Snowpack density modeling is the primary source of uncertainty when mapping basin‐wide SWE with lidar. Issue 8 (22nd April 2017)

• Record Type: Journal Article
• Authors: Raleigh, Mark S.; Small, Eric E.
• Is Part Of: Geophysical research letters. Volume 44:Issue 8(2017)
• Page Start: 3700
• Page End: 3709
• Publication Date: 2017-04-22
• Subjects: snow density -- snow depth -- SWE -- lidar -- uncertainty analysis -- snowpack; Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1002/2016GL071999
• Languages: English
• ISSNs: 0094-8276

Abstract

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. Key Points: Models portrayed dissimilar distributions of snow density across the Tuolumne basin Snow density uncertainty was the primary source of SWE uncertainty where snow depth exceeded 60 cm, covering >70% of the Tuolumne basin Snow density uncertainty dominated SWE uncertainty across a range of snowpack conditions at peak accumulation in the western USA