Introducing drone-based GPR in snow hydrology studies

Abstract

Abstract. Seasonal snowpack deeply influences the distribution of meltwater among watercourses and groundwater. During rain-on-snow (ROS) events, for instance, the structure and properties of the different ice and snow layers dictate the quantity 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 as mild episodes and ROS events’ frequency increases. This study aims to develop a multi-method approach to monitor the key snowpack properties in a non-mountainous environment in a repetitive and non-destructive way. Snowpack evolution was evaluated using a combination of drone-based GPR, photogrammetry surveys and time domain reflectometry (TDR) measurements, tested during the winter of 2020–2021 at the Sainte-Marthe experimental watershed, Quebec, Canada. The experimental watershed is equipped with state-of-the-art automatic weather stations that, together with weekly snow pit measurements, serve as a reference for the multi-method monitoring approach. Drone surveys conducted on a weekly basis are used to generate georeferenced snow depth, relative density, snow water equivalent and average liquid water content maps. In between site visits, snowpack properties are monitored using TDR probes. Despite some limitations, the results show that the approach is very promising in assessing the spatiotemporal evolution of the key hydrological characteristics of the snowpack. Among others, results showed the prevalence of preferential pathways at the early stage of the ablation period, the difference in hydrological reaction to a ROS event between flat and sloped sections of the study area and the hydrological influence of solar radiation at the late stage of the ablation period.

Topographic and vegetation controls of the spatial distribution of snow depth in agro-forested environments by UAV-lidar

“…Much uncertainty still exists about the micro and meso scale spatial variability of snow cover and associated hydrological processes in these landscapes, partly due to lack of detailed and simultaneous micrometeorological and snowpack observations (Brown, 2010;Sena et al, 2017;Valence et al, 2022). To our knowledge, there has been no application of UAV laser scanning to investigate the small-scale snow cover heterogeneity in this type of landscape.…”