We present a methodology that uses pilot and anchor points with probability distributions for saturated hydraulic conductivity in a groundwater contaminant transport model. This approach directly links locations with calibration target data (e.g., water levels and drawdown at monitoring wells) to the most relevant physical parameter(s) that drive behavior, in a way that promotes model parsimony. Distributions for hydraulic conductivity are developed for monitoring well locations with pumping tests in order to reflect the state of uncertainty in the local estimates; these locations are called anchor points. Pilot points are placed between monitoring wells, and because they have more uncertainty these are generally assigned wider distributions that reflect plausible hydraulic conductivity values for the geologic material in which they are located. Scaling issues are considered in the development of these distributions. Pilot points are not randomly or uniformly distributed in the domain; rather they are considered connectors between locations with data (anchor points) and placed strategically between them. For a given model realization, hydraulic conductivity values at both pilot and anchor points are sampled from their respective distributions and all remaining locations are derived using an interpolation scheme (e.g., kriging). This approach to hydraulic conductivity assignment honors location-specific data, geologic heterogeneity, and spatial patterns. Given that inverse analysis of high-dimensional models tends to be ill-posed and thus sensitive to initialization of parameters, the distribution development process plays a critical role in driving the outcome of model calibration.