Expanding PROPAGATOR Cellular Automata based wildfire simulator to represent surface and crown fire transitions

PROPAGATOR is a fire spread simulator designed as a stochastic cellular automaton model for rapid fire risk assessment. The model uses high-resolution data about topography and vegetation cover, accounting for different vegetation types. Key inputs include wind, fuel moisture, and the ignition point. Additionally, the model can incorporate firefighting strategies, such as modifying fuel moisture content or implementing firebreaks. The probability of fire spread is influenced by vegetation type, slope, wind, and fuel moisture content, while fire-propagation speed is calculated using a Rate of Spread model. PROPAGATOR generates independent realizations of a stochastic fire propagation process. At each time step, it produces maps showing the probability of each cell in the domain being affected by fire, along with the potential rate of spread and fire-line intensity. 

The transition from low-intensity surface fires to burning in the vegetation canopy results in significantly larger flame heights, higher energy release rates, and increased rates of spread. Distinguishing between ground fire and canopy fire is therefore crucial for end users, impact evaluations, and the calibration of fire spotting submodels. 

To achieve this, incorporating Canopy Fuel Characteristics—such as canopy base height, canopy fuel load, canopy bulk density, and foliar moisture content—while applying appropriate simplifications, will be a critical step. Implementing Crown Fire Initiation and Spread Models will complement those already used in PROPAGATOR for ground fire, with adaptations of well-established models where feasible. Additionally, Vertical Interaction Mechanisms will be introduced into the probabilistic rules of the cellular automaton to represent conditions under which surface fires escalate to canopy fires and vice versa. 

These improvements will be validated using both synthetic and real case studies to assess their benefits for end users and practitioners. The development of these upgrades is driven by work conducted within the framework of the RETURN extended partnership (Multi-risk science for resilient communities under a changing climate) which aims at strengthening national research chains on environmental, natural, and anthropogenic risks while fostering participation in European and global strategic value chains. 

Keywords: wildfire propagation models, cellular automata, crown fires, wildfire risk, wildfire risk management