Understanding the spatial distribution of snow water equivalent in paired basins in southwest Montana
The goal of this research has been to build upon previous studies focused on what processes control the distribution of snow water equivalent (SWE) in alpine environments. This involved taking a comprehensive look at the widely accepted physiographic variables of: elevation, slope, aspect, solar radiation, and wind exposure, but also avalanche activity, which has been given limited explicit inclusion. The paired basin comparison adopted in this study, between a hypothesized avalanche prone basin and avalanche free basin, has been previously used to correlate avalanche activity with snowmelt runoff. However, it has not been used in an attempt to parse out which variables have the dominant influence on SWE distribution between adjacent areas of very similar physiographic character. While most previous studies have focused on the period of peak SWE to study this distribution, this current research looked at the evolution of the controlling variables throughout snowpack development. A robust dataset of snow depth and SWE measurements were collected during a January 31 - July 10, 2013 field campaign on Cedar Mountain near Big Sky, MT. Physiographic variable values were extracted from a 10 m resolution digital elevation model (DEM) at snow sample points and input as predictors of observed SWE in multiple linear regression (MLR) and binary regression tree (BRT) models to estimate SWE across the study area. Optimal models were selected by various measures of goodness of fit and cross-validation criteria. Calculated R 2 values for MLR models (0.17-0.57) and BRT models (0.33-0.66) were comparable to previous studies indicating a relative level of success in predictive performance. Subsequent analysis of each optimal model's variable selection and predicted SWE distributions revealed differences in the spatial and temporal patterns of this metric between the paired basins, confirming some well-understood processes as well as offering new insights.