16 Nov 2021
16 Nov 2021
Status: this preprint is currently under review for the journal NHESS.

Automated snow avalanche release area delineation in data sparse, remote, and forested regions

John Sykes1, Pascal Haegeli1, and Yves Bühler2 John Sykes et al.
  • 1Geography Department, Simon Fraser University, Burnaby, British Columbia, Canada
  • 2WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland

Abstract. Potential avalanche release area (PRA) modelling is critical for generating automated avalanche terrain maps which provide low-cost large scale spatial representations of snow avalanche hazard for both infrastructure planning and recreational applications. Current methods are not applicable in mountainous terrain where high-resolution elevation models are unavailable and do not include an efficient method to account for avalanche release in forested terrain. This research focuses on expanding an existing PRA model to better incorporate forested terrain using satellite imagery and presents a novel approach for validating the model using local expertise, thereby broadening its application to numerous mountain ranges worldwide. The study area of this research is a remote portion of the Columbia Mountains in southeastern British Columbia, Canada which has no pre-existing high-resolution spatial data sets. Our research documents an open source workflow to generate high-resolution DEM and forest land cover data sets using optical satellite data processing. We validate the PRA model by collecting a polygon dataset of observed potential release areas from local guides, using a method which accounts for the uncertainty of human recollection and variability of avalanche release. The validation dataset allows us to perform a quantitative analysis of the PRA model accuracy and optimize the PRA model input parameters to the snowpack and terrain characteristics of our study area. Compared to the original PRA model our implementation of forested terrain and local optimization improved the percentage of validation polygons accurately modelled by 11.7 percentage points and reduced the number of validation polygons that were underestimated by 14.8 percentage points. Our methods demonstrate substantial improvement in the performance of the PRA model in forested terrain and provide means to generate the requisite input datasets and validation data to apply and evaluate the PRA model in vastly more mountainous regions worldwide than was previously possible.

John Sykes 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 nhess-2021-330', Markus Eckerstorfer, 04 Jan 2022
    • AC1: 'Reply on RC1', John Sykes, 05 Apr 2022
  • RC2: 'Comment on nhess-2021-330', R. S. Purves, 09 Jan 2022
    • AC1: 'Reply on RC1', John Sykes, 05 Apr 2022
  • RC3: 'Comment on nhess-2021-330', Erich Peitzsch, 16 Jan 2022
    • AC1: 'Reply on RC1', John Sykes, 05 Apr 2022

John Sykes et al.

Data sets

Automated snow avalanche release area delineation in data sparse, remote, and forested regions -- Code and Data John Sykes, Pascal Haegeli, Yves Bühler

John Sykes et al.


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Short summary
Automated snow avalanche terrain mapping provides an efficient method for large scale assessment of avalanche hazards, which inform risk management decisions for transportation and recreation. This research reduces the costs of developing avalanche terrain maps by using satellite imagery and open source software as well as improving performance in forested terrain. The research relies on local knowledge to evaluate accuracy, so the methods are broadly applicable in mountainous regions worldwide.