A comparison study between fire-spotting models by a wildfire simulator based on a cellular automata approach

Wildfire propagation is a non-linear and multiscale phenomenon in which there are involved multiple physical and chemical processes. One critical mechanism in the spread of wildfires is the so-called fire-spotting: a random phenomenon which occurs when embers are transported by wind causing the start of new spotting ignitions. Due to its nature, fire-spotting is usually implemented into the fire spread models as a pure probabilistic process regardless the existing physical conditions when the phenomenon occurs. In this work, we have implemented the physical parametrization of fire-spotting RandomFront (Trucchia et al., 2019) into the stochastic operational fire spread software PROPAGATOR (Trucchia et al., 2020), based on cellular automata approach. The research has been conducted in two objetives: (i) To study the impact of macroscale (Egorova et al., 2020) and mesoscale factors (Egorova et al., 2022) over the spot fires generation and its influence over the Rate of Spread within the cellular automaton framework and (ii) compare these results against those by the pure probabilistic model of fire-spotting previously used in literature (Alexandridis et al., 2008), which was explicitly developed in the framework of wildfire spread simulators based on cellular automata. The preliminary results show how the RandomFront parameterization can reproduce the same areas of maximum probability as the model we are comparing but is able to assign a non-zero burning probability to larger areas. The observed long-range fluctuations of the burning probability within RandomFront parametrization create a complex pattern of fire spread for middle and low burning probability areas which is not observed within the Alexandridis et al. (2008) parametrization.

Refrerences:

Alexandridis, A., Vakalis, D., Siettos, C. I., and Bafas, G. V.: A cellular automata model for forest fire spread prediction: The case of the wildfire that swept through Spetses Island in 1990, Appl. Math. Comput., 204, 191–201, https://doi.org/10.1016/j.amc.2008.06.046, 2008.

Egorova, V. N., Trucchia, A., and Pagnini, G.: Fire-spotting generated fires. Part I: The role of atmospheric stability, Appl. Math. Model., 84, 590–609, https://doi.org/10.1016/j.apm.2019.02.010, 2020.

Egorova, V. N., Trucchia, A., and Pagnini, G.: Fire-spotting generated fires. Part II: The role of flame geometry and slope, Appl. Math. Model., 104, 1–20, https://doi.org/10.1016/j.apm.2021.11.010, 2022.

Trucchia, A., Egorova, V., Butenko, A., Kaur, I., and Pagnini, G.: RandomFront 2.3: a physical parameterisation of fire spotting for operational fire spread models – implementation in WRF-SFIRE and response analysis with LSFire+, Geosci. Model Dev., 12, 69–87, https://doi.org/10.5194/gmd-12-69-2019, 2019.

Trucchia, A., D’Andrea, M., Baghino, F., Fiorucci, P., Ferraris, L., Negro, D., Gollini, A., and Severino, M.: PROPAGATOR: An Operational Cellular-Automata Based Wildfire Simulator, Fire, 3, 26, https://doi.org/10.3390/fire3030026, 2020.