Abstract

The physical risk from snow avalanches poses a serious threat to backcountry recreationalists and the winter backcountry recreation industry. Professional guides predominantly manage this risk by 1) assessing avalanche hazard through analysis of the local weather, snowpack, and recent avalanche patterns and 2) selecting appropriate terrain that limits exposure to the avalanche hazard. This process is primarily experience-based, relies considerably on non-explicit and non-formal knowledge, and employs intuitive decision practices. Can we measure such terrain selection decisions of professional guides to produce an avalanche terrain severity classification? We equipped lead guides at Mike Wiegele Helicopter Skiing throughout the 2014/15 and 2015/16 winters with GPS units creating a dataset of 10,592 tracked ski runs. The four main terrain parameters we analyzed were slope, vegetation, down-slope curvature (convexities or concavities), and cross-slope curvature (gullies or ridges). We applied an ordered logistic regression mixed effects model using the above parameters as independent variables and the guide’s PM avalanche hazard forecast as the dependent variable. The guides skied steeper, less dense vegetation, and more convoluted slopes during lower avalanche hazard conditions. The parameter estimates of the regression model were used to combine the terrain raster data in a GIS to create an overall avalanche terrain severity classification. The overall avalanche terrain severity classification compared well to terrain previously classified according to the Avalanche Terrain Exposure Scale. This paper represents a proof-of-concept for how measured professional terrain choices can be analyzed to produce an avalanche terrain severity classification.