Patterns of burned area by a cellular-automata fire simulator: The role of microscale wind field

Abstract

Wildfire dynamics simulated using PROPAGATOR, a quasi-empirical cellular automaton model, is studied by investigating the effects of wind-terrain interactions on the predicted burned area patterns. In order to do so, PROPAGATOR is coupled with WindNinja, a microscale wind simulator that computes spatially varying wind fields by a solver accounting for the conservation of mass and a second one assuming also conservation of momentum. Two historical fires are considered: the first one occurred in quasi-flat terrain in the Molise region of Italy, while the second ignited in the southern area of Avinyo in Spain. The standard fire simulator incorporating solely uniform wind fields and that coupled with the solvers of WindNinja predict similar burning probability maps for the Campomarino fire. The quasi-flatness of the Campomarino terrain is the main cause since the wind pattern is very weakly affected by its topography during fire propagation, resulting in only a slight deviation from the initial uniform wind field. However, in the presence of the complex topography of the Avinyo region, the fire spread simulations incorporating the spatially-varying wind fields predict significantly different burned area shapes for long time regimes and intense winds, where secondary fire spots separated from the main burning zone emerge. Larger spatial extension of the wildfire is observed in the absence of firefighters’ actions, but the predicted patterns seem to be similar regardless of the type of wind field input and its resolution. A 10-fold increase of perturbation magnitude on wind direction yields a contraction of the predicted burned area for all the probability thresholds considered, while a 2-fold and 10-fold increase of the wind speed perturbations lead to a significantly larger burned area and fire spread. Further quantitative analysis of the importance of incorporating spatially-varying wind fields in improving the predictability of cellular automata models in the case of megafires is mandatory.