Improving the Predictivity of Simulating Natural Hazards due to Mass Movements – Optimal Design and Model Selection –

• Applicant: Professorin Dr. Julia Kowalski
• Subject Area: Palaeontology
• Term: from 2020 to 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 441527981

The goal of this project is to improve the predictive power of computational process models for rapid gravity-driven mass movements such as debris flows or other forms of complex landslides as well as rock or snow avalanches. These computational models are used to compute run-out distances, deposition areas and impact pressures, all of which are important when wanting to design hazard mitigation measures. Mass movements are defined as the movement of mobilized surface material caused by gravity. Their dynamic behaviour is highly influenced by their flowing material. Its composition varies depending on its exact type (landslide versus debris flow versus snow avalanche). It is often highly heterogeneous and, due to entrainment and deposition along the way, might even vary with time. Especially the amount of water in the flowing body can have a dramatic impact on the mass movement’s dynamic behaviour. A major challenge when developing computational models for gravity-driven mass movements is hence to adequately reflect material and process complexity in the underlying mathematical process model. To date, research on improving the computational model's predictive power mainly focuses on developing process models of higher complexity levels. Instead of treating the mobilized mass as one bulk mixture, for example, water and solids of different sizes are modeled separately, or the dimensionality of the modeling approach is increased. While such strategies can offer new insights into the process itself, they typically come at the price of an increasing number of model parameters, which are hard to calibrate.Another possible strategy to improve the predictive power is often overlooked, namely to systemically optimize the information content of the data sets used for calibrating model parameters, as well as to exploit the available data sets in a better way. We will address this research gap in the proposed research project by developing optimal design concepts that will result in novel, optimized field scale monitoring set-ups and protocols as well as data sets of higher information content. In a second phase of the project, we will develop model selection routines that allow to utilize available data sets for gravity-driven mass movements to find the most plausible process model out of a collection of candidate models. These two aspects are novel to the field of natural hazards research. They will be developed and validated by a doctoral student, who will be supervised by the applicant in her recently granted Heisenberg group. The value-add of the developed methodologies for the natural hazards engineering community will be demonstrated during a number of national and international collaborations.

• DFG Programme: Research Grants