Water Resources Research最新文献

{"title":"Toward Trustworthy Machine Learning for Daily Sediment Modeling in the Riverine Systems: An Integrated Framework With Enhanced Uncertainty Quantification and Interpretability","authors":"Z. J. Yue, N. N. Wang, B. D. Xu, X. Huang, D. M. Yang, H. B. Xiao, Z. H. Shi","doi":"10.1029/2024wr038650","DOIUrl":"https://doi.org/10.1029/2024wr038650","url":null,"abstract":"Accurately predicting sediment dynamics and understanding their intrinsic contributors are pivotal for sustainable environment and water management. While machine learning (ML) enables precise predictions, its “black-box” nature hinders transparency and credibility, posing challenges in interpretability and uncertainty quantification (UQ). To achieve trustworthy ML for riverine sediment timeseries predictions, this study proposes an integrated ML framework, enhancing key steps: feature selection, UQ, and interpretation. Lagged hydro-environmental variables are incorporated via rigorous feature selection. SHapley Additive exPlanations (SHAP) and conformal prediction are utilized to refine interpretability and UQ, respectively. Based on 41-year multi-source data and three ensemble learning algorithms (LightGBM, XGBoost, and random forest (RF)), this study models daily suspended sediment concentration (SSC) separately for seven subtropical watersheds and evaluates overall and local accuracy. Key findings include: (a) Discharge and precipitation dominate SSC variability (explaining ∼56.8% and ∼18.9% of the variability, respectively). Sampling-day discharge and accumulative lagged precipitation should be prioritized as predictors. Precipitation-discharge interaction effects on SSC exhibit simple threshold effects, whereas the interaction effects of hydrological (precipitation, discharge) and environmental (SPEI, land cover) factors involve complex, bidirectional threshold effects. (b) LightGBM and XGBoost excel in long-term/general prediction, while RF outperform for short-term/extreme value predictions. (c) Conformal prediction-based UQ provides probabilistic information to quantify prediction reliability and efficiency, alongside uncertainty sources: discharge (∼38.9%) > precipitation (∼33.4%) > land cover (∼19.6%) > SPEI (∼8.1%). This framework advances trustworthy ML in riverine sediment modeling, while its algorithm-agnostic design ensures potential scalability to support broader hydrological applications and informed environmental decision-making.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"77 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144066696","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":"A Stochastic Simulation Method for Estimating Vegetation Interception Capacity Based on Mechanical-Geometric Analysis","authors":"Zixi Li, Fuqiang Tian, Di Wang, Zimu Peng","doi":"10.1029/2025wr040267","DOIUrl":"https://doi.org/10.1029/2025wr040267","url":null,"abstract":"Vegetation interception is a vital ecohydrological process, and the interception capacity is a key parameter to many classical interception models. However, current canopy interception capacity estimation methods largely depend on measured interception data, which are high cost and insufficient portability. This study addresses vegetation interception capacity at both leaf and canopy scales. At the leaf scale, a stochastic simulation method that do not depend on measured interception data and can take into account wind loads is developed, incorporating geometric properties and mechanical analysis to estimate water storage per unit leaf area. At the canopy scale, leaf water storage is summed up by using canopy structure parameters including leaf area index, leaf inclination angle distribution, and stem area index obtained by high-precision LiDAR point cloud data. The water storage capacities of 27 different leaf types were measured using the water spray method, and canopy interception from 22 rainfall events were monitored using comprehensive equipment. The proposed approach was successfully validated at both the leaf and canopy scales. In addition, a reasonable parameterization scheme for the model is discussed in detail to facilitate further application.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"153 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144000645","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":"Quantitatively Decoupling the Relationships Between Discharge and Sediment Yield During Flood Events in China's Loess Plateau","authors":"Zelin Li, Guangyao Gao, Anqi Huang, Lishan Ran, Dongfeng Li, Bojie Fu","doi":"10.1029/2024wr039206","DOIUrl":"https://doi.org/10.1029/2024wr039206","url":null,"abstract":"To clarify changes of discharge (<i>Q</i>) and sediment yield (SSY) during flood events provide critical insights for flood disaster prevention and control. However, our understanding of the long-term variations and driving factors of <i>Q</i>-SSY relationships during flood events remains limited. This study examined the variations in <i>Q</i>, SSY, and sediment rating curves (SSY = <i>aQ</i>^<i>b</i>) during maximum one, three, and five flood events (ranked by peak discharge) across 15 catchments in the China's Loess Plateau during 1956–2019. We used the partial least squares-structural equation modeling (PLS-SEM) to quantitatively decouple the effects of driving factors (precipitation, soil, vegetation, topography, and soil and water conservation measures (SWCMs)) on <i>Q</i>-SSY relationships. There was a significant declining trend in both <i>Q</i> and SSY during flood events across catchments, but their contributions to annual SSY significantly increased by 41.48%, underscoring the critical role of floods in sediment transport. The <i>Q</i>-SSY relationship during flood events became weakened over time, with coefficient <i>a</i> decreased and index <i>b</i> increased. The five driving factors explained 44%–49% of the changes in coefficient <i>a</i> and 36%–51% in index <i>b</i>. Significant direct effects of vegetation (path coefficient (<i>β</i>) = −0.921) and precipitation (<i>β</i> = 0.616) on coefficient <i>a</i> were observed (<i>p</i> &lt; 0.05). Index <i>b</i> was principally dominated by SWCMs and vegetation, and the effects diminished with increase in number of flood events. These findings highlight the importance of vegetation cover and SWCMs in mitigating sediment transport, offering valuable insights for sediment management strategies in the Loess Plateau and similar regions.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"8 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144066651","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":"Global River Topology (GRIT): A Bifurcating River Hydrography","authors":"M. Wortmann, L. Slater, L. Hawker, Y. Liu, J. Neal, B. Zhang, J. Schwenk, G. Allen, P. Ashworth, R. Boothroyd, H. Cloke, P. Delorme, S. H. Gebrechorkos, H. Griffith, J. Leyland, S. McLelland, A. P. Nicholas, G. Sambrook-Smith, E. Vahidi, D. Parsons, S. E. Darby","doi":"10.1029/2024wr038308","DOIUrl":"https://doi.org/10.1029/2024wr038308","url":null,"abstract":"Existing global river networks underpin a wide range of hydrological applications but do not represent channels with divergent river flows (bifurcations, multi-threaded channels, canals), as these features defy the convergent flow assumption that elevation-derived networks (e.g., HydroSHEDS, MERIT Hydro) are based on. Yet, bifurcations are important features of the global river drainage system, especially on large floodplains and river deltas, and are also often found in densely populated regions. Here we developed the first raster and vector-based Global RIver Topology that not only represents the tributaries of the global drainage network but also the distributaries, including multi-threaded rivers, canals and deltas. We achieve this by merging a 30 m Landsat-based river mask with elevation-generated streams to ensure a homogeneous drainage density outside of the river mask for rivers narrower than approximately 30 m. Crucially, we employ the new 30 m digital terrain model, FABDEM, based on TanDEM-X, which shows greater accuracy over the traditionally used SRTM derivatives. After vectorization and pruning, directionality is assigned by a series of elevation, flow angle and continuity approaches. The new global network and its attributes are validated using gauging stations, comparison with existing networks, and randomized manual checks. The new network represents 19.6 million km of streams and rivers with drainage areas greater than 50 km² and includes 67,495 bifurcations. With the advent of hyper-resolution modeling and artificial intelligence, GRIT is expected to greatly improve the accuracy of many river-based applications such as flood forecasting, water availability and quality simulations, or riverine habitat mapping.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"54 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144066501","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":"Exploring the Physics of Two Thermodynamic Lake Ice Models","authors":"Arash Rafat, Bin Cheng, Homa Kheyrollah Pour","doi":"10.1029/2024wr038615","DOIUrl":"https://doi.org/10.1029/2024wr038615","url":null,"abstract":"Thermodynamic lake ice models are valuable tools in the simulation of ice formation, growth, and decay. Appropriate application of these models necessitates a thorough understanding of model physics. Here, we examine the physics of two thermodynamic lake ice models, the Canadian Lake Ice Model (CLIMo) and the High-Resolution Snow and Ice Model (HIGHTSI), for understanding key drivers and limitations in modeling of ice evolution. A cold bias in modeled surface temperatures was found to control differences in ice evolution through differences in the magnitudes of radiative and turbulent fluxes. Simplified snow physics and precipitation forcings were found to control the simulation of snow-ice through controlling freeboard. Break-up dates were highly sensitive to the selected melt albedo scheme. Freeze-up dates were controlled by model specific calibration or initialization procedures. Recommendations for advancements to current model processes are presented for future developments to thermodynamic ice models.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"24 7 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143940388","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":"Remotely Sensed High-Resolution Soil Moisture and Evapotranspiration: Bridging the Gap Between Science and Society","authors":"Jingyi Huang, Vinit Sehgal, Laura V. Alvarez, Luca Brocca, Shuohao Cai, Rui Cheng, Xinghua Cheng, Jinyang Du, Bassil El Masri, K. Arthur Endsley, Yilin Fang, Jie Hu, Mahesh Jampani, Md Golam Kibria, Gerbrand Koren, Lingcheng Li, Laibao Liu, Jiafu Mao, Hernan A. Moreno, Angela Rigden, Mingjie Shi, Xiaoying Shi, Yaoping Wang, Xi Zhang, Joshua B. Fisher","doi":"10.1029/2024wr037929","DOIUrl":"https://doi.org/10.1029/2024wr037929","url":null,"abstract":"This paper reviews the current state of high-resolution remotely sensed soil moisture (SM) and evapotranspiration (ET) products and modeling, and the coupling relationship between SM and ET. SM downscaling approaches for satellite passive microwave products leverage advances in artificial intelligence and high-resolution remote sensing using visible, near-infrared, thermal-infrared, and synthetic aperture radar sensors. Remotely sensed ET continues to advance in spatiotemporal resolutions from MODIS to ECOSTRESS to Hydrosat and beyond. These advances enable a new understanding of bio-geo-physical controls and coupled feedback mechanisms between SM and ET reflecting the land cover and land use at field scale (3–30 m, daily). Still, the state-of-the-science products have their challenges and limitations, which we detail across data, retrieval algorithms, and applications. We describe the roles of these data in advancing 10 application areas: drought assessment, food security, precision agriculture, soil salinization, wildfire modeling, dust monitoring, flood forecasting, urban water, energy, and ecosystem management, ecohydrology, and biodiversity conservation. We discuss that future scientific advancement should focus on developing open-access, high-resolution (3–30 m), sub-daily SM and ET products, enabling the evaluation of hydrological processes at finer scales and revolutionizing the societal applications in data-limited regions of the world, especially the Global South for socio-economic development.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"25 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144000646","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":"Characterization of Hydraulic Parameters via Sensitivity Maps for Frequency-Based Oscillatory Pumping in 2D, Confined, Weakly Heterogeneous Aquifers","authors":"Jiong Zhu, Yuanyuan Zha, Dong Xu","doi":"10.1029/2024wr039157","DOIUrl":"https://doi.org/10.1029/2024wr039157","url":null,"abstract":"Multi-frequency oscillatory pumping, in which the groundwater is extracted during a half period, and then reinjected, has recently been used to characterize aquifer heterogeneity. After the initial transition time, a steady periodic head can be observed at the observation well with constant amplitude and phase shift. However, the efficacy of utilizing multiple frequencies to enhance parameter estimation in hydraulic tomography is debatable. Most studies suggest that using multiple frequencies in joint inversion can gradually improve aquifer imaging. However, some researchers argue that additional frequencies add little to the resolution of hydraulic properties. This raises the question: how much non-redundant information do multiple frequencies provide? In this study, we derive closed-form analytical sensitivity maps (i.e., Fréchet kernels) of oscillatory pumping in 2D, unbounded, confined weakly heterogeneous aquifers by the sensitivity equation. It is found that the sensitivity maps of amplitude or phase shift are very similar to the sensitivity of drawdown to hydraulic parameters in constant-rate pumping. Fréchet kernels indicate that multi-frequency information, much like multi-time information, can be instrumental for hydrogeological parameter inversion. According to the Fréchet kernels and their derivatives, we propose a method to select the optimal observation frequency, that is, the distance between the observation well and the pumping well is converted to frequency based on the hydraulic background values. Finally, we compare the effectiveness of single-frequency and multi-frequency observations in inverse transmissivity and storativity modeling using the iterative ensemble smoother. The results show that multi-frequency oscillatory pumping can better characterize aquifer heterogeneity than a single frequency.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"124 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143933341","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":"Seasonality Controls Biogeochemical Shifts in Oxygen, Carbon, and Nitrogen Along a 12-m, 54 hr-Long Hyporheic Flowpath","authors":"S. P. Herzog, A. S. Ward, S. M. Wondzell, S. P. Serchan, R. González-Pinzón, J. P. Zarnetske","doi":"10.1029/2024wr038410","DOIUrl":"https://doi.org/10.1029/2024wr038410","url":null,"abstract":"Hyporheic exchange is critical to river corridor biogeochemistry, but decameter-scale flowpaths (∼10-m long) are understudied due to logistical challenges (e.g., sampling at depth, multi-day transit times). Some studies suggest that decameter-scale flowpaths should have initial hot spots followed by transport-limited conditions, whereas others suggest steady reaction rates and secondary reactions that could make decameter-scale flowpaths important and unique. We investigated biogeochemistry along a 12-m hyporheic mesocosm that allowed for controlled testing of seasonal and spatial water quality changes along a flowpath with fixed geometry and constant flow rate. Water quality profiles of oxygen, carbon, and nitrogen were measured at 1-m intervals along the mesocosm over multiple seasons. The first 6 m of the mesocosm were always oxic and a net nitrogen source to mobile porewater. In winter, oxic conditions persisted to 12 m, whereas the second half of the flowpath became anoxic and a net nitrogen sink in summer. No reactive hot spots were observed in the first meter of the mesocosm. Instead, most reactions were zeroth-order over 12 m and 54 hr of transit time. Influent chemistry had less impact on hyporheic biogeochemistry than expected due to large amounts of in situ reactant sources compared to stream-derived reactant sources. Sorbed or buried carbon likely fueled reactions with rates controlled by temperature and redox conditions. Each reactant showed different hyporheic Damköhler numbers, challenging the characterization of flowpaths being intrinsically reaction- or transport-limited. Future research should explore the prevalence and biogeochemical contributions of decameter-scale flowpaths in diverse field settings.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"28 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932670","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":"A Distributed Machine Learning Model for Blue and Green Water Resources With Transferable Applications in Similar Climatic Zones","authors":"Zhibin Li, Haroon Sahotra, Sajjad Ahmad, Wei Wang, Zhe Yang, Pute Wu, Eakalak Khan, La Zhuo","doi":"10.1029/2024wr039169","DOIUrl":"https://doi.org/10.1029/2024wr039169","url":null,"abstract":"Human activities profoundly impact the terrestrial water cycle and the spatiotemporal dynamics of blue and green water resources. Distributed hydrological models are essential for simulating the water resources within a basin. However, neither process-based nor data-driven hydrological models have fully captured the effects of human activities on the distribution of blue and green water resources in space and time. Here we construct a distributed machine learning model for monthly blue and green water resources, which is trained and calibrated for the Yellow River Basin (YRB) in China, and validated and tested for the transferability to similar climatic zones in the case for Colorado River Basin (CRB) in the United States. The modeling thoroughly accounts for the influence of human activities, incorporating 5 scales (grid, county, city, province, and cluster), 4 algorithms, and 2 model integration methods (Stacking and Bayesian). The <i>R</i>² values reached 0.84 and 0.97 for blue and green water models, respectively, during the test period in the YRB. The corresponding high modeling accuracy maintained with <i>R</i>² values of 0.72 and 0.97 when transferred to the CRB. The model performed better in regions with higher human activity intensity. Precipitation and spatial encoding are respectively the most sensitive feature variables for the green water and blue water models, while nighttime lights and population density are respectively the most significant human activity-related features. The study highlights the non-negligible impacts of socioeconomic factors on spatiotemporal dynamics of blue and green water resources, and the feasibility of machine learning modeling.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"32 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931137","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":"A Probabilistic Approach to Surrogate-Assisted Multi-Objective Optimization of Complex Groundwater Problems","authors":"Reygie Q. Macasieb, Jeremy T. White, Damiano Pasetto, Adam J. Siade","doi":"10.1029/2024wr038554","DOIUrl":"https://doi.org/10.1029/2024wr038554","url":null,"abstract":"Groundwater management involves a complex decision-making process, often with the need to balance the trade-off between meeting society's demand for water and environmental protection. Therefore effective management of groundwater resources often involves some form of multi-objective optimization (MOO). Many existing software tools offer simulation model-enabled optimization, including evolutionary algorithms, for solving MOO problems. However, such analyses involve a huge amount of numerical process-based model runs, which require significant computational effort, depending on the nonlinearity and dimensionality of the problem, in order to seek the optimal trade-off function known as the Pareto front. Surrogate modeling, through techniques such as Gaussian Process Regression (GPR), is an emerging approach to significantly reduce the number of these model evaluations thereby speeding up the optimization process. Yet, surrogate model predictive uncertainty remains a profound challenge for MOO, as it could mislead surrogate-assisted optimization, which may result in either little computational savings from excessive retraining, or lead to suboptimal and/or infeasible solutions. In this work, we present probabilistic Pareto dominance criteria that considers the uncertainty of GPR emulation during MOO, producing a “cloudy” Pareto front which provides an efficient decision space sampling mechanism for retraining the GPR. We then developed a novel acquisition strategy to manage the solution repository from this cloud and generate an ensemble of infill points for retraining. We demonstrate the capabilities of the algorithm through benchmark test functions and a typical density-dependent coastal groundwater management problem.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"22 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932669","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}