Pia Ruttner-Jansen

Snow avalanches threaten people and infrastructure in mountainous areas. For the assessment of temporal protection measures of infrastructure when the avalanche danger is high, local and up-to-date information from the release zones and the avalanche track is crucial. One main factor influencing the avalanche situation is wind-drifted snow, which causes variations in snow depth across a slope. We developed a monitoring system using low-cost lidar and optical sensors to measure snow depth variations in an avalanche release area at a high spatiotemporal scale (centimetre to low decimetre spatial resolution and hourly temporal resolution). We analyse data obtained from such a monitoring system, installed within an avalanche release area at 2200 m a.s.l. close to Davos in the Swiss Alps. The system comprises two measurement stations and has been operational since November 2023. We present the experiences and insights gained from a preliminary analysis of the data obtained so far. The temporal variations of the spatial coverage show the potential and limitations of the system under varying weather conditions. A comparison of the surface elevation models derived from the lidar data and from photogrammetric processing of UAV-based images shows a good agreement, with a mean vertical difference of 0.005 m and standard deviation of 0.15 m. An avalanche event and a period of snowfall with strong winds, chosen as case studies, show the potential and limitations of the proposed system to detect changes in the snow depth distribution on a low decimetre level or better. The results obtained so far indicate that a measurement system with a few setups in or near an avalanche slope can provide information about the small-scale snow depth distribution changes in near real time. We expect that such systems and the related data processing have the potential to support experts in their decisions on avalanche safety measures in the future.

In the domain of avalanche road safety, local hazard experts have to assess the risk of an avalanche hitting the road given the predicted state of the snowpack and changing weather conditions. This project aims to present a proof of concept for a decision-support tool that facilitates risk assessment, provides a reproducible base for expert discussions and enhances the decision-making process by applying numerical models and providing probabilistic maps of avalanche run-out. These maps show the prediction of maximum avalanche impact for specific snow cover conditions. For this feasibility investigation, we consider one avalanche cycle from January 2019, in the Dischma Valley, located in Davos, Switzerland, where three cold avalanches got artificially triggered and reached the road. Photos of the powder cloud and meteorological data from stations nearby the avalanche tracks exist. Within one day after the avalanche events, drone-based photogrammetric measurements of the avalanche outlines and their mass balance, snow pit profiles at the top and bottom of the avalanche tracks were acquired. This unique dataset allows for a robust assessment of the avalanche modelling results. To represent different data availability cases, we investigate two options; a) a dataset in which the input data (new snow, snow temperature) are based on the weather station data collected nearby, and b) a data set derived from SNOWPACK simulation outputs, using weather data from a nearby station. We perform a set of RAMMS avalanche simulations with different values for the predicted average new snow height in the release zone and wind effects. By running multiple simulations for different input parameters, each assigned a specific probability that represents realistic snowcover conditions, we can produce probability-based avalanche run-out results that can be used for decision-making support. To assess the predictive power of the calculated maps, we compare modelled and observed avalanche core outlines.

Wind- and gravity-induced redistribution of snow leads to a high variability in snow depth across a slope, which is one of the major drivers to be taken into account for the assessment of avalanche danger. However, data on snow depth distribution in avalanche release zones at high spatial and temporal resolution are rarely available. We applied a newly developed monitoring system using low cost lidar and optical data that measured the snow depth distribution once per hour over the winter season 2023/24 at the release zone of the Wildi avalanche in Davos, Switzerland. The dataset consists of more than 3’000 epochs, each including an RGB image and lidar scans. In this contribution we present our experiences after one winter season of measurements, focusing on the station stability. By investigating apparent position changes of stable targets in the scan area we can quantify the stability of our measurement system. Additionally, we take first steps towards basic snow depth distribution modeling, by correlating the measured snow depths to terrain parameters. With the topographic position index (TPI) we test the common assumption that the mean snow depth is lower at ridges and hilltops, and higher at gullies and valleys than the mean overall snow depth. Up-to-date information on the snow depth variability in avalanche release zones is valuable for the refinement of avalanche simulation approaches, as well as for practitioners who decide on avalanche safety measures.

In this paper, we present a sensitivity analysis of RAMMS::EXTENDED, a numerical software for avalanche simulations that includes the reconstruction of the avalanche core and powder cloud. It is a further developed version of the well known avalanche simulation tool RAMMS::AVALANCHE. Our results are based on the reconstruction of an avalanche that released in 2019 in the Dischma Valley, Switzerland. The avalanche was surveyed with drone-based photogrammetry and provides detailed information about the expansion of the release zone, snow height and avalanche run-out. First, the analysis is carried out on a planar surface representing the avalanche track, and excludes the effects of the terrain. In a second step, the same parameters are tested on a digital terrain model that represents the real avalanche path. Both topographies are evaluated for a local and a global sensitivity analysis. The analysis is restricted on the input parameters a practitioner can measure, the disposition of mass on the slope (release depth, erosion depth and erosion gradient) and the snow temperature (release temperature and snowpack temperature gradient) were varied. The result of the analysis helps to better understand the influence of each model parameter.

Since people have been living in mountain regions, they are threatened by the danger of snow avalanches. Critical situations may require the closure of traffic routes or the evacuation of settlements as temporary risk mitigation measures. However, the decision-making experts currently have only limited data to base their decision on. A crucial parameter influencing the avalanche situation is the accumulation of wind drifted snow, and the resulting variation of the snow depth and stability across a slope. Herein, we propose a ground-based measurement system which will enable quasi permanent monitoring of the snow depth distribution in avalanche release areas with high spatial and temporal resolution. The system comprises Livox low-cost LiDAR sensors, photo cameras, as well as meteorologic instruments. The setup is designed to operate autonomously, capturing snow depth variations in an avalanche release zone close to the ski resort Jakobshorn in Davos, Switzerland. Using first test datasets, we analyze the specific strengths and weaknesses of the low-cost LiDAR sensors. Drone-based photogrammetry serves as reference for validation. The developed sensor system will serve to build up a snow depth database, which is used in a further step as an input for developing models predicting the snow depth distribution based on meteorological and terrain parameters. All information shall finally be provided to practitioners in a scenario-based platform to aid their decision- making process of traffic route safety measures.