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

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-km (2) Loch Vale watershed (LVWS), Rocky Mountain National Park, Colorado. Bin ary decision trees were used to model the large-scale variations in snow de pth, while the small-scale variations were modeled through kriging interpol ation methods. Binary decision trees related depth to the physically based independent variables of net solar radiation, elevation, slope, and vegetat ion cover type. These decision tree models explained 54-65% of the observed variance in the depth measurements. The tree-based modeled depths were the n subtracted from the measured depths, and the resulting residuals were spa tially distributed across LVWS through kriging techniques. The kriged estim ates of the residuals were added to the tree-based modeled depths to produc e a combined depth model. The combined depth estimates explained 60-85% of the variance in the measured depths. Snow densities were mapped across LVWS using regression analysis. Snow-covered area was determined from high-reso lution aerial photographs. Combining the modeled depths and densities with a snow cover map produced estimates of the spatial distribution of snow wat er equivalence (SWE). This modeling approach offers improvement over previo us methods of estimating SWE distribution in mountain basins.