Preprints
https://doi.org/10.5194/egusphere-2024-744
https://doi.org/10.5194/egusphere-2024-744
03 Apr 2024
 | 03 Apr 2024
Status: this preprint is open for discussion.

Monitoring snow depth variations in an avalanche release area using low cost LiDAR and optical sensors

Pia Ruttner-Jansen, Annelies Voordendag, Thierry Hartmann, Julia Glaus, Andreas Wieser, and Yves Bühler

Abstract. Snow avalanches threaten people and infrastructure in mountainous areas. For the assessment of temporal protection measures of infrastructure in dangerous situations, local and up to date information is crucial. One factor influencing the avalanche situation is wind drifted snow, which causes variations in snow depth across a slope, but this data is rarely available. We present a monitoring system using low cost LiDAR and optical sensors, which we use to monitor snow depth variations in an avalanche release area. The system is operational since November 2023, autonomously measuring and providing data from our study area close to Davos in Switzerland, with high spatiotemporal resolution. In the first three operating months we gained experiences and made a preliminary assessment of the system performance. An analysis of the changes in spatial coverage shows the limitations and potentials of the system under different weather conditions. A comparison of the surface models derived from the LiDAR data and a photogrammetric drone shows a good agreement, achieving a mean of 0.005 m and standard deviation of 0.15 m. Two case studies, including an avalanche event and a period of snowfall with strong winds, show the potential of the proposed system to detect changes in the snow depth distribution on a low decimeter level, or better. In addition, we record meteorological parameters which we will use in future, together with the newly established snow depth database, for attempts to refine and further develop models for wind-induced snow redistribution. The near real time information of the snow depth distribution in avalanche prone slopes will be provided to experts, so it can aid their decisions on avalanche safety measures.

Pia Ruttner-Jansen, Annelies Voordendag, Thierry Hartmann, Julia Glaus, Andreas Wieser, and Yves Bühler

Status: open (until 15 May 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on egusphere-2024-744', Thomas Gölles, 18 Apr 2024 reply
  • RC1: 'Referee Comment on egusphere-2024-744', Katreen Wikstrom Jones, 18 Apr 2024 reply
Pia Ruttner-Jansen, Annelies Voordendag, Thierry Hartmann, Julia Glaus, Andreas Wieser, and Yves Bühler
Pia Ruttner-Jansen, Annelies Voordendag, Thierry Hartmann, Julia Glaus, Andreas Wieser, and Yves Bühler

Viewed

Total article views: 241 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
182 48 11 241 7 4
  • HTML: 182
  • PDF: 48
  • XML: 11
  • Total: 241
  • BibTeX: 7
  • EndNote: 4
Views and downloads (calculated since 03 Apr 2024)
Cumulative views and downloads (calculated since 03 Apr 2024)

Viewed (geographical distribution)

Total article views: 222 (including HTML, PDF, and XML) Thereof 222 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 29 Apr 2024
Download
Short summary
Snow depth variations caused by wind are an important factor in avalanche danger, but detailed and up-to-date information is rarely available. We propose a monitoring system, using LiDAR and optical sensors, to measure the snow depth distribution at high spatial and temporal resolution. First results show that we can quantify snow depth changes with an accuracy on the low decimeter level or better, and to identify events such as avalanches or displacement of snow during periods of strong winds.