Degree Type

Thesis

Date of Award

2009

Degree Name

Master of Science

Department

Geological and Atmospheric Sciences

First Advisor

Kristie J. Franz

Abstract

The primary snow accumulation and ablation model in the US National Weather Service streamflow prediction system is the temperature-based SNOW17 model. In this study, the SNOW17 snowpack heat exchange and melt subroutines are altered using a simplified energy balance approach, while the snow accumulation, water movement, and ground surface heat exchange processes of the SNOW17 are retained. The new model is referred to as the SNOW17 Energy Balance model (SNOW17-EB). Initial model development and testing was conducted with data from Reynold's Creek Experimental Watershed (RCEW). The SNOW17-EB performed comparably to the SNOW17 in six years, but showed a tendency to over predict melt in at least 3 years. An ensemble of models were then created from the SNOW17 and the SNOW17-EB and combined within the Bayesian Model Averaging (BMA) framework. The BMA predictive mean and predictive variance were evaluated for six SNOTEL sites in the western U.S. The models performed best at the colder sites with high winter precipitation and little mid-winter melt. Model weights range from 0-58%, and at most sites all models received some weighting. Although, a single version of the SNOW17 often outperformed the BMA predictive mean, the ability to capture observed SWE within the 95% confidence intervals of the BMA variance was best at sites that gave more or equal weight to versions of the SNOW17-EB.

DOI

https://doi.org/10.31274/etd-180810-529

Copyright Owner

Phillip John Butcher

Language

en

Date Available

2012-04-29

File Format

application/pdf

File Size

95 pages

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