Journal of Hydrology最新文献

{"title":"Model based quantification of salinization dynamics under changing hydrological conditions in the Volturno River (Italy) coastal aquifer","authors":"Mattia Gaiolini ,&nbsp;Abraham Ofori ,&nbsp;Matteo Postacchini ,&nbsp;Micòl Mastrocicco ,&nbsp;Nicolò Colombani","doi":"10.1016/j.jhydrol.2025.133395","DOIUrl":"10.1016/j.jhydrol.2025.133395","url":null,"abstract":"<div><div>This work presents a semi-coupled modelling approach to study salinization dynamics in the Volturno River coastal aquifer (Italy), distinguishing among different salinization mechanisms. The area is of particular interest, given its location in the Mediterranean region, a climate change hot-spot. A 1D HEC-RAS numerical model was built up and run for a decade (2010–2020) to quantify the areal extent and timing of salinization events due to seawater encroachment within the Volturno River mouth. The results were used as input in a 3D SEAWAT model that incorporated salinity variations on a monthly basis for the same period. The SEAWAT model was downscaled from a large calibrated MODFLOW model of the whole Campania region. Both national and worldwide databases were used to constrain the models. The model was then compared with 9 high resolution vertical profiles of porewater salinity obtained using a continuous coring sediment sampler, providing good model performance indicators (R² = 0.867, NSE = 0.808, and RMSE = 3.926 g/L). Results highlight an increasing groundwater salinization pattern due to intrusion from the Volturno riverbed. The classical mechanism of seawater wedge intrusion from the coastline was minimal, while large inland portions of the model domain were characterized by high salinity (up to 75 g/l) due to remnant paleo seawater trapped into peaty and silty-clay aquitards. This physically-based modelling approach could be replicated in any coastal porous aquifer (if hydrological and hydrogeological datasets are available) to identify and quantify the salinization mechanisms and to help water managers to implement tailored solutions in the most affected areas.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"660 ","pages":"Article 133395"},"PeriodicalIF":5.9,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891283","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":"Memory character and predictive period of soil moisture in the root-zone and along hillslope","authors":"Jun Zhang ,&nbsp;Zi Wu ,&nbsp;Yong Li ,&nbsp;Chao Qin ,&nbsp;Junfang Cui","doi":"10.1016/j.jhydrol.2025.133428","DOIUrl":"10.1016/j.jhydrol.2025.133428","url":null,"abstract":"<div><div>Soil moisture plays a crucial role in hydrology, influencing interactions between land and surface, hydrological processes, flood forecasting, and soil degradation. Its behavior varies over different timescales, but most research to date has concentrated on large-scale or long-term data. There is a notable gap in the understanding of soil moisture memory characteristics and its predictive periods, especially at smaller scales like the root zone and hillslopes. This research aims to address this gap by using power spectrum analysis to investigate long-term memory (LTM) characteristics and second-order detrended fluctuation analysis (DFA-2) to assess predictive periods. Data were gathered from greenhouse experiments monitoring soil moisture during the full growth cycle of tomato plants, as well as from field measurements on hillslopes. Findings indicate that the soil moisture predictive period increased from the first (T_s) to the third (T_f) growth stages. Additionally, vegetated slopes showed stronger memory of soil moisture from May to October compared to bare slopes. This study offers essential insights for improving irrigation planning, drought management, and water resource strategies.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"660 ","pages":"Article 133428"},"PeriodicalIF":5.9,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891284","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":"Study on the impact of multi-source uncertainty on the operation of inter-basin water transfer projects and adaptive response strategies","authors":"Yuchen Zhang ,&nbsp;Wenhua Zhou ,&nbsp;Jianan Wang ,&nbsp;Xiaoling Su ,&nbsp;Lianzhou Wu","doi":"10.1016/j.jhydrol.2025.133375","DOIUrl":"10.1016/j.jhydrol.2025.133375","url":null,"abstract":"<div><div>Joint adaptive regulation of water source areas under multi-source uncertainties is key to the efficient operation of inter-basin water transfer projects (IBWTPs). A novel model to quantify the impacts and countermeasures of IBWTPs operations under multi-uncertainty scenarios, was proposed to address the problem of adaptive regulation. This paper focused on the water source area reservoir group of the Hanjiang-to-Weihe River Valley Water Diversion Project (HTWDP). Markov Chain Monte Carlo (MCMC) and three-dimensional Copula functions were used to construct multi-uncertainty scenarios and to quantify the impacts of uncertainties on water resource transfer and power generation during reservoir operations. Additionally, a framework of adaptive simulation–optimization operation model based on multi-uncertainties and hedging rules (AOMU) was established to balance multi objectives of water resource transfer, power generation, and energy consumption under adverse inflow conditions. The results showed (1) significant correlations among inflow, adjustable water volume, and the water resource demand of the HTWDP, allowing the construction of multi-uncertainty scenarios, and (2) maintenance of an average annual water resource transfer volume at 1.35 to 1.52 billion m³, with a 9.2% increase, under multi-source uncertainties. However, the average energy consumption increased by 41.1%, while the average power generation decreased by 5.2%. (3) After optimization, a 14% reduction in total energy consumption and a 2.76% increase in power generation were achieved, while the annual water transfer volume was maintained over 1.3 billion m³ under adverse conditions. This paper provided valuable insight for the effective management of uncertainty risks of IBWTPs while balancing historical and future applicability, and for the optimization of water resource transfers and distribution schemes in the source area.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"660 ","pages":"Article 133375"},"PeriodicalIF":5.9,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886909","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":"Environmental drivers and assembly mechanisms of microplastics in plateau lakes","authors":"Hengchen Li ,&nbsp;Hongwei Lu ,&nbsp;Sansan Feng ,&nbsp;Yuxuan Xue ,&nbsp;Xiaohan Zhang","doi":"10.1016/j.jhydrol.2025.133388","DOIUrl":"10.1016/j.jhydrol.2025.133388","url":null,"abstract":"<div><div>The aggregation of microplastics in plateau lakes was detected, but the environmental fate of microplastics under extreme environments remains unclear. This study systematically explored the fate, assembly process, and driving factors of microplastics in lakes on the Tibetan Plateau. The results revealed that the abundance of microplastics in lakes on the Tibetan Plateau was significantly lower than the global average, and the predominant morphological categories of microplastics were identified as fibers, fragments, and films. The altitude of 4500 m was determined as the critical boundary to distinguish the primary origins of microplastics. Besides, the assembly process of microplastics was influenced by surrounding environmental factors. Salinity, wind speed, altitude, total nitrogen, human activity intensity, precipitation, and pH were the key environmental factors affecting microplastic abundance. This study emphasized the influence of environmental factors on microplastic assembly in plateau lakes.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"660 ","pages":"Article 133388"},"PeriodicalIF":5.9,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886910","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":"Monthly streamflow forecasting with temporal-periodic transformer","authors":"Hanlin Yin ,&nbsp;Qirui Zheng ,&nbsp;Chenxu Wei ,&nbsp;Congcong Liang ,&nbsp;Minhao Fan ,&nbsp;Xiuwei Zhang ,&nbsp;Yanning Zhang","doi":"10.1016/j.jhydrol.2025.133308","DOIUrl":"10.1016/j.jhydrol.2025.133308","url":null,"abstract":"<div><div>Monthly streamflow forecasting is important for water resources planning and management in hydrology. In recent years, deep learning based data-driven approaches have received significant attention, especially the Long Short-Term Memory (LSTM) and the Transformer. Among the above two sorts of models for such a task, hardly any model considers the periodic information from the same month of different years directly. This periodic information is important for monthly streamflow forecasting and we propose a periodic attention mechanism to explore it in this paper. Specifically, we propose a novel Temporal-Periodic Transformer (TPT) model, which has temporal-periodic attention modules exploring the temporal information and the periodic information. As a comparison, the original Transformer-based streamflow forecasting model does not consider such periodic information explicitly. To show the performance of our TPT model, two datasets including the Catchment Attributes and Meteorology for Large-sample Studies in Australia (CAMELS-AUS) and a dataset from the Tangnaihai Hydrological Station located in Qinghai Province of China are employed in this paper. Our TPT model outperforms the benchmark Transformer model significantly, e.g., for Nash–Sutcliffe efficiency, the TPT model improves over the original Transformer-based model in 45.9% and furthermore its NSE achieves 0.9108 in Tangnaihai by pretraining in 20 selected basins in CAMELS-AUS. For monthly streamflow forecasting, the TPT model is a good choice.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"660 ","pages":"Article 133308"},"PeriodicalIF":5.9,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886906","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":"Improving parameter regionalization learning for spatialized differentiable hydrological models by assimilation of satellite-based soil moisture data","authors":"Mouad Ettalbi ,&nbsp;Pierre-André Garambois ,&nbsp;Ngo-Nghi-Truyen Huynh ,&nbsp;Patrick Arnaud ,&nbsp;Emmanuel Ferreira ,&nbsp;Nicolas Baghdadi","doi":"10.1016/j.jhydrol.2025.133300","DOIUrl":"10.1016/j.jhydrol.2025.133300","url":null,"abstract":"<div><div>Accurate and high-resolution hydrological models are crucially needed, especially for important socioeconomic issues related to floods and droughts, but are faced with data and model uncertainties which can be reduced by maximizing information integration from multisource data. This work focuses on improving the integration of satellite and in situ land surface data into spatially distributed hydrological models. The Hybrid Data Assimilation and Parameter Regionalization (HDA-PR) approach incorporating learnable regionalization mappings, based on neural networks into the differentiable spatially distributed hydrological model SMASH, is modified to account for satellite-based moisture maps in addition to discharge at gauging stations and basin physical descriptors maps. Regional optimizations of a spatially distributed conceptual model are performed on a flash-flood-prone area located in the South of France, and their accuracy and robustness are evaluated in terms of simulated discharge and moisture against observations. In general, the integration of satellite-derived soil moisture data alongside traditional observed streamflow measurements during calibration procedures has demonstrated notable improvements in hydrological performance, both in terms of simulated discharge and moisture. This is achieved thanks to an improved learning of regionalization of model conceptual parameters with HDA-PR integrating satellite-based moisture through the RMSE metric adapted to a spatially distributed model with variational data assimilation. This study provides a solid foundation for advanced data assimilation of multi-source data into learnable spatially distributed differentiable geophysical models.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"660 ","pages":"Article 133300"},"PeriodicalIF":5.9,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886908","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":"Displacement prediction and failure mechanism analysis of rainfall-induced colluvial landslides","authors":"Yabo Li,&nbsp;Xinli Hu,&nbsp;Haiyan Zhang,&nbsp;Hongchao Zheng,&nbsp;Ningjie Li","doi":"10.1016/j.jhydrol.2025.133361","DOIUrl":"10.1016/j.jhydrol.2025.133361","url":null,"abstract":"<div><div>Rainfall is the primary cause of colluvial landslides and constitutes approximately 80% of such events. Colluvial landslides are affected by seasonal rainfall patterns and typically exhibit progressive deformation. The causes of these landslides are complex and their destructive mechanisms cannot be controlled easily, thus resulting in catastrophic events. Accurate prediction of the displacement of rainfall-induced colluvial landslides is crucial for mitigating the associated risks. In this study, we consider the Wufeng landslide as an example to elucidate quantitative correlations between rainfall factors and deformation characteristics via the Spearman correlation analysis. Based on seven years of continuous displacement monitoring data, we develop a rainfall-induced colluvial landslide displacement prediction model using the improved complete integrated empirical mode decomposition with adaptive noise (ICEEMDAN) method and a sparrow search algorithm (SSA)-optimized long short-term memory (LSTM) neural network. Furthermore, we examine the progressive deformation mechanisms of rainfall-induced accumulation landslides based on fluid–solid coupling simulations in FLAC3D, supplemented by field investigations and deformation monitoring. The results indicate that (1) cumulative rainfall over 22 d and effective rainfall over 28 d constitute the primary triggering factors for the Wufeng landslide; (2) the ICEEMDAN-SSA-LSTM hybrid model demonstrates outstanding predictive accuracy for rainfall-induced displacement patterns, particularly in characterizing the correlation between intermittent displacements and rainfall signatures; (3) the pipe network infiltration system in the Wufeng landslide establishes preferential seepage pathways, where coupled fluid–solid interactions between infiltration pressure and anti-sliding resistance generate a distinctive “preferential flow–subduction–resistance” deformation sequence. These findings provide a theoretical basis for enhancing early warning systems for rainfall-induced colluvial landslides and offer a new perspective for analyzing water-related landslide deformations worldwide.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"660 ","pages":"Article 133361"},"PeriodicalIF":5.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881975","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":"Applications of percolation-based effective-medium approximation to electrical conductivity in porous media with surface conduction","authors":"David McLachlan ,&nbsp;Meysam Ramezani ,&nbsp;Robert Horton ,&nbsp;Behzad Ghanbarian","doi":"10.1016/j.jhydrol.2025.133384","DOIUrl":"10.1016/j.jhydrol.2025.133384","url":null,"abstract":"<div><div>Electrical conductivity, <span><math><mrow><mi>σ</mi></mrow></math></span>, has been widely used to estimate hydraulic conductivity in porous media as well as to interpret subsurface low- and high-conductivity zones. <span><math><mrow><mi>σ</mi></mrow></math></span> in a porous medium is impacted by the complicated relationship between the surface conductivity of solids, as the low-conductivity component which is significant at dry conditions, and bulk conductivity through the pore space, as the high conductivity component. As water saturation increases from completely dry to fully saturated, the effect of the bulk conductivity on electrical conductivity substantially increases. In this study, for the first time, we propose applications of a percolation-based effective-medium approximation (P-EMA) to describe the saturation dependence of <span><math><mrow><mi>σ</mi></mrow></math></span> in porous media with significant surface conduction. The proposed P-EMA model estimates were compared to 16 data sets including three numerically simulated sets and thirteen measured sets. There was substantial agreement between the theory and the data, with scaling exponents ranging from 0.18 to 2.39, indicating non-universal behavior. The saturation-dependent <span><math><mrow><mi>σ</mi></mrow></math></span> values of packed clay loam soil samples were estimated with the P-EMA model. The P-EMA estimated values were in reasonable agreement with the measured values.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"660 ","pages":"Article 133384"},"PeriodicalIF":5.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878825","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":"Attributing the divergent changes of drought from humid to dry regions across China","authors":"Yao Feng ,&nbsp;Fubao Sun ,&nbsp;Xiaoya Deng","doi":"10.1016/j.jhydrol.2025.133363","DOIUrl":"10.1016/j.jhydrol.2025.133363","url":null,"abstract":"<div><div>Different drought indices can significantly influence our understanding of drought dynamics, particularly as evaporative demand intensifies with ongoing warming. This study investigated long-term drought variations across China using the Standardized Precipitation Index (SPI) and the Standardized Precipitation-Evaporation Index (SPEI). Additionally, we attributed divergent drought dynamics to climate and vegetation variations by applying Multivariate Nonlinear Regression models and Random Forest models. From 1982 to 2022, SPI exhibited a significant wetting trend (0.05/decade, <em>p</em> &lt; 0.01), while SPEI transitioned from wetting to a drying trend in 1993, followed by a significant drying trend (−0.19/decade, <em>p</em> &lt; 0.01), particularly in arid regions. The two indices were highly correlated in humid regions (<em>r</em> = 0.89–0.94) but showed weaker correlations in arid regions (<em>r</em> = 0.57–0.81). The influence of climate variables on SPEI increased progressively from humid (87.6 %) to hyper-arid (95.6 %) regions. Precipitation and relative humidity were the most influential factors in humid and non-humid regions, respectively. While the combined effects of precipitation and relative humidity on SPEI were dominant in most regions, relative humidity and minimum temperature played a more significant role in arid and hyper-arid regions. Stronger drought-vegetation coupling was observed in humid (32.8 %) and hyper-arid (32.5 %) regions. Notably, SPEI was sensitive to evaporation and transpiration in densely vegetated humid regions, while in sparsely vegetated hyper-arid regions, transpiration was the dominant factor. These findings underscore the importance of selecting appropriate drought indices based on regional climatology to enhance drought monitoring and support agricultural and water management strategies.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"660 ","pages":"Article 133363"},"PeriodicalIF":5.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870369","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":"Predicting annual peak daily streamflow in natural basins using quantile regression forests","authors":"Kwan-Hyuck Kim ,&nbsp;Konstantinos M. Andreadis ,&nbsp;Fiachra O’Loughlin","doi":"10.1016/j.jhydrol.2025.133233","DOIUrl":"10.1016/j.jhydrol.2025.133233","url":null,"abstract":"<div><div>Flood risk is characterized by flood inundation areas influenced by hydroclimatic extremes such as peak streamflow events. Predicting peak streamflow discharge in ungauged basins upstream of dams or reservoirs is critical for forecasting inflows, aiding operational management, and mitigating downstream flood risk. We developed a Quantile Regression Forest (QRF) model to predict annual peak daily streamflow in ungauged basins, incorporating uncertainty quantification and variable influence analysis. The model integrates continental-scale data from PRISM, GAGES-II, NWIS Streamflow, and NLCD for the CONUS. Through hyperparameter tuning and recursive feature elimination (RFE), we optimized the QRF model to achieve an adjusted R² of 0.768 with low SMAPE scores (20.512% overall, median 9.444). Results reveal peak precipitation as the dominant driver of flood magnitude (<span><math><mo>&gt;</mo></math></span>50% importance) in streamflow prediction, alongside significant contributions from other explanatory variables. The model effectively captures hydrological relationships and achieves realistic calibration to observed conditions. This approach provides actionable insights for water resources management and flood risk assessment.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"660 ","pages":"Article 133233"},"PeriodicalIF":5.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870516","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}