natcatservice and geo risks research - verisk …...from 1980 until today all loss events; for usa...

NatCatSERVICE®

and Geo Risks Research

Jan Eichner – Geo Risks Research, NatCatSERVICE

Photo: © Marcos Juarez, 2012

From 1980 until today all loss events; for USA and selected countries in Europe all loss events since 1970.

Retrospectively, all great disasters since 1950.

In addition, all major historical events starting from 79 AD –eruption of Mt. Vesuvius (3,000 historical data sets).

Currently ca. 35,000 data sets

The Database Today

NatCatSERVICEOne of the world‘s largest databases on natural catastrophes

© 2015 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

Geophysical

Meteorological

Hydrological

Climatological

Main event

Earthquake

Volcanic eruption

Mass movement dry

Sub Peril

Earthquake (Ground shaking)

Fire following

Tsunami

Volcanic eruption

Subsidence

Rockfall

Landslide

Family

Storm

FloodMass movement wet

Extreme temperature

Drought

Wildfire

Tropical cycloneWinter storm (i.e. extra-trop. cyclone)Tempest/Severe stormHail stormLightningTornadoLocal windstorm (i.e. orographic storm)Sandstorm/Dust stormBlizzard/Snowstorm

General flood

Flash flood

Storm surge

Glacial lake outburst flood

Subsidence

Avalanche

Landslide

Heat wave

Cold wave / frost

Extreme winter conditions

Drought

Wildfire

Unspecified


NatCatSERVICE

Database Structure – Peril Families (updated structure following IRDR DATA Project: work in progress!)

Geophysical events(Earthquake, tsunami, volcanic activity)

Meteorological events (Tropical storm, extratropical storm, convective storm, local storm)

Hydrological events(Flood, mass movement)

Loss events

Climatological events(Extreme temperature, drought, wildfire)

Selection of catastrophesOverall losses ≥ US$ 1,500m

NatCatSERVICE

Loss Events Worldwide 2014 Geographical overview

© 2015 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE – As at January 2015

DroughtBrazil, 2014

Winter damageJapan, 7–16 Feb

Winter damageUSA, Canada, 5–8 Jan

DroughtUSA, 2014

EarthquakeChina, 3 Aug

FloodsIndia, Pakistan,3–15 Sep

FloodsUnited Kingdom,Dec 2013–Feb 2014

Severe stormsFrance, Belgium,Germany,7–10 Jun

Flash floodsUSA,11–13 Aug

Cyclone HudhudIndia, 11–13 Oct

Severe stormsUSA, 18–23 May

Severe stormsUSA, 2–4 Apr

Severe stormsUSA, 27 Apr–1 May

Severe stormsUSA, 3–5 Jun

Typhoon RammasunChina, Philippines, Vietnam, 11–22 Jul

Source: Munich Re, NatCatSERVICE, 2015

Hurricane OdileMexico, 11–17 Sep

980Loss events

Typhoon KalmaegiChina, Philippines, Vietnam,12–20 Sep

FloodsBosnia and Herzegovina, Serbia, Croatia, Romania,13–30 May

11.3.2011 Earthquake, tsunami

Japan 210.000 40.000 15.880

25-30.8.2005 Hurricane Katrina, storm surge

United States 125.000 62.200 1.720

17.1.1995 Earthquake Japan 100.000 3.000 6.430

12.5.2008 Earthquake China 85.000 300 84.000

23-31.10.2012 Hurricane Sandy, storm surge

Bahamas, Cuba, Dominican Republic, Haiti, Jamaica, Puerto Rico, United States, Canada

68.500 29.500 210

17.1.1994 Earthquake United States 44.000 15.300 61

1.8-15.11.2011 Floods, landslides Thailand 43.000 16.000 813

6-14.9.2008 Hurricane Ike United States, Cuba, Haiti, Dominican Republic, Turks and Caicos Islands, Bahamas

38.000 18.500 170


Chile 30.000 8.000 520

23/24/27.10.2004 Earthquake Japan 28.000 760 46

Source: Munich Re NatCatSERVICE, 2015

© 2015 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE As at: January 2015

FatalitiesDate Event Affected countriesOverall losses

in US$ m original values

Insured lossesin US$ m

original values

NatCatSERVICE

Loss Events Worldwide 1980 - 2014 10 costliest events ordered by overall losses

25-30.8.2005 Hurricane Katrina, storm surge

United States 125.000 62.200 1.720


Japan 210.000 40.000 15.880

23-31.10.2012 Hurricane Sandy, storm surge

Bahamas, Cuba, Dominican Republic, Haiti, Jamaica, Puerto Rico, United States, Canada

68.500 29.500 210

6-14.9.2008 Hurricane Ike United States, Cuba, Haiti, Dominican Republic, Turks and Caicos Islands, Bahamas

38.000 18.500 170

23-27.8.1992 Hurricane Andrew United States, Bahamas 26.500 17.000 62

22.2.2011 Earthquake New Zealand 24.000 16.500 185

1.8-15.11.2011 Floods, landslides

Thailand 43.000 16.000 813

17.1.1994 Earthquake United States 44.000 15.300 61

7-21.9.2004 Hurricane Ivan, storm surge

United States, Barbados, Cayman Islands, Cuba, Dominican Republic, Grenada, Haiti

23.000 13.800 120

19-24.10.2005 Hurricane Wilma Bahamas, Cuba, Haiti, Jamaica, Mexico, United States

22.000 12.500 44



FatalitiesDate Event Affected countriesOverall losses



original values

NatCatSERVICE

Loss Events Worldwide 1980 - 2014 10 costliest events ordered by insured losses

12.1.2010 Earthquake Haiti 8.000 200 222.570


Sri Lanka, Indonesia, Thailand, India, Bangladesh, Myanmar, Maldives, Malaysia

10.000 1.000 220.000

2-5.5.2008 Cyclone Nargis, storm surge

Myanmar 4.000 140.000

29-30.4.1991 Tropical cyclone, storm surge

Bangladesh 3.000 100 139.000

8.10.2005 Earthquake Pakistan, India (Kashmir region), Afghanistan 5.200 5 88.000

12.5.2008 Earthquake China 85.000 300 84.000

Jul - Aug 2003 Heat wave, drought

All of Europe 13.800 1.120 70.000

Jul - Sep 2010 Heat wave Russia 400 56.000

20.6.1990 Earthquake Iran 7.100 100 40.000

26.12.2003 Earthquake Iran 500 19 26.200



FatalitiesDate Event Affected areaOverall losses



original values

NatCatSERVICE

Loss Events Worldwide 1980 - 2014 10 deadliest events

NatCatSERVICE

Loss Events Worldwide 1980 – 2014 Number of events


Number

Meteorological events(Tropical storm, extratropicalstorm, convective storm, local storm)

Hydrological events(Flood, massmovement)

Climatological events(Extreme temperature, drought, forest fire)

Geophysical events(Earthquake, tsunami, volcanic activity)

200

400

600

800

1 000

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014

NatCatSERVICE

Loss Events Worldwide 1980 – 2014 Overall and insured losses


US$ bn

Overall losses (in 2014 values)* Insured losses (in 2014 values)* *Losses adjusted to inflation based on country CPI considering ROE of LCU and US$

100

200

300

400

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014

Q: How do we get estimates for direct economic losses?

NatCatSERVICE


NatCatSERVICE

Economic Loss Estimation

Five levels of information quality:

1. Info on insured loss in industrial countries, compiled by institutions such as PCS, Perils AG or various Insurance Associations

2. Partial info on insured loss in developing markets / countries

3. Info on total economic loss, often from governments (no info on insured loss)

4. Partial info on economic loss (e.g. impact on agriculture, infrastructure etc.)

5. Only description of event (e.g. number of houses damaged / destroyed by flood, storm, earthquake etc.)


Economic loss estimation based on insured loss data is of best quality! …and easiest way to scale up

1. Insured loss info2. Up-scaling of insured loss based on insurance penetration info

3. Modulation of economic loss based on event-specific information and/or NatCatSERVICE experience

NatCatSERVICE

Economic Loss EstimationBased on insurance market loss information (info level 1)


Convective storm event in Texas and Ohio in 2013 (Source: PCS)

Major damage in TX from : flash floods and windMajor damage in OH from: wind

Split by state and lines of business:TX: 16.5m (res) + 5.5m (com) + 9.7m (auto) = 31.7m US$OH: 14m (res) + 5 (com) + 0.7m (auto) = 19.7m US$

Insured loss: 51.4m (PCS) + 30.2m (NFIP) = 81.6m US$

Assumptions on insurance penetration rates in affected areas:

For Texas: Insurance penetration residential: 81%Auto and commercial penetration: 95%NFIP penetration: 20%

For Ohio: Insurance penetration residential: 99%Auto and commercial penetration: 95%

Assumption for limits / deductibles: 5%

NatCatSERVICE

Economic Loss EstimationExample for info level 1 (loss event in TX and OH)


Calculating the economic loss based on insured loss:

TX: Residential: 16.5m / (0.81 * 0.95) = 21m US$Auto and commercial: (5.5m + 9.7m) / (0.95 * 0.95) = 17m US$Flood losses: 30.2m / 0.20 = 151m US$

Plus assumption of ~25% infrastructure damage: = 47m US$224m US$

OH: Residential: 14m / (0.99 * 0.95) = 15m US$Auto and commercial: (5m + 0.7m) / (0.95 * 0.95) = 6m US$

Plus assumption of ~10% infrastructure damage: = 2m US$ 23m US$

Combined direct economic loss estimate: 247m US$ rounded: 250m US$

NatCatSERVICE

Economic Loss EstimationExample for info level 1 (loss event in TX and OH)


Q: What is driving the losses?

NatCatSERVICE


Risk ~ Hazard x Vulnerability x Exposure

All three factors can and will change over time!

16© 2015 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

NatCatSERVICE

Definition of Risk in the Insurance Industry

Exposure:- Inflation- Population increase/shift- Increase of wealth- Increase of building stock

1914

2012

Vulnerability:- Building codes- Improved materials- Expensive materials- Flood zones

Hazard:- Natural variability

(rather short time scales)

- Climate change (long time scales)

El Niño La Niña

Jet stream during La Niña:Shift of tornado activity


NatCatSERVICE

Examples of Drivers of NatCat Losses

Increasing exposure

US$

# of events

Reporting effect

US$

# of events

Trend in # of events

Loss event magnitudes today

Two main biasing factors:

a) Improved reporting of events (internet etc.)

b) Increasing wealth, population and destructible assets (socio-economic growth)

past today

today

past

Trend in loss magnitudes© 2015 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE

NatCatSERVICE

Bias in Loss Data over Time

How to overcome these biases when doing time series analysis of loss data?

1. Normalization of loss data

eliminates socio-economic trend bias

2. Introducing a lower threshold to normalized losses

eliminates reporting bias

Procedure is necessary when studying other factors of influence on loss data!


NatCatSERVICE

Bias in Loss Data over Time

Q: What to do with historic loss data?

Sander, J., J. Eichner, E. Faust, and M. Steuer in: Weather, Climate, and Society, March 2013, DOI: 10.1175/WCAS-D-12-00023.1

Example: Thunderstorm losses in the USA

NatCatSERVICE


Source: Munich Re NatCatSERVICE database

Relation between climate variability and losses?Thunderstorm losses in USA (1970 - 2009)


𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑛𝑛𝑙𝑙𝑛𝑛𝑛𝑛𝑛𝑛𝑙𝑙𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝑡𝑡𝑙𝑙𝑛𝑛𝑛𝑛𝑡𝑡 = 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑡𝑡𝑛𝑛 𝑙𝑙𝑜𝑜 𝑛𝑛𝑒𝑒𝑛𝑛𝑛𝑛𝑡𝑡 ∗𝑛𝑛𝑙𝑙𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑙𝑙 𝑛𝑛𝑛𝑛𝑙𝑙𝑡𝑡𝑛𝑛𝑑𝑑𝑑𝑑𝑡𝑡𝑛𝑛𝑑𝑑𝑙𝑙𝑛𝑛 𝑤𝑤𝑛𝑛𝑛𝑛𝑙𝑙𝑡𝑡ℎ𝑡𝑡𝑙𝑙𝑛𝑛𝑛𝑛𝑡𝑡

𝑛𝑛𝑙𝑙𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑙𝑙 𝑛𝑛𝑛𝑛𝑙𝑙𝑡𝑡𝑛𝑛𝑑𝑑𝑑𝑑𝑡𝑡𝑛𝑛𝑑𝑑𝑙𝑙𝑛𝑛 𝑤𝑤𝑛𝑛𝑛𝑛𝑙𝑙𝑡𝑡ℎ𝑡𝑡𝑛𝑛 𝑙𝑙𝑜𝑜 𝑛𝑛𝑒𝑒𝑛𝑛𝑛𝑛𝑡𝑡

• Two proxies for wealth used:

- building stock (BS)(number of home units) x (nominal median value of homes)

- GDP (population) x (nominal GDP per capita)

• To find potential climate signal in loss data time series one has to remove the signal of increasing destructible wealth (= socio-economic growth)

Normalization of past direct economic losses to current levels of wealth:

Relation between climate variability and losses?Normalization of U.S. thunderstorm loss data


© M

unich Re, 2012

Original direct thunderstorm losses,east of 109° W (east of the Rockies), March – Sept.

Normalization using building stock as a proxy for destroyable wealth

© M

unich Re, 2012

Normalisedthunderstorm losses(state-based)

Relation between climate variability and losses?Normalization of U.S. thunderstorm loss data


To ensure homogeneity of normalized loss events over time:

Find threshold selecting sizeable normalized loss events thatwould have been detected at any time.

Here: per-event threshold of US$ 250m (US$ 150m insured) in normalized loss associated with multi-state loss during all of theanalysis period.

Normalized loss events exceeding US$ 250m account for <20% of all events, but >80% of the total loss aggregate in the analysis period.

Relation between climate variability and losses?Removing reporting bias in U.S. thunderstorm loss data


© M

unich Re, 2012

Original direct thunderstorm losses,east of 109° W (east of the Rockies), March – Sept.

Normalization using building stock as a proxy for destroyable wealth

© M

unich Re, 2012

Normalisedthunderstorm losses(state-based)

© M

unich Re, 2012

Normalised thunderstorm losses from events > US$ 250m(state-based)Selecting

sizeable multi-state loss events (> $250m) to ensure homogeneity in detection.

Relation between climate variability and losses?Removing reporting bias in U.S. thunderstorm loss data


NCEP/NCAR reanalysis data: symbiosis of climate model and measurements

Worldwide grid with1.875° x 1.915°

spatial and 6h temporal resolution,

selecting 1970 – 2009, March – September

Parameters for severe convective storm events:

CAPE - Convective Available Potential Energy (temperature and humidity)

DLS - Deep-Layer Wind Shear (up- and down-draft winds, supports convection)

From this we calculate TSP (Thunderstorm Severity Potential) (J. Sander, 2011)

TSP := × DLS6km AGL-GL [J kg-1]

Chosen grid points for analysis:

Relation between climate variability and losses?NCEP/NCAR reanalysis data

�𝟐𝟐 × 𝑪𝑪𝑪𝑪𝑪𝑪𝑪𝑪𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎 𝒍𝒍𝒍𝒍𝒍𝒍𝒎𝒎𝒍𝒍 𝟏𝟏𝟏𝟏𝟏𝟏 𝒉𝒉𝑪𝑪𝒍𝒍

Severe thunderstorm forcing environments defined by very high values of TSP ≥ 3,000 J kg-1, corresponding to the 99.99th percentile of distribution.


Correlations may appear in…

…FREQUENCY of events or…INTENSITY of events or…BOTH

Hence, we have to look at both at the same time:

- NUMBERS of threshold exceedences per time step(counts)

- SUMS of INTENSITIES exceeding the thresholds per time step(aggregated values)

Relation between climate variability and losses?Correlation btw. TSP environment and loss data


Sta

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Sta

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dse

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Seasonal count: TSP, norm. economic losses

Seasonal aggregate: TSP, norm. economic losses

count of TSP per grid point > 3,000 J kg-1

count of norm. loss events ≥ $250m (BS)count of norm. loss events ≥ $250m (GDP)

aggregate of TSP per grid point > 3,000 J kg-1

aggregate of norm. loss events ≥ $250m (BS)aggregate of norm. loss events ≥ $250m (GDP)

BS, GDP: different normalization approaches using either building stock (BS) or GDP (GDP) as a proxy for wealth



Over past 40 years CAPE in North America shows a clear trend that correlates very well with observed warming in the area over the same time span.

Source: NOAA NCEP/NCAR reanalysis data, wmax > 42 m/s

How will CAPE and shear develop in the future?

29

7-year running means



Rise of March-Sept. aggregate of wmax in line with rise of specific humidity in northern hemisphere (WORK IN PROGRESS)

Six-hourly wmax aggregated per March – September season (1970-2009) from analysis domain (NCEP/NCAR reanalysis).

Threshold of SQRT(CAPE) = 42 m s-1

(corresponding to CAPEml ~ 1,764 J kg-1) was applied.

Produced from the new global high-resolution, quality-controlled land surface database HadISDH, available at: http://www.metoffice.gov.uk/hadobs/hadisdh/onlinematerial.html

For infromation on HadISDH see Willett, K.M. et al., 2013, Clim. Past. 9, 657-677.

Findings…

• Increase in variability and mean level of severe thunderstorm-related normalized large losses (USA east of Rockies, 1970 – 2009, March – Sept.)

• Changes in losses reflecting increasing variability and mean level inthunderstorm forcing, i.e. changing climatic conditions. This finding contradicts the opinion that changing socio-economic conditions are the only driver of change in thunderstorm-related losses.

• Changes coincide with rise in low-level specific humidity and in seasonallyaggregated potential convective energy. These effects seem consistent with the modeled effect from anthropogenic climate change that other studies have demonstrated.

Further research that is underway

• Can we identify variability signals in other perils & loss data / in other regions?

• Can we identify variability signals similar to the observation also in climate change projections?


o Collect and analyze worldwide data and info on all types of NatCat losses

o Find correlations between loss patterns and patterns on the hazard side (i.e. meteorological, hydrological, geophysical, …)

o Learn about economic consequences of temporal changes in these patterns

o Learn about impact of both, climate change and climate variability

o Eventually use this info to assist the steering of insurances‘ NatCat business

o …but also as an indicator for changing risks in society and economy in general!

NatCatSERVICE

In SummaryTasks / interests of Geo Risks Research & NatCatSERVICE


Thank You for Your Attention!

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natcatservice and geo risks research - verisk …...from 1980 until today all loss events; for usa...

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