Developing nationwide avalanche terrain maps for Norway

Håvard T. Larsen (), Jordy Hendrikx, Martine S. Slåtten and Rune V. Engeset
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Håvard T. Larsen: Norwegian Water Resources and Energy Directorate
Jordy Hendrikx: Montana State University
Martine S. Slåtten: Norwegian Water Resources and Energy Directorate
Rune V. Engeset: Norwegian Water Resources and Energy Directorate

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2020, vol. 103, issue 3, No 9, 2829-2847

Abstract: Abstract Snow avalanches are a significant natural hazard in Norway. One method to manage the backcountry avalanche hazard is through detailed mapping of avalanche terrain. Avalanche terrain can be mapped using a variety of methods, including using the Avalanche Terrain Exposure Scale (ATES); however, manual classification of terrain using ATES is time consuming. This study has developed and compared a fully automated algorithm to provide ATES mapping for all of Norway. Our new algorithm is based on the technical model for ATES mapping. This model has specific terrain-based thresholds that can be applied for automated terrain-based modeling. Our algorithm expands on prior work by including the potential release area (PRA) model to identify and calculate the likelihood of an avalanche releasing from a start zone. We also use the raster-based TauDEM-model to determine the avalanche runout length. The final product is a 10-m resolution ATES map. We compared this nationwide ATES map with areas that have been manually mapped by avalanche experts, and find that the automated approach yields similar and reliable results. In addition to comparing mapped areas, we also examine manually mapped linear routes and compare these with the automated mapped ATES areas. Our results suggest that for open terrain, the vast majority of the manually classified tracks are predominantly in the same ATES class as our algorithm. For forested areas, we get mixed results, which can be attributed to a lack of suitable vegetation data at an appropriate scale. Despite this limitation, the current ATES algorithm and resulting spatial data are already valuable as a large portion (~ 70%) of the Norwegian backcountry terrain is above tree line. The automated algorithm is also useful to ensure consistent manual classification across different regions in Norway, or globally, and will permit greater reproducibility and easier updating of mapping for the future.

Keywords: ATES; GIS; Algorithm; Mapping; Avalanche; Terrain (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11069-020-04104-7

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