Bushfire impact research – NSW South Coast

Steven George, James O’Brien, Salomé Hussein, Jonathan van Leeuwen, Risk Frontiers

Risk Frontiers deployed a team to the NSW South Coast region in late January, 2020 to undertake damage surveys following the bushfires. This research was supported by the Bushfire and Natural Hazards CRC (BNHCRC). The areas surveyed included Moruya, Mogo, Malua Bay, Rosedale, the Catalina area of Batemans Bay and Lake Conjola. The majority of damage occurred on December 31, 2019 as catastrophic weather conditions (extreme temperatures and strong winds) intensified existing fire fronts. The conditions transported large quantities of embers into vulnerable communities, destroying hundreds of residential and several commercial buildings. In total, the survey identified 426 bushfire affected properties, most of which were destroyed. Industries/infrastructure affected included: bowling/services club, a unit block (12 units), heritage park, industrial complex with numerous businesses and extensive damage to electricity infrastructure (power poles and wires along the Princes Highway). This report complements our report for northern NSW (Risk Frontiers, 2020).

Building age and resilience

As the 2019/2020 fire season progressed, the scale of damage and losses experienced across the country engendered a growing interest in evaluating the resilience of buildings to bushfires. Aspects of buildings such as age, performance of construction materials and a structure’s vulnerability due to its proximity to bushland were the key focus of the NSW South Coast survey. To evaluate the performance of building archetypes impacted by fire, the Insurance Council of Australia (ICA) charted the year of construction of over 25,000 residential buildings located within bushfire impacted areas across four states (Figure 1). Categories range from Old Colonial (pre-Victorian) to post-2009, when bushfire building standards began to be improved and were mandated in certain locations.

Building construction types impacted by 2019 bushfires in Australia
Figure 1: The period of construction for over 25,000 buildings located within the current bushfire impacted areas across four states. Source: Insurance Council of Australia, 2019.

The ICA data shows that only 9.5% of residences were constructed post-2009, when changes were made to Australian Standard 3959 after the Black Saturday fires of February, 2009, to ensure that new buildings in bushfire-prone areas were safer and more likely to survive a fire (BNHCRC, 2019). It was apparent that the scale of residential losses occurring this fire season presented a small window of opportunity to conduct further damage surveys, prior to recovery and debris removal, and would provide a considerable ‘post-2009’ cohort to assess building performance and inform future design. In the near future, further analysis will be undertaken by Risk Frontiers to establish the construction age of the South Coast properties, with a focus on any post-2009, to expand existing research.

Observations of destruction/damage – construction materials

The survey team recorded aspects of fire affected buildings such as construction materials and damage ratios (destroyed/partially destroyed). The field observations from the South Coast survey are compared to those in Rappville (2019) and Tathra (2018) in Figure 2.

Proportion of buildings destroyed  South Coast, Tathra and Rappville
Figure 2: The proportion and number (in column) of buildings categorised as destroyed/partially destroyed. The South Coast and Rappville (2019) damage surveys used a building footprint method where partially destroyed references the building, not the lot. The sampling method figures from the Tathra fire in 2018 assigned partially damaged on the proportion of the whole lot – that is, if a shed was destroyed but the house was undamaged, then a partially damaged rating was assigned. The data shows that once a building is alight, the likelihood of it being destroyed is very high. The total destruction rate across the three events ranged between two-thirds and 100%. The number of properties destroyed also indicates the difference in scale of the fire events between locations.

The South Coast findings reinforce those from the Rappville (2020) and Tathra (2018) surveys, in that, once a building catches fire, regardless of construction material, it will likely be totally destroyed. The official Tathra figures have 68% of all fire affected premises as being ultimately destroyed. Data collected from the South Coast and Rappville surveys provides much stronger indications of this trend, where 92% and 100% respectively, of the buildings observed were destroyed. (The Rappville and South Coast results represent only those properties located and observed, not all fire-affected properties).

In terms of building specifics, The South Coast survey provided numerous examples of fire-affected residences, primarily constructed of ‘non-flammable’ materials (brick and blockwork (piers and walls)). These structures demonstrated some resilience to the fire, at times remaining wholly or partially intact. However, the remaining material comprising the premises (structural roof/wall timbers, internal walls and house contents), once alight, ultimately rendered the entire building unsalvageable (destroyed). Timber beams supporting house roofs and carports were uniformly level on the ground (as though dropped). Metal framed buildings (e.g. sheds) and structural elements (e.g. lintels) did not perform well – failing due to extreme heat and leading to the building warping and impacting brick/masonry when collapsing. There were numerous examples of vehicles completely burnt out in front and rear yards and some isolated examples of aluminium boats that had undergone some degree of melting.

For partially destroyed properties, the building features most often impacted were constructed from timber such as external stairs and decking as well as external cladding. There were numerous examples of destroyed properties categorised as ‘asbestos contaminated’ though this was less common than during the Rappville survey where asbestos was present at over 50% of properties. A large number of asbestos contaminated assessments were speculative based on observations and erred on the side of caution with further assessment and testing usually noted as necessary. The possible exception to this would be Rosedale which experienced near total destruction and where homes predominantly appeared older, were often constructed using fibro or sheeting and surrounded by bushland.

Statistical dependence of bushfire risk on distance to bush and the influence of ember attack

Previous field research conducted by Risk Frontiers (Chen and McAneney, 2004 and other more recent) has established that proximity to bushland is the most important factor in determining a building’s vulnerability. Figure 3 depicts bushfire damage based on aggregated data from recent major bushfires and shows the cumulative distribution of destroyed buildings in relation to distance from bushland.

As with previous fires studied, Figure 3 confirms the significant role that ‘proximity to bushland’ played in the South Coast losses where approximately 38% of destroyed buildings were situated within 1 metre of surrounding bush. The average distance from bushland of all 426 properties surveyed was 55 metres (satellite imagery). However, a feature not obvious from the South Coast data in Figure 3, but apparent in the Rappville and Duffy examples, is the impact of extreme conditions and the capacity of embers to propagate fire over large distances. Witness accounts from fire fighters and locals have described embers being transported by extreme winds across Lake Conjola, over distances greater than 1km. The South Coast survey data would appear to confirm such reports, as two properties surveyed were >1.3km from bushland and 73 were located >100 metres from bush.

Buildings destroyed in relation to distance from bushland
Figure 3: Cumulative distribution of buildings destroyed in relation to distance from nearby bushland for recent major events.

References

BNHCRC 2019. Black Saturday ten years on – what did we discover?

Insurance Council of Australia 2020. Period of residential building construction – chart. Posted by Karl Sullivan.

Risk Frontiers’ Newsletter Vol. 19, Issue 1.

Risk Frontiers’ Newsletter Vol. 17, Issue 3.

Chen, K., and K. J. McAneney, 2004: Quantifying bushfire penetration into urban areas in Australia. Geophys. Res. Lett., 31, L12212.

Acknowledgement

This research was funded through the Bushfire and Natural Hazards CRC Quick Response Fund.

 

Newsletter Volume 19, Issue 2 – April 2020

Bushfire impact research – NSW South Coast

Steven George, James O’Brien, Salomé Hussein, Jonathan van Leeuwen, Risk Frontiers

Risk Frontiers deployed a team to the NSW South Coast region in late January, 2020 to undertake damage surveys following the bushfires. This research was supported by the Bushfire and Natural Hazards CRC (BNHCRC). The areas surveyed included Moruya, Mogo, Malua Bay, Rosedale, the Catalina area of Batemans Bay and Lake Conjola. The majority of damage occurred on December 31, 2019 as catastrophic weather conditions (extreme temperatures and strong winds) intensified existing fire fronts. The conditions transported large quantities of embers into vulnerable communities, destroying hundreds of residential and several commercial buildings. In total, the survey identified 426 bushfire affected properties, most of which were destroyed. Industries/infrastructure affected included: bowling/services club, a unit block (12 units), heritage park, industrial complex with numerous businesses and extensive damage to electricity infrastructure (power poles and wires along the Princes Highway). This report complements our report for northern NSW (Risk Frontiers, 2020).

Building age and resilience

As the 2019/2020 fire season progressed, the scale of damage and losses experienced across the country engendered a growing interest in evaluating the resilience of buildings to bushfires. Aspects of buildings such as age, performance of construction materials and a structure’s vulnerability due to its proximity to bushland were the key focus of the NSW South Coast survey. To evaluate the performance of building archetypes impacted by fire, the Insurance Council of Australia (ICA) charted the year of construction of over 25,000 residential buildings located within bushfire impacted areas across four states (Figure 1). Categories range from Old Colonial (pre-Victorian) to post-2009, when bushfire building standards began to be improved and were mandated in certain locations.

Figure 1: The period of construction for over 25,000 buildings located within the current bushfire impacted areas across four states. Source: Insurance Council of Australia, 2019.

The ICA data shows that only 9.5% of residences were constructed post-2009, when changes were made to Australian Standard 3959 after the Black Saturday fires of February, 2009, to ensure that new buildings in bushfire-prone areas were safer and more likely to survive a fire (BNHCRC, 2019). It was apparent that the scale of residential losses occurring this fire season presented a small window of opportunity to conduct further damage surveys, prior to recovery and debris removal, and would provide a considerable ‘post-2009’ cohort to assess building performance and inform future design. In the near future, further analysis will be undertaken by Risk Frontiers to establish the construction age of the South Coast properties, with a focus on any post-2009, to expand existing research.

Observations of destruction/damage – construction materials

The survey team recorded aspects of fire affected buildings such as construction materials and damage ratios (destroyed/partially destroyed). The field observations from the South Coast survey are compared to those in Rappville (2019) and Tathra (2018) in Figure 2.

Figure 2: The proportion and number (in column) of buildings categorised as destroyed/partially destroyed. The South Coast and Rappville (2019) damage surveys used a building footprint method where partially destroyed references the building, not the lot. The sampling method figures from the Tathra fire in 2018 assigned partially damaged on the proportion of the whole lot – that is, if a shed was destroyed but the house was undamaged, then a partially damaged rating was assigned. The data shows that once a building is alight, the likelihood of it being destroyed is very high. The total destruction rate across the three events ranged between two-thirds and 100%. The number of properties destroyed also indicates the difference in scale of the fire events between locations.

The South Coast findings reinforce those from the Rappville (2020) and Tathra (2018) surveys, in that, once a building catches fire, regardless of construction material, it will likely be totally destroyed. The official Tathra figures have 68% of all fire affected premises as being ultimately destroyed. Data collected from the South Coast and Rappville surveys provides much stronger indications of this trend, where 92% and 100% respectively, of the buildings observed were destroyed. (The Rappville and South Coast results represent only those properties located and observed, not all fire-affected properties).

In terms of building specifics, The South Coast survey provided numerous examples of fire-affected residences, primarily constructed of ‘non-flammable’ materials (brick and blockwork (piers and walls)). These structures demonstrated some resilience to the fire, at times remaining wholly or partially intact. However, the remaining material comprising the premises (structural roof/wall timbers, internal walls and house contents), once alight, ultimately rendered the entire building unsalvageable (destroyed). Timber beams supporting house roofs and carports were uniformly level on the ground (as though dropped). Metal framed buildings (e.g. sheds) and structural elements (e.g. lintels) did not perform well – failing due to extreme heat and leading to the building warping and impacting brick/masonry when collapsing. There were numerous examples of vehicles completely burnt out in front and rear yards and some isolated examples of aluminium boats that had undergone some degree of melting.

For partially destroyed properties, the building features most often impacted were constructed from timber such as external stairs and decking as well as external cladding. There were numerous examples of destroyed properties categorised as ‘asbestos contaminated’ though this was less common than during the Rappville survey where asbestos was present at over 50% of properties. A large number of asbestos contaminated assessments were speculative based on observations and erred on the side of caution with further assessment and testing usually noted as necessary. The possible exception to this would be Rosedale which experienced near total destruction and where homes predominantly appeared older, were often constructed using fibro or sheeting and surrounded by bushland.

Statistical dependence of bushfire risk on distance to bush and the influence of ember attack

Previous field research conducted by Risk Frontiers (Chen and McAneney, 2004 and other more recent) has established that proximity to bushland is the most important factor in determining a building’s vulnerability. Figure 3 depicts bushfire damage based on aggregated data from recent major bushfires and shows the cumulative distribution of destroyed buildings in relation to distance from bushland.

As with previous fires studied, Figure 3 confirms the significant role that ‘proximity to bushland’ played in the South Coast losses where approximately 38% of destroyed buildings were situated within 1 metre of surrounding bush. The average distance from bushland of all 426 properties surveyed was 55 metres (satellite imagery). However, a feature not obvious from the South Coast data in Figure 3, but apparent in the Rappville and Duffy examples, is the impact of extreme conditions and the capacity of embers to propagate fire over large distances. Witness accounts from fire fighters and locals have described embers being transported by extreme winds across Lake Conjola, over distances greater than 1km. The South Coast survey data would appear to confirm such reports, as two properties surveyed were >1.3km from bushland and 73 were located >100 metres from bush.

Figure 3: Cumulative distribution of buildings destroyed in relation to distance from nearby bushland for recent major events.

References

BNHCRC 2019. Black Saturday ten years on – what did we discover?

Insurance Council of Australia 2020. Period of residential building construction – chart. Posted by Karl Sullivan.

Risk Frontiers’ Newsletter Vol. 19, Issue 1.

Risk Frontiers’ Newsletter Vol. 17, Issue 3.

Chen, K., and K. J. McAneney, 2004: Quantifying bushfire penetration into urban areas in Australia. Geophys. Res. Lett., 31, L12212.

Acknowledgement

This research was funded through the Bushfire and Natural Hazards CRC Quick Response Fund.

Future of bushfire fighting in Australia

Andrew Gissing, Risk Frontiers, Neil Bibby, People & Innovation

Australia needs to be ambitious in its thinking about how future bushfires are managed and fought. Recent bushfires caused significant damage and widespread disruption leaving some 3093 homes destroyed (AFAC) and 35 fatalities as well as major damage to community infrastructure. We must learn from this experience.

Today’s management of bushfire risk is largely reliant on long standing approaches that are resource intensive and which struggle to control fires when conditions are catastrophic. This issue is compounded under a warming climate with fire seasons becoming longer, and days of significant fire danger more frequent.

An inherent problem is that bushfire detection is complex and in the time it takes before resources can be tasked and targeted, bushfires have already spread to the point where suppression is difficult. This problem is exacerbated when bushfire ignition occurs in remote areas far from emergency management resources. Making the problem worse still is a growing bushland-urban interface where buildings and community infrastructure are highly vulnerable and exposure is growing.

Innovation to discover the next generation of firefighting capability should be a priority in any government response to the Black Summer bushfires. Our institutions must think big.

To explore blue sky thinking in respect of future firefighting capabilities and enhanced bushfire resilience, Risk Frontiers and People & Innovation hosted a forum with experts in construction, technology, aviation, insurance, risk management, firefighting and information technology. In what follows, insights and questions arising from this forum are outlined.

New thinking is required

There are two stages in considering future capabilities. The first stage is planning and investment to improve capabilities in the short term particularly before the next bushfire season, and the second stage is research and innovation to inspire the next generation of firefighting capability. What is needed is a blueprint of how bushfires will be fought in the future. This blueprint should be focused on a vision whereby bushfires can be rapidly managed and controlled in a coordinated manner informed by advanced predictive intelligence; and where the built environment is resilient. Key research questions to be answered in the development of such a blueprint include:

Bushfire detection and suppression

  • How can bushfires be detected more quickly?
  • How can bushfires be extinguished before they are able to spread?
  • How can the safety of firefighters be improved?

Coordination

  • How can communications enable effective coordination?
  • How can resources be tasked and tracked in a more effective manner?
  • How can situational awareness be enhanced to inform decision-making?

Community resilience

  • How can new buildings be made more resilient?
  • How can existing building stock be retrofitted for resilience?
  • How can community infrastructure such as energy distribution systems, telecommunications, water supplies and sewerage systems be designed with greater resilience?

Short term

It is widely agreed that in the short term there are many technologies and systems already existing that could enhance firefighting and broader disaster management capabilities. Specific opportunities identified by industry experts include:

  • Satellites, such as data sourced from the Himawari satellite, should be evaluated for their ability to enhance fire detection. High Altitude Platform Systems may be another option.
  • In the United States, Unmanned Aerial Vehicles (UAV) have been employed to provide enhanced imagery over firegrounds and if equipped with infrared sensors these can support monitoring of fire conditions at night. The Victorian Government has established a panel contract with UAV providers to assist with real-time fire detection and monitoring. Further policy regarding airspace management is required to support wider demand-based deployments of UAVs.
  • Existing agricultural monitoring technologies could be repurposed to monitor bushfire fuels and soil conditions.
  • Balloons equipped with radio communications could provide coverage when traditional communications technologies have been disrupted. Alternatively, small UAVs could create a mesh network to provide a wireless communications network or equipment fitted to aircraft.
  • Advances in the use of robotics in the mining sector may provide applications to firefighting, for example autonomous trucks.
  • Resource tracking technologies could be implemented to improve coordination and firefighter safety.
  • Emerging fire extinguisher technologies could help to suppress bushfires.

Operational decisions could be improved by enhanced collation and fusion of data already available. There are many data sources that are managed by different organisations, not just government agencies. Collating these datasets to provide a common operating picture across all organisations would improve situational awareness and data analytics.

The widespread adoption of artificial intelligence and greater digital connectedness across the economy and emergency management sector will find new ways to make sense of data and improve decisions. In the built environment, improved information to households about the resilience of their buildings along with programs to implement simple retrofitting measures should be considered. In the aftermath of bushfires, governments should consider land swaps and buy-outs to reduce exposure in high risk areas. Similarly, governments should better plan communities to ensure infrastructure is more resistant to failure when most needed in emergencies.

2030 and beyond

A key area for research and innovation investment over the coming decade should be how to rapidly suppress bushfires once detected. This could see swarms of large capacity UAVs supported by ground-based drones to target suppression and limit fire spread. Resources would be rapidly dispatched and coordinated autonomously once a bushfire was detected. Pre-staging of resources would be informed by advanced predictive analytics and enabled by unmanned traffic management systems. UAVs and drones would have applications beyond fire suppression including for rapid impact assessment, search and rescue, logistics and clearance of supply routes.

The way forward

A research and innovation blueprint is needed that outlines how technologies will be translated to enhance firefighting and resilience in the short term and, beyond this, how the next generation of capability will be designed and built. Its development should involve government, research and industry stakeholders in a collaborative manner. The final blueprint should be integrated with future workforce and asset planning to support broader change management.

Adopting new technologies will not be easy and existing cultural and investment barriers should be considered. In adopting new technologies, it is important to recognise that innovation is an iterative process of improvement and will rarely provide a perfect solution in the first instance.

Public private partnerships will be key to realising opportunities and government must seek to engage a broad range of stakeholders. In the aftermath of Hurricane Sandy in the United States in 2012, the US Government launched a competition called ‘re-build by design’ focused on proactive solutions to minimise risk. Already in Australia, numerous innovation challenges involving businesses and universities are being held to assist in inspiring ideas. There is an opportunity to harness and coordinate such challenges on a grand scale to promote new thinking and collaboration linking directly with responsible agencies.

We need to be bold in our thinking!

Acknowledgements

Forum participants included IAG, SwissRe, IBM, Defence Science and Technology, IAI, Cicada Innovations, Lend Lease and ARUP.

Risk Frontiers Seminar Series 2020
Save the dates

Due to the COVID-19 pandemic Risk Frontiers’ Annual Seminar Series for 2020 will be presented as a series of three one-hour webinars across three weeks.

Webinar 1. Thursday 17th September, 2:30-3:30pm
Webinar 2. Thursday 24th September, 2:30-3:30pm
Webinar 3. Thursday 1st October, 2:30-3:30pm

Further details to follow.

Modelling the Coronavirus Pandemic to Guide Policy in Real Time

Paul Somerville, Risk Frontiers


This briefing presents an article by Martin Enserink and Kai Kupferschmidt entitled “Mathematics of life and death: How disease models shape national shutdowns and other pandemic policies.” It ends by describing a three-way tussle between protecting physical health, protecting the economy, and protecting people’s well-being and emotional health. This tussle is now playing out in Australia and the United States, with state and local governments and their expert advisors ignoring the recommendations of Federal governments by prioritizing the first goal (health) over the second goal (the economy). As of 30 March, it appears that the United States government has now reversed course in favour of health.

As the following article points out, the models shown here, and being used to guide policy, have been posted as drafts on websites in most cases and not yet been peer reviewed. The numbers of these postings probably currently exceed the capacity of the research community to provide peer review. The examples shown here were selected because they are thought to have influenced government policy.

The Australian government has not yet disclosed the modelling methods it has been using to develop its policies, but an example of the modelling that has been done in Australian universities is given by Chang et al. (2020). As indicated in the following article, modelling by Walker et al. (2020) at Imperial College London has influenced current policies in the United Kingdom. In the United States, the government is expected to disclose its modeling methods in the next few days but has stated that its model produces results similar to those of Murray (2020) shown below. At present, New Zealand is the only major English-speaking country that has made its modeling reports and their use in policy making completely open (New Zealand Ministry of Health, 2020).

The underlying approach in these models is illustrated in the two figures below. First, the growth of the number of cases (and other parameters such as numbers of deaths) are modelled for a range of policies ranging from limited, or no action, to complete lockdown.  For example, Figure 1 shows the cumulative number of projected hospitalisations in California as a function of time, and a table of corresponding outcomes after three months, for a set of different policies. Next, the estimated demand is compared with the capacity of the hospital system to meet that demand for the policy that is in effect. Figure 2 shows the excess demand of Intensive Care Units (ICU’s) by state in the United States.  Other charts show the time when excess demand peaks, and the time when the daily death rate falls below 0.3 per million.

Rathi (2020) has pointed out two similarities between the underlying processes governing pandemics and climate charge (and hence the nature of their modelling): both require global models, and both are nonlinear processes, with each incremental increase causing successively severe increases unless mitigated.

Figure 1. Modelling of outcomes of alternative public policies in California. The dashed black line shows the number of available hospital beds. Source: CovidActNow (2020).

Figure 2. Modelling of Intensive Care Unit excess demand in the U.S.  Source: Murray (2020).


Mathematics of life and death: How disease models shape national shutdowns and other pandemic policies

Jacco Wallinga’s computer simulations are about to face a high-stakes reality check. Wallinga is a mathematician and the chief epidemic modeler at the National Institute for Public Health and the Environment (RIVM), which is advising the Dutch government on what actions, such as closing schools and businesses, will help control the spread of the novel coronavirus in the country.

The Netherlands has so far chosen a softer set of measures than most Western European countries; it was late to close its schools and restaurants and hasn’t ordered a full lockdown. In a 16 March speech, Prime Minister Mark Rutte rejected “working endlessly to contain the virus” and “shutting down the country completely.” Instead, he opted for “controlled spread” of the virus among the groups least at risk of severe illness while making sure the health system isn’t swamped with COVID-19 patients. He called on the public to respect RIVM’s expertise on how to thread that needle. Wallinga’s models predict that the number of infected people needing hospitalization, his most important metric, will taper off by the end of the week. But if the models are wrong, the demand for intensive care beds could outstrip supply, as it has, tragically, in Italy and Spain.

COVID-19 isn’t the first infectious disease scientists have modeled—Ebola and Zika are recent examples—but never has so much depended on their work. Entire cities and countries have been locked down based on hastily done forecasts that often haven’t been peer reviewed. “It has suddenly become very visible how much the response to infectious diseases is based on models,” Wallinga says. For the modelers, “it’s a huge responsibility,” says epidemiologist Caitlin Rivers of the Johns Hopkins University Center for Health Security, who co-authored a report about the future of outbreak modeling in the United States that her center released yesterday.

Just how influential those models are became apparent over the past 2 weeks in the United Kingdom. Based partly on modeling work by a group at Imperial College London, the U.K. government at first implemented fewer measures than many other countries—not unlike the strategy the Netherlands is pursuing. Citywide lockdowns and school closures, as China initially mandated, “would result in a large second epidemic once measures were lifted,” a group of modelers that advises the government concluded in a statement. Less severe controls would still reduce the epidemic’s peak and make any rebound less severe, they predicted.

But on 16 March, the Imperial College group published a dramatically revised model that concluded—based on fresh data from the United Kingdom and Italy—that even a reduced peak would fill twice as many intensive care beds as estimated previously, overwhelming capacity. The only choice, they concluded, was to go all out on control measures. At best, strict measures might be periodically eased for short periods, the group said (see graphic, below). The U.K. government shifted course within days and announced a strict lockdown.

Epidemic modelers are the first to admit their projections can be off. “All models are wrong, but some are useful,” statistician George Box supposedly once said—a phrase that has become a cliché in the field.

Textbook mathematics

It’s not that the science behind modeling is controversial. Wallinga uses a well-established epidemic model that divides the Dutch population into four groups, or compartments in the field’s lingo: healthy, sick, recovered, or dead. Equations determine how many people move between compartments as weeks and months pass. “The mathematical side is pretty textbook,” he says. But model outcomes vary widely depending on the characteristics of a pathogen and the affected population.

Because the virus that causes COVID-19 is new, modelers need estimates for key model parameters. These estimates, particularly in the early days of an outbreak, also come from the work of modelers. For instance, by late January several groups had published roughly similar estimates of the number of new infections caused by each infected person when no control measures are taken—a parameter epidemiologists call R0. “This approximate consensus so early in the pandemic gave modelers a chance to warn of this new pathogen’s epidemic and pandemic potential less than 3 weeks after the first Disease Outbreak News report was released by the WHO [World Health Organization] about the outbreak,” says Maia Majumder, a computational epidemiologist at Harvard Medical School whose group produced one of those early estimates.

Wallinga says his team also spent a lot of time estimating R0 for SARS-Cov-2, the virus that causes COVID-19, and feels sure it’s just over two. He is also confident about his estimate that 3 to 6 days elapse between the moment someone is infected and the time they start to infect others. From a 2017 survey of the Dutch population, the RIVM team also has good estimates of how many contacts people of different ages have at home, school, work, and during leisure. Wallinga says he’s least confident about the susceptibility of each age group to infection and the rate at which people of various ages transmit the virus. The best estimates come from a study done in Shenzhen, a city in southern China, he says.

Compartment models assume the population is homogeneously mixed, a reasonable assumption for a small country like the Netherlands. Other modeling groups don’t use compartments but simulate the day-to-day interactions of millions of individuals. Such models are better able to depict heterogeneous countries, such as the United States, or all of Europe. WHO organizes regular calls for COVID-19 modelers to compare strategies and outcomes, Wallinga says: “That’s a huge help in reducing discrepancies between the models that policymakers find difficult to handle.”

Still, models can produce vastly different pictures. A widely publicized, controversial modeling study published yesterday by a group at the University of Oxford [Lourenco et al., 2020] argues that the deaths observed in the United Kingdom could be explained by a very different scenario from the currently accepted one. Rather than SARS-CoV-2 spreading in recent weeks and causing severe disease in a significant percentage of people, as most models suggest, the virus might have been spreading in the United Kingdom since January and could have already infected up to half of the population, causing severe disease only in a tiny fraction. Both scenarios are equally plausible, says Sunetra Gupta, the theoretical epidemiologist who led the Oxford work. “I do think it is missing from the thinking that there is an equally big possibility that a lot of us are immune,” she says. The model itself cannot answer the question, she says; only widespread testing for antibodies can, and that needs to be done urgently.

Adam Kucharski, a modeler at the London School of Hygiene & Tropical Medicine, says the Oxford group’s new scenario is unlikely. Scientists don’t know exactly how many people develop very mild symptoms or none at all, he says, but data from the Diamond Princess—a cruise ship docked in Yokohama, Japan, for 2 weeks that had a big COVID-19 outbreak—and from repatriation flights and other sources argue against a huge number of asymptomatic cases. “We don’t know at the moment, is it 50% asymptomatic or is it 20% or 10%,” he says. “I don’t think the question is: Is it 50%  asymptomatic or 99.5%.”

Riding tigers

In their review of U.S. outbreak modeling, Rivers and her colleagues note that most of the key players are academics with little role in policy. They don’t typically “participate in the decision-making processes … they sort of pivot into a new world when an emergency hits,” she says. “It would be more effective if they could be on-site with the government, working side by side with decision makers.” Rivers argues for the creation of a National Infectious Disease Forecasting Center, akin to the National Weather Service. It would be the primary source of models in a crisis and strengthen outbreak science in “peacetime.”

Policymakers have relied too heavily on COVID-19 models, says Devi Sridhar, a global health expert at the University of Edinburgh. “I’m not really sure whether the theoretical models will play out in real life.” And it’s dangerous for politicians to trust models that claim to show how a little-studied virus can be kept in check, says Harvard University epidemiologist William Hanage. “It’s like, you’ve decided you’ve got to ride a tiger,” he says, “except you don’t know where the tiger is, how big it is, or how many tigers there actually are.”

Models are at their most useful when they identify something that is not obvious, Kucharski says. One valuable function, he says, was to flag that temperature screening at airports will miss most coronavirus-infected people.

There’s also a lot that models don’t capture. They cannot anticipate, say, the development of a faster, easier test to identify and isolate infected people or an effective antiviral that reduces the need for hospital beds. “That’s the nature of modeling: We put in what we know,” says Ira Longini, a modeler at the University of Florida. Nor do most models factor in the anguish of social distancing, or whether the public obeys orders to stay home. Recent data from Hong Kong and Singapore suggest extreme social distancing is hard to keep up, says Gabriel Leung, a modeler at the University of Hong Kong. Both cities are seeing an uptick in cases that he thinks stem at least in part from “response fatigue.”  “We were the poster children because we started early. And we went quite heavy,” Leung says. Now, “It’s 2 months already, and people are really getting very tired.” He thinks both cities may be on the brink of a “major sustained local outbreak”.

Long lockdowns to slow a disease can also have catastrophic economic impacts that may themselves affect public health. “It’s a three-way tussle,” Leung says, “between protecting health, protecting the economy, and protecting people’s well-being and emotional health.”

The economic fallout isn’t something epidemic models address, Longini says—but that may have to change. “We should probably hook up with some economic modelers and try to factor that in,” he says.

References

Chang, S.L., N. Harding, C. Zachreson, O. M. Cliff, and M. Prokopenko (2020). Modelling transmission and control of the COVID-19 pandemic in Australia. Preprint, March 24, 2020

Covid Act Now. https://covidactnow.org/model

Enserink, Martin and Kai Kupferschmidt (2020). Mathematics of life and death: How disease models shape national shutdowns and other pandemic policies. Posted in: HealthCoronavirus, doi:10.1126/science.abb8814, Mar. 25, 2020 , 6:40 PM.

Lourenco, Jose, Robert Paton, Mahan Ghafari, Moritz Kraemer, Craig Thompson, Peter Simmonds, Paul Klenerman, and Sunetra Gupta (2020). Fundamental principles of epidemic spread highlight the immediate need for large-scale serological surveys to assess the stage of the SARS-CoV-2 epidemic. medRxiv preprint doi: https://doi.org/10.1101/2020.03.24.20042291

Murray, Christopher J.L. (2020). Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator-days, and deaths by US state in the next 4 months. Preprint submitted to MedRxiv 03.25.2020 – tracking ID MEDRXIV/2020/043752

http://www.healthdata.org/research-article/forecasting-covid-19-impact-hospital-bed-days-icu-days-ventilator-days-and-deaths

https://covid19.healthdata.org/projections

New Zealand Ministry of Health (2020).

https://www.health.govt.nz/publication/covid-19-modelling-reports

Rathi, A. (2020). The pandemic reveals how the science of risk shapes our lives. 31 March 2020.

https://www.linkedin.com/pulse/pandemic-reveals-how-science-risk-shapes-our-lives-akshat-rathi/

Walker, Patrick G.T. et al. (2020). The Global Impact of COVID-19 and Strategies for Mitigation and Suppression. Imperial College COVID-19 Response Team, March 26, 2020.

Newton did OK working from home during a pandemic

Paul Somerville, Risk Frontiers

William Shakespeare (the “Flower Portrait,” 1609)

Ben Cohen, author of the book “The Hot Hand: The Mystery and Science of Streaks” described “How the plague ravaged William Shakespeare’s world and inspired his work.” The plague closed London’s playhouses and forced Shakespeare’s acting company, the King’s Men, to get creative about performances. As they travelled the English countryside, stopping in rural towns that had not been stricken by the plague, Shakespeare felt that writing was a better use of his time. From the beginning of 1605 to the end of 1606, Shakespeare is thought to have written King Lear, Macbeth, and Antony and Cleopatra. Shakespeare also benefited from the plague because the plague killed off his competition. The King’s Men would eventually take back their indoor theatre spaces because of this disease that preyed on the young.

Gillian Brockell in The Washington Post, 13 March 2020, wrote the following article about Isaac Newton’s experience sixty years later in 1665:

During a pandemic, Isaac Newton had to work from home, too. It was time well spent.

Portrait of Newton at 46 by Godfrey Kneller, 1689

Isaac Newton was in his early 20’s when the Great Plague of London hit. He wasn’t a “Sir” yet, didn’t have that big, formal wig. He was just another college student at Trinity College, Cambridge. Cambridge sent students home to continue their studies. For Newton, that meant Woolsthorpe Manor, the family estate about 100 kilometre north-west of Cambridge.

Without his professors to guide him, Newton apparently thrived. The year-plus he spent away was later referred to as his annus mirabilis, the “year of wonders.”  First, he continued to work on mathematical problems he had begun at Cambridge; the papers he wrote on this became early calculus.

Next, he acquired a few prisms and experimented with them in his bedroom, even going so far as to bore a hole in his shutters so only a small beam could come through. From this sprung his theories on optics. And right outside his window at Woolsthorpe, there was an apple tree. That apple tree.

The story of how Newton sat under the tree, was bonked on the head by an apple and suddenly understood theories of gravity and motion, is largely apocryphal. But according to his assistant, John Conduitt, there’s an element of truth. Here’s how Conduitt later explained it: ” . . . Whilst he was musing in a garden it came into his thought that the same power of gravity (which made an apple fall from the tree to the ground) was not limited to a certain distance from the earth but must extend much farther than was usually thought. ‘Why not as high as the Moon?’ said he to himself..”

In London, a quarter of the population would die of plague from 1665 to 1666. It was one of the last major outbreaks in the 400 years that the Black Death ravaged Europe. Newton returned to Cambridge in 1667, theories in hand. Within six months, he was made a fellow; two years later, a professor.

So if you’re working or studying from home over the next few weeks, perhaps remember the example Newton set. Having time to muse and experiment in unstructured comfort proved life-changing for him – and no one remembers whether he made it out of his pyjamas before noon.

 

URGENT: COVID 19 Risk Frontiers Risk Statement

COVID 19 presents a disruptive risk to the way businesses work. At Risk Frontiers we have been proactive since early February in implementing our pandemic risk management strategies to reduce risks to our staff and to ensure the continuity of our service delivery. This will mean changing some of the ways that we work including increasingly using meeting technologies to interact with our clients and stakeholders.

We appreciate that our clients will also likely suffer some disruption as a consequence of the pandemic and we are committed to working through individual engagements to ensure they can be delivered effectively.

John McAneney

NSW/ACT Large Scale Hail Event, January 2020:  An Overview of Risk Frontiers’ Post-Event Loss Estimate Capabilities

By Salomé Hussein and Foster Langbein

Risk Frontiers’ rapid post-event analysis of radar footprint and damage gives an aggregated loss estimate of $1.2 billion for the January 2020 hail storms that hit Melbourne, Canberra and Sydney simultaneously – see Table 1.

able 1. Risk Frontiers’ post-event loss estimates compared to ICA current estimates for recent hail events. Other events not declared by the ICA have also been analysed by Risk Frontiers but are not shown here.

On January 19, 2020, the Bureau of Meteorology (BoM) issued warnings that a severe convective storm would impact Melbourne. The next day, more hail hammered Canberra and Queanbeyan, then in southwest Sydney shortly after. NSW State Emergency Services (SES) answered 800 calls for assistance in Sutherland, Miranda and Caringbah. The ACT SES received 2200 calls following the event. Images and videos of golf-ball sized (4-5cm) hail and strong wind gusts proliferated through social and general media. 

The Insurance Council of Australia declared three days of storms as a single catastrophe (CAT201) with impacts in Melbourne, Canberra, Queanbeyan, Sydney, and Goulburn (also extreme rain and wind in Queensland). Their current estimated loss is $670 million (from 69,850 claims– 53% ACT, 17% NSW, and 30% Vic). 

Images of damage include the usual dented cars, shattered windscreens, and holes in roofs. CSIRO crop research glasshouses in Canberra fared particularly poorly (some example photos taken by staff are shown in Figure 1.) Reported wind gusts of 116km/h in ACT (https://www.canberratimes.com.au/story/6588613/heavy-storms-and-huge-hailstones-lash-canberra/) will have contributed to the damage, both by breaking drought weakened vegetation, and affecting the impact of the hail itself.

Risk Frontiers performs rapid post-event analysis when storms are declared catastrophic, or before, when alerted to excessive or large hail through other information channels (e.g. SES volunteers, social media). The methodology derives hail footprints from radar data for use in Risk Frontiers’ HailAUS loss model and has been more fully described in prior Newsletters (https://riskfrontiers.com/rf2018/wp-content/uploads/2019/03/RF_Newsletter_Volume18_Issue2_March_2019.pdf). The results are actively compared against evolving ICA estimates.

CAT201  is the second of two ICA hail events to impact Australia in the 2019/20 season and followedCAT196, a smaller storm that occurred in Southeast Queensland in November 2019. Both occurred only a year after the storm that hit Sydney on December 20, 2018, which accrued an estimated $1.357 billion in insurance claims by December last year. As shown in Table 2, the 2018 storm now ranks as the 7th costliest in normalised terms, barely inching ahead of the March 2010 Perth Hail Catastrophe. Thus far Risk Frontiers’ post-event loss estimates compare well to those from the ICA for the December 2018 storm and now the SEQ 2019 storm (Table 1).

The January 2020 post-event analysis proved a more unique challenge. The hours clause of most insurance policies meant that storms (even ones where damage was primarily attributed to rain and wind in Queensland) across 3 days and 3 states were aggregated into one catastrophe. Nonetheless, Risk Frontiers extracted multiple storm footprints from the primary radar in each affected urban centre to yield a current estimate in Table 1. The combined figure of $1.2 billion includes residential loss, commercial and industrial loss including business interruption as well as motor losses. This is in excess of the current ICA estimate, but their reported number is expected to increase substantially given only a fraction of claims have been processed.

Figures 2 through 4 are the sum of the Maximum Estimated Size of Hail (MESH) analysis over the course of the storm for the indicated regions. Animations of the storms development are available on Risk Frontiers’ website. The storm cells in Sydney and Canberra behaved similarly, while the storm in Melbourne exhibited slower movement, an unusual split, and change in travel direction as it passed Northwest of Lancefield. Inspection of the synoptic setup (the plot of Mean Sea Level Pressure in Figure 5) from the BoM shows a steep trough directly over Port Phillip Bay when this phenomena occurred which may offer some explanation. Analysis for this storm required separating the radar volumes in time and allocating a separate footprint to each of the cells that developed after that split.

Risk Frontiers’ latest research shows some exciting possibilities in post-event analysis of hail storms. Our current capability, applied to several recent events, has already demonstrated that. Our current capability, applied to several recent events, has already demonstrated that:

  • Image analysis can be used to rapidly determine a hail storm footprint from widely available radar data,
  • This footprint can be fed into Risk Frontiers’ HailAUS damage module to create a fast loss estimation pipeline,
  • The loss estimate is robust and could be considered for post-event capital allocation considerations or as part of a parametric hail product .
Figure 1. Photographs of damaged cars, windows, and solar panels from Barton and Civic Square in Canberra.
Table 2. The top 10 most costly hail events in Australia with Risk Frontiers’ normalised estimates from the ICA Catastrophe Database.
Figure 2. Cumulative MESH over the storm event in Sydney. The primary suburbs impacted were the Sutherland Shire and Campbelltown, with another hotspot in the far Northeast near Booral.
Figure 3. Cumulative MESH for the Canberra hail-storm. Hailstone sizes estimated up to 8cm landed in the CBD, with another hotspot occuring Northeast of Bungonia and Goulburn.
Figure 4. Cumulative MESH footprints for the Melbourne hail-storm, showing the peak damage areas around Lancefield, Kilmore, Malvern, and Warrandyte. The storm cells split and travelled opposite directions Northeast of Lancefield.
Figure 5. Mean Sea Level Pressure at 5pm AEDT. The low pressure trough occurred over Port Phillips Bay, causing the unusual U-shaped damage swath in Figure 4.

February 2020 East Coast Low: Sydney Impacts

Thomas Mortlock and Paul Somerville

Figure 1. Narrabeen lagoon flooded
Figure 1. Narrabeen lagoon flooded on 9 February leading to inundation of surrounding land. Source: Twitter/Jacqui Kirk.

An East Coast Low (ECL) windstorm event impacted Southeast Queensland, coastal and inland NSW and the ACT over the period 5 to 10 February 2020, with the Insurance Council of Australia declaring the event a catastrophe (ICA, 2020). As of 10 February, over 10,000 insurance claims had been lodged, estimated to be worth $45 million. Most of the claims came from Queensland and coastal NSW for property damage caused principally by strong winds and heavy rain.

More positively, the deluge extinguished most of the remaining bushfires in NSW and made a significant contribution to water storage in the State. There is still some concern over water quality in dam storage due to hydrophobic soils and runoff due to bushfires. However, this is unlikely to be an immediate issue as reservoirs are thermally stratified at this time of year and authorities can draw down ‘older’ water from deeper depths.

In Sydney, heavy rainfall led to flooding of the two main river systems in the area, the Georges and Hawkesbury Rivers, and also Narrabeen Lagoon on Sydney’s Northern Beaches where the lagoon water levels exceeded those of the last major ECL event in June 2016 (Figure 1). Flooding occurred in the Hawkesbury-Nepean valley with a significant contribution from the Grose River. Evacuation orders were in place on 8 and 9 February in these areas. Strong winds also downed trees in the CBD and Potts Point during the afternoon of 9 February. Large wave conditions led to some significant beach erosion and coastal flooding on Sydney’s Northern Beaches, the Illawarra and Central Coast.

This briefing note evaluates the severity of flood and coastal conditions during this event for the Sydney area and provides some context regarding previous notable ECLs and the synoptic setting in Australia.

Synoptic setting

The spring/summer of 2019/2020 was dominated by the devastating bushfires and continuation of drought conditions in NSW. This was facilitated by the strongest positive Indian Ocean Dipole (IOD) in almost 60 years and an anomalously negative Southern Annual Mode (SAM) across Southeast Australia. Both the IOD and SAM combined to reinforce hot, dry conditions with prevailing westerly winds producing dangerous bushfire weather. Since the IOD and SAM both returned to neutral in January, conditions have become more favourable for localised convection. This has led to a spate of hailstorms along coastal NSW, Victoria and the ACT, and it has also coincided with the arrival of the monsoonal trough over Northern Australia.

The February 2020 ECL was an Inland Trough Low extending south down the eastern seaboard from the tropical monsoonal trough (Figure 2). In Figure 2, Tropical Cyclone Damien on the Northwest Shelf of Western Australia and a low-pressure system in the northern Coral Sea off Far North Queensland (later to form into Tropical Cyclone Uesi) were also both embedded in the monsoonal trough.

Figure 2. Synoptic conditions at 17:00 AEDT on 9 February 2020. Source: BoM (2020).

Coastal winds and waves around Sydney during the February 2020 ECL were predominately from the east to north-east. This wind direction not only caused damaging wave conditions, but also drew in moisture from an unusually warm Tasman Sea, which contributed to high rainfall over coastal NSW.

The East Australian Current is an eastern ocean boundary current that transports warm water from the Coral Sea south and extends poleward at this time of year. However, sea surface temperatures (SSTs) on 9 February were 1 – 2 °C warmer than normal around Sydney (up to 26 °C, Figure 3), providing a ready moisture source for this windstorm event.

Figure 2. Sea surface temperature observations
Figure 3. Sea surface temperature (SST) observations (A) and anomalies (B) on 9 February 2020. Source: IMOS (2020).

Rain, wind and flooding

The most widespread impacts from this event arose from wind and wind-driven rain causing localised flash flooding. Figure 4 shows rain and wind observations at Observatory Hill (Sydney CBD) and Sydney Airport between 7 and 9 February.

During the evening of 9 February, a maximum gust of 87 km/hr was recorded at Sydney Airport, with hourly mean wind speeds reaching 60 km/hr. In the CBD, hourly wind speeds reached 65 km/hr (gusts are not measured at the Observatory Hill station). During this period, winds were from the ENE.

1-hour precipitation peaked at 38 mm at the airport and 31 mm in the city. According to Australian Rainfall Runoff Intensity Frequency Duration (IFD) data (Ball et al., 2019), this equates to a 5-year Average Recurrence Interval (ARI) at the airport and 2-year ARI at the city. While these are not uncommon rain rates, the ARI increases when accumulated over longer durations.

Figure 3. Hourly rain and wind observations
Figure 4. Hourly rain and wind observations at Sydney CBD and Sydney Airport. Data were obtained from BoM (2020) and standardised to hourly values. Wind gust refers to the maximum 3-second sustained gust recorded in a 1-hour period.

For example, the greatest 6-hour period saw falls of 93 mm at the airport and 84 mm in the city; this has an approximately 10-year ARI at the airport and 5-year at the CBD. When accumulated over a 24-hour period, 179 mm fell at the airport and 201 mm in the city with corresponding ARIs of 10-years at both locations.

In summary, this event was equivalent to a 5- to 10-year ARI with respect to rainfall at Sydney.

Storm surge, waves and coastal erosion

While winds and rain peaked in the evening of 9 February, coastal conditions were most hazardous in the morning of 9 February due to the coincidence of high waves with spring high tides (which reached approximately 2 m AHD). At HMAS Penguin in Sydney Harbour, a storm surge of over 1 m was recorded on the falling tide after the morning high water (note: this is from live data and is yet to be verified).

A storm surge is the product of low barometric pressure and onshore-directed winds raising water levels above the predicted tide height. Unlike with tropical cyclones, storm surge is often not the main component of coastal flooding with ECLs in Australia. This is because wind speeds are not as great during ECLs and the typical beach dune height along the NSW and Victorian coast is around 3 m AHD, which is usually sufficient to buffer against high water levels during these events.

During the February 2020 event, a maximum wave height of 14 m (that is over 45 feet!) was recorded on the morning of 9 February at the Sydney wave buoy, with significant wave heights (the highest one-third of all waves measured in one hour) peaking at around 7 m (Figure 4).

Figure 4. Observed wave parameters
Figure 4. Observed wave parameters from 3 to 9 February 2020 (right) and directional wave energy spectrum (left) on 9 February at the Sydney wave buoy. Source: MHL (2020). Photo: Wollongong Harbour during the morning of 9 February. Source: T. Mortlock.

A significant wave height of 7 m at Sydney equates to a 5-year ARI, according to Shand et al. (2011), and is in fact a larger peak wave height than was recorded during the infamous June 2016 ECL event (6.4 m). However, the 2020 ECL led to relatively less erosion than in 2016. One of the reasons for this is wave direction.

The mean wave direction during the February 2020 event was from around 100 ° (ESE), although the spectral plot (left inset, Figure 5) suggests there was a wide spread of wave energy between 20 and 180 ° (NNE to S). This means wave energy was dissipated across all sections of the east-facing Sydney beaches, and not just focussed on the narrower and more vulnerable southern sections, as was the case in 2016 when waves predominated from the ENE. Coastal erosion was therefore more limited in the 2020 event, although still caused recession of up to 25 m at some locations (SMH, 2020).

Large waves and strong onshore-directed winds can also contribute to the flooding of coastal lagoons during ECL events. These lagoons – known as ICOLs (Intermittently Closed and Open Lagoons) – are often closed from the ocean by a bar of sand deposited by ocean waves. During storm events, waves and onshore winds can prevent floodwater from escaping the lagoon entrance and further contribute to elevated water levels in the lagoon. The areas adjacent to coastal lagoons are therefore particularly vulnerable to flooding during ECLs.

In order to avert the flooding caused by the lagoon entrance being closed in the 2016 storm, the lagoon entrance was bulldozed open on 8 February. Nevertheless, on 9 February the lagoon flooded prompting an evacuation of approximately 4,000 properties. Floodwaters also inundated parts of Pittwater Road, the main thoroughfare that connects Narrabeen with the City (Figure 6). Similar lagoon flooding was also observed at Fairy Lagoon in North Wollongong and the entrance to Lake Illawarra.

Figure 5. Flooding observed around Narrabeen
Figure 5. Flooding observed around Narrabeen on 9 February 2020. Photos: P. Somerville.

Summary

A summertime East Coast Low (Inland Trough Low) intensified over Sydney on 9 February causing heavy rain, flash and riverine flooding, localised downing of trees and some coastal erosion. Fortunately, there were no associated fatalities. Analysis suggests both rain volumes and coastal wave conditions were equivalent to a 5-to-10-year event magnitude. Unusually warm sea surface temperatures off the coast of Sydney provided a ready moisture source for this event. Given there is now no large-scale climate mode dominant at this time (the IOD, SAM and ENSO are all in neutral territory), we can expect variable weather driven by local-scale processes over the coming months.

Over the past decades, there has been no clear observed trend in the number of East Coast Lows. Climate model projections indicate that fewer East Coast Lows are likely to occur in the future, particularly during winter, with increased rainfall intensities in some cases (ESCC, 2020). However, there is more uncertainty associated with the projections of the future frequency of summertime ECLs such as this event.

Reference

Ball, J., Babister, M., et al. (2019). Australian Rainfall and Runoff: A Guide to Flood Estimation, Commonwealth of Australia.

ESCC (2020). East coast lows and climate change in Australia. Earth System and Climate Change Hub. November 2019, pp 4.

ICA (2020). Insurers delate Catastrophe for east coast storms and flooding. Insurance Council of Australia media release, 10 February 2020.

Shand, T., Mole, M.A., et al. (2011). Coastal Storm Data Analysis: Provision of Extreme Wave Data for Adaptation Planning; Water Research Laboratory (WRL) Technical Report 2011/242; University of New South Wales: Sydney, Australia.

SMH (2020). ‘Critical’ few hours for Sydney beach erosion as storm heads south. Sydney Morning Herald quoting measurements from UNSW Water Research Laboratory. 10 February 2020.

Earthquake risk in Australia 30 years after the 1989 Newcastle Earthquake

Paul Somerville, Risk Frontiers

Newcastle Workers Club. Source: livinghistories.newcastle.edu.au

The 1989 Newcastle Earthquake and its Impact

The Newcastle earthquake occurred at 10:27am local time on December 28, 1989. It had a magnitude Mw of 5.42 (Allen et al., 2018), the epicentre was approximately 15 km SW of the Newcastle CBD (near Boolaroo) and it occurred at a depth of about 11 km.

The earthquake claimed 13 lives: nine people died at the Newcastle Workers Club (pictured above), three people were killed along Beaumont Street in Hamilton and one person died of shock in Broadmeadow. Melchers (2012) showed that collapse of the Newcastle Workers Club would have been unlikely if there had not been significant deficiencies in the structure as built. The number of people in the city on the day of the earthquake was lower than usual, due to a strike by local bus drivers. It is estimated that about 500 people may have died on a normal day.

The earthquake caused damage to over 35,000 homes, 147 schools and 3,000 commercial and other buildings, with significant damage (over $1,000) to 10,000 homes and structural damage to 42 schools within the immediate Newcastle area. About 300 buildings were demolished. Approximately 300,000 people were affected by the earthquake and 1,000 made homeless. 160 people required hospitalisation but the Royal Newcastle Hospital was rendered inoperable by the earthquake. Insured losses are estimated to be $4.25 billion normalised to 2017 values (McAneney et al., 2019).

The effects of the earthquake were felt over an area of about 200,000 sq. km, with isolated reports of shaking felt up to 800 km from Newcastle. Damage to buildings and facilities occurred over a 9000 km2 region. The damage was most severe on soft sediments from the Hunter River, with shaking intensity of MMI VIII observed at many locations.

Lessons learned

As pointed out by Woodside and McCue (2017), the Newcastle earthquake demonstrated that all the basic principles of earthquake engineering design that have been learned abroad also apply to Australia. Specifically, the damage was due to:

  • Failure of unreinforced masonry, especially the failure of galvanised brick ties due to corrosion from the lime mortar
  • The failure of non-structural elements such as ceilings and chimneys
  • The effects of eccentricity and soft stories on the performance of buildings
  • Inadequate seismic design including tying together of the structure.

As described by Brunsdon and Bull (2019), the involvement by New Zealand engineers in the Newcastle earthquake response and recovery prompted a closer look at New Zealand’s earthquake preparedness, particularly through the professional engineering lens. In conjunction with the preceding Loma Prieta earthquake and subsequent Northridge and Kobe earthquakes, the Newcastle earthquake strongly influenced subsequent work in New Zealand, notably the development of capabilities in post-earthquake assessment and placarding and urban search and rescue. As a result, New Zealand was much better prepared to deal with the many challenges presented by the Canterbury Earthquake Sequence of 2010/11, and significant post-earthquake support of urban search and rescue in Christchurch was provided by Australian engineers who had been trained by their New Zealand counterparts.

Contribution to the development of seismic provisions in the Australian Building Code

Prompted initially by the Mw 6.68 Meckering earthquake of 1968 and further by the three Mw 6.3 to 6.6 Tennant Creek earthquakes of 1988, Standards Australia in 1988 decided to revise the Australian Building Code standard AS 2121. The appointed subcommittee first met on 12 December 1989 in Adelaide, about two weeks before the Newcastle earthquake on 28 December 1989 (Woodside and McCue, 2011). The Newcastle earthquake provided impetus to this task, and the revised code was introduced as AS1170.4 in draft form in 1991 and published in 1993. The performance objective was and still is for life safety or better in a rare event, currently defined as one whose ground motion has an annual probability of exceedance (AEP) of 1:500 (return period of 500 years). The code peak accelerations, up to about 0.1g in some cities, are exceeded close to earthquakes having magnitudes above about Mw 4.5.

In many locations in Australia, wind forces, rather than earthquake forces, govern code-based structural design, and so many practicing engineers here do not develop a full understanding of the nature of the forces presented by earthquakes. It is one thing to design a structure to resist the steady force of the wind on the side of a building, and quite another to design it to resist the forces that result from an earthquake, which are equivalent to having the rug you are standing on pulled sideways from under you. Unless the building is strong enough that its roof can follow the abrupt horizontal movement of its foundation within a separation of a few percent of its height as the ground moves back and forth, it will collapse. This requires the careful detailing of connections between columns, beams, floors and walls so that even if the building is damaged in a strong earthquake it does not collapse. In contrast, buildings can easily be designed to withstand the strongest winds even without structural damage let alone collapse.

Motivated by the relatively small (Mw 6.2) Christchurch earthquake of February 22, 2011, which caused major damage and rendered the CBD unusable for a long period of time because it occurred directly underneath the city, Goldsworthy and Somerville (2012) argued for the adoption of a lower probability event (1:2,500 AEP or 2,500 year return period instead of 1:500 AEP or 500 years return period) in Australia in conformance with developments in building codes in Canada, New Zealand and the United States. Unlike the mainly empirical approach to code development based primarily on the past performance of structures in earthquakes, this new generation of codes uses the framework of performance-based design to quantitatively estimate the capacity of buildings to withstand strong ground motion.

Recent developments in seismicprovisions in the Australian Building Code

Major improvements were made in the national seismic hazard map of Australia by Geoscience Australia (NSHA18; Allen et al., 2019). Revision of the magnitudes of historical Australian earthquakes led to the conclusion that for a given magnitude, earthquakes are about half as frequent in Australia as had been previously thought. However, the NSHA18 hazard map was not adopted in the most recent revision of AS1170.4 on August 15, 2019 (Standards Australia, 2019), which contains a minimum peak ground motion level of 0.08g for design. The large reductions in probabilistic seismic hazard estimates in NSHA18 mean that the ground motion levels embodied in AS1170.4 – 2019 are roughly equivalent to an AEP of 1:2,500 (return period of 2,500 years) in most of the capital cities, as shown by Allen et al. (2019), thus largely fulfilling the objective proposed by Goldsworthy and Somerville (2012).

Development of catastrophe loss modeling for the insurance industry

Catastrophe loss modeling for the insurance industry was in its infancy when the Newcastle earthquake occurred. Through the founding of Risk Frontiers in 1994, enabled by the sponsorship of the insurance industry in Australia, the Newcastle earthquake spurred the development in Australia of quantitative methods of estimating catastrophic losses from natural disasters based on validation against comprehensive catalogues of historical losses. Risk Frontiers now has a complete set of catastrophe loss models for all perils in Australia as well as several others in the Asia Pacific region.

Cautionary notes

The beneficial outcome of NSHA18 described above is offset by the fact that in Australia, due to the lack of attention given to seismic design, the performance of some buildings is likely to be poor even in a small event. In Australia, material codes such as the Steel Structures code (Standards Australia, 1998) and the Concrete Structures code (Standards Australia, 2009) do not require designers to use capacity design principles in their design. The implementation of these design principles in New Zealand since the 1980s, in line with the performance requirement for “near collapse” or better under a 2,500 year return period event, is what probably saved many lives in the Christchurch earthquake. Australian building codes do not address single story dwellings.

To further deter complacency, note that there have been 30 known earthquakes with magnitudes larger than the 1989 Newcastle earthquake since 1840, nine of which had magnitudes of Mw 6.2 (the size of the 2011 Christchurch earthquake) or larger. Several Australian capital cities, including Adelaide, Canberra and Melbourne, have known faults in their vicinity that are capable of generating damaging earthquakes. Australian earthquakes have sometimes occurred in clusters; the three Mw 6.3 to 6.6 earthquakes occurred in one day in the 1988 Tennant Creek sequence. Australian earthquakes have also been followed by long aftershock sequences like that of the Canterbury sequence; one occurred off the east coast of Tasmania near Flinders Island from 1884 to 1886 with magnitudes as large as Mw 6.4.

The 1989 Newcastle earthquake, with a revised Mw of 5.42, caused a loss equivalent to $4.25 billion if it were to recur today (McAneney et al., 2019). This is the largest earthquake loss among all of the Australian natural disaster losses spanning 1967 to the present listed by these authors. Although weather related disasters have historically caused larger losses than the 1989 Newcastle earthquake, larger earthquakes could cause larger losses than those of any weather-related disaster.

Challenges for the way forward

The 1989 Newcastle earthquake and the 2011 Christchurch earthquake present challenges for improving the outcomes of future earthquakes in Australia. We need ongoing training of emergency responders in search and rescue, and of engineers in assessing the safety and placarding of buildings in the immediate aftermath of the earthquake. Extending beyond prescriptive code formulas, we need to foster among practicing structural engineers a better understanding of the principles that underly earthquake resistant design. Given the high level of vulnerability of Australian cities to earthquakes, building design and construction need to consider not only the integrity of individual buildings and infrastructure and the life safety of their occupants, but also the role that they play in providing the functionality and viability of whole communities, with advanced focus on recovery. It took several years for Newcastle to recover from its relatively small magnitude earthquake. Almost ten years on, Christchurch is still struggling to regain the functionality that its residents took for granted before the 2011 earthquake. We must do what we can to avoid that fate.

A good way to advance preparedness and mitigation activities is to develop plans for response to and recovery from significant scenario earthquakes in major cities. These plans need to involve emergency responders, structural engineers, architects, city planners, community organisations, and the members of relevant government departments (such as building officials) and elected representatives of the affected cities, states and nation. Members of the public at large also need to be aware of what to do if they experience an earthquake. The message to “drop, cover and hold on” is promoted and practiced in annual “ShakeOut” exercises around the globe.

References

Allen, T. I., Leonard, M., Ghasemi, H, Gibson, G. (2018). The 2018 National Seismic Hazard Assessment for Australia – earthquake epicentre catalogue. Record 2018/30. Geoscience Australia, Canberra. http://dx.doi.org/10.11636/Record.2018.030.

Allen, T., J. Griffin, M. Leonard, D. Clark and H. Ghasemi (2019). The 2018 National Seismic Hazard Assessment: Model overview. Record 2018/27. Geoscience Australia, Canberra. http://dx.doi.org/10.11636/Record.2018.027

Brunsdon, David and Des Bull (2019). Reflections on Thirty Years of Significant Earthquakes in Australasia and Beyond: Earthquake Engineering into the Future. Proceedings of the 2019 Annual Conference of the Aistralian Earthquake Engineering Society, Newcastle, November 30 – December 2, 2019

Goldsworthy, Helen and Paul Somerville (2012). Reassessment of Earthquake Design Philosophy in Australia after the Christchurch Earthquake, Risk Frontiers Briefing Note 232, February 2012.

McAneney, John, Benjamin Sandercock, Ryan Crompton, Thomas Mortlock, Rade Musulin, Roger Pielke Jr & Andrew Gissing (2019). Normalised insurance losses from Australian natural disasters: 1966–2017, Environmental Hazards, 18:5, 414-433, DOI: 10.1080/17477891.2019.1609406
(https://www.tandfonline.com/doi/full/10.1080/17477891.2019.1609406

Melchers, Robert E. (2010). Investigation of the Failure of the Newcastle Workers Club, Australian Journal of Structural Engineering, 11:3, 163-176, DOI: 10.1080/13287982.2010.11465064

Standards Australia (1979). The Design of Earthquake Resistant Buildings, AS2121-1979
Standards Australia (1998). AS4100-1998: Steel Structures.
Standards Australia (1993). Minimum design loads on structures: Part 4 – Earthquake Loads, AS1170.4-1993.
Standards Australia (2009), AS3600-2009: Concrete Structures.
Standards Australia (2019). Structural design actions Part 4: Earthquake actions in Australia. AS1170.4-2019

Woodside, John and Kevin McCue (2017). Early History of Seismic Design and Codes in Australia. Australian Earthquake Engineering Society, 2017.

Officials acquitted of professional negligence in the Fukushima nuclear disaster

Paul Somerville,  Risk Frontiers

Onset of a catastrophe

I was watching the news on NHK TV (Japan’s public broadcaster) on September 11, 2011 when the broadcast was abruptly interrupted by a news flash that a JMA (the Japan Meteorological Agency) magnitude 7.9 earthquake had occurred off the Tohoku coast of northern Japan (Risk Frontiers Briefing Note 217, 2011). It was night in Japan and at first there was not much to see as no reports of extensive shaking damage were shown. As JMA continued to update its estimate of the magnitude from 7.9 to 8.4 and then 8.7, I received an email from my colleague Dr Thio in California estimating the magnitude at 9.0 about 20 minutes after the event began, confirmed by JMA five minutes later. Soon the first arrivals of tsunamis at ports along the Tohoku coast began to appear on the screen, followed by dramatic images of waves and inundation never seen before on TV. The tsunami killed more than 18,000 people along Japan’s north-east coast, including Fukushima. Initially there was little mention of the Fukushima Nuclear Power Plant, operated by Tokyo Electric Power Company (TEPCO), and it took weeks before the dire condition of the five units became clear, as graphically chronicled by Australian journalist Mark Willacy (2013).

Reactors 1 to 3 at the plant suffered nuclear fuel meltdowns, while hydrogen explosions damaged the buildings housing units 1, 3 and 4 (Figure 1). The nuclear meltdowns sent plumes of radiation into the atmosphere and forced the evacuation of 160,000 people living near the plant, 31,000 of whom are still unable to return to their homes. TEPCO has said it will take 40 years to locate and remove the melted fuel from the reactor cores, although some experts believe decommissioning could take longer. The government has estimated that the total cost of dismantling the plant, decontaminating surrounding areas and compensating victims at about $US200bn. TEPCO this week announced that its preferred method of disposing of more than a million tonnes of contaminated water stored at the site is to discharge it into the Pacific ocean, which is strongly opposed by local fishermen who have spent the last eight years rebuilding their industry.

Figure 1. From top: The 15.5 metre high tsunami overtopping the tsunami sea walls on March 11, 2011; The tsunami inundating the reactors; An explosion on March 15 ruptured the reactor components of Reactor no. 2 and breached the main containment; Smoke rising from damaged Reactor No. 3 Source: TEPCO

Acquittal of TEPCO executives

On 19 September 2019, three former top executives of TEPCO were acquitted of professional negligence resulting in death and injury related to the 2011 Fukushima nuclear accident. The trial started in June 2017 after a judicial review panel comprising ordinary citizens ruled that the former executives should be indicted. Initially, prosecutors twice declined to proceed with the case, citing insufficient evidence and a slim chance of conviction. A total of 37 hearings were held for the trial, during which more than 20 witnesses, including current and former TEPCO officials as well as earthquake and tsunami experts, were questioned.

While no one is officially recorded as having died as a direct result of the meltdowns, the former executives were indicted for negligence that allegedly resulted in the deaths of 44 people, including patients who were forced to evacuate from a nearby hospital, as well as injuries suffered by 13 people as a result of the hydrogen explosions.

In concluding the two-year trial, the Tokyo District Court ruled that it was not realistic for the former executives to have predicted all possible tsunami scenarios. The defendants, who were the only people facing prosecution in relation to the nuclear disaster, had all pleaded not guilty to charges of professional negligence resulting in death, arguing that the data available to them before the disaster was unreliable, that the tsunami was unforeseeable and that the meltdowns would have occurred even if they had implemented preventive measures. Prosecutors had sought five-year prison terms for them.

The Fukushima nuclear accident, and what TEPCO knew

In planning the design of the Fukushima plant in 1967, TEPCO decided to reduce the natural 35-metre cliff at the site to just ten metres in height. The 15.5 metre high tsunami generated by the earthquake overtopped the plant’s 5.7-meter tsunami seawall (Figure 2), flooding the basements of the power plant’s turbine buildings and disabling both the main power supply and the emergency diesel generators used for cooling the reactor cores to avoid meltdown. Installation of the emergency diesel generators just ten metres higher may have prevented the meltdowns from occurring.

Figure 2. The height of the tsunami that inundated the power station buildings.

The prosecution claimed that the TEPCO top executives should be held responsible because they could have predicted tsunamis of the height that inundated the Fukushima plant. They claimed that the executives were present at meetings where experts warned of massive tsunamis that could inundate the Fukushima coast. The findings were reported to TEPCO executives, according to a written statement from former TEPCO executive Kazuhiko Yamashita, who said the three executives had approved plans to carry out tsunami safety measures in March 2008. However, in July the same year, according to Yamashita, the trio shelved the plans, saying it would be difficult to convince the government and local residents of the power plant’s safety and that the move could prompt calls for halting operations, implying that the executives had recognized the necessity for such measures.

Figure 3. Stone marker indicating historical tsunami inundation limits, with road descending the slope to a narrow coastal plain. One such marker dates back to the 869 Jogan tsunami.

What was known of the hazard?

The Japanese government’s Headquarters for Earthquake Research Promotion (HERP) released its long-term evaluation in 2002 predicting that a very large tsunami could occur off Tohoku including the area off Fukushima. It was known that a very large tsunami-generating earthquake, the Jogan earthquake, had occurred in the Tohoku region on 9 July 869, about one thousand years earlier. The extent of flooding of the Sendai plain caused by the Jogan tsunami, which had been mapped using dated deposits of sand, extended at least 4 kilometres inland. Its inundated areas closely matched those of the 2011 Tohoku tsunami in Sendai, suggesting that it may have also had a magnitude of 9.0 (Minoura et al., 2001). The Tohoku coast is dotted with markers like the one shown in Figure 2 indicating inundation limits in past earthquakes and warning people not to build at lower levels, an admonition difficult for fishermen to heed.

Dr Kunuhiko Shimazaki, who was a member of HERP’s earthquake research panel in 2002 (and my host when I was a Visiting Research Fellow at Tokyo University’s Earthquake Research Institute in earlier years), told the court that the Cabinet Office pressured the panel shortly before the announcement of the HERP long-term evaluation to state that the assessment was unreliable. The headquarters reported in its introduction to the HERP long-term evaluation that there were problems with the assessment’s reliability and accuracy. In his testimony, Shimazaki pointed out that the Central Disaster Prevention Council’s decision not to adopt the long-term evaluation led to inappropriate tsunami countermeasures, and he stated that many lives would have been saved if the countermeasures based on the HERP long-term evaluation had been in place (Mainichi Newspaper, 2018a).

Failure of regulatory authority

A former safety screening division official of the Ministry of Economy, Trade and Industry’s Nuclear and Industrial Safety Agency (NISA) reported that TEPCO did not accept the agency’s request to assess the tsunami hazard after the release of the HERP report in 2002 (Mainichi Newspaper, 2018b). The official held a hearing on TEPCO the following month as to whether the report would affect safety measures at the Fukushima No. 1 plant. NISA told the utility to calculate a possible earthquake-tsunami disaster off the coast from Fukushima to Ibaraki prefectures. In response, TEPCO representatives showed reluctance, saying that the calculation would “take time and cost money” and that there was no reliable scientific basis in the assessment report. In the end, the agency accepted the utility’s decision to shelve the earthquake-tsunami estimate. In 2006, NISA again requested TEPCO to prepare its nuclear plants for massive tsunamis exceeding envisioned levels, but the company did not comply until finally conducting a calculation in 2008.

Tsunami hazard analysis ignored

Annaka et al. (2007) and Thio et al. (2007) were the first to develop probabilistic methods for tsunami hazard analysis. Dr Annaka worked at Tokyo Electric Power Services Co. (TEPSCO), a subsidiary of TEPCO, and I saw his presentation at a conference in Japan (JNES, 2010) in which he estimated that the return period of a 5.7 metre high tsunami at Fukushima was as little as a few hundred years. In 2007 and 2008, TEPSCO estimated that tsunamis up to 15.7 meters high could inundate the nuclear plant based on the HERP analysis. The TEPSCO witness told the court that he briefed TEPCO headquarters of the outcome of TEPSCO’s estimate of possible tsunami heights in March 2008. An employee at TEPCO headquarters subsequently asked the witness whether the estimated scale of possible tsunami could be lowered by changing the calculation method. He found that it could not, and eventually his prediction was not accepted as TEPCO’s estimate of the height of a possible tsunami (Mainichi Newspaper, 2018c).

The prosecution stated that, although TEPCO headquarters initially considered measures to protect the Fukushima No. 1 nuclear complex from tsunami after being briefed of the outcome of TEPSCO’s tsunami estimate, those who were on the company’s board at the time postponed drawing up tsunami countermeasures, instead commissioning the Japan Society of Civil Engineers to look into the matter. Consequently, TEPCO failed to reflect the 15.7 metre prediction in its tsunami countermeasures at the power station. The Prime Minister’s Cabinet Office’s Central Disaster Prevention Council also did not adopt the long-term evaluation in developing its disaster prevention plan.

Reconciling acquittal with the conclusions of the Nuclear Accident Independent Investigation

At first it seems difficult to reconcile the acquittal with the Message from the Chairman of the Nuclear Accident Independent Investigation Commission (National Diet of Japan, 2012):

“The .. accident at the Fukushima Daiichi Nuclear Power Plant cannot be regarded as a natural disaster. It was a profoundly manmade disaster – that could and should have been foreseen and prevented. And its effects could have been mitigated by a more effective human response….What must be admitted – very painfully – is that this was a disaster “Made in Japan.” Its fundamental causes are to be found in the ingrained conventions of Japanese culture: our reflexive obedience; our reluctance to question authority; our devotion to ‘sticking with the program’; our groupism; and our insularity. [The nuclear power industry’s] regulation was entrusted to the same government bureaucracy responsible for its promotion. This… was reinforced by the collective mindset of Japanese bureaucracy, by which the first duty of any individual bureaucrat is to defend the interests of his organization. Carried to an extreme, this led bureaucrats to put organizational interests ahead of their paramount duty to protect public safety.’

Perhaps his statements that “This report singles out numerous individuals and organizations for harsh criticism, but the goal is not—and should not be—to lay blame,” and “Had other Japanese been in the shoes of those who bear responsibility for this accident, the result may well have been the same” may have contributed to the acquittal.

References

Annaka, T., Satake, K., Sakakiyama, T., Yanagisawa, K., and Shuto, N. (2007). Logic-tree approach for probabilistic tsunami hazard analysis and its applications to the Japanese coasts. Pure Appl. Geophys. 164, 577–592. doi: 10.1007/s00024-006-0174-3.

Japan Nuclear Energy Safety Organisation (JNES, 2010). First Kashiwazaki International Symposium on Seismic Safety of Nuclear Installations. Kashiwazaki, Japan, November 24-26, 2010.

Mainichi Newspaper (2018a). (10 May 2018). Seismologist testifies Fukushima nuclear disaster preventable. https://mainichi.jp/english/articles/20180510/p2a/00m/0na/017000c

Mainichi Newspaper (2018b). (30 January 2018). TEPCO refused in 2002 to calculate possible tsunami hitting Fukushima: ex-gov’t official. https://mainichi.jp/english/articles/20180130/p2a/00m/0na/017000c

Mainichi Newspaper (2018c). (1 March 2018). TEPCO asked subsidiary to underestimate tsunami threat at Fukushima nuke plant: worker. https://mainichi.jp/english/articles/20180301/p2a/00m/0na/003000c

Minoura K, Imamura F, Sugawara D, Kono Y, and Iwashita T (2001) The 869 Jogan tsunami deposit and recurrence interval of large-scale tsunami on the Pacific coast of northeast Japan. Journal of Natural Disaster Science 23: 83”88. Available at: http://jsnds.org/contents/jnds/list.html.

National Diet of Japan (2012). The official report of The Fukushima Nuclear Accident Independent Investigation Commission.

Risk Frontiers (2011). The Mw 9.0 Tohoku, Japan Earthquake of 11 March 2011. Briefing Note 217, March 2011.

Thio, H. K., Somerville, P. G., and Ichinose, G. (2007). Probabilistic analysis of strong ground motion and tsunami hazards in Southeast Asia. Journal of Earthquake And Tsunami, 01,119.https://doi.org/10.1142/S1793431107000080

Willacy, Mark (2013). Fukushima. Macmillan Australia, July 1, 2013.

Risk Frontiers turns 25!

by Russell Blong

Last year Risk Frontiers turned 25 demonstrating the success of what may be Australia’s longest-running insurance industry research collaboration. In this, our 73rd newsletter, Professor Russell Blong, the founder of Risk Frontiers, shares his memories of the early years.

An image in the Natural Hazards Observer (University of Colorado) announcing the start of the NHRC.

In 1988 I was awarded an Australian Research Council Grant to investigate natural hazards in Australia. I thought we could leverage insurance industry involvement to expand the scope of the project, but it soon became clear that the industry really wasn’t interested. I chatted to Gerhard Berz, global head of Geohazards at Munich Re, who agreed the industry should be engaged and urged me to persist.

I decided to spend the first half of 1989 at Munich Re in Sydney. A few days before I was due to start the Newcastle Earthquake (26th December 1989) occurred (see article by Paul Somerville in this issue) making natural hazards research in Australia much more interesting for the insurance industry.

The ARCG team at Macquarie University – Kylie Andrews, Clare Byrnes, De Radford and Lucinda Coates – had already begun work on a natural perils database (now PerilAUS) spanning the period since 1900, when a severe hailstorm on 18th March 1990 caused damage in 130 postcodes in Sydney. Overnight, the Australian insurance industry, which at the time really had no hazards research capacity at all, became interested in academic research. We examined hundreds of residential claims from the 1990 hailstorm and prepared a database of Sydney hailstorms back to 1788 – analyses that years later would become important components of HailAUS.

We also built a scenario-based Sydney residential property earthquake model in a spreadsheet for NRMA. While crude by today’s standards, this was the first Australian earthquake loss model that wasn’t based entirely on hand waving and selective heuristics (i.e., guessing).

In early 1992 I came across a copy of a letter written to all Australian universities by Ray Carless and Rob de Souza from Greig Fester, reinsurance brokers, seeking expressions of interest in developing an earthquake loss model for Australian capital cities. Although the deadline for responses had long passed, I rang up and we eventually produced earthquake loss models for insured residential property in Sydney and Melbourne. This study was published by Greig Fester, with most of the modelling work, including the switch from spreadsheet to Fortran, undertaken by Laraine Hunter.

The NHRC moved to a demountable classroom on the edge of the Macquarie campus (a site now occupied by Macquarie Hospital). We did some research work for our insurance supporters on a range of topics, for Geoscience Australia on landslide and tsunami databases, for Department of Foreign Affairs and Trade on natural disasters in the Solomon Islands, on crop hail damage, on lightning fatalities, the volcanic eruption in Rabaul in 1994, integrated natural peril hazard assessments in Vanuatu and Fiji, and an evaluation of the Australian International Decade for Natural Disaster Reduction program.

While we had been confident that the NHRC would be self-funding by the time the three years were up, all too soon we were talking to our industry partners about another three years, and then another … Meanwhile our insurance industry engagement grew and with it the number of industry sponsors, eventually reaching 10 by 2001 – QBE, Benfield Greig, Swiss Re, Guy Carpenter, NRMA, Aon Re, Employers Re, CGU Insurance, Gerling Global Group, and Royal Sun Alliance – a superb cross-section of the Australian and global insurance industry.

In the early years it would be fair to say the university administration was cautious and could have been more supportive of the NHRC and there was some internal opposition to it being a separate research centre. This, no doubt, stemmed from the uniqueness of the NHRC business model within a university, with perhaps few, if any, similar models in place around the world at the time. We were helped through all of this by Peter Curson, then Head of the School of Earth Sciences. Eventually we were able to show the university administration that NHRC was a profit centre rather than a cost centre, as we were bringing in substantial funds external to the university system in addition to engaging industry.

We also moved out of the ‘tin shed’ and back to the main Earth Sciences building at the University. Luckily this failed to put a dent in the number of lunches and other special occasions we celebrated.

In 1996 NHRC commenced work on flood vulnerability. This work took more years than we intended and even more years to reach valuable agreements with the industry. In the meantime, we commenced a range of flood-related research and evaluation for the NSW Department of Land and Water Conservation and undertook a major effort to understand flood damage to residential and other buildings – all of this at a time when flood insurance in Australia was rare. At the same time, we began the development of HailAUS with additional financial support from Benfield Greig and Hannover Re.

Graduate students including Heather McMaster, Stephen Yeo, Keping Chen, Christina Magill, Sandra Schuster, Andrew Gissing and Ben Miliauskas completed theses on hail damage to crops, flooding in Fiji, geospatial approaches to natural hazards and risk assessment, volcanic risk in Auckland, hail identification and losses in Sydney, commercial flood damage and micro tremors in Newcastle.

In 2001, at the suggestion of Ian Watson, a colleague who had been on the staff of Physical Geography at Macquarie University, the Natural Hazards Research Centre changed its name to Risk Frontiers and a new era began. The insurance industry had changed from an almost entirely analyst-free zone in the early 1990s to workplaces where researchers, analysts and actuaries were thinking about catastrophic events and insurance implications. John McAneney joined Risk Frontiers as Deputy Director and in 2003, shortly after Risk Frontiers-NHRC’s ninth birthday. Russell Blong, worn down by endless strategising and long lunches, called it a day and John took over as Director.

Now, 25 years on, Risk Frontiers is Australia’s leading catastrophe loss modelling and research company, demonstrating the success and impact of industry collaboration. We have swapped spreadsheets for Machine Learning and now provide services globally to a diverse range of clients.

To be continued.

Happy birthday everyone and all the best in the new year!

Farewell to the ‘tin shed’ – from left Russell Blong, Laraine Hunter, De Radford, Carol Robertson, Frank Siciliano, Roy Leigh, Ivan Kuhnel, Stephen Yeo, and Keping Chen. Lucinda Coates – the one person to have survived the whole 25 years of NHRC-Risk Frontiers (and even she failed to turn up for some years) – took the photo in 1999. Sadly, both Laraine and Roy have left this earth – visit https://riskfrontiers.com/people/tributes/ to learn just how important they were to the early years of NHRC-Risk Frontiers and how much we valued them.