International Snow Science Workshop Proceedings - Montana State University Library

Item: BUILDING TRUST IN SNOWPACK MODELS: EVALUATION GUIDELINES FOR RESEARCHERS AND FORECASTERS

• • Title: BUILDING TRUST IN SNOWPACK MODELS: EVALUATION GUIDELINES FOR RESEARCHERS AND FORECASTERS

    Proceedings: Proceedings, International Snow Science Workshop, Whistler, BC, Canada, 2026

    Authors:

    • Simon Horton  [ Simon Fraser University, Burnaby, Canada ]
    • Florian Herla  [ Simon Fraser University, Burnaby, Canada ]
    • Alec van Herwijnen  [ WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland ]
    • Francis Meloche  [ WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland ]  [ Institute for Geotechnical Engineering, ETH Zürich, Switzerland ]
    • Travis J. Morrison  [ Utah Avalanche Center, Salt Lake City, USA ]
    • Louis Quéno  [ WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland ]
    • Vincent Vionnet  [ Environment and Climate Change Canada, Montreal, Canada ]

    Date: 2026-09-28

    Abstract: Physically based snowpack models are increasingly integrated into operational avalanche hazard forecasting, yet fragmented evaluation practices reduce confidence in their outputs. Mismatched spatial and temporal scales, heterogeneous datasets, inconsistent terminology, and custom software pipelines limit reproducibility and prevent groups from transferring findings. To address these challenges, we introduce a structured evaluation framework for snowpack models in support of avalanche hazard forecasting. The framework organizes evaluation design across four core dimensions: Context (why and where an evaluation is done), Model (how the model chain is configured), Data (which observations are used), and Analysis (how model–data comparisons are performed). Applying this framework highlights the critical need to separate physical measurement agreement (Quality) from practical forecasting utility (Value). Effective evaluation requires assessing each stage of the model chain sequentially, aligning spatial and temporal scales, and reporting performance for specific scenarios rather than aggregate summaries. We translate these insights into actionable guidance for researchers, software developers, and forecasters to build trust and advance model-based decision support.

    Object ID: ISSW2026_P3.1.pdf

    DOI: https://doi.org/10.15788/1790099091

    Language of Article: English

    Presenter(s): Simon Horton

    Keywords: snowpack model, model evaluation, avalanche forecasting, decision support

    Page Number(s): 23 - 30

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