Cyrill Hänni

Avalanche terrain maps are becoming increasingly common for large areas due to the availability of high-resolution and high-quality digital elevation models (DEM). Many of these maps use the Avalanche Terrain Exposure Scale (ATES) classification system, which categorizes terrain from simple to extreme, often using simple runout models such as the statistical alpha-beta model to identify potential runout areas. The current automated ATES classification models do not include avalanche dynamics models such as RAMMS, SAMOS or r.avaflow. Since 2018, Classified Avalanche Terrain (CAT) and Avalanche Terrain Hazard (ATH) maps have been introduced in Switzerland, delineating avalanche terrain into potential release areas and runout zones for size class 3 avalanches determined with the numerical avalanche simulation model RAMMS. While these maps have been very well received by recreationists and are widely used in Switzerland, the experience gained over the last years has shown that there is still room for improvements. Specifically, (i) potential release areas were not always well classified, (ii) runout zones may be too long for typical skier-triggered avalanches, and (ii) the ATH map, combining various factors, is complex and the information can be ambiguous. Therefore, we present updated versions of the CAT and ATH maps addressing these issues and allowing users to better assess avalanche risk and plan backcountry trips. These revised maps provide refined representations of potential release areas and improved mapping of avalanche runout zones driven by a new version of RAMMS::EXTENDED. Validation in different regions, focusing on size 3 avalanches, confirm the improved accuracy in delineating potential avalanche runout zones. Additionally, a new iteration of the ATH map simplifies the complexity of avalanche terrain by categorizing the terrain into easy-to-understand classifications that provide a concise overview, which also incorporates the ATES system. The new maps are generated using various layers provided by the RAMMS output, which can also support automatic risk assessment at cruxes. All that is required to produce these new maps is a high-quality elevation model at 5-m resolution, and a reliable map layer for the protective forest cover.

For travelling in the backcountry, good planning is crucial. Besides the current weather and avalanche forecast, terrain characteristics must be assessed to identify cruxes in terms of avalanche risk. Nowadays, support is given by slope angle layers and Geographic Information System (GIS) based avalanche terrain maps. When planning a backcountry tour, it is important to draw the route yourself on a detailed map, consider relevant terrain characteristics and define cruxes. To assist in this task, we developed an automatic crux detector. The method automatically identifies potential cruxes from digital avalanche terrain maps and assesses the terrain at these locations in terms of avalanche triggering and the consequences of being caught. In addition, the various associated terrain characteristics are presented. With this information, an initial risk assessment can already be made during the planning phase. Further, this function improves map reading skills in terms of avalanche risk when drawing routes, and it can analyze downloaded GPS tracks to verify the quality of the route and possibly improve it. The automatic crux detection is implemented in the White Risk application. This streamlines the planning process, which finally contributes to safer backcountry tours. In the future, we plan to link the detected potential cruxes to current snow cover conditions.