Abstract:Our understanding of snow distribution in the mountains is limited as a result of the complex controls leading to extreme spatial variability. More accurate representations of snow distribution are greatly needed for improvements to hydrological forecasts, climate models, and for the future testing and validation of remote-sensing retrieval algorithms. In this study, the relative performances of four spatial interpolation methods were evaluated to estimate snow water equivalent for three 1 km 2 study sites in the Colorado Rocky Mountains. Each study site is representative of different topographic and vegetative characteristics. From 1 to 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 km 2 areas were (1) inverse distance weighting, (2) ordinary kriging, (3) modified residual kriging and cokriging, 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 snow depth. Kriging of the regression tree residuals did not substantially improve the models. Cokriging of the residuals resulted in a less accurate model when compared with the tree-based models alone. 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. Though 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.

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