Spatial Variability Of Hydraulic Conductivity Research Articles

Assessment of the impact of greenhouse rainwater harvesting managed aquifer recharge on the groundwater system in the southern Jeju Island, South Korea: Implication from a numerical modeling approach

Managed aquifer recharge (MAR) is increasingly being adopted worldwide to mitigate groundwater depletion and ensure the sustainability of water resources. Rainwater harvesting (RWH)-MAR can augment aquifer storage and reduce flood damage in rural areas with dense greenhouse facilities. This study has assessed the feasibility of greenhouse RWH-MAR in Namwon agricultural areas in the southern part of Jeju Island, South Korea, by considering the injection rate and location of MAR using a numerical model. The model results showed that groundwater level increases were directly related to the infiltration rate, although spatial differences in head rise were observed owing to the spatial variability of hydraulic conductivity. In addition, installing the RWH-MAR in highland areas (>100 masl) enhanced the water level rise when compared to the expected values, indicating that a higher hydraulic gradient and thick unsaturated zone facilitated more effective MAR outcomes than in lowland areas. To optimize the contribution of source water to the agricultural water demand, placing the RWH-MAR near the pumping well improved the availability of injected rainwater to agricultural wells. This study highlights the importance of designing RWH-MAR schemes considering MAR objectives and the topographic and hydrogeological characteristics of the site.

A Mathematical Model of Interstitial Fluid Flow and Retinal Tissue Deformation in Macular Edema.

This study aims to develop a mathematical model to elucidate fluid circulation in the retina, focusing on the movement of interstitial fluid (comprising water and albumin) to understand the mechanisms underlying exudative macular edema (EME). The model integrates physiological factors such as retinal pigment epithelium (RPE) pumping, osmotic pressure gradients, and tissue deformation. It accounts for spatial variability in hydraulic conductivity (HC) across the retina and incorporates the structural role of Müller cells (MCs) in maintaining retinal stability. The model predicts that tissue deformation is maximal at the center of the fovea despite fluid exudation from blood capillaries occurring elsewhere, aligning with clinical observations. Additionally, the model suggests that spatial variability in HC across the thickness of the retina plays a protective role against fluid accumulation in the fovea. Despite inherent simplifications and uncertainties in parameter values, this study represents a step toward understanding the pathophysiology of EME. The findings provide insights into the mechanisms underlying fluid dynamics in the retina and fluid accumulation in the foveal region, showing that the specific conformation of Müller cells is likely to play a key role.

Numerical modeling of site-scale groundwater flow with stochastic parameterized hydraulic conductivity fields for geological disposal of high-level radioactive waste in China

Deep geological disposal is internationally accepted as a feasible and safe approach for high-level radioactive waste (HLW) disposal. To evaluate hydrogeological conditions in support of safety assessments for HLW disposal at the Xinchang preselected site in China, stochastic inverse models of site-scale groundwater flow were developed in this study to generate highly parameterized hydraulic conductivity fields for the fractured medium by adopting pilot point and null-space Monte Carlo methods. The hydraulic influence zones of the faults were carefully considered to have homogenous parameter values across the zones. A total of 120 multiple random realizations were generated to characterize the 3D spatial variations in hydraulic conductivity by conditioning to 1218 fixed pilot points. The simulated hydraulic heads of the realizations were in good agreement with observational data. The hydraulic conductivity fields close to the boreholes could be characterized in detail and had relatively low uncertainties. Furthermore, the hydraulic conductivity (K) values around underground research laboratory (URL) at the HLW disposal zone depth were less than 10−9 m/s. The K values obtained for most of the influence zones of these faults at this depth were consistent with hydraulic test data and ranged from 10−10 to 10−9 m/s, thereby indicating that the surrounding rock has low permeability and is suitable for HLW disposal. These findings offer new insights into hydrogeological conditions by providing key information on the permeability characteristics that need further focus on HLW disposal.

Stochastic seepage analysis in embankment dams using different types of random fields

Random field theory has become a very popular approach for probabilistic analysis of earth dams. This study performs a stochastic analysis of seepage through an embankment dam to account for the spatial variability of hydraulic parameters. The originality of this study consists in using and comparing three types of Random Fields RFs for the seepage analysis: classical stationary RFs, conditional RFs (to consider the position of available measurements), and non-stationary RFs (to consider the mean variation with depth). The same sample of hydraulic conductivity values is considered to generate the three types of random fields. The hydraulic conductivity RFs are integrated into a Finite Element seepage analysis model to evaluate the flow velocity, hydraulic gradient, pore water pressure, and flow rate. The process is repeated by Monte Carlo simulations to characterize the spatial variability of the seepage results in terms of mean and standard deviation at each point of the studied dam and its foundation. The results show a higher dispersion for the stationary RF case and a lower dispersion for the conditional RF case. For the case study, the results also show that the use of different RFs for modelling the spatial variability of hydraulic conductivity induces differences that can be considered significant for flow velocity, moderate for hydraulic gradient and minor for pore pressure.

Geostatistical simulations of the spatial variability of hydraulic conductivity in an alluvial-marine sedimentary system in Beihai City, China

Sustainable aquifer management in coastal cities is becoming critically important due to the challenges of climate variability amidst population growth. Study of the aquifer heterogeneity is effective in highlighting the aquifer behavior. In this study, we took the case of Beihai to explore the heterogeneity of its subsurface system. 8629 samples from 111 boreholes were divided into four hydrofacies units as discrete geological variables according to their hydraulic conductivities (K) and used as input datasets for the models. Sequential Indicator Simulation (SISIM) and Transition Probability Geostatistical Simulation (T-PROGS) were employed to construct 3D hydrofacies stochastic models. The simulated results realistically represent horizontal river-driven sediment distribution and vertical deposition processes of K in the aquifer, where the northeastern part is mainly composed of low K and the southern part forms the main multi-layered aquifer system with progressively increasing medium and high K. With a comparative analysis on the ability of capturing geological characteristics using both SISIM and T-PROGS, it highlighted that SISIM reduces initial data requirements without the need of Gaussian transformation and is more broadly applicable, while T-PROGS prefers considering the internal correlation of hydrofacies units and is applicable in environments where lateral accumulation is prevalent. This study demonstrates the credibility of stochastic simulation methods in recreating complex costal sedimentary environments, which provide environment for further groundwater modeling and exploration. Multi-interleaved source data and more accurate algorithms will be integrated in aquifer heterogeneity as subsequent research priorities.

Probabilistic Slope Seepage Analysis under Rainfall Considering Spatial Variability of Hydraulic Conductivity and Method Comparison

Due to the spatial variability of hydraulic properties, probabilistic slope seepage analysis becomes necessary. This study conducts a probabilistic analysis of slope seepage under rainfall, considering the spatial variability of saturated hydraulic conductivity. Through this, both the commonly used Monte Carlo simulation method and the proposed first-order stochastic moment approach are tested and compared. The results indicate that the first-order analysis approach is effective and applicable to the study of flow processes in a slope scenario. It is also capable of obtaining statistics such as mean and variance with a high enough accuracy. Using this approach, higher variabilities in the pressure head and the fluctuation of the phreatic surface in the slope are found with a higher value of the correlation length of the saturated hydraulic conductivity. The Monte Carlo simulation is found to be time-consuming: at least 10,000 realizations are required to reach convergence, and the number of realizations needed is sensitive to the grid density. A coarser grid case requires more realizations for convergence. If the number of realizations is not enough, the results are unreliable. Compared with Monte Carlo simulation, the accuracy of the first-order stochastic moment analysis is generally satisfied when the variance and the correlation length of the saturated hydraulic conductivity are not too large. This study highlights the applicability of the proposed first-order stochastic moment analysis approach in the slope scenario.

Risk analysis framework for the optimum remediation of a contaminated aquifer under uncertainty: application in Lake Karla aquifer, Thessaly, Greece

A risk analysis framework is proposed for the optimum remediation of a contaminated aquifer under hydrogeological uncertainty. The limited information and the spatial variation of hydraulic conductivity in a real-world large-scale aquifer create uncertain conditions for decision-making when remediation schemes ought to be accompanied by the minimum possibility of failure. The primary concern is focused on safeguarding public health when groundwater is used for urban drinking purposes from a contaminated aquifer. The proposed framework is based on the conjunctive use of stochastic simulation–optimization modelling followed up by a risk analysis application on remediation trade-offs. The framework includes three main steps/procedures: (i) the model formulation of multiple realizations of groundwater flow and contaminant transport, (ii) the optimal positioning and operation of the clean-up wells determined by the method of stochastic optimization, and (iii) the risk analysis of the optimum remediation strategies through a proposed decision model, so as the one with the minimum cost and risk of failure is chosen as the most appropriate. The proposed framework is tested for two scenarios of nitrogen fertilizer application in the cultivated areas. The strategic target is the groundwater nitrate concentration minimization in an area where exceedances of nitrate concentrations have been observed and water supply wells have been operating for the last twenty years satisfying domestic needs. The results demonstrate that, when decision-making is under hydrogeological uncertainty, the combined use of stochastic optimization and risk-based decision analysis can commend the remediation strategy with the minimum cost and the highest possibility of success.

Probabilistic analysis of water-sealed performance in underground oil storage considering spatial variability of hydraulic conductivity

For underground water-sealed oil storage, the spatial variability of the surrounding rock has a significant impact on the water-sealed effect of a water curtain system. This study presents a methodology for the probabilistic analysis of water curtain performance in underground oil storage, considering the spatial variability of hydraulic conductivity of the surrounding rock based on field data. Anisotropic random fields representing the spatial variability of hydraulic conductivity were established through spatial statistical analysis of field data and introduced into the finite element model of underground oil storage for water-sealed reliability analysis. The water-sealed performance of different water curtain system schemes was studied using Monte Carlo simulation (MCS). The results showed that the difference between the horizontal spatial correlation and the vertical spatial correlation of the surrounding rock has a significant impact on the water-sealed effect of the water curtain system. An excessively large pressure of water curtain boreholes provided a small contribution to improving water curtain performance. The distance between the water curtain holes and the caverns had the less significant affecting the water-sealed reliability of the storage cavern. Finally, the optimal design of the water curtain system is discussed. This study provides valuable insights and a theoretical basis for the optimisation of water curtain system design parameters for underground water-sealed oil storage.

Geostatistical analysis and hydrofacies simulation for estimating the spatial variability of hydraulic conductivity in the Jianghan Plain, central China

Geostatistical analysis and hydrofacies simulation for estimating the spatial variability of hydraulic conductivity in the Jianghan Plain, central China

Revealing vertical aquifer heterogeneity and hydraulic anisotropy by pumping partially penetrating wells

The stratification of sedimentary aquifers introduces spatial variability in hydraulic conductivity, primarily between individual horizontal layers. On larger scales, the vertical heterogeneity enhances hydraulic anisotropy, with the horizontal conductivity typically exceeding the vertical one. In this study, the hydraulic anisotropy of a stratified aquifer is estimated from data of hydraulic tests in which water is sequentially extracted from well sections screened at different depths, and the hydraulic response is measured at various multilevel observation wells. The applicability of the method is demonstrated by field tests in a fluvial gravel aquifer in the Upper Rhine Valley, Germany. A homogeneous anisotropic model, and models with three and five anisotropic layers, are fitted to the measured drawdowns in the steady-shape regime, in which differences in hydraulic head between observation locations do not change over time even though the head values themselves change. The position of the five horizontal layers is based on the lithology of the drilling profile at the pumping-well location. The three-layer model is achieved by merging insensitive or similar layers with sensitive layers. The fits result in estimates of the radial and vertical hydraulic conductivities for all layers of the respective models, which are upscaled to effective parameters over the entire depth in the case of the multilayer models. The homogeneous model shows significantly higher errors than those of the heterogeneous models. The heterogeneous locally anisotropic models not only reveal vertical variability of hydraulic conductivity, but also lead to a three-times larger anisotropy ratio upon upscaling.

Three-Dimensional Seepage Investigation of Riverside Tunnel Construction Considering Heterogeneous Permeability

Seepage is a major concern during the construction of a riverside tunnel, because the seepage may cause both hydraulic and mechanical problems. At present, there are some well-accepted methods combining the hydraulic-mechanical coupling effect. This paper investigated a tunnel near a riverside under seepage forces using hydraulic-mechanical coupling analysis. Two cases were considered: an unlined tunnel without waterproofing, and a tunnel with a cement-treated soil surround acting as water barrier. The spatial variability of hydraulic conductivity was taken into account using random finite-element analysis. Results indicated that the analysis is able to account reasonably for the variation range of soil parameters. The arithmetic mean of hydraulic conductivity could serve as a rule of thumb estimation for a macroscale representative hydraulic conductivity. The results are likely to yield a scientific estimation of the seepage flow as well as a better understanding of the potential tunnel collapse mechanism.

Hydraulic Conductivity of Rock Masses Surrounding Water Curtain Boreholes for Underground Oil Storage Caverns

The assessment of the groundwater flow rate around the cavern periphery is a critical requirement for the design of underground water-sealed oil storage caverns and commonly made through seepage analysis, where a reasonable estimation of the hydraulic conductivity of the host rock is the key issue. However, it is a challenge to accurately determine the hydraulic conductivity of natural rock masses owing to their heterogeneous and anisotropic nature. The underground storage cavern project has a unique favorable condition in that there is a water curtain system that can provide considerable hydraulic test data for inferring hydraulic parameters; however, no well-established method has ever been proposed to exploit these data for characterizing heterogeneity in hydraulic conductivity. This study presents a new approach to evaluate the spatial variation of hydraulic conductivity using water curtain borehole data. This approach treats the peripheral region of each borehole as a homogenous unit with a particular equivalent permeability coefficient that can be back-calculated from the measured injection flow rate of the borehole using a numerically established empirical formula. Besides, the impact of curtain gallery drainage, occurring in the construction stage, on the seepage field was investigated while the effect of the rock fracture configuration on hydraulic conductivity assessment was examined. The proposed method enables robust and accurate mapping of heterogeneity in the hydraulic conductivity of host rocks and provides a new idea of effectively utilizing hydro-geological test data to derive the hydraulic conductivity of rock masses surrounding water-sealed storage caverns.

Deterministic and Probabilistic Investigations of Piping Occurrence during Tunneling through Spatially Variable Soils

Piping is a common phenomenon of seepage failure in underground space construction. Efforts have been made in understanding the mechanism of piping due to seepage gradient force and incorporating these in prediction technologies. In this study, the characteristics of seepage and piping occurrence during the excavation of a riverside tunnel are discussed based on hydraulic-mechanical coupling analysis. Four typical cut-off wall depths are considered to evaluate the effectiveness of different seepage-control measures, and two different water depths are used to assess the impact of water depth. The spatial variability of hydraulic conductivity, which is often the case in reality, is incorporated into the program of the finite-element method. Results reveal that hydraulic conductivity is a key factor affecting the formation of potentially dangerous seepage path and the probability of piping occurrence. When the spatial randomness in hydraulic conductivity is considered, the riverside tunnel is observed as being much more susceptible to piping. By contrast, the assumption of uniformity in hydraulic conductivity may yield an inaccurate estimation of the risk in tunneling. The results make it possible to visualize the potential seepage path and provide a better explanation of the piping mechanism.

Determination of hydraulic conductivity and its spatial variability in the Jianghan Plain using a multi-format, multi-method approach

In recent times, the groundwater in Jianghan Plain, Central China has come under severe stress from agricultural and industrial demand. The spatial variability of hydraulic conductivity (K) in the region is important for better and sustainable groundwater exploration and management. In this study, we determined the spatial distribution of K using a multi-method approach, i.e. analytic and numerical simulation as well as intelligent optimization methods (particle swarm optimization (PSO) and differential evolution (DE)) and geostatistics. Data from 55 multi-format pumping tests situated across the middle-confined aquifer (consisting of late Pleistocene (Qp3s) and middle Pleistocene (Qp2j)) were used. Results showed that for transient pumping test with constant rate by multi-wells, PSO and DE were faster and more precise, especially DE. For transient pumping test with variable rate as well as transient pumping test with constant rate by single well, the numerical method fitted the drawdown better, while the PSO and DE had large deviations at the early stage of pumping. Overall, the values of K vary from 0.032 to 10.64 m/d within Qp3s and 0.33 to 36.24 m/d within Qp2j. The spatial variability model demonstrated that the K values in Qp3s are greater than those in Qp2j. The heterogeneity elucidates the sedimentary development of the basin. These methods and results can provide new insights for estimating the spatial distribution of K for regional groundwater resources assessment in similar sedimentary environments around the world.

Transport Upscaling in Highly Heterogeneous Aquifers and the Prediction of Tracer Dispersion at the MADE Site

AbstractWe present an upscaled Lagrangian approach to predict the plume evolution in highly heterogeneous aquifers. The model is parameterized by transport‐independent characteristics such as the statistics of hydraulic conductivity and the Eulerian flow speed. It can be conditioned on the tracer properties and flow data at the injection region. Thus, the model is transferable to different solutes and hydraulic conditions. It captures the large‐scale non‐Gaussian features for the evolution of the longitudinal mass distribution observed for the bromide and tritium tracer plumes at the Macrodispersion Experiment (MADE) site (Columbus, Mississippi, USA), which are characterized by a slow moving peak and pronounced forward tailing. These large‐scale features are explained by advective tracer propagation due to a broad distribution of spatially persistent Eulerian flow speeds as a result of spatial variability in hydraulic conductivity.

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Groundwater flow has an important impact on the hydrological properties and environmental prediction of seasonally frozen ground in Northeast China, and the hydraulic conductivity of warm frozen soil is the key factor. Due to the complex geology, environment and human activities, the hydraulic conductivity shows spatial variability. This study tests the saturated horizontal hydraulic conductivity (Kh) and saturated vertical hydraulic conductivity (Kv) of seasonally frozen ground in Southern Heilongjiang, Northwest Jilin and Northern Liaoning, Northeast China. The statistical characteristics and variability of Kh and Kv of warm frozen soil under three temperature (−0.1 °C, −0.3 °C and − 0.5 °C) are obtained. Then the scale of fluctuation (SOF) of Kh and Kv of seasonally frozen ground are calculated by random field method. Five theoretical two-dimensional autocorrelation functions (2-DACFs) are used to characterize the spatial variability of hydraulic conductivity. The autocorrelation distance (ACD) and ACF of hydraulic conductivity of seasonally frozen ground in Northeast China are assessed. The results show that the measured Kh and Kv of seasonally frozen ground are discrete. The lower the temperature is, the smaller the hydraulic conductivity is. The statistical characteristics of Kh and Kv are different when the cold temperature is the same. The fitting degree of the linear regression equation of the measured Kh and Kv is very good. The ACD of the Kh and Kv of seasonally frozen ground are different due to different 2-DACFs. The SOF and ACD of the Kh are larger than the SOF and ACD of the Kv. The ACF of hydraulic conductivity decreases with the increase of local average distance. This study clearly clarifies the spatial variability of hydraulic conductivity of seasonally frozen ground and the results can provide reasonable basic data for the random analysis of groundwater flow of seasonally frozen ground in Northeast China.

Assessment of spatial variability of hydraulic conductivity of seasonally frozen ground in Northeast China

Groundwater flow has an important impact on the hydrological properties and environmental prediction of seasonally frozen ground in Northeast China, and the hydraulic conductivity of warm frozen soil is the key factor. Due to the complex geology, environment and human activities, the hydraulic conductivity shows spatial variability. This study tests the saturated horizontal hydraulic conductivity (Kh) and saturated vertical hydraulic conductivity (Kv) of seasonally frozen ground in Southern Heilongjiang, Northwest Jilin and Northern Liaoning, Northeast China. The statistical characteristics and variability of Kh and Kv of warm frozen soil under three temperature (−0.1 °C, −0.3 °C and − 0.5 °C) are obtained. Then the scale of fluctuation (SOF) of Kh and Kv of seasonally frozen ground are calculated by random field method. Five theoretical two-dimensional autocorrelation functions (2-DACFs) are used to characterize the spatial variability of hydraulic conductivity. The autocorrelation distance (ACD) and ACF of hydraulic conductivity of seasonally frozen ground in Northeast China are assessed. The results show that the measured Kh and Kv of seasonally frozen ground are discrete. The lower the temperature is, the smaller the hydraulic conductivity is. The statistical characteristics of Kh and Kv are different when the cold temperature is the same. The fitting degree of the linear regression equation of the measured Kh and Kv is very good. The ACD of the Kh and Kv of seasonally frozen ground are different due to different 2-DACFs. The SOF and ACD of the Kh are larger than the SOF and ACD of the Kv. The ACF of hydraulic conductivity decreases with the increase of local average distance. This study clearly clarifies the spatial variability of hydraulic conductivity of seasonally frozen ground and the results can provide reasonable basic data for the random analysis of groundwater flow of seasonally frozen ground in Northeast China.

Quantifying hydraulic conductivity spatial variability for cement-based solidification/stabilization (S/S) remediation project: case study

This paper presents statistical analyses of hydraulic conductivity data collected from an existing cement-based solidification/stabilization (S/S) system. The goal is to characterize the spatial variability of hydraulic conductivity and to examine sampling recommendations for the quality control (QC) program of that system to achieve target decision error probabilities regarding the acceptance or rejection of the system with respect to hydraulic conductivity. Over 2000 QC hydraulic conductivity samples, taken over an area of 300 000 m2, are used as a basis for these analyses. The hydraulic conductivity spatial variability is described by a marginal lognormal distribution with correlation function parameterized by directional correlation lengths, which are estimated by best fitting an exponentially decaying correlation model to sample correlation functions. The spatial variability associated with hydraulic conductivity of the studied S/S system is then utilized to assess sampling requirements for the QC program of that system. Considering the “worst case” correlation length and the hydraulic conductivity mean and variance, hypothesis test error probabilities are used to provide recommendations for conservative sampling requirements. It is believed that the analysis of this large construction project represents a unique opportunity to review the current practice of S/S field sampling requirements.

Aplicação de Redes Neurais Artificiais Para Simulação de Fluxo e Transporte de Contaminantes Sob Incerteza de Condutividade Hidráulica

A incerteza relacionada à variabilidade espacial da condutividade hidráulica (K) é um importante aspecto a ser considerado nas simulações da migração de plumas de contaminantes e, consequentemente no gerenciamento ambiental de áreas contaminadas. A utilização de modelos estocásticos de K em conjunto com a simulação de fluxo e o transporte de contaminantes é frequente em pesquisas para a previsão do comportamento da pluma, bem como para projetos de remediação sob a incerteza deste parâmetro hidrogeológico. No entanto, as técnicas tradicionais de modelagem de fluxo e de contaminantes, como o MODFLOW e o MT3D, baseadas no método de diferenças finitas, normalmente são lentas e exigem bastante esforço computacional. Dessa maneira, esta pesquisa aplicou redes neurais artificias (RNA) em 100 cenários de condutividade hidráulica, realizados por métodos estocásticos, a fim de avaliar a incerteza de K na migração de uma pluma. Além disso, foi possível validar a utilização de RNA como ferramenta de decisão para a estimativa da concentração média de contaminantes nas águas subterrâneas ao longo de três anos de simulação. Com base nos resultados obtidos, foi possível avaliar possíveis técnicas de remediação para a área de pesquisa em função da migração da pluma sob a incerteza da condutividade hidráulica.

An Appraisal on the Interpolation Methods Used for Predicting Spatial Variability of Field Hydraulic Conductivity

Knowledge of spatial variability of soil hydraulic conductivity is a key parameter for hydrological modeling. Spatial variability of soil hydraulic conductivity, Ks is attributed to soil heterogeneities associated with texture/structure, different initial conditions, meteorological changes, and clogging. The spatial variability is quantified by employing interpolation methods to point measurements performed in the field. There are not many studies reported in the literature that deals with the critical evaluation of the relative performance of different interpolation methods for predicting spatial variability of hydraulic conductivity. The primary objective of this study is to perform a critical evaluation of five interpolation methods, namely Kriging, Inverse Distance Weighted, Natural Neighbor, Spline and Trend for the spatial prediction of hydraulic conductivity. The accuracy of different methods was assessed by comparing the predicted values with the measured hydraulic conductivity of selected locations. It was noted that the Kriging method with exponential model gave a better spatial prediction as compared to other methods. The spatial variability of Ks was found to be in the same pattern as that of the percentage variation of the sand fraction for both the sites investigated in this study. It was further noted that the prediction of Ks was found to be more precise for those stations with a higher percentage of sand. A sudden transition of soil type from sand to silt was found to influence the accuracy of spatial prediction.