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.

Bayesian decision network modeling for environmental risk management: A wildfire case study.

Environmental decision-making requires an understanding of complex interacting systems across scales of space and time. A range of statistical methods, evaluation frameworks and modeling approaches have been applied for conducting structured environmental decision-making under uncertainty. Bayesian Decision Networks (BDNs) are a useful construct for addressing uncertainties in environmental decision-making. In this paper, we apply a BDN to decisions regarding fire management to evaluate the general efficacy and utility of the approach in resource and environmental decision-making. The study was undertaken in south-eastern Australia to examine decisions about prescribed burning rates and locations based on treatment and impact costs. Least-cost solutions were identified but are unlikely to be socially acceptable or practical within existing resources; however, the statistical approach allowed for the identification of alternative, more practical solutions. BDNs provided a transparent and effective method for a multi-criteria decision analysis of environmental management problems.

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.