Cellular Automata Based Simulators for the Design of Prescribed Fire Plans: the Case Study of Liguria, Italy

Abstract Background Socio-economic changes of the last decades have led to fuel buildup in Mediterranean forests. In the context of Climate Change, this situation has resulted in the formation of potential fire traps that could lead to catastrophic events. Several wildfire management systems have therefore started to use prescribed fires for land management and wildfire risk mitigation. Prescribed fires are prepared by designing a plan, where specific objectives are identified, prescriptions are checked, and scenarios are defined. In the plan definition phase, simulation models can provide an additional decision-support tool, allowing the different scenarios to be qualitatively and quantitatively assessed. We used the well-established wildfire simulation tool PROPAGATOR to identify potential areas of treatment and to assess different scenarios of hypothetical prescribed fires. In the present work, we prepared the model for use in prescribed fires by changing the native space-time resolution to a finer scale. We selected a case study in the Liguria region, Italy, where the model is operationally used by the regional wildfire risk management system during emergencies. Results We first used the propagation model to simulate a wildfire event, to show the potentiality of the model as an emergency response tool. We selected the most important fire incident that occurred in the Liguria region in 2022. We then used PROPAGATOR to identify the optimal treatment areas to maximize wildfire risk mitigation effects and reduce the costs of treatment. In the identified areas, we used the model to simulate ignition scenarios in different weather conditions allowed by the regional regulation. The scenarios developed by the model made it possible to differentiate between situations that are under control and those that pose greater risks. Conclusions We showed how PROPAGATOR can provide quantitative and qualitative information that can be used in prescribed fire planning. Our methodology involved incorporating expert opinions throughout the process and providing scenarios based on this information. The possibility of evaluating different scenarios and having quantitative information helps make informed decisions, promoting safer and more efficient fire management practices.