Climate change poses significant challenges in alpine areas, particularly with gliding snow hazards affecting natural habitats and ski resorts. The key mechanism involves a water film forming between the ground and snowpack, which reduces friction and can lead to avalanches. Currently, management involves artificial triggering by deliberately adding water to decrease ground-snow friction. Understanding this water film’s behavior is crucial – its presence and interaction with ground structure enables early detection of potential gliding snow, allowing intervention before visible glide cracks appear and supporting decision-making when cracks are already present.
To address this challenge, a technological approach to improve the detection and monitoring of these water films. A downward-looking radar system is used to detect water films and derive the connection structure within the snowpack.
The development and validation of the system take place in a controlled laboratory setting to precisely determine the key measurement parameters. Materials that match the density of snow and have the ability to retain water are used to simulate realistic snow conditions. In the laboratory, extensive tests are conducted to assess penetration depth and measurement accuracy, with a particular focus on detecting water films of varying thickness. Frequency tests enable deeper measurements, allowing for a more detailed analysis of the structure and stability of the simulated snow layer. The experimental setup includes different material layers with varying water content to capture diverse snow metamorphosis scenarios. The recorded parameters—such as reflection properties, radar signal penetration depth, and variations in water content—are then integrated into a data-driven model. Using machine learning approaches, the development of the snowpack and its stability are analyzed and predicted. The entire approach focuses on the reliable detection of liquid water within the snowpack and the integration of these measurements into predictive algorithms to improve gliding snow forecasting. The development of the measurement system remains centered on the laboratory setup to ensure optimal calibration and validation of measurement methods under controlled conditions.