|Reference : Bayesian Data Fusion for water table interpolation: incorporating a hydrogeological conc...|
|Scientific journals : Article|
|Engineering, computing & technology : Geological, petroleum & mining engineering|
|Bayesian Data Fusion for water table interpolation: incorporating a hydrogeological conceptual model in kriging|
|Peeters, Luk [Katholieke Universiteit Leuven - KUL > Dept. Earth & Environmental Sciences > Hydrogeology > >]|
|Fasbender, Dominique [Institut National de la Recherche Scientifique > Centre Eau, Terre et Environnement > Québec > >]|
|Batelaan, Okke [Katholieke Universiteit Leuven - KUL > Dept. Earth & Environmental Sciences > Hydrogeology > >]|
|Dassargues, Alain [Université de Liège - ULg > Département Argenco : Secteur GEO3 > Hydrogéologie & Géologie de l'environnement >]|
|Water Resources Research|
|American Geophysical Union|
|[en] groundwater ; piezometric map ; Bayesian Data Fusion ; interpolation ; contour map ; geostatistical interpolation ; Brusselian aquifer|
|[en] 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.
|Aquapôle - AQUAPOLE|
|Researchers ; Professionals|
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