Regional Patterns of Snow Water Equivalent in the Colorado River Basin Using Snowpack Telemetry (SNOTEL) Data

Previous clustering of data has typically grouped stations based on spatial proximity or been restricted due to the temporal resolution of snow-course data. This investigation utilizes daily data from 216 snowpack telemetry (SNOTEL) stations located in and around the Colorado River Basin over a 15-year period (1991-2005) to cluster stations - identify regions of homogeneity - based on their patterns and variability. To achieve this, data were submitted to a self-organizing map (SOM), a particular application of artificial neural networks. The number of clusters can be specified to the SOM based on the level of generalization desired. A SOM consisting of a four, six, nine, and sixteen-cluster were constructed as well as a six-cluster derived from physical variables (elevation, aspect, distance to moisture source, etc.) for each station. For each cluster, a linear multivariate regression was used between physiographic variables and SWE to determine a regression equation that best explained the variability in SWE. Results show an unbiased clustering of stations defined not by geographic location, but by each station's particular SWE variability. The established snow climatologies show some general homogenous course-scale clusters, particularly in Wyoming and Arizona, but overall there are no definite spatial patterns to the climatologies, indicating that local topographic variables dominate SWE processes. Regression results indicate southwest barrier height, aspect, elevation, and southwest shield height are the key variables in predicting SWE within each cluster.