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.