Exploring snow model parameter sensitivity using Sobol' variance decomposition. (March 2017) - Houle, Elizabeth S.; Livneh, Ben; Kasprzyk, Joseph R.
Abstract: This study advances model diagnostics for snowmelt-based hydrological systems using Sobol' sensitivity analysis, illuminating parameter sensitivities and contrasting model structural differences. We consider several distinct snow-dominated locations in the western United States, running both SNOW-17, a conceptual degree-day model, and the Variable Infiltration Capacity (VIC) snow model, a physically-based model. Model performance is rigorously evaluated through global sensitivity analysis and a temperature warming analysis is conducted to explore how model parameterizations affect portrayals of climate change. Both VIC and SNOW-17 produce comparable results with SNOW-17 performing slightly better for shallower snowpacks and VIC performing better for deeper snowpacks. However, the lack of sensitivity of SNOW-17 to climate warming suggests that it may not be as reliable as a more sensitive model like VIC. Inter-model differences presented here offer insights into physical features with greatest uncertainty and may inform future model development and planning activities. Highlights: Historically-fixed parameters in the VIC snow model can greatly affect SWE simulations. Calibrating the VIC snow model, although not common practice, improves simulated SWE. Greater model complexity was not necessarily associated with higher model performance.