Automated identification of potential snow avalanche release areas based on digital elevation models

Abstract

The identification of snow avalanche release areas is a very difficult task. The release mechanism of snow avalanches depends on many different terrain, meteorological, snowpack and triggering parameters and their interactions, which are very difficult to assess. In many alpine regions such as the Indian Himalaya, nearly no information on avalanche release areas exists mainly due to the very rough and poorly accessible terrain, the vast size of the region and the lack of avalanche records. However avalanche release information is urgently required for numerical simulation of avalanche events to plan mitigation measures, for hazard mapping and to secure important roads. The Rohtang tunnel access road near Manali, Himachal Pradesh, India, is such an example. By far the most reliable way to identify avalanche release areas is using historic avalanche records and field investigations accomplished by avalanche experts in the formation zones. But both methods are not feasible for this area due to the rough terrain, its vast extent and lack of time. Therefore, we develop an operational, easy-to-use automated potential release area (PRA) detection tool in Python/ArcGIS which uses high spatial resolution digital elevation models (DEMs) and forest cover information derived from airborne remote sensing instruments as input. Such instruments can acquire spatially continuous data even over inaccessible terrain and cover large areas. We validate our tool using a database of historic avalanches acquired over 56 yr in the neighborhood of Davos, Switzerland, and apply this method for the avalanche tracks along the Rohtang tunnel access road. This tool, used by avalanche experts, delivers valuable input to identify focus areas for more-detailed investigations on avalanche release areas in remote regions such as the Indian Himalaya and is a precondition for large-scale avalanche hazard mapping

Automated snow avalanche release area delineation – validation of existing algorithms and proposition of a new object-based approach for large scale hazard indication mapping

“…The validation described in section 2.2 is now also applied to the OBIA algorithm (Table 3). The OBIA algorithm for the frequent scenario is slightly better in POD and POFD than the Bühler et al (2013) algorithm and exhibits an improved 5 delineation of the individual PRA. The comparison to the algorithms of and Voellmy (1955) to the OBIA algorithm reveals a better performance which is quantified by the higher PSS and HSS scores and the lower amount of total delineated area.…”

“…Table 1 gives an overview on PRA delineation algorithms, published in peer-reviewed web of science journals, and the terrain derivatives they apply. considering just the parameter slope after Voellmy (1955) as benchmark and the two recent algorithms taking the terrain roughness into account (Bühler et al, 2013;. The algorithm of Maggioni et al (2002) was also tested but only produces meaningful results with DEM resolutions in the order of 25 m. Furthermore, this algorithm was written in AML script language and cannot be run on current software anymore.…”

“…Therefore, the algorithm of was extended by the option to set a probability threshold 10 for discrete classification and the option to define a minimum release area. The algorithm by Bühler et al (2013) requires a value for the following input parameters: minimum slope angle, maximum slope angle, cell size for the moving window to calculate the roughness, maximum roughness, maximum curvature, minimum release area. To run the algorithms, we apply the swissalti3D digital elevation model from swisstopo with a spatial resolution of 5 m (swisstopo, 2018).…”

“…Low POFD values mean that much less area is delineated as PRA, saving many time consuming numerical simulations. For example, the Bühler et al (2013) approach results in 24 % less PRA area than the slope only approach.…”

“…The slope only and the Veitinger et al (2016) The OBIA algorithm is based on the Bühler et al (2013) algorithm and optimizes the delineation process.…”