Spatiotemporal Variations in Liquid Water Content in a Seasonal Snowpack: Implications for Radar Remote Sensing

Abstract

Radar instruments have been widely used to measure snow water equivalent (SWE) and Interferometric Synthetic Aperture Radar is a promising approach for doing so from spaceborne platforms. Electromagnetic waves propagate through the snowpack at a velocity determined by its dielectric permittivity. Velocity estimates are a significant source of uncertainty in radar SWE retrievals, especially in wet snow. In dry snow, velocity can be calculated from relations between permittivity and snow density. However, wet snow velocity is a function of both snow density and liquid water content (LWC); the latter exhibits high spatiotemporal variability, there is no standard observation method, and it is not typically measured by automated stations. In this study, we used ground-penetrating radar (GPR), probed snow depths, and measured in situ vertically-averaged density to estimate SWE and bulk LWC for seven survey dates at Cameron Pass, Colorado (~3120 m) from April to June 2019. During this cooler than average season, median LWC for individual survey dates never exceeded 7 vol. %. However, in June, LWC values greater than 10 vol. % were observed in isolated areas where the ground and the base of the snowpack were saturated and therefore inhibited further meltwater output. LWC development was modulated by canopy cover and meltwater drainage was influenced by ground slope. We generated synthetic SWE retrievals that resemble the planned footprint of the NASA-ISRO L-band InSAR satellite (NISAR) from GPR using a dry snow density model. Synthetic SWE retrievals overestimated observed SWE by as much as 40% during the melt season due to the presence of LWC. Our findings emphasize the importance of considering LWC variability in order to fully realize the potential of future spaceborne radar missions for measuring SWE.

A Call for More Snow Sampling

“…Advances have been made with structure from motion and photogrammetry to map snow depth [92], including on drones or unmanned aerial vehicles (UAV) [93]. Along transects, ground-penetrating radar (GPR) has been used in various capacities [94,95] and is comparable on different platforms across scales [96,97]. Ground-based techniques such as GPS [98] and neutron probes [99] infer SWE through the signal attenuation by the SWE itself.…”

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

“…Relative permittivity was calculated from coincident snow depth and $$$ twt $$$ cells (Equation ). Previous studies have established a large randomly distributed error in the relative permittivity estimates that results from uncertainties in snow depths and twt , but with sufficient sampling and filtering, a robust estimate can be established (Bonnell et al, 2021; McGrath et al, 2022; Meehan, 2022). Erroneous relative permittivity values (e.g., ε s < 1) were reduced by removing all values outside of the inter‐quartile range.…”

“…GPR measures the two‐way travel time ( twt ) of the radar wave through the snowpack, which can be combined with lidar‐measured snow depths to estimate radar velocity and relative permittivity. Several studies have established this method and converted relative permittivity to LWC by constraining snow density using snow pit measurements (Bonnell et al, 2021; Heilig et al, 2015; Webb et al, 2018; Webb, Wigmore, et al, 2020). More recent studies have used this technique to derive snow density by coupling uncrewed aerial vehicle (UAV) Structure from Motion (SfM) measurements of snow depth with GPR‐measured twt and identified spatial variabilities that were larger than variabilities mapped by previous in‐situ studies (McGrath et al, 2022; Yildiz et al, 2021).…”

“…In dry snow, dielectric permittivity is primarily determined by snow density. However, wet snow permittivity exhibits a large dependence on LWC because the relative permittivity of water is ~60 times that of snow (Bonnell et al, 2021). LWC induces the imaginary component of relative permittivity and acts as a frequency‐dependent attenuator of signal strength (Koch et al, 2014), an attribute which was leveraged by Bradford et al (2009) as the first study to derive spatially distributed LWC along a ground‐penetrating radar (GPR) transect.…”

Watershed memory amplified the Oroville rain-on-snow flood of February 2017

“…Dye tracer experiments carry similar benefits, but the post-hoc nature of these approaches is impractical for hazard and water supply monitoring. We recommend cost-effective automated or semiautomated efforts to map ( 100 , 101 ) and to continuously and noninvasively monitor ( 102 , 103 ) these quantities across watersheds ( 104 ) and land cover types. Process-scale monitoring of basic snow properties—which must be coupled with accurate surface and boundary layer characteristics—can be exploited for more representative modeling frameworks.…”