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Heterogeneity of hydraulic conductivity in aquifers is a topic touched upon by many researchers, but very few analyses were reported on the characteristic parameters of solute transport using stochastic methods. We study he effects of aquifer heterogeneity by running numerical simulations of dipole tracer tests in the software MODFLOW 6, instigated with Python scripts using the FloPy package. Random hydraulic conductivity fields are drawn by the Python package GStools from a log-normal distribution, characterized by a mean, variance and correlation length. A series of ensembles was run with varying variances and correlation lengths. Normalised mean breakthrough curves of the ensembles were compared for homogeneous, stratified and heterogeneous aquifers. Outcomes show that an increase in variance leads to an earlier arrival of the tracer, due to the occurrence of preferential flow paths, but will also lead to more tailing. An increase in correlation length enhances the effects of an increase in variance. For a short correlation length (i.e. much smaller than the distance between the wells) the heterogeneous solution tends towards the solution for a homogeneous medium, while for a longer correlation length, the solution tends towards the solution for a stratified medium. Based on the ensemble results we conclude that a fully heterogeneous aquifer shows a different breakthrough curve than stratified or a homogeneous structured aquifers. Therefore it cannot be treated the same way.

This paper employs stochastic analysis to investigate the combined effect of temporal and spatial variability on the temporal variance of baseflow in large catchments. The study makes use of the well-known aggregated reservoir model, representing the catchment as a network of parallel linear reservoirs. Each reservoir models a sub-catchment as an independent unit whose discharge temporal variation is characterized by a response time. By treating the rainfall-generated recharge and the sub-catchment response times as random variables, the statistical temporal moments of total baseflow are quantified. Comparisons are made between the temporal variance of baseflow in the aggregated reservoir model and that of a single homogeneous reservoir to define an upscaled response time. The analysis of the statistical moments of the random baseflow reveals that the number of reservoirs N has a weak impact on baseflow variance, with ergodic conditions achieved even with a small number of reservoirs. The study highlights that the ratio between the recharge correlation time and the geometric mean of the sub-catchment response times plays a critical role in baseflow damping and the upscaled response. The results indicate that the dynamics of baseflow generation depend not only on the catchment hydro-geological structure but also on the variability of the input signal. This research underscores the importance of understanding the combined influences of hydro-geological factors and recharge input variability for baseflow prediction under uncertainty. The present study should be regarded as a first step, setting the theoretical framework for future research toward incorporating field data., QC 20231229

Aquitards are common hydrogeological features and their hydraulic conductivity is an important property for various groundwater management issues. Predicting their hydraulic conductivity proves challenging, given its dependence on numerous variables. In this study, the dominant factors for predicting aquitard hydraulic conductivity are identified. To this end, a random forest model is trained on a dataset consisting of more than 1000 hydraulic conductivity measurements of core-scale sediment samples from a wide range of stratigraphic units and depths in the Netherlands. The dataset contains textural properties, such as the grain size distribution and porosity, as well as structural data, such as location, sampling depth, stratigraphical unit, lithofacies, organic carbon content, carbonate content and groundwater chloride concentration. Results show that clay fraction, stratigraphic unit, depth, lithofacies and x-coordinate are the most important features for predicting the hydraulic conductivity. Here, x-coordinate is presumably a proxy for distance from marine influence. Using a more detailed grain size distribution or using derived parameters such as the grain size percentiles does not improve the model any further. Our findings indicate that structural properties play a significant role in predicting aquitard conductivity, as they serve as indicators of processes such as compaction and soft-sediment deformation. The model is furthermore an effective method to estimate hydraulic conductivity for sediment samples without conducting costly and time-consuming hydraulic conductivity measurements.

Aquitards are common hydrogeological features and their hydraulic conductivity is an important property for various groundwater management issues. Predicting their hydraulic conductivity proves challenging, given its dependence on numerous variables. In this study, the dominant factors for predicting aquitard hydraulic conductivity are identified. To this end, a random forest model is trained on a dataset consisting of more than 1000 hydraulic conductivity measurements of core-scale sediment samples from a wide range of stratigraphic units and depths in the Netherlands. The dataset contains textural properties, such as the grain size distribution and porosity, as well as structural data, such as location, sampling depth, stratigraphical unit, lithofacies, organic carbon content, carbonate content and groundwater chloride concentration. Results show that clay fraction, stratigraphic unit, depth, lithofacies and x-coordinate are the most important features for predicting the hydraulic conductivity. Here, x-coordinate is presumably a proxy for distance from marine influence. Using a more detailed grain size distribution or using derived parameters such as the grain size percentiles does not improve the model any further. Our findings indicate that structural properties play a significant role in predicting aquitard conductivity, as they serve as indicators of processes such as compaction and soft-sediment deformation. The model is furthermore an effective method to estimate hydraulic conductivity for sediment samples without conducting costly and time-consuming hydraulic conductivity measurements., Water Resources

Aquitards are common hydrogeological features in the subsurface. Typically, pumping tests are used to parameterize the hydraulic conductivity of heterogeneous aquitards. However, they do not take spatial variability and uncertainty into account. Alternatively, core-scale measurements of hydraulic conductivity are used in geostatistical upscaling methods, for which their correlation lengths are needed, but this information is extremely difficult to obtain. This study investigates whether a pumping test can be used to obtain the correlation lengths needed for geostatistical upscaling and account for the uncertainty about heterogeneous aquitard conductivity. Random realizations are generated from core-scale data with varying correlation lengths and inserted into a groundwater flow model which simulates the outcome of an actual pumping test. The realizations yielded a better fit to the pumping test data than the traditional pumping test result, assuming homogeneous layers are selected. Ranges of horizontal and vertical correlation lengths that fit the pumping-test well are found. However, considerable uncertainty regarding the correlation lengths remains, which should be considered when parameterizing a regional groundwater flow model., Water Resources

Aquifer Storage and Recovery systems (ASR) are an alternative source of fresh water to the increasing demand for water. Its performance varies in terms of flow parameters, aquifer conditions and transport processes. An axisymmetric 50 m thick base model is defined to simulate ASR performance by using Modflow, MT3DMS and Seawat. The heterogeneous setting is defined as an alternation of high-K and low-K layers, where different hydraulic conductivities, contrast ratios and anisotropy factors are simulated for homogeneous and heterogeneous cases. Under the numerical approach the anisotropy factor in the homogeneous cases is the independent variable, while in the geological approach, the heterogeneous layers are the independent variable. The numerical approach makes different scenarios comparable and has a greater impact on the high-K layers’ vertical conductivity KV while the geological approach leads to realistic simulations and has a greater impact on the low-K layers’ KV . Higher values of KV lead to a greater buoyancy effect and tilting of the fresh-naive water interface. Homogeneous cases have a better performance than heterogeneous cases, mainly during the first years of simulation. Peaks on RE after 4 or 6 years of simulation in heterogeneous cases can be explained by the transfer of fresh water to a high-K layer from an underneath low-K or high-K layer due to buoyancy. Homogeneous cases have a better performance than heterogeneous cases. RE is more impacted by KV than horizontal hydraulic conductivity KH, as it controls the amount of buoyancy. In heterogeneous cases, buoyancy in high-K layers is the main responsible for the reduction in RE compared to the homogeneous equivalent cases. Keywords

Waterglasinjecties worden vaak toegepast als grondverbeteringstechniek om zandlagen te versterken voor tijdelijke en/of permanente situaties. Het betreft zogenaamde hardgels die worden toegepast als grondkerende injectielagen, als grondverbetering onder (monumentale) fundaties en/of als paalpuntinjectie ter verhoging van het paaldraagvermogen. Tijdens projecten blijkt uit kernboringen in geïnjecteerd zand dat de druksterkte nogal kan variëren met locatie en diepte. De resultaten van het afstudeer-onderzoek van Frank de Kievit (Universiteit Utrecht) laten zien dat organische stof mogelijk een rol speelt bij deze variatie in druksterkte en dat het daarom aan te raden is het organische stofgehalte mee te nemen in het ontwerp, hetzij door dit vooraf te bepalen, hetzij door een proefinjectie uit te voeren.

We present a workflow to estimate geostatistical aquifer parameters from pumping test data using the Python package welltestpy. The procedure of pumping test analysis is exemplified for two data sets from the Horkheimer Insel site and from the Lauswiesen site, Germany. The analysis is based on a semi-analytical drawdown solution from the upscaling approach Radial Coarse Graining, which enables to infer log-transmissivity variance and horizontal correlation length, beside mean transmissivity, and storativity, from pumping test data. We estimate these parameters of aquifer heterogeneity from type-curve analysis and determine their sensitivity. This procedure, implemented in welltestpy, is a template for analyzing any pumping test. It goes beyond the possibilities of standard methods, for example, based on Theis' equation, which are limited to mean transmissivity and storativity. A sensitivity study showed the impact of observation well positions on the parameter estimation quality. The insights of this study help to optimize future test setups for geostatistical aquifer analysis and provides guidance for investigating pumping tests with regard to aquifer statistics using the open-source software package welltestpy.

Empirical porosity-permeability and grain size-permeability relationships have long been used and researched. However, they often under- or overestimate permeability because they simplify complex micro scale porous medium descriptions, such as tortuosity or pore size distribution, into nondescript parameters or constants. This paper aims to highlight the large range of possible permeability values that can be obtained from identical porosity and grain size distributions, but describe different pore scale porous media. To achieve this, numerical flow simulations were performed on irregular grained and circular grained porous media which contain identical grains and porosity but vary in spatial distribution of the grains within the domain. Subsequently, the domain was analyzed in terms of representative elementary volume, pore size distribution, grain size distribution, quantitative grain morphological parameters, porosity, and permeability. The results were compared to five well known empirical relationships: Hazen (1892), Slichter (1898), Beyer (1964), Kozeny-Carman (1953), and Barr (2001). Results showed a linear relationship between permeability on a logarithmic scale and porosity on an arithmetic scale with uncertainty increasing as porosity increased. Porous media containing equally sized circular grains as irregular grains in terms of sieve radius showed higher permeability values. Empirical relationships correctly captured the impact of porosity on permeability, but were unable to yield correct values, and even deviated by over a factor 10 for some porous media. This study emphasizes the need for more extensive research into pore scale processes influencing permeability and provides ideas for future research.

The scope of this work is to discuss the proper choice of macrodispersion coefficients in modeling contaminant transport through the advection dispersion equation (ADE). It is common to model solute concentrations in transport by groundwater with the aid of the ADE. Spreading is quantified by macrodispersivity coefficients, which are much larger than the laboratory observed pore-scale dispersivities. In the frame of stochastic theory, longitudinal macrodispersivity is related to the hydraulic conductivity spatial variability via its statistical moments (mean, variance, integral scales), which are generally determined by geostatistical analysis of field measurements. In many cases, especially for preliminary assessment of contaminant spreading, these data are not available and ad-hoc values are adopted by practitioners. The present study aims at recommending dispersivity values based on a thorough analysis of tens of field experiments. Aquifers are classified as of weak, medium and high heterogeneity and for each class a range of macrodispersivity values is recommended. Much less data are available for the transverse macrodispersivities, which are significantly smaller than the longitudinal one. Nevertheless, a few realistic values based on field data, are recommended for applications. Transport models using macrodispersivities can predict mean concentrations, different from the local ones. They can be used for estimation of robust measures, like plumes spatial moments, longitudinal mass distribution and breakthrough curves at control planes.

Geostatistics as a subfield of statistics accounts for the spatial correlations encountered in many applications of, for example, earth sciences. Valuable information can be extracted from these correlations, also helping to address the often encountered burden of data scarcity. Despite the value of additional data, the use of geostatistics still falls short of its potential. This problem is often connected to the lack of user-friendly software hampering the use and application of geostatistics. We therefore present GSTools, a Python-based software suite for solving a wide range of geostatistical problems. We chose Python due to its unique balance between usability, flexibility, and efficiency and due to its adoption in the scientific community. GSTools provides methods for generating random fields; it can perform kriging, variogram estimation and much more. We demonstrate its abilities by virtue of a series of example applications detailing their use.