Water Resources Research最新文献

{"title":"Fate of Underground Brine Resources in Beach and Neritic Zones in Shallow Aquifers Driven by Salt Pumps","authors":"Yufeng Zhang, Tianyuan Zheng, Xiujun Guo, Jian Luo","doi":"10.1029/2025wr040081","DOIUrl":"https://doi.org/10.1029/2025wr040081","url":null,"abstract":"Underground brine in beach and neritic zones (UBBN), widely distributed on muddy coasts in arid coastal regions, undergoes dynamic salt cycling driven by a “salt pump” system, composed of hydraulic gradients, salinity gradients, and tidal forces. This study investigates the fate of UBBN along the southern coast of Laizhou Bay, China, where salt depletion threatens sustainable resource management. Combining field observations, we developed a bay‐scale numerical model incorporating geomorphological diversity, submarine groundwater discharge hotspots, and sediment heterogeneity to quantify UBBN dynamics during tidal cycles and evaluate the impacts of coastal underground brine (CUB) mining and suspension. Results show that tidal fluctuations control groundwater flow fields, which drive spatiotemporal salt transport and persistent UBBN depletion. Salt outflow rates peak at the sediment‐water interface in beach zones and decline seaward. Discharge hotspots exhibit salt outflow rates 4–8 times greater than adjacent neritic areas. Despite net losses, approximately 9% (offshore)–60% (nearshore) of salt from tidal recharge (evaporated beach salt and seawater) is retained and replenishes the aquifer per tidal cycle, accumulating predominantly in the top‐section of silt cover layers. CUB mining and suspension enhance seawater and evaporated salt influx while reducing leakage from high‐salinity confined brine, effectively slowing UBBN salt loss. These findings advance large‐scale UBBN cycling mechanisms and provide actionable insights for sustainable development of UBBN resources in bay‐scale beach and neritic zones.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"11 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247348","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":"Tracing Nitrate Contamination Sources and Transformations in a Rural−Urban Karst Groundwater System in North China Using Multiple Isotopes and Simmr Modeling","authors":"Jie Zhang, Lin Zhang, Tianyuan Zheng, Menggui Jin, Fengxin Kang, Jinde Jiang, Zhouwei Yuan, Jian Luo","doi":"10.1029/2025wr040156","DOIUrl":"https://doi.org/10.1029/2025wr040156","url":null,"abstract":"In temperate karst aquifers under intensive anthropogenic impacts and high heterogeneity, groundwater contamination tracking has predominantly focused on nitrate, but inadequate evidence for co‐occurring ammonium sources undermines accurate nitrogen pollution assessments. This study pioneered the application of a isotope approach within the groundwater flow framework of the Jinan Spring Catchment, constructed a novel ammonium‐nitrate isotope tracing system (, , and ) for full‐form source tracking, and implemented the Bayesian mixing model (Simmr) to quantitatively apportion nitrate sources in karst groundwater. This integrated system enabled simultaneous NH<jats:sub>4</jats:sub><jats:sup>+</jats:sup>‐N and NO<jats:sub>3</jats:sub><jats:sup>−</jats:sup>‐N pollution source identification, enhancing the resolution of key nitrogen cycling pathways, particularly ammonium‐dominated nitrification. The NO<jats:sub>3</jats:sub><jats:sup>−</jats:sup>‐N was the dominant form of inorganic nitrogen in karst groundwater (0.4−42.2 mg/L), and demonstrated an overall upward trend from 1958 to 2019. and nitrate isotope both confirmed that NH<jats:sub>4</jats:sub><jats:sup>+</jats:sup>‐N and NO<jats:sub>3</jats:sub><jats:sup>−</jats:sup>‐N primarily originated from ammonium fertilizer and soil nitrogen. The Simmr model quantified ammonium fertilizer (28.4%−58.3%) and soil nitrogen (23.1%−62.7%) as primary contributors to groundwater NO<jats:sub>3</jats:sub><jats:sup>−</jats:sup>‐N. Hydrochemical and dual nitrogen isotope evidences revealed that mineralization and re‐nitrification of soil nitrogen, nitrification of ammonium fertilizers, and mineralization‐immobilization‐turnover of nitrate fertilizers all promoted nitrate accumulation in karst groundwater. Groundwater flow analysis identified mixing between shallow and deep karst groundwater as the primary mechanism for nitrate attenuation of spring waters in the urban discharge area, with denitrification playing a negligible role. These findings provide new insights into nitrogen behavior in temperate karst groundwater, offering valuable guidance for water resource management and protection in similar karst systems.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"11 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247479","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":"Addressing Data Imbalance in Hydrological Machine Learning: Impact of Advanced Sampling Methods on Performance and Interpretability","authors":"Xiaoran Yin, Longcang Shu, Zhe Wang, Long Zhou, Shuyao Niu, Huazhun Ren, Bo Liu, Chengpeng Lu","doi":"10.1029/2024wr039848","DOIUrl":"https://doi.org/10.1029/2024wr039848","url":null,"abstract":"Data imbalance poses a severe challenge in hydrological machine learning (ML) applications by limiting model performance and interpretability, whereas solutions remain limited. This study evaluates the impact of advanced sampling methods, particularly feature space coverage sampling (FSCS), on model performance in predicting forest cover types and saturated hydraulic conductivity (Ks); mechanism underlying its efficacy; and impact on model interpretability. Using ML algorithms such as random forest (RF) and LightGBM (LGB) across various training set sizes, we demonstrated that FSCS significantly mitigates data imbalance, enhancing model accuracy, feature importance estimation, and interpretability. Two widely used hydrological data sets were analyzed: a large multiclass forest cover type data set from Roosevelt National Forest (110,393 samples) and continuous-value data set of soil properties from the USKSAT database (18,729 samples). In total, 1,720 models were constructed and optimized, combining different sampling methods, training set sizes, and algorithms. Balanced sampling, conditioned Latin hypercube sampling, and FSCS consistently outperformed simple random sampling. Despite using smaller training sets and simpler RF models, FSCS-trained models matched or surpassed the performance of those using larger data sets or more complex LGB models. SHAP analysis revealed that FSCS enhanced feature–target relationship clarity, emphasizing feature interactions and improving model interpretability. These findings highlight the potential of advanced sampling methods for not only addressing data imbalance but also providing more accurate prior information for model training, thereby enhancing reliability, accuracy, and interpretability in ML for hydrological applications.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"9 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247347","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":"Let's Talk About Dead Pool: How We Discuss the Shallows of Reservoirs","authors":"Adrianne C. Kroepsch, Elanor Andrews, Surabhi Upadhyay, Adrienne Marshall","doi":"10.1029/2025wr041330","DOIUrl":"https://doi.org/10.1029/2025wr041330","url":null,"abstract":"The term “dead pool” has been circulating in water resources discourse in multiple ways, prompting confusion about what it means. In this commentary, we aim to clarify the definition of dead pool (and related terms describing critical reservoir elevations) to encourage clearer conversations about reservoir storage decline going forward. We also make two arguments to animate future research about the shallows of reservoirs. First, we suggest that critical reservoir thresholds such as dead pool are better thought of as dynamic and multifaceted rather than as static and singular elevations. Second, we offer a typology that aims to distinguish among three different types of reservoir storage decline. Taken together, a shared vocabulary about reservoir levels and a more nuanced conceptualization of how reservoirs shrink can better situate water scholars and policymakers to understand and manage reservoirs in an era of water overuse and climate change.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"8 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145241489","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":"Transport Mechanisms of Nanoplastics in Agricultural Soils Under Snowmelt Infiltration Conditions in Cold Regions","authors":"Renjie Hou, Yunjia Hong, Yanling Deng, Bingyu Zhu, Yuxuan Wang, Haihong Zhao, Jian Zhang, Liuwei Wang, Yulu Zhou, Wei Huang","doi":"10.1029/2024wr038827","DOIUrl":"https://doi.org/10.1029/2024wr038827","url":null,"abstract":"This study was conducted to uncover the migration characteristics of nanoplastics (NPs) permeating with snowmelt water in freeze‐thaw soil and their regulatory mechanisms. Luvisol (LCK), chernozem (CCK), and albic soil (ACK) were selected as porous media, and two scenarios of tetracycline and tetracycline plus biochar were established (LTL and LTLB for luvisol, CTL and CTLB for chernozem, and ATL and ATLB for albic). With snowmelt infiltration, soil NPs concentration in the CCK treatment had a peak value of 25.62 mg kg<jats:sup>−1</jats:sup> in the vertical profile, whereas the ACK and LCK treatments were 5.32% and 7.79% higher than the CCK treatment, respectively. The presence of tetracycline and biochar provided additional adsorption sites for NPs, which in turn promoted the deposition and sequestration effects of NPs. This research constructed an innovative migration model of soil NPs under snowmelt infiltration and confirmed that soil NPs would be strongly resolved and re‐migrate under extreme snowfall. Moreover, the NPs in the chernozem would reach 2.12 mg kg<jats:sup>−1</jats:sup> at the vertical profile crest after 20 years, which is 6.19% and 19.88% lower relative to the albic soil and luvisol, respectively. Finally, the Extended Derjaguin‐Landau‐Verwey‐Overbeek (XDLVO) theory measurements demonstrated that the energy barrier heights of the ATL and ATLB treatments in albic soil were 16.80% and 36.91% lower than the ACK treatment, respectively. The lower height of the energy potential barrier makes NPs more accessible to soil particles, which reconfirms that the presence of biochar coupled with tetracycline mediation can effectively inhibit the dissociation release characteristics of soil NPs.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"158 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145241743","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":"Uncertainty Quantification of Fluid Leakage and Fault Instability in Geologic CO2 ${text{CO}}_{2}$ Storage","authors":"Hannah Lu, Lluís Saló‐Salgado, Youssef M. Marzouk, Ruben Juanes","doi":"10.1029/2024wr039275","DOIUrl":"https://doi.org/10.1029/2024wr039275","url":null,"abstract":"Geologic storage is an important strategy for reducing greenhouse gas emissions to the atmosphere and mitigating climate change. In this process, coupling between mechanical deformation and fluid flow in fault zones is a key determinant of fault instability, induced seismicity, and leakage. Using a recently developed methodology, PREDICT, we obtain probability distributions of the permeability tensor in faults from the stochastic placement of clay smears that accounts for geologic uncertainty. We build a comprehensive set of fault permeability scenarios from PREDICT and investigate the effects of uncertainties from the fault zone internal structure and composition on forecasts of permanence and fault stability. To tackle the prohibitively expensive computational cost of the large number of simulations required to quantify uncertainty, we develop a deep‐learning‐based surrogate model capable of predicting flow migration, pressure buildup, and geomechanical responses in storage operations. We also compare our probabilistic estimation of leakage and fault instability with previous studies based on deterministic estimates of fault permeability. The results highlight the importance of including uncertainty and anisotropy in modeling of complex fault structures and improved management of geologic storage projects.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"128 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145241486","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":"Mixing‐Induced Mineral Precipitation in Porous Media: Front Development and Its Impact on Flow and Transport","authors":"Anna Kottsova, Xiang‐Zhao Kong, Pacelli L. J. Zitha, Martin O. Saar, David F. Bruhn, Nils Knornschild, Julien M. Allaz, Corey Archer, Maren Brehme","doi":"10.1029/2024wr039316","DOIUrl":"https://doi.org/10.1029/2024wr039316","url":null,"abstract":"Injectivity decline during brine reinjection poses a significant challenge in the geothermal industry, with reported cases of substantial injectivity reduction and in severe cases, complete well shutdown. Among the reasons behind these issues, chemical processes play a key role due to potential changes in the fluid properties throughout the operation cycle. When reinjected, the fluid with altered chemical composition mixes with in situ fluids, potentially triggering mineral precipitation, which can obstruct flow and reduce injectivity. To better characterize the mechanisms behind the mixing‐induced mineral precipitation processes, we performed a series of core‐flooding experiments combined with high‐resolution imaging techniques. Our study focuses on the direct visualization of barite precipitation fronts in Berea sandstone and characterizes their spatial and temporal evolution under varying flow conditions. Pressure response and time‐resolved 2D scanning were analyzed to capture real‐time changes in the system, whereas post‐experiment micro‐CT scanning, electron microprobe analysis, and mass spectrometry were employed to examine the morphology and distribution of the mineral deposits. Our results highlight the critical role of flow velocities on the kinetics of mixing‐induced precipitation and demonstrate how mineral accumulation may significantly reduce permeability. These findings provide valuable insights into the dynamics of mineral precipitation in porous media, highlighting the impact of flow conditions on formation damage in geothermal systems.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"36 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145235139","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":"Flow Kinematics in Three‐Dimensional Porous Media of Varying Pore Size Distribution Using Smoothed Particle Hydrodynamics","authors":"Fan Chen, Junfeng Sun, Antoine Wautier, Mathieu Souzy","doi":"10.1029/2025wr040413","DOIUrl":"https://doi.org/10.1029/2025wr040413","url":null,"abstract":"The effect of pore size distribution on the flow kinematics and transport properties within a three‐dimensional porous medium is investigated through numerical simulations using the Smoothed Particle Hydrodynamics (SPH) method. The method is first validated for a model porous medium within a monodisperse random spherical packing, for which the velocity distribution of the fluid flowing through the pores (i.e., the interstitial fluid velocity) and the dispersion process are found to be in both qualitative and quantitative agreement with previous experimental results. When varying the pore size distribution of the porous medium by using polydisperse beads (of different diameters), the interstitial fluid velocity distributions get narrower, and the streamlines' tortuosity decreases. This is interpreted as a result of the narrower pore size distribution reported for polydisperse microstructures. Although the dispersion process remains qualitatively the same among the investigated microstructures, with an initial ballistic trend followed by a transient seemingly anomalous regime and eventually a Fickian regime, the transverse dispersion process is found to be quantitatively reduced for polydisperse microstructure (i.e., with a narrower pore size distribution), consistently with the reported decrease in streamlines' tortuosity.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"33 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145241487","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":"Long‐Term Prediction Model for Erosion‐Deposition Topographic Evolution in the Sanmenxia‐To‐Xiaolangdi Reach of the Yellow River Based on Deep Learning","authors":"Xiaojuan Sun, Haojie Jin, Mingyu Gao, Shengde Yu, Jiayi Man, Qiting Zuo, Wei Zhang","doi":"10.1029/2025wr040669","DOIUrl":"https://doi.org/10.1029/2025wr040669","url":null,"abstract":"Reservoirs are essential for global water management and energy regulation, but sedimentation threatens their longevity. This study investigates a 130 km section of the Yellow River between the Sanmenxia and Xiaolangdi dams, using deep learning to predict long‐term erosion and deposition patterns. From 2009 to 2023, we gathered water depth data from 56 sites (840 measurements) with unmanned survey boats and drone‐based LiDAR (Light Detection and Ranging), alongside flow and sediment records. After preprocessing, we evaluated three machine learning models: Convolutional Network for Multimodal Time Series (CNN‐MTS), Convolutional Transformer for Multimodal Time Series (CNN‐Transformer‐MTS), and Convolutional Bi‐LSTM for Multimodal Time Series (CNN‐BiLSTM‐MTS). The CNN‐BiLSTM‐MTS model excelled, achieving a mean absolute error (MAE) of 17.84 m, a coefficient of determination (<jats:italic>R</jats:italic><jats:sup>2</jats:sup>) of 0.9916, and reducing errors by up to 26% compared to alternatives. Key drivers of sediment dynamics included sediment load, maximum sediment concentration, and maximum flow. Data from 2009 to 2023 showed elevation shifts from −0.21 m near the dam to +1.158 m at the reservoir's tail. Predictions for 2024 to 2050 suggest varied riverbed changes, with the Guxian Reservoir's operation in 2036 expanding elevation ranges from −0.625 to 0.875 m. These findings highlight deep learning's potential for efficient sediment management in reservoirs and offer insights for sustainable hydraulic engineering. However, uncertainties persist in scaling the model, improving data resolution, and coordinating across regions.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"5 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145235138","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":"Rethinking Paired-Catchment Studies: Should We Be Replicating Our Controls?","authors":"Steve Wondzell, Sherri Johnson, Gordon Grant, Don Henshaw, Adam Ward","doi":"10.1029/2024wr038981","DOIUrl":"https://doi.org/10.1029/2024wr038981","url":null,"abstract":"Paired-catchment studies are widely used to examine the effects of land management practices (“treatments”) on hydrologic processes. Catchments are matched and a pretreatment calibration regression is used to identify the hydrological relationship between the reference and treated catchments. This method assumes that the calibration regression represents the actual relationship between the catchments (assumption of representativeness) and that the relationship will remain stable over time (assumption of stability). Errors are assumed to be small and similar between reference and treated catchments. Thus, observed differences between the catchments following treatment are assumed to result from that treatment alone. However, calibration periods are often short and it is impossible to know if the calibration period is representative. Further, because the study is unreplicated, it is impossible to determine if stability is maintained. Consequently, it is difficult to determine a minimum detectable effect sizes (MDES) below which estimates of changes in streamflow are statistically uncertain. Here, we use bootstrapped sampling from reference-by-reference (RxR) comparisons in a paired-catchment study design to evaluate the MDES. We generate frequency distributions of the potential changes in flow—changes that cannot be caused by treatment effects. From these, we estimate bootstrapped ±95% confidence intervals encompassing the non-treatment effects which we use as the MDES. We apply this method to long-term paired-catchment studies and reexamine changes in both annual water yields and late summer low flows at the HJA Experimental Forest. This bootstrapping method is widely transferable to any long-term paired catchment study sites where multiple reference catchments exist.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"31 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145229369","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}