Year Established: 2020 Start Date: 2020-03-01 End Date: 2021-02-28
Total Federal Funds: $5,000 Total Non-Federal Funds: Not available
Principal Investigators: Steven Fassnacht
Abstract: The purpose of this proposed study is to develop a scalable snow accumulation model that more accurately predicts snowpack accumulation through better representation of actual processes. Current snow accumulation models rely on bare (no snow) surface characteristics, such as elevation, aspect, vegetation, etc., when determining the drivers of the distribution of snow, but neglect to consider that new snow re-shapes and can significantly change the surface characteristics onto which it falls and is redistributed, etc. Our hypothesis is that a snow model iterating accumulation accounting for the antecedent snow surface characteristics will be more accurate than current models which only rely on the underlying terrain and vegetation properties. This improved model will refine current water forecasting methods and models used by the NOAA River Forecast Centers that interpolate from the NRCS SNOTEL and snow course data. Terrestrial LiDAR and photogrammetry (structure from motion) will be collected at two sites in Colorado (Cameron Pass and Meeker) for several intervals throughout the snow accumulation period. These data will allow for the spatial and temporal reconstruction of successive snow pack accumulation. This is relevant over areas without direct snowpack measurements, and has implications for water resource management, and wildlife habitat.