We introduce a new hydrological index that enables assessment of extreme events every few days from the GRACE Follow‐On (GRACE‐FO) satellite mission. The Mass Change Index (MCI) was developed by standardizing instantaneous satellite gravity anomalies computed directly from orbit perturbations. It is based on hydrology‐related gravity change, namely, total water storage change, and thus equally sensitive to wet and dry anomalies. The key innovation of MCI is its sensitivity to instantaneous mass changes as opposed to monthly mean changes. GRACE‐FO's ground track permits MCI retrievals every 5–6 days in most low and mid latitude regions. We demonstrate the application of MCI to investigate hydrological extremes in the middle‐lower Yangtze River Basin (MLYRB). MCI detects extreme wet conditions (standardized index of 2.0–3.0) along the Yangtze River mainstream related to the catastrophic flood in 2020, consistent with daily streamflow observations. In contrast, a typical GRACE‐FO based monthly drought index significantly underestimates the severity of the event and misidentifies timing of the onset. MCI also detects extreme dry conditions (−2.0 to −2.5) prevailing within MLYRB, related to the unprecedented heatwave and drought event during the summer of 2022. A streamflow index and the monthly drought index both underestimate the severity of the event. MCI retains information in intersatellite range measurements that may be lost when processing monthly gravity solutions. It can also be processed more rapidly, increasing its potential value for hydrological monitoring systems and other operational applications.
{"title":"Mass Change Index for Characterizing Hydrological Extremes Every Few Days From Satellite Gravity Measurements","authors":"Miao Tang, Shin‐Chan Han, Linguo Yuan, Xinghai Yang, In‐Young Yeo, Matthew Rodell, Bailing Li, Eunjee Lee, Zhongshan Jiang","doi":"10.1029/2025wr040534","DOIUrl":"https://doi.org/10.1029/2025wr040534","url":null,"abstract":"We introduce a new hydrological index that enables assessment of extreme events every few days from the GRACE Follow‐On (GRACE‐FO) satellite mission. The Mass Change Index (MCI) was developed by standardizing instantaneous satellite gravity anomalies computed directly from orbit perturbations. It is based on hydrology‐related gravity change, namely, total water storage change, and thus equally sensitive to wet and dry anomalies. The key innovation of MCI is its sensitivity to instantaneous mass changes as opposed to monthly mean changes. GRACE‐FO's ground track permits MCI retrievals every 5–6 days in most low and mid latitude regions. We demonstrate the application of MCI to investigate hydrological extremes in the middle‐lower Yangtze River Basin (MLYRB). MCI detects extreme wet conditions (standardized index of 2.0–3.0) along the Yangtze River mainstream related to the catastrophic flood in 2020, consistent with daily streamflow observations. In contrast, a typical GRACE‐FO based monthly drought index significantly underestimates the severity of the event and misidentifies timing of the onset. MCI also detects extreme dry conditions (−2.0 to −2.5) prevailing within MLYRB, related to the unprecedented heatwave and drought event during the summer of 2022. A streamflow index and the monthly drought index both underestimate the severity of the event. MCI retains information in intersatellite range measurements that may be lost when processing monthly gravity solutions. It can also be processed more rapidly, increasing its potential value for hydrological monitoring systems and other operational applications.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"56 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830034","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}
Forest disturbance can have significant impacts on mountain hydrology. With projections of increases in wildfire severity, intensity, and frequency it is important to quantify how wildfire and wildfire mitigation strategies alter the amount of water available for runoff (WAfR) from montane areas, especially in semi-arid environments. This study focuses on forest disturbance impacts on the water balance using eddy covariance flux tower data provided by three AmeriFlux towers; each can be characterized by varying levels of disturbance including a stand-replacing fire, forest thinning, and undisturbed. Individual water balance mechanisms such as actual evapotranspiration (AET) were evaluated pre- and post-disturbance to determine if any relationships emerged. The site experiencing a stand-replacing fire resulted in a canopy composition change and a significant (p < 0.05) decrease in annual, winter, spring, and fall AET after the fire. The thinned site showed a moderately significant (p < 0.1) decrease in annual AET after thinning, though this was likely controlled by the single year immediately after disturbance. Water balance terms in the undisturbed site did not significantly change over the period of this study, yet this site had the most variable WAfR due to the high rate of water use by vegetation and groundwater subsidy which made AET greater than precipitation inputs for multiple water years. Results indicate that changes in canopy composition in Southwestern US forests following a stand replacing fire, drives changes in water fluxes such as AET whereas a site experiencing changes in canopy density saw little change in water fluxes.
{"title":"Snow Sublimation Significantly Decreases Following Stand-Replacing Fire With Minor Water Balance Impacts From Forest Thinning in a Water Limited Forest","authors":"J. R. Gallais, R. W. Webb, M. E. Litvak","doi":"10.1029/2025wr042119","DOIUrl":"https://doi.org/10.1029/2025wr042119","url":null,"abstract":"Forest disturbance can have significant impacts on mountain hydrology. With projections of increases in wildfire severity, intensity, and frequency it is important to quantify how wildfire and wildfire mitigation strategies alter the amount of water available for runoff (WAfR) from montane areas, especially in semi-arid environments. This study focuses on forest disturbance impacts on the water balance using eddy covariance flux tower data provided by three AmeriFlux towers; each can be characterized by varying levels of disturbance including a stand-replacing fire, forest thinning, and undisturbed. Individual water balance mechanisms such as actual evapotranspiration (AET) were evaluated pre- and post-disturbance to determine if any relationships emerged. The site experiencing a stand-replacing fire resulted in a canopy composition change and a significant (<i>p</i> < 0.05) decrease in annual, winter, spring, and fall AET after the fire. The thinned site showed a moderately significant (<i>p</i> < 0.1) decrease in annual AET after thinning, though this was likely controlled by the single year immediately after disturbance. Water balance terms in the undisturbed site did not significantly change over the period of this study, yet this site had the most variable WAfR due to the high rate of water use by vegetation and groundwater subsidy which made AET greater than precipitation inputs for multiple water years. Results indicate that changes in canopy composition in Southwestern US forests following a stand replacing fire, drives changes in water fluxes such as AET whereas a site experiencing changes in canopy density saw little change in water fluxes.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"45 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145812865","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}
Gabriella Lükő, Eric J. Anderson, Christopher Spence, John D. Lenters, Peter D. Blanken, Erin Nicholls, Péter Torma
Turbulent heat fluxes are affected by and influence the temperature dynamics and ice conditions of lakes. Significant efforts have been made to develop operational hydrodynamic and ice models for large lakes such as the North American Great Lakes. However, the behavior of surface fluxes in these lakes has previously focused on the ice-free season and has not yet been fully assessed during winter conditions in the presence of ice. Given the importance of navigation support and regional weather forecasting, we therefore analyze operational configurations of the Great Lakes for modeled fluxes to evaluate them for open water, ice-covered, and partial ice conditions. We compare the modeled fluxes with eddy covariance-based observed fluxes from the Great Lakes Evaporation Network. While observed latent heat fluxes have periods of high values both during ice-free and ice-covered periods, we find that elevated open water fluxes in early winter can be well modeled. However, the modeled fluxes during ice-covered periods appear less accurate, where the errors are likely related to the simulated ice thickness. Thin ice has many small cracks, resulting in large fluxes nearly as high as over open water; very thick ice can reduce the latent fluxes to near zero, according to observations. Overall, the algorithms used in existing operational models show promise in resolving winter lake fluxes; however, further improvement may require adaptations to underlying ice and hydrodynamic model formulations.
{"title":"Evaluating Winter Turbulent Heat Fluxes in a Hydrodynamic-Ice Model of the Great Lakes","authors":"Gabriella Lükő, Eric J. Anderson, Christopher Spence, John D. Lenters, Peter D. Blanken, Erin Nicholls, Péter Torma","doi":"10.1029/2025wr040624","DOIUrl":"https://doi.org/10.1029/2025wr040624","url":null,"abstract":"Turbulent heat fluxes are affected by and influence the temperature dynamics and ice conditions of lakes. Significant efforts have been made to develop operational hydrodynamic and ice models for large lakes such as the North American Great Lakes. However, the behavior of surface fluxes in these lakes has previously focused on the ice-free season and has not yet been fully assessed during winter conditions in the presence of ice. Given the importance of navigation support and regional weather forecasting, we therefore analyze operational configurations of the Great Lakes for modeled fluxes to evaluate them for open water, ice-covered, and partial ice conditions. We compare the modeled fluxes with eddy covariance-based observed fluxes from the Great Lakes Evaporation Network. While observed latent heat fluxes have periods of high values both during ice-free and ice-covered periods, we find that elevated open water fluxes in early winter can be well modeled. However, the modeled fluxes during ice-covered periods appear less accurate, where the errors are likely related to the simulated ice thickness. Thin ice has many small cracks, resulting in large fluxes nearly as high as over open water; very thick ice can reduce the latent fluxes to near zero, according to observations. Overall, the algorithms used in existing operational models show promise in resolving winter lake fluxes; however, further improvement may require adaptations to underlying ice and hydrodynamic model formulations.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"28 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145801385","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}
Malik A. Dawi, Michele Starnoni, Martina Siena, Giovanni Porta, Xavier Sanchez-Vila
Biofilms in porous media significantly impact solute transport, beyond their role in reducing permeability through bioclogging. Experimental evidence has shown that biofilms can induce anomalous transport behaviors such as increased dispersion and pronounced tailing. These effects arise from the structural heterogeneity of the biofilm and the development of internal convective pathways. Despite being mostly composed of water, biofilms exhibit reduced effective diffusivity due to their complex microstructure. To capture these effects, we develop an original pore-scale transport model combining the micro-continuum approach with Random Walk Particle Tracking. Our simulations show that biofilm permeability, effective diffusivity, and spatial heterogeneity strongly influence solute breakthrough times, highlighting the critical role of biofilm structure in shaping complex transport behavior in porous systems.
{"title":"Direct Numerical Simulation of Solute Transport in Bioclogged Porous Media","authors":"Malik A. Dawi, Michele Starnoni, Martina Siena, Giovanni Porta, Xavier Sanchez-Vila","doi":"10.1029/2025wr041939","DOIUrl":"https://doi.org/10.1029/2025wr041939","url":null,"abstract":"Biofilms in porous media significantly impact solute transport, beyond their role in reducing permeability through bioclogging. Experimental evidence has shown that biofilms can induce anomalous transport behaviors such as increased dispersion and pronounced tailing. These effects arise from the structural heterogeneity of the biofilm and the development of internal convective pathways. Despite being mostly composed of water, biofilms exhibit reduced effective diffusivity due to their complex microstructure. To capture these effects, we develop an original pore-scale transport model combining the micro-continuum approach with Random Walk Particle Tracking. Our simulations show that biofilm permeability, effective diffusivity, and spatial heterogeneity strongly influence solute breakthrough times, highlighting the critical role of biofilm structure in shaping complex transport behavior in porous systems.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"361 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145801380","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}
Due largely to challenges associated with physical interpretability of machine learning (ML) methods, and because model interpretability is key to credibility in management applications, many scientists and practitioners are hesitant to discard traditional physical-conceptual modeling approaches despite their poorer predictive performance. Here, we examine how to develop parsimonious minimally-optimal representations that can facilitate better insight regarding system functioning. The term “minimally-optimal” indicates that the desired outcome can be achieved with the smallest possible effort and resources, while “parsimony” is widely held to support understanding. Accordingly, we suggest that ML-based modeling should use computational units that are inherently physically-interpretable, and explore how generic network architectures comprised of Mass-Conserving-Perceptron can be used to model dynamical systems in a physically-interpretable manner. In the context of spatially-lumped catchment-scale modeling, we find that both physical interpretability and good predictive performance can be achieved using a “distributed-state” network with context-dependent gating and “information-sharing” across nodes. The distributed-state mechanism ensures a sufficient number of temporally-evolving properties of system storage while information-sharing ensures proper synchronization of such properties. The results indicate that MCP-based ML models with only a few layers (up to two) and relativity few physical flow pathways (up to three) can play a significant role in ML-based streamflow modeling.
{"title":"Using Machine Learning to Discover Parsimonious and Physically-Interpretable Representations of Catchment-Scale Rainfall-Runoff Dynamics","authors":"Yuan-Heng Wang, Hoshin V. Gupta","doi":"10.1029/2025wr040178","DOIUrl":"https://doi.org/10.1029/2025wr040178","url":null,"abstract":"Due largely to challenges associated with physical interpretability of machine learning (ML) methods, and because model interpretability is key to credibility in management applications, many scientists and practitioners are hesitant to discard traditional physical-conceptual modeling approaches despite their poorer predictive performance. Here, we examine how to develop parsimonious minimally-optimal representations that can facilitate better insight regarding system functioning. The term “minimally-optimal” indicates that the desired outcome can be achieved with the smallest possible effort and resources, while “parsimony” is widely held to support understanding. Accordingly, we suggest that ML-based modeling should use computational units that are inherently physically-interpretable, and explore how generic network architectures comprised of Mass-Conserving-Perceptron can be used to model dynamical systems in a physically-interpretable manner. In the context of spatially-lumped catchment-scale modeling, we find that both physical interpretability and good predictive performance can be achieved using a “distributed-state” network with context-dependent gating and “information-sharing” across nodes. The distributed-state mechanism ensures a sufficient number of temporally-evolving properties of system storage while information-sharing ensures proper synchronization of such properties. The results indicate that MCP-based ML models with only a few layers (up to two) and relativity few physical flow pathways (up to three) can play a significant role in ML-based streamflow modeling.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"250 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145801381","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}
Global warming has significantly altered the hydrological cycle and increased the frequency and intensity of extreme hydrological events. Therefore, quantifying trends in the variability and extremes of streamflow is crucial for deepening our understanding of changes in the hydrological cycle. However, comprehensive nationwide studies of extreme streamflow trends across China remain scarce. In this study, we explored trends in the variability and extremes of seasonal streamflow from 1961 to 2018 over China using gridded monthly streamflow data. The results revealed a significant increase in the streamflow variability and extremes in approximately half of the country (35.9%–52.9% of the grid points), with particularly strong increases in the Northwest River Basin (over 52% of the grid points). Additionally, we found a strong positive correlation between streamflow variability and extremely high streamflows across China (r > 0.41, p < 0.05). Furthermore, at the seasonal scale, the largest increases in trends of streamflow extremes were observed in summer (mean slope = 0.094 mm mo−1 yr−1), whereas autumn exhibits milder decreases (mean slope = −0.0009 mm mo−1 yr−1) for a large number (>25.8%) of grid points. These findings address a critical knowledge gap by providing a comprehensive nationwide assessment of seasonal streamflow variability and extreme trends in China and offering new perspectives for the development of sustainable strategies for watershed and water resource management.
全球变暖显著改变了水文循环，增加了极端水文事件的频率和强度。因此，量化流量的变异性和极端趋势对于加深我们对水文循环变化的理解至关重要。然而，在全国范围内对中国极端水流趋势的综合研究仍然很少。在这项研究中，我们利用网格化的月度流量数据，探讨了1961 - 2018年中国季节性流量的变化趋势和极端值。结果显示，全国大约一半的地区（35.9%-52.9%的网格点）的流量变异性和极端事件显著增加，其中西北河流流域的增加尤其强烈（超过52%的网格点）。此外，我们发现中国各地的河流流量变异与极高的河流流量之间存在很强的正相关（r > 0.41, p < 0.05）。此外，在季节尺度上，在夏季（平均斜率= 0.094 mm mo - 1 yr - 1）观测到极端流量趋势的最大增加，而秋季在大量（>25.8%）网格点上表现出较温和的减少（平均斜率= - 0.0009 mm mo - 1 yr - 1）。这些发现通过对中国季节性流量变化和极端趋势进行全面的全国性评估，解决了一个关键的知识缺口，并为流域和水资源管理可持续战略的发展提供了新的视角。
{"title":"Exacerbated Variability and Extremes in Streamflow Across Half of China From 1961 to 2018","authors":"Yilin Zhan, Guobao Xu, Bo Wang, Guoju Wu, Jiarui Wu, Tingting Zhao, Xuejiao Wu","doi":"10.1029/2025wr041968","DOIUrl":"https://doi.org/10.1029/2025wr041968","url":null,"abstract":"Global warming has significantly altered the hydrological cycle and increased the frequency and intensity of extreme hydrological events. Therefore, quantifying trends in the variability and extremes of streamflow is crucial for deepening our understanding of changes in the hydrological cycle. However, comprehensive nationwide studies of extreme streamflow trends across China remain scarce. In this study, we explored trends in the variability and extremes of seasonal streamflow from 1961 to 2018 over China using gridded monthly streamflow data. The results revealed a significant increase in the streamflow variability and extremes in approximately half of the country (35.9%–52.9% of the grid points), with particularly strong increases in the Northwest River Basin (over 52% of the grid points). Additionally, we found a strong positive correlation between streamflow variability and extremely high streamflows across China (<i>r</i> > 0.41, <i>p</i> < 0.05). Furthermore, at the seasonal scale, the largest increases in trends of streamflow extremes were observed in summer (mean slope = 0.094 mm mo<sup>−1</sup> yr<sup>−1</sup>), whereas autumn exhibits milder decreases (mean slope = −0.0009 mm mo<sup>−1</sup> yr<sup>−1</sup>) for a large number (>25.8%) of grid points. These findings address a critical knowledge gap by providing a comprehensive nationwide assessment of seasonal streamflow variability and extreme trends in China and offering new perspectives for the development of sustainable strategies for watershed and water resource management.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"38 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145813728","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}
Tidally driven saltwater-freshwater mixing in coastal aquifers can promote denitrification, a key process facilitating attenuation of terrestrially derived nitrate prior to groundwater discharge into coastal waters. However, to date the effect of rainfall recharge, which can greatly alter flow and mixing in intertidal zones, on this mixing-dependent denitrification remains poorly understood. This study employs a numerical variable-density groundwater flow and reactive transport model to evaluate the combined effect of rainfall recharge and spring-neap tides on the spatial and temporal variability of the denitrification process. A systematic sensitivity analysis was conducted by varying the temporal pattern (uniform, random, extreme, and seasonal) and magnitude of rainfall recharge, the reactivity of marine-derived dissolved organic carbon (DOC) and the chemistry of rainfall recharge (represented by four scenarios: no solutes, nitrate only, oxygen only, and both nitrate and oxygen). The results demonstrate that rainfall recharge and spring-neap tides jointly regulate spatial and temporal patterns of denitrification. As DOC reactivity increases, the dominant driver of denitrification variability shifts from rainfall recharge to spring-neap tides. While different rainfall recharge patterns yield similar annual nitrate removal via intertidal denitrification, they may cause significant differences in the variability of daily nitrate removal rates. Increased rainfall recharge generally reduces the nitrate removal rate unless the recharge itself introduces nitrate. Meanwhile, the proportion of nitrate removed, relative to the total terrestrial nitrate input, consistently decreases with increasing rainfall recharge, regardless of the chemistry of rainfall recharge. These findings provide new insights into hydrological and biogeochemical controls on denitrification dynamics in intertidal mixing zones of coastal aquifers, with important implications for estimating chemical fluxes into coastal waters and managing coastal ecosystems.
{"title":"Modeling the Effect of Rainfall Recharge on Denitrification in Intertidal Mixing Zones of Coastal Aquifers","authors":"Huiqiang Wu, Chunhui Lu, Min Yan, Henning Prommer","doi":"10.1029/2025wr040202","DOIUrl":"https://doi.org/10.1029/2025wr040202","url":null,"abstract":"Tidally driven saltwater-freshwater mixing in coastal aquifers can promote denitrification, a key process facilitating attenuation of terrestrially derived nitrate prior to groundwater discharge into coastal waters. However, to date the effect of rainfall recharge, which can greatly alter flow and mixing in intertidal zones, on this mixing-dependent denitrification remains poorly understood. This study employs a numerical variable-density groundwater flow and reactive transport model to evaluate the combined effect of rainfall recharge and spring-neap tides on the spatial and temporal variability of the denitrification process. A systematic sensitivity analysis was conducted by varying the temporal pattern (uniform, random, extreme, and seasonal) and magnitude of rainfall recharge, the reactivity of marine-derived dissolved organic carbon (DOC) and the chemistry of rainfall recharge (represented by four scenarios: no solutes, nitrate only, oxygen only, and both nitrate and oxygen). The results demonstrate that rainfall recharge and spring-neap tides jointly regulate spatial and temporal patterns of denitrification. As DOC reactivity increases, the dominant driver of denitrification variability shifts from rainfall recharge to spring-neap tides. While different rainfall recharge patterns yield similar annual nitrate removal via intertidal denitrification, they may cause significant differences in the variability of daily nitrate removal rates. Increased rainfall recharge generally reduces the nitrate removal rate unless the recharge itself introduces nitrate. Meanwhile, the proportion of nitrate removed, relative to the total terrestrial nitrate input, consistently decreases with increasing rainfall recharge, regardless of the chemistry of rainfall recharge. These findings provide new insights into hydrological and biogeochemical controls on denitrification dynamics in intertidal mixing zones of coastal aquifers, with important implications for estimating chemical fluxes into coastal waters and managing coastal ecosystems.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"184 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145801383","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}
Xiaobing Chen, Jie Yin, Yanzhong Li, Li Chen, Dong Chen
Understanding the spatial variation of streambed sediment median grain size (d50) is essential for understanding hydrological, geomorphological, and ecological processes, yet network-scale patterns remain poorly characterized. Here we combined extensive field sampling (249 locations) with Digital Grain Size analysis and laboratory sieving to build a high-precision d50 database across the 3056 km Xin'anjiang (XAJ) stream network in southeastern China. Using a Spatial Stream Network (SSN) geostatistical model, we produced 1 km resolution predictions of d50 (R2 = 0.92) that explicitly incorporate hydrologic connectivity and spatial autocorrelation. Results reveal high spatial heterogeneity (0.22–100.28 mm), with systematic downstream fining punctuated by stepwise anomalies at tributary confluences. d50 decreases with stream order but increases with flow distance to the outlet, indicating the combined roles of geomorphic scaling and sediment supply contrasts. Sensitivity analysis shows that prediction uncertainty grows as sampling density decreases; a ∼15 km interval provides reliable estimates, while denser sampling is needed near confluences and morphologically complex reaches. This study establishes a transferable framework for network-scale sediment analysis, advancing understanding of spatial grain-size dynamics and guiding efficient sampling strategies in diverse stream networks.
{"title":"Assessing Stream Network-Scale Variability of Streambed Sediment Median Grain Size (d50): Integrating Field Surveys and Geostatistical Modeling","authors":"Xiaobing Chen, Jie Yin, Yanzhong Li, Li Chen, Dong Chen","doi":"10.1029/2025wr041015","DOIUrl":"https://doi.org/10.1029/2025wr041015","url":null,"abstract":"Understanding the spatial variation of streambed sediment median grain size (<i>d</i><sub>50</sub>) is essential for understanding hydrological, geomorphological, and ecological processes, yet network-scale patterns remain poorly characterized. Here we combined extensive field sampling (249 locations) with Digital Grain Size analysis and laboratory sieving to build a high-precision <i>d</i><sub>50</sub> database across the 3056 km Xin'anjiang (XAJ) stream network in southeastern China. Using a Spatial Stream Network (SSN) geostatistical model, we produced 1 km resolution predictions of <i>d</i><sub>50</sub> (<i>R</i><sup>2</sup> = 0.92) that explicitly incorporate hydrologic connectivity and spatial autocorrelation. Results reveal high spatial heterogeneity (0.22–100.28 mm), with systematic downstream fining punctuated by stepwise anomalies at tributary confluences. <i>d</i><sub>50</sub> decreases with stream order but increases with flow distance to the outlet, indicating the combined roles of geomorphic scaling and sediment supply contrasts. Sensitivity analysis shows that prediction uncertainty grows as sampling density decreases; a ∼15 km interval provides reliable estimates, while denser sampling is needed near confluences and morphologically complex reaches. This study establishes a transferable framework for network-scale sediment analysis, advancing understanding of spatial grain-size dynamics and guiding efficient sampling strategies in diverse stream networks.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"254 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145801384","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}
Suzhen Feng, Hao Zheng, Dunwei Gong, Jing Sun, Jinwen Wang
Global climate change and growing water demand exacerbate the imbalances in reservoir resource allocation, necessitating advanced frameworks that move beyond static valuation methods. Traditional valuation methods, constrained by static or homogenized assumptions, fail to capture the spatiotemporal heterogeneity and dynamic trade-offs inherent in cascade reservoir operations. To address this gap, this study develops a novel marginal value framework based on shadow pricing to assess the marginal values of multidimensional resources—water, storage capacity, and turbine capacity—in weekly hydropower scheduling. The key methodological contribution is overcoming the duality gap challenge in mixed-integer linear programming (MILP) models by proposing two practical methods: Method-I fixes integer variables to derive dual multipliers via linear programming, while Method-II computes shadow prices through perturbation analysis. Validated on 26 cascaded reservoirs in Yunnan, China, both methods yield consistent results, with Method-I demonstrating superior computational efficiency. Key findings reveal that: (a) irrigation water values in dry seasons and upstream regions exceed wet seasons and downstream by 1.11–7.34 times; (b) Nuozhadu Reservoir's storage capacity shadow price peaks in week 44, signaling flood control-power generation trade-offs; (c) Wunonglong and Dachaoshan exhibit the highest marginal turbine capacity values for spillage reduction; and (d) reserve capacity costs surge by 32%–45% in weeks 36–37. This work bridges the fields of resource economics and hydraulic engineering, providing actionable insights for dynamic water pricing, infrastructure investment prioritization, and seasonal ancillary service markets.
{"title":"A Dynamic and Multidimensional Framework to Reveal and Interpret Marginal Values in Cascade Reservoir Scheduling Under Competing Demands","authors":"Suzhen Feng, Hao Zheng, Dunwei Gong, Jing Sun, Jinwen Wang","doi":"10.1029/2025wr040598","DOIUrl":"https://doi.org/10.1029/2025wr040598","url":null,"abstract":"Global climate change and growing water demand exacerbate the imbalances in reservoir resource allocation, necessitating advanced frameworks that move beyond static valuation methods. Traditional valuation methods, constrained by static or homogenized assumptions, fail to capture the spatiotemporal heterogeneity and dynamic trade-offs inherent in cascade reservoir operations. To address this gap, this study develops a novel marginal value framework based on shadow pricing to assess the marginal values of multidimensional resources—water, storage capacity, and turbine capacity—in weekly hydropower scheduling. The key methodological contribution is overcoming the duality gap challenge in mixed-integer linear programming (MILP) models by proposing two practical methods: Method-I fixes integer variables to derive dual multipliers via linear programming, while Method-II computes shadow prices through perturbation analysis. Validated on 26 cascaded reservoirs in Yunnan, China, both methods yield consistent results, with Method-I demonstrating superior computational efficiency. Key findings reveal that: (a) irrigation water values in dry seasons and upstream regions exceed wet seasons and downstream by 1.11–7.34 times; (b) Nuozhadu Reservoir's storage capacity shadow price peaks in week 44, signaling flood control-power generation trade-offs; (c) Wunonglong and Dachaoshan exhibit the highest marginal turbine capacity values for spillage reduction; and (d) reserve capacity costs surge by 32%–45% in weeks 36–37. This work bridges the fields of resource economics and hydraulic engineering, providing actionable insights for dynamic water pricing, infrastructure investment prioritization, and seasonal ancillary service markets.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"30 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796295","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}
This study presents a physically based numerical framework for accurately simulating the intake performance of radial collector wells (RCWs) by explicitly accounting for internal flow dynamics and all major head loss mechanisms. Unlike conventional models that often simplify RCW hydraulics, the proposed approach integrates aquifer, filter, slot, frictional, momentum, and caisson head losses into a fully coupled formulation of lateral head and conductance. A Python–MODFLOW 6 coupling scheme, implemented using the FloPy library, iteratively updates lateral head and inflow until convergence, capturing the coupled interaction between the lateral and the surrounding aquifer. Simulation results show that lateral head variation becomes critical under high-intake conditions, leading to nonlinear intake behavior and asymmetric aquifer drawdown. Neglecting key head loss components, especially the caisson and momentum losses, can result in substantial overestimation of intake. In addition to reproducing the head variation within the laterals, the model incorporates the Forchheimer term to account for nonlinear head losses in the aquifer and a slot-loss component to represent structural resistance near screen openings. Although the influence of these factors increases slightly with higher flow rates, their overall impact on total intake remains negligible under the tested conditions. The proposed framework improves the predictive accuracy of RCW modeling and provides a practical tool for design evaluation, performance assessment, and long-term planning of RCW systems.
{"title":"A Fully Coupled Numerical Model for Radial Collector Well Intake Simulation Incorporating Comprehensive Head Loss Mechanisms","authors":"Seonmin Lee, Min-Ho Koo","doi":"10.1029/2025wr041402","DOIUrl":"https://doi.org/10.1029/2025wr041402","url":null,"abstract":"This study presents a physically based numerical framework for accurately simulating the intake performance of radial collector wells (RCWs) by explicitly accounting for internal flow dynamics and all major head loss mechanisms. Unlike conventional models that often simplify RCW hydraulics, the proposed approach integrates aquifer, filter, slot, frictional, momentum, and caisson head losses into a fully coupled formulation of lateral head and conductance. A Python–MODFLOW 6 coupling scheme, implemented using the FloPy library, iteratively updates lateral head and inflow until convergence, capturing the coupled interaction between the lateral and the surrounding aquifer. Simulation results show that lateral head variation becomes critical under high-intake conditions, leading to nonlinear intake behavior and asymmetric aquifer drawdown. Neglecting key head loss components, especially the caisson and momentum losses, can result in substantial overestimation of intake. In addition to reproducing the head variation within the laterals, the model incorporates the Forchheimer term to account for nonlinear head losses in the aquifer and a slot-loss component to represent structural resistance near screen openings. Although the influence of these factors increases slightly with higher flow rates, their overall impact on total intake remains negligible under the tested conditions. The proposed framework improves the predictive accuracy of RCW modeling and provides a practical tool for design evaluation, performance assessment, and long-term planning of RCW systems.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"7 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778092","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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