Yalan Song, Kamlesh Sawadekar, Jonathan M. Frame, Ming Pan, Martyn P. Clark, Wouter J. M. Knoben, Andrew W. Wood, Kathryn E. Lawson, Trupesh Patel, Chaopeng Shen
Recently, a hybrid framework combining machine learning (ML) and process-based equations, termed differentiable modeling, has shown comparable accuracy to pure ML models while offering enhanced interpretability and spatial generalizability. However, it remained unclear how well hybrid models generalize to extreme floods outside of the range of training data, and whether optimizing models for extreme events jeopardizes spatial generalizability and the physical significance of internal variables. Here we evaluated multiple versions of a differentiable model (δHBV1.0 and δHBV1.1p) for predicting unseen extreme events, and benchmarked them against a widely-applied long short-term memory (LSTM) network on the CAMELS data set. We found that both δHBV and LSTM models performed well, with δHBV1.1p outperforming LSTM for events with a return period of 5 years or more. This advantage was more pronounced as the return period increased (0.06 higher median Nash-Sutcliffe efficiency and lower peak flow errors for 80% of the 50-year or rarer events). Loss function choice had a larger impact on δHBV1.1p than on LSTM, and we showed the proper loss led to δHBV models that further surpassed LSTM in different performance aspects. Furthermore, allowing more dynamic parameters improved the extreme metrics, had no negative impact on spatial generalization, and exerted a minimal influence on the untrained variables. We hypothesize that δHBV's mass balance and first-order exchange terms help constrain and inform its responses to mitigate the underestimation of peaks compared to LSTM. We conclude that adopting interpretable structural priors can improve generalizability to unseen cases and thus increase model reliability to better inform stakeholder preparedness.
{"title":"Physics-Informed, Differentiable Hydrologic Models for Capturing Unseen Extreme Events","authors":"Yalan Song, Kamlesh Sawadekar, Jonathan M. Frame, Ming Pan, Martyn P. Clark, Wouter J. M. Knoben, Andrew W. Wood, Kathryn E. Lawson, Trupesh Patel, Chaopeng Shen","doi":"10.1029/2025wr040414","DOIUrl":"https://doi.org/10.1029/2025wr040414","url":null,"abstract":"Recently, a hybrid framework combining machine learning (ML) and process-based equations, termed differentiable modeling, has shown comparable accuracy to pure ML models while offering enhanced interpretability and spatial generalizability. However, it remained unclear how well hybrid models generalize to extreme floods outside of the range of training data, and whether optimizing models for extreme events jeopardizes spatial generalizability and the physical significance of internal variables. Here we evaluated multiple versions of a differentiable model (δHBV1.0 and δHBV1.1p) for predicting unseen extreme events, and benchmarked them against a widely-applied long short-term memory (LSTM) network on the CAMELS data set. We found that both δHBV and LSTM models performed well, with δHBV1.1p outperforming LSTM for events with a return period of 5 years or more. This advantage was more pronounced as the return period increased (0.06 higher median Nash-Sutcliffe efficiency and lower peak flow errors for 80% of the 50-year or rarer events). Loss function choice had a larger impact on δHBV1.1p than on LSTM, and we showed the proper loss led to δHBV models that further surpassed LSTM in different performance aspects. Furthermore, allowing more dynamic parameters improved the extreme metrics, had no negative impact on spatial generalization, and exerted a minimal influence on the untrained variables. We hypothesize that δHBV's mass balance and first-order exchange terms help constrain and inform its responses to mitigate the underestimation of peaks compared to LSTM. We conclude that adopting interpretable structural priors can improve generalizability to unseen cases and thus increase model reliability to better inform stakeholder preparedness.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"40 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146205043","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}
Daniel Broman, Nathalie Voisin, Scott Steinschneider, Jordan Kern, Henry Ssembatya, Sungwook Wi
Hydropower facilities represent a key electricity generating resource in the U.S. Western Interconnection. These facilities rely upon forecasts of inflow when scheduling releases to generate electricity. However, hydropower operations represented in bulk power systems models do not reflect uncertainty in inflow forecasts. This study aims to evaluate how inflow forecast uncertainties impact hydropower generation and revenues at the scale of an entire power grid at a spatial scale relevant to power system modeling. The question is critical and timely as more flexibility is called upon to integrate other technologies without understanding the flexibility already exercised. New advances are needed to represent hydropower contributions under operational uncertainty at the interconnection scale. Our contribution includes the development of consistent and coincident medium-range (0–7 days) inflow forecasts and a generic hydropower scheduler, Forecast-Informed Scheduler for Hydropower (FIScH), that captures non-powered water management objectives and constraints and allows for varying electricity prices. This scheduler was applied at 242 hydropower facilities representing 86% of the conventional nameplate capacity in the Western Interconnection. Hydropower revenues were examined for schedules developed using three sets of inflow forecasts with differing levels of accuracy over a 20-year period from 2000 to 2019. In aggregate, we find that annual hydropower revenue decreases 0.08% when using more skillful forecasts, and 0.11% when using baseline persistence forecasts as compared to revenue using perfect forecasts. Regional and interannual results were more varied and ranged between −1 and 4%. The translation of improved forecast skill into higher revenues is non-linear and varies regionally, with larger revenue changes on the west coast and smaller responses across the interior western U.S. Overall, we demonstrate that scheduling mostly alleviates the impact of inflow forecast errors on hydropower revenue. The study motivates the need for a more detailed evaluation into which specific hydrologic events impact hydropower scheduling and revenue at the system scale.
{"title":"How Hydropower Operations Mitigate Flow Forecast Uncertainties to Maintain Grid Services in the Western U.S.","authors":"Daniel Broman, Nathalie Voisin, Scott Steinschneider, Jordan Kern, Henry Ssembatya, Sungwook Wi","doi":"10.1029/2025wr040943","DOIUrl":"https://doi.org/10.1029/2025wr040943","url":null,"abstract":"Hydropower facilities represent a key electricity generating resource in the U.S. Western Interconnection. These facilities rely upon forecasts of inflow when scheduling releases to generate electricity. However, hydropower operations represented in bulk power systems models do not reflect uncertainty in inflow forecasts. This study aims to evaluate how inflow forecast uncertainties impact hydropower generation and revenues at the scale of an entire power grid at a spatial scale relevant to power system modeling. The question is critical and timely as more flexibility is called upon to integrate other technologies without understanding the flexibility already exercised. New advances are needed to represent hydropower contributions under operational uncertainty at the interconnection scale. Our contribution includes the development of consistent and coincident medium-range (0–7 days) inflow forecasts and a generic hydropower scheduler, Forecast-Informed Scheduler for Hydropower (FIScH), that captures non-powered water management objectives and constraints and allows for varying electricity prices. This scheduler was applied at 242 hydropower facilities representing 86% of the conventional nameplate capacity in the Western Interconnection. Hydropower revenues were examined for schedules developed using three sets of inflow forecasts with differing levels of accuracy over a 20-year period from 2000 to 2019. In aggregate, we find that annual hydropower revenue decreases 0.08% when using more skillful forecasts, and 0.11% when using baseline persistence forecasts as compared to revenue using perfect forecasts. Regional and interannual results were more varied and ranged between −1 and 4%. The translation of improved forecast skill into higher revenues is non-linear and varies regionally, with larger revenue changes on the west coast and smaller responses across the interior western U.S. Overall, we demonstrate that scheduling mostly alleviates the impact of inflow forecast errors on hydropower revenue. The study motivates the need for a more detailed evaluation into which specific hydrologic events impact hydropower scheduling and revenue at the system scale.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"7 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146161076","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}
Qiuheng Xie, Senyou An, Heping Xie, Wendong Wang, Vahid Niasar
Carbon dioxide-rich brine formation during geological carbon sequestration induces calcite dissolution, governed by the physicochemical coupling of fluid flow, reactive transport, and pore structure evolution. Unveiling the mechanisms that control this dissolution, particularly under varying flow and structural conditions, is essential for predicting CO2 plume migration and ensuring long-term storage stability. While previous studies have explored these coupled processes, they often lack explicit resolution of fracture-matrix interactions and are limited by computational scalability. In this study, we present a novel pore-scale numerical framework that integrates the volumetric lattice Boltzmann method with a GPU-CUDA parallel computing architecture, enabling efficient simulations of reactive flow in both fracture-free system and fracture-matrix system. Results reveal that injection velocity governs dissolution morphology and efficiency, with higher velocities reducing reactivity due to preferential flow, while temperature moderately enhances front heterogeneity but has limited impact on overall dissolution behavior. Based on the dissolution profiles observed in two types of 3D carbonate rock cores, three distinct calcite dissolution regimes (uniform, channel widening, and face dissolution) are identified. Moreover, the normalized permeability-porosity relationship exhibits a negative correlation with temperature across all cases, except at higher injection velocities in the fracture-matrix system, where a mixed correlation emerges under the influence of the fracture.
{"title":"Pore-Scale Transition Behavior of Digital Carbonate Rock Dissolution During CO2 Geo-Sequestration","authors":"Qiuheng Xie, Senyou An, Heping Xie, Wendong Wang, Vahid Niasar","doi":"10.1029/2025wr041604","DOIUrl":"https://doi.org/10.1029/2025wr041604","url":null,"abstract":"Carbon dioxide-rich brine formation during geological carbon sequestration induces calcite dissolution, governed by the physicochemical coupling of fluid flow, reactive transport, and pore structure evolution. Unveiling the mechanisms that control this dissolution, particularly under varying flow and structural conditions, is essential for predicting CO<sub>2</sub> plume migration and ensuring long-term storage stability. While previous studies have explored these coupled processes, they often lack explicit resolution of fracture-matrix interactions and are limited by computational scalability. In this study, we present a novel pore-scale numerical framework that integrates the volumetric lattice Boltzmann method with a GPU-CUDA parallel computing architecture, enabling efficient simulations of reactive flow in both fracture-free system and fracture-matrix system. Results reveal that injection velocity governs dissolution morphology and efficiency, with higher velocities reducing reactivity due to preferential flow, while temperature moderately enhances front heterogeneity but has limited impact on overall dissolution behavior. Based on the dissolution profiles observed in two types of 3D carbonate rock cores, three distinct calcite dissolution regimes (uniform, channel widening, and face dissolution) are identified. Moreover, the normalized permeability-porosity relationship exhibits a negative correlation with temperature across all cases, except at higher injection velocities in the fracture-matrix system, where a mixed correlation emerges under the influence of the fracture.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"13 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146161075","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}
Agricultural production is a major consumer of water resources, and the crop water footprint (CWF) serves as a comprehensive metric for assessing agricultural water use efficiency and its associated impacts, thereby providing new insights for agricultural water management. However, quantitative studies of regional CWF require extensive ground observations and are often constrained by scale effects, limited accuracy, and spatiotemporal discontinuities. To address these limitations, we developed a high-precision CWF quantification framework that assimilates remotely sensed leaf area index and downscaled soil moisture into the World Food Studies crop model using the Ensemble Kalman Filter. Application of the proposed framework in the Hetao Irrigation District successfully mapped the high-precision maize production water footprint, revealing a spatial pattern characterized by higher values in the eastern and western regions and lower values in the central area. The mean green water footprint, blue water footprint, and total water footprint of maize were 0.045 m3/kg, 0.660 m3/kg, and 0.705 m3/kg, respectively. Compared with estimates derived from remote sensing evapotranspiration products and the FAO Penman–Monteith method, the data assimilation framework improved the accuracy and spatial representativeness of maize water footprint estimation. Overall, the proposed framework provides a reliable tool for quantifying agricultural water-use efficiency and lays a data and methodological foundation for refined water resources management.
{"title":"A High-Precision Crop Water Footprint Quantification Framework Based on Data Assimilation","authors":"Ting Bai, Shikun Sun, Yali Yin, Dongmei Zhao, Hao Dong, Jing Xue, Jinfeng Zhao, Yubao Wang, Pute Wu","doi":"10.1029/2024wr039817","DOIUrl":"https://doi.org/10.1029/2024wr039817","url":null,"abstract":"Agricultural production is a major consumer of water resources, and the crop water footprint (CWF) serves as a comprehensive metric for assessing agricultural water use efficiency and its associated impacts, thereby providing new insights for agricultural water management. However, quantitative studies of regional CWF require extensive ground observations and are often constrained by scale effects, limited accuracy, and spatiotemporal discontinuities. To address these limitations, we developed a high-precision CWF quantification framework that assimilates remotely sensed leaf area index and downscaled soil moisture into the World Food Studies crop model using the Ensemble Kalman Filter. Application of the proposed framework in the Hetao Irrigation District successfully mapped the high-precision maize production water footprint, revealing a spatial pattern characterized by higher values in the eastern and western regions and lower values in the central area. The mean green water footprint, blue water footprint, and total water footprint of maize were 0.045 m<sup>3</sup>/kg, 0.660 m<sup>3</sup>/kg, and 0.705 m<sup>3</sup>/kg, respectively. Compared with estimates derived from remote sensing evapotranspiration products and the FAO Penman–Monteith method, the data assimilation framework improved the accuracy and spatial representativeness of maize water footprint estimation. Overall, the proposed framework provides a reliable tool for quantifying agricultural water-use efficiency and lays a data and methodological foundation for refined water resources management.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"394 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146160937","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}
Anthony C. Ross, Boris Ochoa-Tocachi, Vivien Bonnesoeur, Braulio Lahuatte, Paola Fuentes, Javier Antiporta, Mauricio F. Villazon, Wouter Buytaert
Land use and land cover change (LUCC) can affect the hydrological response time of rivers. However, it is difficult to generate robust and quantitative evidence of this impact at the catchment scale. This lack of evidence also affects the development of rainfall-runoff models to make ex-ante predictions. Here, we analyze high-frequency observational data from a network of pairwise catchments in the tropical Andes and find a statistically significant impact of intensive land use on the hydrological response time, which can be used for regionalization. First, we isolated individual rainfall response events from 5-min precipitation and discharge time series of 16 catchments (8 pairs). We then fitted unit hydrographs on these events to estimate the catchment response times. These response times were subsequently regionalized by, first, applying a forward stepwise regression to select statistically significant catchment characteristics including land use and land cover, then, fitting a linear mixed-effects model with the selected characteristics to account for within-site variability between pairs. We find that catchments with intensive land use have a significantly quicker response than their natural counterparts. Differences were often sub-hourly, highlighting the value of high-frequency monitoring. Forward stepwise regression identified only catchment area and intensive land use percentage (LUP) as statistically significant predictors. Model coefficients show that, even when considering other catchment characteristics, increasing intensive LUP decreases response times. This study provides solid evidence and a robust methodology to quantify the impacts of LUCC on catchment hydrology.
{"title":"Quantifying and Regionalizing Land Use Impacts on Catchment Response Times With High-Frequency Observations","authors":"Anthony C. Ross, Boris Ochoa-Tocachi, Vivien Bonnesoeur, Braulio Lahuatte, Paola Fuentes, Javier Antiporta, Mauricio F. Villazon, Wouter Buytaert","doi":"10.1029/2025wr040922","DOIUrl":"https://doi.org/10.1029/2025wr040922","url":null,"abstract":"Land use and land cover change (LUCC) can affect the hydrological response time of rivers. However, it is difficult to generate robust and quantitative evidence of this impact at the catchment scale. This lack of evidence also affects the development of rainfall-runoff models to make ex-ante predictions. Here, we analyze high-frequency observational data from a network of pairwise catchments in the tropical Andes and find a statistically significant impact of intensive land use on the hydrological response time, which can be used for regionalization. First, we isolated individual rainfall response events from 5-min precipitation and discharge time series of 16 catchments (8 pairs). We then fitted unit hydrographs on these events to estimate the catchment response times. These response times were subsequently regionalized by, first, applying a forward stepwise regression to select statistically significant catchment characteristics including land use and land cover, then, fitting a linear mixed-effects model with the selected characteristics to account for within-site variability between pairs. We find that catchments with intensive land use have a significantly quicker response than their natural counterparts. Differences were often sub-hourly, highlighting the value of high-frequency monitoring. Forward stepwise regression identified only catchment area and intensive land use percentage (LUP) as statistically significant predictors. Model coefficients show that, even when considering other catchment characteristics, increasing intensive LUP decreases response times. This study provides solid evidence and a robust methodology to quantify the impacts of LUCC on catchment hydrology.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"100 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146161074","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}
Lander Van Tricht, Harry Zekollari, Matthias Huss, Marit van Tiel, Inne Vanderkelen, Rodrigo Aguayo, Loris Compagno, Wim Thiery, Philippe Huybrechts, Daniel Farinotti
Glaciers in the Tien Shan mountains of Central Asia are a crucial source of freshwater for agriculture and local communities, sustaining over 100 million people in the region. Here, we project future water availability for this region by dynamically modeling the evolution of all glaciers and merging their runoff with hydrological runoff simulations from three global hydrological models. We compare this hybrid estimate of water availability with water demand simulations to assess potential risks of water scarcity. Our findings show that Tien Shan glaciers are projected to lose around one-third of their 2020 ice mass before 2040, and between 69% and 93% by 2100, depending on the climate scenario. Compared to a 2000–2020 baseline, the runoff from glacier areas initially increases by 15%–24%, reaching its peak before 2050, followed by a decrease of 14%–20% by 2100. This reduction is particularly pronounced in summer, when glacier areas currently supply up to 45% of total summer runoff, coinciding with peak water demand. A gradual shift in glacier-area runoff toward spring further intensifies the risk of summer water shortages. By the late 21st century, the probability of unmet water demand in summer is projected to increase significantly, especially in heavily glaciated basins, where the increase ranges from 30% to 70% depending on the emissions scenario. These findings highlight the urgent need for adaptive water management strategies and climate mitigation efforts to preserve long-term water security for millions of people reliant on glacier-fed rivers for their water supply.
{"title":"Reduced Future Summer Water Availability in the Tien Shan Due To Glacier Wastage","authors":"Lander Van Tricht, Harry Zekollari, Matthias Huss, Marit van Tiel, Inne Vanderkelen, Rodrigo Aguayo, Loris Compagno, Wim Thiery, Philippe Huybrechts, Daniel Farinotti","doi":"10.1029/2025wr040877","DOIUrl":"https://doi.org/10.1029/2025wr040877","url":null,"abstract":"Glaciers in the Tien Shan mountains of Central Asia are a crucial source of freshwater for agriculture and local communities, sustaining over 100 million people in the region. Here, we project future water availability for this region by dynamically modeling the evolution of all glaciers and merging their runoff with hydrological runoff simulations from three global hydrological models. We compare this hybrid estimate of water availability with water demand simulations to assess potential risks of water scarcity. Our findings show that Tien Shan glaciers are projected to lose around one-third of their 2020 ice mass before 2040, and between 69% and 93% by 2100, depending on the climate scenario. Compared to a 2000–2020 baseline, the runoff from glacier areas initially increases by 15%–24%, reaching its peak before 2050, followed by a decrease of 14%–20% by 2100. This reduction is particularly pronounced in summer, when glacier areas currently supply up to 45% of total summer runoff, coinciding with peak water demand. A gradual shift in glacier-area runoff toward spring further intensifies the risk of summer water shortages. By the late 21st century, the probability of unmet water demand in summer is projected to increase significantly, especially in heavily glaciated basins, where the increase ranges from 30% to 70% depending on the emissions scenario. These findings highlight the urgent need for adaptive water management strategies and climate mitigation efforts to preserve long-term water security for millions of people reliant on glacier-fed rivers for their water supply.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"50 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146508","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}
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}
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