Journal of Hydrology最新文献

{"title":"QDeepGR4J: Quantile-based ensemble of deep learning and GR4J hybrid rainfall-runoff models for extreme flow prediction with uncertainty quantification","authors":"Arpit Kapoor,&nbsp;Rohitash Chandra","doi":"10.1016/j.jhydrol.2025.134434","DOIUrl":"10.1016/j.jhydrol.2025.134434","url":null,"abstract":"<div><div>Conceptual rainfall-runoff models aid hydrologists and climate scientists in modelling streamflow to inform water management practices. Recent advances in deep learning have unravelled the potential for combining hydrological models with deep learning models for better interpretability and improved predictive performance. In our previous work, we introduced DeepGR4J, which enhanced the GR4J conceptual rainfall-runoff model using a deep learning model to serve as a surrogate for the routing component. DeepGR4J had an improved rainfall-runoff prediction accuracy, particularly in arid catchments. Quantile regression models have been extensively used for quantifying uncertainty while aiding extreme value forecasting. In this paper, we extend DeepGR4J using a quantile regression-based ensemble learning framework to quantify uncertainty in streamflow prediction. We also leverage the uncertainty bounds to identify extreme flow events potentially leading to flooding. We further extend the model to multi-step streamflow predictions for uncertainty bounds. We design experiments for a detailed evaluation of the proposed framework using the CAMELS-Aus dataset. The results show that our proposed Quantile DeepGR4J framework improves the predictive accuracy and uncertainty interval quality (interval score) compared to baseline deep learning models. Furthermore, we carry out flood risk evaluation using Quantile DeepGR4J, and the results demonstrate its suitability as an early warning system.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"664 ","pages":"Article 134434"},"PeriodicalIF":6.3,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145340259","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 climate-informed statistical framework to indirectly estimate trends in future seasonal high flows in snow-dominated watersheds using short-term climate variability indices","authors":"Andrés F. Gonzalez-Mora,&nbsp;Etienne Foulon,&nbsp;Alain N. Rousseau","doi":"10.1016/j.jhydrol.2025.134441","DOIUrl":"10.1016/j.jhydrol.2025.134441","url":null,"abstract":"<div><div>The intensification of the hydrological cycle under climate change has brought changes in the temporal variability of flood-generating mechanisms and extreme hydrological events. To better anticipate these changes, modelling approaches integrating climate models, emissions scenarios, and hydrological models have been widely employed. However, their application remains challenging because of inherent uncertainties, in particular from hydrological models. This study aims to use a climate-informed statistical framework to indirectly estimate the temporal variability of seasonal high flows indices (HFI) using a set of short-term climate variability indices (SCI) characterizing likely causative mechanisms over different aggregated look-back periods. An ensemble of climate models, two future scenarios, and 31 SCIs were used to estimate future HFIs trends from 1997 to 2100 using as a proof of concept two snow-dominated watersheds in Southern Quebec, Canada. A statistical framework was used including linear and monotonic partial correlations along with significant trend tests. The results indicated that future temporal variability of HFIs could be anticipated using highly correlated SCIs as proxies. At least 50% of the HFI temporal variability was explained by a single SCI, such as cumulative total precipitation or climatic demands over 1 to 2 weeks, or drought indices like the Effective Drought Index (EDI) over 180 days. Furthermore, significant trends in highly correlated SCIs were consistent with significant trends observed in HFIs. These findings offer valuable insights for future analysis of HFI temporal variability, particularly in more comprehensive water management analyses aimed at informing regional mitigation and adaptation strategies.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"664 ","pages":"Article 134441"},"PeriodicalIF":6.3,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145340257","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":"Towards digital twin of an in-situ experiment: a physics-enhanced machine-learning framework for inverse modelling of mass transport processes","authors":"Haonan Peng ,&nbsp;Ashish Rajyaguru ,&nbsp;Enzo Curti ,&nbsp;Daniel Grolimund ,&nbsp;Sergey V. Churakov ,&nbsp;Nikolaos I. Prasianakis","doi":"10.1016/j.jhydrol.2025.134437","DOIUrl":"10.1016/j.jhydrol.2025.134437","url":null,"abstract":"<div><div>As a prove of concept for experimental geochemistry, an advanced 3D numerical framework, here and after called Digital Twin (DT), of a diffusion experiment conducted at a synchrotron beamline, has been implemented using in-situ measurements data, physics-based modelling, a machine learning (ML) model, and parameter optimization module. The physics-based model enables finely discretized high-resolution 3D mass transport simulations, which provide the training set for the ML model. The resulting ML model greatly accelerates the computationally intensive calculations needed for the interpretation of the experimental observations during inverse modelling. The framework is applied to interpret the in-situ non-destructive micro-X-ray fluorescence (μ-XRF) imaging data from a bromide diffusion experiment through a silica-gel-filled capillary system. The computational framework is refined, and several optimization algorithms are implemented to fit the experimental data. The gain in computational efficiency allows modelling the experiment practically in real-time.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"664 ","pages":"Article 134437"},"PeriodicalIF":6.3,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145340258","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":"Rural river reaches are emission hotspots for greenhouse gases","authors":"Kan Chen ,&nbsp;Yifei Fan ,&nbsp;Qiqi Wei ,&nbsp;Feifei Wang ,&nbsp;Wenfeng Xu ,&nbsp;Lihua Liu ,&nbsp;Shengchang Yang ,&nbsp;Wenzhi Cao","doi":"10.1016/j.jhydrol.2025.134420","DOIUrl":"10.1016/j.jhydrol.2025.134420","url":null,"abstract":"<div><div>Rivers play a pivotal role in the global carbon and nitrogen biogeochemical cycles, contributing disproportionately to the global budget of greenhouse gas (GHG) relative to their areas. Rural river reaches are particularly affected by anthropogenic activities and may serve as significant GHG emission hotspots, while previous studies have focused primarily on GHG emissions from urban sewage treatment and agricultural systems, the effects of decentralized sewage treatment tailwater and agricultural return flow on riverine GHG dynamics remains poorly understood. In this study, we used metagenomic sequencing and microbial taxonomic annotation to investigate GHG production mechanism in a subtropical river receiving agricultural return flow and decentralized sewage treatment tailwater. We found that the global warming potential of rural river reaches surpassed that of other river reaches by 283.6 % and 298.9 % on 20- and 100-year time scales, respectively. These elevated GHG levels were linked to increased nutrient and organic matter loading, along with pronounced shifts in the abundance of functional genes related to GHG production and consumption in the rural river reaches. N₂O production was primarily driven by incomplete denitrification facilitated by abundant denitrification substrates. Concurrently, reduced photosynthesis and aerobic CO₂ fixation, coupled with strong aerobic respiration, led to high CO₂ production. Despite substantial aerobic CH₄ oxidation, the continuous availability of methanogenic substrates and alternative CH₄ oxidation substitutes sustained CH₄ production. These were controlled by methylotrophic and hydrogenotrophic methanogenesis during the wet and dry periods, respectively. These findings emphasize the need to improve the collection rate and treatment efficiency of rural domestic sewage and strengthen the control of agricultural non-point source pollution, so as to better mitigate the indirect greenhouse effect of sewage and fertilization in rural regions.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"664 ","pages":"Article 134420"},"PeriodicalIF":6.3,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145322779","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":"Deciphering spatio-seasonal impacts of agricultural irrigation on water quality evolution and surface water-groundwater interaction in a large irrigation district, northwest China","authors":"Muhan Li ,&nbsp;Keyi Zhang ,&nbsp;Wei Bao ,&nbsp;Shen Qu ,&nbsp;Guanglei Yu ,&nbsp;Yansong Bai ,&nbsp;Xu Yang ,&nbsp;Zeyuan Liu ,&nbsp;Hongli Ma ,&nbsp;Ping Miao ,&nbsp;Fuyang Huang ,&nbsp;Ruihong Yu","doi":"10.1016/j.jhydrol.2025.134423","DOIUrl":"10.1016/j.jhydrol.2025.134423","url":null,"abstract":"<div><div>The irrigation area on the south bank of the Yellow River (IASBYR) in Hangjin Banner, Ordos, China is an important strategic base for grain production in northern China. Yet its complex hydrogeological setting and insufficient monitoring data have limited understanding of water quality dynamics and surface water-groundwater interactions (SWIs). This study employed hydrochemical and isotopic analyses (258 samples), entropy-weighted water quality index (EWQI), positive matrix factorization (PMF), and ²²²Rn mass balance modeling (RMBM) to investigate these processes. Results revealed that groundwater and canal water were predominantly brackish to saline, contrasting with the freshwater of the Yellow River. Water quality degradation was controlled by nitrogen/fluoride contamination and water–rock interactions (WRIs; evaporite dissolution and silicate weathering), with 83 % of groundwater, 60 % of canal water, and 13 % of Yellow River water deemed non-potable due to excessive total nitrogen (TN). Spatially, SWIs exhibited distinct zonation: river leakage dominated in the upper reaches, with the flux rate (q_r) of 5.87 × 10^-4m³/(s∙m), while groundwater discharge flux rate (q_g) is 1.45 × 10^-4m³/(s∙m)). Minimal exchange occurred in the middle reaches (q_r: 2.93 × 10^-4m³/(s∙m), q_g: 0.32 × 10^-4m³/(s∙m)), and groundwater discharged into the river in the lower reaches (q_r: −16.06 × 10^-4m³/(s∙m), q_g: 0.64 × 10^-4m³/(s∙m)). Temporally, groundwater discharge prevailed during the spring and summer irrigation periods, while river leakage and groundwater discharge occurred in the autumn irrigation period. SWIs critically governed TN and F^- migration, with excessive nitrogen inputs driving eutrophication of riparian wetlands and elevated fluoride concentrations can be hazardous to human health. The findings offer critical insights for optimizing agricultural water management and ecological conservation in the Yellow River Basin.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"664 ","pages":"Article 134423"},"PeriodicalIF":6.3,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145322121","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":"Altered rainfall patterns reshape hillslope water balance and amplify carbon and nitrogen losses in a periglacial grassland","authors":"Wencong Lv ,&nbsp;Jianqing Du ,&nbsp;Liyuan Ma ,&nbsp;Fei Wang ,&nbsp;Lizhen Cui ,&nbsp;Haijun Zhang ,&nbsp;Danni Zhou ,&nbsp;Qiang Liu ,&nbsp;Haishan Niu ,&nbsp;Yanbin Hao ,&nbsp;Xiaoyong Cui ,&nbsp;Yanfen Wang","doi":"10.1016/j.jhydrol.2025.134421","DOIUrl":"10.1016/j.jhydrol.2025.134421","url":null,"abstract":"<div><div>Global warming is shifting rainfall patterns towards fewer but more intense events, leading to longer intervals between rainfall events. However, the effects of these changes on hydrological processes and elemental transport in the highly vulnerable periglacial ecosystems remain largely unexplored. This study used a rainfall manipulation experiment to examine how altering rainfall intervals with fixed seasonal total precipitation affects hydrological processes and carbon, nitrogen, and phosphorus export in a periglacial grassland on the Tibetan Plateau. Four treatments were applied on a uniform slope (∼11°): natural rainfall (CK), 3-day interval (P1), 7-day interval (P2), and 11-day interval (P3). Our results revealed that altered rainfall patterns reshape hillslope water balance. Specifically, P1 increased surface runoff (SR), soil water storage (SWS) and soil evaporation while reducing deep percolation (DP), because frequent small rainfall events enhanced soil water retention in the 0-30 cm layer while limiting infiltration into deeper layers. In contrast, P2 and P3 significantly increased SR while reducing SWS, resulting in a non-significant effect on DP. Moreover, distinct thresholds were identified for the generation of SR and DP at 1.8 mm and 1 mm rainfall, respectively. Additionally, altered rainfall patterns significantly increased the losses of dissolved carbon, total nitrogen and nitrate in SR but did not affect ammonium and phosphate. Notably, nitrate losses exhibited a nonlinear response to changing rainfall patterns, which peaked at a 7-day interval instead of 11-day interval with the highest single rainfall amount. This suggests that the extreme rainfall events may not affect the dissolved nitrogen exports in the periglacial grasslands as it requires not only a large single rainfall amount but also high rainfall frequency. Therefore, future studies should consider the interactions between rainfall frequency and single event size to disentangle the responses of hydrological and biogeochemical processes to climate extremes in these fragile periglacial ecosystems, thus supporting sustainable watershed management for the Asian Water Tower.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"664 ","pages":"Article 134421"},"PeriodicalIF":6.3,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145322780","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":"Uncertainty in estimating the relative change of design floods under climate change: a stylized experiment with process-based, deep learning, and hybrid models","authors":"Sandeep Poudel ,&nbsp;Nasser Najibi ,&nbsp;Scott Steinschneider","doi":"10.1016/j.jhydrol.2025.134427","DOIUrl":"10.1016/j.jhydrol.2025.134427","url":null,"abstract":"<div><div>The resilience of water systems to future hydrology depends on reliable projections of hydrological change. Process-based and, more recently, machine learning-based hydrological models are commonly used for such projections. To account for model uncertainties, hydrologists often report relative (i.e., percent) changes in hydrologic design statistics rather than absolute values, assuming model biases cancel out when estimating relative change. While extensive research has addressed uncertainty quantification in hydrologic modeling, little work has examined uncertainty in relative change estimates and its relationship to structural, parametric, and input uncertainties. In this study, we conduct a stylized experiment across 30 basins in Massachusetts, USA to evaluate uncertainty in design flood change estimates from six hydrological models: three process-based models of varying complexity, a deep learning model, and two hybrid models combining process models with deep learning post-processors. We assess each model’s ability to predict changes in design floods under different climate scenarios and levels of historical precipitation error, compared to another model taken as the true hydrologic system. Our findings reveal considerable variance and some bias in estimated design flood change, even with no historical precipitation error. Uncertainty increases only marginally with more precipitation error, suggesting structural limitations and equifinality dominate uncertainty. The deep learning model provides competitive estimates of change, while deep learning post-processors generally reduce bias but not variance of change estimates. Pooling estimates of design flood change across sites significantly reduces error variance, improving reliability. Overall, these insights can guide model and methodological choices for hydrological change assessments supporting long-term planning.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"664 ","pages":"Article 134427"},"PeriodicalIF":6.3,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145322126","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 anatomy of drought in Italy: statistical signatures, spatiotemporal persistence, and forecasting potential","authors":"Francesco Granata,&nbsp;Fabio Di Nunno","doi":"10.1016/j.jhydrol.2025.134428","DOIUrl":"10.1016/j.jhydrol.2025.134428","url":null,"abstract":"<div><div>Drought is a multifaceted hazard with profound socio-environmental consequences in the Mediterranean, where Italy exemplifies a climate vulnerability hotspot shaped by pronounced spatial heterogeneity and intensifying climatic pressures. This study advances drought research by conducting a comprehensive analysis of six-month Standardized Precipitation–Evapotranspiration Index (SPEI-6) time series across Italy, integrating higher-order statistical descriptors, persistence diagnostics based on the Hurst exponent (H) and Detrended Fluctuation Analysis (DFA), advanced clustering algorithms, and deep learning forecasting. Distinct from conventional mean–variance assessments, the analysis emphasizes skewness and other higher-order moments to capture asymmetries in drought intensity and frequency, and employs scaling metrics to quantify long-range dependence and memory in hydroclimatic signals. A comparative suite of clustering approaches, including K-means, Agglomerative Hierarchical, Gaussian Mixture Models, and Spectral Clustering, delineates a coherent tripartite drought structure: a persistent southern and insular regime with strong temporal memory and prolonged droughts, an intermediate northeastern corridor with moderate persistence, and a volatile northwestern Alpine domain characterized by weak persistence, high variability, and abrupt transitions. Forecasting experiments employing Kolmogorov–Arnold Fourier (KAF) networks, benchmarked against Long Short-Term Memory (LSTM) architectures, reveal substantial skill at one-month lead, particularly in persistent southern and insular regions, while performance declines at seasonal horizons and in highly variable northern areas. These findings highlight the necessity of regionally tailored monitoring and adaptive management strategies. The methodological framework presented here, modular and transferable, provides a rigorous and replicable template for drought diagnosis and early warning in Mediterranean and other drought-prone regions facing escalating climate variability.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"664 ","pages":"Article 134428"},"PeriodicalIF":6.3,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145322012","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":"Over half of existing dams in the Tarim River Basin should be removed under changing environment","authors":"Yu Chen ,&nbsp;Qi Liu ,&nbsp;Dongwei Gui ,&nbsp;Junhu Tang ,&nbsp;Xinlong Feng ,&nbsp;Yunfei Liu ,&nbsp;Qian Jin ,&nbsp;Sameh Kotb Abd-Elmabod ,&nbsp;Dongping Xue ,&nbsp;Xiao Zhang","doi":"10.1016/j.jhydrol.2025.134419","DOIUrl":"10.1016/j.jhydrol.2025.134419","url":null,"abstract":"<div><div>Dams are critical hydraulic structures in arid environments to mitigate water shortages for sustainable regional water resource management and socioeconomic development. However, suitable sites for dams would change with global warming and sociodemographic development as the water supply and demand change spatiotemporally. This research develops a data-driven framework combining machine learning (Random Forest) and deep learning (YOLOv7-BiFormer) methods to explore the future optimal location selection of dams across large-scale regions based on multiple environmental and socio-demographic datasets. Focus on the Tarim River Basin, the “water tower” of Central Asia, where hundreds of hydraulic structures have been set up over the past decades and are considered to threaten the basin’s hydrological and ecological security. 142 existing dams, including more than 100 unrecorded dams on the basin, are detected by applying the YOLOv7-BiFormer model to the basin through high-resolution remote sensing imagery (1.2 m). Our results show that cropland and runoff are key to affecting the site of dams, while elevation and climate are behind. The optimal sites of dams on the basin are mainly distributed in the Aksu and upper Yarkant rivers in the future under global warming. However, approximately ninety existing dams in the basin, especially in the Hotan and lower Yarkant rivers, would become useless and require removal by 2100. This research emphasizes the necessity for the management of dam sites in basins to foster the adaptation to social and climate change.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"664 ","pages":"Article 134419"},"PeriodicalIF":6.3,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145322127","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":"Soil moisture response to rainfall and its controls on hillslopes in alpine mountain areas of the Tibetan Plateau","authors":"Tao Xiong ,&nbsp;Jie Tian ,&nbsp;Benniu Niu ,&nbsp;Yizhuo Wang ,&nbsp;Hai Xiang ,&nbsp;Huayi Huang ,&nbsp;Weiming Kang ,&nbsp;Baoqing Zhang ,&nbsp;Chansheng He","doi":"10.1016/j.jhydrol.2025.134425","DOIUrl":"10.1016/j.jhydrol.2025.134425","url":null,"abstract":"<div><div>Understanding the response of hillslope soil moisture (SM) to rainfall and associated control mechanisms is crucial for understanding the partitioning of rainfall into runoff, soil water storage, and groundwater recharge in mountainous areas. However, the mechanisms governing hillslope SM response to rainfall are complex and remain unclear, especially in data-scarce alpine regions. Therefore, we established a slope-scale SM observation network consisting of 15 SM monitoring sites on a grassland slope in the Tibetan Plateau. The sites were categorized into semi-sunny (Sem), middle (Mid), and sunny (Sun) slopes based on slope aspect and position. We evaluated SM response patterns using the rainfall threshold (RT), SM increment (SMI), and soil storage increment (STI). Results revealed a distinct RT distribution across the hillslope, ranging from 0.72 to 8.19 mm, with high spatial heterogeneity (coefficient of variation = 51 %), mainly controlled by the slope aspect. Following rainfall events, lateral unsaturated subsurface flow produced a substantially higher SMI at Mid sites compared to the Sun and Sem sites. Moreover, the STI was concentrated in the surface soil layer (10 cm, 82.27 %), with smaller increases in deeper layers (50 cm, 17.17 %; 100 cm, 0.55 %). Rainfall amount was the primary driver of SMI temporal dynamics, while saturated hydraulic conductivity, bulk density, soil organic matter, antecedent SM, and slope gradient collectively governed its spatial distribution. This study provides new insights into hillslope-scale soil hydrological processes in mountainous areas.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"664 ","pages":"Article 134425"},"PeriodicalIF":6.3,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145322189","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}