Bioaugmented microbially induced carbonate precipitation (MICP) is a potentially useful tool for permeability modification of the subsurface. There is, however, uncertainty surrounding how the transport and mineralization capability of augmenting organisms such as Sporosarcina pasteurii may vary with reservoir properties. Resolving these uncertainties requires further experimental work on natural rock samples; this necessitates, in turn, creative approaches to improving the reproducibility and generalizability of such experimental work. In this study, natural sandstones with different clay contents are processed to narrow grain size ranges and packed into columns, allowing the effect of clay content to be studied independently of pore size. Clay content is shown to have a significant effect on S. pasteurii attachment to rock surfaces, possibly due to the high specific surface area of clay minerals, while the effect of pore size is minor in the absence of straining. Furthermore, differences in S. pasteurii affinity for solid surfaces produce clear differences in the quantity and distribution of precipitate accumulation. When viable S. pasteurii cells are mostly surface-attached, precipitate accumulation begins almost immediately and precipitates appear to form primarily on grain surfaces. When only a small fraction of S. pasteurii is surface-attached, precipitate accumulation begins later but becomes significant with time. In this case, however, precipitates appear to form primarily in suspension, which may produce different precipitation efficiencies and precipitate morphologies based on mass transport conditions.
{"title":"Clay Content Mediates the Contribution of Suspended Sporosarcina Pasteurii to Microbial Mineralization in Sandstones","authors":"E. M. Albalghiti, B. R. Ellis","doi":"10.1029/2025wr040790","DOIUrl":"https://doi.org/10.1029/2025wr040790","url":null,"abstract":"Bioaugmented microbially induced carbonate precipitation (MICP) is a potentially useful tool for permeability modification of the subsurface. There is, however, uncertainty surrounding how the transport and mineralization capability of augmenting organisms such as <i>Sporosarcina pasteurii</i> may vary with reservoir properties. Resolving these uncertainties requires further experimental work on natural rock samples; this necessitates, in turn, creative approaches to improving the reproducibility and generalizability of such experimental work. In this study, natural sandstones with different clay contents are processed to narrow grain size ranges and packed into columns, allowing the effect of clay content to be studied independently of pore size. Clay content is shown to have a significant effect on <i>S. pasteurii</i> attachment to rock surfaces, possibly due to the high specific surface area of clay minerals, while the effect of pore size is minor in the absence of straining. Furthermore, differences in <i>S. pasteurii</i> affinity for solid surfaces produce clear differences in the quantity and distribution of precipitate accumulation. When viable <i>S. pasteurii</i> cells are mostly surface-attached, precipitate accumulation begins almost immediately and precipitates appear to form primarily on grain surfaces. When only a small fraction of <i>S. pasteurii</i> is surface-attached, precipitate accumulation begins later but becomes significant with time. In this case, however, precipitates appear to form primarily in suspension, which may produce different precipitation efficiencies and precipitate morphologies based on mass transport conditions.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"27 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146000896","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}
Distributed acoustic sensing (DAS) with preexisting telecommunication optical fibers (dark fibers) has shown its ability to record rain-induced seismic noise with unprecedented high spatiotemporal resolution. This rain-induced noise exhibits strong correlations with rainfall intensity and rainwater discharge in pipeline sewers, highlighting its potential to infer rainwater flow characteristics. While raindrop impact models exist, a physical model linking stormwater discharge processes to DAS-recorded signals is still lacking. In this study, we introduce a data-driven method, deep embedded clustering (DEC), to automatically detect and classify rain-induced noise from massive DAS data, predicting the presence of moderate to heavy rain and the duration of stormwater discharge. We analyze continuous DAS recordings from 2019 to 2021 from a 4.2 km-long underground fiber-optic array in State College, PA. During training, the DEC model employs an autoencoder to learn the latent features from preprocessed spectrograms and then clusters these latent features into four clusters. Distinct features from spectrograms within each cluster reveal that four clusters correspond to background noise, rain-induced noise of varying rain intensities and stormwater discharge in sewers. Tests on unseen data sets in 2019 and 2021 demonstrate DEC's ability to not only predict rainfall rate levels but also indicate post-rain discharge durations. Furthermore, the model-derived post-rain discharge durations align with synthetic hydrograph estimates, yielding a drainage system time of concentration as 21 min in this region. Finally, we apply this workflow to two more locations to show the potential of spatial monitoring. Our results show that the combination of machine learning and fiber-optic sensing offers a scalable solution for improving stormwater management in urban environments.
{"title":"Unsupervised Characterization of Rain-Induced Seismic Noise in Urban Fiber-Optic Networks Using Deep Embedded Clustering","authors":"Junzhu Shen, Tieyuan Zhu","doi":"10.1029/2025wr041137","DOIUrl":"https://doi.org/10.1029/2025wr041137","url":null,"abstract":"Distributed acoustic sensing (DAS) with preexisting telecommunication optical fibers (dark fibers) has shown its ability to record rain-induced seismic noise with unprecedented high spatiotemporal resolution. This rain-induced noise exhibits strong correlations with rainfall intensity and rainwater discharge in pipeline sewers, highlighting its potential to infer rainwater flow characteristics. While raindrop impact models exist, a physical model linking stormwater discharge processes to DAS-recorded signals is still lacking. In this study, we introduce a data-driven method, deep embedded clustering (DEC), to automatically detect and classify rain-induced noise from massive DAS data, predicting the presence of moderate to heavy rain and the duration of stormwater discharge. We analyze continuous DAS recordings from 2019 to 2021 from a 4.2 km-long underground fiber-optic array in State College, PA. During training, the DEC model employs an autoencoder to learn the latent features from preprocessed spectrograms and then clusters these latent features into four clusters. Distinct features from spectrograms within each cluster reveal that four clusters correspond to background noise, rain-induced noise of varying rain intensities and stormwater discharge in sewers. Tests on unseen data sets in 2019 and 2021 demonstrate DEC's ability to not only predict rainfall rate levels but also indicate post-rain discharge durations. Furthermore, the model-derived post-rain discharge durations align with synthetic hydrograph estimates, yielding a drainage system time of concentration as 21 min in this region. Finally, we apply this workflow to two more locations to show the potential of spatial monitoring. Our results show that the combination of machine learning and fiber-optic sensing offers a scalable solution for improving stormwater management in urban environments.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"30 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001487","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}
Water distribution systems (WDSs) utilize battery-powered sensors to monitor essential parameters like flow rate and pressure. Limited battery life requires reducing data upload frequencies to conserve energy, potentially compromising real-time monitoring vital for system reliability and performance. This challenge is addressed by leveraging temporal redundancies from daily cycles and spatial redundancies from sensor data correlations, enabling data extrapolation instead of continuous transmission. This study proposes an edge computing-based sensor scheduling method that optimizes data transmission frequency while maintaining high data accuracy, thereby extending sensor longevity without sacrificing monitoring capabilities. The proposed approach uses predictive models to forecast future sensor values over multiple time steps based on existing data redundancies. If the deviation between predicted and actual measurements is within a predefined threshold, data transmission is skipped, reducing sensor power consumption; otherwise, data is transmitted to ensure accuracy. Applied to a realistic WDS sensor network, the method achieved up to a 75% reduction in sensor energy consumption with 48 estimation steps and a 0.5 m error threshold, while maintaining a relative data error of only 0.7%. These results demonstrate the method's effectiveness in balancing energy savings with data reliability, suggesting a viable solution for enhancing WDS sustainability and efficiency.
{"title":"Edge Computing for Energy-Efficient Sensor Scheduling in Water Distribution Systems","authors":"Shaosong Wei, Tingchao Yu, Avi Ostfeld, Chengyin Liu, Shipeng Chu, Hao Shen","doi":"10.1029/2025wr040149","DOIUrl":"https://doi.org/10.1029/2025wr040149","url":null,"abstract":"Water distribution systems (WDSs) utilize battery-powered sensors to monitor essential parameters like flow rate and pressure. Limited battery life requires reducing data upload frequencies to conserve energy, potentially compromising real-time monitoring vital for system reliability and performance. This challenge is addressed by leveraging temporal redundancies from daily cycles and spatial redundancies from sensor data correlations, enabling data extrapolation instead of continuous transmission. This study proposes an edge computing-based sensor scheduling method that optimizes data transmission frequency while maintaining high data accuracy, thereby extending sensor longevity without sacrificing monitoring capabilities. The proposed approach uses predictive models to forecast future sensor values over multiple time steps based on existing data redundancies. If the deviation between predicted and actual measurements is within a predefined threshold, data transmission is skipped, reducing sensor power consumption; otherwise, data is transmitted to ensure accuracy. Applied to a realistic WDS sensor network, the method achieved up to a 75% reduction in sensor energy consumption with 48 estimation steps and a 0.5 m error threshold, while maintaining a relative data error of only 0.7%. These results demonstrate the method's effectiveness in balancing energy savings with data reliability, suggesting a viable solution for enhancing WDS sustainability and efficiency.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"142 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001554","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}
Martín Marañón, Alfredo Durán, Rigel Rocha, Monika Winder, Carmen Ledo, Virgilio Martínez, Alfredo Mendoza, Fernando Jaramillo
Water resource assessments are critical for ensuring water security (WS), particularly in rapidly urbanizing regions with increasing water demand and limited water monitoring capabilities. Earth observations and indirect indicators of surface and groundwater changes are valuable tools for developing such assessments. This study examines WS by combining trends in pumping energy consumption and water-induced ground deformation over time and space in the sprawling metropolitan region of Cochabamba, Bolivia. We integrate Interferometric Synthetic Aperture Radar data with pumping energy consumption records from an extensive well network in the period 2012 to 2022. Statistical analysis identifies four trends in energy consumption (increasing, decreasing, stable, and no consumption) and three in ground deformation (uplift, subsidence, and no change). Based on these trends, we define four WS scenarios: WS, Threatened Water Security, water insecurity (WI), and Reversible Water Insecurity. Results reveal predominant domestic groundwater use and an increasing trend in energy consumption by pumping. In more than 1000 of these wells, both unsustainable water use and subsidence occur, implying WI. This study demonstrates the potential of combining InSAR-derived ground deformation and pumping energy consumption as a cost-effective and scalable groundwater monitoring tool for WS assessments.
{"title":"InSAR Ground Deformation and Pumping Energy Consumption Reveal Urban Water Security","authors":"Martín Marañón, Alfredo Durán, Rigel Rocha, Monika Winder, Carmen Ledo, Virgilio Martínez, Alfredo Mendoza, Fernando Jaramillo","doi":"10.1029/2025wr040704","DOIUrl":"https://doi.org/10.1029/2025wr040704","url":null,"abstract":"Water resource assessments are critical for ensuring water security (WS), particularly in rapidly urbanizing regions with increasing water demand and limited water monitoring capabilities. Earth observations and indirect indicators of surface and groundwater changes are valuable tools for developing such assessments. This study examines WS by combining trends in pumping energy consumption and water-induced ground deformation over time and space in the sprawling metropolitan region of Cochabamba, Bolivia. We integrate Interferometric Synthetic Aperture Radar data with pumping energy consumption records from an extensive well network in the period 2012 to 2022. Statistical analysis identifies four trends in energy consumption (increasing, decreasing, stable, and no consumption) and three in ground deformation (uplift, subsidence, and no change). Based on these trends, we define four WS scenarios: WS, Threatened Water Security, water insecurity (WI), and Reversible Water Insecurity. Results reveal predominant domestic groundwater use and an increasing trend in energy consumption by pumping. In more than 1000 of these wells, both unsustainable water use and subsidence occur, implying WI. This study demonstrates the potential of combining InSAR-derived ground deformation and pumping energy consumption as a cost-effective and scalable groundwater monitoring tool for WS assessments.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"178 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145993414","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}
Drought significantly affects water resources, agriculture, energy, and ecosystems, revealing enduring socio-economic vulnerabilities over the centuries. This review synthesizes a century of development and recent advances in drought research (1900–2023), drawing on a bibliometric analysis of over 152,000 peer-reviewed publications. The review begins by exploring ancient and historical droughts, their climatic drivers, and societal impacts, then examines the evolving disciplinary landscape, shifting research priorities, and the progression of drought research over the past century. Key methodological advances are discussed, including statistical and probabilistic modeling, machine learning, and deep learning. Technical milestones such as satellite remote sensing, hydrological and land surface modeling, and global climate modeling have greatly expanded both the scope and precision of drought studies. Research on climate change has deepened understanding of drought processes by examining changes in climate variability and teleconnections, attributing events to human influence, and projecting future risks. Simultaneously, there has been a notable shift from reactive approaches to resilience-oriented management, enhancing preparedness. In the past decade, increasing attention has focused on emerging societal challenges such as environmental degradation, public health risks, social inequities, and resource conflicts. Despite significant progress, critical gaps remain, including the need for stakeholder-informed indicators, improved flash drought detection, a deeper understanding of cascading processes, integration of human-driven factors, enhanced interpretability of AI models, next-generation satellite monitoring, and comprehensive risk management for drought-related compound hazards. This synthesis consolidates a century of progress and presents a forward-looking framework aimed at strengthening resilience and guiding actionable drought risk governance.
{"title":"A Century of Drought Research (1900–2023): Scientific Developments, Methodological Innovations, and Emerging Frontiers","authors":"Amitesh Sabut, Ashok Mishra","doi":"10.1029/2025wr041987","DOIUrl":"https://doi.org/10.1029/2025wr041987","url":null,"abstract":"Drought significantly affects water resources, agriculture, energy, and ecosystems, revealing enduring socio-economic vulnerabilities over the centuries. This review synthesizes a century of development and recent advances in drought research (1900–2023), drawing on a bibliometric analysis of over 152,000 peer-reviewed publications. The review begins by exploring ancient and historical droughts, their climatic drivers, and societal impacts, then examines the evolving disciplinary landscape, shifting research priorities, and the progression of drought research over the past century. Key methodological advances are discussed, including statistical and probabilistic modeling, machine learning, and deep learning. Technical milestones such as satellite remote sensing, hydrological and land surface modeling, and global climate modeling have greatly expanded both the scope and precision of drought studies. Research on climate change has deepened understanding of drought processes by examining changes in climate variability and teleconnections, attributing events to human influence, and projecting future risks. Simultaneously, there has been a notable shift from reactive approaches to resilience-oriented management, enhancing preparedness. In the past decade, increasing attention has focused on emerging societal challenges such as environmental degradation, public health risks, social inequities, and resource conflicts. Despite significant progress, critical gaps remain, including the need for stakeholder-informed indicators, improved flash drought detection, a deeper understanding of cascading processes, integration of human-driven factors, enhanced interpretability of AI models, next-generation satellite monitoring, and comprehensive risk management for drought-related compound hazards. This synthesis consolidates a century of progress and presents a forward-looking framework aimed at strengthening resilience and guiding actionable drought risk governance.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"222 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145993158","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}
Charles R. Kimsal, Enrique R. Vivoni, Osvaldo E. Sala, H. Curtis Monger, Owen P. McKenna
Ephemerally flooded playas are common in the southwestern United States and globally in drylands. Often formed in closed basins, playas are depressions which inundate infrequently from local precipitation and streamflow produced near the playa or from upland areas. Few studies have quantified the hydrologic connectivity between upland catchments and playas using observations. Here, we used rain gauge-corrected precipitation from weather radar and water level measurements in 18 playas of the Chihuahuan Desert to identify precipitation thresholds leading to playa inundation over a 6.4-year period. Geospatial data sets on topography, soil properties, and vegetation cover were employed to determine the controls on inundation. Only 9.4% of all precipitation events above 1 mm led to inundation, with 69.8% of all inundations occurring during the North American monsoon (NAM, July-September). Mean and standard deviations (Std) of runoff ratios at all playas were 2.74 ± 4.08% and 3.29 ± 5.19% for annual and NAM periods. At the annual scale, playa inundation occurred when mean precipitation thresholds of 18.3 ± 7.5 mm (event total) and 12.0 ± 4.5 mm/hr (60-min intensity) were exceeded. Across all playas, inundation occurrence and volume were related most strongly to precipitation metrics and catchment area, with secondary controls of soil and terrain properties. The explanatory power of the derived regressions describing the inundation response across the playas were significantly improved when considering their geological origin. As a result, the inundation response classification system could be applied to ephemeral playas in other arid and semiarid landscapes.
{"title":"Hydrologic Dynamics of Ephemerally Flooded Playas in a Dryland Environment","authors":"Charles R. Kimsal, Enrique R. Vivoni, Osvaldo E. Sala, H. Curtis Monger, Owen P. McKenna","doi":"10.1029/2024wr038848","DOIUrl":"https://doi.org/10.1029/2024wr038848","url":null,"abstract":"Ephemerally flooded playas are common in the southwestern United States and globally in drylands. Often formed in closed basins, playas are depressions which inundate infrequently from local precipitation and streamflow produced near the playa or from upland areas. Few studies have quantified the hydrologic connectivity between upland catchments and playas using observations. Here, we used rain gauge-corrected precipitation from weather radar and water level measurements in 18 playas of the Chihuahuan Desert to identify precipitation thresholds leading to playa inundation over a 6.4-year period. Geospatial data sets on topography, soil properties, and vegetation cover were employed to determine the controls on inundation. Only 9.4% of all precipitation events above 1 mm led to inundation, with 69.8% of all inundations occurring during the North American monsoon (NAM, July-September). Mean and standard deviations (Std) of runoff ratios at all playas were 2.74 ± 4.08% and 3.29 ± 5.19% for annual and NAM periods. At the annual scale, playa inundation occurred when mean precipitation thresholds of 18.3 ± 7.5 mm (event total) and 12.0 ± 4.5 mm/hr (60-min intensity) were exceeded. Across all playas, inundation occurrence and volume were related most strongly to precipitation metrics and catchment area, with secondary controls of soil and terrain properties. The explanatory power of the derived regressions describing the inundation response across the playas were significantly improved when considering their geological origin. As a result, the inundation response classification system could be applied to ephemeral playas in other arid and semiarid landscapes.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"81 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145971974","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}
Sai Veena Sunkara, Jonathan D. Herman, Matteo Giuliani
Recent studies have shown the potential for reservoir control policies to adapt to uncertain future climate and demand by reoptimizing on a fixed time interval. However, this strategy is independent of the system evolution and might implement late or unnecessary adaptation. This study develops a framework to identify dynamic decisions on two levels: an “outer loop” adaptation policy that establishes indicator thresholds for reoptimization based on recently observed data, and an “inner loop” control policy that undergoes reoptimization according to these thresholds. We demonstrate this method for a case study of Oroville Reservoir, California, using an ensemble of climate model projections split into training and testing sets. The control policy uses inputs of storage, day of year, and a 5-day inflow forecast, while the adaptation policy indicators include long-term statistics of climate and demand as well as the recent system performance. Both policy levels are optimized simultaneously using heuristic policy search and analyzed with policy interpretation methods, including Shapley Additive Explanations (SHAP) and global sensitivity analysis. Results show that the adaptation solutions provide equal or better performance compared to the historical benchmark and are robust to out-of-sample scenarios. Additionally, the decision to reoptimize is primarily driven by demand, flood cost and mean annual flow indicators on different timescales. The proposed methodology identifies how control policy reoptimization can be initiated using observed thresholds of climate, demand, and system performance to improve adaptation under future uncertainty.
{"title":"Adaptation Triggers and Indicator Interpretability for Dynamic Reoptimization of Reservoir Control Policies Under Climate Change","authors":"Sai Veena Sunkara, Jonathan D. Herman, Matteo Giuliani","doi":"10.1029/2025wr040531","DOIUrl":"https://doi.org/10.1029/2025wr040531","url":null,"abstract":"Recent studies have shown the potential for reservoir control policies to adapt to uncertain future climate and demand by reoptimizing on a fixed time interval. However, this strategy is independent of the system evolution and might implement late or unnecessary adaptation. This study develops a framework to identify dynamic decisions on two levels: an “outer loop” adaptation policy that establishes indicator thresholds for reoptimization based on recently observed data, and an “inner loop” control policy that undergoes reoptimization according to these thresholds. We demonstrate this method for a case study of Oroville Reservoir, California, using an ensemble of climate model projections split into training and testing sets. The control policy uses inputs of storage, day of year, and a 5-day inflow forecast, while the adaptation policy indicators include long-term statistics of climate and demand as well as the recent system performance. Both policy levels are optimized simultaneously using heuristic policy search and analyzed with policy interpretation methods, including Shapley Additive Explanations (SHAP) and global sensitivity analysis. Results show that the adaptation solutions provide equal or better performance compared to the historical benchmark and are robust to out-of-sample scenarios. Additionally, the decision to reoptimize is primarily driven by demand, flood cost and mean annual flow indicators on different timescales. The proposed methodology identifies how control policy reoptimization can be initiated using observed thresholds of climate, demand, and system performance to improve adaptation under future uncertainty.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"141 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972441","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}
Zili Wang, Chaoyue Li, Ruilong Wei, Binlan Zhang, Peng Cui
Hydrological signatures (HS) have proven to be highly effective in calibrating physically-based hydrological models, enhancing their process consistency. However, their integration into parameter optimization for deep learning (DL)-based hydrological models has been limited. To address this gap, we propose a novel HS-informed framework that dynamically integrates HS into DL parameterization through a multi-task learning approach. This study evaluates the impact of HS integration on model performance using a large-scale, global hydrological data set. The HS-informed model achieved a significant performance improvement, with a median Nash-Sutcliffe Efficiency (NSE) of 0.739, compared to 0.666 for the baseline model across the test set. Notably, the most pronounced improvements in NSE were observed in hydrologically complex basins, including baseflow-dominated (+0.135), drought-prone (+0.148), and flood-prone basins (+0.159). Sensitivity analysis further revealed that the HS-informed model could leverage extended historical input data (over 120 days) to sustain robust performance (median NSE of 0.715) over a 30-day forecast period. Shapley Additive Explanations analysis highlighted two key mechanisms underlying these improvements: the enhanced recognition of long-term hydrological patterns through improved memory and a better representation of catchment heterogeneity by emphasizing non-climatic attributes. These findings demonstrate that integrating HS offers a superior approach to traditional point-error-based calibration in AI-driven hydrological modeling.
{"title":"A Novel Hydrological Signature-Informed Framework for Enhancing Streamflow Prediction Using Multi-Task Learning","authors":"Zili Wang, Chaoyue Li, Ruilong Wei, Binlan Zhang, Peng Cui","doi":"10.1029/2025wr041485","DOIUrl":"https://doi.org/10.1029/2025wr041485","url":null,"abstract":"Hydrological signatures (HS) have proven to be highly effective in calibrating physically-based hydrological models, enhancing their process consistency. However, their integration into parameter optimization for deep learning (DL)-based hydrological models has been limited. To address this gap, we propose a novel HS-informed framework that dynamically integrates HS into DL parameterization through a multi-task learning approach. This study evaluates the impact of HS integration on model performance using a large-scale, global hydrological data set. The HS-informed model achieved a significant performance improvement, with a median Nash-Sutcliffe Efficiency (NSE) of 0.739, compared to 0.666 for the baseline model across the test set. Notably, the most pronounced improvements in NSE were observed in hydrologically complex basins, including baseflow-dominated (+0.135), drought-prone (+0.148), and flood-prone basins (+0.159). Sensitivity analysis further revealed that the HS-informed model could leverage extended historical input data (over 120 days) to sustain robust performance (median NSE of 0.715) over a 30-day forecast period. Shapley Additive Explanations analysis highlighted two key mechanisms underlying these improvements: the enhanced recognition of long-term hydrological patterns through improved memory and a better representation of catchment heterogeneity by emphasizing non-climatic attributes. These findings demonstrate that integrating HS offers a superior approach to traditional point-error-based calibration in AI-driven hydrological modeling.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"15 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145971983","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}
The altitude effect (AE) of stable isotopes in meteoric water (δ18O and δ2H), that is, the depletion of water isotopes with increasing altitude, is an important theoretical assumption of isotope-based paleoaltimetry. However, this assumption has recently been challenged, as many in situ observations fail to consistently demonstrate the expected negative correlation between altitude and isotope values. Here we used 1,255 records of surface water isotopes to investigate AE and inverse altitude effect (IAE) and their mechanisms in arid Central Asia. The results show that isotope altitude gradients across Central Asia are weaker than the global average. Comparisons of the gradients for both the mountain-basin system and mountain system reveal that the windward and leeward slopes of the westerlies consistently exhibit opposite gradients: AE on the windward side and IAE on the leeward. The observed IAE on the leeward slope across all basins is influenced by topography and local circulation. The orientation of mountain ranges perpendicular to large-scale westerly circulation blocks eastward transport of westerly moisture, and the resulting longer moisture pathways weaken AE. Stronger local circulation and sub-cloud evaporation processes enrich water isotopes in the leeward mountain regions, diminishing AE and even leading to the emergence of IAE. Our results highlight the impact of local circulation on water isotopes during different uplift phases when using stable hydrogen and oxygen isotopes to reconstruct paleoelevation.
{"title":"Weakened Isotope Altitude Gradient in the Central Asian Water Tower: Role of Topography and Local Circulation","authors":"Yudong Shi, Shengjie Wang, Xiaokang Liu, Kei Yoshimura, Hayoung Bong, Chenggang Zhu, Yanjun Che, Huawu Wu, Mingjun Zhang","doi":"10.1029/2025wr040283","DOIUrl":"https://doi.org/10.1029/2025wr040283","url":null,"abstract":"The altitude effect (AE) of stable isotopes in meteoric water (<i>δ</i><sup>18</sup>O and <i>δ</i><sup>2</sup>H), that is, the depletion of water isotopes with increasing altitude, is an important theoretical assumption of isotope-based paleoaltimetry. However, this assumption has recently been challenged, as many <i>in situ</i> observations fail to consistently demonstrate the expected negative correlation between altitude and isotope values. Here we used 1,255 records of surface water isotopes to investigate AE and inverse altitude effect (IAE) and their mechanisms in arid Central Asia. The results show that isotope altitude gradients across Central Asia are weaker than the global average. Comparisons of the gradients for both the mountain-basin system and mountain system reveal that the windward and leeward slopes of the westerlies consistently exhibit opposite gradients: AE on the windward side and IAE on the leeward. The observed IAE on the leeward slope across all basins is influenced by topography and local circulation. The orientation of mountain ranges perpendicular to large-scale westerly circulation blocks eastward transport of westerly moisture, and the resulting longer moisture pathways weaken AE. Stronger local circulation and sub-cloud evaporation processes enrich water isotopes in the leeward mountain regions, diminishing AE and even leading to the emergence of IAE. Our results highlight the impact of local circulation on water isotopes during different uplift phases when using stable hydrogen and oxygen isotopes to reconstruct paleoelevation.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"41 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972440","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 flow and solute transport models, governed by partial differential equations (PDEs), are computationally intensive, particularly in large-scale. Traditional numerical models are prohibitively expensive, and existing surrogate models often fail under out-of-distribution (OOD) conditions, such as unseen initial conditions, boundary configurations or altered source terms. To address these challenges, we propose a novel framework based on Operator Inference (OpInf), a physics-informed surrogate modeling approach. OpInf preserves the structure of governing equations, ensuring physical consistency and interpretability, while significantly improving computational efficiency and generalization capabilities. By leveraging Proper Orthogonal Decomposition (POD) for dimensionality reduction and inferring reduced operators directly from simulation data, OpInf enables robust prediction of system behavior. We evaluate the proposed method through two case studies: the two-dimensional and three-dimensional solute transport problem under different point-source concentration fluctuation release conditions with heterogeneous hydraulic conductivity. The inversion framework is further appraised by integrating OpInf with Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) parameterization methods and the Ensemble Smoother (ES) data assimilation. Results demonstrate that OpInf relatively outperforms other surrogate models, particularly under OOD conditions and the inversion efficiency can be increased by over 99%. We establish OpInf as a transformative tool for dynamic surrogate groundwater modeling, offering robust generalization, reduced computational costs, and strong potential for real-world applications.
{"title":"Operator Inference for Physical and Generalized Surrogate Groundwater Modeling","authors":"Yongda Liu, Xi Chen, Zitao Wang, Jianzhi Dong","doi":"10.1029/2025wr039961","DOIUrl":"https://doi.org/10.1029/2025wr039961","url":null,"abstract":"Groundwater flow and solute transport models, governed by partial differential equations (PDEs), are computationally intensive, particularly in large-scale. Traditional numerical models are prohibitively expensive, and existing surrogate models often fail under out-of-distribution (OOD) conditions, such as unseen initial conditions, boundary configurations or altered source terms. To address these challenges, we propose a novel framework based on Operator Inference (OpInf), a physics-informed surrogate modeling approach. OpInf preserves the structure of governing equations, ensuring physical consistency and interpretability, while significantly improving computational efficiency and generalization capabilities. By leveraging Proper Orthogonal Decomposition (POD) for dimensionality reduction and inferring reduced operators directly from simulation data, OpInf enables robust prediction of system behavior. We evaluate the proposed method through two case studies: the two-dimensional and three-dimensional solute transport problem under different point-source concentration fluctuation release conditions with heterogeneous hydraulic conductivity. The inversion framework is further appraised by integrating OpInf with Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) parameterization methods and the Ensemble Smoother (ES) data assimilation. Results demonstrate that OpInf relatively outperforms other surrogate models, particularly under OOD conditions and the inversion efficiency can be increased by over 99%. We establish OpInf as a transformative tool for dynamic surrogate groundwater modeling, offering robust generalization, reduced computational costs, and strong potential for real-world applications.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"101 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146005714","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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