Snowpack density modeling is the primary source of uncertainty when mapping basin-wide SWE with lidar

Abstract

Lidar-measured snow depth and model-estimated snow density can be combined to map snow water equivalent (SWE). This approach has the potential to transform research and operations in snow-dominated regions, but sources of uncertainty need quantification. We compared relative uncertainty contributions from lidar depth measurement and density modeling to SWE estimation, utilizing lidar data from the Tuolumne Basin (California). We found a density uncertainty of 0.048 g cm−3 by comparing output from four models. For typical lidar depth uncertainty (8 cm), density estimation was the dominant source of SWE uncertainty when snow exceeded 60 cm depth, representing >70% of snow cover and 90% of SWE volume throughout the basin in both 2014 and 2016. Density uncertainty accounts for 75% of the SWE uncertainty for a broader range of snowpack characteristics, as measured at SNOTEL stations throughout the western U.S. Reducing density uncertainty is essential for improved SWE mapping with lidar.