Efficiently Estimating Patterns in Wildfire Burn Probability

Wildfires can be dangerous phenomena, creating risks for communities that are likely to be exposed to wildfire. The likelihood of community exposure to a wildfire is influenced by the interaction of fire behaviour factors (weather, fuel and topography) across multiple spatial scales.

Our objective is to develop an index that measures the connectivity of our communities to the multi-scaled interactions of fire behaviour factors in a computationally efficient manner. The index serves as a proxy for relative wildfire likelihood and represents temporally and spatially variable patterns in wildfire likelihood. The index will support wildfire risk assessments, including exploring problems such as optimising landscape treatment placements.

Here, we introduce the connectivity index as a multi-scaled, process-informed spatial aggregation of wildfire hazard properties across a landscape. We use a case study landscape to compare the connectivity index against simulated burn probability and historical burnt areas. Using a historically-informed parameterisation, we find a high correlation (0.83) to simulated burn probability with a fraction of the computational effort (0.3% of the runtime). The connectivity index also demonstrates an improved ability to explain historical burnt areas. We identify opportunities to further improve performance by incorporating the index into data-driven model structures.

Our findings demonstrate that the connectivity index captures structural patterns in wildfire likelihood, as influenced by the interaction of fire behaviour factors across multiple scales. By achieving this in a computationally efficient manner, we believe that the connectivity index can work alongside other measures of wildfire likelihood to inform and plan wildfire risk reduction activities, including in large-scale analysis.