Journal of Hydrology最新文献

{"title":"A hybrid Penman-Monteith and machine learning model for simulating evapotranspiration and its components","authors":"Han Chen ,&nbsp;Stephen Good ,&nbsp;Kelly Caylor ,&nbsp;Richard P. Fiorella ,&nbsp;Lixin Wang","doi":"10.1016/j.jhydrol.2026.134985","DOIUrl":"10.1016/j.jhydrol.2026.134985","url":null,"abstract":"<div><div>Integrating physical processes with machine learning has advanced evapotranspiration (<em>ET</em>) simulation, yet most hybrid models fail to partition total <em>ET</em> into its components: soil evaporation (<em>E</em>) and vegetation transpiration (<em>T</em>). This study introduces Residual Neural Network–Penman–Monteith (RNN-PM), a novel hybrid dual-source <em>ET</em> model designed to overcome this limitation. The model synergizes the physically-based Penman–Monteith framework with three specialized residual neural networks trained to estimate key conductance parameters (canopy conductance, soil surface conductance, and aerodynamic conductance). This explicit parameterization allows for the direct partitioning of total <em>ET</em>. Validation at National Ecological Observatory Network (NEON) flux sites using high-frequency partitioned <em>E</em> and <em>T</em> shows that RNN-PM reliably reproduces <em>ET</em> and the transpiration fraction (<em>T/ET</em>). For <em>ET</em>, the model achieves an average Kling–Gupta efficiency (KGE) of 0.89 and a root-mean-square error (RMSE) of 0.55 mm/day; for <em>T/ET</em>, the KGE is 0.87 with an RMSE of 0.06. Furthermore, RNN-PM demonstrates robust generalization, accurately simulating <em>ET</em> and its components well beyond the initial training dataset, even under extreme climatic conditions. This study extended the analysis by comparing the RNN-PM model with seven established dual-source <em>ET</em> models. The results indicate that RNN-PM outperforms both conventional machine learning models and purely physical process-based models in simulating <em>ET</em> components in most cases. Among the purely physical process-based dual-source models, those based on surface temperature decomposition showed improved performance as the leaf area index (<em>LAI</em>) decreased when evaluated against high-frequency <em>ET</em> component datasets. In contrast, the performance of conductance-based dual-source models declined with decreasing <em>LAI</em>. Although purely machine learning-based models can produce relatively accurate simulations of <em>ET</em> components, they often exhibit limited generalization capability, an issue that the RNN-PM model effectively overcomes. Ultimately, the RNN-PM model represents a significant advance in simulating <em>ET</em> components, offering a novel and scalable approach for improving the representation of land–atmosphere interactions in Earth system models.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"668 ","pages":"Article 134985"},"PeriodicalIF":6.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014993","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":"Ponds greatly influence watershed ecosystem services value: An optimized InVEST model for large-scale","authors":"Xiaofen Bai ,&nbsp;Chen Lin ,&nbsp;Shenmin Wang ,&nbsp;Junfeng Xiong ,&nbsp;Jinduo Xu ,&nbsp;Kun Xue ,&nbsp;DanHua Ma ,&nbsp;Yijun Tong ,&nbsp;Jianchun Chen ,&nbsp;Wenzhuo Cui","doi":"10.1016/j.jhydrol.2026.135026","DOIUrl":"10.1016/j.jhydrol.2026.135026","url":null,"abstract":"<div><div>The correlation between the spatial differentiation of pond-related ecosystem services value (PRESV) and geographic environments is essential for management decisions of ponds. However, the types of ponds and geographic environments are more diverse across a larger scale region, necessitating a systematic approach on PRESV assessment. Therefore, we conducted targeted research on PRESV across a large-scale region to reveal the factors influencing the spatial differentiation of PRESV. To address the lack of a systematic approach on PRESV, this study proposes an optimization method for the aquatic production (AP) through a refined index structure and differentiated yield calculations based on the InVEST model. Notably, the optimized AP evaluation method significantly achieved an R² exceeding 0.6 and demonstrated a marked improvement over the existing method. Consequently, we generated spatial distribution data for the PRESV in China. The results indicate that: (1) The AP exhibits significant spatial differentiation corresponding to the surrounding geographic environments, summarized as “more in the south and less in the north, more in the east and less in the west”. (2) The differentiated surrounding water bodies, elevation and precipitation are potential drivers of the AP function to be disentangled in a way not possible at small watershed scale. (3) Since areas with high PRESV are all associated with high-density AP, ponds should be incorporated into watershed management.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"668 ","pages":"Article 135026"},"PeriodicalIF":6.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072674","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":"Imputation of continuous missing values in water quality data using a temporal embedding-based self-attention variational autoencoder","authors":"Xinghan Xu ,&nbsp;Lei Hu ,&nbsp;Xingyi Miao ,&nbsp;Peng Xiao ,&nbsp;Xiaohui Yan ,&nbsp;Jianwei Liu","doi":"10.1016/j.jhydrol.2026.134937","DOIUrl":"10.1016/j.jhydrol.2026.134937","url":null,"abstract":"<div><div>Missing values in water quality data (WQD), caused by sensor malfunctions, communication failures, and environmental disturbances, undermine the reliability of conventional imputation methods. To address these challenges, we propose the Temporal Embedding-based Self-Attention t-distributed Variational Autoencoder (TE-SAVAE-St), a model designed for robust and temporally consistent data reconstruction. The model incorporates a Student’s-t prior to handle outliers, temporal embeddings (TE) to capture chronological patterns, and multi-head self-attention (MSA) to model long-term dependencies and inter-variable correlations. Extensive experiments on real-world WQD datasets show that TE-SAVAE-St outperforms ten baseline methods under various missing data scenarios, reducing RMSE by 11.8% and SMAPE by 23.6% compared to state-of-the-art models. Ablation studies confirm the complementary benefits of TE, MSA, and Student’s-t components. Additionally, time complexity analysis demonstrates that TE-SAVAE-St achieves an optimal balance between computational efficiency and imputation accuracy, making it suitable for real-time and large-scale monitoring applications. Overall, TE-SAVAE-St offers a domain-aware framework for the accurate reconstruction of incomplete WQD, supporting continuous environmental monitoring.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"668 ","pages":"Article 134937"},"PeriodicalIF":6.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957165","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 impact of rainfall characteristics on combined sewer overflows in wet weather","authors":"Hao Wang ,&nbsp;Zijian Wang ,&nbsp;Pengfei Zeng ,&nbsp;Zilong Liu ,&nbsp;Bin Chen ,&nbsp;Jinjun Zhou","doi":"10.1016/j.jhydrol.2026.134951","DOIUrl":"10.1016/j.jhydrol.2026.134951","url":null,"abstract":"<div><div>Combined sewer overflows (CSO) pose risks to both water quality and public health. However, the impact of varying rainfall characteristics on CSO remains unclear. This study aims to assess and analyze the correlations between CSO and rainfall characteristics, specifically rainfall depth, duration, and the proportion of rainfall occurring during peak periods. The objective is to identify and elucidate the impacts of each main rainfall characteristic on the frequency of CSO. To conduct this analysis, rainfall data spanning 71 years were divided into 1,435 rainfall events and categorized into five representative rainfall patterns based on their temporal distribution. A hydraulic model of sewer network was employed to simulate the CSO results under different rainfall patterns. An indicator termed “rainfall-CSO contribution rate” was introduced to reflect the impact of rainfall on CSO. The neural network method was utilized to establish a relationship model between rainfall characteristics and rainfall-CSO contribution rate (R² &gt; 0.94). Sensitivity analysis and model visualization techniques were used to reveal the relationship between rainfall characteristics and rainfall-CSO contribution rate. Significant differences in contribution rates across rainfall patterns were observed (p = 0.012), indicating a strong association with rainfall characteristics. Specifically, rainfall patterns with a higher proportion of peak period precipitation correspond to greater CSO contribution rates. Within each rainfall pattern, rainfall depth was identified as the most critical factor affecting the CSO contribution rate, followed by rainfall duration, with average sensitivity indices of 0.580 and 0.274, respectively. The peak-period rainfall ratio had a minimal impact on the results, with an average sensitivity index of just 0.024. Furthermore, the study noted that, variations in CSO contribution rates across different patterns intensified with increasing rainfall depth, while the impact of rainfall duration diminished with longer durations. This research provides a methodical approach for quantitatively analyzing the relationship between rainfall characteristics and CSO contribution rates, facilitating rapid assessments of CSO conditions and informing urban planning and drainage management decisions.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"668 ","pages":"Article 134951"},"PeriodicalIF":6.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957292","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":"Time cumulative effects and machine learning–driven forecasting of cyanobacterial blooms in data-sparse eutrophic lakes","authors":"Kaili Zhang ,&nbsp;Tianci Qi ,&nbsp;Ming Shen ,&nbsp;Yinguo Qiu ,&nbsp;Zhigang Cao ,&nbsp;Yaqin Jiao ,&nbsp;Hongtao Duan","doi":"10.1016/j.jhydrol.2026.134966","DOIUrl":"10.1016/j.jhydrol.2026.134966","url":null,"abstract":"<div><div>Cyanobacterial harmful algal blooms (CyanoHABs) critically threaten global freshwater security in lakes. Timely prediction of their magnitude and spatially explicit distribution is essential for mitigating ecological risk. Conventional mechanistic models demand massive data inputs, well-defined boundary conditions, and intensive computation, rendering them impractical for most lakes. Our proposed machine learning framework overcomes these constraints by intelligently integrating sparse monitoring stations and satellite data, and by constructing time cumulative effect variables, which are defined as the average or cumulative values of environmental variables over preceding days. Using 23 years of observations from Lake Chaohu, China, we evaluated four algorithms—Random Forest (RF), XGBoost, Artificial Neural Network (ANN), and Ridge regression, for forecasting CyanoHABs at 1/3/7-day lead times. RF achieved optimal 1-day predictions for CyanoHABs percentage (CBP) (root mean square error (RMSE) = 4.18 %, coefficient of determination (R²) = 0.81) through strategic feature selection from cumulative variables. RF outperformed other models in temporal pattern recognition and sustained robust performance across all forecast horizons (RMSE: 4.18 %–6.26 %, R²: 0.59–0.81). For spatially explicit predictions, the RF model achieved superior 1-day accuracy (RMSE = 5.05 %, F1 score = 0.84) through dynamic interpolation of sparse station data, outperforming 3/7-day forecasts. By leveraging sparse monitoring data, this computationally efficient approach enables reliable 1–7-day forecasting of spatially explicit distributions. It thus offers a transferable solution for proactive CyanoHABs management in data-sparse eutrophic lakes worldwide.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"668 ","pages":"Article 134966"},"PeriodicalIF":6.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995711","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":"Vertical sediment stratification regulates dissolved nutrient dynamics and interface fluxes along the intertidal zone","authors":"Zhenyang Li ,&nbsp;Yueqi Wang ,&nbsp;Yashu Qi ,&nbsp;Kai Xiao ,&nbsp;Guangxuan Han ,&nbsp;Hailong Li ,&nbsp;Liang Liu ,&nbsp;Dejuan Jiang ,&nbsp;Xiaoying Zhang ,&nbsp;Chunmiao Zheng","doi":"10.1016/j.jhydrol.2026.134980","DOIUrl":"10.1016/j.jhydrol.2026.134980","url":null,"abstract":"<div><div>Sediment stratification is a ubiquitous feature in intertidal aquifers, yet its precise role in regulating dissolved nutrient dynamics remains poorly understood. This study examined two typical kinds of intertidal transects including the sandy beach and mudflat, each exhibiting distinct sediment stratification. We analyzed the multi-depth distribution patterns of dissolved nutrients (nitrogen, phosphorus, and silicon) and carbon in intertidal groundwater. We also identified the key drivers using stable isotope tracers (δ¹⁵N-NO₃⁻ and δ¹⁸O-NO₃⁻) and multivariate statistics, and quantified the interface fluxes to the coastal ocean. The findings indicated that the distribution patterns of nutrients and carbon between fine- and coarse-grained sediments in both transects were synthetically influenced by hydrological dynamics and biogeochemical reactions. In the sandy beach, groundwater composition transitioned from being NO₃⁻-dominated in the deep coarse-grained sediments, where attenuation occurred via denitrification, to NH₄⁺-dominated in the surface fine-grained sediments due to organic matter mineralization. In the mudflat, organic-rich sediments and low water exchange rates favored NH₄⁺ dominance, with fine-grained sediments accumulating high concentrations of NH₄⁺, PO₄³⁻, and DSi in groundwater. Moreover, the sandy beach served as hotspots for groundwater discharge, delivering nutrient and dissolved carbon fluxes up to two orders of magnitude higher than the mudflat. The presence of a surface fine-grained sediments enhanced the export of groundwater-derived NH₄⁺ and DIC, potentially exacerbating coastal eutrophication and acidification. These findings demonstrate that sediment stratification fundamentally alters the composition and flux magnitude of nutrients and carbon discharged into coastal waters, thereby influencing coastal solute budgets.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"668 ","pages":"Article 134980"},"PeriodicalIF":6.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001372","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":"Retrieval of snow depth using synthetic aperture radar: past, current, and future","authors":"Zhen Li ,&nbsp;Haiwei Qiao ,&nbsp;Ping Zhang ,&nbsp;Yanan Bai ,&nbsp;Huadong Hu","doi":"10.1016/j.jhydrol.2026.135103","DOIUrl":"10.1016/j.jhydrol.2026.135103","url":null,"abstract":"<div><div>Snow is one of the most dynamic factors in the climate system. In recent years, as climate change has intensified, the distribution of the global snow cover and the timing of snowmelt have changed, seriously affecting the hydrological cycle, ecosystems, carbon balance, and human activities. Snow depth, as a key parameter for measuring snow changes, plays a crucial role in water storage estimation, disaster prediction, and research on global sustainability. Considering the limited spatial representativeness of ground observations and the insufficient spatial resolution of passive microwave remote sensing, synthetic aperture radar (SAR) offers a viable solution for the superior precision and fine-scale retrieval of spatiotemporally continuous snow depth. This paper begins with the interaction between SAR signals and snow, reviewing different types of snow backscattering models and surface scattering models. Then, the SAR data for snow depth retrieval are outlined. Thirdly, snow depth retrieval methods based on polarimetric SAR (PolSAR), interferometric SAR (InSAR), polarimetric interferometric SAR (PolInSAR), and tomographic SAR (TomoSAR) techniques in the last two decades are summarized. Finally, the current opportunities and challenges are envisioned to provide reliable references for future studies.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"669 ","pages":"Article 135103"},"PeriodicalIF":6.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146138399","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":"Climate warming-induced glacier mass loss driving peak runoff variability and cryosphere service value decline","authors":"Jingyao Sun,&nbsp;Jianxia Chang,&nbsp;Aijun Guo,&nbsp;Yimin Wang,&nbsp;Zhehao Li,&nbsp;Dingrong Zhai,&nbsp;Peipei Wang,&nbsp;Jiayang Wang","doi":"10.1016/j.jhydrol.2026.135084","DOIUrl":"10.1016/j.jhydrol.2026.135084","url":null,"abstract":"<div><div>Mountain glaciers play a critical role in freshwater supply and hydrological regulation. Climate warming is projected to accelerate glacier melting in High Mountain Asia, threatening water resource sustainability, particularly in the arid and semi-arid regions. This study employs the integrated ice-dynamic Open Global Glacier Model to investigate glacier responses to past and future climate change. The annual mass balance series of 11,625 glaciers from 1990 to 2019 is reconstructed, revealing their spatiotemporal variability in alpine regions. Additionally, the long-term dynamics of glacier mass, area, and volume are assessed through 2100 under different climate scenarios, focusing on glacier runoff changes and the differences in the timing of peak runoff. Finally, the unit area service pricing method is applied to establish an index for quantifying the ecological value loss resulting from glacier retreat. The results show that climate warming will lead to substantial and irreversible mass loss. This process is characterized by initial thinning followed by retreat, resulting in an overall negative mass balance. By the end of the 21st century, glacier area and volume will decrease by more than 40% in most basins, with peak runoff expected around 2050 under low-emission scenarios. In contrast, delayed peaks are generally associated with high-emission scenarios and regions with substantial glacier reserves. Glacier service values are predicted to decline significantly or be entirely lost, with this trend intensifying from southwest to northeast in the Tarim River Basin. This study provides new insights into glacier dynamical and hydrological responses under climate warming, contributing to regional socio-ecological sustainability and optimized water resource management.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"669 ","pages":"Article 135084"},"PeriodicalIF":6.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110447","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":"Projecting future exposure to compound precipitation and wind extremes using Copula methods with Bayesian model averaging","authors":"Shuyou Liu ,&nbsp;Jun Xia ,&nbsp;Qianzuo Zhao","doi":"10.1016/j.jhydrol.2026.135074","DOIUrl":"10.1016/j.jhydrol.2026.135074","url":null,"abstract":"<div><div>Compound precipitation and wind extremes (CPWEs) are destructive multivariate events whose risks are amplified under climate change. It is urgent but challenging to assess intensity risk and project future exposure due to complex social-meteorological factor interaction and uncertainty of GCM outputs. To deal with these issues, a novel CPWE risk assessment framework was established containing Bayesian Model Averaging (BMA) based GCM outputs ensemble, Copula-based CPWE selection, and intensity assessment. A comprehensive exposure index was built to evaluate future population and economic exposure. Key findings include: (1) The BMA ensemble outperforms individual GCMs, yielding higher accuracy and robustness in simulating precipitation and windspeed; (2) The intensity of future CPWEs is projected to increase, characterized by enhanced variability and spatial heterogeneity. A significantly larger proportion of regions exhibit upward trends under both SSP245 and SSP585 scenarios, particularly under the latter scenario; (3) High exposure risk persists in the North China Plain and southeastern coast, with most regions experiencing increased risk relative to the historical period. Risk peaks around mid-century, indicating a critical period for climate-socioeconomic tipping points. The results provide critical insights into the spatiotemporal patterns of future CPWEs, supporting the development of effective early warning systems and climate resilience strategies.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"669 ","pages":"Article 135074"},"PeriodicalIF":6.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110452","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 interaction between vegetation greenness and hydro-climatic factors in China","authors":"Yuyue Xu ,&nbsp;Jiangling Liao ,&nbsp;Qing Liu ,&nbsp;Yuchen Li ,&nbsp;Junzheng Pi ,&nbsp;Chang-Qing Ke ,&nbsp;Jianli Chen ,&nbsp;Hang Pan ,&nbsp;Lixin Wang ,&nbsp;Liyin He ,&nbsp;Ling Yao ,&nbsp;Chenghu Zhou","doi":"10.1016/j.jhydrol.2026.134956","DOIUrl":"10.1016/j.jhydrol.2026.134956","url":null,"abstract":"<div><div>The interactions between vegetation and hydro-climatic factors play a critical role in key geophysical processes, including carbon and hydrological cycles. However, the interaction between vegetation and hydro-climatic factors in China remains unclear. Here, nonlinear Granger causality tests were employed to analyze the bidirectional relationships between vegetation and key hydro-climatic variables from 2002 to 2021. The results indicate that temperature was the dominant Granger-causal factor influencing vegetation growth (43.49 % of grid cells), followed by terrestrial water storage (16.49 %), soil moisture (11.44 %), precipitation (10.92 %), and solar radiation (3.11 %). In the reverse direction, vegetation exerted the strongest feedback on terrestrial water storage (31.18 %) and precipitation (28.77 %), with weaker effects on soil moisture (20.68 %), solar radiation (9.67 %), and temperature (3.79 %). Bidirectional Granger causality was observed in 33.57 %–46.77 % of the assessed areas, with grasslands showing the highest proportion of significant causal relationships. These findings improve our understanding between vegetation and hydro-climatic factors across China, providing valuable insights that can guide the refinement and optimization of these processes in ecological and hydrological models.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"668 ","pages":"Article 134956"},"PeriodicalIF":6.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957244","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}