Background: Wildfires are a growing threat to many ecosystems, bringing devastation to human safety and health, infrastructure, the environment and wildlife.
Aims: A thorough understanding of the characteristics determining the susceptibility of an area to wildfires is crucial to prevention and management activities. The work focused on a case study of 13 countries in the eastern Mediterranean and southern Black Sea basins.
Methods: A data-driven approach was implemented where a decade of past wildfires was linked to geoclimatic and anthropic descriptors via a machine learning classification technique (Random Forest). Empirical classification of fuel allowed linking of fire intensity and hazard to environmental drivers.
Key results: Wildfire susceptibility, intensity and hazard were obtained for the study area. For the first time, the methodology is applied at a supranational scale characterised by a diverse climate and vegetation landscape, relying on open data.
Conclusions: This approach successfully allowed identification of the main wildfire drivers and led to identification of areas that are more susceptible to impactful wildfire events.
Implications: This work demonstrated the feasibility of the proposed framework and settled the basis for its scalability at a supranational level.