Predicting the number of daily human-caused bushfires to assist suppression planning in south-west Western Australia
M. P. Plucinski A B E , W. L. McCaw C , J. S. Gould A B and B. M. Wotton D
+ Author Affiliations
- Author Affiliations
A CSIRO Ecosystem Sciences and CSIRO Climate Adaptation Flagship, GPO Box 1700, Canberra, ACT 2601, Australia.
B Bushfire Cooperative Research Centre, Level 5, 340 Albert Street, East Melbourne, Vic. 3002, Australia.
C Department of Parks and Wildlife, Locked Bag 2, Manjimup, WA 6258, Australia.
D Natural Resources Canada, Canadian Forest Service, Great Lakes Forestry Centre, 1219 Queen Street East, Sault Ste Marie, ON, P6A 2E5, Canada.
E Corresponding author: Email: matt.plucinski@csiro.au
International Journal of Wildland Fire 23(4) 520-531 https://doi.org/10.1071/WF13090
Submitted: 31 May 2013 Accepted: 19 December 2013 Published: 9 April 2014
Abstract
Data from bushfire incidents in south-west Western Australia from the Departments of Parks and Wildlife and Fire and Emergency Services were used to develop models that predict the number of human-caused bushfires within 10 management areas. Fire incident data were compiled with weather variables, binary classifications of day types (e.g. school days) and counts of the number of fires that occurred over recent days. Models were developed using negative binomial regression with a dataset covering 3 years and evaluated using data from an independent year. A common model form that included variables relating to fuel moisture content, the number of recent human-caused bushfires, work day (binary classification separating weekends and public holidays from other days) and rainfall was applied to all areas. The model had reasonable fit statistics across all management areas, but showed enough day-to-day prediction variability to be of practical use only in the more densely populated management areas, which were dominated by deliberate ignitions. The findings of this study should be of interest to fire managers in Mediterranean climatic regions where a variety of practices are used to manage wildfires.
Additional keywords: accidental ignitions, deliberate ignitions, fuel moisture, negative binomial regression, wildfire occurrence.
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