Groundwater drought represents one of the most pervasive and difficult-to-monitor forms of water scarcity, threatening the reliability of freshwater supply for over 2 billion people worldwide, agricultural productivity, and ecosystem health. Despite its critical importance, monitoring groundwater drought with high spatial and temporal resolution remains challenging due to limited in situ observations, coarse-resolution satellite data, and uncertainties in models. In this study, we introduce an observation-informed approach for producing daily groundwater drought maps at 1/8° resolution across the contiguous United States (CONUS). Leveraging high-performance computing, we jointly assimilate Soil Moisture Active Passive soil moisture and GRACE-FO terrestrial water storage data into the Noah-MP land surface model to enhance the representation of groundwater–surface water interactions while accounting for uncertainties, enabling a more accurate representation of groundwater drought dynamics. Considering the spatial and temporal complexities of drought patterns, we employ the Growing Neural Gas, a machine learning-based pattern recognition algorithm, to identify emergent, evolving, and region-specific behaviors of groundwater drought. The results reveal the onset of distinct and persistent dry clusters in recent years across the contiguous United States (CONUS), identifying the severe groundwater drought conditions that notably impacted large regions of both the Western and Northeastern CONUS. Our findings highlight the need to reassess groundwater resilience strategies, especially as droughts intensify and persist over large domains.
{"title":"An Effective Monitoring of Evolving Groundwater Drought via Multivariate Data Assimilation and Machine Learning","authors":"Parnian Ghaneei, Hamid Moradkhani","doi":"10.1029/2025wr041565","DOIUrl":"https://doi.org/10.1029/2025wr041565","url":null,"abstract":"Groundwater drought represents one of the most pervasive and difficult-to-monitor forms of water scarcity, threatening the reliability of freshwater supply for over 2 billion people worldwide, agricultural productivity, and ecosystem health. Despite its critical importance, monitoring groundwater drought with high spatial and temporal resolution remains challenging due to limited in situ observations, coarse-resolution satellite data, and uncertainties in models. In this study, we introduce an observation-informed approach for producing daily groundwater drought maps at 1/8° resolution across the contiguous United States (CONUS). Leveraging high-performance computing, we jointly assimilate Soil Moisture Active Passive soil moisture and GRACE-FO terrestrial water storage data into the Noah-MP land surface model to enhance the representation of groundwater–surface water interactions while accounting for uncertainties, enabling a more accurate representation of groundwater drought dynamics. Considering the spatial and temporal complexities of drought patterns, we employ the Growing Neural Gas, a machine learning-based pattern recognition algorithm, to identify emergent, evolving, and region-specific behaviors of groundwater drought. The results reveal the onset of distinct and persistent dry clusters in recent years across the contiguous United States (CONUS), identifying the severe groundwater drought conditions that notably impacted large regions of both the Western and Northeastern CONUS. Our findings highlight the need to reassess groundwater resilience strategies, especially as droughts intensify and persist over large domains.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"91 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146138701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. Huang, Y. Gao, L. Zhao, D. Wang, X. Hu, X. Wang
The simulation of karst aquifers is highly challenging due to complex conduit-matrix interactions. This study utilizes KarstFOAM, a high-fidelity, physics-based numerical model, to address these challenges. KarstFOAM integrates (a) a unified single-domain Forchheimer–Darcy–Brinkman–Stokes (FDBS) formulation that transitions naturally across flow regimes, (b) benchmark-level reproduction of interface-scale features (velocity slip and a finite transition layer), and (c) a VOF-based two-phase treatment enabling conduit drying and variably saturated dynamics coupled to matrix retention effects. The model was validated against analytical solutions, laboratory experiments, and applied to a typical karst field site. Results demonstrate that KarstFOAM accurately simulates the conduit-matrix interface velocity and transition zone, as well as dynamic saturation, conduit drying, and matrix water retention effects. The model shows high accuracy for single rainfall events (NSE ≈ 0.97). Given this high fidelity, the model is best suited for academic research requiring precise analysis of local physical mechanisms, rather than for regional water resource management. Future work will focus on developing simplified versions to achieve a better balance between scientific insight and practical application.
{"title":"Modeling Interactions and Dynamic Saturation Processes in Karst Media","authors":"F. Huang, Y. Gao, L. Zhao, D. Wang, X. Hu, X. Wang","doi":"10.1029/2025wr040068","DOIUrl":"https://doi.org/10.1029/2025wr040068","url":null,"abstract":"The simulation of karst aquifers is highly challenging due to complex conduit-matrix interactions. This study utilizes KarstFOAM, a high-fidelity, physics-based numerical model, to address these challenges. KarstFOAM integrates (a) a unified single-domain Forchheimer–Darcy–Brinkman–Stokes (FDBS) formulation that transitions naturally across flow regimes, (b) benchmark-level reproduction of interface-scale features (velocity slip and a finite transition layer), and (c) a VOF-based two-phase treatment enabling conduit drying and variably saturated dynamics coupled to matrix retention effects. The model was validated against analytical solutions, laboratory experiments, and applied to a typical karst field site. Results demonstrate that KarstFOAM accurately simulates the conduit-matrix interface velocity and transition zone, as well as dynamic saturation, conduit drying, and matrix water retention effects. The model shows high accuracy for single rainfall events (NSE ≈ 0.97). Given this high fidelity, the model is best suited for academic research requiring precise analysis of local physical mechanisms, rather than for regional water resource management. Future work will focus on developing simplified versions to achieve a better balance between scientific insight and practical application.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"30 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146138872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Erin M. Dougherty, David Gochis, Michelle Harrold, Sarah A. Tessendorf, Lulin Xue, Jamie Wolff, Bart Geerts
In the western United States, the recent mega-drought and impacts of climate change have resulted in an interest in cloud seeding to enhance water supplies. Studies and field campaigns focused on cloud seeding across the West have quantified the effect on precipitation generation through the release of silver iodide, and these effects can be studied in simulations using WRF-WxMod®, a modeling capability based on the WRF model that includes a cloud-seeding parameterization. Here, we use a 36-member ensemble of WRF-WxMod simulations to force a spatially distributed hydrological model, WRF-Hydro, to study how simulated cloud seeding impacts hydrology in the North Platte and Little Snake River basins of Wyoming during the 2020 water year. WRF-Hydro is configured with a 1-km land surface model, Noah-MP, with the terrain routing grid run at 250 m. Compared to observations, WRF-Hydro shows good performance with an average Kling Gupta Efficiency = 0.80. Over the 2020 water year, snow water equivalent increases by 10 mm over target mountain ranges due to simulated cloud seeding and streamflow increases by 6,921 acre-ft over the entire domain. A water budget analysis shows that increases in ensemble mean precipitation due to simulated cloud seeding result in 78% diverted to increasing streamflow, 21% increasing soil moisture, and 8% going toward evapotranspiration. Such information is critical for water managers looking into the efficacy of cloud seeding to enhance their water resources amidst climate change.
{"title":"Simulated Hydrologic Impacts of Cloud Seeding in the North Platte and Little Snake River Basins of Wyoming","authors":"Erin M. Dougherty, David Gochis, Michelle Harrold, Sarah A. Tessendorf, Lulin Xue, Jamie Wolff, Bart Geerts","doi":"10.1029/2024wr039383","DOIUrl":"https://doi.org/10.1029/2024wr039383","url":null,"abstract":"In the western United States, the recent mega-drought and impacts of climate change have resulted in an interest in cloud seeding to enhance water supplies. Studies and field campaigns focused on cloud seeding across the West have quantified the effect on precipitation generation through the release of silver iodide, and these effects can be studied in simulations using WRF-WxMod<sup>®</sup>, a modeling capability based on the WRF model that includes a cloud-seeding parameterization. Here, we use a 36-member ensemble of WRF-WxMod simulations to force a spatially distributed hydrological model, WRF-Hydro, to study how simulated cloud seeding impacts hydrology in the North Platte and Little Snake River basins of Wyoming during the 2020 water year. WRF-Hydro is configured with a 1-km land surface model, Noah-MP, with the terrain routing grid run at 250 m. Compared to observations, WRF-Hydro shows good performance with an average Kling Gupta Efficiency = 0.80. Over the 2020 water year, snow water equivalent increases by 10 mm over target mountain ranges due to simulated cloud seeding and streamflow increases by 6,921 acre-ft over the entire domain. A water budget analysis shows that increases in ensemble mean precipitation due to simulated cloud seeding result in 78% diverted to increasing streamflow, 21% increasing soil moisture, and 8% going toward evapotranspiration. Such information is critical for water managers looking into the efficacy of cloud seeding to enhance their water resources amidst climate change.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"43 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christopher V. Henri, Graham E. Fogg, Thomas Harter
Understanding the mixing of groundwater age and of nonpoint source (NPS) pollutants in water samples is crucial for interpreting age tracer and NPS pollutant data from production wells. Traditionally, diffusion and mechanical dispersion have been key mixing processes embedded in physical models for simulating and interpreting age tracer and NPS pollutant data. Also, a large body of literature highlights mixing due to aquifer heterogeneity across scales. Importantly, the collection of water samples through wells introduces additional mixing across the internal volume of the well screen. Here, we investigate and quantify the magnitude of mixing due to these four processes—diffusion, mechanical dispersion, aquifer heterogeneity, and in-well mixing—through a Monte Carlo-based modeling framework and sensitivity study. We consider wells in a typical unconsolidated alluvial aquifer system. We find that in-well mixing and aquifer heterogeneity dominate the mixing process in larger production wells. In small production wells and in monitoring wells, diffusion and (sub-grid scale) mechanical dispersion add significantly to age/pollutant mixing. Across an ensemble of larger production wells (e.g., in regional planning), the range of age (or pollutant) mixing observed is dominated by in-well mixing, with aquifer heterogeneity not significantly changing the age mixing distribution. The depth of the well screen has some impact on age mixing only in small (monitoring) wells. Our work suggests that ensemble age (or mixed NPS pollutant concentration) distributions across large sets of production wells can be satisfactorily estimated from well construction information and by considering advective transport in equivalent homogeneous media.
{"title":"Groundwater Age and Nonpoint Source Pollutant Mixing in Alluvial Aquifer Wells: Comparing the Role of Diffusion, Dispersion, Aquifer Heterogeneity, and Well Screen Length","authors":"Christopher V. Henri, Graham E. Fogg, Thomas Harter","doi":"10.1029/2025wr040063","DOIUrl":"https://doi.org/10.1029/2025wr040063","url":null,"abstract":"Understanding the mixing of groundwater age and of nonpoint source (NPS) pollutants in water samples is crucial for interpreting age tracer and NPS pollutant data from production wells. Traditionally, diffusion and mechanical dispersion have been key mixing processes embedded in physical models for simulating and interpreting age tracer and NPS pollutant data. Also, a large body of literature highlights mixing due to aquifer heterogeneity across scales. Importantly, the collection of water samples through wells introduces additional mixing across the internal volume of the well screen. Here, we investigate and quantify the magnitude of mixing due to these four processes—diffusion, mechanical dispersion, aquifer heterogeneity, and in-well mixing—through a Monte Carlo-based modeling framework and sensitivity study. We consider wells in a typical unconsolidated alluvial aquifer system. We find that in-well mixing and aquifer heterogeneity dominate the mixing process in larger production wells. In small production wells and in monitoring wells, diffusion and (sub-grid scale) mechanical dispersion add significantly to age/pollutant mixing. Across an ensemble of larger production wells (e.g., in regional planning), the range of age (or pollutant) mixing observed is dominated by in-well mixing, with aquifer heterogeneity not significantly changing the age mixing distribution. The depth of the well screen has some impact on age mixing only in small (monitoring) wells. Our work suggests that ensemble age (or mixed NPS pollutant concentration) distributions across large sets of production wells can be satisfactorily estimated from well construction information and by considering advective transport in equivalent homogeneous media.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"51 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate streamflow prediction is critical for flood forecasting and water resource management, particularly in data-scarce regions. Deep learning models like Long Short-Term Memory (LSTM) offer a bridge to hydrologic regionalization utilizing climate data and catchment characteristics to improve behavioral insights and constrain predictive uncertainties. Here we evaluate transfer learning (TL) approaches using 441 “donor” basins from regions rich in quality hydrological data (Scotland GB-SCT; Switzerland CH; and Canada's British Columbia; BC) to pre-train LSTM runoff models by fine-tuning in data-poor areas like CA (36 target basins). Pairing measured streamflow (lagged) records with global climate data (ERA-5) boosted the explanatory power of LSTM predictions especially in snowmelt and glacier-influenced basins. A K-Means clustering algorithm was applied to categorize basins into five hydrologically meaningful Clusters (labeled 1–5) based on catchment attributes. The results show that TL-LSTM models perform better when pre-trained using Clusters compared to the locally trained model (NSE = 0.85 and KGE = 0.80). The LSTM model trained on basins Cluster 3 data (LSTM3) most closely resembling those in the target region yielded the most accurate predictions. Fine-tuning with limited local data substantially improved prediction accuracy in the validation split (blind-tested and treated as “ungauged”), evidencing that even short-records of local data can enhance a regional model of hydrological behavior. These results demonstrate that TL-LSTM can effectively enhance streamflow prediction in data-scarce regions. These insights advance understanding of cross-basin generalization and support the development of efficient, scalable modeling strategies for hydrological prediction in regions with limited observational data.
{"title":"Investigating Deep Learning Knowledge Transfer in Streamflow Prediction From Global to Local Catchment","authors":"Jamal Hassan Ougahi, John S. Rowan","doi":"10.1029/2025wr041194","DOIUrl":"https://doi.org/10.1029/2025wr041194","url":null,"abstract":"Accurate streamflow prediction is critical for flood forecasting and water resource management, particularly in data-scarce regions. Deep learning models like Long Short-Term Memory (LSTM) offer a bridge to hydrologic regionalization utilizing climate data and catchment characteristics to improve behavioral insights and constrain predictive uncertainties. Here we evaluate transfer learning (TL) approaches using 441 “donor” basins from regions rich in quality hydrological data (Scotland GB-SCT; Switzerland CH; and Canada's British Columbia; BC) to pre-train LSTM runoff models by fine-tuning in data-poor areas like CA (36 target basins). Pairing measured streamflow (lagged) records with global climate data (ERA-5) boosted the explanatory power of LSTM predictions especially in snowmelt and glacier-influenced basins. A K-Means clustering algorithm was applied to categorize basins into five hydrologically meaningful Clusters (labeled 1–5) based on catchment attributes. The results show that TL-LSTM models perform better when pre-trained using Clusters compared to the locally trained model (NSE = 0.85 and KGE = 0.80). The LSTM model trained on basins Cluster 3 data (LSTM<sub>3</sub>) most closely resembling those in the target region yielded the most accurate predictions. Fine-tuning with limited local data substantially improved prediction accuracy in the validation split (blind-tested and treated as “ungauged”), evidencing that even short-records of local data can enhance a regional model of hydrological behavior. These results demonstrate that TL-LSTM can effectively enhance streamflow prediction in data-scarce regions. These insights advance understanding of cross-basin generalization and support the development of efficient, scalable modeling strategies for hydrological prediction in regions with limited observational data.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"26 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Louisa M. Rochford, Nevenka Bulovic, Steven Flook, Phil J. Hayes, Neil McIntyre
Sustainable management of groundwater resources requires a comprehensive understanding of the groundwater system and its water balance, including groundwater extraction. Bores (otherwise known as wells) used for cattle grazing are not typically metered and extraction is usually estimated using analytical models based on supply and demand-based factors. A world-first program of metering of 36 bores on 24 beef cattle grazing properties and landholder interviews on factors affecting water use was undertaken in semi-arid southern Queensland, Australia, over a 6-year period. This study investigated whether data from this program could be used to develop empirical models that could improve estimates of groundwater extraction. A model to predict cattle numbers was initially developed. The outputs from this model, together with public domain spatial data sets, were then used to predict groundwater extraction using a generalized linear model. Modeled property groundwater extraction ranged from 0.31 to 25.5 ML a−1 and was 7.6 ML a−1 on average. The model explained 83.3% of the variance in metered extraction and was found to be more accurate for the metered properties than models previously applied. The study demonstrates that models developed using data for a representative subset of users can be an effective means of estimating groundwater extraction. The approach could be applied across a range of hydrological environments and agricultural settings globally to improve estimation of unmetered take.
地下水资源的可持续管理需要全面了解地下水系统及其水平衡，包括地下水开采。用于放牧的钻孔（也称为井）通常不计量，提取通常使用基于供需因素的分析模型进行估计。在澳大利亚半干旱的昆士兰州南部，在6年的时间里，对24头肉牛放牧地的36个钻孔进行了测量，并对影响用水的因素进行了土地所有者访谈，这是世界上第一个项目。本研究调查了该项目的数据是否可以用于开发经验模型，以改善地下水开采的估计。最初开发了一个模型来预测牛的数量。该模型的输出与公共领域空间数据集一起，然后使用广义线性模型来预测地下水开采。模拟的地下水提取属性范围为0.31 ~ 25.5 ML a−1，平均为7.6 ML a−1。该模型解释了计量提取中83.3%的方差，并且发现对于计量属性比以前应用的模型更准确。该研究表明，使用具有代表性的用户子集的数据开发的模型可以是估计地下水采掘的有效手段。该方法可以应用于全球一系列水文环境和农业环境，以改进对未计量取水量的估计。
{"title":"Modeling Groundwater Extraction for Cattle Grazing in Semi-Arid Southern Queensland, Australia","authors":"Louisa M. Rochford, Nevenka Bulovic, Steven Flook, Phil J. Hayes, Neil McIntyre","doi":"10.1029/2025wr041588","DOIUrl":"https://doi.org/10.1029/2025wr041588","url":null,"abstract":"Sustainable management of groundwater resources requires a comprehensive understanding of the groundwater system and its water balance, including groundwater extraction. Bores (otherwise known as wells) used for cattle grazing are not typically metered and extraction is usually estimated using analytical models based on supply and demand-based factors. A world-first program of metering of 36 bores on 24 beef cattle grazing properties and landholder interviews on factors affecting water use was undertaken in semi-arid southern Queensland, Australia, over a 6-year period. This study investigated whether data from this program could be used to develop empirical models that could improve estimates of groundwater extraction. A model to predict cattle numbers was initially developed. The outputs from this model, together with public domain spatial data sets, were then used to predict groundwater extraction using a generalized linear model. Modeled property groundwater extraction ranged from 0.31 to 25.5 ML a<sup>−1</sup> and was 7.6 ML a<sup>−1</sup> on average. The model explained 83.3% of the variance in metered extraction and was found to be more accurate for the metered properties than models previously applied. The study demonstrates that models developed using data for a representative subset of users can be an effective means of estimating groundwater extraction. The approach could be applied across a range of hydrological environments and agricultural settings globally to improve estimation of unmetered take.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"72 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maud Demarty, Paul A. del Giorgio, Charles P. Deblois, François Bilodeau, Alain Tremblay
Hydropower is considered essential in meeting the increasing demand in low carbon energy in the context of climate change. Greenhouse gas emissions (GHG) by hydroelectric reservoirs have nevertheless become a major concern to the energy sector. The challenge lies in developing robust estimates and models that account for temporal and spatial heterogeneity of emissions, and validating projections, because comprehensive studies are scarce due to their complexity and cost. Here we present the long term (2005–2022) GHG trajectories and other aspects of the biogeochemical functioning of the Eastmain-1 (or Paix des Braves) reservoir in boreal Québec, Canada. The study considers both seasonal and spatial heterogeneity of diffusive, ebullitive and downstream CO2 and CH4 emissions by combining discrete sampling with continuous, automated monitoring instrumentation. CH4 fluxes represented only 1.2%–6.8% of the total gross emissions, and CO2 diffusive emissions represented 80.1%–90.0% of the total emissions in CO2 equivalents. Our results confirm the projected decreasing trend in dissolved CO2 over the initial years after flooding, a pattern i.e. not observed for CH4 concentrations. Total reservoir emissions after 17 years are on the order of 434 103 tCO2eq yr−1 (i.e., ∼2 gCO2eq m−2.d−1) very close to those initially modeled right after impoundment. Based on a comparison with regional lakes, we estimate that only 45% of the current diffusive GHG emissions are attributable to the impoundment itself. This leads to current generation efficiencies of around 38 to 41 gCO2eq kW h1.
在气候变化的背景下，水电被认为是满足日益增长的低碳能源需求的关键。然而，水力发电水库的温室气体排放（GHG）已成为能源部门关注的主要问题。面临的挑战在于制定强有力的估算和模型来解释排放的时空异质性，并验证预测，因为由于其复杂性和成本，全面的研究很少。在这里，我们提出了长期（2005-2022年）的温室气体轨迹和其他方面的生物地球化学功能的东main1（或paaix des Braves）水库在加拿大quamesbec北部。该研究通过将离散采样与连续自动化监测仪器相结合，考虑了扩散、沸腾和下游CO2和CH4排放的季节和空间异质性。CH4通量仅占总排放量的1.2% ~ 6.8%，CO2扩散排放量占CO2当量总排放量的80.1% ~ 90.0%。我们的结果证实了预估的溶解二氧化碳在洪水后最初几年的下降趋势，即CH4浓度没有观察到这种模式。17年后水库总排放量约为434 103 tCO2eq yr - 1（即~ 2 gCO2eq m - 2）。D−1)非常接近最初在蓄水后的模型。通过与区域湖泊的比较，我们估计目前扩散的温室气体排放中只有45%可归因于蓄水本身。这导致目前的发电效率约为38至41 gCO2eq kW h1。
{"title":"The Long-Term C Footprint Trajectory and Emissions Attribution of a Large Boreal Reservoir (Paix Des Braves (Eastmain-1), Québec)","authors":"Maud Demarty, Paul A. del Giorgio, Charles P. Deblois, François Bilodeau, Alain Tremblay","doi":"10.1029/2025wr041366","DOIUrl":"https://doi.org/10.1029/2025wr041366","url":null,"abstract":"Hydropower is considered essential in meeting the increasing demand in low carbon energy in the context of climate change. Greenhouse gas emissions (GHG) by hydroelectric reservoirs have nevertheless become a major concern to the energy sector. The challenge lies in developing robust estimates and models that account for temporal and spatial heterogeneity of emissions, and validating projections, because comprehensive studies are scarce due to their complexity and cost. Here we present the long term (2005–2022) GHG trajectories and other aspects of the biogeochemical functioning of the Eastmain-1 (or Paix des Braves) reservoir in boreal Québec, Canada. The study considers both seasonal and spatial heterogeneity of diffusive, ebullitive and downstream CO<sub>2</sub> and CH<sub>4</sub> emissions by combining discrete sampling with continuous, automated monitoring instrumentation. CH<sub>4</sub> fluxes represented only 1.2%–6.8% of the total gross emissions, and CO<sub>2</sub> diffusive emissions represented 80.1%–90.0% of the total emissions in CO<sub>2</sub> equivalents. Our results confirm the projected decreasing trend in dissolved CO<sub>2</sub> over the initial years after flooding, a pattern i.e. not observed for CH<sub>4</sub> concentrations. Total reservoir emissions after 17 years are on the order of 434 10<sup>3</sup> tCO<sub>2</sub>eq yr<sup>−1</sup> (i.e., ∼2 gCO<sub>2</sub>eq m<sup>−2</sup>.d<sup>−1</sup>) very close to those initially modeled right after impoundment. Based on a comparison with regional lakes, we estimate that only 45% of the current diffusive GHG emissions are attributable to the impoundment itself. This leads to current generation efficiencies of around 38 to 41 gCO<sub>2</sub>eq kW h<sup>1</sup>.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"47 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christa A. Kelleher, John Patrick Gannon, Dominick Ciruzzi
Supporting undergraduate education in hydrology is crucial to enhancing workforce development, research, and training necessary to advance the future of hydrologic science. Many professionals encounter the subject of hydrology in an undergraduate course that serves as an introduction to this discipline. However, there is limited synthesis regarding how educators design and teach introductory courses in hydrology. In this work, we analyzed 43 syllabi for undergraduate hydrology courses primarily from North America to identify how such approaches may vary and/or converge. We found that course titles varied widely, as do the use and titles of required or recommended textbooks. We also found variability in how instructors structured assessments, with 48% of syllabi reporting a distributed approach to grading rather than favoring a particular type of assessment. Instructors articulated 257 learning objectives spanning the range of Bloom's Taxonomy, as well as a small number of affective and skills-based objectives. The majority of syllabi favored mid-range objectives within Bloom's taxonomy (“understand” and “apply”), with fewer syllabi emphasizing objectives at lower (“remember”) and upper (“evaluate” and “create”) levels. In alignment with emphasis within the water cycle, most courses introduce key processes including precipitation, streamflow generation, groundwater, and evapotranspiration, but tended to diverge in whether they included topics such as climate change, human impacts on the water cycle, and water management. Overall, our synthesis provides a useful starting point for developing a common introductory curriculum in the field of hydrology, and considering what needs may still exist when first introducing undergraduate students to hydrologic science.
{"title":"The Current State of Undergraduate Hydrology Courses in North America: A Path Forward","authors":"Christa A. Kelleher, John Patrick Gannon, Dominick Ciruzzi","doi":"10.1029/2025wr041736","DOIUrl":"https://doi.org/10.1029/2025wr041736","url":null,"abstract":"Supporting undergraduate education in hydrology is crucial to enhancing workforce development, research, and training necessary to advance the future of hydrologic science. Many professionals encounter the subject of hydrology in an undergraduate course that serves as an introduction to this discipline. However, there is limited synthesis regarding how educators design and teach introductory courses in hydrology. In this work, we analyzed 43 syllabi for undergraduate hydrology courses primarily from North America to identify how such approaches may vary and/or converge. We found that course titles varied widely, as do the use and titles of required or recommended textbooks. We also found variability in how instructors structured assessments, with 48% of syllabi reporting a distributed approach to grading rather than favoring a particular type of assessment. Instructors articulated 257 learning objectives spanning the range of Bloom's Taxonomy, as well as a small number of affective and skills-based objectives. The majority of syllabi favored mid-range objectives within Bloom's taxonomy (“understand” and “apply”), with fewer syllabi emphasizing objectives at lower (“remember”) and upper (“evaluate” and “create”) levels. In alignment with emphasis within the water cycle, most courses introduce key processes including precipitation, streamflow generation, groundwater, and evapotranspiration, but tended to diverge in whether they included topics such as climate change, human impacts on the water cycle, and water management. Overall, our synthesis provides a useful starting point for developing a common introductory curriculum in the field of hydrology, and considering what needs may still exist when first introducing undergraduate students to hydrologic science.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"70 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Otto I. Lang, Joachim Meyer, J. Michelle Hu, S. McKenzie Skiles
Snow in the Great Salt Lake Basin is a vital resource for regional agriculture, municipal water use, and the Great Salt Lake. Accumulation of light absorbing particles (LAPs) on mountain snowpacks results in lower albedos and earlier melt compared to clean snow. Though snow darkening is linked to dust events and varies spatially, snowmelt impacts have primarily been studied at the point scale. To address this gap, a spatially distributed process-based snow model (iSnobal) was used to simulate the snowpack under different albedos: a “baseline” estimate uninfluenced by episodic LAP variability, and an “observed” scenario where MODIS snow albedo retrievals informed darkened snow conditions. Shifts in snow disappearance date (SDD) between scenarios were used to quantify the cumulative impact of snow darkening on melt over contrasting water years (WYs). The SDD shifts were greater in WY 2022, with snow disappearing 23–29 days earlier, attributed to sunny weather and darker snow. In WY 2023, SDD shifts were moderate with melt advancing 11–16 days, despite similar melt season albedos to WY 2022. Frequent storms in WY 2023 delayed darkening until later in the season, when melt progressed suddenly due to rapid albedo declines and weak longwave losses. In both years, SDD shifts were pronounced at subalpine elevations (∼2,300–2,900 m), potentially related to snow albedo declines coinciding with high solar irradiance and snowfall patterns. These findings suggest that melt sensitivity to snow darkening shows consistent spatial patterns, but the magnitude of snowmelt impacts is controlled by seasonal variability in meteorology.
{"title":"Darkened Snow Triggers Different Snowmelt Responses Over Contrasting Water Years in Great Salt Lake Headwater Basins","authors":"Otto I. Lang, Joachim Meyer, J. Michelle Hu, S. McKenzie Skiles","doi":"10.1029/2025wr041598","DOIUrl":"https://doi.org/10.1029/2025wr041598","url":null,"abstract":"Snow in the Great Salt Lake Basin is a vital resource for regional agriculture, municipal water use, and the Great Salt Lake. Accumulation of light absorbing particles (LAPs) on mountain snowpacks results in lower albedos and earlier melt compared to clean snow. Though snow darkening is linked to dust events and varies spatially, snowmelt impacts have primarily been studied at the point scale. To address this gap, a spatially distributed process-based snow model (iSnobal) was used to simulate the snowpack under different albedos: a “baseline” estimate uninfluenced by episodic LAP variability, and an “observed” scenario where MODIS snow albedo retrievals informed darkened snow conditions. Shifts in snow disappearance date (SDD) between scenarios were used to quantify the cumulative impact of snow darkening on melt over contrasting water years (WYs). The SDD shifts were greater in WY 2022, with snow disappearing 23–29 days earlier, attributed to sunny weather and darker snow. In WY 2023, SDD shifts were moderate with melt advancing 11–16 days, despite similar melt season albedos to WY 2022. Frequent storms in WY 2023 delayed darkening until later in the season, when melt progressed suddenly due to rapid albedo declines and weak longwave losses. In both years, SDD shifts were pronounced at subalpine elevations (∼2,300–2,900 m), potentially related to snow albedo declines coinciding with high solar irradiance and snowfall patterns. These findings suggest that melt sensitivity to snow darkening shows consistent spatial patterns, but the magnitude of snowmelt impacts is controlled by seasonal variability in meteorology.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"156 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kaushlendra Verma, Simon Munier, Aaron Boone, Patrick Le Moigne
The integration of satellite-based observations into hydrological models offers transformation potential for improving discharge predictions globally, especially in regions lacking in situ measurements. This study presents CTRIP-HyDAS, a global-scale hydrological data assimilation framework that merges SWOT-derived discharge observations with the CTRIP river routing model at 1/12° spatial resolution. The framework was applied at the global scale and evaluated using Observing System Simulation Experiments under controlled discharge observation uncertainty scenarios (10%, 20%, and 40%). Performance metrics computed globally show widespread improvements, with Assimilation Index (AI) values exceeding 0.7 in most regions and relative errors reduced to within 5%–10% under low-error conditions. To illustrate the framework's adaptability, six representative river basins, that is, Amazon, Congo, Ganges, Indus, Mississippi, and Reka, were selected to showcase HyDAS performance under diverse hydrological regimes. A physics-based localization method enabled efficient propagation of corrections beyond the observed swath. These findings confirm the scalability and robustness of CTRIP-HyDAS for global SWOT-based assimilation and underline its potential to enhance discharge prediction and water management in data-scarce regions.
{"title":"CTRIP-HyDAS: A Global-Scale Data Assimilation Framework for SWOT-Derived Discharge Using Synthetic Observations at High Resolution (1/12°)","authors":"Kaushlendra Verma, Simon Munier, Aaron Boone, Patrick Le Moigne","doi":"10.1029/2025wr040888","DOIUrl":"https://doi.org/10.1029/2025wr040888","url":null,"abstract":"The integration of satellite-based observations into hydrological models offers transformation potential for improving discharge predictions globally, especially in regions lacking in situ measurements. This study presents CTRIP-HyDAS, a global-scale hydrological data assimilation framework that merges SWOT-derived discharge observations with the CTRIP river routing model at 1/12° spatial resolution. The framework was applied at the global scale and evaluated using Observing System Simulation Experiments under controlled discharge observation uncertainty scenarios (10%, 20%, and 40%). Performance metrics computed globally show widespread improvements, with Assimilation Index (AI) values exceeding 0.7 in most regions and relative errors reduced to within 5%–10% under low-error conditions. To illustrate the framework's adaptability, six representative river basins, that is, Amazon, Congo, Ganges, Indus, Mississippi, and Reka, were selected to showcase HyDAS performance under diverse hydrological regimes. A physics-based localization method enabled efficient propagation of corrections beyond the observed swath. These findings confirm the scalability and robustness of CTRIP-HyDAS for global SWOT-based assimilation and underline its potential to enhance discharge prediction and water management in data-scarce regions.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"89 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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