Journal of Hydrology-Regional Studies最新文献

{"title":"Examining the complexity of discharge-suspended sediment behaviour of a tropical monsoon-dominated river, India","authors":"Balai Chandra Das ,&nbsp;Aznarul Islam ,&nbsp;Sadik Mahammad ,&nbsp;Edris Alam ,&nbsp;Abu Reza Md Towfiqul Islam ,&nbsp;Biplab Sarkar ,&nbsp;Suman Deb Barman ,&nbsp;Subodh Chandra Pal ,&nbsp;Md Kamrul Islam","doi":"10.1016/j.ejrh.2025.102335","DOIUrl":"10.1016/j.ejrh.2025.102335","url":null,"abstract":"<div><h3>Study region</h3><div>Multiple gauging stations of the Brahmani River Basin, India.</div></div><div><h3>Study focus</h3><div>This study provides an understanding of sediment transport complexities in monsoon-type rivers and supports informed decision-making for effective river management and environmental conservation. It also underscores the impact of surface landscape, the management of reservoirs and the role of anthropogenic impacts on sediment transport dynamics.</div></div><div><h3>New hydrological insights for the region</h3><div>River discharge and sediment concentration are measured at four gauging stations of Brahmani River: Tilga, Panposh, Gomlai, and Jenapur. The first three are located in the upper reaches above the Rengali Dam, while Jenapur is in the plain area below the dam. The analysis reveals diverse correlations for fine sediment (&lt;0.075 mm), ranging from linear at Tilga to logarithmic at Panposh and power-law at Gomlai. Medium sediment (0.075–0.2 mm) exhibits linear and logarithmic correlations at Tilga and Panposh while coarse sediment (&gt;0.2 mm) displays logarithmic correlations at Panposh and Gomlai. Total sediment concentrations showcase both linear and logarithmic relationships at Tilga and Panposh, indicating the complexity of sediment-transport dynamics. The study unveils compelling relationships between river discharge and total sediment load (MT/day), with notable power-lla correlations at Gomlai, emphasising the influence of river discharge variations on sediment transport quantity. It underscores the impact of surface geology, the location of reservoirs and the role of anthropogenic interventions on sediment dynamics.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"59 ","pages":"Article 102335"},"PeriodicalIF":4.7,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing the accuracy and generalizability of reference evapotranspiration forecasting in California using deep global learning","authors":"Arman Ahmadi ,&nbsp;Andre Daccache ,&nbsp;Minxue He ,&nbsp;Peyman Namadi ,&nbsp;Alireza Ghaderi Bafti ,&nbsp;Prabhjot Sandhu ,&nbsp;Zhaojun Bai ,&nbsp;Richard L. Snyder ,&nbsp;Tariq Kadir","doi":"10.1016/j.ejrh.2025.102339","DOIUrl":"10.1016/j.ejrh.2025.102339","url":null,"abstract":"<div><h3>Study region</h3><div>This research focuses on the Central Valley of California, a climatically homogeneous region known for its significant agricultural productivity and reliance on extensive irrigation. Our study utilizes monthly reference evapotranspiration (ET_O) time series data from 55 standardized weather stations as part of the California Irrigation Management Information System (CIMIS).</div></div><div><h3>Study focus</h3><div>ET_O is a critical component of regional water cycles, indicating atmospheric water demand. This study evaluates the potential of deep learning (DL) models for ET_O forecasting, particularly emphasizing the efficacy of a global learning scheme compared to traditional local learning. Global learning involves training forecasting models on pooled data from multiple time series, tested over new instances. We compared the performance of statistical models and advanced DL models, demonstrating significant accuracy enhancements in global learning schemes. We also explored automatic hyperparameter optimization for these models to achieve state-of-the-art forecasting accuracy, yielding RMSE values below 10 mm/month for one-year-ahead forecasts on new, unseen stations.</div></div><div><h3>New hydrological insight for the region</h3><div>Applying global learning methodologies to DL models markedly improved forecasting performance, showcasing an ability to generalize findings to ungauged regions and even newly established weather stations. This suggests a promising avenue for enhancing water resource management efficiency in data-scarce areas. Our findings argue that such data-centric methodological shifts could play a critical role in better managing the irrigation demands of the Central Valley, thereby supporting sustainable water usage and agricultural productivity in the region.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"59 ","pages":"Article 102339"},"PeriodicalIF":4.7,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing runoff simulation by combining superflex with deep learning methods in China's Qinghai Lake Basin, Northeast Tibetan Plateau","authors":"Kaixun Liu ,&nbsp;Na Li ,&nbsp;Sihai Liang","doi":"10.1016/j.ejrh.2025.102331","DOIUrl":"10.1016/j.ejrh.2025.102331","url":null,"abstract":"<div><h3>Study region</h3><div>The Qinghai Lake Basin on the Northeast Tibetan Plateau.</div></div><div><h3>Study focus</h3><div>Coupling physical models with deep learning methods offers potential advantages for runoff simulation, optimizing their interaction remains a crucial challenge. This study investigates hybrid models combining the Superflex hydrological model with Gated Recurrent Unit (GRU) for runoff modeling and simulation in the Qinghai Lake Basin. Our approach leverages Superflex as a pre-training step for the neural networks and incorporates key process variables from the physical model as inputs to the network, creating a physically-driven deep learning framework. We systematically explore various input-output combinations and selections of deep learning models, and comprehensively evaluated the most effective configuration for this specific basin. Furthermore, we use the SHAP method to reveal how meteorological factors influence runoff and their complex relationships, making the results interpretable.</div></div><div><h3>New hydrological insights for the region</h3><div>Compared to hydrological model, hybrid models significantly improve performance by incorporating internal hydrological variables and meteorological data as input features, reducing the error by over 50 % in Buha River Basin. We further observed that although different deep learning architectures exhibit varying performance outcomes, the GRU-based models consistently demonstrated significantly superior predictive capabilities. In addition, we use SHAP to understand the internal operation of the model, revealing how meteorological factors affect runoff and their complex relationships, successfully unveiling the \"black box\" nature of deep learning models.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"59 ","pages":"Article 102331"},"PeriodicalIF":4.7,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Method for identifying non-stationary hydrological drought in regions with intense human activities","authors":"Minhua Ling ,&nbsp;Jiawei Yao ,&nbsp;Cuimei Lv ,&nbsp;Erwei Zheng ,&nbsp;Xingyu Chu","doi":"10.1016/j.ejrh.2025.102330","DOIUrl":"10.1016/j.ejrh.2025.102330","url":null,"abstract":"<div><h3>Study region</h3><div>the middle reaches of the Yellow River Basin (MYRB).</div></div><div><h3>Study focus</h3><div>In regions with intense human activities, the stationarity of runoff sequences was disrupted due to the dual impacts of climate changing and human activities. Therefore, establishment of non-stationary hydrological drought identification methods is urgently required. Previous research infrequently addressed the influence of indirect human activities on the non-stationary hydrological drought. This study comprehensively considered the impacts of climate change, direct human activities, and indirect human activities (underlying surface alterations) on runoff. Consequently, a non-stationary standardized runoff index (NSRI) was constructed based on the Generalized Additive Models for Location, Scale, and Shape, establishing a method for identifying non-stationary hydrological droughts in regions with intense human activities.</div></div><div><h3>New hydrological insights for the region</h3><div>This study uncovered the variability in hydrological drought characteristics in the major sub-basins of the MYRB, where human activities are intense, from 1960–2019.The results demonstrate that the non-stationary hydrological drought identification method constructed in this paper is feasible. The NSRI exhibits superior performance in identifying hydrological drought events across multiple timescales compared with the Standardized Runoff Index. In many sub-basins, the frequency of severe and extreme droughts across various time scales increased, with upward trends measured in both drought severity and intensity. The severity of hydrological drought in the MYRB may further intensify in the future.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"59 ","pages":"Article 102330"},"PeriodicalIF":4.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating the groundwater table threshold for mitigating soil salinization in the Songnen Plain of China","authors":"Yiding Ding ,&nbsp;Haishen Lü ,&nbsp;Ligang Xu ,&nbsp;Robert Horton ,&nbsp;Mingliang Jiang ,&nbsp;Yonghua Zhu ,&nbsp;Junxiang Cheng ,&nbsp;Hongxiang Fan ,&nbsp;Jianbin Su","doi":"10.1016/j.ejrh.2025.102326","DOIUrl":"10.1016/j.ejrh.2025.102326","url":null,"abstract":"<div><h3>Study region</h3><div>The Songnen Plain is a key part of China’s largest plain, situated in northeastern China.</div></div><div><h3>Study focus</h3><div>Soil salinization has become one of the largest ecological issues in the Songnen Plain, and regulating groundwater levels is a crucial strategy for mitigating it. To address this, a comprehensive framework is developed to estimate the groundwater table threshold for soil salinization by integrating field sampling, remote sensing big data, and machine learning models.</div></div><div><h3>New hydrological insights for the region</h3><div>The soil salinity inversion model, which utilizes a random forest algorithm, achieves the highest accuracy (R² = 0.75, d = 0.94, RPD = 2.05), outperforming SVM, LightGBM, and XGBoost algorithms. From 2020–2023, areas with mild salinization accounted for 9.3 % of the total area, while moderate salinization accounted for 3.2 %, severe salinization accounted for 4.0 %, and saline soil areas accounted for 0.6 %. A probabilistic model further identifies groundwater depth thresholds for salinization: 2.3 m for sandy soil, 3.1 m for loamy soil, and 1.1 m for silty soil. Based on current groundwater depths, it is anticipated that 15.5 % of the Songnen Plain area will continue to be affected by soil salinization or remain at risk of potential salinization.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"59 ","pages":"Article 102326"},"PeriodicalIF":4.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Earlier vegetation green-up is intensifying hydrological drought in the Tianshan Mountain basins","authors":"Lilin Zheng ,&nbsp;Ruishan Chen ,&nbsp;Jianhua Xu ,&nbsp;Yinshuai Li ,&nbsp;Nan Jia ,&nbsp;Xiaona Guo","doi":"10.1016/j.ejrh.2025.102321","DOIUrl":"10.1016/j.ejrh.2025.102321","url":null,"abstract":"<div><h3>Study region</h3><div>Two inland basins in the Tianshan Mountains: the Huangshui Basin (HSB) and the Kashi Basin (KSB).</div></div><div><h3>Study focus</h3><div>Earlier green-up driven by climate warming can profoundly alter hydrological processes. However, the isolated impact of phenological changes on hydrological dynamics, independent of climatic factors, remains largely unexplored. To address this gap, we quantified the contributions of earlier green-up to evapotranspiration (ET) and basin streamflow by developing a framework that integrates an NDPI-based phenology model with the RHESSys eco-hydrological model.</div></div><div><h3>New hydrological insights for the region</h3><div>Between 2001 and 2020, the start of the growing season (SOS) advanced at rates of 1.02 days/year in HSB and 0.97 days/year in KSB, while changes in the end of the growing season were minimal (0.17 and − 0.12 days/year, respectively). In HSB, each one-day advancement in green-up increased ET by 2.22 mm and reduced streamflow discharge by 2.35 million m³. In contrast, in KSB, a one-day advancement in green-up resulted in a more significant increase in ET (11.22 mm) and a more substantial reduction in streamflow discharge (58.24 million m³). KSB's westward-facing topography facilitates the inflow of warm, humid Atlantic air, contributing to a more humid climate, higher vegetation cover, and an earlier SOS compared to HSB. These findings highlight the importance of incorporating phenological dynamics into eco-hydrological assessments to develop effective climate adaptation strategies for inland mountain basins.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"59 ","pages":"Article 102321"},"PeriodicalIF":4.7,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Groundwater depletion intensified by irrigation and afforestation in the Yellow River Basin: A spatiotemporal analysis using GRACE and well monitoring data with implications for sustainable management","authors":"Shuitao Guo ,&nbsp;Yingying Yao ,&nbsp;Qiang Ji ,&nbsp;Huijun Jin ,&nbsp;Taihua Wang ,&nbsp;Michele Lancia ,&nbsp;Xianhong Meng ,&nbsp;Chunmiao Zheng ,&nbsp;Dawen Yang","doi":"10.1016/j.ejrh.2025.102324","DOIUrl":"10.1016/j.ejrh.2025.102324","url":null,"abstract":"<div><h3>Study region</h3><div>Yellow River Basin.</div></div><div><h3>Study focus</h3><div>This study analyzes trends in groundwater storage (GWS) changes and their influencing factors in the Yellow River Basin (YRB) using GRACE satellite data and groundwater level measurements. The Soil-Water-Balance model was developed to simulate groundwater recharge (GWR), quantifying the discrepancies between GWS and GWR at the basin scale. Spatiotemporal changes in GWS and GWR are critical indicators for identifying regions at risk of depletion and evaluating groundwater sustainability.</div></div><div><h3>New hydrological insights for the region</h3><div>The results indicate that between 2002 and 2022, the YRB experienced a reduction of 52.28 Gt in terrestrial water storage, with the GWS losing 77.02 Gt. Except for the significant increase in GWS in the source region, GWS decreased at a rate of 5.56 Gt/yr for entire basin. The annual GWR in the YRB was 103 mm/yr, showing a steady increase of 8.5 mm/decade. However, in the middle and lower reaches of the YRB, GWR failed to compensate for consumption. Approximately 73.71 % of the YRB area was identified as a groundwater risk zone. In the source region, natural factors such as precipitation and snowmelt are the primary drivers of groundwater changes. In contrast, afforestation and irrigation play key roles in the middle reaches, while agricultural is the dominant factor in the lower reaches.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"59 ","pages":"Article 102324"},"PeriodicalIF":4.7,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rainfall and streamflow variability in North Benin, West Africa, and its multiscale association with climate teleconnections","authors":"Orou Moctar Ganni Mampo ,&nbsp;Kossi François Guedje ,&nbsp;Bruno Merz ,&nbsp;Ezéchiel Obada ,&nbsp;Ravi Kumar Guntu ,&nbsp;Halissou Yarou ,&nbsp;Adéchina Eric Alamou ,&nbsp;Jean Hounkpe","doi":"10.1016/j.ejrh.2025.102319","DOIUrl":"10.1016/j.ejrh.2025.102319","url":null,"abstract":"<div><h3>Study region</h3><div>Three tributaries of the Niger River, covering 48,000 km² in northern Benin, West Africa.</div></div><div><h3>Study focus</h3><div>Understanding rainfall and streamflow variability in a warming world is crucial for drought-prone West Africa, whose economy relies heavily on rain-fed agriculture. This study explores past changes (1970–2020) in catchment rainfall and streamflow and their association with climate teleconnections.</div></div><div><h3>New hydrological insights for the region</h3><div>We find consistent rainfall patterns across the three catchments, with a recovery from the 1970s-1980s droughts starting in the 1990s. Total rainfall has increased significantly driven by more rainy days, although the wet day rainfall amount has decreased. These results can be summarized as ‘increased total rainfall, but less intense and more variable in space’. More rain, however, does not mean that the drought situation is alleviated, as high interannual and decadal variability persists. Wavelet coherence reveals that rainfall and streamflow variability are modulated by the climate teleconnections ENSO, AMO, and IOD. For rainfall, we find a tendency of a shift from lower-frequency coherence (4–10 years) in earlier decades to higher-frequency coherence (1–3 years) in recent decades. These patterns are less pronounced for streamflow due to indirect climate influences. Unlike many African studies relying on model simulations, these findings are based on quality-checked, dense station data networks, essential for understanding local climate impacts, water management, and early warning systems.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"59 ","pages":"Article 102319"},"PeriodicalIF":4.7,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanisms of climate-induced lake dynamics in the Source Region of Three Rivers, Tibetan Plateau","authors":"Xiankun Zheng ,&nbsp;Sihai Liang ,&nbsp;Dezhao Yan ,&nbsp;Xingxing Kuang ,&nbsp;Li Wan","doi":"10.1016/j.ejrh.2025.102323","DOIUrl":"10.1016/j.ejrh.2025.102323","url":null,"abstract":"<div><h3>Study region</h3><div>The Source Region of the Three Rivers (SRTR), Tibetan Plateau, China.</div></div><div><h3>Study focus</h3><div>Lake variation studies in the SRTR have primarily focused on a few large lakes, with limited understanding of the long-term trends, stages, and main driving mechanisms of change in lakes larger than 1 km², especially the role of groundwater. This study proposes an attributing framework based on trend tests and water balance to investigate the changes in SRTR lakes with areas over 1 km² from 1990 to 2019. Nearly all potential drivers of lake dynamics, including groundwater, precipitation, evapotranspiration, glaciers, permafrost, and geological structure, are incorporated into the analysis.</div></div><div><h3>New hydrological insights for the region</h3><div>Results indicate an upward trend in the number of lakes (approximately 29 %), area (around 17 %), and volume (growing at an estimated 0.23 Gt/yr). Trend tests and water balance methods revealed that changes in groundwater significantly impacted 47 lakes (including 18 endorheic and 7 exorheic basins). Incorporating precipitation, evapotranspiration, and other factors, we further classified these changes into eight expansion patterns (involving 198 lakes) and four shrinkage patterns (involving 4 lakes), highlighting the diverse response mechanisms of lakes. Additionally, a three-phase evolutionary trend in the spatiotemporal dynamics of lakes was identified, providing a scientific basis for precise water resource regulation and lake ecosystem protection under different future scenarios in the SRTR.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"59 ","pages":"Article 102323"},"PeriodicalIF":4.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated framework for non-stationary hydrological drought assessment using time-varying parameter standardized streamflow index and time-varying threshold level method","authors":"Menghao Wang ,&nbsp;Shanhu Jiang ,&nbsp;Liliang Ren ,&nbsp;Junzeng Xu ,&nbsp;Shanshui Yuan ,&nbsp;Chong-Yu Xu","doi":"10.1016/j.ejrh.2025.102329","DOIUrl":"10.1016/j.ejrh.2025.102329","url":null,"abstract":"<div><h3>Study region</h3><div>Weihe River basin (WRB) in northern China.</div></div><div><h3>Study focus</h3><div>In a changing environment, traditional drought assessment methods may not be applicable when assumptions of stationarity are violated. Accordingly, this study proposes a framework that incorporates the time-varying parameter standardized streamflow index (SSI_var) and threshold level method (TL_var) for the non-stationary hydrological drought assessment. Then, the SSI_var and TL_var methods are compared with time-invariant and transplantation parameter SSI (SSI_invar and SSI_tran) and TL (TL_invar and TL_tran) to validate their effectiveness.</div></div><div><h3>New hydrological insights for the region</h3><div>Validation results showed that SSI_var has the highest Kendall correlation coefficients with standardized precipitation index (SPI) and soil moisture index (SSMI) at 0.81 and 0.78, respectively, outperforming SSI_invar (0.67and 0.62) and SSI_tran (0.68 and 0.63). The TL_var method behaves in the same way, indicating that the SSI_var and TL_var methods provide a more accurate assessment of non-stationary hydrological drought. Furthermore, the comparison results show that the mean duration and severity of hydrological drought in the WRB increased by 22.37 % and 13.72 % for SSI_var method and 34.69 % and 19.15 % for TL_var method from 1961–1990 to 1991–2020, respectively, revealing that hydrological drought in the WRB has aggravated over the past 30 years. Overall, the combined use of SSI_var and TL_var provides a comprehensive understanding of non-stationary drought, integrating qualitative (e.g., severity levels) and quantitative (e.g., streamflow deficits) measures.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"59 ","pages":"Article 102329"},"PeriodicalIF":4.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}