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Figure 1: Footprint of peak EHF (EHFmax, top left) and heat load (EHFsum, bottom left) for the January-February 2009 event along with the corresponding Table 1 heatwave severity categories (right).

A heatwave classification for heat related fatality risk

Research undertaken by Risk Frontiers shows that heatwaves are responsible for the largest number of deaths in Australia from natural disasters (Coates et al. 2014). In particular the South East Australia Region has been affected by 6 of the top 10 most deadly Australian events since the beginning of the 20th century.

Surprisingly given this death toll the tools available to communicate about heatwave risk are far less advanced than for other life threatening natural disasters such as tropical cyclones or bushfires: there is as yet no consensus about what constitutes a heatwave event (Perkins 2015) and more transparent ways to inform the community about risk levels are clearly needed. Recognizing this, the Bureau of Meteorology (Nairn and Fawcett, 2015) recently designed a heatwave severity index that takes into account both the ability of the local community to adapt to its climate and the impact of sharp temperature spikes preventing acclimatisation. Using this index, called Excess Heat Factor (EHF), the Bureau proposed a heatwave severity classification scheme that distinguishes low intensity, severe and extreme events. Although this approach is a natural step towards better risk communication, the implications of a high EHF are very dependent on the risk being studied. For applications such as energy demand or infrastructure damage the threshold value above which action needs to be taken will differ significantly from values that can trigger, for instance, human discomfort. We here focus on heat related fatalities and aim to address the following: knowing the peak intensity and accumulated heat load during a heatwave, can we anticipate its impact on human lives?

Figure 1: Footprint of peak EHF (EHFmax, top left) and heat load (EHFsum, bottom left) for the January-February 2009 event along with the corresponding Table 1 heatwave severity categories (right).

Figure 1: Footprint of peak EHF (EHFmax, top left) and heat load (EHFsum, bottom left) for the January-February 2009 event along with the corresponding Table 1 heatwave severity categories (right).

Defining new hazard intensity categories for heat-related fatalities

To specifically link heatwave intensity to heat related fatalities we combine two data products. Risk Frontiers’ PerilAUS archive (Coates et al. 2014) allows extraction of 224 historical occurrences of events with heat-related deaths in Australia, and for each we exploit gridded records of daily minimum and maximum temperature available from the Bureau of Meteorology since 1911 (Jones et al. 2009) to compute EHF estimates. Ranking the most lethal heatwave episodes in terms of heat accumulation (i.e. the heat load, EHFsum) and peak intensity (EHFmax), we define five new severity categories (Table 1). These categories capture conditions that historically led to a higher number of deaths and acknowledge that the most dangerous events will be characterised by both a large peak intensity and a sustained period of severe heat.

To illustrate how this classification can be used we analyse the January – February 2009 heatwave that killed more people that the Black Saturday bushfires taking place during that same period. The footprints of EHFmax and EHFsum (Fig. 1, left) are used to create spatial patterns of the categories following the schema given in Table 1 (Fig. 1, right). The resulting category map allows direct representation of the risk gradient across the event and should enable more efficient risk communication.

Figure 2: Rate of fatalities per 100 000 people (y-axis) as a function of the heatwave category they are exposed to (x-axis). Individual dots represent distinct events while the red dashed line is the expected estimate, representative of all-events combined.

Figure 2: Rate of fatalities per 100 000 people (y-axis) as a function of the heatwave category they are exposed to (x-axis). Individual dots represent distinct events while the red dashed line is the expected estimate, representative of all-events combined.

Developing an EHF-based vulnerability function to project fatalities

To help quantify the risk to human life associated with each of the Table 1 categories, a vulnerability function is derived using census population data from between 2001 and 2011 to normalise the fatality records. The vulnerability development is restricted to that period and the focus is on the Victoria / South Australia region, given the higher quality of information available.

For the 10 biggest events of the period the total population exposed to each of the categories listed in Table 1 is computed, linearly interpolating between records from 2001 and 2011. The corresponding fatalities reported in that same exposed area are then totalled and normalised by the population exposed to derive a death rate by category. Figure 2 shows the expected number of fatalities per 100,000 people exposed for each category.

As all estimates in this study are based on reported fatalities, and because of under-reporting and the likelihood of wrongly categorising deaths to other health-related issues rather than heat stress, a large uncertainty surrounds this fatality curve and all projections should be interpreted as lower bound estimates.

Conclusions

The Excess Heat Factor (EHF) heatwave intensity framework was used in combination with an archive of heat-related fatalities in Australia to provide indicators of heatwave severity. This led to the definition of five severity classes that may be helpful in characterizing and communicating the death potential of heatwaves. In an attempt to quantify heatwave impact beyond the hazard threat, a vulnerability curve is defined to estimate the number of human fatalities to be expected as a function of both the heatwave risk category and the population density. Although our fatality projections are very uncertain the relative increase in magnitude from one category to the next can serve as a powerful communication tool and help communities anticipate the threat to human lives posed by the most extreme heatwave events.

For more information, go to www.riskfrontiers.com

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<p>Thomas Loridan is a lead scientist at Risk Frontiers who specializes in atmospheric hazard modelling with a particular emphasis on Tropical Cyclones (TCs). He holds a PhD from King’s College London where he studied models of the urban boundary layer. In the past 5 years Thomas’ work has led to the development of various techniques to simulate the variability in severe winds observed from TCs across the world’s most active basins. More recently Thomas’ research has also focussed on developing risk metrics for heatwaves in Australia.</p>

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