Advances in Water Resources最新文献

{"title":"A variational framework for inverse modeling: Case study in CO₂ sequestration","authors":"Zhen Zhang ,&nbsp;Xupeng He","doi":"10.1016/j.advwatres.2025.105034","DOIUrl":"10.1016/j.advwatres.2025.105034","url":null,"abstract":"<div><div>This study investigates probabilistic Bayesian inversion methods for CO₂ sequestration problems, focusing on the challenges of estimating subsurface properties in high-dimensional spaces, such as permeability and porosity, and accurately quantifying uncertainties. Traditional approaches, such as gradient-based optimization, Monte Carlo sampling, and Kalman filter-based methods, have notable limitations. Gradient-based methods are computationally efficient but fail to capture uncertainty and are prone to local minima. Monte Carlo methods, while effective in posterior estimation, become computationally infeasible in high-dimensional settings due to the curse of dimensionality. Kalman filter-based methods offer some uncertainty estimation but are limited by their reliance on Gaussian-shaped posterior and weakly nonlinear assumptions for the model-data relationships. To address these challenges, this study explores variational inversion (VI) techniques, recasting Bayesian inference as an optimization problem to efficiently approximate the posterior distribution. Specifically, we apply automatic differentiation variational inference (ADVI) and Stein variational gradient descent (SVGD) to 2D and 3D CO₂ sequestration inverse problems and compare their performance against Monte Carlo sampling and the ensemble smoother with multiple data assimilation (ES-MDA). Our results demonstrate that ADVI and SVGD offer significant computational advantages over traditional methods, while still be able to capture a reliable posterior estimation. ADVI provides more reasonable mean and uncertainty estimates than ES-MDA, even with smaller number of forward runs. SVGD delivers better mean and uncertainty estimates than both ADVI and ES-MDA, with the same computational cost as ES-MDA. Both ADVI and SVGD show better scalability than ES-MDA and MCMC, meaning they are less affected by the increasing dimensionality of the model parameters. These findings highlight the potential of ADVI and SVGD to offer a reliable alternative to traditional MCMC and ES-MDA methods.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"203 ","pages":"Article 105034"},"PeriodicalIF":4.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of relative permeability curves in porous media induced by electroosmotic flow through pore-scale two-phase flow simulations","authors":"Zhenlei Zhang ,&nbsp;Zhigang Sun ,&nbsp;Minghui Gao ,&nbsp;Wei Zhou ,&nbsp;Diansheng Wang ,&nbsp;Lei Zhu ,&nbsp;Ziqiang Wang ,&nbsp;Yudou Wang","doi":"10.1016/j.advwatres.2025.105035","DOIUrl":"10.1016/j.advwatres.2025.105035","url":null,"abstract":"<div><div>Given the limited research on two-phase electroosmotic flow within porous media, this paper conducts numerical simulations to investigate the pore-scale two-phase flow behavior and the relative permeability characteristics induced by electroosmotic flow. A two-phase slip velocity approximation model is employed to simulate water/oil two-phase electroosmotic flow in porous structures. The results indicate that, although the dragging capacity of the water phase increases with water saturation, the influence of capillary force and weak viscous coupling between small oil clusters and the water phase results in a non-monotonic trend in the relative permeability of the oil phase. Due to changes in the flow mechanism, the relative permeability of the water phase increases slowly before a certain level of water saturation. However, once the water saturation exceeds this threshold, the relative permeability of the water phase increases rapidly with further increases in water saturation. The increased flow resistance for the oil phase and the diminished electro-osmotic driving force for the water phase are the two primary factors responsible for the lowest oil-phase relative permeability observed in oil-wet porous media. The relative permeability curves for both the oil and water phases increase with the electric field strength under all three wettability conditions. Moreover, high electric field strength will reduce irreducible water saturation under water-wet conditions, but this phenomenon is not as pronounced under intermediate-wet and oil-wet conditions.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"203 ","pages":"Article 105035"},"PeriodicalIF":4.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Mode Decomposition of 4D imaging data to explore intermittent fluid connectivity in subsurface flows","authors":"Aman Raizada ,&nbsp;Steffen Berg ,&nbsp;Sally M. Benson ,&nbsp;Hamdi A. Tchelepi ,&nbsp;Catherine Spurin","doi":"10.1016/j.advwatres.2025.105013","DOIUrl":"10.1016/j.advwatres.2025.105013","url":null,"abstract":"<div><div>The interaction of multiple fluids within a heterogeneous pore space gives rise to complex pore-scale flow dynamics, such as intermittent pathway flow. Synchrotron imaging has been employed to capture and analyze these dynamics. However, these imaging datasets are often extremely large (on the order of terabytes), and the spatial and temporal characteristics of the relevant flow phenomena are difficult to extract. As a result, identifying the locations of fluctuations that control fluid connectivity remains a significant challenge. In this work, a novel workflow is presented that uses Dynamic Mode Decomposition (DMD) to find critical spatio-temporal regions exhibiting intermittent flow dynamics. DMD is a data-driven algorithm that decomposes complex nonlinear systems into dominant spatio-temporal structures without relying on prior system assumptions.</div><div>The workflow is validated through three test cases, each examining the influence of viscosity ratio on flow dynamics while maintaining a constant capillary number. These scenarios demonstrate the capability of the DMD method to accurately capture underlying flow behavior and extract key intermittent structures from high-dimensional experimental data. DMD offers a powerful and computationally efficient approach for analyzing complex fluid dynamics in heterogeneous pore spaces. The proposed workflow enables rapid and objective identification of relevant time scales and spatial regions of interest. Given its speed and scalability, it holds strong potential as a diagnostic tool for the analysis of large synchrotron imaging datasets.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"203 ","pages":"Article 105013"},"PeriodicalIF":4.0,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based surrogate modeling for underground hydrogen storage","authors":"Shuojia Fu ,&nbsp;Shaowen Mao ,&nbsp;Alvaro Carbonero ,&nbsp;Bharat Srikishan ,&nbsp;Neala Creasy ,&nbsp;Hichem Chellal ,&nbsp;Mohamed Mehana","doi":"10.1016/j.advwatres.2025.105014","DOIUrl":"10.1016/j.advwatres.2025.105014","url":null,"abstract":"<div><div>Underground hydrogen storage (UHS) is critical for integrating intermittent renewable energy sources by storing excess energy as hydrogen during surplus periods and retrieving it during shortages. Effective UHS design requires accurate prediction of hydrogen plume migration and reservoir pressure evolution, typically achieved by high-fidelity numerical simulations. Although these physics-based simulations are accurate, they are computationally expensive and unsuitable for rapid decision-making. To address this, we develop efficient surrogate models for UHS prediction using Swin-Unet, a transformer-based deep learning architecture with a U-Net structure. Our results show that Swin-Unet accurately predicts the spatiotemporal evolution of hydrogen saturation and reservoir pressure in heterogeneous depleted gas reservoirs, offering a fast and reliable alternative to traditional simulations. Compared to surrogate models based on U-Net and Segmentation Transformer (SETR), Swin-Unet achieves higher pressure prediction accuracy while maintaining similar training costs. For hydrogen saturation prediction, Swin-Unet achieves higher accuracy than SETR and comparable accuracy to U-Net, while reducing GPU memory usage by about two-thirds and training time by approximately 75% relative to U-Net. This is the first study to apply Swin-Unet to surrogate modeling of UHS, demonstrating its potential as an accurate and efficient approximation for traditional physics-based simulations. Swin-Unet enables accurate and efficient UHS prediction in heterogenous geological formations, supporting sensitivity analysis, uncertainty quantification, and operational optimization for future UHS projects.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"203 ","pages":"Article 105014"},"PeriodicalIF":4.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144242567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scale-dependent permeability in geologic formations: Renormalization group theory and finite-size scaling analysis","authors":"Misagh Esmaeilpour ,&nbsp;Cheng Chen ,&nbsp;Saeid Sadeghnejad ,&nbsp;Behzad Ghanbarian","doi":"10.1016/j.advwatres.2025.105019","DOIUrl":"10.1016/j.advwatres.2025.105019","url":null,"abstract":"<div><div>Understanding the scale dependence of permeability (<span><math><mi>k</mi></math></span>) of geologic formations at field scales is essential for precise modeling of flow and transport in subsurface, particularly for underground energy storage. In this study, we conducted extensive computations and investigated the scale dependence of <span><math><mi>k</mi></math></span> in random and heterogeneous formations by employing renormalization group theory (RGT) and finite-size scaling analysis. Based on the random permeability field and following the Gaussian distribution of <span><math><mrow><mtext>ln</mtext><mo>(</mo><mi>k</mi><mo>)</mo></mrow></math></span>, we first generated ten formations with different levels of heterogeneity at five dyadic domain sizes, <span><math><mi>L</mi></math></span> = 2ⁱ, <span><math><mrow><mi>i</mi><mi>∈</mi><mo>{</mo><mrow><mn>3</mn><mo>,</mo><mi>…</mi><mo>,</mo><mn>7</mn></mrow><mo>}</mo></mrow></math></span>. The first formation reflects the permeability distribution of an actual reservoir, while the others were generated with varying degrees of heterogeneity. We then applied the RGT to determine the effective permeability (<span><math><msub><mi>k</mi><mrow><mi>e</mi><mi>f</mi><mi>f</mi></mrow></msub></math></span>) of each formation at various occupation probabilities <span><math><mi>p</mi></math></span> = 0.5, 0.6, 0.7, 0.8, 0.9 and 1. We next used finite-size scaling theory to further analyze the scale-dependent <span><math><msub><mi>k</mi><mrow><mi>e</mi><mi>f</mi><mi>f</mi></mrow></msub></math></span>. The <span><math><mrow><msub><mi>k</mi><mrow><mi>e</mi><mi>f</mi><mi>f</mi></mrow></msub><mo>−</mo><mi>L</mi></mrow></math></span> plot for each formation was scattered. However, by applying the finite-size scaling analysis the data collapsed onto a single quasi-universal curve. It means that finite-size scaling theory could successfully incorporate the effect of large-scale heterogeneities in the scale dependence of permeability.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"203 ","pages":"Article 105019"},"PeriodicalIF":4.0,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sedimentation simulation model testing for silty soil water infiltration after Hortonian overland flow","authors":"Stephanos D.V. Giakoumatos,&nbsp;Christina Siontorou,&nbsp;Dimitrios Sidiras","doi":"10.1016/j.advwatres.2025.105020","DOIUrl":"10.1016/j.advwatres.2025.105020","url":null,"abstract":"<div><div>Infiltration is a complex environmental process by which an aquatic solution i.e. rainwater, irrigation, potentially contaminated with insoluble/dispersed particles, enters the ground under the gravity force or the capillary action in deeper soil layers (percolation). Simple Hortonian compression events (when overflow run exceeds land infiltration capacity and depression storage capacity), silty-dominated topsoil with smooth texture characteristics and coarse-grained soil constituents are simplistically simulated by lab columns, in different settlement modes, e.g. packed/non-packed. In the present manuscript, suspended material, compression model equations were tested over laboratory setup columns by optimizing model fitting curves via nonlinear regression analysis, one of which received the lowest Standard Error of Estimate value, among eight overall evaluated models, an indication of the best fitting model performance of the ongoing soil-water suspension compression. By using multiple linear regression, modifications were made of all eight tested models which are presented herein. The modified models under examination, were structured upon the linear dependence of their parameters on the selected variables: silt-water concentration, inner cylinder diameter and effective porosity. A modified Kang et al. model, proposed to predict soil water compression phenomenon, by setting coefficients of linear dependence on independent variables. A higher applicability is to be achieved, due to the better fitting of the tested field values, after the conclusion of a proper field calibration. Practitioners would be able to implement the findings over certain field engineering applications. The observed settlement response, during soil water compression in packed/non packed column operating modes, is in compliance with the relative bibliography.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"203 ","pages":"Article 105020"},"PeriodicalIF":4.0,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144242566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dominant spatiotemporal structures in total water storage anomalies","authors":"G. Libero,&nbsp;V. Ciriello","doi":"10.1016/j.advwatres.2025.105015","DOIUrl":"10.1016/j.advwatres.2025.105015","url":null,"abstract":"<div><div>This study employs Dynamic Mode Decomposition (DMD) to derive a global-scale linear model for the temporal evolution of Total Water Storage Anomaly (TWSA) measured by GRACE satellite missions, with the goal of extracting and analyzing the dominant spatiotemporal structures governing TWSA variability. Our analysis differentiates modes associated with a periodic dynamic – linked to precipitation-driven seasonal cycles and multi-year variations – from those incorporating trend effects indicating, on average, a progressive TWSA decline. Focusing on the latter, we examine patterns associated with extreme TWSA values and their intensification over time. In regions experiencing significant TWSA changes over the past decade, DMD effectively distinguishes natural variability from trends, aligning with previous findings that identify climate change and human impact effects in the same regions. This study underscores DMD’s potential in capturing essential hydrological dynamics in data, thus supporting the interpretation of these dynamics at the scale of the analysis.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"203 ","pages":"Article 105015"},"PeriodicalIF":4.0,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A stochastic modeling framework for radionuclide migration from deep geological repositories considering spatial variability","authors":"Zhibao Zheng ,&nbsp;Xuerui Wang ,&nbsp;Judith Flügge ,&nbsp;Thomas Nagel","doi":"10.1016/j.advwatres.2025.105003","DOIUrl":"10.1016/j.advwatres.2025.105003","url":null,"abstract":"<div><div>Considering the influence of uncertainties on radionuclide migration from deep geological repositories (DGR) is of great significance for safety assessment. However, stochastic modeling for DGR safety assessment remains challenging due to the high computational requirements of handling large regional scale models with multiphysics coupling, high-dimensional random inputs, and long simulated durations. This article introduces an efficient numerical framework to tackle this set of challenges. Specifically, the proposed framework relies on three key components, including efficient solutions of stochastic Darcy equations, propagation of stochastic quantities, and efficient solutions of stochastic mass transport equations. Unknown stochastic solutions are approximated by summing a series of products involving random variables and deterministic components. Alternating iterative algorithms are then proposed to decouple the original stochastic problems into deterministic equations for the spatial components, one-dimensional stochastic algebraic equations for the random variables, and one-dimensional ordinary differential equations for the temporal components. These deterministic equations can be solved efficiently using existing solvers, allowing the handling of large-scale problems. The one-dimensional stochastic algebraic equations can be solved efficiently using a sampling strategy, allowing the handling of high-dimensional stochastic state spaces. The one-dimensional ordinary differential equations can be solved cheaply and further accelerated using a time-parallel algorithm, allowing the handling of long simulated time scales. Furthermore, a similar solution approximation and iterative algorithm are also used to propagate stochastic quantities from stochastic Darcy flow to stochastic mass transport. Numerical examples with up to 122 random variables and a simulated duration of one million years demonstrate the promising performance of the proposed framework. The numerical results demonstrate that the developed stochastic framework achieves accuracy comparable to Monte Carlo simulations while significantly improving computational efficiency by two orders of magnitude. Moreover, the evolutionary probability density functions obtained from our stochastic simulations indicate that the proposed framework could potentially serve as an efficient and robust tool for DGR risk assessment.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"203 ","pages":"Article 105003"},"PeriodicalIF":4.0,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leakage risk assessment in geologic carbon sequestration using a physics-aware ConvLSTM surrogate model","authors":"Jinzheng Kang ,&nbsp;Xiaoqing Shi ,&nbsp;Shaoxing Mo ,&nbsp;Alexander Y Sun ,&nbsp;Lijuan Wang ,&nbsp;Haiou Wang ,&nbsp;Jichun Wu","doi":"10.1016/j.advwatres.2025.105017","DOIUrl":"10.1016/j.advwatres.2025.105017","url":null,"abstract":"<div><div>The secure implementation of geological carbon sequestration (GCS) critically hinges on accurately localization of CO₂ leakage through inverse modeling of plume migration dynamics in heterogeneous reservoirs. This process is inherently challenged by subsurface uncertainties and the complexity of multiphase flow. Advances in various deep-learning-based surrogate models have been made to improve computational efficiency. Especially, Physics-Informed Neural Networks (PINNs) have gained widespread application due to their integration of the partial differential equation (PDE) into the loss function. However, conventional PINNs still face critical limitations in handling two-phase flow dynamics and high-dimensional parameter spaces due to the discretization requirements of PDE. To address these challenges, we propose a Physics-Aware Convolutional LSTM (PA-CLSTM) surrogate model that intrinsically embeds flow gradient information into the ConvLSTM architecture. Unlike PINNs which require PDE discretization as part of the loss function, PA-CLSTM encodes physical constraints through Sobel operator-derived velocity fields in latent space, thereby avoiding the need for PDE discretization, while maintaining compatibility with spatiotemporal feature extraction. Validation with a synthetic 2D saline aquifer demonstrate, PA-CLSTM achieves a five-fold acceleration over numerical simulations (TOUGH2-ECO2N) and a 67% reduction of inversion RMSE (from 1.65 to 0.59) of estimated permeability field in the focused area, compared to purely data-driven ConvLSTM. Meanwhile, PA-CLSTM inversion results accurately localize the CO₂ leakage. Compared to the ConvLSTM, the leakage location estimation RMSE decreased from 7.44 to 1.09, approaching the numerical simulation result of 0.68. In this work, we introduce the PA-CLSTM model in GCS, which significantly improves the inversion speed compared to numerical simulation and enhances accuracy compared to another surrogate model ConvLSTM.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"202 ","pages":"Article 105017"},"PeriodicalIF":4.0,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144166592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characteristics of flow and transport in low-permeability fractured rock based on a channel network model","authors":"Kunwar Mrityunjai Sharma ,&nbsp;Chin-Fu Tsang ,&nbsp;Joel Geier ,&nbsp;Osvaldo Pensado ,&nbsp;Stuart Stothoff ,&nbsp;Auli Niemi","doi":"10.1016/j.advwatres.2025.105016","DOIUrl":"10.1016/j.advwatres.2025.105016","url":null,"abstract":"<div><div>Discrete Fracture Network (DFN) models for evaluating flow and transport in low-permeability fractured rocks are important tools in safety assessments of nuclear waste repositories, and also important for other geoengineering and environmental applications. The well-known phenomena of flow channeling, arising from both intra-fracture and inter-fracture heterogeneities, is in general difficult to implement in these models. The present study uses the Channel Network Model (CNM) concept as a complementary approach to DFN models, with focus on channelized flow within fracture planes and in the fracture network. A method used to generate CNMs based on channels connecting centroids of fracture planes was implemented within a pychan3d library and applied to a 3D DFN model based on field data from Forsmark, Sweden. Three sets of realizations of the channel network are used to characterize the flow and transport system between deformation zones in the granitic host rock. The results indicate the significance of very low-conductivity fractures in providing critical flow connections in these rocks. It is shown that only a few (4 to 6 in our cases) key flow bridges within a network of 9000 or more fractures control its flow and transport. The use of CNMs together with DFN models enhances confidence in safety assessments for nuclear waste repositories and other applications, while providing valuable insights into complex flow and transport behavior in low-fracture-permeability rocks.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"202 ","pages":"Article 105016"},"PeriodicalIF":4.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}