First Results for Simulation Environment using Multi-Sensor Network Observing Hintereisferner

Glaciers are vulnerable to the impacts of climate change, making them a dynamic and rapidly transforming element of the Earth system. The consequences of these changes extend far beyond the polar and mountain regions, affecting ecosystems and water resources globally. Challenges such as flood risks and hazards like rock moraines underscore the importance of understanding this part of the ecosystem. Monitoring and measuring glacial environments are essential not only for mitigating risks but also for advancing scientific knowledge. By studying the dynamics of glaciers, scientists can gain a deeper understanding of their interactions with the Earth's climate system and better predict future changes.

The alpine glaciers have been research areas of several institutes for different geodetic sensors for over 150 years. The current challenge lies in leveraging observational data to develop a glacier model that can assimilate geodetic observations. This research aims to design an optimized geodetic sensor network that enhances the integration of field observations into glacier modeling. Both simulations and real-data processing should be considered. Sensitivity studies evaluate first the data products themselves and second the model’s response to various data inputs, identify observation errors, and refine the network design.

At this stage, a framework for a closed-loop simulation environment tailored to the Hintereisferner is presented. This environment should enable systematic assessment of sensor performance, network accuracy, and future scalability on a simulation basis. Spatial and temporal resolution of the ground truth and the observation methods are discussed. Different sensors are introduced in terms of spatial resolution and measurement accuracy. Initial results from sensitivity studies using different sensors are presented. Additionally, challenges in implementing the simulation environment are discussed.