Land Management Practices Associated with House Loss in Wildfires

Abstract. Wildfires are a natural disturbance in many ecosystems, creating challenges for land management agencies who need to simultaneously reduce risk to people and maintain ecological values. Here we use the PHOENIX RapidFire fire behaviour simulator to compare fuel treatment strategies that meet the twin objectives of reducing wildfire risk to human settlements and a fire sensitive endangered species, the koala (Phascolarctos cinereus) in south-eastern Australia. The local koala population is in decline and a conservation management plan is being prepared to exclude wildfire for a 10-year period to assist with population recovery. Twelve scenarios developed by the land management agencies were compared using four indicators: wildfire size; burn probability; impact from exposure to fire; and treatment cost. Compared with the current risk setting, three treatment scenarios were found to reduce wildfire size and burn probability concurrently to both people and koalas. These strategies worked by increasing the landscape area treated, which came with increased financial cost. However, the impact from exposure to fire for both property and koala habitat remains high. Additional complementary strategies beyond landscape fuel reductions are needed to reduce impact from exposure in the event of a wildfire.

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Is there an inherent conflict in managing fire for people and conservation?

Bentley

Penman

2017

“…Significant research efforts are devoted to improved prediction of housing losses due to wildfires. Studies focus on the analysis of past house losses, either concentrating on the documentation of the events (Lynch 2004;Xanthopoulos 2008) or providing in-depth analysis and discussion (Gibbons et al 2012;Syphard et al 2012). Other studies use spatial simulation of fires (Ager et al 2010;Salis et al 2013;Mitsopoulos et al 2015;Platanianaki et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic prediction of wildfire economic losses to housing in Cyprus using Bayesian network analysis

Papakosta

Xanthopoulos²,

Straub

2017

Abstract. Loss prediction models are an important part of wildfire risk assessment, but have received only limited attention in the scientific literature. Such models can support decision-making on preventive measures targeting fuels or potential ignition sources, on fire suppression, on mitigation of consequences and on effective allocation of funds. This paper presents a probabilistic model for predicting wildfire housing loss at the mesoscale (1 km 2 ) using Bayesian network (BN) analysis. The BN enables the construction of an integrated model based on causal relationships among the influencing parameters jointly with the associated uncertainties. Input data and models are gathered from literature and expert knowledge to overcome the lack of housing loss data in the study area. Numerical investigations are carried out with spatiotemporal datasets for the Mediterranean island of Cyprus. The BN is coupled with a geographic information system (GIS) and the resulting estimated house damages for a given fire hazard are shown in maps. The BN model can be attached to a wildfire hazard model to determine wildfire risk in a spatially explicit manner. The developed model is specific to areas with house characteristics similar to those found in Cyprus, but the general methodology is transferable to any other area, as well as other damages.

“…Empirical research has been conducted into many aspects of fire risk, including the effectiveness of fuel reduction for limiting fire severity, and spread (Thompson et al 2007;Bradstock 2010, 2012), the contribution of house construction, the surrounding environment and the power of the fire to house loss (Gibbons et al 2012;Harris et al 2012;Syphard et al 2012;Price and Bradstock 2013a), the likelihood of ignition (Penman et al 2013), the distance travelled by fires (Price and Bradstock 2013b) and the number of fires experienced at the Wildland Urban Interface (WUI) (Haight et al 2004). However, there has been little research into the likelihood of fires spreading from their ignition point to the WUI (Price and Bradstock 2013b).…”

Section: Introductionmentioning

confidence: 99%

An empirical wildfire risk analysis: the probability of a fire spreading to the urban interface in Sydney, Australia

Price¹,

Borah²,

Bradstock³

et al. 2015

We present a method and case study to predict and map the likelihood of wildfires spreading to the urban interface through statistical analysis of past fire patterns using 15 000 lines from 677 fires with known ignition points and date and random potential end points on the urban interface of Sydney, Australia. A binomial regression approach was used to model whether the fire burnt to the end point of the lines as a function of measures of distance, fuel, weather and barriers to spread. Fire weather had the strongest influence on burning likelihood followed by the percentage of the line that was forested, distance and time since last fire. Fuel treatments would substantially reduce risk from fires starting 1-4 km away from the interface. The model captured 90% of variation in burning with 98% predictive accuracy on test data and was not affected by spatial autocorrelation. We apply the method to map fire risk in Sydney and discuss how the method could be expanded to estimate total risk (from ignition to impact on assets). The method has considerable promise for predicting risk, especially as a complement to simulation methods. AbstractWe present a method and case study to predict and map the likelihood of wildfires spreading to the urban interface through statistical analysis of past fire patterns using 15,000 lines from 677 fires with known ignition points and date and random potential end points on the urban interface of Sydney, Australia. A binomial regression approach was used to model whether the fire burnt to the end point of the lines as a function of measures of distance, fuel, weather and barriers to spread. Fire weather had the strongest influence on burning likelihood followed by the percentage of the line that was forested, distance and time since last fire. Fuel treatments would substantially reduce risk from fires starting 1 -4 km away from the interface. The model captured 90% of variation in burning with 98% predictive accuracy on test data and was not affected by spatial autocorrelation. We apply the method to map fire risk in Sydney and discuss how the method could be expanded to estimate total risk (from ignition to impact on assets). The method has considerable promise for predicting risk, especially as a compliment to simulation methods.