Combining wildfire behaviour simulations and network analysis to support wildfire management: A Mediterranean landscape case study

The recent extreme wildfire events in the Mediterranean region overwhelmed fire suppression capabilities of national authorities, evidencing the need for a paradigm shift in forest and wildfire management. Wildfire spread and behaviour simulations can provide relevant information to assess fire hazard and to guide decision-makers in implementing fuel treatment strategies. Here, we quantify the influence of spatial arrangement of fuels on fire spread connectivity by developing a new graph-based connectivity index and by applying well established graph based metrics. These spatial connectivity metrics provide useful information for wildfire hazard and fuel management plans, since they can be used to map vegetation patches able to generate large and intense wildfires, with consequent large impacts. This approach was applied in the fire-prone landscape of Serra de Monchique, southwestern Portugal, under different fire weather scenarios and various linear fuelbreak layout options. We show that network metrics allow the location of the most important areas of wildfire connectivity whereas wildfire suppression may be challenging under specific fire weather conditions. We further show how the construction of fuelbreaks allows to modify the wildfire spread by directly reducing the overall connectivity by 20% and the amount of area with limited opportunities for ground-based suppression by up to 70%, which may, however, be insufficient under extreme weather conditions. This information can be used to prioritize the implementation of fuelbreak segments given their relevance to contain wildfire spread across the landscape and to provide defensible areas for ground-based wildfire suppression. We anticipate that these network metrics will be helpful to both land planners and wildfire researchers seeking to disrupt fuel connectivity and assess different fuel reduction strategies in fire-prone Mediterranean regions.