SPATIAL DISTRIBUTION AND EVOLUTION OF A SEASONAL SNOWPACK IN COMPLEX TERRAIN: AN EVALUATION OF THE SNODAS MODELING PRODUCT By

The following individuals read and discussed the thesis submitted by student Brian Trail Anderson, and they evaluated his presentation and response to questions during the final oral examination. They found that the student passed the final oral examination. iii ACKNOWLEDGEMENTS I would like to thank those who helped make this work possible. Thanks to my wife Amanda for her patience, understanding, and support during this process. Also, thanks to all my family and friends who have encouraged me to pursue my education, I could not have done it without you. My thanks and appreciation goes out to my advisor Dr. James McNamara for providing me with this opportunity. He was always willing and available for assistance and guidance both on the snow and in the office and provided me with flexibility to pursue my interests in science. Thank you to my committee: Dr. Hans-Peter Marshall whose guidance and expertise in snow science and technology greatly improved this work, and Dr. Alejandro Flores for his suggestions and assistance. I could not have collected these datasets without the many hours of field assistance from: 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 km 2), elevation is the most …