Abstract
The parameterization of spatially distributed hydraulic properties is one of the most crucial steps in groundwater modeling. A common approach is to estimate hydraulic properties at a set of pilot points and interpolate the values at each model cell. Despite the popularity of this method, several questions remain about the optimum number and distribution of pilot points, which are determining factors for the efficiency of the method. This study proposes a strategy for optimal pilot point parameterization that minimizes the number of parameters while maximizing the assimilation of an observed dataset unevenly distributed in space. The performance of different pilot point distributions has been compared with a synthetic groundwater model, considering regular grids of pilot points with different spacings and adaptive grids with different refinement criteria. This work considered both prior and iterative refinements, with a parameter estimation step between successive refinements. The parameter estimation was conducted with the Gauss–Levenberg–Marquardt algorithm, and the strategies were ranked according to the number of model calls to reach the target objective function. The strategy leading to the best fit with the measurement dataset at the minimum computational burden is an adaptive grid of pilot points with prior refinement based on measurement density. This strategy was successfully implemented on a regional, multilayered groundwater flow model in the south-western geological basin of France.