Preprints
https://doi.org/10.5194/egusphere-2024-1510
https://doi.org/10.5194/egusphere-2024-1510
21 Jun 2024
 | 21 Jun 2024
Status: this preprint is open for discussion.

Monitoring snow wetness evolution from satellite with Sentinel-1 multi-track composites

Gwendolyn Dasser, Valentin T. Bickel, Marius Rüetschi, Mylène Jacquemart, Mathias Bavay, Elisabeth D. Hafner, Alec van Herwijnen, and Andrea Manconi

Abstract. Information about snowpack wetness at high temporal and spatial resolutions is important for timely identification of pre-disposing conditions for avalanche release. However, such information is often available only for specific, instrumented locations. Space-borne techniques such as synthetic aperture radar (SAR) allow us acquiring information over large areas and in remote and challenging terrain. Here, we show how Sentinel-1 SAR multi-track composites can be used to monitor snow wetness evolution over multiple seasons for a 5 study site of 400 km2 around Davos in the eastern Swiss Alps. We validate the performance of our method using both in-situ measurements and modelled snowpack data. Moreover, we compared snow wetness maps and time series with wet avalanches records. We found correlations between SAR backscatter and modelled liquid water content between -0.25 and -0.59 for Spearman’s rank coefficient and -0.25 and -0.64 for Pearson’s correlation coefficient. By calculating the percentage of detected wet snow to dry/no snow per elevation, the season-elevation related 10 melting can be tracked. Moreover, we show that a rise of wet snow ratio above 40 % coincides with an increase in wet snow avalanches releases in corresponding elevation bands. Our results suggest that wet snow products derived from Sentinel-1 SAR data may assist in identifying regions featuring a potential increase of wet snow avalanche activity. However, we could not find evidence of precursors of wet avalanche initiation with the accuracy required for operative monitoring applications.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Gwendolyn Dasser, Valentin T. Bickel, Marius Rüetschi, Mylène Jacquemart, Mathias Bavay, Elisabeth D. Hafner, Alec van Herwijnen, and Andrea Manconi

Status: open (until 25 Aug 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Gwendolyn Dasser, Valentin T. Bickel, Marius Rüetschi, Mylène Jacquemart, Mathias Bavay, Elisabeth D. Hafner, Alec van Herwijnen, and Andrea Manconi
Gwendolyn Dasser, Valentin T. Bickel, Marius Rüetschi, Mylène Jacquemart, Mathias Bavay, Elisabeth D. Hafner, Alec van Herwijnen, and Andrea Manconi

Viewed

Total article views: 284 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
222 51 11 284 8 8
  • HTML: 222
  • PDF: 51
  • XML: 11
  • Total: 284
  • BibTeX: 8
  • EndNote: 8
Views and downloads (calculated since 21 Jun 2024)
Cumulative views and downloads (calculated since 21 Jun 2024)

Viewed (geographical distribution)

Total article views: 266 (including HTML, PDF, and XML) Thereof 266 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 24 Jul 2024
Download
Short summary
Understanding snowpack wetness is crucial for predicting wet snow avalanches, but detailed data is often limited to certain locations. Using satellite radar, we monitor snow wetness spatially continuously. By combining different radar tracks from Sentinel-1, we improved spatial resolution and tracked snow wetness over several seasons. Our results indicate higher snow wetness to correlate with increased wet snow avalanche activity, suggesting our method can help identify potential risk areas.