Modeling initial attack success of wildfire suppression in Catalonia, Spain

In southern European regions, the few fires that escape initial attack (IA) account for most of the burned area. Nonetheless, limited effort has been conducted to develop spatiotemporal models aiming at improving pre-positioning and deployment of fire-fighting brigades on the first dispatch. To this end, we calibrated a model to assess the probability of containment of fire by IA in Catalonia (northeastern Spain). The model was trained using machine learning algorithms from georeferenced historical fire ignition locations, fire response and weather conditions. Our results indicated that early detection, ground accessibility, and aerial support governed the broad spatial pattern of fire containment probability, with strong gradients that ranged from lowest chances of containment in northwestern mountains to highest in the coastal belt. In turn, weather conditions and fire simultaneity were crucial to explain the differences during wildfire season. We found that fires igniting above the 85th percentile of temperature and wind speed, during simultaneous fire episodes (n > 10), and 12.5 km away from the nearest fire station will probably escape IA, and grow into large events. These hazardous fire danger conditions were met 13 days per year on average during the period 1998-2015, with 5 fire simultaneous episodes escaping IA that burned 1546 ha in total. Results were provided as a set of high-resolution raster grids (100 m), which replicated the most typical weather and fire occurrence scenarios that first responders are likely to face during the wildfire season. This study reveals existing limitations in the dominant fire exclusion policy of Mediterranean areas and advocates for a comprehensive long-term wildfire management solution. Our model may help inform science-based decision-making on IA and general fire response planning in the study area.