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

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.

Evaluating L-band InSAR Snow Water Equivalent Retrievals with Repeat Ground-Penetrating Radar and Terrestrial Lidar Surveys in Northern Colorado

“…The first option is complicated by snow compaction, while the second option requires accurate bulk snow densities and a snow-off bare-earth digital terrain model, which may be difficult to acquire in densely vegetated areas. We chose the second option because bulk density variability is less of a concern for the relatively small areas surveyed by the TLS (Bonnell et al, 2023). We found the best agreement between UAVSAR and TLS ΔSWE retrievals for surveys that were aligned on the same date, as differential SWE accumulation/redistribution increased uncertainty (Figure 9).…”

“…Engleman spruce (Picea engelmanii), subalpine fir (Abies lasiocarpa), and lodgepole pine (Pinus contorta) are the primary constituents of the forest, with interspersed Aspen (Populus tremuloides) groves (Fassnacht et al, 2018). From August to November 2020, the Cameron Peak fire burned >80 km 2 of the flight line, including the Cameron Peak field site (CP; figure 1a) region (McGrath et al, 2023), which is not accounted for in these land cover estimates.…”

“…We concluded that the snowpack was dry throughout our field campaigns (Section 4.1). We calculated relative permittivity from the Kovacs et al (1995) equation, which was found to have a RMSE = 54 kg m -3 for densities derived in Colorado (Bonnell et al, 2023). The…”