Snow modeling within a coupled multi-layer soil-vegetation-atmosphere model: Influence on potential water resources for crops

Estimates of snow depth, extent, and melt in the Sierra Nevada Mountain Range are critical to estimating the amount of water that will be available for crops during the growing season within California's Central Valley. Numerical weather simulations utilizing a fourth order turbulent closure transport scheme in a multi-layer soil-vegetation-atmosphere model, Advanced Canopy-Atmosphere-Soil algorithm (ACASA), coupled to the globally used, state-of-the-science numerical weather prediction and atmospheric model, Weather Research and Forecasting Advanced Research Model (WRF-ARW) version 3.1, were used to explore snow model improvements in the physics-based parameterization for the Sierra Nevada Range. A set of alterations were made separately to the existing snowpack model within ACASA focusing on improvements to snow cover simulations on complex terrain slopes and over varying canopy cover. Three winter seasons were simulated; a climatological average, dry, and wet winter. The simulated output from the models are compared to MODIS satellite data and SNOTEL data to determine which model alterations made the largest improvements to snow simulations. It will be shown which snow physics modifications lead to the most accurate simulations.