As a critical ecological transition zone between aquatic and terrestrial ecosystems, the water-level fluctuation zone significantly influences flow structure through vegetation morphology. Conventional analytical velocity models inadequately address the variation in vegetation with water depth. In this study, we developed a hydrodynamic coupled model with vertically varying leaf vegetation widths and derived its analytical solutions. We have updated the dynamic invasion width formula in the context of studying vegetation-flow interactions within water-level fluctuation zones. This work quantitatively investigates flow interactions at the main channel-floodplain interface, establishes a dynamic relationship between the resistance coefficient and vegetation geometric parameters, and proposes a modified Kármán coefficient expression incorporating free water layer corrections under submerged conditions. Experimental and numerical validation revealed the shear layer evolution mechanisms and turbulent kinetic energy redistribution patterns (vertical-lateral) under semi-vegetated conditions. This study overcomes the traditional assumption of vegetation homogeneity. The findings will provide a fundamental basis for research on dissolved oxygen variations and pollutant diffusion processes in the littoral zone under vegetation-flow interactions. It also analyzes the vertical variations in vegetation morphology within water-level fluctuation zones, and offering a high-precision analytical tool for eco-hydrological simulations under vertically graded vegetation configurations and associated hydrodynamic impacts in these zones.
{"title":"Analytical Model of Velocity Distribution and Penetration Characteristics in Water-Level Fluctuation Zone With Vegetation","authors":"An-Qi Li, Xiao-Bo Liu, Wei-Jie Wang, Zhuo-Wei Wang, Feng-Jiao Li, Ming-Yang Xu, Wei Huang","doi":"10.1029/2025wr041130","DOIUrl":"https://doi.org/10.1029/2025wr041130","url":null,"abstract":"As a critical ecological transition zone between aquatic and terrestrial ecosystems, the water-level fluctuation zone significantly influences flow structure through vegetation morphology. Conventional analytical velocity models inadequately address the variation in vegetation with water depth. In this study, we developed a hydrodynamic coupled model with vertically varying leaf vegetation widths and derived its analytical solutions. We have updated the dynamic invasion width formula in the context of studying vegetation-flow interactions within water-level fluctuation zones. This work quantitatively investigates flow interactions at the main channel-floodplain interface, establishes a dynamic relationship between the resistance coefficient and vegetation geometric parameters, and proposes a modified Kármán coefficient expression incorporating free water layer corrections under submerged conditions. Experimental and numerical validation revealed the shear layer evolution mechanisms and turbulent kinetic energy redistribution patterns (vertical-lateral) under semi-vegetated conditions. This study overcomes the traditional assumption of vegetation homogeneity. The findings will provide a fundamental basis for research on dissolved oxygen variations and pollutant diffusion processes in the littoral zone under vegetation-flow interactions. It also analyzes the vertical variations in vegetation morphology within water-level fluctuation zones, and offering a high-precision analytical tool for eco-hydrological simulations under vertically graded vegetation configurations and associated hydrodynamic impacts in these zones.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"30 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089465","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}
T. Chen, J. Tian, J. Sun, Z. Zhang, H. Chai, B. Lin, X. Fu
Flooding causes significant loss of life and economic damage and affects healthy development of society. Deep learning (DL) models demonstrate significant advantages in improving computational efficiency while maintaining accuracy. Existing research of predicting dynamic flood evolution still remains some gaps for predicting flooding maps from the initial time step, weak transferability for flood scenarios from unseen breaches, and potential enhancement of common neural network frameworks. This paper proposes a DL model called FloodUnet based on an improved U-Net architecture to achieve rapid and accurate prediction of flood evolution. FloodUnet can predict a series of flooding depth maps and maintain high-precision prediction. It achieves an average root mean square error of 0.2 m and an average Nash-Sutcliffe Efficiency coefficient of 0.9 on testing sets of unseen breaches and inflows through a 4-fold cross validation. It is three orders of magnitude faster than the hydrodynamic model with a 24-hr lead time. It has obvious advantage in prediction accuracy compared to ordinary convolutional neural network and U-Net. Residual module and channel attention mechanism can enhance feature representation for complex flood dynamics and ensures stability during multi-step rolling prediction.
{"title":"FloodUnet: A Rapid Spatio-Temporal Prediction Model for Flood Evolution Based on an Enhanced U-Net","authors":"T. Chen, J. Tian, J. Sun, Z. Zhang, H. Chai, B. Lin, X. Fu","doi":"10.1029/2025wr041427","DOIUrl":"https://doi.org/10.1029/2025wr041427","url":null,"abstract":"Flooding causes significant loss of life and economic damage and affects healthy development of society. Deep learning (DL) models demonstrate significant advantages in improving computational efficiency while maintaining accuracy. Existing research of predicting dynamic flood evolution still remains some gaps for predicting flooding maps from the initial time step, weak transferability for flood scenarios from unseen breaches, and potential enhancement of common neural network frameworks. This paper proposes a DL model called FloodUnet based on an improved U-Net architecture to achieve rapid and accurate prediction of flood evolution. FloodUnet can predict a series of flooding depth maps and maintain high-precision prediction. It achieves an average root mean square error of 0.2 m and an average Nash-Sutcliffe Efficiency coefficient of 0.9 on testing sets of unseen breaches and inflows through a 4-fold cross validation. It is three orders of magnitude faster than the hydrodynamic model with a 24-hr lead time. It has obvious advantage in prediction accuracy compared to ordinary convolutional neural network and U-Net. Residual module and channel attention mechanism can enhance feature representation for complex flood dynamics and ensures stability during multi-step rolling prediction.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"59 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110287","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}
Heng Dai, Yijie Yang, Fangqiang Zhang, Alberto Guadagnini, Jing Yang, Xiaochuang Bu, Lunche Wang, Songhu Yuan, Ming Ye
We rely on a global sensitivity analysis (GSA) approach to identify the dominant physical and biogeochemical controls on dissolved oxygen (DO) dynamics in riparian aquifers. The study is motivated by the observation that availability of DO is key to regulating redox conditions and associated processes in the subsurface. Yet, the complexity of coupled flow and transport models, combined with model input uncertainty challenges our ability to fully characterize system behavior. To address this issue, we integrate Bayesian network-based and variance-based methods into a comprehensive GSA framework, enabling a robust evaluation of parameter and process sensitivities. To overcome the high computational demand of GSA for complex numerical models, we develop surrogate models using deep learning approaches (i.e., multi-layer perceptrons and convolutional neural networks). Application of this framework to a high-resolution model of riparian DO transport reveals that river stage dynamics (i.e., period and amplitude of water level fluctuations) are primary drivers of DO supply to the aquifer system. Hydraulic conductivity, riverine DO concentration, and the maximum DO reaction rate exhibit important but localized effects, influencing different transport pathways including river water infiltration, entrapped air dissolution, and diffusion through the unsaturated zone. In contrast, parameters such as porosity, longitudinal dispersion, and van Genuchten soil parameters exhibit negligible influence. These findings underscore the value of combining deep learning and GSA to efficiently evaluate complex environmental systems and to guide model simplification and diagnosis.
{"title":"Identification of Key Factors Driving Dissolved Oxygen in Riparian Aquifers Through Deep Learning-Assisted Global Sensitivity Analysis","authors":"Heng Dai, Yijie Yang, Fangqiang Zhang, Alberto Guadagnini, Jing Yang, Xiaochuang Bu, Lunche Wang, Songhu Yuan, Ming Ye","doi":"10.1029/2025wr041884","DOIUrl":"https://doi.org/10.1029/2025wr041884","url":null,"abstract":"We rely on a global sensitivity analysis (GSA) approach to identify the dominant physical and biogeochemical controls on dissolved oxygen (DO) dynamics in riparian aquifers. The study is motivated by the observation that availability of DO is key to regulating redox conditions and associated processes in the subsurface. Yet, the complexity of coupled flow and transport models, combined with model input uncertainty challenges our ability to fully characterize system behavior. To address this issue, we integrate Bayesian network-based and variance-based methods into a comprehensive GSA framework, enabling a robust evaluation of parameter and process sensitivities. To overcome the high computational demand of GSA for complex numerical models, we develop surrogate models using deep learning approaches (i.e., multi-layer perceptrons and convolutional neural networks). Application of this framework to a high-resolution model of riparian DO transport reveals that river stage dynamics (i.e., period and amplitude of water level fluctuations) are primary drivers of DO supply to the aquifer system. Hydraulic conductivity, riverine DO concentration, and the maximum DO reaction rate exhibit important but localized effects, influencing different transport pathways including river water infiltration, entrapped air dissolution, and diffusion through the unsaturated zone. In contrast, parameters such as porosity, longitudinal dispersion, and van Genuchten soil parameters exhibit negligible influence. These findings underscore the value of combining deep learning and GSA to efficiently evaluate complex environmental systems and to guide model simplification and diagnosis.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"104 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089467","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}
Interception and ablation of snow in forest canopies significantly influence the quantity, timing, and phase of precipitation that reaches the ground in cold regions forests. Yet current modeling approaches have uncertain transferability across differing climate and forest types. Here, in situ observations from a needleleaf forest in the Canadian Rockies were utilized to evaluate the theories underpinning existing canopy snow ablation models and develop a novel understanding supporting the development of a new canopy snow ablation model. The observations revealed that canopy snow load, wind shear stress, and canopy snowmelt are strongly associated with unloading; however, air temperature and sublimation are not. A new canopy snow ablation model was developed based on these associations and their impact on the canopy snow energy and mass balance. This model demonstrated improved performance in simulating canopy snow load compared with previous approaches, especially during melt- and wind-dominated ablation events. The improved performance in predicting canopy snow load across a wide range of meteorological conditions, compared to existing models, is due to including a comprehensive representation of the mass and energy balance of intercepted snow. In contrast, all existing canopy snow models were found to omit key processes which limited their accuracy in simulating snow load, its ablation and partitioning to sublimation, melt, drip, and unloading.
{"title":"Processes Governing the Ablation of Intercepted Snow","authors":"Alex C. Cebulski, John W. Pomeroy","doi":"10.1029/2025wr042009","DOIUrl":"https://doi.org/10.1029/2025wr042009","url":null,"abstract":"Interception and ablation of snow in forest canopies significantly influence the quantity, timing, and phase of precipitation that reaches the ground in cold regions forests. Yet current modeling approaches have uncertain transferability across differing climate and forest types. Here, in situ observations from a needleleaf forest in the Canadian Rockies were utilized to evaluate the theories underpinning existing canopy snow ablation models and develop a novel understanding supporting the development of a new canopy snow ablation model. The observations revealed that canopy snow load, wind shear stress, and canopy snowmelt are strongly associated with unloading; however, air temperature and sublimation are not. A new canopy snow ablation model was developed based on these associations and their impact on the canopy snow energy and mass balance. This model demonstrated improved performance in simulating canopy snow load compared with previous approaches, especially during melt- and wind-dominated ablation events. The improved performance in predicting canopy snow load across a wide range of meteorological conditions, compared to existing models, is due to including a comprehensive representation of the mass and energy balance of intercepted snow. In contrast, all existing canopy snow models were found to omit key processes which limited their accuracy in simulating snow load, its ablation and partitioning to sublimation, melt, drip, and unloading.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"79 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110284","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}
David Bamidele Olawade, James O. Ijiwade, Ojima Zechariah Wada
Sustainable water management is a critical challenge in space exploration, where the limited availability of resources requires innovative approaches to ensure astronauts' survival on long-duration missions. This narrative review explores the key technologies and methods involved in water recycling, in situ resource utilization (ISRU), and bioregenerative life support systems (BLSS) essential for supporting human life in space. The Environmental Control and Life Support System (ECLSS) aboard the International Space Station has demonstrated significant progress in recycling water from urine, sweat, and humidity, achieving up to 93% recovery. However, challenges remain in reducing energy consumption, improving system durability, and ensuring water quality. ISRU technologies, particularly those aimed at extracting water ice from lunar and Martian environments, offer promising solutions for future missions, but they must overcome scalability and logistical hurdles. This review also highlights the potential of nanotechnology and AI-driven autonomous systems in enhancing water purification and management. Nanomaterials like graphene oxide membranes could revolutionize filtration efficiency, while AI could optimize real-time water quality monitoring and recycling processes. As space agencies push toward establishing colonies on the Moon and Mars, the development of sustainable, closed-loop water systems will be pivotal to the success of these missions. Continued research and innovation are essential to ensuring water resources are efficiently managed for long-term human presence in space.
{"title":"Sustainable Water Systems in Space: A Review of Current Technologies and Future Prospects","authors":"David Bamidele Olawade, James O. Ijiwade, Ojima Zechariah Wada","doi":"10.1029/2025wr041273","DOIUrl":"https://doi.org/10.1029/2025wr041273","url":null,"abstract":"Sustainable water management is a critical challenge in space exploration, where the limited availability of resources requires innovative approaches to ensure astronauts' survival on long-duration missions. This narrative review explores the key technologies and methods involved in water recycling, in situ resource utilization (ISRU), and bioregenerative life support systems (BLSS) essential for supporting human life in space. The Environmental Control and Life Support System (ECLSS) aboard the International Space Station has demonstrated significant progress in recycling water from urine, sweat, and humidity, achieving up to 93% recovery. However, challenges remain in reducing energy consumption, improving system durability, and ensuring water quality. ISRU technologies, particularly those aimed at extracting water ice from lunar and Martian environments, offer promising solutions for future missions, but they must overcome scalability and logistical hurdles. This review also highlights the potential of nanotechnology and AI-driven autonomous systems in enhancing water purification and management. Nanomaterials like graphene oxide membranes could revolutionize filtration efficiency, while AI could optimize real-time water quality monitoring and recycling processes. As space agencies push toward establishing colonies on the Moon and Mars, the development of sustainable, closed-loop water systems will be pivotal to the success of these missions. Continued research and innovation are essential to ensuring water resources are efficiently managed for long-term human presence in space.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"176 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110285","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 rapid rise of machine learning (ML) in hydrology has prompted debate about the discipline's scientific relevance. While ML often outperforms traditional models in streamflow prediction, we argue that this reflects a deeper limitation: persistent fragmentation of hydrological science itself. Narrow focus on isolated components has hindered the development of coherent, scale-relevant understanding of the integrated terrestrial hydrosphere. This is illustrated, for example, by widely divergent estimates of groundwater–streamflow interactions and of water balance-implied ongoing storage changes. We argue that hydrology's future lies not in choosing between ML and physics, but in integrating data-driven and process-based approaches to advance consistent, realistic, and societally relevant understanding of the terrestrial hydrosphere and its multifaceted roles in the Earth System.
{"title":"Hydrology in the Age of Artificial Intelligence: From Fragmentation to Coherent Terrestrial Hydrosphere Science","authors":"Scott L. Painter, Georgia Destouni","doi":"10.1029/2026wr043509","DOIUrl":"https://doi.org/10.1029/2026wr043509","url":null,"abstract":"The rapid rise of machine learning (ML) in hydrology has prompted debate about the discipline's scientific relevance. While ML often outperforms traditional models in streamflow prediction, we argue that this reflects a deeper limitation: persistent fragmentation of hydrological science itself. Narrow focus on isolated components has hindered the development of coherent, scale-relevant understanding of the integrated terrestrial hydrosphere. This is illustrated, for example, by widely divergent estimates of groundwater–streamflow interactions and of water balance-implied ongoing storage changes. We argue that hydrology's future lies not in choosing between ML and physics, but in integrating data-driven and process-based approaches to advance consistent, realistic, and societally relevant understanding of the terrestrial hydrosphere and its multifaceted roles in the Earth System.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"282 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089463","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}
Hong Chen, Xu Zhao, Huiming Zhang, Bangguo Song, Yue Zhao
Targeting the issues of insufficient predictive ability and inefficient computation in two-dimensional shallow water equations (2D-SWEs), this study deeply couples the mesh and hydrodynamic boundary, constructing multiple 2D hydrodynamic models (run 2,640 times). This study proposes and validates, for the first time, a hydrodynamic boundary classification framework (strongly and weakly constrained boundary) based on constraint strength, and systematically quantifies the uncertainty and computational performance of various meshes under different boundaries. Two reasons for insufficient predictive ability were identified: improper boundary setting and mesh selection. Through numerical analysis and theoretical derivation, it was demonstrated that appropriate boundary and mesh choices can reduce the uncertainty of 2D-SWEs. Calculation results indicate that the strongly constrained boundary (water level) significantly reduces model errors; the Unstructured Quadrilateral Mesh (UQM) demonstrates excellent computational robustness, with cumulative deviations in simulated water levels reduced by 30 ∼ 90% compared to the Unstructured Triangular Mesh (UTM). Additionally, the impact of hydrodynamic boundary types on computational efficiency varies with changes in mesh density, type, topography, and other parameters, but the impact of boundary type on computational efficiency does not exceed 4%. UQM improves computational efficiency by 55% ∼ 130% compared to UTM. Additionally, this study identifies the “impossible triangle” region in quadrilateral meshes, which constrains the generation of high-quality meshes. Taking into account the different grid computational performance, flux propagation characteristics, grid quality, and the convenience of large-scale applications, it is recommended to primarily use UQM in river channels and UTM in floodplains.
{"title":"Mesh, Hydrodynamic Boundary, and Uncertainty Analysis of the 2D-SWEs: Taking Numerical Simulation of River Networks as an Example","authors":"Hong Chen, Xu Zhao, Huiming Zhang, Bangguo Song, Yue Zhao","doi":"10.1029/2024wr038993","DOIUrl":"https://doi.org/10.1029/2024wr038993","url":null,"abstract":"Targeting the issues of insufficient predictive ability and inefficient computation in two-dimensional shallow water equations (2D-SWEs), this study deeply couples the mesh and hydrodynamic boundary, constructing multiple 2D hydrodynamic models (run 2,640 times). This study proposes and validates, for the first time, a hydrodynamic boundary classification framework (strongly and weakly constrained boundary) based on constraint strength, and systematically quantifies the uncertainty and computational performance of various meshes under different boundaries. Two reasons for insufficient predictive ability were identified: improper boundary setting and mesh selection. Through numerical analysis and theoretical derivation, it was demonstrated that appropriate boundary and mesh choices can reduce the uncertainty of 2D-SWEs. Calculation results indicate that the strongly constrained boundary (water level) significantly reduces model errors; the Unstructured Quadrilateral Mesh (UQM) demonstrates excellent computational robustness, with cumulative deviations in simulated water levels reduced by 30 ∼ 90% compared to the Unstructured Triangular Mesh (UTM). Additionally, the impact of hydrodynamic boundary types on computational efficiency varies with changes in mesh density, type, topography, and other parameters, but the impact of boundary type on computational efficiency does not exceed 4%. UQM improves computational efficiency by 55% ∼ 130% compared to UTM. Additionally, this study identifies the “impossible triangle” region in quadrilateral meshes, which constrains the generation of high-quality meshes. Taking into account the different grid computational performance, flux propagation characteristics, grid quality, and the convenience of large-scale applications, it is recommended to primarily use UQM in river channels and UTM in floodplains.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"14 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089462","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}
Young-Jin Park, Hyoun-Tae Hwang, Tatsuya Tanaka, Takenori Ozutsumi, Yutaka Morita, Koji Mori, Steven J. Berg, Walter A. Illman
Seepage boundary conditions are commonly used in groundwater simulations to allow groundwater to discharge at the upper surface of the model when groundwater head exceeds atmospheric pressure. However, the extent and transient behavior of the seepage zone are often unknown a priori and difficult to predict. The standard mathematical representation of seepage boundaries defines head as equivalent to elevation only when groundwater pressure exceeds atmospheric pressure, which is a mixed conditional Dirichlet and Neumann boundary condition. While this representation has been widely implemented in groundwater models, it is rarely noted that convergence is guaranteed only when both the efflux and zero-pressure conditions are simultaneously satisfied, often requiring unnecessarily small timestep sizes, resulting in low computational efficiency. This study presents a continuous-differentiable seepage face (CDSF) equation that replaces the conventional mixed boundary condition (or traditional seepage face, TSF) with a head-dependent Robin boundary condition, improving numerical stability and computational performance. It is a refined adaptation of an existing seepage boundary condition approach previously used in integrated surface-subsurface hydrologic models, specifically optimized for saturated flow simulations. Through a series of verification models, we demonstrate that the refined method provides robust and efficient solutions for seepage boundary conditions in saturated flow models. The results suggest that this CDSF approach improves accuracy and computational performance compared to TSF methods, offering a more stable alternative for groundwater modeling. These findings contribute to the advancement of subsurface hydrology by providing a practical framework for handling seepage boundary conditions in groundwater simulations.
{"title":"A Robust and Efficient Continuous-Differentiable Seepage Face Boundary Condition for Dynamic Groundwater Modeling","authors":"Young-Jin Park, Hyoun-Tae Hwang, Tatsuya Tanaka, Takenori Ozutsumi, Yutaka Morita, Koji Mori, Steven J. Berg, Walter A. Illman","doi":"10.1029/2025wr041547","DOIUrl":"https://doi.org/10.1029/2025wr041547","url":null,"abstract":"Seepage boundary conditions are commonly used in groundwater simulations to allow groundwater to discharge at the upper surface of the model when groundwater head exceeds atmospheric pressure. However, the extent and transient behavior of the seepage zone are often unknown a priori and difficult to predict. The standard mathematical representation of seepage boundaries defines head as equivalent to elevation only when groundwater pressure exceeds atmospheric pressure, which is a mixed conditional Dirichlet and Neumann boundary condition. While this representation has been widely implemented in groundwater models, it is rarely noted that convergence is guaranteed only when both the efflux and zero-pressure conditions are simultaneously satisfied, often requiring unnecessarily small timestep sizes, resulting in low computational efficiency. This study presents a continuous-differentiable seepage face (CDSF) equation that replaces the conventional mixed boundary condition (or traditional seepage face, TSF) with a head-dependent Robin boundary condition, improving numerical stability and computational performance. It is a refined adaptation of an existing seepage boundary condition approach previously used in integrated surface-subsurface hydrologic models, specifically optimized for saturated flow simulations. Through a series of verification models, we demonstrate that the refined method provides robust and efficient solutions for seepage boundary conditions in saturated flow models. The results suggest that this CDSF approach improves accuracy and computational performance compared to TSF methods, offering a more stable alternative for groundwater modeling. These findings contribute to the advancement of subsurface hydrology by providing a practical framework for handling seepage boundary conditions in groundwater simulations.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"11 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089464","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 Climate Models (GCMs) are essential for simulating past and future climates but suffer from systematic biases and coarse resolution, limiting direct applications. Bias correction (BC) and downscaling, using dynamical or statistical methods, address these issues. Quantile mapping (QM)-based BC is widely used, yet it distorts dependencies, prompting multivariate approaches whose assumptions remain unclear and results inconsistent. This study evaluates four BC techniques, including one univariate (QM) and three multivariate (dOTC, R2D2, MBCn), in correcting univariate, multivariate, and temporal features of daily precipitation and temperature over India during Indian Summer Monsoon (ISM). For univariate metrics, dOTC effectively corrected temperature mean, variance, and extremes, while QM and dOTC best addressed precipitation variance. Further, R2D2 was most effective for mean correction, and MBCn for dry days and extreme precipitation (P90). Among multivariate methods, R2D2 best preserved inter-variable dependencies, whereas MBCn better captured temporal features, especially precipitation autocorrelation. Additionally, the study evaluates the effectiveness of BC techniques to preserve intervariable dependence, focusing on the Pacific Walker circulation constructed using causal network, crucial for capturing complex climate signals. None of the techniques, however, reproduced the observed network across all GCMs. The overall performance of BC methods was evaluated by averaging ranks across categories since no single approach consistently excelled across all metrics. Among the techniques, dOTC showed the best overall performance, while R2D2 achieved the highest ranks in multivariate evaluations. The findings offer practical insights and highlight challenges in selecting appropriate BC methods for climate applications.
{"title":"Evaluating the Performance of Uni- and Multivariate Bias Correction Techniques: Challenges in Preserving Temporal and Dependence Structures","authors":"Sachidananda Sharma, Akash Singh Raghuvanshi, Ankit Agarwal","doi":"10.1029/2025wr041526","DOIUrl":"https://doi.org/10.1029/2025wr041526","url":null,"abstract":"Global Climate Models (GCMs) are essential for simulating past and future climates but suffer from systematic biases and coarse resolution, limiting direct applications. Bias correction (BC) and downscaling, using dynamical or statistical methods, address these issues. Quantile mapping (QM)-based BC is widely used, yet it distorts dependencies, prompting multivariate approaches whose assumptions remain unclear and results inconsistent. This study evaluates four BC techniques, including one univariate (QM) and three multivariate (dOTC, R2D2, MBCn), in correcting univariate, multivariate, and temporal features of daily precipitation and temperature over India during Indian Summer Monsoon (ISM). For univariate metrics, dOTC effectively corrected temperature mean, variance, and extremes, while QM and dOTC best addressed precipitation variance. Further, R2D2 was most effective for mean correction, and MBCn for dry days and extreme precipitation (P90). Among multivariate methods, R2D2 best preserved inter-variable dependencies, whereas MBCn better captured temporal features, especially precipitation autocorrelation. Additionally, the study evaluates the effectiveness of BC techniques to preserve intervariable dependence, focusing on the Pacific Walker circulation constructed using causal network, crucial for capturing complex climate signals. None of the techniques, however, reproduced the observed network across all GCMs. The overall performance of BC methods was evaluated by averaging ranks across categories since no single approach consistently excelled across all metrics. Among the techniques, dOTC showed the best overall performance, while R2D2 achieved the highest ranks in multivariate evaluations. The findings offer practical insights and highlight challenges in selecting appropriate BC methods for climate applications.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"44 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070150","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}
Zhenya Li, Pengfei Shi, Min Gan, Xijun Lai, Tao Yang
Total Contributing Area (TCA) has been extensively accepted as a crucial terrain attribute in digital terrain analysis and geological simulations. However, existing flow direction algorithms work poorly in the accuracy of TCA estimation due to the irrationality of their empirically designed strategies. To solve this problem, our work proposes a novel method for TCA calculation in a fundamentally different way from existing algorithms. Firstly, general equation of Specific Contributing Area (SCA) along one‐dimensional slope line is deduced based on the constitutive relations between SCA and plane curvature. Its finite difference is revised to enable the calculation of the SCA by just an upslope trace of slope line with only two temporary variables. Secondly, Bicubic B‐spline (BBS) surface is constructed to approximate the terrain surface represented by a digital elevation model (DEM). Finally, the Back‐Trace Numerical (BTN) method is proposed for calculating the TCA of a DEM pixel based on the mathematical relations between pixel‐scale TCA and point‐scale SCA. Particularly note that the outcome of BTN method is the numerical solution of true TCA, which is fundamentally different from the TCAs estimated by existing algorithms based on empirical strategies. Three cases are designed to assess the performances of the BTN method on various terrain surfaces. If fine BTN parameters are adopted, BTN TCAs show extremely high accuracy on all synthetic surfaces with the mean absolute relative errors of <0.20%. Meanwhile, various basin characteristics (e.g., river, valley and ridge lines) could be accurately recognized based on the BTN TCAs for the DEMs of real‐world terrains.
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