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Avalanche Safety for Roads



In this project, we propose a new decision support tool to improve road safety in high alpine areas. Today, decisions on the closure and re-opening of roads, railways and ski resorts in Switzerland are based on the avalanche bulletin. Local experts combine this information with data from measurement networks to decide on the safety state. Hence, there are no strict guidelines which define when a region must be closed or especially can be reopened.



By calculating the avalanche runout distance for different scenarios, we can predict whether an avalanche can reach a road or not. To get the necessary input information we calculate snow depth models for the different slopes and use them as an input for RAMMS:EXTENDEDsimulations to assess the potential runout distances.  

We propose to set-up a remote sensing-based snow depth mapping system with high temporal and spatial resolution to map the snow depth distribution evolution.

The acquired information will be the base for the simulation of specific avalanche scenarios for the Dischma valley (e.g. cold dry slab avalanches, wet snow avalanches, mixed flow regimes) and different return periods with the RAMMS simulation software.


The scenarios will be evaluated by applying mapped outlines from past avalanche events, existing hazard maps and by the local natural hazard experts from the community of Davos,responsible for the Dischma valley.

The key research question is if and how these new tools can efficiently support the decision-making for road closure and re-opening. By combining cutting-edge remote sensing and numerical simulation technology into the decision-making process for avalanche danger estimation, we pave the way for more reliable decisions and higher avalanche safety in alpine valleys.


Mapping, Monitoring and Modelling Snow Depth Distribution

Important input parameters for the avalanche simulation are the snow conditions and the snow depth distribution in the avalanche release area and the avalanche track. To get the best results, the input data must depict the conditions as truthfully as possible. But the snow depth distribution is not evenly distributed across the slope and is also changing a lot with the influence of wind. Therefore, we plan to develop and install a remote sensing monitoring system to map the snow depth distribution with high spatial and temporal resolution. The group has already a lot of experience with drones in alpine terrain (see: Capturing snow depths by using drones), but for a high temporal resolution we plan to additionally install a fixed, ground-based system that is operating autonomously throughout the whole season. With the newly build up snow depth database, meteorological (snowfall, wind, temperature) and terrain information we aim to develop a simulation approach, that allows us to calculate the conditions for any slope, without a costly monitoring system.


Sensitivity Analysis of Input Parameters in RAMMS:EXTENDED

Nowadays, in practice the user version of RAMMS gets applied by engineering offices, authorities and scientists. Currently, the SLF team is developing and testing an SLF internal research version of the software, the so-called RAMMS:EXTENDED. With the new version, many more physical parameters can be incorporated and therefore different flow regimes such as powder- or wet snow avalanches can be simulated.

With the additional parameters, the model gets more sensitive to uncertainties in the input parameters. Hence, a detailed sensitivity analysis for the additional parameters available in RAMMS:EXTENDED must be conducted before the tool can be used by practitioners. The most important parameters will be erodible snow depth in the avalanche track, temperature of the released and entrained snowpack, variation of density during the avalanche flow and the generation of free water in the simulated wet snow avalanches.

The plan is to apply Monte Carlo simulations to assess the sensitivity of the individual parameters on the avalanche runout, deposition depth, velocity and impact pressure. As benchmark to validate the simulation results the RAMMS:USER version, further avalanche dynamic models and measurements from the SLF database will be used.


Definition of Hazard Scenarios and Modelling of Avalanche Runout Scenarios

In a next step, relevant hazard scenarios will be defined in collaboration with the hazard experts of the community Davos who are responsible for closing and opening the roads. The scenarios will be calculated dependent on the new snow amounts, wind and temperature in combination with the new modelling of the snow depth distribution.


A Platform for decision makers

The key output is the provision of all information for those who have to decide on safety measures for roads like closures and re-openings. All measurements and simulations are planned to be integrated in a platform in (near) real-time to support with the most up-to-date data possible. In a first step, this tool will be developed for the test region Dischma. As a future goal, this tool will be upscaled for other regions.