• 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
  • Wilfrid Laurier University
  • Environment and Climate Change Canada
  • University of Calgary
  • Université de Montréal
  • NSF National Center for Atmospheric Research
  • University of Alaska Fairbanks
  • University of Guelph
  • Lawrence Berkeley National Laboratory
  • University of British Columbia
  • University of Manitoba
  • University of Alberta
  • Northern Arizona University
  • Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences
  • Finnish Meteorological Institute
  • Woodwell Climate Research Center
  • Natural Resources Canada
  • Université du Québec à Montréal
  • Czech University of Life Sciences Prague
  • Swedish University of Agricultural Sciences
  • United States Geological Survey
  • University of Helsinki
  • Jet Propulsion Laboratory
  • Universität Innsbruck
  • University of Ottawa
  • University of Northern British Columbia
  • University of Victoria
  • University of Toronto
  • University of Colorado Boulder
  • Wageningen University & Research
  • Agriculture and Agri-Food Canada
  • Western University
  • ETH Zurich
  • Max Planck Institute for Biogeochemistry
  • University of California, Berkeley
  • Université Laval
  • Lund University
  • University of Nebraska–Lincoln
  • Goddard Space Flight Center
  • Chinese Research Academy of Environmental Sciences
  • University of Eastern Finland
  • University of Arizona
  • University of Wisconsin–Madison
  • Vrije Universiteit Amsterdam
  • University of Edinburgh
  • Queen's University
  • Aarhus 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
  • GLOBAL WATER CITIZENSHIP: INTEGRATING NETWORKED CITIZENS, SCIENTISTS AND LOCAL DECISION MAKERS
  • Geogenic contamination of groundwater resources in subarctic regions
  • Hydrological Processes in Frozen Soils
  • Integrated Modelling Program for Canada (IMPC)
  • Knowledge Mobilization Team
  • Lake Futures: Enhancing Adaptive Capacity and Resilience of Lakes and their Watersheds
  • Linking Multiple Stressors to Adverse Ecological Responses Across Watersheds
  • Linking Stream Network Process Models to Robust Data Management Systems for the Purpose of Land-Use Decision Support
  • Linking Water Governance in Canada to Global Economic, Social and Political Drivers
  • Managing Urban Eutrophication Risks under Climate Change: An Integrated Modelling and Decision Support Framework
  • Matawa Water Futures: Developing an Indigenous-Informed Framework for Watershed Monitoring and Stewardship
  • Mountain Water Futures
  • Northern Water Futures
  • Ohneganos – Indigenous ecological knowledge, training and co-creation of mixed method tools
  • Old Meets New: Subsurface Hydrogeological Connectivity and Groundwater Protection
  • Paradigm Shift in Downscaling Climate Model Projections: Building Models and Tools to Advance Climate Change Research in Cold Regions
  • Prairie Water: Enhancing resilience of Prairie communities through sustainable water management
  • Remotely Sensed Monitoring of Northern lake Ice Using RADARSAT Constellation Mission and Cloud Computing Processing
  • SAMMS: Sub-Arctic Metal Mobility Study
  • SPADE: Storms and Precipitation Across the Continental Divide Experiment
  • Saint John river Experiment on cold Season Storms (SaJESS)
  • Sensors and Sensing Systems for Water Quality Monitoring
  • Short‐Duration Extreme Precipitation in Future Climate
  • Significance of Groundwater Dynamics within Hydrologic Models
  • Southern Forests Water Futures
  • Transformative Sensor Technologies and Smart Watersheds for Canadian Water Futures
  • We need more than just water: Assessing sediment limitation in a large freshwater delta
  • What is Water Worth? Valuing Canada’s Water Resources and Aquatic Ecosystem Services
  • Winter Soil Processes in Transition
  • Others

Welcome to the GWF Publications Archive!

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

2024

The temperature sensitivity (Q10) of soil respiration is a critical parameter in modeling soil carbon dynamics; yet the regulating factors and the underlying mechanisms of Q10 in peat soils remain unclear. To address this gap, we conducted a comprehensive synthesis data analysis from 87 peatland sites (350 observations) spanning boreal, temperate, and tropical zones, and investigated the spatial distribution pattern of Q10 and its correlation with climate conditions, soil properties, and hydrology. Findings revealed distinct Q10 values across climate zones: boreal peatlands exhibited the highest Q10, trailed by temperate and then tropical peatlands. Latitude presented a positive correlation with Q10, while mean annual air temperature and precipitation revealed a negative correlation. The results from the structural equation model suggest that soil properties, such as carbon-to-nitrogen ratio (C/N) and peat type, were the primary drivers of the variance in Q10 of peat respiration. Peat C/N ratios negatively correlated with Q10 of peat respiration and the relationship between C/N and Q10 varied significantly between peat types. Our data analyses also revealed that Q10 was influenced by soil moisture levels, with significantly lower values observed for peat soils under wet than dry conditions. Essentially, boreal and temperate peatlands seem more vulnerable to global warming-induced soil organic carbon decomposition than tropical counterparts, with wet peatlands showing higher climate resilience.
The extensive use of road salts as deicers during winter months is causing the salinization of freshwater systems in cold climate regions worldwide. We analyzed 20 years (2001–2020) of data on lake water chemistry, land cover changes, and road salt applications for Lake Wilcox (LW) located in southern Ontario, Canada. The lake is situated within a rapidly urbanizing watershed in which, during the period of observation, on average 785 tons of road salt were applied annually. However, only about a quarter of this salt has reached the lake so far. That is, most salt has been retained in the watershed, likely through accumulation in soils and groundwater. Despite the high watershed salt retention, time series trend analyses for LW show significant increases in the dissolved concentrations of sodium (Na+) and chloride (Cl−), as well as those of sulfate (SO42−), calcium (Ca2+), and magnesium (Mg2+). The relative changes in the major ion concentrations indicate a shift of the lake water chemistry from the mixed SO42–Cl–Ca2+-Mg2+ type to the Na + -Cl- type. Salinization of LW has further been strengthening and lengthening the lake's summer stratification, which, in turn, has been enhancing hypoxia in the hypolimnion and increasing the internal loading of the limiting nutrient phosphorus. The theoretical salinity threshold at which fall overturn would become increasingly unlikely was estimated at around 1.23 g kg−1. A simple chloride mass balance model predicts that, under the current trend of impermeable land cover expansion, LW could reach this salinity threshold by mid-century. Our results also highlight the need for additional research on the accruing salt legacies in urbanizing watersheds because they represent potential long-term threats to water quality for receiving freshwater ecosystems and regional groundwater resources.
A sufficient supply of dissolved silicon (DSi) relative to dissolved phosphorus (DP) may decrease the likelihood of harmful algal blooms in eutrophic waters. Oxidative precipitation of Fe(II) at oxic-anoxic interfaces may contribute to the immobilization of DSi, thereby exerting control over the DSi availability in the overlying water. Nevertheless, the efficacy of DSi immobilization in this context remains to be precisely determined. To investigate the behavior of DSi during Fe(II) oxidation, anoxic solutions containing mixtures of aqueous Fe(II), DSi, and dissolved phosphorus (DP) were exposed to dissolved oxygen (DO) in the batch system. The experimental data, combined with kinetic reaction modeling, indicate that DSi removal during Fe(II) oxidation occurs via two pathways. At the beginning of the experiments, the oxidation of Fe(II)-DSi complexes induces the fast removal of DSi. Upon complete oxidation of Fe(II), further DSi removal is due to adsorption to surface sites of the Fe(III) oxyhydroxides. The presence of DP effectively competes with DSi via both of these pathways during the initial and later stages of the experiments, with as a result more limited removal of DSi during Fe(II) oxidation. Overall, we conclude that at near neutral pH the oxidation of Fe(II) has considerable capacity to immobilize DSi, where the rapid homogeneous oxidation of Fe(II)-DSi results in greater DSi removal compared to surface adsorption. Elevated DP concentration, however, effectively outcompetes DSi in co-precipitation interactions, potentially contributing to enhanced DSi availability within aquatic systems.
Abstract Rare precipitation events with return periods of multiple decades to hundreds of years are particularly damaging to natural and societal systems. Projections of such rare, damaging precipitation events in the future climate are, however, subject to large inter‐model variations. We show that a substantial portion of these differences can be ascribed to the projected warming uncertainty, and can be robustly reduced by using the warming observed during recent decades as an observational constraint, implemented either by directly constraining the projections with the observed warming or by conditioning them on constrained warming projections, as verified by extensive model‐based cross‐validation. The temperature constraint reduces >40% of the warming‐induced uncertainty in the projected intensification of future rare daily precipitation events for a climate that is 2°C warmer than preindustrial across most regions. This uncertainty reduction together with validation of the reliability of the projections should permit more confident adaptation planning at regional levels.
Version 2.1 of Environment and Climate Change Canada's Canadian Surface Reanalysis (CaSR), based on the Regional Deterministic Reforecast System (RDRS), was implemented in 2022 to provide temporally complete meteorological data over 1980–2018 covering Canada at 10 km spatial resolution. Similarly, the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis-Land (ERA5-Land) dataset at ∼9 km spatial resolution became available. To assess their performance in complex topography, this paper undertakes spatiotemporal inter-comparisons between the RDRS and ERA5-Land reanalysis products with station-based data across British Columbia's Skeena and Nechako watersheds for 1980–2018. Results reveal persistent cold biases, ranging from −6.2°C to −1.6°C basin-wide, in reanalysis mean annual air temperatures relative to observations, but biases vary in both space and time. Conversely, reanalysis total annual precipitation shows wet biases, ranging from 25% to 59% basin-wide. Analyses generally show wetting trends for observations and ERA5-Land while RDRS exhibits drying trends. Reanalysis datasets achieve better agreement overall with observations over the Nechako Watershed, likely due to its denser network of meteorological stations and less complex terrain than the Skeena Watershed. Despite some deficiencies, the RDRS and ERA5-Land reanalyses remain particularly useful products to assess regional climate variability and climatic change given their generally skilful representation of spatial patterns and temporal trends in meteorological variables across the Nechako and Skeena watersheds.
An efficient and robust soil moisture (SM) sampling scheme that can capture the spatial variability of SM is required for the accurate calibration and validation of satellite-based SM retrievals. Often, this process requires numerous sampling points, consuming a significant amount of time. Therefore, it is crucial to develop efficient sampling methods for the improvement of satellite-based SM estimations. The objectives of this study were to define an efficient sampling strategy that could be beneficial for the validation of satellite SM estimations; investigate the role of RS covariates in developing such a strategy; and evaluate the performance of the new sampling scheme over various spatial and temporal domains. In this study, we used the conditioned Latin hypercube sampling (cLHS) algorithm to define an efficient sampling strategy. To this end, remote sensing (RS) raster and digital elevation models (DEM) were used to identify numerous environmental covariates to locate sampling points for characterizing spatial variability of SM at the agricultural field scale. A random forest-based technique, the Boruta algorithm, was also applied to select the most important covariates for utilization into the cLHS algorithm. We used the statistical moments (mean and standard deviation, SD) of the field to select the efficient sample size that can best represent SM status in the field. To evaluate the new sampling scheme, a second data set obtained during a different month for the same agricultural field was used. However, because of the potential for high spatial and temporal correlations between training and test covariates when obtained for the same region, we also used different test datasets in New Zealand to evaluate the sampling scheme. Results showed that the RS covariates obtained from SAR and optical imagery were among the most significant covariates for capturing the spatial variability of SM even if they were not acquired on the day of collection. Also, the new sampling scheme could capture the SM spatial pattern of the field for both test datasets with RMSE less than 4% volumetric SM, which is within the range of the expected performance for most satellite SM products. The evaluation of the new sampling scheme on the New Zealand datasets confirmed the functionality of the proposed sampling scheme for a different temporal and spatial domain.
Deltas are hydrologically dynamic landscapes where river floodwaters create a mosaic of productive ecosystems that provide important services. The flood regime, however, is vulnerable to upstream anthropogenic activities, climate change and geomorphic processes. Deciphering the roles of multiple potential stressors on flood regime change is critical for developing appropriate adaptive and mitigative strategies but requires knowledge of hydrological variability at broader scales of space and time than is typically available from instrumental and observational records. At the globally recognized Peace-Athabasca Delta (Canada), the timing, magnitude and causes of reduced flooding and drawdown of perched basin water levels remain an intense focus of investigation. Here we employ novel 'paleofloodscapes', generated from geospatial interpolation of Bayesian mixing model fingerprinting of sediment elemental concentrations, to quantify variation in the delta's flood regime during the past ~140 years. Results reveal that flooding of the delta began to decline several decades before hydroelectric regulation of Peace River flow, not coincident with it, and the influence of floodwaters from the unregulated Athabasca River has declined more than the regulated Peace River. A key discovery is that widespread flooding of perched basins occurs when ice-jam events on the river(s) coincide with a relatively high water-plane in the delta's open-drainage network. Without knowledge of open-drainage water levels, inferred change to the flood regime of perched basins may be inaccurate when derived solely from analyses of Peace River hydrometric data and climatic records. The paleofloodscapes illustrate that rising sediment delivery to the site of a proposed weir, caused by a natural river avulsion in 1982, may undermine the weir's intended purpose. The most recent paleofloodscape, developed from lake surface sediment sampling shortly after widespread flooding, demonstrates the value of the approach as a landscape hydrological monitoring tool, and is readily transferrable to other floodplains to track flood regime change.
Dioxins, furans, and dioxin-like polychlorinated biphenyls (PCBs) are a group of persistent and toxic chemicals that are known to have human health effects at low levels. These chemicals have been produced for commercial use (PCBs) or unintentionally as by-products of industry or natural processes (PCBs, dioxins, and furans). Additionally, dioxin-like PCBs were formerly used in electrical applications before being banned internationally (2004). These chemicals are widely dispersed in the environment as they can contaminate air and travel hundreds to thousands of kilometers before depositing on land or water, thereafter, potentially entering food chains. Community concerns surrounding the safety of traditional foods prompted a human biomonitoring project in Old Crow, Yukon Territory (YT), Canada (2019). Through collaborative community engagement, dioxins and like compounds were identified as a priority for exposure assessment from biobanked samples. In 2022, biobanked plasma samples (n = 54) collected in Old Crow were used to measure exposures to seven dioxins, ten furans, and four dioxin-like PCBs. 1,2,3,6,7,8-HxCDD, 1,2,3,7,8,9-HxCDD, 1,2,3,4,6,7,8-HpCDD, OCDD, 2,3,4,7,8-PeCDF, 1,2,3,6,7,8-HxCDF, PCB 126, and PCB 169 were detected in at least 50 % of samples. Among these analytes, the only congener at elevated levels was PCB 169, which was approximately ∼2-fold higher than the general population of Canada. No significant sex-based or body mass index (BMI) differences in biomarker concentrations were observed. Generally, the concentrations of the detected congeners increased with age, except for 1,2,3,4,6,7,8-HpCDD. For the first time, this research measures dioxin and like-compound exposures in Old Crow, advancing the information available on chemical exposures in the Arctic. Further research could be directed towards the investigation of PCB 169 exposure sources and temporal monitoring of exposures and determinants.
Beavers (Castor canadensis and C. fiber) build dams that modify catchment and pond water balances, and it has been suggested that they can be a nature-based solution for reducing flood hydrographs, enhancing low flow hydrographs and restoring hydrological functioning of degraded streams. How water moves through a beaver dam is determined by its flow state (e.g., overflow, underflow). However, current conceptual models only consider flow state as changing over the beaver site occupation-abandonment cycle. To assess whether flow state changes at shorter timescales and identify possible triggers (e.g., rainfall, animals), we integrated camera trap imagery, machine learning, water level measurements, and hydrometeorological data at beaver dams in a montane peatland in the Canadian Rocky Mountains. Contrary to current models, we found that flow states changed frequently, changing a maximum 12 times during the 139-day study period, but that changes had limited synchronicity amongst the dams in the same stream. More than two-thirds of the changes coincided with rainfall events. We observed no changes in flow state in response to beaver activity or wildlife crossings perhaps due to the camera positioning. Our findings augment the long-term oriented framework, which links changes to the occupancy cycle of a beaver pond and frequent and hydrological-driven changes. To develop realistic predictions of hydrological impacts of beaver dams, ecohydrological models should update their representation of the influence of beaver dams to include short-term dynamism of flow states and potential triggers. Our study advances the understanding of the important, yet understudied, role of beaver dams in stream restoration and climate change initiatives.
Glacier ecosystems are shrinking at an accelerating rate due to changes in climate, and increased darkening from allochthonous and autochthonous carbon is leading to changes in light absorption, associated heat, and microbial communities.
Stand-replacing crown fires are the most prevalent type of fire regime in boreal forests in North America. However, a substantial proportion of low-severity fires are found within fire perimeters. Here we aimed to investigate the effects of low-severity fires on the reproductive potential and seedling recruitment in boreal forests stands in between stand-replacing fire events.
Arsenic accumulation in fish poses concerns for subsistence and recreational fishers worldwide. However, the toxicity of arsenic to consumers strongly depends on the chemical forms, or species, present. Risk assessments often rely on total arsenic concentrations ([As]), adjusting for assumed small percentages of the most harmful inorganic species. While studies on arsenic speciation in marine fish are widespread, and commonly report less toxic arsenobetaine (AsB) as the dominant form, fewer studies have been conducted on freshwater fish, where arsenic speciation may be more variable. To assess these findings, we conducted a systematic literature review on arsenic speciation in freshwater fish using Covidence© review management software. From over 1100 screened studies, 41 were selected for inclusion based on predefined criteria. These studies reported highly variable arsenic speciation patterns in freshwater fish, calling into question the assumption that AsB is the dominant form present. Sites with suspected or known arsenic contamination issues were prominent, with >50% of data reviewed originating from a contaminated river or lake, but the effect of contamination on arsenic speciation was variable. Although AsB and other organic forms typically dominated, some studies (6/41; 15%) identified fish with elevated concentrations of inorganic arsenic (>1 mg/kg dry wt.), most often corresponding to over 20% of total arsenic. Furthermore, arsenic speciation results accounted for a highly variable proportion of total [As] in fish, often less than 50%. Assuming 20% inorganic arsenic appears to be a poor approximation that cannot be applied to all fish. Based on this considerable variability, we recommend the direct measurement of arsenic species whenever possible, especially when total [As] is elevated above relevant guidelines for the most toxic species (e.g., 0.1-2 mg/kg inorganic arsenic wet wt.). We also recommend future works communicate their results in more detail, including complete description of QAQC protocols, to improve the potential for future meta-analyses. Additional work is needed to characterize arsenic speciation in freshwater fish and assess the toxicity of various arsenic species to accurately evaluate the environmental and human health risks associated with arsenic in fish.
Chlorophyll-a concentration (Chl-a) is commonly used as a proxy for phytoplankton abundance in surface waters of large lakes. Mapping spatial and temporal Chl-a distributions derived from multispectral satellite data is therefore increasingly popular for monitoring trends in trophic state of these important ecosystems. We evaluated products of eleven atmospheric correction processors (LEDAPS, LaSRC, Sen2Cor, ACOLITE, ATCOR, C2RCC, DOS 1, FLAASH, iCOR, Polymer, and QUAC) and 27 reflectance indexes (including band-ratio, three-band, and four-band algorithms) recommended for Chl-a concentration retrieval. These were applied to the western basin of Lake Ontario by pairing 236 satellite scenes from Landsat 5, 7, 8, and Sentinel-2 acquired between 2000 and 2022 to 600 near-synchronous and co-located in situ-measured Chl-a concentrations. The in situ data were categorized based on location, seasonality, and Carlson’s Trophic State Index (TSI). Linear regression Chl-a models were calibrated for each processing scheme plus data category. The models were compared using a range of performance metrics. Categorization of data based on trophic state yielded improved outcomes. Furthermore, Sentinel-2 and Landsat 8 data provided the best results, while Landsat 5 and 7 underperformed. A total of 28 Chl-a models were developed across the different data categorization schemes, with RMSEs ranging from 1.1 to 14.1 μg/L. ACOLITE-corrected images paired with the blue-to-green band ratio emerged as the generally best performing scheme. However, model performance was dependent on the data filtration practices and varied between satellites.
The seasonal temperature trends and ice phenology in the Great Slave Lake (GSL) are significantly influenced by inflow from the Slave River. The river undergoes a sequence of mechanical break-ups all the way to the GSL, initiating the GSL break-up process. Additionally, upstream water management practices impact the discharge of the Slave River and, consequently, the ice break-up of the GSL. Therefore, monitoring the break-up process at the Slave River Delta (SRD), where the river meets the lake, is crucial for understanding the cascading effects of upstream activities on GSL ice break-up. This research aimed to use Random Forest (RF) models to monitor the ice break-up processes at the SRD using a combination of satellite images with relatively high spatial resolution, including Landsat-5, Landsat-8, Sentinel-2a, and Sentinel-2b. The RF models were trained using selected training pixels to classify ice, open water, and cloud. The onset of break-up was determined by data-driven thresholds on the ice fraction in images with less than 20% cloud coverage. Analysis of break-up timing from 1984 to 2023 revealed a significant earlier trend using the Mann–Kendall test with a p-value of 0.05. Furthermore, break-up data in recent years show a high degree of variability in the break-up rate using images in recent years with better temporal resolution.
Thawing permafrost releases labile organic carbon and alters groundwater geochemistry and hydrology with uncertain outcomes for the mobility of hazardous metal(loid)s. Managing water quality in thawing permafrost regions is predicated on a detailed understanding of the speciation and abundance of metal(loid)s in permafrost soils and porewaters produced during thaw, which remains limited at present. This study contributes new knowledge on the sources and fate of arsenic during the thaw of organic-rich permafrost using samples collected from a subarctic permafrost region associated with geogenic arsenic (Dawson Range, Yukon, Canada). Several permafrost cores and active-layer samples from this region were analyzed for their solid-phase and aqueous geochemical characteristics and their arsenic speciation. Porewaters were extracted from permafrost cores after thaw under anaerobic conditions for aqueous geochemical analyses. Bedrock samples from the field site were also analyzed for arsenic speciation and mineralogy. X-ray diffraction and X-ray near-edge spectroscopy (XANES) analyses of weathered bedrock upgradient of soil sampling locations contained arsenic(V) hosted in iron-(oxyhydr)oxides and scorodite. XANES and micro X-ray fluorescence analyses of permafrost soils indicated a mixture of arsenic(III) and arsenic(V), indicating redox recycling of arsenic. Soil-bound arsenic was colocated with iron, likely as arseniferous iron-(oxyhydr)oxides that have been encapsulated by aggrading permafrost over geologic time. However, permafrost thaw produced porewater containing elevated dissolved arsenic (median 40 μg L–1, range 2–96 μg L–1). Thawed permafrost porewater also contained elevated dissolved iron (median 5.5 mg L–1, range 0.5–40 mg L–1) and dissolved organic carbon (median 423 mg L–1, range 72–3240 mg L–1), indicative of reducing conditions. This study highlights that arsenic can be found in reactive forms in permafrost soil, and that its thaw can release arsenic and iron to porewater and produce poor water quality.
Data from the International Stormwater Best Management Practices (BMP) Database were used to compare the phosphorus (P) control performance of six categories of stormwater BMPs representing traditional systems (stormwater pond, wetland basin, and detention basin) and low-impact development (LID) systems (bioretention cell, grass swale, and grass strip). Machine learning (ML) models were trained to predict the reduction or enrichment factors of surface runoff concentrations and loadings of total P (TP) and soluble reactive P (SRP) for the different categories of BMP systems. Relative to traditional BMPs, LIDs generally enriched TP and SRP concentrations in stormwater surface outflow and yielded poorer P runoff load control. The SRP concentration reduction and enrichment factors of LIDs also tended to be more sensitive to variations in climate and watershed characteristics. That is, LIDs were more likely to enrich surface runoff SRP concentrations in drier climates, when inflow SRP concentrations were low, and for watersheds exhibiting high impervious land cover. Overall, our results imply that stormwater BMPs do not universally attenuate urban P export and that preferentially implementing LIDs over traditional BMPs may increase TP and SRP export to receiving freshwater bodies, hence magnifying eutrophication risks.
This study introduces the first use of Global Navigation Satellite System Reflectometry (GNSS-R) for monitoring lake ice phenology. This is demonstrated using Qinghai Lake, Tibetan Plateau, as a case study. Signal-to-Noise Ratio (SNR) values obtained from the Cyclone GNSS (CYGNSS) constellation over four ice seasons (2018 to 2022) were used to examine the impact of lake surface conditions on reflected GNSS signals during open water and ice cover seasons. A moving t-test algorithm was applied to time-varying SNR values allowing for the detection of lake ice at daily temporal resolution. Good agreement was achieved between ice phenology records derived from CYGNSS data and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The CYGNSS timings for freeze-up, i.e., the period starting with the first appearance of ice on the lake (freeze-up start; FUS) until the lake becomes fully ice covered (freeze-up end; FUE), as well as those for breakup, i.e., the period beginning with the first pixel of open water (breakup start; BUS) and ending when the whole lake becomes ice-free (breakup end; BUE), were validated against the phenology dates derived from MODIS images. Mean absolute errors are 7, 5, 10, 4 and 5 days for FUS, FUE, BUS, BUE and ice cover duration, respectively. Observations revealed the sensitivity of GNSS reflected signals to surface melt prior to the appearance of open water conditions as determined from MODIS, which explains the larger difference of 10 days for BUS.
Abstract Changes are projected for the boreal biome with complex and variable effects on forest vegetation including drought‐induced tree mortality and forest loss. With soil and atmospheric conditions governing drought intensity, specific drivers of trees water stress can be difficult to disentangle across temporal scales. We used wavelet analysis and causality detection to identify potential environmental controls (evapotranspiration, soil moisture, rainfall, vapor pressure deficit, air temperature and photosynthetically active radiation) on daily tree water deficit and on longer periods of tree dehydration in black spruce and tamarack. Daily tree water deficit was controlled by photosynthetically active radiation, vapor pressure deficit, and air temperature, causing greater stand evapotranspiration. Prolonged periods of tree water deficit (multi‐day) were regulated by photosynthetically active radiation and soil moisture. We provide empirical evidence that continued warming and drying will cause short‐term increases in black spruce and tamarack transpiration, but greater drought stress with reduced soil water availability.
Abstract Forest disturbances can result in very different canopies that carry elevated albedo, thus causing substantial cooling effects on the climate. Unfortunately, the resulting dynamic global warming potential from altered albedo (GWP Δα ) is poorly understood. We examined and modeled the changes in albedo over time after disturbances (i.e., forest age) by forest type, disturbance type and geographic location using direct measurements from 107 sites in temperate and boreal regions. Albedo in undisturbed forests was used as the reference to calculate albedo changes (Δα) and GWP Δα after a disturbance. We found that age is a significant factor for predicting albedo amid the obvious regulations from forest type and geographic locations. We found the strongest cooling GWP Δα in the first 10 years after a disturbance, but it decreased rapidly with time. The changes in GWP Δα were very different from the chronosequence of net ecosystem production (NEP). In the first decade after disturbances, GWP Δα was negative (i.e., cooling) and surprisingly larger in magnitude, with an average of −0.609 kg CO 2 m −2 yr −1 , compared to NEP of −0.166 kg CO 2 m −2 yr −1 . Albedo continued to decrease and approached pre‐disturbance levels until around 50 years, resulting in a nearly zero GWP Δα . This research illustrates that many forests in temperate and boreal regions can be considered significant cooling agents by taking into account the high albedo of young forests following disturbances.

2023

Permafrost thaw/degradation in the Northern Hemisphere due to global warming is projected to accelerate in coming decades. Assessment of this trend requires improved understanding of the evolution and dynamics of permafrost areas. Land surface models (LSMs) are well-suited for this due to their physical basis and large-scale applicability. However, LSM application is challenging because (a) LSMs demand extensive and accurate meteorological forcing data, which are not readily available for historic conditions and only available with significant biases for future climate, (b) LSMs possess a large number of model parameters, and (c) observations of thermal/hydraulic regimes to constrain those parameters are severely limited. This study addresses these challenges by applying the MESH-CLASS modeling framework (Modélisation Environmenntale communautaire—Surface et Hydrology embedding the Canadian Land Surface Scheme) to three regions within the Mackenzie River Basin, Canada, under various meteorological forcing data sets, using the variogram analysis of response surfaces framework for sensitivity analysis and threshold-based identifiability analysis. The study shows that the modeler may face complex trade-offs when choosing a forcing data set; for current and future scenarios, forcing data require multi-variate bias correction, and some data sets enable the representation of some aspects of permafrost dynamics, but are inadequate for others. The results identify the most influential model parameters and show that permafrost simulation is most sensitive to parameters controlling surface insulation and runoff generation. But the identifiability analysis reveals that many of the most influential parameters are unidentifiable. These conclusions can inform future efforts for data collection and model parameterization.
Abstract 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.
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.

DOI bib
Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop
Antara Dasgupta, Louise Arnal, Rebecca Emerton, Shaun Harrigan, Gwyneth Matthews, Ameer Muhammad, Karen O’Regan, Teresa Pérez‐Ciria, Emixi Valdez, Bart van Osnabrugge, Micha Werner, Carlo Buontempo, Hannah Cloke, Florian Pappenberger, Ilias Pechlivanidis, Christel Prudhomme, Maria‐Helena Ramos, Peter Salamon, Antara Dasgupta, Louise Arnal, Rebecca Emerton, Shaun Harrigan, Gwyneth Matthews, Ameer Muhammad, Karen O’Regan, Teresa Pérez‐Ciria, Emixi Valdez, Bart van Osnabrugge, Micha Werner, Carlo Buontempo, Hannah Cloke, Florian Pappenberger, Ilias Pechlivanidis, Christel Prudhomme, Maria‐Helena Ramos, Peter Salamon
Journal of Flood Risk Management

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.
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.
Abstract Permafrost‐underlain watersheds in the subarctic are sensitive to warming as small changes in ground thermal status will alter all components of the hydrological cycle. Globally, observed increases in winter flows and shifting water chemistry have most often been ascribed to permafrost thaw and deepening runoff pathways. However, there remain few studies in headwater catchments that examine coupled flow‐chemistry relations at high frequency over multiple years and seasons to evaluate the implications of environmental change. In this study, we use multi‐year high‐frequency measurement of discharge, specific conductance (SpC) and chromophoric dissolved organic matter (CDOM) along with traditional grab sampling of major ions to understand the sources and pathways of water and evaluate how distinct solutes are mobilized in a well‐studied subarctic basin in Yukon, Canada. Seasonally, the catchment exhibited considerable hysteresis in flow‐solute relations and had both chemostatic and dilution SpC–Q patterns with respect to major ions depending upon season and mobilization CDOM–Q signals. Storm events were extracted from high‐frequency data and normalized C–Q indices were determined and related to flow, catchment and meteorological variables. CDOM–Q events predominantly had an anti‐clockwise hysteresis and increases in DOC concentrations during storms, with some exception in the spring and fall. Conversely, SpC–Q events exhibited clockwise hysteresis and a dilution behaviour during events with less seasonal or inter‐annual variability. Information from this study supports previous conceptual models of thermally regulated runoff generation in a layered soil profile, yet also points to the importance of lateral connectivity and distal sources of solutes.
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.

DOI bib
Assessing and Mitigating Ice-Jam Flood Hazards and Risks: A European Perspective
Karl‐Erich Lindenschmidt, Knut Alfredsen, Dirk Carstensen, Adam Choryński, David Gustafsson, Michał Halicki, Bernd Hentschel, Niina Karjalainen, Michael Kögel, Tomasz Kolerski, Marika Kornaś-Dynia, Michał Kubicki, Zbigniew W. Kundzewicz, Cornelia Lauschke, Albert Malinger, Włodzimierz Marszelewski, Fabian Möldner, Barbro Näslund-Landenmark, Tomasz Niedzielski, Antti Parjanne, Bogusław Pawłowski, Iwona Pińskwar, Joanna Remisz, Maik Renner, Michael Roers, Maksymilian Rybacki, Ewelina Szałkiewicz, Michał Szydłowski, Grzegorz Walusiak, Matylda Witek, Mateusz Zagata, Maciej Zdralewicz, Karl‐Erich Lindenschmidt, Knut Alfredsen, Dirk Carstensen, Adam Choryński, David Gustafsson, Michał Halicki, Bernd Hentschel, Niina Karjalainen, Michael Kögel, Tomasz Kolerski, Marika Kornaś-Dynia, Michał Kubicki, Zbigniew W. Kundzewicz, Cornelia Lauschke, Albert Malinger, Włodzimierz Marszelewski, Fabian Möldner, Barbro Näslund-Landenmark, Tomasz Niedzielski, Antti Parjanne, Bogusław Pawłowski, Iwona Pińskwar, Joanna Remisz, Maik Renner, Michael Roers, Maksymilian Rybacki, Ewelina Szałkiewicz, Michał Szydłowski, Grzegorz Walusiak, Matylda Witek, Mateusz Zagata, Maciej Zdralewicz
Water, Volume 15, Issue 1

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.
Abstract 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 km 2 ); 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.
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.
Abstract 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.
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.

DOI bib
Modeled production, oxidation, and transport processes of wetland methane emissions in temperate, boreal, and Arctic regions
Masahito Ueyama, Sara Knox, Kyle Delwiche, Sheel Bansal, W. J. Riley, Dennis Baldocchi, Takashi Hirano, Gavin McNicol, K. V. Schäfer, L. Windham‐Myers, Benjamin Poulter, Robert B. Jackson, Kuang‐Yu Chang, Jiquen Chen, Housen Chu, Ankur R. Desai, Sébastien Gogo, Hiroki Iwata, Minseok Kang, Ivan Mammarella, Matthias Peichl, Oliver Sonnentag, Eeva‐Stiina Tuittila, Youngryel Ryu, E. S. Euskirchen, Mathias Göckede, Adrien Jacotot, Mats B. Nilsson, Torsten Sachs, Masahito Ueyama, Sara Knox, Kyle Delwiche, Sheel Bansal, W. J. Riley, Dennis Baldocchi, Takashi Hirano, Gavin McNicol, K. V. Schäfer, L. Windham‐Myers, Benjamin Poulter, Robert B. Jackson, Kuang‐Yu Chang, Jiquen Chen, Housen Chu, Ankur R. Desai, Sébastien Gogo, Hiroki Iwata, Minseok Kang, Ivan Mammarella, Matthias Peichl, Oliver Sonnentag, Eeva‐Stiina Tuittila, Youngryel Ryu, E. S. Euskirchen, Mathias Göckede, Adrien Jacotot, Mats B. Nilsson, Torsten Sachs
Global Change Biology, Volume 29, Issue 8

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.

DOI bib
Arctic soil methane sink increases with drier conditions and higher ecosystem respiration
Carolina Voigt, Anna‐Maria Virkkala, Gabriel Hould Gosselin, Kathryn A. Bennett, T. Andrew Black, Matteo Detto, Charles Chevrier-Dion, Georg Guggenberger, Wasi Hashmi, Lukas Kohl, Dan Kou, Charlotte Marquis, Philip Marsh, Maija E. Marushchak, Zoran Nesic, Hannu Nykänen, Taija Saarela, Leopold Sauheitl, Branden Walker, Niels Weiss, Evan J. Wilcox, Oliver Sonnentag, Carolina Voigt, Anna‐Maria Virkkala, Gabriel Hould Gosselin, Kathryn A. Bennett, T. Andrew Black, Matteo Detto, Charles Chevrier-Dion, Georg Guggenberger, Wasi Hashmi, Lukas Kohl, Dan Kou, Charlotte Marquis, Philip Marsh, Maija E. Marushchak, Zoran Nesic, Hannu Nykänen, Taija Saarela, Leopold Sauheitl, Branden Walker, Niels Weiss, Evan J. Wilcox, Oliver Sonnentag
Nature Climate Change

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.

DOI bib
Carbon uptake in Eurasian boreal forests dominates the high‐latitude net ecosystem carbon budget
Jennifer D. Watts, Mary Farina, John S. Kimball, Luke D. Schiferl, Zhihua Liu, Kyle A. Arndt, Donatella Zona, Ashley P. Ballantyne, E. S. Euskirchen, Frans‐Jan W. Parmentier, Manuel Helbig, Oliver Sonnentag, Torbern Tagesson, Janne Rinne, Hiroki Ikawa, Masahito Ueyama, Hideki Kobayashi, Torsten Sachs, Daniel F. Nadeau, John Kochendorfer, M. Jackowicz-Korczyński, Anna Virkkala, Mika Aurela, R. Commane, Brendan Byrne, Leah Birch, Matthew S. Johnson, Nima Madani, Brendan M. Rogers, Jinyang Du, Arthur Endsley, K. E. Savage, Benjamin Poulter, Zhen Zhang, L. M. Bruhwiler, Charles E. Miller, S. J. Goetz, Walter C. Oechel, Jennifer D. Watts, Mary Farina, John S. Kimball, Luke D. Schiferl, Zhihua Liu, Kyle A. Arndt, Donatella Zona, Ashley P. Ballantyne, E. S. Euskirchen, Frans‐Jan W. Parmentier, Manuel Helbig, Oliver Sonnentag, Torbern Tagesson, Janne Rinne, Hiroki Ikawa, Masahito Ueyama, Hideki Kobayashi, Torsten Sachs, Daniel F. Nadeau, John Kochendorfer, M. Jackowicz-Korczyński, Anna Virkkala, Mika Aurela, R. Commane, Brendan Byrne, Leah Birch, Matthew S. Johnson, Nima Madani, Brendan M. Rogers, Jinyang Du, Arthur Endsley, K. E. Savage, Benjamin Poulter, Zhen Zhang, L. M. Bruhwiler, Charles E. Miller, S. J. Goetz, Walter C. Oechel
Global Change Biology, Volume 29, Issue 7

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.

DOI bib
Pan‐Arctic soil moisture control on tundra carbon sequestration and plant productivity
Donatella Zona, Peter M. Lafleur, Koen Hufkens, Beniamino Gioli, Barbara Bailey, George Burba, E. S. Euskirchen, Jennifer D. Watts, Kyle A. Arndt, Mary Farina, John S. Kimball, Martin Heimann, Mathias Göckede, Martijn Pallandt, Torben R. Christensen, Mikhail Mastepanov, Efrèn López‐Blanco, A. J. Dolman, R. Commane, Charles E. Miller, Josh Hashemi, Lars Kutzbach, David Holl, Julia Boike, Christian Wille, Torsten Sachs, Aram Kalhori, Elyn Humphreys, Oliver Sonnentag, Gesa Meyer, Gabriel Hould Gosselin, Philip Marsh, Walter C. Oechel, Donatella Zona, Peter M. Lafleur, Koen Hufkens, Beniamino Gioli, Barbara Bailey, George Burba, E. S. Euskirchen, Jennifer D. Watts, Kyle A. Arndt, Mary Farina, John S. Kimball, Martin Heimann, Mathias Göckede, Martijn Pallandt, Torben R. Christensen, Mikhail Mastepanov, Efrèn López‐Blanco, A. J. Dolman, R. Commane, Charles E. Miller, Josh Hashemi, Lars Kutzbach, David Holl, Julia Boike, Christian Wille, Torsten Sachs, Aram Kalhori, Elyn Humphreys, Oliver Sonnentag, Gesa Meyer, Gabriel Hould Gosselin, Philip Marsh, Walter C. Oechel
Global Change Biology, Volume 29, Issue 5

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.

DOI bib
Blue justice: A review of emerging scholarship and resistance movements
Jessica Blythe, David Gill, Joachim Claudet, Nathan Bennett, Georgina G. Gurney, Jacopo A. Baggio, Natalie C. Ban, Miranda Bernard, Victor Brun, Emily S. Darling, Antonio Di Franco, Graham Epstein, Phil Franks, Rebecca Horan, Stacy D. Jupiter, Jacqueline Lau, Natali Lazzari, Shauna L. Mahajan, Sangeeta Mangubhai, Josheena Naggea, Rachel A. Turner, Noelia Zafra‐Calvo, Jessica Blythe, David Gill, Joachim Claudet, Nathan Bennett, Georgina G. Gurney, Jacopo A. Baggio, Natalie C. Ban, Miranda Bernard, Victor Brun, Emily S. Darling, Antonio Di Franco, Graham Epstein, Phil Franks, Rebecca Horan, Stacy D. Jupiter, Jacqueline Lau, Natali Lazzari, Shauna L. Mahajan, Sangeeta Mangubhai, Josheena Naggea, Rachel A. Turner, Noelia Zafra‐Calvo
Cambridge Prisms: Coastal Futures, Volume 1

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 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.

DOI bib
Differentiable modelling to unify machine learning and physical models for geosciences
Chaopeng Shen, Alison Appling, Pierre Gentine, Toshiyuki Bandai, Hoshin V. Gupta, Alexandre M. Tartakovsky, Marco Baity‐Jesi, Fabrizio Fenicia, Daniel Kifer, Li Li, Xiaofeng Liu, Wei Ren, Yi Zheng, C. J. Harman, Martyn Clark, Matthew W. Farthing, Dapeng Feng, Praveen Kumar, Doaa Aboelyazeed, Farshid Rahmani, Yalan Song, Hylke E. Beck, Tadd Bindas, Dipankar Dwivedi, Kuai Fang, Marvin Höge, Christopher Rackauckas, Binayak P. Mohanty, Tirthankar Roy, Chonggang Xu, Kathryn Lawson, Chaopeng Shen, Alison Appling, Pierre Gentine, Toshiyuki Bandai, Hoshin V. Gupta, Alexandre M. Tartakovsky, Marco Baity‐Jesi, Fabrizio Fenicia, Daniel Kifer, Li Li, Xiaofeng Liu, Wei Ren, Yi Zheng, C. J. Harman, Martyn Clark, Matthew W. Farthing, Dapeng Feng, Praveen Kumar, Doaa Aboelyazeed, Farshid Rahmani, Yalan Song, Hylke E. Beck, Tadd Bindas, Dipankar Dwivedi, Kuai Fang, Marvin Höge, Christopher Rackauckas, Binayak P. Mohanty, Tirthankar Roy, Chonggang Xu, Kathryn Lawson
Nature Reviews Earth & Environment, Volume 4, Issue 8

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.

DOI bib
The Northwest Territories Thermokarst Mapping Collective: A northern-driven mapping collaborative toward understanding the effects of permafrost thaw
Steven V. Kokelj, Tristan Gingras‐Hill, Seamus V. Daly, P D Morse, S A Wolfe, Ashley Rudy, Jurjen van der Sluijs, Niels Weiss, H B O'Neill, Jennifer L. Baltzer, Trevor C. Lantz, Carolyn Gibson, Dieter Cazon, Robert Fraser, Duane G. Froese, Garfield Giff, Charles Klengenberg, Scott F. Lamoureux, W. L. Quinton, M. R. Turetsky, Alexandre Chiasson, C.C. Ferguson, Mike Newton, Mike Pope, Jason Paul, A E Wilson, Joseph M. Young, Steven V. Kokelj, Tristan Gingras‐Hill, Seamus V. Daly, P D Morse, S A Wolfe, Ashley Rudy, Jurjen van der Sluijs, Niels Weiss, H B O'Neill, Jennifer L. Baltzer, Trevor C. Lantz, Carolyn Gibson, Dieter Cazon, Robert Fraser, Duane G. Froese, Garfield Giff, Charles Klengenberg, Scott F. Lamoureux, W. L. Quinton, M. R. Turetsky, Alexandre Chiasson, C.C. Ferguson, Mike Newton, Mike Pope, Jason Paul, A E Wilson, Joseph M. Young, Steven V. Kokelj, Tristan Gingras‐Hill, Seamus V. Daly, P D Morse, S A Wolfe, Ashley Rudy, Jurjen van der Sluijs, Niels Weiss, H B O'Neill, Jennifer L. Baltzer, Trevor C. Lantz, Carolyn Gibson, Dieter Cazon, Robert Fraser, Duane G. Froese, Garfield Giff, Charles Klengenberg, Scott F. Lamoureux, W. L. Quinton, M. R. Turetsky, Alexandre Chiasson, C.C. Ferguson, Mike Newton, Mike Pope, Jason Paul, A E Wilson, Joseph M. Young, Steven V. Kokelj, Tristan Gingras‐Hill, Seamus V. Daly, P D Morse, S A Wolfe, Ashley Rudy, Jurjen van der Sluijs, Niels Weiss, H B O'Neill, Jennifer L. Baltzer, Trevor C. Lantz, Carolyn Gibson, Dieter Cazon, Robert Fraser, Duane G. Froese, Garfield Giff, Charles Klengenberg, Scott F. Lamoureux, W. L. Quinton, M. R. Turetsky, Alexandre Chiasson, C.C. Ferguson, Mike Newton, Mike Pope, Jason Paul, A E Wilson, Joseph M. Young
Arctic Science

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.

DOI bib
The Northwest Territories Thermokarst Mapping Collective: A northern-driven mapping collaborative toward understanding the effects of permafrost thaw
Steven V. Kokelj, Tristan Gingras‐Hill, Seamus V. Daly, P D Morse, S A Wolfe, Ashley Rudy, Jurjen van der Sluijs, Niels Weiss, H B O'Neill, Jennifer L. Baltzer, Trevor C. Lantz, Carolyn Gibson, Dieter Cazon, Robert Fraser, Duane G. Froese, Garfield Giff, Charles Klengenberg, Scott F. Lamoureux, W. L. Quinton, M. R. Turetsky, Alexandre Chiasson, C.C. Ferguson, Mike Newton, Mike Pope, Jason Paul, A E Wilson, Joseph M. Young, Steven V. Kokelj, Tristan Gingras‐Hill, Seamus V. Daly, P D Morse, S A Wolfe, Ashley Rudy, Jurjen van der Sluijs, Niels Weiss, H B O'Neill, Jennifer L. Baltzer, Trevor C. Lantz, Carolyn Gibson, Dieter Cazon, Robert Fraser, Duane G. Froese, Garfield Giff, Charles Klengenberg, Scott F. Lamoureux, W. L. Quinton, M. R. Turetsky, Alexandre Chiasson, C.C. Ferguson, Mike Newton, Mike Pope, Jason Paul, A E Wilson, Joseph M. Young, Steven V. Kokelj, Tristan Gingras‐Hill, Seamus V. Daly, P D Morse, S A Wolfe, Ashley Rudy, Jurjen van der Sluijs, Niels Weiss, H B O'Neill, Jennifer L. Baltzer, Trevor C. Lantz, Carolyn Gibson, Dieter Cazon, Robert Fraser, Duane G. Froese, Garfield Giff, Charles Klengenberg, Scott F. Lamoureux, W. L. Quinton, M. R. Turetsky, Alexandre Chiasson, C.C. Ferguson, Mike Newton, Mike Pope, Jason Paul, A E Wilson, Joseph M. Young, Steven V. Kokelj, Tristan Gingras‐Hill, Seamus V. Daly, P D Morse, S A Wolfe, Ashley Rudy, Jurjen van der Sluijs, Niels Weiss, H B O'Neill, Jennifer L. Baltzer, Trevor C. Lantz, Carolyn Gibson, Dieter Cazon, Robert Fraser, Duane G. Froese, Garfield Giff, Charles Klengenberg, Scott F. Lamoureux, W. L. Quinton, M. R. Turetsky, Alexandre Chiasson, C.C. Ferguson, Mike Newton, Mike Pope, Jason Paul, A E Wilson, Joseph M. Young
Arctic Science

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.
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.
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 vapor) and groundwater flow, advective‐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 (a) 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; (b) 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 (c) 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 modeling framework and simulation results highlight the need to account for coupled thermal‐hydraulic‐mechanical‐chemical behaviors 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.
Abstract Sediment core and water quality data, together with historical information on land use/land cover (LULC), were used to reconstruct changes in phosphorus (P) loading and cycling in Lake Wilcox, Ontario, Canada, since the early 1920s. After first being cleared for farming, the originally forested watershed subsequently underwent urbanization. The large increase in P loading accompanying agricultural intensification after World War II caused the eutrophication of the lake. However, improved soil conservation since the 1980s and urban stormwater management since the 1990s have brought watershed P loading and sediment accumulation down to levels comparable to the early 1900s. Yet, the lake continues to exhibit eutrophication‐like symptoms, especially the intensification of hypoxia in the hypolimnion. Post‐2000 water quality data indicate that the latter is not driven by external P loading from the watershed, but rather by rapid salinization that strengthens the lake's summer stratification and enhances internal P loading. Salinization is caused by the increasing application of deicing agents in the expanding urban area. Curbing salt inputs will therefore be essential to restore the lake. Overall, our results provide new insights into the shifts in lake biogeochemistry associated with LULC changes and the implementation of best management practices. The approaches and findings of our case study have broad applicability for the large number of freshwater ecosystems worldwide that are experiencing salinization.
In recent years, there has been a surge in annual plastic production, which has contributed to growing environmental challenges, particularly in the form of microplastics. Effective management of plastic and microplastic waste has become a critical concern, necessitating innovative strategies to address its impact on ecosystems and human health. In this context, catalytic degradation of microplastics emerges as a pivotal approach that holds significant promise for mitigating the persistent effects of plastic pollution. In this article, we critically explored the current state of catalytic degradation of microplastics and discussed the definition of degradation, characterization methods for degradation products, and the criteria for standard sample preparation. Moreover, the significance and effectiveness of various catalytic entities, including enzymes, transition metal ions (for the Fenton reaction), nanozymes, and microorganisms are summarized. Finally, a few key issues and future perspectives regarding the catalytic degradation of microplastics are proposed.
Cultural eutrophication—the pollution of water bodies with nutrients such as nitrogen and phosphorus from human activities—and associated harmful algal blooms are key issues facing decision-makers, yet costs are often identified as a barrier to restoration. When designed in collaboration with impacted communities, economic valuation of lake ecosystem services can contribute to informed environmental decision-making by quantifying economic benefits of lake restoration and understanding the trade-offs people are willing to make. Here, we collaborate with the local community, stakeholders, and decision-makers to develop and implement a discrete choice experiment survey to estimate people's preferences and willingness to pay for restoring Elk/Beaver Lake, Canada, which has been experiencing worsening harmful algal blooms and other water quality issues. Over half of survey respondents (66%) indicated that water quality issues impact their use of the lake, and many (52%) indicated they did not feel safe swimming in or allowing their pets to drink from the lake (64%). Responses to the choice experiment are analyzed using choice models which reveal that the annual economic benefits of lake restoration across different model specifications ranged from $141 to $292 CAD per household with substantial heterogeneity across people. The aggregate annual benefits of lake restoration are $27 to $55 million which is notably greater than the estimated costs of restoration plans. This study contributes to the growing literature suggesting that there are substantial benefits to society from restoring lakes, thus the perception of cost as an insurmountable barrier to restoration of bloom-affected lakes requires reconsideration.
Climate-driven permafrost degradation and an intensification of the hydrological cycle are rapidly altering the intricate ecohydrological processes of Arctic wetlands, threatening their long-term carbon sequestration capabilities. Addressing this concern through effective management holds immense potential for climate regulation, mitigation, and adaptation efforts. As such, there is growing need for timely spatial inventory data identifying Arctic wetlands with sufficient accuracy, resolution, and detail. Wetland mapping at large scales necessitates the processing of large volumes of Earth observation (EO) data, a challenge known as "Big Data". Consequently, in this study, we present a cloud-based methodology exploiting the remarkable collection of EO data and computational power of Google Earth Engine (GEE) to map Arctic wetlands at 10 m spatial resolution. Our workflow evaluated temporally aggregated optical and radar satellite imagery and novel hydro-physiographic layers as inputs into a robust Random Forest (RF) machine learning (ML) algorithm. Both pixel and object-based classification approaches were assessed, whereby ML models were calibrated with a training dataset of sufficient and comprehensive samples. The study was conducted over Canada's Southern Arctic ecozone (830,000 km2). GEE enabled the efficient preprocessing and classification of large volumes of EO data and resulted in excellent yet similar statistical performance for both pixel and object-based approaches, achieving overall accuracies of > 89 % and mean F1-scores of > 0.79. Moreover, McNemar tests indicated that these classifications were not statistically different, which has significant implications regarding computing time and processing efficiencies. These results demonstrate the efficacy and scalability of our cloud-based GEE methodology, and as such can support future endeavors around Pan-Arctic wetland mapping and monitoring.
Occurrences of near-0°C temperatures (–2°C ≤ T ≤ 2°C) are common in cold regions such as Canada, and these conditions can lead to freeze–thaw events and hazardous precipitation. Many locations in the Coast Mountains within the Canadian province of British Columbia (BC) are especially prone to near-0°C conditions. This study examines the factors that promote anomalously persistent near-0°C conditions, often with precipitation, in Terrace, a small industrial city within the Coast Mountains of northwestern BC. The climatology of near-0°C conditions and associated precipitation over the 1956–2020 period was developed using a combined network of weather stations and field data to study the atmospheric conditions and precipitation during periods of near-0°C conditions. Events with continuous near-0°C conditions generally had long durations, with a climatological mean of 11 h, which increases substantially if accompanied by precipitation (18 h), and even more if accompanied with freezing precipitation (38 h). The longest near-0°C event lasted 233 h and was associated with long-lasting snow and rain. By combining the field data information, large-scale weather conditions and long-term climatology, several factors that contribute to near-0°C conditions at Terrace were identified. These include the ocean's proximity, the surrounding topography, persistent cloudiness, and diabatic processes associated with melting and freezing, although local factors linked with topographic features are also important. Collectively, this study has characterized and improved our understanding of Terrace's near-0°C conditions and its associated precipitation, and these insights can be used for improved forecasting of hazardous events in the area.
Economic valuations of ecosystem services often transfer previously estimated global unit values to the geographical setting of interest. While this approach produces quick results, its reliability depends on how representative the large-scale average unit values are for the given local context. Here, we estimate the values of three ecosystem services (ES)—water filtration, nutrient cycling, and carbon sequestration—in the Grand River watershed (GRW) of southern Ontario, Canada. The watershed covers nearly 7000 km2, has a humid continental climate and a population of close to one million people. Land cover is dominated by agriculture. We compare ES valuations using locally derived (i.e., GRW-specific) unit values to valuations based on unit values from a regional database and those compiled in the global Ecosystem Services Valuation Database (ESVD). The regional database includes mean unit values from three case studies within southern Ontario and one boreal watershed in British Columbia. As expected, the regional database yields average monetary values for the three ES that are close to those obtained using the local unit values but with larger associated uncertainties. Using the ESVD, however, results in significantly higher monetary values for the ES. For water filtration, the ESVD value is more than five times higher than the regional and local estimates. We further illustrate the effect of the extent of aggregation of forested and agricultural land categories on the ES values. For example, by subdividing the forest category into three subcategories (deciduous, coniferous, and mixed forest), the estimated value of the carbon sequestration service from forested areas within the GRW decreases by 7%. Overall, our results emphasize the importance of critically assessing the origin of unit values and the land cover resolution in ES valuation, especially when ES valuation is used as a policy-guiding tool.
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 m (meters) 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 2.37×106 ha (2.37 Mha) burned annually between 2001–2019 over the ABoVE domain (2.87 Mha across all of Alaska and Canada), emitting 79.3 ± 27.96 Tg (±1 standard deviation) of carbon (C) per year, with a mean combustion rate of 3.13 ± 1.17 kg 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 datasets described here (the ABoVE Fire Emissions Database, or ABoVE-FED) can be used for local- to continental-scale applications of boreal fire science.
Abstract Accurate estimations of the precipitation phase at the surface are critical for hydrological and snowpack modelling in cold regions. Precipitation phase partitioning methods (PPMs) vary in their ability to estimate the precipitation phase at around 0°C and can significantly impact simulations of snowpack accumulation and melt. The goal of this study is to evaluate PPMs of varying complexity using high‐quality observations of precipitation phase and to assess the impact on snowpack simulations. We used meteorological data collected in Edmundston, New Brunswick, Canada, during the 2021 Saint John River Experiment on Cold Season Storms (SAJESS). These data were combined with manual observations of snow depth. Five PPMs commonly used in hydrological models were tested against observations from a laser‐optical disdrometer and a Micro Rain Radar. Most PPMs produced similar accuracy in estimating only rainfall and snowfall. Mixed precipitation was the most difficult phase to predict. The multi‐physics model Crocus was then used to simulate snowpack evolution and to diagnose model sensitivity to snowpack accumulation processes (PPM, snowfall density, and snowpack compaction). Sixteen snowpack accumulation periods, including nine warm accumulation events (average temperatures above −2°C) were observed during the study period. When considering all accumulation events, simulated changes in snow water equivalent ( SWE ) were more sensitive to the type of PPM used, whereas simulated changes in snow depth were more sensitive to uncertainties in snowfall density. Choice of PPM was the main source of model sensitivity for changes in SWE and snow depth when only considering warm events. Overall, this study highlights the impact of precipitation phase estimations on snowpack accumulation at the surface during near‐0°C conditions.
Abstract Bare soil evaporation has been studied extensively, but less is certain regarding how site‐specific features, especially the overstory tree canopy and ground covers, mediate evaporation processes. Inspired by recent advances on modelling bare soil evaporative efficiency (SEE), this study explored SEE over a range of soil substrates and ground cover types, with and without the presence of an overstory canopy in three mesic ecosystems in Canadian Rocky Mountains. A significant relationship was found between the critical soil water content and ground cover mass fractions across various ground cover types, both with and without the presence of an overstory canopy. This relationship is expected to be prevalent across various ecosystems. Moreover, a simple approach for modelling SEE of vegetated surfaces and a correction method to account for below‐canopy SEE is also proposed. The model yields satisfactory simulations, and the approach is expected to be widely applicable, given the strength that its parameters are easily acquired, and its formulations are simple and straightforward. While the model may be particularly suited to mesic ecosystems, the underlying mechanism of SEE suggests that this model can also be applied in dryer conditions. This approach will greatly improve ET parameterization in land‐surface models (LSMs) and increase our knowledge of the global water cycle and ecosystem responses under climate change impacts.
With the emergence of Machine Learning, there has been a surge in leveraging its capabilities for problem-solving across various domains. In the code clone realm, the identification of type-4 or semantic clones has emerged as a crucial yet challenging task. Researchers aim to utilize Machine Learning to tackle this challenge, often relying on the Big-CloneBench dataset. However, it's worth noting that BigCloneBench, originally not designed for semantic clone detection, presents several limitations that hinder its suitability as a comprehensive training dataset for this specific purpose. Furthermore, CLCDSA dataset suffers from a lack of reusable examples aligning with real-world software systems, rendering it inadequate for cross-language clone detection approaches. In this work, we present a comprehensive semantic clone and cross-language clone benchmark, GPTCloneBench <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> by exploiting SemanticCloneBench and OpenAI's GPT-3 model. In particular, using code fragments from SemanticCloneBench as sample inputs along with appropriate prompt engineering for GPT-3 model, we generate semantic and cross-language clones for these specific fragments and then conduct a combination of extensive manual analysis, tool-assisted filtering, functionality testing and automated validation in building the benchmark. From 79,928 clone pairs of GPT-3 output, we created a benchmark with 37,149 true semantic clone pairs, 19,288 false semantic pairs(Type-1/Type-2), and 20,770 cross-language clones across four languages (Java, C, C#, and Python). Our benchmark is 15-fold larger than SemanticCloneBench, has more functional code examples for software systems and programming language support than CLCDSA, and overcomes BigCloneBench's qualities, quantification, and language variety limitations. GPTCloneBench can be found here <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Abstract The greenhouse gas (GHG) balance of boreal peatlands in permafrost regions will be affected by climate change through disturbances such as permafrost thaw and wildfire. Although the future GHG balance of boreal peatlands including ponds is dominated by the exchange of both carbon dioxide (CO 2 ) and methane (CH 4 ), disturbance impacts on fluxes of the potent GHG nitrous oxide (N 2 O) could contribute to shifts in the net radiative balance. Here, we measured monthly (April to October) fluxes of N 2 O, CH 4 , and CO 2 from three sites located across the sporadic and discontinuous permafrost zones of western Canada. Undisturbed permafrost peat plateaus acted as N 2 O sinks (−0.025 mg N 2 O m −2 d −1 ), but N 2 O uptake was lower from burned plateaus (−0.003 mg N 2 O m −2 d −1 ) and higher following permafrost thaw in the thermokarst bogs (−0.054 mg N 2 O m −2 d −1 ). The thermokarst bogs had below‐ambient N 2 O soil gas concentrations, suggesting that denitrification consumed atmospheric N 2 O during reduction to dinitrogen. Atmospheric uptake of N 2 O in peat plateaus and thermokarst bogs increased with soil temperature and soil moisture, suggesting sensitivity of N 2 O consumption to further climate change. Four of five peatland ponds acted as N 2 O sinks (−0.018 mg N 2 O m −2 d −1 ), with no influence of thermokarst expansion. One pond with high nitrate concentrations had high N 2 O emissions (0.30 mg N 2 O m −2 d −1 ). Overall, our study suggests that the future net radiative balance of boreal peatlands will be dominated by impacts of wildfire and permafrost thaw on CH 4 and CO 2 fluxes, while the influence from N 2 O is minor.
Abstract Climate change is rapidly altering composition, structure, and functioning of the boreal biome, across North America often broadly categorized into ecoregions. The resulting complex changes in different ecoregions present a challenge for efforts to accurately simulate carbon dioxide (CO 2 ) and energy exchanges between boreal forests and the atmosphere with terrestrial ecosystem models (TEMs). Eddy covariance measurements provide valuable information for evaluating the performance of TEMs and guiding their development. Here, we compiled a boreal forest model benchmarking dataset for North America by harmonizing eddy covariance and supporting measurements from eight black spruce ( Picea mariana )-dominated, mature forest stands. The eight forest stands, located in six boreal ecoregions of North America, differ in stand characteristics, disturbance history, climate, permafrost conditions and soil properties. By compiling various data streams, the benchmarking dataset comprises data to parameterize, force, and evaluate TEMs. Specifically, it includes half-hourly, gap-filled meteorological forcing data, ancillary data essential for model parameterization, and half-hourly, gap-filled or partitioned component flux data on CO 2 (net ecosystem production, gross primary production [GPP], and ecosystem respiration [ER]) and energy (latent [LE] and sensible heat [H]) and their daily aggregates screened based on half-hourly gap-filling quality criteria. We present a case study with the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) to: (1) demonstrate the utility of our dataset to benchmark TEMs and (2) provide guidance for model development and refinement. Model skill was evaluated using several statistical metrics and further examined through the flux responses to their environmental controls. Our results suggest that CLASSIC tended to overestimate GPP and ER among all stands. Model performance regarding the energy fluxes (i.e., LE and H) varied greatly among the stands and exhibited a moderate correlation with latitude. We identified strong relationships between simulated fluxes and their environmental controls except for H, thus highlighting current strengths and limitations of CLASSIC.
The Arctic Council working group, the Conservation of Arctic Flora and Fauna (CAFF) established the Circumpolar Biodiversity Monitoring Programme (CBMP), an international network of scientists, governments, Indigenous organizations, and conservation groups working to harmonize and integrate efforts to extend and develop monitoring and assessment of the Arctic’s biodiversity. Its relevance stretches beyond the Arctic to a broad range of regional and global initiatives and agreements. This paper describes the process and approach taken in the last two decades to develop and implement the CBMP. It documents challenges encountered, lessons learnt, and solutions, and considers how it has been a model for national, regional, and global monitoring programmes; explores how it has impacted Arctic biodiversity monitoring, assessment, and policy and concludes with observations on key issues and next steps. The following are overarching prerequisites identified in the implementation of the CBMP: effective coordination, sufficient and sustained funding, improved standards and protocols, co-production of knowledge and equitable involvement of IK approaches, data management to facilitating regional analysis and comparisons, communication and outreach to raising awareness and engagement in the programme, ensuring resources to engage in international fora to ensuring programme implementation.
Abstract Boreal forests harbor as much carbon (C) as the atmosphere and significant amounts of organic nitrogen (N), the nutrient most likely to limit plant productivity in high‐latitude ecosystems. In the boreal biome, the primary disturbance is wildfire, which consumes plant biomass and soil material, emits greenhouse gasses, and influences long‐term C and N cycling. Climate warming and drying is increasing wildfire severity and frequency and is combusting more soil organic matter (SOM). Combustion of surface SOM exposes deeper older layers of accumulated soil material that previously escaped combustion during past fires, here termed legacy SOM. Postfire SOM decomposition and nutrient availability are determined by these layers, but the drivers of legacy SOM decomposition are unknown. We collected soils from plots after the largest fire year on record in the Northwest Territories, Canada, in 2014. We used radiocarbon dating to measure Δ 14 C (soil age index), soil extractions to quantify N pools and microbial biomass, and a 90‐day laboratory incubation to measure the potential rate of element mineralization and understand patterns and drivers of legacy SOM C decomposition and N availability. We discovered that bulk soil C age predicted C decomposition, where cumulatively, older soil (approximately −450.0‰) produced 230% less C during the incubation than younger soil (~0.0‰). Soil age also predicted C turnover times, with old soil turnover 10 times slower than young soil. We found respired C was younger than bulk soil C, indicating most C enters and leaves relatively quickly, while the older portion remains a stable C sink. Soil age and other indices were unrelated to N availability, but microbial biomass influenced N availability, with more microbial biomass immobilizing soil N pools. Our results stress the importance of legacy SOM as a stable C sink and highlight that soil age drives the pace and magnitude of soil C contributions to the atmosphere between wildfires.
Abstract Questions Rapid climate change in northern latitudes is expected to influence plant functional traits of the whole community (community‐level traits) through species compositional changes and/or trait plasticity, limiting our ability to anticipate climate warming impacts on northern plant communities. We explored differences in plant community composition and community‐level traits within and among four boreal peatland sites and determined whether intra‐ or interspecific variation drives community‐level traits. Location Boreal biome of western North America. Methods We collected plant community composition and functional trait data along dominant topoedaphic and/or hydrologic gradients at four peatland sites spanning the latitudinal extent of the boreal biome of western North America. We characterized variability in community composition and community‐level traits of understorey vascular and moss species both within (local‐scale) and among sites (regional‐scale). Results Against expectations, community‐level traits of vascular plant and moss species were generally consistent among sites. Furthermore, interspecific variation was more important in explaining community‐level trait variation than intraspecific variation. Within‐site variation in both community‐level traits and community composition was greater than among‐site variation, suggesting that local environmental gradients (canopy density, organic layer thickness, etc.) may be more influential in determining plant community processes than regional‐scale gradients. Conclusions Given the importance of interspecific variation to within‐site shifts in community‐level traits and greater variation of community composition within than among sites, we conclude that climate‐induced shifts in understorey community composition may not have a strong influence on community‐level traits in boreal peatlands unless local‐scale environmental gradients are substantially altered.
Results of a 2019 human biomonitoring study indicated that several parameters, including lead, cobalt, manganese, and hexachlorobenzene, were elevated in blood and urine samples in Old Crow, Yukon, in comparison to the general Canadian population. This study aims to identify possible local determinants of levels of these parameters, including consumption of locally harvested traditional foods, lifestyle factors, and demographics, in Old Crow and, for comparison, two other northern populations: communities in the Dehcho and Sahtú regions of the Northwest Territories. We ran generalized linear models to identify possible associations between individual determinants of exposure and key biomarkers, controlling for age and sex. In Old Crow, several variables were associated with elevated exposure levels of these biomarkers, including drinking untreated river water (29% higher blood manganese levels and 120% higher blood lead levels), eating caribou kidneys (22% higher blood manganese levels and 58% higher blood lead levels), and eating whitefish (28% higher blood cobalt levels). Additionally, in order to differentiate results in Old Crow from those in other northern regions and to identify trends across regions, we observed relationships between consumption of moose and caribou organs and lead and hexachlorobenzene levels in the reference populations and pooled population groups. Though levels of particular contaminants may be elevated in some traditional foods, these foods remain an important source of nutrients for members in these communities and provide other benefits, including increased physical activity through harvesting, mental health improvements, and spiritual wellness.
Abstract Despite widespread observations of climate‐change induced treeline migration and shrubification, there remains few direct measurements of transpiration and dynamics of evaporative partitioning in northern climates. Here, we present eddy covariance and sap flow data at a low elevation boreal white spruce forest and a mid‐elevation shrub taiga comprised of tall willow ( Salix spp. ) and birch ( Betula spp. ) in a subarctic, alpine catchment in Yukon Territory, Canada over two hydrologically distinct years. Specific research questions addressed were: (1) How do contributions of T to ET vary between sites and years? and (2) What are the primary meteorological, phenological, and soil moisture controls and limits on ET and T across vegetation covers? In the mid‐growing season, mean T rates were greater at the dense shrub site (2.0 ± 0.75 mm d −1 ) than the forest (1.47 ± 0.52 mm d −1 ). During this time, T:ET was lower at the forest (0.48) than at the tall, dense shrub site (0.80). Of the 2 years, 2020 was considerably wetter and cooler than 2019 during the growing season. At the shrub site, during the mid‐growing season (July 1‐Aug 15), T dropped considerably in 2020 (−26%), as T was suppressed during the short, wet growing season. In contrast, T at the forest was only moderately suppressed (−3%) between years in this same period. Evapotranspiration was more strongly controlled by air temperature during the early and late season at the forest, while ET at the shrub site was more sensitive to warmer temperatures in the mid‐growing season. Distinct differences in sap flux densities, sensitivities to environmental drivers, and stomatal resistances existed between shrub species. Results suggest that warming temperatures, increases in growing season length, and increased rainfall will cause differences in evaporative response and partitioning over complex, heterogenous alpine watersheds.
Abstract In lakes, the rates of gross primary production (GPP), ecosystem respiration (R), and net ecosystem production (NEP) are often controlled by resource availability. Herein, we explore how catchment vs. within lake predictors of metabolism compare using data from 16 lakes spanning 39°N to 64°N, a range of inflowing streams, and trophic status. For each lake, we combined stream loads of dissolved organic carbon (DOC), total nitrogen (TN), and total phosphorus (TP) with lake DOC, TN, and TP concentrations and high frequency in situ monitoring of dissolved oxygen. We found that stream load stoichiometry indicated lake stoichiometry for C : N and C : P ( r 2 = 0.74 and r 2 = 0.84, respectively), but not for N : P ( r 2 = 0.04). As we found a strong positive correlation between TN and TP, we only used TP in our statistical models. For the catchment model, GPP and R were best predicted by DOC load, TP load, and load N : P ( R 2 = 0.85 and R 2 = 0.82, respectively). For the lake model, GPP and R were best predicted by TP concentrations ( R 2 = 0.86 and R 2 = 0.67, respectively). The inclusion of N : P in the catchment model, but not the lake model, suggests that both N and P regulate metabolism and that organisms may be responding more strongly to catchment inputs than lake resources. Our models predicted NEP poorly, though it is unclear why. Overall, our work stresses the importance of characterizing lake catchment loads to predict metabolic rates, a result that may be particularly important in catchments experiencing changing hydrologic regimes related to global environmental change.
Abstract Improving the calculation of land‐atmosphere fluxes of heat and water vapor in mountain terrain requires better resolution of thermally driven diurnal winds (i.e., valley, slope winds) due to differential heating by terrain and radiative fluxes. In this study, the Weather Research and Forecasting model is used to simulate flow in large‐eddy simulation (LES) mode over the complex terrain of the Fortress Mountain and Marmot Creek research basins, Kananaskis Valley, Canadian Rockies, Alberta in mid‐summer. The model was used to examine the temporal and spatial evolution of local winds and near‐surface boundary layer processes with variability in topography and elevation. Numerically resolving complex terrain wind flow effects require smaller grid cell size. However, the use of terrain‐following coordinates in most numerical weather prediction models results in large numerical errors when flow over steep terrain is simulated. These errors propagate through the domain and can result in numerical instability. To avoid this issue when simulating flow over steep terrain a local smoothing approach was used, where smoothing is applied only where slope exceeds some predetermined threshold. LES results from local smoothing were compared with a mesoscale model and LES with global smoothing. Simulations are evaluated using sounding data and meteorological stations. The differences in flow patterns and reversals in two mountain basins suggest that valley geometry and volume is relevant to the break up of inversion layers, removal of cold‐air pools, and strength of thermally driven winds.
Abstract Agriculture is directly related to food security as it determines the global food supply. Research in agriculture to predict crop productivity and losses helps avoid high food demand with little supply and price spikes. Here, we review ten crop models and one intercomparison project used for simulating crop growth and productivity under various impacts from soil–crop–atmosphere interactions. The review outlines food security and production assessments using numerical models for maize, wheat, and rice production. A summary of reviewed studies shows the following: (1) model ensembles provide smaller modeling errors compared to single models, (2) single models show better results when coupled with other types of models, (3) the ten reviewed crop models had improvements over the years and can accurately predict crop growth and yield for most of the locations, management conditions, and genotypes tested, (4) APSIM and DSSAT are fast and reliable in assessing broader output variables, (5) AquaCrop is indicated to investigate water footprint, quality and use efficiency in rainfed and irrigated systems, (6) all models assess nitrogen dynamics and use efficiency efficiently, excluding AquaCrop and WOFOST, (7) JULES specifies in evaluating food security vulnerability, (8) ORYZA is the main crop model used to evaluate paddy rice production, (9) grain filling is usually assessed with APSIM, DAISY, and DSSAT, and (10) the ten crop models can be used as tools to evaluate food production, availability, and security.
Abstract Vegetation structure is considered one of the most important factors shaping the spatial variation of snow accumulation under forest canopies. However, fine scale relationships between canopy density, snow interception, wind redistribution and sub‐canopy accumulation are poorly understood and difficult to observe, and their influence governing stand‐scale snow distributions that determine snow covered area depletion during melt is largely unknown. In this study, fine‐scale observations of forest structure and sub‐canopy snow accumulation were analysed over two mid‐winter snowfalls to a sub‐alpine forest in Marmot Creek Research Basin, Canadian Rockies, Alberta, to identify the impact of snow‐canopy interactions on spatial patterns of sub‐canopy snow accumulation. High spatial resolution (5 and 25 cm) snow accumulation estimates and canopy structure metrics were calculated from the combination of repeated UAV‐lidar observations with snow and photographic surveys, utilizing novel resampling methods including voxel ray sampling of lidar (VoxRS) to improve metric robustness and reduce bias. Over 50% of the spatial variance in forest snow accumulation was found at length scales less than 2 m, supporting the role of local scale canopy structure in governing variation in subcanopy snow accumulation. Additionally, subcanopy snow accumulation showed significant angular spread in relationships with overhead canopy structure; the vertical asymmetry coinciding with local windflow directions during snowfall. Detailed angular analysis showed nontrivial snow‐vegetation relationships that likely reflect multiple snowfall‐vegetation processes, including unloading and entrainment of intercepted snowfall during wind gusts and funnelling of entrained particles by downwind vegetation. These fine‐scale findings suggest several emergent processes which may influence snow accumulation at the scale of forest stands, with novel considerations for representing snow water equivalent distributions under dense evergreen canopies under varying environmental and canopy conditions. Similar studies over a broad range of conditions and forests will help refine and generalize the effects observed here for further snow hydrology and forestry applications.
Abstract Motivated by the limited understanding of future changes in mesoscale convective systems (MCSs), we investigated characteristics of warm‐season (June–August) MCSs in the central United States based on high‐resolution convection‐permitting Weather Research and Forecasting simulations. We examined two 15‐year simulations, which include current simulations (2004–2018) forced by European Centre for Medium‐Range Weather Forecasts Reanalysis version 5 (ERA5) and future simulations (2086–2100) forced by perturbed ERA5 (i.e., ERA5 plus climate change signal derived from 28 Coupled Intercomparison Projected Phase 6 models under the Shared Socioeconomic Pathway–Representative Concentration Pathway 8.5 emission scenario). The initiations and longevities of MCSs were determined using the object‐tracking algorithm MODE‐Time Domain (MTD) from observation, current simulations (ERA), and future simulations (pseudo‐global warming, PGW). Objects identified by MODE‐Time Domain were divided into short‐/long‐lived (based on 75th percentiles of longevity) and daytime (initiated during 0000–1100 UTC)/nighttime (initiated during 1200–2300 UTC). We found that ERA and observation have comparable occurrences of MCSs. MCSs in PGW are associated with intensified rain rates in New Mexico, Colorado, and Kansas and lower rain rates in Texas, Louisiana, and Arkansas than in ERA. Moreover, the statistical analysis based on 15 parameters before MCSs initiation indicates that short‐lived MCSs in PGW are characterized by prominent changes in precipitable water (PW) and the most unstable convective available potential energy. We also found that long‐lived MCSs in PGW are associated with prominent changes in PW, unstable convective available potential energy, and isentropic potential vorticity at 345 K. According to the statistical results, PW is the most important variable in determining the longevity of MCSs and in understanding future changes.
This study analyzed lake surface temperature (LST) trends and spatial distribution across 535 predominantly small to medium lakes across the North Slave Region (NSR) of the Northwest Territories (NWT), Canada. The NWT is characterized by a vast number of lakes covering a significant portion of its spatial extent. However, there is limited knowledge of how LST responds to climate warming in this region. To address this, LST was analyzed in four distinct periods: open water season (OW), ice cover season (IC), and the transitional months of May (TM) and October (TO). LSTs from 1984 to 2021 were retrieved from a lake-specific satellite-derived LST dataset (North Slave LST). LST trend distribution and relationships were analyzed using the Mann-Kendall test and a multilinear regression model. The analysis revealed an overall increase in LST, with average rates (max) of 0.03 °C/year (0.05 °C/year), 0.03 °C/year (0.06 °C/year), and 0.13 °C/year (0.27 °C/year) for OW, TM, and TO, respectively accross study lakes. A faster rate of change was observed in October compared to other periods. Results indicated significant increases in LST for 411 lakes (77%) during OW, 418 lakes (78%) during TO, and 490 lakes (92%) during TM. The spatial distribution and magnitude of LST change were primarily influenced by geographical than morphometric properties. The analysis demonstrated later freeze-up (0.20 day/year) and earlier break-up (−0.17 day/year) of lake ice across the NSR.
The North American prairie region is known for its poorly defined drainage system with numerous surface depressions that lead to variable contributing areas for streamflow generation. Current approaches of representing surface depressions are either simplistic or computationally demanding. In this study, a variable contributing area algorithm is implemented in the HYdrological Predictions for the Environment (HYPE) model and evaluated in the Canadian prairies. HYPE's local lake module is replaced with a Hysteretic Depressional Storage (HDS) algorithm to estimate the variable contributing fractions of subbasins. The modified model shows significant improvements in simulating the streamflows of two prairie basins in Saskatchewan, Canada. The modified model can replicate the hysteretic relationships between the water volume and contributing area of the basins. With the inclusion of the HDS algorithm in HYPE, the global HYPE modelling community can now simulate an important hydrological phenomenon, previously unavailable in the model.
Abstract The proper numerical representation of physical processes in mechanistic hydrological models is essential to produce robust predictions. A common problem with numerical schemes in hydrological models is that multiple concurrent fluxes are calculated sequentially. Although the importance of errors introduced by inappropriate numerical schemes is well recognized in the literature, many hydrological models calculate concurrent fluxes sequentially. Here, two versions of the HYPE model are used to investigate the limitations of sequential calculations. A fourth order Gear‐Nordsieck solution of the continuous state‐space formulation of HYPE (I‐HYPE) is developed to provide a robust solution, and a fixed‐step implicit Euler scheme (IE‐HYPE) is implemented to provide a computationally efficient and robust approximation of the I‐HYPE simulations. In contrast to I‐HYPE, results show that the original HYPE and the sequential calculation implemented in the continuous state‐space formulation of HYPE (SQ‐HYPE) typically simulate no interflow when soil moisture levels exceed the field capacity. The discrepancy between SQ‐HYPE and I‐HYPE grows with the size of the computation time step, and this implies a compromised representation of flow paths by sequential schemes. IE‐HYPE provides responses comparable with I‐HYPE for both daily and hourly time steps. IE‐HYPE and SQ‐HYPE are compared in terms of their groundwater representation, parameter identifiability, and predictive skills for two catchments. The sequential models have larger groundwater contributions to flow than IE‐HYPE because the splitting errors in SQ‐HYPE limit the interflow flux. IE‐HYPE estimates of the groundwater flux are more consistent with literature values of groundwater contributions to flow for the basins studied.
Understanding lead exposure pathways is a priority because of its ubiquitous presence in the environment as well as the potential health risks. We aimed to identify potential lead sources and pathways of lead exposure, including long-range transport, and the magnitude of exposure in Arctic and subarctic communities. A scoping review strategy and screening approach was used to search literature from January 2000 to December 2020. A total of 228 academic and grey literature references were synthesised. The majority of these studies (54%) were from Canada. Indigenous people in Arctic and subarctic communities in Canada had higher levels of lead than the rest of Canada. The majority of studies in all Arctic countries reported at least some individuals above the level of concern. Lead levels were influenced by a number of factors including using lead ammunition to harvest traditional food and living in close proximity to mines. Lead levels in water, soil, and sediment were generally low. Literature showed the possibility of long-range transport via migratory birds. Household lead sources included lead-based paint, dust, or tap water. This literature review will help to inform management strategies for communities, researchers, and governments, with the aim of decreasing lead exposure in northern regions.
Abstract Rapid rates of high latitude warming over the past century have led to widespread research on permafrost thaw and its consequences. Studies from lowland plains environments in the discontinuous permafrost zone have highlighted extensive areal loss of permafrost, largely through observations of the collapse of forested permafrost plateaus into wetland features. These low-relief environments tend to have poor drainage, which initiates runaway thaw as increased soil moisture amplifies permafrost degradation. In contrast to lowland plains, the Taiga Shield landscape features a network of lakes, wetlands, soil-filled lowlands, and forests interspersed with bedrock outcrops. With the exposed (or near-surface) bedrock in this landscape, this region may have greater terrain stability under a warming climate than the lowland plains. The hydrological complexity of the Taiga Shield may also contribute to more varied trajectories for permafrost in this landscape. We investigated land cover change and implications for permafrost in an area that typifies the Taiga Shield. We took intensive ground-based measurements of soil organic layer (SOL) thickness and frost table depth to characterize different land cover types. Archival aerial photographs and recent satellite imagery from the area allowed us to assess land cover change between 1972 and 2017. Associations between permafrost, SOL, and land cover allowed us to use land cover as a proxy for change in permafrost extent. Our results suggest that both aggradation and degradation of permafrost has occurred within the Taiga Shield landscape over this 45 year period, but interestingly we found evidence for a net increase in permafrost extent. Permafrost aggradation in this landscape seems to be driven by a combination of local hydrology and climatic triggers that lead to colder, drier soil conditions that are favourable for the development of permafrost. This study highlights the importance of considering diverse and heterogenous landscapes in the study of changing permafrost ecosystems.
Abstract The objective of this study is to identify the optimal spatial distribution of Best Management Practices (BMPs) to reduce total phosphorus (TP) runoff from agricultural land in the largest Canadian watershed draining into Lake Erie, the Great Lake most vulnerable to eutrophication. BMP measures include reduced fertilizer application, cover crops, buffer strips, and the restoration of wetlands. Environmental SWAT model results feed into a spatial optimization procedure using two separate objective functions to distinguish between public BMP program implementation costs (PIC) on the one hand and farmers’ private pollution abatement costs (PAC) on the other hand. The latter account for the opportunity costs of land retirement and changing land productivity. PAC are initially lower than PIC but exceed the latter after 30% of the annual TP baseline load is eliminated. This suggests that under optimal conditions existing grant and incentive payments cover the economic costs farmers face up to a maximum of 30% of the baseline load reduction. Imposing further reductions of up to 40% results in a cost to farmers of almost $52 million per year. This is 45% higher than the optimal solution based on PIC and therefore not deemed incentive‐compatible under the watershed's existing cost‐sharing scheme.
This paper describes the development of a transdisciplinary knowledge network dedicated to supporting agroecology knowledge exchange and capacity building that is particularly focused on the sustainable use and conservation of agrobiodiversity. The network—Fostering Effective Agroecology for Sustainable Transformation, or FEAST—includes nodes in Brazil, Cuba, Mexico, and Canada’s Northwest Territories and has been engaged in Participatory Action Research activities since 2015. This paper examines the development of the network over time, including a workshop held in 2019 in and around Curitiba, Brazil, and reflects on the outcomes of knowledge exchange activities. We discuss how the development of the FEAST network has informed participants’ local practice and their sense of belonging to a larger-scale, international movement for agroecology, agrobiodiversity conservation, and food system sustainability.
Satellites are designed to monitor geospatial data over large areas at a catchment scale. However, most of satellite validation works are conducted at local point scales with a lack of spatial representativeness. Although upscaling them with a spatial average of several point data collected in the field, it is almost impossible to reorganize backscattering responses at pixel scales. Considering the influence of soil storage on watershed streamflow, we thus suggested watershed-scale hydrological validation. In addition, to overcome the limitations of backscattering models that are widely used for C-band Synthetic Aperture Radar (SAR) soil moisture but applied to bare soils only, in this study, RADARSAT-2 soil moisture was stochastically retrieved to correct vegetation effects arising from agricultural lands. Roughness-corrected soil moisture retrievals were assessed at various spatial scales over the Brightwater Creek basin (land cover: crop lands, gross drainage area: 1540 km2) in Saskatchewan, Canada. At the point scale, local station data showed that the Root Mean Square Errors (RMSEs), Unbiased RMSEs (ubRMSEs) and biases of Radarsat-2 were 0.06~0.09 m3/m3, 0.04~0.08 m3/m3 and 0.01~0.05 m3/m3, respectively, while 1 km Soil Moisture Active Passive (SMAP) showed underestimation at RMSEs of 0.1~0.22 m3/m3 and biases of −0.036~−0.2080 m3/m3. Although SMAP soil moisture better distinguished the contributing area at the catchment scale, Radarsat-2 soil moisture showed a better discharge hysteresis. A reliable estimation of the soil storage dynamics is more important for discharge forecasting than a static classification of contributing and noncontributing areas.
Abstract. The carbon cycle in Arctic–boreal regions (ABRs) is an important component of the planetary carbon balance, with growing concerns about the consequences of ABR warming for the global climate system. The greatest uncertainty in annual carbon dioxide (CO2) budgets exists during winter, 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 winter CO2 fluxes in ABRs over a latitudinal gradient (45∘ 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 g C m2 d−1. To assess the dominant environmental controls governing CO2 fluxes, a random forest machine learning approach was used. We identified soil temperature as the main control of winter CO2 fluxes with 68 % of relative model importance, except when soil liquid water occurred during 0 ∘C curtain conditions (i.e., Tsoil≈0 ∘C and liquid water coexist 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 in fully frozen soils (RMSE=0.024 gCm-2d-1; 70.3 % of mean FCO2) and soils around the freezing point (RMSE=0.286 gCm-2d-1; 112.4 % of mean FCO2). FCO2 increases more rapidly with Tsoil around the freezing point than at Tsoil<5 ∘C. In zero-curtain conditions, the strongest regression was found with soil liquid water content (RMSE=0.137 gCm-2d-1; 49.1 % of mean FCO2). This study shows the role of several variables in the spatio-temporal variability in CO2 fluxes in ABRs during winter and highlights that the complex vegetation–snow–soil interactions in northern environments must be considered when studying what drives the spatial variability in soil carbon emissions during winter.
Abstract. The amount and the phase of cold-season precipitation accumulating in the upper Saint John River (SJR) basin are critical factors in determining spring runoff, ice jams, and flooding. To study the impact of winter and spring storms on the snowpack in the upper SJR basin, the Saint John River Experiment on Cold Season Storms (SAJESS) was conducted during winter–spring 2020–2021. Here, we provide an overview of the SAJESS study area, field campaign, and data collected. The upper SJR basin represents 41 % of the entire SJR watershed and encompasses parts of the US state of Maine and the Canadian provinces of Quebec and New Brunswick. In early December 2020, meteorological instruments were co-located with an Environment and Climate Change Canada station near Edmundston, New Brunswick. This included a separate weather station for measuring standard meteorological variables, an optical disdrometer, and a micro rain radar. This instrumentation was augmented during an intensive observation period that also included upper-air soundings, surface weather observations, a multi-angle snowflake camera, and macrophotography of solid hydrometeors throughout March and April 2021. During the study, the region experienced a lower-than-average snowpack that peaked at ∼ 65 cm, with a total of 287 mm of precipitation (liquid-equivalent) falling between December 2020 and April 2021, a 21 % lower amount of precipitation than the climatological normal. Observers were present for 13 storms during which they conducted 183 h of precipitation observations and took more than 4000 images of hydrometeors. The inclusion of local volunteers and schools provided an additional 1700 measurements of precipitation amounts across the area. The resulting datasets are publicly available from the Federated Research Data Repository at https://doi.org/10.20383/103.0591 (Thompson et al., 2023). We also include a synopsis of the data management plan and a brief assessment of the rewards and challenges of conducting the field campaign and utilizing community volunteers for citizen science.
Abstract. The energy and water vapor exchange between the land surface and atmospheric boundary layer plays a critical role in regional climate simulations. This paper implemented a hybrid data assimilation and machine learning framework (DA-ML method) into the Weather Research and Forecasting (WRF) model to optimize surface soil and vegetation conditions. The hybrid method can integrate remotely sensed leaf area index (LAI), multi-source soil moisture (SM) observations, and land surface models (LSMs) to accurately describe regional climate and land–atmosphere interactions. The performance of the hybrid method on the regional climate was evaluated in the Heihe River basin (HRB), the second-largest endorheic river basin in Northwest China. The results show that the estimated sensible (H) and latent heat (LE) fluxes from the WRF (DA-ML) model agree well with the large aperture scintillometer (LAS) observations. Compared to the WRF (open loop – OL), the WRF (DA-ML) model improved the estimation of evapotranspiration (ET) and generated a spatial distribution consistent with the ML-based watershed ET (ETMap). The proposed WRF (DA-ML) method effectively reduces air warming and drying biases in simulations, particularly in the oasis region. The estimated air temperature and specific humidity from WRF (DA-ML) agree well with the observations. In addition, this method can simulate more realistic oasis–desert boundaries, including wetting and cooling effects and wind shield effects within the oasis. The oasis–desert interactions can transfer water vapor to the surrounding desert in the lower atmosphere. In contrast, the dry and hot air over the desert is transferred to the oasis from the upper atmosphere. The results show that the integration of LAI and SM will induce water vapor intensification and promote precipitation in the upstream of the HRB, particularly on windward slopes. In general, the proposed WRF (DA-ML) model can improve climate modeling by implementing detailed land characterization information in basins with complex underlying surfaces.
Abstract Shifting precipitation patterns, a warming climate, changing snow dynamics and retreating glaciers are occurring simultaneously in glacierized mountain headwaters. To predict future hydrological behavior in an exemplar glacierized basin, a spatially distributed, physically based cold regions process hydrological model including on and off‐glacier process representations was applied to the Peyto Glacier Research Basin in the Canadian Rockies. The model was forced with bias‐corrected outputs from a high‐resolution Weather and Research Forecasting (WRF‐PGW) atmospheric simulation for 2000–2015, and under pseudo‐global warming for 2085–2100 under a business‐as‐usual climate change scenario. The simulations show that the end‐of‐century increase in precipitation nearly compensates for the decreased ice melt associated with almost complete deglaciation, resulting in a decrease in annual streamflow of 7%. However, the timing of streamflow advances drastically, with peak flow shifting from July to June, and August streamflow dropping by 68%. To examine the sensitivity of future hydrology to possible future drainage basin biophysical attributes, the end‐of‐century simulations were run under a range of initial conditions and parameters and showed the highest sensitivity to initial ice volume and surface water storage capacity. This comprehensive examination suggests that hydrological compensation between declining icemelt and increasing rainfall and snowmelt runoff as well as between deglaciation and increasing basin depressional storage capacity play important roles in determining future streamflow in a rapidly deglaciating high‐mountain environment. Conversely, afforestation and soil development had relatively smaller impacts on future hydrology.

DOI bib
Upscaling Wetland Methane Emissions From the FLUXNET‐CH4 Eddy Covariance Network (UpCH4 v1.0): Model Development, Network Assessment, and Budget Comparison
Gavin McNicol, Etienne Fluet‐Chouinard, Zutao Ouyang, Sara Knox, Zhen Zhang, Tuula Aalto, Sheel Bansal, Kuang‐Yu Chang, Min Chen, Kyle Delwiche, Sarah Féron, Mathias Goeckede, Jinxun Liu, Avni Malhotra, Joe R. Melton, W. J. Riley, Rodrigo Vargas, Kunxiaojia Yuan, Qing Ying, Qing Zhu, Pavel Alekseychik, Mika Aurela, David P. Billesbach, David I. Campbell, Jiquan Chen, Housen Chu, Ankur R. Desai, E. S. Euskirchen, Jordan P. Goodrich, Timothy J. Griffis, Manuel Helbig, Takashi Hirano, Hiroki Iwata, Gerald Jurasinski, John S. King, Franziska Koebsch, Randall K. Kolka, Ken W. Krauss, Annalea Lohila, Ivan Mammarella, Mats E Nilson, Asko Noormets, Walter C. Oechel, Matthias Peichl, Torsten Sachs, Ayaka Sakabe, Christopher Schulze, Oliver Sonnentag, Ryan C. Sullivan, Eeva‐Stiina Tuittila, Masahito Ueyama, Timo Vesala, Eric J. Ward, Christian Wille, Guan Xhuan Wong, Donatella Zona, L. Windham‐Myers, Benjamin Poulter, Robert B. Jackson
AGU Advances, Volume 4, Issue 5

Abstract Wetlands are responsible for 20%–31% of global methane (CH 4 ) emissions and account for a large source of uncertainty in the global CH 4 budget. Data‐driven upscaling of CH 4 fluxes from eddy covariance measurements can provide new and independent bottom‐up estimates of wetland CH 4 emissions. Here, we develop a six‐predictor random forest upscaling model (UpCH4), trained on 119 site‐years of eddy covariance CH 4 flux data from 43 freshwater wetland sites in the FLUXNET‐CH4 Community Product. Network patterns in site‐level annual means and mean seasonal cycles of CH 4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash‐Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH 4 emissions of 146 ± 43 TgCH 4 y −1 for 2001–2018 which agrees closely with current bottom‐up land surface models (102–181 TgCH 4 y −1 ) and overlaps with top‐down atmospheric inversion models (155–200 TgCH 4 y −1 ). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH 4 fluxes has the potential to produce realistic extra‐tropical wetland CH 4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid‐to‐arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC ( https://doi.org/10.3334/ORNLDAAC/2253 ).
Abstract Climate change and permafrost thaw may impact the mobilization of terrestrial dissolved organic carbon (DOC), mercury (Hg), and neurotoxic methylmercury (MeHg) into aquatic ecosystems; thus, understanding processes that control analyte export in northern catchments is needed. We monitored water chemistry for 3 years (2019–2021) at a peatland catchment (Scotty Creek) and a mixed catchment (Smith Creek) in the Dehcho (Northwest Territories), within the discontinuous permafrost zone of boreal western Canada. The peatland catchment had higher DOC and dissolved MeHg, but lower total Hg concentrations (mean ± standard deviation; 19 ± 2.6 mg DOC L −1 ; 0.08 ± 0.04 ng DMeHg L −1 ; 1.1 ± 0.3 ng THg L −1 ) than the mixed catchment (12 ± 4.4 mg DOC L −1 ; 0.05 ± 0.01 ng DMeHg L −1 ; 3.1 ± 2.2 ng THg L −1 ). Analyte concentrations increased with discharge at the mixed catchment, suggesting transport limitation and the flushing of near‐surface, organic‐rich flow paths during wet periods. In contrast, analyte concentrations in the peatland catchment were not primarily associated with discharge. MeHg concentrations, MeHg:THg, and MeHg:DOC increased with water temperature, suggesting enhanced Hg methylation during warmer periods. Mean open water season DOC and total MeHg yields were greater and more variable from the peatland than the mixed catchment (1.1–6.6 vs. 1.4–2.4 g DOC m −2 ; 5.2–36 vs. 6.1–10 ng MeHg m −2 ). Crucial storage thresholds controlling runoff generation likely drove greater inter‐annual variability in analyte yields from the peatland catchment. Our results suggest climate change may influence the production and transport of MeHg from boreal‐Arctic catchments as temperatures increase, peatlands thaw, and runoff generation is altered.
Abstract Recognizing that limited literature exists regarding food programs in northern Indigenous communities within Canada, this study draws on a range of sources to map and characterize existing food programs in these contexts. A secondary aim assessed the extent to which traditional food was offered through the identified programs, which has implications for cultural appropriateness and, in turn, food sovereignty. Peer‐reviewed articles and grey literature published between 2000 and 2022 were examined. Frameworks to guide methodologies include PRISMA‐ScR, Arksey and O'Malley, Levac et al., and Godin et al.'s grey literature search strategy. Inclusion criteria were food programs located north of the Northern Boundary Line, programs providing food access, and programs serving Indigenous communities. Data were synthesized based on program type, target population, and whether the program offered or incorporated traditional food. The review yielded 30 records wherein 46 unique food programs were identified and characterized into eight distinct program types. Program success of the identified programs depended on funding availability and continuity, staff/volunteer availability and retention (including program champions), and types of policies that impact traditional food provision. Findings are valuable to organizations and communities interested in using food programs to support Indigenous food security and sovereignty efforts.

2022

Improving understanding of how water use efficiency (WUE), evapotranspiration (ET), and gross primary productivity (GPP) (CO2 exchange) vary across agricultural systems can help farmers better prepare for an uncertain future due to climate change by assessing water requirements for a crop as a function of current environmental conditions. This study: (a) quantified field-scale plant–water–carbon dynamics for silage maize (Zea mays L.) and alfalfa (Medicago sativa L.) crops – two dominant forage crops in southern Ontario, Canada; and (b) identified differences in plant carbon–water dynamics between these two crops, relating these differences to vegetation-driven ecosystem controls. Climate and soil properties were similar between the two study sites, and water availability was not limiting, suggesting that the overall temporal differences in carbon–water relations were driven by vegetation differences, mainly crop choice and management practices. Alfalfa had greater seasonal GPP, ET, and WUE than maize, due to a longer growing season. Differences in daily WUE between maize and alfalfa were driven by differences in GPP rather than ET. Multiple harvests reduced leaf-aging effects and promoted periods of rapid growth in alfalfa. In contrast, late seedling emergence and self-shading reduced GPP in maize. Under a warmer future climate, crop selection (i.e., perennial vs. annual), harvest regimes, and changes in growing season length should be considered when trying to manage for increased WUE. However, longer duration studies to validate these results are required to better address the impacts of climatic variability—especially antecedent conditions—to better inform future crop choices within a climate change context.
Abstract In the Canadian prairies, eutrophication is a widespread issue, with agriculture representing a major anthropogenic nutrient source in many watersheds. However, efforts to mitigate agricultural nutrient export are challenged by the lack of coordinated monitoring programs and the unique hydrological characteristics of the prairies, notably, the dominance of snowmelt in both water flows and nutrient loads, variable runoff, variable contributing area and the issues of understanding how scale affects nutrient concentrations and prevalence of dissolved nutrient transport (over total nutrients). Efforts are being made to integrate these characteristics in process-based water quality models, but the models are often complex and are not yet ready for use by watershed managers for prioritizing implementation of beneficial management practices (BMPs). In this study, a screening and scoping approach based on nutrient export coefficient modeling was used to prioritize BMPs for the 55,700 km2 Qu’Appelle Watershed, Saskatchewan. By integrating land use information, in-stream monitoring data, stakeholder input and nutrient export coefficient modeling, the study assessed potential efficiencies of six BMPs involving fertilizer, manure, grazing, crop and wetland management in nutrient load reductions for nine tributaries of the watershed. Uncertainty around the effectiveness of the BMPs was assessed. Field-level export coefficients were adjusted with nutrient delivery ratios for estimating watershed-level exports. Of the BMPs examined, in general, wetland restoration had the greatest potential to reduce both nitrogen and phosphorus loads in most tributaries, followed by fertilizer management. The importance of wetland restoration was supported by positive, significant, linear correlations between nutrient delivery ratios and drainage intensity in the tributaries (nitrogen: R 2 = 0.67; phosphorus: R 2 = 0.82). Notably, the relative ranking of BMP efficiencies varied with tributaries, as a result of differing landscape characteristics, land uses and nutrient inputs. In conclusion, the approach developed here acknowledges uncertainty, but provides a means to guide management decisions within the context of an adaptive management approach, where BMP implementation is partnered with monitoring and assessment to revise ongoing plans and ensures selected practices are meeting goals for nutrient abatement.
Best Management Practices (BMPs) incentive programs have been introduced to protect agricultural land and reduce nutrient runoff in watersheds. However, their voluntary nature has not led to the expected high participation rates. We examine influencing factors and underlying drivers that are associated with BMP adoption and farmer preferences for specific BMPs. Data are collected through an online survey in Ontario, Canada in 2019. A binary logit model is estimated to explain current participation in BMP schemes and a multinomial logit model to predict preferences for future BMP uptake. Results show that a mix of farmer and farm characteristics and environmental attitudes explain both current participation in BMP schemes and the likelihood of adopting a future BMP. Farmers tend to endorse a BMP if they currently implement that BMP. The findings furthermore suggest that increasing farmers' environmental awareness and sharing positive BMP experiences with other farmers may help expand future BMP adoption in Ontario. • We examine underlying drivers of farmer BMP adoption and preferences in Canada. • We inspect both current participation and future choices using logit models. • Farmers fairly concerned about water pollution are more likely to adopt BMPs. • Farmers tend to endorse a BMP if they currently implement that BMP. • Demographic characteristics are not significant predictors of future adoption.
The wildfire regime in Canada’s boreal region is changing; extended fire seasons are characterized by more frequent large fires (≥200 ha) burning greater areas of land, whilst climate-mediated drying is increasing the vulnerability of peatlands to deep burning. Proactive management strategies, such as fuel modification treatments, are necessary to reduce fire danger at the wildland-human interface (WHI). Novel approaches to fuel management are especially needed in peatlands where deep smouldering combustion is a challenge to suppression efforts and releases harmful emissions. Here, we integrate surface compression within conventional stand treatments to examine the potential for reducing smouldering of near-surface moss and peat. A linear model (adj. R2=0.62, p=2.2e-16) revealed that ground cover (F(2,101)=60.97, p<0.001) and compression (F(1,101)=56.46, p<0.001) had the greatest effects on smouldering potential, while stand treatment did not have a significant effect (F(3,101)=0.44, p=0.727). On average, compressed Sphagnum and feather moss plots showed 57.1% and 58.7% lower smouldering potential, respectively, when compared to uncompressed analogs. While practical evaluation is warranted to better understand the evolving effectiveness of this strategy, these findings demonstrate that a compression treatment can be successfully incorporated within both managed and unmanaged peatlands to reduce fire danger at the WHI.
Indigenous households are 90 times more likely to be without running water than non-Indigenous households in Canada. Current primary indicators of water quality and security for Indigenous Peoples are based on federal boil water advisories, which do not disaggregate at household levels to identify who is most at risk within or between communities. A mixed methods approach was used to assess the level of water insecurity and perceptions of water access by gender and age for a sample of households in Six Nations of the Grand River First Nations in Ontario, Canada. A household survey captured water security using the Household Water InSecurity Experiences (HWISE) scale and Likert-type responses to perceptions of water access, contextualized using semi-structured individual and group interviews. From 2019 to 2020, 66 households participated in the survey, 18 individuals participated in semi-structured individual interviews, and 7 individuals participated in 3 semi-structured group interviews. The survey sample demonstrated high levels of household water insecurity (57.5%, n = 38). Interviews revealed that women were more dissatisfied with their drinking water situations due to quality, source, and cost, though they shared water sharing as a coping strategy. Women faced more physical and mental barriers accessing water for their households, due to their roles as caretakers of their family and knowledge protectors for their communities. Generational divides were found in interviews about what qualified as "good water," with older participants understanding it as relating to traditional water sourcing, and younger participants wanting clean, accessible tap water. Taken together, the participants demonstrated a frustration with the sub-standard drinking water on reserve.
In Canada, Indigenous populations have an increased prevalence of psychiatric disorders and distress. Mental health mobile applications can provide effective, easy-to-access, and low-cost support. Examining grey literature and academic sources, this review found three mobile apps that support mental health for Indigenous communities in Canada. Implications and future directions are discussed.
Advances in scientific domains are led to an increase in the complexity of the experiments. To address this growing complexity, scientists from different domains require to work collaboratively. Scientific Workflow Management Systems (SWfMSs) are popular tools for data-intensive experiments. To the best of our knowledge, very few of the existing SWfMSs support collaboration, and it is not efficient in many cases. Researchers share a single version of the workflow in existing collaborative data analysis systems, which increases the chance of interference as the number of collaborators grows. Moreover, for effective collaboration, contributors require a clear view of the project's status, the information that existing SWfMSs do not provide. Another significant problem is most scientists are not capable of adding collaborative tools to existing SWfMSs, and they need software engineers to take on this responsibility. Even for software engineers such tasks could be challenging and time consuming. In this paper, we attempted to address this crucial issue in scientific workflow composition and doing so in a collaborative setting. Hence, we propose a tool to facilitate collaborative workflow composition. This tool provides branching and versioning, which are standard version control system features to allow multiple researchers to contribute to the project asynchronously. We also suggest some visualizations and a variety of reports to increase group awareness and help the scientists to realize the project's status and issues. As a proof of concept, we developed an API to capture the provenance data and provide collaborative tools. This API is developed as an example for software engineers to help them understand how to integrate collaborative tools into any SWfMS. We collect provenance information during workflow composition and then employ it to track workflow versions using the proposed collaborative tool. Prior to implementing the visualizations, we surveyed to discover how much the proposed visualizations could contribute to group awareness. Moreover, in the survey we investigated to what extent the proposed version control system could help address shortcomings in collaborative experiments. The survey participants provided us with valuable feedback. In future, we will use the survey responses to enhance the proposed version control system and visualizations.
Reading through code, finding relevant methods, classes and files takes a significant portion of software development time. Having good tool support for this code browsing activity can reduce human effort and increase overall developer productivity. To help with program comprehension activities, building an abstract code summary of a software system from its call graph is an active research area. A call graph is a visual representation of the caller-callee relationships between different methods of a software system. Call graphs can be difficult to comprehend for a large code-base. Previous work by Gharibi et al. on abstract code summarizing suggested using the Agglomerative Hierarchical Clustering (AHC) tree for understanding the codebase. Each node in the tree is associated with the top five method names. When we replicated the previous approach, we observed that the number of nodes in the AHC tree is burdensome for developers to explore. We also noticed only five method names for each node is not sufficient to comprehend an abstract node. We propose a technique to transform the AHC tree using cluster flattening for natural grouping and reduced nodes. We also generate a natural text summary for each abstract node derived from method comments. In order to evaluate our proposed approach, we collected developers’ opinions about the abstract code summary tree based on their codebase. The evaluation results confirm that our approach can not only help developers get an overview of their codebases but also could assist them in doing specific software maintenance tasks.
Exploring the source code of a software system is a prevailing task that is frequently done by contributors to a system. Practitioners often use call graphs to aid in understanding the source code of an inadequately documented software system. Call graphs, when visualized, show caller and callee relationships between functions. A static call graph provides an overall structure of a software system and dynamic call graphs generated from dynamic execution logs can be used to trace program behaviour for a particular scenario. Unfortunately a call graph of an entire system can be very complicated and hard to understand. Hierarchically abstracting a call graph can be used to summarize an entire system’s structure and more easily comprehending function calls. In this work, we mine concepts from source code entities (functions) to generate a concept cluster tree with improved naming of cluster nodes to complement existing studies and facilitate more effective program comprehension for developers. We apply three different information retrieval techniques (TFIDF, LDA, and LSI) on function names and function name variants to label the nodes of a concept cluster tree generated by clustering execution paths. From our experiment in comparing automatic labelling with manual labeling by participants for 12 use cases, we found that among the techniques on average, TFIDF performs better with 64% matching. LDA and LSI had 37% and 23% matching respectively. In addition, using the words in function name variants performed at least 5% better in participant ratings for all three techniques on average for the use cases.
Testing software is considered to be one of the most crucial phases in software development life cycle. Software bug fixing requires a significant amount of time and effort. A rich body of recent research explored ways to predict bugs in software artifacts using machine learning based techniques. For a reliable and trustworthy prediction, it is crucial to also consider the explainability aspects of such machine learning models. In this paper, we show how the feature transformation techniques can significantly improve the prediction accuracy and build confidence in building bug prediction models. We propose a novel approach for improved bug prediction that first extracts the features, then finds a weighted transformation of these features using a genetic algorithm that best separates bugs from non-bugs when plotted in a low-dimensional space, and finally, trains the machine learning model using the transformed dataset. In our experiment with real-life bug datasets, the random forest and k-nearest neighbor classifier models that leveraged feature transformation showed 4.25% improvement in recall values on an average of over 8 software systems when compared to the models built on original data.
Software developers often submit questions to technical Q&A sites like Stack Overflow (SO) to resolve their code-level problems. Usually, they include example code segments with their questions to explain the programming issues. When users of SO attempt to answer the questions, they prefer to reproduce the issues reported in questions using the given code segments. However, such code segments could not always reproduce the issues due to several unmet challenges (e.g., too short code segment) that might prevent questions from receiving appropriate and prompt solutions. A previous study produced a catalog of potential challenges that hinder the reproducibility of issues reported at SO questions. However, it is unknown how the practitioners (i.e., developers) perceive the challenge catalog. Understanding the developers’ perspective is inevitable to introduce interactive tool support that promotes reproducibility. We thus attempt to understand developers’ perspectives by surveying 53 users of SO. In particular, we attempt to – (1) see developers’ viewpoints on the agreement to those challenges, (2) find the potential impact of those challenges, (3) see how developers address them, and (4) determine and prioritize tool support needs. Survey results show that about 90% of participants agree to the already exposed challenges. However, they report some additional challenges (e.g., error log missing) that might prevent reproducibility. According to the participants, too short code segment and absence of required Class/Interface/Method from code segments severely prevent reproducibility, followed by missing important part of code. To promote reproducibility, participants strongly recommend introducing tool support that interacts with question submitters with suggestions for improving the code segments if the given code segments fail to reproduce the issues.
Software bug prediction is one of the promising research areas in software engineering. Software developers must allocate a reasonable amount of time and resources to test and debug the developed software extensively to improve software quality. However, it is not always possible to test software thoroughly with limited time and resources to develop high quality software. Sometimes software companies release software products in a hurry to make profit in a competitive environment. As a result the released software might have software defects and can affect the reputation of those software companies. Ideally, any software application that is already in the market should not contain bugs. If it does, depending on its severity, it might cause a great cost. Although a significant amount of work has been done to automate different parts of testing to detect bugs, fixing a bug after it is discovered is still a costly task that developers need to do. Sometimes these bug fixing changes introduce new bugs in the system. Researchers estimated that 80% of the total cost of a software system is spent on fixing bugs [8]. They show that the software faults and failures costs the US economy $59.5 billion a year [9].
AbstractLet M be a two-dimensional table with each cell weighted by a nonzero positive number. A StreamTable visualization of M represents the columns as non-overlapping vertical streams and the rows as horizontal stripes such that the intersection between a stream and a stripe is a rectangle with area equal to the weight of the corresponding cell. To avoid large wiggle of the streams, it is desirable to keep the consecutive cells in a stream to be adjacent. Let B be the smallest axis-aligned bounding box containing the StreamTable. Then the difference between the area of B and the sum of the weights is referred to as the excess area. We attempt to optimize various StreamTable aesthetics (e.g., minimizing excess area, or maximizing cell adjacencies in streams). If the row permutation is fixed and the row heights are given, then we give an O(rc)-time algorithm to optimizes these aesthetics, where r and c are the number of rows and columns, respectively. If the row permutation is fixed but the row heights can be chosen, then we discuss a technique to compute an aesthetic (but not necessarily optimal) StreamTable by solving a quadratically-constrained quadratic program, followed by iterative improvements. If the row heights are restricted to be integers, then we prove the problem to be NP-hard. If the row permutations can be chosen, then we show that it is NP-hard to find a row permutation that optimizes the area or adjacency aesthetics. KeywordsGeometric AlgorithmsTable CartogramStreamgraphs
Software development is largely dependent on libraries to reuse existing functionalities instead of reinventing the wheel. Software developers often need to find analogical libraries (libraries similar to ones they are already familiar with) as an analogical library may offer improved or additional features. Developers also need to search for analogical libraries across programming languages when developing applications in different languages or for different platforms. However, manually searching for analogical libraries is a time-consuming and difficult task. This paper presents a technique, called XLibRec, that recommends analogical libraries across different programming languages. XLibRec collects Stack Overflow question titles containing library names, library usage information from Stack Overflow posts, and library descriptions from a third party website, Libraries.io. We generate word-vectors for each information and calculate a weight-based cosine similarity score from them to recommend analogical libraries. We performed an extensive evaluation using a large number of analogical libraries across four different programming languages. Results from our evaluation show that the proposed technique can recommend cross-language analogical libraries with great accuracy. The precision for the Top-3 recommendations ranges from 62-81% and has achieved 8-45% higher precision than the state-of-the-art technique.
A software release note is one of the essential documents in the software development life cycle. The software release contains a set of information, e.g., bug fixes and security fixes. Release notes are used in different phases, e.g., requirement engineering, software testing and release management. Different types of practitioners (e.g., project managers and clients) get benefited from the release notes to understand the overview of the latest release. As a result, several studies have been done about release notes production and usage in practice. However, two significant problems (e.g., duplication and inconsistency in release notes contents) exist in producing well-written & well-structured release notes and organizing appropriate information regarding different targeted users' needs. For that reason, practitioners face difficulties in writing and reading the release notes using existing tools. To mitigate these problems, we execute two different studies in our paper. First, we execute an exploratory study by analyzing 3,347 release notes of 21 GitHub repositories to understand the documented contents of the release notes. As a result, we find relevant key artifacts, e.g., issues (29%), pull-requests (32%), commits (19%), and common vulnerabilities and exposures (CVE) issues (6%) in the release note contents. Second, we conduct a survey study with 32 professionals to understand the key information that is included in release notes regarding users' roles. For example, project managers are more interested in learning about new features than less critical bug fixes. Our study can guide future research directions to help practitioners produce the release notes with relevant content and improve the documentation quality.
Multi-attribute dataset visualizations are often designed based on attribute types, i.e., whether the attributes are categorical or numerical. Parallel Sets and Parallel Coordinates are two well-known techniques to visualize categorical and numerical data, respectively. A common strategy to visualize mixed data is to use multiple information linked view, e.g., Parallel Coordinates are often augmented with maps to explore spatial data with numeric attributes. In this paper, we design visualizations for mixed data, where the dataset may include numerical, categorical, and spatial attributes. The proposed solution SET-STAT-MAP is a harmonious combination of three interactive components: Parallel Sets (visualizes sets determined by the combination of categories or numeric ranges), statistics columns (visualizes numerical summaries of the sets), and a geospatial map view (visualizes the spatial information). We augment these components with colors and textures to enhance users' capability of analyzing distributions of pairs of attribute combinations. To improve scalability, we merge the sets to limit the number of possible combinations to be rendered on the display. We demonstrate the use of Set-stat-map using two different types of datasets: a meteorological dataset and an online vacation rental dataset (Airbnb). To examine the potential of the system, we collaborated with the meteorologists, which revealed both challenges and opportunities for Set-stat-map to be used for real-life visual analytics.
The susceptible-infected-recovered (SIR) model is perhaps the most basic epidemiological model for the evolution of disease spread within a population. Because of its direct representation of fundamental physical quantities, a true solution to an SIR model possesses a number of qualitative properties, such as conservation of the total population or positivity or monotonicity of its constituent populations, that may only be guaranteed to hold numerically under step-size restrictions on the solver. Operator-splitting methods with order greater than two require backward sub-steps in each operator, and the effects of these backward sub-steps on the step-size restrictions for guarantees of qualitative correctness of numerical solutions are not well studied. In this study, we analyze the impact of backward steps on step-size restrictions for guaranteed qualitative properties by applying third- and fourth-order operator-splitting methods to the SIR epidemic model. We find that it is possible to provide step-size restrictions that guarantee qualitative property preservation of the numerical solution despite the negative sub-steps, but care must be taken in the choice of the method. Results such as this open the door for the design and application of high-order operator-splitting methods to other mathematical models in general for which qualitative property preservation is important.
This study was conducted at an oil sands operation in the Athabasca Oil Sands Region (AOSR), northeastern Alberta, Canada. The mine comprises open pit excavation of bituminous sands at two sites (Mildred Lake, ML, and Aurora North, AN), with a single hot-water extraction circuit connecting extraction plants at each mine. Water samples were collected and analyzed regularly over an eight-year period to establish inventories of site-wide water isotope signatures including seasonal and interannual changes in the recycle water circuit, and to permit future application of an isotope balance model to constrain poorly quantified processes such as evaporation losses, dewatering of tailings, and tailings pond connectivity of the recycle water circuit. Sampling of precipitation inputs over an 8-year period was used to constrain a local meteoric water line for the area. Differences in evaporative isotopic enrichment of tailings ponds at ML and AN are attributed to use of Athabasca River makeup water at the former site versus basal dewatering sources at the latter, with similar atmospheric controls at both. A conceptual model is developed summarizing temporal variations in water balance and isotopic signatures within the recycle water circuit, including accurate simulation of the unique isotopic enrichment of cooling tower blowdown. This study provides foundational evidence for application of stable isotope mass balance to monitor and improve industrial water use efficiency and management. • Detailed summary of stable isotope variations at oil sands mine sites. • New dataset for precipitation, makeup water, and mine circuits. • Updated regressions defining local meteoric water line for district. • Contrasts isotopic variations for nearby mine sites with distinct sources. • Previously unpublished effects of cooling tower blowdown.
The lower Athabasca River was used as a test case using total suspended sediment, chloride and vanadium as the model variables. Upstream model boundary conditions included water from the tributary Clearwater River (right stream tube) and the upper Athabasca River extending upstream of the tributary mouth (left stream tube). This model will be extended to include the Peace-Athabasca Delta (PAD), to determine the implications of mining outfall discharges on a large region of the Athabasca – PAD region. A novel, quasi-two-dimensional surface water-quality modelling approach is presented in which the model domain can be discretised in two dimensions, but a one-dimension solver can still be applied to capture water flow between the discretisation units (segments). The approach requires a river reach to be divided into two stream tubes, along the left and right river sides, with flows exchanging through the segments longitudinally and also laterally between adjacent segments along the two streams. The new method allows the transverse mixing of tributary and outfall water of different constituent concentrations to be simulated along the course of the river. Additional diffuse loading of dissolved vanadium could be determined from the model’s substance balance. A scenario was then simulated in which the transport and fate of vanadium in a floodplain lake and a secondary channel was determined. • Quasi-2D modelling approach proves to be viable for transverse mixing. • Quasi-2D approach allows secondary channels and side lakes to be modelled. • Quasi-2D approach is appropriate to scale up to entire lower Athabasca River reach. • The approach allowed a diffuse loading of dissolved vanadium to be quantified.
This paper synthesizes Canada's environmental valuation literature over the last six decades. Focusing on primary valuation benefit estimates, we link multiple research outputs from the same data collection effort to obtain an accurate measure of unique studies. We identify a total of 269 unique valuation studies conducted in Canada between 1964 and 2019. The number of valuation studies conducted per year has not increased since 1975 and the median data collection year is 1996. Stated preference (SP) methods are the most popular valuation approaches being used in more than 50% of studies and this share has increased to over 80% within the last decade. We discuss numerous gaps in our knowledge for certain environmental resources and regions, in particular Canada's three Northern territories. The paper provides information on the state of environmental valuation research in Canada and identifies future research needs.
A discrete choice experiment was conducted on the non-use value of avoiding climate impacts to coral reefs. • The Northwestern Hawaiian Islands coral reefs were utilized as a case study site. • Decreasing coral cover and fish numbers causes large welfare losses. • Declines to coral health and fish species diversity lead to moderate welfare losses. • Choice behaviour is compared between US mainland and Hawaiian residents. Global climate change is leading to rapid deteriorations of the health and productivity of coral reefs. There is limited research on the associated human welfare implications, particularly in terms of the non-use values that people hold for coral reefs. We examine climate related changes in non-use values of coral health, coral cover, water clarity, fish numbers, fish species diversity and presence of turtles. Using a discrete choice experiment conducted among 1,369 Hawaiian and US mainland residents, we find that climate change induced declines in coral cover and fish numbers result in large welfare losses; whereas, declines in coral health and fish species diversity lead to moderate welfare losses. Deterioration in water clarity results in large welfare losses for US mainland residents but relatively smaller losses for Hawaiian residents. On aggregate, differences in welfare estimates for the US mainland and Hawaii sample are minor. However, we find significant differences in the underlying determinants of willingness-to-pay for partial climate change mitigation including income and beliefs in the need to mitigate climate change. The paper concludes with some recommendations for policy on the basis of these findings.
DNA metabarcoding can provide a high-throughput and rapid method for characterizing responses of communities to environmental stressors. However, within bulk samples, DNA metabarcoding hardly distinguishes live from the dead organisms. Here, both DNA and RNA metabarcoding were applied and compared in experimental freshwater mesocosms conducted for assessment of ecotoxicological responses of zooplankton communities to remediation treatment until 38 days post oil-spill. Furthermore, a novel indicator of normalized vitality (NV), sequence counts of RNA metabarcoding normalized by that of DNA metabarcoding, was developed for assessment of ecological responses. DNA and RNA metabarcoding detected similar taxa richness and rank of relative abundances. Both DNA and RNA metabarcoding demonstrated slight shifts in measured α-diversities in response to treatments. NV presented relatively greater magnitudes of differential responses of community compositions to treatments compared to DNA or RNA metabarcoding. NV declined from the start of the experiment (3 days pre-spill) to the end (38 days post-spill). NV also differed between Rotifer and Arthropoda, possibly due to differential life histories and sizes of organisms. NV could be a useful indicator for characterizing ecological responses to anthropogenic influence; however, the biology of target organisms and subsequent RNA production need to be considered. • RNA normalized by DNA metabarcoding functions as normalized vitality. • Normalized vitality reflected temporal dynamics of zooplankton communities. • Normalized vitality revealed greater community differences between treatments. • Rotifer had greatest normalized vitality compared to Arthropoda.
Abstract Wastewater-based surveillance of SARS-CoV-2 RNA has been implemented at building, neighbourhood, and city levels throughout the world. Implementation strategies and analysis methods differ, but they all aim to provide rapid and reliable information about community COVID-19 health states. A viable and sustainable SARS-CoV-2 surveillance network must not only provide reliable and timely information about COVID-19 trends, but also provide for scalability as well as accurate detection of known or unknown emerging variants. Emergence of the SARS-CoV-2 variant of concern Omicron in late Fall 2021 presented an excellent opportunity to benchmark individual and aggregated data outputs of the Ontario Wastewater Surveillance Initiative in Canada; this public health-integrated surveillance network monitors wastewaters from over 10 million people across major population centres of the province. We demonstrate that this coordinated approach provides excellent situational awareness, comparing favourably with traditional clinical surveillance measures. Thus, aggregated datasets compiled from multiple wastewater-based surveillance nodes can provide sufficient sensitivity (i.e., early indication of increasing and decreasing incidence of SARS-CoV-2) and specificity (i.e., allele frequency estimation of emerging variants) with which to make informed public health decisions at regional- and state-levels.
Abstract Wastewater-based surveillance (WBS) has become an effective tool around the globe for indirect monitoring of COVID-19 in communities. Quantities of viral fragments of SARS-CoV-2 in wastewater are related to numbers of clinical cases of COVID-19 reported within the corresponding sewershed. Variants of Concern (VOCs) have been detected in wastewater by use of reverse transcription quantitative polymerase chain reaction (RT-qPCR) or sequencing. A multiplex RT-qPCR assay to detect and estimate the prevalence of multiple VOCs, including Omicron/Alpha, Beta, Gamma, and Delta, in wastewater RNA extracts was developed and validated. The probe-based multiplex assay, named “N200” focuses on amino acids 199-202, a region of the N gene that contains several mutations that are associated with variants of SARS- CoV-2 within a single amplicon. Each of the probes in the N200 assay are specific to the targeted mutations and worked equally well in single- and multi-plex modes. To estimate prevalence of each VOC, the abundance of the targeted mutation was compared with a non- mutated region within the same amplified region. The N200 assay was applied to monitor frequencies of VOCs in wastewater extracts from six sewersheds in Ontario, Canada collected between December 1, 2021, and January 4, 2022. Using the N200 assay, the replacement of the Delta variant along with the introduction and rapid dominance of the Omicron variant were monitored in near real-time, as they occurred nearly simultaneously at all six locations. The N200 assay is robust and efficient for wastewater surveillance can be adopted into VOC monitoring programs or replace more laborious assays currently being used to monitor SARS- CoV-2 and its VOCs.
Abstract Wastewater monitoring of SARS-CoV-2 allows for early detection and monitoring of COVID-19 burden in communities and can track specific variants of concern. Targeted assays enabled relative proportions of SARS-CoV-2 Omicron and Delta variants to be determined across 30 municipalities covering >75% of the province of Alberta (pop. 4.5M) in Canada, from November 2021 to January 2022. Larger cities like Calgary and Edmonton exhibited a more rapid emergence of Omicron relative to smaller and more remote municipalities. Notable exceptions were Banff, a small international resort town, and Fort McMurray, a more remote northern city with a large fly-in worker population. The integrated wastewater signal revealed that the Omicron variant represented close to 100% of SARS-CoV-2 burden prior to the observed increase in newly diagnosed clinical cases throughout Alberta, which peaked two weeks later. These findings demonstrate that wastewater monitoring offers early and reliable population-level results for establishing the extent and spread of emerging pathogens including SARS-CoV-2 variants.
Cyanobacterial blooms present challenges for water treatment, especially in regions like the Canadian prairies where poor water quality intensifies water treatment issues. Buoyant cyanobacteria that resist sedimentation present a challenge as water treatment operators attempt to balance pre-treatment and toxic disinfection by-products. Here, we used microscopy to identify and describe the succession of cyanobacterial species in Buffalo Pound Lake, a key drinking water supply. We used indicator species analysis to identify temporal grouping structures throughout two sampling seasons from May to October 2018 and 2019. Our findings highlight two key cyanobacterial bloom phases - a mid-summer diazotrophic bloom of Dolichospermum spp. and an autumn Planktothrix agardhii bloom. Dolichospermum crassa and Woronichinia compacta served as indicators of the mid-summer and autumn bloom phases, respectively. Different cyanobacterial metabolites were associated with the distinct bloom phases in both years: toxic microcystins were associated with the mid-summer Dolichospermum bloom and some newly monitored cyanopeptides (anabaenopeptin A and B) with the autumn Planktothrix bloom. Despite forming a significant proportion of the autumn phytoplankton biomass (>60%), the Planktothrix bloom had previously not been detected by sensor or laboratory-derived chlorophyll-a. Our results demonstrate the power of targeted taxonomic identification of key species as a tool for managers of bloom-prone systems. Moreover, we describe an autumn Planktothrix agardhii bloom that has the potential to disrupt water treatment due to its evasion of detection. Our findings highlight the importance of identifying this autumn bloom given the expectation that warmer temperatures and a longer ice-free season will become the norm.
Permafrost plays an important role in the hydrology of arctic/subarctic regions. However, permafrost thaw/degradation has been observed over recent decades in the Northern Hemisphere and is projected to accelerate. Hence, understanding the evolution of permafrost areas is urgently needed. Land surface models (LSMs) are well-suited for predicting permafrost dynamics due to their physical basis and large-scale applicability. However, LSM application is challenging because of the large number of model parameters and the complex memory of state variables. Significant interactions among the underlying processes and the paucity of observations of thermal/hydraulic regimes add further difficulty. This study addresses the challenges of LSM application by evaluating the uncertainty due to meteorological forcing, assessing the sensitivity of simulated permafrost dynamics to LSM parameters, and highlighting issues of parameter identifiability. Modelling experiments are implemented using the MESH-CLASS framework. The VARS sensitivity analysis and traditional threshold-based identifiability analysis are used to assess various aspects of permafrost dynamics for three regions within the Mackenzie River Basin. The study shows that the modeller may face significant trade-offs when choosing a forcing dataset as some datasets enable the representation of some aspects of permafrost dynamics, while being inadequate for others. The results also emphasize the high sensitivity of various aspects of permafrost simulation to parameters controlling surface insulation and soil texture; a detailed list of influential parameters is presented. Identifiability analysis reveals that many of the most influential parameters for permafrost simulation are unidentifiable. These conclusions will hopefully inform future efforts in data collection and model parametrization.
Abstract. Northern peatlands cover approximately four million km2, and about half of these peatlands are estimated to contain permafrost and periglacial landforms, like palsas and peat plateaux. In northeastern Canada, peatland permafrost is predicted to be spatially concentrated in the western interior of Labrador and largely absent along the Labrador Sea and Gulf of St. Lawrence coastline. However, the paucity of observations of peatland permafrost in the interior coupled with ongoing use of perennially frozen peatlands along the coast by Labrador Inuit and Innu cast doubt on the reliability of existing maps of peatland permafrost distribution in the region. In this study, we develop a multi-stage consensus-based inventory of peatland permafrost complexes in coastal Labrador and adjacent parts of Quebec using high-resolution satellite imagery and validate it with extensive field visits and low-altitude aerial photography and videography. A total of 1885 wetland complexes were inventoried, of which 1023 were interpreted as likely containing peatland permafrost. Likely peatland permafrost complexes were mostly found in lowlands within 40 km of the coastline where mean annual air temperatures of up to +1.2 °C are recorded. Evaluation of the geographic distribution of peatland permafrost complexes reveals a clear gradient from the outer coasts, where peatland permafrost is more abundant, to inland peatlands, where permafrost is generally absent. This coastal gradient may be attributed to a combination of climatic and geomorphological influences which lead to lower insolation, thinner snowpacks, and more frost-susceptible materials along the coast. The results of this study also suggest that existing maps of permafrost distribution for southeastern Labrador require adjustment to better reflect the abundance of peatland permafrost complexes which are located to the south of the regional sporadic discontinuous permafrost limit. This study constitutes the first dedicated peatland permafrost inventory for Labrador, and our results provide an important baseline for future mapping, modelling, and climate change adaptation strategy development in the region.
Abstract. Human-controlled reservoirs have a large influence on the global water cycle. While global hydrological models use generic parametrisations to model human dam operations, the representation of reservoir regulation is often still lacking in Earth System Models. Here we implement and evaluate a widely used reservoir parametrisation in the global river routing model mizuRoute, which operates on a vector-based river network resolving individual lakes and reservoirs, and which is currently being coupled to an Earth System Model. We develop an approach to determine the downstream area over which to aggregate irrigation water demand per reservoir. The implementation of managed reservoirs is evaluated by comparing to simulations ignoring inland waters, and simulations with reservoirs represented as natural lakes, using (i) local simulations for 26 individual reservoirs driven by observed inflows, and (ii) global-scale simulations driven by runoff from the Community Land Model. The local simulations show a clear added value of the reservoir parametrisation, especially for simulating storage for large reservoirs with a multi-year storage capacity. In the global-scale application, the implementation of reservoirs shows an improvement in outflow and storage compared to the no-reservoir simulation, but compared to the natural lake parametrisation, an overall similar performance is found. This lack of impact could be attributed to biases in simulated river discharge, mainly originating from biases in simulated runoff from the Community Land Model. Finally, the comparison of modelled monthly streamflow indices against observations highlights that the inclusion of dam operations improves the streamflow simulation compared to ignoring lakes and reservoirs. This study overall underlines the need to further develop and test water management parametrisations, as well as to improve runoff simulations for advancing the representation of anthropogenic interference with the terrestrial water cycle in Earth System Models.
Abstract. Fire is the dominant disturbance agent in Alaskan and Canadian boreal ecosystems and releases large amounts of carbon into the atmosphere. Burned area and carbon emissions have been increasing with climate change, which have the potential to alter the carbon balance and shift the region from a historic sink to a source. It is therefore critically important to track the spatiotemporal changes in burned area and fire carbon emissions over time. Here we developed a new burned area detection algorithm between 2001–2019 across Alaska and Canada at 500 meters (m) resolution that utilizes finer-scale 30 m Landsat imagery to account for land cover unsuitable for burning. This method strictly balances omission and commission errors at 500 m to derive accurate landscape- and regional-scale burned area estimates. Using this new burned area product, we developed statistical models to predict burn depth and carbon combustion for the same period within the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) core and extended domain. Statistical models were constrained using a database of field observations across the domain and were related to a variety of response variables including remotely-sensed indicators of fire severity, fire weather indices, local climate, soils, and topographic indicators. The burn depth and aboveground combustion models performed best, with poorer performance for belowground combustion. We estimate 2.37 million hectares (Mha) burned annually between 2001–2019 over the ABoVE domain (2.87 Mha across all of Alaska and Canada), emitting 79.3 +/- 27.96 (+/- 1 standard deviation) Teragrams of carbon (C) per year, with a mean combustion rate of 3.13 +/- 1.17 kilograms C m-2. Mean combustion and burn depth displayed a general gradient of higher severity in the northwestern portion of the domain to lower severity in the south and east. We also found larger fire years and later season burning were generally associated with greater mean combustion. Our estimates are generally consistent with previous efforts to quantify burned area, fire carbon emissions, and their drivers in regions within boreal North America; however, we generally estimate higher burned area and carbon emissions due to our use of Landsat imagery, greater availability of field observations, and improvements in modeling. The burned area and combustion data sets described here (the ABoVE Fire Emissions Database, or ABoVE-FED) can be used for local to continental-scale applications of boreal fire science.
Abstract. Ice thickness across lake ice is influenced mainly by the presence of snow and its distribution, as it directly impacts the rate of lake ice growth. The spatial distribution of snow depth over lake ice varies and is driven by wind redistribution and snowpack metamorphism, creating variability in the lake ice thickness. The accuracy and consistency of snow depth measurement data on lake ice are challenging and sparse to obtain. However, high spatial resolution lake snow depth observations are necessary for the next generation of thermodynamic lake ice models. Such information is required to improve the knowledge and understanding of snow depth distribution over lake ice. This study maps snow depth distribution over lake ice using ground-penetrating radar (GPR) two-way travel-time (TWT) with ~9 cm spatial resolution along transects totalling ~44 km over four freshwater lakes in Canada’s sub-arctic. The accuracy of the snow depth retrieval is assessed using in situ snow depth observations (n =2,430). On average, the snow depth derived from GPR TWTs for the early winter season is estimated with a root mean square error (RMSE) of 1.58 cm and a mean bias error of -0.01 cm. For the late winter season on a deeper snowpack, the accuracy is estimated with RMSE of 2.86 cm and a mean bias error of 0.41 cm. The GPR-derived snow depths are interpolated to create 1 m spatial resolution snow depth maps. Overall, this study improved lake snow depth retrieval accuracy and introduced a fast and efficient method to obtain high spatial resolution snow depth information, which is essential for the lake ice modelling community.
&lt;p&gt;Climate models are the available tools to assess risks of extreme precipitation events due to climate change. Models simulating historical climate successfully are often reliable to simulate future climate. Here, we assess the performance of CMIP6 models in reproducing the observed annual maxima of daily precipitation (AMP) beyond the commonly used methods. This assessment takes three scales: (1) univariate comparison based on L-moments and relative difference measures; (2) bivariate comparison using Kernel densities of mean and L-variation, and of L-skewness and L-kurtosis, and (3) comparison of the entire distribution function using the Generalized Extreme Value () distribution coupled with a novel application of the Anderson-Darling Goodness-of-fit test. The results depict that 70% of simulations have mean and variation of AMP with a percentage difference within 10&amp;#160;from the observations. Also, the statistical shape properties, defining the frequency and magnitude of AMP, of simulations match well with observations. However, biases are observed in the mean and variation bivariate properties. Several models perform well with the HadGEM3-GC31-MM model performing well in all three scales when compared to the ground-based Global Precipitation Climatology (GPCC) data. Finally, the study highlights biases of CMIP6 models in simulating extreme precipitation in the Arctic, Tropics, arid and semi-arid regions.&lt;/p&gt;
Process-based land surface models are important tools for estimating global wetland methane (CH4) emissions and projecting their behavior across space and time. So far there are no performance assessments of model responses to drivers at multiple time scales. In this study, we apply wavelet analysis to identify the dominant time scales contributing to model uncertainty in the frequency domain. We evaluate seven wetland models at 23 eddy covariance tower sites. Our study first characterizes site-level patterns of freshwater wetland CH4 fluxes (FCH4) at different time scales. A Monte Carlo approach has been developed to incorporate flux observation error to avoid misidentification of the time scales that dominate model error. Our results suggest that 1) significant model-observation disagreements are mainly at short- to intermediate time scales (< 15 days); 2) most of the models can capture the CH4 variability at long time scales (> 32 days) for the boreal and Arctic tundra wetland sites but have limited performance for temperate and tropical/subtropical sites; 3) model error approximates pink noise patterns, indicating that biases at short time scales (< 5 days) could contribute to persistent systematic biases on longer time scales; and 4) differences in error pattern are related to model structure (e.g. proxy of CH4 production). Our evaluation suggests the need to accurately replicate FCH4 variability in future wetland CH4 model developments.
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 es