Estimating snow depth from observations of remotely-sensed snow covered area and the terrain’s snow holding capacity

Abstract:

Snowmelt is the primary water source in the Western United States and mountainous regions globally. Forecasts of streamflow and water supply rely heavily on snow measurements from sparse observation networks that may not provide adequate information during abnormal climatic conditions. Satellite observations of snow covered area are available globally and in near real-time. In this regard, we have developed a method to estimate snow depth from remotely-sensed images of snow covered area by considering the snow holding capacity of the terrain. We show that the relationship between basin-wide average snow depth, as interpolated from snow surveys, and Landsat TM/ETM+-derived basin snow covered area yields an r² of 0.64 and 0.68 in two alpine basins of different climatologies in California and Colorado, respectively. Regression analyses that use fractional snow covered as the independent variable to estimate snow depth from a high resolution Lidar survey result in relative mean squared errors between 39% and 58% of measured snow depth for different roughness classifications near the date of peak accumulation. Future work will look at the changes in the relationship between snow depth and snow covered area through the ablation season to determine the relationship’s utility to water supply forecasting. The importance of this work is illustrated through examples that estimate snow depths for select alpine regions globally.