Fire behaviour modelling in Tasmanian buttongrass moorlands .2. Fire behaviour

An experimental burning program was carried out in Tasmanian buttongrass moorlands to develop fire behaviour prediction models for improving fire management A range of previously developed prediction models were examined, but none provided adequate fire behaviour predictions. Empirical models were then developed to predict rate of fire spread and flame height in flat terrain, using the variables site age, dead fuel moisture content and surface wind speed. The models should provide good predictions for low to moderate intensity fires and adequate predictions for high intensity wildfires. © 1995 IAWF. Printed in U.S.A

Fire modelling Tasmanian buttongrass moorlands. III* Dead fuel moisture

An experimental program was carried out in Tasmanian buttongrass moorlands to develop fire behaviour prediction models for improving fire management. This paper describes the results of the fuel moisture modelling section of this project. A range of previously developed fuel moisture prediction models are examined and three empirical dead fuel moisture prediction models are developed. McArthur's grassland fuel moisture model gave equally good predictions as a linear regression model using humidity and dew-point temperature. The regression model was preferred as a prediction model as it is inherently more robust. A prediction model based on hazard sticks was found to have strong seasonal effects which need further investigation before hazard sticks can be used operationally

Fire behaviour modelling in Tasmanian buttongrass moorlands .1. Fuel characteristics

As part of a program to develop fire management strategies for Tasmanian buttongrass moorlands fuel characteristics were sampled from a wide range of sites in western and southwestern Tasmania. Equations were developed to predict the total fuel loading and the dead fuel loading. These variables are shown in a subsequent paper to be correlated with fire behaviour. The best predictors of fuel loading were found to be geology, vegetation age (i.e. time since the last fire) and vegetation cover. Vegetation cover is difficult to assess consistently. It is shown that reasonable predictions can be male using age and geology alone. The dead fuel loading of a given age was found to be strongly related to the total fuel loading, independent of geology. Statistical techniques used to develop fuel models are discussed. Other fuel characteristics that could be used as inputs for the Rothermel fire behaviour model are also presented. © 1995 IAWF. Printed in U.S.A

Estimating fuel response time and predicting fuel moisture content from field data

We develop a method for estimating equilibrium moisture content (EMC) and fuel moisture response time, using data collected for Eucalyptus twig litter. The method is based on the governing differential equation for the diffusion of water vapour from the fuel, and on a semi-physical formulation for EMC (Nelson 1984), based on the change in Gibbs free energy, which estimates the EMC as a function of fuel temperature and humidity. We then test the model on data collected in Western Australian mallee shrubland and in Tasmanian buttongrass moorland. This method is more generally applicable than those described by Viney and Catchpole (1991) and Viney (1992). The estimates of EMC and response time are in broad agreement with laboratory-based estimates for similar fuels (Anderson 1990a; Nelson 1984). The model can be used to predict fuel moisture content by a book-keeping method. The predictions agree well with the observations for all three of our data sets

Fire modelling in Tasmanian buttongrass moorlands. IV* Sustaining versus non-sustaining fires

Buttongrass moorlands are widespread in western Tasmania. In these moorlands, the ability to conduct burning without having to rely on hard fuel boundaries (e.g. vegetation which is too wet to burn, water courses, mineral earth breaks and/or roads) would be a major advantage to land managers. Such burning relies on fires self-extinguishing and is normally referred to as unbounded burning. The aim of this project was to model the probability of fires extinguishing using the data from 156 buttongrass moorland fires. The variables used were wind speed, dead fuel moisture and site productivity. The model, derived from a combination of logistic regression and classification tree modelling, predicts that fires will self-extinguish over a wide range of conditions in low productivity moorlands but, in medium productivity moorlands, the conditions within which fires will self-extinguish will be much more restrictive. As a result, the technique of unbounded burning should be widely applicable in low productivity moorlands, but will be of marginal utility in medium productivity moorlands

Buttongrass Moorland Fire-Behaviour Prediction and Management

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