Forecasting wet-snow avalanche probability in mountainous terrain

Water percolating through the snow cover can lead to wet-snow instability as well as snowmelt runoff. The accurate prediction of spatial patterns of wet-snow in mountainous terrain therefore has practical applications in both back-country avalanche forecasting and hydrology. Recent research has shown that incident radiation plays a dominant role during the first complete wetting of the snow cover. We therefore investigated if large-scale meteorological forecast data, corrected for subgrid topographic influences on the shortwave radiation balance, together with subgrid mean slopes, can be combined to predict large-scale wet-snow avalanche patterns. Required surface albedo was derived from parameterized snow-covered fraction based on terrain parameters and measured flat field snow depths. We derived avalanche probability density functions for daily mean air temperature and incoming shortwave radiation from detailed observations over six winters using time-lapse photography. Based on probability density functions of these meteorological parameters and of slope angles of previous avalanches, we computed wet-snow probability maps for the entire Swiss Alps at a 2.5 km resolution. The probability maps compared well with observed wet-snow avalanche activity patterns. Even though the approach cannot forecast the onset of a cycle, since this would require snow cover related parameters, it provides a new approach toward an automatic spatial avalanche forecast built upon simple terrain parameters and easy to obtain large-scale meteorological surface variables. The advantage of our method is that it does not require running small-scale models with demanding model input parameters.