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The Forest Fire Danger Index (FFDI) is the main measure used in Australia for estimating fire risk. Recent work by the authors showed that the FFDI forms stable state regimes, nominated as fire climate regimes. These regimes shifted to greater intensity in southern and eastern Australia around the year 2000 and, a decade later, further north. Reductions in atmospheric moisture were the primary contributor. These changes have not been fully incorporated into future projections. This paper compares the recent regime shifts with the most recent national projections of FFDI, published in 2015. They show that for most states and regions, the 2030 upper limit is approached or exceeded by the recent shift, except for two states with large arid zones, South Australia and Western Australia. Methods for attributing past changes, constructing projections, and the inability of climate models to reproduce the recent decreases in atmospheric moisture, all contribute to these underestimates. To address these shortcomings, we make some suggestions to modify efforts aiming to develop seamless predictions and projections of future fire risk. [ABSTRACT FROM AUTHOR]

Numerous hectares of land are destroyed by wildfires every year, causing harm to the environment, the economy, and the ecology. More than fifty million acres have burned in several states as a result of recent forest fires in the Western United States and Australia. According to scientific predictions, as the climate warms and dries, wildfires will become more intense and frequent, as well as more dangerous. These unavoidable catastrophes emphasize how important early wildfire detection and prevention are. The energy management system described in this paper uses an unmanned aircraft system (UAS) with air quality sensors (AQSs) to monitor spot fires before they spread. The goal was to develop an efficient autonomous patrolling system that detects early wildfires while maximizing the battery life of the UAS to cover broad areas. The UAS will send real-time data (sensor readings, thermal imaging, etc.) to a nearby base station (BS) when a wildfire is discovered. An optimization model was developed to minimize the total amount of energy used by the UAS while maintaining the required levels of data quality. Finally, the simulations showed the performance of the proposed solution under different stability conditions and for different minimum data rate types. [ABSTRACT FROM AUTHOR]

The Curse of Conservation: Empirical Evidence Demonstrating That Changes in Land-Use Legislation Drove Catastrophic Bushfires in Southeast Australia. "Victorian Environment Assessment Council Digital Archive" Victorian Environment Assessment Council, The Digital Archive Contains Published Reports and Maps of the Land Conservation Council from 1971 to 1997Available online: https://www.veac.vic.gov.au/digital-archive/investigations(accessed on 22 June 2023) 3 McPhee G. The Land Conservation Council, Conservation or Exploitation? In 1970, the Victorian state government in Australia established the Land Conservation Council (LCC) to study the state's publicly-owned land and make recommendations for its use [[1]]. [Extracted from the article]

The 2012 satellite ASTER geoscience maps of Australia were designed to provide public, web-accessible, and spatially comprehensive surface mineralogy for improved mapping and solutions to geoscience challenges. However, a number of the 2012 products were clearly compromised by variable green and/or dry vegetation cover. Here, we show a strategy to first estimate and then unmix the contributions of both these vegetation components to leave, as residual, the target surface mineralogy. The success of this unmixing process is validated by (i) visual suppression/removal of the regional climate and/or local fire-scar vegetation patterns; and (ii) pixel values more closely matching field sample data. In this process, we also found that the 2012 spectral indices used to gauge the AlOH content, AlOH composition, and water content can be improved. The updated (new indices and vegetation unmixed) maps reveal new geoscience information, including: (i) regional "wet" and "dry" zones that appear to express "deep" geological characters often expressed through thick regolith cover, with one zone over the Yilgarn Craton spatially anti-correlated with Archaean gold deposits; (ii) a ~1000 km wide circular feature over the Lake Eyre region defined by a rim of abundant "muscovite" that appears to coincide with opal deposits; (iii) a N–S zonation across the western half of the continent defined by abundant muscovite in the south and kaolinite in the north, which appears to reflect opposing E ↔ W aeolian sediment transport directions across the high-pressure belt; (iv) various paleo-drainage networks, including those over aeolian sand covered the "lowlands" of the Canning Basin, which are characterized by low AlOH content, as well as those over eroding "uplands", such as the Yilgarn Craton, which have complicated compositional patterns; and (v) a chronological history of Miocene barrier shorelines, back-beach lagoons, and alluvial fans across the Eucla Basin, which, to date, had proved elusive to map using other techniques, with potential implications for heavy mineral sand exploration. Here, we explore the latter three issues. [ABSTRACT FROM AUTHOR]

Climate change has resulted in a worldwide increase in intensity and frequency of extreme weather events including bushfires. Previous research has shown that communities often do not engage in disaster preparedness, even when sufficient education and resources are provided. With the projected increase in natural disasters, preparedness is paramount, and more research is needed to gain an understanding into what impacts community preparedness in the face of climate change. This study investigated one rural Australian community's preparedness for the 2019–2020 bushfires. Thirteen Australian adults who resided within a small rural community in New South Wales during the 2019–2020 bushfires participated in semi-structured interviews. Data were analysed using inductive thematic analysis. Participants reported being unprepared for the 2019–2020 bushfires and that the community has started to prepare for future bushfires. However, they also described a belief in 'climate cycles' rather than climate change, limiting engagement in preparedness for future hazards. Participants also reported that they did not talk about the 2019–2020 bushfires, although described experiencing residual anxiety. Recommendations included support needed for rural communities to help with future preparedness efforts and mental health symptoms. [ABSTRACT FROM AUTHOR]

Wildfires are pivotal to the functioning of many ecosystems globally, including the magnitude of surface erosion rates. This study aims to investigate the relationships between surface erosion rates and wildfire intensity in the tropical north savanna of Australia. The occurrence of fires in western Arnhem Land, Northern Territory, Australia was determined with remotely sensed digital datasets as well as analogue erosion measurement methods. Analysis was performed using satellite imagery to quantify burn severity via a monthly delta normalised burn ratio (dNBR). This was compared and correlated against on-ground erosion measurements (erosion pins) for 13 years. The dNBR for each year (up to +0.4) displayed no relationship with subsequent erosion (up to ±4 mm of erosion/deposition per year). Poor correlation was attributed to low fire severity, patchy burning, significant time between fires and erosion-inducing rainfall. Other influences included surface roughness from disturbances from feral pigs and cyclone impacts. The findings here oppose many other studies that have found that fires increase surface erosion. This accentuates the unique ecosystem characteristics and fire regime properties found in the tropical Northern Territory. Scenarios of late dry season fires with high severity were not observed in this study and require more investigations. Ecosystems such as the one examined here require specialised management practices acknowledging the specific ecosystem functions and processes. The methods employed here combine both analogue and digital sensors to improve understandings of a unique environmental system. [ABSTRACT FROM AUTHOR]

Dust storms and wildfires occur frequently in south-eastern Australia. Their effects on the ecology, environment and population exposure have been the focus of many studies recently. Dust storms do not emit ground-sequestered carbon, but wildfires emit significant quantities of carbon into the atmosphere. However, both natural events promote phytoplankton growth in water bodies because carbon, and other trace elements such as iron, deposit on the surface water of oceans. Carbon dioxide is reabsorbed by phytoplankton via photosynthesis. The carbon balance cycle due to dust storms and wildfires is not well known. Recent studies on the carbon emission of the 2019–2020 summer wildfires in eastern Australia indicated that this megafire event emitted approximately 715 million tonnes of CO2 (195 Tg C) into the atmosphere from burned forest areas. This study focusses on the association of dust storms and wildfires in southeastern Australia with phytoplankton growth in the Tasman Sea due to the February 2019 dust storm event and the 2019–2020 Black Summer wildfires. Central Australia and western New South Wales were the sources of the dust storm emission (11 to 16 February 2019), and the Black Summer wildfires occurred along the coast of New South Wales and Victoria (from early November 2019 to early January 2020). The WRF-Chem model is used for dust storm simulation with the AFWA (Air Force Weather Agency of the US) dust emission version of the GOCART model, and the WRF-Chem model is used for wildfire simulation with FINN (Fire Emission Inventory from NCAR) emission data. The results show the similarities and differences in the deposition of particulate matter, phytoplankton growth and carbon reabsorption patterns in the Tasman Sea from these events. A higher rate of deposition of PM2.5 on the ocean surface corresponds to a higher rate of phytoplankton growth. Using the WRF-Chem model, during the 5-day dust storm event in February 2019, approximately ~1230 tons of total dust was predicted to have been deposited in the Tasman Sea, while ~132,000 tons of PM10 was deposited in the early stage of the wildfires from 1 to 8 November 2019. [ABSTRACT FROM AUTHOR]

Background: Air pollution can cause numerous health problems and increase the need for medicines to treat and prevent asthma in affected areas. There is limited evidence about the association between airborne particles with a diameter of 2.5 micrometres or smaller (PM2.5) and asthma medicine usage. This study examined the potential association between the levels of PM2.5 and the supply of prescription asthma medicines in the Australian Capital Territory (ACT), Australia, during the severe bushfire season between November 2019–January 2020. Methods: Daily data was obtained from an ACT air quality monitoring station from November 2019 to January 2020 (study period) and November 2018 to January 2019 (control period, no bushfire). The number and types of government-funded asthma medicine prescriptions were obtained from the Services Australia (government) website by searching under 'Pharmaceutical Benefits Scheme Item Reports' and using relevant item codes during the study and control periods. Results: The medians for PM2.5 levels for the study period were significantly higher than those for the control period (p < 0.001). There were increases in the number of dispensed prescriptions of short-acting beta-2 agonists (SABA), inhaled corticosteroids, and long-acting beta-2 agonists combined with inhaled corticosteroids. The greatest difference was seen with the inhaled corticosteroids: a 138% increase. Conclusions: The increase in the number of dispensed asthma prescriptions during the bushfire season should be used to inform the stock holdings of these medicines in preparation for future events to ensure access to lifesaving asthma medicines. [ABSTRACT FROM AUTHOR]

Fire danger poses a pressing threat to ecosystems and societies worldwide. Adequate preparation and forewarning can help reduce these threats, but these rely on accurate prediction of extreme fire danger. With the knowledge that climatic conditions contribute heavily to overall fire danger, this study evaluates the skill with which episodes of extreme fire danger in Australia can be predicted from the activity of large-scale climate driver patterns. An extremal dependence index for extreme events is used to depict the historical predictive skill of the Australian Bureau of Meteorology's subseasonal climate prediction system in replicating known relationships between the probability of top-decile fire danger and climate driver states at a lead time of 2–3 weeks. Results demonstrate that the El Niño Southern Oscillation, Southern Annular Mode, persistent modes of atmospheric blocking, Indian Ocean Dipole and Madden-Julian Oscillation are all key for contributing to predictability of fire danger forecasts in different regions during critical fire danger periods. Northwest Australia is found to be particularly predictable, with the highest mean index differences (>0.50) when certain climate drivers are active, compared with the climatological index mean. This integrated approach offers a valuable resource for decision-making in fire-prone regions, providing greater confidence to users relying on fire danger outlooks for key management decisions, such as those involved in the sectors of national park and forest estate management, agriculture, emergency services, health and energy. Furthermore, the results highlight strengths and weaknesses in both the Australian Fire Danger Rating System and the operational climate model, contributing additional information for improving and refining future iterations of these systems. [ABSTRACT FROM AUTHOR]

Every year since the early 1980s, the ozone hole has appeared in late winter/spring over Antarctica. The ozone hole is expected to disappear due to the observed decrease in the concentration of ozone-depleting substances in the stratosphere, which is enforced by the 1987 Montreal Protocol and its subsequent amendments. However, large ozone holes have appeared four years in a row (2020–2023), which may be a signal that Antarctic ozone repair has stopped. Statistical analyses of ozone hole metrics (hole area, minimum total column ozone, ozone mass deficit, and ozone mass deficit per unit area of the hole) are presented to determine how adding the data from these years changes the ozone recovery pattern. Statistically significant trends in the hole metrics were revealed for the short period (2000–2019) but not for the longer period (2000–2023). The modeled time series of metrics from multiple regressions with standard chemical and dynamic explanatory variables indicate that the recovery has slowed since around 2010. Moreover, a sequence of extreme events (wildfires in Australia in the summer of 2019/2020 and the eruption of the Hunga Tonga volcano in January 2022) may have caused additional ozone losses in Antarctica that masked the repair of polar ozone for a while. [ABSTRACT FROM AUTHOR]

Simple Summary: The 2019–2020 Black Summer bushfires had a devastating impact on Australian biodiversity. Many affected human communities felt compelled to intervene by organizing and providing food, water, and/or shelter to affected wildlife in situ ('wildlife provisioning'). While well intentioned, due to the unprecedented scale and intensity of the fires, a lack of institutional support for wildlife provisioning, and what was revealed to be a lack of scientific evidence, this response was largely uncoordinated and a substantial amount of research is required to determine the beneficial and/or negative outcomes of such practices as a response to habitat destruction by bushfires. We propose a 'One Welfare' approach that recognizes the interconnection of human, animal, and environmental welfare and examines the existing literature; local legislation; views of stakeholders; emerging data; and modelling from fire events. There is strong evidence indicating that future bushfire seasons will become longer and more intense in Australia and elsewhere, putting the welfare and survival of millions of wild animals at risk every year. If this approach were implemented, we anticipate that best practice recommendations for stakeholders in different contexts would emerge to determine if, when, and how wildlife provisioning best be conducted, now and into the future. Australia's 2019–2020 bushfires had a devastating impact on animals, humans, and ecosystems. They also demonstrated the lack of evidence or guidance for wildlife provisioning in response to severe fire events when volunteers and wildlife organisations rose to respond. In addition, the unprecedented scale and intensity of the fires and an absence of institutional support for wildlife provisioning meant that well-intentioned interventions were largely uncoordinated and lacked clear short-term, mid-term, and long-term objectives. Fundamentally, a lack of consensus was revealed on whether any such interventions are advisable. Given the strong evidence indicating that future bushfire seasons will become longer and more intense in Australia and elsewhere, the welfare and survival of millions of wild animals are at risk every year. Understanding the impacts of supplementary resource interventions and contributing to the development of best practice information is crucial to inform the response to the next major fire event. Here, we contextualize the arguments for and against provisioning within a 'One Welfare' framework that recognizes that animal welfare, biodiversity, and the environment are intertwined with human welfare and community resilience. We propose that the One Welfare approach can facilitate appropriate consideration of the extant scientific and lay literature; local legislation; views of stakeholders; emerging data; and modelling from historic fire events. As a further step, we see merit in engaging with wildlife provisioners and the broader conservation community to build an evidence base for future wildlife provisioning activities. From an informed position, we can encourage beneficial interventions and reduce the risk of negative outcomes. Finally, we propose controlled experiments (e.g., using hazard reduction burns), ongoing data collection using emergent technology, and longitudinal analysis to address shifting research priorities as the climate changes. We conclude that the ordered collection of the necessary evidence relevant to each of the three stakeholder groups in the One Welfare framework has the greatest potential to support an informed policy platform on wildlife provisioning across Australia that is feasible, legal, and sustainable. [ABSTRACT FROM AUTHOR]

The exacerbation of wildfires, attributed to the effects of climate change, presents substantial risks to ecological systems, infrastructure, and human well-being. In the context of the Sustainable Development Goals (SDGs), particularly those related to climate action, prioritizing the assessment and management of the occurrence and intensity of extensive wildfires is of utmost importance. In recent times, there has been a significant increase in the frequency and severity of widespread wildfires worldwide, affecting several locations, including Australia, Italy, and the United States of America. The presence of complex phenomena marked by limited predictability leads to significant negative impacts on biodiversity and human lives. The utilization of satellite-derived data with neural networks, such as convolutional neural networks (CNNs), is a potentially advantageous approach for augmenting the monitoring capabilities of wildfires. This research examines the generalization capability of four neural network models, namely the fully connected (FC), one-dimensional (1D) CNN, two-dimensional (2D) CNN, and three-dimensional (3D) CNN model. Each model's performance, as measured by accuracy, recall, and F1 scores, is assessed through K-fold cross-validation. Subsequently, T-statistics and p-values are computed based on these metrics to conduct a statistical comparison among the different models, allowing us to quantify the degree of similarity or dissimilarity between them. By using training data from Australia and Sicily, the performances of the trained model are evaluated on the test dataset from Oregon. The results are promising, with cross-validation on the training dataset producing mean precision, recall, and F1 scores ranging between approximately 0.97 and 0.98. Especially, the fully connected model has superior generalization capabilities, whilst the 3D CNN offers more refined and less distorted classifications. However, certain issues, such as false fire detection and confusion between smoke and shadows, persist. The aforementioned methodologies offer significant perspectives on the capabilities of neural network technologies in supporting the detection and management of wildfires. These approaches address the crucial matter of domain transferability and the associated dependability of predictions in new regions. This study makes a valuable contribution to the ongoing efforts in climate change by assisting in monitoring and managing wildfires. [ABSTRACT FROM AUTHOR]

Simple Summary: Bushfires are a regular occurrence in the Australian landscape. The 2019/20 megafires were unprecedented in intensity and scale, impacting many native species. Koalas were particularly impacted by the fires, with many of those coming into care requiring extensive rehabilitation and treatment. Very little is known about the health parameters of rehabilitated koalas following their release and how these parameters may differ between individuals living in a burnt landscape compared to those in an unburnt landscape. This study tested haematological and serum biochemical parameters, chlamydial shedding and body condition scores of rehabilitated koalas and non-rescued residents living in burnt and unburnt habitats. All koalas received a health check 5–9 months post-fire and one 12–16 months post-fire. Rehabilitated koalas also received a health check when coming into care initially. While some variation in parameters was recorded, the majority of measurements in each koala group fell within the normal reference ranges, suggesting that resident and rehabilitated koalas were in good health at the time of release and when recaptured. These results show that koalas can be supported in burnt landscapes (provided there is adequate nutrition) and that the health of rehabilitated koalas is not compromised by returning them to burnt habitats. Many koalas (Phascolarctos cinereus) required rehabilitation after the 2019/20 Australian megafires. Little is known about how the post-release health of rehabilitated koalas compares to non-rescued resident koalas. We evaluated health parameters in rehabilitated koalas and resident koalas in burnt and unburnt habitat in southern New South Wales, Australia. Health checks were undertaken within six weeks of fire (rehabilitated group), 5–9 months post-fire and 12–16 months post-fire. Body condition improved significantly over time in rehabilitated koalas, with similar condition between all groups at 12–16 months. Rehabilitated koalas therefore gained body condition at similar rates to koalas who remained and survived in the wild. The prevalence of Chlamydia pecorum was also similar between groups and timepoints, suggesting wildfire and rehabilitation did not exacerbate disease in this population. While there was some variation in measured serum biochemistry and haematology parameters between groups and timepoints, most were within normal reference ranges. Our findings show that koalas were generally healthy at the time of release and when recaptured nine months later. Landscapes in the Monaro region exhibiting a mosaic of burn severity can support koalas, and rehabilitated koala health is not compromised by returning them to burnt habitats 4–6 months post-fire. [ABSTRACT FROM AUTHOR]

The emission of black carbon (BC) particles, which cause atmospheric warming by affecting radiation budget in the atmosphere, is the result of an incomplete combustion process of organic materials. The recent wildfire event during the summer 2019–2020 in south-eastern Australia was unprecedented in scale. The wildfires lasted for nearly 3 months over large areas of the two most populated states of New South Wales and Victoria. This study on the emission and dispersion of BC emitted from the biomass burnings of the wildfires using the Weather Research Forecast–Chemistry (WRF–Chem) model aims to determine the extent of BC spatial dispersion and ground concentration distribution and the effect of BC on air quality and radiative transfer at the top of the atmosphere, the atmosphere and on the ground. The predicted aerosol concentration and AOD are compared with the observed data using the New South Wales Department of Planning and Environment (DPE) aethalometer and air quality network and remote sensing data. The BC concentration as predicted from the WRF–Chem model, is in general, less than the observed data as measured using the aethalometer monitoring network, but the spatial pattern corresponds well, and the correlation is relatively high. The total BC emission into the atmosphere during the event and the effect on radiation budget were also estimated. This study shows that the summer 2019–2020 wildfires affect not only the air quality and health impact on the east coast of Australia but also short-term weather in the region via aerosol interactions with radiation and clouds. [ABSTRACT FROM AUTHOR]

Smoke pollution from landscape fires is a major health problem, but it is difficult to predict the impact of any particular fire. For example, smoke plumes can be mapped using remote sensing, but we do not know how the smoke is distributed in the air-column. Prescribed burning involves the deliberate introduction of smoke to human communities but the amount, composition, and distribution of the pollution may be different to wildfires. We examined whether mapped plumes produced high levels of particulate pollution (PM2.5) at permanent air quality monitors and factors that influenced those levels. We mapped 1237 plumes, all those observed in 17 years of MODIS imagery over New South Wales, Australia, but this was only ~20% of known fires. Prescribed burn plumes tended to occur over more populated areas than wildfires. Only 18% of wildfire plumes and 4% of prescribed burn plumes passed over a monitor (n = 115). A minority of plumes caused a detectable increase in PM2.5: prescribed burn plumes caused an air quality exceedance for 33% of observations in the daytime and 11% at night, wildfire plumes caused exceedances for 48% and 22% of observations in the day and night-time, respectively. Thus, most plumes remained aloft (did not reach the surface). Statistical modelling revealed that wind speed, temperature, and mixing height influenced whether a plume caused an exceedance, and there was a difference between prescribed and wild fires. In particular, in wind speeds below 1 kmhr−1, exceedance was almost certain in prescribed burns. This information will be useful for planning prescribed burning, preparing warnings, and improving our ability to predict smoke impacts. [ABSTRACT FROM AUTHOR]

Protecting "wilderness" and removing human involvement in "nature" was a core pillar of the modern conservation movement through the 20th century. Conservation approaches and legislation informed by this narrative fail to recognise that Aboriginal people have long valued, used, and shaped most landscapes on Earth. Aboriginal people curated open and fire-safe Country for millennia with fire in what are now forested and fire-prone regions. Settler land holders recognised the importance of this and mimicked these practices. The Land Conservation Act of 1970 in Victoria, Australia, prohibited burning by settler land holders in an effort to protect natural landscapes. We present a 120-year record of vegetation and fire regime change from Gunaikurnai Country, southeast Australia. Our data demonstrate that catastrophic bushfires first impacted the local area immediately following the prohibition of settler burning in 1970, which allowed a rapid increase in flammable eucalypts that resulted in the onset of catastrophic bushfires. Our data corroborate local narratives on the root causes of the current bushfire crisis. Perpetuation of the wilderness myth in conservation may worsen this crisis, and it is time to listen to and learn from Indigenous and local people, and to empower these communities to drive research and management agendas. [ABSTRACT FROM AUTHOR]

This study addresses the difference in media coverage of the Australian bushfires and the pandemic, using an Australian and a Hungarian news site. After a frame analysis of text and imagery, a narration analysis was conducted. Our results provided evidence that crises were covered in different ways. For a distant news portal, it was an obvious option to use the bushfires in order to visualize climate change. In contrast, the bushfire–climate link has been a politicized subject in Australia for decades; hence, the exceptional bushfire season was also unable to get the issue on the agenda. Although the Australian news media in our sample strived to portray a crisis under control, when compared to the pandemic, it was not so effective. Therefore, localization is a major challenge for effective climate communication, where lessons from the pandemic, using more economic and social frames, could be helpful. [ABSTRACT FROM AUTHOR]

The precise information on fuel characteristics is essential for wildfire modelling and management. Satellite remote sensing can provide accurate and timely measurements of fuel characteristics. However, current estimates of fuel load changes from optical remote sensing are obstructed by seasonal cloud cover that limits their continuous assessments. This study utilises remotely sensed Synthetic-Aperture Radar (SAR) (Sentinel-1 backscatter) data as an alternative to optical-based imaging (Sentinel-2 scaled surface reflectance). SAR can penetrate clouds and offers high-spatial and medium-temporal resolution datasets and can hence complement the optical dataset. Inspired by the optical-based Vegetation Structural Perpendicular Index (VSPI), an SAR-based index termed RADAR-VSPI (R-VSPI) is introduced in this study. R-VSPI characterises the spatio-temporal changes in fuel load due to wildfire and the subsequent vegetation recovery thereof. The R-VSPI utilises SAR backscatter (σ°) from the co-polarized (VV) and cross-polarized (VH) channels at a centre frequency of 5.4 GHz. The newly developed index is applied over major wildfire events that occurred during the "Black Summer" wildfire season (2019–2020) in southern Australia. The condition of the fuel load was mapped every 5 (any orbit) to 12 (same orbit) days at an aggregated spatial resolution of 110 m. The results show that R-VSPI was able to quantify fuel depletion by wildfire (relative to healthy vegetation) and monitor its subsequent post-fire recovery. The information on fuel condition and heterogeneity improved at high-resolution by adapting the VSPI on a dual-polarization SAR dataset (R-VSPI) compared to the historic forest fuel characterisation methods (that used visible and infrared bands only for fuel estimations). The R-VSPI thus provides a complementary source of information on fuel load changes in a forest landscape compared to the optical-based VSPI, in particular when optical observations are not available due to cloud cover. [ABSTRACT FROM AUTHOR]

Wildfire is one of the most complex natural hazards. Its origin is a combination of anthropogenic factors, urban development and weather plus climate factors. In particular, weather and climate factors possess many spatiotemporal scales and various degrees of predictability. Due to the complex synergy of the human and natural factors behind the events, every wildfire is unique. However, there are indeed common meteorological and climate factors leading to the high fire risk before certain ignition mechanismfigures occur. From a scientific point of view, a better understanding of the meteorological and climate drivers of wildfire in every region would enable more effective seasonal to annual outlook of fire risk, and in the long term, better applications of climate projections to estimate future scenarios of wildfire. This review has performed a comparison study of two fire-prone regions: southeast Australia including Tasmania, and the Adriatic coast in Europe, especially events in Croatia. The former is well known as part of the 'fire continent', and major resources have been put into wildfire research and forecasting. The Adriatic coast is a region where some of the highest surface wind speeds, under strong topographic effect, have been recorded and, over the years, have coincided with wildfire ignitions. Similar synoptic background and dynamic origins of the meso-micro-scale meteorological conditions of these high wind events as well as the accompanied dryness have been identified between some of the events in the two regions. We have also reviewed how the researchers from these two regions have applied different weather indices and numerical models. The status of estimating fire potential under climate change for both regions has been evaluated. This review aims to promote a global network of information exchange to study the changing anthropogenic and natural factors we have to confront in order to mitigate and adapt the impacts and consequences from wildfire. [ABSTRACT FROM AUTHOR]

The extent and severity of bushfires in a landscape are largely governed by meteorological conditions. An accurate understanding of the interactions of meteorological variables and fire behaviour in the landscape is very complex, yet possible. In exploring such understanding, we used 2693 high-confidence active fire points recorded by a Moderate Resolution Imaging Spectroradiometer (MODIS) sensor for nine different bushfires that occurred in Victoria between 1 January 2009 and 31 March 2009. These fires include the Black Saturday Bushfires of 7 February 2009, one of the worst bushfires in Australian history. For each fire point, 62 different meteorological parameters of bushfire time were extracted from Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) data. These remote sensing and meteorological datasets were fused and further processed in assessing their relative importance using four different tree-based ensemble machine learning models, namely, Random Forest (RF), Fuzzy Forest (FF), Boosted Regression Tree (BRT), and Extreme Gradient Boosting (XGBoost). Google Earth Engine (GEE) and Landsat images were used in deriving the response variable–Relative Difference Normalised Burn Ratio (RdNBR), which was selected by comparing its performance against Difference Normalised Burn Ratio (dNBR). Our findings demonstrate that the FF algorithm utilising the Weighted Gene Coexpression Network Analysis (WGCNA) method has the best predictive performance of 96.50%, assessed against 10-fold cross-validation. The result shows that the relative influence of the variables on bushfire severity is in the following order: (1) soil moisture, (2) soil temperature, (3) air pressure, (4) air temperature, (5) vertical wind, and (6) relative humidity. This highlights the importance of soil meteorology in bushfire severity analysis, often excluded in bushfire severity research. Further, this study provides a scientific basis for choosing a subset of meteorological variables for bushfire severity prediction depending on their relative importance. The optimal subset of high-ranked variables is extremely useful in constructing simplified and computationally efficient surrogate models, which can be particularly useful for the rapid assessment of bushfire severity for operational bushfire management and effective mitigation efforts. [ABSTRACT FROM AUTHOR]

An analysis of a dataset (n = 58) of high-intensity wildfire observations in cured grasslands from southern Australia revealed a simple relationship suitable for quickly obtaining a first approximation of a fire's spread rate under low dead fuel moisture contents and strong wind speeds. It was found that the forward rate of fire spread is approximately 20% of the average 10-m open wind speed. The data on rate of fire spread and 10 m open wind speed ranged from 1.6 to 17 and 20 to 62 km h−1, respectively. The validity of the resulting rule of thumb was examined across a spectrum of burning conditions and its performance was contrasted against that of established empirical-based fire spread models for three different grassland fuel conditions currently used operationally in Australia. The 20% rule of thumb for grassfires produced error statistics comparable to that of the fire spread rate model for grazed or cut grass fuel conditions as recommended for general use during the summer fire season in southern Australia. [ABSTRACT FROM AUTHOR]

The summer season of 2019-2020 has been named Australia's Black Summer because of the large forest fires that burnt for months in southeast Australia, affecting millions of Australia's citizens and hundreds of millions of animals and capturing global media attention. This extensive fire season has been attributed to the global climate crisis, a long drought season and extreme fire weather conditions. Our aim in this study was to examine the factors that have led some of the wildfires to burn over larger areas for a longer duration and to cause more damage to vegetation. To this end, we studied all large forest and non-forest fires (>100 km²) that burnt in Australia between September 2019 and mid-February 2020 (Australia's Black Summer fires), focusing on the forest fires in southeast Australia. We used a segmentation algorithm to define individual polygons of large fires based on the burn date from NASA's Visible Infrared Imaging Radiometer Suite (VIIRS) active fires product and the Moderate Resolution Imaging Spectroradiometer (MODIS) burnt area product (MCD64A1). For each of the wildfires, we calculated the following 10 response variables, which served as proxies for the fires' extent in space and time, spread and intensity: fire area, fire duration (days), the average spread of fire (area/days), fire radiative power (FRP; as detected by NASA's MODIS Collection 6 active fires product (MCD14ML)), two burn severity products, and changes in vegetation as a result of the fire (as calculated using the vegetation health index (VHI) derived from AVHRR and VIIRS as well as live fuel moisture content (LFMC), photosynthetic vegetation (PV) and combined photosynthetic and non-photosynthetic vegetation (PV+NPV) derived from MODIS). We also computed more than 30 climatic, vegetation and anthropogenic variables based on remotely sensed derived variables, climatic time series and land cover datasets, which served as the explanatory variables. Altogether, 391 large fires were identified for Australia's Black Summer. These included 205 forest fires with an average area of 584 km² and 186 non-forest fires with an average area of 445 km²; 63 of the forest fires took place in southeast (SE) Australia (the area between Fraser Island, Queensland, and Kangaroo Island, South Australia), with an average area of 1097 km². Australia's Black Summer forest fires burnt for more days compared with non-forest fires. Overall, the stepwise regression models were most successful at explaining the response variables for the forest fires in SE Australia (n = 63; median-adjusted R² of 64.3%), followed by all forest fires (n = 205; median-adjusted R² of 55.8%) and all non-forest fires (n = 186; median-adjusted R² of 48.2%). The two response variables that were best explained by the explanatory variables used as proxies for fires' extent, spread and intensity across all models for the Black Summer forest and non-forest fires were the change in PV due to fire (median-adjusted R² of 69.1%) and the change in VHI due to fire (median-adjusted R² of 66.3%). Amongst the variables we examined, vegetation and fuel-related variables (such as previous frequency of fires and the conditions of the vegetation before the fire) were found to be more prevalent in the multivariate models for explaining the response variables in comparison with climatic and anthropogenic variables. This result suggests that better management of wildland-urban interfaces and natural vegetation using cultural and prescribed burning as well as planning landscapes with less flammable and more fire-tolerant ground cover plants may reduce fire risk to communities living near forests, but this is challenging given the sheer size and diversity of ecosystems in Australia. [ABSTRACT FROM AUTHOR]

The differential flammability of individual plant species in landscape-scale fire behaviour is an important consideration, but one that is often overlooked. This is in part due to a relative dearth in the availability of plant flammability data. Here, we present a highly accurate predictive model of the likelihood of plant leaves entering flaming combustion as a function of leaf mass per area (LMA), leaf area (LA) and radiant heat flux using species of fire-prone dry sclerophyll forests of south-eastern Australia. We validated the performance of the model on two separate datasets, and on plant species not included in the model building process. Our model gives accurate predictions (75–84%) of leaf flaming with potential application in the next generation of fire behaviour models. Given the global wealth of species' data for LMA and LA, in stark contrast to leaf flammability data, our model has the potential to improve understanding of forest flammability in the absence of leaf flammability information. [ABSTRACT FROM AUTHOR]