit organizations need to be established to enhance co-management between farmers and government.
Algorithms for the generation of a bedfast/floating lake ice product from Sentinel-1A/B synthetic aperture radar (SAR) data were implemented, cross-compared, and validated for various permafrost regions (Alaska, Canada and Russia). The algorithms consisted of: 1) thresholding; 2) Iteration Region Growing with Semantics (IRGS); and 3) K-means. The thresholding algorithm (92.4%) was found to perform slightly better on average than the IRGS algorithm (90.1%), and to outperform K-means (85.3%). The thresholding algorithm was therefore selected for implementation of a processing chain to generate a novel bedfast/floating lake ice product. Using a time series of Sentinel-1 SAR data, the new map product shows the day of year (DOY) when the ice becomes bedfast or remains afloat for individual lake sections.
This work describes a pilot study in southern Ontario, Canada evaluating the use of the ‘Headwall Nano-Hyperspec’ hyperspectral imager onboard a Remotely Piloted Aircraft System (RPAS). Hyperspectral imagers are extremely useful for monitoring vegetation health and water quality, among other environmental parameters. However, guidelines on the use of this specific instrument for these applications are not yet available. As such, recommended operational settings and calibration procedures are presented here, based on nearly 50 flight campaigns over water bodies and vineyards. Using these procedures, spectral reflectance was successfully captured using an RPAS.
Canada has vast water resources that span an enormous range in geography, climate, and ecosystems [1]. Water supply and water quality are the two critical issues relevant to water resources, not only in Canada but globally in a warming climate. The water microsatellite mission described here aims to better prepare end users to respond to the emerging spectrum of water futures issues by revolutionizing remote sensing of water quality and quantity parameters, and permitting unprecedented interconnection and data gathering from Canadian environmental monitoring networks.
Hydrogen peroxide (H 2 O 2 ) is an oxidizing agent used to disinfect recirculated irrigation water during the production of organic crops under controlled environmental systems (e.g., greenhouses). To characterize the phytotoxic effects and define a concentration threshold for H 2 O 2 , three microgreen species [arugula ( Brassica eruca ssp. sativa ), radish ( Raphanus sativus ), and sunflower ( Helianthus annuus ‘ Black Oil’)], and three lettuce ( Lactuca sativa ) cultivars, Othilie, Xandra, and Rouxai, were foliar sprayed once daily with water containing 0, 25, 50, 75, 100, 125, 150, or 200 mg·L −1 of H 2 O 2 from seed to harvest under greenhouse conditions. Leaf damage was assessed at harvest using two distinct methods: 1) the percentage of damaged leaves per tray and 2) a damage index (DI). Applied H 2 O 2 concentrations, starting from 25 mg·L −1 , increased the percentage of damaged leaves in every species except ‘Black Oil’ sunflower, which remained unaffected by any applied concentration. Symptoms of leaf damage manifested in similar patterns on the surface of microgreen cotyledons and lettuce leaves, while mean DI values and extent of damage were unique to each crop. Fresh weight, dry weight, and leaf area of all crops were not significantly affected by daily H 2 O 2 spray. Identifying how foliar H 2 O 2 damage manifests throughout the crop, as well at individual cotyledon or leaf surfaces, is necessary to establish an upper concentration threshold for H 2 O 2 use. On the basis of the aforementioned metrics, maximum recommended concentrations were 150 mg·L −1 (radish), 100 mg·L −1 (arugula) for microgreens and 125 mg·L −1 (‘Othilie’), 75 mg·L −1 (‘Rouxai’), and 125 mg·L −1 (‘Xandra’) lettuce.
Abstract Relatively little is known of how the world's largest vegetation transition zone – the Forest Tundra Ecotone (FTE) – is responding to climate change. Newly available, satellite-derived time-series of the photochemical reflectance index (PRI) across North America and Europe could provide new insights into the physiological response of evergreen trees to climate change by tracking changes in foliar pigment pools that have been linked to photosynthetic phenology. However, before implementing these data for such purpose at these evergreen dominated systems, it is important to increase our understanding of the fine scale mechanisms driving the connection between PRI and environmental conditions. The goal of this study is thus to gain a more mechanistic understanding of which environmental factors drive changes in PRI during late-season phenological transitions at the FTE – including factors that are susceptible to climate change (i.e., air- and soil-temperatures), and those that are not (photoperiod). We hypothesized that late-season phenological changes in foliar pigment pools captured by PRI are largely driven by photoperiod as opposed to less predictable drivers such as air temperature, complicating the utility of PRI time-series for understanding climate change effects on the FTE. Ground-based, time-series of PRI were acquired from individual trees in combination with meteorological variables and photoperiod information at six FTE sites in Alaska. A linear mixed-effects modeling approach was used to determine the significance (α = 0.001) and effect size (i.e., standardized slope b*) of environmental factors on late-seasonal changes in the PRI signal. Our results indicate that photoperiod had the strongest, significant effect on late-season changes in PRI (b* = 0.08, p
Earth System Models (ESMs) are essential tools for understanding and predicting global change, but they cannot explicitly resolve hillslope‐scale terrain structures that fundamentally organize water, energy, and biogeochemical stores and fluxes at subgrid scales. Here we bring together hydrologists, Critical Zone scientists, and ESM developers, to explore how hillslope structures may modulate ESM grid‐level water, energy, and biogeochemical fluxes. In contrast to the one‐dimensional (1‐D), 2‐ to 3‐m deep, and free‐draining soil hydrology in most ESM land models, we hypothesize that 3‐D, lateral ridge‐to‐valley flow through shallow and deep paths and insolation contrasts between sunny and shady slopes are the top two globally quantifiable organizers of water and energy (and vegetation) within an ESM grid cell. We hypothesize that these two processes are likely to impact ESM predictions where (and when) water and/or energy are limiting. We further hypothesize that, if implemented in ESM land models, these processes will increase simulated continental water storage and residence time, buffering terrestrial ecosystems against seasonal and interannual droughts. We explore efficient ways to capture these mechanisms in ESMs and identify critical knowledge gaps preventing us from scaling up hillslope to global processes. One such gap is our extremely limited knowledge of the subsurface, where water is stored (supporting vegetation) and released to stream baseflow (supporting aquatic ecosystems). We conclude with a set of organizing hypotheses and a call for global syntheses activities and model experiments to assess the impact of hillslope hydrology on global change predictions.
Abstract. Meteorological, snow survey, streamflow, and groundwater data are presented from Marmot Creek Research Basin, Alberta, Canada. The basin is a 9.4 km2, alpine–montane forest headwater catchment of the Saskatchewan River basin that provides vital water supplies to the Prairie Provinces of Canada. It was heavily instrumented, experimented upon, and operated by several federal government agencies between 1962 and 1986, during which time its main and sub-basin streams were gauged, automated meteorological stations at multiple elevations were installed, groundwater observation wells were dug and automated, and frequent manual measurements of snow accumulation and ablation and other weather and water variables were made. Over this period, mature evergreen forests were harvested in two sub-basins, leaving large clear cuts in one basin and a “honeycomb” of small forest clearings in another basin. Whilst meteorological measurements and sub-basin streamflow discharge weirs in the basin were removed in the late 1980s, the federal government maintained the outlet streamflow discharge measurements and a nearby high-elevation meteorological station, and the Alberta provincial government maintained observation wells and a nearby fire weather station. Marmot Creek Research Basin was intensively re-instrumented with 12 automated meteorological stations, four sub-basin hydrometric sites, and seven snow survey transects starting in 2004 by the University of Saskatchewan Centre for Hydrology. The observations provide detailed information on meteorology, precipitation, soil moisture, snowpack, streamflow, and groundwater during the historical period from 1962 to 1987 and the modern period from 2005 to the present time. These data are ideal for monitoring climate change, developing hydrological process understanding, evaluating process algorithms and hydrological, cryospheric, or atmospheric models, and examining the response of basin hydrological cycling to changes in climate, extreme weather, and land cover through hydrological modelling and statistical analyses. The data presented are publicly available from Federated Research Data Repository (https://doi.org/10.20383/101.09, Fang et al., 2018).
Abstract Finely resolved geodetic data provide an opportunity to assess the extent and morphology of crevasses and their change over time. Crevasses have the potential to bias geodetic measurements of elevation and mass change unless they are properly accounted for. We developed a framework that automatically maps and extracts crevasse geometry and masks them where they interfere with surface mass-balance assessment. Our study examines airborne light detection and ranging digital elevation models (LiDAR DEMs) from Haig Glacier, which is experiencing a transient response in its crevassed upper regions as the glacier thins, using a self-organizing map algorithm. This method successfully extracts and characterizes ~1000 crevasses, with an overall accuracy of 94%. The resulting map provides insight into stress and flow conditions. The crevasse mask also enables refined geodetic estimates of summer mass balance. From differencing of September and April LiDAR DEMs, the raw LiDAR DEM gives a 9% overestimate in the magnitude of glacier thinning over the summer: −5.48 m compared with a mean elevation change of −5.02 m when crevasses are masked out. Without identification and removal of crevasses, the LiDAR-derived summer mass balance therefore has a negative bias relative to the glaciological surface mass balance.
Abstract Land models are increasingly used and preferred in terrestrial hydrological prediction applications. One reason for selecting land models over simpler models is that their physically based backbone enables wider application under different conditions. This study evaluates the temporal variability in streamflow simulations in land models. Specifically, we evaluate how the subsurface structure and model parameters control the partitioning of water into different flow paths and the temporal variability in streamflow. Moreover, we use a suite of model diagnostics, typically not used in the land modeling community to clarify model weaknesses and identify a path toward model improvement. Our analyses show that the typical land model structure, and their functions for moisture movement between soil layers (an approximation of Richards equation), has a distinctive signature where flashy runoff is superimposed on slow recessions. This hampers the application of land models in simulating flashier basins and headwater catchments where floods are generated. We demonstrate the added value of the preferential flow in the model simulation by including macropores in both a toy model and the Variable Infiltration Capacity model. We argue that including preferential flow in land models is essential to enable their use for multiple applications across a myriad of temporal and spatial scales.
Preferential flowpaths transport phosphorus (P) to agricultural tile drains. However, if and to what extent this may vary with soil texture, moisture conditions, and P placement is poorly understood. This study investigated (a) interactions between soil texture, antecedent moisture conditions, and the relative contributions of matrix and preferential flow and (b) associated P distributions through the soil profile when fertilizers were applied to the surface or subsurface. Brilliant blue dye was used to stain subsurface flowpaths in clay and silt loam plots during simulated rainfall events under wet and dry conditions. Fertilizer P was applied to the surface or via subsurface placement to plots of different soil texture and moisture condition. Photographs of dye stains were analysed to classify the flow patterns as matrix dominated or macropore dominated, and soils within plots were analysed for their water‐extractable P (WEP) content. Preferential flow occurred under all soil texture and moisture conditions. Dye penetrated deeper into clay soils via macropores and had lower interaction with the soil matrix, compared with silt loam soil. Moisture conditions influenced preferential flowpaths in clay, with dry clay having deeper infiltration (92 ± 7.6 cm) and less dye–matrix interaction than wet clay (77 ± 4.7 cm). Depth of staining did not differ between wet (56 ± 7.2 cm) and dry (50 ± 6.6 cm) silt loam, nor did dominant flowpaths. WEP distribution in the top 10 cm of the soil profile differed with fertilizer placement, but no differences in soil WEP were observed at depth. These results demonstrate that large rainfall events following drought conditions in clay soil may be prone to rapid P transport to tile drains due to increased preferential flow, whereas flow in silt loams is less affected by antecedent moisture. Subsurface placement of fertilizer may minimize the risk of subsurface P transport, particularily in clay.
Preferential flow is prevalent in clay soil under both frozen and thawed conditions. Preferential flow dominates the infiltration regime under frozen soil conditions in silt loam. Subsurface placement of fertilizer can limit subsurface nutrient leaching. Subsurface placement is particularly effective in soil with abundant preferential flow. Subsurface placement is recommended for fall fertilizer application.
BACOLI is a Fortran software package for solving one-dimensional parabolic partial differential equations (PDEs) with separated boundary conditions by B-spline adaptive collocation methods. A distinguishing feature of BACOLI is its ability to estimate and control error and correspondingly adapt meshes in both space and time. Many models of scientific interest, however, can be formulated as multiscale parabolic PDE systems, that is, models that couple a system of parabolic PDEs describing dynamics on a global scale with a system of ordinary differential equations describing dynamics on a local scale. This article describes the Fortran software eBACOLI, the extension of BACOLI to solve such multiscale models. The performance of the extended software is demonstrated to be statistically equivalent to the original for purely parabolic PDE systems. Results from eBACOLI are given for various multiscale models from the extended problem class considered.
The speed of mathematical function evaluations can significantly contribute to the overall performance of numerical simulations. Two common approaches to evaluate a mathematical function are by dir...
Identifiability is a fundamental concept in parameter estimation, and therefore key to the large majority of environmental modeling applications. Parameter identifiability analysis assesses whether it is theoretically possible to estimate unique parameter values from data, given the quantities measured, conditions present in the forcing data, model structure (and objective function), and properties of errors in the model and observations. In other words, it tackles the problem of whether the right type of data is available to estimate the desired parameter values. Identifiability analysis is therefore an essential technique that should be adopted more routinely in practice, alongside complementary methods such as uncertainty analysis and evaluation of model performance. This article provides an introductory overview to the topic. We recommend that any modeling study should document whether a model is non-identifiable, the source of potential non-identifiability, and how this affects intended project outcomes.
Abstract The phenomenon of hydrophobicity observed in such surfaces as lotus leaves is typically manifest by hierarchical structures on low-energy surfaces. Sustained interest in fabricating hydrophobic surfaces has resulted in a myriad of processes, which are but limited by their largely referring to soft materials and/or involving multiple process steps. The present work explored the application of electrical discharge machining (EDM) for the single-step manufacture of durable, metallic hydrophobic surfaces. Simple sink EDM in a hydrocarbon dielectric, with no special process kinematic or tooling requirements, is demonstrated to rapidly generate surfaces that are intrinsically water repellent, with contact angles approaching 150°.
Contemporary fire regimes of Canadian forests have been well documented based on forest fire records between the late 1950s to 1990s. Due to known limitations of fire datasets, an analysis of changes in fire-regime characteristics could not be easily undertaken. This paper presents fire-regime trends nationally and within two zonation systems, the homogeneous fire-regime zones and ecozones, for two time periods, 1959–2015 and 1980–2015. Nationally, trends in both area burned and number of large fires (≥200 ha) have increased significantly since 1959, which might be due to increases in lightning-caused fires. Human-caused fires, in contrast, have shown a decline. Results suggest that large fires have been getting larger over the last 57 years and that the fire season has been starting approximately one week earlier and ending one week later. At the regional level, trends in fire regimes are variable across the country, with fewer significant trends. Area burned, number of large fires, and lightning-caused fires are increasing in most of western Canada, whereas human-caused fires are either stable or declining throughout the country. Overall, Canadian forests appear to have been engaged in a trajectory towards more active fire regimes over the last half century.
Abstract. Local-scale advection of energy from warm snow-free surfaces to cold snow-covered surfaces is an important component of the energy balance during snow-cover depletion. Unfortunately, this process is difficult to quantify in one-dimensional snowmelt models. This paper proposes a simple sensible and latent heat advection model for snowmelt situations that can be readily coupled to one-dimensional energy balance snowmelt models. An existing advection parameterization was coupled to a conceptual frozen soil infiltration surface water retention model to estimate the areal average sensible and latent heat advection contributions to snowmelt. The proposed model compared well with observations of latent and sensible heat advection, providing confidence in the process parameterizations and the assumptions applied. Snow-covered area observations from unmanned aerial vehicle imagery were used to update and evaluate the scaling properties of snow patch area distribution and lengths. Model dynamics and snowmelt implications were explored within an idealized modelling experiment, by coupling to a one-dimensional energy balance snowmelt model. Dry, snow-free surfaces were associated with advection of dry air that compensated for positive sensible heat advection fluxes and so limited the net influence of advection on snowmelt. Latent and sensible heat advection fluxes both contributed positive fluxes to snow when snow-free surfaces were wet and enhanced net advection contributions to snowmelt. The increased net advection fluxes from wet surfaces typically develop towards the end of snowmelt and offset decreases in the one-dimensional areal average melt energy that declines with snow-covered area. The new model can be readily incorporated into existing one-dimensional snowmelt hydrology and land surface scheme models and will foster improvements in snowmelt understanding and predictions.
Permafrost distribution in mountains is typically more heterogeneous relative to low‐relief environments due to greater variability in the factors controlling the ground thermal regime, such as topography, snow depth, and sediment grain size (e.g., coarse blocks). Measuring and understanding the geothermal variability in high mountains remains challenging due to logistical constraints. This study presents one of the first applications of distributed temperature sensing (DTS) in periglacial environments to measure ground surface temperatures in a mountain permafrost area at much higher spatial resolution than possible with conventional methods using discrete temperature sensors. DTS measures temperature along a fibre‐optic cable at high spatial resolution (i.e., ≤ 1 m). Its use can be limited by power supply and calibration requirements, although recent methodological developments have relaxed some of these restrictions. Spatially continuous DTS measurements at a studied rock glacier provided greater resolution of geothermal variability and facilitated the interpretation of bottom temperature of snowpack data to map patchy permafrost distribution. This research highlights the potential for DTS to be a useful tool for permafrost mapping, ground thermal regime interpretation, conceptual geothermal model development, and numerical model evaluation in areas of heterogeneous mountain permafrost.
Water quality is increasingly at risk due to nutrient pollution entering river systems from cities, industrial zones and agricultural areas. Agricultural activities are typically the largest non-point source of water pollution. The dynamics of agricultural impacts on water quality are complex and stem from the decisions and activities of multiple stakeholders, often with diverse business plans, values, and attitudes towards practices that can improve water quality. This study proposes a framework to understand and incorporate stakeholders' viewpoints into water quality modeling and management. The framework was applied to the Qu'Appelle River Basin, Saskatchewan, Canada. Q-methodology was used to understand viewpoints of stakeholders, namely agricultural producers (annual croppers, cattle producers, mixed farmers) and cottage owners, regarding a range of agricultural Beneficial Management Practices (BMPs) that can improve water quality, and to identify their preferred BMPs. A System Dynamics (SD) approach was employed to develop a transparent and user-friendly water quality model, SD-Qu'Appelle, to simulate nutrient loads in the region before and after implementation of stakeholder identified BMPs. The SD-Qu'Appelle was used in real-time engagement of stakeholders in model simulations to demonstrate and explore the potential effects of different BMPs in mitigating water pollution. Stakeholder perspectives were explored to understand the functionality and value of the SD-Qu'Appelle, preferred policies and potential barriers to BMP implementation on their land. Results show that although there are differences between viewpoints of stakeholders, they identified wetland restoration/retention, flow and erosion control, and relocation of corrals near creeks to sites more distant from waterways as the most effective BMPs for improving water quality. Economics was identified as a primary factor that causes agricultural producers to either accept or refuse the implementation of BMPs. Agricultural producers believe that incentives rather than regulations are the best policies for increasing the adoption of BMPs. Overall, stakeholders indicated the SD-Qu'Appelle had considerable value for water quality management and provided a set of recommendations to improve the model.
Plants use only a fraction of their photosynthetically derived carbon for biomass production (BP). The biomass production efficiency (BPE), defined as the ratio of BP to photosynthesis, and its variation across and within vegetation types is poorly understood, which hinders our capacity to accurately estimate carbon turnover times and carbon sinks. Here, we present a new global estimation of BPE obtained by combining field measurements from 113 sites with 14 carbon cycle models. Our best estimate of global BPE is 0.41 ± 0.05, excluding cropland. The largest BPE is found in boreal forests (0.48 ± 0.06) and the lowest in tropical forests (0.40 ± 0.04). Carbon cycle models overestimate BPE, although models with carbon-nitrogen interactions tend to be more realistic. Using observation-based estimates of global photosynthesis, we quantify the global BP of non-cropland ecosystems of 41 ± 6 Pg C/year. This flux is less than net primary production as it does not contain carbon allocated to symbionts, used for exudates or volatile carbon compound emissions to the atmosphere. Our study reveals a positive bias of 24 ± 11% in the model-estimated BP (10 of 14 models). When correcting models for this bias while leaving modeled carbon turnover times unchanged, we found that the global ecosystem carbon storage change during the last century is decreased by 67% (or 58 Pg C).
Reported groundwater recovery in South India has been attributed to both increasing rainfall and political interventions. Findings of increasing groundwater levels, however, are at odds with reports of well failure and decreases in the land area irrigated from shallow wells. We argue that recently reported results are skewed by the problem of survivor bias, with dry or defunct wells being systematically excluded from trend analyses due to missing data. We hypothesize that these dry wells carry critical information about groundwater stress that is missed when data are filtered. Indeed, we find strong correlations between missing well data and metrics related to climate stress and groundwater development, indicative of a systemic bias. Using two alternative metrics, which take into account information from dry and defunct wells, our results demonstrate increasing groundwater stress in South India. Our refined approach for identifying groundwater depletion hot spots is critical for policy interventions and resource allocation.
Many DNA-functionalized nanomaterials and biosensors have been reported, but most have ignored the influence of DNA on the stability of nanoparticles. We observed that cytosine-rich DNA oligonucleotides can etch silver nanoparticles (AgNPs). In this work, we showed that phosphorothioate (PS)-modified DNA (PS-DNA) can etch AgNPs independently of DNA sequence, suggesting that the thio-modifications are playing the major role in etching. Compared to unmodified DNA (e.g., poly-cytosine DNA), the concentration of required PS DNA decreases sharply, and the reaction rate increases. Furthermore, etching by PS-DNA occurs quite independent of pH, which is also different from unmodified DNA. The PS-DNA mediated etching could also be controlled well by varying DNA length and conformation, and the number and location of PS modifications. With a higher activity of PS-DNA, the process of etching, ripening, and further etching was taken place sequentially. The etching ability is inhibited by forming duplex DNA and thus etching can be used to measure the concentration of complementary DNA.
Land use change and agricultural intensification have increased food production but at the cost of polluting surface and groundwater. Best management practices implemented to improve water quality have met with limited success. Such lack of success is increasingly attributed to legacy nutrient stores in the subsurface that may act as sources after reduction of external inputs. However, current water‐quality models lack a framework to capture these legacy effects. Here we have modified the SWAT (Soil Water Assessment Tool) model to capture the effects of nitrogen (N) legacies on water quality under multiple land‐management scenarios. Our new SWAT‐LAG model includes (1) a modified carbon‐nitrogen cycling module to capture the dynamics of soil N accumulation, and (2) a groundwater travel time distribution module to capture a range of subsurface travel times. Using a 502‐km2 Iowa watershed as a case study, we found that between 1950 and 2016, 25% of the total watershed N surplus (N Deposition + Fertilizer + Manure + N Fixation − Crop N uptake) had accumulated within the root zone, 14% had accumulated in groundwater, while 27% was lost as riverine output, and 34% was denitrified. In future scenarios, a 100% reduction in fertilizer application led to a 79% reduction in stream N load, but the SWAT‐LAG results suggest that it would take 84 years to achieve this reduction, in contrast to the 2 years predicted in the original SWAT model. The framework proposed here constitutes a first step toward modifying a widely used modeling approach to assess the effects of legacy N on the time required to achieve water‐quality goals.
Deep peat burning at the interface between subhumid Boreal Plains (BP) peatlands and forestlands (margin ecotones) in some hydrogeological settings has brought into question the long‐term stability of these peatlands under current and future predicted climate. Small peatlands located at midtopographic positions on coarse sediments have been identified as hot spots for severe burning, as these peatland margins are not regularly connected to regional groundwater flow. The ability of these peatland systems to recover carbon lost from both the interior and margin within the fire return interval, however, has not yet been investigated. Here we examine peatland soil carbon accumulation along a chronosequence of time since fire for 26 BP ombrotrophic bogs located across a range of hydrogeological settings. Soil organic carbon accumulation following wildfire does not appear to be influenced by hydrogeological setting; however, the ability of a peatland to recover the quantity of carbon lost within the fire return interval is dependent on the amount of carbon that was released through smoldering, which is influenced by hydrogeological setting for peatland margins. Based on published measurements of organic soil carbon loss during wildfire and our soil carbon accumulation rates, we suggest that peatlands located at topographic lows on coarse‐grained glaciofluvial outwash sediments or on low‐relief, fine‐grained sediment deposits from glaciolacustrine or subglacial paleoenvironments are currently resilient to wildfire on the BP landscape. Peatlands that experience severe smoldering at the margins, such as ephemerally perched systems on glaciofluvial outwash sediments, will likely undergo permanent loss of legacy carbon stores.
Abstract Algal blooms in the Great Lakes are a concern due to excess nutrient loading from non-point sources; however, there is uncertainty over the relative contributions of various non-point sources under different types of land use in rural watersheds, particularly over annual time scales. Four nested subwatersheds in Southern Ontario, Canada (one natural woodlot, two agricultural and one mixed agricultural and urban) were monitored over one year to identify peak periods (‘hot moments’) and areas (‘hot spots’) of nutrient (dissolved reactive phosphorus, DRP; total phosphorus, TP; and nitrate, NO3−) export and discharge. Annual nutrient export was small at the natural site (0.001 kg DRP ha−1; 0.004 kg TP ha−1; 0.04 kg NO3—N ha−1) compared to the agricultural and mixed-use sites (0.10–0.15 kg DRP ha−1; 0.70–0.94 kg TP ha−1; 9.15–11.55 kg NO3—N ha−1). Temporal patterns in P concentrations were similar throughout the sites, where spring was the dominant season for P export, irrespective of land use. Within the Hopewell Creek watershed, P and N hot spots existed that were consistently hot spots across all events with the location of these hot spots driven by local land use patterns, where there was elevated P export from a dairy-dominated sub-watershed and elevated N export from both of the two agricultural sub-watersheds. These estimates of seasonal- and event-based nutrient loads and discharge across nested sub-watersheds contribute to the growing body of evidence demonstrating the importance of identifying critical areas and periods in which to emphasize management efforts.
Abstract As a result of increased harmful algal blooms and hypoxia in Lake Erie, the US and Canada revised their phosphorus loading targets under the 2012 Great Lakes Water Quality Agreement. The focus of this paper is the Detroit River and its watershed, a source of 25% of the total phosphorus (TP) load to Lake Erie. Its load declined 37% since 1998, due chiefly to improvements at the regional Great Lakes Water Authority Water Resource Recovery Facility (WRRF) in Detroit and phosphorus sequestered by zebra and quagga mussels in Lake Huron. In addition to the 54% of the load from Lake Huron, nonpoint sources contribute 57% of the TP load and 50% of the dissolved reactive phosphorus load, with the remaining balance from point sources. After Lake Huron, the largest source is the WRRF, which has already reduced its load by over 40%. Currently, loads from Lake Huron and further reductions from the WRRF are not part of the reduction strategy, therefore remaining watershed sources will need to decline by 72% to meet the Water Quality Agreement target - a daunting challenge. Because other urban sources are very small, most of the reduction would have to come from agriculturally-dominated lands. The most effective way to reduce those loads is to apply combinations of practices like cover crops, buffer strips, wetlands, and applying fertilizer below the soil surface on the lands with the highest phosphorus losses. However, our simulations suggest even extensive conservation on those lands may not be enough.
Copying and pasting source code during software development is known as code cloning. Clone fragments with a minimum size of 5 LOC were usually considered in previous studies. In recent studies, clone fragments which are less than 5 LOC are referred as micro-clones. It has been established by the literature that code clones are closely related with software bugs as well as bug replication. None of the previous studies have been conducted on bug-replication of micro-clones. In this paper we investigate and compare bug-replication in between regular and micro-clones. For the purpose of our investigation, we analyze the evolutionary history of our subject systems and identify occurrences of similarity preserving co-changes (SPCOs) in both regular and micro-clones where they experienced bug-fixes. From our experiment on thousands of revisions of six diverse subject systems written in three different programming languages, C, C# and Java we find that the percentage of clone fragments that take part in bug-replication is often higher in micro-clones than in regular code clones. The percentage of bugs that get replicated in micro-clones is almost the same as the percentage in regular clones. Finally, both regular and micro-clones have similar tendencies of replicating severe bugs according to our experiment. Thus, micro-clones in a code-base should not be ignored. We should rather consider these equally important as of the regular clones when making clone management decisions.
Developers often search for relevant code examples on the web for their programming tasks. Unfortunately, they face two major problems. First, the search is impaired due to a lexical gap between their query (task description) and the information associated with the solution. Second, the retrieved solution may not be comprehensive, i.e., the code segment might miss a succinct explanation. These problems make the developers browse dozens of documents in order to synthesize an appropriate solution. To address these two problems, we propose CROKAGE (Crowd Knowledge Answer Generator), a tool that takes the description of a programming task (the query) and provides a comprehensive solution for the task. Our solutions contain not only relevant code examples but also their succinct explanations. Our proposed approach expands the task description with relevant API classes from Stack Overflow Q & A threads and then mitigates the lexical gap problems. Furthermore, we perform natural language processing on the top quality answers and then return such programming solutions containing code examples and code explanations unlike earlier studies. We evaluate our approach using 97 programming queries, of which 50% was used for training and 50% was used for testing, and show that it outperforms six baselines including the state-of-art by a statistically significant margin. Furthermore, our evaluation with 29 developers using 24 tasks (queries) confirms the superiority of CROKAGE over the state-of-art tool in terms of relevance of the suggested code examples, benefit of the code explanations and the overall solution quality (code + explanation).
Abstract. In response to ongoing and future-projected global warming, mid-latitude, nival river basins are expected to transition from a snowmelt-dominated flow regime to a nival–pluvial one with an earlier spring freshet of reduced magnitude. There is, however, a rich variation in responses that depends on factors such as the topographic complexity of the basin and the strength of maritime influences. We illustrate the potential effects of a strong maritime influence by studying future changes in cold season flow variability in the Fraser River Basin (FRB) of British Columbia, a large extratropical watershed extending from the Rocky Mountains to the Pacific Coast. We use a process-based hydrological model driven by an ensemble of 21 statistically downscaled simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5), following the Representative Concentration Pathway 8.5 (RCP 8.5). Warming under RCP 8.5 leads to reduced winter snowfall, shortening the average snow accumulation season by about one-third. Despite this, large increases in cold season rainfall lead to unprecedented cold season peak flows and increased overall runoff variability in the VIC simulations. Increased cold season rainfall is shown to be the dominant climatic driver in the Coast Mountains, contributing 60 % to mean cold season runoff changes in the 2080s. Cold season runoff at the outlet of the basin increases by 70 % by the 2080s, and its interannual variability more than doubles when compared to the 1990s, suggesting substantial challenges for operational flow forecasting in the region. Furthermore, almost half of the basin (45 %) transitions from a snow-dominated runoff regime in the 1990s to a primarily rain-dominated regime in the 2080s, according to a snowmelt pulse detection algorithm. While these projections are consistent with the anticipated transition from a nival to a nival–pluvial hydrologic regime, the marked increase in FRB cold season runoff is likely linked to more frequent landfalling atmospheric rivers in the region projected in the CMIP5 models, providing insights for other maritime-influenced extratropical basins.
Trophic transfer of contaminants dictates concentrations and potential toxic effects in top predators, yet biomagnification behaviour of many trace elements is poorly understood. We examined concentrations of vanadium and thallium, two globally-distributed and anthropogenically-enriched elements, in a food web of the Slave River, Northwest Territories, Canada. We found that tissue concentrations of both elements declined with increasing trophic position as measured by δ15N. Slopes of log [element] versus δ15N regressions were both negative, with a steeper slope for V (-0.369) compared with Tl (-0.099). These slopes correspond to declines of 94% with each step in the food chain for V and 54% with each step in the food chain for Tl. This biodilution behaviour for both elements meant that concentrations in fish were well below values considered to be of concern for the health of fish-eating consumers. Further study of these elements in food webs is needed to allow a fuller understanding of biomagnification patterns across a range of species and systems.
A novel integrated hydro-economic modeling framework that links a bottom-up partial equilibrium (engineering) model with a top-down (economic) general equilibrium model is developed for assessing the regional economic impacts of water resources management and infrastructure development decisions in a transboundary river basin. The engineering model is employed first to solve the water allocation problem for a river system in a partial equilibrium setting. The resulting system-wide changes in optimal water allocation are subsequently fed into the general equilibrium model to provide an economy-wide perspective. This integrated hydro-economic modeling framework is illustrated using the Eastern Nile River basin as a case study. The engineering-based stochastic dual dynamic programming (SDDP) model of the Eastern Nile basin is coupled with the computable general equilibrium (CGE) model GTAP-W to assess the economy-wide impacts of the Grand Ethiopian Renaissance Dam (GERD) on the Eastern Nile economies.
[1] Previous studies of river hydrometric records and Indigenous Knowledge holders claim that flood-induced recharge of ecologically important perched basins decreased across the Peace-Athabasca Delta after 1968 due mainly to hydroelectric regulation of Peace River flow. Natural deltaic processes and climate are acknowledged as additional, lesser contributors, but are challenging to evaluate. We use sediment records spanning ∼115 years from nine perched basins across the Athabasca Delta to test if unidirectional drying coincides with river regulation. Results show bi-directional hydrological changes since the early 1980s, not 1968, to reduced flooding in areas east of the Embarras River confluence with Cree/Mamawi creeks and increased flooding northward along the Cree/Mamawi distributary. The timing and pattern pinpoint the 1982 Embarras Breakthrough, a natural avulsion that diverted flow northward and away from the Athabasca Delta terminus, as the principal cause. The results demonstrate the need to factor natural deltaic processes into impending decisions on the delta’s UNESCO World Heritage status and implementation of a federal Action Plan to mitigate widespread drying.
The potential of high severity wildfires to increase global terrestrial carbon emissions and exacerbate future climatic warming is of international concern. Nowhere is this more prevalent than within high latitude regions where peatlands have, over millennia, accumulated legacy carbon stocks comparable to all human CO2 emissions since the beginning of the industrial revolution. Drying increases rates of peat decomposition and associated atmospheric and aquatic carbon emissions. The degree to which severe wildfires enhance drying under future climates and induce instability in peatland ecological communities and carbon stocks is unknown. Here we show that high burn severities increased post-fire evapotranspiration by 410% within a feather moss peatland by burning through the protective capping layer that restricts evaporative drying in response to low severity burns. High burn severities projected under future climates will therefore leave peatlands that dominate dry sub-humid regions across the boreal, on the edge of their climatic envelopes, more vulnerable to intense post-fire drying, inducing high rates of carbon loss to the atmosphere that amplify the direct combustion emissions.
Depletion of groundwater resources has been identified in numerous global aquifers, suggesting that extractions have exceeded natural recharge rates in critically important global freshwater supplies. Groundwater depletion has been ascribed to groundwater pumping, often ignoring influences of direct and indirect consequences of climate variability. Here, we explore relations between natural and human drivers and spatiotemporal changes in groundwater storage derived from the Gravity Recovery and Climate Experiment (GRACE) satellites using regression procedures and dominance analysis. Changes in groundwater storage are found to be influenced by direct climate variability, whereby groundwater recharge and precipitation exhibited greater influence as compared to groundwater pumping. Weak influence of groundwater pumping may be explained, in part, by quasi-equilibrium aquifer conditions that occur after “long-time” pumping, while precipitation and groundwater recharge records capture groundwater responses linked to climate-induced groundwater depletion. Evaluating groundwater response to climate variability is critical given the reliance of groundwater resources to satisfy water demands and impending changes in climate variability that may threaten future water availability.
Weather and climate are major factors influencing worldwide wildfire activity. This study assesses surface and atmospheric conditions associated with the 2014 extreme wildfires in the Northwest Ter...
Abstract The rapidly warming Arctic is experiencing permafrost degradation and shrub expansion. Future climate projections show a clear increase in mean annual temperature and increasing precipitation in the Arctic; however, the impact of these changes on hydrological cycling in Arctic headwater basins is poorly understood. This study investigates the impact of climate change, as represented by simulations using a high-resolution atmospheric model under a pseudo-global-warming configuration, and projected changes in vegetation, using a spatially distributed and physically based Arctic hydrological model, on a small headwater basin at the tundra–taiga transition in northwestern Canada. Climate projections under the RCP8.5 emission scenario show a 6.1°C warming, a 38% increase in annual precipitation, and a 19 W m−2 increase in all-wave annual irradiance over the twenty-first century. Hydrological modeling results suggest a shift in hydrological processes with maximum peak snow accumulation increasing by 70%, snow-cover duration shortening by 26 days, active layer deepening by 0.25 m, evapotranspiration increasing by 18%, and sublimation decreasing by 9%. This results in an intensification of the hydrological regime by doubling discharge volume, a 130% increase in spring runoff, and earlier and larger peak streamflow. Most hydrological changes were found to be driven by climate change; however, increasing vegetation cover and density reduced blowing snow redistribution and sublimation, and increased evaporation from intercepted rainfall. This study provides the first detailed investigation of projected changes in climate and vegetation on the hydrology of an Arctic headwater basin, and so it is expected to help inform larger-scale climate impact studies in the Arctic.
Governments are struggling to limit global temperatures below the 2°C Paris target with existing climate change policy approaches. This is because conventional climate policies have been predominantly (inter)nationally top-down, which limits citizen agency in driving policy change and influencing citizen behavior. Here we propose elevating Citizen Social Science (CSS) to a new level across governments as an advanced collaborative approach of accelerating climate action and policies that moves beyond conventional citizen science and participatory approaches. Moving beyond the traditional science-policy model of the democratization of science in enabling more inclusive climate policy change, we present examples of how CSS can potentially transform citizen behavior and enable citizens to become key agents in driving climate policy change. We also discuss the barriers that could impede the implementation of CSS and offer solutions to these. In doing this, we articulate the implications of increased citizen action through CSS in moving forward the broader normative and political program of transdisciplinary and co-productive climate change research and policy.

DOI bib
The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty
David M. Lawrence, Rosie A. Fisher, Charles D. Koven, Keith W. Oleson, Sean Swenson, G. B. Bonan, Nathan Collier, Bardan Ghimire, Leo van Kampenhout, Daniel Kennedy, Erik Kluzek, Fang Li, Hongyi Li, Danica Lombardozzi, William J. Riley, William J. Sacks, Mingjie Shi, Mariana Vertenstein, William R. Wieder, Chonggang Xu, Ashehad A. Ali, Andrew M. Badger, Gautam Bisht, Michiel van den Broeke, Michael A. Brunke, Sean P. Burns, Jonathan Buzan, Martyn P. Clark, Anthony P Craig, Kyla M. Dahlin, Beth Drewniak, Joshua B. Fisher, M. Flanner, A. M. Fox, Pierre Gentine, Forrest M. Hoffman, G. Keppel‐Aleks, R. G. Knox, Sanjiv Kumar, Jan T. M. Lenaerts, L. Ruby Leung, William H. Lipscomb, Yaqiong Lü, Ashutosh Pandey, Jon D. Pelletier, J. Perket, James T. Randerson, Daniel M. Ricciuto, Benjamin M. Sanderson, A. G. Slater, Z. M. Subin, Jinyun Tang, R. Quinn Thomas, Maria Val Martin, Xubin Zeng
Journal of Advances in Modeling Earth Systems, Volume 11, Issue 12

The Community Land Model (CLM) is the land component of the Community Earth System Model (CESM) and is used in several global and regional modeling systems. In this paper, we introduce model developments included in CLM version 5 (CLM5), which is the default land component for CESM2. We assess an ensemble of simulations, including prescribed and prognostic vegetation state, multiple forcing data sets, and CLM4, CLM4.5, and CLM5, against a range of metrics including from the International Land Model Benchmarking (ILAMBv2) package. CLM5 includes new and updated processes and parameterizations: (1) dynamic land units, (2) updated parameterizations and structure for hydrology and snow (spatially explicit soil depth, dry surface layer, revised groundwater scheme, revised canopy interception and canopy snow processes, updated fresh snow density, simple firn model, and Model for Scale Adaptive River Transport), (3) plant hydraulics and hydraulic redistribution, (4) revised nitrogen cycling (flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake), (5) global crop model with six crop types and time‐evolving irrigated areas and fertilization rates, (6) updated urban building energy, (7) carbon isotopes, and (8) updated stomatal physiology. New optional features include demographically structured dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator), ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Conclusive establishment of improvement or degradation of individual variables or metrics is challenged by forcing uncertainty, parametric uncertainty, and model structural complexity, but the multivariate metrics presented here suggest a general broad improvement from CLM4 to CLM5.
The concept of using representative hillslopes to simulate hydrologically similar areas of a catchment has been incorporated in many hydrologic models but few Earth system models. Here we describe a configuration of the Community Land Model version 5 in which each grid cell is decomposed into one or more multicolumn hillslopes. Within each hillslope, the intercolumn connectivity is specified, and the lateral saturated subsurface flow from each column is passed to its downslope neighbor. We first apply the model to simulate a headwater catchment and assess the results against runoff and evapotranspiration flux measurements. By redistributing soil water within the catchment, the model is able to reproduce the observed difference between evapotranspiration in the upland and lowland portions of the catchment. Next, global simulations based on hypothetical hillslope geomorphic parameters are used to show the model's sensitivity to differences in hillslope shape and discretization. Differences in evapotranspiration between upland and lowland hillslope columns are found to be largest in arid and semiarid regions, while humid tropical and high‐latitude regions show limited evapotranspiration increases in lowlands relative to uplands.
Increasingly, climate change impact assessments rely directly on climate models. Assessments of future water security depend in part on how the land model components in climate models partition precipitation into evapotranspiration and runoff, and on the sensitivity of this partitioning to climate. Runoff sensitivities are not well constrained, with CMIP5 models displaying a large spread for the present day, which projects onto change under warming, creating uncertainty. Here we show that constraining CMIP5 model runoff sensitivities with observed estimates could reduce uncertainty in runoff projection over the western United States by up to 50%. We urge caution in the direct use of climate model runoff for applications and encourage model development to use regional-scale hydrological sensitivity metrics to improve projections for water security assessments.
The timing and magnitude of snowmelt discharge and subsequent runoff are controlled by both matrix and preferential flows of water through snowpacks. Matrix flow can be estimated using the Richards equation, and recently, preferential flow in snowpacks has been represented in 2D and 3D models. A challenge for representing preferential flow through porous media in 2D or 3D is capillary pressure overshoot in 1D. Soil studies have developed sophisticated and largely realistic approaches to represent capillary pressure overshoot, but it has not been addressed in snowpack water flow models. Here a 1D nonequilibrium Richards equation model is implemented with dynamic capillary pressure and is combined with a new concept of entrapment of liquid water within the pore space. This new model well represented capillary pressure overshoot, as estimated by published capillary pressure measurements in snow samples of various grain sizes under different rates of liquid water infiltration. Three model parameters were calibrated, and their impacts on model outputs were evaluated. This improvement is a substantial step toward better understanding and simulating physical processes occurring while liquid water percolates an initially dry snowpack.
Monitoring diverse components of aquatic ecosystems is vital for elucidation of diversity dynamics and processes, which alter freshwater ecosystems, but such studies are seldom conducted. Phytoplankton and zooplankton are integral components which play indispensable parts in the structure and ecological service function of water bodies. However, few studies were made on how zooplankton and phytoplankton community may respond simultaneously to change of circumstance and their mutual relationship. Therefore, we researched synchronously the phytoplankton communities as well as zooplankton communities based on monthly monitoring data from September 2011 to August 2012 in heavily polluted areas and researched their responses to variation in environmental parameters and their mutual relationship. As indicated by Time-lag analysis (TLA), the long-term dynamics of phytoplankton and zooplankton were undergoing directional variations, what's more, there exists significant seasonal variations of phytoplankton and zooplankton communities as indicated by Non-Metric Multidimensional scaling (NMDS) methods. Also, Redundancy Analysis (RDA) demonstrated that environmental indicators together accounted for 25.6% and 50.1% variance of phytoplankton and zooplankton, respectively, indicating that environmental variations affected significantly on the temporal dynamics of phytoplankton as well as zooplankton communities. What's more, variance partioning suggested that the major environmental factors influencing variation structures of zooplankton communities were water temperature, concentration of nitrogen, revealing the dominating driving mechanism which shaped the communities of zooplankton. It was also found that there was significant synchronization between zooplankton biomass and phytoplankton biomass (expressed as Chl-a concentration), which suggested that zooplankton respond to changes in dynamic structure of phytoplankton community and can initiate a decrease in phytoplankton biomass through grazing in a few months.
Global warming is expected to increase the amount of atmospheric moisture, resulting in heavier extreme precipitation. Various studies have used the historical relationship between extreme precipitation and temperature (temperature scaling) to provide guidance about precipitation extremes in a future warmer climate. Here we assess how much information is required to robustly identify temperature scaling relationships, and whether these relationships are equally effective at different times in the future in estimating precipitation extremes everywhere across North America. Using a large ensemble of 35 North American regional climate simulations of the period 1951–2100, we show that individual climate simulations of length comparable to that of typical instrumental records are unable to constrain temperature scaling relationships well enough to reliably estimate future extremes of local precipitation accumulation for hourly to daily durations in the model's climate. Hence, temperature scaling relationships estimated from the limited historical observations are unlikely to be able to provide reliable guidance for future adaptation planning at local spatial scales. In contrast, well‐constrained temperature scaling relations based on multiple regional climate simulations do provide a feasible basis for accurately projecting precipitation extremes of hourly to daily durations in different future periods over more than 90% of the North American land area.
Climate models project that extreme precipitation events will intensify in proportion to their intensity during the 21st century at large spatial scales. The identification of the causes of this phenomenon nevertheless remains tenuous. Using a large ensemble of North American regional climate simulations, we show that the more rapid intensification of more extreme events also appears as a robust feature at finer regional scales. The larger increases in more extreme events than in less extreme events are found to be primarily due to atmospheric circulation changes. Thermodynamically induced changes have relatively uniform effects across extreme events and regions. In contrast, circulation changes weaken moderate events over western interior regions of North America and enhance them elsewhere. The weakening effect decreases and even reverses for more extreme events, whereas there is further intensification over other parts of North America, creating an “intense gets intenser” pattern over most of the continent.
Understanding the extent and directionality of the impact of human activities on ecosystems is directly related to their management and protection. However, the lack of historical data limits our understanding of ecosystem changes with long-term exposure to human activities. Recently, lake sedimentary DNA (sedDNA) has become a powerful tool for revealing changes in ecosystems at the century and millennium scales. Here, we used sedDNA to reveal the dynamic of the microbial community (including bacteria and micro-eukaryotes) in Lake Chao over the past 150 years, and further explored the effects of long-term nutrient and heavy metal loads on these communities. Our data show that nutrient and heavy metal loads in Lake Chao have increased by ca. 2 to 4-fold since the 1960s. In response, the community structure, diversity, and ecological network of bacteria and micro-eukaryotes changed significantly during the 1960s, the 1980s and the 2010s. Importantly, community structure was more sensitive to human activities than diversity. We also found that the relative abundance of some taxa associated with nitrification and algal blooms (e.g., taxa in Nitrospira sp., Peridinales) has increased ca. 100-fold since the 1960s. Nutrient could better explain the variation in the bacterial community (ca. twice as much as heavy metal), while heavy metal explained micro-eukaryotes better (ca. 3 or 5-fold as much as nutrient). In particular, based on parsimonious models from distance-based linear model (distLM), we further identified that Pb is the key factor affecting the bacterial and micro-eukaryotes community in Lake Chao in addition to nutrient. Our study reveals the impacts of long-term human activities on lake ecosystems from multiple perspectives of nutrient and heavy metal loads, community structure, diversity and ecological network, these findings will contribute to the management and conservation of lakes in the future.
ABSTRACTThis study evaluates the 1981–2010 spatiotemporal differences in six available climate datasets (daily total precipitation and mean air temperature) over the Lower Nelson River Basin (LNRB)...
For ice-jam flood forecasting it is important to differentiate between intact ice covers and ice runs. Ice runs consist of long accumulations of rubble ice that stem from broken up ice covers or ice-jams that have released. A water wave generally travels ahead of the ice run at a faster celerity, arriving at the potentially high flood–risk area much sooner than the ice accumulation. Hence, a rapid detection of the ice run is necessary to lengthen response times for flood mitigation. Intact ice covers are stationary and hence are not an immediate threat to a downstream flood situation, allowing more time for flood preparedness. However, once ice accumulations are moving and potentially pose imminent impacts to flooding, flood response may have to switch from a mitigation to an evacuation mode of the flood management plan. Ice runs are generally observed, often by chance, through ground observations or airborne surveys. In this technical note, we introduce a novel method of differentiating ice runs from intact ice covers using imagery acquired from space-borne radar backscatter signals. The signals are decomposed into different scatter components—surface scattering, volume scattering and double-bounce—the ratios of one to another allow differentiation between intact and running ice. The method is demonstrated for the breakup season of spring 2018 along the Athabasca River, when an ice run shoved into an intact ice cover which led to some flooding in Fort McMurray, Alberta, Canada.
Abstract Dams are typically designed to serve as flood protection, provide water for irrigation, human and animal consumption, and harness hydropower. Despite these benefits, dam operations can have adverse effects on in-reservoir and downstream water temperature regimes, biogeochemical cycling and aquatic ecosystems. We present a water quality dataset of water withdrawal scenarios generated after implementing the 2D hydrodynamic and water quality model, CE-QUAL-W2. The scenarios explore how six water extraction scenarios, starting at 5 m above the reservoir bottom at the dam and increasing upward at 10 m intervals to 55 m, influence water quality in Lake Diefenbaker reservoir, Saskatchewan, Canada. The model simulates daily water temperature, dissolved oxygen, total phosphorus, phosphate as phosphorus, labile phosphorus, total nitrogen, nitrate as nitrogen, labile nitrogen, and ammonium at 87 horizontal segments and at 60 water depths during the 2011–2013 period. This dataset intends to facilitate a broader investigation of in-reservoir nutrient dynamics under dam operations, and to extend the understanding of reservoir nutrient dynamics globally.
We describe a spatially contiguous, temporally consistent high-resolution gridded daily meteorological dataset for northwestern North America. This >4 million km2 region has high topographic relief, seasonal snowpack, permafrost and glaciers, crosses multiple jurisdictional boundaries and contains the entire Yukon, Mackenzie, Saskatchewan, Fraser and Columbia drainages. We interpolate daily station data to 1/16° spatial resolution using a high-resolution monthly 1971-2000 climatology as a predictor in a thin-plate spline interpolating algorithm. Only temporally consistent climate stations with at least 40 years of record are included. Our approach is designed to produce a dataset well suited for driving hydrological models and training statistical downscaling schemes. We compare our results to two commonly used datasets and show improved performance for climate means, extremes and variability. When used to drive a hydrologic model, our dataset also outperforms these datasets for runoff ratios and streamflow trends in several, high elevation, sub-basins of the Fraser River.
Despite ubiquitous warming, the lower Oder River typically freezes over almost every year. Ice jams may occur during freeze-up and ice cover breakup phases, particularly in the middle and lower reaches of the river, with weirs and piers. The slush ice and ice blocks may accumulate to form ice jams, leading to backwater effects and substantial water level rise. The small bottom slope of the lower Oder and the tidal backflow from the Baltic Sea enhance the formation of ice jams during cold weather conditions, jeopardizing the dikes. Therefore, development of an ice jam flood forecasting system for the Oder River is much needed. This commentary presents selected results from an international workshop that took place in Wrocław (Poland) on 26–27 November 2018 that brought together an international team of experts to explore the requirements and research opportunities in the field of ice jam flood forecasting and risk assessment for the Oder River section along the German–Polish border. The workshop launched a platform for collaboration amongst Canadian, German and Polish scientists, government officials and water managers to pave a way forward for joint research focused on achieving the long-term goal of forecasting, assessing and mitigating ice jam impacts along the lower Oder. German and Polish government agencies are in need of new tools to forecast ice jams and assess their subsequent consequences and risks to communities and ship navigation along a river. Addressing these issues will also help research and ice flood management in a Canadian context. A research program would aim to develop a modelling system by addressing fundamental issues that impede the prediction of ice jam events and their consequences in cold regions.
Abstract Forecasting ice jams and their consequential flooding is more challenging than predicting open water flood conditions. This is due to the chaotic nature of ice jam formation since slight changes in water and ice flows, location of the ice jam toe along the river and initial water levels at the time of jam formation can lead to marked differences in the outcome of backwater level elevations and flood severity. In this paper, we introduce a novel, operational real-time flood forecasting system that captures this stochastic nature of ice-jam floods and places the forecasts in a probabilistic context in the form of flood hazard maps (probability of flood extents and depths). This novel system was tested successfully for the ice-cover breakup period in the spring of 2018 along the Athabasca River at the Town of Fort McMurray, Canada.
Clarifying how increased atmospheric CO2 concentration (eCO2) contributes to accelerated land carbon sequestration remains important since this process is the largest negative feedback in the coupled carbon–climate system. Here, we constrain the sensitivity of the terrestrial carbon sink to eCO2 over the temperate Northern Hemisphere for the past five decades, using 12 terrestrial ecosystem models and data from seven CO2 enrichment experiments. This constraint uses the heuristic finding that the northern temperate carbon sink sensitivity to eCO2 is linearly related to the site-scale sensitivity across the models. The emerging data-constrained eCO2 sensitivity is 0.64 ± 0.28 PgC yr−1 per hundred ppm of eCO2. Extrapolating worldwide, this northern temperate sensitivity projects the global terrestrial carbon sink to increase by 3.5 ± 1.9 PgC yr−1 for an increase in CO2 of 100 ppm. This value suggests that CO2 fertilization alone explains most of the observed increase in global land carbon sink since the 1960s. More CO2 enrichment experiments, particularly in boreal, arctic and tropical ecosystems, are required to explain further the responsible processes. The northern temperate carbon sink is estimated to increase by 0.64 PgC each year for each increase in atmospheric CO2 concentrations by 100 ppm, suggests an analysis of data from field experiments at 7 sites constraints.
Polycyclic Aromatic Hydrocarbons (PAH) ubiquitously occur in rivers and threaten the aquatic ecosystem. Understanding their fate and behaviour in rivers can help in improving management strategies. We develop a particle-facilitated transport model considering suspended sediments with sorbed PAH from different origins to investigate the turnover and legacy of sediment-bound PAH in the baseflow-dominated Ammer River in southwest Germany. Our model identifies the contributions of dissolved and particle-bound PAH during wet and dry periods to the annual load. The analysis of in-stream processes enables investigating the average turnover times of sediments and attached PAH for the main stem of the river. The legacy of sediment-bound PAH is studied by running the model assuming a 50% reduction in PAH emissions after the introduction of environmental regulation in the 1970s. Our results show that sediment-bound and dissolved PAH account for 75% and 25% of the annual PAH load, respectively. PAH are mainly emitted from urban areas that contribute over 74% to the total load. In steep reaches, the turnover times of sediments and attached PAH are similar, whereas they differ by 1-2 orders of magnitude in reaches with very mild slopes. Flow rates significantly affect PAH fluxes between the mobile water and the riverbed over the entire river. Total PAH fluxes from the river bed to the mobile water are simulated to occur when the discharge is larger than 5 m3s -1. River segments with large sediment storage show a potential of PAH legacy, which may have caused a PAH release over 10-20 years after the implementation of environmental regulation. This study is useful for assessing environmental impacts of PAH in rivers (e.g., their contribution to the river-water toxicity) and exemplifies that the longitudinal distribution, turnover, and legacy potential of PAH in a river system require a mechanistic understanding of river hydraulics and sediment transport.
Arctic and boreal ecosystems play an important role in the global carbon (C) budget, and whether they act as a future net C sink or source depends on climate and environmental change. Here, we used complementary in situ measurements, model simulations, and satellite observations to investigate the net carbon dioxide (CO2 ) seasonal cycle and its climatic and environmental controls across Alaska and northwestern Canada during the anomalously warm winter to spring conditions of 2015 and 2016 (relative to 2010-2014). In the warm spring, we found that photosynthesis was enhanced more than respiration, leading to greater CO2 uptake. However, photosynthetic enhancement from spring warming was partially offset by greater ecosystem respiration during the preceding anomalously warm winter, resulting in nearly neutral effects on the annual net CO2 balance. Eddy covariance CO2 flux measurements showed that air temperature has a primary influence on net CO2 exchange in winter and spring, while soil moisture has a primary control on net CO2 exchange in the fall. The net CO2 exchange was generally more moisture limited in the boreal region than in the Arctic tundra. Our analysis indicates complex seasonal interactions of underlying C cycle processes in response to changing climate and hydrology that may not manifest in changes in net annual CO2 exchange. Therefore, a better understanding of the seasonal response of C cycle processes may provide important insights for predicting future carbon-climate feedbacks and their consequences on atmospheric CO2 dynamics in the northern high latitudes.
A multitude of disturbance agents, such as wildfires, land use, and climate-driven expansion of woody shrubs, is transforming the distribution of plant functional types across Arctic–Boreal ecosystems, which has significant implications for interactions and feedbacks between terrestrial ecosystems and climate in the northern high-latitude. However, because the spatial resolution of existing land cover datasets is too coarse, large-scale land cover changes in the Arctic–Boreal region (ABR) have been poorly characterized. Here, we use 31 years (1984–2014) of moderate spatial resolution (30 m) satellite imagery over a region spanning 4.7 × 106 km2 in Alaska and northwestern Canada to characterize regional-scale ABR land cover changes. We find that 13.6 ± 1.3% of the domain has changed, primarily via two major modes of transformation: (a) simultaneous disturbance-driven decreases in Evergreen Forest area (−14.7 ± 3.0% relative to 1984) and increases in Deciduous Forest area (+14.8 ± 5.2%) in the Boreal biome; and (b) climate-driven expansion of Herbaceous and Shrub vegetation (+7.4 ± 2.0%) in the Arctic biome. By using time series of 30 m imagery, we characterize dynamics in forest and shrub cover occurring at relatively short spatial scales (hundreds of meters) due to fires, harvest, and climate-induced growth that are not observable in coarse spatial resolution (e.g., 500 m or greater pixel size) imagery. Wildfires caused most of Evergreen Forest Loss and Evergreen Forest Gain and substantial areas of Deciduous Forest Gain. Extensive shifts in the distribution of plant functional types at multiple spatial scales are consistent with observations of increased atmospheric CO2 seasonality and ecosystem productivity at northern high-latitudes and signal continental-scale shifts in the structure and function of northern high-latitude ecosystems in response to climate change.
Climate extremes such as heat waves and droughts are projected to occur more frequently with increasing temperature and an intensified hydrological cycle. It is important to understand and quantify how forest carbon fluxes respond to heat and drought stress. In this study, we developed a series of daily indices of sensitivity to heat and drought stress as indicated by air temperature (Ta ) and evaporative fraction (EF). Using normalized daily carbon fluxes from the FLUXNET Network for 34 forest sites in North America, the seasonal pattern of sensitivities of net ecosystem productivity (NEP), gross ecosystem productivity (GEP) and ecosystem respiration (RE) in response to Ta and EF anomalies were compared for different forest types. The results showed that warm temperatures in spring had a positive effect on NEP in conifer forests but a negative impact in deciduous forests. GEP in conifer forests increased with higher temperature anomalies in spring but decreased in summer. The drought-induced decrease in NEP, which mostly occurred in the deciduous forests, was mostly driven by the reduction in GEP. In conifer forests, drought had a similar dampening effect on both GEP and RE, therefore leading to a neutral NEP response. The NEP sensitivity to Ta anomalies increased with increasing mean annual temperature. Drier sites were less sensitive to drought stress in summer. Natural forests with older stand age tended to be more resilient to the climate stresses compared to managed younger forests. The results of the Classification and Regression Tree analysis showed that seasons and ecosystem productivity were the most powerful variables in explaining the variation of forest sensitivity to heat and drought stress. Our results implied that the magnitude and direction of carbon flux changes in response to climate extremes are highly dependent on the seasonal dynamics of forests and the timing of the climate extremes.
The role of plant phenology as a regulator for gross ecosystem productivity (GEP) in peatlands is empirically not well constrained. This is because proxies to track vegetation development with daily coverage at the ecosystem scale have only recently become available and the lack of such data has hampered the disentangling of biotic and abiotic effects. This study aimed at unraveling the mechanisms that regulate the seasonal variation in GEP across a network of eight European peatlands. Therefore, we described phenology with canopy greenness derived from digital repeat photography and disentangled the effects of radiation, temperature and phenology on GEP with commonality analysis and structural equation modeling. The resulting relational network could not only delineate direct effects but also accounted for possible effect combinations such as interdependencies (mediation) and interactions (moderation). We found that peatland GEP was controlled by the same mechanisms across all sites: phenology constituted a key predictor for the seasonal variation in GEP and further acted as a distinct mediator for temperature and radiation effects on GEP. In particular, the effect of air temperature on GEP was fully mediated through phenology, implying that direct temperature effects representing the thermoregulation of photosynthesis were negligible. The tight coupling between temperature, phenology and GEP applied especially to high latitude and high altitude peatlands and during phenological transition phases. Our study highlights the importance of phenological effects when evaluating the future response of peatland GEP to climate change. Climate change will affect peatland GEP especially through changing temperature patterns during plant phenologically sensitive phases in high latitude and high altitude regions.
The San Joaquin Valley and Tulare basins in California’s Central Valley have intensive agricultural activity and groundwater demand that has caused significant subsidence and depletion of water resources in the past. We measured groundwater pumping-induced land subsidence in the southern Central Valley from March 2015 to May 2017 using Sentinel-1 interferometric synthetic aperture radar (InSAR) data. The InSAR measurements provided fine spatial details of subsidence patterns and displayed a superposition of secular and seasonal variations that were coherent across our study region and correlated with precipitation variability and changes in freshwater demand. Combining InSAR and Global Positioning System (GPS) data, precipitation, and in situ well records showed a broad scale slowdown/cessation of long term subsidence in the wetter winter of 2017, likely reflecting the collective response of the Central Valley aquifer system to heavier-than-usual precipitation. We observed a very good temporal correlation between the Gravity Recovery and Climate Experiment (GRACE) satellite groundwater anomaly (GWA) variation and long-term subsidence records, regardless of local hydrogeology and mechanical properties. This indicates the subsidence from satellite geodesy is a very useful indicator for tracking groundwater storage change. With the continuing acquisition of Sentinel-1 and other satellites, we anticipate decadal-scale subsidence records with a spatial resolution of tens to hundreds of meters will be available in the near future to be combined with basin-averaged GRACE measurements to improve our estimate of time-varying groundwater change.
Abstract Switzerland plans to restore 4000 km of rivers by 2090. Despite the immense investment costs, river restoration benefits have not been valued in monetary terms, and a cost-benefit analysis (CBA) does not exist for any river restoration project in Switzerland. We apply stated preference methods to elicit public preferences and willingness to pay for restoring two specific but representative river sites. The benefits of restoration are compared with its costs. Upscaling the results to the national level shows that the government budget allocated for river restoration (CHF 1200/m) is insufficient to cover the costs of local restoration projects. However, the surveyed local populations are willing to pay substantially more for restoring rivers in their area of residence than they are legally obliged to do. The CBA results demonstrate that the benefits outweigh the costs in the two case studies, and hence that restoration efforts are justified from an economic point of view. A sensitivity analysis shows that the main results and conclusions do not change when we change some of the key assumptions underlying the CBA.
While governments in Canada have a duty to act honourably in the development of legislative actions that may affect Aboriginal or treaty rights, Indigenous peoples’ input and knowledge have largely...
Abstract Snow interception in cold regions needleleaf forest canopies is a crucial process that controls local snow accumulation and redistribution over >20% of the Earth's land surface. Various ground-based methods exist to measure intercepted snow load, however all are based on single-tree measurements and are difficult to implement. No research has focussed on detecting large areal intercepted snow loads and no studies have assessed the use of satellite observations. In this study, four remote sensing indices (NDSI, NDVI, albedo, and land surface temperature (LST)) were retrieved from Landsat images to study their sensitivity to canopy intercepted snow and the possibility of using them to detect the presence of intercepted snow. The results indicate that presence of intercepted snow on canopy increased NDSI and albedo, but decreased NDVI. Intercepted snow presence also decreased the areal variability of NDSI and NDVI while increasing that of albedo. For these three indices, the differences between snow-free and snowcovered canopies were correlated to topography and forest canopy cover. Of these indices, NDSI changed the greatest. Intercepted snow noticeably decreased the LST difference between forest and open areas in springtime while the influence in wintertime was relatively smaller. An intercepted snow detection approach that uses both NDSI and NDVI to classify pixels into either snowcovered canopy or other (snow-free canopy and non-forest areas) is proposed here. A case study applying this approach compared remote sensing detection to simulations by the snow interception and sublimation model implemented in the Cold Regions Hydrological Modelling platform (CRHM). This used local meteorological observations from the pine, spruce and fir forest covered Marmot Creek Research Basin in the Canadian Rockies. The remote sensing detection of intercepted snow agreed well with CRHM simulations for continuous forests (83%) and less well for sparse forests (72%) and clearings with small trees (70%). Therefore, the approach is suitable for intercepted snow detection over continuous evergreen canopies. This technique provides a new capability for large-scale snow interception model validation and data assimilation to cold regions hydrological forecasting models.
Phosphorus (P) loss in agricultural discharge has typically been associated with surface runoff; however, tile drains have been identified as a key P pathway due to preferential transport. Identifying when and where these pathways are active may establish high-risk periods and regions that are vulnerable for P loss. A synthesis of high-frequency, runoff data from eight cropped fields across the Great Lakes region of North America over a 3-yr period showed that both surface and tile flow occurred year-round, although tile flow occurred more frequently. The relative timing of surface and tile flow activation was classified into four response types to infer runoff-generation processes. Response types were found to vary with season and soil texture. In most events across all sites, tile responses preceded surface flow, whereas the occurrence of surface flow prior to tile flow was uncommon. The simultaneous activation of pathways, indicating rapid connectivity through the vadose zone, was seldom observed at the loam sites but occurred at clay sites during spring and summer. Surface flow at the loam sites was often generated as saturation-excess, a phenomenon rarely observed on the clay sites. Contrary to expectations, significant differences in P loads in tiles were not apparent under the different response types. This may be due to the frequency of the water quality sampling or may indicate that factors other than surface-tile hydrologic connectivity drive tile P concentrations. This work provides new insight into spatial and temporal differences in runoff mechanisms in tile-drained landscapes.
Phosphorus (P) plays a crucial role in agriculture as a primary fertilizer nutrient-and as a cause of the eutrophication of surface waters. Despite decades of efforts to keep P on agricultural fields and reduce losses to waterways, frequent algal blooms persist, triggering not only ecological disruption but also economic, social, and political consequences. We investigate historical and persistent factors affecting agricultural P mitigation in a transect of major watersheds across North America: Lake Winnipeg, Lake Erie, the Chesapeake Bay, and Lake Okeechobee/Everglades. These water bodies span 26 degrees of latitude, from the cold climate of central Canada to the subtropics of the southeastern United States. These water bodies and their associated watersheds have tracked trajectories of P mitigation that manifest remarkable similarities, and all have faced challenges in the application of science to agricultural management that continue to this day. An evolution of knowledge and experience in watershed P mitigation calls into question uniform solutions as well as efforts to transfer strategies from other arenas. As a result, there is a need to admit to shortcomings of past approaches, plotting a future for watershed P mitigation that accepts the sometimes two-sided nature of Hennig Brandt's "Devil's Element."
The development of compact and low-cost dissolved oxygen (DO) sensors is essential for the continuous in situ monitoring of environmental water quality and wastewater treatment processes. The optical detection of dynamic and reversible quenching of fluorescent dyes by oxygen has been used for DO sensing. In this paper, we have optimized a multilayer optofluidic device based on the measurement of fluorescence quenching in a Ruthenium-based oxygen sensitive dye by employing total internal reflection (TIR) of the excitation light to achieve sensitivity enhancement for the detection of 0-20-ppm DO in water. The incident angles of light and sensitive layer thickness are optimized experimentally in order to increase the path length of light in the sensitive layer of the device through multiple reflections. A model is developed to demonstrate how light propagates through different layers of the device at varying angles of excitation and to describe the mechanism of fluorescence generation for each of the types of TIR observed. The design principles identified in this paper may be applied to the development and optimization of new multilayered optofluidic sensors by employing TIR for sensitivity enhancement.
Environmental models are used extensively to evaluate the effectiveness of a range of design, planning, operational, management and policy options. However, the number of options that can be evaluated manually is generally limited, making it difficult to identify the most suitable options to consider in decision-making processes. By linking environmental models with evolutionary and other metaheuristic optimization algorithms, the decision options that make best use of scarce resources, achieve the best environmental outcomes for a given budget or provide the best trade-offs between competing objectives can be identified. This Introductory Overview presents reasons for embedding formal optimization approaches in environmental decision-making processes, details how environmental problems are formulated as optimization problems and outlines how single- and multi-objective optimization approaches find good solutions to environmental problems. Practical guidance and potential challenges are also provided.
Abstract Ice phenology, defined as the timing of freeze-up and ice-cover breakup, plays a key role in streamflow regimes in cold-region river catchments. River freeze-up and ice-cover breakup events are controlled by meteorological and hydrological variables. In this study, we present a modelling framework consisting of a physically-based semi-distributed hydrological model and the integration of a 1D stream temperature model that can predict the ice duration in cold region rivers. The hydrological model provides streamflow and hydraulic parameters for the stream temperature model to obtain instream water temperature. The model was successfully applied in the Athabasca River basin in western Canada. Calibration was carried out using the water temperature recorded in the stations at the towns of Hinton, Athabasca and Fort McMurray. Model results show consistent correspondence between simulated freeze-up and breakup dates and the hydrometric station data. In the main tributaries of the basin, freeze-up timing spans from the last week of September to the second week of November and ice-cover breakup occurs from the second week of March to the last week of May. The model presents an application of water temperature and ice phenology simulation which can be incorporated in ice-jam flood forecasting and future climate change studies.
Abstract Many applications of global sensitivity analysis (GSA) do not adequately account for the dynamical nature of earth and environmental systems models. Gupta and Razavi (2018) highlight this fact and develop a sensitivity analysis framework from first principles, based on the sensitivity information contained in trajectories of partial derivatives of the dynamical model responses with respect to controlling factors. Here, we extend and generalize that framework to accommodate any GSA philosophy, including derivative-based approaches (such as Morris and DELSA), direct-response-based approaches (such as the variance-based Sobol’, distribution-based PAWN, and higher-moment-based methods), and unifying variogram-based approaches (such as VARS). The framework is implemented within the VARS-TOOL software toolbox and demonstrated using the HBV-SASK model applied to the Oldman Watershed, Canada. This enables a comprehensive multi-variate investigation of the influence of parameters and forcings on different modeled state variables and responses, without the need for observational data regarding those responses.
Visual notifications are integral to interactive computing systems. With large displays, however, much of the content is in the user's visual periphery, where human capacity to notice visual effects is diminished. One design strategy for enhancing noticeability is to combine visual features, such as motion and colour. Yet little is known about how feature combinations affect noticeability across the visual field, or about how peripheral noticeability changes when a user's primary task involves the same visual features as the notification. We addressed these questions by conducting two studies. Results of the first study showed that noticeability of feature combinations were approximately equal to the better of the individual features. Results of the second study suggest that there can be interference between the features of primary tasks and the visual features in the notifications. Our findings contribute to a better understanding of how visual features operate when used as peripheral notifications.
AIM: Wetland loss and degradation threaten biodiversity to an extent greater than most ecosystems. Science‐supported responses require understanding of interacting effects of land use and climate change on wetland biodiversity. LOCATION: Alberta, Canada. METHODS: We evaluated how current climate, climate change (as a ghost of the past), land use and wetland water quality relate to aquatic macroinvertebrates and birds. RESULTS: Climatic relationships and climate–land use interactions were observed on chironomid abundance, but not macroinvertebrate taxa richness (MTR) or odonate abundance, which responded to land use and water chemistry. Chironomid abundance was positively associated with cropland and negatively associated with total precipitation. Higher cropland cover and dissolved organic carbon synergistically interacted with total precipitation to affect chironomids. MTR was negatively related to salinity, yet greater area of non‐woody riparian vegetation attenuated salinity effects on MTR. Odonate abundance was negatively related to total phosphorus. Higher grassland cover also increased the negative relationship of total phosphorous to odonate abundance. Climatic relationships and climate–land use interactions were observed on bird species richness (BSR) and abundance of several bird functional groups. Higher BSR and abundances of several bird groups were positively related to average rainfall and greater warming temperatures over time. Area of non‐crop cover and wetlands was positively associated with most bird groups and BSR. Warming temperatures over time ameliorated the negative relationship of higher cropland or less shrubland on aerial insectivores and other bird groups. MAIN CONCLUSIONS: Climate patterns and climate change are as important as land use pressures with stronger impacts on birds. Climate change was more influential than current climate and provided novel empirical evidence that progressively warmer, wetter conditions is benefiting some bird groups, including aerial insectivores, a group of conservation concern. Riparian vegetation ameliorated the negative impacts of climate and water quality gradients on MTR and could mitigate global change impacts in agricultural systems.
We expanded the existing one‐dimensional MyLake model by incorporating a vertically resolved sediment diagenesis module and developing a reaction network that seamlessly couples the water column and sediment biogeochemistry. The application of the MyLake‐Sediment model to boreal Lake Vansjø illustrates the model's ability to reproduce daily water quality variables and predict sediment‐water column exchange fluxes over a long historical period. In prognostic scenarios, we assessed the importance of sediment processes and the effects of various climatic and anthropogenic drivers on the lake's biogeochemistry and phytoplankton dynamics. First, MyLake‐Sediment was used to simulate the potential impacts of increasing air temperature on algal growth and water quality. Second, the key role of ice cover in controlling water column mixing and biogeochemical cycles was analyzed in a series of scenarios that included a fully ice‐free end‐member. Third, in another end‐member scenario P loading from the watershed to the lake was abruptly halted. The model results suggest that remobilization of legacy P stored in the bottom sediments could sustain the lake's primary productivity on a time scale of several centuries. Finally, while the majority of management practices to reduce excessive algal growth in lakes focus on reducing external P loads, other efforts rely on the addition of reactive materials that sequester P in the sediment. Therefore, we investigated the effectiveness of ferric iron additions in decreasing the dissolved phosphate efflux from the sediment and, consequently, limit phytoplankton growth in Lake Vansjø.
The change in the empirical distribution of future global precipitation is one of the major implications regarding the intensification of global water cycle. Heavier events are expected to occur more often, compensated by decline of light precipitation and/or number of wet days. Here, we scrutinize a new global, high‐resolution precipitation data set, namely, the Multi‐Source Weighted‐Ensemble Precipitation v2.0, to determine changes in the precipitation distribution over land during 1979–2016. To this end, the fluctuations of wet days precipitation quantiles on an annual basis and their interplay with annual totals and number of wet days were investigated. The results show increase in total precipitation, number of wet days, and heavy events over land, as suggested by the intensification hypothesis. However, the decline in light/medium precipitation or wet days was weaker than expected, debating the “compensation” mechanism.
Selenium (Se) enrichment has been demonstrated to vary by several orders of magnitude among species of planktonic algae. This is a substantial source of uncertainty when modelling Se biodynamics in aquatic systems. In addition, Se bioconcentration data are largely lacking for periphytic species of algae, and for multi-species periphyton biofilms, adding to the challenge of modelling Se transfer in periphyton-based food webs. To better predict Se dynamics in periphyton dominated, freshwater ecosystems, the goal of this study was to assess the relative influence of periphyton community composition on the uptake of waterborne Se oxyanions. Naturally grown freshwater periphyton communities, sampled from five different water bodies, were exposed to environmentally relevant concentrations of selenite [Se(IV)] or selenate [Se(VI)] (nominal concentrations of 5 and 25 μg Se L-1) under similar, controlled laboratory conditions for a period of 8 days. Unique periphyton assemblages were derived from the five different field sites, as confirmed by light microscopy and targeted DNA sequencing of the plastid 23S rRNA gene in algae. Selenium accumulation demonstrated a maximum of 23.6-fold difference for Se(IV) enrichment and 2.1-fold difference for Se(VI) enrichment across the periphyton/biofilm assemblages tested. The assemblage from one field site demonstrated both high accumulation of Se(IV) and iron, and was subjected to additional experimentation to elucidate the mechanism(s) of Se accumulation. Selenite accumulation (at nominal concentrations of 5 and 25 μg Se L-1 and mean pH of 7.5 across all treatment replicates) was assessed in both unaltered and heat-killed periphyton, and in periphyton from the same site grown without light to exclude phototrophic organisms. Following an exposure length of 8 days, all periphyton treatments showed similar levels of Se accumulation, indicating that much of the apparent uptake of Se(IV) was due to non-biological processes (i.e., surface adsorption). The results of this study will help reduce uncertainty in the prediction of Se dynamics and food-chain transfer in freshwater environments. Further exploration of the ecological consequences of extracellular adsorption of Se(IV) to periphyton, rather than intracellular absorption, is recommended to further refine predictions related to Se biodynamics in freshwater food webs.
Aquatic organisms are continuously exposed to multiple environmental stressors working cumulatively to alter ecosystems. Wastewater-dominated environments are often riddled by a myriad of stressors, such as chemical and thermal stressors. The objective of this study was to examine the effects of an environmentally relevant concentration of a commonly prescribed antidepressant, venlafaxine (VFX) [1.0 μg/L], in addition to a 5°C increase in water temperature on zebrafish metabolism. Fish were chronically exposed (21 days) to one of four conditions: (i) 0 μg/L VFX at 27°C; (ii) 1.0 μg/L VFX at 27°C; (iii) 0 μg/L VFX at 32°C; (iv) 1.0 μg/L VFX at 32°C. Following exposure, whole-body metabolism was assessed by routine metabolic rate (RMR) measurements, whereas tissue-specific metabolism was assessed by measuring the activities of major metabolic enzymes in addition to glucose levels in muscle. RMR was significantly higher in the multi-stressed group relative to Control. The combination of both stressors resulted in elevated pyruvate kinase activity and glucose levels, while lipid metabolism was depressed, as measured by 3-hydroxyacyl CoA dehydrogenase activity. Citrate synthase activity increased with the onset of temperature, but only in the group treatment without VFX. Catalase activity was also elevated with the onset of the temperature stressor, however, that was not the case for the multi-stressed group, potentially indicating a deleterious effect of VFX on the anti-oxidant defense mechanism. The results of this study highlight the importance of multiple-stressor research, as it able to further bridge the gap between field and laboratory studies, as well as have the potential of yielding surprising results that may have not been predicted using a conventional single-stressor approach.
It is generally acknowledged in the environmental sciences that the choice of a computational model impacts the research results. In this study of a flood and drought event in the Swiss Thur basin, we show that modeling decisions during the model configuration, beyond the model choice, also impact the model results. In our carefully designed experiment we investigated four modeling decisions in ten nested basins: the spatial resolution of the model, the spatial representation of the forcing data, the calibration period, and the performance metric. The flood characteristics were mainly affected by the performance metric, whereas the drought characteristics were mainly affected by the calibration period. The results could be related to the processes that triggered the particular events studied. The impact of the modeling decisions on the simulations did, however, vary among the investigated sub-basins. In spite of the limitations of this study, our findings have important implications for the understanding and quantification of uncertainty in any hydrological or even environmental model. Modeling decisions during model configuration introduce subjectivity from the modeler. Multiple working hypotheses during model configuration can provide insights on the impact of such subjective modeling decisions.
Western North American (WNA) glaciers outside of Alaska cover 14,384 km2 of mountainous terrain. No comprehensive analysis of recent mass change exists for this region. We generated over 15,000 multisensor digital elevation models from spaceborne optical imagery to provide an assessment of mass change for WNA over the period 2000–2018. These glaciers lost 117 ± 42 gigatons (Gt) of mass, which accounts for up to 0.32 ± 0.11 mm of sea level rise over the full period of study. We observe a fourfold increase in mass loss rates between 2000–2009 [−2.9 ± 3.1 Gt yr−1] and 2009–2018 [−12.3 ± 4.6 Gt yr−1], and we attribute this change to a shift in regional meteorological conditions driven by the location and strength of upper level zonal wind. Our results document decadal‐scale climate variability over WNA that will likely modulate glacier mass change in the future.
As more hydroelectric dams regulate rivers to meet growing energy demands, there is ongoing concern about downstream effects, including impacts on downstream benthic macroinvertebrate (BMI) communities. Hydropeaking is a common hydroelectric practice where short‐term variation in power production leads to large and often rapid fluctuations in discharge and water level. There are key knowledge gaps on the ecosystem impacts of hydropeaking in large rivers, the seasonality of these impacts, and whether dams can be managed to lessen impacts. We assessed how patterns of hydropeaking affect abundance, taxonomic richness, and relative tolerance of BMIs in the Saskatchewan River (Saskatchewan, Canada). Reaches immediately (<2 km) downstream of the dam generally had high densities of BMIs and comparable taxonomic diversity relative to upstream locations but were characterized by lower ratios of sensitive (e.g., Ephemeroptera, Plecoptera, and Trichoptera) to tolerant (e.g., Chironomidae) taxa. The magnitude of effect varied with seasonal changes in discharge. Understanding the effects of river regulation on BMI biodiversity and river health has implications for mitigating the impacts of hydropeaking dams on downstream ecosystems. Although we demonstrated that a hydropeaking dam may contribute to a significantly different downstream BMI assemblage, we emphasize that seasonality is a key consideration. The greatest differences between upstream and downstream locations occurred in spring, suggesting standard methods of late summer and fall sampling may underestimate ecosystem‐scale impacts.
In cold region environments, ice‐jam floods (IJFs) pose a severe risk to local communities, economies, and ecosystems. Previous studies have shown that both climate and regulation affect IJF probabilities, but their relative impacts are poorly understood. This study presents a probabilistic modelling framework that couples hydrologic and hydraulic models to assess the relative role of regulated and naturalized flows on ice‐affected backwater staging. The framework is evaluated at an IJF‐prone town on the Peace River in western Canada, which has been regulated since 1972. Naturalized flows were generated for the comparison, and ice‐affected backwater profiles were calculated along jams of varying length and location and for different combinations of model parameters and boundary conditions. Results show significant differences in backwater staging (~2 m for a return period of T = 1:10 year) between two study time periods (1973–1992 vs 1993–2012) as compared with two different hydraulic flow conditions (regulated vs naturalized), suggesting a larger role of climate than regulation in backwater staging. However, regulation was found to offset flood risk during the 1973–1992 period and exacerbate flood risk during the 1993–2012 period.
Climate change and food security are complex global issues that require multidisciplinary approaches to resolve. A nexus exists between both issues, especially in developing countries, but little prior research has successfully bridged the divide. Existing resolutions to climate change and food security are expensive and resource demanding. Climate modelling is at the forefront of climate change literature and development planning, whereas agronomy research is leading food security plans. The Benin Republic and Nigeria have grown and developed in recent years but may not have all the tools required to implement and sustain long-term food security in the face of climate change. The objective of this paper is to describe the development and outputs of a new model that bridges climate change and food security. Data from the Intergovernmental Panel on Climate Change’s 5th Regional Assessment (IPCC AR5) were combined with a biodiversity database to develop the model to derive these outputs. The model was used to demonstrate what potential impacts climate change will have on the regional food security by incorporating agronomic data from four local underutilized indigenous vegetables (Amaranthus cruentus L., Solanum macrocarpon L., Telfairia occidentalis Hook f., and Ocimum gratissimum L.). The model shows that, by 2099, there is significant uncertainty within the optimal recommendations that originated from the MicroVeg project. This suggests that MicroVeg will not have long-term success for food security unless additional options (e.g., new field trials, shifts in vegetable grown) are considered, creating the need for need for more dissemination tools.
Abstract. Calibration is an essential step for improving the accuracy of simulations generated using hydrologic models. A key modeling decision is selecting the performance metric to be optimized. It has been common to use squared error performance metrics, or normalized variants such as Nash–Sutcliffe efficiency (NSE), based on the idea that their squared-error nature will emphasize the estimates of high flows. However, we conclude that NSE-based model calibrations actually result in poor reproduction of high-flow events, such as the annual peak flows that are used for flood frequency estimation. Using three different types of performance metrics, we calibrate two hydrological models at a daily step, the Variable Infiltration Capacity (VIC) model and the mesoscale Hydrologic Model (mHM), and evaluate their ability to simulate high-flow events for 492 basins throughout the contiguous United States. The metrics investigated are (1) NSE, (2) Kling–Gupta efficiency (KGE) and its variants, and (3) annual peak flow bias (APFB), where the latter is an application-specific metric that focuses on annual peak flows. As expected, the APFB metric produces the best annual peak flow estimates; however, performance on other high-flow-related metrics is poor. In contrast, the use of NSE results in annual peak flow estimates that are more than 20 % worse, primarily due to the tendency of NSE to underestimate observed flow variability. On the other hand, the use of KGE results in annual peak flow estimates that are better than from NSE, owing to improved flow time series metrics (mean and variance), with only a slight degradation in performance with respect to other related metrics, particularly when a non-standard weighting of the components of KGE is used. Stochastically generated ensemble simulations based on model residuals show the ability to improve the high-flow metrics, regardless of the deterministic performances. However, we emphasize that improving the fidelity of streamflow dynamics from deterministically calibrated models is still important, as it may improve high-flow metrics (for the right reasons). Overall, this work highlights the need for a deeper understanding of performance metric behavior and design in relation to the desired goals of model calibration.
Abstract. Permafrost strongly controls hydrological processes in cold regions. Our understanding of how changes in seasonal and perennial frozen ground disposition and linked storage dynamics affect runoff generation processes remains limited. Storage dynamics and water redistribution are influenced by the seasonal variability and spatial heterogeneity of frozen ground, snow accumulation and melt. Stable isotopes are potentially useful for quantifying the dynamics of water sources, flow paths and ages, yet few studies have employed isotope data in permafrost-influenced catchments. Here, we applied the conceptual model STARR (the Spatially distributed Tracer-Aided Rainfall–Runoff model), which facilitates fully distributed simulations of hydrological storage dynamics and runoff processes, isotopic composition and water ages. We adapted this model for a subarctic catchment in Yukon Territory, Canada, with a time-variable implementation of field capacity to include the influence of thaw dynamics. A multi-criteria calibration based on stream flow, snow water equivalent and isotopes was applied to 3 years of data. The integration of isotope data in the spatially distributed model provided the basis for quantifying spatio-temporal dynamics of water storage and ages, emphasizing the importance of thaw layer dynamics in mixing and damping the melt signal. By using the model conceptualization of spatially and temporally variable storage, this study demonstrates the ability of tracer-aided modelling to capture thaw layer dynamics that cause mixing and damping of the isotopic melt signal.
In modern days, mobile applications (apps) have become omnipresent. Components of mobile apps (such as 3rd party libraries) require to be separated and analyzed differently for security issue detection, repackaged app detection, tumor code purification and so on. Various techniques are available to automatically analyze mobile apps. However, analysis of the app's executable binary remains challenging due to required curated database, large codebases and obfuscation. Considering these, we focus on exploring a versatile technique to separate different components with design-based features independent of code obfuscation. Particularly, we conducted an empirical study using design patterns and fuzzy signatures to separate app components such as 3rd party libraries. In doing so, we built a system for automatically extracting design patterns from both the executable package (APK) and Jar of an Android application. The experimental results with various standard datasets containing 3rd party libraries, obfuscated apps and malwares reveal that design features like these are present significantly within them (within 60% APKs including malware). Moreover, these features remain unaltered even after app obfuscation. Finally, as a case study, we found that the design patterns alone can detect 3rd party libraries within the obfuscated apps considerably (F1 score is 32%). Overall, our empirical study reveals that design features might play a versatile role in separating various Android components for various purposes.
Abstract With the era of big data approaching, the number of software systems, their dependencies, as well as the complexity of the individual system is becoming larger and more intricate. Understanding these evolving software systems is thus a primary challenge for cost-effective software management and maintenance. In this paper we perform a case study with evolving code clones. The programmers often need to manually analyze the co-evolution of clone fragments to decide about refactoring, tracking, and bug removal. However, manual analysis is time consuming, and nearly infeasible for a large number of clones, e.g., with millions of similarity pairs, where clones are evolving over hundreds of software revisions. We propose an interactive visual analytics system, Clone-World, which leverages big data visualization approach to manage code clones in large software systems. Clone-World, gives an intuitive yet powerful solution to the clone analytic problems. Clone-World combines multiple information-linked zoomable views, where users can explore and analyze clones through interactive exploration in real time. User studies and experts’ reviews suggest that Clone-World may assist developers in many real-life software development and maintenance scenarios. We believe that Clone-World will ease the management and maintenance of clones, and inspire future innovation to adapt visual analytics to manage big software systems.
Abstract Code clones are identical or nearly similar code fragments in a code-base. According to the existing studies, code clones are directly related to bugs. Code cloning, creating code clones, is suspected to propagate temporarily hidden bugs from one code fragment to another. However, there is no study on the intensity of bug-propagation through code cloning. In this paper, we define two clone evolutionary patterns that reasonably indicate bug propagation through code cloning. By analyzing software evolution history, we identify those code clones that evolved following the bug propagation patterns. According to our study on thousands of commits of seven subject systems, overall 18.42% of the clone fragments that experience bug-fixes contain propagated bugs. Type-3 clones are primarily involved with bug-propagation. Bug propagation is more likely to occur in the clone fragments that are created in the same commit rather than in different commits. Moreover, code clones residing in the same file have a higher possibility of containing propagated bugs compared to those residing in different files. Severe bugs can sometimes get propagated through code cloning. Automatic support for immediately identifying occurrences of bug-propagation can be beneficial for software maintenance. Our findings are important for prioritizing code clones for management.
The identical or nearly similar code fragments in a code-base are called code clones. There is a common belief that code cloning (copy/pasting code fragments) can introduce bugs in a software system if the copied code fragments are not properly adapted to their contexts (i.e., surrounding code). However, none of the existing studies have investigated whether such bugs are really present in code clones. We denote these bugs as Context Adaptation Bugs, or simply Context-Bugs, in our paper and investigate the extent to which they can be present in code clones. We define and automatically analyze two clone evolutionary patterns that indicate fixing of Context-Bugs. According to our analysis on thousands of revisions of six open-source subject systems written in Java, C, and C#, code cloning often introduces Context-Bugs in software systems. Around 50% of the clone related bug-fixes can occur for fixing Context-Bugs. Cloning (copy/pasting) a newly created code fragment (i.e., a code fragment that was not added in a former revision) is more likely to introduce Context-Bugs compared to cloning a preexisting fragment (i.e., a code fragment that was added in a former revision). Moreover, cloning across different files appears to have a significantly higher tendency of introducing Context-Bugs compared to cloning within the same file. Finally, Type 3 clones (gapped clones) have the highest tendency of containing Context-Bugs among the three major clone-types. Our findings can be important for early detection as well as removal of Context-Bugs in code clones.
It has been proposed that Mg2+ and Fe2+ are very similar in interacting with ribozymes and some protein-based enzymes, but their activities with DNAzymes have yet to be studied. Here, the activity of Fe2+ as cofactor for a few RNA-cleaving DNAzymes is investigated. 17E is a well-studied DNAzyme that is active in the presence of many different divalent metal ions; it is highly active with Fe2+ with an apparent Kd of 29.7±2.3 μm and a kobs of 1.12±0.11 min-1 in the presence of 1 mm Fe2+ at pH 7.5. Fe2+ has 21-fold higher activity than Mg2+ . Six different DNAzymes are then tested, and only the DNAzymes active with Mg2+ (17E, 8-17, and E5) are active with Fe2+ . Fe2+ has 25 and one- to twofold higher activity than Mg2+ for the 8-17 and E5 DNAzymes, respectively. In pH>7 buffer and in presence of air, 1 mm Fe2+ results in a nonspecific degradation of the DNA strand due to reactive oxygen species (ROS). Cleavage reactions in anoxic environment and antioxidant ascorbate can be used to overcome the effect of oxidation. The findings provide insights for potential DNAzyme catalysis in the early Earth, and they further support the similarity between Mg2+ and Fe2+ in enzyme catalysis.
• A physically-based semi-distributed hydrological model and a 1D stream water temperature model forced by climate change scenarios is presented here to analyze the effects of stream flow and water temperature changes on fish habitat in the Athabasca River catchment. • Streamflow decreases in most of the catchment will reduce flow velocities and water depths causing current Athabasca Rainbow Trout habitat to be suboptimal. • Increases in water temperature will result in habitat contraction concentrating Athabasca Rainbow Trout in the upper headwaters of the catchment. • Athabasca Rainbow Trout habitat can potentially be reduced as the frequency of occurrence of life threatening and lethal water temperatures tend to increase, particularly in summer. Changes to natural flow and air temperature in the context of climate change can have impacts on physiology, distribution and survival of fish. Of particular interest is the Athabasca River basin, a highly biologically productive basin that includes one of the largest boreal freshwater inland river deltas in the world and serves as habitat for many fish species. Earlier melt events, higher winter and spring flows and lower summer flows are expected as a consequence of climate change in this basin. Here, we model changes in river flow and water temperature under changing climate scenarios through the integration of a physically-based semi-distributed hydrological model and a 1D stream water temperature model forced by climate change scenarios. The modeled changes in streamflow and water temperature are used to predict changes in habitat suitability for the Athabasca Rainbow Trout (ART) ( Oncorhynchus mykiss ), a unique ecotype of trout considered as a ‘species at risk’. The results indicate that future flow decreases in most of the basin can lead to reduced flow velocities and water depths making current ART habitat suboptimal. Also, warming low-land habitats and increasing water temperatures will increase metabolic rates and stress fish forcing them to migrate upstream to cooler waters confining their habitat range.
If the aim of flood risk management (FRM) is to increase society’s resilience to floods, then a holistic treatment of flood risk is required that addresses flood prevention, defence, mitigation, preparation, and response and recovery. Progressing resilience-based management to flood risk requires both diversity and coordination of policy across multiple jurisdictions. Decision makers and the types of FRM policy decisions they make play a key role in implementing FRM policies and strategies that progress flood resilience. This paper explores how policy preferences held by FRM decision makers relate to the characteristics of resilient FRM policy. The research was conducted in three flood-prone provinces in western Canada using a multi-criteria analytical approach. The results show that while decision maker FRM priorities are similar across the Canadian Prairies, their preferred FRM policies differ. Further, preferred FRM policies were largely resistance-based and influenced at least as much by flood experiences and perceptions of flood risk as by more obvious administrative pressures such as cost, public acceptability, and environmental protection. Several observations emerge from these results for advancing a coordinated, diversified approach to FRM which is required for resilience, both for western Canada and for FRM more broadly.
Scientific Workflow Management Systems (SWfMSs) have become popular for accelerating the specification, execution, visualization, and monitoring of data-intensive scientific experiments. Unfortunately, to the best of our knowledge no existing SWfMSs directly support collaboration. Data is increasing in complexity, dimensionality, and volume, and the efficient analysis of data often goes beyond the realm of an individual and requires collaboration with multiple researchers from varying domains. In this paper, we propose a groupware system architecture for data analysis that in addition to supporting collaboration, also incorporates features from SWfMSs to support modern data analysis processes. As a proof of concept for the proposed architecture we developed SciWorCS - a groupware system for scientific data analysis. We present two real-world use-cases: collaborative software repository analysis and bioinformatics data analysis. The results of the experiments evaluating the proposed system are promising. Our bioinformatics user study demonstrates that SciWorCS can leverage real-world data analysis tasks by supporting real-time collaboration among users.
A code clone is a pair of similar code fragments, within or between software systems. To detect each possible clone pair from a software system while handling the complex code structures, the clone detection tools undergo a lot of generalization of the original source codes. The generalization often results in returning code fragments that are only coincidentally similar and not considered clones by users, and hence requires manual validation of the reported possible clones by users which is often both time-consuming and challenging. In this paper, we propose a machine learning based tool 'CloneCognition' (Open Source Codes: https://github.com/pseudoPixels/CloneCognition ; Video Demonstration: https://www.youtube.com/watch?v=KYQjmdr8rsw) to automate the laborious manual validation process. The tool runs on top of any code clone detection tools to facilitate the clone validation process. The tool shows promising clone classification performance with an accuracy of up to 87.4%. The tool also exhibits significant improvement in the results when compared with state-of-the-art techniques for code clone validation.
Software clones are detrimental to software maintenance and evolution and as a result many clone detectors have been proposed. These tools target clone detection in software applications written in a single programming language. However, a software application may be written in different languages for different platforms to improve the application's platform compatibility and adoption by users of different platforms. Cross language clones (CLCs) introduce additional challenges when maintaining multi-platform applications and would likely go undetected using existing tools. In this paper, we propose CLCDSA, a cross language clone detector which can detect CLCs without extensive processing of the source code and without the need to generate an intermediate representation. The proposed CLCDSA model analyzes different syntactic features of source code across different programming languages to detect CLCs. To support large scale clone detection, the CLCDSA model uses an action filter based on cross language API call similarity to discard non-potential clones. The design methodology of CLCDSA is two-fold: (a) it detects CLCs on the fly by comparing the similarity of features, and (b) it uses a deep neural network based feature vector learning model to learn the features and detect CLCs. Early evaluation of the model observed an average precision, recall and F-measure score of 0.55, 0.86, and 0.64 respectively for the first phase and 0.61, 0.93, and 0.71 respectively for the second phase which indicates that CLCDSA outperforms all available models in detecting cross language clones.
Developers often reuse code snippets from online forums, such as Stack Overflow, to learn API usages of software frameworks or libraries. These code snippets often contain ambiguous undeclared external references. Such external references make it difficult to learn and use those APIs correctly. In particular, reusing code snippets containing such ambiguous undeclared external references requires significant manual efforts and expertise to resolve them. Manually resolving fully qualified names (FQN) of API elements is a non-trivial task. In this paper, we propose a novel context-sensitive technique, called COSTER, to resolve FQNs of API elements in such code snippets. The proposed technique collects locally specific source code elements as well as globally related tokens as the context of FQNs, calculates likelihood scores, and builds an occurrence likelihood dictionary (OLD). Given an API element as a query, COSTER captures the context of the query API element, matches that with the FQNs of API elements stored in the OLD, and rank those matched FQNs leveraging three different scores: likelihood, context similarity, and name similarity scores. Evaluation with more than 600K code examples collected from GitHub and two different Stack Overflow datasets shows that our proposed technique improves precision by 4-6% and recall by 3-22% compared to state-of-the-art techniques. The proposed technique significantly reduces the training time compared to the StatType, a state-of-the-art technique, without sacrificing accuracy. Extensive analyses on results demonstrate the robustness of the proposed technique.

DOI bib
Large loss of CO2 in winter observed across the northern permafrost region
Susan M. Natali, Jennifer D. Watts, Brendan M. Rogers, Stefano Potter, S. Ludwig, A. K. Selbmann, Patrick F. Sullivan, Benjamin W. Abbott, Kyle A. Arndt, Leah Birch, Mats Björkman, A. Anthony Bloom, Gerardo Celis, Torben R. Christensen, Casper T. Christiansen, R. Commane, Elisabeth J. Cooper, Patrick Crill, C. I. Czimczik, S. P. Davydov, Jinyang Du, Jocelyn Egan, Bo Elberling, Eugénie Euskirchen, Thomas Friborg, Hélène Genet, Mathias Göckede, Jordan P. Goodrich, Paul Grogan, Manuel Helbig, Elchin Jafarov, Julie Jastrow, Aram Kalhori, Yongwon Kim, John S. Kimball, Lars Kutzbach, Mark J. Lara, Klaus Steenberg Larsen, Bang Yong Lee, Zhihua Liu, M. M. Loranty, Magnus Lund, Massimo Lupascu, Nima Madani, Avni Malhotra, Roser Matamala, J. W. Mcfarland, A. David McGuire, Anders Michelsen, C. Minions, Walter C. Oechel, David Olefeldt, Frans‐Jan W. Parmentier, Norbert Pirk, Benjamin Poulter, William L. Quinton, Fereidoun Rezanezhad, David Risk, Torsten Sachs, Kevin Schaefer, Niels Martin Schmidt, Edward A. G. Schuur, Philipp Semenchuk, Gaius R. Shaver, Oliver Sonnentag, Gregory Starr, Claire C. Treat, Mark P. Waldrop, Yihui Wang, Jeffrey M. Welker, Christian Wille, Xiaofeng Xu, Zhen Zhang, Qianlai Zhuang, Donatella Zona
Nature Climate Change, Volume 9, Issue 11

Recent warming in the Arctic, which has been amplified during the winter1-3, greatly enhances microbial decomposition of soil organic matter and subsequent release of carbon dioxide (CO2)4. However, the amount of CO2 released in winter is highly uncertain and has not been well represented by ecosystem models or by empirically-based estimates5,6. Here we synthesize regional in situ observations of CO2 flux from arctic and boreal soils to assess current and future winter carbon losses from the northern permafrost domain. We estimate a contemporary loss of 1662 Tg C yr-1 from the permafrost region during the winter season (October through April). This loss is greater than the average growing season carbon uptake for this region estimated from process models (-1032 Tg C yr-1). Extending model predictions to warmer conditions in 2100 indicates that winter CO2 emissions will increase 17% under a moderate mitigation scenario-Representative Concentration Pathway (RCP) 4.5-and 41% under business-as-usual emissions scenario-RCP 8.5. Our results provide a new baseline for winter CO2 emissions from northern terrestrial regions and indicate that enhanced soil CO2 loss due to winter warming may offset growing season carbon uptake under future climatic conditions.
The exchanges of water, energy and carbon between the land surface and the atmosphere are tightly coupled, so that errors in simulating evapotranspiration lead to errors in simulating both the water and carbon balances. Areas with seasonally frozen soils present a particular challenge due to the snowmelt‐dominated hydrology and the impact of soil freezing on the soil hydraulic properties and plant root water uptake. Land surface schemes that have been applied in high latitudes often have reported problems with simulating the snowpack and runoff. Models applied at the Boreal Ecosystem Research and Monitoring Sites in central Saskatchewan have consistently over‐predicted evapotranspiration as compared with flux tower estimates. We assessed the performance of two Canadian land surface schemes (CLASS and CLASS‐CTEM) for simulating point‐scale evapotranspiration at an instrumented jack pine sandy upland site in the southern edge of the boreal forest in Saskatchewan, Canada. Consistent with past reported results, these models over‐predicted evapotranspiration, as compared with flux tower observations, but only in the spring period. Looking systematically at soil properties and vegetation characteristics, we found that the dominant control on evapotranspiration within these models was the canopy conductance. However, the problem of excessive spring ET could not be solved satisfactorily by changing the soil or vegetation parameters. The model overestimation of spring ET coincided with the overestimation of spring soil liquid water content. Improved algorithms for the infiltration of snowmelt into frozen soils and plant‐water uptake during the snowmelt and soil thaw periods may be key to addressing the biases in spring ET.
Abstract Extremes are rare and unexpected. This limits observations and constrains our knowledge on their predictability and behavior. Graphical tools are among the many methods developed to study extremes. A major weakness is that they rely on visual-inspection inferences which are subjective and make applications to large datasets time-consuming and impractical. Here, we advance a graphical method, the so-called Mean Excess Function (MEF), into an algorithmic procedure. MEF investigates the mean value of a variable over threshold, and thus, focuses on extremes. We formulate precise and easy-to-apply statistical tests, based on the MEF, to assess if observed data can be described by exponential or heavier tails. As a real-world example, we apply our method in 21,348 daily precipitation records from all over the globe. Results show that the exponential-tail hypothesis is rejected in 75.8% of the records indicating that heavy-tail distributions (alternative hypothesis) can better describe rainfall extremes. The spatial variation of the tail heaviness reveals that heavy tails prevail in regions of Australia and Eurasia, with a “hot spot” found in central Russia and Kazakhstan. We deem this study offers a new diagnostic tool in assessing the behavior of extremes, easy to apply in large databases, and for any variable of interest. Our results on precipitation extremes reinforce past findings and further highlight that exponential tails should be used with caution.
Abstract This study presents a gridded meteorology intercomparison using the State of Hawaii as a testbed. This is motivated by the goal to provide the broad user community with knowledge of interproduct differences and the reasons differences exist. More generally, the challenge of generating station-based gridded meteorological surfaces and the difficulties in attributing interproduct differences to specific methodological decisions are demonstrated. Hawaii is a useful testbed because it is traditionally underserved, yet meteorologically interesting and complex. In addition, several climatological and daily gridded meteorology datasets are now available, which are used extensively by the applications modeling community, thus an intercomparison enhances Hawaiian specific capabilities. We compare PRISM climatology and three daily datasets: new datasets from the University of Hawai‘i and the National Center for Atmospheric Research, and Daymet version 3 for precipitation and temperature variables only. General conclusions that have emerged are 1) differences in input station data significantly influence the product differences, 2) explicit prediction of precipitation occurrence is crucial across multiple metrics, and 3) attribution of differences to specific methodological choices is difficult and limits the usefulness of intercomparisons. Because generating gridded meteorological fields is an elaborate process with many methodological choices interacting in complex ways, future work should 1) develop modular frameworks that allows users to easily examine the breadth of methodological choices, 2) collate available nontraditional high-quality observational datasets for true out-of-sample validation and make them publicly available, and 3) define benchmarks of acceptable performance for methodological components and products.
Abstract Numerical simulations of snow water equivalent (SWE) in mountain systems can be biased, and few SWE observations have existed over large domains. New approaches for measuring SWE, like NASA’s ultra-high-resolution Airborne Snow Observatory (ASO), offer an opportunity to improve model estimates by providing a high-quality validation target. In this study, a computationally efficient snow data assimilation (DA) approach over the western United States at 1.75-km spatial resolution for water years (WYs) 2001–17 is presented. A local ensemble transform Kalman filter implemented as a batch smoother is used with the VIC hydrology model to assimilate the remotely sensed daily MODIS fractional snow-covered area (SCA). Validation of the high-resolution SWE estimates is done against ASO SWE data in the Tuolumne basin (California), Uncompahgre basin (Colorado), and Olympic Peninsula (Washington). Results indicate good performance in dry years and during melt, with DA reducing Tuolumne basin-average SWE percent differences from −68%, −92%, and −84% in open loop to 0.6%, 25%, and 3% after DA for WYs 2013–15, respectively, for ASO dates and spatial extent. DA also improved SWE percent difference over the Uncompahgre basin (−84% open loop, −65% DA) and Olympic Peninsula (26% open loop, −0.2% DA). However, in anomalously wet years DA underestimates SWE, likely due to an inadequate snow depletion curve parameterization. Despite potential shortcomings due to VIC model setup (e.g., water balance mode) or parameterization (snow depletion curve), the DA framework implemented in this study shows promise in overcoming some of these limitations and improving estimated SWE, in particular during drier years or at higher elevations, when most in situ observations cannot capture high-elevation snowpack due to lack of stations there.
Specific ranges of dissolved oxygen (DO) concentrations must be maintained in a waterbody for it to be hospitable for aquatic animals. DO sensor designs can employ selectively permeable membranes to isolate DO from untargeted compounds or organisms in waterbodies. Hence, the DO concentration can be monitored and the health of the water can be evaluated over time. However, the presence of bacteria in natural waterbodies can lead to the formation of biofilms that can block pores and prevent analyte from permeating the membrane, resulting in inaccurate readings. In this work, we demonstrate the implementation of a fluorosilane-based omniphobic lubricant-infused (OLI) coating on a selectively permeable membrane and investigate the rate of biofilm formation for a commercially available DO sensor. Coated and unmodified membranes were incubated in an environment undergoing accelerated bacterial growth, and the change in sensitivity was evaluated after 40, 100, 250, and 500 h. Our findings show that the OLI membranes attenuate biofouling by 70% and maintain sensitivity after 3 weeks of incubation, further demonstrating that oxygen transfer through the OLI coating is achievable. Meanwhile, unmodified membranes exhibit significant biofouling that results in a 3.35 higher rate of decay in oxygen measurement sensitivity and an over 70% decrease in static contact angle. These results show that the OLI coating can be applied on commercially available membranes to prevent biofouling. Therefore, OLI coatings are a suitable candidate to suppress biofilm formation in the widespread use of selectively permeable membranes for environmental, medical, and fluid separation applications.
Abstract This paper provides a comprehensive review of two decades of published research that applies different economic approaches to address forested watershed management decisions. The review takes stock of the applied integrated economic and ecohydrological modeling approaches and assesses the way these approaches capture the complexities involved when linking ecohydrological and economic systems. The implications of integrating watershed services into forest management decisions are discussed, lessons are drawn from existing approaches and future research needs identified. Existing modeling approaches are categorized from independent modular models with a unidirectional flow of information to fully coupled holistic models, and are analyzed, among others, in terms of the efficiency improvement that forest-based investments achieve in watershed services provision. The review shows that the number of studies investigating the relationship between forest management and watershed services in economic decision-support models is very limited. Only 14 studies that were identified examine these relationships for water supply, while 9 studies were found to focus on the impact on water quality, 2 of which addressed water quality in combination with water supply. A shortcoming is that about half of the studies do not clearly specify baseline conditions to test the incremental value of the evaluated forest management actions in terms of watershed services provision, which undermines evaluating their cost-effectiveness or economic efficiency. A promising finding is nevertheless that in 8 of the 10 studies where these relationships were evaluated in terms of their costs and benefits compared to a specified baseline alternative, forest conservation or forest management is shown to be an economically efficient nature-based solution to supply the watershed services of interest. The limited availability of geo-referenced data and information, including the often complex and confidential nature of cost and price data, and the high data demands of more advanced spatial econometric models are among the main barriers to address relevant forest and water economic interactions. Important future extensions of existing integrated approaches include the further coupling of more detailed ecohydrological models and multi-sectoral hydro-economic models that are able to account for the different risks (floods, droughts, wildfires) and uncertainties under climate change and their impact on watershed services and water security.
Thawing permafrost could release large amounts of carbon into the atmosphere, but finding out how much requires better collection and curation of data.
This study evaluates the applicability of the chloride mass balance (CMB) method for groundwater recharge estimation in a semi-arid region in Canada, where recharge largely occurs under topographic depressions. The CMB applicability was tested at three scales: point-scale recharge rates at different topographical positions; average recharge rates incorporating multiple topographical positions on a local scale; and an identification of spatial trends of recharge on a regional scale. Agricultural chloride inputs were shown to be a major factor affecting chloride concentrations at all three scales, where elevated chloride concentrations in the shallow subsurface affected by agricultural inputs surpassed background concentrations by an order of magnitude. The propagation depth of elevated concentrations varied among study sites from being largely confined to the unsaturated zone to extending well into the saturated zone. Lateral chloride redistribution further affected the CMB applicability for point-scale recharge rates. Specific solutions enabling the CMB application in these conditions are presented, including runoff concentration measurements for point-scale estimates, using groundwater age tracers on a local scale, and using the harmonic mean concentration of a large number of samples on a regional scale.
Abstract. Seasonal measurements of glacier mass balance provide insight into the relation between climate forcing and glacier change. To evaluate the feasibility of using remotely sensed methods to assess seasonal balance, we completed tandem airborne laser scanning (ALS) surveys and field-based glaciological measurements over a 4-year period for six alpine glaciers that lie in the Columbia and Rocky Mountains, near the headwaters of the Columbia River, British Columbia, Canada. We calculated annual geodetic balance using coregistered late summer digital elevation models (DEMs) and distributed estimates of density based on surface classification of ice, snow, and firn surfaces. Winter balance was derived using coregistered late summer and spring DEMs, as well as density measurements from regional snow survey observations and our glaciological measurements. Geodetic summer balance was calculated as the difference between winter and annual balance. Winter mass balance from our glaciological observations averaged 1.95±0.09 m w.e. (meter water equivalent), 4 % larger than those derived from geodetic surveys. Average glaciological summer and annual balance were 3 % smaller and 3 % larger, respectively, than our geodetic estimates. We find that distributing snow, firn, and ice density based on surface classification has a greater influence on geodetic annual mass change than the density values themselves. Our results demonstrate that accurate assessments of seasonal mass change can be produced using ALS over a series of glaciers spanning several mountain ranges. Such agreement over multiple seasons, years, and glaciers demonstrates the ability of high-resolution geodetic methods to increase the number of glaciers where seasonal mass balance can be reliably estimated.
Abstract. Natural wetlands constitute the largest and most uncertain source of methane (CH4) to the atmosphere and a large fraction of them are found in the northern latitudes. These emissions are typically estimated using process (“bottom-up”) or inversion (“top-down”) models. However, estimates from these two types of models are not independent of each other since the top-down estimates usually rely on the a priori estimation of these emissions obtained with process models. Hence, independent spatially explicit validation data are needed. Here we utilize a random forest (RF) machine-learning technique to upscale CH4 eddy covariance flux measurements from 25 sites to estimate CH4 wetland emissions from the northern latitudes (north of 45∘ N). Eddy covariance data from 2005 to 2016 are used for model development. The model is then used to predict emissions during 2013 and 2014. The predictive performance of the RF model is evaluated using a leave-one-site-out cross-validation scheme. The performance (Nash–Sutcliffe model efficiency =0.47) is comparable to previous studies upscaling net ecosystem exchange of carbon dioxide and studies comparing process model output against site-level CH4 emission data. The global distribution of wetlands is one major source of uncertainty for upscaling CH4. Thus, three wetland distribution maps are utilized in the upscaling. Depending on the wetland distribution map, the annual emissions for the northern wetlands yield 32 (22.3–41.2, 95 % confidence interval calculated from a RF model ensemble), 31 (21.4–39.9) or 38 (25.9–49.5) Tg(CH4) yr−1. To further evaluate the uncertainties of the upscaled CH4 flux data products we also compared them against output from two process models (LPX-Bern and WetCHARTs), and methodological issues related to CH4 flux upscaling are discussed. The monthly upscaled CH4 flux data products are available at https://doi.org/10.5281/zenodo.2560163 (Peltola et al., 2019).
Abstract. The phase of precipitation and its distribution at the surface can affect water resources and the regional water cycle of a region. A field project was held in March–April 2015 on the eastern slope of the Canadian Rockies to document precipitation characteristics and associated atmospheric conditions. During the project, 60 % of the particles documented were rimed in relatively warm and dry conditions. Rain–snow transitions also occurred aloft and at the surface in sub-saturated conditions. Ice-phase precipitation falling through a saturated atmospheric layer with temperatures > 0 ∘C will start melting. In contrast, if the melting layer is sub-saturated, the ice-phase precipitation undergoes sublimation, which increases the depth of the rain–snow transition. In this context, this study investigates the role of sublimation and riming in precipitation intensity and type reaching the surface in the Kananaskis Valley, Alberta, during March–April 2015. To address this, a set of numerical simulations of an event of mixed precipitation observed at the surface was conducted. This event on 31 March 2015 was documented with a set of devices at the main observation site (Kananaskis Emergency Services, KES), including a precipitation gauge, disdrometer, and micro rain radar. Sensitivity experiments were performed to assess the impacts of temperature changes from sublimation and the role of the production of graupel (riming) aloft in the surface precipitation evolution. A warmer environment associated with no temperature changes from sublimation leads to a peak in the intensity of graupel at the surface. When the formation of graupel is not considered, the maximum snowfall rate occurred at later times. Results suggest that unrimed snow reaching the surface is formed on the western flank and is advected eastward. In contrast, graupel would form aloft in the Kananaskis Valley. The cooling from sublimation and melting by rimed particles increases the vertical shear near KES. Overall, this study illustrated that the presence of graupel influenced the surface evolution of precipitation type in the valley due to the horizontal transport of precipitation particles.
The leaky pipeline phenomenon refers to the disproportionate decline of female scientists at higher academic career levels and is a major problem in the natural sciences. Identifying the underlying causes is challenging, and thus, solving the problem remains difficult. To better understand the reasons for the leaky pipeline, we assess the perceptions and impacts of gender bias and imbalance—two major drivers of the leakage—at different academic career levels with an anonymous survey in geoscience academia (n=1,220). The survey results show that both genders view male geoscientists as substantially more gender biased than female scientists. Moreover, female geoscientists are more than twice as likely to experience negative gender bias at their workplaces and scientific organizations compared to male geoscientists. There are also pronounced gender differences regarding (i) the relevance of role models, (ii) family-friendly working conditions, and (iii) the approval of gender quotas for academic positions. Given the male dominance in senior career levels, our results emphasize that those feeling less impacted by the negative consequences of gender bias and imbalance are the ones in position to tackle the problem. We thus call for actions to better address gender biases and to ensure a balanced gender representation at decision-making levels to ultimately retain more women in geoscience academia.
Abstract. Scotty Creek, Northwest Territories (NWT), Canada, has been the focus of hydrological research for nearly three decades. Over this period, field and modelling studies have generated new insights into the thermal and physical mechanisms governing the flux and storage of water in the wetland-dominated regions of discontinuous permafrost that characterises much of the Canadian and circumpolar subarctic. Research at Scotty Creek has coincided with a period of unprecedented climate warming, permafrost thaw, and resulting land cover transformations including the expansion of wetland areas and loss of forests. This paper (1) synthesises field and modelling studies at Scotty Creek, (2) highlights the key insights of these studies on the major water flux and storage processes operating within and between the major land cover types, and (3) provides insights into the rate and pattern of the permafrost-thaw-induced land cover change and how such changes will affect the hydrology and water resources of the study region.
This study presents diagnostic evaluation of two large‐domain hydrologic models: the mesoscale Hydrologic Model (mHM) and the Variable Infiltration Capacity (VIC) over the contiguous United States (CONUS). These models have been calibrated using the Multiscale Parameter Regionalization scheme in a joint, multibasin approach using 492 medium‐sized basins across the CONUS yielding spatially distributed model parameter sets. The mHM simulations are used as a performance benchmark to examine performance deficiencies in the VIC model. We find that after calibration to streamflow, VIC generally overestimates the magnitude and temporal variability of evapotranspiration (ET) as compared to mHM as well as the FLUXNET observation‐based ET product, resulting in underestimation of the mean and variability of runoff. We perform a controlled calibration experiment to investigate the effect of varying number of transfer function parameters in mHM and to enable a fair comparison between both models (14 and 48 for mHM vs. 14 for VIC). Results of this experiment show similar behavior of mHM with 14 and 48 parameters. Furthermore, we diagnose the internal functioning of the VIC model by looking at the relationship of the evaporative fraction versus the degree of soil saturation and compare it with that of the mHM model, which has a different model structure, a prescribed nonlinear relationship between these variables and exhibits better model skill than VIC. Despite these limitations, the VIC‐based CONUS‐wide calibration constrained against streamflow exhibits better ET skill as compared to two preexisting independent VIC studies.
Abstract How mountain hydrology at different elevations will respond to climate change is a challenging question of great importance to assessing changing water resources. Here, three North American Cordilleran snow-dominated basins—Wolf Creek, Yukon; Marmot Creek, Alberta; and Reynolds Mountain East, Idaho—each with good meteorological and hydrological records, were modeled using the physically based, spatially distributed Cold Regions Hydrological Model. Model performance was verified using field observations and found adequate for diagnostic analysis. To diagnose the effects of future climate, the monthly temperature and precipitation changes projected for the future by 11 regional climate models for the mid-twenty-first century were added to the observed meteorological time series. The modeled future was warmer and wetter, increasing the rainfall fraction of precipitation and shifting all three basins toward rainfall–runoff hydrology. This shift was largest at lower elevations and in the relatively warmer Reynolds Mountain East. In the warmer future, there was decreased blowing snow transport, snow interception and sublimation, peak snow accumulation, and melt rates, and increased evapotranspiration and the duration of the snow-free season. Annual runoff in these basins did not change despite precipitation increases, warming, and an increased prominence of rainfall over snowfall. Reduced snow sublimation offset reduced snowfall amounts, and increased evapotranspiration offset increased rainfall amounts. The hydrological uncertainty due to variation among climate models was greater than the predicted hydrological changes. While the results of this study can be used to assess the vulnerability and resiliency of water resources that are dependent on mountain snow, stakeholders and water managers must make decisions under considerable uncertainty, which this paper illustrates.
Abstract VARS-TOOL is a software toolbox for sensitivity and uncertainty analysis. Developed primarily around the “Variogram Analysis of Response Surfaces” framework, VARS-TOOL adopts a multi-method approach that enables simultaneous generation of a range of sensitivity indices, including ones based on derivative, variance, and variogram concepts, from a single sample. Other special features of VARS-TOOL include (1) novel tools for time-varying and time-aggregate sensitivity analysis of dynamical systems models, (2) highly efficient sampling techniques, such as Progressive Latin Hypercube Sampling (PLHS), that maximize robustness and rapid convergence to stable sensitivity estimates, (3) factor grouping for dealing with high-dimensional problems, (4) visualization for monitoring stability and convergence, (5) model emulation for handling model crashes, and (6) an interface that allows working with any model in any programming language and operating system. As a test bed for training and research, VARS-TOOL provides a set of mathematical test functions and the (dynamical) HBV-SASK hydrologic model.
Recent human-interface wildfires around the world have raised concerns regarding the reliability of freshwater supply flowing from severely burned watersheds. Degraded source water quality can often be expected after severe wildfire and can pose challenges to drinking water facilities by straining treatment response capacities, increasing operating costs, and jeopardizing their ability to supply consumers. Identifying source watersheds that are dangerously exposed to post-wildfire hydrologic changes is important for protecting community drinking-water supplies from contamination risks that may lead to service disruptions. This study presents a spatial index of watershed exposure to wildfires in the province of Alberta, Canada, where growing water demands coupled with increasing fire activity threaten municipal drinking-water supplies. Using a multi-criteria analysis design, we integrated information regarding provincial forest cover, fire danger, source water volume, source-water origin (i.e., forested/un-forested), and population served. We found that (1) >2/3 of the population of the province relies on drinking-water supplies originating in forested watersheds, (2) forest cover is the most important variable controlling final exposure scores, and (3) watersheds supplying small drinking water treatment plants are particularly exposed, especially in central Alberta. The index can help regional authorities prioritize the allocation of risk management resources to mitigate adverse impacts from wildfire. The flexible design of this tool readily allows its deployment at larger national and continental scales to inform broader water security frameworks.
• Little is known about the uptake of the ES concept into policy at a global scale. • GlobaLDES is an open-access database recording ES policy documents. • The database is crowdsourced by learners of a free online ES course. • 136 relevant documents were analyzed, 60% of them were originally not in English. • Many entries refer to multiple ES at once, with an accelerating uptake since 2011. The ecosystem services (ES) concept has gained traction amongst stakeholders involved in environmental regulation, yet little is known about the extent to which the ES concept has been translated into public policy. Here, we present a new online database of policy documents related to ES: GlobaLDES ( https://tinyurl.com/GlobalDES ). The database was created in 2016 and compiled through a crowdsourced process. Learners involved in a Massive Open Online Course (MOOC) were invited to submit documents that explicitly refer to ES. We included in our analysis documents related to laws, regulations, ordonnances, tax incentives, certification, and strategic planning. By early 2018 the database contained 136 relevant entries from 46 countries. Most examples (60%) were in a language other than English. More than 50% of entries addressed multiple ES or the link between biodiversity and ES. There was also a positive temporal trend towards inclusion of multiple ecosystem services. The GlobaLDES database represents the first known snapshot of the mainstreaming of the ES concept at a global scale. Our analysis suggests an accelerating adoption of the ES concept into policy. As the number of entries improves, GlobaLDES will serve as a useful benchmarking tool for monitoring the diffusion of the ES concept into policy-making.
In river ice modelling, deterministic river ice models are often embedded into a Monte-Carlo framework to generate ensembles of backwater staging for jams of varying length and location, and for different combinations of model parameters and boundary conditions. In this approach, values for parameters and boundary conditions are usually sampled independently (of each other) from their probability distributions. However, many of the parameters and boundary conditions are interdependent and thus warrant sampling methods that consider correlation effects. But, such correlation studies have not been previously conducted for river ice models, which is the main motivation for this study. A review of literature was performed to compile data from more than 40 different ice-jam case studies from 24 ice-jam prone locations in Canada and the United States. Then correlations among parameters and boundary conditions in three commonly used river ice models were investigated. The results show that the model parameters in river ice models are ice-jam centric and have varying degrees of correlations, but boundary conditions are independent of each other and, instead, have potentially stronger ties to catchment characteristics, fluvial geomorphology and meteorological conditions. The findings of this study provide important insights in understanding and improving parameterization, calibration and ensemble modelling of river ice models.
ABSTRACTIn cold region environments, any alteration in the hydro-climatic regime can have profound impacts on river ice processes. This paper studies the implications of hydro-climatic trends on ri...
AbstractThe regulation of rivers has always been a controversial issue, with potential benefits but also environmental impacts. In western Canada, the construction of W.A.C. Bennett Dam in the head...
Phragmites australis (Cav.) Trin. ex Steudel subspecies australis is one of the worst plant invaders in wetlands of North America. Remote sensing is the most cost-effective method to track its spread given its widespread distribution and rapid colonization rate. We hypothesize that the morphological and/or physiological features associated with different phenological states of Phragmites can influence their reflectance signal and thus affect mapping accuracies. We tested this hypothesis by comparing classification accuracies of cloud-free images acquired by Landsat 7, Landsat 8, and Sentinel 2 at roughly monthly intervals over a calendar year for two wetlands in southern Ontario. We used the Support Vector Machines classification and employed field observations and image acquired from unmanned aerial vehicle (8 cm) to perform accuracy assessments. The highest Phragmites producer’s, user’s, and overall accuracy (96.00, 91.11, and 88.56% respectively) were provided by images acquired in late summer and fall period. During this period, green, Near Infrared, and Short-Wave Infrared bands generated more unique reflectance signals for Phragmites. Both Normalized Difference Vegetation Index and Normalized Difference Water Index showed significant difference between Phragmites and the most confused classes (cattail; Typha latifolia L., and meadow marsh) during the late summer and fall period. Since meadow marsh separated out best from Phragmites and cattail in the February image, we used it to mask the meadow marsh in the July image to reduce confusion. The unique reflectance signal of Phragmites in late summer and fall is likely due to prolonged greenness of Phragmites when compared to other wetland vegetation, large, distinct inflorescence, and the water content of Phragmites during this period.
Amphibians are declining worldwide, in part because of large-scale degradation of habitat from agriculture and pervasive pathogens. Yet a common North American amphibian, the wood frog (Lithobates sylvaticus), ranges widely and persists in agricultural landscapes. Conventional survey techniques rely on visual encounters and dip-netting efforts, but detectability limits the ability to test for the effects of environmental variables on amphibian habitat suitability. We used environmental DNA to determine the presence of wood frogs and an amphibian pathogen (ranavirus) in Prairie Pothole wetlands and investigated the effects of 32 water quality, wetland habitat, and landscape-level variables on frog presence at sites representing different degrees of agricultural intensity. Several wetland variables influenced wood frog presence, the most influential being those associated with wetland productivity (i.e., nutrients), vegetation buffer width, and proportion of the surrounding landscape that is comprised of other water bodies. Wood frog presence was positively associated with higher dissolved phosphorus (>0.4 mg/L), moderate dissolved nitrogen (0.1-0.2 mg/L), lower chlorophyll a (≤15 µg/L), wider vegetation buffers (≥10 m), and more water on the landscape (≥0.25). These results highlight the effects of environmental factors at multiple scales on the presence of amphibians in this highly modified landscape-namely the importance of maintaining wetland water quality, vegetation buffers, and surrounding habitat heterogeneity. Environ Toxicol Chem 2019;38:2750-2763. © 2019 SETAC.
Abstract. Spatial variability in snowpack properties negatively impacts our capacity to make direct measurements of snow water equivalent (SWE) using satellites. A comprehensive data set of snow microstructure (94 profiles at 36 sites) and snow layer thickness (9000 vertical profiles across nine trenches) collected over two winters at Trail Valley Creek, NWT, Canada, was applied in synthetic radiative transfer experiments. This allowed for robust assessment of the impact of estimation accuracy of unknown snow microstructural characteristics on the viability of SWE retrievals. Depth hoar layer thickness varied over the shortest horizontal distances, controlled by subnivean vegetation and topography, while variability in total snowpack thickness approximated that of wind slab layers. Mean horizontal correlation lengths of layer thickness were less than a metre for all layers. Depth hoar was consistently ∼30 % of total depth, and with increasing total depth the proportion of wind slab increased at the expense of the decreasing surface snow layer. Distinct differences were evident between distributions of layer properties; a single median value represented density and specific surface area (SSA) of each layer well. Spatial variability in microstructure of depth hoar layers dominated SWE retrieval errors. A depth hoar SSA estimate of around 7 % under the median value was needed to accurately retrieve SWE. In shallow snowpacks <0.6 m, depth hoar SSA estimates of ±5 %–10 % around the optimal retrieval SSA allowed SWE retrievals within a tolerance of ±30 mm. Where snowpacks were deeper than ∼30 cm, accurate values of representative SSA for depth hoar became critical as retrieval errors were exceeded if the median depth hoar SSA was applied.
Convection-permitting models (CPM) with at least 4 km horizontal grid spacing enable the cumulus parameterization to be switched off and thus simulate convective processes more realistically than coarse resolution models. This study investigates if a North American scale CPM can reproduce the observed warm season precipitation diurnal cycle on a climate scale. Potential changes in the precipitation diurnal cycle characteristics at the end of the twenty first century are also investigated using the pseudo global warming approach under a high-end anthropogenic emission scenario (RCP8.5). Simulations are performed with the Advanced Research Weather Research and Forecasting (ARW-WRF) model with 4-km horizontal grid spacing. Results from the WRF historical run (2001–2013) are evaluated against hourly precipitation from 2903 weather stations and a gridded hourly precipitation product in the U.S. The magnitude and timing of the diurnal cycle peak are realistically simulated in most of the U.S. and southern Canada. The model also captures the transition from afternoon precipitation peaks eastward of the Rocky Mountains to night peaks in the central U.S., which is related to propagating mesoscale convective systems. However, the historical climate simulation does not capture the observed early morning peaks in the central U.S. and overestimates the magnitude of the diurnal precipitation peak in the southeast region. In the simulation of the future climate, both the precipitation amount of the diurnal cycle and precipitation intensity increase throughout the domain, along with an increase in precipitation frequency in the northern region of the domain in May. These increases indicate a clear intensification of the hydrologic cycle during the warm season with potential impacts on future water resources, agriculture, and flooding.
Abstract To support the 2012 Great Lakes Water Quality Agreement on reducing Lake Erie's phosphorus inputs, we integrated US and Canadian data to update and extend total phosphorus (TP) loads into and out of the St. Clair-Detroit River System for 1998–2016. The most significant changes were decreased loads from Lake Huron caused by mussel-induced oligotrophication of the lake, and decreased loads from upgraded Great Lakes Water Authority sewage treatment facilities in Detroit. By comparing Lake St. Clair inputs and outputs, we demonstrated that on average the lake retains 20% of its TP inputs. We also identified for the first time that loads from resuspended Lake Huron sediment were likely not always detected in US and Canadian monitoring programs due to mismatches in sampling and resuspension event frequencies, substantially underestimating the load. This additional load increased over time due to climate-induced decreases in Lake Huron ice cover and increases in winter storm frequencies. Given this more complete load inventory, we estimated that to reach a 40% reduction in the Detroit River TP load to Lake Erie, accounting for the missed load, point and non-point sources other than that coming from Lake Huron and the atmosphere would have to be reduced by at least 50%. We also discuss the implications of discontinuous monitoring efforts.
Hydrologic models partition flows into surface and subsurface pathways, but their calibration is typically conducted only against streamflow. Here we argue that unless model outcomes are constrained using flow pathway data, multiple partitioning schemes can lead to the same streamflow. This point becomes critical for biogeochemical modeling as individual flow paths may yield unique chemical signatures. We show how information on flow pathways can be used to constrain hydrologic flow partitioning and how improved partitioning can lead to better water quality predictions. As a case study, an agricultural basin in Ontario is used to demonstrate that using tile discharge data could increase the performance of both the hydrology and the nitrogen transport models. Watershed‐scale tile discharge was estimated based on sparse tile data collected at some tiles using a novel regression‐based approach. Through a series of calibration experiments, we show that utilizing tile flow signatures as calibration criteria improves model performance in the prediction of nitrate loads in both the calibration and validation periods. Predictability of nitrate loads is improved even with no tile flow data and by model calibration only against an approximate understanding of annual tile flow percent. However, despite high values of goodness‐of‐fit metrics in this case, temporal dynamics of predictions are inconsistent with reality. For instance, the model predicts significant tile discharge in summer with no tile flow occurrence in the field. Hence, the proposed tile flow upscaling approach and the partitioning‐constrained model calibration are vital steps toward improving the predictability of biogeochemical models in tiled landscapes.
In this study, we assess the impact of forcing data errors, model structure, and parameter choices on 1‐D snow simulations simultaneously within a global variance‐based sensitivity analysis framework. This approach allows inclusion of interaction effects, drawing a more representative picture of the resulting sensitivities. We utilize all combinations of a multiphysics snowpack model to mirror the influence of model structure. Uncertainty ranges of model parameters and input data are extracted from the literature. We evaluate a suite of 230,000 model realizations at the snow monitoring station Kühtai (Tyrol, Austria, 1,920 m above sea level) against snow water equivalent observations. The results show throughout the course of 25 winter seasons (1991–2015) and different model performance criteria a large influence of forcing data uncertainty and its interactions on the model performance. Mean interannual total sensitivity indices are in the general order of parameter choice < model structure < forcing error, with precipitation, air temperature, and the radiative forcings controlling the variance during the accumulation period and air temperature and longwave irradiance controlling the variance during the ablation period, respectively. Model skill is highly sensitive to the snowpack liquid water transport scheme throughout the whole winter period and to albedo representation during the ablation period. We found a sufficiently long evaluation period (>10 years) is required for robust averaging. A considerable interaction effect was revealed, indicating that an improvement in the knowledge (i.e., reduction of uncertainty) of one factor alone might not necessarily improve model results.
Atmospheric rivers (ARs), defined as narrow, transient corridors of strong moisture transport in the lower troposphere, are important phenomena for freshwater recharge and water resources, especially along the west coast of North America. This study presents the variability and trends of landfalling ARs (LARs) along the higher (53.5°–60.0°N) and lower (47.0°–53.5°N) latitudes of British Columbia and southeastern Alaska (BCSAK) during the 1948–2016 period. Moreover, we present the synoptic evolution and distribution of LARs in BCSAK during different phases of ocean–atmosphere climate variability using a six‐hourly AR catalogue from the Scripps Institution of Oceanography and reanalysis data from the National Centers for Environmental Prediction/National Center for Atmospheric Research. During 1948–2016, BCSAK averages 35 ± 5 LARs annually, with the highest frequency during fall (13 ± 2) and lowest during spring (5 ± 2). The frequency of LARs across BCSAK rises during the study period, and the increase between 1979 and 2016 is statistically significant (p < .05). A strong ridge over the Pacific Northwest and BC and a trough over the Gulf of Alaska and the Northeastern Pacific Ocean favours AR landfalls at the higher and lower latitudes, respectively. BCSAK experiences greater numbers of LARs during neutral phases of El Niño/Southern Oscillation, the 2013/2014 Pacific oceanic blob, and during the positive phases of the Pacific Decadal Oscillation and Pacific North American Pattern.
Abstract. Complex, software-intensive, technically advanced, and computationally demanding models, presumably with ever-growing realism and fidelity, have been widely used to simulate and predict the dynamics of the Earth and environmental systems. The parameter-induced simulation crash (failure) problem is typical across most of these models despite considerable efforts that modellers have directed at model development and implementation over the last few decades. A simulation failure mainly occurs due to the violation of numerical stability conditions, non-robust numerical implementations, or errors in programming. However, the existing sampling-based analysis techniques such as global sensitivity analysis (GSA) methods, which require running these models under many configurations of parameter values, are ill equipped to effectively deal with model failures. To tackle this problem, we propose a new approach that allows users to cope with failed designs (samples) when performing GSA without rerunning the entire experiment. This approach deems model crashes as missing data and uses strategies such as median substitution, single nearest-neighbor, or response surface modeling to fill in for model crashes. We test the proposed approach on a 10-parameter HBV-SASK (Hydrologiska Byråns Vattenbalansavdelning modified by the second author for educational purposes) rainfall–runoff model and a 111-parameter Modélisation Environmentale–Surface et Hydrologie (MESH) land surface–hydrology model. Our results show that response surface modeling is a superior strategy, out of the data-filling strategies tested, and can comply with the dimensionality of the model, sample size, and the ratio of the number of failures to the sample size. Further, we conduct a “failure analysis” and discuss some possible causes of the MESH model failure that can be used for future model improvement.
Abstract Dynamical earth and environmental systems models are typically computationally intensive and highly parameterized with many uncertain parameters. Together, these characteristics severely limit the applicability of Global Sensitivity Analysis (GSA) to high-dimensional models because very large numbers of model runs are typically required to achieve convergence and provide a robust assessment. Paradoxically, only 30 percent of GSA applications in the environmental modelling literature have investigated models with more than 20 parameters, suggesting that GSA is under-utilized on problems for which it should prove most useful. We develop a novel grouping strategy, based on bootstrap-based clustering, that enables efficient application of GSA to high-dimensional models. We also provide a new measure of robustness that assesses GSA stability and convergence. For two models, having 50 and 111 parameters, we show that grouping-enabled GSA provides results that are highly robust to sampling variability, while converging with a much smaller number of model runs.
Abstract Despite numerous advances in continental-scale hydrologic modeling and improvements in global Land Surface Models, an accurate representation of regional water table depth (WTD) remains a challenge. Data assimilation of observations from the Gravity Recovery and Climate Experiment (GRACE) mission leads to improvements in the accuracy of hydrologic models, ultimately resulting in more reliable estimates of lumped water storage. However, the usually shallow groundwater compartment of many models presents a problem with GRACE assimilation techniques, as these satellite observations also represent changes in deeper soils and aquifers. To improve the accuracy of modeled groundwater estimates and allow the representation of WTD at finer spatial scales, we implemented a simple, yet novel approach to integrate GRACE data, by augmenting the Variable Infiltration Capacity (VIC) hydrologic model. First, the subsurface model structural representation was modified by incorporating an additional (fourth) soil layer of varying depth (up to 1000 m) in VIC as the bottom ‘groundwater’ layer. This addition allows the model to reproduce water storage variability not only in shallow soils but also in deeper groundwater, in order to allow integration of the full GRACE-observed variability. Second, a Direct Insertion scheme was developed that integrates the high temporal (daily) and spatial (∼6.94 km) resolution model outputs to match the GRACE resolution, performs the integration, and then disaggregates the updated model state after the assimilation step. Simulations were performed with and without Direct Insertion over the three largest river basins in California and including the Central Valley, in order to test the augmented model's ability to capture seasonal and inter-annual trends in the water table. This is the first-ever fusion of GRACE total water storage change observations with hydrologic simulations aiming at the determination of water table depth dynamics, at spatial scales potentially useful for local water management.
Abstract. The interior of western Canada, up to and including the Arctic, has experienced rapid change in its climate, hydrology, cryosphere, and ecosystems, and this is expected to continue. Although there is general consensus that warming will occur in the future, many critical issues remain. In this first of two articles, attention is placed on atmospheric-related issues that range from large scales down to individual precipitation events. Each of these is considered in terms of expected change organized by season and utilizing mainly “business-as-usual” climate scenario information. Large-scale atmospheric circulations affecting this region are projected to shift differently in each season, with conditions that are conducive to the development of hydroclimate extremes in the domain becoming substantially more intense and frequent after the mid-century. When coupled with warming temperatures, changes in the large-scale atmospheric drivers lead to enhancements of numerous water-related and temperature-related extremes. These include winter snowstorms, freezing rain, drought, forest fires, as well as atmospheric forcing of spring floods, although not necessarily summer convection. Collective insights of these atmospheric findings are summarized in a consistent, connected physical framework.
Montane regions throughout western North America have experienced increases in forest canopy closure and forest encroachment into grasslands over the past century; this has been attributed to climate change and fire suppression/exclusion. These changes threaten ecological values and potentially increase probabilities of intense wildfire. Restoration of landscapes to historical conditions has been proposed as a potential solution. We used historical oblique photographs of an area in the Rocky Mountains of Alberta, Canada, to determine the vegetation composition in 1909 and then asked whether restoration to a historical vegetation condition would: (1) reduce the overall burn probability of fire; (2) reduce the probability of high-intensity fires; and (3) change the spatial pattern of burn probabilities, as compared to current conditions. We used the Burn-P3 model to calculate the overall and high-intensity burn probabilities in two scenarios: (1) the baseline (current (2014) vegetation composition) and (2) historical restoration (vegetation in the study area as of 1909 with the surrounding landscape in its current condition). In the baseline, the landscape had 50% less grassland and more coniferous forest than 100 yr ago. Except for the fuel grids, we ensured all input parameters (number and locations of ignitions, weather conditions, etc.) were identical between the two scenarios; therefore, any differences in outputs are solely attributable to the changed fuels. The historical restoration scenario reduced the overall burn probability by only 1.3%, but the probability of high-intensity wildfires was reduced by nearly half (44.2%), as compared to the baseline scenario. There were also differences in the spatial pattern of overall burn probabilities between the two scenarios. While 6.7% of the landscape burned with half (or less) the probability in the restoration scenario (compared to the baseline), other areas (3.2%) had burn probabilities two to five times higher. More than 21.5% had high-intensity burn probabilities that were 20% or less of those in the baseline scenario. Differences in burn probabilities between the two scenarios were largely attributable to the effects of the vegetation difference on rate of fire spread. Restoration to historical vegetation structure significantly lowered wildfire risk to the landscape.
Permafrost degradation in the peat‐rich southern fringe of the discontinuous permafrost zone is catalysing substantial changes to land cover with expansion of permafrost‐free wetlands (bogs and fens) and shrinkage of forest‐dominated permafrost peat plateaux. Predicting discharge from headwater basins in this region depends upon understanding and numerically representing the interactions between storage and discharge within and between the major land cover types and how these interactions are changing. To better understand the implications of advanced permafrost thaw‐induced land cover change on wetland discharge, with all landscape features capable of contributing to drainage networks, the hydrological behaviour of a channel fen sub‐basin in the headwaters of Scotty Creek, Northwest Territories, Canada, dominated by peat plateau–bog complexes, was modelled using the Cold Regions Hydrological Modelling platform for the period of 2009 to 2015. The model construction was based on field water balance observations, and performance was deemed adequate when evaluated against measured water balance components. A sensitivity analysis was conducted to assess the impact of progressive permafrost loss on discharge from the sub‐basin, in which all units of the sub‐basin have the potential to contribute to the drainage network, by incrementally reducing the ratio of wetland to plateau in the modelled sub‐basin. Simulated reductions in permafrost extent decreased total annual discharge from the channel fen by 2.5% for every 10% decrease in permafrost area due to increased surface storage capacity, reduced run‐off efficiency, and increased landscape evapotranspiration. Runoff ratios for the fen hydrological response unit dropped from 0.54 to 0.48 after the simulated 50% permafrost area loss with a substantial reduction of 0.47 to 0.31 during the snowmelt season. The reduction in peat plateau area resulted in decreased seasonal variability in discharge due to changes in the flow path routing, with amplified low flows associated with small increases in subsurface discharge, and decreased peak discharge with large reductions in surface run‐off.
Freezing rain occurs in complex atmospheric conditions when the temperature is close to 0°C. To better understand how its occurrence will change in the future, there is a need to assess how well regional climate models can reproduce those conditions. The goal of the present study is to investigate the influence of horizontal resolution on the simulation of freezing rain using the fifth generation of the Canadian Regional Climate Model (CRCM5). Three CRCM5 simulations driven by the European Centre for Medium-range Weather Forecasts interim reanalysis (ERA-Interim) over eastern North America at resolutions of 0.11°, 0.22°, and 0.44° were conducted over a period of 36 years (1979–2014). Freezing rain is diagnosed using an in-line diagnostic method for precipitation partitioning. A climatological study of annual and seasonal accumulated freezing rain was conducted. In addition, the ability of the three simulations to reproduce individual freezing rain events was evaluated. Our analyses include frequency and partitioning of different precipitation types and comparisons with observations. All simulations reproduced the climatology of freezing rain sufficiently well and show similar large-scale patterns. The number of freezing rain events tends to be overestimated at higher resolution and underestimated at lower resolution. Despite the overestimation, detailed maxima associated with freezing rain are well defined and located at higher resolution, notably in regions of the St. Lawrence River Valley. Overall, this study is consistent with other added-value studies, generally showing a mix of improvement and deterioration in the precipitation fields by the higher resolution simulations.
Time-resolved satellite gravimetry has revolutionized understanding of mass transport in the Earth system. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) has enabled monitoring of the terrestrial water cycle, ice sheet and glacier mass balance, sea level change and ocean bottom pressure variations and understanding responses to changes in the global climate system. Initially a pioneering experiment of geodesy, the time-variable observations have matured into reliable mass transport products, allowing assessment and forecast of a number of important climate trends and improve service applications such as the U.S. Drought Monitor. With the successful launch of the GRACE Follow-On mission, a multi decadal record of mass variability in the Earth system is within reach.
The sudden collapse of thawing soils in the Arctic might double the warming from greenhouse gases released from tundra, warn Merritt R. Turetsky and colleagues. The sudden collapse of thawing soils in the Arctic might double the warming from greenhouse gases released from tundra, warn Merritt R. Turetsky and colleagues.
Peatlands in the Western Boreal Plains act as important water sources in the landscape. Their persistence, despite potential evapotranspiration (PET) often exceeding annual precipitation, is attributed to various water storage mechanisms. One storage element that has been understudied is seasonal ground ice (SGI). This study characterized spring SGI conditions and explored its impacts on available energy, actual evapotranspiration, water table, and near surface soil moisture in a western boreal plains peatland. The majority of SGI melt took place over May 2017. Microtopography had limited impact on melt rates due to wet conditions. SGI melt released 139mm in ice water equivalent (IWE) within the top 30cm of the peat, and weak significant relationships with water table and surface moisture suggest that SGI could be important for maintaining vegetation transpiration during dry springs. Melting SGI decreased available energy causing small reductions in PET (<10mm over the melt period) and appeared to reduce actual evapotranspiration variability but not mean rates, likely due to slow melt rates. This suggests that melting SGI supplies water, allowing evapotranspiration to occur at near potential rates, but reduces the overall rate at which evapotranspiration could occur (PET). The role of SGI may help peatlands in headwater catchments act as a conveyor of water to downstream landscapes during the spring while acting as a supply of water for the peatland. Future work should investigate SGI influences on evapotranspiration under differing peatland types, wet and dry spring conditions, and if the spatial variability of SGI melt leads to spatial variability in evapotranspiration.
Hydrology is still, and for good reasons, an inexact science, even if evolving hydrological understanding has provided a basis for improved water management for at least the last three millennia. The limitations of that understanding have, however, become much more apparent and important in the last century as the pressures of increasing populations, and the anthropogenic impacts on catchment forcing and responses, have intensified. At the same time, the sophistication of hydrological analyses and models has been developing rapidly, often driven more by the availability of computational power and geographical data sets than any real increases in understanding of hydrological processes. This sophistication has created an illusion of real progress but a case can be made that we are still rather muddling along, limited by the significant uncertainties in hydrological observations, knowledge of catchment characteristics and related gaps in conceptual understanding, particularly of the sub-surface. These knowledge gaps are illustrated by the fact that for many catchments we cannot close the water balance without significant uncertainty, uncertainty that is often neglected in evaluating models for practical applications.
Changes in seasonal nutrient dynamics are occurring across a range of climates and land use types. Although it is known that seasonal patterns in nutrient availability are key drivers of both stream metabolism and eutrophication, there has been little success in developing a comprehensive understanding of seasonal variations in nutrient export across watersheds or of the relationship between nutrient seasonality and watershed characteristics. In the present study, we have used concentration and discharge data from more than 200 stations across U.S. and Canadian watersheds to identify (1) archetypal seasonal concentration regimes for nitrate, soluble reactive phosphorus (SRP), and total phosphorus, and (2) dominant watershed controls on these regimes across a gradient of climate, land use, and topography. Our analysis shows that less impacted watersheds, with more forested and wetland area, most commonly exhibit concentration regimes that are in phase with discharge, with concentration lows occurring during summer low‐flow periods. Agricultural watersheds also commonly exhibit in‐phase behavior, though the seasonality is usually muted compared to that seen in less impacted areas. With increasing urban area, however, nutrient concentrations frequently become essentially aseasonal or even exhibit clearly out‐of‐phase behavior. In addition, our data indicate that seasonal SRP concentration patterns may be strongly influenced by proximal controls such as the presence of dams and reservoirs. In all, these results suggest that human activity is significantly altering nutrient concentration regimes, with large potential consequences for both in‐stream metabolism and eutrophication risk in downstream waterbodies.
Ballard et al . argue that our prediction of a 30-year or longer recovery time for Gulf of Mexico water quality is highly uncertain, and that much shorter time lags are equally likely. We demonstrate that their argument, based on the use of a two-component regression model, does not sufficiently consider fundamental watershed processes or multiple lines of evidence suggesting the existence of decadal-scale lags.
Boreal forest fires emit large amounts of carbon into the atmosphere primarily through the combustion of soil organic matter1,2,3. During each fire, a portion of this soil beneath the burned layer can escape combustion, leading to a net accumulation of carbon in forests over multiple fire events4. Climate warming and drying has led to more severe and frequent forest fires5,6,7, which threaten to shift the carbon balance of the boreal ecosystem from net accumulation to net loss1, resulting in a positive climate feedback8. This feedback will occur if organic-soil carbon that escaped burning in previous fires, termed ‘legacy carbon’, combusts. Here we use soil radiocarbon dating to quantitatively assess legacy carbon loss in the 2014 wildfires in the Northwest Territories of Canada2. We found no evidence for the combustion of legacy carbon in forests that were older than the historic fire-return interval of northwestern boreal forests9. In forests that were in dry landscapes and less than 60 years old at the time of the fire, legacy carbon that had escaped burning in the previous fire cycle was combusted. We estimate that 0.34 million hectares of young forests (<60 years) that burned in the 2014 fires could have experienced legacy carbon combustion. This implies a shift to a domain of carbon cycling in which these forests become a net source—instead of a sink—of carbon to the atmosphere over consecutive fires. As boreal wildfires continue to increase in size, frequency and intensity7, the area of young forests that experience legacy carbon combustion will probably increase and have a key role in shifting the boreal carbon balance.
Shrub expansion has occurred across much of the arctic tundra over the past century. Increasing dominance of woody vegetation is expected to have global influences on climate patterns and lead to local changes in hydrological function and nutrient cycling. Changing abiotic conditions associated with shrubs will likely alter the relative fitness of neighbouring plants resulting in distinct community composition. Here, we use an extensive set of paired abiotic and biotic data to investigate the capacity for Alnus alnobetula (green alder) patches to modify the habitat of the local plant community at the taiga–tundra ecotone of the Northwest Territories, Canada. Plots were established across topographic positions in ten alder patches and adjacent, alder-free tundra. Habitat corresponded to the strongest gradient of among-site variation in abiotic measures and plant community composition, indicating that alder patch growing conditions were distinct from those of alder-free tundra. Slope position was generally unimportant in determining environmental conditions. Alder patches changed the vertical structure of the understory by increasing the maximum height of birch. Tall shrubs also decreased the richness of tundra specialists, suggesting that these species face competitive pressures from shrub expansion at the southern edge of their ranges. Our findings demonstrate that tall shrub patches can substantially modify their local environment in taiga–tundra ecotone systems, altering available habitat and acting as niche constructors for the local plant community. These habitats will therefore be important to consider in regional predictions of hydrology, nutrient cycling, and biodiversity as shrubs continue to expand across the arctic.
Phenology plays a fundamental role in regulating photosynthesis, evapotranspiration, and surface energy fluxes and is sensitive to climate change. The global mean surface air temperature data indicate a global warming hiatus between 1998 and 2012, while its impacts on global phenology remains unclear. Here we use long-term satellite and FLUXNET records to examine phenology trends in the northern hemisphere before and during the warming hiatus. Our results based on the satellite record show that the phenology change rate slowed down during the warming hiatus. The analysis of the long-term FLUXNET measurements, mainly within the warming hiatus, shows that there were no widespread advancing (or delaying) trends in spring (or autumn) phenology. The lack of widespread phenology trends partly led to the lack of widespread trends in spring and autumn carbon fluxes. Our findings have significant implications for understanding the responses of phenology to climate change and the climate-carbon feedbacks.
Catalysts that can work without the need of light and additional oxidants such as H2O2 to degrade organic pollutants have been long sought. In this work, we report that at acidic condition, CeO2 na...
Fluoride boosts the oxidase-like activity of hydrolyzed Ce(<sc>iv</sc>) but inhibits the activity of Ce(<sc>iv</sc>), allowing intentional hydrolysis to be performed for consistent analysis of Ce(<sc>iv</sc>).
The present study analyses the impacts of past and future climate change on extreme weather events for southern parts of Canada from 1981 to 2100. A set of precipitation and temperature‐based indices were computed using the downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) multi‐model ensemble projections at 8 km resolution over the 21st Century for two representative concentration pathway (RCP) scenarios: RCP4.5 and RCP8.5. The results show that this region is expected to experience stronger warming and a higher increase in precipitation extremes in future. Generally, projected changes in minimum temperature will be greater than changes in maximum temperature, as shown by respective indices. A decrease in frost days and an increase in warm nights will be expected. By 2100 there will be no cool nights and cool days. Daily minimum and maximum temperatures will increase by 12 and 7°C, respectively, under the RCP8.5 scenario, when compared with the reference period 1981–2000. The highest warming in minimum temperature and decrease in cool nights and days will occur in Ontario and Quebec provinces close to the Great Lakes and Hudson Bay. The highest warming in maximum temperature will occur in the southern parts of Alberta and Saskatchewan. Annual total precipitation is expected to increase by about 16% and the occurrence of heavy precipitation events by five days. The highest increase in annual total precipitation will occur in the northern parts of Ontario and Quebec and in western British Columbia.
AbstractAlthough the Laurentian Great Lakes Basin contains the largest global store of fresh water, long-term groundwater storage (GWS) decline has been observed in some aquifers supplying communit...
Storage and removal of nutrients by wetlands and riparian areas is an important process in understanding catchment nutrient fluxes and in helping to mitigate current issues of eutrophication in man...
Spring snowmelt is the most important hydrological event in agricultural cold regions, recharging soil moisture and generating the majority of annual runoff. Melting agricultural snowcovers are pat...
AbstractIn cold conditions, early winter precipitation occurs as snowfall and contributes to the accumulating seasonal snowpack. In a warming climate, precipitation may occur as rainfall in mountai...
Abstract Global fire regimes are changing, with increases in wildfire frequency and severity expected for many North American forests over the next 100 years. Fires can result in dramatic changes to carbon (C) stocks and can restructure plant and microbial communities, with long-lasting effects on ecosystem functions. We investigated wildfire effects on soil microbial communities (bacteria and fungi) in an extreme fire season in the northwestern Canadian boreal forest, using field surveys, remote sensing, and high-throughput amplicon sequencing in upland and wetland sites. We hypothesized that vegetation community and soil pH would be the most important determinants of microbial community composition, while the effect of fire might not be significant, and found that fire occurrence, along with vegetation community, moisture regime, pH, total carbon, and soil texture are all significant predictors of soil microbial community composition. Burned communities become increasingly dissimilar to unburned communities with increasingly severe burns, and the burn severity index (an index of the fractional area of consumed organic soils and exposed mineral soils) best predicted total bacterial community composition, while whether a site was burned or not was the best predictor for fungi. Globally abundant taxa were identified as significant positive fire responders in this system, including the bacteria Massilia sp. (64 × more abundant with fire) and Arthrobacter sp. (35 × ), and the fungi Penicillium sp. (22 × ) and Fusicladium sp. (12 × ). Bacterial and fungal co-occurrence network modules were characterized by fire responsiveness as well as pH and moisture regime. Building on the efforts of previous studies, our results consider a particularly wide range of soils, vegetation, and burn severities, and we identify specific fire-responsive microbial taxa and suggest that accounting for burn severity improves our understanding of microbial response to fires.
The overall spatial and temporal influence of shrub expansion on permafrost is largely unknown due to uncertainty in estimating the magnitude of many counteracting processes. For example, shrubs shade the ground during the snow-free season, which can reduce active layer thickness. At the same time, shrubs advance the timing of snowmelt when they protrude through the snow surface, thereby exposing the active layer to thawing earlier in spring. Here, we compare 3056 in situ frost table depth measurements split between mineral earth hummocks and organic inter-hummock zones across four dominant shrub–tundra vegetation types. Snow-free date, snow depth, hummock development, topography, and vegetation cover were compared to frost table depth measurements using a structural equation modeling approach that quantifies the direct and combined interacting influence of these variables. Areas of birch shrubs became snow free earlier regardless of snow depth or hillslope aspect because they protruded through the snow surface, leading to deeper hummock frost table depths. Projected increases in shrub height and extent combined with projected decreases in snowfall would lead to increased shrub protrusion across the Arctic, potentially deepening the active layer in areas where shrub protrusion advances the snow-free date.
Wildfire represents the largest areal disturbance of forested boreal peatlands and the spatial variability in the severity of these peat fires is both a leading source of uncertainty in boreal wildfire carbon emissions and a major challenge for regional wildfire management. Peat smouldering can emit large quantities of carbon and smoke to the atmosphere, and therefore can contribute to hazardous air quality. The wildland-industry interface and wildland-urban interface are both extensive across the sub-humid boreal plains (BP) ecozone where one-third of the area is covered by peatlands. As such, there is a growing research need to identify drivers of variability in smouldering combustion. This study uses hydrophysical peat properties to assess the drivers of cross-scale variability in peat smouldering combustion vulnerability in forested peatlands across the BP. Using a space-for-time chronosequence across the 120-year fire return interval and three main hydrogeological settings, and by incorporating hummock, hollow and margin locations, cross-scale variability is studied. We find that, based on peat properties such as specific yield (Sy) and gravimetric water content, forested peatland margins represent areas of high peat smouldering vulnerability, and that this is exacerbated with an increasing time-since-fire (stand-age). Although increasing Sy with time-since-fire in peatland middles may buffer water table drawdown, when accounting for increases in canopy fuel load, transpiration, and feather moss dominance forested peatland middles also become more vulnerable to smouldering combustion with time-since-fire. Moreover, the interaction of peatland margins with coarse- and heterogeneous-grained hydrogeological settings leads to lower Sy and higher density margin peat than in fine-grained settings, further increasing smouldering vulnerability. We estimate that forested peatland margins are vulnerable to combustion throughout their entire profile i.e. burn-out, under moderate-high water deficits in the BP. Furthermore, we identify peatland margin: total area ratio as a driver of smouldering vulnerability where small peatlands that are periodically disconnected from regional groundwater systems are the most vulnerable to high total peat carbon loss. We suggest that these drivers of cross-scale variability should be incorporated into peatland and wildfire management strategies, especially in areas near the wildland-industry and wildland-urban interface.
A suite of autogenic ecohydrological feedbacks and moss traits are important for protecting vast peatland carbon stocks following wildfire disturbance. Here, we examine how peat burn severity and water table depth (WTD) affect the strength of one such feedback—the hydrophobicity–evaporation feedback (HEF). The HEF is an evaporation‐limiting feedback known to minimize water loss following wildfire. The peatland surface becomes hydrophobic creating an evaporative cap and thereby reducing post‐fire evaporation; however, recent studies hypothesize that this is dependent on peat burn severity. To test this hypothesis, we studied plots along a peat burn severity gradient in a partially drained black spruce peatland that burned during the 2016 Fort McMurray Horse River wildfire. Evaporation rates were significantly lower in plots where hydrophobicity was present. Hydrophobicity was lowest in the severely burned area, and the average instantaneous evaporation rate (2.75 mm day−1) was significantly higher than moderately and typical‐lightly burned areas (0.82 and 1.64 mm day−1, respectively). Based on lab results, increasing WTD affected hydrophobicity within lightly burned (singed) feather moss samples but not in heavily burned feather moss, showing the importance of post‐fire ground cover and in situ WTD. Our results provide evidence of a burn severity threshold where increased depth of burn removes the feather moss evaporative cap and causes the HEF to break down. We argue that this threshold has important implications for boreal peatlands, which are predicted to undergo climate‐mediated pre‐fire drying and increasing burn severities, potentially leading to further carbon losses due to enhanced post‐fire drying and concomitant decomposition.
In this study we assess the total storage, landscape distribution, and vertical partitioning of soil organic carbon (SOC) stocks on the Brogger Peninsula, Svalbard. This type of high Arctic...
Abstract Carotenoid pigments play an important role in the seasonal regulation of photosynthesis and photoprotection of overwintering conifers. Because the seasonal changes in the rate of photosynthetic CO2 assimilation are linked to changes in carotenoid pigment composition, it has been suggested that carotenoid sensitive vegetation indices might be used to track the otherwise “invisible” phenology of photosynthesis of conifer forests through remote sensing of leaf spectral reflectance. In this study we aimed to assess differences in the seasonal regulation of photosynthesis and the associated variation of carotenoids and chlorophylls at the leaf-scale for eastern white pine, red maple and white oak, in order to understand if photosynthetic and photoprotective processes are adequately represented by different vegetation indices over the course of the year. For this purpose we measured maximum rates of CO2 assimilation (Amax), quantified photosynthetic pigments, estimated photochemical and non-photochemical quenching processes via chlorophyll fluorescence and determined leaf spectral reflectance in pine, maple and oak trees over the course of two years. Seasonal variation in Amax, used here as a proxy for photosynthetic phenology, and photosynthetic pigments were adequately represented by the normalized difference vegetation index (NDVI) for the deciduous trees. For pine, NDVI overestimated photosynthetic activity for most of the year and was hence not able to represent photosynthetic phenology, due to the fact that needle chlorophyll content shows only little variation over the course of the year. By contrast, using the photochemical reflectance index (PRI) and the chlorophyll/carotenoid index (CCI), which both detect variations in carotenoids, we were able to observe an improved representation of the seasonal variation of CO2 assimilation and photosynthetic phenology for the two deciduous and the conifer species. Based on the accurate detection of the seasonal regulation of leaf-scale photosynthetic activity for all three species, we conclude that carotenoid-sensitive vegetation indices are promising tools to improve monitoring of phenology in both deciduous and conifer forests.
In our recent study in Global Change Biology (Li et al., ), we examined the relationship between solar-induced chlorophyll fluorescence (SIF) measured from the Orbiting Carbon Observatory-2 (OCO-2) and gross primary productivity (GPP) derived from eddy covariance flux towers across the globe, and we discovered that there is a nearly universal relationship between SIF and GPP across a wide variety of biomes. This finding reveals the tremendous potential of SIF for accurately mapping terrestrial photosynthesis globally.
Abstract Due to complex natural water flux processes and the ambiguous explanation of Bouchet’s complementary theory, site-level investigations on evapotranspiration (ET) and related climate variables assist in understanding the regional hydrological response to climate change. In this study, site specific empirical parameters were incorporated in the Bouchet’s complementary relationship (CR) and potential and actual ET were estimated by CR method and subsequently validated by 6 years of ground-based vapor flux observations. Time series analysis, correlation analysis and principal regression analysis were conducted to reveal the characteristics of climate change and the controlling factor(s) of the variations of potential ET and actual ET. The results show that this region is exhibiting a combined warming and drying trend over the past decades with two change points that occurred in 1993 and in 2000. Potential ET was predominantly influenced by temperature and vapor pressure deficit, while actual ET was mostly influenced by vegetation activity. Potential ET was found to be increasing concurrently with declining actual ET to constitute nearly a symmetric complementary relationship over the past decades. This study help to enhance our understanding of the regional hydrological response to climate change. Further studies are needed to partition the actual ET into transpiration and other components and to reveal the role of vegetation activity in determining regional ET as well as water balance.
Acid mine drainage (AMD) is one of the most hazardous byproducts of some types of mining. However, research on how AMD affects the bacterial community structure of downstream riverine ecosystems and the distribution of metal resistance genes (MRGs) along pollution gradient is limited. Comprehensive geochemical and high-throughput next-generation sequencing analyses can be integrated to characterize spatial distributions and MRG profiles of sediment bacteria communities along the AMD-contaminated Hengshi River. We found that (1) diversities of bacterial communities significantly and gradually increased along the river with decreasing contamination, suggesting community composition reflected changes in geochemical conditions; (2) relative abundances of phyla Proteobacteria and genus Halomonas and Planococcaceae that function in metal reduction decreased along the AMD gradient; (3) low levels of sediment salinity, sulfate, aquatic lead (Pb), and cadmium (Cd) were negatively correlated with bacterial diversity despite pH was in a positive manner with diversity; and (4) arsenic (As) and copper (Cu) resistance genes corresponded to sediment concentrations of As and Cu, respectively. Altogether, our findings offer initial insight into the distribution patterns of sediment bacterial community structure, diversity and MRGs along a lotic ecosystem contaminated by AMD, and the factors that affect them.
Gating or threshold selection is very important in analyzing data from a microflow cytometer, which is especially critical in analyzing weak signals from particles/cells with small sizes. It has been reported that using the amplitude gating alone may result in false positive events in analyzing data with a poor signal-to-noise ratio. Transit time (τ) can be set as a gating threshold along with side-scattered light or fluorescent light signals in the detection of particles/cells using a microflow cytometer. In this study, transit time of microspheres was studied systematically when the microspheres passed through a laser beam in a microflow cytometer and side-scattered light was detected. A clear linear relationship between the inverse of the average transit time and total flow rate was found. Transit time was used as another gate (other than the amplitude of side-scattering signals) to distinguish real scattering signals from noise. It was shown that the relative difference of the measured microsphere concentration can be reduced significantly from the range of 3.43%–8.77% to the range of 8.42%–111.76% by employing both amplitude and transit time as gates in analysis of collected scattering data. By using optimized transit time and amplitude gate thresholds, a good correlation with the traditional hemocytometer-based particle counting was achieved (R2 > 0.94). The obtained results suggest that the transit time could be used as another gate together with the amplitude gate to improve measurement accuracy of particle/cell concentration for microfluidic devices.
Abstract Porous polyamide functionalized by plasma or various coatings has been investigated for oil/water separation. In literature, polyamide has rarely been studied for oil removal, and this work investigated the performance of bare polyamide 6.6 (nylon 6.6) in terms of the oil/water separation efficiency and the intrusion pressure, inspiring cost-effective solutions for large-scale oil removal in the industry. Both polyamide meshes possessing two-dimensional (2D) one-layer pores and nonwoven fabrics with three-dimensional (3D) irregular pores were found to be able to separate oil/water with a high efficiency above 98.5%. This finding was attributed to the dual underwater oleophobicity and underoil hydrophobicity of these polyamide samples. The roles of 2D and 3D structures in oil/water separation were illustrated, to provide a new insight into filter designing. Due to its greater intrusion pressure, the 3D netting structure was suggested as being more beneficial for oil/water separation than the 2D structure.
Abstract This work investigated a two-step surface modification of polyamide meshes and nonwoven fabrics for oil/water separation and looked into the durability of such modified polyamide. The two-step modification included 1) pre-etching the polyamide surface using plasma treatment and 2) coating the pre-etched surface by eco-friendly polydopamine (PDA)/cellulose. The pre-etching increased the surface roughness, which further improved the underwater superoleophobicity of the coating. Therefore, the modified polyamide was able to separate various oil/water mixtures and showed a higher intrusion pressure than the original sample and the samples which were only etched or only coated. The grooves on the surface that resulted from the pre-etching prevented the coating from peeling off. In durability tests, after 6 repeated uses, the modified nonwoven sample lost its underwater oleophobicity due to severe oil fouling, coming to a complete failure in oil/water separation. After 19 cycles, the modified mesh was still able to separate a certain amount of oil/water but showed reduced intrusion pressure because of slight oil contamination. Filters with different structures, like meshes with one layer of pores and nonwoven fabrics with complex three dimensional pores, had different oil fouling levels that affected oil/water separation. The recoverability of filters from oil contamination should be considered for practical applications.
The flow of fresh groundwater to the ocean through the coast (fresh submarine groundwater discharge or fresh SGD) plays an important role in global biogeochemical cycles and coastal water quality. In addition to delivering dissolved elements from land to sea, fresh SGD forms a natural barrier against salinization of coastal aquifers. Here we estimate groundwater discharge rates through the near‐global coast (60°N to 60°S) at high resolution using a water budget approach. We find that tropical coasts export more than 56% of all fresh SGD, while midlatitude arid regions export only 10%. Fresh SGD rates from tectonically active margins (coastlines along tectonic plate boundaries) are also significantly greater than passive margins, where most field studies have been focused. Active margins combine rapid uplift and weathering with high rates of fresh SGD and may therefore host exceptionally large groundwater‐borne solute fluxes to the coast.
An arsenic-binding aptamer named Ars-3 was reported in 2009, and it has been used for detection of As(III) in more than two dozen papers. In this work, we performed extensive binding assays using isothermal titration calorimetry, various DNA-staining dyes, and gold nanoparticles. By carefully comparing Ars-3 and a few random control DNA sequences, no specific binding of As(III) was observed in each case. Therefore, we conclude that Ars-3 cannot bind As(III). Possible reasons for some of the previously reported binding and detection were speculated to be related to the adsorption of As(III) onto gold surfaces, which were used in many related sensor designs, and As(III)/Au interactions were not considered before. The selection data in the original paper were then analyzed in terms of sequence alignment, secondary structure prediction, and dissociation constant measurement. These steps need rigorous testing before confirming specific binding of newly selected aptamers. This study calls for attention to the gap between aptamer selection and biosensor design, and the gap needs to be filled by careful binding assays to further the growth of the aptamer field.
In the subarctic tundra, soil moisture information can benefit permafrost monitoring and ecological studies, but fine-scale remote-sensing approaches are lacking. We explore the suitability of C-band SAR, paying attention to two challenges soil moisture retrieval faces. First, the microtopography and the heterogeneous organic soils impart unique microwave scattering properties, even in absence of noteworthy shrub cover. Empirically, we find the polarimetric response is highly random (entropies >0.7). The randomness limits the applicability of purely polarimetric approaches to soil moisture estimation, as it causes a tailor-made decomposition to break down. For comparison, the L-band scattering response is more surfacelike, also in terms of its angular characteristics. The second challenge concerns the large spatial but small temporal variability of soil moisture observed at our site. Accordingly, the Radarsat-2 C-band backscatter has a limited dynamic range (~2 dB). However, contrary to polarimetric indicators, it shows a clear surface soil moisture signal. To account for the small dynamic range while retaining a 100-m spatial resolution, we embed an empirical time-series model in a Bayesian framework. This framework adaptively pools information from neighboring grid cells, thus increasing the precision. The retrieved soil moisture index achieves correlations of 0.3–0.5 with in situ data at 5 cm depth and, upon calibration, root-mean-square errors of <0.04 m3m−3. As this approach is applicable to Sentinel-1 data, it can potentially provide frequent soil moisture estimates across large regions. In the long term, L-band data hold greater promise for operational retrievals.
Knowledge of soil moisture conditions is important for modeling soil temperatures, as soil moisture influences the thermal dynamics in multiple ways. However, in permafrost regions, soil moisture is highly heterogeneous and difficult to model. Satellite soil moisture data may fill this gap, but the degree to which they can improve permafrost modeling is unknown. To explore their added value for modeling soil temperatures, we assimilate fine‐scale satellite surface soil moisture into the CryoGrid‐3 permafrost model, which accounts for the soil moisture's influence on the soil thermal properties and the surface energy balance. At our study site in the Canadian Arctic, the assimilation improves the estimates of deeper (>10 cm) soil temperatures during summer but not consistently those of the near‐surface temperatures. The improvements in the deeper temperatures are strongly contingent on soil type: They are largest for porous organic soils (30%), smaller for thin organic soil covers (20%), and they essentially vanish for mineral soils (only synthetic data available). That the improvements are greatest over organic soils reflects the strong coupling between soil moisture and deeper temperatures. The coupling arises largely from the diminishing soil thermal conductivity with increasing desiccation thanks to which the deeper soil is kept cool. It is this association of dry organic soils being cool at depth that lets the assimilation revise the simulated soil temperatures toward the actually measured ones. In the future, the increasing availability of satellite soil moisture data holds promise for the operational monitoring of soil temperatures, hydrology, and biogeochemistry.
Bitumen extraction via surface mining in the Athabasca Oil Sands Region results in permanent alteration of boreal forests and wetlands. As part of their legal requirements, oil companies must reclaim disturbed landscapes into functioning ecosystems. Despite considerable work establishing upland forests, only two pilot wetland-peatland systems integrated within a watershed have been constructed to date. Peatland reclamation is challenging as it requires complete reconstruction with few guidelines or previous work in this region. Furthermore, the variable sub-humid climate and salinity of tailings materials present additional challenges. In 2012, Syncrude Canada Ltd. constructed a 52-ha pilot upland-wetland system, the Sandhill Fen Watershed, which was designed with a pump and underdrain system to provide freshwater and enhance drainage to limit salinization from underlying soft tailings materials that have elevated electrical conductivity (EC) and Na+. The objective of this research is to evaluate the hydrochemical response of a constructed wetland to variations in hydrology and water management with respect to water sources, flow pathways and major chemical transformations in the three years following commissioning. Results suggest that active water management practices in 2013 kept EC relatively low, with most wetland sites <1000 μS/cm with Na+ concentrations <250 mg/L. With limited management in 2014 and 2015, the EC increased in the wetland to >1000 μS/cm in 2014 and >2000 μS/cm in 2015. The most notable change was the emergence of several Na+ enriched zones in the margins. Here, Na+ concentrations were two to three times higher than other sites. Stable isotopes of water support that the Na+ enriched areas arise from underlying process-affected water in the tailings, providing evidence of its upward transport and seepage under a natural hydrologic regime. In future years, salinity is expected to evolve in its flow pathways and diffusion, yet the timeline and extent of these changes are uncertain.
Beavers ingeniously alter environments to suit their needs of predator protection and food access, creating widespread effects on surface waters throughout their range. Beaver are thus considered the quintessential ecosystem engineer. They “engineer” landscapes largely by building dams across low-order streams to retain water. Dam building changes a wide range of ecological, hydrologic, and geomorphic processes that transform rivers into complex wetland systems capable of supporting a diversity of aquatic and terrestrial species. Although less studied, beavers live in and can significantly impact landscape processes in large rivers, wetlands, and lakes and unexpected places like landslides, brackish deltas, and glacial discharge environments. The earliest works on beaver are from a time when beaver were very much still being trapped to supply the fashion market in Europe with pelts (c. late 1800s to early 1900s). Works from this period primarily document the natural history of beaver. Research interest in beaver waned for several decades, coincident with low beaver populations. In the 1980s and 1990s, however, researcher interest in beaver was again piqued, which led to a little over a decade of studies documenting a range of ecosystem effects of beaver. Research on beaver ecosystem engineering was reinvigorated again in the mid- to late-2000s, coincident with rewilding efforts in Europe, beaver use in stream restoration activities in the United States, and rapid spread of the exotic, invasive beaver population in Tierra del Fuego. This encyclopedia entry provides a summary of the hydrogeomorphic processes known to be beaver-mediated, as well as the state of knowledge of how beaver form stream valleys and shape wetland ecosystems. Included are brief annotations of key literature. Ecological and biogeochemical impacts of beaver ponds are extensive, but a full description of them are beyond the scope of this annotated bibliography. The topic could benefit from greater synergistic and integrative research among biologists, geomorphologists, ecologists, and hydrologists.
In this paper, the hydrological impacts of future socio-economic and climatic development are assessed for a regional-scale Alpine catchment (Brixental, Tyrol, Austria). Therefore, coupled storylines of future land use and climate scenarios were developed in a transdisciplinary stakeholder process by means of questionnaire analyses and interviews with local experts from various relevant societal sectors. Resulting future land use maps for each decade were used as spatial input in the hydrological model WaSiM, to which a new module for the consideration of snow-canopy interaction processes has been added. Simulation results for three developed storylines, each combined with a moderate (A1B) and an extreme (RCP8.5) climate future, show that in a warmer and dryer climate the amount of annual simulated streamflow at the gauge of the catchment undergoes a significant reduction. The (mainly natural) reforestation of the catchment - caused by abandonment of previously cultivated areas - leads to additional losses of water by enhanced interception and evapotranspiration processes. Further cultivation of the current mountain pasture areas has a certain potential to attenuate undesirable long-term impacts of climate change on the catchment water balance.
This paper presents a novel data fusion technique for improving the snow cover monitoring for a mesoscale Alpine region, in particular in those areas where two information sources disagree. The presented methodological innovation consists in the integration of remote-sensing data products and the numerical simulation results by means of a machine learning classifier (support vector machine), capable to extract information from their quality measures. This differs from the existing approaches where remote sensing is only used for model tuning or data assimilation. The technique has been tested to generate a time series of about 1300 snow maps for the period between October 2012 and July 2016. The results show an average agreement between the fused product and the reference ground data of 96%, compared to 90% of the moderate-resolution imaging spectroradiometer (MODIS) data product and 92% of the numerical model simulation. Moreover, one of the most important results is observed from the analysis of snow cover area (SCA) time series, where the fused product seems to overcome the well-known underestimation of snow in forest of the MODIS product, by accurately reproducing the SCA peaks of winter season.
Detection of changes in spatial processes has long been of interest to quantitative geographers seeking to test models, validate theories, and anticipate change. Given the current “data-rich” environment of today, it may be time to reconsider the methodological approaches used for quantifying change in spatial processes. New tools emerging from computer vision research may hold particular potential to make significant advances in quantifying changes in spatial processes. In this article, two comparative indices from computer vision, the structural similarity (SSIM) index, and the complex wavelet structural similarity (CWSSIM) index were examined for their utility in the comparison of real and simulated spatial data sets. Gaussian Markov random fields were simulated and compared with both metrics. A case study into comparison of snow water equivalent spatial patterns over northern Canada was used to explore the properties of these indices on real-world data. CWSSIM was found to be less sensitive than SSIM to changing window dimension. The CWSSIM appears to have significant potential in characterizing change and/or similarity; distinguishing between map pairs that possess subtle structural differences. Further research is required to explore the utility of these approaches for empirical comparison cases of different forms of landscape change and in comparison to human judgments of spatial pattern differences.
Abstract In mountain terrain, well-configured high-resolution atmospheric models are able to simulate total annual rain and snowfall better than spatial estimates derived from in situ observational networks of precipitation gauges, and significantly better than radar or satellite-derived estimates. This conclusion is primarily based on comparisons with streamflow and snow in basins across the western United States and in Iceland, Europe, and Asia. Even though they outperform gridded datasets based on gauge networks, atmospheric models still disagree with each other on annual average precipitation and often disagree more on their representation of individual storms. Research to address these difficulties must make use of a wide range of observations (snow, streamflow, ecology, radar, satellite) and bring together scientists from different disciplines and a wide range of communities.
Mountain regions with complex orography are a particular challenge for regional climate simulations. High spatial resolution is required to account for the high spatial variability in meteorological conditions. This study presents a very high-resolution regional climate simulation (5 km) using the Weather Research and Forecasting Model (WRF) for the central part of Europe including the Alps. Global boundaries are dynamically downscaled for the historical period 1980–2009 (ERA-Interim and MPI-ESM), and for the near future period 2020–2049 (MPI-ESM, scenario RCP4.5). Model results are compared to gridded observation datasets and to data from a dense meteorological station network in the Berchtesgaden Alps (Germany). Averaged for the Alps, the mean bias in temperature is about −0.3 °C, whereas precipitation is overestimated by +14% to +19%. R 2 values for hourly, daily and monthly temperature range between 0.71 and 0.99. Temporal precipitation dynamics are well reproduced at daily and monthly scales (R 2 between 0.36 and 0.85), but are not well captured at hourly scale. The spatial patterns, seasonal distributions, and elevation-dependencies of the climate change signals are investigated. Mean warming in Central Europe exhibits a temperature increase between 0.44 °C and 1.59 °C and is strongest in winter and spring. An elevation-dependent warming is found for different specific regions and seasons, but is absent in others. Annual precipitation changes between −4% and +25% in Central Europe. The change signals for humidity, wind speed, and incoming short-wave radiation are small, but they show distinct spatial and elevation-dependent patterns. On large-scale spatial and temporal averages, the presented 5 km RCM setup has in general similar biases as EURO-CORDEX simulations, but it shows very good model performance at the regional and local scale for daily meteorology, and, apart from wind-speed and precipitation, even for hourly values.
We determined the streamflow transit time and the subsurface water storage volume in the glacierized high-elevation catchment of the Rofenache (Oetztal Alps, Austria) with the lumped parameter transit time model TRANSEP. Therefore we enhanced the surface energy-balance model ESCIMO to simulate the ice melt, snowmelt and rain input to the catchment and associated δ18O values for 100 m elevation bands. We then optimized TRANSEP with streamflow volume and δ18O for a four-year period with input data from the modified version of ESCIMO at a daily resolution. The median of the 100 best TRANSEP runs revealed a catchment mean transit time of 9.5 years and a mobile storage of 13,846 mm. The interquartile ranges of the best 100 runs were large for both, the mean transit time (8.2–10.5 years) and the mobile storage (11,975–15,382 mm). The young water fraction estimated with the sinusoidal amplitude ratio of input and output δ18O values and delayed input of snow and ice melt was 47%. Our results indicate that streamflow is dominated by the release of water younger than 56 days. However, tracers also revealed a large water volume in the subsurface with a long transit time resulting to a strongly delayed exchange with streamflow and hence also to a certain portion of relatively old water: The median of the best 100 TRANSEP runs for streamflow fraction older than five years is 28%.
This paper presents a new concept to derive the snow water equivalent (SWE) based on the joint use of snow model (AMUNDSEN) simulation, ground data, and auxiliary products derived from remote sensing. The main objective is to characterize the spatial-temporal distribution of the model-derived SWE deviation with respect to the real SWE values derived from ground measurements. This deviation is due to the intrinsic uncertainty of any theoretical model, related to the approximations in the analytical formulation. The method, based on the k-NN algorithm, computes the deviation for some labeled samples, i.e., samples for which ground measurements are available, in order to characterize and model the deviations associated to unlabeled samples (no ground measurements available), by assuming that the deviations of samples vary depending on the location within the feature space. Obtained results indicate an improved performance with respect to AMUNDSEN model, by decreasing the RMSE and the MAE with ground data, on average, from 154 to 75 mm and from 99 to 45 mm, respectively. Furthermore, the slope of regression line between estimated SWE and ground reference samples reaches 0.9 from 0.6 of AMUNDSEN simulations, by reducing the data spread and the number of outliers.
Water is of uttermost importance for human well-being and a central resource in sustainable development. Many simulation models for sustainable water management, however, lack explanatory and predictive power because the two-way dynamic feedbacks between human and water systems are neglected. With Agent-based Modelling of Resources (Aqua.MORE; here, of the resource water), we present a platform that can support understanding, interpretation and scenario development of resource flows in coupled human–water systems at the catchment scale. Aqua.MORE simulates the water resources in a demand and supply system, whereby water fluxes and socioeconomic actors are represented by individual agents that mutually interact and cause complex feedback loops. First, we describe the key steps for developing an agent-based model (ABM) of water demand and supply, using the platform Aqua.MORE. Second, we illustrate the modelling process by application in an idealized Alpine valley, characterized by touristic and agricultural water demand sectors. Here, the implementation and analysis of scenarios highlights the possibilities of Aqua.MORE (1) to easily deploy case study-specific agents and characterize them, (2) to evaluate feedbacks between water demand and supply and (3) to compare the effects of different agent behavior or water use strategies. Thereby, we corroborate the potential of Aqua.MORE as a decision-support tool for sustainable watershed management.
Scientific workflow management system (SWFMS) is one of the inherent parts of Big Data analytics systems. Analyses in such data intensive research using workflows are very costly. SWFMSs or workflows keep track of every bit of executions through logs, which later could be used on demand. For example, in the case of errors, security breaches, or even any conditions, we may need to trace back to the previous steps or look at the intermediate data elements. Such fashion of logging is known as workflow provenance. However, prominent workflows being domain specific and developed following different programming paradigms, their architectures, logging mechanisms, information in the logs, provenance queries, and so on differ significantly. So, provenance technology of one workflow from a certain domain is not easily applicable in another domain. Facing the lack of a general workflow provenance standard, we propose a programming model for automated workflow logging. The programming model is easy to implement and easily configurable by domain experts independent of workflow users. We implement our workflow programming model on Bioinformatics research—for evaluation and collect workflow logs from various scientific pipelines’ executions. Then we focus on some fundamental provenance questions inspired by recent literature that can derive many other complex provenance questions. Finally, the end users are provided with discovered insights from the workflow provenance through online data visualization as a separate web service.
Big Data analytics or systems developed with parallel distributed processing frameworks (e.g., Hadoop and Spark) are becoming popular for finding important insights from a huge amount of heterogeneous data (e.g., image, text, and sensor data). These systems offer a wide range of tools and connect them to form workflows for processing Big Data. Independent schemes from different studies for managing programs and data of workflows have been already proposed by many researchers and most of the systems have been presented with data or metadata management. However, to the best of our knowledge, no study particularly discusses the performance implications of utilizing intermediate states of data and programs generated at various execution steps of a workflow in distributed platforms. In order to address the shortcomings, we propose a scheme of Big Data management for micro-level modular computation-intensive programs in a Spark and Hadoop-based platform. In this paper, we investigate whether management of the intermediate states can speed up the execution of an image processing pipeline consisting of various image processing tools/APIs in Hadoop Distributed File System (HDFS) while ensuring appropriate reusability and error monitoring. From our experiments, we obtained prominent results, e.g., we have reported that with the intermediate data management, we can gain up to 87% computation time for an image processing job.
Abstract Western Boreal Canada could experience drier hydrometeorological conditions under future climatic changes, and the drying of nonpermafrost peatlands can lead to higher frequency and extent of wildfires. Despite increasing pressures, our understanding of the impact of fire on dissolved organic carbon (DOC) concentration and quality across boreal peatlands is not consistent. This study capitalizes on the rare opportunity of having 3 years of prefire and 3 years of postfire DOC data at a treed, moderate‐rich fen in the Western Boreal Plain, northern Alberta, to investigate wildfire effects on peatland DOC dynamics. We investigated whether a wildfire facilitated any changes in the pore water DOC concentration and quality. There was very little impact of the fire directly, with no significant changes in DOC concentrations postfire. We highlight that DOC patterns are more likely to be controlled by local hydrogeological factors than any effect of fire. Fall hydrological conditions and subsequent winter storage processes impose a strong control on DOC concentrations the following year. We suggest that the presence or absence of concrete ground frost in the fen (determined by fall water table position) influences overwinter storage changes, controlling the effect that DOC‐poor snowmelt may have on pore water concentrations. However, an increase in SUVA 254 was found 2 years postfire, indicating an increase in aromaticity. These results highlight the need for careful consideration of the local hydrogeologic setting and hydrological regime when predicting and analysing trends in DOC concentrations and quality.
Abstract In the sub‐humid Western Boreal Plains of Alberta, where evapotranspiration often exceeds precipitation, trembling aspen ( Populus tremuloides Michx.) uplands often depend on adjacent peatlands for water supply through hydraulic redistribution. Wildfire is common in the Boreal Plains, so the resilience of the transfer of water from peatlands to uplands through roots immediately following wildfire may have implications for aspen succession. The objective of this research was to characterize post‐fire peatland‐upland hydraulic connectivity and assess controls on aspen transpiration (as a measure of stress and productivity) among landscape topographic positions. In May 2011, a wildfire affected 90,000 ha of north central Alberta, including the Utikuma Region Study Area (URSA). Portions of an URSA glacio‐fluval outwash lake catchment were burned, which included forests and a small peatland. Within 1 year after the fire, aspen were found to be growing in both the interior and margins of this peatland. Across recovering land units, transpiration varied along a topographic gradient of upland midslope (0.42 mm hr −1 ) > upland hilltop (0.29 mm hr −1 ) > margin (0.23 mm hr −1 ) > peatland (0.10 mm hr −1 ); similar trends were observed with leaf area and stem heights. Although volumetric water content was below field capacity, P. tremuloides were sustained through roots present, likely before fire, in peatland margins through hydraulic redistribution. Evidence for this was observed through the analysis of oxygen (δ 18 O) and hydrogen (δ 2 H) isotopes where upland xylem and peat core signatures were −10.0‰ and −117.8‰ and −9.2‰ and −114.0‰, respectively. This research highlights the potential importance of hydraulic redistribution to forest sustainability and recovery, in which the continued delivery of water may result in the encroachment of aspen into peatlands. As such, we suggest that through altering ecosystem services, peatland margins following fire may be at risk to aspen colonization during succession.
Abstract. The hummock–hollow classification framework used to categorize peatland ecosystem microtopography is pervasive throughout peatland experimental designs and current peatland ecosystem modeling approaches. However, identifying what constitutes a representative hummock–hollow pair within a site and characterizing hummock–hollow variability within or between peatlands remains largely unassessed. Using structure from motion (SfM), high-resolution digital elevation models (DEMs) of hummock–hollow microtopography were used to (1) examine how much area needs to be sampled to characterize site-level microtopographic variation; and (2) examine the potential role of microtopographic shape/structure on biogeochemical fluxes using plot-level data from nine northern peatlands. To capture 95 % of site-level microtopographic variability, on average, an aggregate sampling area of 32 m2 composed of 10 randomly located plots was required. Both site- (i.e. transect data) and plot-level (i.e. SfM-derived DEM) results show that microtopographic variability can be described as a fractal at the submeter scale, where contributions to total variance are very small below a 0.5 m length scale. Microtopography at the plot level was often found to be non-bimodal, as assessed using a Gaussian mixture model (GMM). Our findings suggest that the non-bimodal distribution of microtopography at the plot level may result in an undersampling of intermediate topographic positions. Extended to the modeling domain, an underrepresentation of intermediate microtopographic positions is shown to lead to potentially large flux biases over a wide range of water table positions for ecosystem processes which are non-linearly related to water and energy availability at the moss surface. Moreover, our simple modeling results suggest that much of the bias can be eliminated by representing microtopography with several classes rather than the traditional two (i.e. hummock/hollow). A range of tools examined herein can be used to easily parameterize peatland models, from GMMs used as simple transfer functions to spatially explicit fractal landscapes based on simple power-law relations between microtopographic variability and scale.
Abstract. Climate change poses great risks to western Canada's ecosystem and socioeconomical development. To assess these hydroclimatic risks under high-end emission scenario RCP8.5, this study used the Weather Research Forecasting (WRF) model at a convection-permitting (CP) 4 km resolution to dynamically downscale the mean projection of a 19-member CMIP5 ensemble by the end of the 21st century. The CP simulations include a retrospective simulation (CTL, 2000–2015) for verification forced by ERA-Interim and a pseudo-global warming (PGW) for climate change projection forced with climate change forcing (2071–2100 to 1976–2005) from CMIP5 ensemble added on ERA-Interim. The retrospective WRF-CTL's surface air temperature simulation was evaluated against Canadian daily analysis ANUSPLIN, showing good agreements in the geographical distribution with cold biases east of the Canadian Rockies, especially in spring. WRF-CTL captures the main pattern of observed precipitation distribution from CaPA and ANUSPLIN but shows a wet bias near the British Columbia coast in winter and over the immediate region on the lee side of the Canadian Rockies. The WRF-PGW simulation shows significant warming relative to CTL, especially over the polar region in the northeast during the cold season, and in daily minimum temperature. Precipitation changes in PGW over CTL vary with the seasons: in spring and late autumn precipitation increases in most areas, whereas in summer in the Saskatchewan River basin and southern Canadian Prairies, the precipitation change is negligible or decreased slightly. With almost no increase in precipitation and much more evapotranspiration in the future, the water availability during the growing season will be challenging for the Canadian Prairies. The WRF-PGW projected warming is less than that by the CMIP5 ensemble in all seasons. The CMIP5 ensemble projects a 10 %–20 % decrease in summer precipitation over the Canadian Prairies and generally agrees with WRF-PGW except for regions with significant terrain. This difference may be due to the much higher resolution of WRF being able to more faithfully represent small-scale summer convection and orographic lifting due to steep terrain. WRF-PGW shows an increase in high-intensity precipitation events and shifts the distribution of precipitation events toward more extremely intensive events in all seasons. Due to this shift in precipitation intensity to the higher end in the PGW simulation, the seemingly moderate increase in the total amount of precipitation in summer east of the Canadian Rockies may underestimate the increase in flooding risk and water shortage for agriculture. The change in the probability distribution of precipitation intensity also calls for innovative bias-correction methods to be developed for the application of the dataset when bias correction is required. High-quality meteorological observation over the region is needed for both forcing high-resolution climate simulation and conducting verification. The high-resolution downscaled climate simulations provide abundant opportunities both for investigating local-scale atmospheric dynamics and for studying climate impacts on hydrology, agriculture, and ecosystems.

2018

The ideas presented by Stahl (2018) are intriguing. There is a wealth of information that supports that groundwater pumping has perturbed the hydrologic cycle at a global scale (Konikow 2011; Rodell et al. 2018) and perturbations to global elemental cycles would not be unexpected. However, the analysis presented by Stahl (2018) is problematic. Stahl assumes that the 45% of produced waters from oil field operations that were not used in enhanced oil recovery (EOR) are released into the more active portion of the hydrological cycle based on 2007 figures for the United States from Clark and Veil (2009). This figure is substantially lower in reality. Clark and Veil (2009) report that 38.2% of produced waters were injected into nonproducing strata. This injection occurs almost exclusively through Class II disposal wells, which are typically installed in saline aquifers (EPA 2018). Similar practices have been noted in Canada, where there has been a net gain in the amount of water in the Western Canada Sedimentary Basin (Ferguson 2015). In addition, Stahl states that 45% of Shell’s produced water is discharged at the surface based on an estimate from Khatib and Verbeek (2003). However, that study also noted that much of this discharge was to the ocean as part of offshore drilling activities. The overestimation of addition of produced water to the active portion of global elemental cycles will have a notable effect on estimates of fluxes of elements such as Li, Na, Cl, and Ca, which are found in high concentrations
Brines are commonly found at depth in sedimentary basins. Many of these brines are known to be connate waters that have persisted since the early Paleozoic Era. Yet questions remain about their distribution and mechanisms for retention at depth in the Earth's crust. Here we demonstrate that there is insufficient topography to drive these dense fluids from the bottom of deep sedimentary basins. Our assessment based on driving force ratio indicates that sedimentary basins with driving force ratio > 1 contain connate waters and frequently host large evaporite deposits. These stagnant conditions appear to be relatively stable over geological time and insensitive to factors such as glaciations, erosion, compaction, and hydrocarbon generation.
Groundwater resources are being stressed from the top down and bottom up. Declining water tables and near-surface contamination are driving groundwater users to construct deeper wells in many US aquifer systems. This has been a successful short-term mitigation measure where deep groundwater is fresh and free of contaminants. Nevertheless, vertical salinity profiles are not well-constrained at continental-scales. In many regions, oil and gas activities use pore spaces for energy production and waste disposal. Here we quantify depths that aquifer systems transition from fresh-to-brackish and where oil and gas activities are widespread in sedimentary basins across the United States. Fresh-brackish transitions occur at relatively shallow depths of just a few hundred meters, particularly in eastern US basins. We conclude that fresh groundwater is less abundant in several key US basins than previously thought; therefore drilling deeper wells to access fresh groundwater resources is not feasible extensively across the continent. Our findings illustrate that groundwater stores are being depleted not only by excessive withdrawals, but due to injection, and potentially contamination, from the oil and gas industry in areas of deep fresh and brackish groundwater.
Extensive dissolution of evaporites has occurred in the Williston Basin, Canada, but it is unclear what effect this has had on bulk permeability. The bulk of this dissolution has occurred from the Prairie Evaporite Formation, which is predominantly halite and potash. However, minor evaporite beds and porosity infilling have also been removed from the overlying Dawson Bay and Souris River formations, which are predominantly carbonates. This study examines whether permeability values in the Dawson Bay and Souris River formations have been affected by dissolution, by analyzing 142 drillstem tests from those formations. For both the Dawson Bay and Souris River formations, the highest permeabilities were found in areas where halite dissolution had occurred. However, the mean permeabilities were not statistically different in areas of halite dissolution compared to those containing connate water. Subsequent precipitation of anhydrite is known to have clogged pore spaces and fractures in some instances. Geochemical relationships found here support this idea but there is no statistically significant relationship between anhydrite saturation and permeability. Geomechanical effects, notably closure of fractures due to collapse, could be a mitigating factor. The results indicate that coupling dissolution and precipitation to changes in permeability in regional flow models remains a significant challenge.
We thank Audrey Innes for isotope analysis at University of Aberdeen for Bruntland Burn and Krycklan sites, Johannes Tiwari (SLU) for isotope sampling in Krycklan, Pernilla Lofvenius (SLU) for providing PET data for Krycklan (via SITES), and Jeff McDonnell and Kim Janzen (University of Saskatchewan) for soil water isotope analysis for the Dorset and Wolf Creek sites. The Krycklan part was funded by the KAW Branch-Point project. We acknowledge the funding from the European Research Council (ERC, project GA 335910 VeWa). We thank the Editor and three anonymous reviewers for their critical comments during the peer-review process.
Use of isotopes to quantify the temporal dynamics of the transformation of precipitation into run-off has revealed fundamental new insights into catchment flow paths and mixing processes that influence biogeochemical transport. However, catchments underlain by permafrost have received little attention in isotope-based studies, despite their global importance in terms of rapid environmental change. These high-latitude regions offer limited access for data collection during critical periods (e.g., early phases of snowmelt). Additionally, spatio-temporal variable freeze-thaw cycles, together with the development of an active layer, have a time variant influence on catchment hydrology. All of these characteristics make the application of traditional transit time estimation approaches challenging. We describe an isotope-based study undertaken to provide a preliminary assessment of travel times at Siksik Creek in the western Canadian Arctic. We adopted a model-data fusion approach to estimate the volumes and isotopic characteristics of snowpack and meltwater. Using samples collected in the spring/summer, we characterize the isotopic composition of summer rainfall, melt from snow, soil water, and stream water. In addition, soil moisture dynamics and the temporal evolution of the active layer profile were monitored. First approximations of transit times were estimated for soil and streamwater compositions using lumped convolution integral models and temporally variable inputs including snowmelt, ice thaw, and summer rainfall. Comparing transit time estimates using a variety of inputs revealed that transit time was best estimated using all available inflows (i.e., snowmelt, soil ice thaw, and rainfall). Early spring transit times were short, dominated by snowmelt and soil ice thaw and limited catchment storage when soils are predominantly frozen. However, significant and increasing mixing with water in the active layer during the summer resulted in more damped steam water variation and longer mean travel times (~1.5 years). The study has also highlighted key data needs to better constrain travel time estimates in permafrost catchments.
Geochemical and isotopic tracers were often used in mixing models to estimate glacier melt contributions to streamflow, whereas the spatio‐temporal variability in the glacier melt tracer signature and its influence on tracer‐based hydrograph separation results received less attention. We present novel tracer data from a high‐elevation catchment (17 km2, glacierized area: 34%) in the Oetztal Alps (Austria) and investigated the spatial, as well as the subdaily to monthly tracer variability of supraglacial meltwater and the temporal tracer variability of winter baseflow to infer groundwater dynamics. The streamflow tracer variability during winter baseflow conditions was small, and the glacier melt tracer variation was higher, especially at the end of the ablation period. We applied a three‐component mixing model with electrical conductivity and oxygen‐18. Hydrograph separation (groundwater, glacier melt, and rain) was performed for 6 single glacier melt‐induced days (i.e., 6 events) during the ablation period 2016 (July to September). Median fractions (±uncertainty) of groundwater, glacier melt, and rain for the events were estimated at 49±2%, 35±11%, and 16±11%, respectively. Minimum and maximum glacier melt fractions at the subdaily scale ranged between 2±5% and 76±11%, respectively. A sensitivity analysis showed that the intraseasonal glacier melt tracer variability had a marked effect on the estimated glacier melt contribution during events with large glacier melt fractions of streamflow. Intra‐daily and spatial variation of the glacier melt tracer signature played a negligible role in applying the mixing model. The results of this study (a) show the necessity to apply a multiple sampling approach in order to characterize the glacier melt end‐member and (b) reveal the importance of groundwater and rainfall–runoff dynamics in catchments with a glacial flow regime.
Abstract Blowing snow particle transport responds to wind motions across many length and time scales. This coupling is nonlinear by nature and complicated in atmospheric flows where eddies of many sizes are superimposed. In mountainous terrain, wind flow descriptions are further complicated by topographically influenced or enhanced flows. To improve the current understanding and modeling of blowing snow transport in complex terrain, statistically significant timing and frequencies of wind–snow coupling were identified in high-frequency observations of surface blowing snow and near-surface turbulence from a mountain field site in the Canadian Rockies. Investigation of the mechanisms influencing near-surface, high-frequency turbulence and snow concentration fluctuations provided strong evidence for amplitude modulation from large-scale motions. The large-scale atmospheric motions modulating near-surface turbulence and snow transport were then compared to specific quadrant analysis structures recently identified as relevant for outdoor blowing snow transport. The results suggest that large atmospheric structures modulate the amplitude of high-frequency turbulence and modify turbulence statistics typically used to model blowing snow. Additionally, blowing snow was preferentially redistributed under the footprint of these same sweep motions, with both low- and high-frequency coherence increasing in their presence.
Earth‐orbiting satellites provide valuable observations of upstream river conditions worldwide. These observations can be used in real‐time applications like early flood warning systems and reservoir operations, provided they are made available to users with sufficient lead time. Yet the temporal requirements for access to satellite‐based river data remain uncharacterized for time‐sensitive applications. Here we present a global approximation of flow wave travel time to assess the utility of existing and future low‐latency/near‐real‐time satellite products, with an emphasis on the forthcoming SWOT satellite mission. We apply a kinematic wave model to a global hydrography data set and find that global flow waves traveling at their maximum speed take a median travel time of 6, 4, and 3 days to reach their basin terminus, the next downstream city, and the next downstream dam, respectively. Our findings suggest that a recently proposed ≤2‐day data latency for a low‐latency SWOT product is potentially useful for real‐time river applications.
Roughly 3% of the Earth’s land surface burns annually, representing a critical exchange of energy andmatter between the land and atmosphere via combustion. Fires range from slow smouldering peatfires, to low-intensity surface fires, to intense crown fires, depending on vegetation structure, fuelmoisture, prevailing climate, and weather conditions. While the links between biogeochemistry,climate and fire are widely studied within Earth system science, these relationships are also mediatedby fuels—namely plants and their litter—that are the product of evolutionary and ecologicalprocesses. Fire is a powerful selective force and, over their evolutionary history, plants have evolvedtraits that both tolerate and promote fire numerous times and across diverse clades. Here we outline aconceptual framework of how plant traits determine the flammability of ecosystems and interact withclimate and weather to influence fire regimes. We explore how these evolutionary and ecologicalprocesses scale to impact biogeochemical and Earth system processes. Finally, we outline severalresearch challenges that, when resolved, will improve our understanding of the role of plant evolutionin mediating the fire feedbacks driving Earth system processes. Understanding current patterns of fireand vegetation, as well as patterns of fire over geological time, requires research that incorporatesevolutionary biology, ecology, biogeography, and the biogeosciences.
Decades of studies on endocrine disruption have suggested the need to manage the release of key estrogens from municipal wastewater treatment plants (WWTP). However, the proposed thresholds are below the detection limits of most routine chemical analysis, thereby restricting the ability of watershed managers to assess the environmental exposure appropriately. In this study, we demonstrated the utility of a mechanistic model to address the data gaps on estrogen exposure. Concentrations of the prominent estrogenic contaminants in wastewaters (estrone, estradiol, and ethinylestradiol) were simulated in the Grand River in southern Ontario (Canada) for nine years, including a period when major WWTP upgrades occurred. The predicted concentrations expressed as total estrogenicity (E2 equivalent concentrations) were contrasted to a key estrogenic response (i.e., intersex) in rainbow darter (Etheostoma caeruleum), a wild sentinel fish species. A predicted total estrogenicity in the river of ≥10 ng/L E2 equivalents was associated with high intersex incidence and severity, whereas concentrations <0.1 ng/L E2 equivalents were associated with minimal intersex expression. Exposure to a predicted river concentration of 0.4 ng/L E2 equivalents, the environmental quality standard (EQS) proposed by the European Union for estradiol, was associated with 34% (95% CI:30-38) intersex incidence and a very low severity score of 0.6 (95% CI:0.5-0.7). This exposure is not predicted to cause adverse effects in rainbow darter. The analyses completed in this study were only based on the predicted presence of three major estrogens (E1, E2, EE2), so caution must be exercised when interpreting the results. Nevertheless, this study illustrates the use of models for exposure assessment, especially when measured data are not available.
In this study, the estrogenicity of two major wastewater treatment plant (WWTP) effluents located in the central reaches of the Grand River watershed in southern Ontario was estimated using population demographics, excretion rates, and treatment plant-specific removals. Due to the lack of data on estrogen concentrations from direct measurements at WWTPs, the treatment efficiencies through the plants were estimated using the information obtained from an effects-directed analysis. The results show that this approach could effectively estimate the estrogenicity of WWTP effluents, both before and after major infrastructure upgrades were made at the Kitchener WWTP. The model was then applied to several possible future scenarios including population growth and river low flow conditions. The scenario analyses showed that post-upgrade operation of the Kitchener WWTP will not release highly estrogenic effluent under the 2041 projected population increase (36%) or summer low flows. Similarly, the Waterloo WWTP treatment operation is also expected to improve once the upgrades have been fully implemented and is expected to effectively treat estrogens even under extreme scenarios of population growth and river flows. The developed model may be employed to support decision making on wastewater management strategies designed for environmental protection, especially on reducing the endocrine effects in fish exposed to WWTP effluents.
The large mediatic coverage of recent massive wildfires across the world has emphasized the vulnerability of freshwater resources. The extensive hydrogeomorphic effects from a wildfire can impair the ability of watersheds to provide safe drinking water to downstream communities and high-quality water to maintain riverine ecosystem health. Safeguarding water use for human activities and ecosystems is required for sustainable development; however, no global assessment of wildfire impacts on water supply is currently available. Here, we provide the first global evaluation of wildfire risks to water security, in the form of a spatially explicit index. We adapted the Driving forces-Pressure-State-Impact-Response risk analysis framework to select a comprehensive set of indicators of fire activity and water availability, which we then aggregated to a single index of wildfire-water risk using a simple additive weighted model. Our results show that water security in many regions of the world is potentially vulnerable, regardless of socio-economic status. However, in developing countries, a critical component of the risk is the lack of socio-economic capability to respond to disasters. Our work highlights the importance of addressing wildfire-induced risks in the development of water security policies; the geographic differences in the components of the overall risk could help adapting those policies to different regional contexts.
Abstract. Drought is a recurring extreme climate event and among the most costly natural disasters in the world. This is particularly true over Canada, where drought is both a frequent and damaging phenomenon with impacts on regional water resources, agriculture, industry, aquatic ecosystems, and health. However, nationwide drought assessments are currently lacking and impacted by limited ground-based observations. This study provides a comprehensive analysis of historical droughts over the whole of Canada, including the role of large-scale teleconnections. Drought events are characterized by the Standardized Precipitation Evapotranspiration Index (SPEI) over various temporal scales (1, 3, 6, and 12 consecutive months, 6 months from April to September, and 12 months from October to September) applied to different gridded monthly data sets for the period 1950–2013. The Mann–Kendall test, rotated empirical orthogonal function, continuous wavelet transform, and wavelet coherence analyses are used, respectively, to investigate the trend, spatio-temporal patterns, periodicity, and teleconnectivity of drought events. Results indicate that southern (northern) parts of the country experienced significant trends towards drier (wetter) conditions although substantial variability exists. Two spatially well-defined regions with different temporal evolution of droughts were identified – the Canadian Prairies and northern central Canada. The analyses also revealed the presence of a dominant periodicity of between 8 and 32 months in the Prairie region and between 8 and 40 months in the northern central region. These cycles of low-frequency variability are found to be associated principally with the Pacific–North American (PNA) and Multivariate El Niño/Southern Oscillation Index (MEI) relative to other considered large-scale climate indices. This study is the first of its kind to identify dominant periodicities in drought variability over the whole of Canada in terms of when the drought events occur, their duration, and how often they occur.
Abstract. Arctic and subarctic regions are amongst the most susceptible regions on Earth to global warming and climate change. Understanding and predicting the impact of climate change in these regions require a proper process representation of the interactions between climate, carbon cycle, and hydrology in Earth system models. This study focuses on land surface models (LSMs) that represent the lower boundary condition of general circulation models (GCMs) and regional climate models (RCMs), which simulate climate change evolution at the global and regional scales, respectively. LSMs typically utilize a standard soil configuration with a depth of no more than 4 m, whereas for cold, permafrost regions, field experiments show that attention to deep soil profiles is needed to understand and close the water and energy balances, which are tightly coupled through the phase change. To address this gap, we design and run a series of model experiments with a one-dimensional LSM, called CLASS (Canadian Land Surface Scheme), as embedded in the MESH (Modélisation Environmentale Communautaire – Surface and Hydrology) modelling system, to (1) characterize the effect of soil profile depth under different climate conditions and in the presence of parameter uncertainty; (2) assess the effect of including or excluding the geothermal flux in the LSM at the bottom of the soil column; and (3) develop a methodology for temperature profile initialization in permafrost regions, where the system has an extended memory, by the use of paleo-records and bootstrapping. Our study area is in Norman Wells, Northwest Territories of Canada, where measurements of soil temperature profiles and historical reconstructed climate data are available. Our results demonstrate a dominant role for parameter uncertainty, that is often neglected in LSMs. Considering such high sensitivity to parameter values and dependency on the climate condition, we show that a minimum depth of 20 m is essential to adequately represent the temperature dynamics. We further show that our proposed initialization procedure is effective and robust to uncertainty in paleo-climate reconstructions and that more than 300 years of reconstructed climate time series are needed for proper model initialization.
Boreal woodland caribou (Rangifer tarandus caribou) are currently listed as threatened in Canada, with populations in the province of Alberta expected to decline as much as 50 percent over the next 8–15 yr. We assessed the future of caribou habitat across a region of northeast Alberta using a model of habitat-quality and projections of future climate from three general circulation models. We used mapped climatic and topo-edaphic properties to project future upland vegetation cover and a fire simulation model to project the frequency and extent of wildfires. Based on those projections, we quantified the future habitat of caribou according to estimates of nutritional resources and predation risk derived from vegetation cover type and stand age. Grassland vegetation covered up to half of the study area by the 2080s, expanding from >1% in the present and contributing to a significant contraction in mixedwood and coniferous forests. This change in vegetation would increase the risk of predation and disease, as habitat becomes more suitable for white-tailed deer (Odocoileus virginianus) and, consequently, gray wolves (Canis lupus). Borne out, these changes would severely compromise the long-term persistence of caribou in the boreal forest of Alberta.
Abstract A freezing rain event, in which the Meteorological Centre of Canada’s 2.5-km numerical weather prediction system significantly underpredicted the quantity of freezing rain, is examined. The prediction system models precipitation types explicitly, directly from the Milbrandt–Yau microphysics scheme. It was determined that the freezing rain underprediction for this case was due primarily to excessive refreezing of rain, originating from melting snow and graupel, in and under the temperature inversion of the advancing warm front ultimately depleting the supply of rain reaching the surface. The refreezing was caused from excessive collisional freezing between rain and graupel. Sensitivity experiments were conducted to examine the effects of a temperature threshold for collisional freezing and on varying the values of the collection efficiencies between rain and ice-phase hydrometeors. It was shown that by reducing the rain–graupel collection efficiency and by imposing a temperature threshold of −5°C, above which collisional freezing is not permitted, excessive rain–graupel collection and graupel formation can be controlled in the microphysics scheme, leading to an improved simulation of freezing rain at the surface.
Abstract Probable maximum precipitation (PMP) is the key parameter used to estimate the probable maximum flood (PMF), both of which are important for dam safety and civil engineering purposes. The usual operational procedure for obtaining PMP values, which is based on a moisture maximization approach, produces a single PMP value without an estimate of its uncertainty. We therefore propose a probabilistic framework based on a bivariate extreme value distribution to aid in the interpretation of these PMP values. This 1) allows us to evaluate estimates from the operational procedure relative to an estimate of a plausible distribution of PMP values, 2) enables an evaluation of the uncertainty of these values, and 3) provides clarification of the impact of the assumption that a PMP event occurs under conditions of maximum moisture availability. Results based on a 50-yr Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) simulation over North America reveal that operational PMP estimates are highly uncertain and suggest that the assumption that PMP events have maximum moisture availability may not be valid. Specifically, in the climate simulated by CanRCM4, the operational approach applied to 50-yr data records produces a value that is similar to the value that is obtained in our approach when assuming complete dependence between extreme precipitation efficiency and extreme precipitable water. In contrast, our results suggest weaker than complete dependence. Estimates from the operational approach are 15% larger on average over North America than those obtained when accounting for the dependence between precipitation efficiency and precipitable water extremes realistically. A difference of this magnitude may have serious implications in engineering design.
In this study we evaluate a Random Forest (RF) model for characterizing the spatial variability of soil moisture based on model derived from in situ soil moisture samples, geophysical data and RADAR observations. The RF model is run with and without C-band SAR backscatter to understand the importance of the inclusion of SAR data for mapping of soil moisture at field scale. The inclusion of SAR data in the RF resulted in a modest improvement however the geophysical parameters (e.g. soil types and terrain properties) were of greater importance.
Clumping index (CI) is a measure of foliage aggregation relative to a random distribution of leaves in space. The CI can help with estimating fractions of sunlit and shaded leaves for a given value of leaf area index (LAI). Both the CI and LAI can be obtained from global Earth Observing (EO) systems such as the Moderate Resolution Imaging Spectrometer (MODIS). Here, the compatibility between CI and LAI products derived from EO data is examined independently using the theory of spectral invariants, also referred to as photon recollision probability theory (i.e. ‘ $p$ -theory’), along with raw LAI-2000/2200 Plant Canopy Analyzer data from 75 sites distributed across a range of plant functional types (PFTs). The $p$ -theory describes the probability (p-value) that a photon, having intercepted an element in the canopy, will recollide with another canopy element rather than escape the canopy. Our results indicate that the integration of empirically-based CI maps with the MODIS LAI product is feasible, providing a potential means to improve the accuracy of LAI EO data products. Given the strong results for the large range of PFTs explored here, we demonstrate the capacity to obtain p-values for any location solely from EO data. This is relevant for future applications of the photon recollision probability concept for global and local monitoring of vegetation using EO data.
Forests dominate carbon (C) exchanges between the terrestrial biosphere and the atmosphere on land. In the long term, the net carbon flux between forests and the atmosphere has been significantly impacted by changes in forest cover area and structure due to ecological disturbances and management activities. Current empirical approaches for estimating net ecosystem productivity (NEP) rarely consider forest age as a predictor, which represents variation in physiological processes that can respond differently to environmental drivers, and regrowth following disturbance. Here, we conduct an observational synthesis to empirically determine to what extent climate, soil properties, nitrogen deposition, forest age and management influence the spatial and interannual variability of forest NEP across 126 forest eddy-covariance flux sites worldwide. The empirical models explained up to 62% and 71% of spatio-temporal and across-site variability of annual NEP, respectively. An investigation of model structures revealed that forest age was a dominant factor of NEP spatio-temporal variability in both space and time at the global scale as compared to abiotic factors, such as nutrient availability, soil characteristics and climate. These findings emphasize the importance of forest age in quantifying spatio-temporal variation in NEP using empirical approaches.
The impact of urbanization on stream channels is of interest due to the growth of cities and the sensitivity of stream morphology and ecology to hydrologic change. Channel enlargement is a commonly observed effect and channel evolution models can help guide management efforts, but the models must be used in the proper geologic and climatic context. Semi‐alluvial channels characterized by a relatively thin alluvial layer over clay till and a convex channel profile in a temperate climate are not represented in currently available models. In this study we: (i) assess channel enlargement; and (ii) propose a channel evolution model for an urban semi‐alluvial creek in Toronto, Canada. The system is 90% developed with an imperviousness of approximately 47%. Channel enlargement is assessed by comparing 50 year old construction surveys, a recent survey of a relic channel, low‐precision surveys of channel change over a 15 year period, and high‐precision surveys over a three year period. The enlargement ratio of the channel since 1958 is 2.6, but could be as high 8.2 in comparison with the pre‐urban channel. When the increase in flow capacity is considered, the enlargement ratio is 1.9 since 1958 and up to 6.0 in comparison with the pre‐urban channel. Channel enlargement continues in the contemporary channel at an estimated rate of 0.23 m2/year. A five stage model is presented to describe channel evolution in the lower reaches. In this model the coarse lag material from glacial sources provides a natural resilience to the bed and incision occurs only after the increased flows from urbanization are combined with higher slopes as a result of channel straightening or avulsions. Further research should be done to assess stream behaviour close to an identified geologic control point. Copyright © 2018 John Wiley & Sons, Ltd.
Time series remote sensing vegetation indices derived from SPOT 5 data are compared with vegetation structure and eddy covariance flux data at 15 dry to wet reclamation and reference sites within the Oil Sands region of Alberta, Canada. This comprehensive analysis examines the linkages between indicators of ecosystem function and change trajectories observed both at the plot level and within pixels. Using SPOT imagery, we find that higher spatial resolution datasets (e.g. 10 m) improves the relationship between vegetation indices and structural measurements compared with interpolated (lower resolution) pixels. The simple ratio (SR) vegetation index performs best when compared with stem density-based indicators (R2 = 0.65; p < 0.00), while the normalised difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI) are most comparable to foliage indicators (leaf area index (LAI) and canopy cover (R2 = 0.52-0.78; p > 0.02). Fluxes (net ecosystem production (NEP) and gross ecosystem production (GEP)) are most related to NDVI and SAVI when these are interpolated to larger 20 m × 20 m pixels (R2 = 0.44-0.50; p < 0.00). As expected, decreased sensitivity of NDVI is problematic for sites with LAI > 3 m2 m-2, making this index more appropriate for newly regenerating reclamation areas. For sites with LAI < 3 m2 m-2, trajectories of vegetation change can be mapped over time and are within 2.7% and 3.3% of annual measured LAI changes observed at most sites. This study demonstrates the utility of remote sensing in combination with field and eddy covariance data for monitoring and scaling of reclaimed and reference site productivity within and beyond the Oil Sands Region of western Canada.
A nutrient mass balance and a three‐dimensional, coupled hydrodynamic‐ecological model, calibrated and validated for Lake St. Clair with observations from 2009 and 2010, were integrated to estimate monthly lake‐scale nutrient loss rates, and to calculate 3 monthly transport time scales: flushing time, water age, and water residence time. While nutrient loss rates had statistically significant relationships with all transport time scale measures, water age had the strongest explanatory power, with water age and nutrient loss rates both smaller in spring and fall and larger in summer. We show that Lake St. Clair is seasonally divided into two discrete regions of contrasting water age and productivity. The north‐western region is dominated by oligotrophic waters from the St. Clair River, and south‐eastern region is dominated by the nutrient enriched, more productive waters from the Thames‐Sydenham River complex. The spatial and temporal variations in local transport scales and nutrient loss rates, coupled with strong seasonal variations in discharge and nutrient loads from the major tributaries, suggest the need for different load reduction strategies for different tributaries.
There is movement in engineering fields and in Indigenous communities for enhancement of local participation in the design of community infrastructure. Inclusion of community priorities and unique cultural, spiritual, and traditional values harmonize the appearance, location, and functionality of developments with the social and cultural context in which they are built and contribute to holistic wellness. However, co-design processes that align community values and the technical needs of water facilities are difficult to find. A scoping review was conducted to explore the state of knowledge on co-design of water infrastructure in Indigenous Canada to build a knowledge base from which practices and processes could emerge. The scoping results revealed that articles and reports emerged only in recent years, contained case studies and meta-reviews with primary (qualitative) data, and involved community members in various capacities. Overall, 13 articles were reviewed that contributed to understanding co-design for water infrastructure in Indigenous Canada. Barriers to co-design included funding models for Indigenous community infrastructure, difficulties in engineers and designers understanding Indigenous worldviews and paradigms, and a lack of cooperation among stakeholders that contribute to ongoing design failures. A working definition of co-design for Indigenous water infrastructure is presented.
Complex interactions between water, society, the economy, and the environment necessitate attention to how water issues are framed, and the limitations of a water-centric framework for analyzing or solving problems. We explore this complexity through an example of an existing complex, or wicked, policy problem - the case of agricultural wetland drainage in the Canadian Prairies. Agricultural wetland drainage expands the amount of productive agricultural land, increasing agricultural efficiency and productivity. Drainage is also one of the primary drivers of the loss of Canada’s wetlands and is a hotly contentious issue between actors with divergent views and values in the Canadian Prairies. Using the nuances of drainage as an exemplar, we discuss how fragmented framings of water foster perspectives and solutions that fail to consider the full range of aspects and interactions, and contribute to the enduring conflicts that accompany drainage debates in many regions. First, we discuss agricultural wetland drainage as practiced in the province of Saskatchewan, where significant regulatory and governance changes are in progress. Next, we discuss the challenges of policy and governance fragmentation, both specific to water and to the surrounding system. Finally, we note potential alternative framings that, while specific to prairie water governance, provide guidance for how other complex social-ecological challenges might be approached.
Abstract A global meta-analysis consisting of almost three decades of groundwater quality valuation studies is presented. New in this study is the focus on the uncertainties surrounding different groundwater quality levels and the control included for groundwater contaminants originating from agriculture and other sources. Separate meta-regression models are estimated for the USA, Europe and the World, detecting sensitivity to scope and reference dependence. Public willingness to pay appears more sensitive to uncertainty in the baseline scenario than in the policy scenario. The high explanatory power of the estimated meta-regression models and low prediction errors provide confidence in their usefulness for reliable benefits transfer.
Abstract Improving groundwater quality is expected to yield direct use benefits to society (e.g. clean and safe drinking water) and groundwater dependent ecosystems. Ten years after the adoption of the European Groundwater Directive (GWD), policymaker and public understanding of the societal value of groundwater protection is still rather limited. This is partly due to the invisible and intangible nature of groundwater resources and the sheer lack of valuation studies. This study contributes to the limited number of groundwater valuation studies in Europe by estimating the public benefits from improved groundwater quality in the Aveiro Quaternary Aquifer (AQA) in Portugal. This is the first and only economic valuation study of groundwater in Portugal. In order to communicate the various benefits provided by groundwater resources in easy understandable terms to lay people, and to assess public perception and willingness to pay (WTP) for groundwater protection, a groundwater quality ladder was developed based on the threshold values proposed in the GWD. The ladder reflects the different use and non-use values of groundwater quality improvements and accounts for natural background levels of chemicals in groundwater. The large-scale survey targets a representative sample of residents in the AQA. Split samples are used to assess the impact of framing groundwater protection in a broader regional water resources management context, giving part of the sample furthermore time to think about their WTP for the different groundwater threshold levels. Although use values dominate public WTP for the different groundwater threshold values, substantial non-use values are also found. Public WTP is considerable, varying between 20 and 30% over and above the current water bill residents pay for safe drinking water quality and natural background levels, respectively. Giving respondents time to think and framing groundwater protection as part of the improvement of all water resources in the region results in a more conservative WTP estimate. Public WTP is higher for better informed private well owners in rural areas. Aggregated across the entire aquifer the estimated total economic value is 1.5 million euros annually for safe drinking water quality and 3.5 million euros annually for groundwater containing natural background levels only.
The influence of humans on the boreal forest has altered the temporal and spatial patterns of wildfire activity through modification of the physical environment and through fire management for the protection of human and economic values. Wildfires are actively suppressed in areas with higher human influence, but, paradoxically, these areas have more numerous ignitions than low-impact ones because of the high rates of human-ignited fires, especially during the springtime. The aim of this study is to evaluate how humans have altered the temporal patterns of wildfire activity in the Canadian boreal forest by comparing two adjacent areas of low and high human influence, respectively: Wood Buffalo National Park (WBNP) and the Lower Athabasca Plains (LAP). We carried out Singular Spectrum Analysis to identify trends and cycles in wildfires from 1970 to 2015 for the two areas and examined their association with climate conditions. We found human influence to be reflected in wildfire activity in multiple ways: (1) by dampening (i.e., for area burned)—and even reversing (i.e., for the number of fires)—the increasing trends of fire activity usually associated with drier and warmer conditions; (2) by shifting the peak of fire activity from the summer to the spring; (3) by altering the fire-climate association; and (4) by exhibiting more recurrent ( 9 years).
Abstract. This article presents the development of a sub-hourly database of hydrometeorological conditions collected in British Columbia's (BC's) Cariboo Mountains and surrounding area extending from 2006 to present. The Cariboo Alpine Mesonet (CAMnet) forms a network of 11 active hydrometeorological stations positioned at strategic locations across mid- to high elevations of the Cariboo Mountains. This mountain region spans 44 150 km2, forming the northern extension of the Columbia Mountains. Deep fjord lakes along with old-growth western redcedar and hemlock forests reside in the lower valleys, montane forests of Engelmann spruce, lodgepole pine and subalpine fir permeate the mid-elevations, while alpine tundra, glaciers and several large ice fields cover the higher elevations. The automatic weather stations typically measure air and soil temperature, relative humidity, atmospheric pressure, wind speed and direction, rainfall and snow depth at 15 min intervals. Additional measurements at some stations include shortwave and longwave radiation, near-surface air, skin, snow, or water temperature, and soil moisture, among others. Details on deployment sites, the instrumentation used and its precision, the collection and quality control process are provided. Instructions on how to access the database at Zenodo, an online public data repository, are also furnished (https://doi.org/10.5281/zenodo.1195043). Information on some of the challenges and opportunities encountered in maintaining continuous and homogeneous time series of hydrometeorological variables and remote field sites is provided. The paper also summarizes ongoing plans to expand CAMnet to better monitor atmospheric conditions in BC's mountainous terrain, efforts to push data online in (near-)real time, availability of ancillary data and lessons learned thus far in developing this mesoscale network of hydrometeorological stations in the data-sparse Cariboo Mountains.
Workflows are frequently built and used to systematically process large datasets using workflow management systems (WMS). A workflow (i.e., a pipeline) is a finite set of processing modules organized as a series of steps that is applied to an input dataset to produce a desired output. In a workflow management system, users generally create workflows manually for their own investigations. However, workflows can sometimes be lengthy and the constituent processing modules might often be computationally expensive. In this situation, it would be beneficial if users could reuse intermediate stage results generated by previously executed workflows for executing their current workflow.In this paper, we propose a novel technique based on association rule mining for suggesting which intermediate stage results from a workflow that a user is going to execute should be stored for reusing in the future. We call our proposed technique, RISP (Recommending Intermediate States from Pipelines). According to our investigation on hundreds of workflows from two scientific workflow management systems, our proposed technique can efficiently suggest intermediate state results to store for future reuse. The results that are suggested to be stored have a high reuse frequency. Moreover, for creating around 51% of the entire pipelines, we can reuse results suggested by our technique. Finally, we can achieve a considerable gain (74% gain) in execution time by reusing intermediate results stored by the suggestions provided by our proposed technique. We believe that our technique (RISP) has the potential to have a significant positive impact on Big-Data systems, because it can considerably reduce execution time of the workflows through appropriate reuse of intermediate state results, and hence, can improve the performance of the systems.
Abstract This study presents the energy, water, and carbon (C) flux dynamics of a young afforested temperate white pine (Pinus strobus L.) forest in southern Ontario, Canada during the initial fourteen years (2003–2016) of establishment. Energy fluxes, namely, net radiation (Rn), latent heat (LE), and sensible heat (H) flux increased over time, due to canopy development. Annual values of ground heat flux (G) peaked in 2007 and then gradually declined in response to canopy closure. The forest became a consistent C-sink only 5 years after establishment owing in part to low respiratory fluxes from the former agricultural, sandy soils with low residual soil organic matter. Mean annual values of gross ecosystem productivity (GEP), ecosystem respiration (RE), and net ecosystem productivity (NEP) ranged from 494 to 1913, 515 to 1774 and −126 to 216 g C m−2 year−1 respectively, over the study period. Annual evapotranspiration (ET) values ranged from 328 to 429 mm year−1 over the same period. Water use efficiency (WUE) increased with stand age with a mean WUE value of 3.92 g C kg−1 H2O from 2008 to 2016. Multivariable linear regression analysis conducted using observed data suggested that the overall, C and water dynamics of the stand were primarily driven by radiation and temperature, both of which explained 77%, 48%, 28%, and 76% of the variability in GEP, RE, NEP, and ET, respectively. However, late summer droughts, which were prevalent in the region, reduced NEP. The reduction in NEP was enhanced when summer drought events were accompanied by increased heat such as those in 2005, 2012 and 2016. This study contributes to our understanding of the energy, water and C dynamics of afforested temperate conifer plantations and how these forests may respond to changing climate conditions during the crucial initial stage of their life cycle. Our findings also demonstrate the potential of pine plantation stands to sequester atmospheric CO2 in eastern North America.
Ecosystem trajectories are inextricably linked to hydrology; however, water availability is not easily observed within the landscape. The response of vegetation to soil water availability may provide an indicator of local hydrology and the resilience or sensitivity of ecosystems to long‐term changes in water balance. In this study, vegetation trajectories derived from Landsat Modified Soil Adjusted Vegetation Index over a 22‐year period are used as an indicator of spatio‐temporal changes of watershed water balance and surface water storage within 6 proximal watersheds of the Boreal Plains ecozone of Alberta, Canada. The interactions between hydrology, topography, geology, and land cover type are examined as they relate to vegetation change.
Environmental pollutants are known as disruptors of gut microbiota. However, it remains unexplored whether the dysbiosis of gut microbiota by pollutants is durable and transgenerational in teleost. Therefore, this study exposed eggs of marine medaka to environmentally realistic concentrations (0, 1.0, 2.9, or 9.5 μg/L) of perfluorobutanesulfonate (PFBS), a persistent organic pollutant of emerging concern, until sexual maturity. A proportion of F0 adults was dissected after exposure (F0-exposed). Remaining fish were depurated in clean seawater (F0-depurated). F1 offspring were also cultured in clean seawater for a complete life-cycle. Substantial amounts of PFBS were accumulated in F0-exposed intestines, while F1 intestines contained no PFBS. Significant alterations were observed in physiological activities of F0-exposed and F1 medaka. The gut microbial community in F0-exposed, F0-depurated, and F1 medaka were restructured in a concentration-dependent manner by PFBS exposure. Dysbiosis of gut microbiota ca...
Author(s): Chu, H; Baldocchi, DD; Poindexter, C; Abraha, M; Desai, AR; Bohrer, G; Arain, MA; Griffis, T; Blanken, PD; O'Halloran, TL; Thomas, RQ; Zhang, Q; Burns, SP; Frank, JM; Christian, D; Brown, S; Black, TA; Gough, CM; Law, BE; Lee, X; Chen, J; Reed, DE; Massman, WJ; Clark, K; Hatfield, J; Prueger, J; Bracho, R; Baker, JM; Martin, TA | Abstract: Aerodynamic canopy height (ha) is the effective height of vegetation canopy for its influence on atmospheric fluxes and is a key parameter of surface-atmosphere coupling. However, methods to estimate ha from data are limited. This synthesis evaluates the applicability and robustness of the calculation of ha from eddy covariance momentum-flux data. At 69 forest sites, annual ha robustly predicted site-to-site and year-to-year differences in canopy heights (R2n=n0.88, 111nsite-years). At 23 cropland/grassland sites, weekly ha successfully captured the dynamics of vegetation canopies over growing seasons (R2ngn0.70 in 74nsite-years). Our results demonstrate the potential of flux-derived ha determination for tracking the seasonal, interannual, and/or decadal dynamics of vegetation canopies including growth, harvest, land use change, and disturbance. The large-scale and time-varying ha derived from flux networks worldwide provides a new benchmark for regional and global Earth system models and satellite remote sensing of canopy structure.
Agronomy Journa l • Volume 110 , I s sue 3 • 2018 Elevated levels of P transported into aquatic ecosystems from agricultural watersheds have contributed to the eutrophication of surface water bodies in Canada as well as freshwater habitats around the world (Smith et al., 1998; Schindler et al., 2012; Michalak et al., 2013; Jarvie et al., 2017). In salt water environments, eutrophication has been linked to anthropogenic N inputs (Gruber and Galloway, 2008; Congreves and Van Eerd, 2015). Eutrophication is problematic as it impacts both ecosystem and human health (Anderson et al., 2002), which reduces the recreational value of lakes as well as their potential use as drinking water sources (Smith et al., 1998, 2015). Consequently, there is significant pressure to reduce the export of P and N from surrounding watersheds (International Joint Commission, 2014). Although researchers and environmental managers have attempted to manage nutrient export for decades (Sharpley et al., 1994), the occurrence of large algal blooms has increased due to a combination of climate drivers as well as the large intensity of agricultural land use in surrounding watersheds (e.g. Michalak et al., 2013; Smith et al., 2015). There is also evidence that the elevated P loads from agricultural systems may be an unintended consequence of conservation practices (Jarvie et al., 2017). Thus, an improved understanding of the efficacy of best management practices (BMPs) is needed, and potential unintended consequences must be identified and quantified. The use of CC is a conservation practice that is growing in popularity (Wayman et al., 2016; Statistics Canada, 2017). Cover crops are plants grown by farmers for their benefits to the soil, environment, and future crop yields (Snapp et al., 2005; Blanco-Canqui et al., 2015). Cover crops have the benefit of reducing particulate P losses associated with soil erosion; however, concern has been raised about their potential to release dissolved reactive P (Tukey, 1970; Sharpley et al., 1994; Sturite et al., 2007; Liu et al., 2014). Phosphorus loss through leaching is typically minor; however, certain conditions and mechanical processes have been found to increase the concentration of nutrients in leachate (Tukey and Morgan, 1963; Bechmann et al., 2005; Lozier and Macrae, 2017; Lozier et al., 2017). Of particular importance for northern temperate regions is the effect of freezing on P release from plants, Nutrient Release from Living and Terminated Cover Crops Under Variable Freeze–Thaw Cycles
Abstract. Transfer functions are generally used to adjust for the wind-induced undercatch of solid precipitation measurements. These functions are derived based on the variation of the collection efficiency with wind speed for a particular type of gauge, either using field experiments or based on numerical simulation. Most studies use the wind speed alone, while others also include surface air temperature and/or precipitation type to try to reduce the scatter of the residuals at a given wind speed. In this study, we propose the use of the measured precipitation intensity to improve the effectiveness of the transfer function. This is achieved by applying optimized curve fitting to field measurements from the Marshall field-test site (CO, USA). The use of a non-gradient optimization algorithm ensures optimal binning of experimental data according to the parameter under test. The results reveal that using precipitation intensity as an explanatory variable significantly reduce the scatter of the residuals. The scatter reduction as indicated by the Root Mean Square Error (RMSE) is confirmed by the analysis of the recent quality controlled data from the WMO/SPICE campaign, showing that this approach can be applied to a variety of locations and catching-type gauges. We demonstrate the physical basis of the relationship between the collection efficiency and the measured precipitation intensity, due to the correlation of large particles with high intensities, by conducting a Computational Fluid-Dynamics (CFD) simulation. We use a Reynolds Averaged Navier-Stokes SST k-ω model coupled with a Lagrangian particle-tracking model. Results validate the hypothesis of using the measured precipitation intensity as a key parameter to improve the correction of wind-induced undercatch. Findings have the potential to improve operational measurements since no additional instrument other than a wind sensor is required to apply the correction. This improves the accuracy of precipitation measurements without the additional cost of ancillary instruments such as particle counters.

DOI bib
Environmental and taxonomic controls of carbon and oxygen stable isotope composition in <i>Sphagnum</i> across broad climatic and geographic ranges
Gustaf Granath, Håkan Rydin, Jennifer L. Baltzer, Fia Bengtsson, Nicholas Boncek, Luca Bragazza, Zhao‐Jun Bu, S. J. M. Caporn, Ellen Dorrepaal, О. В. Галанина, Mariusz Gałka, Anna Ganeva, David P. Gillikin, Irina Goia, N. D. Goncharova, Michal Hájek, Akira Haraguchi, Lorna I. Harris, Elyn Humphreys, Martin Jiroušek, Katarzyna Kajukało, Edgar Karofeld, Natalia G. Koronatova, Natalia P. Kosykh, Mariusz Lamentowicz, Е. Д. Лапшина, Juul Limpens, Maiju Linkosalmi, Jinze Ma, Marguerite Mauritz, Tariq Muhammad Munir, Susan M. Natali, Rayna Natcheva, Maria​ Noskova, Richard J. Payne, Kyle Pilkington, Sean M. Robinson, Bjorn J. M. Robroek, Line Rochefort, David Singer, Hans K. Stenøien, Eeva‐Stiina Tuittila, Kai Vellak, Anouk Verheyden, J. M. Waddington, Steven K. Rice

Abstract. Rain-fed peatlands are dominated by peat mosses (Sphagnum sp.), which for their growth depend on elements from the atmosphere. As the isotopic composition of carbon (12,13C) and oxygen (16,18O) of these Sphagnum mosses are affected by environmental conditions, the dead Sphagnum tissue accumulated in peat constitutes a potential long-term archive that can be used for climate reconstruction. However, there is a lack of adequate understanding of how isotope values are influenced by environmental conditions, which restricts their current use as environmental and palaeoenvironmental indicators. Here we tested (i) to what extent C and O isotopic variation in living tissue of Sphagnum is species-specific and associated with local hydrological gradients, climatic gradients (evapotranspiration, temperature, precipitation), and elevation; (ii) if the C isotopic signature can be a proxy for net primary productivity (NPP) of Sphagnum; and (iii) to what extent Sphagnum tissue δ18O tracks the δ18O isotope signature of precipitation. In total, we analysed 337 samples from 93 sites across North America and Eurasia using two important peat-forming Sphagnum species (S. magellanicum, S. fuscum) common to the Holartic realm. There were differences in δ13C values between species. For S. magellanicum δ13C decreased with increasing height above the water table (HWT, R2 = 17 %) and was positively correlated to productivity (R2 = 7 %). Together these two variables explained 46 % of the between-site variation in δ13C values. For S. fuscum, productivity was the only significant predictor of δ13C (total R2 = 6 %). For δ18O values, ca. 90 % of the variation was found between sites. Globally-modelled annual δ18O values in precipitation explained 69% of the between-site variation in tissue δ18O. S. magellanicum showed lower δ18O enrichment than S. fuscum (−0.83 ‰ lower) . Elevation and climatic variables were weak predictors of tissue δ18O values after controlling for δ18O values of the precipitation. To summarise, our study provides evidence for (a) good predictability of tissue δ18O values from modelled annual δ18O values in precipitation, and (b) the possibility to relate tissue δ13C values to HWT and NPP, but this appears to be species-dependent. These results suggest that isotope composition can be used at a large scale for climatic reconstructions but that such models should be species-specific.
Abstract. Warm-season precipitation over the Canadian Prairies plays a crucial role in activities in environment and society and has particular importance to agricultural production over the region. This research investigates how a warm season precipitation deficit over the Canadian Prairies is related to tropical Pacific forcing in the early summer 2015 drought. The significant deficit of precipitation in May and June of 2015 were coincident with a warm phase of El Nino–Southern Oscillation (ENSO) and a negative phase of Madden–Julian Oscillation (MJO)-4 index as they both favor a positive geopotential height anomaly in western Canada. Further investigation during the instrumental record period (1979–2015) shows that the warm-season precipitation in the Canadian Prairies and the corresponding atmospheric circulation anomalies over western Canada teleconnected with the lower boundary conditions in the tropical western Pacific. MJO may play a crucial role in determining the summer precipitation anomaly in the western Canadian Prairie when equatorial central Pacific is warmer than normal (NINO4 > 0) and MJO is more active. The mechanism of this teleconnection may be due to the propagation of stationary Rossby wave that is generated in the MJO-4 index region. When the tropical convection around MJO-4 index regions (western tropical Pacific, centered over 140 E) is more active than normal when NINO4 > 0, a Rossby wave train originates from western Pacific and propagates into the midlatitude North America causing an anomalous ridge in the upper level over western Canada.
Abstract. Snowpack accumulation and depletion are important elements of the hydrological cycle in the prairies. The surface runoff generated during snowmelt is transformed into streamflow or fills numerous depressions driving the focused recharge of groundwater in this dry setting. The snowpack in the prairies can undergo several cycles of accumulation and depletion in a winter. The timing of the melt affects the mechanisms of snowpack depletion and their hydrological implications. The effects of midwinter melt were investigated at three sites in the Canadian prairies. Unlike net radiation-driven snowmelt during spring melt, turbulent sensible heat fluxes were the dominant source of energy inputs for midwinter melt occurring in the period with low solar radiation inputs. Midwinter melt events had lower runoff ratios than subsequent spring melt events and had strong impacts on the timing of the focussed recharge. Remote sensing data have shown that midwinter melt events regularly occur under the present climate throughout the Canadian prairies.
Abstract. Drought is a recurring extreme climate event and among the most costly natural disasters in the world. This is particularly true over Canada, where drought is both a frequent and damaging phenomenon with impacts on regional water resources, agriculture, industry, aquatic ecosystems and health. However, nation-wide drought assessments are currently lacking and impacted by limited ground-based observations. This study provides a comprehensive analysis of historical droughts over the whole of Canada, including the role of large-scale teleconnections. Drought events are characterized by the Standardized Precipitation-Evapotranspiration Index (SPEI) over various temporal scales (1, 3, 6, and 12 consecutive months, 6 months from April to September, and 12 months from October to September) applied to different gridded monthly data sets for the period 1950–2013. The Mann Kendall test, Rotated Empirical Orthogonal Function, Continuous Wavelet Transform, and Wavelet Coherence analyses are used, respectively, to investigate the trend, spatiotemporal patterns, periodicity, and teleconnectivity of drought events. Results indicate that southern (northern) parts of the country experienced significant trends towards drier (wetter) conditions although substantial variability exists. Two spatially well-defined regions with different temporal evolution of droughts were identified―the Canadian Prairies and Northern-central Canada. The analyses also revealed the presence of a dominant periodicity of between 8–32 months in the Prairie region, and 8–40 months in the Northern central region. These cycles of low-frequency variability are found to be associated principally to the Pacific-North American (PNA) and Multivariate El Niño/Southern Oscillation Index (MEI) relative to other considered large-scale climate indices. This study is the first of its kind to identify dominant periodicities in drought variability over the whole of Canada in terms of when the drought events occur, the duration, and how often they do so.
Abstract. This article presents the development of a sub-hourly database of hydrometeorological conditions collected in British Columbia's Cariboo Mountains and surrounding area extending from 2006 to present. The Cariboo Alpine Mesonet (CAMnet) forms a network of 11 active hydrometeorological stations positioned at strategic locations across mid- to high elevations of the Cariboo Mountains. This mountain range spans 44,150 km2 forming the northern extension of the Columbia Mountains. Deep fjord lakes along with old-growth redcedar and hemlock forests reside in the lower valleys, montane forests of Engelmann spruce, lodgepole pine and subalpine fir permeate the mid-elevations while alpine tundra, glaciers and several large icefields cover the higher elevations. The automatic weather stations typically measure air and soil temperature, relative humidity, atmospheric pressure, wind speed and direction, rainfall, and snow depth at 15 minute intervals. Additional measurements at some stations include shortwave and longwave radiation, near-surface air, skin, snow or water temperature, and soil moisture among others. Details on deployment sites, the instrumentation used and its precision, the collection and quality control process are provided. Instructions on how to access the database at Zenodo, an online public data repository, are also furnished (https://doi.org/10.5281/zenodo.1195043). Information on some of the challenges and opportunities encountered in maintaining continuous and homogeneous time series of hydrometeorological variables and remote field sites is provided. The paper also summarizes ongoing plans to expand CAMnet to better monitor atmospheric conditions in BC's mountainous terrain, efforts to push data online in (near)real-time, availability of ancillary data, and lessons learned thus far in developing this mesoscale network of hydrometeorological stations in the data-sparse Cariboo Mountains.
Measurements of active layer thickness (ALT) are typically taken at the end of summer, a time synonymous with maximum thaw depth. By definition, the active layer is the layer above permafrost that freezes and thaws annually. This study, conducted in peatlands of subarctic Canada, in the zone of thawing discontinuous permafrost, demonstrates that the entire thickness of ground atop permafrost does not always refreeze over winter. In these instances, a talik exists between the permafrost and active layer, and ALT must therefore be measured by the depth of refreeze at the end of winter. As talik thickness increases at the expense of the underlying permafrost, ALT is shown to simultaneously decrease. This suggests that the active layer has a maximum thickness that is controlled by the amount of energy lost from the ground to the atmosphere during winter. The taliks documented in this study are relatively thin (<2 m) and exist on forested peat plateaus. The presence of taliks greatly affects the stability of the underlying permafrost. Vertical permafrost thaw was found to be significantly greater in areas with taliks (0.07 m year−1) than without (0.01 m year−1). Furthermore, the spatial distribution of areas with taliks increased between 2011 and 2015 from 20% to 48%, a phenomenon likely caused by an anomalously large ground heat flux input in 2012. Rapid talik development and accelerated permafrost thaw indicates that permafrost loss may exhibit a nonlinear response to warming temperatures. Documentation of refreeze depths and talik development is needed across the circumpolar north.
Abstract Forest clearings are common features of evergreen forests and produce snowpack accumulation and melt differing from that in adjacent forests and open terrain. This study has investigated the challenges in specifying the turbulent fluxes of sensible and latent heat to snowpacks in forest clearings. The snowpack in two forest clearings in the Canadian Rockies was simulated using a one-dimensional (1D) snowpack model. A trade-off was found between optimizing against measured snow surface temperature or snowmelt when choosing how to specify the turbulent fluxes. Schemes using the Monin–Obukhov similarity theory tended to produce negatively biased surface temperature, while schemes that enhanced turbulent fluxes, to reduce the surface temperature bias, resulted in too much melt. Uncertainty estimates from Monte Carlo experiments showed that no realistic parameter set could successfully remove biases in both surface temperature and melt. A simple scheme that excludes atmospheric stability correction was required to successfully simulate surface temperature under low wind speed conditions. Nonturbulent advective fluxes and/or nonlocal sources of turbulence are thought to account for the maintenance of heat exchange in low-wind conditions. The simulation of snowmelt was improved by allowing enhanced latent heat fluxes during low-wind conditions. Caution is warranted when snowpack models are optimized on surface temperature, as model tuning may compensate for deficiencies in conceptual and numerical models of radiative, conductive, and turbulent heat exchange at the snow surface and within the snowpack. Such model tuning could have large impacts on the melt rate and timing of the snow-free transition in simulations of forest clearings within hydrological and meteorological models.
Abstract A distributed snow model is applied to simulate the spatiotemporal evolution of the Austrian snow cover at 1 km × 1 km spatial and daily temporal resolution for the period 1948–2009. After a comprehensive model validation, changes in snow cover conditions are analyzed for all of Austria as well as for different Austrian subregions and elevation belts focusing on the change in snow cover days (SCDs). The comparison of SCDs for the period 1950–79 to those achieved for 1980–2009 for all of Austria shows a decrease in SCDs with a maximum of >35 SCDs near Villach (Carinthia). The analysis of SCD changes in different subregions of Austria reveals mean changes between −11 and −15 days with highest absolute change in SCDs for southern Austria. Two decrease maxima could be identified in elevations of 500–2000 m MSL (between −13 and −18 SCDs depending on the subregion considered) and above 2500 m MSL (over −20 SCDs in the case of central Austria). The temporal distribution of SCD change in the Austrian subregions is characterized by a reduction of SCDs in midwinter and at the end of winter rather than by fewer SCDs in early winter. With respect to the temporal distribution of SCD change in different elevation belts, changes in elevations below 1000 m MSL are characterized by a distinct reduction of SCDs in January. With increasing elevation the maximum change in SCDs shifts toward the summer season, reaching a maximum decrease in the months of June–August above 2500 m MSL.
Abstract Early ionic pulse during spring snowmelt can account for a significant portion of the total annual nutrient load in seasonally snow-covered areas. Ionic pulses are a consequence of snow grain core to surface ion segregation during metamorphism, a process commonly referred to as ion exclusion. While numerous studies have provided quantitative measurements of this phenomenon, very few process-based mathematical models have been proposed for diagnostic and prognostic investigations. A few early modelling attempts have been successful in capturing this process assuming transport through porous media with variable porosity. However, this process is represented in models in ways that misalign with the mechanistic view of the process described in the literature. In this research, a process-based model is proposed that can simulated ionic pulses in runoff by emulating solute leaching from snow grains during melt and the subsequent vertical solute transport by meltwater through the snowpack. To facilitate its use without the need for snow-physics’ models, simplified alternative methods are proposed to estimate some of the variables required by the model. The model was applied to two regions, and a total of 4 study sites, that are subject to significantly different winter climatic and hydrological conditions. Comparison between observations and simulation results suggest that the model can capture well the overall snow melt runoff concentration pattern, including both the timing and magnitude of the early melt ionic pulse. The model enables the prediction of concentration profiles of the dry (snow) and liquid (wet) fractions within the snow matrix for the first time. Although there is a computational cost associated with the proposed modelling framework, this study demonstrates that it can provide more detailed information about the reallocation and transport of ions through snowpacks, which can ultimately be used to improve nutrient transport predictions during snowmelt.
Abstract. The Fraser River Basin (FRB) of British Columbia is one of the largest and most important watersheds in western North America, and home to a rich diversity of biological species and economic assets that depend implicitly upon its extensive riverine habitats. The hydrology of the FRB is dominated by snow accumulation and melt processes, leading to a prominent annual peak streamflow invariably occurring in May–July. Nevertheless, while annual peak daily streamflow (APF) during the spring freshet in the FRB is historically well correlated with basin-averaged, 1 April snow water equivalent (SWE), there are numerous occurrences of anomalously large APF in below- or near-normal SWE years, some of which have resulted in damaging floods in the region. An imperfect understanding of which other climatic factors contribute to these anomalously large APFs hinders robust projections of their magnitude and frequency. We employ the Variable Infiltration Capacity (VIC) process-based hydrological model driven by gridded observations to investigate the key controlling factors of anomalous APF events in the FRB and four of its subbasins that contribute nearly 70 % of the annual flow at Fraser-Hope. The relative influence of a set of predictors characterizing the interannual variability of rainfall, snowfall, snowpack (characterized by the annual maximum value, SWEmax), soil moisture and temperature on simulated APF at Hope (the main outlet of the FRB) and at the subbasin outlets is examined within a regression framework. The influence of large-scale climate modes of variability (the Pacific Decadal Oscillation (PDO) and the El Niño–Southern Oscillation – ENSO) on APF magnitude is also assessed, and placed in context with these more localized controls. The results indicate that next to SWEmax (univariate Spearman correlation with APF of ρ^ = 0.64; 0.70 (observations; VIC simulation)), the snowmelt rate (ρ^ = 0.43 in VIC), the ENSO and PDO indices (ρ^ = −0.40; −0.41) and (ρ^ = −0.35; −0.38), respectively, and rate of warming subsequent to the date of SWEmax (ρ^ = 0.26; 0.38), are the most influential predictors of APF magnitude in the FRB and its subbasins. The identification of these controls on annual peak flows in the region may be of use in understanding seasonal predictions or future projected streamflow changes.
Ice jams are critical components of the hydraulic regimes of rivers in cold regions. In addition to contributing to the maintenance of wetland ecology, including aquatic animals and waterfowl, ice jams provide essential moisture and nutrient replenishment to perched lakes and ponds in northern inland deltas. However, river ice-jam flooding can have detrimental impacts on in-stream aquatic ecosystems, cause damage to property and infrastructure, and present hazards to riverside communities. In order to maintain sustainable communities and ecosystems, ice-jam flooding must be both mitigated and promoted. This study reviews various flood management strategies used worldwide, and points to the knowledge gaps in these strategies. The main objective of the paper is to provide a framework for a sustainable ice-jam flood management strategy in order to better protect riverine socio-economic and socio-ecological systems. Sustainable flood management must be a carefully adopted and integrated strategy that includes both economic and ecological perspectives in order to mitigate ice-jam flooding in riverside socio-economic systems, while at the same time promoting ice-jam flooding of riverine socio-ecological systems such as inland deltas.
Abstract Greenhouse gas (GHG) emissions from agricultural soils in the Canadian Prairie region are generally low and, due to dry, well aerated soil conditions, can be quite variable. Compared to dryland (rainfed) crop production, irrigated cropping has potential to contribute greater quantities of soil derived nitrous oxide (N2O), carbon dioxide (CO2), and methane (CH4) to the atmosphere as producers target higher yields by minimizing soil moisture limitations and applying greater amounts of nitrogen fertilizers. However, the actual GHG dynamics from irrigated soils in this region are not well understood as there have been few field-based studies in the semi-arid prairies of western Canada. The goal of this study was to identify how emissions of soil derived N2O, CO2, and CH4 are influenced by changes in soil temperature, water status, and nitrogen rates brought about by irrigated crop management. This was achieved through continuous, in-situ monitoring of soil conditions and chamber-based measurements of soil GHG flux. The most notable change in soil conditions brought about by irrigation was elevated moisture levels, which appeared to influence the flux dynamics of all three agricultural greenhouse gases—specifically, a reduction in CH4 uptake and periodic increases in CO2 and N2O emissions. Despite the reduced soil moisture limitation, annual N2O emissions from the irrigated cropping system were much lower than those calculated using the current Canadian National GHG Inventory Reporting. This suggests that annual emissions are limited more by N availability rather than moisture deficits, as the current method for emissions accounting assumes. Consequently, our results indicate that emissions from irrigated cropping systems in the semi-arid Canadian Prairies are overestimated by the current inventory approach. Moreover, because irrigated crop production involves more than just the application of water, our results demonstrate that a more systems-oriented approach to GHG accounting is required to capture the combined effects of water-soil-crop management on GHG emissions from irrigated cropping systems.
The Boreal Plains (BP) of Western Canada have been exposed to increasing disturbance by wildfire and host a mixture of upland‐wetland‐pond complexes with substantial quantities of trembling aspen (Populus tremuloides Michx.) throughout the terrestrial areas. The ability of these tree species to regenerate within both upland and wetland areas of the BP following wildfire is unclear. The purpose of this study was to investigate the influence of fire on nutrient dynamics in soil and water in peatlands and forested landscapes in the BP and relate this to aspen regeneration. Nutrient concentrations, nutrient supply rates, and net nutrient mineralization rates were determined in burned and unburned sections of a peatland and forest and compared with the regeneration of aspen. NO3−, NH4+, and P varied spatially throughout the landscape, and differences were observed between peatland and upland areas. In general, differences in nutrient dynamics were not observed between burned and unburned areas, with the exception of P. Nutrient and growth data suggest that aspen do not require nutrient‐rich conditions for regeneration and instead relied on forest litter to satisfy nutrient demands. Although the peatlands contained high nutrients, aspen did not flourish in the combination of anoxic and aerobic organic‐rich soils present in this area. Although aspen may use peat water and nutrients through their rooting zones, peatlands are unsuitable for aspen re‐establishment in the long‐term. However, the combination of abundant nutrients in surface mineral soils in peat margins may indicate the vulnerability of margins to upland transformations in later successional stages.
Evapotranspiration (ET) is a key component of the water cycle, whereby accurate partitioning of ET into evaporation and transpiration provides important information about the intrinsically coupled carbon, water, and energy fluxes. Currently, global estimates of partitioned evaporative and transpiration fluxes remain highly uncertain, especially for high‐latitude ecosystems where measurements are scarce. Forested peat plateaus underlain by permafrost and surrounded by permafrost‐free wetlands characterize approximately 60% (7.0 × 107 km2) of Canadian peatlands. In this study, 22 Picea mariana (black spruce) individuals, the most common tree species of the North American boreal forest, were instrumented with sap flow sensors within the footprint of an eddy covariance tower measuring ET from a forest–wetland mosaic landscape. Sap flux density (JS), together with remote sensing data and in situ measurements of canopy structure, was used to upscale tree‐level JS to overstorey transpiration (TBS). Black spruce trees growing in nutrient‐poor permafrost peat soils were found to have lower mean JS than those growing in mineral soils. Overall, TBS contributed less than 1% to landscape ET. Climate‐change‐induced forest loss and the expansion of wetlands may further minimize the contributions of TBS to ET and increase the contribution of standing water.
Author(s): Fisher, JB; Hayes, DJ; Schwalm, CR; Huntzinger, DN; Stofferahn, E; Schaefer, K; Luo, Y; Wullschleger, SD; Goetz, S; Miller, CE; Griffith, P; Chadburn, S; Chatterjee, A; Ciais, P; Douglas, TA; Genet, H; Ito, A; Neigh, CSR; Poulter, B; Rogers, BM; Sonnentag, O; Tian, H; Wang, W; Xue, Y; Yang, ZL; Zeng, N; Zhang, Z | Abstract: NASA has launched the decade-long Arctic-Boreal Vulnerability Experiment (ABoVE). While the initial phases focus on field and airborne data collection, early integration with modeling activities is important to benefit future modeling syntheses. We compiled feedback from ecosystem modeling teams on key data needs, which encompass carbon biogeochemistry, vegetation, permafrost, hydrology, and disturbance dynamics. A suite of variables was identified as part of this activity with a critical requirement that they are collected concurrently and representatively over space and time. Individual projects in ABoVE may not capture all these needs, and thus there is both demand and opportunity for the augmentation of field observations, and synthesis of the observations that are collected, to ensure that science questions and integrated modeling activities are successfully implemented.
Abstract Hysteresis is a widely reported phenomenon in natural and engineered systems across different temporal and spatial scales. Its definition is non-unique and rather context-dependent, while systems with hysteretic behavior, including hydrological systems, are commonly referred to as path-dependent systems or systems with memory. Despite widespread existence of hysteretic processes, the current generation of hydrologic models do not directly account for hysteresis. In this paper, we review the fundamentals, theories, and general properties of hysteresis in the broad scientific literature and then focus on its representations in hydrological sciences. Through illustrative examples, we show how an incomplete understanding or representation of the underlying processes in a system can lead to considering the system as being path-dependent. We argue that, in most cases, hysteresis is a manifestation of our dimensionality-reducing approach to process understanding and representation. We further explain that modelling hysteresis in an ideal world requires a full-dimensional process representation, based on a perfect understanding of the processes, their heterogeneity, and their spatio-temporal scale dependency. We discuss, however, that the missing dimensions/physics in a hydrologic model may be compensated to some extent by enabling the model with formal hysteretic components. Moreover, we show that the conventional model structure and parameterization may be designed in a way to partially reproduce a desired hysteretic behavior.
Permafrost vulnerability to climate change may be underestimated unless effects of wildfire are considered. Here we assess impacts of wildfire on soil thermal regime and rate of thermokarst bog expansion resulting from complete permafrost thaw in western Canadian permafrost peatlands. Effects of wildfire on permafrost peatlands last for 30 years and include a warmer and deeper active layer, and spatial expansion of continuously thawed soil layers (taliks). These impacts on the soil thermal regime are associated with a tripled rate of thermokarst bog expansion along permafrost edges. Our results suggest that wildfire is directly responsible for 2200 ± 1500 km2 (95% CI) of thermokarst bog development in the study region over the last 30 years, representing ~25% of all thermokarst bog expansion during this period. With increasing fire frequency under a warming climate, this study emphasizes the need to consider wildfires when projecting future circumpolar permafrost thaw.
Ecohydrological functioning of natural Boreal forest in Canada's Boreal Plains is a product of interactions between soil hydrophysical characteristics and hydrogeochemical processes. These interactions create a moisture–nutrient gradient within the surface soils, increasing along low‐relief transitions from upland to riparian zone, and in turn influence the distribution of vegetation communities. It is not yet known if/when analogous ecohydrological functions can be achieved in constructed uplands following industrial disturbance, such as that following oil sands development. Hence, to assess this, we studied interactions between hydrogeochemical processes and vegetation colonization in a constructed upland relative to hydrophysical properties of 2 reclamation cover substrates during a typical continental climate's growing season. Our results indicated that in 3 years of postconstruction, the establishment of a moisture–nutrient gradient that supports vegetation colonization along slope positions was still limited by heterogeneity of cover substrates. Portions of the upland under peat–mineral mix were characterized by lower nutrient availability, high moisture content, and establishment of planted shrubs and trees. In contrast, forest floor materials plots were characterized by poor soil quality, but higher nutrient availability and greater colonization of invasive grasses and native shrubs. We suggest that the colonization of underdeveloped soils by invasive grasses may facilitate pedogenic processes and thus should be accepted by reclamation managers as a successional milestone in the recovery of ecohydrological functioning of constructed uplands. Poor soil structure under forest floor materials could not support edaphic conditions required by plants to efficiently utilize fertilizer, making this practise futile at the early stage of soil development.
Northern peatlands are important global carbon stores, but there is concern that these boreal peat reserves are at risk due to increased fire frequency and severity as predicted by climate change models. In a subhumid climate, hydrogeological position is an important control on peatland hydrology and wildfire vulnerability. Consequently, we hypothesized that in a coarse‐textured glaciofluvial outwash, isolated peatlands lacking the moderating effect of large‐scale groundwater flow would have greater water table (WT) variability and would also be more vulnerable to deep WT drawdown and wildfire during dry climate cycles. A holistic approach was taken to evaluate 3 well‐accepted factors that are associated with smouldering in boreal peatlands: hollow microform coverage, peatland margin morphometry, and gravimetric water content. Using a combination of field measurements (bulk density, humification, WT position, hummock–hollow distribution, and margin width) and modelling (1‐D vertical unsaturated flow coupled with a simple peat–fuel energy balance equation), we assessed the vulnerability of peat to smouldering. We found that a peatland in the regionally intermediate topographic position is the most vulnerable to smouldering due to the interaction of variable connectivity to large‐scale groundwater flow and the absence of mineral stratigraphy for limiting WT declines during dry conditions. Our findings represent a novel assessment framework and tool for fire managers by providing a priori knowledge of potential peat smouldering hot spot locations in the landscape to efficiently allocate resources and reduce emergency response time to smouldering events.
Ag10c is a recently reported RNA-cleaving DNAzyme obtained from in vitro selection. Its cleavage activity selectively requires Ag+ ions, and thus it has been used as a sensor for Ag+ detection. However, the previous selection yielded very limited information regarding its sequence requirement, since only ∼0.1% of the population in the final library were related to Ag10c and most other sequences were inactive. In this work, we performed a reselection by randomizing the 19 important nucleotides in Ag10c in such a way that a purine has an equal chance of being A or G, whereas a pyrimidine has an equal chance of being T or C. The round 3 library of the reselection was carefully analyzed and a statistic understanding of the relative importance of each nucleotide was obtained. At the same time, a more active mutant was identified, containing two mutated nucleotides. Further analysis indicated new base pairs leading to an enzyme with smaller catalytic loops but with ∼200% activity of the original Ag10c, and also excellent selectivity for Ag+. Therefore, a more active mutant of Ag10c was obtained and further truncations were successfully performed, which might be better candidates for developing new biosensors for silver. A deeper biochemical understanding was also obtained using this reselection method.
A winter time series of ground-based (X- and Ku-bands) scatterometer and spaceborne synthetic aperture radar (SAR) (C-band) fully polarimetric observations coincident with in situ snow and ice measurements are used to identify the dominant scattering mechanism in bubbled freshwater lake ice in the Hudson Bay Lowlands near Churchill, Manitoba. Scatterometer observations identify two physical sources of backscatter from the ice cover: the snow–ice and ice–water interfaces. Backscatter time series at all frequencies show increases from the ice–water interface prior to the inclusion of tubular bubbles in the ice column based on in situ observations, indicating scattering mechanisms independent of double-bounce scatter. The co-polarized phase difference of interactions at the ice–water interface from both scatterometer and SAR observations is centered at 0° during the time series, also indicating a scattering regime other than double bounce. A Yamaguchi three-component decomposition of the RADARSAT-2 C-band time series is presented, which suggests the dominant scattering mechanism to be single-bounce off the ice–water interface with appreciable surface roughness or preferentially oriented facets, regardless of the presence, absence, or density of tubular bubble inclusions. This paper builds on newly established evidence of single-bounce scattering mechanism for freshwater lake ice and is the first to present a winter time series of ground-based and spaceborne fully polarimetric active microwave observations with polarimetric decompositions for bubbled freshwater lake ice.
This paper investigates the problem of global sensitivity analysis (GSA) of Dynamical Earth System Models and proposes a basis for how such analyses should be performed. We argue that (a) performance metric‐based approaches to parameter GSA are actually identifiability analyses, (b) the use of a performance metric to assess sensitivity unavoidably distorts the information provided by the model about relative parameter importance, and (c) it is a serious conceptual flaw to interpret the results of such an analysis as being consistent and accurate indications of the sensitivity of the model response to parameter perturbations. Further, because such approaches depend on availability of system state/output observational data, the analysis they provide is necessarily incomplete. Here we frame the GSA problem from first principles, using trajectories of the partial derivatives of model outputs with respect to controlling factors as the theoretical basis for sensitivity, and construct a global sensitivity matrix from which statistical indices of total period time‐aggregate parameter importance, and time series of time‐varying parameter importance, can be inferred. We demonstrate this framework using the HBV‐SASK conceptual hydrologic model applied to the Oldman basin in Canada and show that it disagrees with performance metric‐based methods regarding which parameters exert the strongest controls on model behavior. Further, it is highly efficient, requiring less than 1,000 base samples to obtain stable and robust parameter importance assessments for our 10‐parameter example.
The timing, extent, and severity of forest wildfires have increased in many parts of the world in recent decades. These wildfires can have substantial and devastating impacts on water supply, ecohydrological systems, and sociohydrosystems. Existing frameworks to assess the magnitude and spatial extent of these effects generally focus on local processes or services and are not readily transferable to other regions. However, there is a growing need for regional, continental, and global scale indices to assess the potential effect of wildfires on freshwater availability and water supply resilience. Such indices must consider both the individual and compound effects of wildfires. In so doing, this will enable comprehensive insights on the water security paradigm and the value of hydrological services in fire‐affected areas around the globe.
Abstract Over the last decade, considerable progress has been made in developing vulnerability assessment tools and in applying these methodologies to identify and implement climate change adaptation approaches for forest ecosystems and forest management organizations in Canada and the United States. However, given that adaptation processes are in early stages, evaluation of approaches across agency, organizational, and geographic boundaries is critical. Thus, we conducted a qualitative comparison of three conceptual frameworks for climate change vulnerability assessment and adaptation efforts in the Canadian and United States forestry agency contexts. We focus our comparison on components of the conceptual frameworks, development process, intended users, similarities and differences in institutional contexts (geographic and organizational), and implementation. Finally, we present case studies to illustrate how the frameworks have been implemented on the ground and in different contexts. Despite different trajectories of development, the Canadian and US forest agencies have developed similar conceptual frameworks for vulnerability assessment and adaptation. We found that key components of the conceptual frameworks included: establishing a science-management partnership; evaluating current forest conditions and management objectives; conducting detailed science-based vulnerability assessments; developing adaptation approaches and on-the-ground tactics; implementing adaptation tactics; and monitoring outcomes and adjusting as needed. However, the contexts in which these frameworks are implemented vary considerably within and between countries, mostly because of differences in land ownership, management norms, and organizational cultures. On-the-ground applications, although slow to develop, are beginning to proliferate, providing examples that can be emulated by others. A strategy for accelerating implementation of adaptation in Canada and the United States is suggested, building on successes by federal agencies and extending to public, private, and crown lands.
Abstract On the Canadian Prairies, agricultural practices result in millions of hectares of standing crop stubble that gradually emerges during snowmelt. The importance of stubble in trapping wind-blown snow and retaining winter snowfall has been well demonstrated. However, stubble is not explicitly accounted for in hydrological or energy balance snowmelt models. This paper relates measurable stubble parameters (height, width, areal density, and albedo) to the snowpack energy balance and snowmelt with the new, physically based Stubble–Snow–Atmosphere Model (SSAM). Novel process representations of SSAM quantify the attenuation of shortwave radiation by exposed stubble, the sky and vegetation view factors needed to solve longwave radiation terms, and a resistance scheme for stubble–snow–atmosphere fluxes to solve for surface temperatures and turbulent fluxes. SSAM results were compared to observations of radiometric snow-surface temperature, stubble temperature, snow-surface solar irradiance, areal-average turbulent fluxes, and snow water equivalent from two intensive field campaigns during snowmelt in 2015 and 2016 over wheat and canola stubble in Saskatchewan, Canada. Uncalibrated SSAM simulations compared well with these observations, providing confidence in the model structure and parameterization. A sensitivity analysis conducted using SSAM revealed compensatory relationships in energy balance terms that result in a small increase in net snowpack energy as stubble exposure increases.
Groundwater flow through coarse blocky landforms contributes to streamflow in mountain watersheds, yet its role in the alpine hydrologic cycle has received relatively little attention. This study examines the internal structure and hydrogeological characteristics of an inactive rock glacier in the Canadian Rockies using geophysical imaging techniques, analysis of the discharge hydrograph of the spring draining the rock glacier, and chemical and stable isotopic compositions of source waters. The results show that the coarse blocky sediments forming the rock glacier allow the rapid infiltration of snowmelt and rain water to an unconfined aquifer above the bedrock surface. The water flowing through the aquifer is eventually routed via an internal channel parallel to the front of the rock glacier to a spring, which provides baseflow to a headwater stream designated as a critical habitat for an at‐risk cold‐water fish species. Discharge from the rock glacier spring contributes up to 50% of basin streamflow during summer baseflow periods and up to 100% of basin streamflow over winter, despite draining less than 20% of the watershed area. The rock glacier contains patches of ground ice even though it may have been inactive for thousands of years, suggesting the resiliency of the ground thermal regime under a warming climate.
Conservation agriculture, especially no‐tillage, has proven to become sustainable farming in many agricultural environments globally. In spite of advantages of no‐till systems, this practice may result in excess infiltration into the soil and can enhance the movement of mobile nutrients and some pesticides to subsurface drains and groundwater along preferential pathways. The goal of this study was to evaluate the capacity of DRAINMOD‐N II to simulate subsurface nitrate‐N leaching in no‐till fields in Truro, Nova Scotia, Canada, from 2003 to 2006. The model performance was first evaluated by comparing observed and simulated drain outflow data that is an essential prerequisite for the model to obtain a proper prediction of NO₃‐N movement, and then by comparing observed and simulated NO₃‐N concentration in no‐till fields using three statistical indices, relative root mean square error (RRMSE), average absolute deviation (AAD) and the correlation coefficient (R²). The RRMSE, AAD and R² for the validation period were determined to be 1.09, 1.85 and 0.83 mm for drain outflow, and 1.43, 0.51 and 0.79 mg l⁻¹ for NO₃‐N concentration respectively. The results showed that DRAINMOD‐N II predicted NO₃‐N leaching reasonably well in drainage outflow of no‐till fields over the whole period. Copyright © 2018 John Wiley & Sons, Ltd.
Herein, the excellent Na+ selectivity of a few RNA-cleaving DNAzymes was exploited, where Na+ can be around 3000-fold more effective than K+ for promoting catalysis. By using a double mutant based on the Ce13d DNAzyme, and by lowering the temperature, increased 2-aminopurine (2AP) fluorescence was observed with addition of both Na+ and K+. The fluorescence increase was similar for these two metals at below 10 mM, after which K+ took a different pathway. Since 2AP probes its local base stacking environment, K+ can be considered to induce misfolding. Binding of both Na+ and K+ was specific, since single base mutations could fully inhibit 2AP fluorescence for both metals. The binding thermodynamics was measured by temperature-dependent experiments revealing enthalpy-driven binding for both metals and less coordination sites compared to G-quadruplex DNA. Cleavage activity assays indicated a moderate cleavage activity with 10 mM K+, while further increase of K+ inhibited the activity, also supporting its misfolding of the DNAzyme. For comparison, a G-quadruplex DNA was also studied using the same system, where Na+ and K+ led to the same final state with only around 8-fold difference in Kd. This study provides interesting insights into strategies for discriminating Na+ and K+.
Resources allocated to natural resource management often fluctuate, requiring the types and numbers of parameters used in monitoring programs (e.g., indicators of ecosystem health) to be frequently reassessed. Conventional approaches to selecting monitoring indicators are often biased and non-inclusive. A new Criteria-based Ranking (CBR) process for selecting and/or prioritizing indicators was tested in the Muskoka River Watershed (Ontario, Canada). The CBR process is based on two environmental assessment tools, Simple Weighted and Leopold matrices. It incorporates environmental components and criteria for assessing each indicator, which generate a score per indicator. The process tested in this study was concluded to be an effective way to prioritize and/or select environmental monitoring indicators. A different set of indicators emerged when a common set of criteria was used to assess monitoring indicators. Benefits of the CBR process include: •Standardization of indicator selection process with less bias and lower cost (e.g., time and human resources).•Indicators that are representative of the community and more relevant for decision-making (e.g., more resilient to socio-political change).•Adaptability: (1) to other goals, e.g., selecting from a list of Valued Ecosystem Components (VECs), and (2) to any context through localized scoring criteria. Easily integrated into existing practice.
Abstract Climate is changing at an unprecedented rate with impacts being felt in social and ecological systems around the world. Opportunities for building climate resilience of the social-ecological system surrounding freshwater areas are assessed using the aquatic monitoring and reporting programs of Muskoka River Watershed (Ontario, Canada) as a case study. A three-step study design was used: establishment of a knowledge baseline (i.e., what has been done), confirmation of the baseline to ensure perspectives that emerged were inclusive of multiple stakeholders (i.e., broadly applicable) and an exploratory workshop to disseminate recommendations and discuss implementation with key stakeholders. Two themes are discussed: the strengthening of watershed-scale monitoring approaches, and improved communication with stakeholders (e.g., through ‘state of the watershed’ reporting). This study offers an evaluation of watershed-scale aquatic monitoring and reporting and provides concrete examples from the case study. We test a new process for refining, selecting, or prioritizing indicators for aquatic monitoring. Cumulative effects assessment and monitoring (CEAM) is considered as the suggested monitoring approach at a watershed-scale. Recommendations for developing CEAM in the Muskoka River Watershed include considerations for selection of monitoring indicators, consistent communication of indicators, and implementing a metadatabase. Ways to enhance education of, and engagement with, local stakeholders through improved ‘state of the watershed’ report cards are highlighted. Resilience is strengthened by addressing two goals in the case study: engaging with the community and improving knowledge of stressor-effect relationships in the watershed via stronger aquatic monitoring.
If two or more program entities (such as files, classes, methods) co-change (i.e., change together) frequently during software evolution, then it is likely that these two entities are coupled (i.e., the entities are related). Such a coupling is termed as evolutionary coupling in the literature. The concept of traditional evolutionary coupling restricts us to assume coupling among only those entities that changed together in the past. The entities that did not co-change in the past might also have coupling. However, such couplings can not be retrieved using the current concept of detecting evolutionary coupling in the literature. In this paper, we investigate whether we can detect such couplings by applying transitive rules on the evolutionary couplings detected using the traditional mechanism. We call these couplings that we detect using our proposed mechanism as transitive evolutionary couplings. According to our research on thousands of revisions of four subject systems, transitive evolutionary couplings combined with the traditional ones provide us with 13.96% higher recall and 5.56% higher precision in detecting future co-change candidates when compared with a state-of-the-art technique.
A code clone is a pair of code fragments, within or between software systems that are similar. Since code clones often negatively impact the maintainability of a software system, a great many numbers of code clone detection techniques and tools have been proposed and studied over the last decade. To detect all possible similar source code patterns in general, the clone detection tools work on syntax level (such as texts, tokens, AST and so on) while lacking user-specific preferences. This often means the reported clones must be manually validated prior to any analysis in order to filter out the true positive clones from task or user-specific considerations. This manual clone validation effort is very time-consuming and often error-prone, in particular for large-scale clone detection. In this paper, we propose a machine learning based approach for automating the validation process. In an experiment with clones detected by several clone detectors in several different software systems, we found our approach has an accuracy of up to 87.4% when compared against the manual validation by multiple expert judges. The proposed method shows promising results in several comparative studies with the existing related approaches for automatic code clone validation. We also present our experimental results in terms of different code clone detection tools, machine learning algorithms and open source software systems.
In today's open source era, developers look forsimilar software applications in source code repositories for anumber of reasons, including, exploring alternative implementations, reusing source code, or looking for a better application. However, while there are a great many studies for finding similarapplications written in the same programming language, there isa marked lack of studies for finding similar software applicationswritten in different languages. In this paper, we fill the gapby proposing a novel modelCroLSimwhich is able to detectsimilar software applications across different programming lan-guages. In our approach, we use the API documentation tofind relationships among the API calls used by the differentprogramming languages. We adopt a deep learning based word-vector learning method to identify semantic relationships amongthe API documentation which we then use to detect cross-language similar software applications. For evaluating CroLSim, we formed a repository consisting of 8,956 Java, 7,658 C#, and 10,232 Python applications collected from GitHub. Weobserved thatCroLSimcan successfully detect similar softwareapplications across different programming languages with a meanaverage precision rate of 0.65, an average confidence rate of3.6 (out of 5) with 75% high rated successful queries, whichoutperforms all related existing approaches with a significantperformance improvement.
Abstract The Randle Reef contaminated site, located in the southwest corner of Hamilton Harbour, is approximately 60 hectares in size. This site contains approximately 695,000 m3 of sediment contaminated with polycyclic aromatic hydrocarbons (PAHs) and metals. The complex Randle Reef sediment remediation project is finally coming to fruition after more than 30 years of study, discussion, collaborations, stakeholder consensus-building, and debate. This paper unravels the reasons behind the delays associated with implementing sediment management at the Randle Reef site. In-depth interviews with experts and professionals from organizations who are/were involved in the project were conducted to identify the nature of performance in five theme areas that are important for successful action namely: (1) participation of appropriate actors with common objectives; (2) funding and resources; (3) decision-making process; (4) research and technology development; and (5) public and political support. It is evident from this study that the hurdles to progress with addressing contaminated sediment sites involve technical, political, regulatory as well as social challenges. We offer potential solutions and a series of recommendations based on experts' first-hand experience with the management of such complex sites to inform how future remediation projects can overcome obstacles. This article has been made Open Access thanks to the kind support of CAWQ/ACQE (https://www.cawq.ca).
Metal ions play a critical role in the RNA-cleavage reaction by interacting with the scissile phosphate and stabilizing the highly negatively charged transition state. Many metal-dependent DNAzymes have been selected for RNA cleavage. Herein, we report that the Ce13d DNAzyme can use nonmetallic iodine (I2) to cleave a phosphorothioate (PS)-modified substrate. The cleavage yield exceeded 60% for both the Rp and Sp stereoisomers in 10 s, while the yield without the enzyme strand was only ∼10%. The Ce13d cleavage with I2 also required Na+, consistent with the property of Ce13d and confirming the similar role of I2 as a metal ion. Ce13d had the highest yield among eight tested DNAzymes, with the second highest DNAzyme showing only 20% cleavage. The incomplete cleavage was due to competition from desulfurization and isomerization reactions. This DNAzyme was engineered for fluorescence-based I2 detection. With EDTA for masking metal ions, I2 was selectively detected down to 4.7 nM. Oxidation of I- with Fe3+ produced I2 in situ, allowing detection of Fe3+ down to 78 nM. By harnessing nonelectrostatic interactions, such as the I2/sulfur interaction observed here, more nonmetal species might be discovered to assist DNAzyme-based RNA cleavage.
Destruction of human-built structures occurs in the ‘wildland–urban interface’ (WUI) – where homes or other burnable community structures meet with or are interspersed within wildland fuels. To mitigate WUI fires, basic information such as the location of interface areas is required, but such information is not available in Canada. Therefore, in this study, we produced the first national map of WUI in Canada. We also extended the WUI concept to address potentially vulnerable industrial structures and infrastructure that are not traditionally part of the WUI, resulting in two additional maps: a ‘wildland–industrial interface’ map (i.e. the interface of wildland fuels and industrial structures, denoted here as WUI-Ind) and a ‘wildland–infrastructure interface’ map (i.e. the interface of wildland fuels and infrastructure such as roads and railways, WUI-Inf). All three interface types (WUI, WUI-Ind, WUI-Inf) were defined as areas of wildland fuels within a variable-width buffer (maximum distance: 2400m) from potentially vulnerable structures or infrastructure. Canada has 32.3 million ha of WUI (3.8% of total national land area), 10.5 million ha of WUI-Ind (1.2%) and 109.8 million ha of WUI-Inf (13.0%). The maps produced here provide a baseline for future research and have a wide variety of practical applications.
Parties to the United Nations Framework Convention on Climate Change have agreed to hold the “increase in global average temperature to well below 2°C above preindustrial levels and to pursue efforts to limit the temperature increase to 1.5°C.” Comparison of the costs and benefits for different warming limits requires an understanding of how risks vary between warming limits. As changes in risk are often associated with changes in exposure due to projected changes in local or regional climate extremes, we analyze differences in the risks of extreme daily temperatures and extreme daily precipitation amounts under different warming limits. We show that global warming of 2°C would result in substantially larger changes in the probabilities of the extreme events than global warming of 1.5°C. For example, over the global land area, the probability of a warm extreme that occurs once every 20 years on average in the current climate is projected to increase 130% and 340% at the 1.5°C and 2.0°C warming levels, respectively (median values). Moreover, the relative changes in probability are larger for rarer, more extreme events, implying that risk assessments need to carefully consider the extreme event thresholds at which vulnerabilities occur.
Abstract Recent advancement in the understanding of snow-microwave interactions has helped to isolate the considerable potential for radar-based retrieval of snow water equivalent (SWE). There are however, few datasets available to address spatial uncertainties, such as the influence of snow microstructure, at scales relevant to space-borne application. In this study we introduce measurements from SnowSAR, an airborne, dual-frequency (9.6 and 17.2 GHz) synthetic aperture radar (SAR), to evaluate high resolution (10 m) backscatter within a snow-covered tundra basin. Coincident in situ surveys at two sites characterize a generally thin snowpack (50 cm) interspersed with deeper drift features. Structure of the snowpack is found to be predominantly wind slab (65%) with smaller proportions of depth hoar underlain (35%). Objective estimates of snow microstructure (exponential correlation length; lex), show the slab layers to be 2.8 times smaller than the basal depth hoar. In situ measurements are used to parametrize the Microwave Emission Model of Layered Snowpacks (MEMLS3&a) and compare against collocated SnowSAR backscatter. The evaluation shows a scaling factor (ϕ) between 1.37 and 1.08, when applied to input of lex, minimizes MEMLS root mean squared error to
Abstract Clumping index (CI) is a measure of foliage aggregation relative to a random distribution of leaves in space. The CI can help with estimating fractions of sunlit and shaded leaves for a given leaf area index (LAI) value. Both the CI and LAI can be obtained from global Earth Observation data from sensors such as the Moderate Resolution Imaging Spectrometer (MODIS). Here, the synergy between a MODIS-based CI and a MODIS LAI product is examined using the theory of spectral invariants, also referred to as photon recollision probability (‘p-theory’), along with raw LAI-2000/2200 Plant Canopy Analyzer data from 75 sites distributed across a range of plant functional types. The p-theory describes the probability (p-value) that a photon, having intercepted an element in the canopy, will recollide with another canopy element rather than escape the canopy. We show that empirically-based CI maps can be integrated with the MODIS LAI product. Our results indicate that it is feasible to derive approximate p-values for any location solely from Earth Observation data. This approximation is relevant for future applications of the photon recollision probability concept for global and local monitoring of vegetation using Earth Observation data.
Water quality problems are frequently influenced by hydrological processes, particularly in landscapes in which land drainage has been modified. The expansion of agricultural tile drainage in the Northern Great Plains of North America is occurring, yet is controversial due to persistent water quality problems such as eutrophication. Runoff‐generating mechanisms in North American tile‐drained landscapes in vertisolic soils have not been investigated but are important for understanding the impacts of tile drainage on water quantity and quality. This study evaluated the role of climate drivers on the activation of overland (OF) and tile (TF) flow and groundwater flow responses (GWT) on tile‐drained and nontile‐drained farm fields in Southern Manitoba, Canada. The response times of different flow paths (OF, TF, and GWT) were compared for 23 hydrological events (April–September 2015, 2016) to infer dominant runoff generation processes. Runoff responses (all pathways) were more rapid following higher intensity rainfall. Subsurface responses were hastened by wetter antecedent conditions in spring and delayed by the seasonal soil–ice layer. The activation of OF did not differ between the tiled and nontiled fields, suggesting that tile drains do little to reduce the occurrence of OF in this landscape. Rapid vertical preferential flow into tiles via preferential flow pathways was uncommon at our site, and the soil profile instead wet up from the top down. These conclusions have implications for the expansion of tile drainage and the impact of such an expansion on hydrological and biogeochemical processes in agricultural landscapes.
This is the peer reviewed version of the following article: Kompanizare M, Petrone RM, Shafii M, Robinson DT, Rooney RC. Effect of climate change and mining on hydrological connectivity of surficial layers in the Athabasca Oil Sands Region. Hydrological Processes. 2018;32:3698–3716. https://doi.org/10.1002/hyp.13292, which has been published in final form at https://doi.org/10.1002/hyp.13292. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
Abstract. The impact of transient changes in climate and vegetation on the hydrology of small Arctic headwater basins has not been investigated before, particularly in the tundra–taiga transition region. This study uses weather and land cover observations and a hydrological model suitable for cold regions to investigate historical changes in modelled hydrological processes driving the streamflow response of a small Arctic basin at the treeline. The physical processes found in this environment and explicit changes in vegetation extent and density were simulated and validated against observations of streamflow discharge, snow water equivalent and active layer thickness. Mean air temperature and all-wave irradiance have increased by 3.7 ∘C and 8.4 W m−2, respectively, while precipitation has decreased 48 mm (10 %) since 1960. Two modelling scenarios were created to separate the effects of changing climate and vegetation on hydrological processes. Results show that over 1960–2016 most hydrological changes were driven by climate changes, such as decreasing snowfall, evapotranspiration, deepening active layer thickness, earlier snow cover depletion and diminishing annual sublimation and soil moisture. However, changing vegetation has a significant impact on decreasing blowing snow redistribution and sublimation, counteracting the impact of decreasing precipitation on streamflow, demonstrating the importance of including transient changes in vegetation in long-term hydrological studies. Streamflow dropped by 38 mm as a response to the 48 mm decrease in precipitation, suggesting a small degree of hydrological resiliency. These results represent the first detailed estimate of hydrological changes occurring in small Arctic basins, and can be used as a reference to inform other studies of Arctic climate change impacts.
Abstract. Suspended sediments impact stream water quality by increasing the turbidity and acting as a vector for strongly sorbing pollutants. Understanding their sources is of great importance to developing appropriate river management strategies. In this study, we present an integrated sediment transport model composed of a catchment-scale hydrological model to predict river discharge, a river-hydraulics model to obtain shear stresses in the channel, a sediment-generating model, and a river sediment-transport model. We use this framework to investigate the sediment contributions from catchment and in-stream processes in the Ammer catchment close to Tübingen in southwestern Germany. The model is calibrated to stream flow and suspended-sediment concentrations. We use the monthly mean suspended-sediment load to analyze seasonal variations of different processes. The contributions of catchment and in-stream processes to the total loads are demonstrated by model simulations under different flow conditions. The evaluation of shear stresses by the river-hydraulics model allows the identification of hotspots and hot moments of bed erosion for the main stem of the Ammer River. The results suggest that the contributions of suspended-sediment loads from urban areas and in-stream processes are higher in the summer months, while deposition has small variations with a slight increase in summer months. The sediment input from agricultural land and urban areas as well as bed and bank erosion increase with an increase in flow rates. Bed and bank erosion are negligible when flow is smaller than the corresponding thresholds of 1.5 and 2.5 times the mean discharge, respectively. The bed-erosion rate is higher during the summer months and varies along the main stem. Over the simulated time period, net sediment trapping is observed in the Ammer River. The present work is the basis to study particle-facilitated transport of pollutants in the system, helping to understand the fate and transport of sediments and sediment-bound pollutants.
Soil‐surface temperature acts as a master variable driving nonlinear terrestrial ecohydrological, biogeochemical, and micrometeorological processes, inducing short‐lived or spatially isolated extremes across heterogeneous landscape surfaces. However, subcanopy soil‐surface temperatures have been, to date, characterized through isolated, spatially discrete measurements. Using spatially complex forested northern peatlands as an exemplar ecosystem, we explore the high‐resolution spatiotemporal thermal behavior of this critical interface and its response to disturbances by using Fiber‐Optic Distributed Temperature Sensing. Soil‐surface thermal patterning was identified from 1.9 million temperature measurements under undisturbed, trees removed and vascular subcanopy removed conditions. Removing layers of the structurally diverse vegetation canopy not only increased mean temperatures but it shifted the spatial and temporal distribution, range, and longevity of thermal hot spots and hot moments. We argue that linking hot spots and/or hot moments with spatially variable ecosystem processes and feedbacks is key for predicting ecosystem function and resilience.
NiO nanoparticles can quickly catalyze oxidation of Amplex red to produce fluorescent products for intracellular imaging, much more efficiently than other types of tested nanozymes.
Rivers are among the most threatened freshwater ecosystems, and anthropogenic activities are affecting both river structures and water quality. While assessing the organisms can provide a comprehensive measure of a river's ecological status, it is limited by the traditional morphotaxonomy-based biomonitoring. Recent advances in environmental DNA (eDNA) metabarcoding allow to identify prokaryotes and eukaryotes in one sequencing run, and could thus allow unprecedented resolution. Whether such eDNA-based data can be used directly to predict the pollution status of rivers as a complementation of environmental data remains unknown. Here we used eDNA metabarcoding to explore the main stressors of rivers along which community structure changes, and to identify the method's potential for predicting pollution status based on eDNA data. We showed that a broad range of taxa in bacterial, protistan, and metazoan communities could be profiled with eDNA. Nutrients were the main driving stressor affecting communities' structure, alpha diversity, and the ecological network. We specifically observed that the relative abundance of indicative OTUs was significantly correlated with nutrient levels. These OTUs data could be used to predict the nutrient status up to 79% accuracy on testing data sets. Thus, our study gives a novel approach to predicting the pollution status of rivers by eDNA data.
This study evaluates regional-scale projections of climate indices that are relevant to climate change impacts in Canada. We consider indices of relevance to different sectors including those that describe heat conditions for different crop types, temperature threshold exceedances relevant for human beings and ecological ecosystems such as the number of days temperatures are above certain thresholds, utility relevant indices that indicate levels of energy demand for cooling or heating, and indices that represent precipitation conditions. Results are based on an ensemble of high-resolution statistically downscaled climate change projections from 24 global climate models (GCMs) under the RCP2.6, RCP4.5, and RCP8.5 emissions scenarios. The statistical downscaling approach includes a bias-correction procedure, resulting in more realistic indices than those computed from the original GCM data. We find that the level of projected changes in the indices scales well with the projected increase in the global mean temperature and is insensitive to the emission scenarios. At the global warming level about 2.1 °C above pre-industrial (corresponding to the multi-model ensemble mean for 2031–2050 under the RCP8.5 scenario), there is almost complete model agreement on the sign of projected changes in temperature indices for every region in Canada. This includes projected increases in extreme high temperatures and cooling demand, growing season length, and decrease in heating demand. Models project much larger changes in temperature indices at the higher 4.5 °C global warming level (corresponding to 2081–2100 under the RCP8.5 scenario). Models also project an increase in total precipitation, in the frequency and intensity of precipitation, and in extreme precipitation. Uncertainty is high in precipitation projections, with the result that models do not fully agree on the sign of changes in most regions even at the 4.5 °C global warming level.
Solar-induced chlorophyll fluorescence (SIF) has been increasingly used as a proxy for terrestrial gross primary productivity (GPP). Previous work mainly evaluated the relationship between satellite-observed SIF and gridded GPP products both based on coarse spatial resolutions. Finer resolution SIF (1.3 km × 2.25 km) measured from the Orbiting Carbon Observatory-2 (OCO-2) provides the first opportunity to examine the SIF–GPP relationship at the ecosystem scale using flux tower GPP data. However, it remains unclear how strong the relationship is for each biome and whether a robust, universal relationship exists across a variety of biomes. Here we conducted the first global analysis of the relationship between OCO-2 SIF and tower GPP for a total of 64 flux sites across the globe encompassing eight major biomes. OCO-2 SIF showed strong correlations with tower GPP at both midday and daily timescales, with the strongest relationship observed for daily SIF at the 757 nm (R2 = 0.72, p < 0.0001). Strong linear relationships between SIF and GPP were consistently found for all biomes (R2 = 0.57–0.79, p < 0.0001) except evergreen broadleaf forests (R2 = 0.16, p < 0.05) at the daily timescale. A higher slope was found for C4 grasslands and croplands than for C3 ecosystems. The generally consistent slope of the relationship among biomes suggests a nearly universal rather than biome-specific SIF–GPP relationship, and this finding is an important distinction and simplification compared to previous results. SIF was mainly driven by absorbed photosynthetically active radiation and was also influenced by environmental stresses (temperature and water stresses) that determine photosynthetic light use efficiency. OCO-2 SIF generally had a better performance for predicting GPP than satellite-derived vegetation indices and a light use efficiency model. The universal SIF–GPP relationship can potentially lead to more accurate GPP estimates regionally or globally. Our findings revealed the remarkable ability of finer resolution SIF observations from OCO-2 and other new or future missions (e.g., TROPOMI, FLEX) for estimating terrestrial photosynthesis across a wide variety of biomes and identified their potential and limitations for ecosystem functioning and carbon cycle studies.
Climate warming and drying is associated with increased wildfire disturbance and the emergence of megafires in North American boreal forests. Changes to the fire regime are expected to strongly increase combustion emissions of carbon (C) which could alter regional C balance and positively feedback to climate warming. In order to accurately estimate C emissions and thereby better predict future climate feedbacks, there is a need to understand the major sources of heterogeneity that impact C emissions at different scales. Here, we examined 211 field plots in boreal forests dominated by black spruce (Picea mariana) or jack pine (Pinus banksiana) of the Northwest Territories (NWT), Canada after an unprecedentedly large area burned in 2014. We assessed both aboveground and soil organic layer (SOL) combustion, with the goal of determining the major drivers in total C emissions, as well as to develop a high spatial resolution model to scale emissions in a relatively understudied region of the boreal forest. On average, 3.35 kg C m−2 was combusted and almost 90% of this was from SOL combustion. Our results indicate that black spruce stands located at landscape positions with intermediate drainage contribute the most to C emissions. Indices associated with fire weather and date of burn did not impact emissions, which we attribute to the extreme fire weather over a short period of time. Using these results, we estimated a total of 94.3 Tg C emitted from 2.85 Mha of burned area across the entire 2014 NWT fire complex, which offsets almost 50% of mean annual net ecosystem production in terrestrial ecosystems of Canada. Our study also highlights the need for fine-scale estimates of burned area that represent small water bodies and regionally specific calibrations of combustion that account for spatial heterogeneity in order to accurately model emissions at the continental scale.
Monitoring spatiotemporal variations of river ice covers is critical for selecting safe ice transportation routes. The coherence of synthetic aperture radar (SAR) interferometry (InSAR) conveys imp...
The monitoring of river ice development is a crucial guidance indicator to establish safe crossings along river ice covers. This is the first study, based on our knowledge, to understand the interactions between river ice cover structures and radar signals and to further monitor ice development using C-band synthetic aperture radar images. The study was applied to the Slave River, Canada, using the Freeman–Durden decomposition of quad-pol C-band Radarsat-2 FQ14W images and ice core crystallography analysis. Results demonstrate that the combination of volume and surface scattering can be used to monitor ice cover development that cannot be interpreted from single polarization images, such as Radarsat-2 spotlight images used in this study. These results indicate that the decomposed quad-pol Radarsat-2 images can provide a more effective guide than the single-pol Radarsat-2 SLA images to select safe ice transportation routes. This decomposition approach can be extended to other snow and ice covered rivers.
Abstract. Soils in Arctic and boreal ecosystems store twice as much carbon as the atmosphere, a portion of which may be released as high-latitude soils warm. Some of the uncertainty in the timing and magnitude of the permafrost–climate feedback stems from complex interactions between ecosystem properties and soil thermal dynamics. Terrestrial ecosystems fundamentally regulate the response of permafrost to climate change by influencing surface energy partitioning and the thermal properties of soil itself. Here we review how Arctic and boreal ecosystem processes influence thermal dynamics in permafrost soil and how these linkages may evolve in response to climate change. While many of the ecosystem characteristics and processes affecting soil thermal dynamics have been examined individually (e.g., vegetation, soil moisture, and soil structure), interactions among these processes are less understood. Changes in ecosystem type and vegetation characteristics will alter spatial patterns of interactions between climate and permafrost. In addition to shrub expansion, other vegetation responses to changes in climate and rapidly changing disturbance regimes will affect ecosystem surface energy partitioning in ways that are important for permafrost. Lastly, changes in vegetation and ecosystem distribution will lead to regional and global biophysical and biogeochemical climate feedbacks that may compound or offset local impacts on permafrost soils. Consequently, accurate prediction of the permafrost carbon climate feedback will require detailed understanding of changes in terrestrial ecosystem distribution and function, which depend on the net effects of multiple feedback processes operating across scales in space and time.
Author(s): Luo, X; Chen, JM; Liu, J; Black, TA; Croft, H; Staebler, R; He, L; Arain, MA; Chen, B; Mo, G; Gonsamo, A; McCaughey, H | Abstract: Evapotranspiration (ET) is commonly estimated using the Penman-Monteith equation, which assumes that the plant canopy is a big leaf (BL) and the water flux from vegetation is regulated by canopy stomatal conductance (Gs). However, BL has been found to be unsuitable for terrestrial biosphere models built on the carbon-water coupling principle because it fails to capture daily variations of gross primary productivity (GPP). A two-big-leaf scheme (TBL) and a two-leaf scheme (TL) that stratify a canopy into sunlit and shaded leaves have been developed to address this issue. However, there is a lack of comparison of these upscaling schemes for ET estimation, especially on the difference between TBL and TL. We find that TL shows strong performance (r2n=n0.71, root-mean-square errorn=n0.05nmm/h) in estimating ET at nine eddy covariance towers in Canada. BL simulates lower annual ET and GPP than TL and TBL. The biases of estimated ET and GPP increase with leaf area index (LAI) in BL and TBL, and the biases of TL show no trends with LAI. BL miscalculates the portions of light-saturated and light-unsaturated leaves in the canopy, incurring negative biases in its flux estimation. TBL and TL showed improved yet different GPP and ET estimations. This difference is attributed to the lower Gs and intercellular CO2 concentration simulated in TBL compared to their counterparts in TL. We suggest to use TL for ET modeling to avoid the uncertainty propagated from the artificial upscaling of leaf-level processes to the canopy scale in BL and TBL.
Abstract The landscape freeze/thaw (FT) state plays an important role in local, regional and global weather and climate, but is difficult to monitor. The Soil Moisture Active Passive (SMAP) satellite mission provides hemispheric estimates of landscape FT state at a spatial resolution of approximately 36 2  km 2 . Previous validation studies of SMAP and other satellite FT products have compared satellite retrievals with point estimates obtained from in-situ measurements of air and/or soil temperature. Differences between the two are attributed to errors in the satellite retrieval. However, significant differences can occur between satellite and in-situ estimates solely due to differences in scale between the measurements; these differences can be viewed as ‘representativeness errors’ in the in-situ product, caused by using a point estimate to represent a large-scale spatial average. Most previous validation studies of landscape FT state have neglected representativeness errors entirely, resulting in conservative estimates of satellite retrieval skill. In this study, we use a variant of triple collocation called ‘categorical triple collocation’ – a technique that uses model, satellite and in-situ estimates to obtain relative performance rankings of all three products, without neglecting representativeness errors – to validate the SMAP landscape FT product. Performance rankings are obtained for nine sites at northern latitudes. We also investigate differences between using air or soil temperatures to estimate FT state, and between using morning (6 AM) or evening (6 PM) estimates. Overall, at most sites, the SMAP product or in-situ FT measurement is ranked first, and the model FT product is ranked last (although rankings vary across sites). These results suggest SMAP is adding value to model simulations, providing higher-accuracy estimates of landscape FT states compared to models and, in some cases, even in-situ estimates, when representativeness errors are properly accounted for in the validation analysis.
Groundwater discharge can be a major source of nutrients to river systems. Although quantification of groundwater nitrate loading to streams is common, the dependence of surface water silicon (Si) and phosphorus (P) concentrations on groundwater sources has rarely been determined. Additionally, the ability of groundwater discharge to drive surface water Si:P ratios has not been contextualized relative to riverine inputs or in-stream transformations. In this study, we quantify the seasonal dynamics of Si and P cycles in the Grand River (GR) watershed, the largest Canadian watershed draining into Lake Erie, to test our hypothesis that regions of Si-rich groundwater discharge increase surface water Si:P ratios. Historically, both the GR and Lake Erie have been considered stoichiometrically P-limited, where the molar Si:P ratio is greater than the ~16:1 phytoplankton uptake ratio. However, recent trends suggest that eastern Lake Erie may be approaching Si-limitation. We sampled groundwater and surface water for dissolved and reactive particulate Si as well as total dissolved P for 12months within and downstream of a 50-km reach of high groundwater discharge. Our results indicate that groundwater Si:P ratios are lower than the corresponding surface water and that groundwater is a significant source of bioavailable P to surface water. Despite these observations, the watershed remains P-limited for the majority of the year, with localized periods of Si-limitation. We further find that groundwater Si:P ratios are a relatively minor driver of surface water Si:P, but that the magnitude of Si and P loads from groundwater represent a large proportion of the overall fluxes to Lake Erie.
Abstract Chinooks are the North American variety of foehn: strong, warm and dry winds that descend lee mountain slopes. The strong wind speeds, high temperatures and substantial humidity deficits have been hypothesized to remove important prairie near-surface water storage from agricultural fields via evaporation, sublimation and blowing snow, as well as change the phase of near surface water via snowmelt and ground thaw. This paper presents observations of surface energy and water balances from eddy covariance instrumentation deployed at three open sites in southern Alberta, Canada during winter 2011–2012. Energy balances, snow and soil moisture budgets of three select chinook events were analysed in detail. These three events ranged in duration from two to nine days, and are representative of winter through early spring chinooks. Precipitation data from gauges and reanalyses (CaPA and ERA-interim) were used to assess water balances. Variations in precipitation and snowpacks caused the greatest differences in energy and water balances. Cumulative winter precipitation varied by a factor of two over the three sites: heaviest at the more northern site immediately east of the Rocky Mountains and lightest at the easternmost and southernmost site. The temporal progression of chinook-driven surface water loss is explained, beginning with strong blowing snow events through to evaporation of meltwater as snowpacks disappear. At the two sites with considerable winter precipitation and snowcover, large upward latent heat fluxes, often exceeding 100 W m−2, were driven by downward sensible heat fluxes but were unrelated to net radiation. Conversely, at the southernmost site with little snowcover, upward latent heat fluxes were much smaller (less than 50 W m−2) and were associated with periods of positive net radiation. Upward sensible heat fluxes during periods of positive net radiation were observed at this site throughout winter, but were not observed at the more northerly sites until March when the snowcovers ablated. Daily sublimation plus evaporation rates during chinooks at the sites with heaviest and lightest precipitation were 1.3–2.1 mm/day and 0.1–0.3 mm/day, respectively. Evaporation of soil water occurred while soils were partially to fully unfrozen in November. There was little change in soil water content between fall freeze-up and spring thaw (December through most of March), indicating that over-winter infiltration was balanced by soil water evaporation and both terms were likely to be small. Winter precipitation resulted in only 2% to 4% increases in near-surface water storage at the more northern sites with greater precipitation, whereas there was a net loss over winter at the southernmost site.
Abstract Widespread growth of cities, the association of trace metals with urban runoff, and the potentially deleterious effect of metals on aquatic ecology have made it important to understand the distribution and transport of metals through surface water channel networks. The Don River in Toronto, Canada has been identified as an Area of Concern for pollution to Lake Ontario, with historically high levels of metal contamination. Sampling programs are sparse, therefore a model is needed to understand the spatial and temporal variability of metals in the river network. The objectives of the current study are to: i) describe the sampled spatial and temporal variability of metals in the Don River and ii) develop a modelling strategy to describe within flood metal transport dynamics. A model setup tool is developed that links Storm Water Management Model (SWMM) with the Environmental Fluid Dynamics Code (EFDC) to allow a seamless transition from catchment hydrology to in-stream hydraulic and chemical processes. Results show that lead pollution in the Don River is decreasing, likely as a result of policy changes and sediment dredging in the mouth of the river. However, zinc and copper pollution are increasingly problematic, with copper exceeding recommended lower guidelines, particularly during floods. Model results confirm that most of the sediment and metals are transported in relatively short bursts within longer flood durations and are stored in depositional hotspots within the Lower Don River. A better monitoring strategy is needed to understand and more accurately parametrize these processes in an urban river system.
Abstract Unstructured triangular meshes are an efficient and effective landscape representation that are suitable for use in distributed hydrological and land surface models. Their variable spatial resolution provides similar spatial performance to high-resolution structured grids while using only a fraction of the number of elements. Many existing triangulation methods either sacrifice triangle quality to introduce variable resolution or maintain well-formed uniform meshes at the expense of variable triangle resolution. They are also generally constructed to only fulfil topographic constraints. However, distributed hydrological and land surface models require triangles of varying resolution to provide landscape representations that accurately represent the spatial heterogeneity of driving meteorology, physical parameters and process operation in the simulation domain. As such, mesh generators need to constrain the unstructured mesh to not only topography but to other important surface and sub-surface features. This work presents novel multi-objective unstructured mesh generation software that allows mesh generation to be constrained to an arbitrary number of important features while maintaining a variable spatial resolution. Triangle quality is supported as well as a smooth gradation from small to large triangles. Including these additional constraints results in a better representation of spatial heterogeneity than from classic topography-only constraints.
The absence of a rigorous mechanism for prioritizing investment in endangered species management is a major implementation hurdle affecting recovery. Here, we present a method for prioritizing strategies for endangered species management based on the likelihood of achieving species' recovery goals per dollar invested. We demonstrate our approach for 15 species listed under Canada's Species at Risk Act that co-occur in Southwestern Saskatchewan. Without management, only two species have >50% probability of meeting recovery objectives; whereas, with management, 13 species exceed the >50% threshold with the implementation of just five complementary strategies at a cost of $126m over 20 years. The likelihood of meeting recovery objectives rarely exceeded 70% and two species failed to reach the >50% threshold. Our findings underscore the need to consider the cost, benefit, and feasibility of management strategies when developing recovery plans in order to prioritize implementation in a timely and cost-effective manner.
Accurate and detailed knowledge of California's groundwater is of paramount importance for statewide water resources planning and management, and to sustain a multi-billion-dollar agriculture industry during prolonged droughts. In this study, we use water supply and demand information from California's Department of Water Resources to develop an aggregate groundwater storage model for California's Central Valley. The model is evaluated against 34 years of historic estimates of changes in groundwater storage derived from the United States Geological Survey's Central Valley Hydrologic Model (USGS CVHM) and NASA's Gravity Recovery and Climate Experiment (NASA GRACE) satellites. The calibrated model is then applied to predict future changes in groundwater storage for the years 2015-2050 under various precipitation scenarios from downscaled climate projections. We also discuss and project potential management strategies across different annual supply and demand variables and how they affect changes in groundwater storage. All simulations support the need for collective statewide management intervention to prevent continued depletion of groundwater availability.
Ice-jam floods (IJFs) are important hydrological and hydraulic events in the northern hemisphere that are of major concern for citizens, authorities, insurance companies and government agencies. In recent years, there have been advances in assessing and quantifying climate change impacts on river ice processes, however, an understanding of climate change and regulation impacts on the timing and magnitude of IJFs remains limited. This study presents a global overview of IJF case studies and discusses IJF risks in North America, one of the most IJF prone regions according to literature. Then an assessment of shifts in the timing and magnitude of IJFs in Canada is presented analyzing flow data from 1107 hydrometric stations across Canada for the period from 1903 to 2015. The analyses show clear signals of climate change and regulation impacts in the timing and magnitude of IJFs, particularly in small basins.
Abstract. Decoupling the integrated microwave signal originating from soil and vegetation remains a challenge for all microwave remote sensing applications. To improve satellite and airborne microwave data products in forest environments, a precise and reliable estimation of the relative permittivity (ε=ε′-iε′′) of trees is required. We developed an open-ended coaxial probe suitable for in situ permittivity measurements of tree trunks at L-band frequencies (1–2 GHz). The probe is characterized by uncertainty ratios under 3.3 % for a broad range of relative permittivities (unitless), [2–40] for ε′ and [0.1–20] for ε′′. We quantified the complex number describing the permittivity of seven different tree species in both frozen and thawed states: black spruce, larch, red spruce, balsam fir, red pine, aspen and black cherry. Permittivity variability is substantial and can range up to 300 % for certain species. Our results show that the permittivity of wood is linked to the freeze–thaw state of vegetation and that even short winter thaw events can lead to an increase in vegetation permittivity. The open-ended coaxial probe proved to be precise enough to capture the diurnal cycle of water storage inside the trunk for the length of the growing season.
Canada’s Boreal Plains peatland vegetation species assemblages are characterized by their functional ecosystem roles and feedbacks, which are important for carbon and water storage in a sub-humid climate. The vegetation communities at the peatland-upland interface, or the peatland margin ecotone, have not been extensively delineated or characterized as a distinct ecotone. Because these ecotones constitute a smouldering “hotspot” during wildfire, with carbon loss from these margins accounting for 50–90% of total peatland carbon loss, their delineation is critical. Post-fire, areas of severe peat smouldering have previously been shown to undergo shifts in vegetation community composition, resulting in a loss of key peatland ecohydrological functions. The aim of this study was to delineate Boreal Plains peatland margin ecotones and assess their prevalence across the landscape. Using split moving window analysis on vegetation transect data from a chronosequence of study sites, the margin ecotones were delineated at sites having different times since fire. No significant differences were identified in margin width over time or margin peat depths across hydrogeological settings. However, with peat depths of up to 2.46 m in small peatlands characteristic of moraine and glaciofluvial deposits, vulnerable margin peat has been demonstrated to represent a significant carbon store. Fire managers employing peatland fuel treatments for wildfire abatement and community protection should consider these confined peatlands more carefully to mitigate catastrophic carbon losses. Further, we suggest that a greater understanding is needed of the roles of peatland margin ecotones in sustaining peatland autogenic feedback mechanisms that promote paludification and recovery following wildfire.
Abstract Tree growth rings from three specimens in two different aged (14- and 77-year old) white pine plantation forests were analyzed for stable carbon isotope ratios to identify both short- and long-term variations in physiological response to changing environmental conditions. Three isotopic (δ13Ccorr) time series records were constructed from whole wood samples extracted from paths parallel to the growth rings in each forest. These δ13Ccorr records were corrected for the long-term anthropogenically induced CO2 and compared to historical climate (temperature, precipitation) data from 1935 to 2016. High resolution inter-annual variations in trees in each stand displayed similar intra-annual cycles in δ13Ccorr, demonstrating the seasonal physiological response of these forests to environmental stressors. In both stands, growing season temperature acted as a significant control (p
The objective of this study was to assess the effects of municipal wastewater treatment plant effluent on the energetics and stress response of rainbow darter (Etheostoma caeruleum). Male and female rainbow darter were collected upstream and downstream of the Waterloo WWTP in the Grand River watershed, ON, Canada. To assess the effects of wastewater treatment plant effluent on whole-body and tissue specific metabolic capacity, closed-chamber respirometry and muscle-enzyme activity analyses were performed. Plasma cortisol was also collected from fish before and after an acute air-exposure stressor to evaluate the cortisol stress response in fish exposed to additional stressors. Male and female rainbow darter collected downstream of the effluent had higher oxygen consumption rates, while differences in enzyme activities were primarily associated with sex rather than collection site. No impairment in the cortisol stress response between downstream and upstream fish was observed, however baseline cortisol levels in female fish from the downstream site were significantly higher compared to other baseline groups. Stress-induced cortisol levels were also higher in female fish from both sites when compared to their male counterparts. Overall, this study demonstrates that chronic exposure to WWTP effluent impacts whole-body metabolic performance. This study was also able to demonstrate that sex-differences are a key determinant of various metabolic changes in response to physiological stress, thereby, providing a novel avenue to be considered and further explored.
Abstract. Extreme climatic events, such as droughts and heat stress, induce anomalies in ecosystem–atmosphere CO2 fluxes, such as gross primary production (GPP) and ecosystem respiration (Reco), and, hence, can change the net ecosystem carbon balance. However, despite our increasing understanding of the underlying mechanisms, the magnitudes of the impacts of different types of extremes on GPP and Reco within and between ecosystems remain poorly predicted. Here we aim to identify the major factors controlling the amplitude of extreme-event impacts on GPP, Reco, and the resulting net ecosystem production (NEP). We focus on the impacts of heat and drought and their combination. We identified hydrometeorological extreme events in consistently downscaled water availability and temperature measurements over a 30-year time period. We then used FLUXNET eddy covariance flux measurements to estimate the CO2 flux anomalies during these extreme events across dominant vegetation types and climate zones. Overall, our results indicate that short-term heat extremes increased respiration more strongly than they downregulated GPP, resulting in a moderate reduction in the ecosystem's carbon sink potential. In the absence of heat stress, droughts tended to have smaller and similarly dampening effects on both GPP and Reco and, hence, often resulted in neutral NEP responses. The combination of drought and heat typically led to a strong decrease in GPP, whereas heat and drought impacts on respiration partially offset each other. Taken together, compound heat and drought events led to the strongest C sink reduction compared to any single-factor extreme. A key insight of this paper, however, is that duration matters most: for heat stress during droughts, the magnitude of impacts systematically increased with duration, whereas under heat stress without drought, the response of Reco over time turned from an initial increase to a downregulation after about 2 weeks. This confirms earlier theories that not only the magnitude but also the duration of an extreme event determines its impact. Our study corroborates the results of several local site-level case studies but as a novelty generalizes these findings on the global scale. Specifically, we find that the different response functions of the two antipodal land–atmosphere fluxes GPP and Reco can also result in increasing NEP during certain extreme conditions. Apparently counterintuitive findings of this kind bear great potential for scrutinizing the mechanisms implemented in state-of-the-art terrestrial biosphere models and provide a benchmark for future model development and testing.
NASA’s Gravity Recovery and Climate Experiment (GRACE) has already proven to be a powerful data source for regional groundwater assessments in many areas around the world. However, the applicability of GRACE data products to more localized studies and their utility to water management authorities have been constrained by their limited spatial resolution (~200,000 km2). Researchers have begun to address these shortcomings with data assimilation approaches that integrate GRACE-derived total water storage estimates into complex regional models, producing higher-resolution estimates of hydrologic variables (~2500 km2). Here we take those approaches one step further by developing an empirically based model capable of downscaling GRACE data to a high-resolution (~16 km2) dataset of groundwater storage changes over a portion of California’s Central Valley. The model utilizes an artificial neural network to generate a series of high-resolution maps of groundwater storage change from 2002 to 2010 using GRACE estimates of variations in total water storage and a series of widely available hydrologic variables (PRISM precipitation and temperature data, digital elevation model (DEM)-derived slope, and Natural Resources Conservation Service (NRCS) soil type). The neural network downscaling model is able to accurately reproduce local groundwater behavior, with acceptable Nash-Sutcliffe efficiency (NSE) values for calibration and validation (ranging from 0.2445 to 0.9577 and 0.0391 to 0.7511, respectively). Ultimately, the model generates maps of local groundwater storage change at a 100-fold higher resolution than GRACE gridded data products without the use of computationally intensive physical models. The model’s simulated maps have the potential for application to local groundwater management initiatives in the region.
In California, new groundwater legislation—the 2014 Sustainable Groundwater Management Act (SGMA)—mandates that groundwater sustainability agencies (GSAs) employ the concept of sustainable yield as their primary management goal. However, SGMA’s current definition of sustainable yield does not offer clear guidance for new agencies and lacks grounding in physics. This study presents a novel hydrologically based framework for quantifying sustainable yield under SGMA that is derived from a synthesis of scientific inquiry and analysis. We introduce a flexible three-step approach that basin managers can rely on to quantify sustainable yield values, incorporate the impact of “undesirable results”, and analyze groundwater sustainability over SGMA’s implementation horizon. Our framework is illustrated through a case study of the South San Joaquin Irrigation District, a proposed GSA in one of California’s critically overdrafted groundwater basins. We calculate sustainable yield for three different management scenarios and assess the impact of each scenario on future groundwater sustainability by performing an annual water groundwater balance through 2040. Our sustainable yield framework can be used as a basis for the development of SGMA’s groundwater management plans throughout California.
Review highlights the hydrological importance of macropore flow in frozen soils. Governing flow mechanisms and infiltration and refreezing dynamics are discussed. Research is needed to integrate macropore flow and soil freeze–thaw theory. Dual‐domain models of macropore flow should be adapted to frozen ground. A conceptual framework for modeling frozen macroporous soils is proposed.
Ephemeral ponds in depressions are the foci of groundwater recharge in the Canadian Prairies. Freeze–thaw processes influence snowmelt runoff and depression‐focused recharge. A new water balance model was developed to represent these processes. The water balance model successfully simulated the observed soil processes. This model will provide a tool to estimate recharge in the prairie landscape.
Document readers with linear navigation controls do not work well when users need to navigate to previously-visited locations, particularly when documents are long. Existing solutions - bookmarks, search, history, and read wear - are valuable but limited in terms of effort, clutter, and interpretability. In this paper, we investigate artificial landmarks as a way to improve support for revisitation in long documents - inspired by visual augmentations seen in physical books such as coloring on page edges or indents cut into pages. We developed several artificial-landmark visualizations that can represent locations even in documents that are many hundreds of pages long, and tested them in studies where participants visited multiple locations in long documents. Results show that providing two columns of landmark icons led to significantly better performance and user preference. Artificial landmarks provide a new mechanism to build spatial memory of long documents - and can be used either alone or with existing techniques like bookmarks, read wear, and search.
Climate and land-use changes modify the physical functioning of river basins and, in particular, influence the transport of nutrients from land to water. In large-scale basins, where a variety of climates, topographies, soil types and land uses co-exist to form a highly heterogeneous environment, a more complex nutrient dynamic is imposed by climate and land-use changes. This is the case of the South Saskatchewan River (SSR) that, along with the North Saskatchewan River, forms one of the largest river systems in western Canada. The SPAtially Referenced Regression On Watershed (SPARROW) model is therefore implemented to assess water quality in the basin, in order to describe spatial and temporal patterns and identify those factors and processes that affect water quality. Forty-five climate and land-use change scenarios comprehended by five General Circulation Models (GCMs) and three Representative Concentration Pathways (RCPs) were incorporated into the model to explain how total nitrogen (TN) and total phosphorus (TP) export could vary across the basin in 30, 60 and 90 years from now. According to model results, annual averages of TN and TP export in the SSR are going to increase in the range 0.9–1.28 kg km − 2 year − 1 and 0.12–0.17 kg km − 2 year − 1 , respectively, by the end of the century, due to climate and land-use changes. Higher increases of TP compared to TN are expected since TP and TN are going to increase ∼36% and ∼21%, respectively, by the end of the century. This research will support management plans in order to mitigate nutrient export under future changes of climate and land use.
Major flood events are likely to happen more frequently and be more severe under changing land use and climatic conditions. Adapting to floods using resilience-based flood risk management (FRM) pol...
Scientific Workflow Management Systems are being widely used in recent years for data-intensive analysis tasks or domain-specific discoveries. It often becomes challenging for an individual to effectively analyze the large scale scientific data of relatively higher complexity and dimensions, and requires a collaboration of multiple members of different disciplines. Hence, researchers have focused on designing collaborative workflow management systems. However, consistency management in the face of conflicting concurrent operations of the collaborators is a major challenge in such systems. In this paper, we propose a locking scheme (e.g., collaborator gets write access to non-conflicting components of the workflow at a given time) to facilitate consistency management in collaborative scientific workflow management systems. The proposed method allows locking workflow components at a granular level in addition to supporting locks on a targeted part of the collaborative workflow. We conducted several experiments to analyze the performance of the proposed method in comparison to related existing methods. Our studies show that the proposed method can reduce the average waiting time of a collaborator by up to 36.19% in comparison to existing descendent modular level locking techniques for collaborative scientific workflow management systems.
Both climate and statistical models play an essential role in the process of demonstrating that the distribution of some atmospheric variable has changed over time and in establishing the most likely causes for the detected change. One statistical difficulty in the research field of detection and attribution resides in defining events that can be easily compared and accurately inferred from reasonable sample sizes. As many impacts studies focus on extreme events, the inference of small probabilities and the computation of their associated uncertainties quickly become challenging. In the particular context of event attribution, the authors address the question of how to compare records between the counterfactual “world as it might have been” without anthropogenic forcings and the factual “world that is.” Records are often the most important events in terms of impact and get much media attention. The authors will show how to efficiently estimate the ratio of two small probabilities of records. The inferential gain is particularly substantial when a simple hypothesis-testing procedure is implemented. The theoretical justification of such a proposed scheme can be found in extreme value theory. To illustrate this study’s approach, classical indicators in event attribution studies, like the risk ratio or the fraction of attributable risk, are modified and tailored to handle records. The authors illustrate the advantages of their method through theoretical results, simulation studies, temperature records in Paris, and outputs from a numerical climate model.
Water stress has been identified as a key mechanism of the contemporary increase in tree mortality rates in northwestern North America. However, a detailed analysis of boreal tree hydrodynamics and their interspecific differences is still lacking. Here we examine the hydraulic behaviour of co-occurring larch (Larix laricina) and black spruce (Picea mariana), two characteristic boreal tree species, near the southern limit of the boreal ecozone in central Canada. Sap flux density (Js), concurrently recorded stem radius fluctuations and meteorological conditions are used to quantify tree hydraulic functioning and to scrutinize tree water-use strategies. Our analysis revealed asynchrony in the diel hydrodynamics of the two species with the initial rise in Js occurring 2 h earlier in larch than in black spruce. Interspecific differences in larch and black spruce crown architecture explained the observed asynchrony in their hydraulic functioning. Furthermore, the two species exhibited diverging stomatal regulation strategies with larch and black spruce employing relatively isohydric and anisohydric behaviour, respectively. Such asynchronous and diverging tree-level hydrodynamics provide new insights into the ecosystem-level complementarity in tree form and function, with implications for understanding boreal forests' water and carbon dynamics and their resilience to environmental stress.
Le sud-ouest de la France n’a pas connu d’incendie majeur depuis plusieurs decennies, mais il presente un fort potentiel de feu de grande ampleur. Cette menace concerne la securite de la population...
In the boreal plains ecozone, black spruce (Picea mariana (Mill.) Britton, Sterns & Poggenb.) peatlands can represent large parts of the expanding wildland–urban interface (WUI) and wildland–indust...
Abstract This paper applies a social return on investment (SROI) analysis to the issue of flood control and wetland conservation in the Smith Creek basin of southeastern Saskatchewan, Canada. Basin hydrological modeling applied to wetland loss and restoration scenarios in the study area provides local estimates of the ecosystem service (ES) provision related to flood control and nutrient removal. Locally appropriate monetary values are applied to these services to gauge the cost effectiveness of wetland conservation funding at two levels: flood control capacity alone and then incorporating a suite of ES. SROI ratios for flood control alone provide ratios between 3.17 (retention) and 0.80 (full restoration) over 30 years; when other ES are included, the ratios increase, ranging from 7.70 (retention) to 2.98 (full restoration) over 30 years. Retention of existing wetlands provides the highest SROI and therefore we argue that government policy should focus on preventing further loss of wetlands as a strategic investment opportunity. Overall, these results indicate that wetland retention is an economically viable solution to limit the financial, social and environmental damages of flooding in Saskatchewan specifically and the Prairie Pothole Region (PPR) generally.
Abstract Application of stable isotope methods to evaluate the contribution of different water sources to groundwater recharge relies on the knowledge about isotopic signatures of these sources. The data collected at study sites in the Canadian Prairies show that snowpack isotopic signatures exhibit a high spatial variability over a small scale (
Trees play a key role in the global hydrological cycle and measurements performed with the thermal dissipation method (TDM) have been crucial in providing whole-tree water-use estimates. Yet, different data processing to calculate whole-tree water use encapsulates uncertainties that have not been systematically assessed. We quantified uncertainties in conifer sap flux density (Fd ) and stand water use caused by commonly applied methods for deriving zero-flow conditions, dampening and sensor calibration. Their contribution has been assessed using a stem segment calibration experiment and 4 yr of TDM measurements in Picea abies and Larix decidua growing in contrasting environments. Uncertainties were then projected on TDM data from different conifers across the northern hemisphere. Commonly applied methods mostly underestimated absolute Fd . Lacking a site- and species-specific calibrations reduced our stand water-use measurements by 37% and induced uncertainty in northern hemisphere Fd . Additionally, although the interdaily variability was maintained, disregarding dampening and/or applying zero-flow conditions that ignored night-time water use reduced the correlation between environment and Fd . The presented ensemble of calibration curves and proposed dampening correction, together with the systematic quantification of data-processing uncertainties, provide crucial steps in improving whole-tree water-use estimates across spatial and temporal scales.
As display environments become larger and more diverse - now often encompassing multiple walls and room surfaces - it is becoming more common that users must find and manipulate digital artifacts not directly in front of them. There is little understanding, however, about what techniques and devices are best for carrying out basic operations above, behind, or to the side of the user. We conducted an empirical study comparing two main techniques that are suitable for full-coverage display environments: mouse-based pointing, and ray-cast 'laser' pointing. Participants completed search and pointing tasks on the walls and ceiling, and we measured completion time, path lengths and perceived effort. Our study showed a strong interaction between performance and target location: when the target position was not known a priori the mouse was fastest for targets on the front wall, but ray-casting was faster for targets behind the user. Our findings provide new empirical evidence that can help designers choose pointing techniques for full-coverage spaces.
Phosphorus (P) mobilization in agricultural landscapes is regulated by both hydrologic (transport) and biogeochemical (supply) processes interacting within soils; however, the dominance of these controls can vary spatially and temporally. In this study, we analyzed a 5-yr dataset of stormflow events across nine agricultural fields in the lower Great Lakes region of Ontario, Canada, to determine if edge-of-field surface runoff and tile drainage losses (total and dissolved reactive P) were limited by transport mechanisms or P supply. Field sites ranged from clay loam, silt loam, to sandy loam textures. Findings indicate that biogeochemical processes (P supply) were more important for tile drain P loading patterns (i.e., variable flow-weighted mean concentrations ([]) across a range of flow regimes) relative to surface runoff, which trended toward a more chemostatic or transport-limited response. At two sites with the same soil texture, higher tile [] and greater transport limitations were apparent at the site with higher soil available P (STP); however, STP did not significantly correlate with tile [] or P loading patterns across the nine sites. This may reflect that the fields were all within a narrow STP range and were not elevated in STP concentrations (Olsen-P, ≤25 mg kg). For the study sites where STP was maintained at reasonable concentrations, hydrology was less of a driving factor for tile P loadings, and thus management strategies that limit P supply may be an effective way to reduce P losses from fields (e.g., timing of fertilizer application).
Abstract Phosphorus (P) losses from agricultural soils are a growing economic and water-quality concern in the Lake Erie watershed. While recent studies have explored edge-of-field and watershed P losses related to land-use and agricultural management, the potential for soils developed from contrasting parent materials to retain or release P to runoff has not been examined. A field-based study comparing eight agricultural fields in contrasting glacial landscapes (hummocky coarse-textured till-plain, lacustrine and fine-textured till-plain) showed distinct physical and geochemical soil properties influencing inorganic P (Pi) partitioning throughout the soil profile between the two regions. Fields located on the coarse-textured till-plain in mid-western Ontario, Canada had alkaline calcareous soils with the highest Total-Pi concentrations and the majority of soil Pi stored in an acid-soluble pool (up to 91%). In contrast, loosely to moderately soluble Pi concentrations were higher in soils of the lacustrine and fine-textured till-plain in southwestern Ontario, northeast Indiana and northwestern Ohio, US. Overall, soils on the lacustrine and fine-textured till-plain had a greater shrink swell-capacity, likely creating preferential flow to minimize Pi interaction with the more acidic, lower carbonate and lower sorption capacity soils. These differences in soil Pi retention and transport pathways demonstrate that in addition to management, the natural landscape may exert a significant control on how Pi is mobilized throughout the Lake Erie watershed. Further, results indicate that careful consideration of region-specific hydrology and soil biogeochemistry may be required when designing appropriate management strategies to minimize Pi losses across the lower Great Lakes region.
Abstract Human activities have significantly modified the inputs of land-derived phosphorus (P) and nitrogen (N) to the Mediterranean Sea (MS). Here, we reconstruct the external inputs of reactive P and N to the Western Mediterranean Sea (WMS) and Eastern Mediterranean Sea (EMS) over the period 1950–2030. We estimate that during this period the land derived P and N loads increased by factors of 3 and 2 to the WMS and EMS, respectively, with reactive P inputs peaking in the 1980s but reactive N inputs increasing continuously from 1950 to 2030. The temporal variations in reactive P and N inputs are imposed in a coupled P and N mass balance model of the MS to simulate the accompanying changes in water column nutrient distributions and primary production with time. The key question we address is whether these changes are large enough to be distinguishable from variations caused by confounding factors, specifically the relatively large inter-annual variability in thermohaline circulation (THC) of the MS. Our analysis indicates that for the intermediate and deep water masses of the MS the magnitudes of changes in reactive P concentrations due to changes in anthropogenic inputs are relatively small and likely difficult to diagnose because of the noise created by the natural circulation variability. Anthropogenic N enrichment should be more readily detectable in time series concentration data for dissolved organic N (DON) after the 1970s, and for nitrate (NO3) after the 1990s. The DON concentrations in the EMS are predicted to exhibit the largest anthropogenic enrichment signature. Temporal variations in annual primary production over the 1950–2030 period are dominated by variations in deep-water formation rates, followed by changes in riverine P inputs for the WMS and atmospheric P deposition for the EMS. Overall, our analysis indicates that the detection of basin-wide anthropogenic nutrient concentration trends in the MS is rendered difficult due to: (1) the Atlantic Ocean contributing the largest reactive P and N inputs to the MS, hence diluting the anthropogenic nutrient signatures, (2) the anti-estuarine circulation removing at least 45% of the anthropogenic nutrients inputs added to both basins of the MS between 1950 and 2030, and (3) variations in intermediate and deep water formation rates that add high natural noise to the P and N concentration trajectories.
Abstract Accurate estimation of global evapotranspiration (ET) is essential to understand water cycle and land-atmosphere feedbacks in the Earth system. Satellite-driven ET models provide global estimates, but many of the ET algorithms have been designed independently of soil moisture observations. As water for ET is sourced from the soil, incorporating soil moisture into global remote sensing algorithms of ET should, in theory, improve performance, especially in water-limited regions. This paper presents an update to the widely-used Priestley Taylor-Jet Propulsion Laboratory (PT-JPL) ET algorithm to incorporate spatially explicit daily surface soil moisture control on soil evaporation and canopy transpiration. The updated algorithm is evaluated using 14 AmeriFlux eddy covariance towers co-located with COsmic-ray Soil Moisture Observing System (COSMOS) soil moisture observations. The new PT-JPLSM model shows reduced errors and increased explanation of variance, with the greatest improvements in water-limited regions. Soil moisture incorporation into soil evaporation improves ET estimates by reducing bias and RMSE by 29.9% and 22.7% respectively, while soil moisture incorporation into transpiration improves ET estimates by reducing bias by 30.2%, RMSE by 16.9%. We apply the algorithm globally using soil moisture observations from the Soil Moisture Active Passive Mission (SMAP). These new global estimates of ET show reduced error at finer spatial resolutions and provide a rich dataset to evaluate land surface and climate models, vegetation response to changes in water availability and environmental conditions, and anthropogenic perturbations to the water cycle.
Abstract Accurate, efficient, inexpensive, and multi-parameter monitoring of water quality parameters is critical for continued water safety from developed urban regions to resource-limited or sparsely populated areas. This study describes an integrated sensing system with solution-processed pH, free chlorine, and temperature sensors on a common glass substrate. The pH and temperature sensors are fabricated by low-cost inkjet printing of palladium/palladium oxide and silver. The potentiometric pH sensor has a high sensitivity of 60.6 mV/pH and a fast response of 15 s. The Wheatstone-bridge-based temperature sensor shows an immediate response of 3.35 mV/°C towards temperature change. The free chlorine sensor is based on an electrochemically modified pencil lead, which exhibits a stable and reproducible sensitivity of 342 nA/ppm for hypochlorous acid. Such a free chlorine sensor is potentiostat-free and calibration-free, so it is easy-to-use. The three sensors are connected to a field-programmable gate array board for data collection, analysis and display, with real-time pH and temperature compensation for free chlorine sensing. The developed sensing system is user-friendly, cost-effective, and can monitor water samples in real-time with an accuracy of >82%. This platform enables water quality monitoring by nonprofessionals in a simple manner.
Community-based projects place emphasis on a collaborative approach and facilitate research among Indigenous populations regarding local issues and challenges, such as traditional foods consumption, climate change and health safety. Country foods (locally harvested fish, game birds, land animals and plants), which contribute to improved food security, can also be a primary route of contaminant exposure among populations in remote regions. A community-based project was launched in the Dehcho and Sahtù regions of the Northwest Territories (Canada) to: 1) assess contaminants exposure and nutrition status; 2) investigate the role of country food on nutrient and contaminant levels and 3) understand the determinants of message perception on this issue. Consultation with community members, leadership, local partners and researchers was essential to refine the design of the project and implement it in a culturally relevant way. This article details the design of a community-based biomonitoring study that investigates country food use, contaminant exposure and nutritional status in Canadian subarctic First Nations in the Dehcho and Sahtù regions. Results will support environmental health policies in the future for these communities. The project was designed to explore the risks and benefits of country foods and to inform the development of public health strategies.
Human biomonitoring represents an important tool for health risk assessment, supporting the characterization of contaminant exposure and nutrient status. In communities where country foods (locally harvested foods: land animals, fish, birds, plants) are integrated in the daily diet, as is the case in remote northern regions where food security is a challenge, such foods can potentially be a significant route of contaminant exposure. To assess this issue, a biomonitoring project was implemented among Dene/Métis communities of the Dehcho region of the Northwest Territories, Canada.Participants completed dietary surveys (i.e., a food frequency questionnaire and 24-h recall) to estimate food consumption patterns as well as a Health Messages Survey to evaluate the awareness and perception of contaminants and consumption notices. Biological sampling of hair, urine and blood was conducted. Toxic metals (e.g., mercury, lead, cadmium), essential metals (e.g., copper, nickel, zinc), fatty acids, and persistent organic pollutants (POPs) were measured in samples.The levels of contaminants in blood, hair and urine for the majority of participants were below the available guidance values for mercury, cadmium, lead and uranium. However, from the 279 participants, approximately 2% were invited to provide follow up samples, mainly for elevated mercury level. Also, at the population level, blood lead (GM: 11 μg/L) and blood cadmium (GM: 0.53 μg/L) were slightly above the Canadian Health Measures Survey data. Therefore, although country foods occasionally contain elevated levels of particular contaminants, human exposures to these metals remained similar to those seen in the Canadian general population. In addition, dietary data showed the importance and diversity of country foods across participating communities, with the consumption of an average of 5.1% of total calories from wild-harvested country foods.This project completed in the Mackenzie Valley of the Northwest Territories fills a data gap across other biomonitoring studies in Canada as it integrates community results, will support stakeholders in the development of public health strategies, and will inform environmental health issue prioritization.
Abstract Prewhitening, the process of eliminating or reducing short-term stochastic persistence to enable detection of deterministic change, has been extensively applied to time series analysis of a range of geophysical variables. Despite the controversy around its utility, methodologies for prewhitening time series continue to be a critical feature of a variety of analyses including: trend detection of hydroclimatic variables and reconstruction of climate and/or hydrology through proxy records such as tree rings. With a focus on the latter, this paper presents a generalized approach to exploring the impact of a wide range of stochastic structures of short- and long-term persistence on the variability of hydroclimatic time series. Through this approach, we examine the impact of prewhitening on the inferred variability of time series across time scales. We document how a focus on prewhitened, residual time series can be misleading, as it can drastically distort (or remove) the structure of variability across time scales. Through examples with actual data, we show how such loss of information in prewhitened time series of tree rings (so-called “residual chronologies”) can lead to the underestimation of extreme conditions in climate and hydrology, particularly droughts, reconstructed for centuries preceding the historical period.
Climate‐driven decline in freshwater supplied by rivers draining the hydrographic apex of western North America has ramifications for downstream ecosystems and society. For the Peace‐Athabasca Delta (PAD), floods from the Peace and Athabasca rivers are critical for sustaining abundant shallow water habitat, but their frequency has been in decline for decades over much of its area. Here, we assess current hydrological and limnological status in the PAD by integrating spatial and temporal data. Analysis of water isotope compositions and water chemistry measured at numerous lakes across the delta shows that hydro‐limnological effects of the large‐scale ice‐jam flood event of 2014 failed to persist beyond the early ice‐free season of 2015. Isotope‐inferred paleohydrological records from five hydrologically representative lakes in the PAD indicate that periodic desiccation during the Little Ice Age occurred at the most elevated basin in response to locally arid climatic conditions, yet other lower elevation sites were influenced by high water level on Lake Athabasca owing to increased snowmelt‐ and glacier‐derived river discharge. In contrast, water isotope data during the past 15 yr at all five lakes consistently document the strong role of evaporation, a trend which began in the early to mid‐20th century according to sediment records and is indicative of widespread aridity unprecedented during the past 400 yr. We suggest that integration of hydrological and limnological approaches over space and time is needed to inform assessment of contemporary lake conditions in large, complex floodplain landscapes.
Freshwater availability is changing worldwide. Here we quantify 34 trends in terrestrial water storage observed by the Gravity Recovery and Climate Experiment (GRACE) satellites during 2002-2016 and categorize their drivers as natural interannual variability, unsustainable groundwater consumption, climate change or combinations thereof. Several of these trends had been lacking thorough investigation and attribution, including massive changes in northwestern China and the Okavango Delta. Others are consistent with climate model predictions. This observation-based assessment of how the world's water landscape is responding to human impacts and climate variations provides a blueprint for evaluating and predicting emerging threats to water and food security.
Almost 60% of the rivers in the northern hemisphere experience significant seasonal effects of river ice. In many of these northern rivers, ice-jam floods (IJFs) pose serious threats to riverine communities. Since the inundation elevations associated with ice-jam events can be several meters higher than open-water floods for the same or even lower discharges, IJFs can be more disastrous to local communities and economies, especially as their occurrence is often very sudden and difficult to anticipate. In the last several decades, there have been many important advances in river ice hydrology, resulting in improved knowledge and capacity to deal with IJFs. This paper presents a review of IJF literature available on the Web of Science. Nature and scope of scholarly research on IJF are analysed, and an agenda for research that better integrates IJF challenges with research and mitigation opportunities is suggested.
Abstract A field campaign was conducted October 30th to November 13th, 2015 with the intention of capturing diurnal soil freeze/thaw state at multiple scales using ground measurements and remote sensing measurements. On four of the five sampling days, we observed a significant difference between morning (frozen scenario) and afternoon (thawed scenario) ground-based measurements of the soil relative permittivity. These results were supported by an in situ soil moisture and temperature network (installed at the scale of a spaceborne passive microwave pixel) which indicated surface soil temperatures fell below 0 °C for the same four sampling dates. Ground-based radiometers appeared to be highly sensitive to F/T conditions of the very surface of the soil and indicated normalized polarization index (NPR) values that were below the defined freezing values during the morning sampling period on all sampling dates. The Scanning L-band Active Passive (SLAP) instrumentation, flown over the study region, showed very good agreement with the ground-based radiometers, with freezing states observed on all four days that the airborne observations covered the fields with ground-based radiometers. The Soil Moisture Active Passive (SMAP) satellite had morning overpasses on three of the sampling days, and indicated frozen conditions on two of those days. It was found that >60% of the in situ network had to indicate surface temperatures below 0 °C before SMAP indicated freezing conditions. This was also true of the SLAP radiometer measurements. The SMAP, SLAP and ground-based radiometer measurements all indicated freezing conditions when soil temperature sensors installed at 5 cm depth were not frozen.
Abstract Algal simulations in many water quality models perform poorly because of oversimplifications in the process descriptions of the algae growth mechanisms. In this study, algae simulations were improved by implementing variable chlorophyll a/algal biomass ratios in the CE-QUAL-W2 model, a sophisticated two-dimensional laterally-averaged water quality model. Originally a constant in the model, the chlorophyll a/algal biomass ratio was reprogrammed to vary according to the nutrient and light limiting conditions in the water column. The modified model was tested on Lake Diefenbaker, a prairie reservoir in Saskatchewan, Canada, where, similar to many other lakes in the world, field observations confirm variable spatiotemporal ratios between chlorophyll a and algal biomass. The modified version yielded more accurate simulations compared to the standard version and provides a promising algorithm to improve results for many lakes and reservoirs globally.
Forest fire risk estimation constitutes an essential process to prevent high-intensity fires which are associated with severe implications to the natural and cultural environment. The primary aim of this research was to determine fire risk levels based on the local features of an island, namely, the impact of fuel structures, slope, aspects, as well as the impact of the road network and inhabited regions. The contribution of all the involved factors to forest fires ignition and behavior highlight certain regions which are highly vulnerable. In addition, the influence of both natural and anthropogenic factors to forest fire phenomena is explored. In this study, natural factors play a dominant role compared to anthropogenic factors. Hence essential preventative measures must focus on specific areas and established immediately. Indicative measures may include: the optimal allocation of watchtowers as well as the spatial optimization of mobile firefighting vehicles; and, forest fuel treatments in areas characterized by extremely high fire risk. The added value of this fire prediction tool is that it is highly flexible and could be adopted elsewhere with the necessary adjustments to local characteristics.
Anonymous review of scientific manuscripts was intended to encourage reviewers to speak freely, but other models may be better for accountability and inclusivity.
Under changing climate conditions, understanding local adaptation of plants is crucial to predicting the resilience of ecosystems. We selected black spruce (Picea mariana), the most dominant tree species in the North American boreal forest, in order to evaluate local adaptation vs. plasticity across regions experiencing some of the most extreme climate warming globally. Seeds from three provenances across the latitudinal extent of this species in northwestern Canada were planted in a common garden study in growth chambers. Two levels of two resource conditions were applied (low/high nutrient and ambient/elevated CO2) in a fully factorial design and we measured physiological traits, allocational traits, growth and survival. We found significant differences in height, root length and biomass among populations, with southern populations producing the largest seedlings. However, we did not detect meaningful significant differences among nutrient or CO2 treatments in any traits measured, and there were no consistent population-level differences in physiological traits or allocation patterns. We found that there was greater mortality after simulated winter in the high nutrient treatment, which may reflect an important shift in seedling growth strategies under increased resource availability. Our study provides important insight into how this dominant boreal tree species might respond to the changing climate conditions predicted in this region.
Surface mining in northern Alberta transforms wetlands and forests into open pits, tailings ponds, and overburden. As part of their license to operate, mine operators are required to reclaim this altered landscape to a predisturbance capacity. In 2012, Syncrude Canada Limited constructed one of the first of two reclaimed wetlands, the Sandhill Fen Watershed (SFW), to evaluate wetland reclamation strategies. SFW is a 52-ha system atop soft-tailings that includes an inflow/outflow pump system, underdrains, upland hummocks, and a fen lowland. In this study, water table dynamics of the fen lowland were evaluated in the 2 years following commissioning (2014–2015) to assess whether this newly constructed watershed has hydrological conditions that facilitate hydric soils with water table regimes similar to reference systems. Results indicate that the location and hydrophysical properties of placed materials control water table responses to both water management and precipitation. This differential water table response in the SFW lowland drove lateral fluxes between adjacent landforms, suggesting periods of intermittent water supply from uplands to wetlands along hummock margins. As in natural systems, the lowland fen exhibited several lateral flow reversals over the 2 years depending upon water level. Water tables on-average were greater than those observed in natural analogues. Comparison during these first 2 years following commissioning contribute to the increasing insight as to how construction and management practices support reclamation postmining.
Abstract. Precipitation events that bring rain and snow to the Banff–Calgary area of Alberta are a critical aspect of the region's water cycle and can lead to major flooding events such as the June 2013 event that was the second most costly natural disaster in Canadian history. Because no special atmospheric-oriented observations of these events have been made, a field experiment was conducted in March and April 2015 in Kananaskis, Alberta, to begin to fill this gap. The goal was to characterize and better understand the formation of the precipitation at the surface during spring 2015 at a specific location in the Kananaskis Valley. Within the experiment, detailed measurements of precipitation and weather conditions were obtained, a vertically pointing Doppler radar was deployed and weather balloons were released. Although 17 precipitation events occurred, this period was associated with much less precipitation than normal (−35 %) and above-normal temperatures (2.5 ∘C). Of the 133 h of observed precipitation, solid precipitation occurred 71 % of the time, mixed precipitation occurred 9 % and rain occurred 20 %. An analysis of 17 504 precipitation particles from 1181 images showed that a wide variety of crystals and aggregates occurred and approximately 63 % showed signs of riming. This was largely independent of whether flows aloft were upslope (easterly) or downslope (westerly). In the often sub-saturated surface conditions, hydrometeors containing ice occurred at temperatures as high as 9 ∘C. Radar structures aloft were highly variable with reflectivity sometimes >30 dBZe and Doppler velocity up to −1 m s−1, which indicates upward motion of particles within ascending air masses. Precipitation was formed in this region within cloud fields sometimes having variable structures and within which supercooled water at least sometimes existed to produce accreted particles massive enough to reach the surface through the relatively dry sub-cloud region.
Forecasting changes to ecological communities is one of the central challenges in ecology. However, nonlinear dependencies, biotic interactions and data limitations have limited our ability to assess how predictable communities are. Here, we used a machine learning approach and environmental monitoring data (biological, physical and chemical) to assess the predictability of phytoplankton cell density in one lake across an unprecedented range of time-scales. Communities were highly predictable over hours to months: model R2 decreased from 0.89 at 4 hours to 0.74 at 1 month, and in a long-term dataset lacking fine spatial resolution, from 0.46 at 1 month to 0.32 at 10 years. When cyanobacterial and eukaryotic algal cell densities were examined separately, model-inferred environmental growth dependencies matched laboratory studies, and suggested novel trade-offs governing their competition. High-frequency monitoring and machine learning can set prediction targets for process-based models and help elucidate the mechanisms underlying ecological dynamics.
Haunted by the past Reducing the extent of hypoxia in the Gulf of Mexico will not be as easy as reducing agricultural nitrogen use. Van Meter et al. report that so much nitrogen from runoff has accumulated in the Mississippi River basin that, even if future agricultural nitrogen inputs are eliminated, it will still take 30 years to realize the 60% decrease in load needed to reduce eutrophication in the Gulf. This legacy effect means that a dramatic shift in land-use practices, which may not be compatible with current levels of agricultural production, will be needed to control hypoxia in the Gulf of Mexico. Science , this issue p. 427
Abstract Water managers are actively incorporating climate change information into their long- and short-term planning processes. This is generally seen as a step in the right direction because it supplements traditional methods, providing new insights that can help in planning for a non-stationary climate. However, the continuous evolution of climate change information can make it challenging to use available information appropriately. Advice on how to use the information is not always straightforward and typically requires extended dialogue between information producers and users, which is not always feasible. To help navigate better the ever-changing climate science landscape, this review is organized as a set of nine guidelines for water managers and planners that highlight better practices for incorporating climate change information into water resource planning and management. Each DOs and DON'Ts recommendation is given with context on why certain strategies are preferable and addresses frequently asked questions by exploring past studies and documents that provide guidance, including real-world examples mainly, though not exclusively, from the United States. This paper is intended to provide a foundation that can expand through continued dialogue within and between the climate science and application communities worldwide, a two-way information sharing that can increase the actionable nature of the information produced and promote greater utility and appropriate use.
Increased fire frequency, extent and severity are expected to strongly affect the structure and function of boreal forest ecosystems. In this study, we examined 213 plots in boreal forests dominated by black spruce (Picea mariana) or jack pine (Pinus banksiana) of the Northwest Territories, Canada, after an unprecedentedly large area burned in 2014. Large fire size is associated with high fire intensity and severity, which would manifest as areas with deep burning of the soil organic layer (SOL). Our primary objectives were to estimate burn depth in these fires and then to characterise landscapes vulnerable to deep burning throughout this region. Here we quantify burn depth in black spruce stands using the position of adventitious roots within the soil column, and in jack pine stands using measurements of burned and unburned SOL depths. Using these estimates, we then evaluate how burn depth and the proportion of SOL combusted varies among forest type, ecozone, plot-level moisture and stand density. Our results suggest that most of the SOL was combusted in jack pine stands regardless of plot moisture class, but that black spruce forests experience complete combustion of the SOL only in dry and moderately well-drained landscape positions. The models and calibrations we present in this study should allow future research to more accurately estimate burn depth in Canadian boreal forests.
Lake ice is a significant component of the cryosphere due to its large spatial coverage in high-latitude regions during the winter months. The Laurentian Great Lakes are the world’s largest supply of freshwater and their ice cover has a major impact on regional weather and climate, ship navigation, and public safety. Ice experts at the Canadian Ice Service (CIS) have been manually producing operational Great Lakes image analysis charts based on visual interpretation of the synthetic aperture radar (SAR) images. In that regard, we have investigated the performance of the semi-automated segmentation algorithm “glocal” Iterative Region Growing with Semantics (IRGS) for lake ice classification using dual polarized RADARSAT-2 imagery acquired over Lake Erie. Analysis of various case studies indicated that the “glocal” IRGS algorithm could provide a reliable ice-water classification using dual polarized images with a high overall accuracy of 90.4%. However, lake ice types that are based on stage of development were not effectively identified due to the ambiguous relation between backscatter and ice types. The slight improvement of using dual-pol as opposed to single-pol images for ice-water discrimination was also demonstrated.
Endorheic (hydrologically landlocked) basins spatially concur with arid/semi-arid climates. Given limited precipitation but high potential evaporation, their water storage is vulnerable to subtle flux perturbations, which are exacerbated by global warming and human activities. Increasing regional evidence suggests a probably recent net decline in endorheic water storage, but this remains unquantified at a global scale. By integrating satellite observations and hydrological modelling, we reveal that during 2002–2016 the global endorheic system experienced a widespread water loss of about 106.3 Gt yr−1, attributed to comparable losses in surface water, soil moisture and groundwater. This decadal decline, disparate from water storage fluctuations in exorheic basins, appears less sensitive to El Nino–Southern Oscillation-driven climate variability, which implies a possible response to longer-term climate conditions and human water management. In the mass-conserved hydrosphere, such an endorheic water loss not only exacerbates local water stress, but also imposes excess water on exorheic basins, leading to a potential sea level rise that matches the contribution of nearly half of the land glacier retreat (excluding Greenland and Antarctica). Given these dual ramifications, we suggest the necessity for long-term monitoring of water storage variation in the global endorheic system and the inclusion of its net contribution to future sea level budgeting.
Hyporheic exchange is important in increasing stream water transit time through basins and enhancing redox-sensitive biogeochemical reactions influencing downstream water quality. Such exchange may be enhanced by beaver dams which are common throughout low order streams including those originating in peatlands. To understand the influence of beaver dams on hyporheic flows and biogeochemical properties, nitrogen (N), dissolved organic nitrogen (DOC) and N cycling rates were observed along a beaver dammed, third-order stream draining Canadian Rocky Mountain peatland. Beaver dams enlarged the hyporheric zone from ≤1.5 to ≥7.5 m. The looping hyporheic flow path created a zone of N and DOC depletion adjacent to the dams. As a result, nitrification rates were lowest in this zone. Where hyporheic flows exited the riparian area and flowed back to the stream channel downstream of a dam, the adjacent riparian area served as a source of N and DOC to the stream. Enhanced nutrient influx to streams owing to beaver dam modified hyporheic flow paths has implications for stream biogeochemical cycling and ecological integrity, which need further exploration.
Abstract Horizontal and altitudinal redistribution of snow by wind transport and avalanches can be important controls on small- and large-scale snow accumulation patterns that control meltwater supply in alpine environments. Redistribution processes control the spatial variability of snow accumulation, which not only controls meltwater supply, but also regulates snowmelt timing, duration, and rates, as well as snow-covered area depletion and the variable contributing area for meltwater runoff generation. However, most hydrological models and land surface schemes do not consider snow redistribution processes, and those that do are difficult to verify without spatially distributed snow depth measurements. These are rarely available in both high resolution and covering large scales. As an increased number of hydrological models include snow redistribution processes there is a need for additional snowcover metrics to verify snow redistribution schemes over large areas using readily available data. This study develops novel high-resolution (20 m), snowcover indices from remotely sensed imagery (Landsat-8 and Sentinel-2) to evaluate snow redistribution models over alpine areas without in-situ or airborne snow observations. A snowcover absence (SA) index, calculated from snow-free areas during the winter, identifies areas of wind erosion or avalanche source areas. A snowcover persistence (SP) index, calculated from snow-covered areas during the summer, identifies snow deposition in drifts and avalanche deposits. The snowcover indices captured the relative differences in surface observations of snow presence and absence between exposed and sheltered sites on an intensely instrumented ridge in the Canadian Rockies Hydrological Observatory. Within the Tuolumne River Basin in central California (1100 km2), the SP index captured roughly half of the spatial variability (R2 = 0.49 to 0.56) in peak SWE as estimated from airborne LiDAR-derived snow depths. At the individual mountain ridge scale (~800 m), variability in both ablation and snow redistribution controlled the SP patterns over 7979 ridges. Differences in shortwave irradiance explained 76% of the SP variance across ridges, but could not explain smaller-scale (~100 m) SP peaks that are associated with snowdrifts and avalanche deposits. The snowcover indices can be used to evaluate snow redistribution models of the finer scale impacts of snow redistribution by wind and gravity as long as the larger scale influences of spatially variable solar irradiance effects are also simulated.
Wildfires, which constitute the most extensive natural disturbance of the boreal biome, produce a broad range of ecological impacts to vegetation and soils that may influence post-fire vegetation assemblies and seedling recruitment. We inventoried post-fire understory vascular plant communities and tree seedling recruitment in the northwestern Canadian boreal forest and characterized the relative importance of fire effects and fire history, as well as non-fire drivers (i.e., the topoedaphic context and climate), to post-fire vegetation assemblies. Topoedaphic context, pre-fire forest structure and composition, and climate primarily controlled the understory plant communities and shifts in the ranked dominance of tree species (***8% and **13% of variance explained, respectively); however, fire and fire-affected soils were significant secondary drivers of post-fire vegetation. Wildfire had a significant indirect effect on understory vegetation communities through post-fire soil properties (**5%), and fire history and burn severity explained the dominance shifts of tree species (*7%). Fire-related variables were important explanatory variables in classification and regression tree models explaining the dominance shifts of four tree species (R2 = 0.43–0.65). The dominance of jack pine (Pinus banksiana Lamb.) and trembling aspen (Populus tremuloides Michx.) increased following fires, whereas that of black spruce (Picea mariana (Mill.) BSP.) and white spruce (Picea glauca (Moench) Voss) declined. The overriding importance of site and climate to post-fire vegetation assemblies may confer some resilience to disturbed forests; however, if projected increases in fire activity in the northwestern boreal forest are borne out, secondary pathways of burn severity, fire frequency, and fire effects on soils are likely to accelerate ongoing climate-driven shifts in species compositions.
Climate change mediated drying of boreal peatlands is expected to enhance peatland afforestation and wildfire vulnerability. The water table depth–afforestation feedback represents a positive feedback that can enhance peat drying and consolidation and thereby increase peat burn severity; exacerbating the challenges and costs of wildfire suppression efforts and potentially shifting the peatland to a persistent source of atmospheric carbon. To address this wildfire management challenge, we examined burn severity across a gradient of drying in a black spruce dominated peatland that was partially drained in 1975−1980 and burned in the 2016 Fort McMurray Horse River wildfire. We found that post-drainage black spruce annual ring width increased substantially with intense drainage. Average (±SD) basal diameter was 2.6 ± 1.2 cm, 3.2 ± 2.0 cm and 7.9 ± 4.7 cm in undrained (UD), moderately drained (MD) and heavily drained (HD) treatments, respectively. Depth of burn was significantly different between treatments (p < 0.001) and averaged (±SD) 2.5 ± 3.5 cm, 6.4 ± 5.0 cm and 36.9 ± 29.6 cm for the UD, MD and HD treatments, respectively. The high burn severity in the HD treatment included 38% of the treatment that experienced combustion of the entire peat profile, and we estimate that overall 51% of the HD pre-burn peat carbon stock was lost. We argue that the HD treatment surpassed an ecohydrological tipping point to high severity peat burn that may be identified using black spruce stand characteristics in boreal plains bogs. While further studies are needed, we believe that quantifying this threshold will aid in developing effective adaptive management techniques and protecting boreal peatland carbon stocks.
Assessment of remote sensing derived freeze/thaw products from L-band radiometry requires ground validation. There is growing interest in utilizing soil moisture networks to meet this validation re...
Payments for Environmental Services (PES) constitute an innovative economic intervention to counteract the global loss of biodiversity and ecosystem functions. In theory, some appealing features should enable PES to perform well in achieving conservation and welfare goals. In practice, outcomes depend on the interplay between context, design and implementation. Inspecting a new global dataset, we find that some PES design principles pre-identified in the social-science literature as desirable, such as spatial targeting and payment differentiation, are only partially being applied in practice. More importantly, the PES-defining principle of conditionality—monitoring compliance and sanctioning detected non-compliance—is seldom being implemented. Administrative ease, multiple non-environmental side objectives and social equity concerns may jointly help explain the reluctance to adopt more sophisticated, theoretically informed practices. However, by taking simplifying shortcuts in design and implementation, PES programmes may become less environmentally effective and efficient as economic incentives, thus underperforming their conservation potential.
Conventional assessment and evaluation of sediment quality are based on laboratory-based ecotoxicological and chemical measurements with lack of concern for ecological relevance. Microbiotas in sediment are responsive to pollutants and can be used as alternative ecological indicators of sediment pollutants; however, the linkage between the microbial ecology and ecotoxicological endpoints in response to sediment contamination has been poorly evaluated. Here, in situ microbiotas from the Three Gorges Reservoir (TGR) area of the Yangtze River were characterized by DNA metabarcoding approaches, and then, changes of in situ microbiotas were compared with the ecotoxicological endpoint, aryl hydrocarbon receptor (AhR) mediated activity, and level of polycyclic aromatic hydrocarbons (PAHs) in sediments. PAHs and organic pollutant mixtures mediating AhR activity had different effects on the structures of microbiotas. Specifically, Shannon indices of protistan communities were negatively correlated with the levels of AhR mediated activity and PAHs. The sediment AhR activity was positively correlated with the relative abundance of prokaryotic Acetobacteraceae, but had a negative correlation with protistan Oxytrichidae. Furthermore, a quantitative classification model was built to predict the level of AhR activity based on the relative abundances of Acetobacteraceae and Oxytrichidae. These results suggested that in situ Protista communities could provide a useful tool for monitoring and assessing ecological stressors. The observed responses of microbial community provided supplementary evidence to support that the AhR-active pollutants, such as PAHs, were the primary stressors of the aquatic community in TGR area.
Oil spills offshore can cause long-term ecological effects on coastal marine ecosystems. Despite their important ecological roles in the cycling of energy and nutrients in food webs, effects on bacteria, protists or arthropods are often neglected. Environmental DNA (eDNA) metabarcoding was applied to characterize changes in the structure of micro- and macro-biota communities of surface sediments over a 7-year period since the occurrence of Hebei Spirit oil spill on December 7, 2007. Alterations in diversities and structures of micro- and macro-biota were observed in the contaminated area where concentrations of polycyclic aromatic hydrocarbons were greater. Successions of bacterial, protists and metazoan communities revealed long-term ecological effects of residual oil. Residual oil dominated the largest cluster of the community-environment association network. Presence of bacterial families (Aerococcaceae and Carnobacteriaceae) and the protozoan family (Platyophryidae) might have conferred sensitivity of communities to oil pollution. Hydrocarbon-degrading bacterial families (Anaerolinaceae, Desulfobacteraceae, Helicobacteraceae and Piscirickettsiaceae) and algal family (Araphid pennate) were resistant to adverse effects of spilt oil. The protistan family (Subulatomonas) and arthropod families (Folsomia, Sarcophagidae Opomyzoidea, and Anomura) appeared to be positively associated with residual oil pollution. eDNA metabarcoding can provide a powerful tool for assessing effects of anthropogenic pollution, such as oil spills on sediment communities and its long-term trends in coastal marine environments.
Environmental indicators are powerful tools for tracking environmental changes, measuring environmental performance, and informing policymakers. Many diverse environmental indicators, including agricultural environmental indicators, are currently in use or being developed. This special collection of technical papers expands on the peer-reviewed literature on environmental indicators and their application to important current issues in the following areas: (i) model-derived indicators to indicate phosphorus losses from arable land to surface runoff and subsurface drainage, (ii) glutathione-ascorbate cycle-related antioxidants as early-warning bioindicators of polybrominated diphenyl ether toxicity in mangroves, and (iii) assessing the effectiveness of using organic matrix biobeds to limit herbicide dissipation from agricultural fields, thereby controlling on-farm point-source pollution. This introductory review also provides an overview of environmental indicators, mainly for agriculture, with examples related to the quality of the agricultural soil-water-air continuum and the application of model-derived indicators. Current knowledge gaps and future lines of investigation are also discussed. It appears that environmental indicators, particularly those for agriculture, work efficiently at the field, catchment, and local scales and serve as valuable metrics of system functioning and response; however, these indicators need to be refined or further developed to comprehensively meet community expectations in terms of providing a consistent picture of relevant issues and/or allowing comparisons to be made nationally or internationally.
Soil moisture plays an important role in modulating regional climate from sub-seasonal to seasonal timescales. Particularly important, soil moisture deficits can amplify summer heatwaves (HWs) through soil moisture-temperature feedback which has critical impacts on society, economy and human health. In this study, we evaluate decade-long convection-permitting Weather Research and Forecast (WRF) model simulations over the contiguous US on simulating heatwaves and their relationship with antecedent soil moisture using a dense observational network. We showed that the WRF model is capable of capturing the spatial patten of temperature threshold to define HWs, though the simulation shows a warm bias in the Midwest and cold bias in western mountainous regions. Two HW indices, based on frequency (HWF) and magnitude (HWM), are evaluated. Significant anti-correlations between antecedent soil moisture and both HW indices have been found in most parts of the domain except the South Pacific Coast. A detailed study has been conducted for the Midwest and South Great Plains regions, where two heatwaves had occurred in the last decade. In both regions, the high quantile of the HWF distribution shows a strong dependence on antecedent soil moisture: drier soil leads to much larger increase on the upper quantile of HWF than it does on the lower quantile. Soil moisture effects on the higher end of HWM are not as strong as on the lower end: wetter antecedent soil corresponds to a larger decrease on the lower quantile of HWM. WRF captures the heterogeneous responses to dry soil on HWF distribution in both regions, but overestimates these HWM responses in the Midwest and underestimates them in the South Great Plains. Our results show confidence in WRF’s ability to simulate HW characteristics and the impacts of antecedent soil moisture on HWs. These are also important implications for using high-resolution convection-permitting mode to study the coupling between land and atmosphere.
In this paper, we develop a radar snow water equivalent (SWE) retrieval algorithm based on a parameterized forward model of bicontinuous dense media radiative transfer (Bic-DMRT). The algorithm is based on retrieving the absorption loss of the snowpack which is directly proportional to the SWE. In the algorithm, Bic-DMRT is first applied to generate a lookup table (LUT) of snowpack backscattering at X- and Ku-band. Regression training is applied to the LUT to transform the dual-frequency backscatter into functions of two parameters: the scattering albedo at X-band and SWE. The background scattering is subtracted from the SnowSAR data to give the volume scattering of snow. Classification of SnowSAR data is applied to provide a priori information. Based on the obtained volume scattering and the priori information, a cost function is established to find SWE. Performance of the retrieval algorithm was tested using three sets of airborne SnowSAR data acquired over mixed areas in Finland and open tundra landscape in Canada. It is shown that the retrieval algorithm has a root-mean-square error below 30 mm of SWE and a correlation coefficient above 0.64.

DOI bib
ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks
Gerhard Krinner, Chris Derksen, Richard Essery, M. Flanner, Stefan Hagemann, Martyn P. Clark, Alex Hall, Helmut Rott, Claire Brutel‐Vuilmet, Hyungjun Kim, Cécile B. Ménard, Lawrence Mudryk, Chad W. Thackeray, Libo Wang, Gabriele Arduini, Gianpaolo Balsamo, Paul Bartlett, Julia Boike, Aaron Boone, F. Chéruy, Jeanne Colin, Matthias Cuntz, Yongjiu Dai, Bertrand Decharme, Jeff Derry, Agnès Ducharne, Emanuel Dutra, Xing Fang, Charles Fierz, Josephine Ghattas, Yeugeniy M. Gusev, Vanessa Haverd, Anna Kontu, Matthieu Lafaysse, R. M. Law, David M. Lawrence, Weiping Li, Thomas Marke, Danny Marks, Martin Ménégoz, О. Н. Насонова, Tomoko Nitta, Michio Niwano, John W. Pomeroy, Mark S. Raleigh, Gerd Schaedler, В. А. Семенов, Tanya Smirnova, Tobias Stacke, Ulrich Strasser, Sean Svenson, Dmitry Turkov, Tao Wang, Nander Wever, Hua Yuan, Wenyan Zhou, Dan Zhu
Geoscientific Model Development, Volume 11, Issue 12

Abstract. This paper describes ESM-SnowMIP, an international coordinated modelling effort to evaluate current snow schemes, including snow schemes that are included in Earth system models, in a wide variety of settings against local and global observations. The project aims to identify crucial processes and characteristics that need to be improved in snow models in the context of local- and global-scale modelling. A further objective of ESM-SnowMIP is to better quantify snow-related feedbacks in the Earth system. Although it is not part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), ESM-SnowMIP is tightly linked to the CMIP6-endorsed Land Surface, Snow and Soil Moisture Model Intercomparison (LS3MIP).
Permafrost thaw in the Arctic enables the biogeochemical transformation of vast stores of organic carbon into carbon dioxide (CO2). This CO2 release has significant implications for climate feedbacks, yet the potential counterbalance from CO2 fixation via chemical weathering of minerals exposed by thawing permafrost is entirely unstudied. We show that thermokarst in the western Canadian Arctic can enable rapid weathering of carbonate tills, driven by sulfuric acid from sulfide oxidation. Unlike carbonic acid‐driven weathering, this caused significant and previously undocumented CO2 production and outgassing in headwater streams. Increasing riverine solute fluxes correspond with long‐term intensification of thermokarst and reflect the regional predominance of sulfuric acid‐driven carbonate weathering. We conclude that thermokarst‐enhanced mineral weathering has potential to profoundly disrupt Arctic freshwater carbon cycling. While thermokarst and sulfuric acid‐driven carbonate weathering in the western Canadian Arctic amplify CO2 release, regional variation in sulfide oxidation will moderate the effects on the permafrost carbon‐climate feedback.
We present a new model extension for the Water balance Simulation Model, WaSiM, which features (i) snow interception and (ii) modified meteorological conditions under coniferous forest canopies, co...
Abstract. This article presents analyses of retrospective seasonal forecasts of snow accumulation. Re-forecasts with 4 months' lead time from two coupled atmosphere–ocean general circulation models (NCEP CFSv2 and MetOffice GloSea5) drive the Alpine Water balance and Runoff Estimation model (AWARE) in order to predict mid-winter snow accumulation in the Inn headwaters. As snowpack is hydrological storage that evolves during the winter season, it is strongly dependent on precipitation totals of the previous months. Climate model (CM) predictions of precipitation totals integrated from November to February (NDJF) compare reasonably well with observations. Even though predictions for precipitation may not be significantly more skilful than for temperature, the predictive skill achieved for precipitation is retained in subsequent water balance simulations when snow water equivalent (SWE) in February is considered. Given the AWARE simulations driven by observed meteorological fields as a benchmark for SWE analyses, the correlation achieved using GloSea5-AWARE SWE predictions is r = 0.57. The tendency of SWE anomalies (i.e. the sign of anomalies) is correctly predicted in 11 of 13 years. For CFSv2-AWARE, the corresponding values are r = 0.28 and 7 of 13 years. The results suggest that some seasonal prediction of hydrological model storage tendencies in parts of Europe is possible.
Abstract. A physically based hydroclimatological model (AMUNDSEN) is used to assess future climate change impacts on the cryosphere and hydrology of the Ötztal Alps (Austria) until 2100. The model is run in 100 m spatial and 3 h temporal resolution using in total 31 downscaled, bias-corrected, and temporally disaggregated EURO-CORDEX climate projections for the representative concentration pathways (RCPs) 2.6, 4.5, and 8.5 scenarios as forcing data, making this – to date – the most detailed study for this region in terms of process representation and range of considered climate projections. Changes in snow coverage, glacierization, and hydrological regimes are discussed both for a larger area encompassing the Ötztal Alps (1850 km2, 862–3770 m a.s.l.) as well as for seven catchments in the area with varying size (11–165 km2) and glacierization (24–77 %). Results show generally declining snow amounts with moderate decreases (0–20 % depending on the emission scenario) of mean annual snow water equivalent in high elevations (> 2500 m a.s.l.) until the end of the century. The largest decreases, amounting to up to 25–80 %, are projected to occur in elevations below 1500 m a.s.l. Glaciers in the region will continue to retreat strongly, leaving only 4–20 % of the initial (as of 2006) ice volume left by 2100. Total and summer (JJA) runoff will change little during the early 21st century (2011–2040) with simulated decreases (compared to 1997–2006) of up to 11 % (total) and 13 % (summer) depending on catchment and scenario, whereas runoff volumes decrease by up to 39 % (total) and 47 % (summer) towards the end of the century (2071–2100), accompanied by a shift in peak flows from July towards June.
Abstract. The mountain cryosphere of mainland Europe is recognized to have important impacts on a range of environmental processes. In this paper, we provide an overview on the current knowledge on snow, glacier, and permafrost processes, as well as their past, current, and future evolution. We additionally provide an assessment of current cryosphere research in Europe and point to the different domains requiring further research. Emphasis is given to our understanding of climate–cryosphere interactions, cryosphere controls on physical and biological mountain systems, and related impacts. By the end of the century, Europe's mountain cryosphere will have changed to an extent that will impact the landscape, the hydrological regimes, the water resources, and the infrastructure. The impacts will not remain confined to the mountain area but also affect the downstream lowlands, entailing a wide range of socioeconomical consequences. European mountains will have a completely different visual appearance, in which low- and mid-range-altitude glaciers will have disappeared and even large valley glaciers will have experienced significant retreat and mass loss. Due to increased air temperatures and related shifts from solid to liquid precipitation, seasonal snow lines will be found at much higher altitudes, and the snow season will be much shorter than today. These changes in snow and ice melt will cause a shift in the timing of discharge maxima, as well as a transition of runoff regimes from glacial to nival and from nival to pluvial. This will entail significant impacts on the seasonality of high-altitude water availability, with consequences for water storage and management in reservoirs for drinking water, irrigation, and hydropower production. Whereas an upward shift of the tree line and expansion of vegetation can be expected into current periglacial areas, the disappearance of permafrost at lower altitudes and its warming at higher elevations will likely result in mass movements and process chains beyond historical experience. Future cryospheric research has the responsibility not only to foster awareness of these expected changes and to develop targeted strategies to precisely quantify their magnitude and rate of occurrence but also to help in the development of approaches to adapt to these changes and to mitigate their consequences. Major joint efforts are required in the domain of cryospheric monitoring, which will require coordination in terms of data availability and quality. In particular, we recognize the quantification of high-altitude precipitation as a key source of uncertainty in projections of future changes. Improvements in numerical modeling and a better understanding of process chains affecting high-altitude mass movements are the two further fields that – in our view – future cryospheric research should focus on.
Winter weather events with temperatures near $$0\,^\circ\mathrm{{C}}$$ are often associated with freezing rain. They can have major impacts on the society by causing power outages and disruptions to the transportation networks. Despite the catastrophic consequences of freezing rain, very few studies have investigated how their occurrences could evolve under climate change. This study aims to investigate the change of freezing rain and ice pellets over southern Québec using regional climate modeling at high resolution. The fifth-generation Canadian Regional Climate Model with climate scenario RCP 8.5 at $$0.11^\circ$$ grid mesh was used. The precipitation types such as freezing rain, ice pellets or their combination are diagnosed using five methods (Cantin and Bachand, Bourgouin, Ramer, Czys and, Baldwin). The occurrences of the diagnosed precipitation types for the recent past (1980–2009) are found to be comparable to observations. The projections for the future scenario (2070–2099) suggested a general decrease in the occurrences of mixed precipitation over southern Québec from October to April. This is mainly due to a decrease in long-duration events ( $$\ge 6\,\mathrm{{h}}$$ ). Overall, this study contributes to better understand how the distribution of freezing rain and ice pellets might change in the future using high-resolution regional climate model.
Traditional foods have significant nutritional, sociocultural and economic value in subarctic First Nations communities of the Northwest Territories, and play a crucial role in promoting cultural continuity and sovereignty. Omega-3 polyunsaturated fatty acids (N-3 PUFAs), including eicosapentaenoic (EPA) and docosahexaenoic acid (DHA), carry significant benefits for neurocognitive development and cardiovascular health. However, the health risks posed by methylmercury may serve to undermine the benefits of fish consumption in Northern Indigenous communities. The objective of this study was to characterize profiles for mercury (Hg) and fatty acids in fish species harvested across lakes of the Dehcho Region, in the Mackenzie Valley of the Northwest Territories, to better understand the risks and benefits associated with traditional foods. Hg levels increased with trophic position, with the highest levels found in Burbot, Lake Trout, Walleye, and Northern Pike. Lake Trout, along with planktivorous species including Lake Whitefish, Cisco, and Sucker, demonstrated higher N-3 PUFAs than other species. Negative associations were observed between Hg and N-3 PUFAs in Lake Trout, Northern Pike, Walleye and Burbot. Further stratifying these relationships revealed significant interactions by lake. Significant differences observed in fatty acid and Hg profiles across lakes underscore the importance of considering both species- and lake-specific findings. This growing dataset of freshwater fish of the Dehcho will inform future efforts to characterize human Hg exposure profiles using probabilistic dose reconstruction models.
Detection, tracking, and refactoring of code clones (i.e., identical or nearly similar code fragments in the code-base of a software system) have been extensively investigated by a great many studies. Code clones have often been considered bad smells. While clone refactoring is important for removing code clones from the code-base, clone tracking is important for consistently updating code clones that are not suitable for refactoring. In this research we investigate the importance of micro-clones (i.e., code clones of less than five lines of code) in consistent updating of the code-base. While the existing clone detectors and trackers have ignored micro clones, our investigation on thousands of commits from six subject systems imply that around 80% of all consistent updates during system evolution occur in micro clones. The percentage of consistent updates occurring in micro clones is significantly higher than that in regular clones according to our statistical significance tests. Also, the consistent updates occurring in micro-clones can be up to 23% of all updates during the whole period of evolution. According to our manual analysis, around 83% of the consistent updates in micro-clones are non-trivial. As micro-clones also require consistent updates like the regular clones, tracking or refactoring micro-clones can help us considerably minimize effort for consistently updating such clones. Thus, micro-clones should also be taken into proper consideration when making clone management decisions.
The design and maintenance of APIs are complex tasks due to the constantly changing requirements of its users. Despite the efforts of its designers, APIs may suffer from a number of issues (such as incomplete or erroneous documentation, poor performance, and backward incompatibility). To maintain a healthy client base, API designers must learn these issues to fix them. Question answering sites, such as Stack Overflow (SO), has become a popular place for discussing API issues. These posts about API issues are invaluable to API designers, not only because they can help to learn more about the problem but also because they can facilitate learning the requirements of API users. However, the unstructured nature of posts and the abundance of non-issue posts make the task of detecting SO posts concerning API issues difficult and challenging. In this paper, we first develop a supervised learning approach using a Conditional Random Field (CRF), a statistical modeling method, to identify API issue-related sentences. We use the above information together with different features of posts and experience of users to build a technique, called CAPS, that can classify SO posts concerning API issues. Evaluation of CAPS using carefully curated SO posts on three popular API types reveals that the technique outperforms all three baseline approaches we consider in this study. We also conduct studies to test the generalizability of CAPS results and to understand the effects of different sources of information on it.
Copying code and then pasting with large number of edits is a common activity in software development, and the pasted code is a kind of complicated Type-3 clone. Due to large number of edits, we consider the clone as a large-gap clone. Large-gap clone can reflect the extension of code, such as change and improvement. The existing state-of-the-art clone detectors suffer from several limitations in detecting large-gap clones. In this paper, we propose a tool, CCAligner, using code window that considers e edit distance for matching to detect large-gap clones. In our approach, a novel e-mismatch index is designed and the asymmetric similarity coefficient is used for similarity measure. We thoroughly evaluate CCAligner both for large-gap clone detection, and for general Type-1, Type-2 and Type-3 clone detection. The results show that CCAligner performs better than other competing tools in large-gap clone detection, and has the best execution time for 10MLOC input with good precision and recall in general Type-1 to Type-3 clone detection. Compared with existing state-of-the-art tools, CCAligner is the best performing large-gap clone detection tool, and remains competitive with the best clone detectors in general Type-1, Type-2 and Type-3 clone detection.
Recent findings from a user study suggest that IR-based bug localization techniques do not perform well if the bug report lacks rich structured information such as relevant program entity names. On the contrary, excessive structured information such as stack traces in the bug report might always not be helpful for the automated bug localization. In this paper, we conduct a large empirical study using 5,500 bug reports from eight subject systems and replicating three existing studies from the literature. Our findings (1) empirically demonstrate how quality dynamics of bug reports affect the performances of IR-based bug localization, and (2) suggest potential ways (e.g., query reformulations) to overcome such limitations.
Despite the great number of clone detection approaches proposed in the literature, few have the scalability and speed to analyze large inter-project source datasets, where clone detection has many potential applications. Furthermore, because of the many uses of clone detection, an approach is needed that can adapt to the needs of the user to detect any kind of clone. We propose a clone detection approach designed for user-guided clone detection by exploiting the power of source transformation in a plugin based source processing pipeline. Clones are detected using a simple Jaccard-based clone similarity metric, and users customize the representation of their source code as sets of terms to target particular types or kinds of clones. Fast and scalable clone detection is achieved with indexing, sub-block filtering and input partitioning.

DOI bib
Towards long-term standardised carbon and greenhouse gas observations for monitoring Europe’s terrestrial ecosystems: a review
Daniela Franz, Manuel Acosta, Núria Altimir, Nicola Arriga, Dominique Arrouays, Marc Aubinet, Mika Aurela, Edward Ayres, Ana López‐Ballesteros, Mireille Barbaste, Daniel Berveiller, Sébastien Biraud, Hakima Boukir, Thomas S. Brown, Christian Brümmer, Nina Buchmann, George Burba, Arnaud Carrara, A. Cescatti, Éric Ceschia, Robert Clement, Edoardo Cremonese, Patrick Crill, Eva Dařenová, Sigrid Dengel, Petra D’Odorico, Gianluca Filippa, Stefan Fleck, Gerardo Fratini, Roland Fuß, Bert Gielen, Sébastien Gogo, J. Grace, Alexander Graf, Achim Grelle, Patrick Gross, Thomas Grünwald, Sami Haapanala, Markus Hehn, Bernard Heinesch, Jouni Heiskanen, Mathias Herbst, Christine Herschlein, Lukas Hörtnagl, Koen Hufkens, Andreas Ibrom, Claudy Jolivet, Lilian Joly, Michael B. Jones, Ralf Kiese, Leif Klemedtsson, Natascha Kljun, Katja Klumpp, Pasi Kolari, Olaf Kolle, Andrew S. Kowalski, Werner L. Kutsch, Tuomas Laurila, Anne De Ligne, Sune Linder, Anders Lindroth, Annalea Lohila, Bernhard Longdoz, Ivan Mammarella, Tanguy Manise, Sara Marañón-Jiménez, Giorgio Matteucci, Matthias Mauder, Philip Meier, Lutz Merbold, Simone Mereu, Stefan Metzger, Mirco Migliavacca, Meelis Mölder, Leonardo Montagnani, Christine Moureaux, David D. Nelson, Eiko Nemitz, Giacomo Nicolini, Mats Nilsson, Maarten Op de Beeck, Bruce Osborne, Mikaell Ottosson Löfvenius, Marián Pavelka, Matthias Peichl, Olli Peltola, Mari Pihlatie, Andrea Pitacco, Radek Pokorný, Jukka Pumpanen, Céline Ratié, Corinna Rebmann, Marilyn Roland, Simone Sabbatini, Nicolas Saby, Matthew Saunders, Hans Peter Schmid, Marion Schrumpf, Pavel Sedlák, Penélope Serrano-Ortiz, Lukas Siebicke, Ladislav Šigut, Hanna Silvennoinen, Guillaume Simioni, Ute Skiba, Oliver Sonnentag, Kamel Soudani, Patrice Soulé, R. Steinbrecher, Tiphaine Tallec, Anne Thimonier, Eeva‐Stiina Tuittila, Juha‐Pekka Tuovinen, Patrik Vestin, Gaëlle Vincent, Caroline Vincke, Domenico Vitale, Peter Waldner, Per Weslien, Lisa Wingate, Georg Wohlfahrt, M. S. Zahniser, Timo Vesala
International Agrophysics, Volume 32, Issue 4

Abstract Research infrastructures play a key role in launching a new generation of integrated long-term, geographically distributed observation programmes designed to monitor climate change, better understand its impacts on global ecosystems, and evaluate possible mitigation and adaptation strategies. The pan-European Integrated Carbon Observation System combines carbon and greenhouse gas (GHG; CO 2 , CH 4 , N 2 O, H 2 O) observations within the atmosphere, terrestrial ecosystems and oceans. High-precision measurements are obtained using standardised methodologies, are centrally processed and openly available in a traceable and verifiable fashion in combination with detailed metadata. The Integrated Carbon Observation System ecosystem station network aims to sample climate and land-cover variability across Europe. In addition to GHG flux measurements, a large set of complementary data (including management practices, vegetation and soil characteristics) is collected to support the interpretation, spatial upscaling and modelling of observed ecosystem carbon and GHG dynamics. The applied sampling design was developed and formulated in protocols by the scientific community, representing a trade-off between an ideal dataset and practical feasibility. The use of open-access, high-quality and multi-level data products by different user communities is crucial for the Integrated Carbon Observation System in order to achieve its scientific potential and societal value.
Abstract The Integrated Carbon Observation System is a Pan-European distributed research infrastructure that has as its main goal to monitor the greenhouse gas balance of Europe. The ecosystem component of Integrated Carbon Observation System consists of a multitude of stations where the net greenhouse gas exchange is monitored continuously by eddy covariance measurements while, in addition many other measurements are carried out that are a key to an understanding of the greenhouse gas balance. Amongst them are the continuous meteorological measurements and a set of non-continuous measurements related to vegetation. The latter include Green Area Index, aboveground biomass and litter biomass. The standardized methodology that is used at the Integrated Carbon Observation System ecosystem stations to monitor these vegetation related variables differs between the ecosystem types that are represented within the network, whereby in this paper we focus on forests, grasslands, croplands and mires. For each of the variables and ecosystems a spatial and temporal sampling design was developed so that the variables can be monitored in a consistent way within the ICOS network. The standardisation of the methodology to collect Green Area Index, above ground biomass and litter biomass and the methods to evaluate the quality of the collected data ensures that all stations within the ICOS ecosystem network produce data sets with small and similar errors, which allows for inter-comparison comparisons across the Integrated Carbon Observation System ecosystem network.
Abstract. Warm-season precipitation on the Canadian Prairies plays a crucial role in agricultural production. This research investigates how the early summer 2015 drought across the Canadian Prairies is related to the tropical Pacific forcing. The significant deficit of precipitation in May and June 2015 coincided with a warm phase of the El Niño–Southern Oscillation (ENSO) and a negative phase of Madden–Julian Oscillation (MJO)-4 index, which favour a positive geopotential height (GPH) anomaly in western Canada. Our further investigation during the instrumental record (1979–2016) shows that warm-season precipitation in the Canadian Prairies and the corresponding atmospheric circulation anomalies over western Canada teleconnected with the lower boundary conditions in the tropical western Pacific. Our results indicate that MJO can play a crucial role in determining the summer precipitation anomaly in the western Canadian Prairies when the equatorial central Pacific is warmer than normal (NINO4 > 0) and MJO is more active. This teleconnection is due to the propagation of a stationary Rossby wave that is generated in the MJO-4 index region. When the tropical convection around MJO-4 index region (western tropical Pacific, centred over 140∘ E) is more active than normal (NINO4 > 0), Rossby wave trains originate from the western Pacific with wavenumbers determined by the background mean wind and meridional absolute vorticity gradient. Under warm NINO4 conditions waves are generated with smaller wavenumbers compared to cold NINO4 conditions. These waves under warm NINO4 can propagate into the mid-latitudes over North America, causing a persistent anomalous ridge in the upper level over western Canada, which favours dry conditions over the region.
The creation of Remedial Action Plans for the Great Lakes Areas of Concern was an experiment in addressing anthropogenic stress on human and nonhuman uses of the nearshore zones, invoking new governance paradigms. This article examines how positive governance attributes and negative governance deficits can benefit from an adaptive governance approach. More specifically, it explores best practises in governance for environmental management and suggests a framework in which Areas of Concern approaches can achieve adaptive capacity. This research also aims to identify gaps in current governance arrangements in the ongoing effort to regenerate excellence in the Areas of Concern, with a view forward to nearshore governance frameworks under both Annex 1 and Annex 2 of the Great Lakes Water Quality Agreement Protocol of 2012.

2017

Intersex in fish downstream of municipal wastewater treatment plants (MWWTPs) is a global concern. Consistent high rates of intersex in male rainbow darter (Etheostoma caeruleum) have been reported for several years in the Grand River, in southern Ontario, Canada, in close proximity to two MWWTPs. The larger MWWTP (Kitchener) recently underwent upgrades that included the conversion from a carbonaceous activated sludge to nitrifying activated sludge treatment process. This created a unique opportunity to assess whether upgrades designed to improve effluent quality could also remediate the intersex previously observed in wild fish. Multiple years (2007-2012) of intersex data on male rainbow darter collected before the upgrades at sites associated with the MWWTP outfall were compared with intersex data collected in postupgrade years (2013-2015). These upgrades resulted in a reduction from 70 to 100% intersex incidence (preupgrade) to <10% in postupgrade years. Although the cause of intersex remains unknown, indicators of effluent quality including nutrients, pharmaceuticals, and estrogenicity improved in the effluent after the upgrades. This study demonstrated that investment in MWWTP upgrades improved effluent quality and was associated with an immediate change in biological responses in the receiving environment. This is an important finding considering the tremendous cost of wastewater infrastructure.
While turbulent bursts are considered critical for blowing-snow transport and initiation, the interaction of the airflow with the snow surface is not fully understood. To better characterize the coupling of turbulent structures and blowing-snow transport, observations collected in natural environments at the necessary high-resolution time scales are needed. To address this, high-frequency measurements of turbulence, blowing-snow density and particle velocity were made in the Canadian Rockies. During blowing-snow storms, modified variable-interval time averaging enabled identification of periods of near-surface blowing-snow coupling with shear-stress-producing motions in the lowest 2 m of the atmospheric surface layer. The identification of those turbulent motions responsible for blowing snow yields a better understanding of the event-driven mechanics of initiation and sustained transport. The type of coherent structures generating the Reynolds stress are just as important as the magnitude of the Reynolds stress in initiating and sustaining near-surface blowing snow. Our results suggest that blowing-snow models driven by merely the time-averaged shear stress lack physical realism in the near-surface region. The next phase of the development of blowing-snow models should incorporate parametrizations of coherent turbulent structures.
Cities are under pressure to operate their services effectively and project costs of operations across various timeframes. In high-latitude and high-altitude urban centers, snow management is one of the larger unknowns and has both operational and budgetary limitations. Snowfall and snow depth observations within urban environments are important to plan snow clearing and prepare for the effects of spring runoff on cities’ drainage systems. In-house research functions are expensive, but one way to overcome that expense and still produce effective data is through citizen science. In this paper, we examine the potential to use citizen science for snowfall data collection in urban environments. A group of volunteers measured daily snowfall and snow depth at an urban site in Saskatoon (Canada) during two winters. Reliability was assessed with a statistical consistency analysis and a comparison with other data sets collected around Saskatoon. We found that citizen-science-derived data were more reliable and relevant for many urban management stakeholders. Feedback from the participants demonstrated reflexivity about social learning and a renewed sense of community built around generating reliable and useful data. We conclude that citizen science holds great potential to improve data provision for effective and sustainable city planning and greater social learning benefits overall.
Abstract The Global Precipitation Measurement (GPM) mission offers new opportunities for modeling a range of physical/hydrological processes at higher resolutions, especially for remote river systems where the hydrometeorological monitoring network is sparse and weather radar is not readily available. In this study, the recently released Integrated Multisatellite Retrievals for GPM [version 03 (V03) IMERG Final Run] product with high spatiotemporal resolution of 0.1° and 30 min is evaluated against ground-based reference measurements (at the 6-hourly, daily, and monthly time scales) over different terrestrial ecozones of southern Canada within a 23-month period from 12 March 2014 to 31 January 2016. While IMERG and ground-based observations show similar regional variations of mean daily precipitation, IMERG tends to overestimate higher monthly precipitation amounts over the Pacific Maritime ecozone. Results from using continuous as well as categorical skill metrics reveal that IMERG shows more satisfactory agreement at the daily and the 6-hourly time scales for the months of June–September, unlike November–March. In terms of precipitation extremes (defined by the 75th percentile threshold for reference data), apart from a tendency toward overdetection of heavy precipitation events, IMERG captured well the distribution of heavy precipitation amounts and observed wet/dry spell length distributions over most ecozones. However, low skill was found over large portions of the Montane Cordillera ecozone and a few stations in the Prairie ecozone. This early study highlights a potential applicability of V03 IMERG Final Run as a reliable source of precipitation estimates in diverse water resources and hydrometeorological applications for different regions in southern Canada.
Anthropogenic climate change is anticipated to increase severe thunderstorm potential in North America, but the resulting changes in associated convective hazards are not well known. Here, using a novel modelling approach, we investigate the spatiotemporal changes in hail frequency and size between the present (1971–2000) and future (2041–2070). Although fewer hail days are expected over most areas in the future, an increase in the mean hail size is projected, with fewer small hail events and a shift toward a more frequent occurrence of larger hail. This leads to an anticipated increase in hail damage potential over most southern regions in spring, retreating to the higher latitudes (that is, north of 50° N) and the Rocky Mountains in the summer. In contrast, a dramatic decrease in hail frequency and damage potential is predicted over eastern and southeastern regions in spring and summer due to a significant increase in melting that mitigates gains in hail size from increased buoyancy.
This study was part of the European Framework 7 funded project ‘Restoring Rivers for Effective Catchment Management’ (REFORM).
The accuracy of digital elevation models (DEMs) plays an important role in many terrain-related applications, particular in high northern latitudes where there is uncertainty in DEMs. Using the interferometric synthetic aperture radar techniques, this study examined how different RADARSAT-2 beam modes can be used to generate DEMs with high accuracy. Using a conventional interferometry method, the Spotlight DEM shows the highest accuracy among all studied DEM products, with the root-mean-square error (RMSE) ranging from 13.9 to 17.4 m, followed by the F0W3 DEM and U26W2 DEM. The error sources in DEM generation due to uncertainty in perpendicular baseline and atmospheric delay are likely more important than the random phase noise caused by volume scattering and environmental changes during synthetic aperture radar (SAR) acquisitions. The small baselines subset (SBAS) method did not significantly improve DEM quality due to the limitation of the number of SAR images in this study. The integration of both Spotlight conventional DEMs and SBAS DEM considerably improved results yielding high-quality DEMs for the study area, with an RMSE of 9.7 m. Further studies are necessary to quantitatively evaluate the effects of surface motion as well as the orbital and atmospheric errors on the DEM accuracy. The Slave River Delta in the Northwest Territories of Canada was used as a test case.
Abstract Tree ring data provide proxy records of historical hydroclimatic conditions that are widely used for reconstructing precipitation time series. Most previous applications are limited to annual time scales, though information about daily precipitation would enable a range of additional analyses of environmental processes to be investigated and modelled. We used statistical downscaling to simulate stochastic daily precipitation ensembles using dendrochronological data from the western Canadian boreal forest. The simulated precipitation series were generally consistent with observed precipitation data, though reconstructions were poorly constrained during short periods of forest pest outbreaks. The proposed multiple temporal scale precipitation reconstruction can generate annual daily maxima and persistent monthly wet and dry episodes, so that the observed and simulated ensembles have similar precipitation characteristics (i.e. magnitude, peak, and duration)—an improvement on previous modelling studies. We discuss how ecological disturbances may limit reconstructions by inducing non-linear responses in tree growth, and conclude with suggestions of possible applications and further development of downscaling methods for dendrochronological data.
Modeling nutrient transport during snowmelt in cold regions remains a major scientific challenge. A key limitation of existing nutrient models for application in cold regions is the inadequate representation of snowmelt, including hydrological and biogeochemical processes. This brief period can account for more than 80% of the total annual surface runoff in the Canadian Prairies and Northern Canada and processes such as atmospheric deposition, over-winter redistribution of snow, ion exclusion from snow crystals, frozen soils, and snowcovered area depletion during melt influence the distribution and release of snow and soil nutrients, thus affecting the timing and magnitude of snowmelt runoff nutrient concentrations.Research in cold regions suggests that nitrate (NO3) runoff at the field scale can be divided into five phases during snowmelt. In the first phase, water and ions originating from ion-rich snow layers travel and diffuse through the snowpack. This process causes ion concentrations in runoff to gradually increase. The second phase occurs when this snow ion meltwater front has reached the bottom of the snowpack and forms runoff to the edge-of-the-field (EOF). During the third and fourth phases, the main source of NO3 transitions from the snowpack to the soil. Finally, the fifth and last phase occurs when the snow has completely melted, and the thawing soil becomes the main source of NO3 to the stream.In this research, a process-based model was developed to simulate hourly export based on this five-phase approach. Results from an application in the Red River Basin of southern Manitoba, Canada shows that the model can adequately capture the dynamics and rapid changes of NO3 concentrations during this period at relevant temporal resolutions. This is a significant achievement to advance the current nutrient modeling paradigm in cold climates, which is generally limited to satisfactory results at monthly or annual resolutions. The approach can inform catchment-scale nutrient models to improve simulation of this critical snowmelt period.Nutrient exports Winter Snow Nitrate Agriculture Nutrient model
Abstract The spatial heterogeneity of mountain snow cover and ablation is important in controlling patterns of snow cover depletion (SCD), meltwater production, and runoff, yet is not well-represented in most large-scale hydrological models and land surface schemes. Analyses were conducted in this study to examine the influence of various representations of snow cover and melt energy heterogeneity on both simulated SCD and stream discharge from a small alpine basin in the Canadian Rocky Mountains. Simulations were performed using the Cold Regions Hydrological Model (CRHM), where point-scale snowmelt computations were made using a snowpack energy balance formulation and applied to spatial frequency distributions of snow water equivalent (SWE) on individual slope-, aspect-, and landcover-based hydrological response units (HRUs) in the basin. Hydrological routines were added to represent the vertical and lateral transfers of water through the basin and channel system. From previous studies it is understood that the heterogeneity of late winter SWE is a primary control on patterns of SCD. The analyses here showed that spatial variation in applied melt energy, mainly due to differences in net radiation, has an important influence on SCD at multiple scales and basin discharge, and cannot be neglected without serious error in the prediction of these variables. A single basin SWE distribution using the basin-wide mean SWE ( SWE ‾ ) and coefficient of variation (CV; standard deviation/mean) was found to represent the fine-scale spatial heterogeneity of SWE sufficiently well. Simulations that accounted for differences in ( SWE ‾ ) among HRUs but neglected the sub-HRU heterogeneity of SWE were found to yield similar discharge results as simulations that included this heterogeneity, while SCD was poorly represented, even at the basin level. Finally, applying point-scale snowmelt computations based on a single SWE depth for each HRU (thereby neglecting spatial differences in internal snowpack energetics over the distributions) was found to yield similar SCD and discharge results as simulations that resolved internal energy differences. Spatial/internal snowpack melt energy effects are more pronounced at times earlier in spring before the main period of snowmelt and SCD, as shown in previously published work. The paper discusses the importance of these findings as they apply to the warranted complexity of snowmelt process simulation in cold mountain environments, and shows how the end-of-winter SWE distribution represents an effective means of resolving snow cover heterogeneity at multiple scales for modelling, even in steep and complex terrain.
This study proposes an integrated modeling system consisting of the physically-based MIKE SHE/MIKE 11 model, a cellular automata model, and general circulation models (GCMs) scenarios to investigate the independent and combined effects of future climate and land-use/land-cover (LULC) changes on the hydrology of a river system. The integrated modelling system is applied to the Elbow River watershed in southern Alberta, Canada in conjunction with extreme GCM scenarios and two LULC change scenarios in the 2020s and 2050s. Results reveal that LULC change substantially modifies the river flow regime in the east sub-catchment, where rapid urbanization is occurring. It is also shown that the change in LULC causes an increase in peak flows in both the 2020s and 2050s. The impacts of climate and LULC change on streamflow are positively correlated in winter and spring, which intensifies their influence and leads to a significant rise in streamflow, and, subsequently, increases the vulnerability of the watershed to spring floods. This study highlights the importance of using an integrated modeling approach to investigate both the independent and combined impacts of climate and LULC changes on the future of hydrology to improve our understanding of how watersheds will respond to climate and LULC changes.
Abstract. Lakes might be sentinels of climate change, but the uncertainty in their main feedback to the atmosphere – heat-exchange fluxes – is often not considered within climate models. Additionally, these fluxes are seldom measured, hindering critical evaluation of model output. Analysis of the Canadian Small Lake Model (CSLM), a one-dimensional integral lake model, was performed to assess its ability to reproduce diurnal and seasonal variations in heat fluxes and the sensitivity of simulated fluxes to changes in model parameters, i.e., turbulent transport parameters and the light extinction coefficient (Kd). A C++ open-source software package, Problem Solving environment for Uncertainty Analysis and Design Exploration (PSUADE), was used to perform sensitivity analysis (SA) and identify the parameters that dominate model behavior. The generalized likelihood uncertainty estimation (GLUE) was applied to quantify the fluxes' uncertainty, comparing daily-averaged eddy-covariance observations to the output of CSLM. Seven qualitative and two quantitative SA methods were tested, and the posterior likelihoods of the modeled parameters, obtained from the GLUE analysis, were used to determine the dominant parameters and the uncertainty in the modeled fluxes. Despite the ubiquity of the equifinality issue – different parameter-value combinations yielding equivalent results – the answer to the question was unequivocal: Kd, a measure of how much light penetrates the lake, dominates sensible and latent heat fluxes, and the uncertainty in their estimates is strongly related to the accuracy with which Kd is determined. This is important since accurate and continuous measurements of Kd could reduce modeling uncertainty.
Abstract This paper investigates the commonly overlooked “sensitivity” of sensitivity analysis (SA) to what we refer to as parameter “perturbation scale”, which can be defined as a prescribed size of the sensitivity-related neighbourhood around any point in the parameter space (analogous to step size Δ x for numerical estimation of derivatives). We discuss that perturbation scale is inherent to any (local and global) SA approach, and explain how derivative-based SA approaches (e.g., method of Morris) focus on small-scale perturbations, while variance-based approaches (e.g., method of Sobol) focus on large-scale perturbations. We employ a novel variogram-based approach, called Variogram Analysis of Response Surfaces (VARS), which bridges derivative- and variance-based approaches. Our analyses with different real-world environmental models demonstrate significant implications of subjectivity in the perturbation-scale choice and the need for strategies to address these implications. It is further shown how VARS can uniquely characterize the perturbation-scale dependency and generate sensitivity measures that encompass all sensitivity-related information across the full spectrum of perturbation scales.
Complex hydrological models are being increasingly used nowadays for many purposes such as studying the impact of climate and land-use change on water resources. However, building a high-fidelity model, particularly at large scales, remains a challenging task, due to complexities in model functioning and behavior and uncertainties in model structure, parameterization, and data. Global Sensitivity Analysis (GSA), which characterizes how the variation in the model response is attributed to variations in its input factors (e.g., parameters, forcing data), provides an opportunity to enhance the development and application of these complex models. In this paper, we advocate using GSA as an integral part of the modelling process by discussing its capabilities as a tool for diagnosing model structure and detecting potential defects, identifying influential factors, characterizing uncertainty, and selecting calibration parameters. Accordingly, we conduct a comprehensive GSA of a complex land surface-hydrology model, Modelisation Environmentale–Surface et Hydrologie (MESH), which combines the Canadian Land Surface Scheme (CLASS) with a hydrological routing component, WATROUTE. Various GSA experiments are carried out using a new technique, called Variogram Analysis of Response Surfaces (VARS), for alternative hydroclimatic conditions in Canada using multiple criteria, various model configurations, and a full set of model parameters. Results from this study reveal that, in addition to different hydroclimatic conditions and SA criteria, model configurations can also have a major impact on the assessment of sensitivity. GSA can identify aspects of the model internal functioning that are counter-intuitive, and thus, help the modeler to diagnose possible model deficiencies and make recommendations for improving development and application of the model. As a specific outcome of this work, a list of the most influential parameters for the MESH model is developed. This list, along with some specific recommendations, is expected to assist the wide community of MESH and CLASS users, to enhance their modelling applications.
The breakup of snow cover into patches during snowmelt leads to a dynamic, heterogeneous land surface composed of melting snow, and wet and dry soil and plant surfaces. Energy exchange with the atmosphere is therefore complicated by horizontal gradients in surface temperature and humidity as snow surface temperature and humidity are regulated by the phase change of melting snow unlike snow-free areas. Airflow across these surface transitions results in local-scale advection of energy that has been documented as sensible heat during snowmelt, while latent heat advection has received scant attention. Herein, results are presented from an experiment measuring near-surface profiles of air temperature and humidity across snow-free to snow-covered transitions that demonstrates that latent heat advection can be the same order of magnitude as sensible heat advection and is therefore an important source of snowmelt energy. Latent heat advection is conditional on an upwind source of water vapor from a wetted snow-free surface.
The thermal regimes of alpine streams remain understudied and have important implications for cold-water fish habitat which is expected to decline due to climatic warming. Previous research has focused on the effects of distributed energy fluxes and meltwater from snowpacks and glaciers on the temperature of mountain streams. This study presents the effects of the groundwater spring discharge from an inactive rock glacier containing little ground ice on the temperature of an alpine stream. Rock glaciers are coarse blocky landforms that are ubiquitous in alpine environments and typically exhibit low groundwater discharge temperatures and resilience to climatic warming. Water temperature data indicate that the rock glacier spring cools the stream by an average of 3°C during July and August and reduces maximum daily temperatures by an average of 5°C during the peak temperature period of the first two weeks in August, producing a cold-water refuge downstream of the spring. The distributed stream surface and streambed energy fluxes are calculated for the reach along the toe of the rock glacier, and solar radiation dominates the distributed stream energy budget. The lateral advective heat flux generated by the rock glacier spring is compared to the distributed energy fluxes over the study reach, and the spring advective heat flux is the dominant control on stream temperature at the reach scale. This study highlights the potential for coarse blocky landforms to generate climatically-resilient cold-water refuges in alpine streams.
About a fifth of the global wetland methane emissions originate from boreal peatlands, which represent an important land cover type in boreal landscapes in the sporadic permafrost zone. There, rising air temperatures could lead to warmer spring and longer growing seasons, changing landscape methane emissions. To quantify the effect of warmer spring conditions on methane emissions of a boreal peat landscape in the sporadic permafrost zone of northwestern Canada, we analyzed four years (2013 – 2016) of methane fluxes measured with the eddy covariance technique and long-term (1951-2016) meteorological observations from a nearby climate station. In May, after snowmelt was complete, mean air temperatures were more than 2 °C warmer in 2013, 2015, and 2016 than in 2014. Mean growing season (May-August) air temperatures, in contrast, differed by less than 1 °C over the four years. Warmer May air temperatures caused earlier wetland soil warming, with temperatures rising from ~0 °C to g12 °C 25 to 40 days earlier and leading to ~6 °C warmer mean soil temperatures between May and June. However, from July to August, soil temperatures were similar among years. Mean May to August and annual methane emissions (6.4 g CH4 m-2 and 9.4 g CH4 m-2, respectively) of years with warmer spring (i.e., May) temperatures exceeded emissions during the cooler year by 20-30 % (4.5 g CH4 m-2 and 7.2 g CH4 m-2, respectively). Among years with warmer springs, growing season methane emissions varied little (0.5 g CH4 m-2). The observed interannual differences are most likely caused by a strong soil temperature control on methane fluxes and large soil temperature differences during the spring. Thus, in a warming climate, methane emissions from waterlogged boreal peat landscapes at the southern limit of permafrost are likely to increase in response to more frequent occurrences of warm springs.
A multi-country, multi-sector computable general equilibrium (CGE) model is employed to evaluate the economy-wide impacts of climate change under the IPCC’s A2 and B1 scenarios and existing irrigation development plans in the Nile basin. The study reveals that climate change adversely affects mainly downstream Egypt and to a lesser extent Sudan, while it results in a limited impact in the upstream countries Ethiopia and the Equatorial Lakes region, where irrigated agriculture is still limited. The economic consequences for Egypt are especially substantial if the river basin countries pursue a unilateral irrigation development strategy. In order to prevent water use conflicts and ease water scarcity conditions, a cooperative water development strategy is needed as well as economic diversification in favor of less water-intensive sectors, combined with investments in water-saving infrastructure and improved irrigation efficiency.
A multi-country, multi-sector computable general equilibrium (CGE) model is used for the first time to evaluate the economic and water resource availability effects of trade liberalization (removal of import tariffs) and facilitation (reducing non-tariff barriers) under climate change in the Nile Basin. The analysis uses the GTAP 9 Database and the GTAP-W model that distinguishes between rainfed and irrigated agriculture and implements water as a factor of production directly substitutable in the production process of irrigated agriculture. A full trade liberalization and improved trade facilitation scenario is considered with and without climate change. The study reveals that trade liberalization and facilitation generates substantial economic benefits and enhances economic growth and welfare in the Nile basin. The effect of instituting a free trade policy on water savings is found to be limited, while climate change improves water supply and hence irrigation water use, enhancing economic growth and welfare in the basin.
Beaver dams are known to raise water tables in mineral soil environments but very little is known about their impact in wetlands, such as peatlands. Peatlands tend to have shallow water tables, and the position and tendency of the water table to fluctuate (i.e. stability) is a factor controlling the system's ability to store carbon and water. Many peatland environments, especially fens, offer ideal habitat for beaver and the potential for beaver dams to influence this link by manipulating water table dynamics requires investigation. Our objective was to determine the influence of beaver dams on water table dynamics of a Rocky Mountain fen. We monitored water tables in the peatland for four years while beaver dams were intact and two years after they were breached by an extreme flood event. We found that, because of the unique way in which dams were built, they connected the peatland to the stream and raised and stabilized already high water tables within a 150-m radius. Beaver-mediated changes to peatland water table regimes have the potential to enhance carbon sequestration and the peatland's ability to respond to external pressures such as climate change. Furthermore, beaver dams increased surface and groundwater storage, which has implications for regional water balances, especially in times of drought.
Abstract Freezing precipitation and ice pellet events on the Canadian Prairies and Arctic territories of Canada often lead to major disruptions to air and ground transportation, damage power grids and prevent arctic caribou and other animals from accessing the plants and lichen they depend on for survival. In a warming climate, these hazards and associated impacts will continue to happen, although their spatial and temporal characteristics may vary. In order to address these issues, the occurrence of freezing rain, freezing drizzle, and ice pellets from 1964 to 2005 is examined using hourly weather observations at 27 manned 24 h weather stations across the different climatic regions of the Prairie Provinces and Arctic Territories of Canada. Because of the enormous size of the area and its diverse climatic regions, many temporal and spatial differences in freezing precipitation and ice pellet characteristics occur. The 12 most widespread freezing rain events over the study area are associated with only two atmospheric patterns with one linked to strong warm advection between low and high pressure centres and the other pattern associated with chinooks occurring east of the Rocky Mountains. Given the annual patterns of freezing rain occurrence found in this study, it is proposed that a maximum of five regimes exist and three occur within the Prairies and Arctic.
Abstract A better understanding of cold regions hydrological processes and regimes in transitional environments is critical for predicting future Arctic freshwater fluxes under climate and vegetation change. A physically based hydrological model using the Cold Regions Hydrological Model platform was created for a small Arctic basin in the tundra-taiga transition region. The model represents snow redistribution and sublimation by wind and vegetation, snowmelt energy budget, evapotranspiration, subsurface flow through organic terrain, infiltration to frozen soils, freezing and thawing of soils, permafrost and streamflow routing. The model was used to reconstruct the basin water cycle over 28 years to understand and quantify the mass fluxes controlling its hydrological regime. Model structure and parameters were set from the current understanding of Arctic hydrology, remote sensing, field research in the basin and region, and calibration against streamflow observations. Calibration was restricted to subsurface hydraulic and storage parameters. Multi-objective evaluation of the model using observed streamflow, snow accumulation and ground freeze/thaw state showed adequate simulation. Significant spatial variability in the winter mass fluxes was found between tundra, shrubs and forested sites, particularly due to the substantial blowing snow redistribution and sublimation from the wind-swept upper basin, as well as sublimation of canopy intercepted snow from the forest (about 17% of snowfall). At the basin scale, the model showed that evapotranspiration is the largest loss of water (47%), followed by streamflow (39%) and sublimation (14%). The models streamflow performance sensitivity to a set of parameter was analysed, as well as the mean annual mass balance uncertainty associated with these parameters.
Abstract The relatively low water flow velocities in reservoirs cause them to have high capacities for retaining sediments and pollutants, which can lead to a reduction in downstream nutrient loading. Hence, nutrients can progressively accumulate in reservoirs, resulting in the deterioration of aquatic ecosystems and water quality. Lake Diefenbaker (LD) is a large multipurpose reservoir, located on the South Saskatchewan River (SSR), that serves as a major source of freshwater in Saskatchewan, Canada. Over the past several years, changes in land use (e.g. expansion of urban areas and industrial developments) in the reservoir’s catchment have heightened concerns about future water quality in the catchment and in the reservoir. Intensification of agricultural activities has led to an increase in augmented the application of manure and fertilizer for crops and pasture. Although previous research has attempted to quantify nutrient retention in LD, there is a knowledge gap related to the identification of major nutrient sources and quantification of nutrient export from the catchment at different spatial scales. Using the SPAtially Referenced Regression On Watershed (SPARROW) model, this gap has been addressed by assessing water quality regionally, and identifying spatial patterns of factors and processes that affect water quality in the LD catchment. Model results indicate that LD retains about 70% of the inflowing total nitrogen (TN) and 90% of the inflowing total phosphorus (TP) loads, of which fertilizer and manure applied to agricultural fields contribute the greatest proportion. The SPARROW model will be useful as a tool to guide the optimal implementation of nutrient management plans to reduce nutrient inputs to LD.
Wet bulb Globe Temperature (WBGT) accounts for the effect of environmental temperature and humidity on thermal comfort, and can be directly related to the ability of the human body to dissipate excess metabolic heat and thus avoid heat stress. Using WBGT as a measure of environmental conditions conducive to heat stress, we show that anthropogenic influence has very substantially increased the likelihood of extreme high summer mean WBGT in northern hemispheric land areas relative to the climate that would have prevailed in the absence of anthropogenic forcing. We estimate that the likelihood of summer mean WGBT exceeding the observed historical record value has increased by a factor of at least 70 at regional scales due to anthropogenic influence on the climate. We further estimate that, in most northern hemispheric regions, these changes in the likelihood of extreme summer mean WBGT are roughly an order of magnitude larger than the corresponding changes in the likelihood of extreme hot summers as simply measured by surface air temperature. Projections of future summer mean WBGT under the RCP8.5 emissions scenario that are constrained by observations indicate that by 2030s at least 50% of the summers will have mean WBGT higher than the observed historical record value in all the analyzed regions, and that this frequency of occurrence will increase to 95% by mid-century.
Abstract A devastating, flood-producing rainstorm occurred over southern Alberta, Canada, from 19 to 22 June 2013. The long-lived, heavy rainfall event was a result of complex interplays between topographic, synoptic, and convective processes that rendered an accurate simulation of this event a challenging task. In this study, the Weather Research and Forecasting (WRF) Model was used to simulate this event and was validated against several observation datasets. Both the timing and location of the model precipitation agree closely with the observations, indicating that the WRF Model is capable of reproducing this type of severe event. Sensitivity tests with different microphysics schemes were conducted and evaluated using equitable threat and bias frequency scores. The WRF double-moment 6-class microphysics scheme (WDM6) generally performed better when compared with other schemes. The application of a conventional convective/stratiform separation algorithm shows that convective activity was dominant during the early stages, then evolved into predominantly stratiform precipitation later in the event. The HYSPLIT back-trajectory analysis and regional water budget assessments using WRF simulation output suggest that the moisture for the precipitation was mainly from recycling antecedent soil moisture through evaporation and evapotranspiration over the Canadian Prairies and the U.S. Great Plains. This analysis also shows that a small fraction of the moisture can be traced back to the northeastern Pacific, and direct uptake from the Gulf of Mexico was not a significant source in this event.
The circulation patterns of persistent cold weather spells with durations longer than 10 days in central–eastern North America (United States and Canada; 32°–52°N, 95°–65°W) are investigated by using NCEP reanalysis data from 1948 to 2014. The criteria for the persistent cold spells are: (1) three-day averaged temperature anomalies for the regional average over the central–eastern United States and Canada must be below the 10th percentile, and (2) such extreme cold spells must last at least 10 days. The circulation patterns associated with these cold spells are examined to find the common signals of these events. The circulation anomaly patterns of these cold spells are categorized based on the El Nino–Southern Oscillation, Arctic Oscillation (AO), and other climate indices. The atmospheric circulation patterns that favor the cold spells are identified through composites of geopotential height maps for the cold spells. Negative AO phases favor persistent cold spells. Phases of sea surface temperature (SST) modes that are associated with warm SSTs in the eastern extratropical Pacific also favor persistent cold events in the study region. Stratospheric polar vortex breakdown alone is not a good predictor for the regional extreme cold spells in central–eastern North America. The meridional dispersions of quasi-stationary Rossby waves in the Pacific–North America sector in terms of cut-off zonal wavenumber modulated by background flow are analyzed to provide insight into the difference in evolution of the cold spells under different mean AO phases. The waveguide for AO > 1 is in a narrow latitudinal band centered on 40°N, whereas the waveguide for AO <–1 is in a broader latitudinal band from 40° to 65°N. The circulation patterns and lower boundary conditions favorable for persistent cold spells identified by this study can be a stepping-stone for improving winter subseasonal forecasting in North America.
The present study examined in vitro 11-ketotestosterone and testosterone production by the testes of rainbow darter (Etheostoma caeruleum) collected from selected reference sites and downstream of 2 municipal wastewater treatment plants (MWWTPs; Waterloo and Kitchener) on the central Grand River (Ontario, Canada), over a 6-yr period (2011-2016). The main objective was to investigate if infrastructure upgrades at the Kitchener MWWTP in 2012 resulted in a recovery of this response in the post-upgrade period (2013-2016). Two supporting studies showed that the fall season is appropriate for measuring in vitro sex steroid production because it provides stable detection of steroid patterns, and that the sample handling practiced in the present study did not introduce a bias. Infrastructure upgrades of the Kitchener MWWTP resulted in significant reductions in ammonia and estrogenicity. After the upgrades, 11-ketotestosterone production by MWWTP-exposed fish increased in 2013 and it continued to recover throughout the study period of 2014 through 2016, returning to levels measured in reference fish. Testosterone production was less sensitive and it lacked consistency. The Waterloo MWWTP underwent some minor upgrades but the level of ammonia and estrogenicity remained variable over time. The production of 11-ketotestosterone and testosterone in rainbow darter below the Waterloo MWWTP was variable and without a clear recovery pattern over the course of the present study. The results of the present study demonstrated that measuring production of sex steroids (especially 11-ketotestosterone) over multiple years can be relevant for assessing responses in fish to environmental changes such as those resulting from major infrastructure upgrades. Environ Toxicol Chem 2018;37:501-514. © 2017 SETAC.
Abstract Wildfire is the largest disturbance affecting peatlands, with northern peat reserves expected to become more vulnerable to wildfire as climate change enhances the length and severity of the fire season. Recent research suggests that high water table positions after wildfire are critical to limit atmospheric carbon losses and enable the re-establishment of keystone peatland mosses (i.e. Sphagnum). Post-fire recovery of the moss surface in Sphagnum-feathermoss peatlands, however, has been shown to be limited where moss type and burn severity interact to result in a water repellent surface. While in situ measurements of moss water repellency in peatlands have been shown to be greater for feathermoss in both a burned and unburned state in comparison to Sphagnum moss, it is difficult to separate the effect of water content from species. Consequently, we carried out a laboratory based drying experiment where we compared the water repellency of two dominant peatland moss species, Sphagnum and feathermoss, for several burn severity classes including unburned samples. The results suggest that water repellency in moss is primarily controlled by water content, where a sharp threshold exists at gravimetric water contents (GWC) lower than ∼1.4 g g−1. While GWC is shown to be a strong predictor of water repellency, the effect is enhanced by burning. Based on soil water retention curves, we suggest that it is highly unlikely that Sphagnum will exhibit strong hydrophobic conditions under field conditions.
In river catchments, sediment fluxes facilitate the transport of nutrients and pollutants and reduce water quality, potentially impacting water body health and altering ecosystem functioning. Sediment transport processes also modify the morphology of catchments, and sediment deposition can reduce flow capacity in rivers and water storage capacity in reservoirs and lakes. In this paper, estimates of suspended sediment yields and concentrations in the South Saskatchewan River catchment located in western Canada are presented. The results stem from a SPARROW model, which indicates that the dominant sources of sediment are represented by agricultural fields and urbanized lands. Analyses of sediment retention in the major catchment reservoirs indicate that, as expected, reservoir storage capacity is negatively correlated with reservoir storage reduction and positively correlated with retention rate. Additionally, reservoir lifespans range from less than 100 years to over 9000 years. The results presented here will be useful to complement local environmental guidelines to allow better management of sediment erosion and deposition in the South Saskatchewan River catchment.
Abstract. Anthropogenic nutrient enrichment has caused phosphorus (P) accumulation in many freshwater sediments, raising concerns that internal loading from legacy P may delay the recovery of aquatic ecosystems suffering from eutrophication. Benthic recycling of P strongly depends on the redox regime within surficial sediment. In many shallow environments, redox conditions tend to be highly dynamic as a result of, among others, bioturbation by macrofauna, root activity, sediment resuspension and seasonal variations in bottom-water oxygen (O2) concentrations. To gain insight into the mobility and biogeochemistry of P under fluctuating redox conditions, a suspension of sediment from a hypereutrophic freshwater marsh was exposed to alternating 7-day periods of purging with air and nitrogen gas (N2), for a total duration of 74 days, in a bioreactor system. We present comprehensive data time series of bulk aqueous- and solid-phase chemistry, solid-phase phosphorus speciation and hydrolytic enzyme activities demonstrating the mass balanced redistribution of P in sediment during redox cycling. Aqueous phosphate concentrations remained low ( ∼ 2.5 µM) under oxic conditions due to sorption to iron(III) oxyhydroxides. During anoxic periods, once nitrate was depleted, the reductive dissolution of iron(III) oxyhydroxides released P. However, only 4.5 % of the released P accumulated in solution while the rest was redistributed between the MgCl2 and NaHCO3 extractable fractions of the solid phase. Thus, under the short redox fluctuations imposed in the experiments, P remobilization to the aqueous phase remained relatively limited. Orthophosphate predominated at all times during the experiment in both the solid and aqueous phase. Combined P monoesters and diesters accounted for between 9 and 16 % of sediment particulate P. Phosphatase activities up to 2.4 mmol h−1 kg−1 indicated the potential for rapid mineralization of organic P (Po), in particular during periods of aeration when the activity of phosphomonoesterases was 37 % higher than under N2 sparging. The results emphasize that the magnitude and timing of internal P loading during periods of anoxia are dependent on both P redistribution within sediments and bottom-water nitrate concentrations.
In this study, we built a two-dimensional sediment transport model of Lake Diefenbaker, Saskatchewan, Canada. It was calibrated by using measured turbidity data from stations along the reservoir and satellite images based on a flood event in 2013. In June 2013, there was heavy rainfall for two consecutive days on the frozen and snow-covered ground in the higher elevations of western Alberta, Canada. The runoff from the rainfall and the melted snow caused one of the largest recorded inflows to the headwaters of the South Saskatchewan River and Lake Diefenbaker downstream. An estimated discharge peak of over 5200 m3/s arrived at the reservoir inlet with a thick sediment front within a few days. The sediment plume moved quickly through the entire reservoir and remained visible from satellite images for over 2 weeks along most of the reservoir, leading to concerns regarding water quality. The aims of this study are to compare, quantitatively and qualitatively, the efficacy of using turbidity data and satellite images for sediment transport model calibration and to determine how accurately a sediment transport model can simulate sediment transport based on each of them. Both turbidity data and satellite images were very useful for calibrating the sediment transport model quantitatively and qualitatively. Model predictions and turbidity measurements show that the flood water and suspended sediments entered upstream fairly well mixed and moved downstream as overflow with a sharp gradient at the plume front. The model results suggest that the settling and resuspension rates of sediment are directly proportional to flow characteristics and that the use of constant coefficients leads to model underestimation or overestimation unless more data on sediment formation become available. Hence, this study reiterates the significance of the availability of data on sediment distribution and characteristics for building a robust and reliable sediment transport model.
© American Geophysical Union: Shafii, M., Basu, N., Craig, J. R., Schiff, S. L., & Van Cappellen, P. (2017). A diagnostic approach to constraining flow partitioning in hydrologic models using a multiobjective optimization framework. Water Resources Research, 53(4), 3279–3301. https://doi.org/10.1002/2016WR019736
Efficient sampling strategies that scale with the size of the problem, computational budget, and users needs are essential for various sampling-based analyses, such as sensitivity and uncertainty analysis. In this study, we propose a new strategy, called Progressive Latin Hypercube Sampling (PLHS), which sequentially generates sample points while progressively preserving the distributional properties of interest (Latin hypercube properties, space-filling, etc.), as the sample size grows. Unlike Latin hypercube sampling, PLHS generates a series of smaller sub-sets (slices) such that (1) the first slice is Latin hypercube, (2) the progressive union of slices remains Latin hypercube and achieves maximum stratification in any one-dimensional projection, and as such (3) the entire sample set is Latin hypercube. The performance of PLHS is compared with benchmark sampling strategies across multiple case studies for Monte Carlo simulation, sensitivity and uncertainty analysis. Our results indicate that PLHS leads to improved efficiency, convergence, and robustness of sampling-based analyses. A new sequential sampling strategy called PLHS is proposed for sampling-based analysis of simulation models.PLHS is evaluated across multiple case studies for Monte Carlo simulation, sensitivity and uncertainty analysis.PLHS provides better performance compared with the other sampling strategies in terms of convergence rate and robustness.PLHS can be used to monitor the performance of the associated sampling-based analysis and to avoid over- or under-sampling.
AbstractThe high impact of river ice phenomena on the hydrology of cold regions has led to the extensive use of numerical models in simulating and predicting river ice processes. Consequently, ther...
Weather and climate extremes are identified as major areas necessitating further progress in climate research and have thus been selected as one of the World Climate Research Programme (WCRP) Grand Challenges. Here, we provide an overview of current challenges and opportunities for scientific progress and cross-community collaboration on the topic of understanding, modeling and predicting extreme events based on an expert workshop organized as part of the implementation of the WCRP Grand Challenge on Weather and Climate Extremes. In general, the development of an extreme event depends on a favorable initial state, the presence of large-scale drivers, and positive local feedbacks, as well as stochastic processes. We, therefore, elaborate on the scientific challenges related to large-scale drivers and local-to-regional feedback processes leading to extreme events. A better understanding of the drivers and processes will improve the prediction of extremes and will support process-based evaluation of the representation of weather and climate extremes in climate model simulations. Further, we discuss how to address these challenges by focusing on short-duration (less than three days) and long-duration (weeks to months) extreme events, their underlying mechanisms and approaches for their evaluation and prediction.
The science of event attribution meets a mounting demand for reliable and timely information about the links between climate change and individual extreme events. Studies have estimated the contribution of human-induced climate change to the magnitude of an event as well as its likelihood, and many types of event have been investigated including heatwaves, floods, and droughts. Despite this progress, such approaches have been criticised for being unreliable and for being overly conservative. We argue that such criticisms are misplaced. Rather, a false dichotomy has arisen between “conventional” approaches and new alternative framings. We have three points to make about the choice of statistical paradigm for event attribution studies. First, different approaches to event attribution may choose to occupy different places on the conditioning spectrum. Providing this choice of conditioning is communicated clearly, the value of such choices depends ultimately on their utility to the user concerned. Second, event attribution is an estimation problem for which either frequentist or Bayesian paradigms can be used. Third, for hypothesis testing, the choice of null hypothesis is context specific. Thus, the null hypothesis of human influence is not inherently a preferable alternative to the usual null hypothesis of no human influence.
Unprecedented decreases in atmospheric nitrogen (N) deposition together with increases in agricultural N-use efficiency have led to decreases in net anthropogenic N inputs in many eastern US and Canadian watersheds as well as in Europe. Despite such decreases, N concentrations in streams and rivers continue to increase, and problems of coastal eutrophication remain acute. Such a mismatch between N inputs and outputs can arise due to legacy N accumulation and subsequent lag times between implementation of conservation measures and improvements in water quality. In the present study, we quantified such lag times by pairing long-term N input trajectories with stream nitrate concentration data for 16 nested subwatersheds in a 6800 km2, Southern Ontario watershed. Our results show significant nonlinearity between N inputs and outputs, with a strong hysteresis effect indicative of decadal-scale lag times. The mean annual lag time was found to be 24.5 years, with lags varying seasonally, likely due to differences in N-delivery pathways. Lag times were found to be negatively correlated with both tile drainage and watershed slope, with tile drainage being a dominant control in fall and watershed slope being significant during the spring snowmelt period. Quantification of such lags will be crucial to policy-makers as they struggle to set appropriate goals for water quality improvement in human-impacted watersheds.
Abstract. Over recent decades, the global population has been rapidly increasing and human activities have altered terrestrial water fluxes to an unprecedented extent. The phenomenal growth of the human footprint has significantly modified hydrological processes in various ways (e.g. irrigation, artificial dams, and water diversion) and at various scales (from a watershed to the globe). During the early 1990s, awareness of the potential for increased water scarcity led to the first detailed global water resource assessments. Shortly thereafter, in order to analyse the human perturbation on terrestrial water resources, the first generation of large-scale hydrological models (LHMs) was produced. However, at this early stage few models considered the interaction between terrestrial water fluxes and human activities, including water use and reservoir regulation, and even fewer models distinguished water use from surface water and groundwater resources. Since the early 2000s, a growing number of LHMs have incorporated human impacts on the hydrological cycle, yet the representation of human activities in hydrological models remains challenging. In this paper we provide a synthesis of progress in the development and application of human impact modelling in LHMs. We highlight a number of key challenges and discuss possible improvements in order to better represent the human–water interface in hydrological models.
The operational history of one of Canada’s longest operating hydrometric stations is reviewed in detail, including flood estimates that precede formal hydrometric monitoring. The assessment inspects the early and operational history, the published streamflow record and the stage-discharge measurements collected since 1909. Methods used to estimate pre-operational high flows and the operational history are reviewed to establish potential issues with changes in technology, location and measurement sections. The streamflow record is screened for discontinuities and change. The stage-discharge measurements used to establish the rating curve for open-water and ice-covered periods are assessed and used to establish the degree of support for the published data over the period of record. In the period 1882 to 1909, occasional high-stage estimates were used to estimate peak discharge, but with considerable uncertainty due to lack of stream velocity measurements and bed profiles. For the period 1909–1914 it is diff...
Abstract Several large in-situ soil moisture-monitoring networks currently exist over seasonally frozen regions that may have use for the validation of remote sensing soil freeze/thaw (F/T) products. However, further understanding of how the existing network instrumentation responds to changes in near surface soil F/T is recommended. This case study describes the results of a small plot-scale (7 × 7 m) study from November 2013 through April 2014 instrumented with 36 impedance probes. Soil temperature and real dielectric permittivity ϵr' were measured every 15 minutes during F/T transition periods at shallow soil depths (0–10 cm). Categorical soil temperature and real dielectric permittivity techniques were used to define the soil F/T state during these periods. Results demonstrate that both methods for detecting soil F/T have strong agreement (84.7–95.6%) during the fall freeze but weak agreement (53.3–60.9%) during the spring thaw. Bootstrapping results demonstrated both techniques showed a mean difference within ±1.0°C and ±1.4 ϵr' between the standard 5 cm below surface measurement depth and probes at 2, 10 and integrated 0–5.7 cm depths installed within the same study plot. Overall this study demonstrates that the Hydra Probe offers promise for near surface soil F/T detection using existing soil moisture monitoring networks particularly for the fall freeze.
Abstract. A number of global and regional gridded climate products based on multiple data sources are available that can potentially provide reliable estimates of precipitation for climate and hydrological studies. However, research into the consistency of these products for various regions has been limited and in many cases non-existent. This study inter-compares several gridded precipitation products over 15 terrestrial ecozones in Canada for different seasons. The spatial and temporal variability of the errors (relative to station observations) was quantified over the period of 1979 to 2012 at a 0.5° and daily spatio-temporal resolution. These datasets were assessed in their ability to represent the daily variability of precipitation amounts by four performance measures: percentage of bias, root mean square error, correlation coefficient, and standard deviation ratio. Results showed that most of the datasets were relatively skilful in central Canada. However, they tended to overestimate precipitation amounts in the west and underestimate in the north and east, with the underestimation being particularly dominant in northern Canada (above 60° N). The global product by WATCH Forcing Data ERA-Interim (WFDEI) augmented by Global Precipitation Climatology Centre (GPCC) data (WFDEI [GPCC]) performed best with respect to different metrics. The Canadian Precipitation Analysis (CaPA) product performed comparably with WFDEI [GPCC]; however, it only provides data starting in 2002. All the datasets performed best in summer, followed by autumn, spring, and winter in order of decreasing quality. Findings from this study can provide guidance to potential users regarding the performance of different precipitation products for a range of geographical regions and time periods.
Hydrologic model development and calibration have continued in most cases to focus only on accurately reproducing streamflows. However, complex models, for example, the so-called physically based models, possess large degrees of freedom that, if not constrained properly, may lead to poor model performance when used for prediction. We argue that constraining a model to represent streamflow, which is an integrated resultant of many factors across the watershed, is necessary but by no means sufficient to develop a high-fidelity model. To address this problem, we develop a framework to utilize the Gravity Recovery and Climate Experiment's (GRACE) total water storage anomaly data as a supplement to streamflows for model calibration, in a multiobjective setting. The VARS method (Variogram Analysis of Response Surfaces) for global sensitivity analysis is used to understand the model behaviour with respect to streamflow and GRACE data, and the BORG multiobjective optimization method is applied for model calibration. Two subbasins of the Saskatchewan River Basin in Western Canada are used as a case study. Results show that the developed framework is superior to the conventional approach of calibration only to streamflows, even when multiple streamflow-based error functions are simultaneously minimized. It is shown that a range of (possibly false) system trajectories in state variable space can lead to similar (acceptable) model responses. This observation has significant implications for land-surface and hydrologic model development and, if not addressed properly, may undermine the credibility of the model in prediction. The framework effectively constrains the model behaviour (by constraining posterior parameter space) and results in more credible representation of hydrology across the watershed.
Abstract. Extreme climatic events, such as droughts and heat stress induce anomalies in ecosystem-atmosphere CO2 fluxes, such as gross primary production (GPP) and ecosystem respiration (Reco), and, hence, can change the net ecosystem carbon balance. However, despite our increasing understanding of the underlying mechanisms, the magnitudes of the impacts of different types of extremes on GPP and Reco within and between ecosystems remain poorly predicted. Here we aim to identify the major factors controlling the amplitude of extreme event impacts on GPP, Reco, and the resulting net ecosystem production (NEP). We focus on the impacts of heat and drought and their combination. We identified hydrometeorological extreme events in consistently downscaled water availability and temperature measurements over a 30 year time period. We then used FLUXNET eddy-covariance flux measurements to estimate the CO2 flux anomalies during these extreme events across dominant vegetation types and climate zones. Overall, our results indicate that short-term heat extremes increased respiration more strongly than they down-regulated GPP, resulting in a moderate reduction of the ecosystem’s carbon sink potential. In the absence of heat stress, droughts tended to have smaller and similarly dampening effects on both GPP and Reco, and, hence, often resulted in neutral NEP responses. The combination of drought and heat typically led to a strong decrease in GPP, whereas heat and drought impacts on respiration partially offset each other. Taken together, compound heat and drought events led to the strongest C sink reduction compared to any single-factor extreme. A key insight of this paper, however, is that duration matters most: for heat stress during droughts, the magnitude of impacts systematically increased with duration, whereas under heat stress without drought, the response of Reco over time turned from an initial increase to a down-regulation after about two weeks. This confirms earlier theories that not only the magnitude but also the duration of an extreme event determines its impact. Our study corroborates the results of several local site-level case studies, but as a novelty generalizes these findings at the global scale. Specifically, we find that the different response functions of the two antipodal land-atmosphere fluxes GPP and Reco can also result in increasing NEP during certain extreme conditions. Apparently counterintuitive findings of this kind bear great potential for scrutinizing the mechanisms implemented in state-of-the-art terrestrial biosphere models and provide a benchmark for future model development and testing.
Abstract. The Fraser River basin (FRB) of British Columbia is one of the largest and most important watersheds in Western North America, and is home to a rich diversity of biological species and economic assets that depend implicitly upon its extensive riverine habitats. The hydrology of the FRB is dominated by snow accumulation and melt processes, leading to a prominent annual peak streamflow invariably occurring in June–July. However, while annual peak daily streamflow (APF) during the spring freshet in the FRB is historically well correlated with basin-averaged, April 1 snow water equivalent (SWE), there are numerous occurrences of anomalously large APF in below- or near-normal SWE years, some of which have resulted in damaging floods in the region. An imperfect understanding of which other climatic factors contribute to these anomalously large APFs hinders robust projections of their magnitude and frequency. We employ the Variable Infiltration Capacity (VIC) process-based hydrological model driven by gridded observations to investigate the key controlling factors of anomalous APF events in the FRB and four of its subbasins that contribute more than 70 % of the annual flow at Fraser-Hope. The relative influence of a set of predictors characterizing the interannual variability of rainfall, snowfall, snowpack (characterized by the annual maximum value, SWEmax), soil moisture and temperature on simulated APF at Hope (the main outlet of the FRB) and at the subbasin outlets is examined within a regression framework. The influence of large-scale climate modes of variability (the Pacific Decadal Oscillation (PDO) and the El Niño-Southern Oscillation (ENSO)) on APF magnitude is also assessed, and placed in context with these more localized controls. The results indicate that next to SWEmax (which strongly controls the annual maximum of soil moisture), the snowmelt rate, the ENSO and PDO indices, and rate of warming subsequent to the date of SWEmax are the most influential predictors of APF magnitude in the FRB and its subbasins. The identification of these controls on annual peak flows in the region may be of use in the context of seasonal prediction or future projected streamflow behaviour.
Abstract Forecasting anthropogenic changes to ecological communities is one of the central challenges in ecology. However, nonlinear dependencies, biotic interactions and data limitations have limited our ability to assess how predictable communities are. Here we used a machine learning approach and environmental monitoring data (biological, physical and chemical) to assess the predictability of phytoplankton cell density in one lake across an unprecedented range of time scales. Communities were highly predictable over hours to months: model R 2 decreased from 0. 89 at 4 hours to 0.75 at 1 month, and in a long-term dataset lacking fine spatial resolution, from 0.46 at 1 month to 0.32 at 10 years. When cyanobacterial and eukaryotic algal cell density were examined separately, model-inferred environmental growth dependencies matched laboratory studies, and suggested novel trade-offs governing their competition. High-frequency monitoring and machine learning can help elucidate the mechanisms underlying ecological dynamics and set prediction targets for process-based models.
Landscape freeze/thaw (FT) state is a key variable in Earth's carbon cycle. NASA's Soil Moisture Active Passive (SMAP) satellite mission, launched in January 2015, provides global retrievals of FT state every two to three days. Validating SMAP FT observations with in-situ observations is difficult due to the substantial scale mismatch between a point estimate and a satellite footprint, inducing “representativeness errors” in the in-situ observations. Triple collocation (TC) is a validation technique that addresses this problem by combining estimates from in-situ, model and spaceborne estimates to obtain error estimates for all three products, without assuming that any product is error-free. Unfortunately, it fails when applied to binary or categorical variables, such as landscape FT state. In this study, we use a new variant of TC — categorical triple collocation (CTC) — that can be applied to binary variables, to validate the SMAP FT product across northern land regions (>45N).
In this study we quantified the sensitivity of snow to climate warming in selected mountain sites having a Mediterranean climate, including the Pyrenees in Spain and Andorra, the Sierra Nevada in Spain and California (USA), the Atlas in Morocco, and the Andes in Chile. Meteorological observations from high elevations were used to simulate the snow energy and mass balance (SEMB) and calculate its sensitivity to climate. Very different climate sensitivities were evident amongst the various sites. For example, reductions of 9%–19% and 6–28 days in the mean snow water equivalent (SWE) and snow duration, respectively, were found per °C increase. Simulated changes in precipitation (±20%) did not affect the sensitivities. The Andes and Atlas Mountains have a shallow and cold snowpack, and net radiation dominates the SEMB; and explains their relatively low sensitivity to climate warming. The Pyrenees and USA Sierra Nevada have a deeper and warmer snowpack, and sensible heat flux is more important in the SEMB; this explains the much greater sensitivities of these regions. Differences in sensitivity help explain why, in regions where climate models project relatively greater temperature increases and drier conditions by 2050 (such as the Spanish Sierra Nevada and the Moroccan Atlas Mountains), the decline in snow accumulation and duration is similar to other sites (such as the Pyrenees and the USA Sierra Nevada), where models project stable precipitation and more attenuated warming. The snowpack in the Andes (Chile) exhibited the lowest sensitivity to warming, and is expected to undergo only moderate change (a decrease of <12% in mean SWE, and a reduction of < 7 days in snow duration under RCP 4.5). Snow accumulation and duration in the other regions are projected to decrease substantially (a minimum of 40% in mean SWE and 15 days in snow duration) by 2050.
Use of crystal nanocellulose to stabilize nano-ZVI has tremendous potential to improve the capability and applicability of nano-ZVI based subsurface remediation systems in an environmentally sustainable way.

2016

This study assesses projected changes to drought characteristics in Alberta, Saskatchewan and Manitoba, the prairie provinces of Canada, using a multi-regional climate model (RCM) ensemble available through the North American Regional Climate Change Assessment Program. Simulations considered include those performed with six RCMs driven by National Center for Environmental Prediction reanalysis II for the 1981–2003 period and those driven by four Atmosphere–Ocean General Circulation Models for the 1970–1999 and 2041–2070 periods (i.e. eleven current and the same number of corresponding future period simulations). Drought characteristics are extracted using two drought indices, namely the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). Regional frequency analysis is used to project changes to selected 20- and 50-year regional return levels of drought characteristics for fifteen homogeneous regions, covering the study area. In addition, multivariate analyses of drought characteristics, derived on the basis of 6-month SPI and SPEI values, are developed using the copula approach for each region. Analysis of multi-RCM ensemble-averaged projected changes to mean and selected return levels of drought characteristics show increases over the southern and south-western parts of the study area. Based on bi- and trivariate joint occurrence probabilities of drought characteristics, the southern regions along with the central regions are found highly drought vulnerable, followed by the southwestern and southeastern regions. Compared to the SPI-based analysis, the results based on SPEI suggest drier conditions over many regions in the future, indicating potential effects of rising temperatures on drought risks. These projections will be useful in the development of appropriate adaptation strategies for the water and agricultural sectors, which play an important role in the economy of the study area.
The effects of climate change on April–October short- and long-duration precipitation extremes over the Canadian Prairie Provinces were evaluated using a multi-Regional Climate Model (RCM) ensemble available through the North American Regional Climate Change Assessment Program. Simulations considered include those performed with six RCMs driven by the National Centre for Environmental Prediction (NCEP) reanalysis II product for the 1981–2000 period and those driven by four Atmosphere–Ocean General Circulation Models (AOGCMs) for the current 1971–2000 and future 2041–2070 periods (i.e. a total of 11 current-to-future period simulation pairs). A regional frequency analysis approach was used to develop 2-, 5-, 10-, 25-, and 50-year return values of precipitation extremes from NCEP and AOGCM-driven current and future period simulations that respectively were used to study the performance of RCMs and projected changes for selected return values at regional, grid-cell and local scales. Performance errors due to internal dynamics and physics of RCMs studied for the 1981–2000 period reveal considerable variation in the performance of the RCMs. However, the performance errors were found to be much smaller for RCM ensemble averages than for individual RCMs. Projected changes in future climate to selected regional return values of short-duration (e.g. 15- and 30-min) precipitation extremes and for longer return periods (e.g. 50-year) were found to be mostly larger than those to the longer duration (e.g. 24- and 48-h) extremes and short return periods (e.g. 2-year). Overall, projected changes in precipitation extremes were larger for southeastern regions followed by southern and northern regions and smaller for southwestern and western regions of the study area. The changes to return values were also found to be statistically significant for the majority of the RCM–AOGCM simulation pairs. These projections might be useful as a key input for the future planning of urban drainage infrastructure and development of strategic climate change adaptation measures.
The significance of spatial variability of rainfall on runoff is explored as a function of catchment scale and type, and antecedent conditions via the continuous time, semi-distributed probability distributed model (PDM) hydrological model applied to the Upper Lee catchment, UK. The impact of catchment scale and type is assessed using 11 nested catchments, and further assessed by artificially changing the catchment characteristics and translating these to model parameters (MPs) with uncertainty using model regionalisation. Dry and wet antecedent conditions are represented by ‘warming up’ the model under different rainfall time series. Synthetic rainfall events are introduced to directly relate the change in simulated runoff to the spatial variability of rainfall. Results show that runoff volume and peak are more sensitive to the spatial rainfall for more impermeable catchments; however, this sensitivity is significantly undermined under wet antecedent conditions. Although there is indication that the impact of spatial rainfall on runoff varies as a function of catchment scale, the variability of antecedent conditions between the synthetic catchments seems to mask this significance. Parameter uncertainty analysis highlights the importance of accurately representing the spatial variability of the catchment properties and their translation to MPs when investigating the effects of spatial properties of rainfall on runoff.
Northern ecosystem processes play out across scales that are rare elsewhere on contemporary earth: large ranging predator–prey systems are still operational, invasive species are rare, and large-scale natural disturbances occur extensively. Disturbances in the far north affect huge areas of land and are difficult to control or manage. Historically, disturbance patterns and processes ranging across a number of spatio-temporal scales have played an important role in the resilience of northern ecosystems. However, due to interactions with a warming climate, these disturbances are now erasing key legacies of the last millennia of ecosystem processes. Building on the concepts of legacies and cross-scale interactions, we highlight several general conceptual issues that represent key challenges for the future of northern ecosystem science, but that also have relevance to other biomes.
The June 2013 flood in the Canadian Rockies featured rain‐on‐snow (ROS) runoff generation at alpine elevations that contributed to the high streamflows observed during the event. Such a mid‐summer ROS event has not been diagnosed in detail, and a diagnosis may help to understand future high discharge‐producing hydrometeorological events in mountainous cold regions. The alpine hydrology of the flood was simulated using a physically based model created with the modular cold regions hydrological modelling platform. The event was distinctive in that, although at first, relatively warm rain fell onto existing snowdrifts inducing ROS melt; the rainfall turned to snowfall as the air mass cooled and so increased snowcover and snowpacks in alpine regions, which then melted rapidly from ground heat fluxes in the latter part of the event. Melt rates of existing snowpacks were substantially lower during the ROS than during the relatively sunny periods preceding and following the event as a result of low wind speeds, cloud cover and cool temperatures. However, at the basin scale, melt volumes increased during the event as a result of increased snowcover from the fresh snowfall and consequent large ground heat contributions to melt energy, causing snowmelt to enhance rainfall–runoff by one fifth. Flow pathways also shifted during the event from relatively slow sub‐surface flow prior to the flood to an even contribution from sub‐surface and fast overland flow during and immediately after the event. This early summer, high precipitation ROS event was distinctive for the impact of decreased solar irradiance in suppressing melt rates, the contribution of ground heat flux to basin scale snowmelt after precipitation turned to snowfall, the transition from slow sub‐surface to fast overland flow runoff as the sub‐surface storage saturated and streamflow volumes that exceeded precipitation. These distinctions show that summer, mountain ROS events should be considered quite distinct from winter ROS and can be important contributors to catastrophic events. Copyright © 2016 John Wiley & Sons, Ltd.
In June 2013, excessive rainfall associated with an intense weather system triggered severe flooding in southern Alberta, which became the costliest natural disaster in Canadian history. This article provides an overview of the climatological aspects and large-scale hydrometeorological features associated with the flooding event based upon information from a variety of sources, including satellite data, upper air soundings, surface observations and operational model analyses. The results show that multiple factors combined to create this unusually severe event. The event was characterized by a slow-moving upper level low pressure system west of Alberta, blocked by an upper level ridge, while an associated well-organized surface low pressure system kept southern Alberta, especially the eastern slopes of the Rocky Mountains, in continuous precipitation for up to two days. Results from air parcel trajectory analysis show that a significant amount of the moisture originated from the central Great Plains, transported into Alberta by a southeasterly low level jet. The event was first dominated by significant thunderstorm activity, and then evolved into continuous precipitation supported by the synoptic-scale low pressure system. Both the thunderstorm activity and upslope winds associated with the low pressure system produced large rainfall amounts. A comparison with previous similar events occurring in the same region suggests that the synoptic-scale features associated with the 2013 rainfall event were not particularly intense; however, its storm environment was the most convectively unstable. The system also exhibited a relatively high freezing level, which resulted in rain, rather than snow, mainly falling over the still snow-covered mountainous areas. Melting associated with this rain-on-snow scenario likely contributed to downstream flooding. Furthermore, above-normal snowfall in the preceding spring helped to maintain snow in the high-elevation areas, which facilitated the rain-on-snow event.
A devastating flood struck Southern Alberta in late June 2013, with much of its streamflow generation in the Front Ranges of the Rocky Mountains, west of Calgary. To better understand streamflow generation processes and their sensitivity to initial conditions, a physically based hydrological model was developed using the Cold Regions Hydrological Modelling platform (CRHM) to simulate the flood for the Marmot Creek Research Basin (~9.4 km2). The modular model includes major cold and warm season hydrological processes including snow redistribution, sublimation, melt, runoff over frozen and unfrozen soils, evapotranspiration, subsurface runoff on hillslopes, groundwater recharge and discharge and streamflow routing. Uncalibrated simulations were conducted for eight hydrological years and generally matched streamflow observations well, with a NRMSD of 52%, small model bias (−3%) and a Nash–Sutcliffe efficiency (NSE) of 0.71. The model was then used to diagnose the responses of hydrological processes in 2013 flood from different ecozones in Marmot Creek: alpine, treeline, montane forest and large and small forest clearings to better understand spatial variations in the flood runoff generation mechanisms. To examine the sensitivity to antecedent conditions, ‘virtual’ flood simulations were conducted using a week (17 to 24 June 2013) of flood meteorology imposed on the meteorology of the same period in other years (2005 to 2012), or switched with the meteorology of one week in different months (May to July) of 2013. Sensitivity to changing precipitation and land cover was assessed by varying the precipitation amount during the flood and forest cover and soil storage capacity in forest ecozone. The results show that runoff efficiency increases rapidly with antecedent snowpack and soil moisture storage with the highest runoff response to rainfall from locations in the basin where there are recently melted or actively melting snowpacks and resulting high soil moisture or frozen soils. The impact of forest canopy on flooding is negligible, but flood peak doubles if forest canopy removal is accompanied by 50% reduction in water storage capacity in the basin. Copyright © 2016 John Wiley & Sons, Ltd.
Snow cover dynamics in alpine regions play a crucial role in view of the water balance of head water catchments. The temporal storage of water in form of snow and ice leads to a decoupling of precipitation and runoff. Changes in the volume and the temporal dynamics of the snow storage lead to modified runoff regimes and can influence the frequency of low flow events and floods. For a better estimation of the possible range and direction of future changes, projection runs can be realized by using process-based models. In this study, the Cold Regions Hydrological Modelling platform (CRHM) is used to compile such a model for simulating the snow cover development within research catchment Zugspitze (RCZ; 11.4 km2/Germany). Therefore, the catchment is divided into four hydrological response units (HRUs), able to cover the physiographic characteristics in four elevation zones. The model is evaluated over snow depth measurements. The range of variability within and differences between the HRUs are analyzed, and future projections (2001–2100) are performed on the basis of three different WETTREG realizations. It could be shown that CRHM is able to reproduce the snow cover dynamics very well and that the ongoing climate change does have an identifiable influence on the average extent and size of the snow storage. Furthermore, it could be shown that variations in snow cover dynamics within the RCZ are strongly connected to NAO.
Abstract The snow surface temperature (SST) is essential for estimating longwave radiation fluxes from snow. SST can be diagnosed using finescale multilayer snow physics models that track changes in snow properties and internal energy; however, these models are heavily parameterized, have high predictive uncertainty, and require continuous simulation to estimate prognostic state variables. Here, a relatively simple model to estimate SST that is not reliant on prognostic state variables is proposed. The model assumes that the snow surface is poorly connected thermally to the underlying snowpack and largely transparent for most of the shortwave radiation spectrum, such that a snow surface energy balance among only sensible heat, latent heat, longwave radiation, and near-infrared radiation is possible and is called the radiative psychrometric model (RPM). The RPM SST is sensitive to air temperature, humidity, ventilation, and longwave irradiance and is secondarily affected by absorption of near-infrared radiation at the snow surface that was higher where atmospheric deposition of particulates was more likely to be higher. The model was implemented with neutral stability, an implicit windless exchange coefficient, and constant shortwave absorption factors and aerodynamic roughness lengths. It was evaluated against radiative SST measurements from the Canadian Prairies and Rocky Mountains, French Alps, and Bolivian Andes. With optimized and global shortwave absorption and aerodynamic roughness length parameters, the model is shown to accurately predict SST under a wide range of conditions, providing superior predictions when compared to air temperature, dewpoint, or ice bulb calculation approaches.
The origins and results of the scientific experiments in Marmot Creek Experimental Watershed, now the Marmot Creek Research Basin, over more than 50 years are reviewed. Marmot Creek was established to better understand how forest manipulations could be used to manage streamflow hydrographs and was actively manipulated in the 1970s and 1980s. While small forest clearings were shown to increase snow accumulation consistently, the impacts on melt rates depended on clearing size, slope and aspect. As a result, clearing treatments whether through large cutblocks or small clearings had modest impacts on the hydrograph timing and variability and only local impacts on streamflow volume. Changes in climate are primarily manifested as warming which has substantially reduced snowpacks at low elevations. These climate changes have not been evident in hydrograph change and there is no trend to volumes or timing of streamflow over the last 50 years. Overall the basin shows remarkable resiliency to climate and land use change due to its wide range of elevations, slopes, snow environments and sub-surface storage. The basin has become a hydrological process observatory where multi-scale models are developed and evaluated for operation over larger areas. It has served an invaluable role for this and the scientific results from Marmot Creek have supported the development of global climate models and hydrological models that are now applied throughout the world.