Wildfire prediction is essential for reducing environmental risks. A recent study, the result of a collaboration between CIMA Research Foundation and BCAM, has enhanced the PROPAGATOR simulator with new fire spotting models, making prevention and intervention strategies more effective. The Molise case study demonstrates the tangible impact of this innovation.
In the field of applied scientific research for environmental protection, the continuous improvement of operational tools is essential to effectively address natural hazards. To enhance the accuracy of wildfire predictions, CIMA Research Foundation collaborated with the Basque Center for Applied Mathematics (BCAM) to refine existing modeling techniques and introduce new parameterizations. Through this synergy, advanced fire spotting models have been integrated into the PROPAGATOR simulator, improving predictive accuracy and providing essential support for operational decision-making in the field.
From models to the field: the role of PROPAGATOR in wildfire risk management
Accurately predicting wildfire dynamics is crucial for protecting communities and ecosystems. In this context, PROPAGATOR stands out as a key tool, developed over the past 10 years by CIMA Research Foundation for the Italian Civil Protection Department. With a solid scientific foundation, researchers continuously update the model in line with the latest scientific advancements to address the growing challenges posed by wildfires.
PROPAGATOR is a simulator based on cellular automata that provides probabilistic indications of how a fire may spread under specific conditions and in a given area. The model accounts for variables such as fuel moisture content, wind speed and direction, terrain slope, and vegetation type. These factors collectively influence fire spread velocity and direction, making each wildfire a unique event.
Andrea Trucchia, researcher in Wildfire Science and Forest Biodiversity Conservation at CIMA Research Foundation, explains: “Wildfire propagation is a complex phenomenon, where atmospheric conditions, fuel characteristics, and terrain morphology interact. PROPAGATOR allows us to model this complexity, combining cellular automata approaches with experimental data and numerical simulations. The continuous development of the simulator has led to the integration of new parameterizations, which consider the spatial variability of vegetation and its response to meteorological conditions during a fire event. This enables increasingly accurate predictive scenarios, providing a robust scientific foundation for operational wildfire management.”
The fire spotting case study in Molise
One crucial aspect of wildfire propagation is the phenomenon of fire spotting, in which burning embers are transported by the wind over considerable distances, igniting new fire outbreaks far from the main front. This process results in discontinuous propagation, forming a “patchy” fire pattern. Fire spotting significantly complicates suppression operations and requires advanced predictive models for effective management.
In 2021, a significant wildfire affected the Campomarino area in Molise, where fire spotting played a decisive role in fire spread. This event was the subject of an in-depth study conducted in collaboration with BCAM, the Italian Civil Protection Department, and the Molise Region. The results were published in the scientific paper Fire-spotting modelling in operational wildfire simulators based on Cellular Automata: A comparison study. Researchers integrated new fire spotting parameterizations into the PROPAGATOR simulator, including the RandomFront model, based on physical principles and applied in this context for the first time.
The study’s results demonstrated how integrating these advanced parameterizations into the simulator improved the ability to predict fire spread, offering more detailed patterns of ignition probabilities in areas subject to fire spotting. These advancements not only enhance the scientific understanding of wildfire dynamics but also provide more precise operational tools for risk management and emergency response planning.
Continuous innovation for effective protection
The integration of scientific research with operational applications can lead to significant advancements in understanding natural disasters, enabling the translation of cutting-edge scientific discoveries into increasingly effective operational tools.
Andrea Trucchia concludes: “Staying up to date with the latest scientific research and integrating this knowledge into our operational tools is fundamental for effectively tackling the challenges posed by wildfires. This proactive approach ensures that prevention and response strategies are always based on the best available evidence, helping protect both human communities and natural ecosystems from the devastating effects of wildfires.”