Underpinnings of fire management for biodiversity conservation in ...

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Assessing the effectiveness of fuel reduction for fire suppression
In the last section, the direct effects of prescribed fire on fuels was considered. Here, the focus is on
the efficacy of prescribed burning for fire suppression and how that might be measured.
Difficulty of suppression, and therefore control, is difficult to define because of the numbers of
variables involved. For example, Gould et al. (2007, p. 32) mention visibility through a forest, access
and difficulty of working machinery, flame height and spotting potential. Fire intensity is a convenient,
single measure of the fire side of the story, because, in forests at least, suppression is likely to fail
when spotting occurs and this may be linked to fire intensity (e.g. Gould et al. 2007, p. 117; see
Chapter 2 also). McCarthy et al. (1999, p. 27) provide equivalent fuel loads for various components
of the forest-fuel array so that they can be linked with fire behaviour guides. Thus an array intensity
could be estimated. However, the focus of their method is to predict the probability of success of first
suppression actions based on the overall fuel hazard rating.
Three groups of methods for assessing the effectiveness of fuel modification and decreasing fire
intensity to aid suppression are suggested:
1. Analysis of case histories in which suppression is usually explicit
2. Scenario modelling in which the reduction in the extent of unplanned fires is the measure
3. Analysis of fire-area statistics in which the measure can be the same as in (2).
Group one includes:
• Probability modelling of the effectiveness of initial attack by firefighters (McCarthy and Tolhurst
1998)
• Direct observation of fire behaviour when it reaches an area that has been prescribed burnt (e.g.
Grant and Wouters 1993)
• Probability modelling of the decline in the rate of heading-fire rate of spread and its consequences
for suppression (McCarthy and Tolhurst 2001).
All these methods show an effect of fuel characteristics. The higher the fuel load (or score) and the
Forest Fire Danger Index (FFDI) – a measure of weather effect on fire behaviour – the lower the chance
of success, in general. Benefits for the few years after fire in forests and shrublands are summarised
by Fernandes and Botelho (2003) as increasing the safety of firefighters, decreasing the extent of
firefighting resources needed, moderating the overall suppression strategy and lessening the amount
of mopping up.
In group two (the modelling of prescribed-burning scenarios in relation to the extent of unplanned
fires) there are various levels of detail, including:
• Conceptual, graphical models (Bradstock et al. 1995; Cary and Bradstock 2003)
• Site-based models, such as those of Bradstock et al. (1998b), which use the number of days a fire
was deemed to be uncontrollable as an index – a measure that includes weather, slope, aspect and
suppression thresholds
• Full landscape depiction in a sophisticated GIS simulation – King (2004) and King et al. (2006)
simulated fire-area frequency distributions with and without prescribed burning in south-west
Tasmania, and found a decrease in unplanned fire areas as prescribed-fire area increased.
These methods provide useful ways of exploring options and comparing unplanned fire sizes, with
varying proportions of the landscape being subject to fuel reduction by burning.
In group three (a set based on fire-regime ideas using landscape data) there are:
• Comparisons within a fire season – The effect in the monsoon tropics of burning fuel in early
dry season (mostly of low intensity) on fires in late dry season (mostly of high intensity). Gill et al.
(2000) showed that there was a trade-off between the area burnt in the early dry season with that
burned in the late dry season in lowland Kakadu National Park, east of Darwin, Northern Territory
irrespective of ignition sources.
Fire and adaptive management Underpinnings of fire management for biodiversity conservation in reserves