An efficient method to simulate wildfire propagation using irregular grids

Abstract

Climate change and land use changes are projected to make wildfires more frequent and intense, with a global increase in the number of extreme fires of up to 14 % by 2030, 27 % by the end of 2050, and 57 % by the end of the century (United Nations Environment Programme & GRID-Arendal, 2022). These values were forecasted by a Representative Concentration Pathway with a radiative forcing value of 6 W m⁻², which represents the energy imbalance in Earth's energy system caused by greenhouse gases and other factors, in the year 2100. This latest information has increased interest regarding how large-scale, often catastrophic, events can be reduced and more effectively managed. One critical area revolves around real-time fire line prediction and how resources can be better deployed to reduce the propagation of wildfires. In this paper a novel software platform called the Irregular Grid Software (IGS) was developed, which allows for the simulation of wildfires on a configurable grid using mathematical models for fire propagation. The aim of the IGS was to explore computational differences between different grid types and a comparison to preexisting software while producing outputs of an acceptable similarity to that preexisting software. The configurable grid was built using a Voronoi diagram, where a fire can spread between polygons, propagating throughout the grid. The configurable grid allows for cross comparison of both regular grids, such as square, hexagonal, and triangular, and irregular grids, such as a randomly seeded Voronoi diagram and a newly developed flammable resolution grid (FRG). The FRG was adapted to focus attention on areas of higher importance, which provides greater precision at the cost of extra computing time. The irregular grid approach and ForeFire, an existing industry-standard program, were compared. The comparison included simulations of wildfires located in the Wicklow Mountains, in Ireland, a region used by fire services for exercises. The performance of the grid-based techniques was examined using a set of experiments to characterise the model's response to key factors such as wind, elevation, and fuel type. The objective of this paper was to compare the various grid types on the metrics of similarity with ForeFire and computational time while also comparing the FRG to ForeFire on the same metrics with multiple sample wildfires. From the sample wildfires tested in this paper the results show that the IGS runs on average 34 times more quickly than ForeFire on the same computing platform while retaining an average result similarity of 80 % with ForeFire.