Cost-effective prescribed burning solutions vary between landscapes in eastern Australia

Fire management agencies undertake a range of fire management strategies in an attempt to reduce the risk posed by future wildfires. This can include fuel treatments (prescribed burning and mechanical removal), suppression and community engagement. However, no agency has an unlimited budget and numerically optimal solutions can rarely be implemented or may not even exist. Agencies are trying to quantify the extent to which their management actions reduce risk across multiple values in the most cost-effective manner. In this paper, we examine the cost-effectiveness of a range of prescribed burning strategies across multiple landscapes in south-eastern Australia. Landscapes considered include vegetated areas surrounding the cities of Hobart, Melbourne, Adelaide, Canberra, and Sydney. Using a simulation approach, we examine the potential range of fires that could occur in a region with varying levels of edge and landscape prescribed burning treatment regimes. Damages to assets are measured for houses, lives, transmission lines, carbon and ecological assets. Costs of treatments are estimated from published models and all data are analyzed using multi-criteria decision analysis. Cost-effectiveness of prescribed burning varies widely between regions. Variations primarily relate to the spatial configuration of assets and natural vegetation. Regions with continuous urban interface adjacent to continuous vegetation had the most cost-effective fuel treatment strategies. In contrast, those regions with fragmented vegetation and discontinuous interfaces demonstrated the lowest cost-effectiveness of treatments. Quantifying the extent to which fuel treatments can reduce the risk to assets is vital for determining the location and extent of treatments across a landscape

Quantification of inter-regional differences in risk mitigation from prescribed burning across multiple management values

Fire agencies are moving towards planning systems based on risk assessment; however, knowledge of the most effective way to quantify changes in risk to key values by application of prescribed fire is generally lacking. We present a quantification and inter-regional comparison of how risk to management values responds to variations in prescribed burning treatment rate. Fire simulations were run using the PHOENIX RapidFire fire behaviour simulator for two case study landscapes in interface zones in Tasmania and the Australian Capital Territory (ACT), Australia. A Bayesian network approach used these data to explore the influence of treatment and weather on risk from wildfire. Area burnt, length of powerline damaged and length of road damaged responded more strongly to treatment in the ACT than in Tasmania, whereas treatment mitigated house loss and life loss more strongly in Tasmania than the ACT. The effect of prescribed burning treatment rate on area burnt below minimum tolerable fire interval was similar in each case study landscape. Our study shows that the effectiveness of prescribed burning at mitigating area burnt by wildfire and other key values varies considerably across landscapes and values

Developing and testing models of the drivers of anthropogenic and lightning-caused wildfire ignitions in south-eastern Australia

Considerable investments are made in managing fire risk to human assets, including a growing use of fire behaviour simulation tools to allocate expenditure. Understanding fire risk requires estimation of the likelihood of ignition, spread of the fire and impact on assets. The ability to estimate and predict risk requires both the development of ignition likelihood models and the evaluation of these models in novel environments. We developed models for natural and anthropogenic ignitions in the south-eastern Australian state of Victoria incorporating variables relating to fire weather, terrain and the built environment. Fire weather conditions had a consistently positive effect on the likelihood of ignition, although they contributed much more to lightning (57%) and power transmission (55%) ignitions than the 7 other modelled causes (8–32%). The built environment played an important role in driving anthropogenic ignitions. Housing density was the most important variable in most models and proximity to roads had a consistently positive effect. In contrast, the best model for lightning ignitions included a positive relationship with primary productivity, as represented by annual rainfall. These patterns are broadly consistent with previous ignition modelling studies. The models developed for Victoria were tested in the neighbouring fire prone states of South Australia and Tasmania. The anthropogenic ignition model performed well in South Australia (AUC = 0.969) and Tasmania (AUC = 0.848), whereas the natural ignition model only performed well in South Australia (AUC = 0.972; Tasmania AUC = 0.612). Model performance may have been impaired by much lower lightning ignition rates in South Australia and Tasmania than in Victoria. This study shows that the spatial likelihood of ignition can be reliably predicted based on readily available meteorological and biophysical data. Furthermore, the strong performance of anthropogenic and natural ignition models in novel environments suggests there are some universal drivers of ignition likelihood across south-eastern Australia

Developing and testing models of the drivers of anthropogenic and lightning-caused wildfire ignitions in south-eastern Australia

Considerable investments are made in managing fire risk to human assets, including a growing use of fire behaviour simulation tools to allocate expenditure. Understanding fire risk requires estimation of the likelihood of ignition, spread of the fire and impact on assets. The ability to estimate and predict risk requires both the development of ignition likelihood models and the evaluation of these models in novel environments. We developed models for natural and anthropogenic ignitions in the south-eastern Australian state of Victoria incorporating variables relating to fire weather, terrain and the built environment. Fire weather conditions had a consistently positive effect on the likelihood of ignition, although they contributed much more to lightning (57%) and power transmission (55%) ignitions than the 7 other modelled causes (8-32%). The built environment played an important role in driving anthropogenic ignitions. Housing density was the most important variable in most models and proximity to roads had a consistently positive effect. In contrast, the best model for lightning ignitions included a positive relationship with primary productivity, as represented by annual rainfall. These patterns are broadly consistent with previous ignition modelling studies. The models developed for Victoria were tested in the neighbouring fire prone states of South Australia and Tasmania. The anthropogenic ignition model performed well in South Australia (AUC = 0.969) and Tasmania (AUC = 0.848), whereas the natural ignition model only performed well in South Australia (AUC = 0.972; Tasmania AUC = 0.612). Model performance may have been impaired by much lower lightning ignition rates in South Australia and Tasmania than in Victoria. This study shows that the spatial likelihood of ignition can be reliably predicted based on readily available meteorological and biophysical data. Furthermore, the strong performance of anthropogenic and natural ignition models in novel environments suggests there are some universal drivers of ignition likelihood across south-eastern Australia

The 2019–2020 Australian forest fires are a harbinger of decreased prescribed burning effectiveness under rising extreme conditions

There is an imperative for fire agencies to quantify the potential for prescribed burning to mitigate risk to life, property and environmental values while facing changing climates. The 2019–2020 Black Summer fires in eastern Australia raised questions about the effectiveness of prescribed burning in mitigating risk under unprecedented fire conditions. We performed a simulation experiment to test the effects of different rates of prescribed burning treatment on risks posed by wildfire to life, property and infrastructure. In four forested case study landscapes, we found that the risks posed by wildfire were substantially higher under the fire weather conditions of the 2019–2020 season, compared to the full range of long-term historic weather conditions. For area burnt and house loss, the 2019–2020 conditions resulted in more than a doubling of residual risk across the four landscapes, regardless of treatment rate (mean increase of 230%, range 164–360%). Fire managers must prepare for a higher level of residual risk as climate change increases the likelihood of similar or even more dangerous fire seasons

Health costs of wildfire smoke to rise under climate change

Abstract The global health burden from wildfire smoke is expected to worsen under climate change, yet we lack quantitative estimates of the economic costs of increased mortality and hospital admissions for cardiovascular and respiratory conditions. Using a quantitative wildfire risk assessment framework and a 12-member climate model ensemble, we find a median increase in wildfire smoke health costs of 1–16% by 2070 across diverse landscapes in south-eastern Australia. Ensemble maximum cost increases (5–38%) often exceed abatements from fuel treatment, while costs decline moderately (0–7%) for the ensemble minimum. Unmitigated climate change will increase the health burden of wildfire smoke and undermine prescribed burning effectiveness

Conditional Performance Evaluation: Using Wildfire Observations for Systematic Fire Simulator Development

Faster than real-time wildland fire simulators are being increasingly adopted by land managers to provide decision support for tactical wildfire management and assist with strategic risk planning. These simulators are typically based on simple forward rate-of-spread algorithms that were predominantly developed using observations of experimental fires. Given their operational use, it is important that fire simulators be assessed in terms of their performance for their intended use; predicting the spatial progression of wildfires. However, the conditions under which wildfires occur cannot be easily replicated experimentally. We describe and demonstrate a method for use in model development, whereby a dataset comprised of wildfire case-studies is used for evaluating the predictive performance of fire simulators. Two different versions of the model PHOENIX RapidFire were assessed, one incorporating a novel algorithm that accounts fine-scale spatial variation in landscape dryness. Evaluation was done by comparing simulator predictions against contemporaneous observations of 9 different wildfires that occurred in Australia. Performance was quantified using the sum of the Area Difference Indices—a measure of prediction overlap calculated for each prediction/observation pair. The two versions of the model performed similarly, with the newer version being marginally (but not statistically significantly) better when outcomes were summarised across all fires. Despite this, it did not perform better in all cases, with three of the 9 fires better predicted using the original model. Wildfire evaluation datasets were demonstrated to provide valuable feedback for model development, however the limited availability of data means power is lacking for detailed comparisons. With increasingly extreme weather conditions resulting from climate change, conditions under which wildfires occur are likely to continue to extend well beyond those under which fire models algorithms were developed. Consequently, the adoption of improved methods for collecting and utilising wildfire data is critical to ensure fire simulators retain relevance

Quantification of inter-regional differences in risk mitigation from prescribed burning across multiple management values

Fire agencies are moving towards planning systems based on risk assessment; however, knowledge of the most effective way to quantify changes in risk to key values by application of prescribed fire is generally lacking. We present a quantification and inter-regional comparison of how risk to management values responds to variations in prescribed burning treatment rate. Fire simulations were run using the PHOENIX RapidFire fire behaviour simulator for two case study landscapes in interface zones in Tasmania and the Australian Capital Territory (ACT), Australia. A Bayesian network approach used these data to explore the influence of treatment and weather on risk from wildfire. Area burnt, length of powerline damaged and length of road damaged responded more strongly to treatment in the ACT than in Tasmania, whereas treatment mitigated house loss and life loss more strongly in Tasmania than the ACT. The effect of prescribed burning treatment rate on area burnt below minimum tolerable fire interval was similar in each case study landscape. Our study shows that the effectiveness of prescribed burning at mitigating area burnt by wildfire and other key values varies considerably across landscapes and values

A flexible framework for cost-effective fire management

Fire management aims to change fire regimes. However, the challenge is to provide the optimal balance between the mitigation of risks to life and property, while ensuring a healthy environment and the protection of other key values in any given landscape. Incorporating cost-effectiveness and climate change impacts magnifies this task. We present an objective framework for quantitative comparison of the risk mitigation potential of alternative fuel treatment scenarios in south-eastern Australia. There is no single optimal strategy for all values in a given region, nor for any individual value in all regions. Trade-offs are required and cost-effectiveness is highly sensitive to the addition of management values. Climate change is likely to decrease prescribed burning effectiveness and increase total costs, therefore a rethink of best practice is required. Our study highlights the need for flexibility in the development and implementation of fire management strategies, which is something that risk-based approaches can provide. We discuss prospects of extending our framework to values for which we currently lack robust quantitative information and issues of compatibility with Aboriginal cultural burning and by implication other approaches that do not stem from within the prevailing fire management paradigm