Modelling avalanche danger and understanding snow depth variability

Schirmer, Michael (Author); Schneider, Christoph (Thesis advisor)

Aachen / Publikationsserver der RWTH Aachen University (2010, 2011) [Dissertation / PhD Thesis]

Page(s): IV, 105 S. : Ill., graph. Darst., Kt.


This thesis addresses the causes of avalanche danger at a regional scale. Modelled snow stratigraphy variables were linked to [1] forecasted avalanche danger and [2] observed snowpack stability. Spatial variability of snowpack parameters in a region is an additional important factor that influences the avalanche danger. Snow depth and its change during individual snow fall periods are snowpack parameters which can be measured at a high spatial resolution. Hence, the spatial distribution of snow depth and snow depth change due to individual snow storms were observed [3]. Furthermore, this spatial dataset was characterised with a fractal analysis and results were related to deposition processes [4]. In the following, each subject is described in more detail: [1] In the past, numerical prediction of regional avalanche danger using statistical methods with meteorological input variables has shown insufficiently accurate results, possibly due to the lack of snow stratigraphy data. Detailed snow-cover data were rarely used because they were not readily available (manual observations). With the development and increasing use of snow-cover models this deficiency can now be rectified and model output can be used as input for forecasting models. We used the output of the physically based snow cover model SNOWPACK combined with meteorological variables to investigate and establish a link to regional avalanche danger. Snow stratigraphy was simulated for the location of an automatic weather station near Davos (Switzerland) over nine winters. Only dry-snow situations were considered. A variety of selection algorithms was used to identify the most important simulated snow variables. Data mining and statistical methods, including classification trees, artificial neural networks, support vector machines, hidden Markov Models and nearest-neighbour methods were trained on the forecasted regional avalanche danger (European avalanche danger scale). The best results were achieved with a nearest neighbour method which used the avalanche danger level of the previous day as additional input. A cross-validated accuracy (hit rate) of 73% was obtained. This study suggests that modelled snow-stratigraphy variables, as provided by SNOWPACK, are able to improve numerical avalanche forecasting.[2] Snow stability, or the probability of avalanche release, is one of the key factors defining avalanche danger. Most snow stability evaluations are based on field observations, which are time-consuming and sometimes dangerous. Through numerical modelling of the snow cover stratigraphy, the problem of having sparsely measured regional stability information can be overcome. In this study we compared numerical model output with observed stability. Overall, 775 snow profiles combined with Rutschblock scores and release types for the area surrounding five weather stations were rated into three stability classes. Snow stratigraphy data were then produced for the locations of these five weather stations using the snow cover model SNOWPACK. We observed that (i) an existing physically based stability interpretation implemented in SNOWPACK was applicable for regional stability evaluation; (ii) modelled variables equivalent to those manually observed variables found to be significantly discriminatory with regard to stability, did not demonstrated equal strength of classification; (iii) additional modelled variables that cannot be measured in the field discriminated well between stability categories. Finally, with objective feature selection, a set of variables was chosen to establish an optimal link between the modelled snow stratigraphy data and the stability rating through the use of classification trees. Cross-validation was then used to assess the quality of the classification trees. A true skill statistic of 0.5 and 0.4 was achieved by two models that detected "rather stable" or "rather unstable" conditions, respectively. The interpretation derived could be further developed into a support tool for avalanche warning services for the prediction of regional avalanche danger.[3] Terrestrial and Airborne Laser Scanning (TLS and ALS) techniques have only recently developed to the point where they allow wide-area measurements of snow distribution in varying terrain. Multiple TLS measurements are presented showing the snow depth development for a series of precipitation events. We observe that the pattern of maximum accumulation is similar for the two years presented here (correlation up to r=0.97). Storms arriving from the Northwest show persistent snow depth distributions and contribute most to the final accumulation pattern. Snow depth patterns of maximum accumulation for the two years is more similar than the distribution created by any two pairs of individual storms. A decrease in variance of snow depth change with time was observed, while variance of snow depth was increasing. Based on the strong link between accumulation patterns and terrain, we investigated the ability of a model based on terrain and wind direction to predict accumulation patterns. This approach of Winstral, which describes wind exposure and shelter, was able to predict the general accumulation pattern over scales of slopes but failed to match observed variance. Furthermore, a high sensitivity to the local wind direction was demonstrated. We suggest that Winstral's model could form a useful tool for application from hydrology and avalanche risk assessment to glaciology.[4] We present analysis of high resolution laser scanning data of snow depths in the Wannengrat catchment (introduced in [3]) using omni-directional and directional variograms for three specific terrain features; cross-loaded slopes, lee slopes and windward slopes. A break in scaling behavior was observed in all sub-areas, which can be seen as the roughness scale of summer terrain which is modified by the snow cover. In the wind-protected lee slope a different scaling behavior was observed, compared to the two wind-exposed areas. The wind-exposed areas have a smaller ordinal intercept, a smaller short range fractal dimension D and a larger scale break distance L than the wind-protected lee slope. Snow depth structure inherits characteristics of dominant NW storms, which results e.g. in a trend towards larger break distances in the course of the accumulation season. This can be interpreted as a result of surface smoothing at increasing scales. Similar scaling characteristics were obtained for two different years at the end of the accumulation season. Since snow depth structure is altered strongly by NW storms, this inter-annual consistency may strongly depend on their frequency in an accumulation period. The analysis of directional variograms suggests that existing anisotropies can be explained by the orientation of terrain features with respect to the predominant wind direction.


  • URN: urn:nbn:de:hbz:82-opus-36352