Pub Date : 2026-01-06DOI: 10.1016/j.ejrh.2025.103097
Chunmei Ma , Shilei Ma , Yonghong Hao , Junfeng Zhu , Qinghua Lei , Jitao Sang , Huiqing Hao
Study region
Shentou springs, China, are one of the typical karst springs in northern China.
Study focus
This study focuses on Shentou springs discharge prediction based on the multi-scale precipitation. A framework called MCT that is composed of Multi-scale Convolutional network and Transformer is proposed. First, an adaptive denoising method for precipitation data is put forward. Second, a multi-scale convolutional network is designed to extract multi-temporal scale features of precipitation. Then, the Transformer is used to explore the spatial relationship between spring discharge and precipitation. Finally, the output of the coupled model is used to predict spring discharge.
New hydrological insights for the region
Results show that (1) denoising precipitation data can improve the accuracy of spring discharge prediction. (2) Precipitation and spring discharge exhibit obvious multi-temporal scale relationships, precipitation typically affects spring discharge after a lag of 6 months, and precipitation has a temporally persistent influence on spring discharge. (3) Precipitation in areas closer to the spring have an intense impact on spring discharge, while areas far from the spring but located in topographic depressions have volume-driven influence on spring discharge.
{"title":"Deep learning based multi-temporal scale precipitation modeling for spring discharge prediction, Shentou springs, China","authors":"Chunmei Ma , Shilei Ma , Yonghong Hao , Junfeng Zhu , Qinghua Lei , Jitao Sang , Huiqing Hao","doi":"10.1016/j.ejrh.2025.103097","DOIUrl":"10.1016/j.ejrh.2025.103097","url":null,"abstract":"<div><h3>Study region</h3><div>Shentou springs, China, are one of the typical karst springs in northern China.</div></div><div><h3>Study focus</h3><div>This study focuses on Shentou springs discharge prediction based on the multi-scale precipitation. A framework called MCT that is composed of Multi-scale Convolutional network and Transformer is proposed. First, an adaptive denoising method for precipitation data is put forward. Second, a multi-scale convolutional network is designed to extract multi-temporal scale features of precipitation. Then, the Transformer is used to explore the spatial relationship between spring discharge and precipitation. Finally, the output of the coupled model is used to predict spring discharge.</div></div><div><h3>New hydrological insights for the region</h3><div>Results show that (1) denoising precipitation data can improve the accuracy of spring discharge prediction. (2) Precipitation and spring discharge exhibit obvious multi-temporal scale relationships, precipitation typically affects spring discharge after a lag of 6 months, and precipitation has a temporally persistent influence on spring discharge. (3) Precipitation in areas closer to the spring have an intense impact on spring discharge, while areas far from the spring but located in topographic depressions have volume-driven influence on spring discharge.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"63 ","pages":"Article 103097"},"PeriodicalIF":5.0,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926074","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 : 2026-01-06DOI: 10.1016/j.ejrh.2025.103076
Pinjian Li , Haojun Xi , Qiqi Ding , Chuanzhe Feng , Yulong Yang , Fenglin Wang , Fulei Han , Tianhong Li
Study region
This study focuses on the middle Yangtze River's ecologically vital but hydrologically vulnerable wetlands: the Poyang Lake Plain and Dongting Lake Plain, China.
Study focus
Hydrological connectivity is essential for sustaining wetland ecological integrity, especially under accelerating climate change and intensified human activities. However, few studies have provided long-term, quantitative assessments of wetland hydrological connectivity and its driving mechanisms. This study examined the long-term (1993–2022) patterns and drivers of wetland hydrological connectivity in the Poyang Lake and Dongting Lake plains, and quantified natural and anthropogenic factor impacts using random forest (RF), partial least squares structural equation modeling (PLS-SEM), and Copula methods.
New hydrological insights for the region
The results revealed a significant long-term decline in hydrological connectivity in the Poyang Lake Plain, whereas no consistent trend was observed in the Dongting Lake Plain. Both regions experienced marked reductions in connectivity following the operation of the Three Gorges Dam, particularly during the wet season. Climate factors predominated drivers of hydrological connectivity changes across both plains. Although human-induced land use change accounted for less than 5 % of the total effect, it served as an important amplifying stressor on connectivity loss. Critical water level thresholds are 9.8 m and 23.5 m for Poyang Lake and Dongting Lake respectively to maintain moderate or higher connectivity. These insights provide a scientific basis for region-specific wetland management and restoration.
{"title":"Long-term variations of hydrological connectivity and its drivers in the middle reach wetlands of the Yangtze River","authors":"Pinjian Li , Haojun Xi , Qiqi Ding , Chuanzhe Feng , Yulong Yang , Fenglin Wang , Fulei Han , Tianhong Li","doi":"10.1016/j.ejrh.2025.103076","DOIUrl":"10.1016/j.ejrh.2025.103076","url":null,"abstract":"<div><h3>Study region</h3><div>This study focuses on the middle Yangtze River's ecologically vital but hydrologically vulnerable wetlands: the Poyang Lake Plain and Dongting Lake Plain, China.</div></div><div><h3>Study focus</h3><div>Hydrological connectivity is essential for sustaining wetland ecological integrity, especially under accelerating climate change and intensified human activities. However, few studies have provided long-term, quantitative assessments of wetland hydrological connectivity and its driving mechanisms. This study examined the long-term (1993–2022) patterns and drivers of wetland hydrological connectivity in the Poyang Lake and Dongting Lake plains, and quantified natural and anthropogenic factor impacts using random forest (RF), partial least squares structural equation modeling (PLS-SEM), and Copula methods.</div></div><div><h3>New hydrological insights for the region</h3><div>The results revealed a significant long-term decline in hydrological connectivity in the Poyang Lake Plain, whereas no consistent trend was observed in the Dongting Lake Plain. Both regions experienced marked reductions in connectivity following the operation of the Three Gorges Dam, particularly during the wet season. Climate factors predominated drivers of hydrological connectivity changes across both plains. Although human-induced land use change accounted for less than 5 % of the total effect, it served as an important amplifying stressor on connectivity loss. Critical water level thresholds are 9.8 m and 23.5 m for Poyang Lake and Dongting Lake respectively to maintain moderate or higher connectivity. These insights provide a scientific basis for region-specific wetland management and restoration.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"63 ","pages":"Article 103076"},"PeriodicalIF":5.0,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926196","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 : 2026-01-06DOI: 10.1016/j.ejrh.2025.103075
Constantinos F. Panagiotou , Giorgia Guerrisi , Davide De Santis , Fabio Del Frate , Marios Tzouvaras
Study region
The island of Cyprus is dominated by small-scale watersheds that favor the occurrence of flash floods. Climate projections indicate the increase in frequency and intensity of these events.
Study focus
The development of rapid flood screening tools is essential for better urban planning. This study uses four different machine learning algorithms, namely support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), and multilayer perceptron (MLP), to build models based on data collected from eight watersheds to enhance their within-region (Cyprus) generalization. Seven features were selected for tuning and testing the performance of these models. T-based confidence intervals were calculated to quantify uncertainty.
New hydrological insights for the region
All models achieved good agreement with the inventory database. RF model was selected to build multi-level susceptibility maps. Half of the Georskipou watershed is classified as highly susceptible to flooding, mostly urban and semi-urban regions, whereas 38 % of the test watershed is not expected to experience severe flood events. Simplified RF models were developed by selecting different combinations of the most important features, revealing that land-use, terrain slope, terrain elevation, and flow accumulation are sufficient to achieve good accuracy (95 %) with flood inventory data. The results highlight the ability of simple, computationally efficient data-driven models to provide rapid predictions, thus avoiding the compilation of fully detailed physically-based models.
{"title":"Investigating the mechanisms of flood susceptibility with the use of multi-basin machine learning models in data-scarce environments in Cyprus","authors":"Constantinos F. Panagiotou , Giorgia Guerrisi , Davide De Santis , Fabio Del Frate , Marios Tzouvaras","doi":"10.1016/j.ejrh.2025.103075","DOIUrl":"10.1016/j.ejrh.2025.103075","url":null,"abstract":"<div><h3>Study region</h3><div>The island of Cyprus is dominated by small-scale watersheds that favor the occurrence of flash floods. Climate projections indicate the increase in frequency and intensity of these events.</div></div><div><h3>Study focus</h3><div>The development of rapid flood screening tools is essential for better urban planning. This study uses four different machine learning algorithms, namely support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), and multilayer perceptron (MLP), to build models based on data collected from eight watersheds to enhance their within-region (Cyprus) generalization. Seven features were selected for tuning and testing the performance of these models. T-based confidence intervals were calculated to quantify uncertainty.</div></div><div><h3>New hydrological insights for the region</h3><div>All models achieved good agreement with the inventory database. RF model was selected to build multi-level susceptibility maps. Half of the Georskipou watershed is classified as highly susceptible to flooding, mostly urban and semi-urban regions, whereas 38 % of the test watershed is not expected to experience severe flood events. Simplified RF models were developed by selecting different combinations of the most important features, revealing that land-use, terrain slope, terrain elevation, and flow accumulation are sufficient to achieve good accuracy (95 %) with flood inventory data. The results highlight the ability of simple, computationally efficient data-driven models to provide rapid predictions, thus avoiding the compilation of fully detailed physically-based models.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"63 ","pages":"Article 103075"},"PeriodicalIF":5.0,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926200","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 : 2026-01-06DOI: 10.1016/j.ejrh.2025.103074
Shuai Du, Yuanyuan Zha, Yuzhe Ji, Yue Wang, Xiangsen Xu, Yuhan Liu, Meijun Zheng, Yang Zhang, Shenshen Wu
Study Region
Qingtongxia Irrigation District (QID), Ningxia, China, a fully irrigated and arid region.
Study Focus
The Optical Trapezoid Model (OPTRAM) estimates surface soil moisture (SSM) using optical remote sensing data by linking SSM to Shortwave Infrared Transformed Reflectance (STR). It defines linear dry and wet edges of the STR-NDVI trapezoidal space under minimum dry and maximum wet soil conditions. However, these edges may not always be linear, and OPTRAM's long-term performance in large-scale irrigation areas remains underexplored. This study, set in Ningxia’s Qingtongxia Irrigation District, uses Sentinel 2 and Landsat 8 images (2022–2024) across crop growth and fallow periods. A modified OPTRAM model introduces a quadratic function to better capture non-linear STR-NDVI edges, improving long-term SSM estimates.
New Hydrological Insights for the region
Our result showed that the modified OPTRAM achieved the highest accuracy in SSM estimation, especially with Sentinel 2 data, compared with OPTRAM and TOTRAM models. Despite cloud cover, the model captured field-scale SSM dynamics, including irrigation events. It also showed potential for crop type mapping, growth stage analysis, and irrigation detection. By incorporating the entire crop growth and fallow periods, a distinct STR-NDVI feature space for QID was revealed. These results offer new insights into soil moisture heterogeneity and water use patterns in irrigated dryland regions, supporting improved irrigation management and precision agriculture.
{"title":"Retrieving long-term topsoil moisture in Qingtongxia irrigation district using a modified OPTRAM model","authors":"Shuai Du, Yuanyuan Zha, Yuzhe Ji, Yue Wang, Xiangsen Xu, Yuhan Liu, Meijun Zheng, Yang Zhang, Shenshen Wu","doi":"10.1016/j.ejrh.2025.103074","DOIUrl":"10.1016/j.ejrh.2025.103074","url":null,"abstract":"<div><h3>Study Region</h3><div>Qingtongxia Irrigation District (QID), Ningxia, China, a fully irrigated and arid region.</div></div><div><h3>Study Focus</h3><div>The Optical Trapezoid Model (OPTRAM) estimates surface soil moisture (SSM) using optical remote sensing data by linking SSM to Shortwave Infrared Transformed Reflectance (STR). It defines linear dry and wet edges of the STR-NDVI trapezoidal space under minimum dry and maximum wet soil conditions. However, these edges may not always be linear, and OPTRAM's long-term performance in large-scale irrigation areas remains underexplored. This study, set in Ningxia’s Qingtongxia Irrigation District, uses Sentinel 2 and Landsat 8 images (2022–2024) across crop growth and fallow periods. A modified OPTRAM model introduces a quadratic function to better capture non-linear STR-NDVI edges, improving long-term SSM estimates.</div></div><div><h3>New Hydrological Insights for the region</h3><div>Our result showed that the modified OPTRAM achieved the highest accuracy in SSM estimation, especially with Sentinel 2 data, compared with OPTRAM and TOTRAM models. Despite cloud cover, the model captured field-scale SSM dynamics, including irrigation events. It also showed potential for crop type mapping, growth stage analysis, and irrigation detection. By incorporating the entire crop growth and fallow periods, a distinct STR-NDVI feature space for QID was revealed. These results offer new insights into soil moisture heterogeneity and water use patterns in irrigated dryland regions, supporting improved irrigation management and precision agriculture.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"63 ","pages":"Article 103074"},"PeriodicalIF":5.0,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926073","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 : 2026-01-06DOI: 10.1016/j.ejrh.2025.103077
Dongxu Yang , Baowei Yan , Donglin Gu , Jianbo Chang , Shixiong Du
Study region
The upper reaches of the Hanjiang River, China
Study focus
To enhance the accuracy and physical consistency of reservoir inflow forecasting, this study proposes a hybrid modeling framework that couples an enhanced Muskingum model with a bidirectional long short-term memory (BiLSTM) network. The Muskingum model was restructured via differentiable programming to allow dynamic calibration of physical parameters across river sub-reaches. This physics-based layer was embedded within the BiLSTM network to learn the relationship between meteorological forcing inputs and runoff dynamics. Bayesian Optimization (BO) was adopted to co-optimize the Muskingum parameters and neural network hyperparameters, mitigating error propagation and thus enhancing predictive robustness.
New hydrological insights for the region
The proposed framework was evaluated on a reach of the upper Hanjiang River between Ankang and Danjiangkou Reservoirs. Results showed that model performance initially improved with finer segmentation, peaking with a four-segment configuration, after which performance declined—likely due to over-parameterization. The optimal four-segment hybrid model achieved a Nash–Sutcliffe efficiency (NSE) of 0.94 during the test period, representing a 4.4 % improvement over both the pure BiLSTM model and the one-way coupled model. In addition, it achieved a Kling–Gupta efficiency (KGE) of 0.95 and a Root Mean Square Error (RMSE) of 598 m³ /s, exhibiting more stable predictive behavior. This further demonstrates the framework’s capability for accurate runoff characterization and high-precision inflow forecasting in complex reservoir systems.
{"title":"A hybrid deep learning-Muskingum framework for enhanced runoff prediction: Model coupling and hydrological process integration","authors":"Dongxu Yang , Baowei Yan , Donglin Gu , Jianbo Chang , Shixiong Du","doi":"10.1016/j.ejrh.2025.103077","DOIUrl":"10.1016/j.ejrh.2025.103077","url":null,"abstract":"<div><h3>Study region</h3><div>The upper reaches of the Hanjiang River, China</div></div><div><h3>Study focus</h3><div>To enhance the accuracy and physical consistency of reservoir inflow forecasting, this study proposes a hybrid modeling framework that couples an enhanced Muskingum model with a bidirectional long short-term memory (BiLSTM) network. The Muskingum model was restructured via differentiable programming to allow dynamic calibration of physical parameters across river sub-reaches. This physics-based layer was embedded within the BiLSTM network to learn the relationship between meteorological forcing inputs and runoff dynamics. Bayesian Optimization (BO) was adopted to co-optimize the Muskingum parameters and neural network hyperparameters, mitigating error propagation and thus enhancing predictive robustness.</div></div><div><h3>New hydrological insights for the region</h3><div>The proposed framework was evaluated on a reach of the upper Hanjiang River between Ankang and Danjiangkou Reservoirs. Results showed that model performance initially improved with finer segmentation, peaking with a four-segment configuration, after which performance declined—likely due to over-parameterization. The optimal four-segment hybrid model achieved a Nash–Sutcliffe efficiency (NSE) of 0.94 during the test period, representing a 4.4 % improvement over both the pure BiLSTM model and the one-way coupled model. In addition, it achieved a Kling–Gupta efficiency (KGE) of 0.95 and a Root Mean Square Error (RMSE) of 598 m³ /s, exhibiting more stable predictive behavior. This further demonstrates the framework’s capability for accurate runoff characterization and high-precision inflow forecasting in complex reservoir systems.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"63 ","pages":"Article 103077"},"PeriodicalIF":5.0,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926195","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 : 2026-01-06DOI: 10.1016/j.ejrh.2025.103095
Andrew John , Avril Horne , Leah Traill , Keirnan Fowler , Rory Nathan
Study region
The Murray-Darling Basin (MDB) is Australia’s most significant river system. Its’ scale and complexity are such that climate change impact assessments using traditional water resource modelling have been restricted to a small number of scenarios, which limits the understanding of climate uncertainty and hinders the identification of robust adaptation responses.
Study focus
This study implements a bottom-up climate vulnerability assessment for the MDB. The approach is designed to overcome the computational and data constraints of traditional top-down methods and offers insights into system robustness to climate uncertainty. It uses computationally efficient machine learning-based emulators, trained on outputs from complex water resource models, to conduct the extensive simulations required. The emulators, driven by conceptual rainfall-runoff models, enable the rapid simulation of the regulated river system to explore a wide range of climate uncertainties, which retaining high accuracy.
New hydrological insights for the region
The bottom-up assessment reveals significant system sensitivities, and non-linearities and thresholds in how ecological metrics respond to climate change. Results contrast differences in hydrological response across the north and south MDB. A key insight is the importance of precipitation reductions of 15 %, which represents a threshold beyond which the long-term performance of environmental targets is significantly compromised across the basin. Such outcomes may be missed in traditional top-down assessments but are crucial for future planning to develop robust water management practices.
{"title":"Bottom-up assessment of climate change vulnerability of a large and complex river basin using emulator models","authors":"Andrew John , Avril Horne , Leah Traill , Keirnan Fowler , Rory Nathan","doi":"10.1016/j.ejrh.2025.103095","DOIUrl":"10.1016/j.ejrh.2025.103095","url":null,"abstract":"<div><h3>Study region</h3><div>The Murray-Darling Basin (MDB) is Australia’s most significant river system. Its’ scale and complexity are such that climate change impact assessments using traditional water resource modelling have been restricted to a small number of scenarios, which limits the understanding of climate uncertainty and hinders the identification of robust adaptation responses.</div></div><div><h3>Study focus</h3><div>This study implements a bottom-up climate vulnerability assessment for the MDB. The approach is designed to overcome the computational and data constraints of traditional top-down methods and offers insights into system robustness to climate uncertainty. It uses computationally efficient machine learning-based emulators, trained on outputs from complex water resource models, to conduct the extensive simulations required. The emulators, driven by conceptual rainfall-runoff models, enable the rapid simulation of the regulated river system to explore a wide range of climate uncertainties, which retaining high accuracy.</div></div><div><h3>New hydrological insights for the region</h3><div>The bottom-up assessment reveals significant system sensitivities, and non-linearities and thresholds in how ecological metrics respond to climate change. Results contrast differences in hydrological response across the north and south MDB. A key insight is the importance of precipitation reductions of 15 %, which represents a threshold beyond which the long-term performance of environmental targets is significantly compromised across the basin. Such outcomes may be missed in traditional top-down assessments but are crucial for future planning to develop robust water management practices.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"63 ","pages":"Article 103095"},"PeriodicalIF":5.0,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926513","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}
Dokriani Glacier Catchment (DGC), Central Himalaya.
Study focus
This study evaluates and corrects biases of ERA5 and ERA5-Land (ERA5L) mean temperature (TMEAN) data for the DGC, using high-resolution daily observations from three Automatic Weather Stations (AWSs) in distinct settings: glacierized, proglacial, and forested. Five methods including Delta Change (DC), Linear Regression (LR), Empirical Quantile Mapping (EQM), Quantile Delta Mapping (QDM), and Generalized Additive Models (GAM) were used to identify the most effective method for correcting reanalysis data based on AWS observations at daily, monthly, and seasonal timescales (2011–2014) using bias, Root Mean Square Error (RMSE), correlation coefficient, and coefficient of determination.
New hydrological insights for the region
LR and GAM were the most effective, reducing biases to near zero and RMSE by up to 86 % at the seasonal scale, enabling more reliable climate-driven hydrological modeling, glaciological studies, and water-resource management in this monsoon-influenced region. Seasonal drivers can differentially influence dataset-specific corrections, with ERA5L showing substantial reductions in RMSE during monsoon periods. Hydrological models incorporating such improvements provide vital information for downstream river systems that are critical for South Asian livelihoods, agriculture, and hydropower. In contrast, ERA5 showed slight improvements, with biases that were significantly dependent on grid size; finer resolutions resulted in better error reduction. The validation based on 2014–2015 data confirmed that the LR and GAM methods effectively minimized the errors in the reanalysis dataset.
喜马拉雅中部多克里亚尼冰川集水区（DGC）研究区域。本研究利用三个自动气象站（aws）在不同环境下（冰川化、原冰川化和森林化）的高分辨率每日观测数据，评估和纠正了DGC的ERA5和ERA5- land （ERA5L）平均温度（TMEAN）数据的偏差。采用Delta变化（DC）、线性回归（LR）、经验分位数映射（EQM）、分位数Delta映射（QDM）和广义加性模型（GAM） 5种方法，利用偏差、均方根误差（RMSE）、相关系数和决定系数，确定了基于日、月和季节时间尺度（2011-2014）的AWS观测数据校正再分析数据的最有效方法。区域lr和GAM的新水文见解是最有效的，在季节尺度上将偏差减少到接近零，RMSE减少高达86% %，从而在这个受季风影响的地区实现更可靠的气候驱动的水文建模、冰河学研究和水资源管理。季节性驱动因素对数据集特定修正的影响不同，ERA5L显示季风期间RMSE大幅减少。包含这些改进的水文模型为下游河流系统提供了重要信息，而下游河流系统对南亚的生计、农业和水电至关重要。相比之下，ERA5表现出轻微的改善，偏差明显依赖于网格大小；更精细的分辨率可以更好地减少错误。基于2014-2015年数据的验证证实，LR和GAM方法有效地减少了再分析数据集的误差。
{"title":"Bias corrections of ERA5 and ERA5-land temperature using automatic weather station data in the Higher Central Himalaya: implications for hydro-meteorological and glaciological research","authors":"Soumya Satyapragyan , Jairam Singh Yadav , Rakesh Bhambri","doi":"10.1016/j.ejrh.2025.103079","DOIUrl":"10.1016/j.ejrh.2025.103079","url":null,"abstract":"<div><h3>Study region</h3><div>Dokriani Glacier Catchment (DGC), Central Himalaya.</div></div><div><h3>Study focus</h3><div>This study evaluates and corrects biases of ERA5 and ERA5-Land (ERA5L) mean temperature (T<sub>MEAN</sub>) data for the DGC, using high-resolution daily observations from three Automatic Weather Stations (AWSs) in distinct settings: glacierized, proglacial, and forested. Five methods including Delta Change (DC), Linear Regression (LR), Empirical Quantile Mapping (EQM), Quantile Delta Mapping (QDM), and Generalized Additive Models (GAM) were used to identify the most effective method for correcting reanalysis data based on AWS observations at daily, monthly, and seasonal timescales (2011–2014) using bias, Root Mean Square Error (RMSE), correlation coefficient, and coefficient of determination.</div></div><div><h3>New hydrological insights for the region</h3><div>LR and GAM were the most effective, reducing biases to near zero and RMSE by up to 86 % at the seasonal scale, enabling more reliable climate-driven hydrological modeling, glaciological studies, and water-resource management in this monsoon-influenced region. Seasonal drivers can differentially influence dataset-specific corrections, with ERA5L showing substantial reductions in RMSE during monsoon periods. Hydrological models incorporating such improvements provide vital information for downstream river systems that are critical for South Asian livelihoods, agriculture, and hydropower. In contrast, ERA5 showed slight improvements, with biases that were significantly dependent on grid size; finer resolutions resulted in better error reduction. The validation based on 2014–2015 data confirmed that the LR and GAM methods effectively minimized the errors in the reanalysis dataset.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"63 ","pages":"Article 103079"},"PeriodicalIF":5.0,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926590","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 : 2026-01-06DOI: 10.1016/j.ejrh.2025.103090
Jianan Shan , Rui Zhu , Zhenliang Yin , Chunshuang Fang , Rong Li , Ganlin Zhou
Study region
Northwest China
Study focus
Understanding the propagation mechanism from meteorological to groundwater drought is crucial for groundwater management and drought early warning. However, scant research exists for mechanism of the unseen groundwater drought propagation. This study applied drought indices including the Standardized Precipitation Index (SPI) and Groundwater Drought Index (GDI), and utilized methods such as run theory, convergent cross mapping (CCM), Copula function, and Bayesian network, as well as several open-source data sources to analyze the drought characteristics, propagation rule, threshold, and recovery time of meteorological-groundwater drought in Northwest China (NWC) from 1960 to 2024. Specifically, the 'compound meteorological-groundwater drought event' is defined as the period from the onset of groundwater drought to the end of meteorological drought, aiming to highlight the full system response time from the initiation of deep water deficit to shallow water recovery. The contributions of driving factors were further quantified using XGBoost-SHAP, a game theory-based approach for interpreting model outputs and quantifying feature importance.
New hydrological insights for the region
The number of meteorological-groundwater drought events is lower than that of meteorological droughts but higher than that of groundwater droughts, with the shortest average duration (2.29 months) and the lowest severity (3.94). The propagation time (PT) of meteorological-groundwater drought is 4.69 months. The average probabilities of the meteorological drought triggering mild, moderate, severe, and extreme groundwater droughts are 30.27 %, 20.60 %, 9.63 %, and 5.50 %. The propagation threshold is dominated by extreme meteorological drought, accounting for 55.69 %. The recovery time for compound meteorological-groundwater droughts reached up to 3.05 months, exceeding that of individual meteorological or groundwater drought events. ENSO has the strongest influence on the groundwater drought. The interaction between climate change and human activities has the largest average contribution at 64 %, with Digital Elevation Model (DEM), precipitation (Pre), soil moisture (SM), and Gross Domestic Product (GDP) being the primary factors. These findings highlight the importance of drought monitoring and differentiated groundwater management in arid and semi-arid regions.
{"title":"Spatial-temporal dynamics of meteorological and groundwater drought in Northwest China: Propagation, threshold, recovery time, drivers","authors":"Jianan Shan , Rui Zhu , Zhenliang Yin , Chunshuang Fang , Rong Li , Ganlin Zhou","doi":"10.1016/j.ejrh.2025.103090","DOIUrl":"10.1016/j.ejrh.2025.103090","url":null,"abstract":"<div><h3>Study region</h3><div>Northwest China</div></div><div><h3>Study focus</h3><div>Understanding the propagation mechanism from meteorological to groundwater drought is crucial for groundwater management and drought early warning. However, scant research exists for mechanism of the unseen groundwater drought propagation. This study applied drought indices including the Standardized Precipitation Index (SPI) and Groundwater Drought Index (GDI), and utilized methods such as run theory, convergent cross mapping (CCM), Copula function, and Bayesian network, as well as several open-source data sources to analyze the drought characteristics, propagation rule, threshold, and recovery time of meteorological-groundwater drought in Northwest China (NWC) from 1960 to 2024. Specifically, the 'compound meteorological-groundwater drought event' is defined as the period from the onset of groundwater drought to the end of meteorological drought, aiming to highlight the full system response time from the initiation of deep water deficit to shallow water recovery. The contributions of driving factors were further quantified using XGBoost-SHAP, a game theory-based approach for interpreting model outputs and quantifying feature importance.</div></div><div><h3>New hydrological insights for the region</h3><div>The number of meteorological-groundwater drought events is lower than that of meteorological droughts but higher than that of groundwater droughts, with the shortest average duration (2.29 months) and the lowest severity (3.94). The propagation time (PT) of meteorological-groundwater drought is 4.69 months. The average probabilities of the meteorological drought triggering mild, moderate, severe, and extreme groundwater droughts are 30.27 %, 20.60 %, 9.63 %, and 5.50 %. The propagation threshold is dominated by extreme meteorological drought, accounting for 55.69 %. The recovery time for compound meteorological-groundwater droughts reached up to 3.05 months, exceeding that of individual meteorological or groundwater drought events. ENSO has the strongest influence on the groundwater drought. The interaction between climate change and human activities has the largest average contribution at 64 %, with Digital Elevation Model (DEM), precipitation (Pre), soil moisture (SM), and Gross Domestic Product (GDP) being the primary factors. These findings highlight the importance of drought monitoring and differentiated groundwater management in arid and semi-arid regions.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"63 ","pages":"Article 103090"},"PeriodicalIF":5.0,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926071","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}
In the Oum Er Rbia watershed, Morocco, dam water resources play a crucial role in prolonged drought conditions, particularly in the case of the Al Massira Dam, which has been a strategic reservoir for drought resilience since its inauguration.
Study focus
Optimized pipelines of explainable artificial intelligence (XAI) models were developed for monthly forecasts of water resource variations at the Al Massira dam, which has been affected by unprecedented drought since 2019. The architectures of the models developed incorporate Bayesian optimization via Optuna for identifying the best hyperparameters, advanced feature selection methods, and lagged regressors of teleconnection indices, drought indices, and hydroclimatic variables. The performance of the models was first evaluated in terms of their ability to forecast dam water volume up to 6 months ahead under near-normal hydroclimate conditions. Next, model performance was assessed under a scenario of unusual changes in time series.
New hydrological insights for the region
The light gradient boosting machine (LightGBM) showed high uncertainty when forecasting water volumes under unusual drought conditions, with Skill= 75.1 % and NMAE= 11.2 %. The Bayesian probabilistic LSTM (ProbLSTM) reached the maximum predictive skill score (Skill=86.2 %, NMAE=3.6 %), followed by the generalized additive model (GAM) (Skill=85.3 % and NMAE=3.4 %). Overall, from an operational perspective, ProbLSTM and the GAM are preferable for seasonal forecasting because of their low performance variability under a scenario of unusual changes in time series and their high predictive performance.
{"title":"Seasonal forecasting of dam water resources using optimized hybrid models under unprecedented drought conditions","authors":"Ismaguil Hanadé Houmma , Abdessamad Hadri , Abdelghani Boudhar , El Mahdi El Khalki , Ismail Karaoui , Sabir Oussaoui , Mohamed Samih , Christophe Kinnard","doi":"10.1016/j.ejrh.2025.103091","DOIUrl":"10.1016/j.ejrh.2025.103091","url":null,"abstract":"<div><h3>Study region</h3><div>In the Oum Er Rbia watershed, Morocco, dam water resources play a crucial role in prolonged drought conditions, particularly in the case of the Al Massira Dam, which has been a strategic reservoir for drought resilience since its inauguration.</div></div><div><h3>Study focus</h3><div>Optimized pipelines of explainable artificial intelligence (XAI) models were developed for monthly forecasts of water resource variations at the Al Massira dam, which has been affected by unprecedented drought since 2019. The architectures of the models developed incorporate Bayesian optimization via Optuna for identifying the best hyperparameters, advanced feature selection methods, and lagged regressors of teleconnection indices, drought indices, and hydroclimatic variables. The performance of the models was first evaluated in terms of their ability to forecast dam water volume up to 6 months ahead under near-normal hydroclimate conditions. Next, model performance was assessed under a scenario of unusual changes in time series.</div></div><div><h3>New hydrological insights for the region</h3><div>The light gradient boosting machine (LightGBM) showed high uncertainty when forecasting water volumes under unusual drought conditions, with Skill= 75.1 % and NMAE= 11.2 %. The Bayesian probabilistic LSTM (ProbLSTM) reached the maximum predictive skill score (Skill=86.2 %, NMAE=3.6 %), followed by the generalized additive model (GAM) (Skill=85.3 % and NMAE=3.4 %). Overall, from an operational perspective, ProbLSTM and the GAM are preferable for seasonal forecasting because of their low performance variability under a scenario of unusual changes in time series and their high predictive performance.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"63 ","pages":"Article 103091"},"PeriodicalIF":5.0,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926076","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 : 2026-01-06DOI: 10.1016/j.ejrh.2025.103084
Tingting Huang , Yu Liu , Zhifeng Jia , Jiaru Shi , Yulin Wei , Pengcheng Sun
Study region
The Jing River basin on China's Loess Plateau is an arid to semi-arid region strongly influenced by climate change and human activities.
Study focus
Understanding of how human activities alter drought development and recovery mechanisms remains limited, particularly in complex and dynamic environments. We employed the Soil and Water Assessment Tool (SWAT) to simulate natural runoff in the Jing River basin, aiming to establish a baseline natural runoff model and isolate anthropogenic influences. We quantified the evolution and mitigation processes of hydrological drought through an integrated framework combining range theory and the Human Activity Impact Index (HADI), enabling separate assessments of human activity impacts during drought development and recovery phases. Furthermore, employing methods such as correlation analysis, we investigated how changes in environmental factors regulate the propagation mechanisms of meteorological-hydrological drought.
New hydrological insights for the region
Human activities exerted a stronger influence on short-term than on long-term hydrological drought (mean HADI = 16.28 %) and generally aggravated drought by intensifying and accelerating the development phase. Although human activities slightly reduced recovery duration and increased recovery speed, these modest mitigating effects during recovery were insufficient to compensate for the stronger aggravating effects during drought development. Human activities also weakened the linkage between meteorological and hydrological drought and shortened drought propagation time. Among the examined factors, precipitation and vegetation cover emerged as key controls on drought propagation. These findings provide a quantitative basis for managing human activities in arid and semi-arid basins and for improving early warning and forecasting of hydrological drought.
{"title":"Hydrological drought characteristics and its propagation from meteorological drought in the Jing river basin under environmental change","authors":"Tingting Huang , Yu Liu , Zhifeng Jia , Jiaru Shi , Yulin Wei , Pengcheng Sun","doi":"10.1016/j.ejrh.2025.103084","DOIUrl":"10.1016/j.ejrh.2025.103084","url":null,"abstract":"<div><h3>Study region</h3><div>The Jing River basin on China's Loess Plateau is an arid to semi-arid region strongly influenced by climate change and human activities.</div></div><div><h3>Study focus</h3><div>Understanding of how human activities alter drought development and recovery mechanisms remains limited, particularly in complex and dynamic environments. We employed the Soil and Water Assessment Tool (SWAT) to simulate natural runoff in the Jing River basin, aiming to establish a baseline natural runoff model and isolate anthropogenic influences. We quantified the evolution and mitigation processes of hydrological drought through an integrated framework combining range theory and the Human Activity Impact Index (HADI), enabling separate assessments of human activity impacts during drought development and recovery phases. Furthermore, employing methods such as correlation analysis, we investigated how changes in environmental factors regulate the propagation mechanisms of meteorological-hydrological drought.</div></div><div><h3>New hydrological insights for the region</h3><div>Human activities exerted a stronger influence on short-term than on long-term hydrological drought (mean HADI = 16.28 %) and generally aggravated drought by intensifying and accelerating the development phase. Although human activities slightly reduced recovery duration and increased recovery speed, these modest mitigating effects during recovery were insufficient to compensate for the stronger aggravating effects during drought development. Human activities also weakened the linkage between meteorological and hydrological drought and shortened drought propagation time. Among the examined factors, precipitation and vegetation cover emerged as key controls on drought propagation. These findings provide a quantitative basis for managing human activities in arid and semi-arid basins and for improving early warning and forecasting of hydrological drought.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"63 ","pages":"Article 103084"},"PeriodicalIF":5.0,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925995","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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