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TM Probabilistic catastrophe models for disaster risk reduction Disaster Risk Reduction for Natural Hazards Putting Research into Practice UCL Nov 5 th 2009 Robert Muir-Wood Chief Research Officer, RMS

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Probabilistic catastrophe models for disaster risk reduction

Disaster Risk Reduction for Natural HazardsPutting Research into Practice

UCLNov 5th 2009

Robert Muir-Wood

Chief Research Officer, RMS

CONFIDENTIAL© 2007 Risk Management Solutions, Inc.

TM

Insurance is based on the ability to transfer potential unforeseen „accidental‟ costs to a counterparty in exchange for an ex-ante fee

Counterparty requires less capital gearing to support a payout if the risk can be diversified

Diversification assumption breaks down under risk correlation = catastrophe

– which is why insurers buy insurance (from reinsurers)

The insurance food-chain is driven by the need to transfer risk onto an entity who can achieve further diversification

– Insured to Insurer to Reinsurer (to Capital Markets)

The function of insurance

2

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Before the early 1990s insurers and reinsurers would:

– Use the concept of the „probable maximum loss‟ PML

– Employ (recent) historical scenarios for looking at „expected losses‟

– Measure their aggregate accumulations in zones

Catastrophe Modelling

– Replaced the PML with the exceedance probability EP curve

– Recognised that the next catastrophe will not be a repeat of a historical catastrophe

– Recognised that aggregates can only be managed probabilistically

– Emerged through the 1990s to become mainstream for how insurers and reinsurers managed catastrophe risk

The motivation of Catastrophe Modeling

3

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4

Catastrophe Modeling tools

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Catastrophe Loss Modeling of Hurricane

Assess Wind

Speed Calculate DamageDefine Hurricane Quantify Financial

Loss

9 0 %$ Loss

Stochastic

100,000 years of simulation of

physical parameters of

each storm

Hazard

Wind and flood

footprints

Vulnerability

Damage as a function of

hazard value and exposure

Financial

Loss distribution

after contract structures

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Exceedance Probability (EP) OutputAnnual Pro

babili

ty o

f Exc

eedence

(%

)

Loss Amount

0

2

4

6

10

8

0 50M 100M 150M 200M 250M 300M

100 year loss = $131 million

CONFIDENTIAL© 2007 Risk Management Solutions, Inc.

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7Confidential

RiskLink® v4.3: Geography of Risk

AL

CT

DEDC

FL

GA

HI

LA

ME

MA

MS

NH

NJ

NY

NC

PA

RI

SC

TX

VT

VA

WV

> 10.0

5.0 to 10.0

2.0 to 5.0

1.0 to 2.0

0.5 to 1.00.05 to 0.50

0.01 to 0.05

< 0.01

Residential WoodframeLosscost ($/1,000)

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A specific Scenario – the windfield of 2008 Hurricane Ike

As a new smaller eyewalldevelops, high windsalso move to the leftof the track

Stochastic events may be modelled to have less random complexity than real events

However may need to capture localized correlation in hazard field

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Alternative perspectives of:

– Hazard maps

– The EP curve

– Expected Loss at key return period

– Average Annualised Loss

– Mapped risk costs

– Scenario events

Each perspective is useful

No single perspective provides all the information needed to manage catastrophe risk

– One needs to scroll through the perspectives

Perspectives on Catastrophe risk

9

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Hazard Mapping (Step 1 in Risk Quantification)

Examples of Best Practice

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Hazard Map – Producing AuthorityType of map Either risk based (probabilistic/historical) or forecast

Parameters Damaging agent A, damaging agent B, damaging agent C

Risk Boundaries Boundaries for damaging agent A, boundaries for damaging agents B and C

Information Preparedness, evacuation or response information

Retrieval Method How the data is retrieved by the user (e.g. address search or coordinates)

Access How the user initially locates the map or the search form

CoverageStars approximately represent the coverage of the risk area e.g. US hurricane maps achieve

coverage despite only including the East/Gulf Coast

Best Practices

Very Good Very difficult to improve

Good Sufficient but minor improvements possible

Average Functional but requires some improvement

Poor Very limited requires substantial improvement

Key to tables

Key to colours

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Tsunami Threat Map – FESA Western AustraliaType of map Probabilistic

Parameters Wave height

Risk Boundaries 100/500/1000/2000 year RP

Information None

Retrieval Method Not searchable

Access Map available on FESA website, full report linked from GA website

Coverage Covers entire area at risk from tsunamis

Tsunamis

Maximum wave heights along the WA coast for return periods of a) 100 years, b) 500 years, c)

1000 years, d) 2000 years

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My Hazards – CALEMAType of map Probabilistic

Parameters Shaking, liquefaction, landslide, fault rupture

Risk Boundaries High/medium, at risk areas

Information Preparedness information tailored to each risk level, multi-format

Retrieval Method Search by zip/address/coordinates

Access Prominent banner link from California state and emergency website

Coverage Covers all areas of the state of California

Earthquakes

Screenshot of the information page showing the risk level of hazards,

the checklist of suggested steps to prepare and the

tabs to view flood and fire risk

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Hurricane Preparedness – NHC/NOAAType of map Probabilistic

Parameters Intensity (Saffir-Simpson scale)

Risk Boundaries Exact return periods for 75nm areas

Information None (available in other sections of NHC website)

Retrieval Method Selectable by three areas of coast (Gulf, Southeast, Northeast)

Access Link from NHC website

Coverage Covers all of East and Gulf coast but only at discrete points

Tropical Storms

NHC map of the Gulf Coast showing return periods for

Category 5 hurricanes

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New Orleans Risk – US Army Corps of EngineersType of map Probabilistic

Parameters Height of inundation

Risk Boundaries 2%/1%/0.2% annual probabilities on separate maps (50/100/500 year RP)

Information None (available in other sections of New Orleans Risk website)

Retrieval Method Selectable by city area

AccessWhilst this is easily available on NOR website, links should be available from more obvious sites such as

Louisiana or New Orleans disaster planning sites

Coverage Covers New Orleans and Plaquemines but not entire NO metro area

Storm Surges

Example of 1% flood heights for the French Quarter, New Orleans

CONFIDENTIAL© 2007 Risk Management Solutions, Inc.

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Flood Map – England and Wales EAType of map Probabilistic

Parameters No information

Risk Boundaries 1%/0.5%/0.1% annual chance of flooding (100/200/1000 year RP)

Information Tailored information for each flood risk category

Retrieval Method Search by postcode or browse map

Access Prominent link from Environment Agency website

Coverage Covers all of England and Wales

Floods

Screenshot of map showing regions of likely

flooding, extreme flooding and protected zones. Data on specific location risk is

available by clicking on the map

CONFIDENTIAL© 2007 Risk Management Solutions, Inc.

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Mt. Rainier – USGSType of map Probabilistic

Parameters Lahars, all other volcanic hazards

Risk Boundaries 1-100/100-500/500-1000 year RP, 100-1000 year RP

Information None (available on other sections of USGS volcano site)

Retrieval Method Viewed by volcano

Access Easily located from main USGS hazards page

Coverage Detailed mapping only covers areas surrounding major volcanoes

Volcanoes

Extract from hazard map for Mt. Rainier, Washington

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Progetto IFFI – ISPRAType of map Probabilistic/Historical

Parameters Superficial landslides, subsidence, collapse, expansion

Risk Boundaries Only given as ‘at risk’ areas

Information None

Retrieval Method By address or area

Access Theoretically easily accessed but site frequently goes down

Coverage All of Italy covered CHECK THIS

Landslides

Extract from the IFFI GIS map. Dark green areas are

at risk from superficial landslides, Light green

areas have COLAMENTO RAPIDO, Purple hatching

represents urban development and Red

areas have experienced collapse or tipping.

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Indonesia – Eq, Fl, Ls, Ts, Vo

Earthquakes

Probabilistic Ground shaking 475/2500 year RP

Well reported on the BMG website, some

information on hazardsNo search function

Linked from the PVMBG website. More

detailed map from USGS

Floods

P/F Flooding High/Medium/Low risk

Forecast for up to 2 months available,

information availableSelectable by area

Maps linked from BMG website, but

information harder to find

LandslidesProbabilistic Landslide High/Medium/Low risk

Plenty of information available on PVMBG

websiteSelectable by area Linked from the PVMBG website

Tsunamis

Historical Damaged area -

Preparedness road shows/online

information and early warningSelectable by area

Not directly available on Indonesian

websites

Volcanoes

Worst Case Debris avalanches, pyroclastic flows,

lateral blasts, tephra, lahars

Worst case limits, 200/300/500/700 m3/s

volume flux

Hazard levels and information for major

volcanoes is on PVMBG.PVMBG information selectable by volcano

PVMBG information easily available.

Detailed Merabi map in a paper.

Indonesia

Earthquake, 27th May 2006Casualties: 5,800Losses: US$3100million•Magnitude 6.2 event some 25km South-West of Yogyakarta•Despite relatively low intensity, extremely shallow depth meant it was very damaging•Most casualties in the Bantul district

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Myanmar – Eq, Fl, Tr/Ss, Ts

Earthquakes

Probabilistic Ground shaking 475 year RP

Reports of significant seismic activity

on DMH website. No informationNo search function

Not linked from any Myanmar

government website

Floods

None - - -

DMH provides flood warnings. -Information links on DMH website do

not function

Tropical Storm

Storm Surge

None - - -

DMH provides tropical storm and

storm surge warnings.-

Information links on DMH website do

not function

TsunamiNone - - -

None - None

Myanmar

Cyclone Nargis, 2nd May 2008Casualties: Estimates range from 100,000 upwardsLosses: Estimated US$4billion-10billion•Most damage and casualties caused by storm surge• No accurate loss/casualty figures available• Several million left homeless

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Implications of the hazard information deficit

Cyclone Nargis in May 2007 highlighted the implications of a situation where hazard data is unavailable

No flood hazard maps in the Irrawaddy Delta

– People did not know they lived in a storm surge flood zone

– No evacuation plans

– No monitoring of cyclone forecasts

As a result c 100,000 died – through an „information deficit‟

Economic and health consequences will endure for years

Accessible information on its own can provide powerful disaster risk reduction

Mapped hazard information should be a 21st Century „universal right‟

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22

Probabilistic Catastrophe risk modelingwith application for improved disaster

resistance

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The megacities of South America

23Caracas – 7.0MRMS Research Trip

Lima – 8.9MRMS Research Trip

Bogotá – 8.5Mwww.fotopaises.com

Santiago– 5.9Mwww.skyscraperlife.com

Quito – 1.8MRMS Research Trip

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Why Capital Cities? ~ 80% of South America‟s

population lives in urban areas

Capital cities are major economic centers and act as magnets for rural poor and refugees

Significant fraction of entire nation‟s population resides in capitals

– Santiago, Chile 5.9 M 36%

– Lima, Peru 8.9 M 30%

– Bogota, Colombia 8.6 M 19%

– La Paz, Bolivia 1.8 M 19%

– Caracas, Venezuela 4.9 M 19%

– Quito, Ecuador 1.7M 12%

24

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With their dense, highly vulnerable neighborhoods…

25

Caracas, VenezuelaRMS Research Trip

Lima, PeruRMS Research Trip

Bogotá, ColombiaRMS Research Trip

La Paz, BoliviaMyplanetaustralia.com

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Probabilistic Hazard Completed as Part of v9.0 EQ Model

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Loss Cost - SFDRiskLink® 9.0

Caracas, VZ

La Paz, BOLima, PE

Quito, EC

Bogota, CO

Santiago, CL

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Economic RES Loss Cost (AAL/$1000 exposure value) -- Capital Cities

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Project Objectives

Phase I – Quantify risk Extend v9.0 Americas EQ model to quantify humanitarian impacts as well as economic losses for 6 seismically at-risk, South American capital cities

– Potential fatalities & injuries

– Displaced households

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Phase II – Implement mitigation solutions Use analyses to raise risk awareness and forge partnerships with NGOs and other local stakeholders to design mitigation strategies and programs to reduce future losses and suffering

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Typical “Barrios” Housing Conditions

House millions of people

Informal construction using cheap, readily available materials

Often built on landslide-prone hillsides on city margins

Many are squatters

Shared walls

Unplanned additions over time

Few roads through housing hampering emergency response

29

Images of some homes

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“Superbloques” of Caracas, Venezuela24,000 people live in these 40-story MFDs

Not Just a Barrios Problem …

30

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More 20-story “superbloques” of CaracasResidents often remove interior walls to create space

Not Just a Barrios Problem …

31

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QuitoPichincha Volcano

Quito fault an active reverse faultWell-determined 1 mm/year slip rateFrom geologyNo such event recorded in historic time ~500 yrs

M6.5 eq expected based on fault length and elapsed time

Individual EQ Scenarios: Quito + Caracas

Caracas

M 7.1 March 25, 1812 earthquake near Caracas26,000-40,000 dead, city nearly destroyedSan Sebastian strike-slip fault offshore,slip rate 10 mm/yr~ 2 m slip accumulated => M7+ earthquake

1967 07 29 M 6.5 –Coast of Venezuela , 30 mi W of Caracas.

240 dead , hundreds injured $100 M property damage in Caracas ~ 80,000 persons homeless. 4 major apt, buildings, 10-12 stories high, collapsed.

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Hazard : EQ-induced Landslides Will Compound the Looming Catastrophe

Challenge to adequately model landslide susceptibility and vulnerability

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A window into future impacts: cat models as a tool to explore impacts and benefits of adaptation

WindfieldStochastic

500,000 tracks

Storm Surge

Wind

Vulnerability

Flood

Vulnerability

Financial

9 0 %$ Loss

Exposure

Modify vulnerability to explore impacts of individual adaptation /

hard defences

Modify inventory to explore impacts of

socio-economic growth w/wo land use policy

Quantify future loss scenarios

Modify hazard to explore physical climate change

impacts

e.g. future coastal flood risk

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Modelling Benefits (£) of Adaptation to Climate Change

35

Vulnerability Inventory

Model Changes to the Vulnerability of Buildings

Hazard

Model Changes to Hazard

Model Changes to Inventory

(changes to primary and secondary modifiers)

e.g. Flood Defences e.g. Flood Resistance & Resilience Measures

e.g. Risk Averse Land Planning

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Comparing individual measures

Individual measures can substantially reduce losses (both in terms of average annual losses and losses from extreme events).

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TemporaryFlood

Wall

RaisedGround Floor

(0-0.5m)

DryFlood

Proofing

MovingVulnerable

Contents

Engineered

Foundations

ProtectedExternal

Equipment

Reinforced

Cladding

No

adaptation

0%

10%

15%

22%

25%

28%

35%

40%

Reduction inaverage loss (individual)

Tem

pora

ry f

lood w

all

Dry

flo

od p

roofing

Rais

ed g

round f

loor

(0-0

.5m

)

Movin

g v

uln

era

ble

conte

nts

Engin

eere

d foundations

Pro

tect

ed e

xte

rnal

equip

ment

Rein

forc

ed

claddin

g

% AAL reduction

Flood loss reduction by adaptation method

CONFIDENTIAL© 2007 Risk Management Solutions, Inc.

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Examples of spontaneous adaptation(Bahamas)

Queen’s Cove canal estate

North Grand Bahama

Flooded by surges 3 times in 6 years

No longer insurable

The response

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Cannot identify where best to invest in risk reduction, without first understanding the peaks in the „landscape‟ of risk

Cannot identify the best value for money in risk reduction (adaptation) without exploring alternative mitigative options

Disaster Risk reduction may also have an insurance component to spread risk – example of the Caribbean Catastrophe Risk Insurance Facility

Commercial Cat models are expanding beyond the developed world

Open Source Cat models coming online – in particular for EQ

New models for drought, heatwave etc

The future of disaster risk reduction will be probabilistic!

Disaster Risk reduction and Cat Modelling

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