Interpolation of contaminant data can present a significant challenge due to sample clustering and sharp gradients in concentration. The research presented in this paper represents a study of commonly used interpolation schemes applied to three-dimensional plume characterization. Kriging, natural neighbor, and inverse distance weighted interpolation were tested on four actual data sets. The accuracy of each scheme was gauged using the cross-validation approach. Each scheme was compared to the other schemes and the effect of various interpolation parameters was studied. The kriging approach resulted in the lowest error at three of the four sites. The simpler and quicker inverse distance weighted approach resulted in a lower interpolation error on the other site and performed well overall. The natural neighbor method had the highest average error at all four sites in spite of the fact that it has been shown to perform well with clustered data. Another unexpected result was that the computationally expensive high order nodal functions resulted in reduced accuracy for the inverse distance weighted and natural neighbor approaches.