The Proximal Drivers of Large Fires: A Pyrogeographic Study

Abstract

Variations in global patterns of burning and fire regimes are relatively well measured, however, the degree of influence of the complex suite of biophysical and human drivers of fire remains controversial and incompletely understood. Such an understanding is required in order to support current fire management and to predict the future trajectory of global fire patterns in response to changes in these determinants. In this study we explore and compare the effects of four fundamental controls on fire, namely the production of biomass, its drying, the influence of weather on the spread of fire and sources of ignition. Our study area is southern Australia, where fire is currently limited by either fuel production or fuel dryness. As in most fire-prone environments, the majority of annual burned area is due to a relatively small number of large fires. We train and test an Artificial Neural Network's ability to predict spatial patterns in the probability of large fires (>1,250 ha) in forests and grasslands as a function of proxies of the four major controls on fire activity. Fuel load is represented by predicted forested biomass and remotely sensed grass biomass, drying is represented by fraction of the time monthly potential evapotranspiration exceeds precipitation, weather is represented by the frequency of severe fire weather conditions and ignitions are represented by the average annual density of reported ignitions. The response of fire to these drivers is often non-linear. Our results suggest that fuel management will have limited capacity to alter future fire occurrence unless it yields landscape-scale changes in fuel amount, and that shifts between, rather than within, vegetation community types may be more important. We also find that increased frequency of severe fire weather could increase the likelihood of large fires in forests but decrease it in grasslands. These results have the potential to support long-term strategic planning and risk assessment by fire management agencies.

Causes of the Widespread 2019–2020 Australian Bushfire Season

“…This study uses empirical approaches to identifying variables that intensified the bushfire season. Previous studies on past bushfires have evaluated independent relationships among Australian bushfires and climatic variables such as Lewis et al (2020), Clarke et al (2020), and Clarke and Evans (2019) and fuel moisture (Nolan et al, 2016a; de Dios et al, 2015). This study, on the other hand, evaluated all possible causes/factors contributing to the catastrophic bushfires accounting for climatic and geomorphic characteristics, which is also a novelty of this study.…”

“…With the advancement of computational ability of modern computers, bushfire models have become complicated and account for several variables including the interactions among climate, vegetation, terrain, and land use (Boer et al, 2019; Clarke et al, 2019; Penman et al, 2013). Similarly, machine learning approaches are also widely accepted in bushfire applications such as Clarke et al (2020) and Dutta et al (2016). While employing such complex models over a large geographical domain is cumbersome, a simplistic model such as McArthur's FFDI index is generally more effective and hence operational in Australian context.…”

“…and climatic variables such as Lewis et al (2020), Clarke et al (2020), and Clarke and Evans (2019) and fuel moisture (Nolan et al, 2016a;de Dios et al, 2015). This study, on the other hand, evaluated all possible causes/factors contributing to the catastrophic bushfires accounting for climatic and geomorphic characteristics, which is also a novelty of this study.…”

Editorial: Climate, Land Use, and Fire: Can Models Inform Management?

“…Since fuels reduction is a hot topic issue and is often brandished as the one size fits all solution to the extreme fire behavior, we highlight two articles (Clarke et al, 2020;O'Connor et al, 2020) that show the importance of fuels reduction in one dryland ecosystem and the only short term success in another. A second grouping of studies highlighted the various ways in which modeling can more broadly inform management decisions, including a review of various modeling efforts to help managers assess and address ecosystem stability (Loehman et al, 2020), the identification of non-stationarity in extreme fire seasons that emphasizes the need for modernizing fire risk approaches (Barbero et al, 2020), and the importance of ecosystem threshold behavior in savanna ecosystems to changing fire frequency that will require agile models forecasting such drastic change in conditions (Gomes et al, 2020).…”

Counting the Dead: 17 Million Vertebrates Directly Killed by the 2020’s Wildfires in the Pantanal Wetland, Brazil

“…Besides the climate change implications for the wild re increased frequency, many ongoing drivers at regional scales are to blame as well, including short to long-term anthropogenic activities such as deforestation, incorrect ignition and use of re, absence of or inadequate landscape management strategies, vegetation encroachment, increased need of re as management tool, and release of greenhouse gases which, in turn, contributes to climate change 7,8,9,10,11,12 . In fact, during the last few years we have been witnessing an astonishing increase in intensity and frequency of wild res, leading to a globally unprecedented amount of burnt area 3,5,6 .…”