Item: WHERE TO PUT THE WEATHER STATION? OPTIMIZING THE LOCATION FOR AUTOMATED SNOW DEPTH MEASUREMENTS BASED ON REMOTE SENSING, AVALANCHE MODELING AND TERRAIN CHARACTERISTICS
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Title: WHERE TO PUT THE WEATHER STATION? OPTIMIZING THE LOCATION FOR AUTOMATED SNOW DEPTH MEASUREMENTS BASED ON REMOTE SENSING, AVALANCHE MODELING AND TERRAIN CHARACTERISTICS
Proceedings: International Snow Science Workshop 2024, Tromsø, Norway
Authors:
- Yves Buhler [ WSL Institute for Snow and Avalanche Research SLF ] [ Climate Change, Extremes and Natural Hazards in Alpine Regions Research Center CERC ]
- Andreas Stoffel [ WSL Institute for Snow and Avalanche Research SLF ] [ Climate Change, Extremes and Natural Hazards in Alpine Regions Research Center CERC ]
- David Liechti [ WSL Institute for Snow and Avalanche Research SLF ]
Date: 2024-09-23
Abstract: Automated weather stations (AWS) measuring snow-related parameters are essential for avalanche warning in many regions. Key data include new snow accumulation, wind direction, and wind velocity, especially in remote, high-elevation terrain. This information is critical for decisions such as when to close and reopen roads. Given the high spatial variability of snow depth distribution in mountain areas, the positioning of AWS is crucial. High-resolution, spatially coherent snow depth measurements acquired by drones, airplanes, or satellites reveal significant variability within short distances of just a few meters. Therefore, it is important to place weather stations at relatively flat locations with representative snow depth values. Areas where the snowpack is strongly influenced by wind or avalanches, either removing or depositing large amounts of snow, are unsuitable. We developed an automated approach combining remotely sensed snow depth maps with terrain characteristics (e.g., slope or homogeneity) and simulated avalanche scenarios to identify optimal positions for AWS. We demonstrate how this approach can enhance safety-relevant information in the Dischma Valley near Davos, Switzerland. This approach could be applied globally wherever high-quality digital elevation models are available and spatially coherent snow depth maps can be acquired. Currently, the positioning of AWS relies heavily on expert judgment. Our tool could help make these decisions more comprehensible and serve as a second opinion, ensuring the optimal placement of AWS.
Object ID: ISSW2024_O8.10.pdf
Language of Article: English
Presenter(s): Yves Buehler
Keywords: Weather station, spatial variability, remote sensing, snow depth, hazard indication mapping, avalanche warning
Page Number(s): 1054 - 1060
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