Journal of Hydrology最新文献

{"title":"Quantitative assessment and analysis of the impact of inter-basin water transfer on regional water resource stress","authors":"Lichuan Wang ,&nbsp;Fan He ,&nbsp;Yong Zhao ,&nbsp;Jianhua Wang ,&nbsp;Meng Hao ,&nbsp;Peiyi Lu ,&nbsp;Yage Jia ,&nbsp;Kuan Liu ,&nbsp;Haodong Deng","doi":"10.1016/j.jhydrol.2025.133090","DOIUrl":"10.1016/j.jhydrol.2025.133090","url":null,"abstract":"<div><div>With the increasingly serious water shortage and other problems, Inter-basin water transfer (IBWT) has become an important measure to alleviate regional water stress. In this study, based on the improved water stress index (WSI), we comprehensively assessed the multi-scale (urban, tertiary basin zones) spatio-temporal status of WSI in the four major basins of the Huang, Huai, Hai and Yangtze River Basins (HHHYRB) from 1965 to 2020, and analysed the contribution of Inter-basin water transfer to alleviate regional water stress. The Dagum Gini coefficient method was used to determine the differences and sources of WSI distribution. Finally, the impact of each water transfer project was quantitatively analysed through bottom-up scenario derivation. The results show that 35.8% of the cities in the HHHYRB, and 40.4% of the tertiary basins are at medium or higher risk of water stress. The impacts of external inter-basin water transfers in the HHHYRB are all less than 0.002. The IBWT effectively mitigated the WSI in the Hai River Basin (75%) and the Huaihe River Basin (15.6%), negatively affected the WSI in the Yellow River Basin (−6.4%), and had only a 2% impact on the Yangtze River Basin. There is obvious spatial heterogeneity in the WSI of the HHHYRB, and the coefficients between groups (48.3%–66%) are higher than the coefficients within groups (17.3%–23.7%) and hypervariable density coefficients (10.8%–31%). IBWT projects effectively moderate the degree of inequality in water resources, with intra-basin impacts ranging from 1.77% to 33.69% and inter-basin impacts reaching 2.29%–7.28%. Most of the IBWT projects tend to transfer water from areas with low WSI to areas with high WSI with positive impacts, but there are still 10 water transfer projects with negative impacts. Therefore, the impacts of IBWT should be considered comprehensively when formulating water resources management policies in order to achieve long-term sustainable use of water resources.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"657 ","pages":"Article 133090"},"PeriodicalIF":5.9,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143654846","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":"Assessing compound flood hazards in the Pearl river Delta: A Scenario-Based Integration of trivariate fluvial conditions and extreme storm events","authors":"Haoxuan Du ,&nbsp;Kai Fei ,&nbsp;Liang Gao","doi":"10.1016/j.jhydrol.2025.133104","DOIUrl":"10.1016/j.jhydrol.2025.133104","url":null,"abstract":"<div><div>In coastal delta regions, typhoons not only generate storm surges but also threaten local communities by causing extreme fluvial flooding due to intense rainfall. Traditionally, upstream river discharges are neglected and not linked to these compound events, leading to an underestimation of flood extent and impacts. Quantifying the joint occurrences of extreme fluvial and coastal conditions is essential for accurately predicting future compound flood hazards. This study proposes an integrated statistical-numerical modeling approach to assess compound fluvial-coastal flood hazards. The approach combines a trivariate statistical analysis to characterize extreme fluvial conditions with a numerical model to simulate coastal storm surges and their compound effects. The methodology is applied to the Pearl River Delta in southern China, a region highly vulnerable to compound flooding. Trivariate joint statistical relationships are developed using historical river discharge records during typhoon events from 1957 to 2022 to characterize extreme fluvial conditions. While the correlation among the three connected rivers is weak under low flow conditions, a high dependence exists during extreme events, increasing the likelihood of concurrent flooding. The trivariate fluvial conditions are then integrated with univariate coastal storm surge conditions to project the compound flood hazard scenario. The results show that high-risk scenarios (100-year river discharge combined with 100-year storm tide) could inundate over 24% of the Pearl River Delta’s land area. Neglecting river discharges underestimates the floodplain extent by up to 32%. Transition zones influenced by both river flow and storm surges are identified along midstream river networks and upstream floodplains. These regions experience significant expansion with rising hazard levels, suggesting larger compound flood areas under future extreme conditions. This scenario-based approach provides valuable insights into characterizing and mapping compound flooding risks in vulnerable coastal regions.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"657 ","pages":"Article 133104"},"PeriodicalIF":5.9,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143654643","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":"Efficient glacial lake mapping by leveraging deep transfer learning and a new annotated glacial lake dataset","authors":"Donghui Ma ,&nbsp;Jie Li ,&nbsp;Liguang Jiang","doi":"10.1016/j.jhydrol.2025.133072","DOIUrl":"10.1016/j.jhydrol.2025.133072","url":null,"abstract":"<div><div>Glacial lakes, crucial components of the cryosphere, are recognized as key sentinels of climate change. While satellite imagery offers a straightforward method for monitoring their dynamics, traditional approaches are often subjective and time-consuming. Deep learning techniques, though promising, have been hindered by the scarcity of labeled glacial lake datasets. To address this limitation, we present the Glacial Lake Image Dataset (GLID), the first publicly available collection of its kind. This dataset comprises 18,367 (512 × 512 pixels) sample pairs (lake polygons and corresponding images) derived from 36 scenes from across multiple sources (WorldView-2, Sentinel-2, Landsat-8, and Gaofen-2), covering the entire Himalayan region. We then propose a transferable deep learning network for glacial lake extraction. Our findings underscore the critical role of high-quality training data in model performance. The GLID-trained model achieved superior results, demonstrating a Precision of 95.36 %, Recall of 87.50 %, F1 score of 91.66 %, and mIoU of 82.07 %. Notably, this method exhibits promising transferability across diverse regions, including North America, South America, Greenland, and High Mountain Asia. The GLID dataset provides a valuable resource for advancing machine learning-based glacial mapping research. By offering a large-scale, publicly accessible collection of labeled data, we aim to facilitate the development of more accurate and efficient methods for monitoring and understanding the impacts of climate change on glacial lake ecosystems.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"657 ","pages":"Article 133072"},"PeriodicalIF":5.9,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636995","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":"Widespread consistent but rapid response of terrestrial ecosystem photosynthesis and respiratory to drought","authors":"Wenwen Guo ,&nbsp;Shengzhi Huang ,&nbsp;Laibao Liu ,&nbsp;Feilong Hu ,&nbsp;Liang Gao ,&nbsp;Jianfeng Li ,&nbsp;Qiang Huang ,&nbsp;Guohe Huang ,&nbsp;Mingjiang Deng ,&nbsp;Guoyong Leng ,&nbsp;Ji Li ,&nbsp;Xiaoting Wei ,&nbsp;Yifei Li ,&nbsp;Jian Peng","doi":"10.1016/j.jhydrol.2025.133107","DOIUrl":"10.1016/j.jhydrol.2025.133107","url":null,"abstract":"<div><div>Drought significantly threatens terrestrial ecosystems health, through influencing both photosynthesis and respiratory processes. However, whether these processes have changed in response to intensified drought and the driving mechanisms remain unclear, even though the inconsistent responses may indicate an increased potential for unstable carbon sinks. The knowledge gap would hinder accurate prediction of the size of China’s future terrestrial ecosystems carbon sink under increasing extreme droughts, thus impacting the realization of China’s carbon neutrality goal. Here, we combined observational-based data and dynamic global vegetation model data to explore the response time (RT) of Gross Primary Productivity (GPP) and Ecosystem Respiration (TER) to meteorological drought in China and their dynamics over the past 40 years. Results reveal consistent spatial distribution patterns in GPP and TER responses to drought. During 1982–2021, widespread declines in the RT of both GPP and TER to drought were observed, indicating an increased likelihood of vegetation converting from a carbon sink into a carbon source under droughts. GPP responds slightly faster than TER, notably in arid regions influenced by land cover change and climate change. Hotspots of decreasing RT trends, such as the Tibetan Plateau and Yellow River Basin, underscore the diverse impacts of climate and land cover changes. Our findings shed new insights into ecosystem carbon fluxes mechanisms, thus providing accurate carbon budget for China’s carbon neutrality goal.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"657 ","pages":"Article 133107"},"PeriodicalIF":5.9,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642349","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":"Enhanced prediction of discharge coefficient in sharp-edged width constrictions using a novel hybrid SVR-IWOA and machine learning models","authors":"Ehsan Afaridegan ,&nbsp;Reza Fatahi-Alkouhi ,&nbsp;Paymaneh Azarm ,&nbsp;Nosratollah Amanian","doi":"10.1016/j.jhydrol.2025.133103","DOIUrl":"10.1016/j.jhydrol.2025.133103","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Sharp-Edged Width Constrictions (SEWC) are hydraulic structures designed to measure flow in open channels. Accurate prediction of the discharge coefficient (&lt;em&gt;C&lt;sub&gt;d&lt;/sub&gt;&lt;/em&gt;) in SEWC is crucial for determining water discharge in these channels. This information plays a key role in effective water resource management, supporting decision-making regarding the allocation and conservation of water for agricultural, industrial, and municipal purposes. This study introduces a novel hybrid machine learning model, combining Support Vector Regression (SVR) with the Improved Whale Optimization Algorithm (IWOA). Additionally, advanced machine learning models such as NGBoost, AutoInt, and TabNet were employed to predict &lt;em&gt;C&lt;sub&gt;d&lt;/sub&gt;&lt;/em&gt; in SEWC. The SVR-IWOA model offers automatic hyperparameter tuning, significantly enhancing prediction accuracy in complex flow conditions. To develop these models, a dataset consisting of 156 laboratory data points from SEWC experiments was utilized, with 75 % of the data allocated for training and 25 % for testing. The Isolation Forest (IF) algorithm was applied to detect and remove outliers, leading to the exclusion of 5.1 % of the original dataset. Dimensional analysis identified critical factors influencing &lt;em&gt;C&lt;sub&gt;d&lt;/sub&gt;&lt;/em&gt;, including the ratio of upstream depth to opening width (&lt;em&gt;h&lt;/em&gt;/&lt;em&gt;b&lt;/em&gt;) and the constriction ratio (&lt;em&gt;β&lt;/em&gt; = &lt;em&gt;b&lt;/em&gt;/&lt;em&gt;B&lt;/em&gt;, where &lt;em&gt;B&lt;/em&gt; is the channel width). The validity of these dimensionless parameters was confirmed using ANOVA and SHAP analyses, which highlighted &lt;em&gt;β&lt;/em&gt; as the most influential factor affecting &lt;em&gt;C&lt;sub&gt;d&lt;/sub&gt;&lt;/em&gt;. Model performance was rigorously evaluated using multiple metrics, including the coefficient of determination (&lt;em&gt;R&lt;/em&gt;&lt;sup&gt;2&lt;/sup&gt;), Root Mean Squared Error (RMSE), Scatter Index (SI), Weighted Mean Absolute Percentage Error (WMAPE), and symmetric Mean Absolute Percentage Error (sMAPE). Comparative evaluations were conducted using Taylor Diagrams, Residual Error Curves (REC), and the Performance Index (PI). In the training stage, NGBoost demonstrated superior performance with a PI of 4994 and a normalized Root Mean Squared Error (&lt;em&gt;E′&lt;/em&gt;) of 0.00021, followed by SVR-IWOA with a PI of 2490 and &lt;em&gt;E′&lt;/em&gt; of 0.00035. During the testing stage, the SVR-IWOA model maintained strong performance, achieving a PI of 1986 and a low &lt;em&gt;E′&lt;/em&gt; of 0.00046, while TabNet closely followed with a PI of 1986 and &lt;em&gt;E′&lt;/em&gt; of 0.00047. In terms of &lt;em&gt;R&lt;/em&gt;&lt;sup&gt;2&lt;/sup&gt; values, the models ranked as follows during testing: SVR-IWOA and TabNet tied for first with &lt;em&gt;R&lt;/em&gt;&lt;sup&gt;2&lt;/sup&gt; = 0.993, followed by NGBoost (&lt;em&gt;R&lt;/em&gt;&lt;sup&gt;2&lt;/sup&gt; = 0.992) and AutoInt (&lt;em&gt;R&lt;/em&gt;&lt;sup&gt;2&lt;/sup&gt; = 0.973). These findings highlight the effectiveness of the proposed SVR-IWOA model in accurately predicting &lt;em&gt;C&lt;sub&gt;d&lt;/sub&gt;&lt;/em&gt; and its strong generalization capabilities, positioning it as a robust tool for hydraulic applications.&lt;/div&gt;&lt;/","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"657 ","pages":"Article 133103"},"PeriodicalIF":5.9,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642471","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":"Multi-Scale domain adaptation for high-resolution soil moisture retrieval from synthetic aperture radar in data-scarce regions","authors":"Liujun Zhu ,&nbsp;Qi Cai ,&nbsp;Junliang Jin ,&nbsp;Shanshui Yuan ,&nbsp;Xiaoji Shen ,&nbsp;Jeffrey P. Walker","doi":"10.1016/j.jhydrol.2025.133073","DOIUrl":"10.1016/j.jhydrol.2025.133073","url":null,"abstract":"<div><div>Remote sensing of soil moisture plays an important role in advancing various hydrology applications, with Synthetic Aperture Radar (SAR) being the most promising technique for high-resolution soil moisture estimation. The growing adoption of machine learning methods has further enhanced this field, though their effectiveness heavily relies on the availability and quality of in-situ measurements. A recent study has demonstrated that pretrained models at 9-km resolution, based on the Soil Moisture Active Passive (SMAP) soil moisture products, can be transferred to 1-km using fewer in-situ measurements. Despite the success of this cross-resolution framework, its performance in data-scarce regions at high resolutions remains poor. To address this limitation, a multi-scale domain adaption (MSDA) method was proposed for soil moisture retrieval from data-scarce regions at a resolution of 50 m, taking the pretrained 9 km models as the starting points. Two modifications were made: the integration of multi-scale losses at both 9-km and 50-m resolutions, and the application of domain loss to bridge the gap between training and testing datasets. The MSDA was evaluated in both transductive and inductive modes where transductive mode involves adapting the model using a portion of the unlabeled test data, and inductive mode involves generalizing the model to entirely new, unseen data. A total of 66,547 daily averaged soil moisture measurements from 480 stations of 7 networks across the Contiguous United States were used. In the transductive mode, the use of a single training station in the MSDA achieved an R and RMSE of 0.67 and 0.088 m³/m³ respectively, which were improved to 0.81 and 0.071 m³/m³ when using data from 45 training stations. An acceptable R and RMSE of 0.76 and 0.078 m³/m³ was achieved in the inductive mode. The joint use of the two modifications achieved significantly better results (p &lt; 0.01), with a relative improvement of 5.8 – 20.0 %, overall, and a lower risk of performance deterioration in data-scarce scenarios.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"657 ","pages":"Article 133073"},"PeriodicalIF":5.9,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642467","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":"The contribution of floods to streamflow at yearly timescales: A global assessment","authors":"Caleb Dykman ,&nbsp;Ashish Sharma ,&nbsp;Rory Nathan ,&nbsp;Conrad Wasko","doi":"10.1016/j.jhydrol.2025.133099","DOIUrl":"10.1016/j.jhydrol.2025.133099","url":null,"abstract":"<div><div>Much of the world’s population faces significant threats to water security that are likely to be exacerbated under a warmer climate. To understand the impact of climate change on water security, we characterise the relationship between total annual streamflow, a surrogate for water supply, and low frequency flood events. We first calculate the proportion of annual streamflow attributable to flood events at approximately 5000 stations globally, then characterise this relationship as a function of climate and catchment characteristics. To explore the impact of climate changes we investigate trends in these relationships. We find that across the world on average 25% of annual streamflow comes from a single flood event and this proportion is well described by the variability in streamflow and precipitation, which is a function of catchment aridity. In arid to semi-arid catchments, where on average over 40% of annual streamflow comes from a single flood event, we conclude that water availability may be reasonably approximated by focusing on a selection of low frequency events, simplifying the projection of water supply changes under a future climate. Flood generating mechanism is crucial in determining trends of annual streamflow volumes from low frequency events. Where snowmelt is a significant flood process, increasing temperatures are causing a reduction in the proportion of total annual streamflows accounted for by flood events. Where snow is not a significant flood driver, trends are dominated by hydroclimatic variability and aridity, which is expected to be exacerbated with a shift to rainfall driven flooding under climate change.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"657 ","pages":"Article 133099"},"PeriodicalIF":5.9,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143654613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"On the accuracy requirement of surrogate models for adequate global sensitivity analysis of urban low-impact development model","authors":"Ke Yi ,&nbsp;Pan Yang ,&nbsp;Siyuan Yang ,&nbsp;Shenxu Bao ,&nbsp;Zhihao Xu ,&nbsp;Qian Tan","doi":"10.1016/j.jhydrol.2025.133102","DOIUrl":"10.1016/j.jhydrol.2025.133102","url":null,"abstract":"<div><div>Global sensitivity analysis (GSA) is crucial for understanding, simplifying, and applying high fidelity process-based (Hifi) hydrological models. Its application has been hindered by the extensive model evaluations needed for convergence and the associated computational cost. Surrogate models (SMs) can significantly reduce the computational burden of GSA, but its approximation errors can introduce usually uninvestigated GSA errors. We address this gap by investigating SMs-induced GSA error in parameter screening, sensitivity ranking, and sensitivity index valuation. By comparing the converged GSA results from a support vector regression surrogate model (SVR-SM) and the storm water management model (SWMM) in simulating the hydrological response of a small urban watershed to changes in low-impact development (LID) parameters, this study finds that SMs-induced GSA errors increase with higher SMs approximation errors. The relationship between SMs-induced GSA error and SMs approximation error (measured by R²) is consistent across various flow metrics and rainfall intensities. SMs can adequately reproduce the converged GSA results of a Hifi SWMM with only one-thousandth of the original computation time. However, SMs-induced GSA errors may become unacceptable if the R² of SVR-SMs is below 0.96. Our findings highlight the importance of surrogate models’ accuracy in GSA and provide valuable guidance for future GSA applications.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"657 ","pages":"Article 133102"},"PeriodicalIF":5.9,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143654609","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":"Effects of surface vegetation and litter on rainfall redistribution during the rainy season in semiarid grasslands","authors":"Yang Luo ,&nbsp;Yang Chen ,&nbsp;Chunxia Jian ,&nbsp;Junjie Zhou ,&nbsp;Yingkun Mou ,&nbsp;Yuan Jin ,&nbsp;Shaoyan Wang ,&nbsp;Bingcheng Xu","doi":"10.1016/j.jhydrol.2025.133079","DOIUrl":"10.1016/j.jhydrol.2025.133079","url":null,"abstract":"<div><div>Studying the characteristics of rainfall redistribution and their influencing factors in grassland communities is crucial for understanding rainfall effectiveness and ecohydrological processes in semiarid regions. Small plot-scale experiments were conducted in three typical grassland communities (<em>Bothriochloa ischaemum</em>, <em>Artemisia gmelinii</em>, and <em>Stipa bungeana</em>) using three simulated rainfall amounts (10 mm, 20 mm, and 40 mm). Four treatments were applied: PR (surface plants removed), LR (litter removed), LR + PR (both plants and litter removed), and CK (no treatment). These treatments were used to measure the percentages of surface plant interception (PI%), litter interception (LI%), runoff (RO%), and soil water storage (SWS%) during the rainy season (July to September) in the Zhifanggou watershed, Shaanxi Province, China. Results indicated that the average PI%, LI%, RO%, and SWS% for the three community types were 7.94 %, 9.55 %, 17.65 %, and 64.87 %, respectively. Growth month, community type, and rainfall amount significantly influenced these four components (<em>p</em> &lt; 0.05). Specifically, interception losses and soil moisture storage were highest in August. The <em>A. gmelinii</em> community exhibited higher interception and soil water storage than the other two grassland communities. Rainfall amount negatively affected PI%, LI%, and SWS% while positively influencing RO%. Stepwise regression analysis revealed that aboveground biomass was the best predictor for PI%, and litter thickness was the best predictor for LI%. Soil water content in the 0∼30 cm layer was the primary factor affecting RO% and SWS%. Furthermore, the PR treatment significantly reduced LI% by approximately 2.02 % and increased RO% by about 9.89 % compared to CK, while the LR treatment increased RO% by about 9.62 %. The LR + PR treatment significantly reduced SWS% by approximately 15.72 % and increased RO% by 30.33 %. These findings demonstrate that grassland plants and litter contribute to soil water replenishment, outweighing interception losses during the rainy season. This suggests that vegetation rehabilitation enhances soil water storage and is beneficial for managing water resources in semiarid regions.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"657 ","pages":"Article 133079"},"PeriodicalIF":5.9,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642348","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":"Beyond water supply augmentation: Environmental benefits of infrastructure investment for a regional water supply system","authors":"Hui Wang ,&nbsp;Tirusew Asefa ,&nbsp;Nisai Wanakule ,&nbsp;Solomon Erkyihun","doi":"10.1016/j.jhydrol.2025.133091","DOIUrl":"10.1016/j.jhydrol.2025.133091","url":null,"abstract":"<div><div>This paper presents an examination of the environmental benefits of enhanced surface water production on groundwater systems in a metropolitan region, Tampa Bay Region, in Florida in the United States. The regional water supply agency, Tampa Bay Water, has decided to expand facility production capacity of its surface water treatment plant by 20 million gallons per day (MGD), starting in 2028. This capacity expansion has the potential to significantly reduce groundwater production in future years. Reduced groundwater production can lead to higher water levels in both the Surficial aquifer and the Upper Floridan aquifer, mitigating potential adverse environmental impacts such as ecosystem degradation. A simulation–optimization framework is used to determine production from different supply sources under two scenarios: the baseline scenario (Scenario A), and the supply augmentation scenario (Scenario B). Results reveal a considerable increase in the annual reliability of the water supply system, while simultaneously maintaining higher groundwater levels. Specifically, for the annual median water level in the Surficial aquifer, increased water levels in Scenario B are observed at 25 out of the 41 (61%) stations. Similarly, tor the 25th percentile water level in the Surficial aquifer, increased water levels occur at 30 out of the 41 (75%) stations. In the Upper Floridan aquifer, higher water levels in Scenario B are expected at 13 out of 18 (72%) monitoring sites. The approaches presented here can serve as a blueprint for similar efforts worldwide, particularly in regions facing growing water demand, groundwater depletion, and environmental degradation<strong>.</strong></div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"657 ","pages":"Article 133091"},"PeriodicalIF":5.9,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636997","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}