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Snowpack relative permittivity and density derived from near-coincident lidar and ground-penetrating radar

October 16, 2023

Depth-based and radar-based remote sensing methods (e.g., lidar, synthetic aperture radar) are promising approaches for remotely measuring snow water equivalent (SWE) at high spatial resolution. These approaches require snow density estimates, obtained from in-situ measurements or density models, to calculate SWE. However, in-situ measurements are operationally limited, and few density models have seen extensive evaluation. Here, we combine near-coincident, lidar-measured snow depths with ground-penetrating radar (GPR) two-way travel times (twt) of snowpack thickness to derive >20 km of relative permittivity estimates from nine dry and two wet snow surveys at Grand Mesa, Cameron Pass, and Ranch Creek, Colorado. We tested three equations for converting dry snow relative permittivity to snow density and found the Kovacs et al. (1995) equation to yield the best comparison with in-situ measurements (RMSE = 54 kg m−3). Variogram analyses revealed a 19 m median correlation length for relative permittivity and snow density in dry snow, which increased to >30 m in wet conditions. We compared derived densities with estimated densities from several empirical models, the Snow Data Assimilation System (SNODAS), and the physically based iSnobal model. Estimated and derived densities were combined with snow depths and twt to evaluate density model performance within SWE remote sensing methods. The Jonas et al. (2009) empirical model yielded the most accurate SWE from lidar snow depths (RMSE = 51 mm), whereas SNODAS yielded the most accurate SWE from GPR twt (RMSE = 41 mm). Densities from both models generated SWE estimates within ±10% of derived SWE when SWE averaged >400 mm, however, model uncertainty increased to >20% when SWE averaged <300 mm. The development and refinement of density models, particularly in lower SWE conditions, is a high priority to fully realize the potential of SWE remote sensing methods.

Publication Year 2023
Title Snowpack relative permittivity and density derived from near-coincident lidar and ground-penetrating radar
DOI 10.1002/hyp.14996
Authors Randall Bonnell, Daniel McGrath, Andrew Hedrick, Ernesto Trujillo, Tate Meehan, Keith Williams, Hans-Peter Marshall, Graham A. Sexstone, John Fulton, Michael Ronayne, Steven R. Fassnacht, Ryan Webb, Katherine Hale
Publication Type Article
Publication Subtype Journal Article
Series Title Hydrological Processes
Index ID 70249590
Record Source USGS Publications Warehouse
USGS Organization Colorado Water Science Center