New research from the Bioeconomy Science Institute Maiangi Taiao aims to improve New Zealand’s wildfire preparedness and response.
The institute has developed national-scale wildfire hazard potential data layers to help communities, businesses, and wildfire managers better understand areas more likely to burn under high-risk conditions and the severity of fire behaviour.
The data were generated by simulating more than 500,000 potential ignitions and wildfire growth across New Zealand.
Fire scientist Laura Kiely, who led the work, said the team modelled potential fires under set conditions, not actual fires that have happened.
“We’re not saying this is the number of fires that will happen at any time. Rather, we’re considering: if an ignition occurs, what happens? What does that look like?” said Kiely.
“Combining data from a high number of simulations allows us to consider the potential fire behaviour and the probability of it occurring.”
The data combined information on likely ignition drivers, fuel and weather conditions, and potential fire behaviour to provide a consistent, documented baseline for planning, prevention, preparedness, and risk communication. The team is designing these layers so they can be shared and reused in operational tools and scientific workflows.
Kiely added that this will help identify which parts of New Zealand burn repeatedly under historical locally extreme weather conditions in the simulations, and which parts experience high- or low-intensity fires, indicating what a wildfire could look like in a particular area if one were to occur there.
“This can enable safer wildfire management, resilience planning, better-prepared responses and more transparent conversations about mitigation. Knowing the potential hazard that could occur allows us to better prepare for a wildfire.”
Consistent hazard layers can help explain why defensible space, building design choices and protection of evacuation routes matter, supporting resilience. The team has been working with councils and risk analysis companies that have already expressed interest in using this data.
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