Articles | Volume 18, issue 1
https://doi.org/10.5194/gmd-18-193-2025
https://doi.org/10.5194/gmd-18-193-2025
Methods for assessment of models
 | 
17 Jan 2025
Methods for assessment of models |  | 17 Jan 2025

Clustering simulated snow profiles to form avalanche forecast regions

Simon Horton, Florian Herla, and Pascal Haegeli

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Cited articles

Bouchayer, C.: Synthesis of distributed snowpack simulations relevant for avalanche hazard forecasting, Master's thesis, University Grenoble Alpes, 2017. a, b
Brun, E., David, P., Sudul, M., and Brunot, G.: A numerical model to simulate snow-cover stratigraphy for operational avalanche forecasting, J. Glaciol., 38, 13–22, 1992. a
Canadian Avalanche Association: Technical aspects of snow avalanche risk management – Resources and guidelines for avalanche practioners in Canada, Canadian Avalanche Association, Revelstoke, BC, ISBN 978-1-926497-00-6, https://www.avalancheassociation.ca/page/GuidelinesStandards (last access: 10 October 2024), 2016. a
Chavent, M., Kuentz-Simonet, V., Labenne, A., and Saracco, J.: ClustGeo: an R package for hierarchical clustering with spatial constraints, Comput. Stat., 33, 1799–1822, https://doi.org/10.1007/s00180-018-0791-1, 2018. a
Feurer, M. and Hutter, F.: Hyperparameter Optimization, pp. 3–33, Cham, Switzerland, ISBN 978-3-030-05318-5, https://doi.org/10.1007/978-3-030-05318-5_1, 2019. a
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Short summary
We present a method for avalanche forecasters to analyze patterns in snowpack model simulations. It uses fuzzy clustering to group small regions into larger forecast areas based on snow characteristics, locations, and temporal history. Tested in the Columbia Mountains in two winter seasons, it closely matched real forecast regions regions and identified major avalanche hazard patterns. This approach simplifies complex model outputs, helping forecasters make informed decisions.
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