1. Katie Collins1, Trent Penman2, Owen Price1
Exploring mitigation strategies to reduce the likelihood of house losses from wildfires
Katie Collins1, Trent Penman2, Owen Price1
1 Centre for Environmental Risk Management of Bushfires, University of Wollongong, Wollongong, NSW 2522
2 School of Ecosystem and Forest Sciences, University of Melbourne, Creswick, Victoria 3363

2. Wildfire
Natural process
People and property

3. House Losses 2017 Napa Valley Ca. >5000 2009 Black Saturday 2133
2003 Canberra 501
2013 Blue Mountains 205
NSW 699 houses destroyed in 81 fires the last 15 years

4. Mitigation Treatments
Fire Suppression e.g. trucks, aircraft
Fuel Treatment
e.g. prescribed burning,
clearing
Ignition management
e.g. restricting access, restricting activities,
patrolling ignition hot spots

5. Aim
Develop a Bayesian Network model using existing data and models to predict the probability of house loss
Identify the combination of wildfire mitigation treatments that provide the greatest reduction in house loss

6. Study area

7. BN Conceptual framework
Ignition Arson, Powerline, Lightning, Other
Containment
House Loss
Ignition Management
Suppression
Fuel Treatment

9. Vegetation
Forest Grass

10. Probability of ignition
Models developed based on empirical analyses of Victorian ignition data (Penman, Gibson and Bradstock, Modelling the drivers of ignition across Victoria, Australia, in prep.)

12. Probability of Containment
Models developed based on empirical analyses of NSW fire incident data (Collins, Price and Penman, Factors influencing containment of forest and grass fires, in prep.)

14. Probability of House Loss
Models developed based on NSW & Victorian house loss data (Collins, Penman and Price, 2016, Some wildfire ignition causes pose more risk of destroying houses than others, PLOS One, doi: /journal.pone )

16. Results – Forest fires

17. Results – Forest fires
Best result from increasing the number of trucks, prescribed burn effort and reducing arson
Increasing trucks > reducing response time
Little difference between the current level of prescribed burning and increasing prescribed burn effort by 1 and 2%

18. Results – Grass Fires

19. Results – Grass Fires Fuel treatment had no effect
Reducing arson ignitions and increasing response time had minimal effect
Increasing the number of trucks had major effect

20. P(house loss) by FFDI

21. Findings Fuel treatment has limited effect
Response time more important for forest fires than for grass fires
Reducing ignitions is not always possible
Increasing suppression resources has economic and social cost
Firefighters are largely a volunteer resource – ageing, declining volunteer numbers

22. Next steps
Include house based risk reduction strategies e.g. construction standard, fuel loads immediately adjacent to and around the house, defensive actions taken to protect the house.
Economic analysis
Spatially explicit network - fire simulation