@article {bnh-7343, title = {Effect of weather forecast errors on fire growth model projections}, journal = {International Journal of Wildland Fire}, year = {2020}, month = {08/2020}, abstract = {

Fire management agencies use fire behaviour simulation tools to predict the potential spread of a fire in both risk planning and operationally during wildfires. These models are generally based on underlying empirical or quasi-empirical relations and rarely are uncertainties considered. Little attention has been given to the quality of the input data used during operational fire predictions. We examined the extent to which error in weather forecasts can affect fire simulation results. The study was conducted using data representing the State of Victoria in south-eastern Australia, including grassland and forest conditions. Two fire simulator software packages were used to compare fire growth under observed and forecast weather. We found that error in the weather forecast data significantly altered the predicted size and location of fires. Large errors in wind speed and temperature resulted in an overprediction of fire size, whereas large errors in wind direction resulted in an increased spatial error in the fire{\textquoteright}s location. As the fire weather intensified, fire predictions using forecast weather under predicted fire size, potentially resulting in greater risks to the community. These results highlight the importance of on-ground intelligence during wildfires and the use of ensembles to improve operational fire predictions.

}, keywords = {Bayesian Network, fire prediction, meteorological forecast, sensitivity, simulation}, doi = {https://doi.org/10.1071/WF19199}, url = {https://www.publish.csiro.au/wf/wf19199}, author = {Trent Penman and Dan Ababei and Jane Cawson and Brett Cirulis and Thomas Duff and Swedosh, W and James Hilton} } @article {bnh-6854, title = {Cost effectiveness of fire management strategies in southern Australia}, journal = {International Journal of Wildland Fire}, volume = {29}, year = {2019}, month = {06/2019}, pages = {427-439}, abstract = {

Fire-management agencies invest significant resources to reduce the impacts of future fires. There has been increasing public scrutiny over how agencies allocate fire-management budgets and, in response, agencies are looking to use quantitative risk-based approaches to make decisions about expenditure in a more transparent manner. Advances in fire-simulation software and computing capacity of fire-agency staff have meant that fire simulators have been increasingly used for quantitative fire-risk analysis. Here we analyse the cost trade-offs of future fire management in the Australian Capital Territory (ACT) and surrounding areas by combining fire simulation with Bayesian Decision Networks. We compare potential future-management approaches considering prescribed burning, suppression and fire exclusion. These data combined costs of treatment and impacts on assets to undertake a quantitative risk analysis. The proposed approach for fuel treatment in ACT and New South Wales (NSW) provided the greatest reduction in risk and the most cost-effective approach to managing fuels in this landscape. Past management decisions have reduced risk in the landscape and the legacy of these treatments will last for at least 3 years. However, an absence of burning will result in an increased risk from fire in this landscape.

}, keywords = {Bayesian Network, house loss, life loss, prescribed fire, risk}, doi = { https://doi.org/10.1071/WF18128}, url = {https://www.publish.csiro.au/WF/WF18128}, author = {Trent Penman and Brett Cirulis} }