Models of forest fire spread generally only consider the contribution of fine fuels (i.e. <6 mm in diameter). A recent study in the CSIRO Pyrotron showed that the presence of larger fuel elements such as twigs and branches 6-50 mm in diameter greatly affect the speed of a fire compared to when these elements are not present. This is not an issue for empirically-based fire spread models in which such fuels are included implicitly but may cause theoretically-based models that do not explicitly incorporate the effect of these fuels to over-predict a fire’s spread rate.
Knowledge of the suitability and effectiveness of different suppression actions is essential for sound planning and response decisions that reduce the impact of wildfires. The effect of suppression actions at a tactical level during a wildfire are difficult to measure and assess. The development of novel methods for collecting operational data from a range of new and traditional sources will extend our understanding of suppression effectiveness and improve firefighter safety and wildfire outcomes.
Predictions of fire propagation across the landscape are often used to support planning of fire suppression activities and warn communities of impending threats. However, in many situations there is little time or access to the necessary data to undertake detailed predictions of fire spread. We derived a simple rule of thumb that can be quickly applied to a broad range of fuel types: the rate of forward spread of an established bushfire is equal to approximately 10% of the average 10 m open wind speed.
The effectiveness of wildfire suppression is difficult to evaluate as it can be assessed against a range of objectives and purposes at many scales. There is a strong and growing need for such information to support suppression planning, resource prioritisation and decision making. Two recent review articles provide a summary of the current state of suppression effectiveness research.
Generalised statements about the state of a science are often given in publications to provide a simplified context for the reader. When such statements are repeated without the necessary critical assessment to determine if the statement is still valid, it can become a mantra. In bushfire science, such statements have impacted research directions, end-user expectations and the value of applied research results. A recent article analysed the truthfulness of five such statements about bushfire behaviour modelling commonly found in the literature.
The effect of grass fuel load on fire behaviour and fire danger has been a contentious issue for some time in Australia. Existing operational models have placed different emphases on the effect of fuel load on model predictions, creating uncertainty in the operational assessment of fire potential and distrust in model results. Analysis of a series of field experiments across a broad range of grasslands found an inverse relationship between fuel load and fire spread rate. A fuel load effect function has been developed that can be applied to grassfire spread models used in Australia.
Accurate estimates of the moisture content of dead grass fuels is critical for accurate predictions of grassland fire behaviour. Six dead fuel moisture prediction models were assessed using measurements of dead grass moisture content collected across eastern Australia. The best-performing model was that derived from Alan McArthur’s 1960 tables, which is built into the Mk 2 CSIRO Grassland Fire Spread Meter.
The ability to accurately predict the behaviour and spread of a bushfire enables fire managers to develop and implement safe and effective suppression strategies and to release timely and effective public warnings. We compared the predictive capability of five older rate of fire spread models with newer versions and found a reduction in prediction bias and prediction error, highlighting the value of new, improved models.
Many methods are available to measure the time and distance of travel of a fire front and thus determine its speed. The choice depends on measurement objectives, scale, location (in the laboratory or field), desired accuracy and availability of resources. The performance of three common methods were quantitatively compared using fires burning in eucalypt litter in the CSIRO Pyrotron.
Current systems for determining fire danger nationally have been found to be inadequate and are presently being revised. Recent advances in spatial statistical analysis, in particular extreme event modelling using ‘max-stable’ processes, may provide an avenue for refining fire danger rating determination. This study used the spatial behaviour of extreme values of a common fire danger index to investigate the utility of employing spatially-varying thresholds for defining fire danger ratings.
Laboratory experiments enable study of the mechanisms of propagation in free-burning fires which are difficult to isolate in the field. Highly uniform beds of ‘artificial’ fuel (such as wood shavings) are often used to reduce experimental variability but results can be difficult to translate into the real world. This study showed that fire behaviour in natural heterogeneous fuel beds was highly repeatable and suitable for experimentation.
There are many aspects of the behaviour of bushfires for which we do not have a complete understanding. These gaps in knowledge need to be overcome when undertaking operational fire behaviour predictions to ensure meaningful results. Compensation strategies for a number of critical knowledge gaps are provided.
Large plantations of radiata pine (Pinus radiata D. Don) are susceptible to wildfires that compromise the sustainability of forest production and downstream industries. We evaluated the effectiveness of fuel management zones characterised by an intensive silvicultural prescription. The silvicultural prescription resulted in significantly lower predicted fireline intensity and likelihood of crowning under a broad range of burning conditions.
A reliable fire spread prediction requires a sound understanding of the current scientific knowledge of fire behaviour and the prudent application of expert judgement. Scientific knowledge is embedded in fire behaviour models. Expert judgement compensates for deficiencies in scientific knowledge and input data, allowing adaptation of a prediction to specific situations. A recent paper outlines the important aspects of fire behaviour knowledge that help improve the reliability of operational fire spread predictions.
A raging high intensity bushfire is one of the most spectacular and dangerous natural phenomena in the world. Understanding the processes involved in the sustained combustion of vegetation is essential to developing systems to predict the behaviour and spread of bushfires over the landscape. A recent two-part synthesis provides insight into the current state of our understanding and highlights many of the processes that contribute to the difficulties in obtaining accurate predictions of fire spread.
Bushfires, both prescribed and wild, are a major source of non-industrial greenhouse gas emissions in Australia and play a major role in the global carbon cycle. Understanding how such emissions are affected by the behaviour of bushfire is essential to identifying ways to use fire as tool to mitigate emissions and positively influence the carbon cycle.
Australian land and rural fire agencies have recognised the need for a national-level bushfire fuel classification system to enable consistent characterisation and categorisation of fuels across the country. This would support a wide range of fire and fuel management activities including risk planning, fire danger rating and prediction of bushfire behaviour. The Bushfire Fuel Classification (BFC) system leverages existing vegetation data from a range of sources to classify bushfire fuel attributes within a hierarchical system of increasing detail.
A methodology for testing and comparing the direct attack effectiveness of wildfire suppressants has been developed. The method determines the minimum volume of suppressant required to extinguish ‘standard’ fires in the CSIRO Pyrotron. This measure can be used to quantify and compare effectiveness of different suppressants.
Fires burning in forests of ribbon bark eucalypts such as Eucalyptus viminalis (manna gum) and E. rubida (candlebark) have a notorious reputation for producing firebrands that can travel enormous distances and start spotfires up to 40 kilometres downwind of the main fire. Recent work has investigated the mechanisms by which ribbon bark might travel these distances and determined that a key factor is the shape of the bark.
The state of curing of a grass sward has long been known to have a direct effect on the speed of a fire, with fires spreading faster in more fully cured swards than those that are less cured. A research partnership between CSIRO and the Victorian Country Fire Authority has shown that current systems for incorporating the effect of grass curing on fire behaviour under predict fire potential when grasses are not fully cured. A new mathematical function to described this effect has been developed for incorporation into grassfire rate of spread predictions.
Amicus is a new software package designed to help make the prediction of fire behaviour more reliable. It combines existing state-of-the-art fire models for major fuel types found in Australia with an intuitive interface that enables make quick predictions to be made with estimates of output reliability and graphical visualisations. A beta version for testing is available for download.
The Dry Eucalypt Forest Fire Model (DEFFM), developed from Project Vesta, predicts the rate of spread of wildfires based on estimates of wind speed, fine dead fuel moisture content and a visual assessment of surface and near-surface fuel characteristics. Fuel characteristics are described using a numeric fuel hazard score (from 0 to 4) or fuel hazard rating (Low to Extreme) and the height of the near-surface fuel layer. Example default fuel values for a modest productivity eucalypt forest with a shrubby understorey are given for when site-specific data are not available.
Models to predict likely fire spread and behaviour are key tools in a fire manager’s toolbox. But how well do we know the models we use? A new book and companion scientific paper detail all available models, their performance, and application bounds, to support their informed use. A digital copy of the book in the form of a PDF file is available here.
Understanding when spotfires are likely to occur is essential for suppression planning and determining the potential impact on fire behaviour and firefighter safety. New research reveals critical fuel bed moisture contents for spotfire ignition success.