Journal of Hydrology-Regional Studies最新文献

{"title":"Regional base-flow index in arid landscapes using machine learning and instrumented records","authors":"Caelum Mroczek ,&nbsp;Abraham E. Springer ,&nbsp;Neha Gupta ,&nbsp;Temuulen Sankey ,&nbsp;Benjamin Lucas","doi":"10.1016/j.ejrh.2025.102778","DOIUrl":"10.1016/j.ejrh.2025.102778","url":null,"abstract":"<div><h3>Study region</h3><div>This study focuses on Arizona, a dryland state in the southwestern United States with marked variability in climate, elevation, and hydrogeology. Arizona spans two major physiographic regions, the Colorado Plateau and the Basin and Range, each exhibiting distinct hydrologic behavior.</div></div><div><h3>Study focus</h3><div>We quantify long-term base-flow index (BFI) patterns and trends across Arizona and develop a predictive framework for ungauged basins. BFI was calculated at 205 USGS stream gauges using a recursive digital filter applied to multi-decadal streamflow records. Coincident trends in precipitation, temperature, and evapotranspiration were analyzed to assess climate–base-flow relationships. We trained an eXtreme Gradient Boosting (XGBoost) model on hydroclimatic and physiographic variables to estimate long-term BFI from 1991 to 2020 at the 8-digit Hydrologic Unit Code (HUC) scale.</div></div><div><h3>New hydrological insights for the region</h3><div>Groundwater discharge accounts for approximately 32 % of streamflow in Arizona, with substantial spatial variability linked to topography, land cover, and climate. High BFI values are found in forested headwaters with spring-fed and snowmelt-driven systems, while low values dominate the state’s arid lowlands. Declining BFI trends were most pronounced in monsoon-dominated, warm-dry, and low-slope basins. Precipitation was the strongest climate correlate of BFI trends, underscoring the importance of climate variability for dryland base flow. This integration of observational records and machine learning provides new insights into groundwater–surface water interactions and offers a transferable framework for water resource assessment in data-scarce dryland regions globally.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"62 ","pages":"Article 102778"},"PeriodicalIF":5.0,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145108016","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":"Energy and mass balance of glaciers in the Ulugh Muztagh driven by climate warming over 44 years","authors":"Lailei Gu ,&nbsp;Ninglian Wang ,&nbsp;Anan Chen ,&nbsp;Xuewen Yang ,&nbsp;Mingjun Zhang ,&nbsp;Yanjun Che ,&nbsp;Sugang Zhou","doi":"10.1016/j.ejrh.2025.102771","DOIUrl":"10.1016/j.ejrh.2025.102771","url":null,"abstract":"<div><h3>Study region</h3><div>This study focuses on the glaciers of the Ulugh Muztagh region, a remote high-altitude area in the north-central Tibetan Plateau.</div></div><div><h3>Study focus</h3><div>Using calibrated and downscaled meteorological reanalysis data together with the COSIPY energy and mass balance model, this study systematically investigates the mass balance and energy exchange characteristics of the Ulugh Muztagh glaciers from 1980 to 2023. The analysis covers long-term trends, interannual variability, and the dominant meteorological and glaciological processes affecting glacier change in this region.</div></div><div><h3>New hydrological insights for the region</h3><div>Results indicate that the mean glacier mass balance was −0.06 ± 0.05 m w.e./yr over the past 44 years, with accelerated mass loss after 2000. Net radiation accounts for 66 % of the glacier energy budget, highlighting its predominant role. July and August are the main ablation months, while accumulation mainly occurs from September to the following March. A significant negative correlation exists between air temperature and glacier mass balance. Snowfall is the major input, and melt is the primary output; notably, sublimation dominates mass loss during the accumulation season. These findings clarify the mechanisms driving glacier change in Ulugh Muztagh and provide critical evidence to assess the region's hydrological response to climate change.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"62 ","pages":"Article 102771"},"PeriodicalIF":5.0,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107709","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 physics-informed neural network approach to predicting land subsidence-rebound in Dezhou City under different climate scenarios","authors":"Haotong Wang ,&nbsp;Huili Gong ,&nbsp;Beibei Chen ,&nbsp;Chaofan Zhou ,&nbsp;Yabin Yang ,&nbsp;Xiaoxiao Sun","doi":"10.1016/j.ejrh.2025.102773","DOIUrl":"10.1016/j.ejrh.2025.102773","url":null,"abstract":"<div><h3>Study region</h3><div>Dezhou, China, is one of the typical areas of land subsidence in the North China Plain.</div></div><div><h3>Study focus</h3><div>This study focuses on the changes in subsidence patterns following groundwater level (GWL) recovery in Dezhou City, developing a dualistic water cycle framework that integrates both climate and human factors. Two Physics-Informed Neural Network (PINN) models are constructed to simulate: (1) the relationship between precipitation, evapotranspiration, groundwater (GW) extraction, and GWLs (2) the coupling between GWLs and land subsidence. Trained with meteorological, hydrogeological, and Interferometric Synthetic Aperture Radar (InSAR) deformation data, the models use Shared Socioeconomic Pathway–Representative Concentration Pathway (SSP-RCP) scenario data and simulated GW extraction data to predict future GWLs and subsidence under different scenarios.</div></div><div><h3>New hydrological insights for the region</h3><div>Shallow GWLs are highly sensitive to climate change, showing significant seasonal fluctuations under the SSP5-RCP8.5 scenario, with a maximum amplitude of 2.79 m. In contrast, deep GWLs have a slower response, though long-term trends gradually emerge under the SSP5-RCP8.5 scenario, up to 0.872 m/yr. Groundwater extraction directly drives GWL decline, suppressing seasonal fluctuations and extending the response time to precipitation, with a maximum lag of 8 months. Precipitation indirectly affects subsidence through the multi-aquifer system, with subsidence-rebound variations mainly influenced by groundwater extraction and GWL fluctuations. Overall, climate change affects subsidence fluctuations, while groundwater extraction remains the primary factor for long-term subsidence trends.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"62 ","pages":"Article 102773"},"PeriodicalIF":5.0,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107708","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":"Geochemical, hydrochemical and remote sensing study of an Andean calcareous wetland in Huanta, Peru","authors":"Bruno K. Cardenas Morales ,&nbsp;John Forrest ,&nbsp;Walter V. Castro Aponte ,&nbsp;Henry E. Sanchez Cornejo ,&nbsp;Braulio La Torre ,&nbsp;Jorge Jhoncon Kooyip ,&nbsp;Patrick Byrne ,&nbsp;T.T. Nguyen ,&nbsp;Crispin H.W. Barnes ,&nbsp;Luis De Los Santos Valladares","doi":"10.1016/j.ejrh.2025.102767","DOIUrl":"10.1016/j.ejrh.2025.102767","url":null,"abstract":"<div><h3>Study region</h3><div>The Huaper Wetland is located in the Huanta Province, Ayacucho Region, in the Central Peruvian Andes at 2353 m above sea level. This calcareous highland ecosystem has a key role for irrigation, biodiversity conservation, and local water supply. However, it is increasingly affected by unregulated tourism, agricultural runoff, and poor waste management.</div></div><div><h3>Study focus</h3><div>This study presents the first integrated geochemical, hydrochemical, and remote sensing assessment of the Huaper Wetland. Water samples were collected during four campaigns across two hydrological years (March and November 2023–2024), representing both Austral summer and winter. Parameters analyzed included pH (6.92–7.22), electrical conductivity (0.87–0.94 dS/m), total dissolved solids, dissolved oxygen (min. 1.43 mg/L), and potentially toxic elements. Seventeen sediment samples were characterized using Energy Dispersive X-ray Spectroscopy (EDX) and X-ray Diffraction (XRD), confirming dominance of calcite (up to 43.8 %) and magnesium calcite (32.8 %), with traces of nitratine (NaNO₃) suggesting agricultural influence. Surface moisture dynamics were assessed using the Normalized Difference Water Index (NDWI) from Sentinel-2 imagery (2016–2023).</div></div><div><h3>New hydrological insights for the region</h3><div>The results indicate initial signs of water quality deterioration, with nitrate levels in November 2023 (8.09 mg/L) exceeding national standards and a decline in the Water Quality Index from “Excellent” (94.92–100.00) in 2023 to “Good” (92.04) in 2024. NDWI analysis revealed a persistent decrease in surface moisture, with a minimum in 2017 (–0.4699; STD = 0.0716). Elevated sodium concentrations and low dissolved oxygen levels may destabilize redox conditions, potentially mobilizing arsenic and lead. These findings suggest a weakening of the wetland’s geochemical buffering capacity and highlight the urgency of implementing land-use regulation, salinity control, and cost-effective long-term monitoring in calcareous Andean wetlands.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"62 ","pages":"Article 102767"},"PeriodicalIF":5.0,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145108013","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":"Reconstruction and inversion of lake water depth based on ICESat-2 photon and Sentinel-2 — A case study of Caiduochaka Lake on the Tibetan Plateau","authors":"Tianjiao Du,&nbsp;Jinzhu Li,&nbsp;Zhongxuan Wang,&nbsp;Yuqi Zhang,&nbsp;Baojin Qiao","doi":"10.1016/j.ejrh.2025.102776","DOIUrl":"10.1016/j.ejrh.2025.102776","url":null,"abstract":"<div><h3>Study region</h3><div>Caiduochaka Lake (CK) on the Tibetan Plateau (TP).</div></div><div><h3>Study focus</h3><div>This study reconstructs the bathymetry of CK by integrating Sentinel-2's large-area, spatially continuous spectral data with precise but spatially discontinuous depth references from ICESat-2. Three machine learning models were developed to invert water depth from Sentinel-2 spectral reflectance, trained on ICESat-2-derived and in situ bathymetry. The aim is to evaluate whether ICESat-2-derived bathymetry can substitute field measurements, and to demonstrate the combined value of both datasets for robust, large-scale bathymetric mapping.</div></div><div><h3>New hydrological insights</h3><div>Reconstruction of water depth in CK revealed a maximum depth of 14.73 m and an average depth of 3.90 m, with ICESat-2-derived bathymetry showing strong agreement with in situ bathymetry (R²=0.985, RMSE=0.534 m). When using ICESat-2-derived bathymetry as training data, the KAN model yielded the best performance (R²=0.911, RMSE=1.064 m), whereas the RF model trained on in situ bathymetry achieved the highest overall accuracy. The bathymetry and water storage estimates obtained from both approaches were highly consistent, indicating that ICESat-2-derived bathymetry can reliably substitute for traditional field measurements in shallow water areas. From 2000–2023, lake water storage increased by 0.529–0.555 km³, reflecting significant long-term hydrological changes in the region. These findings provide a robust and scalable approach for reconstructing lake water depth, monitoring lake water resources, and evaluating hydrological responses to climate variability on the TP.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"62 ","pages":"Article 102776"},"PeriodicalIF":5.0,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145108014","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":"PrecipNet: A transformer-based downscaling framework for improved precipitation prediction in San Diego County","authors":"AmirHossein Adibfar ,&nbsp;Hassan Davani","doi":"10.1016/j.ejrh.2025.102738","DOIUrl":"10.1016/j.ejrh.2025.102738","url":null,"abstract":"<div><h3>Study region</h3><div>San Diego County (California, USA), with its complex topography and coastal climate variability, requires high-resolution precipitation data to support hydrological modeling and climate adaptation planning. However, the coarse spatial resolution of Global Climate Models (GCMs) limits their applicability in such a diverse and hydrologically sensitive region.</div></div><div><h3>Study focus</h3><div>This study introduces a two-stage hybrid statistical downscaling framework that combines Transformer-based deep learning with traditional machine learning for localized precipitation prediction. The goal is to downscale coarse-resolution CMIP5 precipitation data (2° × 2.5°, 3-h intervals) to a finer 10 km × 10 km grid appropriate for regional hydrological applications. The first stage employs HydroFusionNet, a Transformer-based classifier, to detect rainfall occurrence using spatial atmospheric predictors, thereby filtering out non-rain periods and improving computational efficiency. The second stage applies two regression models: a Random Forest with linear bias adjustment and PrecipNet, a Transformer-based model.</div></div><div><h3>New hydrological insights for the region</h3><div>PrecipNet achieved a Mean Absolute Error (MAE) of 1.24 mm, Root Mean Square Error (RMSE) of 1.62 mm, and R² of 0.94, outperforming the Random Forest baseline in accuracy and spatial generalization. HydroFusionNet demonstrated 92.75 % classification accuracy, enhancing rainfall detection. The framework reduces false positives, captures complex rainfall dynamics, and provides context-aware uncertainty estimation—offering a scalable, hydrologically meaningful tool for regional climate impact assessments and water resource decision-making in topographically complex areas like San Diego County.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"62 ","pages":"Article 102738"},"PeriodicalIF":5.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050400","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":"Relationships between surface water area and hydrologic fluxes in a cold region terminal lake basin","authors":"Md Helal Ahmmed ,&nbsp;Taufique H. Mahmood ,&nbsp;Alexis L. Archambault ,&nbsp;Sharhad Wainty","doi":"10.1016/j.ejrh.2025.102766","DOIUrl":"10.1016/j.ejrh.2025.102766","url":null,"abstract":"<div><h3>Study region</h3><div>Northern Great Plains (NGP), North Dakota, USA</div></div><div><h3>Study focus</h3><div>Recent climate shifts have caused a prolonged wet period in the NGP since 1993, significantly expanding surface water area and hydrological connectivity. However, the spatiotemporal links between the water connectivity and hydrologic fluxes remain poorly understood in terminal basins like the Devils Lake Basin (DLB), and accurately estimating open water evaporation (OWE) in this cold region is challenging due to winter ice cover. In this study, we developed a modified framework for estimating OWE in cold regions and investigated the spatiotemporal dynamics of water connectivity and hydrological fluxes.</div></div><div><h3>New hydrologic Insights</h3><div>The modified framework produced an average annual net evaporation of 785 mm (2000–2015), closely matched with the USGS estimation (825 mm). A clear hysteresis loop between OWE and both permanent and summer water areas reflected wetting (1996–2011) and drying (2013–2018) phases, highlighting a nonlinear response to shifting climate conditions. Strong nonlinear correlations were found between water connectivity and streamflow across the DLB (R² = 0.49–0.79). While Random Forest model captured this nonlinearity in training (NSE = 0.37–0.68), predictive performance declined during testing (NSE = 0.15–0.46), underscoring the region’s climatic and geomorphic complexity. Multiple Linear Regression revealed spatial heterogeneity in streamflow drivers: water connectivity in western subbasins, precipitation in the north-central, and temperature in the east. These findings highlight the need to consider nonlinearity and spatial variability in cold-region water modeling under climate change.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"62 ","pages":"Article 102766"},"PeriodicalIF":5.0,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046744","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":"Hybrid heterogeneous ensemble learning framework for flood susceptibility mapping in Balochistan, Pakistan","authors":"Muhammad Afaq Hussain ,&nbsp;Zhanlong Chen ,&nbsp;Biswajeet Pradhan ,&nbsp;Sansar Raj Meena ,&nbsp;Yulong Zhou","doi":"10.1016/j.ejrh.2025.102718","DOIUrl":"10.1016/j.ejrh.2025.102718","url":null,"abstract":"<div><h3>Study region</h3><div>The National Highways 85 and 50, key routes of the China–Pakistan Economic Corridor (CPEC) in Balochistan, Pakistan.</div></div><div><h3>Study focus</h3><div>Flooding is a natural disaster that is becoming increasingly frequent and severe. The National Highways 85 and 50 are vulnerable, necessitating accurate flood susceptibility mapping (FSM). Current machine learning (ML) models for FSM often suffer from low efficiency and overfitting. This study introduces an innovative hybrid FSM approach using four heterogeneous ensemble learning (HEL) techniques combined with three ML models: Random Forest (RF), Support Vector Machine (SVM), and Light Gradient Boosting Machine (LGBM). The proposed method was tested using satellite data from Sentinel-1, Sentinel-2, and Landsat-8, analyzing 1371 flood locations and 12 contributing variables. RF, variable importance factors (VIF), and information gain ratio (IGR) were applied to assess multicollinearity. The dataset was split (70:30) for model training and testing, with HEL-based models achieving superior performance over single ML models.</div></div><div><h3>New hydrological insights for the region</h3><div>The stacking model yielded the highest AUROC (0.98), Kappa (0.82), accuracy (0.927), precision (0.963), Matthew’s correlation coefficient (0.820), and F1-score (0.950). HEL-based models proved more stable and resistant to overfitting. IGR analysis identified slope and distance from streams as key factors in FSM. The resulting flood-prone maps provide insights for disaster management adaptation strategies, demonstrating the broader applicability of the developed approach to enhance FSM accuracy and reliability.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"61 ","pages":"Article 102718"},"PeriodicalIF":5.0,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144864540","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":"Stratification and mixing dynamics of hypersaline Lake Urmia (Iran)","authors":"Peygham Ghaffari ,&nbsp;Jafar Azizpour ,&nbsp;Evgeniy Yakushev ,&nbsp;Hamid A.K. Lahijani","doi":"10.1016/j.ejrh.2025.102697","DOIUrl":"10.1016/j.ejrh.2025.102697","url":null,"abstract":"<div><h3>Study region:</h3><div>Lake Urmia, northwestern Iran</div></div><div><h3>Study focus:</h3><div>This study investigates the stratification and vertical mixing dynamics of Lake Urmia, a transboundary hypersaline lake under critical ecological stress. Using multi-year, high-resolution in-situ temperature and salinity measurements (2016–2019), we characterize seasonal mixing patterns and quantify the relative contributions of salinity and temperature to vertical water column stability. A lake-specific density formulation and thermal energy estimates are applied to assess buoyancy structure and overturn dynamics across contrasting seasonal phases.</div></div><div><h3>New hydrogeological insights from the region:</h3><div>Lake Urmia exhibits a dual-phase mixing regime, polymictic during the warm season with recurrent full-depth mixing, and inverse meromictic during the cold season due to salinity-enhanced density stratification. Salinity is shown to be the dominant stabilizing factor, while temperature plays a supporting role, primarily as a long-term tracer. The lake is classified as <em>Hyperhalimictic</em>, where mixing is governed by salinity rather than classical thermal stratification. A seasonal salinity pump mechanism is identified—winter brine rejection deepens stratification, while summer halite re-dissolution erodes it. Thermal inertia in lakebed sediments contributes to persistent cold-phase stratification. Findings indicate a trend toward increasing vertical decoupling, reduced overturning, and elevated risk of ecological stress. These insights support improved understanding of hypersaline lake behavior and can guide monitoring and resilience strategies in similar terminal lakes worldwide.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"61 ","pages":"Article 102697"},"PeriodicalIF":5.0,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144864541","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":"Assessment of horizontal shifting errors in open-source DEMs and their impact on 1D hydraulic modeling of meandering river channel","authors":"Md. Ismail Firoz ,&nbsp;Md. Latifur Rahman Sarker ,&nbsp;Janet Nichol ,&nbsp;Eko Siswanto","doi":"10.1016/j.ejrh.2025.102707","DOIUrl":"10.1016/j.ejrh.2025.102707","url":null,"abstract":"<div><h3>Study Region</h3><div>The Ganges River, Bangladesh</div></div><div><h3>Study Focus</h3><div>Horizontal shifting error in open-access GDEMs has received very little attention for detecting and assessing its impact on hydraulic modeling. This study uses three integrated techniques to detect horizontal shifting errors in ASTER and SRTM DEMs by comparing DEM river centerlines and real-time river centerlines, measuring river channel dynamics, and analyzing water level elevation profiles. Four strategies were applied to assess the impacts: evaluating DEM-generated river channels, creating a 1D hydraulic model for inundation mapping, assessing inundation accuracy, and visualizing the horizontal shifts.</div></div><div><h3>New Hydrological Insights for the Region</h3><div>The results reveal several significant issues. Firstly, both DEMs exhibited notable horizontal shifting errors, with observed maximum, average, and minimum errors of 3200 m, 1800 m, and 680 m, respectively, though the intensity of these errors varied spatiotemporally. Secondly, the maximum riverbed effectiveness (≥73.38 %) was observed in 2000, but subsequently decreased to ≤ 41.25 %, ≤ 47.94 %, ≤ 46.75 %, and ≤ 54.08 % in 2006, 2010, 2015, and 2020, respectively. Thirdly, the horizontal shifting error was found to have severe implications for 1D inundation mapping accuracy. Lastly, strong negative correlation was observed between river channel dynamics and DEM river channel effectiveness (r ≥ -0.963). The results indicate that both DEMs have horizontal shifting errors, which are expected to produce severe inaccurate results in hydraulic and flood modeling, especially in areas with dynamic topography.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"61 ","pages":"Article 102707"},"PeriodicalIF":5.0,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858308","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}