06 Apr 2022
06 Apr 2022
Status: this preprint is currently under review for the journal NHESS.

Large-scale risk assessment on snow avalanche hazard in alpine regions

Gregor Ortner1,2,3, Michael Bründl1,2, Chahan M. Kropf3,4, Thomas Röösli3,4, Yves Bühler1,2, and David N. Bresch3,4 Gregor Ortner et al.
  • 1WSL Institute for Snow and Avalanche Research SLF, 7260 Davos Dorf, Switzerland
  • 2Climate Change, Extremes and Natural Hazards in Alpine Regions Research Center CERC, 7260 Davos Dorf, Switzerland
  • 3Institute for Environmental Decisions, ETH Zurich, Universitätstr. 16, 8092 Zurich, Switzerland
  • 4Federal Office of Meteorology and Climatology MeteoSwiss, Operation Center 1, P.O. Box 257, 8058 Zurich-Airport, Switzerland

Abstract. Snow avalanches are recurring natural hazards that affect the population and transport infrastructure in alpine regions during the winter months such as in the most recent avalanche winters of 2018 and 2019, where large damages were caused by avalanches throughout the Alps. Decision makers need detailed information on the spatial distribution of the hazard and risk in order to prioritize and apply appropriate adaptation strategies and mitigation measures to minimize impacts. Here, we present a novel risk assessment approach for assessing the spatial distribution of avalanche risk by combining large-scale hazard mapping with a state-of-the-art risk assessment tool, where risk is understood as the product of hazard, exposure, and vulnerability. Hazard disposition is modeled using the large-scale hazard indication mapping method RAMMS::LSHIM, and risks are assessed using the probabilistic Python-based risk assessment platform CLIMADA, developed at ETH Zürich. The avalanche hazard mapping for scenarios with a 30, 100, and 300 year return period is based on a high-resolution terrain model, 3-day snow depth increase, automatically determined potential release areas, and protection forest information. Avalanche hazard for 40,000 single snow avalanches is assessed in avalanche intensity measured as pressure. Exposure is represented with a detailed building layer indicating the spatial distribution of monetary assets. Vulnerability of the buildings is defined by damage functions based on the software EconoMe, which is in operational use in Switzerland. The outputs of the hazard, exposure, and vulnerability analyses are combined to quantify the risk in spatially explicit risk maps. The risk considers the probability and intensity of snow avalanche occurrence as well as the concentration of vulnerable, exposed buildings. Uncertainty and sensitivity analyses were performed to capture inherent variability in the input parameters. This new risk assessment approach allows for the quantification of avalanche risk on large scales and results in maps that show the spatial distribution of risk at specific locations. Large-scale risk maps can assist decision makers in identifying areas where hazard mitigation and/or adaption is needed to address current and future avalanche risk.

Gregor Ortner et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-112', Pascal Haegeli, 06 Aug 2022
    • AC1: 'Reply on RC1', Gregor Ortner, 08 Aug 2022
    • AC2: 'Reply on RC1 and RC2', Gregor Ortner, 21 Dec 2022
  • RC2: 'Comment on nhess-2022-112', Anonymous Referee #2, 10 Nov 2022
    • AC1: 'Reply on RC1', Gregor Ortner, 08 Aug 2022
    • AC3: 'Reply on RC1 and RC2', Gregor Ortner, 21 Dec 2022

Gregor Ortner et al.

Gregor Ortner et al.


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Short summary
This paper presents a new approach to assess avalanche risk on a large scale in mountainous regions. It combines a large scale avalanche modeling method with a state of the art probilistic risk tool. Over 40'000 individual avalanches were simulated and a building dataset with over 13'000 single buildings was investigated. With this new method, risk hotspots can be identified and surveyed. This enables current and future risk analysis to assist decision makers in risk reduction and adaptation.