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See detailRelationship between sedimentary features and permeability at different scales in the Brussels Sands
Possemiers, Mathias; Huysmans, Marijke; Peeters, Luk et al

in Geologica Belgica (2012), 15(3), 156-164

The Brussels Sands display a complex three-dimensional subsurface architecture. This sedimentological heterogeneity induces a highly heterogeneous spatial distribution of hydrogeological parameters at ... [more ▼]

The Brussels Sands display a complex three-dimensional subsurface architecture. This sedimentological heterogeneity induces a highly heterogeneous spatial distribution of hydrogeological parameters at different scales and may consequently influence subsurface fluid flow and solute migration. This study aims at characterizing spatial variability of permeability at different scales in the Brussels Sands. Firstly, a literature review on the permeability distribution of the Brussels Sands was performed. Secondly, a field campaign was carried out consisting of field observations of the small-scale sedimentary structures and in situ measurements of air permeability. A total of 6550 cm-scale air permeability measurements were carried out in situ in three Brussels Sands quarries in the central part of Belgium: Bierbeek, Mont Saint Guibert and Chaumont Gistoux. On the large basin scale, substantial differences in permeability are observed. A literature data analysis shows that there is no clear correlation between hydraulic conductivity and sedimentary facies. At the small scale, results show that permeability heterogeneity and anisotropy are strongly influenced by sedimentary heterogeneity in all three quarries. Clay-rich sedimentary features such as bottomsets and distinct mud drapes exhibit a different statistical and geostatistical permeability distribution compared to the cross-bedded lithofacies, where the permeability anisotropy is dominated by the foreset lamination orientation. [less ▲]

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See detailApplication of a multi-model approach to account for conceptual model and scenario uncertainties in groundwater modelling
Rojas, Rodriguo; Kahunde, Samalie; Peeters, Luk et al

in Journal of Hydrology (2010), 394(3-4), 416-435

Groundwater models are often used to predict the future behaviour of groundwater systems. These models may vary in complexity from simplified system conceptualizations to more intricate versions. It has ... [more ▼]

Groundwater models are often used to predict the future behaviour of groundwater systems. These models may vary in complexity from simplified system conceptualizations to more intricate versions. It has been recently suggested that uncertainties in model predictions are largely dominated by uncertainties arising from the definition of alternative conceptual models. Different external factors such as climatic conditions or groundwater abstraction policies, on the other hand, may also play an important role. Rojas et al. (2008) proposed a multimodel approach to account for predictive uncertainty arising from forcing data (inputs), parameters and alternative conceptualizations. In this work we extend upon this approach to include uncertainties arising from the definition of alternative future scenarios and we apply the extended methodology to a real aquifer system underlying the Walenbos Nature Reserve area in Belgium. Three alternative conceptual models comprising different levels of geological knowledge are considered. Additionally, three recharge settings (scenarios) are proposed to evaluate recharge uncertainties. A joint estimation of the predictive uncertainty including parameter, conceptual model and scenario uncertainties is estimated for groundwater budget terms. Finally, results obtained using the improved approach are compared with the results obtained from methodologies that include a calibration step and which use a model selection criterion to discriminate between alternative conceptualizations. Results showed that conceptual model and scenario uncertainties significantly contribute to the predictive variance for some budget terms. Besides, conceptual model uncertainties played an important role even for the case when a model was preferred over the others. Predictive distributions showed to be considerably different in shape, central moment and spread among alternative conceptualizations and scenarios analysed. This reaffirms the idea that relying on a single conceptual model driven by a particular scenario, will likely produce bias and under-dispersive estimations of the predictive uncertainty. Multimodel methodologies based on the use of model selection criteria produced ambiguous results. In the frame of a multimodel approach, these inconsistencies are critical and can not be neglected. These results strongly advocate the idea of addressing conceptual model uncertainty in groundwater modelling practice. Additionally, considering alternative future recharge uncertainties will permit to obtain more realistic and, possibly, more reliable estimations of the predictive uncertainty. [less ▲]

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See detailBayesian Data Fusion for water table interpolation: incorporating a hydrogeological conceptual model in kriging
Peeters, Luk; Fasbender, Dominique; Batelaan, Okke et al

in Water Resources Research (2010), 46(8), 08532

The creation of a contour map of the water table in an unconfined aquifer based on head measurements is often the first step in any hydrogeological study. Geostatistical interpolation methods (e.g ... [more ▼]

The creation of a contour map of the water table in an unconfined aquifer based on head measurements is often the first step in any hydrogeological study. Geostatistical interpolation methods (e.g. kriging) may provide exact interpolated groundwater levels at the measurement locations, but often fail to represent the hydrogeological flow system. A physically based, numerical groundwater model with spatially variable parameters and inputs is more adequate in representing a flow system. Due to the difficulty in parameterization and solving the inverse problem however, an often considerable difference between calculated and observed heads will remain. In this study the water table interpolation methodology presented by Fasbender et al. (2008), in which the results of a kriging interpolation are combined with information from a drainage network and a Digital Elevation Model (DEM), using the Bayesian Data Fusion framework (Bogaert and Fasbender, 2007), is extended to incorporate information from a tuned analytic element groundwater model. The resulting interpolation is exact at the measurement locations while the shape of the head contours is in accordance with the conceptual information incorporated in the groundwater flow model. The Bayesian Data Fusion methodology is applied to a regional, unconfined aquifer in Central Belgium. A cross-validation procedure shows that the predictive capability of the interpolation at unmeasured locations benefits from the Bayesian Data Fusion of the three data sources (kriging, DEM and groundwater model), compared to the individual data sources or any combination of two data sources. [less ▲]

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See detailWater table mapping using Bayesian data fusion with auxiliary data
Fasbender, Dominique; Bogaert, Patrick; Peeters, Luk et al

in Water 2010 symposium, International Symposium on Stochastic Hydraulics (2010)

Water table elevations are usually sampled in space using piezometric measurements, that are unfortunately both expensive to drill and monitor and consequently are thus scarce over space. Most of the time ... [more ▼]

Water table elevations are usually sampled in space using piezometric measurements, that are unfortunately both expensive to drill and monitor and consequently are thus scarce over space. Most of the time, piezometric data are sparsely distributed over large areas, thus providing limited direct information about the level of the corresponding water table. As a consequence, there is a real need for approaches that are able at the same time to (i) provide spatial predictions at unsampled locations and (ii) enable the user to account in a meaningful way for all potentially available secondary information sources that are in some way related to water table elevations. Advantages of these auxiliary information sources are their cheapest prices and their better spatial coverage, thus allowing the user to improve the quality of subsequent mapping provide that a meaningful way of merging these data is made available. In this paper, a recently developed Bayesian Data Fusion technique (BDF) is applied to the problem of water table spatial mapping. After a brief presentation of the underlying theory, specific assumptions are made and discussed in order to account for a digital elevation model as well as for the geometry of a corresponding river network. Based on a data set for the Dyle basin in the north part of Belgium, the suggested model is then implemented by accounting for two secondary information sources, i.e., a spatially exhaustive high resolution digital elevation model and a metric allowing us to account for the whole geometry of the river network as auxiliary information. Results are compared to those of standard spatial mapping techniques like ordinary kriging and cokriging. Respective accuracies and precisions of these estimators are finally evaluated using a leave-one-out cross-validation procedure. They show one one side the obvious benefit of incorporating additional information sources, but more interesting they also emphasize the limitations of traditional multivariate methods (like, e.g., cokriging methods) that fail to efficiently take benefit of these addditional information due to restrictive modeling hypotheses, whereas BDF has no difficulty on that side. Though the BDF methodology was illustrated here for the integration of only two secondary information sources, the method can also be applied for incorporating an arbitrary number of auxiliary variables. It has also been successfully applied in other fields like remote-sensing and air pollution, thus opening new avenues for the important and general topic of data integration in a spatial mapping context. Extension towards a space-time context for dynamic mapping is also possible. [less ▲]

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See detailSmall-scale sedimentary structures and permeability in a cross-bedded aquifer
Huysmans, Marijke; Peeters, Luk; Moermans, Gert et al

in Journal of Hydrology (2008), (361), 41-51

The objective of this study is to investigate the relation between small-scale sedimentary structures and permeability in the Brussels Sands formation, an early Middle-Eocene shallow marine sand deposit ... [more ▼]

The objective of this study is to investigate the relation between small-scale sedimentary structures and permeability in the Brussels Sands formation, an early Middle-Eocene shallow marine sand deposit in Central Belgium that constitutes a major groundwater source in the region. A field campaign was carried out consisting of field observations of the sedimentary structures and in situ measurements of air permeability. The sedimentary structures were interpreted, sketched, digitally photographed and measured in a representative outcrop. Additionally, a total of 2750 cm-scale air permeability measurements were carried out in situ. Analysis of the spatial distribution of sedimentary structures and permeability shows that clay-rich sedimentary features such as bottomsets and distinct mud drapes exhibit a different statistical and geostatistical permeability distribution compared to the other lithofacies in the cross-bedded sands. Spatial analysis of the air permeability data shows that permeability anisotropy in the cross-bedded lithofacies is dominated by the foreset lamination orientation. These results show that smallscale sedimentary heterogeneity strongly influences the local spatial distribution of the hydraulic properties and results in permeability heterogeneity and stratification that would produce anisotropy in upscaled permeability values. [less ▲]

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See detailBayesian data fusion applied to water table spatial mapping
Fasbender, D.; Peeters, Luk; Bogaert, P. et al

in Water Resources Research (2008), 44

Water table elevations are usually sampled in space using piezometric measurements that are unfortunately expensive to obtain and are thus scarce over space. Most of the time, piezometric data are ... [more ▼]

Water table elevations are usually sampled in space using piezometric measurements that are unfortunately expensive to obtain and are thus scarce over space. Most of the time, piezometric data are sparsely distributed over large areas, thus providing limited direct information about the level of the corresponding water table. As a consequence, there is a real need for approaches that are able at the same time to (1) provide spatial predictions at unsampled locations and (2) enable the user to account for all potentially available secondary information sources that are in some way related to water table elevations. In this paper, a recently developed Bayesian data fusion (BDF) framework is applied to the problem of water table spatial mapping. After a brief presentation of the underlying theory, specific assumptions are made and discussed to account for a digital elevation model and for the geometry of a corresponding river network. On the basis of a data set for the Dijle basin in the north part of Belgium, the suggested model is then implemented and results are compared to those of standard techniques such as ordinary kriging and cokriging. Respective accuracies and precisions of these estimators are finally evaluated using a ‘‘leave-one-out’’ cross-validation procedure. Although the BDF methodology was illustrated here for the integration of only two secondary information sources (namely, a digital elevation model and the geometry of a river network), the method can be applied for incorporating an arbitrary number of secondary information sources, thus opening new avenues for the important topic of data integration in a spatial mapping context. [less ▲]

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See detailExploratory data analysis and clustering of multivariate spatial hydrogeological data by means of GEO3DSOM, a variant of Kohonen's Self-Organizing Map
Peeters, Luk; Bacao, R.; Lobo, V. et al

in Hydrology & Earth System Sciences (2007), 11(4), 1309-1321

The use of unsupervised artificial neural network techniques like the self-organizing map (SOM) algorithm has proven to be a useful tool in exploratory data analysis and clustering of multivariate data ... [more ▼]

The use of unsupervised artificial neural network techniques like the self-organizing map (SOM) algorithm has proven to be a useful tool in exploratory data analysis and clustering of multivariate data sets. In this study a variant of the SOM-algorithm is proposed, the GEO3DSOM, capable of explicitly incorporating three-dimensional spatial knowledge into the algorithm. The performance of the GEO3DSOM is compared to the performance of the standard SOM in analyzing an artificial data set and a hydrochemical data set. The hydrochemical data set consists of 131 groundwater samples collected in two detritic, phreatic, Cenozoic aquifers in Central Belgium. Both techniques succeed very well in providing more insight in the groundwater quality data set, visualizing the relationships between variables, highlighting the main differences between groups of samples and pointing out anomalous wells and well screens. The GEO3DSOM however has the advantage to provide an increased resolution while still maintaining a good generalization of the data set. [less ▲]

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See detailIdentification and quantification of sources of major solutes in a sandy, phreatic aquifer in Central Belgium through ionic ratios and geochemical mass-balance modelling
Peeters, Luk; Batelaan, Okke; Dassargues, Alain ULg

in Groundwater and Ecosystems, Proc. of the XXXV IAH Congress (2007)

In this study the processes affecting groundwater chemistry in the Eocene Brussels sands aquifer in Central Belgium are identified based on evaluation of ionic ratios of major solutes. Based on these ... [more ▼]

In this study the processes affecting groundwater chemistry in the Eocene Brussels sands aquifer in Central Belgium are identified based on evaluation of ionic ratios of major solutes. Based on these results, in combination with mineralogical and hydrogeological information of the aquifer, a geochemical mass-balance model is created to quantify the contribution of each of the processes to the observed composition of groundwater. After a rigorous validation process, a dataset of 99 groundwater samples is obtained from observation and pumping wells in the Eocene Brussels sands aquifer, which is one of the main aquifers for drinking water production in Belgium. The aquifer consists of heterogeneous alteration of calcified and silicified coarse sands, with local presence of clay drapes and glauconite-rich zones (Laga et al. 2001). The entire aquifer is overlain by Quaternary eolian deposits, mainly consisting of loam with the exception of the north east, where the Quaternary deposits are sandy loam. The groundwater in this aquifer is of Ca-Mg-HCO3-type with locally elevated nitrate concentrations. Based on the evaluation of ionic ratios and the mineralogy of the aquifer, a conceptual geochemical model is developed for mass-balance modeling, including (1) concentration of precipitation by a factor 1 to 5 due to evaporation, (2) dissolution of a pure calcite phase and a calcite phase containing 25 % magnesium by both carbonic acid and sulfuric acid, (3) anthropogenic inputs for all major cations and anions except bicarbonate, (4) dissolution of glauconite, (5) cation exchange of sodium and potassium for calcium and magnesium. The two calcite phases can be thought of as end-members of a solid solution of magnesium in calcite. The mass-balance modeling consists of a mole-balance equation for each considered element according to: [Obs] = p[Prec] + p1[Phase 1] + ... + pi[Phase i] + a [Anthropogenic] +/- c[Cation Exchange] This set of linear equations is additionally constrained by (1) defining a range for concentration factors p based on measured and calculated evaporation rates, (2) charge balance for the anthropogenic sources and (3) pi being positive or negative according to whether the phase dissolves or precipitates. The set of linear equations with the given constraints is solved using a least squares optimization. Based on the possible processes and reactions several geochemical models are tested for each sample and a model is considered adequate if the root mean squared error (RMSE) between observed and calculated concentrations is less than 10-10 mol/L and the charge balance of the calculated composition is less than 5 %. If several models are able to explain the observed concentrations, the RMSE provides an objective measure to compare the quality of the models. The best model for each sample is selected and the spatial distribution of these models is compared to the spatial variations in lithology and land-use to asses the feasibility of the proposed models. [less ▲]

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See detailComparison of Kohonen's Self-Organizing Map algorithm and principal component analysis in the exploratory data analysis of a groundwater quality dataset
Peeters, Luk; Dassargues, Alain ULg

(2006)

Groundwater monitoring networks typically yield large, multivariate datasets. Analysis and interpretation of these datasets starts with an exploratory data analysis in order to summarize the available ... [more ▼]

Groundwater monitoring networks typically yield large, multivariate datasets. Analysis and interpretation of these datasets starts with an exploratory data analysis in order to summarize the available data, extract useful information and formulate hypotheses for further research. Exploratory data analysis is mostly focussed on finding related variables and groupings of similar observations. Traditionally multivariate statistical techniques like principal component analysis (PCA) are used for this purpose. In PCA a linear dimensionality reduction of the original, high dimensional dataset is carried out in order to identify orthogonal directions (principal components) of maximum variance in the dataset based on linear combinations of correlated variables. Projections of the original data in the subspace defined by the principal components can be used to identify groups in the data and to reveal relationships between variables (Davis, 1986). In this study, principal component analysis is compared to Kohonen's self-organizing map (SOM) algorithm. The SOM-algorithm is an artificial neural network technique designed to carry out a non-parametric regression process that is mainly used to represent high-dimensional, nonlinearly related data items in a topology-preserving, often two-dimensional display, and to perform unsupervised classification and clustering (Kohonen, 1995). Both PCA and SOM are applied to a hydrochemical dataset from a monitoring network in two sandy, phreatic aquifers in Central Belgium. The monitoring network consists of 47 monitoring wells each equipped with three filters at different depths, in which 14 variables are measured. The first aquifer, the Diest sands aquifer is of Late Miocene age and consists of coarse, glauconiferous sands and sandstones (Laga et al., 2001). The second aquifer, the Brussels sands aquifer, is of Middle Eocene age and is an heterogeneous formation consisting of an alteration of highly and poorly calcareous sands, locally silicified (Laga et al., 2001). Both techniques succeed in distinguishing between both aquifers and reveal the relationships between variables. The main advantage of PCA is the mathematical quantification of correlation between variables and the expression of the original data in the subspace defined by the principal components. The visualization of the SOM-analysis on the other hand allows a straightforward interpretation of the dataset structure in which even non-linear relationships between variables can be identified. Additionally, the SOM-algorithm can handle a limited amount of missing values in the dataset, contrary to PCA. [less ▲]

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See detailClassification of groundwater samples in wetlands using selforganizing maps
Peeters, Luk; Dassargues, Alain ULg

in Stauffer, Fr; Dassargues, Alain (Eds.) Quantitative Geology from Multiple Sources: S10 Use of multiple sources in conditioning/calibrating groundwater flow and transport models (2006)

Groundwater chemistry in groundwater-fed wetlands often is the result of mixing of discharging groundwater and rainfall in combination with chemical reactions altering the chemical composition, mostly due ... [more ▼]

Groundwater chemistry in groundwater-fed wetlands often is the result of mixing of discharging groundwater and rainfall in combination with chemical reactions altering the chemical composition, mostly due to changes in redox conditions. In this study, a Self-Organizing Map is used to classify chemical groundwater samples of three groundwaterfed wetlands in Belgium in order to identify the origin of groundwater and to deduce redox conditions in the wetlands. The Self-Organizing Map (SOM) algorithm is an unsupervised neural network technique to represent a multidimensional dataset on a two-dimensional grid in a topology-preserving way, allowing investigation of non-linear, complex relationships between variables and grouping of the data (Kohonen, 1995). The SOM is trained with data from a regional groundwater monitoring network and rainfall data. The resulting SOM is able to distinguish between samples of different origin or redox conditions within the regional aquifers. Subsequently, samples of the three wetlands are shown to the SOM and each sample is classified as having a chemical composition comparable to rainfall or to one of the regional aquifers. [less ▲]

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See detailModelling seasonal variations in nitrate and sulphate concentrations in a vulnerable alluvial aquifer
Peeters, Luk; Haerens, Bruno; Van der Sluys, Jan et al

in Environmental Geology (2004), 46(6-7), 951-961

The Eisden-Meeswijk region in Belgium has been affected by mining subsidence due to the deep coal mining activities. Groundwater levels in the alluvial plain of the Meuse River are maintained below the ... [more ▼]

The Eisden-Meeswijk region in Belgium has been affected by mining subsidence due to the deep coal mining activities. Groundwater levels in the alluvial plain of the Meuse River are maintained below the ground surface by drainage installations and municipal well fields. A correlation between the water level in the Meuse River and the variation in nitrate and sulphate concentrations in the aquifer has been observed. A transient groundwater model is developed for the period May 1998-May 2002 and advective transport simulations have been carried out using this model. During dry periods, the major groundwater flow is directed towards the Meuse River, thereby feeding the river. During wet periods, however, groundwater flows in the opposite direction. Due to these variations in groundwater flow direction and to the extraction of groundwater, zones of higher solute concentration exist of which the position and extension vary both spatially and temporally. [less ▲]

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