With the growing world population, managing the risk associated with natural hazards (e.g. floods, tsunamis, landslides, wildfires, earthquakes, volcanic eruptions) becomes a top priority in research and applications. Moreover, the massive increase in sensing technologies has enabled the production of large volumes of geospatial data at high spatial and temporal resolutions. These offer the possibility for their assimilation in increasingly complex models, able to capture finer details of the physical processes underlying the natural hazards, thus improving their predictive capabilities.

The session welcomes contributions about advances in computational modelling for natural hazards and risk, including:

• Numerical models to simulate the onset and evolution of hazardous phenomena;
• Source characterization and sensitivity analysis of model input parameters;
• Uncertainty quantification and reduction;
• Data assimilation techniques, with focus on remote sensing data (fixed monitoring stations, satellites, manned and unmanned aerial vehicles, etc);
• Multi-scale hazard and risk assessment;
• Risk mitigation through long and short-term strategies.