Articles | Volume 23, issue 6
https://doi.org/10.5194/nhess-23-2089-2023
https://doi.org/10.5194/nhess-23-2089-2023
Research article
 | 
13 Jun 2023
Research article |  | 13 Jun 2023

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

Gregor Ortner, Michael Bründl, Chahan M. Kropf, Thomas Röösli, Yves Bühler, and David N. Bresch

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This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
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Cited articles

Accastello, C., Poratelli, F., Renner, K., Cocuccioni, S., D’Amboise, C. J. L., and Teich, M.: Risk-Based Decision Support for Protective Forest and Natural Hazard Management, in: Protective Forests as Ecosystem-based Solution for Disaster Risk Reduction (Eco-DRR), IntechOpen, https://doi.org/10.5772/intechopen.99512, 2022. a
Aznar-Siguan, G. and Bresch, D. N.: CLIMADA v1: a global weather and climate risk assessment platform, Geosci. Model Dev., 12, 3085–3097, https://doi.org/10.5194/gmd-12-3085-2019, 2019 (data available at: https://github.com/CLIMADA-project/climada_python (last access: 2 May 2023). a, b, c, d, e, f
Ballesteros-Cánovas, J. A., Trappmann, D., Madrigal-González, J., Eckert, N., and Stoffel, M.: Climate warming enhances snow avalanche risk in the Western Himalayas, P. Natl. Acad. Sci. USA, 115, 3410–3415, https://doi.org/10.1073/pnas.1716913115, 2018. a
Bartelt, P., Christen, M., Bühler, Y., Deubelbeiss, Y., Salz, M., Schneider, M., and Schumacher, L.: RAMMS::AVALANCHE User Manual, WSL Institute for Snow and Avalanche Research SLF, v1.7.0 edn., 2017. a
Bebi, P., Kulakowski, D., and Rixen, C.: Snow avalanche disturbances in forest ecosystems-State of research and implications for management, Forest Ecol. Manag., 257, 1883–1892, https://doi.org/10.1016/j.foreco.2009.01.050, 2009. a
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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 probabilistic 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.
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