Journal of Hydrology-Regional Studies最新文献

{"title":"Unraveling spatiotemporal distribution of extreme precipitation in the southern Tibetan Plateau: Synergistic effects between atmospheric circulation and topography","authors":"Peng Liu ,&nbsp;Zhenjiang Wu ,&nbsp;Kang Xie ,&nbsp;Qixiao Zhang ,&nbsp;Cuishan Liu ,&nbsp;Peng Liu ,&nbsp;Guoqing Wang","doi":"10.1016/j.ejrh.2025.102809","DOIUrl":"10.1016/j.ejrh.2025.102809","url":null,"abstract":"<div><h3>Study region</h3><div>The Yarlung Zangbo River Basin.</div></div><div><h3>Study focus</h3><div>Amid global climate change, the intensity of extreme precipitation across the Tibetan Plateau has increased. However, accurately forecasting the spatiotemporal patterns of extreme precipitation in high-altitude basins with complex terrain remains challenging. This study selects the Yarlung Zangbo River Basin in the southern Tibetan Plateau as a case study and analyzes the spatiotemporal trends of extreme precipitation intensity from 1961 to 2022. A convolutional neural network–long short-term memory model incorporating dynamic atmospheric circulation indices and static topographic characteristics is developed to predict monthly spatiotemporal variations in extreme precipitation intensity.</div></div><div><h3>New hydrological insights for the region</h3><div>The results indicate that the monthly maximum 1-day precipitation (Rx1day) and the monthly maximum 5-day precipitation (Rx5day) extreme precipitation intensity indices exhibit overall non-significant increasing trends across the basin, although significant upward trends are observed in the central and eastern regions. Regarding model performance, the average Nash–Sutcliffe efficiency for spatiotemporal predictions of Rx1day and Rx5day are 0.62 and 0.67, respectively, while the corresponding Pearson correlation coefficients reach 0.78 and 0.81, demonstrating satisfactory predictive accuracy. Simulation results reveal that atmospheric circulation indices combined with high-resolution topography significantly improve the model’s predictive accuracy, increasing the average Nash–Sutcliffe efficiency for Rx5day by over 6 %. High-resolution topographic data enhance the model’s ability to capture spatial features, thereby improving prediction accuracy. This study establishes an innovative framework for predicting extreme precipitation in high-altitude basins with complex terrain, offering important implications for regional disaster prevention/mitigation and water resource management.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"62 ","pages":"Article 102809"},"PeriodicalIF":5.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221574","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":"Long-term decline in the proportion of particulate organic matter in Yunnan-Guizhou plateau lakes from 2000 to 2024","authors":"Fangfang Chen ,&nbsp;Jie Xi ,&nbsp;Shijiao Lu ,&nbsp;Chenyang Wei ,&nbsp;Huaiqing Liu ,&nbsp;Heng Lyu ,&nbsp;Yunmei Li ,&nbsp;Wenyu Liu ,&nbsp;Yuxin Zhu ,&nbsp;Qingxia Miao ,&nbsp;Yiling Zheng","doi":"10.1016/j.ejrh.2025.102802","DOIUrl":"10.1016/j.ejrh.2025.102802","url":null,"abstract":"<div><h3>Study region</h3><div>Yunnan-Guizhou Plateau lakes(YGPL)</div></div><div><h3>Study focus</h3><div>Particulate composition is critical for understanding the changes in underwater light fields and primary productivity. In this study, an empirical algorithm was developed based on a band ratio (555 nm/645 nm) of atmospherically corrected surface reflectance of the Moderate Resolution Imaging Spectro-radiometer(MODIS), using 1215 in-situ samples to estimate particulate composition (Organic Suspended Matter/Total Suspended Matter, OSM/TSM). The study further explored the intra-annual and inter-annual variability of OSM/TSM using General Linear Model(GLM) and Partial Least Squares Structural Equation Modeling (PLS-SEM), and examined its linkages by analyzing the covariation between OSM/TSM and submerged vegetation (SV) in Erhai from 2000 to 2024.</div></div><div><h3>New hydrological insights</h3><div>The OSM/TSM algorithm demonstrated satisfactory performance (R²=0.71, RMSE=0.14, UMAPE= 31.23 %). Results showed that Urban lakes exhibited 1.3 times higher annual mean OSM/TSM than natural lakes, with all lakes exhibiting consistent seasonal peaks during the summer and autumn. A weak but consistent long-term declining trend in OSM/TSM (-0.003 yr⁻¹) was observed across most lakes, except for Dianchi and Yangzonghai. GLM analysis revealed that wind speed was the dominant factor driving intra-annual OSM/TSM variability, accounting for 45.58 ± 15.94 %. PLS-SEM results indicated that climate factors were the primary inter-annual driving in natural and semi-urban lakes, while human activity, such as lake expansion and nighttime light, dominated in urban lakes. OSM/TSM was shown to be a potential influencing factor for the growth of submerged vegetation.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"62 ","pages":"Article 102802"},"PeriodicalIF":5.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221663","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":"A novel method for estimating film-flow-controlled bare soil evaporation","authors":"Chengcheng Gong ,&nbsp;Markus Berli ,&nbsp;Zaiyong Zhang ,&nbsp;Wenke Wang ,&nbsp;Yunquan Wang","doi":"10.1016/j.ejrh.2025.102784","DOIUrl":"10.1016/j.ejrh.2025.102784","url":null,"abstract":"<div><h3>Study region</h3><div>Arid and semi-arid regions.</div></div><div><h3>Study focus</h3><div>The precise estimation of bare soil evaporation is essential for effective water resource management, particularly in arid and semi-arid regions. Although stage 2 evaporation (using three evaporation stage notation), characterized by film flow following the breakdown of capillary flow, is an important process, it is often neglected due to the difficulties associated with accurately estimating it. This study focuses on proposing an innovative method that explicitly integrates film flow processes to enhance the estimation of stage 2 evaporation.</div></div><div><h3>New hydrological insights for the region</h3><div>We proposed a method to estimate stage 2 evaporation rates. The proposed method represents actual evaporation as a linear function of potential evapotranspiration, incorporating a critical threshold that signifies the transition from capillary to film flow, and uses one of the following as an input variable: soil water content, pressure head, or relative humidity near the soil surface. The proposed method was examined through data obtained from three laboratory experiments and a large-scale weighing lysimeter located in the Mojave Desert (an arid region), USA. The results show that evaporation rates in stage 2 can be accurately reproduced across various experimental setups and soil textures, yielding regression coefficients (<em>b</em>₀) between 0.89 and 1.08, coefficients of determination (<em>R</em>²) values up to 0.99, and <em>RMSE</em> as low as 0.06–1.3 mm/day. This study addresses a critical gap in the estimation of evaporation by offering a simple and field-applicable tool for accurately quantifying stage 2 evaporation, which is beneficial for improving water resource management in arid and semi-arid regions. In addition, the proposed method relies on readily measurable surface variables, such as soil moisture, which can be obtained through remote sensing, making the approach especially practical for large-scale and field applications in the future.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"62 ","pages":"Article 102784"},"PeriodicalIF":5.0,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159646","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":"Assessing blue-green infrastructures for urban flood and drought mitigation under changing climate scenarios","authors":"Xuan Wu,&nbsp;Patrick Willems","doi":"10.1016/j.ejrh.2025.102798","DOIUrl":"10.1016/j.ejrh.2025.102798","url":null,"abstract":"<div><h3>Study region</h3><div>Arenberg III campus of KU Leuven and its neighboring area in Belgium.</div></div><div><h3>Study focus</h3><div>Blue and Green Infrastructures (BGIs) are natural or semi-natural systems recognized as effective solutions for stormwater management and climate change adaptation. This study evaluates the potential of green roofs, rain tanks and permeable pavements as BGIs to mitigate floods and droughts under various climate scenarios. A fine-scale surface water balance model was integrated with a groundwater model to simulate surface runoff and groundwater levels. The integrated model offers high spatial and temporal resolution while remaining computationally efficient, capturing both short-term extreme events and long-term trends. Applied to a university campus in Belgium, the model simulated surface runoff and groundwater levels under present and future climate conditions. The future climate conditions were based on an ensemble of 30 regional climate model runs after quantile perturbation statistical downscaling.</div></div><div><h3>New hydrological insights for the region</h3><div>Results show that the BGIs significantly reduce the monthly average total discharge volume and lower the peak discharge rates across all climate scenarios. Additionally, the BGIs substantially enhance groundwater recharge, leading to increases in both monthly average and low groundwater levels under various climate conditions. These findings highlight the potential for BGIs to enhance stormwater management and water sustainability in similar urbanized catchments in the region, offering valuable guidance for regional adaptation strategies under future climate variability.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"62 ","pages":"Article 102798"},"PeriodicalIF":5.0,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159647","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 hydrothermal-driven dynamics of gross primary productivity in the Ta-Pieh mountains based on explainable artificial intelligence","authors":"Shaowei Ning ,&nbsp;Lichang Xu ,&nbsp;Xiaoyan Xu ,&nbsp;Yuliang Zhou ,&nbsp;Yuliang Zhang ,&nbsp;Shengyi Zhang ,&nbsp;Rujian Long ,&nbsp;Juliang Jin ,&nbsp;Bhesh Raj Thapa","doi":"10.1016/j.ejrh.2025.102797","DOIUrl":"10.1016/j.ejrh.2025.102797","url":null,"abstract":"<div><h3>Study region</h3><div>The study area is the Ta-Pieh Mountains, located in central China at the northern margin of the East Asian monsoon region.</div></div><div><h3>Study focus</h3><div>This study integrates multi-source remote-sensing and meteorological data (2000 – 2022) to investigate the hydrothermal-driven dynamics of Gross Primary Productivity (GPP). We applied Theil–Sen trend analysis, Mann–Kendall tests, and least-squares cross-wavelet analysis to assess spatiotemporal variations in GPP and climate variables. A per-pixel sliding-window modeling framework was developed using four tree-ensemble algorithms—Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Category Boosting (CatBoost). TreeSHAP was employed to quantify the relative contributions of temperature, precipitation, Root-Zone Soil Moisture (RZSM), and Vapor-Pressure Deficit (VPD) to GPP.</div></div><div><h3>New hydrological insights for the region</h3><div>Results show that temperature consistently dominates GPP variability, with VPD contributions closely tracking temperature fluctuations. Precipitation exhibits a one-month lagged effect on GPP, while reduced precipitation and lower RZSM strongly limit carbon uptake during droughts, exemplified by the 2019 autumn drought. The sliding-window framework achieved high predictive accuracy and delineated the spatiotemporal influence of hydrothermal drivers across different climate scenarios. These findings highlight the critical role of hydrothermal variability in regulating ecosystem carbon uptake and provide a transferable toolkit for vegetation-climate interaction analysis and ecosystem management in mountainous and transitional regions.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"62 ","pages":"Article 102797"},"PeriodicalIF":5.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158898","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":"Co-designed farming scenarios show that vegetation is more effective than drainage regulating water excess in the inner Argentinean Pampas","authors":"Sebastián López ,&nbsp;Horacio Videla-Mensegue ,&nbsp;Nicolas F. Guillen ,&nbsp;Javier Álvarez ,&nbsp;José Corigliano ,&nbsp;Alejandra Macchiavello ,&nbsp;Betsy Romero-Verastegui ,&nbsp;Timo Kroon ,&nbsp;Ab Veldhuizen ,&nbsp;Analía Salafía ,&nbsp;Paola Blanco ,&nbsp;Alejandra Canale ,&nbsp;Carlos Marcelo García ,&nbsp;Esteban Jobbagy","doi":"10.1016/j.ejrh.2025.102811","DOIUrl":"10.1016/j.ejrh.2025.102811","url":null,"abstract":"<div><h3>Study region</h3><div>La Picasa endorheic basin (6 820 km²), Inner Argentine Pampas, one of South America’s most productive rain-fed plains.</div></div><div><h3>Study focus</h3><div>We evaluated how land-use change, rainfall variability and drainage infrastructure control shallow-groundwater behaviour in ultra-flat landscapes. The Soil Water Balance (SWB) crop model was linked to the iMOD groundwater model through a one-way coupling to simulate daily water-table dynamics (2009–2019) under four stakeholder-defined scenarios: current practice (E0), agricultural intensification (E1), pasture intensification with + 20 % perennial cover (E2) and a six-fold drainage-network expansion (E3).</div></div><div><h3>New hydrological insights for the region</h3><div>Pasture intensification deepened the median water table by ∼0.9 m and halved the area with depths &lt; 1 m, outperforming intensified cropping (–0.3 m) and extensive drainage (negligible basin-wide effect). Drainage channels acted only as local, short-term buffers during extreme floods. Inter-annual rainfall variability remained the dominant driver of groundwater fluctuations, yet adaptive land-use mixes can increase storage ahead of wet periods. Results indicate that nature-based vegetation strategies are more effective and sustainable than additional “grey” infrastructure for regulating water excess in the Inner Pampas and similar dry-plain agro-ecosystems.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"62 ","pages":"Article 102811"},"PeriodicalIF":5.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159661","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":"Understanding orographic precipitation pattern in Arizona: Implications for climate change","authors":"Aida H. Baghanam ,&nbsp;Amin Mohebbi","doi":"10.1016/j.ejrh.2025.102793","DOIUrl":"10.1016/j.ejrh.2025.102793","url":null,"abstract":"<div><h3>Study area</h3><div>This study focused on Arizona, encompassing diverse landscapes such as high-elevation mountains, low-elevation deserts, and intermediate plateaus. Precipitation data from four representative stations, Baldy, Maricopa, Safford, and Workman Creek, were analyzed to reflect the region’s climatic and topographic variability.</div></div><div><h3>Study Focus</h3><div>The study investigated precipitation patterns, particularly orographic influences, over the past three decades and projected future changes under climate change scenarios. Statistical and machine learning methods were utilized, including Self-Organizing Maps (SOM) for clustering, the Mann-Kendall test for trend analysis, and a Convolutional Neural Network and Long Short-term Memory (CNN-LSTM) model for downscaling. Mutual information (MI) analysis assessed relationships between precipitation and climatic predictors. Projections under Representative Concentration Pathway (RCP)4.5 and RCP8.5 explored impacts of varying emission pathways.</div></div><div><h3>New hydrological insights for the region</h3><div>Analysis of the past 33 years showed no statistically significant trends in total annual precipitation across Arizona. Nonetheless, noticeable shifts in mean precipitation and seasonal patterns were observed at several locations, which may indicate emerging signals of climate variability. Climate model projections under RCP8.5 suggested a general decline in precipitation, especially in mountainous regions, along with reduced interannual variability. In contrast, desert regions such as Maricopa may experience increased variability. These findings highlight the importance of localized water resource planning and adaptive strategies to address potential climate-induced hydrological changes in Arizona.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"62 ","pages":"Article 102793"},"PeriodicalIF":5.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158899","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":"Mediterranean rapid warming drives abrupt runoff decline in South China around 2002","authors":"Zichen Song ,&nbsp;Cheng Sun ,&nbsp;Menghao Dong ,&nbsp;Wei Lou ,&nbsp;Linfeng Shi","doi":"10.1016/j.ejrh.2025.102775","DOIUrl":"10.1016/j.ejrh.2025.102775","url":null,"abstract":"<div><h3>Study region</h3><div>South China is located in humid region.</div></div><div><h3>Study focus</h3><div>Decadal Abrupt Changes (DACs) in regional hydroclimate significantly impact water resources and ecosystem stability. This study investigated a pronounced DAC in summer runoff over South China around 2002 with moving t-test technique, characterized by an abrupt transition to reduced runoff. We explored the teleconnection mechanisms linking this hydrological shift with concurrent Mediterranean Sea surface temperature (SST) rapid warming using comprehensive observational analysis, Random Forest model, and the SPEEDY-Vegas coupled model, supplemented by 16 Atmospheric Model Intercomparison Project (AMIP) model validations.</div></div><div><h3>New hydrological insights from the region</h3><div>Results revealed a significant summer runoff decreased DAC around 2002 across the region. Concurrent Mediterranean SST exhibited a warming DAC, demonstrating a latent temporal synchronization. Random Forest analysis identified precipitation changes as the primary driver. The SPEEDY-Vegas model experiments successfully reproduced the observed runoff DAC when forced with realistic Mediterranean SST warming patterns. Both model and observational results revealed the physical mechanism: Mediterranean warming triggers an eastward-propagating atmospheric wave train that establishes an anomalous high-pressure system over East Asia, inducing regional moisture divergence and enhanced surface drying. AMIP model ensemble (16 models) further confirmed this teleconnection pathway. This circulation anomaly ultimately drives a reduction in regional moisture convergence, explaining the observed runoff decline. These findings demonstrate a teleconnection pathway through which Mediterranean warming modulates East Asian decadal hydroclimate via atmospheric wave dynamics and land-atmosphere feedbacks.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"62 ","pages":"Article 102775"},"PeriodicalIF":5.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158900","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":"Intensified water scarcity in the Asian Water Tower driven by increased water withdrawals","authors":"Yunfei Wang,&nbsp;Aizhong Ye,&nbsp;Fan Yang,&nbsp;Xiaohong Zeng,&nbsp;Huiying Zhu","doi":"10.1016/j.ejrh.2025.102795","DOIUrl":"10.1016/j.ejrh.2025.102795","url":null,"abstract":"<div><h3>Study region</h3><div>The Asian Water Tower (AWT), encompassing the Tibetan Plateau and surrounding mountain ranges, provides water for nearly 2 billion people. It spans transboundary basins such as the Indus, Ganges-Brahmaputra, and Mekong, with notable variability in water resources.</div></div><div><h3>Study focus</h3><div>This study investigates the spatiotemporal dynamics of water availability and withdrawals during 1980–2022 using the PCR-GLOBWB v2.0 model. Water scarcity dynamics are examined across multiple spatial and temporal scales, and key drivers of change are identified.</div></div><div><h3>New hydrological insights for the region</h3><div>The long-term average annual water availability and withdrawal are 1211 km³ and 545 km³ , respectively. A cross-basin comparison reveals significant heterogeneity in water stress patterns, with scarcity concentrated in the lower parts of the catchment, particularly in the downstream regions of the Indus and Ganges-Brahmaputra basins. The water stress index has increased over past four decades, especially during the wet season (May to October). Although downstream water availability increased by 96 km³ between 1980–2000 and 2001–2022, withdrawals increased by 152 km³ , resulting in persistent water shortages. Downstream irrigation is the dominant driver of water scarcity in most basins, while upstream runoff influence wet season scarcity in some basins. By comparing eight major basins under a consistent analytical framework, this study identifies basin-specific variability in water scarcity drivers, and highlighting the structural dominance of downstream demand across the AWT.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"62 ","pages":"Article 102795"},"PeriodicalIF":5.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158902","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":"Enhancing streamflow prediction in a dam-regulated river by integrating mechanism and machine learning models","authors":"Wei Gao ,&nbsp;Feilong Li ,&nbsp;Yanpeng Cai ,&nbsp;Xikang Hou","doi":"10.1016/j.ejrh.2025.102799","DOIUrl":"10.1016/j.ejrh.2025.102799","url":null,"abstract":"<div><h3>Study region</h3><div>Dongjiang River Basin, China</div></div><div><h3>Study focus</h3><div>Daily streamflow prediction in dam-regulated rivers remains a critical challenge in contemporary hydrology, particularly given the growing global prevalence of regulated river systems. In order to reduce high-flow prediction errors while mitigating RF overfitting through HSPF constraints and maintaining robust validation performance, this work develops a hybrid streamflow prediction framework combining Hydrological Simulation Program-FORTRAN (HSPF) and Random Forest (RF) to improve daily streamflow prediction in dam-regulated rivers.</div></div><div><h3>New hydrological insights for the region</h3><div>The proposed hybrid methodology strategically integrates the Hydrological Simulation Program—FORTRAN (HSPF), which provides physics-based simulations of watershed-scale rainfall-runoff processes, with the Random Forest (RF) algorithm, which effectively captures nonlinear dam operation patterns. This integration addresses key limitations associated with standalone modeling approaches. Validation through multiple metrics demonstrates the integrated framework's statistically superior performance compared to individual HSPF and RF (NSE=0.83) models across all flow regimes. Notably, the ensemble approach reduces extreme flow prediction errors by 4–25 % while mitigating RF's overfitting tendency (validation NSE decline: 0.83→0.49) through mechanistic constraints. This nested simulation paradigm establishes a novel pathway for reconciling data-driven flexibility with physical consistency in regulated basin modeling.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"62 ","pages":"Article 102799"},"PeriodicalIF":5.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159648","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}