ALBERT

Description: Analysis of the snow-atmosphere energy balance during wet-snow instabilities and implications for avalanche prediction The Cryosphere Discussions, 6, 2715-2749, 2012 Author(s): C. Mitterer and J. Schweizer Wet-snow avalanches are notoriously difficult to predict, as their formation mechanism is poorly understood and in-situ measurements closely related to instability are inexistent. Instead, air temperature is commonly used as predictor variable for days with high wet-snow avalanche danger – with limited success. As melt water is a major driver of wet-snow instability and snow melt depends on the energy input into the snow cover, we computed the energy balance and study whether it is a better proxy than meteorological parameters such as air temperature for predicting periods with high wet-snow avalanche activity. The energy balance was partly measured and partly modelled for virtual slopes at different elevations for the aspects south and north using the 1-D snow cover model SNOWPACK. We used measured meteorological variables and computed energy balance and its components to compare wet-snow avalanche days to non-avalanche days for four consecutive winter seasons in the surroundings of Davos, Switzerland. Air temperature, the net shortwave radiation and the energy input integrated over 3 or 5 days showed best results in discriminating event from non-event days. Multivariate statistics, however, revealed that for better predicting avalanche days, information on the cold content of the snowpack is necessary. Wet-snow avalanche activity was closely related to periods when large parts of the snowpack reached an isothermal state (0 °C) and energy input exceeded a maximum value of 200 kJ m −2 in one day, or the 3-day sum of positive energy input was larger than 1.2 MJ m −2 . Prediction accuracy with measured meteorological variables was as good as with computed energy balance parameters, but simulated energy balance variables accounted better for different aspects, slopes and elevations than meteorological data.