Abstract: Plume interpolation consists of estimating contaminant concentrations at unsampled locations using the available contaminant data surrounding those locations. The goal of groundwater plume interpolation is to maximize the accuracy in estimating the spatial distribution of the contaminant plume given the data limitations associated with sparse monitoring networks with irregular geometries. Beyond data limitations, contaminant plume interpolation is a difficult task because contaminant concentration fields are highly heterogeneous, anisotropic, and nonstationary phenomena. This study provides a comprehensive performance analysis of 6 interpolation methods for scatter-point concentration data, ranging in complexity from intrinsic kriging based on intrinsic random function theory to a traditional implementation of inverse-distance weighting. High resolution simulation data of perchloroethylene (PCE) contamination in a highly heterogeneous alluvial aquifer were used to generate 3 test cases, which show how each interpolation method performs as a function of the number of available sample data. Overall, the variability of PCE samples and preferential sampling controlled how well each of the interpolation schemes performed. Quantile Kriging was the most robust of the interpolation methods, showing the least bias from both of these factors. Additionally, the method’s non-parametric uncertainty estimates successfully predicted zones of high estimation error for each test case. This study provides guidance to practitioners balancing opposing theoretical perspectives, ease-of-implementation, and effectiveness when choosing a plume interpolation method.