Item: Quantitative Risk Reduction Method (QRM), a data-driven avalanche risk estimator
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Title: Quantitative Risk Reduction Method (QRM), a data-driven avalanche risk estimator
Proceedings: International Snow Science Workshop Proceedings 2018, Innsbruck, Austria
Authors:
- Günter Schmudlach [ bfu - Swiss Council for Accident Prevention, Bern, Switzerland ]
- Kurt Winkler [ WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland ]
- Jochen Köhler [ Norwegian University of Science and Technology, Trondheim, Norway ]
Date: 2018-10-07
Abstract: Strategic methods are well established aids for planning backcountry ski tours. They typically combine the avalanche danger level and the slope angle to a "risk category". The broad application of these classical methods reduces the frequency of avalanche accidents. However, they can’t represent the risk, because they are based exclusively on accident data and neglect the terrain usage by the skier community. In this paper we present the Quantitative Reduction Method (QRM), which allows the estimation of the relative avalanche risk on backcountry ski tours. The method is based upon data: Human involved avalanche accidents (1469), GPS tracks of backcountry ski tours (47’530 km) and avalanche conditions (taken from 4656 avalanche warning forecasts). First, we introduce two continuous indicators: the "Danger Indicator" (DI) to describe the danger predicted in the avalanche bulletin and the "Terrain Indicator" (TI) to describe the extent to which a certain point and its surround - ings are typical avalanche terrain. Then, we compute pairs of DI and TI for both, the release areas of the avalanche accidents and discrete points along the GPS tracks. The latter gives information about the terrain usage by backcountry recreationists. For probabilistic interpretation we use Kernel Density Estimations (KDE). Dividing accident KDE by terrain usage KDE gives the QRM. At a first glance, the QRM resembles earlier strategic methods. However, the QRM shows, that in the orange and red zones the risk increases exponentially with the danger indicator and terrain indicator. On the other hand, the relative risk remains close to zero in the green zone. The new method is suitable for computer applications and separates unambiguously low risk zones from high risk zones. Approximately 50% of the avalanche accidents could be avoided, by abstaining from only 1.9% of route segments.
Language of Article: English
Presenters:
Keywords: Reduction Method, Strategic Method, Avalanche Risk, Backcountry Skiing
Page Number(s): 1272-1278
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Digital Abstract Not Available
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