Snow gliding is a key component leading to natural hazards, i.e. avalanches and erosions, and due to ongoing global changes has become a topic of major concern. Spatial information on snow gliding is important for management purposes, but, to date, lack of knowledge about key drivers for the snow-glide process hindered the development of a spatial snow-glide model (SSGM). We report the most important drivers for snow gliding derived from analyzing snow-glide distances taken over five winter periods in two climatically different study areas by ordinary least-squares regression. Six variables (forest stand, slope angle, winter precipitation, surface roughness, slope aspect west, slope aspect east) were revealed as key drivers and enabled us for the first time to establish a SSGM. Both model development (R2 = 0.838) and model validation (R2 = 0.823) exhibit outstanding accuracy of prediction. Hence, the SSGM was used to model snow-glide maps for both study areas: the ‘Kaserstattalm’ (Stubai Valley, North Tyrol, Austria) and the drier and warmer area of the ‘Waltner Mähder’ (Passeier Valley, South Tyrol, Italy). The reliability of these maps was validated by intersection with mapped erosions attributed to snow gliding. Therewith, such potential snow-glide maps have management relevance and are useful and necessary for risk assessment as well as to raise awareness about snow gliding to the land owners and regional managers.
116.121.4 (Schneemechanik, Kräfte und Spannungen in der Schneedecke (einschl. der Wirkungen dieser Kräfte auf Boden, Vegetation und Bauten)) 116.122.2 (Lawinendynamik, Lawinenkräfte und Lawinenbahnen (einschl. der Wirkungen dieser Kräfte auf Boden, Vegetation und Buten))
