Modelling the probability of Australian grassfires escaping initial attack to aid deployment decisions
Matt P. PlucinskiA CSIRO Ecosystem Sciences and CSIRO Climate Adaptation Flagship, GPO Box 1700, Canberra, ACT 2601, Australia. Email: matt.plucinski@csiro.au
B Bushfire Cooperative Research Centre, Level 5, 340 Albert Street, East Melbourne, Vic. 3002, Australia.
International Journal of Wildland Fire 22(4) 459-468 https://doi.org/10.1071/WF12019
Submitted: 3 February 2012 Accepted: 18 September 2012 Published: 23 November 2012
Abstract
Most grassfires that occur in southern Australia are contained to small areas by local suppression resources. Those that are not require extra resources from neighbouring districts. Identifying these fires at the start of initial attack can prompt early resource requests so that resources arrive earlier when they can more effectively assist with containment. This study uses operational data collected from Australian grassfires that used ground tankers and aircraft for suppression. Variables were limited to those available when the first situation report is provided to incident controllers and included weather parameters, resource response times, slope, curing state, pasture condition and estimated fire area at initial attack. Logistic regression and classification trees were used to identify grassfires likely to escape initial attack by (a) becoming large (final area ≥100 ha), (b) being of long duration (containment time ≥4 h) or (c) either or both of these. These fires would benefit from having more resources deployed to them than are normally available. The best models used initial fire area and Grassland Fire Danger Index as predictor variables. Preliminary operational decision guides developed from classification trees could be used by fire managers to make quick assessments of the need for extra resources at early stages of a fire.
Additional keywords : classification trees, grasslands, logistic regression, wildfire containment, wildfire suppression.
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