Preprints
https://doi.org/10.5194/egusphere-2024-771
https://doi.org/10.5194/egusphere-2024-771
22 Apr 2024
 | 22 Apr 2024
Status: this preprint is open for discussion.

Simulation of cold powder avalanches considering daily snowpack and weather situations to enhance road safety

Julia Glaus, Katreen Wikstrom Jones, Perry Bartelt, Marc Christen, Lukas Stoffel, Johan Gaume, and Yves Bühler

Abstract. Snow avalanches are rapid gravitational mass movements that represent a significant hazard to both humans and infrastructure, including traffic lines. In this context, risk management in mountainous region usually relies on experience of avalanche experts, observations in the field, weather and snowpack measurements and numerical simulations, which are typically based on shallow water equations.

Ensuring road safety requires considering daily weather conditions, snowpack characteristics, and terrain features. To include a numerical model in the decision process for road safety, it is essential to incorporate these aspects and provide insights into utilizing measurements as input parameters for the simulations.

This study investigates the predictive capabilities of the numerical simulation model RAMMS::EXTENDED, an extended version of the well established RAMMS software developed at the WSL Institute for Snow and Avalanche research SLF over the past fifteen years, to estimate avalanche run-out distances. Specifically tailored to cold powder avalanches dynamics, taking into account the temperature of the snowpack and erosion, our inquiry utilizes meteorological station measurements as an input to evaluate the model's performance.

We begin by providing an overview of the model, examining its physical and practical aspects. We then conduct a sensitivity analysis on input and system parameters, focusing on avalanche dynamics representation. Leveraging drone-based observational data, we perform a comparative analysis to validate the simulation results.

Additional to the recalculation of avalanches due to the sensitivity analysis, we show that we achieve meaningful predictions of the avalanche run-out distance for cold powder avalanches incorporating snow height and snow temperature measured by weather stations at two different altitudes. In the future, a further refined and validated version of this approach could allow for near real time hazard assessments to improve the decision making for road-closer and re-opening. Additionally, we plan to calibrate the model for wet-snow avalanches, to cover a larger range of weather scenarios.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Julia Glaus, Katreen Wikstrom Jones, Perry Bartelt, Marc Christen, Lukas Stoffel, Johan Gaume, and Yves Bühler

Status: open (until 12 Jun 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Julia Glaus, Katreen Wikstrom Jones, Perry Bartelt, Marc Christen, Lukas Stoffel, Johan Gaume, and Yves Bühler
Julia Glaus, Katreen Wikstrom Jones, Perry Bartelt, Marc Christen, Lukas Stoffel, Johan Gaume, and Yves Bühler

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Short summary
This study assesses RAMMS::EXTENDED's predictive power in estimating avalanche run-out distances critical for mountain road safety. Leveraging meteorological data and sensitivity analysis, it offers meaningful predictions, aiding near real-time hazard assessments and future model refinement for improved decision-making.