Pub Date : 2026-04-01Epub Date: 2026-01-30DOI: 10.1016/j.ejrh.2026.103178
Bartholomew Hill , Qiuhua Liang , Huili Chen , Lee Bosher
Study region
Cocker River catchment (145 km²), Lake District, Cumbria, United Kingdom.
Study focus
This study investigates the influence that Natural Flood Management (NFM) features have on flood behaviour at the catchment scale using a high-resolution, two-dimensional hydrodynamic modelling approach. A high-performance computing framework, based on the High-Performance Integrated hydrodynamic Modelling System (HiPIMS), was applied to simulate two events; 1) pre-NFM implementation - Storm Desmond flood in 2015, and 2) post-NFM implementation – High rainfall event in 2021. Leaky wooden barriers and other NFM features were explicitly represented using UAV-derived digital terrain data at 2 m and 4 m spatial resolutions.
Hydrological insights
The simulations indicate that the hydrological response to NFM within the Cocker catchment is strongly event dependent. Clearer flow attenuation and hydrograph smoothing were observed during the smaller 2021 event, while impacts during the extreme 2015 event were modest and spatially variable. Localised water retention within the Whinlatter sub-catchment translated into small but measurable downstream changes in flood levels, alongside indications that delayed flows may interact with contributions from other tributaries. These findings highlight the importance of event magnitude, spatial configuration, and flow timing when assessing the role of NFM in catchment-scale flood risk management.
{"title":"From reach to catchment-scale impacts: High-resolution hydrodynamic modelling of Nature-based solutions in the Cocker Catchment, UK","authors":"Bartholomew Hill , Qiuhua Liang , Huili Chen , Lee Bosher","doi":"10.1016/j.ejrh.2026.103178","DOIUrl":"10.1016/j.ejrh.2026.103178","url":null,"abstract":"<div><h3>Study region</h3><div>Cocker River catchment (145 km²), Lake District, Cumbria, United Kingdom.</div></div><div><h3>Study focus</h3><div>This study investigates the influence that Natural Flood Management (NFM) features have on flood behaviour at the catchment scale using a high-resolution, two-dimensional hydrodynamic modelling approach. A high-performance computing framework, based on the High-Performance Integrated hydrodynamic Modelling System (HiPIMS), was applied to simulate two events; 1) pre-NFM implementation - Storm Desmond flood in 2015, and 2) post-NFM implementation – High rainfall event in 2021. Leaky wooden barriers and other NFM features were explicitly represented using UAV-derived digital terrain data at 2 m and 4 m spatial resolutions.</div></div><div><h3>Hydrological insights</h3><div>The simulations indicate that the hydrological response to NFM within the Cocker catchment is strongly event dependent. Clearer flow attenuation and hydrograph smoothing were observed during the smaller 2021 event, while impacts during the extreme 2015 event were modest and spatially variable. Localised water retention within the Whinlatter sub-catchment translated into small but measurable downstream changes in flood levels, alongside indications that delayed flows may interact with contributions from other tributaries. These findings highlight the importance of event magnitude, spatial configuration, and flow timing when assessing the role of NFM in catchment-scale flood risk management.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"64 ","pages":"Article 103178"},"PeriodicalIF":5.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079807","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}
Pub Date : 2026-04-01Epub Date: 2026-01-30DOI: 10.1016/j.ejrh.2026.103175
Peng Wang , Xizhen Wu , Haowen Tan , Hongyu Zhi , Zhangli Chen , Lei Huang
Study region
Gulou district, Nanjing, China.
Study focus
Facing escalating extreme precipitation and urbanization, urban infrastructure resilience is critical. While previous recovery strategies often used optimization models, they largely overlooked the underlying mechanisms driving efficient recovery. This study addresses this gap by developing an optimal strategy for post-extreme precipitation infrastructure recovery. We establish a multi-objective optimization model and a novel recovery efficiency index to evaluate recovery combinations, specifically focusing on Gulou District. The analysis considers recovery objectives, infrastructure categories, and spatial distribution to identify efficient recovery methods.
New hydrological insights for the region
Findings reveal significant spatial and categorical disparities in recovery efficiency, emphasizing the need for context-specific strategies. Optimal recovery methods depend on the interplay between infrastructure type and their geographic clustering. Strategic prioritization of spatially interconnected infrastructure and category-specific recovery sequences can enhance overall efficiency by 15–20 %. These insights provide actionable guidance for policymakers to design resilient recovery plans under climate change pressures. Future refinements will incorporate dynamic interactions between adjacent infrastructure systems to further optimize recovery outcomes.
{"title":"Urban infrastructure recovery strategy under extreme precipitation based on multi-objective optimization algorithm","authors":"Peng Wang , Xizhen Wu , Haowen Tan , Hongyu Zhi , Zhangli Chen , Lei Huang","doi":"10.1016/j.ejrh.2026.103175","DOIUrl":"10.1016/j.ejrh.2026.103175","url":null,"abstract":"<div><h3>Study region</h3><div>Gulou district, Nanjing, China.</div></div><div><h3>Study focus</h3><div>Facing escalating extreme precipitation and urbanization, urban infrastructure resilience is critical. While previous recovery strategies often used optimization models, they largely overlooked the underlying mechanisms driving efficient recovery. This study addresses this gap by developing an optimal strategy for post-extreme precipitation infrastructure recovery. We establish a multi-objective optimization model and a novel recovery efficiency index to evaluate recovery combinations, specifically focusing on Gulou District. The analysis considers recovery objectives, infrastructure categories, and spatial distribution to identify efficient recovery methods.</div></div><div><h3>New hydrological insights for the region</h3><div>Findings reveal significant spatial and categorical disparities in recovery efficiency, emphasizing the need for context-specific strategies. Optimal recovery methods depend on the interplay between infrastructure type and their geographic clustering. Strategic prioritization of spatially interconnected infrastructure and category-specific recovery sequences can enhance overall efficiency by 15–20 %. These insights provide actionable guidance for policymakers to design resilient recovery plans under climate change pressures. Future refinements will incorporate dynamic interactions between adjacent infrastructure systems to further optimize recovery outcomes.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"64 ","pages":"Article 103175"},"PeriodicalIF":5.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079829","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}
Pub Date : 2026-04-01Epub Date: 2026-01-21DOI: 10.1016/j.ejrh.2026.103145
Xinxin Zheng , Yiwei Luo , Kan Zhang , Zhenying Zeng , Xiaoying Yang , Xiaogang Li
Study Region
This study was conducted in the ecologically critical and climate-sensitive Poyang Lake Region of China.
Study Focus
Global climate change has increased extreme hydrological events worldwide, necessitating advanced hydrological models to manage escalating risks. This study proposed a hybrid model, SWAT-GCN-BiLSTM, integrating the strengths of SWAT (simulating physical hydrological processes), GCN (capturing spatial topological relationships), and BiLSTM (modeling complex temporal dynamics).
New Hydrological Insights
The hybrid SWAT-GCN-BiLSTM model outperformed the standalone SWAT and BiLSTM models, with significantly higher NSE and R2 values of around 0.90. The hybrid model particularly excelled in simulating extreme flows, reducing RMSE by over 20 % for extremely high flows (≥ Q10, Q10 represents streamflow magnitude with a 10 % exceedance probability). Based on the ensemble mean of four Global Climate Models, the hybrid model predicted a substantial increase in streamflow during the wet months of April (24.9 %-44.1 %) and May (11.5 %-20.2 %) compared to the baseline. Furthermore, under all considered climate change scenarios, the Q10 of the 7-day flow was projected to increase by 9.5–19.5 %. Conversely, streamflow in the dry months of November and December was projected to decrease by 21.0–34.7 %. This indicates climate change may exacerbate hydrological extremes, necessitating robust adaptive management strategies to address both increased spring flooding risk and heightened drought conditions during late autumn/early winter in the region under a changing climate.
{"title":"Enhancing hydrological simulation and climate change impact assessment for the Poyang Lake Region, China: A novel hybrid SWAT-GCN-BiLSTM framework","authors":"Xinxin Zheng , Yiwei Luo , Kan Zhang , Zhenying Zeng , Xiaoying Yang , Xiaogang Li","doi":"10.1016/j.ejrh.2026.103145","DOIUrl":"10.1016/j.ejrh.2026.103145","url":null,"abstract":"<div><h3>Study Region</h3><div>This study was conducted in the ecologically critical and climate-sensitive Poyang Lake Region of China.</div></div><div><h3>Study Focus</h3><div>Global climate change has increased extreme hydrological events worldwide, necessitating advanced hydrological models to manage escalating risks. This study proposed a hybrid model, SWAT-GCN-BiLSTM, integrating the strengths of SWAT (simulating physical hydrological processes), GCN (capturing spatial topological relationships), and BiLSTM (modeling complex temporal dynamics).</div></div><div><h3>New Hydrological Insights</h3><div>The hybrid SWAT-GCN-BiLSTM model outperformed the standalone SWAT and BiLSTM models, with significantly higher NSE and R<sup>2</sup> values of around 0.90. The hybrid model particularly excelled in simulating extreme flows, reducing RMSE by over 20 % for extremely high flows (≥ Q10, Q10 represents streamflow magnitude with a 10 % exceedance probability). Based on the ensemble mean of four Global Climate Models, the hybrid model predicted a substantial increase in streamflow during the wet months of April (24.9 %-44.1 %) and May (11.5 %-20.2 %) compared to the baseline. Furthermore, under all considered climate change scenarios, the Q10 of the 7-day flow was projected to increase by 9.5–19.5 %. Conversely, streamflow in the dry months of November and December was projected to decrease by 21.0–34.7 %. This indicates climate change may exacerbate hydrological extremes, necessitating robust adaptive management strategies to address both increased spring flooding risk and heightened drought conditions during late autumn/early winter in the region under a changing climate.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"64 ","pages":"Article 103145"},"PeriodicalIF":5.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024544","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}
Pub Date : 2026-04-01Epub Date: 2026-01-20DOI: 10.1016/j.ejrh.2026.103113
Francisco Haces-Garcia , Craig L. Glennie , Hanadi S. Rifai , Vedhus Hoskere
Study Region: Houston, TX, USA
Study Focus: The data-driven quantification of evolving urban flood hazard is challenging. Historical flooding data is readily available from the US National Flood Insurance Program, which has mapped Flood Hazard Areas (FHAs) since the 1970s. However, estimated FHAs are generally not used in modern flood studies due to the lack of georeferencing information. This poses a key impediment for fine-scale floodplain analysis, with critical implications for the study of urban flood change. This research develops a framework to automatically georeference historical Flood Insurance Rate Maps, and extract their floodplain data using photogrammetry, geomatics, and artificial intelligence. The registration framework is systematically validated to ensure the accurate extraction of longitudinal flood data. A median georeferencing residual of 23.1 m was obtained, which was smaller than the validation dataset accuracy. The framework provides an avenue towards the widespread assessment of longitudinal flood hazard, with significant implications for the study of urban flood resilience. Three flood-prone case studies are presented to exemplify the usefulness of the framework; Brays Bayou, Hunting Bayou, and Cypress Creek in Greater Houston.
New hydrological insights for the region: The case studies quantify the change of flood hazard within these watersheds. Floodplain expansion had significant flood resilience consequences. Population exposure was estimated to have risen by up to 635%, with a concurrent increase in the vulnerability of critical infrastructure.
{"title":"The longitudinal assessment of flood hazard in cities: Unlocking the floodplain record of Houston, TX, USA","authors":"Francisco Haces-Garcia , Craig L. Glennie , Hanadi S. Rifai , Vedhus Hoskere","doi":"10.1016/j.ejrh.2026.103113","DOIUrl":"10.1016/j.ejrh.2026.103113","url":null,"abstract":"<div><div>Study Region: Houston, TX, USA</div><div>Study Focus: The data-driven quantification of evolving urban flood hazard is challenging. Historical flooding data is readily available from the US National Flood Insurance Program, which has mapped Flood Hazard Areas (FHAs) since the 1970s. However, estimated FHAs are generally not used in modern flood studies due to the lack of georeferencing information. This poses a key impediment for fine-scale floodplain analysis, with critical implications for the study of urban flood change. This research develops a framework to automatically georeference historical Flood Insurance Rate Maps, and extract their floodplain data using photogrammetry, geomatics, and artificial intelligence. The registration framework is systematically validated to ensure the accurate extraction of longitudinal flood data. A median georeferencing residual of 23.1 m was obtained, which was smaller than the validation dataset accuracy. The framework provides an avenue towards the widespread assessment of longitudinal flood hazard, with significant implications for the study of urban flood resilience. Three flood-prone case studies are presented to exemplify the usefulness of the framework; Brays Bayou, Hunting Bayou, and Cypress Creek in Greater Houston.</div><div>New hydrological insights for the region: The case studies quantify the change of flood hazard within these watersheds. Floodplain expansion had significant flood resilience consequences. Population exposure was estimated to have risen by up to 635%, with a concurrent increase in the vulnerability of critical infrastructure.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"64 ","pages":"Article 103113"},"PeriodicalIF":5.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024542","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}
The Middle Route of the South-to-North Water Diversion Project in China.
Study focus
Long-distance water transfer projects are characterized by complex and variable hydraulic conditions, making them vulnerable to sudden incidents. Effective emergency regulation requires gate operations that are simple and minimize drainage losses. However, most existing studies do not constrain the gate-closure method, often allowing rapid continuous closure, which differs from the staged closure used in actual operations, and may alter hydraulic responses. In this study, an optimal emergency control model was developed by coupling a hydrodynamic model with a multi-objective genetic algorithm. Under a large-flow interruption scenario triggered by a pollution accident, the study examined hydraulic responses to staged gate closure and evaluated optimized schemes using synchronized staged closure as the baseline.
New hydrological insights for the region
Under identical regulation intervals, optimizing drainage increases gate operation frequency but reduces water level fluctuation. Extending the interval reduces operation frequency while maintaining low fluctuations. Compared to the baseline, the optimized scheme achieved moderate reductions in gate operations, reduced drainage volume by 42.1 % and maximum water level fluctuation by 48.8 %. Gate operations generally followed a two-stage pattern—initial rapid adjustment followed by slower regulation—with asynchronous operation of gates and staged drainage adjustments. The proposed model and strategies provide practical guidance for safer and more efficient emergency regulation in long-distance water transfer systems.
{"title":"Optimization of stage-controlled gate operations for upstream canal cascades post-contingency: A case study of MR-SNWDP hydraulic system","authors":"Wei Cui, Yuling Lei, Xiangpeng Mu, Wenxue Chen, Zhinan Ding, Xiaochen Li, Hui Liu","doi":"10.1016/j.ejrh.2026.103212","DOIUrl":"10.1016/j.ejrh.2026.103212","url":null,"abstract":"<div><h3>Study region</h3><div>The Middle Route of the South-to-North Water Diversion Project in China.</div></div><div><h3>Study focus</h3><div>Long-distance water transfer projects are characterized by complex and variable hydraulic conditions, making them vulnerable to sudden incidents. Effective emergency regulation requires gate operations that are simple and minimize drainage losses. However, most existing studies do not constrain the gate-closure method, often allowing rapid continuous closure, which differs from the staged closure used in actual operations, and may alter hydraulic responses. In this study, an optimal emergency control model was developed by coupling a hydrodynamic model with a multi-objective genetic algorithm. Under a large-flow interruption scenario triggered by a pollution accident, the study examined hydraulic responses to staged gate closure and evaluated optimized schemes using synchronized staged closure as the baseline.</div></div><div><h3>New hydrological insights for the region</h3><div>Under identical regulation intervals, optimizing drainage increases gate operation frequency but reduces water level fluctuation. Extending the interval reduces operation frequency while maintaining low fluctuations. Compared to the baseline, the optimized scheme achieved moderate reductions in gate operations, reduced drainage volume by 42.1 % and maximum water level fluctuation by 48.8 %. Gate operations generally followed a two-stage pattern—initial rapid adjustment followed by slower regulation—with asynchronous operation of gates and staged drainage adjustments. The proposed model and strategies provide practical guidance for safer and more efficient emergency regulation in long-distance water transfer systems.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"64 ","pages":"Article 103212"},"PeriodicalIF":5.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174226","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}
Pub Date : 2026-04-01Epub Date: 2026-01-19DOI: 10.1016/j.ejrh.2026.103137
Linqiao Ran , Fangleng He , Xingwei Chen , Ying Chen , Meibing Liu , Haijun Deng
Study area
The Southeast River Basin is one of China's nine major river basins. As a typical monsoon humid region, it has exhibited significant actual evapotranspiration（ETa） growth in recent years.
Study focus
Existing studies in this basin predominantly rely on publicly available global datasets, lacking process-based mechanism analyses. This study employs Penman-Monteith-Leuning (PML) model to simulate daily-scale ETa from 1981 to 2021. Owing to insufficient observed ETa data, the three-cornered hat method was employed to assess simulation accuracy,then use detrending experiment to systematically analyse the spatiotemporal evolution and component variations of ETa, identifying the dominant factors.
New hydrological insights for the region
The PML model demonstrated the highest stability in cross-validation across three datasets. Since 1981, ETa in this basin has exhibited significant growth (4.39 mm year−1, p < 0.05), with the fastest increase occurring in spring (1.45 mm year−1). Spatially, trends are most pronounced in the northwestern and central regions, while northeastern and coastal areas exhibit weaker trends or even declines. Component-wise, transpiration accounts for 71.20 % of ETa. Trend analysis indicates that vapour pressure deficit (VPD) is the primary driver (contributing 38.70 %), followed by leaf area index (LAI, 18.41 %). These findings mark a shift from trend description to mechanistically driven quantitative understanding, providing robust data support and theoretical foundations for comprehending ETa dynamics and ecohydrological processes in the southeastern of China.
研究区域东南河流域是中国九大流域之一。作为典型的季风湿润区，近年来实际蒸散量（ETa）增长显著。该盆地的现有研究主要依赖于公开的全球数据集，缺乏基于过程的机制分析。本文采用Penman-Monteith-Leuning （PML）模型模拟了1981 - 2021年的日尺度ETa。由于ETa观测数据不足，采用三角帽法对模拟精度进行评估，然后利用去趋势实验系统分析ETa的时空演变和成分变化，找出主导因素。在跨三个数据集的交叉验证中，PML模型显示出最高的稳定性。1981年以来，该盆地ETa呈显著增长（4.39 mm year−1,p <； 0.05），其中春季增长最快（1.45 mm year−1）。从空间上看，西北和中部地区趋势最为明显，东北和沿海地区趋势较弱甚至下降。在组分方面，蒸腾作用占ETa的71.20 %。趋势分析表明，水汽压亏缺（VPD）是主要驱动因子（贡献38.70 %），其次是叶面积指数（LAI，贡献18.41 %）。这些发现标志着从趋势描述到机制驱动的定量理解的转变，为理解中国东南部ETa动态和生态水文过程提供了强有力的数据支持和理论基础。
{"title":"Vapor pressure deficit dominated actual evapotranspiration changes in the Southeast River Basin of China from 1981 to 2021: A PML-based attribution analysis","authors":"Linqiao Ran , Fangleng He , Xingwei Chen , Ying Chen , Meibing Liu , Haijun Deng","doi":"10.1016/j.ejrh.2026.103137","DOIUrl":"10.1016/j.ejrh.2026.103137","url":null,"abstract":"<div><h3>Study area</h3><div>The Southeast River Basin is one of China's nine major river basins. As a typical monsoon humid region, it has exhibited significant actual evapotranspiration（ET<sub>a</sub>） growth in recent years.</div></div><div><h3>Study focus</h3><div>Existing studies in this basin predominantly rely on publicly available global datasets, lacking process-based mechanism analyses. This study employs Penman-Monteith-Leuning (PML) model to simulate daily-scale ET<sub>a</sub> from 1981 to 2021. Owing to insufficient observed ET<sub>a</sub> data, the three-cornered hat method was employed to assess simulation accuracy,then use detrending experiment to systematically analyse the spatiotemporal evolution and component variations of ET<sub>a</sub>, identifying the dominant factors.</div></div><div><h3>New hydrological insights for the region</h3><div>The PML model demonstrated the highest stability in cross-validation across three datasets. Since 1981, ET<sub>a</sub> in this basin has exhibited significant growth (4.39 mm year<sup>−1</sup>, p < 0.05), with the fastest increase occurring in spring (1.45 mm year<sup>−1</sup>). Spatially, trends are most pronounced in the northwestern and central regions, while northeastern and coastal areas exhibit weaker trends or even declines. Component-wise, transpiration accounts for 71.20 % of ET<sub>a</sub>. Trend analysis indicates that vapour pressure deficit (VPD) is the primary driver (contributing 38.70 %), followed by leaf area index (LAI, 18.41 %). These findings mark a shift from trend description to mechanistically driven quantitative understanding, providing robust data support and theoretical foundations for comprehending ET<sub>a</sub> dynamics and ecohydrological processes in the southeastern of China.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"64 ","pages":"Article 103137"},"PeriodicalIF":5.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024493","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}
Pub Date : 2026-04-01Epub Date: 2026-01-30DOI: 10.1016/j.ejrh.2026.103183
Sheng Wang , Xingyu Wang , Yuzhe Wang , Tandong Yao , Jianchen Pu , Jinfeng Wang
Study region
Qiyi Glacier in the Qilian Mountains, China.
Study focus
Glacier flow is one of the important processes in the glacier development and evolution. It provides a scientific basis for assessing glacial disaster risks, and is of great significance for formulating adaptive strategies in response to glacial environmental variations. In this study, a two-dimensional higher-order flow-band glacier flow model (PoLIM) was constructed to analyze the spatio-temporal patterns of surface flow velocity on Qiyi Glacier, and the future dynamics and dominant influencing factors of flow velocity under various climate scenarios were investigated.
New hydrological insights for the region
Impacted by glacier (ice) volume and thickness, the annual variation in surface flow velocity of Qiyi Glacier exhibits a decreasing trend, declining from 16 m a−1 in 1958–5.97 m a−1 in 2021, with significantly higher flow velocity during the ablation season compared to the non-ablation season. The mean glacier surface flow velocity along the main flowline was 6.92 ± 0.13 m a−1 from 2017 to 2021. Under three future scenarios, this velocity is projected to decrease to 0.71 ± 0.13 m a−1, 0.73 ± 0.12 m a−1 and 0.47 ± 0.09 m a−1 by 2050, respectively. The spatio-temporal patterns of glacier flow velocity are primarily related to glacier scale (ice thickness) and its variation, with climate warming-induced basal sliding serving as a principal driver of velocity changes in certain part of the glacier.
祁连山七一冰川研究区冰川流动是冰川发育演化的重要过程之一。这为评估冰川灾害风险提供了科学依据，对制定应对冰川环境变化的适应策略具有重要意义。本文通过构建二维高阶流带冰川流动模型（PoLIM），分析了七一冰川地表流速的时空格局，探讨了不同气候情景下地表流速的未来动态及其主导影响因素。受冰川（冰）体积和厚度的影响，七宜冰川地表流速年际变化呈减小趋势，从1958年的16 m a−1下降至2021年的5.97 m a−1，消融期流速明显高于非消融期。2017 - 2021年冰川地表流速平均值为6.92 ± 0.13 m a−1。三个未来情景下,这个速度预计将下降到0.71 ±0.13 m−1, 0.73±0.12 m −1和0.47±0.09 m−1到2050年,分别。冰川流速的时空格局主要与冰川尺度（冰厚）及其变化有关，气候变暖引起的基底滑动是部分冰川流速变化的主要驱动因素。
{"title":"Diagnostic and prognostic modeling of glacier dynamics and the driving factors in the Qilian Mountains, China","authors":"Sheng Wang , Xingyu Wang , Yuzhe Wang , Tandong Yao , Jianchen Pu , Jinfeng Wang","doi":"10.1016/j.ejrh.2026.103183","DOIUrl":"10.1016/j.ejrh.2026.103183","url":null,"abstract":"<div><h3>Study region</h3><div>Qiyi Glacier in the Qilian Mountains, China.</div></div><div><h3>Study focus</h3><div>Glacier flow is one of the important processes in the glacier development and evolution. It provides a scientific basis for assessing glacial disaster risks, and is of great significance for formulating adaptive strategies in response to glacial environmental variations. In this study, a two-dimensional higher-order flow-band glacier flow model (PoLIM) was constructed to analyze the spatio-temporal patterns of surface flow velocity on Qiyi Glacier, and the future dynamics and dominant influencing factors of flow velocity under various climate scenarios were investigated.</div></div><div><h3>New hydrological insights for the region</h3><div>Impacted by glacier (ice) volume and thickness, the annual variation in surface flow velocity of Qiyi Glacier exhibits a decreasing trend, declining from 16 m a<sup>−1</sup> in 1958–5.97 m a<sup>−1</sup> in 2021, with significantly higher flow velocity during the ablation season compared to the non-ablation season. The mean glacier surface flow velocity along the main flowline was 6.92 ± 0.13 m a<sup>−1</sup> from 2017 to 2021. Under three future scenarios, this velocity is projected to decrease to 0.71 ± 0.13 m a<sup>−1</sup>, 0.73 ± 0.12 m a<sup>−1</sup> and 0.47 ± 0.09 m a<sup>−1</sup> by 2050, respectively. The spatio-temporal patterns of glacier flow velocity are primarily related to glacier scale (ice thickness) and its variation, with climate warming-induced basal sliding serving as a principal driver of velocity changes in certain part of the glacier.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"64 ","pages":"Article 103183"},"PeriodicalIF":5.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079719","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}
Pub Date : 2026-04-01Epub Date: 2026-01-31DOI: 10.1016/j.ejrh.2026.103197
Dekang Zhao , Fan Miao , Guorui Feng , Xiang Li , Jiaying Cai , Shuning Dong , Yujiang Zhang , Yongqi Chen , Ruoyu Niu , Ziqing Yang
Study region
Liulin County, located on the eastern bank of the Yellow River (YR) in Shanxi Province, China, is characterized by complex terrain including mountains and loess plateaus under a semi-arid climate.
Study focus
To mitigate seasonal water supply instability and identify reliable groundwater sources, this study proposes a novel machine-learning framework integrating the Sparrow Search Algorithm (SSA) with Extreme Gradient Boosting (XGBoost). Utilizing Geographic Information System (GIS) and field surveys, a comprehensive hydrogeological dataset was constructed. The Boruta Algorithm (BA) was employed to eliminate redundant variables, while Random Forest (RF) evaluated feature importance. The proposed model was rigorously benchmarked against five alternative methods, including hybrids optimized by the Grey Wolf Optimizer (GWO). Furthermore, SHapley Additive exPlanations (SHAP) were applied to decipher the "black-box" nature of the models, quantifying feature contributions and non-linear interactions.
New hydrological insights for the region
The results demonstrate that the SSA-XGBoost model achieved superior predictive accuracy, yielding a maximum Area Under the Curve (AUC) of 0.8812. Consensus from RF and SHAP analyses identified lithology and altitude as the dominant controlling factors, while the Normalized Difference Vegetation Index (NDVI) and rainfall provided essential spatial variability. GIS-based zonation revealed that approximately 22.06 % of the study area possesses high groundwater potential. This framework effectively balances high predictive accuracy with transparency, providing a scientifically robust tool for sustainable groundwater management in complex terrain regions.
{"title":"Interpretable groundwater spring potential mapping in complex terrain of Liulin County, China, using SSA-XGBoost with GIS-based validation","authors":"Dekang Zhao , Fan Miao , Guorui Feng , Xiang Li , Jiaying Cai , Shuning Dong , Yujiang Zhang , Yongqi Chen , Ruoyu Niu , Ziqing Yang","doi":"10.1016/j.ejrh.2026.103197","DOIUrl":"10.1016/j.ejrh.2026.103197","url":null,"abstract":"<div><h3>Study region</h3><div>Liulin County, located on the eastern bank of the Yellow River (YR) in Shanxi Province, China, is characterized by complex terrain including mountains and loess plateaus under a semi-arid climate.</div></div><div><h3>Study focus</h3><div>To mitigate seasonal water supply instability and identify reliable groundwater sources, this study proposes a novel machine-learning framework integrating the Sparrow Search Algorithm (SSA) with Extreme Gradient Boosting (XGBoost). Utilizing Geographic Information System (GIS) and field surveys, a comprehensive hydrogeological dataset was constructed. The Boruta Algorithm (BA) was employed to eliminate redundant variables, while Random Forest (RF) evaluated feature importance. The proposed model was rigorously benchmarked against five alternative methods, including hybrids optimized by the Grey Wolf Optimizer (GWO). Furthermore, SHapley Additive exPlanations (SHAP) were applied to decipher the \"black-box\" nature of the models, quantifying feature contributions and non-linear interactions.</div></div><div><h3>New hydrological insights for the region</h3><div>The results demonstrate that the SSA-XGBoost model achieved superior predictive accuracy, yielding a maximum Area Under the Curve (AUC) of 0.8812. Consensus from RF and SHAP analyses identified lithology and altitude as the dominant controlling factors, while the Normalized Difference Vegetation Index (NDVI) and rainfall provided essential spatial variability. GIS-based zonation revealed that approximately 22.06 % of the study area possesses high groundwater potential. This framework effectively balances high predictive accuracy with transparency, providing a scientifically robust tool for sustainable groundwater management in complex terrain regions.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"64 ","pages":"Article 103197"},"PeriodicalIF":5.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079717","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}
Pub Date : 2026-04-01Epub Date: 2026-02-06DOI: 10.1016/j.ejrh.2026.103215
Omur Faruq , Nahrin Jannat Hossain , Abdul Majed Sajib , Mir Talas Mahammad Diganta , Md. Moniruzzaman , Agnieszka I. Olbert , Md Galal Uddin
Study region
The Bhairab River is located in the south of Bangladesh. It is an active tidal river that supports a wide range of aquatic environments.
Study focus
The present research utilized a holistic approach by incorporating the optimized root mean squared water quality index (RMS-WQI) model and machine learning/artificial intelligence (ML/AI) techniques to assess the water quality (WQ) of the Bhairab River. The study utilized four years (2021–2024) of WQ data, including temperature, pH, electrical conductivity, chloride, total solids, dissolved oxygen, and biochemical oxygen demand, from 8 monitoring sites of the Bhairab River.
New hydrological insights of the region
The results of the RMS-WQI model showed a decreasing trend of water quality index (WQI) scores between 2021 and 2024, with most of the monitoring sites rated as ‘fair’ to ‘poor’ WQ categories, indicating that the majority of WQ indicators failed to meet World Health Organization (WHO 2022) and Environmental Conservation Rules (ECR, 2023) standards. The declining trend of WQI scores was statistically validated by the Friedman test statistic of 21.75 (p-value < 0.05) and the Mann-Kendall Tau value of -1.0 (p-value < 0.05). Moreover, the study utilized eight ML/AI algorithms with the Optuna optimizer, where the Artificial Neural Network (ANN) model demonstrated excellent performance with high accuracy and reliability in predicting WQI scores. In terms of reliability assessment, the ANN-Optuna model showed high effectiveness (Average model efficiency factor: MEF = 0.47; average percentage of relative error index: PREI = 0.39), excellent sensitivity (Average coefficient of determination: R2 = 0.95), and low uncertainty throughout the study period. In addition to assessing WQ trends, the study identified major pollution hotspots along the Bhairab River, where various types of industrial activities, brick kilns, and urban dumping stations were identified as the major sources of pollution around the monitoring sites. In summary, the declining trend of WQ indicated that the Bhairab River was under notable pressure from various point and non-point pollution sources during the study period, which requires site-specific WQ management strategies to protect the Bhairab River ecosystem and living organisms.
{"title":"An integrated approach for water quality assessment and pollution source identification using optimized machine learning and water quality index model in a Tidal River of Bangladesh","authors":"Omur Faruq , Nahrin Jannat Hossain , Abdul Majed Sajib , Mir Talas Mahammad Diganta , Md. Moniruzzaman , Agnieszka I. Olbert , Md Galal Uddin","doi":"10.1016/j.ejrh.2026.103215","DOIUrl":"10.1016/j.ejrh.2026.103215","url":null,"abstract":"<div><h3>Study region</h3><div>The Bhairab River is located in the south of Bangladesh. It is an active tidal river that supports a wide range of aquatic environments.</div></div><div><h3>Study focus</h3><div>The present research utilized a holistic approach by incorporating the optimized root mean squared water quality index (RMS-WQI) model and machine learning/artificial intelligence (ML/AI) techniques to assess the water quality (WQ) of the Bhairab River. The study utilized four years (2021–2024) of WQ data, including temperature, pH, electrical conductivity, chloride, total solids, dissolved oxygen, and biochemical oxygen demand, from 8 monitoring sites of the Bhairab River.</div></div><div><h3>New hydrological insights of the region</h3><div>The results of the RMS-WQI model showed a decreasing trend of water quality index (WQI) scores between 2021 and 2024, with most of the monitoring sites rated as ‘fair’ to ‘poor’ WQ categories, indicating that the majority of WQ indicators failed to meet World Health Organization (WHO 2022) and Environmental Conservation Rules (ECR, 2023) standards. The declining trend of WQI scores was statistically validated by the Friedman test statistic of 21.75 (p-value < 0.05) and the Mann-Kendall Tau value of -1.0 (p-value < 0.05). Moreover, the study utilized eight ML/AI algorithms with the Optuna optimizer, where the Artificial Neural Network (ANN) model demonstrated excellent performance with high accuracy and reliability in predicting WQI scores. In terms of reliability assessment, the ANN-Optuna model showed high effectiveness (Average model efficiency factor: MEF = 0.47; average percentage of relative error index: PREI = 0.39), excellent sensitivity (Average coefficient of determination: R<sup>2</sup> = 0.95), and low uncertainty throughout the study period. In addition to assessing WQ trends, the study identified major pollution hotspots along the Bhairab River, where various types of industrial activities, brick kilns, and urban dumping stations were identified as the major sources of pollution around the monitoring sites. In summary, the declining trend of WQ indicated that the Bhairab River was under notable pressure from various point and non-point pollution sources during the study period, which requires site-specific WQ management strategies to protect the Bhairab River ecosystem and living organisms.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"64 ","pages":"Article 103215"},"PeriodicalIF":5.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173783","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}
Pub Date : 2026-04-01Epub Date: 2026-01-15DOI: 10.1016/j.ejrh.2025.103089
Yuxuan Gao , Wen Wang , Futing Wu , Fuxiong Guo , Yanjun Hu
Study region
China.
Study focus
This study investigates the spatiotemporal evolution of precipitation efficiency (PE) over China (1979–2022) using ERA5 reanalysis and CHM_PRE precipitation data. Combining the moisture budget and moist static energy frameworks, we quantify the thermodynamic and dynamic controls on PE variability and assess how climate warming influences moisture transport and atmospheric stability, leading to distinct regional and temporal responses.
New hydrological insights for the region
PE exhibits pronounced regional contrasts, with the highest values over the Tibetan Plateau and the lowest in humid southern China. Over the past four decades, PE has generally increased across China, with the strongest rise in arid regions (0.25 % yr⁻¹) and weaker trends in semi-arid (0.12 % yr⁻¹) and humid regions (0.11 % yr⁻¹). As temperature rises, the increase in precipitation lags behind the rapid growth of precipitable water, leading to a decline in monthly PE. Vertical moisture advection dominates PE variability, exerting far stronger influence than horizontal transport. Further decomposition reveals that thermodynamic moistening tends to enhance PE nationwide, whereas dynamic changes determine its regional differences. In humid regions, weakened upward motion limits PE growth; in contrast, strengthened ascent and enhanced thermodynamic effects jointly increase PE in arid and high-altitude regions. These findings clarify the physical controls of precipitation efficiency under a warming climate and provide a process-based understanding of regional hydrological responses in China.
{"title":"Dynamic and thermodynamic mechanisms of precipitation efficiency variations in China under global warming","authors":"Yuxuan Gao , Wen Wang , Futing Wu , Fuxiong Guo , Yanjun Hu","doi":"10.1016/j.ejrh.2025.103089","DOIUrl":"10.1016/j.ejrh.2025.103089","url":null,"abstract":"<div><h3>Study region</h3><div>China.</div></div><div><h3>Study focus</h3><div>This study investigates the spatiotemporal evolution of precipitation efficiency (PE) over China (1979–2022) using ERA5 reanalysis and CHM_PRE precipitation data. Combining the moisture budget and moist static energy frameworks, we quantify the thermodynamic and dynamic controls on PE variability and assess how climate warming influences moisture transport and atmospheric stability, leading to distinct regional and temporal responses.</div></div><div><h3>New hydrological insights for the region</h3><div>PE exhibits pronounced regional contrasts, with the highest values over the Tibetan Plateau and the lowest in humid southern China. Over the past four decades, PE has generally increased across China, with the strongest rise in arid regions (0.25 % yr⁻¹) and weaker trends in semi-arid (0.12 % yr⁻¹) and humid regions (0.11 % yr⁻¹). As temperature rises, the increase in precipitation lags behind the rapid growth of precipitable water, leading to a decline in monthly PE. Vertical moisture advection dominates PE variability, exerting far stronger influence than horizontal transport. Further decomposition reveals that thermodynamic moistening tends to enhance PE nationwide, whereas dynamic changes determine its regional differences. In humid regions, weakened upward motion limits PE growth; in contrast, strengthened ascent and enhanced thermodynamic effects jointly increase PE in arid and high-altitude regions. These findings clarify the physical controls of precipitation efficiency under a warming climate and provide a process-based understanding of regional hydrological responses in China.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"64 ","pages":"Article 103089"},"PeriodicalIF":5.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981840","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}
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