Millions of acres of land are destroyed by wildfires every year, and they pose a significant threat to humans both in terms of property damage and potential loss of life. Of course, there is a significant impact on the ecology of areas affected by wildfires as well. The risk of devastating fires and their costs have increased due to trends in land development near the boundary between wildland and urban areas, which is known wildland urban interface (WUI). Additionally, there is evidence that wildfires are becoming bigger and more common, and it is predicted that this trend will continue because of global warming. This is presenting ever greater challenges to those who must manage, mitigate, and predict fires at the WUI.

Modeling wildfire spread requires an understanding of the pathways in which fires propagate. Generally speaking, there are three ways for a fire to spread: convective heat transfer (where the flame directly contacts a source of suitable fuel); radiant exposure (where heat from nearby large flames ignites suitable fuel); and firebrand shower (or "spotting," when new ignition occurs far from the current fire). The first two mechanisms spread the fire in a somewhat continuous manner throughout the landscape, but firebrand shower spotting leads to new fires away from the primary fire front and has been demonstrated to be the primary factor in significant conflagrations at the WUI that causes rapid spread.

Our group Is building data-driven Bayesian models for wildfire spread that Incorporate mechanistic Information via statistical cellular automata and level-set dynamics. Such models can accommodate uncertainty In observations, understanding of mechanisms, and Interactions with the environment.