Fig 1.
Ignition of a receiver leaf by a burning donor.
The flame from the donor produces a convective plume following a direction described by the flame angle (broken arrow), where the temperature of the air in the plume decreases with distance from the donor (solid curve) in a pattern determined by the flame produced from that leaf. The time of heating required for ignition of the receiver increases as the temperature decreases, at a rate determined by the ignitability of the leaf. The plume temperature model is taken from [37], and the time to ignition modelled from [35], where ignitability is a function of plume temperature and the Ignitability Coefficient (IC = leaf moisture (% Oven Dry Weight) * thickness (mm) / number of sides on the leaf).
Fig 2.
Effects of donor flammability, receiver ignitability and overstorey sheltering on fire severity.
Wind speed is shown above and below the canopy by the solid arrows, with thicker and longer arrows showing greater wind speed. The trajectory of convective heat transfer is shown by the broken line. Four scenarios b to e are shown relative to the left scene a. The convective plume produced by the donor plant in a. intersects the receiver; however it is insufficient to ignite it. This is changed when in b the donor flammability is increased to give a larger flame that ignites the receiver, and in c when the donor flame is the same as in a but the receiver ignitability is greater. The flammability of the plants in scenarios d and e is the same as in a, but the wider tree spacing has reduced the overstorey sheltering so that the wind speed is greater at the level of the flame. This directs the plume through its neighbouring plants in d so that the flame depth is increased and the resulting larger flame ignites the receiver stratum. In scenario e, the plume passes over neighbouring shrubs or elevated stratum [7] so that they are not ignited and the flame dimensions remain unchanged from a The more acute angle of the plume, however, increases the distance to the receiver stratum so that the heat dissipates and that stratum is even less likely to ignite than in a.
Table 1.
Fuel and structural parameters used in this study.
Table 2.
Leaf traits used to model flammability parameters in this study.
Fig 3.
Location of the study sites (open circles) in relation to Canberra, Sydney and the area affected by the 2003 bushfires (shaded).
Table 3.
Forest classes examined in the study, as given in the original survey.
Table 4.
Exogenous factors used in modelling of fire behaviour for this study.
Table 5.
Leaf traits driving differences between FSL and FS treatments in this study.
Table 6.
Predictors used in the LASSO regression of change in predicted flame height.
Fig 4.
Median estimated vs. predicted flame height for the three treatments.
Using surface fuel only (F), all predictions were for low flame heights (R2 = 0.11). Including structure with that fuel (FS) enabled the prediction of large flames, but the accuracy was low (R2 = 0.24). The inclusion of leaf traits (FSL) significantly improved the accuracy of predictions (R2 = 0.80), producing a MAE 3.80 times smaller than F, (p < 0.01, paired t-test), and 4.67 times smaller than FS (p < 0.01). The line of exact agreement is shown as solid, and the trend of the data is shown as a broken line, with R2 reported under the treatment name.
Fig 5.
Violin plots showing error in flame height prediction for 58 plots modelled using three treatments of the FFM.
As per [58], the box plots indicate data range, quartiles, and median, and the shaded area shows the density trace.
Table 7.
Mean Error and Mean Absolute Error (m) in flame height predictions for three model treatments of 58 sites, with standard error shown in brackets.
Fig 6.
Proportion of correct predictions for each model treatment for the full flame height dataset (PCP) and for flames ≥1m (PCP1).
PCP calculations were based on all 58 sites; PCP1 calculations were based on the 40 sites with flame heights ≥1m. Error bars show one standard error above and below the mean.
Table 8.
Regression coefficients for the predictors used in the LASSO regression, with lower (2.5%) and upper (97.5%) quantiles.
Predictor groups are DF–donor flammability, RI–receiver ignitability, OS–overstorey shelter and General–exogenous, structural and surface parameters. Results are shown from 1000 regressions.
Fig 7.
Donor flame height per species, stratum and site.
Showing the five sites where the difference in prediction (FSL—FS) was most positive (a), and the five sites where it was most negative (b). Grey bars show flame heights per species from FSL and horizontal lines show flame heights from mean species in FS. Sites are ordered by delta flame height and the site number is given at the top of each plot.