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}
The flow of liquid water through snow is a complex and poorly understood problem in snow hydrology. This paper reviews current understanding of the lateral flow of water through snow. We determined that the main physical processes producing lateral flow are: (a) hydraulic barriers at layer interfaces, (b) soil saturation overland/through-snow flow, and (c) infiltration excess through-snow flow. These processes result in increased potential for lateral flow where snowpacks have more complex stratigraphy and the rate of snowmelt input is greater than the storage or infiltration capacity of the underlying soil. A global snow classification shows lateral flow through snow is important for consideration in 75% of the total global cryosphere and 50% of global seasonal snow coverage. Lateral flow is important for 70% of the cryosphere in North America and 46% of the Cryosphere in Europe and Asia. Knowledge gaps in current understanding outline future research needs which include: (a) improving hydrologic model structures to include lateral flow through snow, (b) expanded research in parameterizing the hydraulic properties of snow, and (c) further understanding of the spatial and temporal scale of lateral flow through snow processes.
{"title":"Review: The Importance of Lateral Flow Through Snow in Hydrological Processes Globally","authors":"R. W. Webb, N. Ohara, H. P. Marshall, J. McNamara","doi":"10.1029/2025wr040776","DOIUrl":"https://doi.org/10.1029/2025wr040776","url":null,"abstract":"The flow of liquid water through snow is a complex and poorly understood problem in snow hydrology. This paper reviews current understanding of the lateral flow of water through snow. We determined that the main physical processes producing lateral flow are: (a) hydraulic barriers at layer interfaces, (b) soil saturation overland/through-snow flow, and (c) infiltration excess through-snow flow. These processes result in increased potential for lateral flow where snowpacks have more complex stratigraphy and the rate of snowmelt input is greater than the storage or infiltration capacity of the underlying soil. A global snow classification shows lateral flow through snow is important for consideration in 75% of the total global cryosphere and 50% of global seasonal snow coverage. Lateral flow is important for 70% of the cryosphere in North America and 46% of the Cryosphere in Europe and Asia. Knowledge gaps in current understanding outline future research needs which include: (a) improving hydrologic model structures to include lateral flow through snow, (b) expanded research in parameterizing the hydraulic properties of snow, and (c) further understanding of the spatial and temporal scale of lateral flow through snow processes.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"1 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146021780","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}
{"title":"Spatial Covariability of Extreme Floods Over the Coterminous United States: Co-Dependency Measures and Their Statistical Significance","authors":"Kichul Bae, Jeongwoo Hwang, A. Sankarasubramanian","doi":"10.1029/2025wr041262","DOIUrl":"https://doi.org/10.1029/2025wr041262","url":null,"abstract":"Understanding the spatial structure of extreme floods is critical both for reliable design flood estimation and for coordinated development of regional response and flood mitigation strategies. Yet, analysis of rare, high-magnitude floods is challenged by the limited sample size. This study investigates the spatial covariability of extreme floods across the coterminous United States (CONUS) for large return periods (2–100 years) by proposing three distinct co-dependency measures: (a) annual co-occurrence probability (<span data-altimg=\"/cms/asset/7027a4c3-d219-4443-8ee2-318f8f214c3e/wrcr70683-math-0001.png\"></span><mjx-container ctxtmenu_counter=\"188\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/wrcr70683-math-0001.png\"><mjx-semantics><mjx-mrow><mjx-msub data-semantic-children=\"0,1\" data-semantic- data-semantic-role=\"unknown\" data-semantic-speech=\"CP Subscript annual\" data-semantic-type=\"subscript\"><mjx-mtext data-semantic-annotation=\"clearspeak:unit\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"unknown\" data-semantic-type=\"text\"><mjx-c></mjx-c><mjx-c></mjx-c></mjx-mtext><mjx-script style=\"vertical-align: -0.15em;\"><mjx-mtext data-semantic-annotation=\"clearspeak:unit\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"unknown\" data-semantic-type=\"text\" size=\"s\"><mjx-c></mjx-c><mjx-c></mjx-c><mjx-c></mjx-c><mjx-c></mjx-c><mjx-c></mjx-c><mjx-c></mjx-c></mjx-mtext></mjx-script></mjx-msub></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:00431397:media:wrcr70683:wrcr70683-math-0001\" display=\"inline\" location=\"graphic/wrcr70683-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mrow><msub data-semantic-=\"\" data-semantic-children=\"0,1\" data-semantic-role=\"unknown\" data-semantic-speech=\"CP Subscript annual\" data-semantic-type=\"subscript\"><mtext data-semantic-=\"\" data-semantic-annotation=\"clearspeak:unit\" data-semantic-font=\"normal\" data-semantic-parent=\"2\" data-semantic-role=\"unknown\" data-semantic-type=\"text\">CP</mtext><mtext data-semantic-=\"\" data-semantic-annotation=\"clearspeak:unit\" data-semantic-font=\"normal\" data-semantic-parent=\"2\" data-semantic-role=\"unknown\" data-semantic-type=\"text\">annual</mtext></msub></mrow>${text{CP}}_{text{annual}}$</annotation></semantics></math></mjx-assistive-mml></mjx-container>), (b) 7-day co-occurrence probability (<span data-altimg=\"/cms/asset/29d52946-17f1-4017-ae55-0afeff5d9d68/wrcr70683-math-0002.png\"></span><mjx-container ctxtmenu_counter=\"189\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/wrcr70683-math-0002.png\"><mjx-semantics><mjx-mrow><mjx-msub data-semantic-children=\"0,1\" data-semantic- d","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"45 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146005671","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}
Huihui Feng, Jianhong Zhou, Zhiyong Wu, Jianzhi Dong, Luca Brocca, Long Zhao, Hai He, Hui Fan
Hydrological models are typically calibrated using historical ground-based streamflow observations to constrain model uncertainty. However, such a calibration strategy can lead to unrealistic model parameters and is not applicable in data-sparse regions where streamflow observations are unavailable. Motivated by this limitation, a novel model calibration approach that leverages remote sensing (RS) soil moisture retrievals has been recently developed based on the assumption of perfect rank correlation. It calibrates model parameters by maximizing the rank correlation between RS pre-storm soil moisture and modeled storm-scale runoff coefficient (i.e., the ratio of runoff to precipitation). However, this calibration approach has so far been limited to basin-scale applications and evaluated only in terms of storm-scale runoff coefficients rather than actual streamflow simulations. Here, we extend the calibration approach to a grid-by-grid parameter calibration framework within the Variable Infiltration Capacity (VIC) model and incorporate a routing scheme to enable streamflow simulation. The model simulations are evaluated against independent ground-based streamflow observations and other hydrological variables, including ground-based soil moisture and RS-based terrestrial water storage (TWS) and evapotranspiration (ET). Results show that the RS-based calibration approach produces VIC streamflow simulations comparable to the conventional calibration using ground-based streamflow in semi-humid and humid basins—achieving a mean Nash-Sutcliffe coefficient above 0.68. In addition, the calibration method leads to improvements in both VIC TWS and ET estimates (with average correlation increments of 0.06 and 0.07, respectively). The study offers valuable insights for streamflow modeling in data-sparse regions.
{"title":"Optimizing Soil Moisture-Runoff Coupling Strength With Remotely Sensed Soil Moisture for Improved Hydrological Modeling","authors":"Huihui Feng, Jianhong Zhou, Zhiyong Wu, Jianzhi Dong, Luca Brocca, Long Zhao, Hai He, Hui Fan","doi":"10.1029/2024wr039571","DOIUrl":"https://doi.org/10.1029/2024wr039571","url":null,"abstract":"Hydrological models are typically calibrated using historical ground-based streamflow observations to constrain model uncertainty. However, such a calibration strategy can lead to unrealistic model parameters and is not applicable in data-sparse regions where streamflow observations are unavailable. Motivated by this limitation, a novel model calibration approach that leverages remote sensing (RS) soil moisture retrievals has been recently developed based on the assumption of perfect rank correlation. It calibrates model parameters by maximizing the rank correlation between RS pre-storm soil moisture and modeled storm-scale runoff coefficient (i.e., the ratio of runoff to precipitation). However, this calibration approach has so far been limited to basin-scale applications and evaluated only in terms of storm-scale runoff coefficients rather than actual streamflow simulations. Here, we extend the calibration approach to a grid-by-grid parameter calibration framework within the Variable Infiltration Capacity (VIC) model and incorporate a routing scheme to enable streamflow simulation. The model simulations are evaluated against independent ground-based streamflow observations and other hydrological variables, including ground-based soil moisture and RS-based terrestrial water storage (TWS) and evapotranspiration (ET). Results show that the RS-based calibration approach produces VIC streamflow simulations comparable to the conventional calibration using ground-based streamflow in semi-humid and humid basins—achieving a mean Nash-Sutcliffe coefficient above 0.68. In addition, the calibration method leads to improvements in both VIC TWS and ET estimates (with average correlation increments of 0.06 and 0.07, respectively). The study offers valuable insights for streamflow modeling in data-sparse regions.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"63 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146005670","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}
A. Lackner, W. Lidberg, A. M. Ågren, I. F. Creed, K. Bishop
Decadal trends in the concentration of dissolved organic carbon (DOC) in surface water have gained considerable attention due to their significance for aquatic ecology and drinking water quality. Spatial patterns in DOC dynamics hold clues to the causes of DOC variation. Recent developments in digital mapping provide high-resolution information on soil moisture and how the length of stream networks, including drainage ditches, changes with discharge. This study characterized riparian corridors across multiple flow conditions and spatial extents, showing that although soil moisture became wetter closer to the stream, between-catchment differences in soil moisture composition were similar across 10, 100 m, and whole-catchment extents. The study explored how catchment factors influencing spatial and temporal variation in DOC in 145 Swedish watercourses could be explained using high-resolution spatial data in corridors along stream networks that expand and contract with flow. Catchment-wide characteristics mapped at coarser scales, combined with meteorological factors and stream flow, explained 64%–77% of observed mean DOC and the influences of seasonality and discharge. Adding high-resolution soil moisture data and considering them in corridors of different widths did not improve explanation of DOC variation. However, variation in high-resolution soil moisture contained information important for explaining mean DOC and daily DOC variation. Ditch density and changes in mesic soil moisture class were important for explaining mean DOC, while stream density affected the influence of discharge. Although high-resolution soil moisture data did not add explanatory power beyond coarser-scale information, they deepened understanding of how soil moisture and topography influence DOC dynamics.
{"title":"Scales of Landscape Influence on Dissolved Organic Carbon Dynamics in Boreal Surface Water","authors":"A. Lackner, W. Lidberg, A. M. Ågren, I. F. Creed, K. Bishop","doi":"10.1029/2025wr041513","DOIUrl":"https://doi.org/10.1029/2025wr041513","url":null,"abstract":"Decadal trends in the concentration of dissolved organic carbon (DOC) in surface water have gained considerable attention due to their significance for aquatic ecology and drinking water quality. Spatial patterns in DOC dynamics hold clues to the causes of DOC variation. Recent developments in digital mapping provide high-resolution information on soil moisture and how the length of stream networks, including drainage ditches, changes with discharge. This study characterized riparian corridors across multiple flow conditions and spatial extents, showing that although soil moisture became wetter closer to the stream, between-catchment differences in soil moisture composition were similar across 10, 100 m, and whole-catchment extents. The study explored how catchment factors influencing spatial and temporal variation in DOC in 145 Swedish watercourses could be explained using high-resolution spatial data in corridors along stream networks that expand and contract with flow. Catchment-wide characteristics mapped at coarser scales, combined with meteorological factors and stream flow, explained 64%–77% of observed mean DOC and the influences of seasonality and discharge. Adding high-resolution soil moisture data and considering them in corridors of different widths did not improve explanation of DOC variation. However, variation in high-resolution soil moisture contained information important for explaining mean DOC and daily DOC variation. Ditch density and changes in mesic soil moisture class were important for explaining mean DOC, while stream density affected the influence of discharge. Although high-resolution soil moisture data did not add explanatory power beyond coarser-scale information, they deepened understanding of how soil moisture and topography influence DOC dynamics.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"43 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146021996","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}
J. Huang, M. Barandun, J. Richard-Cerda, M. Hoelzle, E. Pohl
The Pamir Mountains, a critical water source for Central Asia, require accurate quantification of runoff components for water resource management under climate change. Uncertainties in precipitation data are known to greatly affect hydrological model accuracy, leading to the widespread use of multi-data calibration methods to avoid internal error compensation effects between snow and glacier accumulation and melt processes. Traditional approaches incorporating runoff, snow cover fraction, and glacier mass balance are frequently employed in the region's hydrological model calibration; yet we find this calibration approach to still result in significant uncertainties in the quantification of baseflow, snowmelt, and glacier runoff. Here we show winter baseflow calibration to provide a previously overlooked yet powerful constraint on model parameters, not only constraining baseflow but also enhancing the estimation of snowmelt and glacier runoff through groundwater parameters' control on hydrograph characteristics. Even with low-quality forcing data, winter baseflow calibration guides parameters toward more realistic values of runoff estimates, improving model reliability. Using five different forcing data sets, we show that incorporating winter baseflow alongside traditional calibration variables (runoff, snow cover, and glacier mass balance) reduces uncertainty ranges from 34%–61% to 8%–21% for snowmelt, 5%–17% to 3%–11% for glacier runoff, and 33%–50% to 7%–21% for baseflow estimates. Though parameter equifinality remains a challenge, winter baseflow calibration consistently enhances model accuracy, emphasizing its vital role in refining hydrological predictions in alpine, data-scarce, and climate-sensitive regions.
{"title":"Winter Baseflow Calibration's Critical Role in Hydrological Modeling for the Pamir Region","authors":"J. Huang, M. Barandun, J. Richard-Cerda, M. Hoelzle, E. Pohl","doi":"10.1029/2025wr040043","DOIUrl":"https://doi.org/10.1029/2025wr040043","url":null,"abstract":"The Pamir Mountains, a critical water source for Central Asia, require accurate quantification of runoff components for water resource management under climate change. Uncertainties in precipitation data are known to greatly affect hydrological model accuracy, leading to the widespread use of multi-data calibration methods to avoid internal error compensation effects between snow and glacier accumulation and melt processes. Traditional approaches incorporating runoff, snow cover fraction, and glacier mass balance are frequently employed in the region's hydrological model calibration; yet we find this calibration approach to still result in significant uncertainties in the quantification of baseflow, snowmelt, and glacier runoff. Here we show winter baseflow calibration to provide a previously overlooked yet powerful constraint on model parameters, not only constraining baseflow but also enhancing the estimation of snowmelt and glacier runoff through groundwater parameters' control on hydrograph characteristics. Even with low-quality forcing data, winter baseflow calibration guides parameters toward more realistic values of runoff estimates, improving model reliability. Using five different forcing data sets, we show that incorporating winter baseflow alongside traditional calibration variables (runoff, snow cover, and glacier mass balance) reduces uncertainty ranges from 34%–61% to 8%–21% for snowmelt, 5%–17% to 3%–11% for glacier runoff, and 33%–50% to 7%–21% for baseflow estimates. Though parameter equifinality remains a challenge, winter baseflow calibration consistently enhances model accuracy, emphasizing its vital role in refining hydrological predictions in alpine, data-scarce, and climate-sensitive regions.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"28 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146042630","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}
Hannah Besso, Ross Mower, Justin M. Pflug, Jessica D. Lundquist
Real-time estimates of peak snow water equivalent (SWE) are critical to spring runoff forecasts in snow-dominated basins, but large uncertainties remain due to the high spatial and temporal variability of interannual peak SWE. Here we introduce new methods for calculating real-time distributed 1 April SWE in the Western US using patterns in annual SWE anomalies, which are consistent over large regions. Our methods capitalize on the high accuracy of SWE reanalysis products by combining historical (1990–2021) 1 April SWE from a reanalysis product with real-time point measurements from in situ snow stations to estimate current-year 1 April SWE. First, we used a clustering algorithm to determine which regions of the Western US historically have similar SWE anomalies. Then we tested several ways to estimate 1 April SWE in the Upper Colorado River Basin (UCRB). We tested historical SWE distributions using (a) parametric and (b) nonparametric distribution assumptions, combined with current-year observations from: (a) the geographically closest station to each grid cell, (b) the collection of stations within the same cluster as each grid cell, and (c) all stations in the UCRB. The most accurate method used a parametric distribution and the collection of stations from the same cluster. This produced distributed 1 April SWE with a median R2 of 0.64, percent bias of 0.49%, and a root mean squared error of 0.13 m compared to the SWE reanalysis data in withheld years. The methods demonstrated here could be used wherever historical gridded data and real-time point measurements exist.
{"title":"Mapping 1 April SWE in the Western US Using Standardized Anomalies and Quantiles From SWE Reanalysis and In Situ Stations","authors":"Hannah Besso, Ross Mower, Justin M. Pflug, Jessica D. Lundquist","doi":"10.1029/2025wr040902","DOIUrl":"https://doi.org/10.1029/2025wr040902","url":null,"abstract":"Real-time estimates of peak snow water equivalent (SWE) are critical to spring runoff forecasts in snow-dominated basins, but large uncertainties remain due to the high spatial and temporal variability of interannual peak SWE. Here we introduce new methods for calculating real-time distributed 1 April SWE in the Western US using patterns in annual SWE anomalies, which are consistent over large regions. Our methods capitalize on the high accuracy of SWE reanalysis products by combining historical (1990–2021) 1 April SWE from a reanalysis product with real-time point measurements from in situ snow stations to estimate current-year 1 April SWE. First, we used a clustering algorithm to determine which regions of the Western US historically have similar SWE anomalies. Then we tested several ways to estimate 1 April SWE in the Upper Colorado River Basin (UCRB). We tested historical SWE distributions using (a) parametric and (b) nonparametric distribution assumptions, combined with current-year observations from: (a) the geographically closest station to each grid cell, (b) the collection of stations within the same cluster as each grid cell, and (c) all stations in the UCRB. The most accurate method used a parametric distribution and the collection of stations from the same cluster. This produced distributed 1 April SWE with a median <i>R</i><sup>2</sup> of 0.64, percent bias of 0.49%, and a root mean squared error of 0.13 m compared to the SWE reanalysis data in withheld years. The methods demonstrated here could be used wherever historical gridded data and real-time point measurements exist.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"51 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001530","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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