• Clear All
  • 2024
  • 2023
  • 2022
  • 2021
  • 2020
  • 2019
  • 2018
  • 2017
  • 2016
  • Clear All
  • 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
  • Clear All
  • (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
  • 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  | 


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.


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.


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.

DOI bib
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.

DOI bib
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 infere