Comparison of Geostatistical and Binary Regression Tree Methods
in Estimating Snow Water Equivalence Distribution
in a Mountain Watershed
Benjamin Balk1, Kelly Elder1, and Jill Baron2
Understanding snow water equivalence (SWE) distribution is imperative for snowmelt models to
efficiently predict the volume and timing of runoff. Many factors contribute to the variation of SWE,
including elevation, slope, aspect, vegetation type, surface roughness, and energy exchange. Studies
of seasonal snow cover are manageable in areas with gentle terrain because the importance of factors
controlling snow distribution is greatly diminished. However, understanding the processes control-
ling the spatial distribution of snow is difficult in rugged alpine regions.
The spatial distribution of peak SWE in a mountain watershed was modeled and compared using
geostatistical and binary regression tree methods. In April 1997 and 1998, intensive snow surveys
were conducted in the Loch Vale Watershed (6.9 km2), Rocky Mountain National Park, Colorado.
This glacially scoured watershed lies in the Front Range immediately east of the Continental Divide
with elevations between 3091 and 4003 m. Sample locations of snow depth and density were chosen
to be representative of the range of elevations, slopes, and aspects of the watershed with safety
constraints. All field measurements were registered to a 10-m resolution digital elevation model.
In the geostatistical approach to modeling SWE distribution, snow depths were spatially distributed