Articles | Volume 18, issue 1
https://doi.org/10.5194/gmd-18-193-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-193-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Clustering simulated snow profiles to form avalanche forecast regions
School of Resource & Environmental Management, Simon Fraser University, Burnaby, BC, Canada
Avalanche Canada, Revelstoke, BC, Canada
Florian Herla
School of Resource & Environmental Management, Simon Fraser University, Burnaby, BC, Canada
Pascal Haegeli
School of Resource & Environmental Management, Simon Fraser University, Burnaby, BC, Canada
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We present a spatial framework for extracting information about avalanche problems from detailed snowpack simulations and compare the numerical results against operational assessments from avalanche forecasters. Despite good agreement in seasonal summary statistics, a comparison of daily assessments revealed considerable differences, while it remained unclear which data source represented reality the best. We discuss how snowpack simulations can add value to the forecasting process.
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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.
We present a method for avalanche forecasters to analyze patterns in snowpack model simulations....