Water Resources Research最新文献

{"title":"Tidal Response of Groundwater in an Anisotropic Leaky Aquifer","authors":"Guoliang Wang, Chi-Yuen Wang, Yan Zhang, Jiangwei Zhang, Xiuyu Liang","doi":"10.1029/2024wr038851","DOIUrl":"https://doi.org/10.1029/2024wr038851","url":null,"abstract":"Groundwater tidal response analysis is a valuable tool for monitoring leakage in groundwater systems, yet the interpretation of this response has often been incomplete. Notably, the impact of anisotropic aquifer permeability on tidal response has not been addressed in existing models. This study presents an analytical model to examine the effect of anisotropy on the tidal response of an aquifer overlain by a semi-confined aquitard with finite storage. After verifying our model against previous models and numerical simulations, we fund: (a) At high vertical aquifer conductivity and aquitard leakage, the amplitude ratio of the tidal response is small, and the phase shift is positive, making our solution closely align with the existing leaky aquifer model. (b) As the vertical aquifer conductivity decreases, the amplitude ratio increases and the phase shift decreases and becomes negative at relatively low leakage, similar to that of a confined aquifer. (c) When the vertical aquifer conductivity is smaller relative to the horizontal one, the existing leaky aquifer model tends to underestimate the amplitude and overestimate the phase shift. (d) The aquitard storage has a significant effect on the tidal response of the aquifer when the aquitard leakage is large, but a negligible impact when the vertical aquifer conductivity is small. Applying our model to field data from four monitoring wells in the North China Plain, we find that when the shale content in the aquifer reaches 40.09%, our anisotropic model more effectively fits the observed phase shift compared to the existing leaky aquifer model.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"122 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841895","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":"Classifying Flash Flood Disasters From Disaster-Prone Environments to Support Mitigation Measures","authors":"Xiaoyan Zhai, Yongyong Zhang, Yongqiang Zhang, Ronghua Liu, Changjun Liu, Xiaoxiang Zhang, Yuehong Chen, Xiekang Wang, Nigel Wright","doi":"10.1029/2024wr037389","DOIUrl":"https://doi.org/10.1029/2024wr037389","url":null,"abstract":"Spatiotemporal heterogeneities in climatic, physiographic, and socio-economic environments cause complex and varied formation mechanisms in flash flood disasters. However, previous studies were usually conducted at event or catchment scale in specific environments. Investigation on disaster formation mechanisms in climatic, physiographic, and socio-economic environments with different combinations and quantities at large scale is not available, which further affects the decision-making of mitigation measures. Our study develops a type-based analytical framework of flash flood disasters and their causes from disaster-prone environments using ten-fold multivariate analysis including cluster analysis, analysis of similarities, and ordination analysis. Application of this framework to environment factors and losses of 37,332 disaster events across China revealed three disaster-prone environment types, contributing 55.5% ± 0.3%, 55.9% ± 0.3%, and 50.9% ± 0.2% to variations in disaster attributes, respectively. The events with low disaster intensities (24.6%) in undeveloped northwestern China were governed by short rainfall, low retention capacity, and low prevention investments, and their mitigation focused on afforestation and construction of rainfall and flash flood monitoring systems. Those with high disaster intensities (38.5%) in developed and disturbed central and southeastern China were interpreted by frequent intense rainfall and good flood prevention infrastructures, and their mitigation prioritized development of flash flood forecasting warning models, and grain for green, etc. Those with intermediate disaster intensities (36.9%) in undeveloped southwestern and central China were shaped by frequent short intense rainfall and steep rivers, and their mitigation required satellites or radars in alpine regions, multi-disaster prevention technology development, and dam construction.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"61 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143837124","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":"Linking Surface Processes, Solute Generation, and CO2 Budgets Across Lithological and Land Cover Gradients in Rocky Mountain Watersheds","authors":"John R. Slosson, Isaac J. Larsen, Matthew J. Winnick, José M. Marmolejo-Cossío, Kenneth H. Williams","doi":"10.1029/2023wr036850","DOIUrl":"https://doi.org/10.1029/2023wr036850","url":null,"abstract":"Chemical weathering in mountain critical zones controls river chemistry and regulates long-term climate. Mountain landscapes contain diverse landforms created by geomorphic processes, including landslides, glacial moraines, and rock glaciers. These landforms generate unique flowpaths and water-rock interactions that modify water chemistry as precipitation is transformed to streamflow. Variations in lithology and vegetation also strongly control water chemistry. Prior work has shown that landslides generate increased dissolved solute concentrations in rapidly uplifting mountains. However, there is still uncertainty regarding the magnitude which different geomorphic processes and land cover variations influence solute chemistry across tectonic and climatic regimes. We measured ion concentrations in surface water from areas that drain a variety of landforms and across land cover gradients in the East River watershed, a tributary of the Colorado River. Our results show that landslides produce higher solute concentrations than background values measured in streams draining soil-mantled hillslopes and that elevated concentrations persist centuries to millennia after landslide occurrence. Channels with active bedrock incision also generate high solute concentrations, whereas solute concentrations in waters draining moraines and rock glaciers are comparable to background values. Solute fluxes from landslides and areas of bedrock incision are 1.6–1.8 times greater than nearby soil-mantled hillslopes. Carbonic acid weathering dominates surface water samples from watersheds with greater vegetation coverage. Geomorphically enhanced weathering generates hotspots for net CO₂ release or sequestration, depending on lithology, that are 1.5–3.5 times greater than background values, which has implications for understanding links among surface processes, chemical weathering, and carbon cycle dynamics in alpine watersheds.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"60 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831876","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":"Cascaded Machine Learning of Soil Moisture and Salinity Prediction in Estuarine Wetlands Based on In Situ Internet of Things Monitoring","authors":"Jie Song, Yujun Yi","doi":"10.1029/2024wr038271","DOIUrl":"https://doi.org/10.1029/2024wr038271","url":null,"abstract":"Estuarine wetlands, formed by the interaction of fluvial and tidal processes, exhibit complex spatiotemporal variations in soil moisture and salinity. Predicting soil moisture and salinity in estuarine wetlands is key for ecosystem management and assessing environmental impacts, while traditional methods have limitations in resolution and complexity. The elucidation of transport pattern and prediction of water and salt in estuarine wetland soils remain significant challenges. To address these challenges and improve our ability to predict and manage wetland soil properties, this study employs an in situ Internet of Things (IoT)-based monitoring network and a interpretable, cascaded machine learning model to predict these critical soil parameters. The IoT platform facilitates real-time and longitudinal tracking of soil volumetric moisture content, salinity, and groundwater depth in the Yellow River Delta salt marsh wetlands, and the high-fidelity monitoring data are used to build a two-stage machine learning model. Artificial Neural Networks, Support Vector Machines, Random Forests (RF), and Gradient Boosting Decision Trees (GBDT) were used to develop the soil moisture and salinity prediction models. The cascaded framework, in combination with a moisture and a salinity sub-model, which inspired by soil water and salt transport processes, was found to be an effective approach for capturing moisture-salinity dynamics. The Gradient Boosting Decision Tree (GBDT) algorithm predicted moisture best (<i>R</i>² = 0.846), while the GBDT-RF model predicted salinity best (<i>R</i>² = 0.875). To enhance model interpretability, SHAP (Shapley Additive exPlanations) analysis was applied, revealing that groundwater depth is the most significant positive driver of soil moisture, while water content is the dominant negative driver of soil salinity. These findings align with established eco-hydrological processes, validating the models' ability to capture physically meaningful relationships. Sensitivity analysis revealed critical groundwater depth thresholds that strongly influence soil moisture and salinity. Specifically, as the water table rises, soil moisture increases to saturation at −0.5 m. Salt accumulates rapidly at −0.8 m (27% soil moisture) and becomes stable and close to seawater salinity. With real-time in situ monitoring and the cascaded soil property prediction model, the method framework can accurately simulate and predict wetland soil moisture and salinity patterns, providing a valuable tool for monitoring and managing these vulnerable ecosystems and better understanding of wetland responses to environmental changes and supports evidence-based conservation.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"558 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831879","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":"Foundation Models as Assistive Tools in Hydrometeorology: Opportunities, Challenges, and Perspectives","authors":"Lujia Zhang, Yurong Song, Hanzhe Cui, Mengqian Lu, Chenyue Li, Binhang Yuan, Bin Wang, Upmanu Lall, Jing Yang","doi":"10.1029/2024wr039553","DOIUrl":"https://doi.org/10.1029/2024wr039553","url":null,"abstract":"Most state-of-the-art AI applications in hydrometeorology are based on classic deep learning approaches. However, such approaches cannot automatically integrate multiple functions to construct a single intelligent agent, as each function is enabled by a separate model trained on independent data sets. Foundation models (FMs), which can process diverse inputs and perform different tasks, present a substantial opportunity to overcome this challenge. In this commentary, we evaluate how three state-of-the-art FMs, specifically GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro, perform across four key task types in hydrometeorology: data processing, event diagnosis, forecast and prediction, and decision-making. The models perform well in the first two task types and offer valuable information for decision-makers but still face challenges in generating reliable forecasts. Moreover, this commentary highlights the concerns regarding the use of FMs: hallucination, responsibility, over-reliance, and openness. Finally, we propose that enhancing human-AI collaboration and developing domain-specific FMs could drive the future of FM applications in hydrometeorology. We also provide specific recommendations to achieve the perspectives.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"19 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143827214","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":"Identifying and Quantifying the Impact of Climatic and Non-Climatic Drivers on River Discharge in Europe","authors":"Julie Collignan, Jan Polcher, Sophie Bastin, Pere Quintana-Segui","doi":"10.1029/2024wr038220","DOIUrl":"https://doi.org/10.1029/2024wr038220","url":null,"abstract":"Our water resources have changed over the last century through a combination of water management evolution and climate change. Understanding and decomposing the drivers of discharge changes is essential to preparing and planning adaptive strategies. To separate the response of catchment dynamics between climate change-related and other factors in discharge observations, we propose a methodology to compare discharge observations to discharge from a physically based model. The novelty lies in the fact that, to keep the comparison pertinent despite systematic biases in physically based model outputs, we compare both systems using a common framework of interpretation, a parsimonious model, which allows us to isolate trends in catchment dynamics from trends due to average changes in annual climate variables. The modeled system stands as the reference to reproduce changes only due to evolving climate dynamics. Comparing it to the interpretation framework applied to the observation system highlights the effect of the non-modeled factors on catchment dynamics and discharge, such as human intervention in rivers and water uptakes. We show that over Europe, especially in the South, the dominant explanations for discharge trends are non-climatic factors. Still, in some catchments of Northern Europe, climate change seems to be the dominating driver of change. We hypothesize that the dominating non-climatic factors are irrigation development, groundwater pumping and other human water usage. These results show the importance of including non-climatic factors in physically based models to understand the main drivers of discharge better and accurately project future changes.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"32 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823106","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":"Streamflow Monitoring at High Temporal Resolution Based on Non-Contact Instruments and Manually Surveyed Bathymetry in a River Prone to Bathymetric Shifts","authors":"G. Nord, S. Safdar, M. Hasanyar, K. O. Eze, R. Biron, G. Freche, H. Denis, C. Legout, A. Hauet, M. Esteves","doi":"10.1029/2024wr037536","DOIUrl":"https://doi.org/10.1029/2024wr037536","url":null,"abstract":"This study presents a proof of concept of a reliable methodology for monitoring streamflow in a dynamic river of the Alps prone to bathymetric changes using non-contact instruments. The method relies on water level and surface velocity radar monitoring, discharge measurements by Large-Scale Particle Image Velocimetry (LSPIV), and topographic surveys. A single proportional relation, stable under bathymetric changes, is established between maximum surface velocity (<i>V</i>_s,max) and bulk velocity (<i>U</i>_mean) using LSPIV measurements. The location of the maximum surface velocity is also shown to be relatively stable under bathymetric changes. The Isovel model, a theoretical approach which requires minimal information (i.e., bathymetry, water level and bed roughness) is also used to assess its capacity to predict the <i>V</i>_s,max–<i>U</i>_mean relation and the location of the maximum surface velocity. Such model could be useful for applying the method in the absence of LSPIV measurements in the future. The applicability of the method is finally tested over a 2.5-year data set. Discharge is calculated at a time step of 10 min by multiplying the bulk velocity and the wetted cross-sectional area. The results are compared to the specific discharge time series at the historical station located 2.5 km further upstream, which has a stage-discharge rating curve, to assess the credibility of the proposed method. Good agreement is generally observed when surface velocity is above 0.7 m/s, but accuracy decreases for lower velocities. A simplified uncertainty analysis estimates a 25% relative error on discharge calculated with the presented method.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"55 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823108","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":"Improved Water Level Retrieval in Complex Riverine Environments: Sentinel-3 and Sentinel-6 Altimetry Over China's Rivers","authors":"Chenqi Fang, Di Long, Qi Huang, Fanyu Zhao, Huaichuan Liu, Xingwu Duan, Aizhong Hou","doi":"10.1029/2024wr039705","DOIUrl":"https://doi.org/10.1029/2024wr039705","url":null,"abstract":"The decline in in situ water level measurements since the 1980s has impeded our ability to fully understand hydrological and hydrodynamic processes, particularly in ungauged river reaches, and how global and regional water cycles respond to climate change. Satellite altimetry offers a valuable means of supplementing these gaps in river water level data, both temporally and spatially. However, existing radar waveform retracking techniques often struggle to accommodate rivers with varying morphologies and surrounding environments. This study presents an Improved Multiple Subwaveform Analysis (IMSA) algorithm based on the 50% Threshold and Ice-1 Combined (TIC) algorithm, incorporating noise filtering into the subwave search module and refining the retracking strategy for multiple subwaves, independent of coarse digital elevation models (DEMs). We validated the IMSA algorithm using in situ data from 23 gauging stations and applied it to Sentinel-3 and Sentinel-6 altimetry across 57 virtual stations (VSs) in China, covering rivers with widths ranging from 20 to 1,500 m, generating 79 validation results (each representing an RMSE value comparing altimetry with in situ measurements). The IMSA algorithm demonstrated significant enhancements at over 48 VSs with more than 64 validation results compared to the original TIC, achieving the lowest median RMSE of 0.61 m (0.13–0.50 m lower than the OCOG, Threshold, MWaPP, and TIC algorithms), with strong resilience to environmental noise. Error analysis revealed that the altimetric accuracy is primarily influenced by the underlying surface characteristics of VSs, with built-up areas exerting significant interference. Additional disturbances stem from surrounding waters, large slopes, river channels running parallel to the satellite's ground track, and unique features such as sandbars, braided and ice-covered rivers, and hydroelectric stations. The synthetic aperture radar (SAR) mode was found to mitigate some of these land cover impacts, further improving water level retrieval accuracy. Finally, the results show that river width and topography (whether mountainous or flat) do not inherently affect altimetric accuracy, provided that the on-board tracking system is supported by accurate prior DEMs and minimal slope interference.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"75 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823135","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":"Statistical Learning and Topkriging Improve Spatio-Temporal Low-Flow Estimation","authors":"J. Laimighofer, G. Laaha","doi":"10.1029/2024wr038329","DOIUrl":"https://doi.org/10.1029/2024wr038329","url":null,"abstract":"This study evaluates the potential of a novel hierarchical space-time model for predicting monthly low-flow in ungauged basins. The model decomposes the monthly low-flows into a mean field and a residual field, where the mean field represents the seasonal low-flow regime plus a long-term trend component. We compare four statistical learning approaches for the mean field, and three geostatistical methods for the residual field. All model combinations are evaluated using a hydrologically diverse dataset of 260 stations in Austria and the predictive performance is validated using nested 10-fold cross-validation. The best model for monthly low-flow prediction is a combination of a model-based boosting approach for the mean field and topkriging for the residual field. This model reaches a median &lt;span data-altimg=\"/cms/asset/bd07921f-b370-432a-af44-864f4985be52/wrcr70042-math-0001.png\"&gt;&lt;/span&gt;&lt;mjx-container ctxtmenu_counter=\"285\" 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/wrcr70042-math-0001.png\"&gt;&lt;mjx-semantics&gt;&lt;mjx-mrow&gt;&lt;mjx-msup data-semantic-children=\"0,1\" data-semantic- data-semantic-role=\"latinletter\" data-semantic-speech=\"upper R squared\" data-semantic-type=\"superscript\"&gt;&lt;mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mi&gt;&lt;mjx-script style=\"vertical-align: 0.363em;\"&gt;&lt;mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"integer\" data-semantic-type=\"number\" size=\"s\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mn&gt;&lt;/mjx-script&gt;&lt;/mjx-msup&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:wrcr70042:wrcr70042-math-0001\" display=\"inline\" location=\"graphic/wrcr70042-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"&gt;&lt;semantics&gt;&lt;mrow&gt;&lt;msup data-semantic-=\"\" data-semantic-children=\"0,1\" data-semantic-role=\"latinletter\" data-semantic-speech=\"upper R squared\" data-semantic-type=\"superscript\"&gt;&lt;mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic-parent=\"2\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"&gt;R&lt;/mi&gt;&lt;mn data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic-parent=\"2\" data-semantic-role=\"integer\" data-semantic-type=\"number\"&gt;2&lt;/mn&gt;&lt;/msup&gt;&lt;/mrow&gt;${R}^{2}$&lt;/annotation&gt;&lt;/semantics&gt;&lt;/math&gt;&lt;/mjx-assistive-mml&gt;&lt;/mjx-container&gt; of 0.73 across all stations, outperforming an XGBoost model on the same data set. Model performance is generally higher for stations with a winter regime (median &lt;span data-altimg=\"/cms/asset/9899636a-7236-4a2c-900e-7b39fea3ce82/wrcr70042-math-0002.png\"&gt;&lt;/span&gt;&lt;mjx-container ctx","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"5 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823109","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":"Has the Three Gorges Reservoir Impacted Regional Moisture Recycling?","authors":"Jia Wei, Weiguang Wang, Mingzhu Cao, Jianyun Zhang, Junliang Jin, Guoqing Wang, Hongbin Li, Xiaolong Pan, Zongchao Ye, Adriaan J. Teuling, Shuo Wang","doi":"10.1029/2024wr038208","DOIUrl":"https://doi.org/10.1029/2024wr038208","url":null,"abstract":"The Three Gorges Dam (TGD) and its impoundment significantly alter natural river properties and local land cover, drawing considerable concerns regarding its climatic and environmental effects. However, with the role of the Three Gorges Reservoir (TGR) in narrowing temperature ranges and changing precipitation patterns is well understood, its impact on moisture recycling is little known. Here, we tracked precipitation in the TGR basin back to evaporated moisture to explore the features of moisture recycling and quantify local evaporation ratios in the pre-dam (1980–2002) and post-dam (2003–2022) periods. The influences of the forcing data, simulation time steps and different tracking models on evaporation recycling are investigated. Relevant mechanisms are analyzed in terms of atmospheric motion, surface radiation, land cover changes and climate variability impacts. Results indicate that the precipitationshed shows a reduction in both summer and winter during the post-dam period. Local evaporation recycling ratios (ERRs) in TGR basin decrease by 0.46%, 1.07%, 0.59, 0.94% during the post-TGD period relative to the pre-TGD period in spring, summer, autumn and winter, respectively. Local evaporation contributions are limited in both the pre-dam and post-dam periods, especially in dry years. The reduced precipitation in TGR region is more dependent on upwind moisture, which results from the enhanced sinking motion and moisture divergence. Although different forcing data and simulation time steps show good agreement in spatial and temporal variations in the recycled moisture, the local ERRs are larger when calculated from the UTrack model than from the WAM-2layers model.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"108 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143819922","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}