Factors influencing large wildland fire suppression expenditures
- 1 January 2008
- journal article
- research article
- Published by CSIRO Publishing in International Journal of Wildland Fire
- Vol. 17 (5), 650-659
- https://doi.org/10.1071/wf07010
Abstract
There is an urgent and immediate need to address the excessive cost of large fires. Here, we studied large wildland fire suppression expenditures by the US Department of Agriculture Forest Service. Among 16 potential non-managerial factors, which represented fire size and shape, private properties, public land attributes, forest and fuel conditions, and geographic settings, we found only fire size and private land had a strong effect on suppression expenditures. When both were accounted for, all the other variables had no significant effect. A parsimonious model to predict suppression expenditures was suggested, in which fire size and private land explained 58% of variation in expenditures. Other things being equal, suppression expenditures monotonically increased with fire size. For the average fire size, expenditures first increased with the percentage of private land within burned area, but as the percentage exceeded 20%, expenditures slowly declined until they stabilised when private land reached 50% of burned area. The results suggested that efforts to contain federal suppression expenditures need to focus on the highly complex, politically sensitive topic of wildfires on private land.Keywords
This publication has 8 references indexed in Scilit:
- Estimating Suppression Expenditures for Individual Large Wildland FiresWestern Journal of Applied Forestry, 2007
- THE WILDLAND–URBAN INTERFACE IN THE UNITED STATESEcological Applications, 2005
- Forest Service Large Fire Area Burned and Suppression Expenditure Trends, 1970–2002Journal of Forestry, 2005
- Hierarchical Partitioning Public-domain SoftwareBiodiversity and Conservation, 2004
- Regression and model-building in conservation biology, biogeography and ecology: The distinction between – and reconciliation of – ‘predictive’ and ‘explanatory’ modelsBiodiversity and Conservation, 2000
- An Economic Evaluation of Public and Organized Wildfire Detection in WisconsinInternational Journal of Wildland Fire, 1998
- EDF Statistics for Goodness of Fit and Some ComparisonsJournal of the American Statistical Association, 1974
- THE PROBABLE ERROR OF A MEANBiometrika, 1908