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All good things come from above: Drones open up new opportunities for snow research

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Drones buzz over our heads increasingly often and also keep the media buzzing – whether it has to do with protecting personal privacy or a drone crash that narrowly misses hitting a ski-racer, as actually happened during a World Cup slalom in Madonna di Campiglio (I) in 2015.

SLF researchers also rely on drones – not to film people, but to study snow. Yves Bühler, a remote-sensing specialist at SLF says, “The advantages of using drones are obvious. Drones are cheap and take high-resolution pictures.  They can be used quickly and flexibly in regions that are inaccessible or difficult to get to.”

Measuring avalanches safely

Yves invests a lot of time into making sure that his drones fly without a hitch. Before deploying them, he uses maps and terrain models to program on the computer where he later wants his drone to fly. In the field he then adapts the flight plan to the prevailing terrain, wind and temperature conditions before the drone automatically searches for the predefined route using the built-in GPS. On 7 February 2015, for example, he and his co-pilot Andreas Stoffel ‘flew over’ the release zone and snow deposits of the Wildi avalanche, which had come down in the Dischma Valley in Davos three days earlier. The researchers then created a digital surface model from the camera images using photogrammetric software. About three months later, they flew over the same area again, which was by then free of snow. By comparing the two surface models, Yves could calculate the release depths of the Wildi Avalanche’s snow deposits with an accuracy close to 20 cm. Yves explains, “This method enables us to document the course of an avalanche accurately and efficiently without us having to set foot in dangerous terrain.” If detailed surface models of the snow-free area prior to the avalanche are available, the dimensions of the avalanche deposits can be determined directly after the event.


Technology of the future

Surface models calculated using information from drone flights can also improve computer simulations of natural Alpine hazards, such as rockfall, debris flows and avalanches. The more precise the model of the terrain is – and precision is a basic prerequisite for such simulations – the more exactly natural hazards can be simulated on the screen using the simulations-software RAMMS. The simulation provides indications about how seriously threatened residential areas and transport routes are, and how they can best be protected. Another interesting use of drones is determining the snow depth across a complete area. If you want to know how much snow there is somewhere, and how the snow depth has changed during the winter, you used to have to rely primarily on data from the automatic weather stations. Snow depths for the areas between the stations then had to be interpolated using mathematical functions – with corresponding inaccuracies since the snow cover sometimes varies considerably over very small areas. Yves managed to determine snow depths very accurately in very different terrains based on several surface models obtained from drone flights under snow-free and snow-covered conditions. His estimates have been confirmed by manual measurements in the field.

Snow monitoring from the air opens up new opportunities for snow research. Not only can avalanche warning benefit from such data, but better forecasts can be made of how much water is stored in the snow at particular sites, and thus how much may be released when the snow melts. This is important information for the hydro-electric power industry and for flood warning. It may also help optimize the preparation of ski-runs or decide where automatic avalanche release systems should be placed. In the meantime Yves has received requests from people interested in the measurement technology for practical purposes. Canton Grisons, for example, gave him the task of flying over an area in the Engadine to find out how windbreaks influence the deposition of transported snow. (Christine Huovinen, Diagonal 1/17)