Geo-electrical data fusion by stochastic co-conditioning simulations for delineating groundwater protection zones

In hydrogeology, advances in the delimitation of protection zones are made by the use of stochastic simulations integrating all available data. In practice, due to the few available measurements of the main parameters (hard data), it is very useful to integrate several secondary properties of the media as indirect data (soft data) to reduce the uncertainty of the results. In aquifers, most of the solute spreading is governed by the hydraulic conductivity (K) spatial variability, which is generally considered as the main uncertain parameter. A stochastic approach integrating hydraulic conductivity measurements (hard data), head observations and shallow electrical resistivity tomography (soft data) is presented. Results are discussed on a synthetic and on a practical case. It is shown in practice how the uncertainty of the well capture zone probability distribution (CaPD) can be reduced. Since geophysical data and head observations are easier to collect on the field then hydraulic conductivity measurements, they are generally more abundant. The methodology presented can even be used in real applications when little or no information is available about the hydraulic properties, through the conditioning on geophysical data and/or head observations.