Using informative priors in facies inversion: The case of C-ISR method

Inverse problems involving the characterization of hydraulic properties of groundwater flow systems by conditioning on observations of the state variables are mathematically ill-posed because they have multiple solutions and are sensitive to small changes in the data. In the framework of McMC methods for nonlinear optimization and under an iterative spatial resampling transition kernel, we present an algorithm for narrowing the prior and thus producing improved proposal realizations. To achieve this goal, we cosimulate the facies distribution conditionally to facies observations and normal scores transformed hydrologic response measurements, assuming a linear coregionalization model. The approach works by creating an importance sampling effect that steers the process to selected areas of the prior. The effectiveness of our approach is demonstrated by an example application on a synthetic underdetermined inverse problem in aquifer characterization.     

Geologic heterogeneity and a comparison of two geostatistical models: Sequential Gaussian and transition probability-based geostatistical simulation

A covariance-based model-fitting approach is often considered valid to represent field spatial variability of hydraulic properties. This study examines the representation of geologic heterogeneity in two types of geostatistical models under the same mean and spatial covariance structure, and subsequently its effect on the hydraulic response to a pumping test based on 3D high-resolution numerical simulation and field data. Two geostatistical simulation methods, sequential Gaussian simulation (SGS) and transition probability indicator simulation (TPROGS) were applied to create conditional realizations of alluvial fan aquifer systems in the Lawrence Livermore National Laboratory (LLNL) area. The simulated K fields were then used in a numerical groundwater flow model to simulate a pumping test performed at the LLNL site. Spatial connectivity measures of high-K materials (channel facies) captured connectivity characteristics of each geostatistical model and revealed that the TPROGS model created an aquifer (channel) network having greater lateral connectivity. SGS realizations neglected important geologic structures associated with channel and overbank (levee) facies, even though the covariance model used to create these realizations provided excellent fits to sample covariances computed from exhaustive samplings of TPROGS realizations. Observed drawdown response in monitoring wells during a pumping test and its numerical simulation shows that in an aquifer system with strongly connected network of high-K materials, the Gaussian approach could not reproduce a similar behavior in simulated drawdown response found in TPROGS case. Overall, the simulated drawdown responses demonstrate significant disagreement between TPROGS and SGS realizations. This study showed that important geologic characteristics may not be captured by a spatial covariance model, even if that model is exhaustively determined and closely fits the exponential function.     

An interactive multi-objective optimization framework for groundwater inverse modeling

The groundwater inverse problem of estimating heterogeneous groundwater model parameters (hydraulic conductivity in this case) given measurements of aquifer response (such as hydraulic heads) is known to be an ill-posed problem, with multiple parameter values giving similar fits to the aquifer response measurements. This problem is further exacerbated due to the lack of extensive data, typical of most real-world problems. In such cases, it is desirable to incorporate expert knowledge in the estimation process to generate more reasonable estimates. This work presents a novel interactive framework, called the ‘Interactive Multi-Objective Genetic Algorithm’ (IMOGA), to solve the groundwater inverse problem considering different sources of quantitative data as well as qualitative expert knowledge about the site. The IMOGA is unique in that it looks at groundwater model calibration as a multi-objective problem consisting of quantitative objectives – calibration error and regularization – and a ‘qualitative’ objective based on the preference of the geological expert for different spatial characteristics of the conductivity field. All these objectives are then included within a multi-objective genetic algorithm to find multiple solutions that represent the best combination of all quantitative and qualitative objectives. A hypothetical aquifer case-study (based on the test case presented by Freyberg [Freyberg DL. An exercise in ground-water model calibration and prediction. Ground Water 1988;26(3)], for which the ‘true’ parameter values are known, is used as a test case to demonstrate the applicability of this method. It is shown that using automated calibration techniques without using expert interaction leads to parameter values that are not consistent with site-knowledge. Adding expert interaction is shown to not only improve the plausibility of the estimated conductivity fields but also the predictive accuracy of the calibrated model.     

Using direct current resistivity sounding and geostatistics to aid in hydrogeological studies in the Choshuichi alluvial fan, Taiwan

Ground water reservoirs in the Choshuichi alluvial fan, central western Taiwan, were investigated using direct-current (DC) resistivity soundings at 190 locations, combined with hydrogeological measurements from 37 wells. In addition, attempts were made to calculate aquifer transmissivity from both surface DC resistivity measurements and geostatistically derived predictions of aquifer properties. DC resistivity sounding data are highly correlated to the hydraulic parameters in the Choshuichi alluvial fan. By estimating the spatial distribution of hydraulic conductivity from the kriged well data and the cokriged thickness of the correlative aquifer from both resistivity sounding data and well information, the transmissivity of the aquifer at each location can be obtained from the product of kriged hydraulic conductivity and computed thickness of the geoelectric layer. Thus, the spatial variation of the transmissivities in the study area is obtained. Our work is more comparable to Ahmed et al. (1988) than to the work of Niwas and Singhal (1981). The first "constraint" from Niwas and Singhal's work is a result of their use of linear regression. The geostatistical approach taken here (and by Ahmed et al. [1988]) is a natural improvement on the linear regression approach.     

Stochastically conditioned flow paths and travel time distribution in highly heterogeneous synthesized aquifers / Chemins d’écoulement et distribution des temps de parcours au sein d’aquifères synthétiques hautement hétérogènes sous conditions stochastiques

Abstract

Abstract Characterization of heterogeneity at the field scale generally requires detailed aquifer properties such as transmissivity and hydraulic head. An accurate delineation of these properties is expensive and time consuming, and for many if not most groundwater systems, is not practical. As an alternative approach, stochastic representation of random fields is used and presented in this paper. Specifically, an iterative stochastic conditional simulation approach was applied to a hypothetical and highly heterogeneous pre-designed aquifer system. The approach is similar to the classical co-kriging technique; it uses a linear estimator that depends on the covariance functions of transmissivity (T), and hydraulic head (h), as well as their cross-covariances. A linearized flow equation along with a conditional random field generator constitutes the iterative process of the conditional simulation. One hundred equally likely realizations of transmissivity fields with pre-specified geostatistical parameters were generated, and conditioned to both limited transmissivity and head data. The successful implementation of the approach resulted in conditioned flow paths and travel-time distribution under different degrees of aquifer heterogeneity. This approach worked well for fields exhibiting small variances. However, for random fields exhibiting large variances (greater than 1.0), an iterative procedure was used. The results show that, as the variance of the ln[T] increases, the flow paths tend to diverge, resulting in a wide spectrum of flow conditions, with no direct discernable relationship between the degree of heterogeneity and travel time. The applied approach indicates that high errors may result when estimation of particle travel times in a heterogeneous medium is approximated by an equivalent homogeneous medium.     

Towards Improved Remedial Outcomes in Categorical Aquifers with an Iterative Ensemble Smoother

Categorical parameter distributions consisting of geologic facies with distinct properties, for example, high-permeability channels embedded in a low-permeability matrix, are common at contaminated sites. At these sites, low-permeability facies store solute mass, acting as secondary sources to higher-permeability facies, sustaining concentrations for decades while increasing risk and cleanup costs. Parameter estimation is difficult in such systems because the discontinuities in the parameter space hinder the inverse problem. This paper presents a novel approach based on Traveling Pilot Points (TRIPS) and an iterative ensemble smoother (IES) to solve the categorical inverse problem. Groundwater flow and solute transport in a hypothetical aquifer with a categorical parameter distribution are simulated using MODFLOW 6. Heads and concentrations are recorded at multiple monitoring locations. IES is used to generate posterior ensembles assuming a TRIPS prior and an approximate multi-Gaussian prior. The ensembles are used to predict solute concentrations and mass into the future. The evaluation also includes an assessment of how the number of measurements and the choice of the geological prior determine the characteristics of the posterior ensemble and the resulting predictions. The results indicate that IES was able to efficiently sample the posterior distribution and showed that even with an approximate geological prior, a high degree of parameterization and history matching could lead to parameter ensembles that can be useful for making certain types of predictions (heads, concentrations). However, the approximate geological prior was insufficient for predicting mass. The analysis demonstrates how decision-makers can quantify uncertainty and make informed decisions with an ensemble-based approach.     

An efficiency-improved genetic algorithm and its application on multimodal functions and a 2D common reflection surface stacking problem

Although Genetic Algorithms have found many successful applications in the field of exploration geophysics, the convergence speed remains a big challenge as Genetic Algorithms usually require a huge amount of fitness function evaluations. In this paper, we propose an efficiency-improved Genetic Algorithm, which has both a good global search capability and a good local search capability, and is also capable of robustly handling the premature convergence challenge commonly seen in linear and directed non-linear optimization methods. In our new genetic algorithm, the global search capability is performed via a modified island model, while the local search capability is provided by a novel self-adaptive differential evolution fine tuning scheme. Premature convergence is dealt with via a local exhaustive search method. We first demonstrate the much improved convergence speed of this efficiency-improved Genetic Algorithm over that of our previously proposed advanced Genetic Algorithm on several multimodal functions. We further demonstrate the effectiveness of our efficiency-improved Genetic Algorithm by applying it to a two-dimensional common reflection surface stacking problem, which is a highly nonlinear geophysical optimization problem, to obtain very encouraging results.     

Imaging geochemical heterogeneities using inverse reactive transport modeling: An example relevant for characterizing arsenic mobilization and distribution

The spatial distribution of reactive minerals in the subsurface is often a primary factor controlling the fate and transport of contaminants in groundwater systems. However, direct measurement and estimation of heterogeneously distributed minerals are often costly and difficult to obtain. While previous studies have shown the utility of using hydrologic measurements combined with inverse modeling techniques for tomography of physical properties including hydraulic conductivity, these methods have seldom been used to image reactive geochemical heterogeneities. In this study, we focus on As-bearing reactive minerals as aquifer contaminants. We use synthetic applications to demonstrate the ability of inverse modeling techniques combined with mechanistic reactive transport models to image reactive mineral lenses in the subsurface and quantify estimation error using indirect, commonly measured groundwater parameters. Specifically, we simulate the mobilization of arsenic via kinetic oxidative dissolution of As-bearing pyrite due to dissolved oxygen in the ambient groundwater, which is an important mechanism for arsenic release in groundwater both under natural conditions and engineering applications such as managed aquifer recharge and recovery operations. The modeling investigation is carried out at various scales and considers different flow-through domains including (i) a 1D lab-scale column (80 cm), (ii) a 2D lab-scale setup (60 cm × 30 cm) and (iii) a 2D field-scale domain (20 m × 4 m). In these setups, synthetic dissolved oxygen data and forward reactive transport simulations are used to image the spatial distribution of As-bearing pyrite using the Principal Component Geostatistical Approach (PCGA) for inverse modeling.     

基于子空间的二维大地电磁量子遗传反演法研究

量子遗传算法作为一种高效的优化算法,仍存在容易陷入局部极值的缺点.为提高算法的高效性,并探讨将算法应用于大地电磁二维反演的可行性和有效性,本文对算法进行了改进,并通过一维两层D型和四层HK型模型数值试验验证了改进的有效性.然后将改进后的算法引入二维大地电磁反演,在引入滑动子空间思想,同时只考虑最简化反演条件的前提下,对...     

Combining 3D Hydraulic Tomography with Tracer Tests for Improved Transport Characterization

Hydraulic tomography (HT) is a method for resolving the spatial distribution of hydraulic parameters to some extent, but many details important for solute transport usually remain unresolved. We present a methodology to improve solute transport predictions by combining data from HT with the breakthrough curve (BTC) of a single forced‐gradient tracer test. We estimated the three dimensional (3D) hydraulic‐conductivity field in an alluvial aquifer by inverting tomographic pumping tests performed at the Hydrogeological Research Site Lauswiesen close to Tübingen, Germany, using a regularized pilot‐point method. We compared the estimated parameter field to available profiles of hydraulic‐conductivity variations from direct‐push injection logging (DPIL), and validated the hydraulic‐conductivity field with hydraulic‐head measurements of tests not used in the inversion. After validation, spatially uniform parameters for dual‐domain transport were estimated by fitting tracer data collected during a forced‐gradient tracer test. The dual‐domain assumption was used to parameterize effects of the unresolved heterogeneity of the aquifer and deemed necessary to fit the shape of the BTC using reasonable parameter values. The estimated hydraulic‐conductivity field and transport parameters were subsequently used to successfully predict a second independent tracer test. Our work provides an efficient and practical approach to predict solute transport in heterogeneous aquifers without performing elaborate field tracer tests with a tomographic layout.     

Statistical scale-up of 3D particle-tracking simulation for non-Fickian dispersive solute transport modeling

Numerical techniques for subsurface flow and transport modeling are often limited by computational limitations including fine mesh and small time steps to control artificial dispersion. Particle-tracking simulation offers a robust alternative for modeling solute transport in subsurface formations. However, the modeling scale usually differs substantially from the rock measurement scale, and the scale-up of measurements have to be made accounting for the pattern of spatial heterogeneity exhibited at different scales. Therefore, it is important to construct accurate coarse-scale simulations that are capable of capturing the uncertainties in reservoir and transport attributes due to scale-up. A statistical scale-up procedure developed in our previous work is extended by considering the effects of unresolved (residual) heterogeneity below the resolution of the finest modeling scale in 3D. First, a scale-up procedure based on the concept of volume variance is employed to construct realizations of permeability and porosity at the (coarse) transport modeling scale, at which flow or transport simulation is performed. Next, to compute various effective transport parameters, a series of realizations exhibiting detailed heterogeneities at the fine scale, whose domain size is the same as the transport modeling scale, are generated. These realizations are subjected to a hybrid particle-tracking simulation. Probabilistic transition time is considered, borrowing the idea from the continuous time random walk (CTRW) technique to account for any sub-scale heterogeneity at the fine scale level. The approach is validated against analytical solutions and general CTRW formulation. Finally, coarse-scale transport variables (i.e., dispersivities and parameterization of transition time distribution) are calibrated by minimizing the mismatch in effluent history with the equivalent averaged models. Construction of conditional probability distributions of effective parameters is facilitated by integrating the results over the entire suite of realizations. The proposed method is flexible, as it does not invoke any explicit assumption regarding the multivariate distribution of the heterogeneity. In contrast to other hierarchical CTRW formulation for modeling multi-scale heterogeneities, the proposed approach does not impose any length scale requirement regarding sub-grid heterogeneities. In fact, it aims to capture the uncertainty in effective reservoir and transport properties due to the presence of heterogeneity at the intermediate scale, which is larger than the finest resolution of heterogeneity but smaller than the representative elementary volume, but it is often comparable to the transport modeling scale.     

Studies on structural vibration control with MR dampers using μGA

A new approach to reducing the seismic response of spatial structures with magneto-rheological (MR) dampers is presented in this paper. The Genetic Algorithm with small populations (μGA) is used to optimize the control for the MR dampers to reduce structural vibration, which is difficult to achieve using classical optimal control. The advantages of μGA are the use of global properties and that fewer conditions are required to obtain the optimal function. Numerical results demonstrate the effectiveness of the proposed method in reducing the seismic response of structures.     

Delineating alluvial aquifer heterogeneity using resistivity and GPR data

Conceptual geological models based on geophysical data can elucidate aquifer architecture and heterogeneity at meter and smaller scales, which can lead to better predictions of preferential flow pathways. The macrodispersion experiment (MADE) site, with >2000 measurements of hydraulic conductivity obtained and three tracer tests conducted, serves as an ideal natural laboratory for examining relationships between subsurface flow characteristics and geophysical attributes in fluvial aquifers. The spatial variation of hydraulic conductivity measurements indicates a large degree of site heterogeneity. To evaluate the usefulness of geophysical methods for better delineating fluvial aquifer heterogeneities and distribution of preferential flow paths, a surface grid of two-dimensional ground penetrating radar (GPR) and direct current (DC) resistivity data were collected. A geological model was developed from these data that delineate four stratigraphic units with distinct electrical and radar properties including (from top to bottom) (1) a meandering fluvial system (MFS); (2) a braided fluvial system (BFS); (3) fine-grained sands; and (4) a clay-rich interval. A paleochannel, inferred by other authors to affect flow, was mapped in the MFS with both DC resistivity and GPR data. The channel is 2 to 4 m deep and, based on resistivity values, is predominantly filled with clay and silt. Comparing previously collected hydraulic conductivity measurements and tracer-plume migration patterns to the geological model indicates that flow primarily occurs in the BFS and that the channel mapped in the MFS has no influence on plume migration patterns.     

Predicting DNAPL entrapment and recovery: the influence of hydraulic property correlation

The influence of aquifer property correlation on multiphase fluid migration, entrapment and recovery was explored by incorporating correlated and uncorrelated porosity, permeability, and capillary pressure-saturation (P_c-Sat) parameter fields in a cross-sectional numerical multiphase flow model. Comparison of two-dimensional entrapped organic saturation distributions for a simulated tetrachloroethylene (PCE) spill in ensembles of aquifer realizations suggests that the degree of spatial correlation in P_c-Sat parameters exerts a controlling influence on dense nonaqueous phase liquid (DNAPL) spreading and redistribution in saturated aquifers. The predicted evolution of DNAPL source zones and resultant remediation efficiency under surfactant enhanced aquifer remediation (SEAR) also appear to be strongly influenced by the spatial correlation of aquifer parameters and multiphase flow constitutive relationships. Results for a limited number of realizations selected from each ensemble showed that removal of 60% to 99% of entrapped PCE could reduce dissolved contaminant concentration and mass flux by approximately two orders of magnitude under natural gradient conditions. Aqueous phase contaminant mass flux did not vary uniformly as a function of % DNAPL removed, however, and notable differences in behavior were observed for models incorporating correlated versus uncorrelated P_c-Sat and permeability fields. Although these results must be confirmed through analysis of additional realizations, it is likely that similar or larger differences between correlated and uncorrelated system behavior will be observed in aquifers with greater spatially variability than that of the nonuniform, homogeneous sand aquifer studied here. Funding for this research was provided by the United States Environmental Protection Agency, Great Lakes and Mid-Atlantic Center for Hazardous Substance Research under Grant No. R-825540, the Michigan Department of Environmental Quality under Contract No. Y80011, and the Strategic Environmental Research and Development Program under Project No. CU-1293. The content of this publication does not necessarily represent the views of these agencies and has not been subject to agency review.     

Numerical modelling of stream–aquifer interaction: Quantifying the impact of transient streambed permeability and aquifer heterogeneity

Stream–aquifer interaction plays a vital role in the water cycle, and a proper study of this interaction is needed for understanding groundwater recharge, contaminants migration, and for managing surface water and groundwater resources. A model‐based investigation of a field experiment in a riparian zone of the Schwarzbach river, a tributary of the Rhine River in Germany, was conducted to understand stream–aquifer interaction under alternative gaining and losing streamflow conditions. An equivalent streambed permeability, estimated by inverting aquifer responses to flood waves, shows that streambed permeability increased during infiltration of stream water to aquifer and decreased during exfiltration. Aquifer permeability realizations generated by multiple‐point geostatistics exhibit a high degree of heterogeneity and anisotropy. A coupled surface water groundwater flow model was developed incorporating the time‐varying streambed permeability and heterogeneous aquifer permeability realizations. The model was able to reproduce varying pressure heads at two observation wells near the stream over a period of 55 days. A Monte Carlo analysis was also carried out to simulate groundwater flow, its age distribution, and the release of a hypothetical wastewater plume into the aquifer from the stream. Results of this uncertainty analysis suggest (a) stream–aquifer exchange flux during the infiltration periods was constrained by aquifer permeability; (b) during exfiltration, this flux was constrained by the reduced streambed permeability; (c) the effect of temporally variable streambed permeability and aquifer heterogeneity were found important to improve the accurate capture of the uncertainty; and (d) probabilistic infiltration paths in the aquifer reveal that such pathways and the associated prediction of the extent of the contaminant plume are highly dependent on aquifer heterogeneity.     

A stochastic optimization framework for the restoration of an over-exploited aquifer

ABSTRACT

This study investigates the impact of hydraulic conductivity uncertainty on the sustainable management of the aquifer of Lake Karla, Greece, using the stochastic optimization approach. The lack of surface water resources in combination with the sharp increase in irrigation needs in the basin over the last 30 years have led to an unprecedented degradation of the aquifer. In addition, the lack of data regarding hydraulic conductivity in a heterogeneous aquifer leads to hydrogeologic uncertainty. This uncertainty has to be taken into consideration when developing the optimization procedure in order to achieve the aquifer’s sustainable management. Multiple Monte Carlo realizations of this spatially-distributed parameter are generated and groundwater flow is simulated for each one of them. The main goal of the sustainable management of the ‘depleted’ aquifer of Lake Karla is two-fold: to determine the optimum volume of renewable groundwater that can be extracted, while, at the same time, restoring its water table to a historic high level. A stochastic optimization problem is therefore formulated, based on the application of the optimization method for each of the aquifer’s multiple stochastic realizations in a future period. In order to carry out this stochastic optimization procedure, a modelling system consisting of a series of interlinked models was developed. The results show that the proposed stochastic optimization framework can be a very useful tool for estimating the impact of hydraulic conductivity uncertainty on the management strategies of a depleted aquifer restoration. They also prove that the optimization process is affected more by hydraulic conductivity uncertainty than the simulation process.

Editor Z.W. Kundzewicz; Guest editor S. Weijs     

Surface NMR within a geophysical study of an aquifer at Haldensleben (Germany)

The surface nuclear magnetic resonance (SNMR) method has been tested at a site in Haldensleben, northern Germany, to assess the suitability of this new method for groundwater exploration and environmental investigations. More information is obtained by SNMR, particularly with respect to aquifer parameters, than with other geophysical techniques. SNMR measurements were carried out at three borehole locations, together with 2D and 1D direct current geoelectrics, as well as ground-penetrating radar, and well logging (induction log, gamma-ray log and pulsed neutron-gamma log). Permeabilities were calculated from the grain-size distributions of core material determined in the laboratory. It is demonstrated that the SNMR method is able to detect groundwater and the results are in good agreement with other geophysical and hydrogeological data. Using the SNMR method, the water content of the unsaturated and saturated zones (i.e. porosity of an aquifer) can be reliably determined. This information and resistivity data permit in situ determination of other aquifer parameters. Comparison of the SNMR results with borehole data clearly shows that the water content determined by SNMR is the free or mobile water in the pores. The permeabilities estimated from the SNMR decay times are similar to those derived from sieve analysis of core material. Thus, the combination of SNMR with geoelectric methods promises to be a powerful tool for studying aquifer properties.     

Influence of aquifer properties and the spatial and temporal distribution of recharge and abstraction on sustainable yields in semi‐arid regions

This paper aims to contribute to understanding the importance of four factors on the determination of sustainable yields: (i) aquifer properties; (ii) temporal distribution of recharge; (iii) temporal distribution of groundwater pumping; and (iv) spatial distribution of pumping wells. It is important to comprehend how the present‐day and future vulnerability of groundwater systems to pumping activities depend on these critical factors and what the risks are of considering sustainable yield as a fixed percentage of mean annual recharge (MAR). A numerical model of the Querença–Silves aquifer in Portugal is used to develop hypothetical scenarios with which these factors are studied. Results demonstrate the aquifer properties, particularly the storage coefficient, have an important role in determining the resilience of an aquifer and therefore to which degree it is dependent on the spatial and temporal distribution of abstraction and recharge, as well as the occurrence of extreme events. Sustainable yields are determined for the developed scenarios based on specific criteria rather than a fraction of MAR. Under simplified current recharge and abstraction conditions, the sustainable yield was determined at approximately 73% of MAR or 76 million m³. When considering a concentration of rainfall in time, as predicted by climate scenarios for the region, sustainable yield could drop to ca 70% of MAR. However, a more even distribution of pumping volumes throughout the year could increase this value. The location of the pumping wells is seen to affect the distribution of hydraulic heads in the aquifer, albeit without significant changes in sustainable yield. Copyright © 2011 John Wiley & Sons, Ltd.     

A methodology for regionalizing 3‐D effective porosity at watershed scale in crystalline aquifers

An innovative approach for regionalizing the 3‐D effective porosity field is presented and applied to two large, overexploited, and deeply weathered crystalline aquifers located in southern India. The method derives from earlier work on regionalizing a 2‐D effective porosity field in that part of an aquifer where the water table fluctuates, which is now extended over the entire aquifer using a 3‐D approach. A method based on geological and geophysical surveys has also been developed for mapping the weathering profile layers (saprolite and fractured layers). The method for regionalizing 3‐D effective porosity combines water table fluctuation and groundwater budget techniques at various cell sizes with the use of satellite‐based data (for groundwater abstraction), the structure of the weathering profile, and geostatistical techniques. The approach is presented in detail for the Kudaliar watershed (983 km²) and tested on the 730 km² Anantapur watershed. At watershed scale, the effective porosity of the aquifer ranges from 0.5% to 2% in Kudaliar and between 0.3% and 1% in Anantapur, which agrees with earlier works. Results show that (a) depending on the geology and on the structure of the weathering profile, the vertical distribution of effective porosity can be very different and that the fractured layers in crystalline aquifers are not necessarily characterized by a rapid decrease in effective porosity and (b) that the lateral variations in effective porosity can be larger than the vertical ones. These variations suggest that within a same weathering profile, the density of open fractures and/or degree of weathering in the fractured zone may significantly vary from a place to another. The proposed method provides information on the spatial distribution of effective porosity that is of prime interest in terms of flux and contaminant transport in crystalline aquifers. Implications for mapping groundwater storage and scarcity are also discussed, which should help in improving groundwater resource management strategies.