Assessing improvements in models used to operationally predict wildland fire rate of spread

• Published in: Environmental modelling & software : with environment data news Vol. 105; pp. 54 - 63
• Main Authors: Cruz, Miguel G., Alexander, Martin E., Sullivan, Andrew L., Gould, James S., Kilinc, Musa
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.07.2018; Elsevier Science Ltd
• Subjects: Aridity; Coniferous forests; Continuous improvement; Crown fire; Fire behaviour; Fire propagation; Fire weather; Forest & brush fires; Forest management; Forests; Fuel type; Grasslands; Landscape; Mathematical models; Model accuracy; Model error; Predictions; Reversion; Topography; Wildfires; Fire propagation; Fire weather; Model error; Crown fire; Fire behaviour; Fuel type
• ISSN: 1364-8152; 1873-6726
• DOI: 10.1016/j.envsoft.2018.03.027

The prediction of fire propagation across landscapes is necessary for safe and effective fire management. We analyzed the predictive accuracy of models currently used operationally in Australia for predicting fire spread rates in five different fuel types (grasslands, temperate and semi-arid shrublands, dry eucalypt and conifer forests) compared to their previous counterparts. We calculated error statistics and contrasted model predictions against observed spread rates of field observations of wildfires and prescribed fires. We then compared the changes in error metrics of older models to newer ones. Evaluation results show newer models to have improved prediction accuracy. Mean absolute errors were reduced by 56%, 68% and 70% in dry eucalypt forests, grasslands and crown fires in conifer forests, respectively. The most significant improvement was the reversion of under-prediction bias achieved with newer models. This study has highlighted the value of continuous improvement when it comes to developing operational wildland fire spread models. •We analyzed the predictive accuracy of wildfire rate of spread models used operationally.•We observed newer models to have improved prediction accuracy over previous counterparts.•Mean errors were reduced between 56% and 70%.•This study has highlighted the value of continuous improvement in fire behaviour modelling.