McArthur Forest Fire Danger Research Articles

Australian Fire Danger Rating System: implementing fire behaviour calculations to forecast fire danger in a research prototype†

Background The Australian Fire Danger Rating System (AFDRS) was implemented operationally throughout Australia in September 2022, providing calculation of fire danger forecasts based on peer-reviewed fire behaviour models. The system is modular and allows for ongoing incorporation of new scientific research and improved datasets. Aims Prior to operational implementation of the AFDRS, a Research Prototype (AFDRSRP), described here, was built to test the input data and systems and evaluate the performance and potential outputs. Methods Fire spread models were selected and aligned with fuel types in a process that captured bioregional variation in fuel characteristics. National spatial datasets were created to identify fuel types and fire history in alignment with existing spatial weather forecast layers. Key results The AFDRSRP demonstrated improvements over the McArthur Forest and Grass Fire Danger systems due to its use of improved fire behaviour models, as well as more accurately reflecting the variation in fuels. Conclusions The system design was robust and allowed for the incorporation of updates to the models and datasets prior to implementation of the AFDRS.

Projections of wildfire risk and activities under 1.5 °C and 2.0 °C global warming scenarios

Wildfires are important ecosystem processes that have a significant impact on terrestrial vegetation, environment, and climate. This study investigates how future wildfire risk and activities could change under 1.5 °C and 2.0 °C warming scenarios relative to pre-industrial levels using a modified McArthur Forest Fire Danger Index (FFDIn) and the CLM4.5-BGC land surface model. Sixteen Earth System Models (ESMs) from CMIP5 and CMIP6 were employed to supply the variables of climate change under low, middle, and high greenhouse emission scenarios in the 1.5 °C and 2.0 °C scenarios. The ensemble means from the FFDIn and results from the CLM4.5-BGC with multiple forcings show that the dry areas in the southwestern US, Brazilian Highlands, and Arabian islands are projected to face higher wildfire risk with larger burned areas and more carbon emissions under a warmer climate. The Congo Basin and part of the Amazon could have a lower wildfire risk with smaller burned areas and less carbon emissions. The absolute changes in the projected FFDIn are small, although large increases are observed in boreal areas, particularly in the winter and spring. Burned area and carbon emissions are projected to increase in general in the boreal area but decrease in northeastern Asia. Compared to the 1.5 °C scenario, the wildfire risk and burned area levels are projected to increase under the 2.0 °C scenario except in the western Amazon. However, fire carbon emissions are projected to decrease more in tropical areas under the 2.0 °C scenario. The different change directions in eastern North America and eastern China produced by the FFDIn and CLM4.5-BGC suggest the potential effect of non-meteorological elements on fire activities.

Prediction of Meteorological Parameter Time Series Data for the Forest Fire Early Warning System

A forest fire early warning system must be developed to reduce the impact of greater community losses. One effort to develop an early warning system is to use a forest fire hazard index as a potential assessment guide. The main factor which is a parameter in the fire hazard index calculation method is the meteorological parameter. In general, to know today’s fire hazard index is calculated from today’s weather conditions, but the need for an early warning system is to know the future fire hazard index. Based on a series of meteorological conditions data held for thirty-six months, using the backpropagation algorithm, it is estimated that the meteorological conditions will be several months to come. Several meteorological parameters have their respective roles, the unknown contribution of which is calculated. In this study, each parameter will be measured by predicting time series data and compared with the results of calculations. The method of calculating the forest fire index used is the McArthur Forest Fire Danger Index with the meteorological parameter elements are temperature, relative humidity, wind speed, and drought factor. Each parameter was trained in artificial neural networks and tested its predictions to produce accuracy for data series temperatures of 91.67%, the relative humidity of 83.33%, and wind speed of 50%.

Evolution of a pyrocumulonimbus event associated with an extreme wildfire in Tasmania, Australia

Abstract. Extreme fires have substantial adverse effects on society and natural ecosystems. Such events can be associated with the intense coupling of fire behaviour with the atmosphere, resulting in extreme fire characteristics such as pyrocumulonimbus cloud (pyroCb) development. Concern that anthropogenic climate change is increasing the occurrence of pyroCbs globally is driving more focused research into these meteorological phenomena. Using 6 min scans from a nearby weather radar, we describe the development of a pyroCb during the afternoon of 4 January 2013 above the Forcett–Dunalley fire in south-eastern Tasmania. We relate storm development to (1) near-surface weather using the McArthur forest fire danger index (FFDI) and the C-Haines index, the latter of which is a measure of the vertical atmospheric stability and dryness, both derived from gridded weather reanalysis for Tasmania (BARRA-TA); and (2) a chronosequence of fire severity derived from remote sensing. We show that the pyroCb rapidly developed over a 24 min period on the afternoon of 4 January, with the cloud top reaching a height of 15 km. The pyroCb was associated with a highly unstable lower atmosphere (C-Haines value of 10–11) and severe–marginally extreme (FFDI 60–75) near-surface fire weather, and it formed over an area of forest that was severely burned (total crown defoliation). We use spatial patterns of elevated fire weather in Tasmania and fire weather during major runs of large wildfires in Tasmania for the period from 2007 to 2016 to geographically and historically contextualise this pyroCb event. Although the Forcett–Dunalley fire is the only known record of a pyroCb in Tasmania, our results show that eastern and south-eastern Tasmania are prone to the conjunction of high FFDI and C-Haines values that have been associated with pyroCb development. Our findings have implications for fire weather forecasting and wildfire management, and they highlight the vulnerability of south-east Tasmania to extreme fire events.

A Comparative Study of the Forest Fire Danger Index Calculation Methods Using Backpropagation

Forest fires are one of the threats of disasters in Indonesia. Rising earth temperatures add to the higher potential contribution of forest fires. Many forest fire hazard index calculation methods have been developed to analyze the impact categories arising from changes in meteorological parameters. Each calculation method has advantages in calculating the magnitude of the potential. One method of calculating the forest fire hazard index is needed from many choices of methods that have the highest level of accuracy to predict the potential for a fire. A comparative study of the methods will be validated and guide the best calculating procedure to be implemented for the prediction. Predictions that have excellent accuracy and precision can be used as an early warning system. This study will predict forest fires for each calculation method using the Backpropagation algorithm, then analyze the accuracy of the prediction results using Relative Operating Characteristics (ROC). The methods compared include the methods that have been used in Indonesia as a country that has tropical rainforests, namely Keetch-Byram Drought Index (KBDI), Standard Precipitation Index (SPI), McArthur Forest Fire Danger Index (MFFDI), and Fire Weather Index (FWI). Through a comparative study of this calculation model, it is concluded that MFFDI is the best method of calculating the fire hazard index with an accuracy value of 0.917 and a precision value of 0.667.

Evaluation of the Weather Research and Forecasting model in simulating fire weather for the south-west of Western Australia

The Weather Research and Forecasting (WRF) model was used to simulate fire weather for the south-west of Western Australia (SWWA) over multiple decades at a 5-km resolution using lateral boundary conditions from the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA)-Interim reanalysis. Simulations were compared with observations at Australian Bureau of Meteorology meteorological stations and the McArthur Forest Fire Danger Index (FFDI) was used to quantify fire weather. Results showed that, overall, the WRF reproduced the annual cumulative FFDI at most stations reasonably well, with most biases in the FFDI ranging between –600 and 600. Biases were highest at stations within the metropolitan region. The WRF simulated the geographical gradients in the FFDI across the domain well. The source of errors in the FFDI varied markedly between the different stations, with no one particular variable able to account for the errors at all stations. Overall, this study shows that the WRF is a useful model for simulating fire weather for SWWA, one of the most fire-prone regions in Australia.

Understanding the variability of Australian fire weather between 1973 and 2017.

Australian fire weather shows spatiotemporal variability on interannual and multi-decadal time scales. We investigate the climate factors that drive this variability using 39 station-based historical time series of the seasonal 90th-percentile of the McArthur Forest Fire Danger Index (FFDI) extending from 1973 through 2017. Using correlation analyses, we examine the relationship of these time series to the El Niño Southern Oscillation (ENSO), the Southern Annular Mode (SAM) and the Indian Ocean Dipole (IOD), considering both concurrent and time-lagged relationships. Additionally, longer term behaviour of the time series using linear trend analysis is discussed in the context of the climate drivers, Interdecadal Pacific Oscillation (IPO) and anthropogenic climate change. The results show that ENSO is the main driver for interannual variability of fire weather, as defined by FFDI in this study, for most of Australia. In general, El Niño-like conditions lead to more extreme fire weather, with this effect stronger in eastern Australia. However, there are significant regional variations to this general rule. In NSW, particularly along the central coast, negative SAM is a primary influence for elevated fire weather in late-winter and spring. In the southeast (VIC and TAS), the El Niño-like impact is exacerbated when positive IOD conditions are simultaneously observed. The spring conditions are key, and strongly influence what is observed during the following summer. On longer time scales (45 years), linear trends are upward at most stations; this trend is strongest in the southeast and during the spring. The positive trends are not driven by the trends in the climate drivers and they are not consistent with hypothesized impacts of the IPO, either before or after its late-1990s shift to the cold phase. We propose that anthropogenic climate change is the primary driver of the trend, through both higher mean temperatures and potentially through associated shifts in large-scale rainfall patterns. Variations from interannual factors are generally larger in magnitude than the trend effects observed to date.

Future changes in extreme weather and pyroconvection risk factors for Australian wildfires

Extreme wildfires have recently caused disastrous impacts in Australia and other regions of the world, including events with strong convective processes in their plumes (i.e., strong pyroconvection). Dangerous wildfire events such as these could potentially be influenced by anthropogenic climate change, however, there are large knowledge gaps on how these events might change in the future. The McArthur Forest Fire Danger Index (FFDI) is used to represent near-surface weather conditions and the Continuous Haines index (CH) is used here to represent lower to mid-tropospheric vertical atmospheric stability and humidity measures relevant to dangerous wildfires and pyroconvective processes. Projected changes in extreme measures of CH and FFDI are examined using a multi-method approach, including an ensemble of global climate models together with two ensembles of regional climate models. The projections show a clear trend towards more dangerous near-surface fire weather conditions for Australia based on the FFDI, as well as increased pyroconvection risk factors for some regions of southern Australia based on the CH. These results have implications for fields such as disaster risk reduction, climate adaptation, ecology, policy and planning, noting that improved knowledge on how climate change can influence extreme wildfires can help reduce future impacts of these events.

Selection Drought Index Calculation Methods Using Electre, Topsis, and Analytic Hierarchy Process

The drought index is an essential indicator for calculating forest fires’ potential. Many methods are developed to maintain the drought index. However, they provide less suitable at many places. Every area has their own character, and each of methods has their own specification. The spot problem is how to find the right method for those places. The forest of Bukit Suharto, has particular character as one of the rain tropical forests, and it needs suitable method. Furthermore, this study is conducted to examine the right methods that compatible for the forest. They are: Palmer Drought Severity Index (PDSI), Keetch Byram Drought Index (KBDI), Reconnaissance Drought Index (RDI), Standard Precipitation Index (SPI), Effective Drought Index (EDI), McArthur Forest Fire Danger Index (MFFDI), and Standard Precipitation Evapotranspiration Index (SPEI). Every method has specific variables for the calculation, namely, the period, the data’s type, the formula’s complexity, the usability, and scale results’ type. On processing the seven methods, the researcher uses other techniques to asses them, namely, ELECTRE, TOPSIS, and Analytic Hierarchy Process. In final process, the conclusion is compared through the result. In summary, the results show that KBDI’s method is the most recommended, and TOPSIS is the best technique for recommendations.

Exploring the future change space for fire weather in southeast Australia

High-resolution projections of climate change impacts on fire weather conditions in southeast Australia out to 2080 are presented. Fire weather is represented by the McArthur Forest Fire Danger Index (FFDI), calculated from an objectively designed regional climate model ensemble. Changes in annual cumulative FFDI vary widely, from − 337 (− 21%) to + 657 (+ 24%) in coastal areas and − 237 (− 12%) to + 1143 (+ 26%) in inland areas. A similar spread is projected in extreme FFDI values. In coastal regions, the number of prescribed burning days is projected to change from − 11 to + 10 in autumn and − 10 to + 3 in spring. Across the ensemble, the most significant increases in fire weather and decreases in prescribed burn windows are projected to take place in spring. Partial bias correction of FFDI leads to similar projections but with a greater spread, particularly in extreme values. The partially bias-corrected FFDI performs similarly to uncorrected FFDI compared to the observed annual cumulative FFDI (ensemble root mean square error spans 540 to 1583 for uncorrected output and 695 to 1398 for corrected) but is generally worse for FFDI values above 50. This emphasizes the need to consider inter-variable relationships when bias-correcting for complex phenomena such as fire weather. There is considerable uncertainty in the future trajectory of fire weather in southeast Australia, including the potential for less prescribed burning days and substantially greater fire danger in spring. Selecting climate models on the basis of multiple criteria can lead to more informative projections and allow an explicit exploration of uncertainty.

Climatological Variability of Fire Weather in Australia

AbstractLong-term variations in fire weather conditions are examined throughout Australia from gridded daily data from 1950 to 2016. The McArthur forest fire danger index is used to represent fire weather conditions throughout this 67-yr period, calculated on the basis of a gridded analysis of observations over this time period. This is a complementary approach to previous studies (e.g., those based primarily on model output, reanalysis, or individual station locations), providing a spatially continuous and long-term observations-based dataset to expand on previous research and produce climatological guidance information for planning agencies. Long-term changes in fire weather conditions are apparent in many regions. In particular, there is a clear trend toward more dangerous conditions during spring and summer in southern Australia, including increased frequency and magnitude of extremes, as well as indicating an earlier start to the fire season. Changes in fire weather conditions are attributable at least in part to anthropogenic climate change, including in relation to increasing temperatures. The influence of El Niño–Southern Oscillation (ENSO) on fire weather conditions is found to be broadly consistent with previous studies (indicating more severe fire weather in general for El Niño conditions than for La Niña conditions), but it is demonstrated that this relationship is highly variable (depending on season and region) and that there is considerable potential in almost all regions of Australia for long-range prediction of fire weather (e.g., multiweek and seasonal forecasting). It is intended that improved understanding of the climatological variability of fire weather conditions will help lead to better preparedness for risks associated with dangerous wildfires in Australia.

Comparison of soil wetness from multiple models over Australia with observations

AbstractThe McArthur Forest Fire Danger Index used in Australia for operational fire warnings has a component representing fuel availability called the Drought Factor (DF). The DF is partly based on soil moisture deficit, calculated as either the Keetch‐Byram Drought Index (KBDI) or Mount's Soil Dryness Index (MSDI). The KBDI and MSDI are simplified water balance models driven by observation based daily rainfall and temperature. In this work, gridded KBDI and MSDI analyses are computed at a horizontal resolution of 5 km and are verified against in‐situ soil moisture observations. Also verified is another simple model called the Antecedent Precipitation Index (API). Soil moisture analyses from the Australian Community Climate and Earth System Simulator (ACCESS) global Numerical Weather Prediction (NWP) system as well as remotely sensed soil wetness retrievals from the Advanced Scatterometer (ASCAT) are also verified. The verification shows that the NWP soil wetness analyses have greater skill and smaller biases than the KBDI, MSDI and API analyses. This is despite the NWP system having a coarse horizontal resolution and not using observed precipitation. The average temporal correlations (root mean square difference) between cosmic ray soil moisture monitoring facility observations and modeled or remotely sensed soil wetness are 0.82 (0.15 ±0.02), 0.66 (0.33 ±0.07), 0.77 (0.20 ±0.03), 0.74 (0.22 ±0.03) and 0.83 (0.18 ±0.04) for NWP, KBDI, MSDI, API and ASCAT. The results from this study suggests that analyses of soil moisture can be greatly improved by using physically based land surface models, remote sensing measurements and data assimilation.

Forest fire danger, life satisfaction and feelings of safety: evidence from Australia

Employing data from the Household, Income and Labour Dynamics in Australia survey and the McArthur Forest Fire Danger Index, this study tests: (1) the association between forest fire danger and an individual’s life satisfaction; (2) the association between forest fire danger and an individual’s feeling of safety; and (3) whether the association between forest fire danger and an individual’s life satisfaction is explained by feelings of safety. Further, this study employs the experienced preference method to estimate, in monetary terms, the psychological costs associated with forest fire danger. We find negative and significant associations between life satisfaction and forest fire danger, as well as between forest fire danger and feelings of safety. When feelings of safety are included in the life satisfaction regression, however, the forest fire danger variable is no longer statistically significant – suggesting that the link between forest fire danger and life satisfaction can be largely explained by an individual’s feelings of safety. The experienced preference method yields an implicit willingness-to-pay of $10 per year to avoid a one unit increase in the spatially weighted average of the average daily value of the Fire Danger Index over the previous 12 months.

An investigation of future fuel load and fire weather in Australia

We present an assessment of the impact of future climate change on two key drivers of fire risk in Australia, fire weather and fuel load. Fire weather conditions are represented by the McArthur Forest Fire Danger Index (FFDI), calculated from a 12-member regional climate model ensemble. Fuel load is predicted from net primary production, simulated using a land surface model forced by the same regional climate model ensemble. Mean annual fine litter is projected to increase across all ensemble members, by 1.2 to 1.7 t ha−1 in temperate areas, 0.3 to 0.5 t ha−1 in grassland areas and 0.7 to 1.1 t ha−1 in subtropical areas. Ensemble changes in annual cumulative FFDI vary widely, from 57 to 550 in temperate areas, −186 to 1372 in grassland areas and −231 to 907 in subtropical areas. These results suggest that uncertainty in FFDI projections will be underestimated if only a single driving model is used. The largest increases in fuel load and fire weather are projected to occur in spring. Deriving fuel load from a land surface model may be possible in other regions, when this information is not directly available from climate model outputs.

Measurement of inter- and intra-annual variability of landscape fire activity at a continental scale: the Australian case

Climate dynamics at diurnal, seasonal and inter-annual scales shape global fire activity, although difficulties of assembling reliable fire and meteorological data with sufficient spatio-temporal resolution have frustrated quantification of this variability. Using Australia as a case study, we combine data from 4760 meteorological stations with 12 years of satellite-derived active fire detections to determine day and night time fire activity, fire season start and end dates, and inter-annual variability, across 61 objectively defined climate regions in three climate zones (monsoon tropics, arid and temperate). We show that geographic patterns of landscape burning (onset and duration) are related to fire weather, resulting in a latitudinal gradient from the monsoon tropics in winter, through the arid zone in all seasons except winter, and then to the temperate zone in summer and autumn. Peak fire activity precedes maximum lightning activity by several months in all regions, signalling the importance of human ignitions in shaping fire seasons. We determined median daily McArthur forest fire danger index (FFDI50) for days and nights when fires were detected: FFDI50 varied substantially between climate zones, reflecting effects of fire management in the temperate zone, fuel limitation in the arid zone and abundance of flammable grasses in the monsoon tropical zone. We found correlations between the proportion of days when FFDI exceeds FFDI50 and the Southern Oscillation index across the arid zone during spring and summer, and Indian Ocean dipole mode index across south-eastern Australia during summer. Our study demonstrates that Australia has a long fire weather season with high inter-annual variability relative to all other continents, making it difficult to detect long term trends. It also provides a way of establishing robust baselines to track changes to fire seasons, and supports a previous conceptual model highlighting multi-temporal scale effects of climate in shaping continental-scale pyrogeography.

Estimating Spatially Varying Severity Thresholds of a Forest Fire Danger Rating System Using Max-Stable Extreme-Event Modeling

AbstractFire danger indices are used in many countries to estimate the potential fire danger and to issue warnings to local regions. The McArthur fire danger rating system is used in Australia. The McArthur forest fire danger index (FFDI) uses only meteorological elements. It combines information on wind speed, temperature, relative humidity, and recent rainfall to produce a weather index of fire potential. This index is converted into fire danger categories to serve as warnings to the local population and to estimate potential fire-suppression difficulty. FFDI values above the threshold of 75 are rated as extreme. The spatial behavior of large values of the FFDI is modeled to investigate whether a varying threshold across space may serve as a better guide for determining the onset of elevated fire danger. The authors modify and apply a statistical method that was recently developed for spatial extreme events, using a “max-stable” process to model FFDI data at approximately 17 000 data sites. The method that is described here produces a quantile map that can be employed as a spatially varying fire danger threshold. It is found that a spatially varying threshold may serve to more accurately represent high fire danger, and an adjustment is proposed that varies by local government area. Temporal change was also investigated, and evidence was found of a recent increase in extreme fire danger in southwestern Australia.

English

The McArthur Forest Fire Danger Indices (FFDI) have been used in Australia since was developed in 1960s and used by fire authority to measure the level of fire danger. Its value depend on the drought factor which is based on dryness of the fuel and other weather variable factors such as temperature, relative humidity, rainfall, wind speed and wind directions. The accuracy measurement of FFDI output depends on the accuracy of the input variables, inaccuracy and uncertainty of these inputs may result in incorrect of the FFDI value. As the value of the FFDI increased the more effected from the inaccuracy of the wind speed, temperature and humidity. Bushfires prediction systems sensitivity depends on the instruments accuracy used to measure the input data.

Future fire danger climatology for Tasmania, Australia, using a dynamically downscaled regional climate model

Daily values of McArthur Forest Fire Danger Index were generated at ~10-km resolution over Tasmania, Australia, from six dynamically downscaled CMIP3 climate models for 1961–2100, using a high (A2) emissions scenario. Multi-model mean fire danger validated well against observations for 2002–2012, with 99th percentile fire dangers having the same distribution and largely similar values to those observed over the same time. Model projections showed a broad increase in fire danger across Tasmania, but with substantial regional variation – the increase was smaller in western Tasmania (district mean cumulative fire danger increasing at 1.07 per year) compared with parts of the east (1.79 per year), for example. There was also noticeable seasonal variation, with little change occurring in autumn, but a steady increase in area subject to springtime 99th percentile fire danger from 6% in 1961–1980 to 21% by 2081–2100, again consistent with observations. In general, annually accumulated fire danger behaved similarly. Regional mean sea level pressure patterns resembled observed patterns often associated with days of dangerous fire weather. Days of elevated fire danger displaying these patterns increased in frequency during the simulated twenty-first century: in south-east Tasmania, for example, the number of such events detected rose from 101 (across all models) in 1961–1980 to 169 by 2081–2100. Correspondence of model output with observations and the regional detail available suggest that these dynamically downscaled model data are useful projections of future fire danger for landscape managers and the community.

Use of the Extreme Value Analysis in Determining Annual Probability of Exceedance for Bushfire Protection Design

Historically extreme value analysis has been used to guide human activities against many forms of natural hazards such as floods, storms, heat waves and wind. However, it has not been used for extreme fire weather assessment. This may be because forest fire danger index is a composite of differing parameters which may not be regarded as being associated with adverse fire weather individually, however, collectively may result in Extreme fire weather conditions. This paper offers new insights in the use of the extreme value analysis for the McArthur Forest Fire Danger Index (FFDI) so as to provide a scientific basis for planning, development and building construction in bushfire prone areas. The underlying principle is to use the recurrence of fire weather as measured by FFDI as the planning or design fire weather reference. The recurrence is determined by the applying the Generalised Extreme Value Analysis to local historical weather data of limited time period. The resultant distribution is fitted with an appropriate regression curve which allows the extrapolation beyond the available weather data recording period. The method is applied to a number of weather districts in the state of New South Wales of Australia. The derived FFDIs corresponding to the nominated recurrence is compared with the FFDIs based on traditional empirical methods.

Environmental circumstances surrounding bushfire fatalities in Australia 1901–2011

Abstract This paper describes the development and analysis of a dataset covering bushfire related life loss in Australia over the past 110 years (1901–2011). Over this time period 260 bushfires have been associated with a total of 825 known civilian and firefighter fatalities. This database was developed to provide an evidence base from which an Australian national fire danger rating system can be developed and has benefits in formalising our understanding of community exposure to bushfire. The database includes detail of the spatial, temporal and localised context in which the fatalities occurred. This paper presents the analysis of 674 civilian fatalities. The analysis has focused on characterising the relationship between fatal exposure location, weather conditions (wind speed, temperature, relative humidity and drought indices), proximity to fuel, activities and decision making leading up to the death. The analysis demonstrates that civilian fatalities were dominated by several iconic bushfires that have occurred under very severe weather conditions. The fatalities from Australia's 10 worst bushfire days accounted for 64% of all civilian fatalities. Over 50% of all fatalities occurred on days where the McArthur Forest Fire Danger Index (FFDI) exceeded 100 (the current threshold for declaring a day as ‘catastrophic’) proximal to the fatality. The dominant location category was open air representing 58% of all fatalities followed by 28% in structures, and 8% in vehicles (6% are unknown). For bushfires occurring under weather conditions exceeding an FFDI value of 100, fatalities within structures represented over 60% of all fatalities. These were associated with people dying while attempting to shelter mainly in their place of residence. Of the fatalities that occurred inside a structure in a location that was specifically known, 41% occurred in rooms with reduced visibility to the outside conditions. Over 78% of all fatalities occurred within 30 m of the forest.