Empirical models of initial attack success are not suitable for triaging ignitions in Victoria, Australia

Background Initial attack refers to the first firefighting actions undertaken at a newly reported fire, with the intention to control the fire’s spread. When multiple ignitions are reported and resources constrained, decisions are made about triaging fires for an escalated response. Aims We developed empirical initial attack models for grass and forest fires for the specific purpose of triaging fires, and evaluated the models relative to their intended application. Methods We used a recently developed dataset of spreading fires and tested three statistical modelling techniques to model the probability of unsuccessful initial attack. Model performance was evaluated with case studies, including major fire days. Key results The models showed limited skill overall. The best performance was observed for lightning-ignited remote forest fires, but performance was not adequate to support implementation on days with extreme fire behaviour. Conclusions Our approach did not yield suitable models for operational triage of fires, because there are many on-the-ground factors that influence the success of initial attack, and they are difficult to capture as data. Implications This study shows the importance of developing and testing empirical models relative to their intended application. It also demonstrates the significance of unmeasured bottlenecks in wildfire suppression on initial attack outcome.