Snowpack Relative Permittivity and Density Derived from Near-Coincident Lidar and Ground-Penetrating Radar

Authors

Randall Bonnell, Colorado State University
Daniel McGrath, Colorado State University
Andrew R. Hedrick, USDA Agricultural Research Service
Ernesto Trujillo, Boise State UniversityFollow
Tate G. Meehan, U.S. Army Corps of Engineers
Keith Williams, UNAVCO Inc.
Hans-Peter Marshall, Boise State UniversityFollow
Graham Sexstone, U.S. Geological Survey
John Fulton, U.S. Geological Survey
Michael J. Ronayne, Colorado State University
Steven R. Fassnacht, Colorado State University
Ryan Webb, University of Wyoming
Katherine E. Hale, University of Vermont

Document Type

Article

Publication Date

10-2023

Abstract

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.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Publication Information

Bonnell, Randall; McGrath, Daniel; Hedrick, Andrew R.; Trujillo, Ernesto; Meehan, Tate G.; Williams, Keith; . . . and Hale, Katherine E. (2023). "Snowpack Relative Permittivity and Density Derived from Near-Coincident Lidar and Ground-Penetrating Radar". Hydrological Processes, 37(10), e14996. https://doi.org/10.1002/hyp.14996