Matthew Tiller, Lucy Reading, Marc Miska, Prasanna Egodawatta
Rainfall infiltration is a key hydrological process influencing agriculture, pollutant transport, and flood modeling. Accurate prediction of rainfall losses, defined as rainfall that does not contribute to surface runoff is critical in rainfall-runoff models. Rainfall–runoff models are typically calibrated using historical data to estimate loss parameters, which often deviate from physically realistic infiltration behavior as they compensate for other sources of error and uncertainty in the model. This study addresses this gap by investigating infiltration losses based on physical attributes under controlled rainfall conditions. Seventy-five sites in southeastern Queensland, Australia, were subjected to rainfall of approximately 60 mm/hr for 1-hr, allowing detailed analysis of infiltration responses. Key predictors of infiltration included grass cover, leaf litter, soil organic carbon, and bulk density, while slope had minimal predictive power. Findings indicate that, during short, high-intensity rainfall events, initial losses were relatively low, with runoff beginning within 10–30 min, while continuing loss rates exceeded expectations within the first hour. Multiple Linear Regression (MLR) techniques were used to develop prediction equations for several loss models, including lumped loss, initial loss–continuing loss, and Horton infiltration. These equations explained approximately 60% of the variance between observed and predicted losses. The equations provide a practical tool for estimating infiltration losses in ungauged catchments. The prediction equations are suitable for 1-hr, 60 mm/hr intensity rainfall events, with limited applicability to longer, low-intensity rainfall. The results offer insights for improving flash flood predictions, particularly in ungauged catchments experiencing intense, short-duration storms.
{"title":"Predicting Rainfall Infiltration Losses: A Rainfall Simulation Study of Land Cover, Slope and Soil Type","authors":"Matthew Tiller, Lucy Reading, Marc Miska, Prasanna Egodawatta","doi":"10.1029/2025wr040920","DOIUrl":"https://doi.org/10.1029/2025wr040920","url":null,"abstract":"Rainfall infiltration is a key hydrological process influencing agriculture, pollutant transport, and flood modeling. Accurate prediction of rainfall losses, defined as rainfall that does not contribute to surface runoff is critical in rainfall-runoff models. Rainfall–runoff models are typically calibrated using historical data to estimate loss parameters, which often deviate from physically realistic infiltration behavior as they compensate for other sources of error and uncertainty in the model. This study addresses this gap by investigating infiltration losses based on physical attributes under controlled rainfall conditions. Seventy-five sites in southeastern Queensland, Australia, were subjected to rainfall of approximately 60 mm/hr for 1-hr, allowing detailed analysis of infiltration responses. Key predictors of infiltration included grass cover, leaf litter, soil organic carbon, and bulk density, while slope had minimal predictive power. Findings indicate that, during short, high-intensity rainfall events, initial losses were relatively low, with runoff beginning within 10–30 min, while continuing loss rates exceeded expectations within the first hour. Multiple Linear Regression (MLR) techniques were used to develop prediction equations for several loss models, including lumped loss, initial loss–continuing loss, and Horton infiltration. These equations explained approximately 60% of the variance between observed and predicted losses. The equations provide a practical tool for estimating infiltration losses in ungauged catchments. The prediction equations are suitable for 1-hr, 60 mm/hr intensity rainfall events, with limited applicability to longer, low-intensity rainfall. The results offer insights for improving flash flood predictions, particularly in ungauged catchments experiencing intense, short-duration storms.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"284 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146042631","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}
Zi Wu, Jie Zhan, Zhenduo Zhu, Man Zhang, Lu Chang, Xudong Fu
Drifting fish eggs are a type of fish egg with a slightly higher density than water, requiring floating for successful hatching. While it is acknowledged that interaction with the riverbed surface can cause mortality of the eggs, the impact of this process on their downstream transport remains unclear. In this paper, we theoretically explore the transport of drifting fish eggs in turbulent open channel flows, taking into account both gravitational settling and riverbed mortality effects. This is done by incorporating a vertical drift term in the governing advection-diffusion equation and an absorbing boundary condition for the riverbed surface, respectively. For the first time, we derive an analytical solution by the method of separation of variables for the vertical distribution of eggs during transport. Our analysis shows that in principle, settling can lead to egg accumulation near the riverbed, reducing the population's mean velocity, while conversely, riverbed mortality can decrease near-bed accumulation and accelerate drifting to some extent. However, by estimating values of the mortality rate parameter in the real rivers, we conclude that while it can significantly affect the population size, it has a negligible effect on the vertical concentration distribution in practice, allowing for a considerable simplification of the analytical solution. Furthermore, we deduce an analytical solution for the mean velocity of the egg population, indicating variations of the deceleration rate compared to mean flow velocity, which is capable of assisting in the identification of spawning grounds. The obtained analytical solutions are validated by various numerical and experimental results.
{"title":"Effects of Gravitational Settling and Riverbed-Induced Mortality on the Transport of Drifting Fish Eggs in Rivers","authors":"Zi Wu, Jie Zhan, Zhenduo Zhu, Man Zhang, Lu Chang, Xudong Fu","doi":"10.1029/2025wr041343","DOIUrl":"https://doi.org/10.1029/2025wr041343","url":null,"abstract":"Drifting fish eggs are a type of fish egg with a slightly higher density than water, requiring floating for successful hatching. While it is acknowledged that interaction with the riverbed surface can cause mortality of the eggs, the impact of this process on their downstream transport remains unclear. In this paper, we theoretically explore the transport of drifting fish eggs in turbulent open channel flows, taking into account both gravitational settling and riverbed mortality effects. This is done by incorporating a vertical drift term in the governing advection-diffusion equation and an absorbing boundary condition for the riverbed surface, respectively. For the first time, we derive an analytical solution by the method of separation of variables for the vertical distribution of eggs during transport. Our analysis shows that in principle, settling can lead to egg accumulation near the riverbed, reducing the population's mean velocity, while conversely, riverbed mortality can decrease near-bed accumulation and accelerate drifting to some extent. However, by estimating values of the mortality rate parameter in the real rivers, we conclude that while it can significantly affect the population size, it has a negligible effect on the vertical concentration distribution in practice, allowing for a considerable simplification of the analytical solution. Furthermore, we deduce an analytical solution for the mean velocity of the egg population, indicating variations of the deceleration rate compared to mean flow velocity, which is capable of assisting in the identification of spawning grounds. The obtained analytical solutions are validated by various numerical and experimental results.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"4 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034139","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}
Marie-Madeleine Stettler, Marco Dentz, Olaf A. Cirpka
Spatial Markov models (SMM) are an efficient approach to simulate transport in heterogeneous media across scales. They represent particle transport by equidistant spatial transitions with correlated random velocities, which renders the associated transition times correlated random variables. While SMM perform excellently for modeling purely advective transport, incorporating diffusion remains challenging. So far, applying SMM to advective-diffusive transport in porous media has been mostly restricted to using empirical transition matrices based on numerical simulations. Using a transition matrix for advective-diffusive transport obliterates the fundamental differences between the two transport processes and is conflicting with the goal of replacing explicit transport simulations by a SMM. Here, we present an advective-diffusive SMM that conceptualizes diffusion as jumps between advective trajectories, that is, diffusion competes with advection for changing the particle velocity. At each particle transition, a random diffusion time is compared to the current advection time. If the advection time is shorter than the diffusion time, the particle remains on its current SMM trajectory and the longitudinal velocity correlation is kept. If the diffusion time is shorter, the particle velocity is reset. Breakthrough curves and their first and second moments calculated with the advective-diffusive SMM are in agreement with three-dimensional numerical simulations in heterogeneous log-conductivity fields with isotropic, exponential covariance function with variances up to five.
{"title":"Spatial Markov Model of Advective-Diffusive Transport in Heterogeneous Domains","authors":"Marie-Madeleine Stettler, Marco Dentz, Olaf A. Cirpka","doi":"10.1029/2025wr041175","DOIUrl":"https://doi.org/10.1029/2025wr041175","url":null,"abstract":"Spatial Markov models (SMM) are an efficient approach to simulate transport in heterogeneous media across scales. They represent particle transport by equidistant spatial transitions with correlated random velocities, which renders the associated transition times correlated random variables. While SMM perform excellently for modeling purely advective transport, incorporating diffusion remains challenging. So far, applying SMM to advective-diffusive transport in porous media has been mostly restricted to using empirical transition matrices based on numerical simulations. Using a transition matrix for advective-diffusive transport obliterates the fundamental differences between the two transport processes and is conflicting with the goal of replacing explicit transport simulations by a SMM. Here, we present an advective-diffusive SMM that conceptualizes diffusion as jumps between advective trajectories, that is, diffusion competes with advection for changing the particle velocity. At each particle transition, a random diffusion time is compared to the current advection time. If the advection time is shorter than the diffusion time, the particle remains on its current SMM trajectory and the longitudinal velocity correlation is kept. If the diffusion time is shorter, the particle velocity is reset. Breakthrough curves and their first and second moments calculated with the advective-diffusive SMM are in agreement with three-dimensional numerical simulations in heterogeneous log-conductivity fields with isotropic, exponential covariance function with variances up to five.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"31 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034150","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}
Small (<10 ha) and medium (10–100 ha) inland waterbodies are crucial for water storage and flood regulation, necessitating an improved understanding of their water volume. Traditional water volume measurement methods, modeling techniques, and advanced altimetry missions struggle to capture the non-linear volume changes in these waterbodies, leading to inaccurate and temporally sparse volume estimates. Using 86 in situ bathymetry and spatio-temporal water spread area data, the study addresses this gap by developing a machine learning model that estimates monthly volume changes (1988–2023) in 914 waterbodies of the Adyar-Chennai basin, India. The machine learning model demonstrated superior performance (R2 = 0.94), outperforming global (R2 = 0.57) and regional models (R2 = 0.24). The water volume of small and medium waterbodies in the Adyar-Chennai basin nearly halved from ∼102.28 (95% CI: 93.38–114.28) million cubic meters (MCM) in January 1988 to ∼40.13 (32.25–61.42) MCM in December 2023, which is possibly driven by urbanization, vegetation loss, and increasing vapor pressure. The first-ever future volumes were projected for these waterbodies (R2 = 0.62). While analyzing small and medium waterbodies' flood mitigation potential, the peak flood rate in the basin increased by 50% in their absence, highlighting their crucial role in flood control. To completely mitigate floods in the basin, we propose (a) creating 90 new waterbodies and (b) deepening existing waterbodies by 1 m. Suitable sites for creating new waterbodies were identified using hydrology and a land-use-based novel tankshed overflow index. This research advances water volume estimation and flood mitigation strategies of small and medium inland waterbodies.
{"title":"Small and Medium-Sized Inland Waterbodies: Water Volume Predictions and Flood Implications","authors":"Ankit Sharma, Idhayachandhiran Ilampooranan","doi":"10.1029/2024wr038283","DOIUrl":"https://doi.org/10.1029/2024wr038283","url":null,"abstract":"Small (<10 ha) and medium (10–100 ha) inland waterbodies are crucial for water storage and flood regulation, necessitating an improved understanding of their water volume. Traditional water volume measurement methods, modeling techniques, and advanced altimetry missions struggle to capture the non-linear volume changes in these waterbodies, leading to inaccurate and temporally sparse volume estimates. Using 86 in situ bathymetry and spatio-temporal water spread area data, the study addresses this gap by developing a machine learning model that estimates monthly volume changes (1988–2023) in 914 waterbodies of the Adyar-Chennai basin, India. The machine learning model demonstrated superior performance (<i>R</i><sup>2</sup> = 0.94), outperforming global (<i>R</i><sup>2</sup> = 0.57) and regional models (<i>R</i><sup>2</sup> = 0.24). The water volume of small and medium waterbodies in the Adyar-Chennai basin nearly halved from ∼102.28 (95% CI: 93.38–114.28) million cubic meters (MCM) in January 1988 to ∼40.13 (32.25–61.42) MCM in December 2023, which is possibly driven by urbanization, vegetation loss, and increasing vapor pressure. The first-ever future volumes were projected for these waterbodies (<i>R</i><sup>2</sup> = 0.62). While analyzing small and medium waterbodies' flood mitigation potential, the peak flood rate in the basin increased by 50% in their absence, highlighting their crucial role in flood control. To completely mitigate floods in the basin, we propose (a) creating 90 new waterbodies and (b) deepening existing waterbodies by 1 m. Suitable sites for creating new waterbodies were identified using hydrology and a land-use-based novel tankshed overflow index. This research advances water volume estimation and flood mitigation strategies of small and medium inland waterbodies.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"32 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034152","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}
Zhongzheng Wang, Yixiang Gan, Jean-Michel Pereira, Scott McCue, Anna Herring, Emilie Sauret
Cyclic injection of immiscible fluids in porous media is a key process in applications such as carbon geosequestration and underground hydrogen storage, where understanding and predicting the residual trapping efficiency are critical. This study develops a modified invasion percolation algorithm incorporating a pore coupling coefficient to simulate quasi-static, cyclic fluid displacement in porous media. The coefficient captures the effects of pore-scale cooperative pore-filling mechanisms by modifying the capillary pore entry pressures based on the filling status of neighboring pores. Systematic simulations reveal that the displacement morphology and saturation hysteresis are strongly influenced by the pore coupling strength. Phase diagrams highlight regimes for which cyclic injections significantly enhance residual gas trapping. Results also establish connections between the pore coupling coefficient and physical parameters such as wettability and porosity. This work provides new insights into the pore-scale origins of saturation hysteresis and its implications for optimizing fluid injection strategies in subsurface applications.
{"title":"Saturation Hysteresis During Cyclic Injections of Immiscible Fluids in Porous Media: An Invasion Percolation Study","authors":"Zhongzheng Wang, Yixiang Gan, Jean-Michel Pereira, Scott McCue, Anna Herring, Emilie Sauret","doi":"10.1029/2025wr041271","DOIUrl":"https://doi.org/10.1029/2025wr041271","url":null,"abstract":"Cyclic injection of immiscible fluids in porous media is a key process in applications such as carbon geosequestration and underground hydrogen storage, where understanding and predicting the residual trapping efficiency are critical. This study develops a modified invasion percolation algorithm incorporating a pore coupling coefficient to simulate quasi-static, cyclic fluid displacement in porous media. The coefficient captures the effects of pore-scale cooperative pore-filling mechanisms by modifying the capillary pore entry pressures based on the filling status of neighboring pores. Systematic simulations reveal that the displacement morphology and saturation hysteresis are strongly influenced by the pore coupling strength. Phase diagrams highlight regimes for which cyclic injections significantly enhance residual gas trapping. Results also establish connections between the pore coupling coefficient and physical parameters such as wettability and porosity. This work provides new insights into the pore-scale origins of saturation hysteresis and its implications for optimizing fluid injection strategies in subsurface applications.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"53 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022043","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}
Machine learning models are highly potential to substitute computationally intensive numerical simulation models in saltwater intrusion (SWI) remediation optimization. However, uncertainty inherent in machine learning models can propagate through predictions into optimization, resulting in inaccurate solutions. Unlike deterministic modeling that ignores uncertainty with fixed outputs, this study proposes a computationally efficient mixed integer multiobjective stochastic optimization (MIMOSO) method, which uniquely bridges the gap between Bayesian multi-model uncertainty quantification and risk-aware decision-making. The method captures stochastic uncertainty propagation from model prediction to optimization by integrating with Bayesian model averaging (BMA). In contrast to traditional single-surrogate approaches, the proposed method incorporates multiple machine learning approaches to alleviate computational burden. The framework enables to derive optimal but robust extraction-injection strategies by considering various constraint-violation levels. Two conflicting goals are addressed: minimizing total extraction-injection and maximizing SWI remediation effect. Binary variables are introduced to control discrete operation states of the well system. The developed method is demonstrated in a “1,500-foot” sand aquifer located in Baton Rouge, USA. Results exhibit that Pareto optimal remediation strategies are identified with associated SWI risk levels. MIMOSO advances the field by simultaneously resolving computational bottlenecks through machine learning surrogates and rigorously propagating multi-source uncertainties via BMA. Compared to numerical simulation based optimization (≥2,000 hr), machine learning assisted model reduces computation time to 87 hr, achieving a 23-fold efficiency improvement. Three metrics (hypervolume, spacing, and maximum spread) validate superior performance regarding both convergence and diversity. The methodology provides a promising way for risk-aware real-world aquifer remediation design.
{"title":"A Computationally Efficient Stochastic Method for Quantifying the Effects of Multi-Surrogate Model Uncertainty on Saltwater Remediation Optimization","authors":"Yulu Huang, Jina Yin, Chunhui Lu","doi":"10.1029/2025wr041251","DOIUrl":"https://doi.org/10.1029/2025wr041251","url":null,"abstract":"Machine learning models are highly potential to substitute computationally intensive numerical simulation models in saltwater intrusion (SWI) remediation optimization. However, uncertainty inherent in machine learning models can propagate through predictions into optimization, resulting in inaccurate solutions. Unlike deterministic modeling that ignores uncertainty with fixed outputs, this study proposes a computationally efficient mixed integer multiobjective stochastic optimization (MIMOSO) method, which uniquely bridges the gap between Bayesian multi-model uncertainty quantification and risk-aware decision-making. The method captures stochastic uncertainty propagation from model prediction to optimization by integrating with Bayesian model averaging (BMA). In contrast to traditional single-surrogate approaches, the proposed method incorporates multiple machine learning approaches to alleviate computational burden. The framework enables to derive optimal but robust extraction-injection strategies by considering various constraint-violation levels. Two conflicting goals are addressed: minimizing total extraction-injection and maximizing SWI remediation effect. Binary variables are introduced to control discrete operation states of the well system. The developed method is demonstrated in a “1,500-foot” sand aquifer located in Baton Rouge, USA. Results exhibit that Pareto optimal remediation strategies are identified with associated SWI risk levels. MIMOSO advances the field by simultaneously resolving computational bottlenecks through machine learning surrogates and rigorously propagating multi-source uncertainties via BMA. Compared to numerical simulation based optimization (≥2,000 hr), machine learning assisted model reduces computation time to 87 hr, achieving a 23-fold efficiency improvement. Three metrics (hypervolume, spacing, and maximum spread) validate superior performance regarding both convergence and diversity. The methodology provides a promising way for risk-aware real-world aquifer remediation design.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"13 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146021994","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}
Swelling potential (SP) has long been used as a terminology to describe a soil's expansibility. It is commonly defined in terms of pressure or deformation under certain constraints. However, fundamentally, SP originates from the soil-water interactions in the interlayer space of expansive minerals and should not depend on displacement or force constraints. Here, the writers propose a SP based on the concepts of soil sorptive potential, unitary definition of matric potential, and water retention hysteresis. Water retention hysteresis in low matric potential is the result of interlayer hydration against the interlayer energy barrier. This energy barrier prevents water from entering the interlayer space. SSP synthesizes all the known sources of water adsorption, which provides the energy for soil swelling and can be determined under the unified definition of matric potential. The SP is defined as the energy hysteresis of interlayer hydration during wetting and drying. It is a function of relative humidity and can be calculated solely from the soil water isotherm (SWI). The SWI data of a wide variety of fine-grained soils are used to determine and assess the proposed SP. For validation, the SP index (SPI), defined as the maximum energy consumed to overcome the energy barrier during wetting, is used. The SPI compares well with several expansive soil classification systems, confirming the validity of the SP. This study provides a scientific basis for linking soil water potential and energy used for swelling and understanding the volumetric behavior of expansive soil under varying humidity environments.
{"title":"Swelling Potential of Fine-Grained Soil: Theory, Determination, and Validation","authors":"Yijie Wang, Liming Hu, Chao Zhang, Ning Lu","doi":"10.1029/2024wr038985","DOIUrl":"https://doi.org/10.1029/2024wr038985","url":null,"abstract":"Swelling potential (SP) has long been used as a terminology to describe a soil's expansibility. It is commonly defined in terms of pressure or deformation under certain constraints. However, fundamentally, SP originates from the soil-water interactions in the interlayer space of expansive minerals and should not depend on displacement or force constraints. Here, the writers propose a SP based on the concepts of soil sorptive potential, unitary definition of matric potential, and water retention hysteresis. Water retention hysteresis in low matric potential is the result of interlayer hydration against the interlayer energy barrier. This energy barrier prevents water from entering the interlayer space. SSP synthesizes all the known sources of water adsorption, which provides the energy for soil swelling and can be determined under the unified definition of matric potential. The SP is defined as the energy hysteresis of interlayer hydration during wetting and drying. It is a function of relative humidity and can be calculated solely from the soil water isotherm (SWI). The SWI data of a wide variety of fine-grained soils are used to determine and assess the proposed SP. For validation, the SP index (SPI), defined as the maximum energy consumed to overcome the energy barrier during wetting, is used. The SPI compares well with several expansive soil classification systems, confirming the validity of the SP. This study provides a scientific basis for linking soil water potential and energy used for swelling and understanding the volumetric behavior of expansive soil under varying humidity environments.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"71 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146042629","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}
Defining lentic and lotic system types is critical for understanding hydrological, ecological, and biochemical processes. Traditional classification methods rely on non-generalizable site-specific parameters such as visual characteristics, historical inventory, and residence time. While machine learning and deep learning models address these challenges to some extent, they are limited by high data requirements, unverified training data sets, computational demands, and the inability to accurately detect inland waters smaller than 3 ha. To address this gap, this study introduces a novel Automated Data Efficient Morphometric Approach (ADEMA) that classifies inland waters into lentic and lotic system types globally up to 0.09 ha (33 times smaller than previous studies) using multi-dimensional morphometric interpretations. ADEMA was developed and validated using 17,391 expert-labeled inland waters spanning 66 globally diverse locations and compared against state-of-the-art, comprehensively optimized machine learning, deep learning, and global models. Results show ADEMA equivalently performed to the machine learning and deep learning models, achieving F1 scores of 92%, 95%, and 71% in small, medium, and large inland waters, respectively. Across 17,391 expert-labeled samples, ADEMA maintained a high performance with a precision of 89%, a recall of 99%, and an F1 score of 94%. Analysis across four decadal intervals (1991–2021) demonstrated ADEMA's temporal invariance, with consistently high F1 scores (90%–93%) and negligible omission errors (0%–2%). Further, ADEMA surpassed global classification products (average F1 score: 97% vs. 62%). These findings emphasize ADEMA's potential for accurately classifying global inland waters into lentic and lotic system types.
{"title":"An Automated Data Efficient Morphometric Approach to Define Global Lentic and Lotic Inland Waters","authors":"Ankit Sharma, Mukund Narayanan, Idhayachandhiran Ilampooranan","doi":"10.1029/2025wr040137","DOIUrl":"https://doi.org/10.1029/2025wr040137","url":null,"abstract":"Defining lentic and lotic system types is critical for understanding hydrological, ecological, and biochemical processes. Traditional classification methods rely on non-generalizable site-specific parameters such as visual characteristics, historical inventory, and residence time. While machine learning and deep learning models address these challenges to some extent, they are limited by high data requirements, unverified training data sets, computational demands, and the inability to accurately detect inland waters smaller than 3 ha. To address this gap, this study introduces a novel Automated Data Efficient Morphometric Approach (ADEMA) that classifies inland waters into lentic and lotic system types globally up to 0.09 ha (33 times smaller than previous studies) using multi-dimensional morphometric interpretations. ADEMA was developed and validated using 17,391 expert-labeled inland waters spanning 66 globally diverse locations and compared against state-of-the-art, comprehensively optimized machine learning, deep learning, and global models. Results show ADEMA equivalently performed to the machine learning and deep learning models, achieving <i>F</i>1 scores of 92%, 95%, and 71% in small, medium, and large inland waters, respectively. Across 17,391 expert-labeled samples, ADEMA maintained a high performance with a precision of 89%, a recall of 99%, and an <i>F</i>1 score of 94%. Analysis across four decadal intervals (1991–2021) demonstrated ADEMA's temporal invariance, with consistently high <i>F</i>1 scores (90%–93%) and negligible omission errors (0%–2%). Further, ADEMA surpassed global classification products (average <i>F</i>1 score: 97% vs. 62%). These findings emphasize ADEMA's potential for accurately classifying global inland waters into lentic and lotic system types.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"50 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034151","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}
Wetlands, though covering only 2% of the Earth's surface, store over 20% of global organic carbon, making them vital reservoirs in the global carbon cycle. Despite this significance, the role of hydrological connectivity in wetland vegetation carbon storage remains poorly understood. This study addresses this gap by quantitatively assessing the impact of hydrological connectivity on wetland vegetation carbon sequestration in Poyang Lake, China's largest freshwater lake, based on multi-source remote sensing data fusion. It reveals that total carbon storage in Poyang Lake increased from 2000 to 2020 at a rate of 0.09 Tg/year, with a more pronounced rise after the Three Gorges Dam began operation. Hydrological connectivity explained 73% variation in vegetation carbon storage, with connectivity functions (CFs, defined as the probability of water connection between surface units as a function of distance and direction) during the receding period having the most significant impact, and near-distance CFs contributing more to carbon sequestration than middle- and far-distance CFs. Additionally, enhancing hydrological connectivity does not necessarily result in higher carbon sequestration, as low-connected seasonal isolated lakes (SILs) sequestered up to 2,051.18 g C/m2/year, exceeding the 1,593.75 g C/m2/year in high-connected SILs. These findings challenge conventional understanding and offer actionable insights for optimizing wetland management strategies aimed at enhancing carbon sequestration, particularly through targeted hydrological regulation.
湿地虽然只占地球表面的2%，却储存了全球20%以上的有机碳，使它们成为全球碳循环的重要储存库。尽管具有这一意义，但水文连通性在湿地植被碳储量中的作用仍然知之甚少。本研究基于多源遥感数据融合，定量评估了鄱阳湖水文连通性对湿地植被固碳的影响，填补了这一空白。结果表明：2000 ~ 2020年鄱阳湖总碳储量以0.09 Tg/年的速率增加，三峡大坝开通后碳储量增加更为明显；水文连通性解释了73%的植被碳储量变化，其中连通性函数（CFs，定义为地表单元之间水连接的概率，作为距离和方向的函数）在后退期间的影响最为显著，近距离CFs对碳封存的贡献大于中距离和远距离CFs。此外，加强水文连通性并不一定会带来更高的碳固存，因为低连通性的季节性隔离湖（SILs）的碳固存量高达2,051.18 g C/m2/年，超过了高连通性的湖泊的1,593.75 g C/m2/年。这些发现挑战了传统的认识，并为优化旨在加强碳封存的湿地管理策略提供了可行的见解，特别是通过有针对性的水文调节。
{"title":"Linking Hydrological Connectivity to Wetland Vegetation Carbon Storage: Insights From the Largest Freshwater Lake in China","authors":"Zhiqiang Tan, Yaling Lin, Leiqiang Gong, Jing Yao, Yunliang Li, Xiaolong Wang, Xianghu Li, Yongjiu Cai","doi":"10.1029/2024wr039631","DOIUrl":"https://doi.org/10.1029/2024wr039631","url":null,"abstract":"Wetlands, though covering only 2% of the Earth's surface, store over 20% of global organic carbon, making them vital reservoirs in the global carbon cycle. Despite this significance, the role of hydrological connectivity in wetland vegetation carbon storage remains poorly understood. This study addresses this gap by quantitatively assessing the impact of hydrological connectivity on wetland vegetation carbon sequestration in Poyang Lake, China's largest freshwater lake, based on multi-source remote sensing data fusion. It reveals that total carbon storage in Poyang Lake increased from 2000 to 2020 at a rate of 0.09 Tg/year, with a more pronounced rise after the Three Gorges Dam began operation. Hydrological connectivity explained 73% variation in vegetation carbon storage, with connectivity functions (CFs, defined as the probability of water connection between surface units as a function of distance and direction) during the receding period having the most significant impact, and near-distance CFs contributing more to carbon sequestration than middle- and far-distance CFs. Additionally, enhancing hydrological connectivity does not necessarily result in higher carbon sequestration, as low-connected seasonal isolated lakes (SILs) sequestered up to 2,051.18 g C/m<sup>2</sup>/year, exceeding the 1,593.75 g C/m<sup>2</sup>/year in high-connected SILs. These findings challenge conventional understanding and offer actionable insights for optimizing wetland management strategies aimed at enhancing carbon sequestration, particularly through targeted hydrological regulation.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"69 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146021995","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}
Reliable and efficient simulation and optimization (SO) approach are crucial for groundwater management. Traditionally, SO of groundwater system relies on process-based numerical models, which often feature low computational efficiency, and unsatisfactory accuracy under limited amount of supportive data and time-varying aquifer conditions. This study establishes a bidirectional SO approach for adaptive prediction, diagnostics and optimization of groundwater system. Prediction module uses recurrent and convolutional neural network to tackle the spatiotemporal relationship between well operations and responses; particularly the lightweight model is developed under limited historical observations, and transfer learning is leveraged for model updating based on new observations to accommodate evolving aquifer conditions. Diagnostics module uses expected gradient algorithm to detect abnormal situation that the predicted well responses are biased from the object, and to identify controlling factors (e.g., well positions and pumping rates) sensitive to the abnormal responses. Optimization module uses iterative ensemble smoother to optimize the controlling factors. The effects of this real-time analysis approach are exemplified in a uranium leaching system in north China. The forward lightweight model facilitated with transfer learning achieves fast and accurate prediction of uranium concentrations under varying pumping rates. The diagnostics module allows for the dynamic detection of well positions and pumping rates controlling the uranium production, and are explainable in comparison to residual uraninite distribution in the aquifer simulated by reactive transport model. Finally, the optimization of pumping rates at the controlling wells in real time enhances the uranium production by approximately 20% higher than that without SO.
{"title":"An Integrated Machine Learning Approach for Real-Time Prediction, Diagnostics and Optimization of Uranium-Leaching Groundwater System","authors":"Zhenjiao Jiang, Jinxin Wang, Jiangjiang Zhang, Mengdi Chen, Bin Yu, Tianfu Xu","doi":"10.1029/2024wr038747","DOIUrl":"https://doi.org/10.1029/2024wr038747","url":null,"abstract":"Reliable and efficient simulation and optimization (SO) approach are crucial for groundwater management. Traditionally, SO of groundwater system relies on process-based numerical models, which often feature low computational efficiency, and unsatisfactory accuracy under limited amount of supportive data and time-varying aquifer conditions. This study establishes a bidirectional SO approach for adaptive prediction, diagnostics and optimization of groundwater system. Prediction module uses recurrent and convolutional neural network to tackle the spatiotemporal relationship between well operations and responses; particularly the lightweight model is developed under limited historical observations, and transfer learning is leveraged for model updating based on new observations to accommodate evolving aquifer conditions. Diagnostics module uses expected gradient algorithm to detect abnormal situation that the predicted well responses are biased from the object, and to identify controlling factors (e.g., well positions and pumping rates) sensitive to the abnormal responses. Optimization module uses iterative ensemble smoother to optimize the controlling factors. The effects of this real-time analysis approach are exemplified in a uranium leaching system in north China. The forward lightweight model facilitated with transfer learning achieves fast and accurate prediction of uranium concentrations under varying pumping rates. The diagnostics module allows for the dynamic detection of well positions and pumping rates controlling the uranium production, and are explainable in comparison to residual uraninite distribution in the aquifer simulated by reactive transport model. Finally, the optimization of pumping rates at the controlling wells in real time enhances the uranium production by approximately 20% higher than that without SO.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"47 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146021779","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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