Investigating the role of snow stratigraphy and water transport schemes in modelling wet-snow instability

Wet-snow avalanches are a significant hazard in mountainous regions, especially for infrastructure. Wet-snow stability can change within a couple of minutes and is thought to be strongly dependent on water movement through the snowpack. Furthermore, the onset of wet-snow avalanche activity is highly correlated to the first wetting of the snowpack. Therefore knowledge on the water content within the snowpack is crucial to determine wet-snow instabilities. Since field measurements of wet snow are hard to obtain, recent studies focused on predicting the onset of wet-snow avalanches by forcing the 1-D physically based snowpack model SNOWPACK with data from numerical weather prediction models. For this master thesis we use this modelling approach to look into the limits of the 1-D physically based snowpack model SNOWPACK regarding wet-snow mechanics, investigate how snow stratigraphy effects the distribution of liquid water content and compare three recent wet-snow indices to predict the onset of wet-snow avalanches. Therefore, we use a meteorological data set during a rain-on-snow event to force SNOWPACK simulations of four different initial snow stratigraphies. Modelled liquid water content and natural stability is compared for small changes in snow stratigraphy and different model setups. Most striking differences in liquid water content for the different model setups occurred between the two different averaging methods for hydraulic conductivity differing up to a factor two, however this difference decreases when modelling in high resolution. In terms of snow stratigraphy only varying slab height led to significant differences in modelled liquid water content. Stability indices based on liquid water content showed promising results regarding their robustness throughout the simulations, whereas the index based on natural stability is too dependent on pre-wetting stability of the snowpack and hence not advised for forecasting wet-snow avalanches.
by Hannes Hohenwarter
Masterarbeit University of Innsbruck 2021