Journal of Hydrology最新文献

{"title":"Evolution of eutrophication in boreal lakes: results from a half-century of in-situ measurements","authors":"Milad Shamsi Anboohi ,&nbsp;Roohollah Noori ,&nbsp;Mohammad Reza Badamian ,&nbsp;Sayed M. Bateni ,&nbsp;Fan Xia ,&nbsp;Mehran Mahdian ,&nbsp;Peiman Kianmehr ,&nbsp;Qiuhong Tang","doi":"10.1016/j.jhydrol.2025.134323","DOIUrl":"10.1016/j.jhydrol.2025.134323","url":null,"abstract":"<div><div>Eutrophication remains a critical threat to aquatic ecosystems globally. However, <em>in-situ</em> water quality measurements are often limited by short-duration or low temporal-frequency, resulting in long-term evolution of lake eutrophication remains largely unknown. This has led to major gaps in understanding how lakes respond to climatic, geomorphometric, and human influences at regional scales. Here, by leveraging approximately a half-century of <em>in-situ</em> water quality data measured at 135 stations in 31 Finnish lakes, we aim to provide a comprehensive, multi-decadal assessment of eutrophication evolution using four well-established trophic state indices (TSIs). Also, we utilize a random forest model to quantify the relative contribution of anthropogenic and climatic drivers to the changing TSIs across the lakes from 1980 to 2024. Our findings indicated that while countrywide average TSIs remained relatively stable over the study period, the proportion of eutrophic lakes increased significantly since the early 2010s, particularly in southern and central Finland. This indicates localized and spatially clustered eutrophication trends are not captured by national means. Spatial analysis revealed a pronounced latitudinal and elevational gradient of the TSIs, with eutrophic conditions dominating southern regions and oligotrophic states persisting in northern basins. Human activities, particularly land-use change, population density, and vegetation variation play a dominant role in the southern lakes’ eutrophication, while northern lakes are more sensitive to climatic variables. In human-impacted regions, reducing nutrient inputs through targeted land-use regulation is critical, whereas climate-sensitive areas require adaptive strategies to enhance ecohydrological resilience. Our findings provide actionable insights for protecting freshwater ecosystems under accelerating environmental change.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"663 ","pages":"Article 134323"},"PeriodicalIF":6.3,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145216363","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 multivariate copula framework quantifies the augmenting joint risks of compound high temperature and dry/wet events under climate change in China","authors":"Min Liu ,&nbsp;Tao Li ,&nbsp;Peter Goethals ,&nbsp;Fengjiao Song ,&nbsp;Jianying Ma ,&nbsp;Wei Sun","doi":"10.1016/j.jhydrol.2025.134321","DOIUrl":"10.1016/j.jhydrol.2025.134321","url":null,"abstract":"<div><div>As climate change intensifies, the urgency to accurately quantify the risks associated with compound climate events increases, yet the multi-dimensional integrated risk assessment of these events remains very weak. Here, we assessed the occurrence characteristics of compound high temperature and dry events (CHTDE) and compound high temperature and wet events (CHTWE) in the historical and future using daily data from the Coupled Model Intercomparison Project Phase 6 (CMIP6), and constructed a three-dimensional copula model to investigate their joint occurrence risks in China. In future emission scenarios, the precipitation threshold line are projected to shift northward, and the extent of high temperatures will continuously expand. These changes directly influenced the spatial distribution of two types of compound events (CEs). Under the highest emission scenario, China is likely to experiences an increase in the total/maximum duration, frequency, and magnitude of both types of CEs, with CHTDE and CHTWE rising by 25%-55% and 92%-677%, respectively. If emissions were controlled under the SSP245 scenario, the occurrence of CHTDE could decrease overall by 11.5%-30%. Specifically, 87% and 93% of regions will experience more frequent, larger, and more intense of CHTDE and CHTWE. Historically, CEs were concentrated in July and August, but it is likely to occur more frequently across a broader range of months in future. The three-dimensional copula analysis highlights the complex interdependencies among the duration, frequency, and intensity of CEs under different climate scenarios. Our findings further emphasize the inrease in severity of CEs in future scenarios, with a noticeable reduction in their return periods, compared to two-dimensional copula analysis. We quantitatively assessesed the occurrence risk of CEs, explored the mechanisms of their occurrence and persistence, and emphasized that CHTWE should warrant greater attention.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"663 ","pages":"Article 134321"},"PeriodicalIF":6.3,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145155350","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 graph Fourier Kolmogorov-Arnold Network (G-FourierKAN) and its application to spring discharge simulation","authors":"Yaping Yin ,&nbsp;Xingchao Deng ,&nbsp;Huiqing Hao ,&nbsp;Yonghong Hao ,&nbsp;Huibin Chang ,&nbsp;Tian-Chyi Jim Yeh","doi":"10.1016/j.jhydrol.2025.134313","DOIUrl":"10.1016/j.jhydrol.2025.134313","url":null,"abstract":"<div><div>Karst spring discharge, a vital indicator of regional groundwater dynamics, is influenced by both anthropogenic activities and climate variability. It exhibits nonlinear and nonstationary behaviors, making accurate simulation challenging even with machine learning methods. To overcome this challenge, this study develops a G-FourierKAN model, which introduces the Fourier Kolmogorov-Arnold Network (FourierKAN) into Graph Neural Networks (GNNs) by replacing the conventional Multilayer Perceptrons (MLPs). This G-FourierKAN model enhances the extraction and representation of node features within GNNs. Specifically, the FourierKAN layer represents precipitation and karst spring discharge as a combination of multi-frequency features, enabling the model to automatically learn the importance of each frequency component and extract transformed node features. Subsequently, the transformed node features are aggregated with information from neighboring nodes to enable high-precision simulation of spring discharge.</div><div>Following this approach, a precipitation-driven spring discharge prediction model is established using monthly precipitation and spring discharge data from 1959 to 2003 (the period of groundwater overexploitation) at Xin’an Spring, China. The data from 2004 to 2022 (the groundwater exploitation relieving period after implementation of sustainable development policies) are used to assess the model’s adaptability to policy changes.</div><div>The results of this application demonstrate that the model achieves high accuracy in simulating karst spring discharge, with a Nash-Sutcliffe Efficiency (NSE) of 0.72 during the testing period (1996–2003). Moreover, the model demonstrates good adaptability in simulating spring discharge from 2004 to 2022. A comparative analysis with Graph Convolutional Network (GCN) reveals that the NSE of G-FourierKAN is 0.05 higher than that of GCN in testing period. The model shows excellent stability and accuracy in simulating spring discharge dynamics in karst terrain.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"664 ","pages":"Article 134313"},"PeriodicalIF":6.3,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145264590","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 soil erosion-based framework for assessing mercury nonpoint pollution in mining watersheds","authors":"Xianwu Zheng ,&nbsp;Kouping Chen ,&nbsp;Bowen Luo ,&nbsp;Jichun Wu ,&nbsp;Huali Chen ,&nbsp;Lei Xiang","doi":"10.1016/j.jhydrol.2025.134309","DOIUrl":"10.1016/j.jhydrol.2025.134309","url":null,"abstract":"<div><div>In the Wanshan mining watershed, erosion is an important surface process transporting mercury contaminants from soil to river, posing substantial pollution risk to ecosystem. The erosion-driven mercury pollution is influenced by rainfall, soil erodibility, vegetation coverage, topography, mercury pollutant, and human activity. Quantifying the contribution of these factors is vital to evaluate erosion-driven mercury pollution. Previous studies over-relied on expert-based subjective method to assess factor contributions, yielding discrepancies between mercury pollution risk and observed fluvial contamination. Here, this study applied the CRITIC method to quantify objective factor contributions. Results indicate that rainfall, human activity, and mercury pollutant predominantly determine pollution risk, with contribution of 28 %, 20 %, and 18 %, respectively. Based on objective contribution results, we develop a novel risk index to evaluate mercury pollution. The evaluated mercury pollution risk and observed fluvial mercury contamination show a consistent distribution pattern. These findings provide effective reference for pollution control within mining watersheds.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"663 ","pages":"Article 134309"},"PeriodicalIF":6.3,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154856","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":"Near real-time satellite soil moisture estimation via residual learning integrated with sensor networks","authors":"Soumita Sengupta,&nbsp;Hone-Jay Chu","doi":"10.1016/j.jhydrol.2025.134302","DOIUrl":"10.1016/j.jhydrol.2025.134302","url":null,"abstract":"<div><div>Soil moisture (SM) is crucial for climate dynamics, hydrological processes, agricultural productivity, drought and flood management. However, real-time SM monitoring remains challenging due to sparse in-situ observations. This study presents a novel sensor driven residual learning framework that integrates multi-source data—including in-situ measurements (COSMOS-UK), satellite information (SMAP, AMSR2/GCOM-W1, SMOPS, and MODIS), and meteorological variables to generate high-precision, near real-time SM estimates across the United Kingdom (UK). The methodology employs a two-stage machine learning approach: the first stage utilizes an ensemble model to generate initial SM estimates, while the second stage applies residual learning informed by automated sensor networks to refine these estimates by correcting systematic deviations observed in the UK. Unlike conventional approaches that rely on historical time-series data, this framework demonstrates that reliable SM estimation can be achieved using single-time satellite observations with in-situ data, enabling near real-time monitoring. Initial SM estimates achieved an R² of 0.75 across 40 stations, with 37 stations achieving &gt;70 % relative accuracy. Interestingly, residual analysis within the model revealed comparatively large residuals in central and southern UK regions, and the final refined SM estimations through residual learning improved the R² to 0.94. This computationally efficient, scalable framework offers a robust solution for data-sparse regions, advancing near real-time hydrological forecasting, drought assessment, and climate resilience strategies.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"663 ","pages":"Article 134302"},"PeriodicalIF":6.3,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154857","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":"An ecohydrological framework for balancing hydropower and aquatic health using machine learning and multi-objective optimization","authors":"Jinglin Zeng ,&nbsp;Yulei Xie ,&nbsp;Hang Wan ,&nbsp;Ran Li ,&nbsp;Yanpeng Cai ,&nbsp;Zhifeng Yang","doi":"10.1016/j.jhydrol.2025.134307","DOIUrl":"10.1016/j.jhydrol.2025.134307","url":null,"abstract":"<div><div>Dam construction and operation alter flow regimes and dissolved gas saturation, threatening downstream fish habitats and aquatic ecosystem safety. Efforts to mitigate these impacts while balancing reservoir operation objectives have drawn significant attention. Although previous studies have considered ecological flow and supersaturated total dissolved gas (TDG) impacts, comprehensive analyses of power generation-ecological protection trade-offs and intelligent methods for impact prediction and scheduling optimization are still lacking. Hence, a multi-objective optimization model was established in the paper, combining machine learning for supersaturated TDG level prediction and a genetic algorithm for reservoir operation, aiming to maximize power generation and minimize supersaturated TDG levels. The results showed that XGBoost (Extreme Gradient Boosting) model outperformed other machine learning models in predicting supersaturated TDG levels, with a mean absolute error of 1.3923, a mean square error of 3.4899, and an R² value of 0.8845. SHapley Additive exPlanations (SHAP) analysis revealed that total flow was the key factor affecting the prediction accuracy of the XGBoost model for supersaturated TDG levels, followed by flood discharge, with average SHAP values of 0.093 and 0.033. The multi-objective optimization results showed that the maximum supersaturated TDG levels during the optimized flood discharge process remained below 125 %, with a duration of only 8 h, while fish exposure time were shorter than their median lethal times. Furthermore, full-load power generation was achieved, highlighting a synergistic enhancement of both ecological and economic benefits in dam operations. The results showed that the established method combining machine learning predictions with genetic algorithm optimization could effectively address multi-objective ecological scheduling problems. This study also recognized challenges in real-time scheduling under limited data contexts and the need for stakeholder input and policy integration. While not fully addressed here, the proposed framework establishes a foundation for extending to hydrological forecasting, adaptive scheduling, and policy-relevant decision support.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"663 ","pages":"Article 134307"},"PeriodicalIF":6.3,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145216448","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":"Improvement of CMIP6 water vapor accuracy by the digital twin innovation based on GNSS","authors":"Ao Guo ,&nbsp;Yan Xu ,&nbsp;Nan Jiang ,&nbsp;Yubo Wang ,&nbsp;Jiangteng Wang ,&nbsp;Tianhe Xu ,&nbsp;Harald Schuh","doi":"10.1016/j.jhydrol.2025.134305","DOIUrl":"10.1016/j.jhydrol.2025.134305","url":null,"abstract":"<div><div>The accelerating pace of global warming poses unprecedented challenges to climate prediction and environmental sustainability. The ensuing development of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) has empowered climate research into a new era, enabling simulation and projection of the global atmosphere. However, the Global Climate Models (GCMs) database is built upon physical models, inevitably with limitations of deficient observational restraints, insufficient regional simulation capacities and low spatio-temporal resolution. In contrast, the Global Navigation Satellite System (GNSS) is characterized by high precision, high temporal resolution and all-weather availability. Therefore, we propose a GNSS-integrated approach that leverages the high-precision feature of GNSS observations to enhance CMIP6 water vapor accuracy and demonstrate the improved performances of the digital twin of atmospheric Precipitable Water Vapor (PWV) over the Turkey with comprehensive validations. The results show that the Root Mean Square Errors (RMSEs) of CMIP6 water vapor improved from CNN, XGBoost and LSTM algorithm digital twins are 4.57 mm, 4.04 mm and 4.93 mm against GNSS-PWV and 5.40 mm, 5.66 mm and 5.41 mm against ERA5-PWV, which are improved by 22.27 %, 18.51 % and 22.12 %, respectively. Spatio-temporal analysis reveals the pronounced improvements during winter and in mid-altitude regions. Notably, low RMSEs were recorded in the eastern and central inland areas (improved by 50 % upon XGBoost). Across all digital twin implementations, this study pioneers GNSS into CMIP6 water vapor correction, improving the accuracy of future water vapor projections from GCMs obviously. These breakthroughs promote the contribution of GNSS in meteorology and geodesy for climate research.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"663 ","pages":"Article 134305"},"PeriodicalIF":6.3,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145155421","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":"MamGA: a deep neural network architecture for dual-channel parallel monthly runoff prediction based on mamba and depth-gated attention layer","authors":"Wen-chuan Wang ,&nbsp;Wei-can Tian ,&nbsp;Ming-lei Ren ,&nbsp;Dong-mei Xu","doi":"10.1016/j.jhydrol.2025.134304","DOIUrl":"10.1016/j.jhydrol.2025.134304","url":null,"abstract":"<div><div>Monthly runoff prediction is crucial in water resource management, involving both short-term hydrological dynamics and long-term planning. It has a decisive impact on flood prevention, resource allocation, and ecological protection. In the context of increasing uncertainties in runoff due to climate change and human activities, accurate monthly runoff forecasting becomes even more essential. Therefore, this paper proposes a novel dual-channel parallel monthly runoff prediction deep neural network architecture—MamGA—built on the significant application value of deep neural networks in runoff prediction. The architecture first introduces the Mamba model, which employs a selection mechanism to achieve selective information propagation and suppression, effectively enhancing the processing capability of global feature information while reducing the computational complexity of modelling long sequences. Furthermore, this paper incorporates a Depth-gated Attention Layer that combines bidirectional depth-gated modules and linear attention mechanisms to address the shortcomings of the Mamba network in unidirectional information processing. Integrating an Embedded Coding layer and a Sequential Decoding layer constructs an efficient coding and decoding system, further strengthening the model’s ability to capture global features and temporal information. To validate the effectiveness and advancement of the MamGA model, this study selected the Manwan Station (MW), Xiaowan Station (XW) in China, and the Thunder Creek Station (TC) in the United States as experimental subjects. Five evaluation metrics were employed for comparative analysis against nine benchmark models. The experimental results indicate that the MamGA model exhibits significant superiority across all cases. For instance, at the MW station, compared to the Long Short-Term Memory (LSTM) model, the MamGA model reduced the Mean Absolute Error (MAE) and Normalized Root Mean Square Error (NRMSE) by 33.08% and 23.93%, respectively. Meanwhile, the Nash Efficiency Coefficient (NSE), correlation coefficient (R), and Kling-Gupta Efficiency (KGE) improved by 8.41%, 3.93%, and 8.36%, respectively, with both R and NSE exceeding 0.9. The MamGA model also demonstrated significant performance improvements at other stations compared to the competing models. The study suggests that the MamGA model, as an advanced tool for monthly runoff prediction, can significantly enhance the accuracy of runoff forecasting, providing robust support for the optimal allocation and management of water resources.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"663 ","pages":"Article 134304"},"PeriodicalIF":6.3,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145155348","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":"Simulating and predicting lake dynamics by fusing HBV modeling, machine learning approach and remote sensing data","authors":"Muhammad Naeem ,&nbsp;Yongqiang Zhang ,&nbsp;Ning Ma ,&nbsp;Zixuan Tang ,&nbsp;Ping Miao ,&nbsp;Xiaoqiang Tian ,&nbsp;Congcong Li ,&nbsp;Qi Huang ,&nbsp;Zhenwu Xu ,&nbsp;Longhao wang ,&nbsp;Zhen Huang","doi":"10.1016/j.jhydrol.2025.134303","DOIUrl":"10.1016/j.jhydrol.2025.134303","url":null,"abstract":"<div><div>This study provides a comprehensive analysis of the hydrological dynamics and land use changes in the Hongjiannao Lake Basin from 1990 to 2023, with projections extending to 2060. By integrating advanced hydrological modeling Hydrologiska Byrans Vattenbalansavdelning (HBV), a machine learning algorithm Random Forest (RF), Cellular Automata (CA) Markov, and remote sensing data, this research offers a robust framework for understanding the interactions between climate change, anthropogenic activities, and ecosystem responses. The historical analysis revealed remarkable fluctuations in the lake’s area, including a 25.5 % reduction between 2000 and 2011, followed by a recovery from 2012 to 2023. The lake area increased by 26.2 % during the recovery phase, highlighting a partial reversal of decline. Projections indicate that, under various future climate scenarios, the lake area could increase by 29 % by 2060, showcasing the resilience of the ecosystem despite ongoing climate and anthropogenic pressures. The RF model demonstrated strong predictive capabilities, with R² values of 0.92 during 1990–2013 calibration and 0.76 during 2014–2023 validation, coupled with root mean square errors of 0.12 km² and 0.26 km², respectively. Additionally, the CA-Markov model predicted vegetation growth and urbanization, highlighting potential for significant landscape changes. These findings stress the need for water management strategies to preserve the lake’s ecological health, advocating for the integration of climate, land use, and hydrological factors in management plans for sustainable conservation and restoration in semi-arid regions.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"663 ","pages":"Article 134303"},"PeriodicalIF":6.3,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118373","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":"Unraveling river-tide process connectivity in complex deltaic networks","authors":"Yajun Wang ,&nbsp;Jianliang Lin ,&nbsp;Yu Yan ,&nbsp;Shuxian Wang ,&nbsp;Zhenyan She ,&nbsp;Chengyu Jin ,&nbsp;Kairong Lin ,&nbsp;Tongtiegang Zhao ,&nbsp;Giovanni Coco ,&nbsp;Huayang Cai","doi":"10.1016/j.jhydrol.2025.134301","DOIUrl":"10.1016/j.jhydrol.2025.134301","url":null,"abstract":"<div><div>The Pearl River Delta (PRD) is one of the world’s most complex deltaic systems, shaped by the dynamic interaction between river discharge and tidal forces. However, the mechanisms governing river-tide connectivity within this system remain unclear, particularly with respect to the nonlinear feedback processes and spatiotemporal lag effects. This study employs an information-theoretic framework to investigate process connectivity in the PRD, integrating relative mutual information and relative transfer entropy to quantify synchrony, causality, and directional information flow among river discharge, tides, and water levels. The results reveal that river discharge predominantly governs water level synchrony in the upper PRD, while tidal dynamics exert stronger causal effects downstream water levels. Since the 1990s, human interventions have weakened the influence of river discharge, while tidal impacts have remained relatively stable. Furthermore, water level connectivity is modulated by seasonal and tidal cycles, with discharge effects dominating during flood seasons and tidal forces prevailing during dry seasons, particularly under spring tide conditions. By integrating time-lag effects, our framework reveals delayed yet physically consistent driver-response pathways and refines the spatial structure of hydrodynamic connectivity. This work presents the first lag-aware, information-theoretic quantification of river-tide connectivity in a complex deltaic system. These insights, constituting the first lag-aware, information-theoretic quantification of river-tide connectivity in a complex delta, enhance our understanding of deltaic hydrodynamics and provide a stronger basis for hydrodynamic modeling, adaptive management, and resilience planning in deltas.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"663 ","pages":"Article 134301"},"PeriodicalIF":6.3,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145155416","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}