Water Resources Research最新文献

{"title":"Abrupt Ecological Shift and Recovery Trajectory of a Peri-Urban Lake in the Anthropocene: Insights From Paleoecology and Modeling Projection","authors":"Shixin Huang, Qi Lin, Ke Zhang, Yaoyao Han, Chenliang Du, Ji Shen","doi":"10.1029/2024wr038925","DOIUrl":"https://doi.org/10.1029/2024wr038925","url":null,"abstract":"Urban and peri-urban lakes are undergoing significant ecological deterioration in the fast-changing Anthropocene, leading to toxic algal proliferation jeopardizing ecosystem services and public health. Nevertheless, the ecological response of these lakes to anthropogenic disturbances, management interventions, and climate change remains inadequately understood. This study examined the dynamic trajectory of the algal community from Luoma Lake, a representative peri-urban lake in eastern China, from the 1900s to 2050, based on comprehensive paleoecological investigations and model projections. Phototrophic pigment analysis indicated an exponential increase in algal abundances since the early 2000s, coupled with an abrupt community shift toward eutrophic taxa driven by rapid urbanization, agricultural and fishery practices. Recent rate-based observations suggested a reversal in algal production and cyanobacterial proliferation due to management efforts, signaling an early ecological recovery. However, model projections under two representative climate scenarios (SSP1-2.6 and SSP5-8.5) suggested continued algal abundance growth and accelerated ecological response rates until 2050. This highlighted that the anticipated benefits of nutrient reductions may be diluted by climate warming, posing a significant challenge for future urban lake management. This study underscores the necessity of incorporating climate adaptation into rate-focused management strategies to mitigate adverse ecological impacts. Our findings provide valuable insights for policymakers and contribute to the broader understanding of urban lake ecosystem responses to combined anthropogenic and climatic stressors, offering new perspectives for effective lake restoration in the context of global climate change.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"8 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144165702","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 New Model for Water Retention and Hydraulic Conductivity Curves of Deformable Unsaturated Soils","authors":"Zhenxing Chang, Chao Zhou","doi":"10.1029/2024wr037826","DOIUrl":"https://doi.org/10.1029/2024wr037826","url":null,"abstract":"The water retention and hydraulic conductivity curves of unsaturated soils are important parameters for seepage analysis. Experimental results in the literature generally show that with increasing density, the air-entry value and adsorption/desorption rate of the water retention curve increase and the relative hydraulic conductivity (<i>k</i>_<i>r</i>) at a given degree of saturation changes. The above phenomena, except the density-dependency of air-entry value, have not been considered in existing models. This study aims to address these problems by developing new hydraulic models based on experimental evidence from microscopic analysis. First of all, a new equation was proposed to model the evolution of pore size distribution with soil density. For a given pore, the ratio of its initial to final sizes is higher when the initial size is larger and when there is a greater increase in density. Based on this equation, a new and simple water retention equation was derived to predict the increase in air-entry value (resulting from the reduction in pore size) and the adsorption/desorption rate (due to a more uniform pore size distribution) as density increases. Then, a new equation for <i>k</i>_<i>r</i> was developed by incorporating the evolution of pore size distribution and tortuosity upon soil deformation, and therefore it can capture the changes of <i>k</i>_<i>r</i>. To validate the above equations, test data from several soils with distinct properties were used. The measured and calculated results are well-matched.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"14 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154310","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":"Multi-Frequency Oscillatory Hydraulic Tomography Improves Heterogeneity Imaging and Resolution and Reduces Uncertainty","authors":"Jeremy R. Patterson, Michael Cardiff","doi":"10.1029/2024wr039606","DOIUrl":"https://doi.org/10.1029/2024wr039606","url":null,"abstract":"Understanding subsurface heterogeneity is crucial to predicting groundwater flow pathways, mixing, and other processes in aquifers and other fluid reservoirs. Despite significant effort developing geophysical tools to understand this heterogeneity, geophysical mapping of aquifer flow parameters—transmissivity &lt;span data-altimg=\"/cms/asset/3bae40bb-f7e9-4275-a67d-d89d1fbe9c81/wrcr70156-math-0001.png\"&gt;&lt;/span&gt;&lt;mjx-container ctxtmenu_counter=\"177\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"&gt;&lt;mjx-math aria-hidden=\"true\" location=\"graphic/wrcr70156-math-0001.png\"&gt;&lt;mjx-semantics&gt;&lt;mjx-mrow data-semantic-children=\"1\" data-semantic-content=\"0,2\" data-semantic- data-semantic-role=\"leftright\" data-semantic-speech=\"left parenthesis upper T right parenthesis\" data-semantic-type=\"fenced\"&gt;&lt;mjx-mo data-semantic- data-semantic-operator=\"fenced\" data-semantic-parent=\"3\" data-semantic-role=\"open\" data-semantic-type=\"fence\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mo&gt;&lt;mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"3\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mi&gt;&lt;mjx-mo data-semantic- data-semantic-operator=\"fenced\" data-semantic-parent=\"3\" data-semantic-role=\"close\" data-semantic-type=\"fence\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mo&gt;&lt;/mjx-mrow&gt;&lt;/mjx-semantics&gt;&lt;/mjx-math&gt;&lt;mjx-assistive-mml display=\"inline\" unselectable=\"on\"&gt;&lt;math altimg=\"urn:x-wiley:00431397:media:wrcr70156:wrcr70156-math-0001\" display=\"inline\" location=\"graphic/wrcr70156-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"&gt;&lt;semantics&gt;&lt;mrow data-semantic-=\"\" data-semantic-children=\"1\" data-semantic-content=\"0,2\" data-semantic-role=\"leftright\" data-semantic-speech=\"left parenthesis upper T right parenthesis\" data-semantic-type=\"fenced\"&gt;&lt;mo data-semantic-=\"\" data-semantic-operator=\"fenced\" data-semantic-parent=\"3\" data-semantic-role=\"open\" data-semantic-type=\"fence\" stretchy=\"false\"&gt;(&lt;/mo&gt;&lt;mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic-parent=\"3\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"&gt;T&lt;/mi&gt;&lt;mo data-semantic-=\"\" data-semantic-operator=\"fenced\" data-semantic-parent=\"3\" data-semantic-role=\"close\" data-semantic-type=\"fence\" stretchy=\"false\"&gt;)&lt;/mo&gt;&lt;/mrow&gt;$(T)$&lt;/annotation&gt;&lt;/semantics&gt;&lt;/math&gt;&lt;/mjx-assistive-mml&gt;&lt;/mjx-container&gt;, and storativity &lt;span data-altimg=\"/cms/asset/6a5d9933-1178-41c2-abd7-1bb517162224/wrcr70156-math-0002.png\"&gt;&lt;/span&gt;&lt;mjx-container ctxtmenu_counter=\"178\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"&gt;&lt;mjx-math aria-hidden=\"true\" location=\"graphic/wrcr70156-math-0002.png\"&gt;&lt;mjx-semantics&gt;&lt;mjx-mrow data-semantic-children=\"1\" data-semantic-content=\"0,2\" da","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"149 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144136944","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":"Bidirectional Translations Between Observational and Topography-Based Hydrographic Data Sets: MERIT-Basins and the SWOT River Database (SWORD)","authors":"Jeffrey Wade, Cédric H. David, Elizabeth H. Altenau, Elyssa L. Collins, Hind Oubanas, Stephen Coss, Arnaud Cerbelaud, Manu Tom, Michael Durand, Tamlin M. Pavelsky","doi":"10.1029/2024wr038633","DOIUrl":"https://doi.org/10.1029/2024wr038633","url":null,"abstract":"The recently launched Surface Water and Ocean Topography (SWOT) Mission is expected to provide transformative observations of water surface elevation, width, and slope and produce derived estimates of discharge for global rivers along rivers in the SWOT River Database (SWORD). However, the hydrographic representation of rivers in SWORD differs from hydrography data sets commonly used for modeling purposes, such as Multi-Error-Removed Improved Terrain (MERIT)-Basins. Here, we develop links between the river networks of SWORD and MERIT-Basins (MB) to enable interoperability between SWOT data products and hydrologic modeling frameworks. This data set, termed MERIT-SWORD, identifies a subset of ∼277,000 global MB river reaches that most closely represent the location and extent of the SWORD river network and establishes bidirectional, one-to-many translations between reaches in the two hydrographic data sets. The MERIT-SWORD data set serves to unite SWOT observations with river routing models, allowing for the seamless and standardized assimilation of SWOT vector products into global river simulations and the provision of improved a priori discharge estimates for SWOT discharge computation.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"13 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144136942","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 Controlling Factors of Watershed Streamflow Variability Using Hydrological and Machine Learning Models","authors":"Bingbing Ding, Xinxiao Yu, Guodong Jia","doi":"10.1029/2024wr039734","DOIUrl":"https://doi.org/10.1029/2024wr039734","url":null,"abstract":"Studying streamflow processes and controlling factors is crucial for sustainable water resource management. This study demonstrated the potential of integrating hydrological models with machine learning by constructing two machine learning methods, Extreme Gradient Boosting (XGBoost) and Random Forest (RF), based on the input and output data from the Soil and Water Assessment Tool (SWAT) and comparing their streamflow simulation performances. The Shapley Additive exPlanations (SHAP) method identified the controlling factors and their interactions in streamflow variation, whereas scenario simulations quantified the relative contributions of climate and land use changes. The results showed that when integrated with the SWAT model, XGBoost demonstrated better streamflow simulation performance than RF. Among the key factors influencing streamflow variation, area was the most important, with precipitation having a stronger impact than temperature, positively affecting streamflow when exceeding 550 mm. Different land use types exerted nonlinear impacts on streamflow, with notable differences and threshold effects. Specifically, grassland, cropland, and forest positively contributed to streamflow when their proportions were below 50%, above 20%, and between 30% and 50%, respectively. Nonlinear interaction effects on streamflow between land use types resulted in positive or negative contributions at specific proportion thresholds. Furthermore, precipitation was not dominant in the interaction with land use. Streamflow changes were primarily driven by drastic land use changes, which contributed 55.71%, while climate change accounted for 44.27%. This integration of hydrological models with machine learning revealed the complex impacts of climate and land use changes on streamflow, offering scientific insights for watershed water resource management.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"21 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130397","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":"Estimation of Extreme Floods Using a Statistical and Conceptual Model of the Hydrological Response","authors":"Pietro Devò, Stefano Basso, Marco Marani","doi":"10.1029/2024wr038667","DOIUrl":"https://doi.org/10.1029/2024wr038667","url":null,"abstract":"The robust estimation of flood peak discharge values is critical for designing mitigation measures and increasing preparedness to natural hazards. Traditional flood estimation methods are, however, severely limited by data series shorter than the return period of interest, as they only use annual maxima or a few values above a high threshold. Here we couple two recent advances in flood estimation from short data samples, namely the Metastatistical Extreme Value Distribution (MEVD) and a conceptual model of flood generation processes, the Physically based Extreme Value (PhEV) distribution of river flows. The result is a methodology, defined through a few hydrologic attributes describing runoff generation and catchment response, to estimate extreme discharge in poorly gauged basins, which we test on data from 178 catchments in Germany. We find that extremes are best estimated when PhEV runoff-generation parameters are set using the long-term mean discharge and precipitation depth, while catchment response parameters are estimated by statistical fitting to observed peak streamflow values. This estimation method interestingly outperforms a methodology in which all parameters are tuned to optimize the reproduction of the statistics of observed peak streamflow values. Our results show that the median relative error associated with MEVD-PhEV, across the large data set explored, consistently remains between −50% and +50%. Hence, MEVD-PhEV yields useful estimates of extreme flows with limited observational information and with no need of preselecting a suitable distribution for ordinary peak discharge values, a step that is substituted by the inclusion of catchment hydrologic information.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"25 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130563","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":"Bio-Mediated Flocculation of Freshwater Microplastics: Effects of Microalgae With Exopolymer Attachments","authors":"Jianwei Zhang, Xiaoteng Shen, Peter Robins, Xiaorong Li, Byung Joon Lee, Qilong Bi, Ying Zhang, Qiqing Chen, Jisheng Zhang","doi":"10.1029/2024wr039115","DOIUrl":"https://doi.org/10.1029/2024wr039115","url":null,"abstract":"Transparent exopolymer particles (TEPs) are crucial for enhancing the flocculation of microplastics (MPs). However, quantitatively evaluating the influence of TEP on the flocculation process and addressing these effects in a flocculation model are challenging. In this study, three freshwater microalgae (<i>Scenedesmus</i> sp., <i>Aulacoseira granulata</i>, and <i>Melosira varians</i>) with various levels of TEP production were incubated to investigate the biologically mediated flocculation process with MPs in a mixing chamber. The results revealed that the three microalgal species significantly increased flocculation, with floc size increasing notably (one-way analysis of variance, <i>p</i> value &lt; 0.001) at later incubation periods (12, 16, 20, 24, and 30 days), compared with the early incubation periods (after 6 and 9 days), when TEP production was lower. A critical TEP concentration (0.42 mg/L) was observed, beyond which further increases in TEP production had minimal effects on the flocculation process. Among the selected microalgae, the <i>Scenedesmus</i> sp.-MPs mixture presented a faster floc growth rate than <i>Aulacoseira granulata</i> and <i>Melosira varians</i>. Furthermore, a modified population balance equation model was proposed to incorporate the ratio of the TEP concentration to the microplastic concentration into the aggregation and maximum specific growth rate parameters. The modified model revealed that the floc growth rate and equilibrium mean size are dependent on the TEP concentration when the MP concentration is fixed, which is in good agreement with the experimental data. The modified model illustrates the potential to simulate exopolymer-driven interactions between microalgae and MPs and provides insights into the mechanisms of bio-mediated flocculation.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"17 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130398","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 Flood Projection and Socioeconomic Implications Under a Deep Learning Framework","authors":"Shengyu Kang, Jiabo Yin, Louise Slater, Pan Liu, Fubao Sun, Dedi Liu, Jun Xia","doi":"10.1029/2024wr037139","DOIUrl":"https://doi.org/10.1029/2024wr037139","url":null,"abstract":"As the planet warms, the frequency and severity of weather-related hazards such as floods are intensifying, posing substantial threats to communities around the globe. Rising flood peaks and volumes claim lives, damage infrastructure, and compromise access to essential services. However, the physical mechanisms behind global flood evolution are still uncertain, and their implications for socioeconomic systems remain unclear. In this study, we leverage a supervised machine learning technique to identify the dominant factors influencing daily streamflow. We then develop a physics-constrained cascade model chain which assimilates water and heat transport processes to project the bivariate risk of flood peak and volume, along with its socioeconomic consequences. To achieve this, we develop a hybrid deep-learning-hydrological model with bias-corrected outputs from 20 global climate models from CMIP6 under four shared socioeconomic pathways. Our results project considerable increases in flood risk under the medium to high-end emission scenario (SSP3-7.0) over most catchments of the globe. The median future joint return period decreases from 50 years to around 27.6 years, with 186 trillion USD and 4 billion people exposed. Downwelling shortwave radiation is identified as the dominant factor driving changes in daily streamflow, accelerating both terrestrial evapotranspiration and snowmelt. As future scenarios project enhanced global warming along with an increase in precipitation extremes, a heightened risk of widespread flooding is foreseen. This study aims to provide valuable insights for policymakers developing proactive strategies to mitigate the risks associated with river flooding under climate change.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"6 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144122565","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":"Predicting the Transport Time of Supersaturated Total Dissolved Gas in a Large Deep Reservoir","authors":"Chonglin Wang, Jin Wang, Jingjie Feng, Haobai Wang, Ran Li, Yuanming Wang, Kefeng Li","doi":"10.1029/2024wr038631","DOIUrl":"https://doi.org/10.1029/2024wr038631","url":null,"abstract":"During dam discharge, supersaturated total dissolved gas (TDG) is generated in the plunge pool and transported downstream for a long distance. Fish living in supersaturated TDG water may suffer from gas bubble disease and even death. Investigating the transport time of supersaturated TDG helps to predict better the downstream impact range and duration of TDG supersaturation and then facilitate mitigation measures for fish in time. From another perspective, it also contributes to optimizing flood discharge management and enhancing the effectiveness of ecological management strategies. This is essential for improving the effectiveness of ecological management strategies. This study utilized a laterally averaged numerical model to investigate the factors influencing the supersaturated TDG transport time in a large deep reservoir. The Baihetan (BHT)–Xiluodu (XLD) hydropower stations were selected as the research object. Simulations under various BHT–XLD joint operation strategies were conducted to analyze the supersaturated TDG transport time in the XLD Reservoir. It is revealed that the transport time of supersaturated TDG is associated with the discharge flow and reservoir characteristics. A power function relationship was identified between the transport time and the transport distance as well as the discharge flow. The relationship among the transport time, water depth, and total volume follows a linear function. Furthermore, a quantitative relationship between the transport time and these four influencing factors was established. The results provide a scientific basis for the accurate prediction of the supersaturated TDG transport process in large deep reservoirs and the formulation of effective regulation and early warning schemes.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"33 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144122568","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":"Assessment of Large-Scale Reservoirs' Impact on the Local Precipitation","authors":"Han Zhou, Jun Qiu, Mengjia Li, Houliang Lu, Fangfang Li","doi":"10.1029/2025wr039938","DOIUrl":"https://doi.org/10.1029/2025wr039938","url":null,"abstract":"Reservoir operations have complex and profound impacts on local climate, particularly precipitation. Quantifying this impact is challenging because it requires the reconstruction of natural precipitation prior to reservoir operation. Instead of assuming that the natural variability of the contrast region and the study region is identical, this study develops an interpretable machine learning model to investigate relationships between precipitation-influencing factors and precipitation itself, including both stable components (sum of trend and seasonality from STL decomposition) and random components (residuals after removing trend and seasonality), which is then used to forecast natural precipitation in the absence of reservoir operation. The application in the contrast region verifies the forecast's accuracy, even in mountainous areas. The proposed method is used to analyze the impact of three large-scale reservoirs along the Yangtze River on local precipitation, collectively having a total storage capacity of 17.86 × 10⁹ m³. The results indicate that reservoir operation leads to a 14% increase in the trend and seasonal components of precipitation, which would be underestimated by previous methods. In addition, there is a noticeable shift in the precipitation center toward the reservoir. Further comparisons suggest that reservoir operation shifts the key influencing factors of local precipitation patterns from those characterized by high variability to those characterized by low variability. Changes in soil water retention capacity likely play a significant role in these precipitation changes. We also found a significant positive coupling between soil moisture and precipitation in the study area, which has been a focal point of recent research. These findings provide new insights into the mechanisms through which reservoir construction impacts precipitation.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"44 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144122567","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}