Author Archives: Robert Muir-Wood

About Robert Muir-Wood

Chief Research Officer, RMS
Robert Muir-Wood works to enhance approaches to natural catastrophe modeling, identify models for new areas of risk, and explore expanded applications for catastrophe modeling. Recently, he has been focusing on identifying the potential locations and consequences of magnitude 9 earthquakes worldwide. In 2012, as part of Mexico's presidency of the G20, he helped promote government usage of catastrophe models for managing national disaster risks. Robert has more than 20 years of experience developing probabilistic catastrophe models. He was lead author for the 2007 IPCC 4th Assessment Report and 2011 IPCC Special Report on Extremes, is a member of the Climate Risk and Insurance Working Group for the Geneva Association, and is vice-chair of the OECD panel on the Financial Consequences of Large Scale Catastrophes. He is the author of six books, as well as numerous papers and articles in scientific and industry publications. He holds a degree in natural sciences and a PhD in Earth sciences, both from Cambridge University.

The Lessons From “Last Year’s” Catastrophes

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.

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How to Maintain Awareness of Tsunami Risk

Today is World Tsunami Awareness Day — designated by the United Nations General Assembly, and according to the United Nations Office for Disaster Risk Reduction (UNISDR), on average, tsunami events have a higher mortality rate than any other hazard. Over the past 20 years (1998-2017) tsunamis have claimed more than 250,000 lives and are also attributable for US$280 billion of the US$661 billion of total recorded economic losses for earthquakes and tsunamis. Between 1978-1997, tsunamis claimed 998 lives, and US$2.7 billion in losses. Overall, tsunamis are rare, but as the UN points out, when they occur they are deadly and hugely damaging. This infrequency makes building awareness and preparedness more of a challenge.

The UN has promoted World Tsunami Awareness Day since 2015, and the UN Secretary-General’s Special Representative for Disaster Risk Reduction, Mami Mizutori, stated that “…it is an occasion to promote greater understanding of tsunami risk to avoid future loss of life. This year we also want to bring attention to the economic losses tsunamis can inflict as a result of damage to critical infrastructure located along vulnerable, densely populated coastlines.”

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Ultra-liquefaction Changes Everything

It turns out the biggest killer in the Palu earthquake on the island of Sulawesi, Indonesia, may not have been the tsunami after all — but liquefaction. Two thousand victims of the earthquake and tsunami are confirmed but 5,000 people remain missing, many of them presumed swallowed up in extraordinary ground deformation and mudflows, which took off when the underlying solid ground liquefied. Some buildings were transported hundreds of meters, others were ripped apart, many collapsed into fragments that then became absorbed into the mud. Media reports state that in Balaroa, just a few kilometers from Palu City, many of the 1,747 houses in the village appear to have sunk into the earth. In Petobo, a village to the east of Palu, many of the village’s 744 houses have disappeared.

What we have witnessed at Palu merits the term “ultra-liquefaction”, as witnessed in the 2011 Christchurch, New Zealand earthquake when perhaps half the total insurance loss costs were a consequence of liquefaction. For Christchurch, in the eastern suburbs it was single storey houses, ripped apart by the ground movements. In the Central Business District (CBD), many mid-rise buildings had to be demolished because underlying liquefaction had led to one corner of the structure sinking by ten or twenty centimeters (four to eight inches).

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Hurricane Michael: Speed Demon

It’s hard to believe that Hurricane Michael, the thirteenth named storm of the 2018 North Atlantic hurricane season, only achieved tropical storm status just two days ago on Sunday, October 7. Tracked by the National Hurricane Center (NHC) since October 2, Michael started out as a broad area of low pressure over the southwestern Caribbean Sea, a couple hundred miles north of Panama.

Becoming more organized as it began to move toward the Yucatán Peninsula, by October 6 it achieved Potential Tropical Cyclone status. Between October 7 and October 8, rapid intensification saw sustained wind speeds jump from 35 miles per hour to 75 miles per hour (120 kilometers per hour) by midday local time on October 8. Skirting between the eastern tip of the Yucatán Peninsula, and the western tip of Cuba, Michael entered the Gulf of Mexico late evening local time on Monday, October 8.

As of 09:00 UTC today (Tuesday 9), the latest NHC advisory located Michael at about 420 miles (680 kilometers) south of Panama City, Florida and about 390 miles (630 kilometers) south of Apalachicola, Florida, with sustained winds at 90 miles per hour (150 kilometers per hour), placing it as a Category 1 hurricane on the Saffir-Simpson Hurricane Wind Scale (SSHWS). Michael is moving toward the north-northwest at close to 12 miles per hour (19 kilometers per hour). Hurricane-force winds extended outward up to 40 miles (65 kilometers) from the center and tropical-storm-force winds extended outward up to 195 miles (315 kilometers).

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The Tragedy at Palu

A version of this article was originally published in Insurance Day

The Mw7.5 earthquake in Sulawesi, Indonesia on September 28 reminds us that fourteen years after the terrible Indian Ocean tsunami, and despite significant investment in systems intended to provide tsunami warnings, the risk to life and property is not going away. To understand why the destruction and loss of life in the city of Palu, with a population of 350,000, is so great (1,300 and rising) we need to understand why this location has proved such a nexus of vulnerabilities.

First, Palu is located less than one degree south of the equator. That means it is in the “shadow zone” for tropical cyclones. In most of the world’s oceans, no tropical cyclone can exist within ten degrees of the equator, although in the western Pacific the typhoon exclusion zone can narrow down to six to eight degrees from the equator. The lack of Coriolis force at the equator prevents a collection of thunderstorms gaining a structured rotation (and tropical cyclones spin in opposite directions in the northern and southern hemispheres).

The lack of tropical cyclones means there are no significant storm surges, or even much in the way of significant wind-driven waves, and as a result people build their houses right down to sea level. This means, in comparison even with a coastal city in Philippines or China, there were many more seafront buildings exposed to a tsunami that reached no more than three to five meters above sea level.

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The Three “Protection Gaps” (And the Role of Protection Gap Analytics)

The rallying cry has sounded — to “close the protection gap”, the difference between what is paid out by insurance and the total cost of some incident or disaster. Here is an issue that can unite and promote the insurance industry, extending benefits to those in peril by expanding the insurance sector. Having ex-post access to funding after a loss, we know, can bring important benefits.

Yet in reality, there is not just one, but three distinct insurance “protection gaps”, each with separate causes and each requiring different remedies. These protection gaps are so different to one another that we should stop treating them as a single category. Lumping them together can cause confusion.

In this series of four blogs, I will explore each of these three distinct gaps, together with the role of protection gap analytics, and the actions we can plan to address these protection gaps.

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The Emerging Markets Protection Gap

This is the second blog in a series of four blogs examining three potential “protection gaps” and the importance of “protection gap analytics”. To read the first blog post in this series, click here.

Year-by-year, we can check to see if the gap between insured and economic disaster losses in emerging economies is starting to shrink. The gap remains resolutely stuck in the range 80 to 100 percent uninsured. Even a 90 percent average flatters the proportion, as coverage is concentrated in high value hotels, factories and central business districts whereas almost all ordinary houses are without insurance.

We should not be surprised how the emerging markets gap stays so wide.

See what happened in Japan. Unregulated mass rebuilding after the war led to a rising toll of flood disasters. In one single year in the 1950s, more than a million properties were flooded. Then in 1959 there was Typhoon Vera and the Ise Bay storm surge flood catastrophe in which more than 5,000 died. In 1960 the Government declared the level of risk to be intolerable and directed that seven to eight percent of government expenditure should be invested in funding disaster risk reduction. The annual investment proved successful and by the 1980s the annual number of houses flooded had reduced to only three percent of its 1950s level.

For any emerging economy the question can be asked: when did the nation reach the equivalent of Japan in 1960 and start to invest in disaster risk reduction. China passed the point of “intolerable disaster risk” towards the end of the 1990s, while India is undergoing that transition today. This is not just investment in physical disaster risk reduction, but also good risk governance and education.

Insurance is a product of this disaster risk management culture.

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The Intangibles Protection Gap

This is the third blog in a series of four blogs examining three potential “protection gaps” and the importance of “protection gap analytics”. To read the first blog post in this series, click here.

In 1975, 83 percent of the value of the S&P 500 companies was invested in physical assets: factories, refineries, ships and offices. By 2015 that percentage had fallen to 16 percent, leaving 84 percent of the assets as intangible. Intangibles included intellectual property, data on clients, brand value and innovation potential. This massive shift has had huge significance for insurance.

The insurance product was designed to cover tangible risks: first ships and their cargoes, then houses, factories, cars and airplanes. Each item could be independently valued. A claims assessor could be sent out to inspect the damage and measure the costs of repair and replacement.

Now, much of business value is intangible. The “Intangibles Protection Gap” includes all those situations where insurance fails to cover losses suffered by non-physical business assets. How does one assess the value of intangibles — how does one measure loss? Some intellectual property (IP) has been stolen — how much is it worth? You are a cloud service provider hit by a deadly cyberattack which has released some confidential data. What is the value of your lost business, the damage to your reputation and of the penalties levied by the regulator and your customers.

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Protection Gap Analytics: The Role of Risk Modeling

This is the final blog in a series of four blogs examining three potential “protection gaps” and the importance of “protection gap analytics”. To read the first blog post in this series, click here.

We are not going to be able to take effective action to reduce any of these three protection gaps unless we can first learn how to consistently measure the difference between insured and total loss. Such measurement means we can know the current situation as well as set appropriate targets and monitor progress in reducing the gap. It can also help to focus investment and action.

At present, the only form of measurement is to acknowledge the difference between insured loss and the estimated total economic loss once the claims have settled, one or two years after a significant disaster.

In the same way that probabilistic catastrophe risk models were developed to enable insurers and reinsurers to look beyond the latest event loss, so the same models are now required to monitor the protection gap. This is the focus of “protection gap analytics”.

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Schrödinger’s Cat Model

Schrödinger’s cat inhabits a thought-experiment designed to reveal the paradox of quantum properties. A hypothetical cat is sealed in a windowless box, in which there is a device that will administer a lethal poison, according to whether a single atom undergoes radioactive decay. Should the atom decay the cat will be dead. If the atom survives so will the cat. Only the quantum state of the atom is completely unknowable. So, the cat — in principle at least, is half dead and half alive. The simultaneous state of being both alive and dead is called a “superposition”.

While quantum behavior is not an average insurance coverage, (at least until future quantum computer cyber cover emerges), there are situations in the world of risk modeling that come close to Schrödinger’s cat — or perhaps that should better be Schrödinger’s “Cat” (short for Catastrophe)?

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