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

Wildfire Natural process People and property

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

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

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

Study area

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

Vegetation Forest Grass

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.)

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.)

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:10.1371/journal.pone.0162083)

Results – Forest fires

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%

Results – Grass Fires

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

P(house loss) by FFDI

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

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