Assessing improvements in models used to operationally predict wildland fire rate of spread
Document Type: Journal Article
Author(s): Miguel G. Cruz; Martin E. Alexander; Andrew L. Sullivan; James S. Gould; Musa Kilinc
Publication Year: 2018

Cataloging Information

  • Australia
  • conifer forests
  • crown fire
  • dry eucalypt forests
  • fire propagation
  • fuel type
  • grasslands
  • model error
  • ROS - rate of spread
  • wildfires
  • International
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Record Last Modified: October 13, 2020
FRAMES Record Number: 62089


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.

Online Link(s):
Cruz, M.G.; Alexander, M.E.; Sullivan, A.L.; Gould, J.S.; Kilinc, M. 2018. Assessing model improvements in predicting wildland fire rates of spread. Environmental Modelling & Software 105: 54-63.