Oscillatory Pumping Test to Estimate Aquifer Hydraulic Parameters in a Bayesian Geostatistical Framework

Comprehensive information about the spatial distribution of the subsurface hydraulic properties is crucial to model groundwater flow, to predict solute transport in aquifers and to design remediation actions. In this work, a Bayesian Geostatistical approach, as implemented in bgaPEST, was adopted to estimate the hydraulic properties of a well field located at the Campus of Science and Technology of the University of Parma (Northern Italy), in a contest of a highly parameterized inversion. Head data, collected by means of multi frequency oscillatory pumping tests, were used to both estimate the hydraulic parameters and validate the results. The groundwater flow processes were modelled by means of MODFLOW 2005 and an adjoint-state formulation of the same software was used to efficiently calculate the sensitivity matrix, required by the inverse procedure. The Bayesian Geostatistical approach estimated the hydraulic conductivity and specific storage fields, handling a large number of parameters. The results of the inversion are consistent with the alluvial nature of the investigated aquifer and the preliminary traditional pumping tests carried out at the site.