Wildfire activity in the United States incurs substantial costs and losses, and presents challenges to federal, state, tribal and local agencies that have responsibility for wildfire management. Beyond the potential socioeconomic and ecological losses, and the monetary costs to taxpayers due to suppression, wildfire management is a dangerous occupation. Aviation resources, in particular large airtankers, currently play a critical role in wildfire management, and account for a relatively large share of both suppression expenditure and firefighting fatalities. A recent airtanker modernisation strategy released by the US Department of Agriculture Forest Service and the US Department of Interior highlighted cost effectiveness as the fundamental tenet of both the replacement strategy and the use of aerial firefighting resources. However, determining the cost effectiveness of alternative airtanker fleets is challenging due to limited data and substantial uncertainty regarding aerial firefighting effectiveness. In this paper, we significantly expand on current airtanker usage and effectiveness knowledge, by incorporating spatially explicit drop location data linked to firefighting resource orders to better identify the period in the fire history when drops occurred, and through characterisation of the resulting outcomes of fires that received drops during initial attack. Our results confirm earlier work suggesting extensive use of large airtankers on extended attack, despite policy suggesting priority use in initial attack. Further, results suggest that containment rates for fires receiving large airtanker use during initial attack are quite low. We explore possible causes for these results, address potential limitations with our methods and data, and offer recommendations for improvements in data collection and aviation management.
Calkin DE, Stonesifer CS, Thompson MP, McHugh CW. Large airtanker use and outcomes in suppressing wildland fires in the United States. International Journal of Wildland Fire. 2014 ;On-line early.