Reducing the risk of house loss due to wildfires. (May 2015)

• Record Type: Journal Article
• Authors: Penman, T.D.; Nicholson, A.E.; Bradstock, R.A.; Collins, L.; Penman, S.H.; Price, O.F.
• Is Part Of: Environmental modelling & software. Volume 67(2015:May)
• Page Start: 12
• Page End: 25
• Publication Date: 2015-05
• Subjects: Bayesian Network -- Community engagement -- Suppression -- Planning -- Wildfire -- Fire management; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2014.12.020
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Wildfires will continue to reach people and property regardless of management effort in the landscape. House-based strategies are therefore required to complement the landscape strategies in order to reduce the extent of house loss. Here we use a Bayesian Network approach to quantify the relative influence of preventative and suppressive management strategies on the probability of house loss in Australia. Community education had a limited effect on the extent to which residents prepared their property hence a limited effect on the reduction in risk of house loss, however hypothetically improving property preparedness did reduce the risk of house loss. Increasing expenditure on suppression resources resulted in a greater reduction in the risk of loss than preparedness. This increase had an interaction effect with increasing the distance between vegetation and the houses. The extent to which any one action can be implemented is limited by social, environmental and economic factors. Highlights: Bayesian Network models provide a powerful tool for analysing fire risk scenarios. Combining multiple data sources allow for the exploration of a range of scenarios that cannot be empirically tested. Fire risk reduction strategies can reduce risk, but other factors limit their implementation. Approaches used here are broadly applicable to other natural hazards.