Advances in Water Resources最新文献_第3页

Chaotic advection in regional groundwater flow under periodic water table fluctuations: An analytical model with depth-dependent aquifer properties 地下水位周期性波动下区域地下水流动的混沌平流：一个含深度依赖含水层性质的解析模型

IF 4.2 2区环境科学与生态学

Advances in Water Resources Pub Date : 2026-02-01 Epub Date: 2026-01-10 DOI: 10.1016/j.advwatres.2026.105215

Saurabh Maurya, Ratan Sarmah, Ickkshaanshu Sonkar

{"title":"Chaotic advection in regional groundwater flow under periodic water table fluctuations: An analytical model with depth-dependent aquifer properties","authors":"Saurabh Maurya, Ratan Sarmah, Ickkshaanshu Sonkar","doi":"10.1016/j.advwatres.2026.105215","DOIUrl":"10.1016/j.advwatres.2026.105215","url":null,"abstract":"<div><div>Reliable prediction of groundwater flow dynamics is often hindered by the assumption of uniform aquifer parameters, leading to biased estimates of hydraulic head. Despite their well-established depth-dependence relationship, most analytical solutions remain restricted to homogeneous or single-layer aquifer systems. This study develops a two-dimensional transient analytical model for a multilayer regional aquifer driven by a fluctuating water table, explicitly incorporating depth-dependent hydraulic conductivity and specific storage while accounting for pumping. The analytical solution is derived using the Generalized Integral Transform Technique (GITT), ensuring implicit continuity of head and flux across layer interfaces without requiring iterative eigenvalue estimation, providing a rigorous framework to capture the complexities of stratified aquifers. Model verification is performed against a benchmark single-layer solution, and independent validation is carried out with COMSOL Multiphysics simulations, showing excellent agreement. Parameter influence and reliability are further assessed through global sensitivity analysis and uncertainty evaluation. A novel contribution of this work is the identification of chaotic flow behaviour within a three-layer Tóthian basin, analyzed using the Finite-Time Lyapunov Exponent (FTLE). Results show that chaotic dynamics are most pronounced near the upper boundary, where FTLE values are significantly higher than in deeper zones and further enhancement is observed under periodic injection. Moreover, specific storage is found to enhance the swirly motion of particle trajectories, increasing residence times near pseudo-stagnation zones. The proposed analytical framework bridges a critical gap in multilayer transient flow modeling, advances the theoretical understanding of stratified aquifer systems, and provides a robust benchmark for numerical and field-scale groundwater investigations.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"208 ","pages":"Article 105215"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956936","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}

引用次数: 0

Generative adversarial network-based super-resolution of subsurface rock images: Visual, petrophysical, and flow simulation assessment 基于生成对抗网络的地下岩石图像超分辨率：视觉、岩石物理和流体模拟评估

IF 4.2 2区环境科学与生态学

Advances in Water Resources Pub Date : 2026-01-01 Epub Date: 2025-12-01 DOI: 10.1016/j.advwatres.2025.105184

Mohammad Hamidian, Rohaldin Miri, Hossein Fazeli

{"title":"Generative adversarial network-based super-resolution of subsurface rock images: Visual, petrophysical, and flow simulation assessment","authors":"Mohammad Hamidian, Rohaldin Miri, Hossein Fazeli","doi":"10.1016/j.advwatres.2025.105184","DOIUrl":"10.1016/j.advwatres.2025.105184","url":null,"abstract":"<div><div>High-resolution (HR) micro-CT imaging of porous reservoir rocks plays a critical role in digital rock physics and flow simulations, yet it is limited by the resolution–field-of-view trade-off. To address this challenge, we propose a Pore-Preserving Denoised Generative Adversarial Network (PPD-GAN), trained on denoised, HR two-dimensional computed tomography (CT) slices to recover fine-scale pore structures from low-resolution (LR) images. The PPD-GAN model is systematically compared against seven convolutional neural network (CNN)-based SR methods and bicubic interpolation using both image-based metrics—including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Learned Perceptual Image Patch Similarity (LPIPS)—and a comprehensive set of petro-physical metrics, including porosity, connectivity, pore size distribution, skeleton topology, and grain boundary preservation. Results show that although CNN models yield high pixel-wise fidelity, the PPD-GAN model achieves superior perceptual quality and reconstructs structural features that are consistently closer to ground truth across all physical metrics. Furthermore, pore-scale transport simulations on three-dimensional (3D) core images confirm that PPD-GAN outputs accurately replicate ground-truth (GT) properties such as permeability, diffusivity, and relative permeability—substantially outperforming bicubic interpolation. These findings demonstrate that the proposed PPD-GAN model not only enhances visual resolution but also preserves physically meaningful characteristics, enabling reliable downstream simulation and analysis.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"207 ","pages":"Article 105184"},"PeriodicalIF":4.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145657972","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}

引用次数: 0

Decision-making under uncertainty for LT-ATES systems in complex subsurface settings: application to a low-transmissivity aquifer 复杂地下环境中LT-ATES系统的不确定性决策：在低透射率含水层中的应用

IF 4.2 2区环境科学与生态学

Advances in Water Resources Pub Date : 2026-01-01 Epub Date: 2025-12-06 DOI: 10.1016/j.advwatres.2025.105193

Luka Tas , Jef Caers , Thomas Hermans

{"title":"Decision-making under uncertainty for LT-ATES systems in complex subsurface settings: application to a low-transmissivity aquifer","authors":"Luka Tas , Jef Caers , Thomas Hermans","doi":"10.1016/j.advwatres.2025.105193","DOIUrl":"10.1016/j.advwatres.2025.105193","url":null,"abstract":"<div><div>Low-temperature aquifer thermal energy storage systems (LT-ATES) store excess thermal energy in the subsurface and recover it when needed to heat and cool buildings sustainably. Complex subsurface settings are increasingly targeted for storage. Existing design guidelines cannot be readily applied because they were developed for homogeneous sandy aquifers. In complex settings, subsurface uncertainty translates into uncertainty about system feasibility or efficiency. This paper introduces a method to establish a decision tree for ATES based on uncertainty analysis. It combines Monte Carlo simulations of groundwater flow and heat transport with sensitivity analysis, joint probability distribution estimation and kernel density estimation. For illustration the method is applied to a low-transmissivity aquifer where hydraulic and thermal feasibility are the main prediction targets. Sensitivity analyses based on clustering reveal the impact of model parameters and engineering actions on both prediction targets. In the specific case study, we find for example that storage conditions with transmissivity below 20 m²/d lead to inefficient systems. Desirable storage conditions have transmissivity above 40 m²/d, while intermediate values require thorough risk analysis. Thermal breakthrough risk is higher when longitudinal dispersivity is above 3 m. Further, our approach results in some minimum requirements in terms of subsurface properties that have to be reached for which an investment is justified. On top, the decision tree proposes target engineering actions to decrease investment risk while optimizing return. As such, the proposed method effectively guides early investment decisions for LT-ATES in complex subsurface settings without requiring field data.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"207 ","pages":"Article 105193"},"PeriodicalIF":4.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689898","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}

引用次数: 0

A Physics-Informed Adaptive-Wavelet Neural Network (PIAWNN) for reservoir simulation with two-phase flow 基于物理信息的自适应小波神经网络（PIAWNN）在两相流油藏模拟中的应用

IF 4.2 2区环境科学与生态学

Advances in Water Resources Pub Date : 2026-01-01 Epub Date: 2025-11-14 DOI: 10.1016/j.advwatres.2025.105174

Haifeng Zhu , Zhiming Chen , Xin Gao , Xing Chen , Wei Yu , Kamy Sepehrnoori

{"title":"A Physics-Informed Adaptive-Wavelet Neural Network (PIAWNN) for reservoir simulation with two-phase flow","authors":"Haifeng Zhu , Zhiming Chen , Xin Gao , Xing Chen , Wei Yu , Kamy Sepehrnoori","doi":"10.1016/j.advwatres.2025.105174","DOIUrl":"10.1016/j.advwatres.2025.105174","url":null,"abstract":"<div><div>The flow of oil and water represents the most common two-phase regime in reservoir development. The frontal position and diffusion law of water saturation and pressure are typically delineated using nonlinear partial differential equations. However, these equations exhibit strong nonlinearity. Conventional numerical approaches demand substantial computational resources, posing challenges in handling such equations. Recently, physics - informed neural networks (PINNs) have opened new avenues for solving partial differential equations, while existing PINNs methods still suffer from high computational expenses. To address this, we propose a physics - informed adaptive wavelet neural network (PIAWNN) as a simulator for oil - water two - phase flow. The solution of pressure at the subsequent time step is primarily achieved by iterating the loss function structured in the form of the finite volume method. Additionally, the implicit pressure explicit saturation method is employed to update saturation. For two-dimensional problems, results demonstrate that the mean absolute error and mean relative error reach a low level. When solve three-dimensional complex scenarios, we find the proposed PIAWNN remains applicable. Comparing training performances across different networks, a notable finding emerges: as grid size increases, PIAWNN's training time and accuracy do not deteriorate significantlyand it outperforms physics-informed convolutional neural network (PICNN) and PINN. These results suggest PIAWNN holds promise for yielding novel insights into solving nonlinear systems.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"207 ","pages":"Article 105174"},"PeriodicalIF":4.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145531299","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}

引用次数: 0

A simple derivation of the geometric theorems for averaging and Errata for the article Adv. Water Res., 32 (2013) 340-352 平均几何定理的简单推导及文章的勘误表。水利学报，32 (2013):340-352

IF 4.2 2区环境科学与生态学

Advances in Water Resources Pub Date : 2026-01-01 Epub Date: 2025-12-04 DOI: 10.1016/j.advwatres.2025.105181

Francisco J. Valdés-Parada , Didier Lasseux , Brian D. Wood

引用次数: 0

Analytical investigation of pumping in finite coastal aquifers under heterogeneous recharge and various boundary conditions 非均质补给和不同边界条件下有限海岸含水层抽水分析研究

IF 4.2 2区环境科学与生态学

Advances in Water Resources Pub Date : 2026-01-01 Epub Date: 2025-11-08 DOI: 10.1016/j.advwatres.2025.105171

Yuening Tang , Mengfan Wang , Chunhui Lu , Jian Luo

{"title":"Analytical investigation of pumping in finite coastal aquifers under heterogeneous recharge and various boundary conditions","authors":"Yuening Tang , Mengfan Wang , Chunhui Lu , Jian Luo","doi":"10.1016/j.advwatres.2025.105171","DOIUrl":"10.1016/j.advwatres.2025.105171","url":null,"abstract":"<div><div>Effective pumping management is critical for coastal aquifers to address high freshwater demand in densely populated areas while mitigating seawater intrusion risks. Previous analytical studies have not considered the effects of heterogeneous recharge for pumping in coastal aquifers. We present the first analytical solutions for groundwater withdrawal under heterogeneous recharge conditions in finite rectangular aquifers with diverse boundary conditions. The solutions allow determination of toe positions for single- and multi-well systems, as well as the critical pumping rate for single wells. Results show that the maximum allowable pumping rate increases with the domain aspect ratio and eventually stabilizes, with stabilized values differing by <5 % across domains with varying numbers of sea boundaries for a centrally located well at an aspect ratio of 5. Boundary conditions exert a distinct influence on system behavior. The critical pumping rates of wells adjacent to sea boundaries show minimal sensitivity to heterogeneous recharge patterns, whereas those near no-flow boundaries exhibit high sensitivity. Uncertainty analysis confirms the contrasting influences of these two boundary types and further demonstrates that inter-percentile differences remain largely insensitive to recharge heterogeneity under the studied configurations. These analytical solutions extend classical potential theory to more realistic recharge and boundary conditions, offering a computationally efficient and physically transparent tool for preliminary design, sensitivity analysis, and sustainable groundwater management in coastal aquifers.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"207 ","pages":"Article 105171"},"PeriodicalIF":4.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145473287","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}

引用次数: 0

Generative modeling of three-dimensional multiphase flow in porous media 多孔介质中三维多相流的生成建模

IF 4.2 2区环境科学与生态学

Advances in Water Resources Pub Date : 2026-01-01 Epub Date: 2025-12-12 DOI: 10.1016/j.advwatres.2025.105196

Serveh Kamrava

{"title":"Generative modeling of three-dimensional multiphase flow in porous media","authors":"Serveh Kamrava","doi":"10.1016/j.advwatres.2025.105196","DOIUrl":"10.1016/j.advwatres.2025.105196","url":null,"abstract":"<div><div>Understanding multiphase flow in porous media requires models that capture complex pore geometries and reproduce realistic fluid distributions under diverse boundary conditions. In this paper, we propose a generative framework that combines variational machine learning with a denoising diffusion approach to build 3D multiphase pore structures directly from micro-CT images. The model is trained on small subvolumes to balance resolution with computational feasibility, enabling efficient training and rapid generation of new realizations. Quantitative comparisons show good agreement between generated and experimental samples across morphological metrics and statistical functions, while permeability distributions are reproduced within the range of variability. The variational component of the proposed method provides a compact latent representation that accelerates sampling and also increases the diversity of multiphase configurations that the model can generate, capturing a wider range of pore-scale fluid distributions. The framework allows tiling strategies to produce larger domains that remain consistent with pore-scale data, to scale from local heterogeneity to larger representative volumes. This method generates ensembles of realistic multiphase structures that can be used as input for sampling, conditioning, and optimization workflows in porous media applications.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"207 ","pages":"Article 105196"},"PeriodicalIF":4.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731387","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}

引用次数: 0

Impact of observation and surrogate-model noises on deep learning-based subsurface heterogeneous structure identification through monitoring network optimization 观测噪声和替代模型噪声对基于深度学习的地下非均质结构监测网络优化识别的影响

IF 4.2 2区环境科学与生态学

Advances in Water Resources Pub Date : 2026-01-01 Epub Date: 2025-12-30 DOI: 10.1016/j.advwatres.2025.105204

Yuzhou Xia , Chuanjun Zhan , Zhenxue Dai , Jichun Wu , Xiaoying Zhang , Huichao Yin , Jiahe Yan , Junjun Chen , Zihao Wang , Mohamad Reza Soltanian , Kenneth C. Carroll

{"title":"Impact of observation and surrogate-model noises on deep learning-based subsurface heterogeneous structure identification through monitoring network optimization","authors":"Yuzhou Xia , Chuanjun Zhan , Zhenxue Dai , Jichun Wu , Xiaoying Zhang , Huichao Yin , Jiahe Yan , Junjun Chen , Zihao Wang , Mohamad Reza Soltanian , Kenneth C. Carroll","doi":"10.1016/j.advwatres.2025.105204","DOIUrl":"10.1016/j.advwatres.2025.105204","url":null,"abstract":"<div><div>Accurate identification of subsurface structures is essential for enhancing geoscientific modeling and monitoring, thereby deepening the understanding of earth system. Although recent advances in deep learning have greatly enhanced the computational efficiency of structure identification, the identification accuracy can still be degraded by the spatial stochasticity of monitoring locations, observation noise and surrogate model noise. To address these challenges, this study develops a transition probability and entropy-based monitoring network optimization method, which can determine monitoring locations with high data worth. Based on this method, a synthetic contaminant transport experiment was conducted to obtain dynamic observations of hydraulic head, solute concentration, and electrical resistivity tomography (ERT). The impacts of observation and model noise on inversion results for different data types were then quantitatively analyzed. The results indicate that ERT data exhibit strong noise resistance, with average identification accuracy reductions of only 1.59% and 2.24% under 15% observation noise and model noise, respectively. Moreover, incorporating ERT data into data fusion scenarios enhances the robustness of other observation types, particularly solute concentration data, for which the accuracy decrease is limited to 1.24% even under the highest observation noise level. Model noise not only reduces the accuracy of structure identification but also increases uncertainty, and this negative effect is further amplified in data fusion scenarios.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"207 ","pages":"Article 105204"},"PeriodicalIF":4.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880963","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}

引用次数: 0

Multiscale projection-based embedded discrete fracture modeling approach for CO2 storage in deep saline aquifers 基于多尺度投影的深部含盐含水层CO2封存嵌入离散裂缝建模方法

IF 4.2 2区环境科学与生态学

Advances in Water Resources Pub Date : 2026-01-01 Epub Date: 2025-12-23 DOI: 10.1016/j.advwatres.2025.105200

Mengjie Zhao , Ryan Haagenson , Marc Gerritsma , Hadi Hajibeygi

{"title":"Multiscale projection-based embedded discrete fracture modeling approach for CO2 storage in deep saline aquifers","authors":"Mengjie Zhao , Ryan Haagenson , Marc Gerritsma , Hadi Hajibeygi","doi":"10.1016/j.advwatres.2025.105200","DOIUrl":"10.1016/j.advwatres.2025.105200","url":null,"abstract":"<div><div>This study introduces a multiscale simulation framework, termed Projection-based Embedded Discrete Fracture Modeling with Algebraic Dynamic Multilevel method (pEDFM-ADM), which integrates an embedded discrete fracture network representation with a fully algebraic, front-tracking-based mesh adaptation strategy. Incorporating a fully implicit scheme, compositional thermodynamics, and algebraic multilevel operators, the framework captures essential subsurface processes such as buoyancy-driven migration, convective dissolution, phase partitioning, and fracture-matrix interactions under geologically realistic conditions. The method constructs a hierarchy of multilevel grids and localized multiscale basis functions that introduce fine-scale heterogeneities at each coarse level. Adaptive mesh refinement and coarsening are driven by local variations in CO<sub>2</sub> mass fraction and executed through algebraic prolongation and restriction operators, enabling efficient projection between grid levels. The framework is systematically evaluated across a sequence of test cases with increasing complexity, including systems with low-permeability flow barriers, highly conductive fractures, striking a trade-off between computational resource and detailed simulation accuracy. Overall, the pEDFM-ADM framework provides a scalable, fully algebraic, and physically adaptive modeling tool for large-scale CO<sub>2</sub> storage simulations in fractured porous media, supporting predictive simulation and risk assessment for long-term carbon sequestration.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"207 ","pages":"Article 105200"},"PeriodicalIF":4.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823478","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}

引用次数: 0

A statistical and simulation-informed model for estimating permeability from pore size distribution in saturated geomaterials 基于孔隙尺寸分布估算饱和岩土渗透率的统计和模拟模型

IF 4.2 2区环境科学与生态学

Advances in Water Resources Pub Date : 2026-01-01 Epub Date: 2025-12-15 DOI: 10.1016/j.advwatres.2025.105197

Mengwei Liu, Yongkoo Seol

{"title":"A statistical and simulation-informed model for estimating permeability from pore size distribution in saturated geomaterials","authors":"Mengwei Liu, Yongkoo Seol","doi":"10.1016/j.advwatres.2025.105197","DOIUrl":"10.1016/j.advwatres.2025.105197","url":null,"abstract":"<div><div>Accurate permeability estimation is essential across subsurface engineering applications but remains challenging due to the complex pore structures of natural geomaterials. Traditional empirical methods and simplified theoretical models often inadequately capture the role of pore size distribution and connectivity. This study develops a statistical and simulation-informed permeability model that collapses pore-scale complexity into a compact scaling of the form <em>k</em> = <em>αϕμ<sub>d</sub></em><sup>2</sup>, where <em>ϕ</em> is porosity, <em>μ<sub>d</sub></em> is mean pore size, and <em>α</em> is a weakly varying coefficient. By combining pore network simulations with statistical analysis of unimodal and bimodal pore size distributions, we identify three key findings: (i) permeability is much more sensitive to mean pore size than to porosity; (ii) across extensive datasets, the ratio <em>σ<sub>d</sub></em>/<em>μ<sub>d</sub></em> (standard deviation to mean) clusters around a characteristic value ∼0.4, allowing the effects of the full pore size distribution to be represented by <em>μ<sub>d</sub></em>and a narrowly varying <em>α</em> ≈ 0.05; and (iii) for bimodal systems, there exists a critical fraction of small pores ∼0.78 above which flow becomes small-pore dominated, enabling the definition of an effective flow-controlling pore population and facilitating simplified permeability estimation for such systems. The resulting model, which requires only porosity and a representative mean pore size as inputs, is validated against comprehensive experimental datasets (>1700 samples) spanning diverse soils and rocks and achieves good predictive accuracy. Overall, this work provides a physically grounded yet practically simple permeability estimator suitable for subsurface engineering, environmental protection, and resource management applications.</div></div>","PeriodicalId":7614,"journal":{"name":"Advances in Water Resources","volume":"207 ","pages":"Article 105197"},"PeriodicalIF":4.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880964","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}

引用次数: 0