Reducing the risk of house loss due to wildfires
Introduction
Wildfires can cause considerable damage to people and property with the effects on communities and individuals lasting for many years after the event. The Black Saturday fires in Victoria, Australia, resulted in the damage or destruction of over 2000 houses and the loss of 173 lives (Gibbons et al., 2012, Leonard et al., 2009b, Price and Bradstock, 2012). Similarly, the 2007 wildfires in California resulted in the evacuation of 300 000 people and the loss of 2223 houses (McCaffrey and Rhodes, 2009). In the following years, wildfires have considerable economic impacts on communities, local business and production (e.g. agriculture and forestry) (Ganewatta, 2008). Societal impacts continue for decades as many residents suffer post-traumatic stress as a result of the wildfire (Langley and Jones, 2005, McFarlane et al., 1997, Papadatou et al., 2012). Minimising the damage of wildfires to people and property will therefore have a range of economic and social benefits.
Fire management agencies have large budgets devoted to landscape fire management in an attempt to reduce the risk of fires reaching property (Berry et al., 2006, Calkin et al., 2005). These are primarily fuel treatment (e.g. thinning, clearing, prescribed burning) and fire suppression (i.e. the coordinated use of fire-fighting resources such as trucks, helicopters and aircraft, in an attempt to contain or extinguish the fire). Optimised placement of fuel treatments and resources can reduce the risk to the interface, i.e. those houses which form the boundary between native vegetation and urban areas (Bradstock et al., 2012, Finney et al., 2007, Penman et al., 2014, Plucinski, 2012, Wilson and Wiitala, 2005). However, these actions are not expected to contain all wildfires, particularly under more severe fire weather conditions (Cary et al., 2009, LaCroix et al., 2006, Penman et al., 2011a, Price and Bradstock, 2010). Given that wildfires under severe fire weather conditions are generally responsible for the majority of area burned and greatest loss of houses (Blanchi et al., 2010, Bradstock et al., 2009, Mees and Strauss, 1992, Podur and Martell, 2007), wildfires will continue to reach houses regardless of the extent of management intervention in the landscape (Bradstock et al., 2012, Cary et al., 2009, Penman et al., 2014, Syphard et al., 2011). The frequency with which fire impacts upon the interface is predicted to increase due to the expansion of populations into native vegetation and the severity of fire weather increases (Clarke et al., 2013, Penman et al., 2013a, Syphard et al., 2007). Therefore house-based strategies are required to complement the landscape strategies in order to minimise house loss.
Management strategies that may reduce the risk of individual property loss can be considered to be preventative or defensive, because they are predicated on the assumption that fires will reach the vicinity of houses. Considered decisions about placement of property relative to flammable vegetation and building construction (Blanchi and Leonard, 2008, Cohen, 2000, Radeloff et al., 2005, Ramsay et al., 1987) will affect the level of exposure to fire, hence the probability of loss. In the short term, the primary preventative option is educating land owners to prepare their property for wildfire by reducing or removing fuels within their property (Blanchi and Leonard, 2008, Gibbons et al., 2012, Gill, 2005, McGee, 2011) to reduce both the risk of ignition within the property and the severity of subsequent fire(s). Other defensive actions include fire suppression in and around houses, although the level of suppression can vary from work carried out by individual residents through to volunteer or professional fire agency resources, e.g. fire trucks, helicopters etc.
All these strategies are considered to reduce the risk of house loss however there has been no quantification of the individual or interactive effects. Fire management agencies require this information to determine how to invest limited budgets in order to reduce the risk of house loss. There are limited data available to address the issue, primarily because houses are lost during emergency situations where the focus is on protecting life and property, rather than data collection. Generating such a data set after an event relies on methods such as detailed structured interviews of a large number of individuals in an attempt to reconstruct the range of actions and responses. Furthermore, generation of suitable data that covers sufficient events for a quantitative analysis has generally been considered too difficult and expensive (Gill et al., 2013). An alternative to reconstruction is to use a formal elicitation process to generate meaningful values for quantitative analysis (Burgman et al., 2011, Martin et al., 2012, Wintle et al., 2013).
Here we use a process based model combined with a formal elicitation process to quantify the relative influence of preventative and suppressive management strategies on the probability of house loss. Specifically, we ask the questions:
- 1.
What is the optimal strategy or strategies at the interface to reduce the risk of house loss in the event of a fire?
- 2.
Does this capacity differ between urban interface and intermix communities (low density housing within extensive native vegetation)? (Radeloff et al., 2005)
Section snippets
Study area
The study was conducted in the Sydney Basin Bioregion (Environment Australia, 2000), the most populated area of Australia. Within the Sydney Basin Bioregion are three large urban centres (Sydney, Newcastle and Wollongong) which support a combined population of 5.5 million people (www.abs.gov.au, Accessed March 2011). Fire prone native vegetation, predominantly dry Eucalypt forest (Keith, 2004), surrounds all three urban centres creating a large and complex urban interface (Fig. 1). Between 2000
Elicitation
Opinions of the participants suggested that the education campaigns considered were likely to be ineffective in influencing preparedness by residents (Fig. 4). The greatest change in the distribution was predicted to occur if an education campaign included a ‘street walk’, with much smaller changes predicted when either the letterbox drop or television advertising were included in the mix. Regardless of the advertising campaign very few houses were considered to be in the “Good” category of
Discussion
There are a range of strategies in and around properties that will reduce the risk of house loss in the event of a fire. Increasing the distance between vegetation and structures had the strongest influence as they reduce the exposure and increase the ability of fire suppression to address fires when they occur. Residents have the potential to reduce their own risk if they prepare property for wildfire, yet they often fail to do so. While there were no differences in the relative role of
Community education
Community education campaigns were predicted to be relatively unsuccessful in altering the extent to which property preparedness and thereby reducing the risk of house loss. Participants in the elicitation exercise believed that few residents were likely to respond to any of the proposed education campaigns which is consistent with empirical studies elsewhere (McLennan et al., 2012). The idealised education campaign which hypothetically improved the overall community standard of house condition
Model limitations
The model presented here attempted to quantify the risk of loss that can be attributed to management actions of various spatial and temporal time scales. There is a lack of empirical data that can be used for such a model and this resulted in the need to use expert elicitation for some of the key nodes in the model. However, this is not necessarily a limitation as Bayesian Networks built purely on empirical data tend overfit the data to the situation in which data were collected and as result
Conclusion
In the study, we have brought together spatial data, a process model and expert opinion in order to undertake a risk assessment of various fire management strategies. BNs provide the ideal framework for such a task and have been increasingly used in the field of risk assessment (Borsuk et al., 2004, Burgman et al., 2010, Chen and Pollino, 2012, Dlamini, 2010, Ejsing et al., 2008, Johnson et al., 2010, Jolma et al., 2014, Lucas, 2004, Oatley and Ewart, 2003, Punt and Hilborn, 1997). The approach
Acknowledgements
The work was funded by the NSW Rural Fire Service. The probability elicitation exercise was approved by the University of Wollongong of Human Research Ethics Committee approval number HE12/149. All participants in the elicitation provided written consent prior to the exercise. Anonymity of participants has been guaranteed.
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