Comparison of spatial interpolation methods for estimating snow distribution in the Colorado Rocky Mountains

In this study, the relative performances of four spatial interpolation methods were evaluated to estimate snow water equivalent (SWE) for three 1 km2 study sites in the Colorado Rocky Mountains. Each study site is representative of different topographic and vegetative characteristics. From 1-11 April 2001, 550 snow depth measurements and approximately 16 snow density profiles were obtained within each study site. The analytical methods used to estimate snow depth over the 1 km2 areas were 1) inverse distance weighting, 2) ordinary kriging,3) modified residual kriging and co-kriging, and 4) a combined method using binary regression trees and geostatistical methods. The independent variables used were elevation, slope, aspect, net solar radiation, and vegetation. Using cross-validation procedures, each method was assessed for accuracy. The tree-based models provided the most accurate estimates for all study sites, explaining 18-30 of the observed variability in snowdepth. Binary regression trees may have generated the most accurate estimates out of all methods evaluated, however, substantial portions of the variability in observed snow depth were left unexplained by the models. While the data may have simply lacked spatial structure, it is recommended that the characteristics of the study sites, sampling strategy, and independent variables be explored further to evaluate the causes for the relatively poor model results.