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

Authors: BONNELL, RANDELL - Colorado State University; MCGRATH, DANIEL - Colorado State University; Hedrick, Andrew; TRUJILLO, ERNESTO - Boise State University; MEEHAN, TATE - Us Army Corp Of Engineers (USACE); WILLIAMS, KEITH - Unavco; MARSHALL, HANS-PETER - Boise State University; SEXSTONE, GRAHAM - Us Geological Survey (USGS); FULTON, JOHN - Us Geological Survey (USGS); RONAYNE, MICHAEL - Colorado State University; FASSNACHT, STEVEN - Colorado State University; WEBB, RYAN - University Of Wyoming; HALE, KATHERINE - University Of Vermont

Submitted to: Hydrological Processes
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/5/2023
Publication Date: 10/16/2023
Citation: Bonnell, R., McGrath, D., Hedrick, A., Trujillo, E., Meehan, T., Williams, K., Marshall, H., Sexstone, G., Fulton, J., Ronayne, M., Fassnacht, S., Webb, R., Hale, K. 2023. Snowpack relative permittivity and density derived from near-coincident lidar and ground-penetrating radar. Hydrological Processes. 37(10). Article e14996. https://doi.org/10.1002/hyp.14996.
DOI: https://doi.org/10.1002/hyp.14996

Interpretive Summary: The remote sensing of snow water equivalent requires estimates of snow density, but snow density models have not been extensively evaluated within remote sensing algorithms. We derived snow densities from combined lidar and ground-penetrating radar across airborne, terrestrial, and UAV lidar platforms. We estimated the spatial variability of derived densities from variogram analyses and used the derived densities to estimate the accuracy of snow density models. Derived snow density was found to have larger variability than what previous in situ methods have established. This approach can be used to evaluate the accuracy of snow density models with implications for global snow remote sensing.

Technical Abstract: Depth-based and radar-based remote sensing methods (e.g., lidar, InSAR) represent promising approaches for 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, while 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, CO. 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), while 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.