2020
Frank Techel, Karsten Müller, and Jürg Schweizer
Abstract
Consistency in assigning an avalanche danger level when forecasting or locally assessing avalanche hazard is essential but challenging to achieve, as relevant information is often scarce and must be interpreted in light of uncertainties. Furthermore, the definitions of the danger levels, an ordinal variable, are vague and leave room for interpretation. Decision tools developed to assist in assigning a danger level are primarily experience-based due to a lack of data. Here, we address this lack of quantitative evidence by exploring a large data set of stability tests (N=9310) and avalanche observations (N=39 017) from two countries related to the three key factors that characterize avalanche danger: snowpack stability, the frequency distribution of snowpack stability, and avalanche size. We show that the frequency of the most unstable locations increases with increasing danger level. However, a similarly clear relation between avalanche size and danger level was not found. Only for the higher danger levels did the size of the largest avalanche per day and warning region increase. Furthermore, we derive stability distributions typical for the danger levels 1-Low to 4-High using four stability classes (very poor, poor, fair, and good) and define frequency classes describing the frequency of the most unstable locations (none or nearly none, a few, several, and many). Combining snowpack stability, the frequency of stability classes and avalanche size in a simulation experiment, typical descriptions for the four danger levels are obtained. Finally, using the simulated stability distributions together with the largest avalanche size in a stepwise approach, we present a data-driven look-up table for avalanche danger assessment. Our findings may aid in refining the definitions of the avalanche danger scale and in fostering its consistent usage.