Journal of Hydrology-Regional Studies最新文献

{"title":"Deconstructing the spatiotemporal characteristics of extreme precipitation events from multiple data products during Indian summer monsoon","authors":"Sandipan Paul ,&nbsp;Priyank J. Sharma ,&nbsp;Ramesh S.V. Teegavarapu","doi":"10.1016/j.ejrh.2025.102667","DOIUrl":"10.1016/j.ejrh.2025.102667","url":null,"abstract":"<div><h3>Study Region</h3><div>India</div></div><div><h3>Study Focus</h3><div>The rising frequency of extreme precipitation events (EPEs) alters Earth systems processes and poses growing risks to socio-economic stability, intensified by climate change. This study analyzes the spatiotemporal characteristics of EPEs across the Indian subcontinent during the monsoon season, critical for the region’s water resources and agriculture. Using observational (IMD, APHRODITE), reanalysis (IMDAA, GLDAS, ERA5-Land), satellite (CHIRPS, PERSIANN-CDR), and hybrid (MSWEP) datasets, we assess their ability to reproduce EPE intensity, detectability, timing, trends, and statistical properties. Results identify MSWEP as the most reliable alternative to IMD in data-scarce regions, providing valuable insights for hydrologic studies, climate impact assessments, disaster risk management and enhancing socio-economic resilience.</div></div><div><h3>New Hydrological Insights for the Region</h3><div>The study reveals that EPE intensity and frequency are highest along India’s western coast and northeast, moderate in central regions, and lowest in arid western and peninsular areas. Wet-to-wet, dry-to-dry, and wet-to-dry transitions follow similar regional patterns. Satellite datasets generally underestimate, while reanalysis datasets overestimate EPE intensities, introducing wet and dry biases in moderate-intensity event frequencies, respectively. In contrast, both datasets report an overestimation of low-intensity event frequencies. MSWEP shows the best performance with the lowest bias and highest detectability, while MSWEP and APHRODITE best preserve spatial patterns of median EPEs. No consistent EPE trend clusters are found. These findings support adaptive hydrologic design and disaster risk mitigation to combat climate change.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"61 ","pages":"Article 102667"},"PeriodicalIF":5.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771161","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":"Accuracy of HEC-HMS and Artificial Neural Network models in simulating runoffs in upper valley of the Medjerda-Tunisia","authors":"Mohamed Lassaad Kotti,&nbsp;Taoufik Hermassi","doi":"10.1016/j.ejrh.2025.102639","DOIUrl":"10.1016/j.ejrh.2025.102639","url":null,"abstract":"<div><h3>Study region</h3><div>The Ghardimaou-Jendouba section of the upper Medjerda valley watershed is located in the extreme north-west of Tunisia. The upstream section of this river has special topographical and hydrographical features that make it particularly vulnerable to flooding.</div></div><div><h3>Study focus</h3><div>This study aimed to replicate daily streamflow historical records using two distinct modeling approaches: the HEC-HMS and Artificial Neural Network (ANN) models. The effectiveness of both models was rigorously evaluated during their calibration and validation phases using key statistical metrics, namely Root-Mean-Square Error (RMSE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE).</div></div><div><h3>New hydrological insights for the region</h3><div>Model performance varied significantly. Post-validation, the HEC-HMS model yielded R2, NSE, and RMSE values of 0.3668, 0.573, and 0.664, respectively. In contrast, the ANN model ([2−4−1] architecture) showed substantially superior calibration performance: R2 of 0.978, NSE of 0.979, and RMSE of 8.46. These statistics unequivocally point to the ANN model's superior predictive capability. Further analysis revealed HEC-HMS overestimates low flows and underestimates high flows. Conversely, the ANN model accurately estimated both extreme and general flow conditions. This highlights the ANN model's strong potential for precise streamflow forecasting and water resource management in the Ghardimaou-Jendouba Basin. Future studies should compare other advanced machine learning models against HEC-HMS to refine daily streamflow forecasts.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"61 ","pages":"Article 102639"},"PeriodicalIF":5.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771160","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":"An econometric approach toward water policy assessment: A case of groundwater meter installation (GWMI) in Iran","authors":"Soorena Naderi ,&nbsp;Ali Moridi","doi":"10.1016/j.ejrh.2025.102635","DOIUrl":"10.1016/j.ejrh.2025.102635","url":null,"abstract":"<div><h3>Study region</h3><div>The basins of Kor and Sivand Rivers, Iran.</div></div><div><h3>Study focus</h3><div>More than two decades of groundwater depletion in Iran, prompted the Supreme Water Council (ISWC) approve the GWMI policy, aiming to reduce the groundwater withdrawal. Currently, although a decade has passed since the approval of the GWMI and significant financial resources have been invested in it, yet, no study has attempted to evaluate its effectiveness. To address this gap, this study evaluated the performance of the GWMI in reducing groundwater withdrawal using various propensity score-based econometric models.</div></div><div><h3>New Hydrological Insights for the Region</h3><div>Although all models showed acceptable fit and met their assumptions, results indicate that the GWMI —as one of Iran's most important water policies— has failed to achieve its primary goal of reducing groundwater withdrawal under all socioeconomic and water resources circumstances. This conclusion is based on the fact that the Average Treatment Effect on the Treated (ATT), used to assess GWMI’s effects, was not statistically significant in any model, despite indicating a decrease in well water withdrawal ranging from 763 m³ to 17,219 m³ in some areas. This study showed that this failure was mainly due to the mismatch between permitted volume of water extraction of wells and the dynamic storage of the aquifers, as well as as weaknesses in water pricing and low accuracy of meters.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"61 ","pages":"Article 102635"},"PeriodicalIF":5.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144772047","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":"Variable spatio-temporal source contributions during storm hydrographs revealed by composite fingerprinting","authors":"Fatemeh Akbari Emamzadeh ,&nbsp;Abdulvahed Khaledi Darvishan ,&nbsp;Mehdi Vafakhah ,&nbsp;Kazem Nosrati ,&nbsp;Adrian L. Collins","doi":"10.1016/j.ejrh.2025.102662","DOIUrl":"10.1016/j.ejrh.2025.102662","url":null,"abstract":"<div><h3>Study Region</h3><div>The Kasilian watershed with an area of 67.22 km² is located in the eastern part of Mazandaran province.</div></div><div><h3>Study Focus</h3><div>This study investigated the spatio-temporal variations in sediment sources during rainfall events. For this purpose, source samples were collected from various land uses, including rangeland, natural forest, hand-planted forest, and agriculture, as well as from the river bed. Suspended sediment sampling was conducted at 60-minute intervals during three rainfall events at two monitoring stations: MS1 (mid-watershed) and MS2 (watershed outlet). The concentration of 59 geochemical elements in source and sediment samples was measured, and composite fingerprints were selected using statistical tests in the FingerPro package in R software.</div></div><div><h3>New Hydrological Insights for the Region</h3><div>The study found that the contributions of rangelands, natural forests, hand-planted forests, and the river bed at MS1 were 46 %, 24 %, 19 %, and 11 %, respectively, while at MS2, the contributions were 72 %, 7 %, 4 %, 14 %, and 3 % for agricultural lands. Additionally, intra-event variations showed that at MS1, rangelands contributed the most at the hydrograph peak, whereas during the rising and falling limbs, both rangelands and natural forests were dominant. At MS2, rangelands had the highest contribution throughout all hydrograph phases. These findings provide valuable information for managers in developing management programs.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"61 ","pages":"Article 102662"},"PeriodicalIF":5.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144772078","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":"Exploring thresholds and drivers of water-energy-food nexus across China: Insights from 2005 to 2020","authors":"Duoduo Zhou ,&nbsp;Qiang Li ,&nbsp;Yaping Wang ,&nbsp;Xinyue Zhang ,&nbsp;Xiaoqian Xu ,&nbsp;Han Zhang ,&nbsp;Fubo Zhao ,&nbsp;Dengfeng Liu ,&nbsp;Weiguo Liu","doi":"10.1016/j.ejrh.2025.102660","DOIUrl":"10.1016/j.ejrh.2025.102660","url":null,"abstract":"<div><h3>Study region</h3><div>This study is conducted at the provincial level in mainland China from 2005 to 2020.</div></div><div><h3>Study focus</h3><div>The water-energy-food (WEF) nexus is fundamental to sustainable development of human-nature systems, yet whether its evolving drivers exhibit threshold remains poorly understood. To address this gap, we developed an indicator-based analytical framework to quantify the sustainable levels of WEF nexus. We applied K-means clustering to identify regional patterns, used the Coupling Coordination Degree (CCD) model to assess inter-sectoral synergy, and conducted threshold regression to explore the threshold of the key drivers.</div></div><div><h3>New Hydrological Insights for the Region</h3><div>Four clusters were identified based on divergent sustainable levels across WEF sectors. While the performance of WEF sectors and the CCD of the nexus significantly improved, marked heterogeneities persisted across these clusters. For Cluster 1, a CCD threshold was observed in 2017 when per capita consumption and urbanization rates reached 20,417 yuan and 53.3 %, respectively. For Cluster 3, a threshold manifested in 2007 when the NDVI reached 0.7. In Cluster 4, a threshold occurred in 2010 when the proportion of environmental investment reached 1.7 %. Threshold analysis suggests that enhancing key drivers in regions below the threshold could significantly improve WEF nexus synergy. Furthermore, evolving WEF dynamics reduce policy effectiveness, necessitating adaptive region-specific strategies. These findings provide valuable insights for tailoring resource management and development strategies in China’s diverse regions.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"61 ","pages":"Article 102660"},"PeriodicalIF":5.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771162","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":"Unveiling plant adaptations: How limestone outperforms dolomite in water supply capacity","authors":"Yali Ding ,&nbsp;Yunpeng Nie ,&nbsp;Wei Zhang ,&nbsp;Kelin Wang ,&nbsp;Li Wen ,&nbsp;Hongsong Chen","doi":"10.1016/j.ejrh.2025.102641","DOIUrl":"10.1016/j.ejrh.2025.102641","url":null,"abstract":"<div><h3>Study region</h3><div>Typical karst landscapes in southwest China with differing lithologies: dolomite and limestone.</div></div><div><h3>Study focus</h3><div>Karst landscapes, characterized by thin soils atop soluble carbonate bedrock, exhibit rapid hydrological dynamics. Lithology is known to influence vegetation growth by affecting regolith water retention, but the impact of lithological composition on plant adaptation strategies and fluctuations in water availability remains poorly understood.</div></div><div><h3>New hydrological insights for the region</h3><div>We sampled 13 dominant woody plant species (8 on dolomite and 9 on limestone), including 4 overlapping species, on a monthly basis. Key functional traits, including leaf water content (LWC), leaf area (LA), specific leaf area (SLA), chlorophyll, and hydrogen (δD) and oxygen (δ¹⁸O) isotope ratios of xylem water, were measured to assess plant adaptation strategies to their respective lithologies. Plants on dolomite exhibited significantly lower LWC and smaller LA (P &lt; 0.01), indicating limited access to root-zone water. Dolomite plants had lower SLA and chlorophyll (P &lt; 0.01), and showed shifts in water source use between dry and wet seasons, whereas limestone plants exhibited fewer variations in traits beyond water source shifts. The findings suggest that species in limestone-derived forests may be more resilient to water stress and climate variability, benefiting from a more stable water supply compared to species in dolomite habitats. This research underscores the importance of considering lithological variations in predicting the vulnerability of karst ecosystems to climate change.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"61 ","pages":"Article 102641"},"PeriodicalIF":5.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771211","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-step-ahead forecasting of daily reference evapotranspiration using hybrid deep learning models for the Taklamakan Desert oasis","authors":"Maosong Tang,&nbsp;Zhenghu Ma,&nbsp;Pengrui Ai,&nbsp;Tong Heng,&nbsp;Yingjie Ma","doi":"10.1016/j.ejrh.2025.102663","DOIUrl":"10.1016/j.ejrh.2025.102663","url":null,"abstract":"<div><h3>Study region</h3><div>This research focuses on the Taklamakan Desert oasis in southern Xinjiang, China, which represents one of the most hydrologically challenging and climatically extreme agricultural regions in the world.</div></div><div><h3>Study focus</h3><div>In this study, we address the short-term forecasting of daily reference evapotranspiration (ET₀) using six advanced hybrid deep learning models—LSTM, BiLSTM, CNN-LSTM, CNN-BiLSTM, CNN-LSTM-Attention, and CNN-BiLSTM-Attention. Historical daily meteorological data collected from 25 stations were utilized, with records from 1991 to 2020 used for model training and data from 2021 to 2023 reserved exclusively for independent testing. Forecasts were produced for 1-, 3-, and 5-day lead times. Model performance was evaluated using the Global Performance Index (GPI), and interpretability was further enhanced by applying Shapley Additive Explanations (SHAP) to analyze feature contributions under different climatic conditions.</div></div><div><h3>New hydrological insight for the region</h3><div>BiLSTM-based models demonstrated higher ET₀ forecasting accuracy than long short-term memory networks (LSTM)-based models, while the incorporation of convolutional neural networks (CNN) and attention mechanisms further improved forecasting performance. The CNN-BiLSTM-Attention model consistently exhibited the highest accuracy and robustness across different stations and forecast horizons, making it the most suitable for operational deployment in desert oasis regions. SHAP analysis indicated that temperature and solar radiation are the principal drivers of ET₀ at most stations, highlighting the spatial heterogeneity in feature importance. Thus, this study provides robust and interpretable ET₀ forecasting models along with new hydrological insights, offering practical support for localized water resource management and enhancing confidence in the deployment of precision irrigation models in hyper-arid agricultural systems.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"61 ","pages":"Article 102663"},"PeriodicalIF":5.0,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144772075","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 spatial explainable deep learning framework for prediction classification of hydrological drought in ungauged basin","authors":"Qianyu Wang ,&nbsp;Xiaoling Su ,&nbsp;Haijiang Wu ,&nbsp;Yue Xiao ,&nbsp;Yang Yang","doi":"10.1016/j.ejrh.2025.102653","DOIUrl":"10.1016/j.ejrh.2025.102653","url":null,"abstract":"<div><h3>Study Region</h3><div>The Upper Yellow River Basin (UYRB), China</div></div><div><h3>Study focus</h3><div>The increasing frequency, spatial extent, and intensity of hydrological droughts pose devastating impacts on water security, ecosystem stability, and sustainable development. While deep learning models have demonstrated significant promise in drought forecasting, particularly in data-scarce basins, their inherent opacity hinders the understanding of drought mechanisms. Therefore, we proposed a Spatial Explainable Deep Learning (SEDL) framework suitable for ungauged basins that can be integrated with various deep learning models. This framework aims to quantitatively analyze the spatial driving mechanisms that govern hydrological drought occurrence and enhances the accuracy of categorical drought prediction.</div></div><div><h3>New hydrological insights for the region</h3><div>This study quantitatively demonstrated that natural runoff (with the mean contribution of 42.45 %) was the primary driving factor of hydrological droughts, surpassing the effects of precipitation (26.90 %) and average temperature (31.05 %). Crucially, regional hydrological drought occurrence was spatially influenced by temperature-runoff interactions in upstream headwater catchments and local precipitation variations. The improved model based on the SEDL framework achieved significant improvements in performance, with maximum increments of 8.7 % in accuracy, 62.7 % in Kappa coefficient (<em>K</em>), and 10.3 % in <em>F</em>₁-score. By integrating deep learning with explainable artificial intelligence, the SEDL framework revealed the spatial physical driving factors of hydrological droughts while achieving 85.2 % accuracy on the test set, thus establishing a new research paradigm for explainable drought prediction.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"61 ","pages":"Article 102653"},"PeriodicalIF":5.0,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739471","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":"Impact of artificial modifications on the structure and robustness of the Haihe River network","authors":"Shuhui Shi ,&nbsp;Fawen Li","doi":"10.1016/j.ejrh.2025.102670","DOIUrl":"10.1016/j.ejrh.2025.102670","url":null,"abstract":"<div><h3>Study region</h3><div>Haihe River Basin, China.</div></div><div><h3>Study focus</h3><div>This study employed complex network theory to investigate the impact of artificial modifications on the Haihe River Network. We constructed topological models of the natural and current river networks, assessed key functions (flood protection, water supply, and ecological), and analyzed changes in network hierarchy, clustering, and transmission. Network robustness was assessed through node and edge removal simulations under static and dynamic attack scenarios, including function-weighted and centrality-based strategies. This analytical method, integrating network indicators and functional weights, innovatively reveals differences in robustness between natural and current river networks and offers a new perspective for river network optimization.</div></div><div><h3>New hydrological insights for the region</h3><div>Artificial modifications have enhanced the Haihe River Basin's connectivity (average degree increased by 20 %), strengthened network clustering (largest component size rose from 0.218 to 0.981), and improved transmission (global efficiency increased fivefold). Static attack simulations highlighted the critical role of the Yongding River, particularly its tributary, the Sanggan River, whose removal resulted in a 42.25 % decrease in the largest component size in the natural network. Dynamic attack simulations identified vulnerability thresholds, emphasizing the importance of high-betweenness nodes and water supply-weighted edges. These thresholds are especially evident in the network's sensitivity to disruptions of the Middle Route of the South-to-North Water Diversion Project and the Beijing-Hangzhou Grand Canal.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"61 ","pages":"Article 102670"},"PeriodicalIF":5.0,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739582","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":"Rainfall thresholds model for predicting urban local pluvial flooding","authors":"Xuelian Zhang ,&nbsp;Aiqing Kang ,&nbsp;Xiaohui Lei ,&nbsp;Hao Wang ,&nbsp;Guoxin Chen","doi":"10.1016/j.ejrh.2025.102651","DOIUrl":"10.1016/j.ejrh.2025.102651","url":null,"abstract":"<div><h3>Study region</h3><div>Guangzhou, China</div></div><div><h3>Study focus</h3><div>This study adapts the empirical ID/ED (Intensity-Duration/Accumulated Rainfall-Duration) threshold framework—commonly used for landslide prediction—to urban pluvial flood forecasting.</div></div><div><h3>New hydrological insights</h3><div>Traditional ID/ED thresholds face challenges in flood prediction due to hydrological complexity. This study evaluates their applicability to urban pluvial flooding, noting that existing empirical flood thresholds essentially remain within parameter optimization under the ID/ED framework rather than representing a theoretical change—supporting the framework’s applicability. To refine the model, the study also tested the inclusion of maximum rainfall intensity (<em>I</em>_<em>max</em>) and antecedent rainfall (<em>E</em>_<em>a</em>, reflecting pre-rainfall drainage conditions). However, stepwise regression analysis of 38–91 observed events from five stations rejected these variables. The proposed model uses accumulated event rainfall (<em>E</em>) and duration (<em>D</em>) to estimate flood peak depths (<em>P</em>), achieving a balance of accuracy (observation-based: R² = 0.66–0.89, RMSE = 0.028–0.104 m; simulation-based: R² = 0.89–0.99, RMSE = 0.005–0.072 m), generalizability, and robustness, while effectively minimizing overfitting. K-fold cross-validation ensured model stability, while classification modeling offered potential for performance improvement. The simple ED model structure improves flood risk communication for non-experts, balancing interpretability and feasibility. Though slightly less precise than conventional models, its operational advantages support disaster response in resource-limited areas, making it suitable for wider community-level use.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"61 ","pages":"Article 102651"},"PeriodicalIF":5.0,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144749516","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}