The goal of groundwater contaminant plume interpolation is to provide an accurate representation of the spatial distribution of the plume given the data limitations associated with sparse monitoring networks with irregular geometries. Currently available methods for plume estimation cannot fully take advantage of prior knowledge of flow and transport information or the location of a contaminant source. This paper presents two new geostatistical tools for incorporating transport information in estimating the spatial distribution of groundwater contaminant plumes. The methods account for the spatial/temporal covariance of the contaminant plume in defining a best estimate of the plume distribution and its associated uncertainty. Overall, the methods require that the estimated plume distribution be physically feasible given both the available concentration measurements and the flow and transport in the affected aquifer. Sample applications in homogeneous and heterogeneous formations are presented. Even with relatively few observations, the new methods yield results that are superior to those obtained by kriging, with a better reproduction of the true plume shape and lower uncertainty.