A data - Model fusion methodology for mapping bushfire fuels for smoke emissions forecasting in forested landscapes of south-eastern Australia

Multi-decadal increase of forest burned area in Australia is linked to climate change

Fire activity in Australia is strongly affected by high inter-annual climate variability and extremes. Through changes in the climate, anthropogenic climate change has the potential to alter fire dynamics. Here we compile satellite (19 and 32 years) and ground-based (90 years) burned area datasets, climate and weather observations, and simulated fuel loads for Australian forests. Burned area in Australia's forests shows a linear positive annual trend but an exponential increase during autumn and winter. The mean number of years since the last fire has decreased consecutively in each of the past four decades, while the frequency of forest megafire years (>1 Mha burned) has markedly increased since 2000. The increase in forest burned area is consistent with increasingly more dangerous fire weather conditions, increased risk factors associated with pyroconvection, including fire-generated thunderstorms, and increased ignitions from dry lightning, all associated to varying degrees with anthropogenic climate change.

Predictively modelling the distribution of the threatened brush-tailed rock-wallaby (

Abstract Context Species Distribution Models (SDM) can be used to investigate and understand relationships between species occurrence and environmental variables, so as to predict potential distribution. These predictions can facilitate conservation actions and management decisions. Oxley Wild Rivers National Park (OWRNP) is regarded as an important stronghold for the threatened brush-tailed rock-wallaby (Petrogale penicillata), on the basis of the presence of the largest known metapopulation of the species. Adequate knowledge of the species’ ecology and distribution in OWRNP is a key objective in the national recovery plan for the species occurring in the Park. Aims To model distribution using key GIS-derived environmental factors for the brush-tailed rock-wallaby in OWRNP and to ground-truth its presence through field surveys in areas of high habitat suitability. Methods We used Maxent to model the distribution of the brush-tailed rock-wallaby within OWRNP on the basis of 282 occurrence records collected from an online database, elicitation of informal records from experts, helicopter surveys and historic records. Environmental variables used in the analysis were aspect, distance to water, elevation, geology type, slope and vegetation type. Key results Vegetation type (37.9%) was the highest contributing predictor of suitable habitat, whereas aspect (4.8%) contributed the least. The model produced an area under the curve (AUC) of the receiver operating characteristic (ROC) of 0.780. The model was able to discriminate between suitable and non-suitable habitat for brush-tailed rock-wallabies. Areas identified in our model as being highly suitable yielded eight new occurrence records during subsequent ground-truthing field surveys. Conclusions Brush-tailed rock-wallaby distribution in OWRNP is primarily associated with vegetation type, followed by distance to water, elevation, geology, slope and aspect. Field surveys indicated that the model was able to identify areas of high habitat suitability. Implications This model represents the first predicted distribution of brush-tailed rock-wallaby in OWRNP. By identifying areas of high habitat suitability, it can be used to survey and monitor the species in OWRNP, and, thus, contribute to its management and conservation within the Park.

Fire intensity effects on post-fire fuel recovery in Eucalyptus open forests of south-eastern Australia

This is a study of the re-accumulation of bushfire fuels following both prescribed fire of low fireline intensity (<700 kW m^-1) and wildfire of high intensity (>10,000 kW m^-1) in Australian Eucalyptus open forests of differing annual rainfall. Repeated measurements over 5 to 7 years of litter, elevated fuels, coarse woody debris, and bark revealed more rapid fuel recovery in higher rainfall forests compared with lower rainfall forests, following prescribed fire. In prescribed-burnt forests with mean annual rainfall 900-950 mm all fuel categories recovered to very high within seven years, with elevated fuels exceeding pre-fire loads by up to 200%. No fuels in prescribed-burnt forests with mean annual rainfall 600-650 mm recovered to pre-fire loads after six years suggesting that rainfall is an important driver of the rate of fuels recovery. High intensity wildfire in lower rainfall forests (600-650 mm) stimulated the rapid recovery of elevated fuels to over 600% of pre-fire loads - effectively transforming open forest formations into shrublands over the 6 years after fire. The recovery of elevated fuels following both prescribed fire in high rainfall forests and wildfire in low rainfall forests did not follow a gradual negative exponential increase often approximated by an Olson curve, but peaked early after fires. This suggests that the Olson recovery function, the default for predicting loads for these fuels in the operational fire behaviour models in use in south-eastern Australia, may not be appropriate in all cases. Fire simulations were run for forests burnt in wildfires using default (forest) and observed (shrubland) vegetation types. Under weather conditions similar to the previous wildfire, predictions for fireline intensities and the rate of spread would be at least 50% greater in transitional shrubland than forest, emphasizing the importance of accounting for vegetation dynamics for safe response management.