Jim Gould

A physics-based approach to modelling grassland fires

Physics-based coupled fire–atmosphere models are based on approximations to the governing equations of fluid dynamics, combustion, and the thermal degradation of solid fuel. They require significantly more computational resources than the most commonly used fire spread models, which are semi-empirical or empirical. However, there are a number of fire behaviour problems, of increasing relevance, that are outside the scope of empirical and semi-empirical models. Examples are wildland–urban interface fires, assessing how well fuel treatments work to reduce the intensity of wildland fires, and investigating the mechanisms and conditions underlying blow-up fires and fire spread through heterogeneous fuels. These problems are not amenable to repeatable full-scale field studies. Suitably validated coupled atmosphere–fire models are one way to address these problems. This paper describes the development of a three-dimensional, fully transient, physics-based computer simulation approach for modelling fire spread through surface fuels. Grassland fires were simulated and compared to findings from Australian experiments. Predictions of the head fire spread rate for a range of ambient wind speeds and ignition line-fire lengths compared favourably to experiments. In addition, two specific experimental cases were simulated in order to evaluate how well the model predicts the development of the entire fire perimeter.

The Dead-Man Zone—a neglected area of firefighter safety

Summary Firefighters engaged in parallel or indirect attack are working in a “dead-man zone” if they do not appreciate the time and space required to find a safe refuge. In this zone, if the wind direction changes, the fire can advance so rapidly that the firefighters have very little time to seek refuge in the burnt area behind a suppressed portion of line, or egress elsewhere, before the fire overwhelms them. We discuss three bushfire incidents in Australia where firefighters were trapped and killed, and the development of fire spread in forest fuels from a line start. A table illustrating the distance that a line fire can travel in five minutes under different fire danger conditions is presented. Factors that affect the speed of the firefighters reaction to changed circumstances, and safe work practices, arc discussed.

A generic, empirical-based model for predicting rate of fire spread in shrublands

A shrubland fire behaviour dataset was assembled using data from experimental studies in Australia, New Zealand, Europe and South Africa. The dataset covers a wide range of heathlands and shrubland species associations and vegetation structures. Three models for rate of spread are developed using 2-m wind speed, a wind reduction factor, elevated dead fuel moisture content and either vegetation height (with or without live fuel moisture content) or bulk density. The models are tested against independent data from prescribed fires and wildfires and found to predict fire spread rate within acceptable limits (mean absolute errors varying between 3.5 and 9.1 m min–1). A simple model to predict dead fuel moisture content is evaluated, and an ignition line length correction is proposed. Although the model can be expected to provide robust predictions of rate of spread in a broad range of shrublands, the effects of slope steepness and variation in fuel quantity and composition are yet to be quantified. The model does not predict threshold conditions for continuous fire spread, and future work should focus on identifying fuel and weather factors that control transitions in fire behaviour.

Simple models for predicting dead fuel moisture in eucalyptus forests.

Fire behaviour prediction requires models of dead fuel moisture that are both accurate and suitable for use for operational applications. The paper investigates two methods of developing a simple operational fine fuel moisture model from a more complex process-based model. The first simple model is a table of fuel moisture predictions for values of air temperature, relative humidity, wind speed and solar radiation. The second model reduces the original model to a single differential equation, which may be used on low-powered computers. The simple models are tested against the output of the original model and against observations from two case studies in dry eucalyptus forest in south-western Australia. The single differential equation model was capable of reproducing the prediction of the process-based model at all times of the day, with mean error (ME) in predictions of –0.1% and mean absolute error (MAE) of 0.6%. The table model performed less well, with ME = –0.7% and MAE = 1.1% at 1500 hours, and ME = –1.2% and MAE = 3.0% at other times of the day.

The effect of aerial suppression on the containment time of Australian wildfires estimated by fire management personnel

The addition of aerial firefighting resources to wildfire suppression operations does not always result in faster fire containment. In this paper, containment times of fires with aerial suppression are compared with estimated containment times for the same fires without aerial suppression. Senior firefighting personnel who had worked on each fire estimated whether fires could have been contained within a time class if aircraft were not available. Data from 251 wildfires were analysed based on four fire-containment time classes: ≤2, 2–4, 4–8 and 8–24 h from the start of initial attack. Aircraft were perceived to reduce time to containment when firefighting conditions were more challenging owing to fuel hazard rating, weather conditions, slope, resource response times and area burning at initial attack. Comparisons of containment time with and without aircraft can be used to develop operational tools to help dispatchers decide when aircraft should be deployed to newly detected fires.

Effects of curing on grassfires: II. Effect of grass senescence on the rate of fire spread

The capacity to predict fire dynamics in fuel beds comprised of live and dead fuel components is constrained by our limited understanding of the effects of live fuels on fire propagation. A field-based experimental burning program was conducted to specifically address the effect of the degree of curing, the proportion of dead fuels in the fuel bed, on fire propagation in grasslands. Experimental fires were conducted at two sites characterised by structurally distinct fuels with curing levels varying between 20% and 100%. Fire sustainability experiments showed that fire propagation can occur down to curing levels as low as 20%. Rate of fire spread varied between 41.7 and 102 m min–1 in fully cured fuels and between 2.8 and 43.5 m min–1 in partially cured grasslands. The degree of curing was found to be the best variable describing the damping effect of live fuels in a natural, senescing grassland. Live fuel moisture content by itself was not found to be related to the damping effect of live fuels on the rate of fire spread. Existing models for the effect of grass curing on fire behaviour presently used in Australia were found to under-predict the rate of forward fire spread in partially cured grasslands. A new curing relationship for southern Australian grasslands derived from the study results is proposed.

Numerical Simulations of Grassland Fire Behavior from the LANL-FIRETEC and NIST-WFDS Models

Grassland fires on level terrain offer a good basic scenario for test wildland fire behavior models, due to the simplicity and homogeneity of the fuels and terrain. Two physics based models, FIRETEC and WFDS, are briefly described, applied fire spread in grassland fuel, followed by a discussion of the results. It is important to note that both models have undergone appreciable development since the writing of this conference paper in 2005.

Predicting the number of daily human-caused bushfires to assist suppression planning in south-west Western Australia

Data from bushfire incidents in south-west Western Australia from the Departments of Parks and Wildlife and Fire and Emergency Services were used to develop models that predict the number of human-caused bushfires within 10 management areas. Fire incident data were compiled with weather variables, binary classifications of day types (e.g. school days) and counts of the number of fires that occurred over recent days. Models were developed using negative binomial regression with a dataset covering 3 years and evaluated using data from an independent year. A common model form that included variables relating to fuel moisture content, the number of recent human-caused bushfires, work day (binary classification separating weekends and public holidays from other days) and rainfall was applied to all areas. The model had reasonable fit statistics across all management areas, but showed enough day-to-day prediction variability to be of practical use only in the more densely populated management areas, which were dominated by deliberate ignitions. The findings of this study should be of interest to fire managers in Mediterranean climatic regions where a variety of practices are used to manage wildfires.

Effects of curing on grassfires: I. Fuel dynamics in a senescing grassland

Grass senescence, or grassland curing, is a dynamic process in which grass fuels transition from a live to dead state and, in turn, influence fire dynamics. In the present study we examined the process of curing with specific consideration of changes in fuel structure that will affect potential fire behaviour. Our sampling protocol expanded the fuel component groups from two (live and dead) to four (green, senescing, new dead and old dead fuel). We found that all these components had significant fuel moisture content differences, thereby justifying our sampling protocol. Visual curing assessment predominantly resulted in an over-prediction bias of curing level and failed to capture the effect of the senescing process on fuel availability to combust due to misclassification of fuel components (e.g. senescing fuels with high fuel moisture content were classified as dead fuels because of their colouration). Models were developed to estimate the: (1) proportion of senescing and green fuels from knowledge of the current year’s dead fuel proportion; and (2) actual curing level from fuel moisture content and soil dryness level.

Comparison of three methods to quantify the fire spread rate in laboratory experiments

Different methods can be used to measure the time and distance of travel of a fire and thus its speed. The selection of a particular method will depend on the experimental objectives, design, scale, location (in the laboratory or field), required accuracy and resources available. In this study, measurements from ocular observation (directly by eye), visible spectrum video imagery and thermocouple instrumentation were used to compare their performance in quantifying the time of arrival and rate of spread of a fire burning across a eucalypt forest litter fuel bed in a combustion wind tunnel. All methods gave similar results, but there were some significant differences depending on the dryness of the fuel and speed of the wind.