Probabilistic catastrophe models for
disaster risk reduction
Robert Muir-Wood
Chief Research Officer, RMS
Disaster Risk Reduction for Natural Hazards
Putting Research into Practice
Nov 5th
UCL
2009
TM
The function of insurance

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)
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The motivation of Catastrophe Modeling

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
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Catastrophe Modeling tools
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Catastrophe Loss Modeling of Hurricane
$ Loss
90%
Define Hurricane
Assess Wind
Speed
Calculate Damage
Quantify Financial
Loss
Stochastic
Hazard
Vulnerability
Financial
100,000 years of
simulation of
physical
parameters of
each storm
Wind and
flood
footprints
Damage as a
function of
hazard value
and exposure
Loss
distribution
after contract
structures
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Annual Probability of Exceedence (%)
Exceedance Probability (EP) Output
10
8
6
100 year loss = $131 million
4
2
0
0
50M
100M
150M
200M
250M
300M
Loss Amount
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RiskLink® v4.3: Geography of Risk
ME
VT
NH
NY
MA
CT
RI
PA
NJ
DC
WV
DE
VA
NC
SC
MS
TX
AL
GA
LA
FL
HI
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Residential Woodframe
Losscost ($/1,000)
> 10.0
5.0 to 10.0
2.0 to 5.0
1.0 to 2.0
0.5 to 1.0
0.05 to 0.50
0.01 to 0.05
< 0.01
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A specific Scenario – the windfield of 2008
Hurricane Ike
 Stochastic events may be
modelled to have less
random complexity than
real events
 However may need to
capture localized correlation
in hazard field
As a new smaller eyewall
develops, high winds
also move to the left
of the track
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Perspectives on Catastrophe risk

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
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Hazard Mapping
(Step 1 in Risk Quantification)
Examples of Best Practice
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Best Practices
Key to tables
Hazard Map – Producing Authority
Type of map
Parameters
Risk Boundaries
Information
Retrieval Method
Access
Coverage
Either risk based (probabilistic/historical) or forecast
Damaging agent A, damaging agent B, damaging agent C
Boundaries for damaging agent A, boundaries for damaging agents B and C
Preparedness, evacuation or response information
How the data is retrieved by the user (e.g. address search or coordinates)
How the user initially locates the map or the search form
Stars approximately represent the coverage of the risk area e.g. US hurricane maps achieve 
coverage despite only including the East/Gulf Coast
Key to colours
Very Good
Good
Average
Poor
Very difficult to improve
Sufficient but minor improvements possible
Functional but requires some improvement
Very limited requires substantial improvement
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Tsunamis
Maximum wave heights
along the WA coast for
return periods of a) 100
years, b) 500 years, c)
1000 years, d) 2000 years
Tsunami Threat Map – FESA Western Australia
Type of map
Parameters
Risk Boundaries
Information
Retrieval Method
Access
Probabilistic
Wave height
100/500/1000/2000 year RP
None
Not searchable
Map available on FESA website, full report linked from GA website
Coverage
 Covers entire area at risk from tsunamis
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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
My Hazards – CALEMA
Type of map
Parameters
Risk Boundaries
Information
Retrieval Method
Access
Probabilistic
Shaking, liquefaction, landslide, fault rupture
High/medium, at risk areas
Preparedness information tailored to each risk level, multi-format
Search by zip/address/coordinates
Prominent banner link from California state and emergency website
Coverage
 Covers all areas of the state of California
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Tropical Storms
NHC map of the Gulf Coast
showing return periods for
Category 5 hurricanes
Hurricane Preparedness – NHC/NOAA
Type of map
Parameters
Risk Boundaries
Information
Retrieval Method
Access
Probabilistic
Intensity (Saffir-Simpson scale)
Exact return periods for 75nm areas
None (available in other sections of NHC website)
Selectable by three areas of coast (Gulf, Southeast, Northeast)
Link from NHC website
Coverage
 Covers all of East and Gulf coast but only at discrete points
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Storm Surges
Example of 1% flood
heights for the French
Quarter, New Orleans
New Orleans Risk – US Army Corps of Engineers
Type of map
Parameters
Risk Boundaries
Information
Retrieval Method
Access
Coverage
Probabilistic
Height of inundation
2%/1%/0.2% annual probabilities on separate maps (50/100/500 year RP)
None (available in other sections of New Orleans Risk website)
Selectable by city area
Whilst this is easily available on NOR website, links should be available from more obvious sites such as
Louisiana or New Orleans disaster planning sites
 Covers New Orleans and Plaquemines but not entire NO metro area
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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
Flood Map – England and Wales EA
Type of map
Parameters
Risk Boundaries
Information
Retrieval Method
Access
Probabilistic
No information
1%/0.5%/0.1% annual chance of flooding (100/200/1000 year RP)
Tailored information for each flood risk category
Search by postcode or browse map
Prominent link from Environment Agency website
Coverage
 Covers all of England and Wales
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Volcanoes
Extract from hazard map
for Mt. Rainier, Washington
Mt. Rainier – USGS
Type of map
Parameters
Risk Boundaries
Information
Retrieval Method
Access
Probabilistic
Lahars, all other volcanic hazards
1-100/100-500/500-1000 year RP, 100-1000 year RP
None (available on other sections of USGS volcano site)
Viewed by volcano
Easily located from main USGS hazards page
Coverage
 Detailed mapping only covers areas surrounding major volcanoes
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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.
Progetto IFFI – ISPRA
Type of map
Parameters
Risk Boundaries
Information
Retrieval Method
Access
Probabilistic/Historical
Superficial landslides, subsidence, collapse, expansion
Only given as ‘at risk’ areas
None
By address or area
Theoretically easily accessed but site frequently goes down
Coverage
 All of Italy covered CHECK THIS
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Indonesia
Earthquake, 27th May 2006
Casualties: 5,800
Losses: US$3100million
•Magnitude 6.2 event some 25km SouthWest of Yogyakarta
•Despite relatively low intensity, extremely
shallow depth meant it was very damaging
•Most casualties in the Bantul district
Indonesia – Eq, Fl, Ls, Ts, Vo
Earthquakes
Floods
Landslides
Tsunamis
Probabilistic

Well reported on the BMG website, some
information on hazards
P/F

Forecast for up to 2 months available,
information available
Probabilistic

Plenty of information available on PVMBG
website
Historical

Preparedness road shows/online
information and early warning
Worst Case
Volcanoes

Hazard levels and information for major
volcanoes is on PVMBG.
© 2007 Risk Management Solutions, Inc.
Ground shaking
Landslide
475/2500 year RP
Linked from the PVMBG website. More
detailed map from USGS
High/Medium/Low risk
Maps linked from BMG website, but
information harder to find
High/Medium/Low risk
Selectable by area
Linked from the PVMBG website
Damaged area
Not directly available on Indonesian
websites
Worst case limits, 200/300/500/700 m3/s
volume flux
PVMBG information easily available.
Detailed Merabi map in a paper.
No search function
Flooding
Selectable by area
Selectable by area
Debris avalanches, pyroclastic flows,
lateral blasts, tephra, lahars
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PVMBG
information selectable by volcano
CONFIDENTIAL
Myanmar
Cyclone Nargis, 2nd May 2008
Casualties: Estimates range from 100,000
upwards
Losses: Estimated US$4billion-10billion
•Most damage and casualties caused by storm
surge
• No accurate loss/casualty figures available
• Several million left homeless
Myanmar – Eq, Fl, Tr/Ss, Ts

Probabilistic
Earthquakes
Reports of significant seismic activity
on DMH website. No information
None
Floods
Tsunami
475 year RP
No search function
Not linked from any Myanmar
government website
-
-
-
Information links on DMH website do
not function
-
-
-
Information links on DMH website do
not function
-
None
-
DMH provides flood warnings.
None
Tropical Storm
Storm Surge
Ground shaking
-
DMH provides tropical storm and
storm surge warnings.
None
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None
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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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Probabilistic Catastrophe risk modeling
with application for improved disaster
resistance
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The megacities of South
America
Quito – 1.8M
RMS Research Trip
Bogotá – 8.5M
Santiago– 5.9M
www.fotopaises.com
www.skyscraperlife.com
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Caracas
– 7.0M
RMS Research Trip
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Lima – 8.9M
RMS Research Trip
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…
Caracas, Venezuela
RMS Research Trip
La Paz, Bolivia
Myplanetaustralia.com
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Lima, PeruCONFIDENTIAL
RMS Research Trip
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Bogotá, Colombia
RMS Research Trip
Probabilistic Hazard
Completed as Part of v9.0 EQ Model
Caracas, VZ
Bogota, CO
Quito, EC
Lima, PE
La Paz, BO
Santiago, CL
Loss Cost - SFD
RiskLink® 9.0
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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
 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
Images of
some
homes
 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
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Not Just a Barrios Problem …
“Superbloques” of Caracas, Venezuela
24,000 people live in these 40-story MFDs
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Not Just a Barrios Problem …
More 20-story “superbloques” of Caracas
Residents often remove interior walls to create space
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Individual EQ Scenarios: Quito + Caracas
Pichincha Volcano
Quito
Caracas
Quito fault an active reverse fault
Well-determined 1 mm/year slip rate
From geology
No such event recorded in historic
time ~500 yrs
M6.5 eq expected based on fault
length and elapsed time
© 2007 Risk Management Solutions, Inc.
M 7.1 March 25, 1812 earthquake near Caracas
26,000-40,000 dead, city nearly destroyed
San 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.
CONFIDENTIAL
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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
Modify vulnerability to explore
impacts of individual adaptation /
hard defences
Modify hazard to
explore physical
climate change
impacts
Quantify future loss
scenarios
Storm Surge
Flood
Vulnerability
500,000
tracks
Stochastic
90%
Exposure
Windfield
Financial
Wind
Vulnerability
Modify inventory to
explore impacts of
socio-economic growth
w/wo land use policy
e.g. future coastal flood risk
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Modelling Benefits (£) of Adaptation to
Climate Change
Model Changes to
Hazard
Hazard
e.g. Flood Defences
Model Changes to the
Vulnerability of Buildings
Vulnerability
e.g. Flood Resistance &
Resilience Measures
Model Changes to
Inventory
Inventory
e.g. Risk Averse
Land Planning
(changes to primary and
secondary modifiers)
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Comparing individual measures

Individual measures can substantially reduce losses (both in terms of average
Reduction in
annual losses and losses from
extreme
events).
average loss (individual)
40%
Flood loss reduction by adaptation
method
35%
% AAL reduction
28%
25%
Engineered foundations
Protected external
equipment
Reinforced
cladding
Temporary
Flood
Wall
Moving vulnerable contents
0%
Dry flood proofing
10%
Temporary flood wall
15%
Raised ground floor (0-0.5m)
22%
Raised
Ground Floor
(0-0.5m)
Dry
Flood
Proofing
Moving
Vulnerable
Contents
Engineered
Foundations
Protected
External
Equipment
Reinforced
Cladding
No
adaptation
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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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Disaster Risk reduction and Cat Modelling
 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!
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