@article {bnh-8115, title = {Derivation of a Bayesian fire spread model using large-scale wildfire observations}, journal = {Environmental Modelling \& Software}, year = {2021}, month = {07/2021}, abstract = {

Models that predict wildfire rate of spread (ROS) play an important role in decision-making during firefighting operations, including fire crew placement and timing of community evacuations. Here, we use a large set of remotely sensed wildfire observations, and explanatory data (focusing on weather), to demonstrate a Bayesian probabilistic ROS modelling approach. Our approach has two major advantages: (1) Using actual wildfire observations, instead of controlled fire observations, makes models developed well-suited to wildfire prediction; (2) Bayesian modelling accounts for the complex nature of wildfire spread by explicitly considering uncertainty in the data to produce probabilistic ROS predictions. We show that highly informative probabilistic predictions can be made from a simple Bayesian model containing wind speed, relative humidity and soil moisture. We also compare Bayesian model predictions to those of widely used deterministic ROS models in Australia.

}, keywords = {Wildfire Bushfire Fire behaviour Bayesian Bayesian modelling Rate of Spread}, doi = {https://doi.org/10.1016/j.envsoft.2021.105127}, url = {https://www.sciencedirect.com/science/article/abs/pii/S1364815221001705}, author = {Michael Storey and Bedward, M. and Owen Price and Ross Bradstock and Jason J. Sharples} } @article {bnh-1360, title = {National Fire Danger Rating System Probabalistic Framework: year two report}, number = {008}, year = {2014}, abstract = {

Fire danger ratings in Australia currently rely heavily on McArthur{\textquoteright}s indices for forests and grasslands. Advances in fire and in meteorological science means that these indices do not adequately reflect current knowledge or recent technological advances. We now have the capability to provide information on fire danger at a much greater spatial and temporal resolution than previously, but do not fully utilise it. The indices are used for a wider range of purposes than originally envisaged, and growing societal expectations and changes in communication technology provide both the impetus and opportunity for a more detailed and more accurate service.

This is the final report for year two of this research project.

}, author = {Trent Penman and Bedward, M. and Ross Bradstock} } @article {BF-2397, title = {Prediction of the probability of large fires in the Sydney region of south-eastern Australia using fire weather}, journal = {International Journal of Wildland Fire}, volume = {18}, year = {2009}, month = {2009}, pages = {932}, abstract = {The probability of large-fire (>=1000 ha) ignition days, in the Sydney region, was examined using historical records. Relative influences of the ambient and drought components of the Forest Fire Danger Index (FFDI) on large fire ignition probability were explored using Bayesian logistic regression. The preferred models for two areas (Blue Mountains and Central Coast) were composed of the sum of FFDI (Drought Factor, DF = 1) (ambient component) and DF as predictors. Both drought and ambient weather positively affected the chance of large fire ignitions, with large fires more probable on the Central Coast than in the Blue Mountains. The preferred, additive combination of drought and ambient weather had a marked threshold effect on large-fire ignition and total area burned in both localities. This may be due to a landscape-scale increase in the connectivity of available fuel at high values of the index. Higher probability of large fires on the Central Coast may be due to more subdued terrain or higher population density and ignitions. Climate scenarios for 2050 yielded predictions of a 20{\textendash}84\% increase in potential large-fire ignitions days, using the preferred model.}, doi = {10.1071/WF08133}, author = {Ross Bradstock and Cohn, J. S. and Gill, A. Malcolm and Bedward, M. and Chris Lucas} }