DECOMPOSING AND DOWNSCALING SOLAR RADIATION COMPONENTS TO IMPROVE MOUNTAIN SNOWPACK PREDICTION IN THE UPPER COLORADO RIVER BASIN

Seasonal mountain snow serves as a critical water storage system across the western United States. Given the growing pressures from climate change and societal demand, water managers increasingly depend on accurate freshwater supply forecasting from snowmelt. To increase the forecasting abilities, snow models, such as the physically based energy balance model iSnobal, are being integrated into operations for regional water resource management decisions. These models require spatially and temporally complete forcing data, such as the outputs from the High-Resolution Rapid Refresh numerical weather prediction model. However, modeled inputs are typically at a coarser spatial resolution than the targeted snow model output, requiring downscaling techniques. Current methods in iSnobal employ standard downscaling of forcing data as a single flux, potentially overlooking important physical processes at the sub-grid scale. This study presents a new implementation of iSnobal by decomposing modeled radiation components, which are downscaled and adjusted with high-resolution terrain modeling information. We evaluate this physically based radiation partitioning approach against alternative iSnobal configurations to assess improvements in the ability to predict snow melt timing and magnitude for a single water year within a watershed of the Upper Colorado River Basin.