Combining binary decision tree and geostatistical methods to estimate snow distribution in a mountain watershed

TitleCombining binary decision tree and geostatistical methods to estimate snow distribution in a mountain watershed
Publication TypeConference Proceedings
Year of Conference1999
AuthorsBalk, B., Elder K., Winstrall A., and Elder K.
Conference Name67th Annual Western Snow Conference
Series TitleProceedings of the 67th Annual Western Snow Conference
Date PublishedApril 1999
PublisherWestern Snow Conference
Conference LocationSouth Lake Tahoe, California
KeywordsModeling, Snow covered area, Snow distribution, Statistical methods

We model the spatial distribution of snow across a mountain basin using an approach that combines binary decision tree and geostatistical techniques. In April 1997 and 1998, intensive snow surveys were conducted in the 6.9 km2 Loch Vale watershed (L VWS), Rocky Mountain National Park, Colorado. Binary decision trees were used to model the large-scale variations in snow depth while the small-scale variations were modeled through kriging interpolation methods. Binary decision trees related depth to the physically based independent variables of net solar radiation, elevation, slope and vegetation cover type. These decision tree models explained 54-65% of the observed variance in the depth measurements. The tree-based modeled depths were then subtracted from the measured depths, and the resulting residuals were spatially distributed across L VWS through kriging techniques. The kriged estimates of the residuals were added to the tree-based modeled depths to produce a combined depth model. The combined depth estimates explained 60-85% of the variance in the measured depths. Snow densities were mapped across L VWS using regression analysis. Snow-covered area (SCA) was determined from high- resolution aerial photographs. Combining the modeled depths and densities with a snow cover map produced estimates of the spatial distribution of snow water equivalence (SWE). This modeling approach offers improvement over previous methods of estimating SWE distribution in mountain basins.