Drone-based ground-penetrating radar (GPR) application to snow hydrology

Abstract

Abstract. Seasonal snowpack deeply influences the distribution of meltwater among watercourses and groundwater. During rain-on-snow (ROS) events, the structure and properties of the different snow and ice layers dictate the quantity and timing of water flowing out of the snowpack, increasing the risk of flooding and ice jams. With ongoing climate change, a better understanding of the processes and internal properties influencing snowpack outflows is needed to predict the hydrological consequences of winter melting episodes and increases in the frequency of ROS events. This study develops a multi-method approach to monitor the key snowpack properties in a non-mountainous environment in a repeated and non-destructive way. Snowpack evolution during the winter of 2020–2021 was evaluated using a drone-based, ground-penetrating radar (GPR) coupled with photogrammetry surveys conducted at the Ste-Marthe experimental watershed in Quebec, Canada. Drone-based surveys were performed over a 200 m2 area with a flat and a sloped section. In addition, time domain reflectometry (TDR) measurements were used to follow water flow through the snowpack and identify drivers of the changes in snowpack conditions, as observed in the drone-based surveys. The experimental watershed is equipped with state-of-the-art automatic weather stations that, together with weekly snow pit measurements over the ablation period, served as a reference for the multi-method monitoring approach. Drone surveys conducted on a weekly basis were used to generate georeferenced snow depth, density, snow water equivalent and bulk liquid water content maps. Despite some limitations, the results show that the combination of drone-based GPR, photogrammetric surveys and TDR is very promising for assessing the spatiotemporal evolution of the key hydrological characteristics of the snowpack. For instance, the tested method allowed for measuring marked differences in snow pack behaviour between the first and second weeks of the ablation period. A ROS event that occurred during the first week did not generate significant changes in snow pack density, liquid water content and water equivalent, while another one that happened in the second week of ablation generated changes in all three variables. After the second week of ablation, differences in density, liquid water content (LWC) and snow water equivalent (SWE) between the flat and the sloped sections of the study area were detected by the drone-based GPR measurements. Comparison between different events was made possible by the contact-free nature of the drone-based measurements.

Spatially distributed snow depth, bulk density, and snow water equivalent from ground-based and airborne sensor integration at Grand Mesa, Colorado, USA

“…The expense of acquiring airborne remote sensing data is a crux of the technique, and it may not be feasible to fly entire catchments across the breadth of snow climates. Less expensive techniques for estimating SWE distribution, such as drone-based radar retrievals of dielectric permittivity (e.g., Valence et al, 2022), and in situ measurement campaigns combined with learned-regression models (e.g., Wetlaufer et al, 2016;Broxton et al, 2019) should be utilised where appropriate and examined for the physical basis. Empirical models of this type are often distributed over vast areas with little validation or consideration to the underlying physical processes.…”

UAS remote sensing applications to abrupt cold region hazards

“…UAS-based LiDAR and photogrammetry have been widely used to monitor spatial distribution of snow depth (Vander Jagt et al, 2015;Bühler et al, 2016;Harder et al, 2016;Harder, Pomeroy, and Helgason, 2020;Cho, McCrary, and Jacobs, 2021;Jacobs et al, 2021). To estimate SWE and LWC maps, recent studies utilized ground penetrating radar (GPR) coupled with UAS photogrammetry (Yildiz, Akyurek, and Binley, 2021;McGrath et al, 2022;Valence et al, 2022). UAS-based GPR systems can observe permittivity measures which are physically related to snow density and LWC (Prager et al, 2022;Valence et al, 2022).…”

“…To estimate SWE and LWC maps, recent studies utilized ground penetrating radar (GPR) coupled with UAS photogrammetry (Yildiz, Akyurek, and Binley, 2021;McGrath et al, 2022;Valence et al, 2022). UAS-based GPR systems can observe permittivity measures which are physically related to snow density and LWC (Prager et al, 2022;Valence et al, 2022). UASmounted synthetic aperture radar (SAR) (Koo et al, 2012) is another potential application to directly measure river flow and flood propagation for near-real snowmelt flood monitoring.…”

“…In addition to explosive payloads, restrictions also exist for sensor payloads. Valence et al (2022) explored the potential applications of GPR for collecting data on snowpack hydrological characteristics in Quebec, Canada. Most GPR applications to date have required the instrument to be towed by the operator, but airborne and vehicle based GPR applications have risen in popularity (Valence et al, 2022).…”

“…Valence et al (2022) explored the potential applications of GPR for collecting data on snowpack hydrological characteristics in Quebec, Canada. Most GPR applications to date have required the instrument to be towed by the operator, but airborne and vehicle based GPR applications have risen in popularity (Valence et al, 2022). UAS-mounted GPR can increase the capability to survey avalanche conditions by reducing the need for operators to travel in avalanche-prone terrain and increasing the number of slopes that can be evaluated during a survey compared to hand dug pits (McCormack and Vaa, 2019).…”