Date of Award
Master of Science (MS)
Water supply in the western United States is dominated by snowmelt, and as a result water management is increasingly reliant on numerical modeling of snowmelt processes, including snow accumulation and ablation. We seek to advance a framework for providing model diagnostics for such systems by combining an improved understanding of model structural differences (i.e., conceptual vs. physically based) and parameter sensitivities. The two snow models used in this study are SNOW-17, a conceptual degree-day model, and the Variable Infiltration Capacity (VIC) snow model, which is physically based and solves the full water and energy balances. To better understand the performance of these models, global sensitivity analysis and multi-objective calibration methods were applied to identify important parameters and show calibrated parameter values. For the physically based model, we contribute a novel exploration of some parameters that can be adjusted within the model, including the liquid water holding capacity, the density of newly fallen snow, snow roughness, and snow albedo decay parameters. For each model run, snow sensitivities and errors (i.e., snow water equivalent validation) are visualized to better understand the effect of changing parameters on model outputs. The sensitivity analyses and multi-objective calibrations resulted in model parameterizations that produced Nash-Sutcilffe Efficiency values up to 0.88 through 0.95 across all site locations. Additionally, a temperature change analysis was conducted for each model to explore how model parameterizations affect portrayals of climate change. Accurately predicting water yield from snowpack is essential for water management, and it is used here as a practical measure to determine the importance of model parameter sensitivity and calibration. The analysis was conducted across a range of snow-dominated locations representing a variety of climates across the western United States (e.g. continental, maritime, intermountain).
Houle, Elizabeth S., "Inter-Model Diagnostics for Two Snow Models and Implications for Management" (2015). Civil Engineering Graduate Theses & Dissertations. 128.