Verification of regional snowpack stability and avalanche danger

Schweizer, Jürg; Kronholm, Kalle; Wiesinger, Thomas

doi:10.1016/s0165-232x(03)00070-3

Cited by 86 publications

(81 citation statements)

References 6 publications

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“…Nevertheless for avalanche forecasting, the snow depth needs to be modeled with some confidence since the depth of critical layers such as surface hoar layers and crusts is required for assessing the propensity of humantriggered slab avalanches (e.g. Schweizer et al, 2003). The simulations of the snow depth with the snow cover model SNOWPACK (Fig.…”

Section: Discussionmentioning

confidence: 99%

Forcing the snow-cover model SNOWPACK with forecasted weather data

Bellaire

Jamieson

Fierz³

2011

The Cryosphere

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Abstract. Avalanche danger is often estimated based on snow cover stratigraphy and snow stability data. In Canada, single forecasting regions are very large (>50 000 km 2 ) and snow cover data are often not available. To provide additional information on the snow cover and its seasonal evolution the Swiss snow cover model SNOWPACK was therefore coupled with a regional weather forecasting model GEM15. The output of GEM15 was compared to meteorological as well as snow cover data from Mt. Fidelity, British Columbia, Canada, for five winters between 2005 and 2010. Precipitation amounts are most difficult to predict for weather forecasting models. Therefore, we first assess the capability of the model chain to forecast new snow amounts and consequently snow depth. Forecasted precipitation amounts were generally over-estimated. The forecasted data were therefore filtered and used as input for the snow cover model. Comparison between the model output and manual observations showed that after pre-processing the input data the snow depth and new snow events were well modelled. In a case study two key factors of snow cover instability, i.e. surface hoar formation and crust formation were investigated at a single point. Over half of the relevant critical layers were reproduced. Overall, the model chain shows promising potential as a future forecasting tool for avalanche warning services in Canadian data sparse areas and could thus well be applied to similarly large regions elsewhere. However, a more detailed analysis of the simulated snow cover structure is still required.

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Section: Discussionmentioning

confidence: 99%

Forcing the snow-cover model SNOWPACK with forecasted weather data

Bellaire

Jamieson

Fierz³

2011

The Cryosphere

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“…The regional snow cover stability had been assessed and the verified avalanche danger rated as "Moderate, 2" above 2300 m a.s.l. on slopes of aspects W-N-E (Schweizer et al, 2003b). For this exemplary analysis, the relative threshold sum approach (Monti et al, 2014a, b) was used to detect the potential weak layers within the simulated snow stratigraphy, then the SK 38 and the SK ML 38 were calculated to evaluate the stability at the depth of the weak layer.…”

Section: Applicability Of the Sk ML 38 To The 1-d Snow Cover Model Snmentioning

confidence: 99%

Snow instability evaluation: calculating the skier-induced stress in a multi-layered snowpack

Monti¹,

Gaume²,

Herwijnen³

et al. 2016

Nat. Hazards Earth Syst. Sci.

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Abstract. The process of dry-snow slab avalanche formation can be divided into two phases: failure initiation and crack propagation. Several approaches tried to quantify slab avalanche release probability in terms of failure initiation based on shear stress and strength. Though it is known that both the properties of the weak layer and the slab play a major role in avalanche release, most previous approaches only considered slab properties in terms of slab depth, average density and skier penetration. For example, for the skier stability index, the additional stress (e.g. due to a skier) at the depth of the weak layer is calculated by assuming that the snow cover can be considered a semi-infinite, elastic, half-space. We suggest a new approach based on a simplification of the multi-layered elasticity theory in order to easily compute the additional stress due to a skier at the depth of the weak layer, taking into account the layering of the snow slab and the substratum. We first tested the proposed approach on simplified snow profiles, then on manually observed snow profiles including a stability test and, finally, on simulated snow profiles. Our simple approach reproduced the additional stress obtained by finite element simulations for the simplified profiles well -except that the sequence of layering in the slab cannot be replicated. Once implemented into the classical skier stability index and applied to manually observed snow profiles classified into different stability classes, the classification accuracy improved with the new approach. Finally, we implemented the refined skier stability index into the 1-D snow cover model SNOWPACK. The two study cases presented in this paper showed promising results even though further verification is still needed. In the future, we intend to implement the proposed approach for describing skier-induced stress within a multi-layered snowpack into more complex models which take into account not only failure initiation but also crack propagation.

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“…With regard to the location of the failure surface, 42 % released within the snowpack and 58 % of the avalanches at the snow-soil interface. We calculated a daily avalanche activity index (AAI) using a weighted sum of recorded avalanches per day with weights of 0.01, 0.1, 1 and 10 for size classes 1 to 4, respectively (Schweizer et al, 2003b). In order to exclude days with either many small avalanches or with a single large event, only days with an AAI > 2 were considered as wet-snow avalanche days for the statistical analyses (see below).…”

Section: Avalanche Occurrencementioning

confidence: 99%

Analysis of the snow-atmosphere energy balance during wet-snow instabilities and implications for avalanche prediction

Mitterer¹,

Schweizer²

2013

The Cryosphere

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Abstract. Wet-snow avalanches are notoriously difficult to predict; their formation mechanism is poorly understood since in situ measurements representing the thermal and mechanical evolution are difficult to perform. Instead, air temperature is commonly used as a predictor variable for days with high wet-snow avalanche danger -often with limited success. As melt water is a major driver of wet-snow instability and snow melt depends on the energy input into the snow cover, we computed the energy balance for predicting periods with high wet-snow avalanche activity. The energy balance was partly measured and partly modelled for virtual slopes at different elevations for the aspects south and north using the 1-D snow cover model SNOWPACK. We used measured meteorological variables and computed energy balance and its components to compare wet-snow avalanche days to non-avalanche days for four consecutive winter seasons in the surroundings of Davos, Switzerland. Air temperature, the net shortwave radiation and the energy input integrated over 3 or 5 days showed best results in discriminating event from non-event days. Multivariate statistics, however, revealed that for better predicting avalanche days, information on the cold content of the snowpack is necessary. Wet-snow avalanche activity was closely related to periods when large parts of the snowpack reached an isothermal state (0 • C) and energy input exceeded a maximum value of 200 kJ m −2 in one day, or the 3-day sum of positive energy input was larger than 1.2 MJ m −2 . Prediction accuracy with measured meteorological variables was as good as with computed energy balance parameters, but simulated energy balance variables accounted better for different aspects, slopes and elevations than meteorological data.

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Verification of regional snowpack stability and avalanche danger

Cited by 86 publications

References 6 publications

Forcing the snow-cover model SNOWPACK with forecasted weather data

Forcing the snow-cover model SNOWPACK with forecasted weather data

Snow instability evaluation: calculating the skier-induced stress in a multi-layered snowpack

Analysis of the snow-atmosphere energy balance during wet-snow instabilities and implications for avalanche prediction

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