Spatial patterns in snowpack stability and their causes
Are meteorological processes the key to forecasting stability patterns?
In the densely populated Alpine region, slab avalanches are one of the major natural hazards. Although regional avalanche danger forecasts are now quite reliable, it remains impossible to pinpoint the actual location and timing of an avalanche event in advance. Reliable forecasting continues to depend on close observation of the snowpack’s structure and stability on-site; however, knowledge of snowpack variations at other locations is also critical.
In principle, spatial and geographical variations in the terrain can be measured by remote exploratory devices using satellites; or by producing a 3D model of the snowpack. However, neither of these approaches is sufficiently sophisticated to be applied in avalanche-forecast practice. That is to say, the stability of the snowpack cannot be determined directly by remote measurements; and the requirements for accurate modelling are currently too complex. The modelling of spatial patterns in snowpack stability depends on prior knowledge of the patterns' appearance and an understanding of how they are brought about by precipitation, wind and solar/outgoing radiation. Not until these parameters are more fully understood will it be possible to evaluate how the snowpack and its stability are likely to vary in a given situation or predict its response to changing weather conditions.
2016 - 2016
Measuring campaign high above Davos
In the context of a dissertation sponsored by the Swiss National Science Foundation, SLF researchers have now examined the patterns and causes of these local disparities in snowpack stability. In Steintälli, a small high valley above Davos, they conducted a series of measuring campaigns (Figure 1). They measured the snow depth with terrestrial laser scanners, and assessed the snowpack stability by way of snow profiles and stability tests. They also used the SnowMicroPen (SMP) to measure changes in the snow’s resistance to penetration, which discloses the snowpack layering. Approximately 150 measurements were performed in a single day. Several weather stations in the area supplied additional data for modelling the snowpack.
For the first time, the research team was able to extract information linked to snowpack stability from the SMP data. They devised a method to determine both the stability index and the critical crack length from the signal indicating the resistance to penetration. These two variables are closely associated with two avalanche-generating processes, namely, the fracture’s formation and its propagation, thus serve as useful indicators of snowpack stability. The researchers verified the calculated values by cross-referencing them with field observations (Figure 2).
Snow stability maps
With the aid of a geostatistical model the researchers subsequently mapped the stability index and critical crack length on the basis of the point measurements. The key factor in this effort was the slope aspect, which – alongside snow depth and slope angle – enabled them to improve the interpolation. In other words, the slope's compass-orientation is the most significant terrain characteristic relevant to stability. For the first time ever, these maps illustrate snowpack stability with a high resolution of approximately 1 m (Figure 3) and can now be used for comparisons with the results of snowpack modelling . It came as no surprise to find that a correlation exists between mean stability and danger level, and that significant local variations can exist in an area where a uniform danger level is indicated.
In a further step, the researchers used the Alpine3D model developed at the SLF to simulate how and in what meteorological conditions the snowpack properties vary. Despite the complexity of the processes and an initially rudimentary modelling approach, some patterns have already been reproduced. The measured variations in snowpack stability were chiefly attributed to differences in the radiation balance and the distribution of precipitation. Before this project can be developed to the next step, the modelling approach needs to be further refined. The differences in snow depth in the area, which are a crucial factor for stability, can be captured only if the modelling of snow transportation reflects reality as accurately as possible.
Forecasting spatial variation in snowpack stability
These research findings demonstrate that snowpack models used in conjunction with high-resolution meteorological models can serve as an aid to avalanche forecasting. In future, forecasters should therefore be able to describe not only the severity (level) of the avalanche danger, but also local variations which threaten. Until that point is reached, however, additional research is required. Both snow quantities relevant to its movements and concepts describing the mechanics of fractures must be integrated in the snowpack modelling, for example. Moreover, the forecasting of meteorological factors in the mountains urgently need to be highly detailed.