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Fire spread models

The effect of surface fuel load on the fire spread rate in Vesta Mk 2 model

The effect of surface fuel load—the quantity of fine fuel in the surface and near-surface layers—on fire spread rate in Vesta Mk 2 model.

Background

The New South Wales Rural Fire Service (RFS) currently uses 7 operational fire spread models. These models each have a different set of input variables that predict fundamental patterns of fire behavior, like spread rate and flame length.

Many of the inputs to the fire spread models are vegetation fuels datasets. These include variables that describe vegetation structure, like overstorey height or fuel cover percentage, variables that are derived from interactions between weather and vegetation structure, like wind reduction factor, or variables that describe fuel abundance, like bark fuel loads or litter fuel loads.

Below is a summary of each of the fire spread models, including the input variables used to drive each model. This information is mostly courtesy of David Field, the Acting Manager of Predictive Services for RFS.

Fire spread models

Vesta Mk2

Cruz MG, Cheney NP, Gould JS, McCaw WL, Kilinc M, Sullivan AL (2022) An empirical-based model for predicting the forward spread rate of wildfires in eucalypt forests. International Journal of Wildland Fire 31. doi:10.1071/WF21068

PDF link to the paper Vesta Mk2 user guide

Inputs:

  • Dry formations/sub-forms
    • Surface Fuel Hazard Score (0-4)
    • Near-surface Hazard Score (0-4)
    • Cover-weighted average height of the combined near-surface fuel (cm)
    • Elevated Fuel Hazard Score (0-4)
    • Mean Elevated Fuel Height (m)
    • Bark Hazard Score (0-4)
    • Overstorey Foliage Projective Cover (%)
    • Overstorey Height (m)
    • Tall Understorey (>3m) Cover (%)
  • Wet formations/sub-forms
    • Surface Fuel Hazard Score (0-4)
    • Near-surface Hazard Score (0-4)
    • Cover-weighted average height of the combined near-surface fuel (cm)
    • Elevated Fuel Hazard Score (0-4)
    • Mean Elevated Fuel Height (m)
    • Bark Hazard Score (0-4)
    • Overstorey Foliage Projective Cover (%)
    • Overstorey Height (m)
    • Tall Understorey (>3m) Cover (%)
    • Keetch-Byram Drought Index (KBDI) (0-200)
    • Aspect of Slope (azimuth) (degrees TN)
    • Slope steepness (degrees)

Vesta Mk1

Also known as the Dry eucalypt forest fire model.

Cheney, N.P., Gould, J.S., McCaw, W.L. and Anderson, W.R. (2012) Predicting fire behaviour in dry eucalypt forest in southern Australia. Forest Ecology and Management 280, 120-131. doi:10.1016/j.foreco.2012.06.012

PDF link to the paper

Inputs:

  • Surface Fuel Hazard Score (0-4)
  • Near-surface Hazard Score (0-4)
  • Near-surface Fuel Height (cm)
  • Elevated Fuel Hazard Score (0-4)
  • Elevated Fuel Height (m)
  • Bark Hazard Score (0-4)

Buttongrass model

Marsden-Smedley, J.B. and Catchpole, W.R. (1995b) Fire behaviour modelling in Tasmanian buttongrass moorlands I. Fuel characteristics. International Journal of Wildland Fire 5, 202-214. doi:10.1071/WF9950203

Marsden-Smedley, J.B. and Catchpole, W.R. (1995a) Fire behaviour modelling in Tasmanian buttongrass moorlands II. Fire behaviour. International Journal of Wildland Fire 5, 215-228. doi:10.1071/WF995021

Inputs:

  • Buttongrass Age (yrs)
  • Fuel Cover (%)
  • Productivity (low=1, med=2)

CSIRO grassland fire spread meter

Also known as the Fire spread meter for Northern Australia.

Cheney, N.P., Gould, J.S. and Catchpole, W.R. (1998) Prediction of fire spread in grasslands. International Journal of Wildland Fire 8, 1-15. doi:10.1071/WF9980001

PDF link to the paper

Inputs:

  • Degree of Curing (%)
  • Grass condition (Natural / Grazed)
  • Wind Reduction Factor

Heathland model

Anderson, W.R., Cruz, M.G., Fernandes, P.M., McCaw, W.L., Vega, J.A., Bradstock, R.A., Fogarty, L., Gould, J., McCarthy, G., Marsden-Smedley, J.B., Matthews, S., Mattingley, G., Pearce, G. and van Wilgen, B.W. (2015) A generic, empirical-based model for predicting rate of fire spread in shrublands. International Journal of Wildland Fire 24 (4), 443-460. doi:10.1071/WF14130

PDF link to the paper

Inputs:

  • Vegetation height
  • Wind reduction factor

or

  • Bulk density
  • Wind reduction factor

Mallee heath model

Cruz, M.G. and Alexander, M.E. (2013) Uncertainty associated with model predictions of surface and crown fire rates of spread. Environmental Modelling & Software 47, 16-28. doi:10.1016/j.envsoft.2013.04.004

PDF link to the paper

Inputs:

  • Overstorey Cover (%)
  • Overstorey Height (m)
  • Combined Surface Fine Fuel Load (t/ha)

Pine plantation pyrometrics

Cruz, M.G., Alexander, M.E. and Fernandes, P. (2008) Development of a model system to predict wildfire behaviour in pine plantations. Australian Forestry 71, 113-121. doi:10.1080/00049158.2008.10676278

PDF link to paper

Inputs:

  • Stand height
  • Canopy Cover
  • Canopy Bulk Density
  • Canopy Base Height
  • (Surface) Fuel Load
  • (Surface) Fuel Load Height
  • (Surface) Size class distribution (or fuel model)

Spinifex model

Burrows N, Gill M, Sharples J (2018). Development and validation of a model for predicting fire behaviour in spinifex grasslands of arid Australia. International Journal of Wildland Fire 27, 271–279. doi:10.1071/WF17155

Inputs:

Spinifex cover

  • Spinifex height
  • % of cover live
  • % of cover dead
  • % of cover other
  • Age (yrs)
  • Spinifex and other fine fuel load
  • 2m wind speed
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