Why the Saffir-Simpson Hurricane Intensity Scale had five levels we don’t know. The digits on a hand? Better than three, but lower resolution than the dozen rungs for wind speeds or earthquake intensity? Whatever the reason it seems to work.
In the late 1960s, Herbert Saffir, a Florida building engineer, was sent by the United Nations to study the hurricane vulnerability of low-cost housing in the Caribbean. He realized something was needed to rank hurricane destructiveness. Saffir had some “Richter envy” from observing the ease with which seismologists now communicated with the public. In 1971, he contacted Robert Simpson, head of the National Hurricane Center to help link damage levels with wind speeds.
Seeing the opportunity to communicate evacuation warnings, Simpson also added details around the height of advancing storm surges. Better information was clearly needed, after the loss of life in Hurricane Camille on the Mississippi coast in 1969.
Professor Ilan Noy holds a unique ”Chair in the Economics of Disasters” at the Victoria University of Wellington, New Zealand. He has proposed in a couple of research papers that instead of counting disaster deaths and economic costs, we should report the “expected life-years” lost, not only for human casualties but also for the life-years of work that will be required to repair all the damage to buildings and infrastructure.
The idea is based on the World Health Organization’s Disability Adjusted Life Years (DALYs) lost through disease and injury (WHO 2013). The motivation is to escape from the distortion introduced by measuring the impact of global disasters in dollars, as loss from the richest countries will always dominate this metric. Noy’s proposal converts injuries into life-years lost, based on how long it takes for the injured to return to complete health, while also factoring the degree of permanent disability multiplied by its duration. This is topped up by a “welfare reduction weight” for all those exposed to a disaster. The final component of the index attempts to capture how many years of human endeavor is lost to recovering the buildings and assets destroyed in the disaster.
There is plenty to argue over in terms of how deaths, injury and damage should be combined. In particular, the assumption that additional work to rebuild a city, is the same as a shortened life, seems somewhat reductive.
Across the global risk management community, we are bombarded by new information every day. As risk professionals we have to prioritize how we give our attention to new information. From an RMS perspective, when we release new model insights, we know there is a need to be concise and boil down huge research projects into just the important details. But there is a concern that the top-level results get taken as a uniform value that can be applied across the board, losing vital nuance.
When RMS released its New Zealand Earthquake High-Definition (HD) model in mid-2016, an important message was that the annual average loss (AAL) had increased by 30 percent. The ground-up, all-lines, countrywide AAL increased 30 percent relative to the previous version of the model released in 2007. An increase in loss came as no surprise after the Canterbury Earthquake Sequence of 2010/11 – see our New Zealand earthquake blogs.
The HD model was launched at two industry seminars in Wellington and Auckland and came with online documentation: some 44 pages of Understanding Changes in Results and 114 pages of model methodology, supplementary materials on our RMS OWL client portal and a team of modelers happy to talk about their work.
Faced with this information, one approach is to note that the New Zealand market is very consolidated so industry figures should be useful guides for actual portfolios. Let’s just use the old model and scale it by 30 percent. “She’ll be right”, as they like to say in New Zealand. But with two models being so different, this scaling-up would not make sense. Why are they so different?
Around 98 percent of residential homes in New Zealand have earthquake insurance. This remarkable achievement is due to a unique partnership between the New Zealand government Earthquake Commission (EQC) working together with the insurance industry. From its origins in 1945 as the Earthquake and War Damage Commission – renamed as the EQC in 1993, the Commission is supported by an Act of Parliament which sees the Crown as the insurer of first resort for earthquakes in New Zealand. The EQC provides the first layer of coverage for 1.84 million residential properties across the country, with the private market delivering cover over this initial layer.
The EQC administers the New Zealand Natural Disaster Fund (NDF) which receives monies directly passed on by private insurers, from a flat rate levy imposed on all households who purchase a homeowner insurance policy. The EQC is also responsible for investing the fund and ensuring there is adequate reinsurance cover available.
The NDF has supported the country’s homeowners through a series of damaging events since the start of this decade, providing NZ$100,000 (US$67,332) of buildings and NZ$20,000 (US$13,466) of contents cover for each event. Before the Canterbury earthquakes in 2011-12, the NDF had NZ$6.4 billion (US$4.27 billion). By 2018, including payments for the Kaikoura earthquake in 2016, the NDF had just NZ$287 million (US$195 million) left and was perilously close to the NZ$200 million limit where the government is mandated to top up the fund.
It is now exactly a quarter of a century, on January 17, 1994, since the last significant U.S. earthquake disaster. A previously unknown blind thrust ruptured beneath Northridge, in the San Fernando Valley north of Los Angeles. Casualties were fortunately modest (57 deaths) because the Mw6.7 shock happened at 4.30 a.m. local time, but the damage was significant – estimated as at least US$30 billion in 1994 prices, as the fault lay directly underneath the city.
Sooner or later California will experience another Mw6.7-7.5 earthquake disaster, in the highly populated San Francisco Bay Area or under sprawling greater Los Angeles. Year-on-year, while the probability rises, the proportion of the affected population with any previous disaster experience dwindles. When it happens, in all senses of the word – it will be a great shock.
One prediction is inevitable: after the next big Bay Area or LA earthquake, there will be large numbers of uninsured homeowners, landlords and small business owners looking for compensation. Given the high deductible and low take-up rates for earthquake insurance, as much as 90 percent of the residential losses will not be covered by insurance payouts: a far higher percentage than in 1994.
And the question is then, will the Federal Government response match that which followed Hurricane Maria, or can we expect it to be more like the aftermath of Hurricane Katrina. Or to put it another way: will California be “Puerto Rico” or “New Orleans”?
Indonesia was beset by disasters in 2018, including two high casualty local tsunamis: in coastal western Sulawesi – impacting the city of Palu, on September 28, and around the Sunda Strait, between Java and Sumatra, on December 22. These events may have appeared unusual, but the great subduction zone tsunamis, like those in the Indian Ocean in 2004 and Japan in 2011, have reset our imagination. Before 2004, forty years had passed without any transoceanic tsunamis. Overall, local tsunamis are more common, presenting many challenges in how they can be anticipated.
The Palu tsunami reminds us how “strike-slip” faults, involving only horizontal displacement can still generate tsunamis, first as a result of vertical displacement at “jogs”, where the fault rupture jumps alignment, as well as from triggered submarine landslides. It seems both factors were important in driving the Sulawesi tsunami that became amplified to more than four meters (13 feet) in the funnel-shaped Palu embayment.
The December 22 Sunda Strait tsunami was caused by a submarine landslide on the erupting Anak Krakatoa volcano and arrived without warning, in the dark of mid-evening. More than 400 people drowned mainly around a series of beach resorts in Banten and Lampung provinces, although water levels in the tsunami only reached a meter or two above sea level. An audience of 200 enjoying a concert at the Tanjung Lesung Beach Resort, staged directly on the beach by Indonesian rock band Seventeen were caught unaware. 29 concertgoers were killed together with four people associated with the band.
I am in Wellington, New Zealand, looking out from a rainy hotel window high over the city, admiring the older wooden houses on the forested slopes. Below there are four to eight story office and retail buildings, a number of which are shrouded in scaffolding, still repairing damage from the 2016 Kaikoura earthquake. The earthquake epicenter was some distance from the city, but the pattern of fault ruptures propelled long period ground shaking into the heart of Wellington.
In 1848, only eight years after the city was founded, a Mw7.5 earthquake on the far side of Cook Strait, shattered the town’s brick buildings. The Lieutenant Governor, Edward Eyre, forgetting his official role as colonial booster, declared the “… town of Wellington is in ruins … Terror and despair reign everywhere. Ships now in port … (are) crowded to excess with colonists abandoning the country.” However, the tremors declined, and the town survived.
Many ordinary houses were rebuilt using wood instead of brick. As a result, they suffered far less damage from a larger and closer Mw8.2 earthquake in 1855, that struck at the end of a two-day public holiday to celebrate the fifteenth anniversary of the city’s formation. This ruined all the remaining brick and stone commercial buildings including churches, barracks, the jail, and the colonial hospital. However, the earthquake delivered a tectonic bounty, raising the city by one to two meters (3.2 to 6.5 feet), turning the harbor into new land for development.
Many of us across the risk management industry actively help communities in need after natural disasters, through donations, working with organizations to promote resilience, or through on-the-ground assistance. Our intimate understanding of the power of these catastrophes makes us acutely aware of the need to act.
This is true for everyone here at RMS, where our values embrace the need to understand risk, build resiliency, and make an impact to help improve the lives of communities who live with the threat of natural disasters. One of the ways we live our values is through our annual RMS Impact Trek, where both RMS employees and our clients work with the social enterprise Build Change in some of the world’s most catastrophe-prone areas.
If you are an RMS client, I would like to extend an invitation to our annual RMS Impact Trek. This is the fourth year that we are sponsoring representatives from our clients to join RMS employees and Build Change so that their skills can be used to build stronger communities.
When I was still a teenager – summer brave, full of sport, hot and bold – I hitchhiked from Lithuania to Armenia and back again. Outbound via the former Soviet Union and the Caucasus; home via Turkey and the Balkans.
Time rich and cash poor, I took risks I wouldn’t today. All the same, my gambles paid off and I look back on that adventure fondly.
The journey was filled with comparisons and contrasts. Some things, like being invited in basic Russian to squeeze into a crammed Lada Riva, remained almost constant from country to country. Others, like the landscapes and local delicacies, evolved with every new ride.
When I found myself back in Istanbul last month for the first time since my hitchhiking days, I was again struck by these contrasts. Here I was, a guest of the United Nations, discussing disaster risk reduction financing with the finance ministers of those countries through which I’d once hitchhiked. And here I was, marveling afresh at the cultural, political, economic and geographical diversity of a vast region which yet shares so much.
Catastrophe modeling remains work in progress. With each upgrade we aim to build a better model, employing expanded data sets for hazard calibration, longer simulation runs, more detailed exposure data, and higher resolution digital terrain models (DTMs).
Yet the principal way that the catastrophe model “learns” still comes from the experience of actual disasters. What elements, or impacts, were previously not fully appreciated? What loss pattern is new? How do actual claims relate to the severity of the hazard, or change with time through shifts in the claiming process?
After a particularly catastrophic season we give presentations around ”the lessons from last year’s catastrophes.” We should make it a practice, a few years later, to recount how those lessons became implemented in the models.