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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.
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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
CONFIDENTIAL© 2007 Risk Management Solutions, Inc.
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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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Catastrophe Modeling tools
CONFIDENTIAL© 2007 Risk Management Solutions, Inc.
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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
CONFIDENTIAL© 2007 Risk Management Solutions, Inc.
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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)
CONFIDENTIAL© 2007 Risk Management Solutions, Inc.
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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
CONFIDENTIAL© 2007 Risk Management Solutions, Inc.
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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
CONFIDENTIAL© 2007 Risk Management Solutions, Inc.
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Hazard Mapping (Step 1 in Risk Quantification)
Examples of Best Practice
CONFIDENTIAL© 2007 Risk Management Solutions, Inc.
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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
CONFIDENTIAL© 2007 Risk Management Solutions, Inc.
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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
CONFIDENTIAL© 2007 Risk Management Solutions, Inc.
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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.
CONFIDENTIAL© 2007 Risk Management Solutions, Inc.
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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
CONFIDENTIAL© 2007 Risk Management Solutions, Inc.
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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
CONFIDENTIAL© 2007 Risk Management Solutions, Inc.
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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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CONFIDENTIAL© 2007 Risk Management Solutions, Inc.
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22
Probabilistic Catastrophe risk modelingwith application for improved disaster
resistance
CONFIDENTIAL© 2007 Risk Management Solutions, Inc.
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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
CONFIDENTIAL© 2007 Risk Management Solutions, Inc.
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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%
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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
CONFIDENTIAL© 2007 Risk Management Solutions, Inc.
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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
CONFIDENTIAL© 2007 Risk Management Solutions, Inc.
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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
CONFIDENTIAL© 2007 Risk Management Solutions, Inc.
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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 …
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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
CONFIDENTIAL© 2007 Risk Management Solutions, Inc.
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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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