Under intensifying climate change, demand management policies are likely to become increasingly important means for increasing the resilience of surface water systems during scarcity periods. Yet, there is little knowledge focused on how these policies might function in the face of climate change. In this sense, understanding the likely effectiveness of demand management policies under climate change is critical for ensuring sustainable and equitable long-term planning of water systems. Here, we build an agent-based model to evaluate the reservoir storage in the cities of Denver (Colorado), Las Vegas (Nevada), and Phoenix (Arizona) under climate change and demand management scenarios. The results indicate that in some scenarios, demand management policies can counteract the negative impacts of climate change. In others, however, the demand management policies have minimal impact, suggesting that demand management alone is not always enough to counteract climate change. Overall, the model can be used to test other demand management policies, while also serving as a basis for improved understanding of the interactions between climate change, water conservation attitudes, and demand management policies within the Colorado River Basin.
{"title":"Exploring the Impacts of Climate Change and Water Conservation Attitudes on Urban Water Supply in the Colorado River Basin","authors":"Renee Obringer, Grace Peterson, Dave D. White","doi":"10.1029/2024wr039403","DOIUrl":"https://doi.org/10.1029/2024wr039403","url":null,"abstract":"Under intensifying climate change, demand management policies are likely to become increasingly important means for increasing the resilience of surface water systems during scarcity periods. Yet, there is little knowledge focused on how these policies might function in the face of climate change. In this sense, understanding the likely effectiveness of demand management policies under climate change is critical for ensuring sustainable and equitable long-term planning of water systems. Here, we build an agent-based model to evaluate the reservoir storage in the cities of Denver (Colorado), Las Vegas (Nevada), and Phoenix (Arizona) under climate change and demand management scenarios. The results indicate that in some scenarios, demand management policies can counteract the negative impacts of climate change. In others, however, the demand management policies have minimal impact, suggesting that demand management alone is not always enough to counteract climate change. Overall, the model can be used to test other demand management policies, while also serving as a basis for improved understanding of the interactions between climate change, water conservation attitudes, and demand management policies within the Colorado River Basin.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"325 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796236","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}
Wenguang Shi, Quanrong Wang, Maria Klepikova, Dylan J. Irvine, Aohan Jin, Yanxin Wang
Local thermal nonequilibrium (LTNE) effects in heterogeneous media can affect subsurface temperature distributions, as well as the capacity of the heat transport model to solve the inverse problem of estimating groundwater fluxes. We present a synthetic coupled flow and heat transport numerical model with five scenarios to analyze the influence of subsurface hydraulic and thermal property variations on heat transport in heterogeneous streambed sediments, while also evaluating the role of LTNE effects in heat transport processes within heterogeneous streambed sediments and their impact on streambed fluxes estimation. Heterogeneous streambed sediments with varying sand-gravel-clay fractions are stochastically generated using a Markov Chain model. Synthetic streambed temperature-time series are produced to estimate effective thermal diffusivity and thermal front velocity using a heat transport model based on homogeneous and local thermal equilibrium assumptions, and these estimates were compared to known values from numerical models of flow fields analogous to losing streams. Results show that neglecting thermal heterogeneity in streambed sediments leads to significant errors in streambed fluxes estimation, where the effective thermal diffusivity can be underestimated by about 40%, while the thermal front velocity can be overestimated by more than two times. In addition to the effects of streambed heterogeneity, LTNE effects further amplify these errors. Furthermore, the influences of streambed heterogeneity on LTNE effects are primarily influenced by flow velocity, with higher clay content reducing Darcian velocity and weakening LTNE effects.
{"title":"Effects of Local Thermal Nonequilibrium and Sediment Heterogeneity on Heat Tracer-Based Downwelling Flux Quantification in Streambeds","authors":"Wenguang Shi, Quanrong Wang, Maria Klepikova, Dylan J. Irvine, Aohan Jin, Yanxin Wang","doi":"10.1029/2025wr041536","DOIUrl":"https://doi.org/10.1029/2025wr041536","url":null,"abstract":"Local thermal nonequilibrium (LTNE) effects in heterogeneous media can affect subsurface temperature distributions, as well as the capacity of the heat transport model to solve the inverse problem of estimating groundwater fluxes. We present a synthetic coupled flow and heat transport numerical model with five scenarios to analyze the influence of subsurface hydraulic and thermal property variations on heat transport in heterogeneous streambed sediments, while also evaluating the role of LTNE effects in heat transport processes within heterogeneous streambed sediments and their impact on streambed fluxes estimation. Heterogeneous streambed sediments with varying sand-gravel-clay fractions are stochastically generated using a Markov Chain model. Synthetic streambed temperature-time series are produced to estimate effective thermal diffusivity and thermal front velocity using a heat transport model based on homogeneous and local thermal equilibrium assumptions, and these estimates were compared to known values from numerical models of flow fields analogous to losing streams. Results show that neglecting thermal heterogeneity in streambed sediments leads to significant errors in streambed fluxes estimation, where the effective thermal diffusivity can be underestimated by about 40%, while the thermal front velocity can be overestimated by more than two times. In addition to the effects of streambed heterogeneity, LTNE effects further amplify these errors. Furthermore, the influences of streambed heterogeneity on LTNE effects are primarily influenced by flow velocity, with higher clay content reducing Darcian velocity and weakening LTNE effects.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"83 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796253","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}
Accurately estimating water flux in variably saturated soils is crucial, yet traditional methods, such as the Richardson-Richards equation, often suffer from some limitations. As an advancement of Physics-Informed Neural Networks (PINNs), we introduce the Adaptive Constrained Neural Network (ACNN), designed to estimate variably saturated soil water flux using sparse volumetric water content (VWC) data alone, without requiring initial conditions (ICs), boundary conditions (BCs), or soil hydraulic constitutive relationships. The framework integrates domain decomposition methods and adaptive constraints within a sequential training framework. Testing on synthetic cases and soil column experiments demonstrates its capability to achieve unified modeling of variably saturated flow, seamlessly linking the unsaturated and saturated zones. It effectively addresses issues of incomplete knowledge and noisy data, not only mitigating ill-conditioning compared to traditional PINNs, but also reducing flux estimation errors caused by uncertainties in ICs and BCs compared to numerical methods. Notably, under conditions of rapid moisture content change, ACNN successfully captures instantaneous flux changes at the boundary with an accuracy of approximately R2 = 0.9, highlighting its potential in applications such as estimating surface infiltration, evapotranspiration, and groundwater recharge.
{"title":"Estimating the Soil Water Flux in Variably Saturated Flow With Sparse Soil Moisture Observations","authors":"Yujie Wang, Liangsheng Shi, Yanling Wang, Xiaolong Hu, Wenxiang Song, Leilei He, Lijun Wang, Yuanyuan Zha, Jiong Zhu","doi":"10.1029/2025wr039879","DOIUrl":"https://doi.org/10.1029/2025wr039879","url":null,"abstract":"Accurately estimating water flux in variably saturated soils is crucial, yet traditional methods, such as the Richardson-Richards equation, often suffer from some limitations. As an advancement of Physics-Informed Neural Networks (PINNs), we introduce the Adaptive Constrained Neural Network (ACNN), designed to estimate variably saturated soil water flux using sparse volumetric water content (VWC) data alone, without requiring initial conditions (ICs), boundary conditions (BCs), or soil hydraulic constitutive relationships. The framework integrates domain decomposition methods and adaptive constraints within a sequential training framework. Testing on synthetic cases and soil column experiments demonstrates its capability to achieve unified modeling of variably saturated flow, seamlessly linking the unsaturated and saturated zones. It effectively addresses issues of incomplete knowledge and noisy data, not only mitigating ill-conditioning compared to traditional PINNs, but also reducing flux estimation errors caused by uncertainties in ICs and BCs compared to numerical methods. Notably, under conditions of rapid moisture content change, ACNN successfully captures instantaneous flux changes at the boundary with an accuracy of approximately <i>R</i><sup>2</sup> = 0.9, highlighting its potential in applications such as estimating surface infiltration, evapotranspiration, and groundwater recharge.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"41 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145813727","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}
Mário J. Franca, Rose Cook, Ali Pourzangbar, Xilin Xia, Daniel Valero, Davide Vanzo, Arnau Bayón, Nigel Wright
Climate change, urbanization, and inadequate infrastructure exacerbate urban flood risks, yet one critical factor remains largely overlooked: hazardous debris such as cars, construction materials, wood, plastic containers among others. In the Valencia 2024 flood alone, the Spanish Insurance Compensation Consortium reported about 144,000 vehicles damaged or destroyed, many of them mobilized by the flow, which demonstrates the scale of large-debris impacts during floods. Debris alters and intensifies flooding impacts by clogging drainage systems and streets, decreasing flow conveyance, and causing direct damage to infrastructure, lives, and ecosystems. Nevertheless, debris dynamics are largely absent from flood risk assessments and management strategies. This Commentary highlights the urgent need to integrate debris considerations into urban flood planning and emergency response. Using case studies from recent catastrophic floods, we illustrate how debris amplifies hazard. We explore emerging scientific insights into the influence of debris in different flood types (flash, fluvial, coastal, tsunamis), and discuss why current management strategies fail to incorporate this factor. A solution-oriented roadmap is possible and we propose an actionable strategy toward the integration of debris into flood risk management, contributing to adapting cities toward higher levels of safety and resilience.
{"title":"Why Should Urban Debris Dynamics Be Considered in Urban Flood Management?","authors":"Mário J. Franca, Rose Cook, Ali Pourzangbar, Xilin Xia, Daniel Valero, Davide Vanzo, Arnau Bayón, Nigel Wright","doi":"10.1029/2025wr041574","DOIUrl":"https://doi.org/10.1029/2025wr041574","url":null,"abstract":"Climate change, urbanization, and inadequate infrastructure exacerbate urban flood risks, yet one critical factor remains largely overlooked: hazardous debris such as cars, construction materials, wood, plastic containers among others. In the Valencia 2024 flood alone, the Spanish Insurance Compensation Consortium reported about 144,000 vehicles damaged or destroyed, many of them mobilized by the flow, which demonstrates the scale of large-debris impacts during floods. Debris alters and intensifies flooding impacts by clogging drainage systems and streets, decreasing flow conveyance, and causing direct damage to infrastructure, lives, and ecosystems. Nevertheless, debris dynamics are largely absent from flood risk assessments and management strategies. This Commentary highlights the urgent need to integrate debris considerations into urban flood planning and emergency response. Using case studies from recent catastrophic floods, we illustrate how debris amplifies hazard. We explore emerging scientific insights into the influence of debris in different flood types (flash, fluvial, coastal, tsunamis), and discuss why current management strategies fail to incorporate this factor. A solution-oriented roadmap is possible and we propose an actionable strategy toward the integration of debris into flood risk management, contributing to adapting cities toward higher levels of safety and resilience.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"29 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796255","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}
Effective flood forecasting is essential for implementing proactive flood management and risk reduction strategies. However, conventional artificial neural networks often fail to capture the complex spatiotemporal dependencies among hydrometeorological variables, resulting in system biases and time-lag errors, especially during extreme flood events. This study introduces a spatiotemporal Pathformer-based deep learning framework for multi-step-ahead flood forecasting that dynamically adapts to flood magnitude and duration. The model integrates a dual self-attention mechanism and adaptive path selection, enhancing its ability to model nonlinear rainfall-runoff relationships and long horizon dependencies. Using a case study in the Jianxi basin with 25,341 hydrometeorological records at a 3-hr resolution, the spatiotemporal Pathformer's performance was evaluated across 1- to 7-step forecast horizons. A comparative analysis with Long Short-Term Memory (LSTM) and Transformer models demonstrates the spatiotemporal Pathformer's superior predictive accuracy and stability. It improves nash-sutcliffe efficiency by 3.0% and 7.4%, increases Volume Efficiency by 3.4% and 9.6%, reduces root mean square error by 18.4% and 34.9%, and lowers mean absolute error by 17.5% and 36.1% compared to LSTM and Transformer, respectively. By effectively mitigating time-lag errors and prediction bottlenecks, the spatiotemporal Pathformer ensures robust and reliable forecasting, even during extreme flood events. The application of SHapley Additive exPlanations analysis increases the model's interpretability, transparency, and trustworthiness by revealing the key hydrometeorological drivers behind its predictions. These results establish the spatiotemporal Pathformer as an advanced solution for next-generation flood forecasting, with strong potential to improve real-world applications in flood prevention and water resource management.
{"title":"A Spatiotemporal Pathformer-Based Deep Learning Framework for Watershed Flood Forecasting","authors":"Tianyu Xia, Yanlai Zhou, Chong-Yu Xu, Pan Liu, Yuxuan Luo, Fi-John Chang","doi":"10.1029/2025wr040193","DOIUrl":"https://doi.org/10.1029/2025wr040193","url":null,"abstract":"Effective flood forecasting is essential for implementing proactive flood management and risk reduction strategies. However, conventional artificial neural networks often fail to capture the complex spatiotemporal dependencies among hydrometeorological variables, resulting in system biases and time-lag errors, especially during extreme flood events. This study introduces a spatiotemporal Pathformer-based deep learning framework for multi-step-ahead flood forecasting that dynamically adapts to flood magnitude and duration. The model integrates a dual self-attention mechanism and adaptive path selection, enhancing its ability to model nonlinear rainfall-runoff relationships and long horizon dependencies. Using a case study in the Jianxi basin with 25,341 hydrometeorological records at a 3-hr resolution, the spatiotemporal Pathformer's performance was evaluated across 1- to 7-step forecast horizons. A comparative analysis with Long Short-Term Memory (LSTM) and Transformer models demonstrates the spatiotemporal Pathformer's superior predictive accuracy and stability. It improves nash-sutcliffe efficiency by 3.0% and 7.4%, increases Volume Efficiency by 3.4% and 9.6%, reduces root mean square error by 18.4% and 34.9%, and lowers mean absolute error by 17.5% and 36.1% compared to LSTM and Transformer, respectively. By effectively mitigating time-lag errors and prediction bottlenecks, the spatiotemporal Pathformer ensures robust and reliable forecasting, even during extreme flood events. The application of SHapley Additive exPlanations analysis increases the model's interpretability, transparency, and trustworthiness by revealing the key hydrometeorological drivers behind its predictions. These results establish the spatiotemporal Pathformer as an advanced solution for next-generation flood forecasting, with strong potential to improve real-world applications in flood prevention and water resource management.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"9 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145777830","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}
Jiachuan Wang, Qiang Li, Yu Liu, Hans W. Linderholm, Chenxi Xu, Yang Xu, Zichun Jia, Hanying Li, Jingyao Zhao, Huiming Song, Changfeng Sun, Shuheng Li, Wei Guo, Meng Ren, Qiufang Cai, Wenxuan Pang, Yifan Wu, Hai Cheng
As the capital city of China, Beijing is confronted with significant water scarcity challenges, primarily attributed to its substantial population growth and accelerated industrial expansion. The Miyun Reservoir, which serves as the principal water source for Beijing's domestic supply, is primarily replenished by the White River. Using Chinese pine (Pinus tabuliformis Carr.) tree-ring samples collected near the upper reaches of river, we analyzed inter-annual variations in δ18O, to access the variability of reservoir outflow discharge from 1961 to 2018. Statistical analysis revealed a significant positive correlation between tree-ring δ18O and the mean minimum temperature during February-March, demonstrating a pronounced “temperature effect” on isotopic fractionation associated with cellulose synthesis. A significant negative correlation was observed between δ18O and summer outflow discharge, primarily driven by the coupled effects of snowmelt dynamics on both hydrological regimes and isotopic partitioning processes. Tree-ring δ18O record revealed distinct anthropogenic disturbances in outflow discharge patterns during two critical periods: 1976–1985 and 1997–2002, characterized by significant deviations from natural hydrological variability. Outflow discharge was found to be fundamentally modulated by the natural variability of large-scale atmospheric circulation patterns.
{"title":"Reservoir Perturbation to Natural River in Beijing, China, as Recorded by Tree-Ring δ18O","authors":"Jiachuan Wang, Qiang Li, Yu Liu, Hans W. Linderholm, Chenxi Xu, Yang Xu, Zichun Jia, Hanying Li, Jingyao Zhao, Huiming Song, Changfeng Sun, Shuheng Li, Wei Guo, Meng Ren, Qiufang Cai, Wenxuan Pang, Yifan Wu, Hai Cheng","doi":"10.1029/2025wr041592","DOIUrl":"https://doi.org/10.1029/2025wr041592","url":null,"abstract":"As the capital city of China, Beijing is confronted with significant water scarcity challenges, primarily attributed to its substantial population growth and accelerated industrial expansion. The Miyun Reservoir, which serves as the principal water source for Beijing's domestic supply, is primarily replenished by the White River. Using Chinese pine (<i>Pinus tabuliformis</i> Carr.) tree-ring samples collected near the upper reaches of river, we analyzed inter-annual variations in δ<sup>18</sup>O, to access the variability of reservoir outflow discharge from 1961 to 2018. Statistical analysis revealed a significant positive correlation between tree-ring δ<sup>18</sup>O and the mean minimum temperature during February-March, demonstrating a pronounced “temperature effect” on isotopic fractionation associated with cellulose synthesis. A significant negative correlation was observed between δ<sup>18</sup>O and summer outflow discharge, primarily driven by the coupled effects of snowmelt dynamics on both hydrological regimes and isotopic partitioning processes. Tree-ring δ<sup>18</sup>O record revealed distinct anthropogenic disturbances in outflow discharge patterns during two critical periods: 1976–1985 and 1997–2002, characterized by significant deviations from natural hydrological variability. Outflow discharge was found to be fundamentally modulated by the natural variability of large-scale atmospheric circulation patterns.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"16 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778093","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}
Molly E. Tedesche, Travis A. Dahl, Jeremy J. Giovando
Climate change is impacting snow phenology in the Contiguous US (CONUS) and altering locations of elevated risk for floods driven by snowmelt. Our study uses a new spatial snow regime classification system to track climate driven changes in snow phenology across CONUS over 40 years (1981–2020). Using cloud-based computing and reanalysis data, regime classes are calculated annually, with changes evaluated across decadal and 30-year normal time scales. The snow regime classification designates areas across CONUS as either rain dominated, snow dominated, transitional, or perennial snow. Classifications are determined using a ratio of maximum snow water equivalent (SWE) over accumulated cool-season precipitation. Results indicate that average snow cover duration generally became shorter in each decade over our evaluation period, with rates of decline increasing at higher elevations. The snow-dominated spatial extents, compared to the 30-year normal, decreased over the first three decades, while areas that are classified as rain dominated increased. Also, previously snow-dominated areas have shifted to transitional areas, with boundary lines moving up in latitude and elevation. This classification will provide the land, water, and wildlife resource management communities with a method to monitor changes in snow accumulation and melt regimes that are important for runoff amount and timing.
{"title":"Changing Snow Regime Classifications Across the Contiguous United States","authors":"Molly E. Tedesche, Travis A. Dahl, Jeremy J. Giovando","doi":"10.1029/2025wr041066","DOIUrl":"https://doi.org/10.1029/2025wr041066","url":null,"abstract":"Climate change is impacting snow phenology in the Contiguous US (CONUS) and altering locations of elevated risk for floods driven by snowmelt. Our study uses a new spatial snow regime classification system to track climate driven changes in snow phenology across CONUS over 40 years (1981–2020). Using cloud-based computing and reanalysis data, regime classes are calculated annually, with changes evaluated across decadal and 30-year normal time scales. The snow regime classification designates areas across CONUS as either rain dominated, snow dominated, transitional, or perennial snow. Classifications are determined using a ratio of maximum snow water equivalent (SWE) over accumulated cool-season precipitation. Results indicate that average snow cover duration generally became shorter in each decade over our evaluation period, with rates of decline increasing at higher elevations. The snow-dominated spatial extents, compared to the 30-year normal, decreased over the first three decades, while areas that are classified as rain dominated increased. Also, previously snow-dominated areas have shifted to transitional areas, with boundary lines moving up in latitude and elevation. This classification will provide the land, water, and wildlife resource management communities with a method to monitor changes in snow accumulation and melt regimes that are important for runoff amount and timing.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"22 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796254","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}
Permafrost-affected peatlands on the central Qinghai-Tibet Plateau (QTP) store globally significant amounts of carbon but face climate-induced hydrological changes. The mechanisms enabling slope peatlands to sustain waterlogging under low net precipitation (precipitation minus evapotranspiration) remain uncertain. We combined field measurements (drone-based topography, peat cores and porewater δ18O) with a modified DigiBog_Boreal model to elucidate water balance in Chadam peatland, a representative permafrost slope peatland on the central QTP. Laboratory analyses reveal that Chadam peatland is characterized by exceptionally high dry bulk density (0.48 ± 0.21 g cm−3 (n = 8), compared to 0.12 ± 0.09 g cm−3 in northern peatlands (n = 1,318)) and low horizontal hydraulic conductivity (Kh), indicating distinct hydraulic properties. Model simulations parameterized with local steep slopes (5°), low net precipitation (mean 165 mm yr−1), and site-specific peat properties demonstrated that only the low Kh (initial 0.01 cm s−1; as measured in this study) simulation scenario can sustain millennial-scale waterlogging. These conditions facilitate continued peat accumulation, matching observed peat thickness. In contrast, high-conductivity scenarios (initial Kh = 0.15 cm s−1 and 3 cm s−1), due to their low water retention capacity, fail to maintain elevated water tables both during peat initiation and over subsequent centuries (300–600 years) under identical climatic and terrain conditions. Aligning with model results, stable isotope (δ2H and δ18O) profiles corroborate strong evaporation and prolonged subsurface water residence times. These findings indicate that peatland hydrological stability depends on either low Kh, sufficient net precipitation, or gentle topography to counteract destabilizing influences. This study identifies peat hydraulic conductivity as the primary control on hydrological stability in semi-arid, high-elevation permafrost peatlands, providing new insights into QTP peatland resilience under warming conditions.
在低净降水量（降水量减去蒸散）条件下，斜坡泥炭地维持内涝的机制仍不确定。我们将野外测量（基于无人机的地形、泥炭岩心和孔隙水δ18O）与改进的DigiBog_Boreal模型相结合，以解释Chadam泥炭地的水平衡，Chadam泥炭地是QTP中部具有代表性的多年冻土斜坡泥炭地。实验室分析表明，Chadam泥炭地具有极高的干容重(0.48±0.21 g cm - 3 （n = 8)，而北部泥炭地的干容重为0.12±0.09 g cm - 3 (n = 1318)）和低水平水力导电性（Kh），表明其具有独特的水力特性。以当地陡坡（5°）、低净降水量（平均165 mm yr - 1）和特定地点泥炭特性为参数的模式模拟表明，只有低Kh（本研究测量的初始值为0.01 cm s - 1）模拟情景才能维持千年尺度的内涝。这些条件有利于泥炭的持续积累，与观测到的泥炭厚度相匹配。相比之下，高导电性情景（初始Kh = 0.15 cm s - 1和3 cm s - 1），由于其低保水能力，在相同的气候和地形条件下，在泥炭形成期间和随后的几个世纪（300-600年）都无法保持较高的地下水位。与模型结果一致，稳定同位素（δ2H和δ18O）剖面证实了强烈的蒸发和较长的地下水停留时间。这些发现表明，泥炭地的水文稳定性取决于低钾、充足的净降水或平缓的地形来抵消不稳定的影响。本研究确定泥炭的水力传导性是半干旱、高海拔永久冻土泥炭地水文稳定性的主要控制因素，为QTP泥炭地在变暖条件下的恢复能力提供了新的见解。
{"title":"Peat Hydraulic Structure Maintains the Stability of Permafrost Slope Peatlands in the Central Qinghai-Tibet Plateau","authors":"Yuefeng Li, Zhengyu Xia, Jingjing Sun, Tingwan Yang, Zicheng Yu","doi":"10.1029/2025wr041170","DOIUrl":"https://doi.org/10.1029/2025wr041170","url":null,"abstract":"Permafrost-affected peatlands on the central Qinghai-Tibet Plateau (QTP) store globally significant amounts of carbon but face climate-induced hydrological changes. The mechanisms enabling slope peatlands to sustain waterlogging under low net precipitation (precipitation minus evapotranspiration) remain uncertain. We combined field measurements (drone-based topography, peat cores and porewater δ<sup>18</sup>O) with a modified DigiBog_Boreal model to elucidate water balance in Chadam peatland, a representative permafrost slope peatland on the central QTP. Laboratory analyses reveal that Chadam peatland is characterized by exceptionally high dry bulk density (0.48 ± 0.21 g cm<sup>−3</sup> (<i>n</i> = 8), compared to 0.12 ± 0.09 g cm<sup>−3</sup> in northern peatlands (<i>n</i> = 1,318)) and low horizontal hydraulic conductivity (<i>K</i><sub><i>h</i></sub>), indicating distinct hydraulic properties. Model simulations parameterized with local steep slopes (5°), low net precipitation (mean 165 mm yr<sup>−1</sup>), and site-specific peat properties demonstrated that only the low <i>K</i><sub><i>h</i></sub> (initial 0.01 cm s<sup>−1</sup>; as measured in this study) simulation scenario can sustain millennial-scale waterlogging. These conditions facilitate continued peat accumulation, matching observed peat thickness. In contrast, high-conductivity scenarios (initial <i>K</i><sub><i>h</i></sub> = 0.15 cm s<sup>−1</sup> and 3 cm s<sup>−1</sup>), due to their low water retention capacity, fail to maintain elevated water tables both during peat initiation and over subsequent centuries (300–600 years) under identical climatic and terrain conditions. Aligning with model results, stable isotope (δ<sup>2</sup>H and δ<sup>18</sup>O) profiles corroborate strong evaporation and prolonged subsurface water residence times. These findings indicate that peatland hydrological stability depends on either low <i>K</i><sub><i>h</i></sub>, sufficient net precipitation, or gentle topography to counteract destabilizing influences. This study identifies peat hydraulic conductivity as the primary control on hydrological stability in semi-arid, high-elevation permafrost peatlands, providing new insights into QTP peatland resilience under warming conditions.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"29 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778094","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}
Xin Wang, Shaohua Li, Pengfei Lv, Cai Liang, Jintao Xu, Menglan Shi, Baocai Tong, Lanlan Jiang, Yu Liu, Yongchen Song
Understanding two‐phase flow in porous media is essential for optimizing subsurface storage efficiency. Pore‐scale flow properties significantly influence macroscopic plume migration behavior and trapping performance. However, the inherent complexity of porous media impedes real‐time tracking of pore‐scale flow dynamics, leaving the mechanisms governing CO 2 ‐brine steady‐state flow largely unexplored. In this study, CO 2 ‐brine co‐injection experiments were conducted to explore fluid flow and distribution at the pore scale using X‐ray computed tomography (X‐ray CT) imaging. The results reveal that both the nonwetting and wetting phases, as well as the intermittent flow with CT grayscale values between those of nonwetting and wetting, are strongly affected by the capillary number. The nonwetting phase primarily occupies larger pores, while the wetting phase exhibits a bimodal distribution across small and big pores. This bimodal distribution is attributed to the core's heterogeneity and the presence of water layers along the pore walls, which also provides a unique insight into water layer identification at the pore scale. Additionally, the experiments reveal a distinct phenomenon where the morphology of the nonwetting phase transitions from clusters to singlets and then to ganglia as nonwetting phase capillary numbers vary. This transition highlights the role of the intermittent flow in modifying nonwetting phase morphology, leading to disconnections and reconnections that alter connectivity and relative permeability.
{"title":"Pore‐Scale Intermittent Flow and Its Impact on Two‐Phase Fluid Distribution in Porous Media","authors":"Xin Wang, Shaohua Li, Pengfei Lv, Cai Liang, Jintao Xu, Menglan Shi, Baocai Tong, Lanlan Jiang, Yu Liu, Yongchen Song","doi":"10.1029/2025wr040858","DOIUrl":"https://doi.org/10.1029/2025wr040858","url":null,"abstract":"Understanding two‐phase flow in porous media is essential for optimizing subsurface storage efficiency. Pore‐scale flow properties significantly influence macroscopic plume migration behavior and trapping performance. However, the inherent complexity of porous media impedes real‐time tracking of pore‐scale flow dynamics, leaving the mechanisms governing CO <jats:sub>2</jats:sub> ‐brine steady‐state flow largely unexplored. In this study, CO <jats:sub>2</jats:sub> ‐brine co‐injection experiments were conducted to explore fluid flow and distribution at the pore scale using X‐ray computed tomography (X‐ray CT) imaging. The results reveal that both the nonwetting and wetting phases, as well as the intermittent flow with CT grayscale values between those of nonwetting and wetting, are strongly affected by the capillary number. The nonwetting phase primarily occupies larger pores, while the wetting phase exhibits a bimodal distribution across small and big pores. This bimodal distribution is attributed to the core's heterogeneity and the presence of water layers along the pore walls, which also provides a unique insight into water layer identification at the pore scale. Additionally, the experiments reveal a distinct phenomenon where the morphology of the nonwetting phase transitions from clusters to singlets and then to ganglia as nonwetting phase capillary numbers vary. This transition highlights the role of the intermittent flow in modifying nonwetting phase morphology, leading to disconnections and reconnections that alter connectivity and relative permeability.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"26 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145765470","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}
Long-term lake ice evolution under climate change has attracted global attention. However, despite the widespread occurrence of lake shrinkage in endorheic regions worldwide, few studies have explicitly addressed its effects on lake ice regimes. This study fills this research gap by investigating the long-term evolution of lake ice in Lake Daihai—a large shrinking endorheic lake in China—by integrating six decades (1960–2022) of hydrometeorological data, retrieved Landsat images, and experiments with a three-dimensional hydrodynamics-ice numerical model. Our results show that Lake Daihai experienced accelerated shrinkage at an average rate of −2.18 km2 yr−1 from 1960 to 2022, which was primarily driven by intensified anthropogenic activities and increased evaporation. Concurrently, the annual average lake ice thickness exhibited an accelerated decreasing trend at an average rate of −0.39 cm yr−1. This ice-thinning trend was attributed to the processes of atmospheric warming (air temperature increase: 2.5°C), salinization (increase in salinity: 451.3%), and morphological changes associated with lake shrinkage (water depth reduction: −12 m; surface area reduction: −72.9%). Model experiments reveal3ed that the representative factors (i.e., air temperature, salinity, and average water depth) of these processes were significantly correlated with ice phenology metrics (i.e., ice-on date, ice-off date, and ice duration); their relative contributions to ice thinning were 36.1%, 18.9%, and −15.2%, respectively, and the wind speed contributed 3.5%. Ice thinning was driven mainly by atmospheric warming but slowed by lake shrinkage characterized by a decrease in the average water depth. Under ongoing global warming, ice-thinning is projected to accelerate by 2031 because of the nonlinear increase in the contribution of salinization in this shrinking lake. These findings highlight that traditional climate-centric models may underestimate or overestimate lake ice dynamics if they fail to account for salinization or morphological changes, underscoring the necessity of developing integrated assessment frameworks tailored to shrinking endorheic lakes.
气候变化下湖冰的长期演变引起了全球的关注。然而，尽管湖泊萎缩在全球内陆地区广泛发生，但很少有研究明确指出其对湖泊冰况的影响。本研究利用60年（1960-2022年）水文气象资料、陆地卫星遥感影像和三维水动力-冰数值模型实验资料，对中国大型内陆湖岱海湖冰的长期演变进行了研究，填补了这一研究空白。结果表明：1960 ~ 2022年，岱海湖面积以平均2.18 km2 / yr的速率加速萎缩，主要受人为活动加剧和蒸发增加的驱动。同时，年平均湖冰厚度呈加速减少趋势，平均速度为- 0.39 cm / yr - 1。这种冰变薄趋势归因于大气变暖（气温升高2.5°C）、盐碱化（盐度增加451.3%）和湖泊萎缩相关的形态变化（水深减少- 12 m，表面积减少- 72.9%）。模式实验表明，这些过程的代表因子（气温、盐度和平均水深）与冰物候指标（结冰日期、停冰日期和冰期）呈显著相关；它们对冰变薄的相对贡献分别为36.1%、18.9%和- 15.2%，风速贡献为3.5%。海冰变薄主要是由大气变暖驱动的，但以平均水深下降为特征的湖泊萎缩减缓了海冰变薄。在全球持续变暖的情况下，预计到2031年，由于这个不断缩小的湖泊的盐碱化贡献的非线性增加，冰的变薄将加速。这些发现强调，传统的以气候为中心的模型如果不能考虑盐碱化或形态变化，可能会低估或高估湖冰动态，这强调了开发针对内河湖泊萎缩的综合评估框架的必要性。
{"title":"Long-Term Lake Ice Evolution in a Large Endorheic Lake Undergoing Accelerated Shrinkage in a Semiarid Region of China","authors":"Tingfeng Wu, Anning Huang, Qi Zhang, Justin Brookes, Wenming Yan, Boqiang Qin, Dequan Han, Xiaofei Hu","doi":"10.1029/2024wr038954","DOIUrl":"https://doi.org/10.1029/2024wr038954","url":null,"abstract":"Long-term lake ice evolution under climate change has attracted global attention. However, despite the widespread occurrence of lake shrinkage in endorheic regions worldwide, few studies have explicitly addressed its effects on lake ice regimes. This study fills this research gap by investigating the long-term evolution of lake ice in Lake Daihai—a large shrinking endorheic lake in China—by integrating six decades (1960–2022) of hydrometeorological data, retrieved Landsat images, and experiments with a three-dimensional hydrodynamics-ice numerical model. Our results show that Lake Daihai experienced accelerated shrinkage at an average rate of −2.18 km<sup>2</sup> yr<sup>−1</sup> from 1960 to 2022, which was primarily driven by intensified anthropogenic activities and increased evaporation. Concurrently, the annual average lake ice thickness exhibited an accelerated decreasing trend at an average rate of −0.39 cm yr<sup>−1</sup>. This ice-thinning trend was attributed to the processes of atmospheric warming (air temperature increase: 2.5°C), salinization (increase in salinity: 451.3%), and morphological changes associated with lake shrinkage (water depth reduction: −12 m; surface area reduction: −72.9%). Model experiments reveal3ed that the representative factors (i.e., air temperature, salinity, and average water depth) of these processes were significantly correlated with ice phenology metrics (i.e., ice-on date, ice-off date, and ice duration); their relative contributions to ice thinning were 36.1%, 18.9%, and −15.2%, respectively, and the wind speed contributed 3.5%. Ice thinning was driven mainly by atmospheric warming but slowed by lake shrinkage characterized by a decrease in the average water depth. Under ongoing global warming, ice-thinning is projected to accelerate by 2031 because of the nonlinear increase in the contribution of salinization in this shrinking lake. These findings highlight that traditional climate-centric models may underestimate or overestimate lake ice dynamics if they fail to account for salinization or morphological changes, underscoring the necessity of developing integrated assessment frameworks tailored to shrinking endorheic lakes.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"116 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145777831","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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