乐播传媒

Raleigh, Mark听¹听;听Small, Eric听²

¹听乐播传媒
²听乐播传媒

Lidar-measured snow depth and model-estimated snow density can be combined to map snow water equivalent (SWE) across basins. This approach has the potential to transform watershed 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 Airborne Snow Observatory in California, we evaluated a range of levels of depth uncertainty and calculated density uncertainty using four snow models. For uncertainties of 8 cm (depth) and 0.058 g cm-3 (density), density uncertainty dominated SWE estimation for snowpack exceeding 50 cm depth, effectively 80% of snow cover and nearly 95% of SWE volume in the basin. Across a broader range of conditions, including peak snowpack at SNOTEL stations, density remained the primary source of SWE uncertainty. Thus, reducing density uncertainty is essential for improved SWE mapping with lidar.