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

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 shrubla...

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Bibliographic Details
Published in:Environmental modelling & software : with environment data news 2018-07, Vol.105, p.54-63
Main Authors: Cruz, Miguel G., Alexander, Martin E., Sullivan, Andrew L., Gould, James S., Kilinc, Musa
Format: Article
Language:English
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
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Summary: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.
ISSN:1364-8152
1873-6726
DOI:10.1016/j.envsoft.2018.03.027