Tag Archives: Flood

High Tides a Predictor for Storm Surge Risk

On February 21, 2015, locations along the Bristol Channel experienced their highest tides of the first quarter of the 21st century, which were predicted to reach as high as 14.6 m in Avonmouth. When high tides are coupled with stormy weather, the risk of devastating storm surge is at its peak.

Storm surge is an abnormal rise of water above the predicted astronomical tide generated by a storm, and the U.K. is subject to some of the largest tides in the world, which makes its coastlines very prone to storm surge.


A breach at Erith, U.K. after the 1953 North Sea Flood

The sensitivity of storm surge to extreme tides is an important consideration for managing coastal flood risk. While it’s not possible to reliably predict the occurrence or track of windstorms—even a few days before they strike land—it is at least possible to predict years with a higher probability of storm surge well in advance—which can help in risk mitigation operation planning, insurance risk management, and pricing.

Perfect timing is the key to a devastating storm surge. The point at which a storm strikes a coast relative to the time and magnitude of the highest tide will dictate the size of the surge. A strong storm on a neap tide can produce a very large storm surge without producing dangerously high water levels. Conversely, a medium storm on a spring tide may produce a smaller storm surge, but the highest water level can lead to extensive flooding. The configuration of the coastal geometry, topography, bathymetry, and sea defenses can all have a significant impact on the damage caused and the extent of any coastal flooding.

This weekend’s high tides in the U.K. remind us of the prevailing conditions of the catastrophic 1607 Flood, which also occurred in winter. The tides reached an estimated 14.3 m in Avonmouth which, combined with stormy conditions at the time, produced a storm surge that caused the largest loss of life in the U.K. from a sudden onset natural catastrophe. Records estimate between 500 and 2,000 people drowned in villages and isolated farms on low-lying coastlines around the Bristol Channel and Severn Estuary. The return period of such an event is probably over 500 years and potentially longer.

The catastrophic 1953 Flood is another example of a U.K. storm surge event. These floods caused unprecedented property damage along the North Sea coast in the U.K. and claimed more than 2,000 lives along northern European coastlines. This flood occurred close to a Spring tide, but not on an exceptional tide. Water level return periods along the east coast are varied, peaking at just over 200 years in Essex and just less than 100 years in the Thames. So, while the 1953 event is rightfully a benchmark event for the insurance industry, it was not as “extreme” as the 1607 Flood, which coincided with an exceptionally high astronomical tide.

Thankfully, there were no strong storms that struck the west coast of the U.K. this weekend. So, while the high tides may have caused some coastal flooding, they were not catastrophic.

Serial Clustering Activity around the Baja Peninsula during September 2014

In the past two weeks, two major hurricanes have impacted the Baja Peninsula in Mexico. Hurricane Norbert bypassed a large portion of the west coast of the peninsula from September 5 to 7, and Hurricane Odile made landfall near Cabo San Lucas on September 14th as a Category 3 hurricane on the Saffir-Simpson Wind Scale. A third system, Hurricane Polo, formed Tuesday, September 16 and is forecasted to follow a similar track to Norbert and Odile, making it the third such tropical cyclone to develop in the region since the beginning of the month.

This serial cluster of storms has been driven primarily by steady, favorable conditions for tropical cyclone development and consistent atmospheric patterns present over the Eastern Pacific. A serial cluster is defined as a set of storms that form in the same part of a basin, and subsequently follow one another in an unbroken sequence over a relatively short period of time. To qualify as a cluster, there needs to be measurable consistency between the tracks. This is typically a result of steady, predominant atmospheric steering currents, which play a major role in influencing the speed and direction of tropical cyclones. One example of a serial cluster is the four major hurricanes (Charley, Francis, Ivan, and Jeanne) that impacted Florida during a six-week period in 2004.

During this recent two-week period, the area off the west coast of Mexico has maintained high sea-surface temperatures near 85.1 degree Fahrenheit and limited vertical wind shear, leading to an active tropical development region. A mid-level atmospheric ridge over northern Mexico has provided a consistent steering pattern towards the north-northwest, producing similar observed tracks for Norbert and Odile and forecasted track for Polo. Devastating amounts of rainfall have occurred with these storms. Hurricane Odile dropped nearly 18 inches of rain in areas around Cabo San Lucas, representing nearly 21 months-worth of typical rainfall. This cluster, while generating significant wind and flood damage along the Baja Peninsula, has also caused torrential rainfall in the southwestern U.S., including Arizona, southern Nevada, and southern California. Last week, Phoenix, AZ, one of the hardest hit areas, experienced over 3 inches of rain in a 7 hour span due to the remnants of Hurricane Norbert. This was the most rainfall to occur in a 24-hour period in the city since 1911, an estimated 1-in-200 year event by the National Oceanic and Atmospheric Administration. Significant rainfall and inland flooding is forecast to continue as the remnants of Odile and Polo move inland, which may lead to widespread flood losses and the potential for compound post-event loss amplification.

How is the 2014 North West Pacific Typhoon Season Shaping Up?

July’s Typhoon Matmo was the 10th named typhoon of 2014 and the 5th to make landfall in the West Pacific basin. Typhoons can occur throughout the year, but the peak of the season is July through October, when nearly 70 percent of all typhoons develop, so we expect to see more activity in the region in the coming months.

Let’s take a look at recent activity and typhoon risk in China, the Philippines, Japan, and Taiwan.

China

To date, China has been impacted by three landfalling typhoons in 2014, the strongest of which was Rammasun, a Category 4 strength storm, with maximum sustained winds of 135 mph impacting Hainan and Guangdong provinces, and the autonomous region of Guangxi.

The southeastern coastal provinces of Guangdong, Fujian, and Zhejiang are most vulnerable to landfalling typhoons. They also represent some of China‘s most economically developed areas. Typhoon Rammasun impacted Guangdong province in July, bringing damaging wind and heavy rain. Overall in China, typhoon-induced flooding is the biggest driver of risk in high-exposure areas such as Guangdong, driving approximately 80 percent of the average annual losses from typhoon.

Insurance penetration is extremely low in China, varying by province. On average, about 15 percent of residential property risk is insured. Hainan, where Typhoon Rammasun first made landfall, has one of the lowest insurance penetrations in China, while Guangdong, one of the more prosperous provinces, is the second largest province for property insurance purchases with 41.7 billion Yuan ($6.8 billion) in direct premiums in 2012, according to the China Insurance Regulatory Commission.

Philippines

Typhoon activity kicked off early this year in the Philippines with Tropical Storm Kajiki in January. More recently, the second storm to make landfall was Typhoon Rammasun, which hit Legaspi City in the Albay Province, south of the capital Manila, as a Category 3 storm. In a 36-hour period it brought 11.6 inches of rainfall, leading to flash flooding and landslides. The provinces impacted by Rammasun contain over $180 billion of insurable commercial and industrial building exposure, and over $215 billion of residential building exposure.

Like China, the Philippines lags behind some other markets in Asia in relation to insurance expenditure – non-life insurance penetration is 0.09 percent – though with higher proportionally for commercial and industrial businesses, which are centred around Manila and the industrial zones.

Japan

Tropical Storm Neoguri made landfall over the Kumamoto Prefecture on Kyushu Island in southwest Japan as the country’s first landfall this season. Neoguri brought strong winds, heavy rains, flooding, landslides, and mudslides to parts of southwest Japan. On Kyushu, the city of Ebino reported 13 inches of rain in the first 24 hours, and on Okinawa, heavy rainfall triggered flash flooding.

The southwestern parts of the country are the most vulnerable, particularly Shikoku, Kyushu, and San-in. Tokyo is rarely hit by typhoons and much of the coastline is protected from by the tsunami walls designed to protect from a four-meter storm surge.

Japan is the second largest non-life market in gross premium terms behind the U.S., and there is relatively high penetration of personal lines insurance, with over 50 percent of households buying building insurance. However, corporate Japan is massively under-insured compared to its western equivalents. Many large corporations only insure their property on an indemnity basis, while many small to medium-sized enterprises are completely uninsured.

Taiwan

So far this season, Taiwan has only been impacted by Typhoon Matmo, which passed through the center of the country as a Category 2 storm, bringing heavy rain and strong winds.

Storms typically travel towards the northwest from the Philippines, losing speed when they encounter the mountain chain running north-south down the center of Taiwan, and dropping most of their rain on the eastern side, causing rivers to overflow due to the extra water runoff from the mountains.

The most dangerous typhoons are those that approach from the south. The north-south mountain chain funnels them north up the Taiwan Straits so that they hit the western and northwestern parts of the island, including Taipei, where large industrial and commercial exposure is situated, such as the Hsin Chu Industrial Park in the province of Hsinchu which reportedly has a combined property/business interruption accumulation of $33.33 billion. However, insurers have reported few insured losses arising from wind damage alone, as the main damages are a result of flooding. Most of the losses caused by typhoons in Taiwan are agricultural, and thus uninsured. Insurance penetration is very low compared to some other markets in South East Asia in relation to insurance expenditure, with insurance penetration for non-life at 0.08 percent.

Rammasun is One of the Strongest Typhoons to Hit Southeast China in Recent Years

RMS closely monitored typhoon Rammasun last week as it picked up strength en route to the Philippines. The world also watched, remembering the catastrophic damage typhoon Haiyan caused last November. While Rammasun did not wreak as much havoc as Haiyan, it still left a trail of damaged buildings and flooded crop fields in the Phillipines, southeast China and Vietnam. Below, RMS looks at the property damage and insurance industry implications as the typhoon hit both denser commercial metropolitan areas and agricultural provinces.

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RMS reports that on Friday, July 19 Super typhoon Rammasun, one of the strongest to hit southeast China in recent years, made three landfalls in the provinces of Hainan, Guangdong, and Guangxi.

Rammasun has significantly impacted the Philippines, southeast China and Vietnam. Rammasun brought strong winds, heavy rain, and storm surge to some coastal areas with close to 300,000 buildings damaged in the affected countries.

In China, damaging wind and floods have destroyed at least 37,000 homes and ravaged 468,500 hectares of crops in Hainan, Guangdong and Guangxi provinces. Virtually all brick-and-tile houses in the town of Wengtian, Hainan were either destroyed or had their roofs removed. Within a 24 hour period up to 15 inches of rain fell in the city of Haikou; the week before Rammasun hit, the southeastern provinces were reportedly experiencing heavy floods, which have only been exacerbated by the typhoon.

“Typhoon-related flood, which includes both rainfall driven and coastal flooding, contributes as much as 80% to typhoon average annual loss in China, with the coastal provinces driving the loss,” said Nikki Chambers, hazards scientist at RMS. “July to October are the most active months for typhoons in this region. On average 6 typhoons make landfall a year in China and typhoon Rammasun highlights the importance of accounting for all sources of typhoon losses, of which flood is the main driver.” Insurance penetration is extremely low in China particularly for residential risk, slightly higher for commercial and industrial lines of business. On average, about 15% of property risk in China is insured. Insurance penetration varies by province; Hainan has one of the lowest insurance penetrations in China. Guangdong is one of the more prosperous provinces; it is the second largest province for property insurance purchases, with 41.7 billion yuan (US$6.8 billion) in direct premiums for property insurance in 2012, according to the China Insurance Regulatory Commission.

Philippines

The typhoon wreaked havoc earlier in the week in the northern Philippines, which is still rebuilding after Typhoon Haiyan. Rammasun made its first landfall in the largely agricultural provinces south of the capitol Manila, leaving 94 people dead, and over 111,000 houses damaged, of which nearly 28,000 have been totally destroyed and 83,000 have been partially damaged., Based on analysis from the RMS Philippines Economic Exposure Database, the impacted provinces in the Philippines from Rammasun contains over 100 bn USD of insurable commercial building exposure, 80 bn USD of industrial building insurable exposure, and over 215bn USD of residential building exposure. Based on the RMS Philippines industrial cluster catalog, industry is clustered around metro Manila and in areas to the north and south of the capital in Central Luzon, which are located within the affected area of Rammasun. The insurance penetration rates in the Philippines is relatively low, though higher for commercial and industrial lines of business and will be centred around Manila and the industrial zones.

Vietnam

In northern Vietnam, Typhoon Rammasun made landfall Saturday morning, causing heavy flooding. At least eight people have died and it has affected more than 6,000 homes. The typhoon has damaged 3,300 hectares of rice and other crops and disrupted traffic in the region. Typhoon, Matmo, with maximum winds of 150km/h, is now threatening the area ravaged by Rammasun. RMS is monitoring the situation closely.

Understanding the Potential Impact of the Next Catastrophic European Flood

Over the past year, Europe has intermittently but consistently suffered from significant flooding.

Most recently, the Balkans experienced widespread devastation in May due to some of the region’s heaviest precipitation on record. Three months worth of rain fell in just three days. The subsequent flooding was so severe that entire towns were submerged. While it is too soon to estimate the full impact, the economic and humanitarian costs will be high.

This event follows one of the stormiest and wettest winters on record for the U.K. Remote locations bore the worst of it, and for now, the U.K. government and insurance industry appear to have largely escaped a sizeable bill, at least on the scale of previous flood events.

The events come just one year after the costliest natural catastrophe of 2013 for the insurance industry, when flooding inundated Central and Eastern Europe in late May and early June. The event caused around $20 billion (€12 billion) in economic losses, of which it is estimated that approximately 20 percent was insured.

As with the more recent Balkans and U.K. events, the May 2013 flooding followed a period of extreme rainfall; consequently, groundwater and soil moisture levels were saturated. As more rain fell in late May and early June, the precipitation had nowhere to go except to flow through catchments into the river network as runoff. The Danube, Elbe, and other rivers overflowed, resulting in significant flooding across Germany and the Czech Republic, and, to lesser extents, Austria, Switzerland, Poland, Slovakia, Hungary, Croatia, and Serbia.

Each of these events highlighted the importance of understanding the impact of precipitation, whether from a short, intense period of rainfall, prolonged wet conditions, or a combination of these characteristics. In each case, to evaluate flood risk, it is vital to understand how antecedent wetness conditions influences subsequent flooding.

In 2002, Central Europe was similarly inundated by severe flooding, producing economic losses of over $28 billion (€17 billion). Both events were triggered by similar meteorological phenomena, Genoa type-lows. However, the antecedent conditions in 2002 were comparatively dry compared to those in 2013, and the precipitation that triggered the eventual flooding was more severe in 2002 compared to 2013.

Both events had significant impacts, but what would happen if we combined the worst features of both to create a “perfect storm” type of flood event?

Combining the antecedent wetness of spring 2013 with the extreme precipitation of the August 2002 event, RMS researchers estimated how severe this “perfect flood” could be. Results of this study show a substantial increase in peak flow (more than 50 percent on average) for both the Elbe and Danube rivers.

Elbe River flood hazard map for a "perfect flood event," Riesa, Germany

Elbe River flood hazard map for a “perfect flood event,” Riesa, Germany

In certain locations, this scenario would be characterized by a flood extent (shown above for the area surrounding Riesa, Germany) of about 2.5 times that observed in 2002. But given the remarkable non-linearity between hazard and damage, RMS research estimates that the increased losses could aggregate to a total economic loss of approximately four times the 2002 losses. While this is a theoretical scenario, it is also an entirely realistic one.

The events that have occurred since May 2013 are a stark reminder that flood is a peril from which much can be lost.

After the 2002 flooding, flood defenses were improved in some locations, such as Prague, resulting in less severe flooding. However, because both the flood hazard itself and the physical environment change over time, Europe’s flood risk must be continually and holistically assessed to ensure that we are prepared for when, not if, a similar event occurs again.

What Lies Beneath?

As we approach the first anniversary of Superstorm Sandy, I’ve been reflecting on my own experience of the event.

Living in New York at the time, I sat tight in my apartment as the storm headed toward the New Jersey coastline. A meteorologist at heart, I watched with concern and fascination as the disaster unfolded on TV, until my power cut out.

The following morning, with no power and most of lower Manhattan shut down, I took a walk downtown to explore the impact of the storm.

I passed many downed trees and the signs of flood inundation from the surge were clear to see.

Downed trees after Superstorm Sandy

Downed trees in the village on Houston Street, NYC after Sandy.

As I walked down Broad Street in the financial district, a very noticeable consequence of the flooding could be smelled in the air and observed across the ground. An oily sheen covered the street as basement oil tanks in commercial buildings in the area had flooded and leaked, their contents subsequently spread by the floodwaters.

Bentley parked in Tribeca. The back seat shows signs that the whole car had flooded.

In the year after Sandy, this contamination issue has also been observed in other flood events.

After the significant summer flooding that impacted central Europe, RMS sent a reconnaissance team to inspect the damage. Basement-level heating tanks leaking oil were commonly observed, adding to the cost of cleanup, due to the cost of replacement and decontamination.

Contamination on a much larger scale occurred three months later, in the devastating Colorado floods. During this event, floodwaters reached oil and gas wells in the region, prompting concerns over contamination and significant potential environmental and financial costs.

Water pumping in the financial district, NYC, after Superstorm Sandy.

While the physical damage and business interruption from flood events are significant, each of these events highlights how important the issue of contamination can be. Contaminated properties will take longer and cost more to repair but the negative environmental and health consequences can also be significant both in their impact and cost.

Contamination coverage may not be included in all property insurance policies, but where it is provided, it could represent an unexpected additional cost from these events. However, it is the potential liability cost associated with this hazard that should perhaps be of most concern to the insurance industry.

Various forms of advice exist surrounding how to design properties to protect them against flood damage but there is no guarantee that a risk will be compliant with a proposed guideline. The onus must be for the insurance industry to fully understand the risks they are providing coverage to.

Contamination poses an issue for the insurance industry, as modeling this risk would be very complex. The mode of damage and probability of occurrence will be difficult to represent and the combination of policy terms stretches beyond the realms existing solutions.

It has been widely noted in recent years that a proportion (either the peril itself or a component of the loss from a modeled peril) of global insured losses are not modeled.

Looking to the future, the industry will need tools that have the potential to evaluate all sources of risk; the exposures, the relevant policy terms and the non-modeled sources of loss.

While the industry may not be able to avoid surprises in the future, such as a large contamination loss, with improved technology (re)insurers should at least be equipped with the tools to explore such potential surprises.

The Dam-biguity

Catastrophe modeling of floods is not just a problem of stochastic rainfalls, run-off and channel flows. It also requires anticipating the actions of the human factors; for flood is as much a man-made peril as it is a natural peril.

The passive human interventions, such as the permanent flood defenses, are less of a challenge to model than the active interventions. Will the portable flood defenses be installed in time?

Perhaps they have already been borrowed by some upstream community, as happened for one town in the U.K. along the River Severn in the summer 2007 floods.

In “active flood management,” land and properties upstream get deliberately sacrificed to protect a downstream concentration of value. In modeling one can assume the decisions are rational and involve carefully calculated trade-offs. The same cannot always be said for human actions.

In 1927 the grandees of New Orleans, concerned that the city was about to be inundated by the Mississippi river, blew up the levees 13 miles downstream of the city with 39 tons of dynamite (with the idea of speeding up the flow of water through the city). The action proved completely unnecessary.

The Great Mississippi Flood of 1927 in Natchez, Mississippi, showing a submerged train with boats brought in for rescue (Courtesy of NOAA’s National Weather Service Collection from the family of Captain Jack Sammons, Coast and Geodetic Survey)

One of the biggest of all these challenges of flood modeling concerns how to factor in the role of dams.

  • What is the water level in the dam likely to be when the flood wave arrives?
  • How are the operators of the dam likely to have behaved ahead of the flood?

In many low latitude countries the problem for the operators is they often have two irreconcilable objectives.

Objective 1: The dam operator has to hold onto as much water as possible through the rainy period so that water remains available to all agricultural, industrial and domestic users through the dry season. The reservoir should be completely full the day the rain stops.

Objective 2: The dam operator is expected to hold back a large proportion of a flood wave, releasing the water after the wave has passed. To be effective the operator needs to have as little water as possible in the reservoir before the flood arrives.

Simply because dry years tend to happen more often than extreme floods, most operators work to the first objective.

In Thailand there was a drought in 2010 and the dam operators were accused of not holding enough water in reserve, so they topped up their reservoirs at the start of 2011 and had little capacity to manage the flood waves of the ensuing summer and autumn.

Earlier the same year, much the same situation happened in Brisbane, Australia. After catastrophic floods in 1974 a main branch of the Brisbane River had been dammed to create Lake Wivenhoe. Over the years the dam was increasingly used for water retention. When the intense rains came in early January 2011 the dam operators soon ran out of any storage capacity. In March 2011 the Insurance Council of Australia claimed that “release from Wivenhoe Dam raised water levels in the Brisbane River by up to 10 meters” – and that the January flood event could be classed as a “dam release flood.”

“Dammed if you do and dammed if you don’t.”

Being a dam operator can be a very stressful function! Ideally dam operators need optimization software to assist in this process – including long-range rainfall forecasts to determine their optimum strategy and costs for the flooding as well as an expected price of water in a period of low rainfall.

For now, when developing a river flood catastrophe loss model it is safest to assume that the dam will not be functioning at the optimum for flood wave reduction. In episodes of prolonged heavy rainfall the reservoir will cease to have any capacity for water retention – so that the flooding downstream will be as if the dam did not exist.