Journal of Hydrology-Regional Studies最新文献

{"title":"Spatial and temporal characteristics of water conservation services and rapid response framework for water yield in key ecological zones of the Yiluo River basin","authors":"Junqiang Xu ,&nbsp;Fan Wang ,&nbsp;Chao Ren ,&nbsp;Jianmin Bian ,&nbsp;Tao Li ,&nbsp;Zikai Ping","doi":"10.1016/j.ejrh.2025.102542","DOIUrl":"10.1016/j.ejrh.2025.102542","url":null,"abstract":"<div><h3>Study region</h3><div>Yiluo River basin, an important water source and ecological barrier in China.</div></div><div><h3>Study focus</h3><div>In this study, we analyzed the spatial and temporal patterns of water yield and the main influencing factors of the Yiluo River Basin based on the water yield response framework constructed by the SWAT model and the intelligent optimization algorithm.</div></div><div><h3>New hydrological insights for the region</h3><div>The results indicated that the average annual total of water-source containment per unit area in the district was 330.03 mm from 2019 to 2023, with a Nash-Sutcliffe Efficiency (NSE) of 0.77, based on the average of two hydrological sites in the SWAT model. The high value of water conservation goes mainly in the forested mountainous areas of the upper reaches of the Yi River and concentrated in July–October, seasonal differences in the amount of water conservation are mainly influenced by precipitation (correlation of 0.79), and potential evapotranspiration determines its lower limit value. Urban land uses and riparian areas with high levels of hydraulic erosion are areas with low water yield concentration. The artificial neural network-based prediction framework achieved high performance with Pearson correlation coefficients exceeding 0.90 across all datasets. The average relative error was 1.31 % (training), 1.39 % (validation), and 1.24 % (test), with MAPE values below 2 %. This approach allows flexible scenario modeling and has been successfully applied to seven cases, offering valuable early-warning insights for regional ecological planning and water resource management.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"60 ","pages":"Article 102542"},"PeriodicalIF":4.7,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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-scale spatiotemporal pattern and its causes of meteorological drought over a typical steppe in the Inner Mongolia Plateau","authors":"Yixuan Wang ,&nbsp;Shuyue Shi ,&nbsp;Tingxi Liu ,&nbsp;Limin Duan ,&nbsp;Jianguo Ji ,&nbsp;Shiyu Zhang","doi":"10.1016/j.ejrh.2025.102550","DOIUrl":"10.1016/j.ejrh.2025.102550","url":null,"abstract":"<div><h3>Study region</h3><div>Against the backdrop of intensifying climate change, extreme drought events have become increasingly frequent in arid and semi-arid grasslands, posing significant threats to ecological security and sustainable development. This study focused on a typical steppe in the eastern Inner Mongolia Plateau.</div></div><div><h3>Study focus</h3><div>Based on the Standardized Precipitation Index, the spatiotemporal evolution of meteorological drought across multiple scales was analyzed through principal component analysis and time-varying moment model. Furthermore, the influences of atmospheric circulations on regional drought variabilities were elucidated using covariate-incorporated modeling.</div></div><div><h3>New hydrological insights for the region</h3><div>The results reveal the pronounced spatial heterogeneity in droughts across the study area, which can be categorized into three distinct sub-regions, including a western sub-region characterized by moderate drought, a southeastern sub-region prone to severe droughts, and a northeastern sub-region susceptible to extreme droughts. Temporally, a significant linear trend in droughts was observed in the western sub-region, primarily marked by the aggravation of droughts in summer and the alleviation of droughts in the non-growing season. Drought dynamics in the southeastern sub-region were mainly manifested as the linear extremization in summer and the nonlinear alleviation in the non-growing season. In the northeastern sub-region, drought conditions remained relatively stable overall, but showed a trend toward extreme drought during the growing season. The most dominant atmospheric circulation drivers were identified as the North Atlantic Oscillation (NAO), the Pacific-North American Oscillation (PNA), and the Southern Oscillation (SO). Specifically, both the positive phase of NAO and the negative phase of PNA contributed to the aggravation of regional drought, while the positive and negative phases of SO respectively exacerbated droughts in the western and eastern regions. These findings provide a theoretical reference for enhancing the accuracy of drought prediction and advancing risk management strategies in grassland ecosystems.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"60 ","pages":"Article 102550"},"PeriodicalIF":4.7,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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-model ensemble machine learning-based downscaling and projection of GRACE data reveals groundwater decline in Saudi Arabia throughout the 21st century","authors":"Arfan Arshad ,&nbsp;Muhammad Shafeeque ,&nbsp;Thanh Nhan Duc Tran ,&nbsp;Ali Mirchi ,&nbsp;Zaichen Xiang ,&nbsp;Cenlin He ,&nbsp;Amir AghaKouchak ,&nbsp;Jessica Besnier ,&nbsp;Md Masudur Rahman","doi":"10.1016/j.ejrh.2025.102552","DOIUrl":"10.1016/j.ejrh.2025.102552","url":null,"abstract":"<div><h3>Study region</h3><div>Saudi Arabia.</div></div><div><h3>Study focus</h3><div>The major goal of this study is to downscale GRACE (Gravity Recovery and Climate Experiment) groundwater storage (GWS) anomalies to assess the local-scale vulnerabilities of groundwater changes across western regions of Saudi Arabia (Al Jumum, Makkah, Jeddah, and Bahrah). This was accomplished by using multi-model ensemble machine learning (ML) approach leveraging Random Forest, CART, and Gradient Tree Boosting algorithms within Google Earth Engine (GEE). Additionally, we used the downscaled GWS and CMIP6 climate data with the Generalized Additive Model (GAM) to project the future GWS changes under climate change.</div></div><div><h3>New hydrological insights for the region</h3><div>The ensemble results demonstrated robust performance (R² = 0.92 and RMSE = 20 mm) compared to the individual model (R² = 0.84–0.88 and RMSE = 25–28 mm). The areas of higher groundwater depletion were predominantly observed in Jeddah and Makkah, with average annual rates of − 165 mm/year and − 150 mm/year, respectively, from 2002 to 2023. The total volumetric losses range from 11.38 km³ to 15.31 km³ across different sub-regions. Seasonally, the peak GWS drop (-90 to − 125 mm) was detected during the summer months (April–July), aligning with periods of maximum water demand. Several key drivers that control the GWS changes were also identified, including anthropogenic effects, local climate anomalies, and large-scale climate oscillations. Projections for GWS reveal an irreversible decline throughout the 21st Century with potential reductions surpassing − 216 mm/year in high-emission scenarios (SSP5-8.5). The developed approach is transferable to other regions for quantitative assessment of local groundwater changes.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"60 ","pages":"Article 102552"},"PeriodicalIF":4.7,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative analysis of deep learning and machine learning models for one-day-ahead streamflow forecasting in the Krishna River basin","authors":"Sukhsehaj Kaur,&nbsp;Sagar Rohidas Chavan","doi":"10.1016/j.ejrh.2025.102549","DOIUrl":"10.1016/j.ejrh.2025.102549","url":null,"abstract":"<div><h3>Study region</h3><div>Karad, Keesara, Sarati and T.Ramapuram catchments located in the Krishna River basin, India</div></div><div><h3>Study focus</h3><div>This study focused on 1-day ahead streamflow forecasting in four distinct catchments using a wide array of Deep Learning (DL) and Machine Learning (ML) models. A comprehensive evaluation of eleven models was conducted to assess their strengths and limitations across different datasets.</div></div><div><h3>New hydrological insights</h3><div>The study implemented Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), Bidirectional GRU, Convolutional Neural Network, WaveNet, K-Nearest Neighbours, Random Forest (RF), Support Vector Regression, Adaptive Boosting, and Extreme Gradient Boosting (XGBoost) to forecast streamflow at each site. Lagged precipitation and antecedent streamflow emerged as key predictors. Model performance was assessed using multiple evaluation metrics and visualization techniques. Bi-LSTM achieved the highest performance in three catchments with Nash-Sutcliffe efficiency (NSE) of 0.864 in Karad, 0.708 in Keesara, and 0.702 in T. Ramapuram, while GRU performed best in Sarati with NSE close to 0.7. The best model achieved \"very good\" accuracy in one catchment and \"good\" in three, as indicated by performance metrics. However, even the best-performing DL models struggled to capture peak flow events, revealing limitations in extrapolation. The study also highlights the potential of ML models based on ensemble techniques, such as RF and XGBoost, which demonstrated performance comparable to that of complex DL architectures.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"60 ","pages":"Article 102549"},"PeriodicalIF":4.7,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 spatiotemporally varying parameters for grid-based distributed hydrologic models","authors":"Xiaojing Zhang ,&nbsp;Pan Liu ,&nbsp;Kang Xie ,&nbsp;Weibo Liu ,&nbsp;Lele Deng ,&nbsp;Huan Xu ,&nbsp;Qian Cheng ,&nbsp;Liting Zhou","doi":"10.1016/j.ejrh.2025.102536","DOIUrl":"10.1016/j.ejrh.2025.102536","url":null,"abstract":"<div><h3>Study region</h3><div>Xiangjiang and Baihe River basins, China.</div></div><div><h3>Study focus</h3><div>Hydrologic models often use either time-varying or spatially heterogeneous parameter methods to improve runoff simulations. However, few methods account for both dimensions simultaneously, limiting model accuracy and reducing insight into the effects of climate change and human activities on rainfall-runoff relationships. To fill this gap, a spatiotemporally varying parameter estimation method, DAKG-SWD-DP, is proposed here. This method involves three steps: (1) division of the dataset into sub-periods using a sliding window-based split-sample calibration (SWD-SSC) method; (2) calibration of spatially heterogeneous parameters for each sub-period using a dimension-adaptive key grid (DAKG) strategy; and (3) optimization of spatiotemporal parameter variations through dynamic programming to consider both simulation accuracy and parameter continuity.</div></div><div><h3>New hydrological insights for the region</h3><div>(1) the DAKG-SWD-DP method significantly improves runoff simulation compared to the constant parameter, DAKG, and SWD-SSC methods. Specifically, the NSE increases by 0.05 in the Xiangjiang River basin and 0.09 in the Baihe River basin compared to the constant parameter method; (2) the DAKG-SWD-DP method outperforms the DAKG-SWD method in capturing the relationships between hydrologic parameters and environmental factors, due to enhanced parameter continuity. Additionally, the DAKG-SWD-DP method efficiently identifies climatic factors as key drivers of parameter variations in both basins, while human activities, such as reservoir construction, are also key drivers in the Baihe River basin.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"60 ","pages":"Article 102536"},"PeriodicalIF":4.7,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monitoring and modeling hydrologic conditions in Ukraine for hydropower generation","authors":"Matthew J. McCarthy ,&nbsp;Jesus D. Gomez-Velez ,&nbsp;David Hughes ,&nbsp;Shannon Meade","doi":"10.1016/j.ejrh.2025.102518","DOIUrl":"10.1016/j.ejrh.2025.102518","url":null,"abstract":"<div><h3>Study region</h3><div>The Dnieper and Dniester Rivers of Ukraine.</div></div><div><h3>Study focus</h3><div>The ongoing conflict in Ukraine has caused disruptions to electricity generation, of which hydroelectric sources contribute approximately 9 % to the country’s needs. With the takeover of the Zaporizhzhia nuclear power plant by enemy forces, the loss of the Kakhovka hydroelectric dam, and the future impacts of the conflict on electricity generation unclear, it may be valuable for the Ukrainian government to better understand how it could leverage hydroelectric power sources in the near future. Unfortunately, measurements of river discharge throughout Ukraine ceased data collection in the late 1980’s to early 1990’s. To address this data gap, we developed a protocol that combined satellite-based time-series measurements of river width at seven locations throughout Ukraine from 2013 to 2023 with reanalysis data, climate-model predictions, and hydrologic models to both provide a means of monitoring a proxy for near-real-time discharge and also predict near-term (i.e., 2023–2030) hydrologic patterns for the region.</div></div><div><h3>New hydrological insights for the region</h3><div>We ran new algorithms on 144 WorldView-2 and WorldView-3 satellite images to map rivers and extract width, one of which was validated against river gauge data located along the same river but in a neighboring country. Hydrologic models using two climate scenarios found minimal change in annual discharge at all sites, but magnitude and timing of peak discharge showed a moderate trend. The results suggest that hydropower is underutilized in Ukraine.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"60 ","pages":"Article 102518"},"PeriodicalIF":4.7,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Innovative use of reanalysis data in Slovenia: Enhancing precipitation time series with a multiplicative cascade model","authors":"Hannes Müller-Thomy ,&nbsp;Jana Kellner ,&nbsp;Patrick Nistahl ,&nbsp;Nejc Bezak ,&nbsp;Katarina Zabret ,&nbsp;Kai Schröter","doi":"10.1016/j.ejrh.2025.102530","DOIUrl":"10.1016/j.ejrh.2025.102530","url":null,"abstract":"<div><h3>Study region</h3><div>Slovenia</div></div><div><h3>Study focus</h3><div>Precipitation reanalysis products (PRP) with hourly resolution as ERA5-Land and COSMO-REA6 are a promising solution for hydrologic applications in data scarce regions as Slovenia. Both data sets are validated against observed hourly time series from 5 rain gauges and areal precipitation in 20 catchments in Slovenia regarding continuous and event-based precipitation characteristics as well as extreme values. Both, station and catchment precipitation are not well-represented by PRP, with a worse representation by ERA5-Land than COSMO-REA6. The aim of this study is to explore how if the PRP time series can, instead of being directly appliedtime series, from precipitation reanalysis products (PRP) can be used for the generation of high-resolution precipitation time series in unobserved catchments. A new approach is proposed to estimate parameters of a micro-canonical cascade model from ERA5-Land and COSMO-REA6 time series to generate hourly time series from daily observations.</div></div><div><h3>New hydrologic insights for the region</h3><div>The proposed parameter estimation approach leads to a general better representation of precipitation characteristics. For some stations the disaggregation based on COSMO-REA6 parameters even outperforms disaggregation results with parameters estimated from observed time series at these stations. Significant spatial patterns of PRP errors are identified which can help improving future PRP. The parameterization approach is not limited to the study region and can be used for precipitation generation in unobserved catchments, particularly in catchments with complex terrain which are usually not well represented by PRP.</div></div><div><h3>Plain language summary</h3><div>For many hydrological applications precipitation data with hourly resolution are required. Unfortunately, this kind of data exists often only for a few stations or only for short periods in the region of interest. Precipitation reanalysis data (PRP), which are produced by simulating the earth’s atmosphere over the last decades, are a promising solution. However, the quality of PRP depends on multiple factors and has to be tested for each region before application. In this study widely used ERA5-Land and COSMO-REA6 are tested for five rain gauges and 20 catchments in Slovenia. The validation of PRP with available observations for the study area indicated strong deviations. So instead of using the PRP directly for hydrological applications, a precipitation generator was parameterized with information from the PRP. Its validation shows a better representation of the observations, which suggests their usage for subsequent hydrological applications.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"60 ","pages":"Article 102530"},"PeriodicalIF":4.7,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving regional soil water-salt management by modeling soil freezing-thawing processes in a cold-arid irrigation district, upper Yellow River basin","authors":"Lvyang Xiong ,&nbsp;Yao Jiang ,&nbsp;Junyu Qi ,&nbsp;Guanhua Huang","doi":"10.1016/j.ejrh.2025.102544","DOIUrl":"10.1016/j.ejrh.2025.102544","url":null,"abstract":"<div><h3>Study region</h3><div>The Hetao Irrigation District (Hetao), a super-large cold-arid irrigation district located in the upper Yellow River basin, China.</div></div><div><h3>Study focus</h3><div>For cold-arid irrigation districts, understanding soil freezing-thawing processes is important for the reasonable management of non-growing season irrigation to improve soil water-salt conditions. This study aims to conduct regional agro-hydrological modeling with considering soil freezing-thawing processes in Hetao. The SWAT-AG model was adopted and enhanced by introducing a physically-based soil temperature module. Using the enhanced SWAT-AG, the agro-hydrological processes in Hetao were simulated and predicted under both the current autumn irrigation scheme (CAIS) and some water-saving autumn irrigation scenarios (WAIS).</div></div><div><h3>New hydrological insights for the region</h3><div>The results proved that the enhanced SWAT-AG model effectively described the agro-hydrological processes during the soil freezing-thawing period. The simulated results of CAIS indicated that autumn irrigation was efficient in replenishing soil water and controlling soil salinity. The predictions from WAIS indicated that soil water content decreased, soil salt content increased, and groundwater depth became deeper compared to those under CAIS. A 20 % reduction in the current autumn irrigation amount did not compromise the effectiveness of autumn irrigation on soil water-salt and groundwater conditions, making it an appropriate autumn irrigation scheme for achieving both soil water-salt management and water saving in Hetao.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"60 ","pages":"Article 102544"},"PeriodicalIF":4.7,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying turbidity dynamics in lake water using OLS regression: A landsat 8 OLI-based remote sensing approach","authors":"Imran Ahmad ,&nbsp;Amnah A. Alasgah ,&nbsp;Martina Zelenakova ,&nbsp;Mithas Ahmad Dar ,&nbsp;Minwagaw Damtie ,&nbsp;Marshet Berhan","doi":"10.1016/j.ejrh.2025.102523","DOIUrl":"10.1016/j.ejrh.2025.102523","url":null,"abstract":"<div><h3>Study region</h3><div>Lake Tana, Ethiopia’s largest freshwater lake, has experienced a notable increase in water turbidity. This issue highlights the need for an in-depth understanding of how human activities and environmental changes are impacting its ecological balance. Addressing these turbidity challenges is crucial for safeguarding the sustainability of this vital resource.</div></div><div><h3>Study focus</h3><div>This research utilized Landsat 8 satellite imagery to examine turbidity levels in Lake Tana. Six bands from Landsat OLI—band 2, band 3, band 4, band 5, band 6, and band 7—were analyzed both individually and in combination. Ordinary least squares (OLS) regression modeling was applied to investigate the relationships between these bands and in-situ turbidity data.</div></div><div><h3>New hydrological insights</h3><div>Our findings reveal that the combined use of specific bands—particularly band 2 + band 5 - band 6—accounted for 87 % of the variance in turbidity as explained by the OLS regression model. Additionally, the Koenker- (Breusch-Pagan) statistic indicated no conflicting relationships (p &gt; 0.005) within the model, affirming its reliability. To further validate the model’s impartiality, the Jarque-Bera test was performed. Polynomial and exponential regression analyses were also conducted, leading to the identification of an optimal regression equation for predicting the spatial distribution of turbidity in Lake Tana.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"60 ","pages":"Article 102523"},"PeriodicalIF":4.7,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation and prediction of hydrological processes in Kaidu River Basin based on DHSVM model","authors":"Qiyue Zhang ,&nbsp;Changchun Xu ,&nbsp;Hongyu Wang ,&nbsp;Qian Wang ,&nbsp;Lin Li ,&nbsp;Yu Luo","doi":"10.1016/j.ejrh.2025.102537","DOIUrl":"10.1016/j.ejrh.2025.102537","url":null,"abstract":"<div><h3>Study Region</h3><div>The Kaidu River Basin in Xinjiang, China.</div></div><div><h3>Study Focus</h3><div>This study is focused on simulating future climate and underlying surface changes using CMIP6 climate model data and the PLUS model, and modeling historical and future runoff in the study region using the Distributed Hydrology Soil Vegetation Model (DHSVM). The double cumulative curve method is applied to quantify the respective contributions of climate change and reservoir operations to runoff variation, and to reveal the mechanisms by which reservoir cluster regulation alters the spatiotemporal distribution of runoff.</div></div><div><h3>New Hydrological Insights</h3><div>Our findings reveal that the PLUS model exhibits high reliability in simulating land use in the study region, providing accurate land surface inputs for hydrological modeling. The performance of the DHSVM model was significantly improved through parameter optimization, with the Nash–Sutcliffe efficiency (NSE) during the validation period increasing to 0.64 at the daily scale, and reaching 0.77 at the monthly scale. This confirms its suitability for simulating hydrological processes in small to medium-sized arid basins. The cascade reservoirs adopt a multi-stage winter-spring storage and summer-autumn coordinated release operation strategy, which shifts the natural runoff peak from June–July to July–August, effectively aligning peak water supply with agricultural water demand. Human activities, represented by reservoir operations, account for 20.30 % of the runoff variation, while climatic factors, primarily precipitation, contribute 79.70 %, highlighting the regulatory role of reservoirs in regional hydrological processes.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"60 ","pages":"Article 102537"},"PeriodicalIF":4.7,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}