Preprints
https://doi.org/10.5194/tc-2022-42
https://doi.org/10.5194/tc-2022-42
 
23 Feb 2022
23 Feb 2022
Status: this preprint is currently under review for the journal TC.

Introducing drone-based GPR in snow hydrology studies

Eole Valence1,2, Michel Baraer1,2, Eric Rosa2,3, Florent Barbecot2,4, and Chloe Monty1 Eole Valence et al.
  • 1Departement de genie de la construction, Ecole de Technologie Superieure, Montreal, H3C 1K3, Canada
  • 2Geotop, Montreal, H2X 3Y7, Canada
  • 3Groupe de recherche sur l’eau souterraine, Universite du Quebec en Abitibi-Temiscamingue, Rouyn-Noranda, J9X 5E4, Canada
  • 4Departement des sciences de la Terre et de l’atmosphere, Universite du Quebec a Montreal, Montreal, H2L 2C4, Canada

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.

Eole Valence et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-42', Anonymous Referee #1, 24 Mar 2022
    • AC1: 'Reply to RC1', Eole Valence, 06 Apr 2022
      • RC2: 'Reply on AC1', Anonymous Referee #1, 14 Apr 2022
        • AC1: 'Reply to RC1', Eole Valence, 06 Apr 2022
    • AC3: 'Reply on RC1', Eole Valence, 22 May 2022
  • RC3: 'Comment on tc-2022-42', Anonymous Referee #2, 15 Apr 2022
    • AC2: 'Reply on RC3', Eole Valence, 22 May 2022

Eole Valence et al.

Eole Valence et al.

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Short summary
The internal properties of the snow cover shape the annual hygrogram of northern and alpine regions. This study aims to develop a multimethod approach to measure the evolution of snowpack internal properties. The snowpack hydrological properties evolution was evaluated with dronebased ground-penetrating radar (GPR) measurement. In addition, the combination between GPR observation and time domain reflectometry (TDR) measurement has shown to be adapted to monitor the snowpack moisture winterlong.