Evaluating the Representativeness of Snow Sampling Sites in the Western United States and Alaska
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Authors
Shelor, Griffin
Issue Date
2025
Type
Thesis
Language
en_US
Keywords
SHAP , snow water equivalent , SWE , SWE classification
Alternative Title
Abstract
Snowpack is a critical water resource threatened by climate change. This isparticularly true in the western United States. NASA’s SnowEx program measured
snow cover at numerous sites in the western United States and Alaska in preparation
for future space-based missions. These snow cover sites were chosen largely based
on snow cover classes created using subjectively defined thresholds. However, there
has not been a systematic classification of snow cover in the US or SnowEx sites
in terms of variables that affect snow water equivalent (SWE). Random Forest
is a machine learning method that uses groups of decision trees to create robust
predictions and identify important variables. SHAP (Shapley Additive Explanatory
Values) is a modeling framework which uses game theory to evaluate the local
importances of different predictors in a model by estimating their contributions in
different coalitions of predictors. Using these advanced machine learning methods,
I have created new snow cover classifications for the western United States and
Alaska based on key predictor variables of peak SWE for Water Years 1993-2020
and assessed the representativeness of SnowEx sites in terms of these classes. These
new snow classes are compared with the snow cover classification system created
by Sturm and Liston (2021). This work will help NASA identify data gaps and
enhance future snow monitoring efforts