Journal of Hydrology最新文献

{"title":"Attribution of streamflow and its seasonal variation to dual nature-society drivers using CMIP6 data and hydrological models","authors":"Menghao Wang ,&nbsp;Shanhu Jiang ,&nbsp;Liliang Ren ,&nbsp;Hao Cui ,&nbsp;Shanshui Yuan ,&nbsp;Junzeng Xu ,&nbsp;Chong-Yu Xu","doi":"10.1016/j.jhydrol.2025.133314","DOIUrl":"10.1016/j.jhydrol.2025.133314","url":null,"abstract":"<div><div>Attributing changes in streamflow processes is crucial for water resource management as well as for understanding and mitigation of flood and drought risks. However, most existing attribution methods lack a unified approach to handle various causal variables, making them unsuitable for comprehensive attribution assessments. Therefore, this study proposed a framework to quantitatively attribute the impacts of natural and anthropogenic climate change, land use and cover change (LUCC), and human water withdrawal on streamflow and its seasonality. The framework consists of three steps: (1) bias correction of Coupled Model Intercomparison Project Phase 6 (CMIP6) data and construction of a dualistic nature-society water cycle model; (2) simulation of streamflow processes and identification of streamflow seasonality under different climate forcing and LUCC scenarios; and (3) quantitative attribution of streamflow evolution characteristics. The Weihe River Basin (WRB) in China has been selected as a case study area for the proposed attribution framework. The quantitative analysis indicates that natural and anthropogenic climate change, LUCC, and human water withdrawal account for 20.8%, 27.9%, 4.6%, and 46.7% of the decreasing trend in streamflow volume and –42.4%, –28.1%, –5.1%, and 175.6% of the weakening trend in streamflow seasonality in the WRB, respectively. These results suggest that human water withdrawal reduces streamflow and weakens its seasonality, while the other three factors contribute to streamflow reduction but enhance its seasonality. Overall, this study effectively distinguishes the impacts of anthropogenic and natural climate change on streamflow processes, thus providing a deep understanding of the influences of human-induced hydro-climate change.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"659 ","pages":"Article 133314"},"PeriodicalIF":5.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integral delay inspired deep learning model for single pool water level prediction","authors":"Xiaohui Lei ,&nbsp;Jiahao Wu ,&nbsp;Yan Long ,&nbsp;Lingqiang Chen ,&nbsp;Xiaowei Liu ,&nbsp;Huimin Xu","doi":"10.1016/j.jhydrol.2025.133328","DOIUrl":"10.1016/j.jhydrol.2025.133328","url":null,"abstract":"<div><div>Accurate water level prediction is essential for optimizing water resource allocation in large-scale water transfer projects. Although traditional hydrodynamic models can accurately predict water level changes, they heavily rely on foundational data such as topography and model parameters, and come with high computational costs. In contrast, deep learning models overcome the limitations of traditional ones in capturing complex water level dynamics by extensively learning long-term temporal dependencies. However, most deep learning models ignore hydraulic time-delay characteristics, making it difficult to accurately predict abrupt changes. To address this issue, this study proposes a Hydrological Physics-informed Attention (HPA) model for predicting single-step water level of specific channel pools in the South-to-North Water Diversion Project in China. HPA uses Integral Delay (ID) theory as the physical foundation, which constructs a linear relationship between upstream and downstream hydrological information with respect to time-delay. HPA leverages the powerful representational capacity of deep learning to address the challenges in prior knowledge acquisition and computational efficiency posed by traditional hydrodynamic models. Specifically, HPA integrates attention mechanisms with ID theory to dynamically represent complex spatiotemporal interactions and delay effects between upstream and downstream attributes. Moreover, HPA mines the periodicity of hydraulic data by adding the time information of the day and week. To learn time-delay information, HPA applies attention on long-term upstream flow data. Besides, it builds short-term attribute correlations within downstream hydrological data. This study validates the proposed model using sensor data from three stations along the Middle Route of the South-to-North Water Diversion Project. Experimental results demonstrate that HPA significantly reduces three key metrics MAE, RMSE, and MAPE compared to existing deep learning models. The MAE, MAPE, and RMSE exhibit average reductions of 45.36 %, 45.35 %, and 49.80 %, respectively. These results show that the physics-informed mechanism used in HPA can improve water level prediction accuracy and stability across various scenarios, offering its superior practicality and reliability over existing models.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"659 ","pages":"Article 133328"},"PeriodicalIF":5.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143854414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring controls on precipitation-runoff dependencies: Implications for non-stationary and spatially heterogeneous analyses","authors":"Tian Lan ,&nbsp;Xinyue Du ,&nbsp;Xue Xie ,&nbsp;Kairong Lin ,&nbsp;Hongbo Zhang ,&nbsp;Xinghui Gong ,&nbsp;Yongqin David Chen ,&nbsp;Chong-Yu Xu","doi":"10.1016/j.jhydrol.2025.133333","DOIUrl":"10.1016/j.jhydrol.2025.133333","url":null,"abstract":"<div><div>Climate change and complex anthropogenic activities present significant challenges to understanding Precipitation-Runoff Dependencies (PRD). Traditional methods, which often assume stationary and linear conditions, may not fully capture these complex relationships. To address this limitation, we propose an integrated framework that incorporates non-stationary and spatially heterogeneous analyses to identify the controlling mechanisms influencing PRD. This framework was applied to eleven sub-basins within the Illinois River Basin, a region characterized by high spatiotemporal variability and intense anthropogenic activity. The study introduces the novel Controlling Index for Changes in Precipitation-Runoff Dependencies (CC-PRD) and employs a geographical detector model to effectively identify and analyze these controlling factors. Our findings reveal that under non-stationary conditions, baseflow (BF) is the primary driver of PRD across all sub-basins. However, its impact varies by basin type: in urban sub-basins, BF weakens PRD, while in rural sub-basins, BF enhances PRD. Beyond BF, anthropogenic factors, such as impervious surface percentage (ISP) and rural area percentage (RAP), emerge as the key drivers of PRD variation in urban sub-basins, whereas in rural sub-basins, natural factors, particularly potential evapotranspiration (PET), play a dominant role in shaping PRD. Spatially, PET, BF, and RAP emerge as the main determinants of PRD, with their interactions with other factors significantly amplifying their influence, accounting for over 90% of its variability. This comprehensive approach enhances our understanding of the non-stationarity and spatial heterogeneity in PRD, while considering the challenges posed by climate change and human activities to the stationary assumption, thereby supporting strategies for sustainable watershed development.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"659 ","pages":"Article 133333"},"PeriodicalIF":5.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Annual multi-objective optimization model and strategy for scheduling cascade reservoirs on the Yellow River mainstream","authors":"Gang Liu ,&nbsp;Enhui Jiang ,&nbsp;Donglin Li ,&nbsp;Jieyu Li ,&nbsp;Yuanjian Wang ,&nbsp;Wanjie Zhao ,&nbsp;Zhou Yang","doi":"10.1016/j.jhydrol.2025.133306","DOIUrl":"10.1016/j.jhydrol.2025.133306","url":null,"abstract":"<div><div>To enhance the water use efficiency in the Yellow River basin and alleviate the competition among the scheduling objectives of cascade reservoirs, we developed a comprehensive annual multi-objective optimization model based on basin systems science. This model integrated multiple objectives water and sediment management, ecological sustainability, and economic considerations. It was applied to the primary cascade reservoirs along the Yellow River mainstream: the Longyangxia, Liujiaxia, Haibowan, Wanjiazhai, Sanmenxia, and Xiaolangdi reservoirs. Using the Pareto solution set, we examined the optimization scope and potential adjustments under varying hydrologic frequencies, obtaining an optimized scheduling strategy informed by the “runoff conditions − trade-off relationships − optimization space”. The results indicated that as the runoff increased, both the optimization space and magnitude increased. A competitive relationship was observed between power generation and sediment discharge, as well as between ecological needs and sediment discharge, with competition intensifying as runoff increased. During dry years, power generation and ecological objectives were competitive, and this relationship gradually collaborated with increased runoff. A comparison between the segmented and established models demonstrated the superiority of the segmented approach. This study contributes to alleviating water use conflicts in the Yellow River basin, enhancing water utilization efficiency, and supporting effective management of the Yellow River.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"659 ","pages":"Article 133306"},"PeriodicalIF":5.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 novel NMR-capillary pressure method for quantifying pore connectivity and its impact on permeability evolution","authors":"Huimin Wang ,&nbsp;Jiali Tian ,&nbsp;Jianguo Wang ,&nbsp;Xiaolin Wang ,&nbsp;Jinchang Sheng","doi":"10.1016/j.jhydrol.2025.133330","DOIUrl":"10.1016/j.jhydrol.2025.133330","url":null,"abstract":"<div><div>Pore connectivity within rocks is critical for predicting permeability and plays a significant role in evaluating geological reservoirs and engineering applications. However, quantifying pore connectivity remains challenging due to the multiscale pore structures. This study proposes a novel method combining nuclear magnetic resonance (NMR) and capillary pressure (P_c) to quantitatively characterize pore connectivity. Initially, a joint function was established using the relaxation spectra (T₂) of movable water obtained at various capillary pressures. Subsequently, a novel NMR method considering fluid trapping and migration was proposed to quantitatively characterize the pore connectivity of sandstone samples. Finally, a positive correlation between pore connectivity and permeability confirmed the validity of the proposed NMR method. The experimental results indicate that permeability evolution is more strongly correlated with pore connectivity than with porosity. The contribution of pore connectivity to permeability exhibits different stages, influenced by the proportion of pores across multiple scales. The RZ sample, with a pore connectivity of 0.02, suggests that the high percentage of residual water saturation in meso- and micropores is the primary limiting factor. The sorting coefficients of the six samples are ranked as DY &lt; YB &lt; WH &lt; ZG &lt; JN &lt; RZ, reflecting a more homogeneous pore size distribution and demonstrating the best pore connectivity in the DY sample.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"660 ","pages":"Article 133330"},"PeriodicalIF":5.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hydraulic river model calibration and validation for comprehensive hydrograph simulation: Evaluating accuracy across discharge ranges","authors":"Parisa Khorsandi Kuhanestani,&nbsp;Anouk Bomers,&nbsp;Martijn J. Booij,&nbsp;Suzanne J.M.H. Hulscher","doi":"10.1016/j.jhydrol.2025.133210","DOIUrl":"10.1016/j.jhydrol.2025.133210","url":null,"abstract":"<div><div>This study evaluates the performance of a hydraulic model across discharge ranges, including those outside the calibrated range, to assess its robustness beyond calibration conditions. Using the Differential Split-Sample Test (DSST), we systematically assessed the model’s ability to simulate water levels across different discharge ranges while calibrated for specific ranges. The application of the DSST method to hydraulic models introduces a structured framework for evaluating model performance across a wider range of discharge conditions, offering clearer insights into the role of discharge-related roughness calibration. A case study on a lowland river in the Netherlands, employing a two-dimensional depth-averaged (2D) hydraulic model, revealed both the strengths and limitations of 2D simulations with shallow water equations, especially under extreme flow conditions. The findings show that calibrating for moderate discharges yields reliable results for other moderate flows and performs better for extreme low discharges than for high ones. Calibration for low or moderate flows always results in Y-KGE values larger than 0.9 and a MAE less than 10 cm for all moderate and low flows. However, this is not the case for high flows in the validation. While model accuracy declines at the extremes high flows, this approach ensures reliable performance across a broad range of hydraulic conditions and highlights the need for gradual calibration for extreme high flows due to the significant variations in accuracy. Consequently, this approach aids water managers in optimizing resource allocation, predicting and mitigating flood risks, and supply across diverse discharge scenarios.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"660 ","pages":"Article 133210"},"PeriodicalIF":5.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the role of temporal resolution and multi-model ensemble data on WRF/XGB integrated snowfall prediction for the Northeast United States","authors":"Ummul Khaira ,&nbsp;Marina Astitha","doi":"10.1016/j.jhydrol.2025.133313","DOIUrl":"10.1016/j.jhydrol.2025.133313","url":null,"abstract":"<div><div>Predicting snowfall accumulation in the Northeast United States, especially during extreme weather events like rapidly intensifying storms, is a significant challenge. Accurate snowfall predictions are crucial for public safety, infrastructure planning, and economic stability, yet they are difficult to achieve due to the complex processes of snowfall formation and forecasting. This study focused on the impact of enhancing temporal resolution and integrating multi-model ensemble data to improve snowfall prediction accuracy. We combined outputs from the Weather Research and Forecasting (WRF) model with machine learning (ML) algorithms and gridded snowfall products. Specifically, we explored the impact of finer temporal resolutions, such as 6-hour versus 24-hour feature intervals, on snowfall predictions and examined the impact of incorporating ensemble snowfall data that provided various percentile snowfall accumulations and probabilities of exceeding specific thresholds. Results demonstrated that the 6-hour model significantly reduced the overall prediction error, particularly during rapidly intensifying storms, by up to 30%. The inclusion of ensemble data further enhanced the prediction of 24-hour snowfall, particularly in reducing the bias. Despite these advancements, challenges persist in accurately forecasting heavy snowfall amounts and capturing complex atmospheric dynamics during extreme events.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"659 ","pages":"Article 133313"},"PeriodicalIF":5.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inconsistencies in global soil moisture products and discrepancies in their relationship with vegetation productivity","authors":"Xinyao Xu ,&nbsp;Xufeng Wang ,&nbsp;Jingfeng Xiao ,&nbsp;Songlin Zhang ,&nbsp;Yanpeng Yang ,&nbsp;Xing Li ,&nbsp;Te Sha ,&nbsp;Zongxing Li","doi":"10.1016/j.jhydrol.2025.133298","DOIUrl":"10.1016/j.jhydrol.2025.133298","url":null,"abstract":"<div><div>Soil moisture is one of the critical environmental variables influencing ecosystem function and plays a vital role in regulating vegetation dynamics. In recent years, various soil moisture datasets have been developed using different methodologies, including land surface modeling, remote sensing-based retrievals, and data assimilation techniques. These datasets have been widely applied to study vegetation responses to water availability. However, their consistency has not been thoroughly evaluated, which introduces biases and inconsistencies in vegetation response analyses. Such inconsistencies may lead to biases when interpreting vegetation response patterns and long-term environmental trends.</div><div>To address this issue, this study explores the differences among multiple soil moisture products and assesses their consistency in evaluating vegetation water stress responses. We focus on five widely used soil moisture products European Centre for Medium-Range Weather Forecasts Fifth-Generation Land Reanalysis Dataset (ERA5-Land), Global Land Evaporation Amsterdam Model (GLEAM) soil moisture dataset, the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2), Global Land Data Assimilation System (GLDAS) soil moisture dataset, and a globally gap-filled surface soil moisture dataset and in-situ soil moisture observations were collected for this analysis. These products were chosen to represent different soil moisture estimation approaches, ensuring a comprehensive assessment of their consistency.</div><div>To evaluate discrepancies among these datasets, we applied statistical correlation analysis, trend comparisons, and spatial pattern assessments using satellite-derived vegetation index and solar-induced fluorescence were used as proxies for vegetation activity. The results indicate that correlations between each product and observed data varied seasonally, with stronger performance during the growing season compared to the non-growing season. These products showed conflicting long-term trends at global scale. Additionally, there were significant discrepancies in the relationships between different moisture products and vegetation indices, particularly in spatial patterns. In about half of the global regions, conflicting correlations emerged between different products and vegetation indices. These inconsistencies highlight the challenges of using a single soil moisture dataset for ecological studies, emphasizing the necessity for cross-product comparisons to improve data reliability and integration. The findings of this paper provide new perspectives for future research on soil moisture and atmospheric dryness and help improve the effectiveness of data integration strategies.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"659 ","pages":"Article 133298"},"PeriodicalIF":5.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Changes in future ice-jam flood severity of a regulated cascade reservoir-river system","authors":"Mingwen Liu ,&nbsp;Karl-Erich Lindenschmidt ,&nbsp;Haishen Lü ,&nbsp;Tingxing Chen ,&nbsp;Yonghua Zhu ,&nbsp;Yu Lin","doi":"10.1016/j.jhydrol.2025.133307","DOIUrl":"10.1016/j.jhydrol.2025.133307","url":null,"abstract":"<div><div>Ice-jam flooding (IJF) is a complex and hazardous phenomenon that can result in devastating impacts on riverine communities and infrastructure. With the increasing concern about climate change and its potential implications on hydrological processes, there is a growing need to assess the future risks of IJF. In this study, we developed an integrated probabilistic modelling framework that combines a river ice model with bias-corrected CMIP6-GCMs data and a machine learning model to investigate the impacts of climate change and flow regulation on IJF backwater levels. The simulation of the IJF employs extensive Monte Carlo analysis (MOCA) simulations to capture a wide range of historical (1970–2019) and future (2020–2069) scenarios in the Sanhuhekou bend reach (SBR) of the Yellow River. Firstly, the results reveal that current flow regulation plays a significant role in increasing IJF backwater levels, outweighing the effects of 3 future climate scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5), which tended to reduce IJF backwater levels. Secondly, the findings suggest that any caps on regulated flows during the ice period may be eased in the future to partially meet growing demand for hydropower. Finally, the flow regulation strategy adapted from the MOCA simulation results can both increase hydropower generation and meet the high flood design criteria under SSP5-8.5 scenario.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"659 ","pages":"Article 133307"},"PeriodicalIF":5.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 the impacts of climate change on groundwater evapotranspiration in mid-to-high latitude regions","authors":"Luis Carlos Serrano Diaz ,&nbsp;Brian Smerdon ,&nbsp;Daniel S. Alessi ,&nbsp;Monireh Faramarzi","doi":"10.1016/j.jhydrol.2025.133294","DOIUrl":"10.1016/j.jhydrol.2025.133294","url":null,"abstract":"<div><div>In arid and semi-arid regions of mid-to-high latitude zones, actual evapotranspiration (AET) dominates the water balance, posing risks to the hydrologic budget and leading to potential groundwater depletion. Despite numerous studies on AET, the evapotranspiration from groundwater (GWET) and surface water (SWET) remains poorly understood at a regional scale. This study developed, calibrated, and validated an integrated surface and groundwater model to study AET in the North Saskatchewan River Basin (NSRB) in western Canada, covering historical (1983-2013) and mid-future (2043-2073) periods. The study addresses the temporal variation and feedback mechanisms affecting AET and its water sources across different ecohydro(geo)logical (EHG) regions often present in large watersheds, including Mountains, Foothills, and Plains. Results show that subsurface transpiration and evaporation are the primary contributors to AET, while surface water evaporation contributes the least across all EHG regions. In terms of water sources, SW is the largest contributor to AET in most areas across all EHG regions. However, GW is the primary source of water contributing to AET in riparian areas and regions with high atmospheric evapotranspiration demand, large Leaf Area Index, and deep-rooted plants accessing shallow groundwater. In the mid-future period, AET is projected to increase across the NSRB, with the greatest change occurring in the Mountains. In riparian areas and discharge zones of northeast Plains, GWET contribution is expected to increase. However, in the Foothills and Plains, the projected increases in AET may lead to groundwater depletion, reducing the amount of GWET. The most significant future impacts on groundwater are anticipated in the Plains.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"660 ","pages":"Article 133294"},"PeriodicalIF":5.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}