Stochastic decision trigger modelling to assess the probability of wildland fire impact

Abstract

The ability to estimate both the time and probability of a wildfire reaching an area to be protected is critically important to preventing loss of human life and property, and damage to ecological and economic assets. Wildfire decision trigger modelling has been used to assess fire exposure and create evacuation trigger buffers around the communities providing a specific amount of warning time. This approach has been applied in multiple scenarios including household-level and community-level evacuation planning and during suppression operations. However, little attention has been paid to input data uncertainty using this modelling approach. This study presents an innovative stochastic fire simulation decision trigger modelling method that produces a probability map of the fire arrival to areas to be protected by simulating (n) wildfire decision trigger buffers with varied input data according to a potential range of deviations. The Tubbs fire (USA) was used as case study to show the applicability of this approach to estimate the probability of wildland fire impact. Our results highlighted the importance of considering input data uncertainty in operational environments to estimate fire progression and decision trigger buffers to better develop suppression tactic and strategy. The method presented may be solved in real-time and used with any empirical fire propagation model as a core engine. Practical real-time implications of this fire simulation mode are discussed.