Journal of Hydrology最新文献

{"title":"Estimating irrigation consumptive use for the conterminous United States: coupling satellite-sourced estimates of actual evapotranspiration with a national hydrologic model","authors":"Donald J. Martin ,&nbsp;Richard G. Niswonger ,&nbsp;R. Steve Regan ,&nbsp;Justin L. Huntington ,&nbsp;Thomas Ott ,&nbsp;Charles Morton ,&nbsp;Gabriel B. Senay ,&nbsp;MacKenzie Friedrichs ,&nbsp;Forrest S. Melton ,&nbsp;Jonathan Haynes ,&nbsp;Wesley Henson ,&nbsp;Amy Read ,&nbsp;Yanhua Xie ,&nbsp;Tyler Lark ,&nbsp;Michael Rush","doi":"10.1016/j.jhydrol.2025.133909","DOIUrl":"10.1016/j.jhydrol.2025.133909","url":null,"abstract":"<div><div>Irrigation consumptive use is crucial information for water resource management and this information is generally not available for most regional and national assessments. From 2000 to 2020, daily estimates of consumptive use were produced for the conterminous U.S. at the 12-digit hydrologic unit code watershed level. Using the Simplified Surface Energy Balance model within the OpenET tool on Google Earth Engine, actual evapotranspiration estimates were derived from Landsat land surface temperature data. These estimates were mapped to the National Hydrologic Model (NHM) spatial units for irrigated areas defined by the Landsat Irrigation Dataset (LANID). The NHM allows for simultaneous estimation of consumptive use and effective precipitation. Results were compared to the 2015 USGS water use report, revealing annual maximum difference in consumptive use of 37 cm, with an average absolute difference of 13 cm and a bias of 2.7 %. Comparisons to field resolution irrigation withdrawals reported by growers in 4 major irrigation regions across the nation were made using efficiencies reported in the literature. These comparisons resulted in root mean square errors that ranged between 10 and 18 cm per year and biases of −18.2 to 15.8 %. Significant differences in these different reported estimates represent challenges in accurate data availability and inconsistent methods and definitions used in assessments. Annual consumptive use in the U.S. can vary by 37 % due to droughts, particularly in regions more heavily dependent on precipitation. Significant increases in consumptive use were noted over the 20-year period in western states, especially the Colorado River basin from 2010 to 2020, exacerbating water supply declines.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"662 ","pages":"Article 133909"},"PeriodicalIF":5.9,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670128","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":"Regime shift in river-tide dynamics of longitudinal and transverse channels over the Pearl River Delta, China","authors":"Bo Li ,&nbsp;Hao Yang ,&nbsp;Huayang Cai ,&nbsp;Suying Ou ,&nbsp;Feng Liu ,&nbsp;Tongtiegang Zhao ,&nbsp;Kairong Lin ,&nbsp;Jianliang Lin","doi":"10.1016/j.jhydrol.2025.133883","DOIUrl":"10.1016/j.jhydrol.2025.133883","url":null,"abstract":"<div><div>Understanding the impact of upstream river discharge on river-tide dynamics is vital for sustainable freshwater management in river deltas, including flood control, salinity intrusion, navigation, etc. In this study, we applied the R_TIDE data-driven analysis tool to the Pearl River Delta (PRD), China, to quantify the stepwise alterations in river-tide dynamics, focusing on changes in water levels, tidal amplitudes, phases, and damping rates. The results identify three distinct periods (Pre-Development Period: 1965–1988, Transitional Period: 1989–1998, Post-Development Period: 1999–2017), which correspond to shifts in river-tide interactions, as indicated by changes in water levels and tidal amplitudes. These changes reflect alterations in effective friction, driven by the nonlinear modulation of river discharge and morphology, significantly altering the relationship between tidal damping rate and river discharge. This regime shift suggests a significant transformation in the balance between riverine and tidal forces, especially in the transverse channels, highlighting their roles in flood regulation and tidal storage. The successful application of the R_TIDE in the PRD provides insights for sustainable water resources management and highlights its potential applicability to other estuaries subject to intensive human interventions worldwide.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"662 ","pages":"Article 133883"},"PeriodicalIF":5.9,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670129","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":"Impact of land cover and sediments on GHG emissions from Indonesian rivers","authors":"Andriamanantena R. Vonihanitrinaina D.Z. ,&nbsp;Junun Sartohadi ,&nbsp;Hojeong Kang","doi":"10.1016/j.jhydrol.2025.133918","DOIUrl":"10.1016/j.jhydrol.2025.133918","url":null,"abstract":"<div><div>The evasion of methane (CH₄) and carbon dioxide (CO₂) from tropical rivers represents a substantial contribution, where land covers and riverbed biogeochemistry significantly apply <em>in situ</em> controls. However, CH₄ and CO₂ emissions from tropical rivers are less constrained. Riverine emissions from Indonesia—the world’s most populous tropical island—are undergoing rapid land cover changes and have not been adequately studied. Here, we investigated the spatial variations of concentration and fluxes of CH₄ and CO₂ from rivers with different land cover types in Yogyakarta. Our result showed an intense CH₄ ebullition rate (94.51 ± 126.09 mmol m⁻² d⁻¹) approximately 47 times higher than the estimated global mean value for streams and rivers. Elevated ebullition rates were controlled by enriched clayey sediments through agricultural activities, which increase organic carbon availability, foster reduced conditions, and support methanogenic archaea. CO₂ diffusion was preponderant in agricultural-dominated rivers. CO₂ emissions were governed by sediment texture, microbial respiration, and lateral CO₂ input from river corridors. These findings highlight the role of the target river in shaping CH₄ and CO₂ dynamics, particularly the impact of agricultural practices and sedimentation. This study not only tackles the scarcity of data on riverine greenhouse gas emissions in Indonesia but also highlights the importance of managing agricultural activities and sedimentary processes to mitigate CH₄ and CO₂ in tropical systems.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"662 ","pages":"Article 133918"},"PeriodicalIF":5.9,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144669931","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":"High-frequency dual-tracer approach to identify contaminant transport pathways and quantify migration behaviors in karst underground river system","authors":"Huaisong Ji ,&nbsp;Gabriele Chiogna ,&nbsp;Beatrice Richieri ,&nbsp;Xinyang Fan ,&nbsp;Kun Huang ,&nbsp;Chen Chen ,&nbsp;Huaishui Yang ,&nbsp;Mingming Luo ,&nbsp;Heng Zhao","doi":"10.1016/j.jhydrol.2025.133935","DOIUrl":"10.1016/j.jhydrol.2025.133935","url":null,"abstract":"<div><div>Karst aquifers heterogeneity presents significant challenges in deciphering pollutant transport mechanisms. To better understand the multi-pathway transport of pollutants and their lag effects in karst underground river systems, we propose a dual-tracer approach that integrates the dynamics of natural pollutants and artificial tracer experiments. Here, we couple the continuous input characteristics of acid mine drainage (AMD) with the pulse injection of dye tracers at the Qingxisi (QXS) underground river system. By using high-frequency hydrogeochemical data and dual-tracer quantitative analysis, this study systematically investigates the different transport behaviors of pollutants in the complex karst system. Results show that: (i) Three hydraulically distinct conduit pathways are newly revealed in the study sites, two of which are found to transport the AMD pollutants for more than 10 km. The migration velocities of the two conduits differ significantly by 3 to 6 times, resulting in a two-stage lagged response in the pollutant peaks during storm events; (ii) The transient pulses shown in the breakthrough curves (BTC) of the artificial tracers strongly contrast to the sustained characteristics of AMD pollutants. We found that the combined use of artificial tracers and AMD enables a comprehensive characterization of the transport dynamics in karst systems; (iii) A conceptual model of pollutant migration is proposed. This model captures a sequence of interconnected processes including intermittent pollutant input driven by stormflow, preferential migration through fast and slow conduits, transient storage within fractures and matrix, and subsequent secondary release. This study underscores the complementarity and applicability of adopting a high-frequency dual-tracer framework that contributes to improved understanding of the dynamic pollutant transport processes and mechanisms in karst systems.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"662 ","pages":"Article 133935"},"PeriodicalIF":5.9,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679533","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":"Harnessing the strengths of machine learning and geostatistics to improve streamflow prediction in ungauged basins; the best of both worlds","authors":"Vaughn Grey ,&nbsp;Tim D. Fletcher ,&nbsp;Kate Smith-Miles ,&nbsp;Belinda E. Hatt ,&nbsp;Rhys A. Coleman","doi":"10.1016/j.jhydrol.2025.133936","DOIUrl":"10.1016/j.jhydrol.2025.133936","url":null,"abstract":"<div><div>Streamflow is a key driver of stream health, influencing water quality, physical form and habitat to support healthy populations of freshwater-dependent biota. However, while available at locations with hydrographic gauging, measurements of streamflow are often unavailable at the majority of reaches across a catchment, hindering the interpretation of flow-related variables, and subsequently, the effectiveness of management interventions. This study constructed models for predicting daily streamflow at ungauged reaches using two contrasting statistical algorithms: a long short-term memory (LSTM) machine learning algorithm and a spatial stream network (SSN) geostatistical algorithm. Instance Space Analysis (ISA) was used to explore the suitability of the machine learning and geostatistical algorithms for the prediction of daily streamflow across a range of catchment characteristics. Both LSTM and SSN algorithms were capable of accurately predicting daily streamflow in ungauged basins in a leave-one-out cross-validation. LSTM models reached the “good” performance benchmark more frequently than SSN models. However, the SSN models could outperform LSTM models where there was a sufficient density of nearby hydrographic sites and localised flow perturbations. ISA proved a useful new method to accurately predict the best performing algorithm for ungauged reaches, improving overall prediction performance compared to using either individual algorithms or an ensemble. ISA also provided guidance of the reliability of predictions, allowing for informed decisions when using model outputs, and insights into the drivers of algorithm suitability to particular scenarios. This approach harnesses the best attributes of machine learning and geostatistics for the prediction of streamflow in ungauged basins.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"662 ","pages":"Article 133936"},"PeriodicalIF":5.9,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686873","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":"Grounded perspectives on water infrastructures and drought imaginaries in the semi-arid Northeast of Brazil","authors":"Veronica Mitroi ,&nbsp;Daniela Michelle Encamilla Henriquez ,&nbsp;Laudemira Silva Rabelo ,&nbsp;Isabelle Tritsch ,&nbsp;Francisco das Chagas Vasconcelos Júnior ,&nbsp;Marcel Kuper ,&nbsp;Eduardo Sávio P.R. Martins","doi":"10.1016/j.jhydrol.2025.133894","DOIUrl":"10.1016/j.jhydrol.2025.133894","url":null,"abstract":"<div><div>This paper discusses how water infrastructure became central to drought management in the state of Ceará in Brazil’s Northeast region, and how this status has been maintained over the decades. It emphasizes the importance of social imaginaries and institutional arrangements in defining water ontologies and developing subjectivities that play in the moralization of water use and rights in crisis contexts. Our empirically grounded, interdisciplinary approach demonstrates how the co-evolution of infrastructure and institutional arrangements contributes to the maintainance of the infrastructure’s centrality in drought management, primarily to increase water availability. Consequently, despite the establishment of participatory bodies and an alternative approach to manage droughts in rural communities, large-scale water infrastructure remains a key pillar in preparing for future droughts. While these infrastructures provide the state with some ’control capacities’ over the water resources, they also have considerable uncontrolled and intertwined territorial effects. We argue that further interdisciplinary research is required to understand the complex role of infrastructures and imaginaries in water management.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"662 ","pages":"Article 133894"},"PeriodicalIF":5.9,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656072","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":"Influence of fluid density on the estimation of vertical streambed fluxes and sediment thermal properties","authors":"Chong Ma ,&nbsp;Wenguang Shi ,&nbsp;Hongbin Zhan","doi":"10.1016/j.jhydrol.2025.133915","DOIUrl":"10.1016/j.jhydrol.2025.133915","url":null,"abstract":"<div><div>Freshwater salinization alters the fluid density in the river and affects subsurface flow, solute and heat transport. Studies for quantifying vertical streambed fluxes (VSFs) and the effective thermal diffusivity of streambed sediments using heat as a tracer, considering the influence of fluid density, are limited. This study introduces a coupled conceptual model of subsurface flow-solute-heat transport within a streambed of losing streams, with the numerical solution derived using the finite element method. To investigate the impact of fluid density on parameter estimation, a combination of particle swarm optimization (PSO) and an existing analytical solution is employed to estimate VSFs and effective thermal diffusivity based on temperature–time series across varying salt concentrations in the river water. The accuracy and reliability of the VFLUX2 method are assessed under the influence of the fluid density effect. Results show that fluid density has a more significant influence on the estimation of VSFs than on the effective thermal diffusivity, and the impacts of fluid density on VSFs increase with increasing depth. As river water density increases, the use of traditional analytical models that quantify VSFs could result in an underestimation of VSF, especially when the observation depth is greater than 0.2 m. When using the VFLUX2 method to estimate effective thermal diffusivities of streambed sediments amidst fluid density variations, the VSFs may also be underestimated significantly when temperature measurements are taken at depths greater than 0.2 m. In the lower portion (less than 0.2 m), parameter estimation results indicated that the effect of density effects failed to appear due to the computational errors and short seepage times. Once the fluid density within the streambed stabilizes, even in the presence of higher density than before, the VSFs estimated by VFLUX2 or conventional analytical models remain reliable. This study provides new insights into the use of heat as a tracer to quantify VSFs when subject to fluid density variation.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"662 ","pages":"Article 133915"},"PeriodicalIF":5.9,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663555","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":"Contrasting influences of diurnal patterns and rainfall events on riverine CO2 degassing: insights from in situ high-resolution monitoring","authors":"Jianhong Li ,&nbsp;Tao Zhang ,&nbsp;Junbing Pu ,&nbsp;Changchun Huang ,&nbsp;Shi Yu ,&nbsp;Kun Ren ,&nbsp;Ping’an Sun ,&nbsp;Qiong Xiao","doi":"10.1016/j.jhydrol.2025.133916","DOIUrl":"10.1016/j.jhydrol.2025.133916","url":null,"abstract":"<div><div>High-resolution measurements of CO₂ flux are critical for accurate estimation of CO₂ degassing in inland waters. However, persistent challenges remain due to limited high-frequency datasets and incomplete understanding of diurnal CO₂ dynamics and rainfall influences. This study implemented 35 days high-resolution monitoring campaign targeting hydrological, hydrogeochemical, and atmospheric parameters across three transects of the Lijiang River (LJR), a representative karst river in southwestern China, during the monsoon season. Key findings demonstrate that: (1) The LJR consistently functioned as a CO₂ source. Neglecting diurnal <em>p</em>CO_2air variability introduced systematic biases, causing daytime flux underestimation (136.9 mg·m⁻²·h⁻¹) and nighttime overestimation (72.6 mg·m⁻²·h⁻¹). Site-specific continuous <em>p</em>CO_2air monitoring reduced these errors by 18–32 % compared to fixed atmospheric defaults; (2) Flood events amplified CO₂ emissions by 200–300 %, primarily driven by turbulence-enhanced gas transfer velocity (<span><math><msub><mi>k</mi><msub><mrow><mi>C</mi><mi>O</mi></mrow><mn>2</mn></msub></msub></math></span>, contributing 91.0–94.6 % to evasion). In contrast, non-flood periods were governed by metabolic <em>p</em>CO_2water signals (71.0–86.9 % flux contribution). This mechanistic shift necessitates phase-specific sampling strategies; (3) Excluding flood events caused 34.1–45 % flux underestimation in this karst basin, exceeding parametric uncertainties (<em>V</em>, <em>S</em>, <em>p</em>CO_2water and <em>p</em>CO_2air) by 2.2–4.5 times. DIC-enriched floods delivered &gt; 62 % of annual CO₂ evasion, aligning with global observations of 25–45 % tropical flux underestimation from flood neglect; (4) Monitoring ≥ 3 flood events per hydrological year and at the same time extend the monitoring to the transition before and after the flood, quantifying that carbonate weathering pulses dominate the annual escape can reduce flux deviation by 60–70 %. Combining dynamic <em>p</em>CO_2water and <em>p</em>CO_2air measurements with specific stage models can reduce the uncertainty of global river fluxes by 30–50 %, especially in carbonate-rich catchments. Conventional approaches risk overestimating CO₂ degassing through neglect of diurnal variability during stable hydrologic conditions while underestimating fluxes by disregarding rainfall impacts. These results underscore the necessity of high-frequency monitoring protocols to refine greenhouse gas (GHG) flux quantifications in fluvial systems.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"662 ","pages":"Article 133916"},"PeriodicalIF":5.9,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656652","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":"Shapelet-based decomposition stack machine learning model explains more middle river reaches water level hydrological process with high accuracy early warning","authors":"Songhua Huan","doi":"10.1016/j.jhydrol.2025.133927","DOIUrl":"10.1016/j.jhydrol.2025.133927","url":null,"abstract":"<div><div>Flooding remains one of the most devastating natural hazards worldwide, yet understanding the complex hydrological processes that lead to flooding poses a significant challenge, hindering effective prevention efforts. To address this issue, this study proposes a stacked machine learning framework that integrates the Offline Shapelet Discovery (OSD) technique. Hydrological time series data are first decomposed using Empirical Wavelet Transform (EWT), and OSD is applied to generate a pool of potential shapelets for training. These shapelets are then processed using a deep learning model to produce preliminary predictions. Finally, an ensemble machine learning approach integrates these sub-predictions to generate the final forecast. The model is evaluated in the Pearl River Basin, a representative watershed encompassing several major urban areas. Compared with traditional machine learning methods, the proposed model demonstrates superior predictive performance across six stations located in the upper, middle and lower reaches of the basin. In the upper reaches, the model achieves a mean absolute error (MAE) of 0.2265, mean square error (MSE) of 0.0723, root mean square error (RMSE) of 0.2679, mean absolute percentage error (MAPE) of 0.0038, percent bias (PBIAS) of 0.0034 and Nash-Sutcliffe efficiency (NSE) of 0.8103. In the lower reaches, the respective values are 0.1766, 0.0619, 0.2720, 0.0415, −0.0007 and 0.8739, while in the middle reaches, they are 0.1239, 0.0362, 0.1890, 0.0059, 0.0007 and 0.9228. The shapelet pool reveals distinctive water level patterns, notably “up-down-up-up” and “down-down-up-down” types across various river segments. This study contributes to a deeper understanding of complex hydrological behaviors and provides new insights for enhancing flood prediction and prevention strategies through innovative data decomposition and pattern recognition techniques.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"662 ","pages":"Article 133927"},"PeriodicalIF":5.9,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663554","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 correlation for the matrix-driven increase in hydraulic permeability of rough-walled fractures","authors":"Carlos A.S. Ferreira,&nbsp;Hamidreza M. Nick","doi":"10.1016/j.jhydrol.2025.133790","DOIUrl":"10.1016/j.jhydrol.2025.133790","url":null,"abstract":"<div><div>Fractured porous media models often underestimate fracture permeability by neglecting the contribution of matrix-driven fluid pathways, relying instead on slip boundary conditions to account for matrix influence. Previous studies have demonstrated that such fluid pathways through permeable matrix regions can significantly affect fracture flow and enhance fracture permeability, a phenomenon that is not captured by conventional models that are based solely on slip boundary conditions. In this study, we develop an empirical correction to upscaled fracture permeability that incorporates the effect of matrix flow. We quantify the increase in fracture permeability through numerical simulations of a discrete fracture-matrix (DFM) system with varying matrix permeability. Our results reveal a substantial increase in apparent fracture permeability, which cannot be captured by traditional models based on impermeable walls. The empirical correction is validated by applying it to a fracture network system, demonstrating its robustness in both single-fracture and network-scale settings. This study emphasizes the necessity of accounting for matrix flow when estimating fracture permeability, ensuring more accurate predictions for exploiting fractured porous media.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"662 ","pages":"Article 133790"},"PeriodicalIF":5.9,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703960","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}