@article {bnh-5648, title = {Developing and testing models of the drivers of anthropogenic and lightning-caused wildfire ignitions in south-eastern Australia}, journal = {Journal of Environmental Management}, volume = {235}, year = {2019}, month = {01/2019}, chapter = {34-41}, abstract = {

Considerable investments are made in managing fire risk to human assets, including a growing use of fire behaviour simulation tools to allocate expenditure. Understanding fire risk requires estimation of the likelihood of ignition, spread of the fire and impact on assets. The ability to estimate and predict risk requires both the development of ignition likelihood models and the evaluation of these models in novel environments. We developed models for natural and anthropogenic ignitions in the south-eastern Australian state of Victoria incorporating variables relating to fire weather, terrain and the built environment. Fire weather conditions had a consistently positive effect on the likelihood of ignition, although they contributed much more to lightning (57\%) and power transmission (55\%) ignitions than the 7 other modelled causes (8{\textendash}32\%). The built environment played an important role in driving anthropogenic ignitions. Housing density was the most important variable in most models and proximity to roads had a consistently positive effect. In contrast, the best model for lightning ignitions included a positive relationship with primary productivity, as represented by annual rainfall. These patterns are broadly consistent with previous ignition modelling studies. The models developed for Victoria were tested in the neighbouring fire prone states of South Australia and Tasmania. The anthropogenic ignition model performed well in South Australia (AUC = 0.969) and Tasmania (AUC = 0.848), whereas the natural ignition model only performed well in South Australia (AUC = 0.972; Tasmania AUC = 0.612). Model performance may have been impaired by much lower lightning ignition rates in South Australia and Tasmania than in Victoria. This study shows that the spatial likelihood of ignition can be reliably predicted based on readily available meteorological and biophysical data. Furthermore, the strong performance of anthropogenic and natural ignition models in novel environments suggests there are some universal drivers of ignition likelihood across south-eastern Australia.

}, keywords = {Bushfire, ignition, Risk modelling, Wildfire}, doi = {https://doi.org/10.1016/j.jenvman.2019.01.055}, url = {https://www.sciencedirect.com/science/article/pii/S0301479719300568}, author = {Hamish Clarke and Rebecca Gibson and Brett Cirulis and Ross Bradstock and Trent Penman} } @article {bnh-5615, title = {Quantification of inter-regional differences in risk mitigation from prescribed burning across multiple management values}, journal = {International Journal of Wildland Fire}, year = {2019}, month = {06/2019}, abstract = {

Fire agencies are moving towards planning systems based on risk assessment; however, knowledge of the most effective way to quantify changes in risk to key values by application of prescribed fire is generally lacking. We present a quantification and inter-regional comparison of how risk to management values responds to variations in prescribed burning treatment rate. Fire simulations were run using the PHOENIX RapidFire fire behaviour simulator for two case study landscapes in interface zones in Tasmania and the Australian Capital Territory (ACT), Australia. A Bayesian network approach used these data to explore the influence of treatment and weather on risk from wildfire. Area burnt, length of powerline damaged and length of road damaged responded more strongly to treatment in the ACT than in Tasmania, whereas treatment mitigated house loss and life loss more strongly in Tasmania than the ACT. The effect of prescribed burning treatment rate on area burnt below minimum tolerable fire interval was similar in each case study landscape. Our study shows that the effectiveness of prescribed burning at mitigating area burnt by wildfire and other key values varies considerably across landscapes and values.

}, keywords = {Bushfire, Climate change, trends, Wildfire, wildland fire}, doi = {https://doi.org/10.1071/WF18135}, url = {http://www.publish.csiro.au/WF/WF18135}, author = {Brett Cirulis and Hamish Clarke and Matthias M. Boer and Trent Penman and Owen Price and Ross Bradstock} } @article {bnh-6856, title = {Quantification of inter-regional differences in risk mitigation from prescribed burning across multiple management values}, journal = {International Journal of Wildland Fire}, volume = {29}, year = {2019}, month = {09/2019}, pages = {414-426}, abstract = {

Fire agencies are moving towards planning systems based on risk assessment; however, knowledge of the most effective way to quantify changes in risk to key values by application of prescribed fire is generally lacking. We present a quantification and inter-regional comparison of how risk to management values responds to variations in prescribed burning treatment rate. Fire simulations were run using the PHOENIX RapidFire fire behaviour simulator for two case study landscapes in interface zones in Tasmania and the Australian Capital Territory (ACT), Australia. A Bayesian network approach used these data to explore the influence of treatment and weather on risk from wildfire. Area burnt, length of powerline damaged and length of road damaged responded more strongly to treatment in the ACT than in Tasmania, whereas treatment mitigated house loss and life loss more strongly in Tasmania than the ACT. The effect of prescribed burning treatment rate on area burnt below minimum tolerable fire interval was similar in each case study landscape. Our study shows that the effectiveness of prescribed burning at mitigating area burnt by wildfire and other key values varies considerably across landscapes and values.

}, keywords = {Bushfire, Climate change, trends, Wildfire, wildland fire}, doi = {https://doi.org/10.1071/WF18135}, url = {https://www.publish.csiro.au/WF/WF18135}, author = {Brett Cirulis and Hamish Clarke and Matthias M. Boer and Trent Penman and Owen Price and Ross Bradstock} }