Abstract. Usually, subsoil data for groundwater models are generated from borehole data, using upscaling techniques. Since the assumed hydraulic properties for litho-classes in boreholes are uncertain, and upscaling may add inaccuracies, the groundwater model has to be calibrated. In this paper, a method is presented that uses a calibrated groundwater model to improve the quality of a hydrogeological model (layer thickness and hydraulic properties) as obtained from borehole data. To achieve this, all borehole data are defined by random variables and related to aquifer and aquitard properties at the same support as the groundwater model, using complete probability density functions. Subsequently, the calibrated parameter values of the groundwater model are assumed to be the truth and are used to find the most likely combination of layer thicknesses and hydraulic conductivities for the lithological layers making up the aquifer or aquitard. The presented example is an application of the proposed method to aquitards. Nevertheless, the method can be applied to aquifers as well. The analysis of the results gives rise to the discussion about the correctness of the hydrogeological interpretation of the borehole data as well as the correctness of the calibration results of the groundwater flow model. In order to make the problem tractable, computationally feasible, and avoid assumptions about the distribution form, piecewise linear probability density functions are used, instead of parametrized functions.