Modeling spatially distributed snow instability at a regional scale using Alpine3D

Abstract

Assessing the avalanche danger level requires snow stratigraphy and instability data. As such data are usually sparse, we investigated whether distributed snow cover modeling can be used to provide information on spatial instability patterns relevant for regional avalanche forecasting. Using Alpine3D, we performed spatially distributed simulations to evaluate snow instability for the winter season 2016–17 in the region of Davos, Switzerland. Meteorological data from automatic weather stations were interpolated to 100 m horizontal resolution and precipitation was scaled with snow depth measurements from airborne laser scanning. Modeled snow instability metrics assessed for two different weak layers suggested that the weak layer closer to the snow surface was more variable. Initially, it was less stable than the weak layer closer to the ground, yet it stabilized faster as the winter progressed. In spring, the simulated snowpack on south-facing slopes stabilized faster than on north-facing slopes, in line with the regional avalanche forecast. In the winter months January to March 2017, simulated instability metrics did not suggest that the snowpack on south-facing slopes was more stable, as reported in the regional avalanche forecast. Although a validation with field data is lacking, these model results still show the potential and challenges of distributed modeling for supporting operational avalanche forecasting.

A random forest model to assess snow instability from simulated snow stratigraphy

“…In particular, there are no validated threshold values for a combination of both indices in the case of simulated snow profiles. Moreover, SK 38 provides meaningful results only for weak layers that are not deeply buried (< 80 cm) (Schweizer et al, 2016;Richter et al, 2021).…”

A comparison of hydrological models with different level of complexity in Alpine regions in the context of climate change

“…Alpine3D is a good example of a physically based model that accurately describes many Alpine surface processes. As it has been designed from the start for avalanche warning applications (Lehning et al, 2006), it must describe the snow metamorphism and microstructure, the snow density, temperature and liquid water content (Köhler et al, 2018), the liquid water transport in snow (Wever et al, 2017), the liquid water preferential flow (Würzer et al, 2017), the turbulent kinetic energy exchanges at the surface (Schlögl et al, 2018) and, of course, the snow stability (Richter et al, 2021). Besides, in view of its use for avalanche risk forecasting (Morin et al, 2020), it is continuously being tested during the snow season.…”

Spatial snowpack properties in a snow-avalanche release area: An extreme dry-slab avalanche case on Mt. Nodanishoji, Japan, in 2021

“…One may use computational tools, such as Alpine3D (Lehning et al, 2006), as they numerically simulate spatial snowpack distributions based on the blowing snow process. However, the current models do not always provide realistic snowpack distributions (Richter et al, 2021), perhaps because the topography effect on the precipitation (Houze, 2012) is not significantly considered.…”

“…For instance, using the SNOWPACK model with meteorological data observed at ~2,700 m away from the release area of an avalanche by seeking the minimum SI across snowpack layers, Takeuchi et al (2011) determined a WL made of faceted crystals (FC) and a thickness of slab accumulated over the WL that caused an extreme avalanche in Japan. Richter et al (2021) analyzed temporal evolutions of CCLs estimated by SNOWPACK that potentially related to avalanche events during the winter season in Switzerland.…”