Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 26, issue 3
Articles | Volume 26, issue 3
https://doi.org/10.5194/nhess-26-1161-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-1161-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 3
Research article
 | 
05 Mar 2026
Research article |  | 05 Mar 2026

The EAWS matrix, a decision support tool to determine the regional avalanche danger level (Part B): operational testing and use

Frank Techel, Karsten Müller, Christopher Marquardt, and Christoph Mitterer

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Cited articles

Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J.: Classification and Regression Trees, Chapman and Hall/CRC, New York, https://doi.org/10.1201/9781315139470, ISBN 9781315139470, 2017. a, b, c
Campbell, D. T. and Fiske, D. W.: Convergent and discriminant validation by the multitrait-multimethod matrix, Psychol. Bull., 56, 81–105, https://doi.org/10.1037/h0046016, 1959. a
Chicco, D. and Jurman, G.: The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation, BMC Genomics, 21, 6, https://doi.org/10.1186/s12864-019-6413-7, 2020. a
Clark, T.: Exploring the Link between the Conceptual Model of Avalanche Hazard and the North American Public Avalanche Danger Scale, Master's thesis, Simon Fraser University, https://summit.sfu.ca/_flysystem/fedora/sfu_migrate/18786/etd20073.pdf (last access: 23 December 2025), 2019. a
Clark, T. and Haegeli, P.: Establishing the link between the Conceptual Model of Avalanche Hazard and the North American Public Avalanche Danger Scale: initial explorations from Canada, in: Proceedings ISSW 2018. International Snow Science Workshop, Innsbruck, Austria, 7–12 October 2018, 1116–1120, https://arc.lib.montana.edu/snow-science/objects/ISSW2018_O12.4.pdf (last access: 3 March 2026), 2018. a
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We examined how avalanche forecasters across Europe use the EAWS (European Avalanche Warning Services) Matrix, a decision-support tool for determining regional avalanche danger levels. Although warning services apply the Matrix differently, we identified both consistent patterns and notable inconsistencies in its application. Our findings highlight where the Matrix works well and where clarification is needed, supporting more consistent and transparent avalanche information for the public.
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