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Regional Patterns of Snow Water Equivalent in the Colorado River Basin
Using Snowpack Telemetry (SNOTEL) Data
Jeffrey E. Derry and Steven R.
Fassnacht
Watershed Science Program, College of Natural Resources, Colorado State
University, Fort Collins
Abstract. Typically the grouping of station measuring
snow properties is based on spatial proximity or has been restricted due
to the temporal resolution, in particular the monthly sampling of snow
course data. This investigation utilizes daily data from 216 snowpack
telemetry (SNOTEL) stations located in and around the Colorado River Basin
for a 15-year period (1991-2005) to group stations. The grouping or clustering
identifies 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. This methodology
represents a learning algorithm that is non-linear, non-parametric, unsupervised,
and learns through an iterative training process. 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 snow pack descriptor variables (peak
SWE, length of snow season, etc.) and physical variables (elevation, aspect,
distance to moisture source, etc.) for each station.
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. The descriptor
variables that best represent daily time-step classifications are peak
SWE, April 1st SWE, and physical variables.
For more information contact: Horacio Toniolo (907) 474
7977
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