T E N PERCEN T ROS R U LE E VA LU ATIO N
CLOSING REMARKS The 10% rule of thumb was developed to provide first approximations of wildfire propagation rates for situations when there is little or no time to apply more comprehensive and accepted fire behavior prediction methods. As shown in the evaluation study, its application works well for the dry and windy conditions associated with fast-spreading wildfires, but will result in large over-predictions when applied in low to moderate wind speeds (i.e. <20 km/h or 12 mi/h) and fine dead fuel moisture conditions >7%. ACKNOWLEDGEMENTS
Thanks to Paulo Fernandes (UTAD), Musa Kilinc (CFA) and Ângelo Sil (UTAD) for their contributions to the 10% rule of thumb evaluation study and to Matt Plucinski and Andrew Sullivan (CSIRO) for their comments on a draft of this article. Vincent Pastor with the Service Départemental d’Incendie et de Secours – 13, Bouches-du-Rhône, France, kindly provided details on the 2016 Rognac Fire.
ABOUT THE AUTHORS
Miguel Cruz is a research scientist, with Australia’s Commonwealth Scientific and Industrial Research Organisation (CSIRO), specializing in the study of bushfire behavior (miguel.cruz@csiro.au) Marty Alexander is a semi-retired Canadian wildland fire behavior researcher (mea2@telus.net). The two of them have been cooperating on various projects for the the past 24 years. Box 1. Interpreting Percent Error Associated With Predictions of Wildfire Rate of Spread
REFERENCES Alexander, M.E. and M.G. Cruz. 2019. A rule of thumb for estimating a wildfire’s forward spread rate. Wildfire 28(5): 36-39. Cruz, M.G. and M.E. Alexander. 2013. Uncertainty associated with model predictions of surface and crown fire rates of spread. Environmental Modelling & Software 47: 16-28. Cruz, M.G. and M.E. Alexander. 2019. The 10% wind speed rule of thumb for estimating a wildfire’s forward rate of spread in forests and shrublands. Annals of Forest Science 76(2): 44. 11 p. Cruz, M.G., M.E. Alexander, P.M. Fernandes, M. Kilinc and Â. Sil. 2020. Evaluating the 10% wind speed rule of thumb for estimating a wildfire’s forward rate of spread against an extensive independent set of observations. Environmental Modelling & Software 133: 104818. 15 p. Fernandes, P.M., Â. Sil, C.G. Rossa, D. Ascoli, M.G. Cruz and M.E. Alexander. 2020. Characterizing fire behavior across the globe. In: Hood, S., S. Drury, T. Steelman and R. Steffens (Editors), The Fire Continuum – Preparing for the Future of Wildland Fire: Proceedings of the Fire Continuum Conference, 2018 May 21-24, Missoula, MT. Proceedings RMRS-P-78. Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station. pp. 258-263. Harris, S., W. Anderson, M. Kilinc and L. Fogarty. 2011. Establishing a link between the power of fire and community loss: the first step towards developing a bushfire severity scale. Report 89. Melbourne, VIC: Victorian Government, Department of Sustainability and Environment. 75 p. Kilinc, M., W. Anderson and B. Price. 2012. The applicability of bushfire behaviour models in Australia. Technical Report 1. Melbourne, VIC: Victorian Government, Department of Sustainability and Environment, DSE Schedule 5: Fire Severity Rating Project. 60 p. 50
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JANUARY - MARCH 2021
The spread rate of wildfires can vary over a wide spectrum. The error produced by predictive models is best interpreted on a percent basis relative to an observed rate of spread. This can be calculated as the difference between the predicted and observed fire spread rates, divided by the observed fire spread rate, multiplied by a 100: Percent Error = Predicted Value – Observed Value x 100 Observed Value The above formula will result in negative errors if the fire spread rate is under-predicted and positive errors if the prediction is higher than the observed value. For example, if the observed rate of fire spread is 1.0 km/h (0.6 mi/h), a prediction of 1.3 km/h (0.8 mi/h) equates to an over-prediction error of 30%, whereas a prediction of 0.7 km/h (0.4 mi/h) would constitute an underprediction error of -30%.
