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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/nhess-2019-344
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2019-344
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 20 Nov 2019

Submitted as: research article | 20 Nov 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Natural Hazards and Earth System Sciences (NHESS).

Enhancing the operational value of snowpack models with visualization design principles

Simon Horton, Stan Nowak, and Pascal Haegeli Simon Horton et al.
  • Simon Fraser University, Burnaby, BC, Canada

Abstract. Forecasting snow avalanches requires a reliable stream of field observations, which are often difficult and expensive to collect. Despite the increasing capability of simulating snowpack conditions with physical models, models have seen limited adoption by avalanche forecasters. Feedback from forecasters suggest model data is presented in ways that are difficult to interpret and irrelevant to operational needs. We apply a visualization design framework to enhance the value of snowpack models to avalanche forecasters. An established risk-based workflow for avalanche forecasting is used to define the ways forecasters solve problems with snowpack data. We address common forecasting tasks such as identifying snowpack features related to avalanche problems, summarizing snowpack features within a forecast area, and locating problems in terrain. Examples of visualizations that support these tasks are presented and follow established perceptual and cognitive principles from the field of information visualization. Interactive designs play a critical role in understanding these complex datasets and are well suited for forecasting workflows. Preliminary feedback suggests these design principles produce visualizations that are more relevant and interpretable for avalanche forecasters, but additional operational testing is needed to evaluate their effectiveness. By addressing issues with interpretability and relevance, this work sets the stage for implementing snowpack models into workstations where forecasters can test their operational value and learn their capabilities and deficiencies.

Simon Horton et al.
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Simon Horton et al.
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Latest update: 07 Dec 2019
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Short summary
Numeric snowpack models currently offer limited value to operational avalanche forecasters. To improve the relevance and interpretability of model data, we introduce and discuss visualization principles that map model data into visual representations that can inform avalanche hazard assessments.
Numeric snowpack models currently offer limited value to operational avalanche forecasters. To...
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