Katie Collins 1 Trent Penman 2 Owen Price 1 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 ID: 660971
Download Presentation The PPT/PDF document "Exploring mitigation strategies to reduc..." is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
Slide1
Exploring mitigation strategies to reduce the likelihood of house losses from wildfires
Katie Collins
1
, Trent Penman
2
, Owen Price
1
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 3363Slide2
WildfireNatural process
People and propertySlide3
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 yearsSlide4
Mitigation Treatments
Fire Suppression
e.g. trucks, aircraft
Fuel Treatment
e.g. prescribed burning,
clearingIgnition management e.g. restricting access,
restricting activities,
patrolling ignition hot spotsSlide5
AimDevelop 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 Slide6
Study areaSlide7
BN Conceptual framework
Ignition Management
Suppression
Fuel TreatmentSlide8Slide9
Vegetation
Forest
GrassSlide10
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.)Slide11Slide12
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.)Slide13Slide14
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)Slide15Slide16
Results – Forest firesSlide17
Results – Forest firesBest 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% Slide18
Results – Grass FiresSlide19
Results – Grass FiresFuel treatment had no effect
Reducing arson ignitions and increasing response time had minimal effect
Increasing the number of trucks had major effectSlide20
P(house loss) by FFDISlide21
FindingsFuel 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 Slide22
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 simulationSlide23