Publication Date

5-2011

Type of Culminating Activity

Thesis

Degree Title

Master of Science in Hydrologic Sciences

Department

Geosciences

Supervisory Committee Chair

James P. McNamara, Ph.D.

Abstract

Hydrologists and water managers have been attempting to accurately estimate watershed scale snow water equivalent (SWE) for over a century. Extensive monitoring networks, remote sensing technology, and sophisticated modeling approaches have greatly improved these estimates; however, water inputs from snow in mountainous areas are still subject to considerable uncertainty due to SWE spatial variability. In an attempt to improve the understanding of physical processes and controls influencing SWE spatial variability, a field campaign to measure the spatial and temporal distribution of SWE within the Dry Creek Experimental Watershed (DCEW) was conducted during 2009 and 2010. These measurements are compared to a distributed SWE data assimilation and modeling product from the National Weather Service called the Snow Data Assimilation System (SNODAS) to estimate the sub-pixel variability and accuracy of the model estimates, as well as attempt to understand model deviation from observed conditions. These data are evaluated using the variogram to assess the evolution of SWE variability and spatial correlation lengths throughout the winter. Correlations between snow depth and landscape characteristics are explored to determine the most influential physical processes influencing SWE distribution. Specifically, this work indentifies the relative importance of differential accumulation, redistribution, and differential ablation at three spatial scales. Results from this work indicate that at the watershed scale (27 km2), elevation is the most important control on snow distribution, while at the SNODAS pixel scale (1 km2), and 1 meter spaced transect scale, differential solar radiation is a stronger control on SWE distribution during ablation. Comparison of transect scale and SNODAS pixel scale observations with SNODAS show the model under-predicts SWE throughout the winter at two out of three sites, and over-predicts during ablation at one site. SNODAS captures the watershed scale elevation trend, but under-predicts the magnitude of SWE at assumed maximum accumulation.

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