RMSI_DisasterManagement-1.ppt

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Disaster Management Framework for Preparedness
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Inderjit Claire
Vice President
RMSI
October, 2007

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Need for Mainstreaming Pre-hazard Risks Management
 Frequency and magnitude of
losses from natural disasters have
been constantly increasing
 Losses from recent natural
disasters have been a great deal
higher than those that occurred
earlier in time
 This trend is expected to continue
because of an increasing higher
concentration of population and
property in areas susceptible to
natural hazards
Losses from major natural disasters world-
wide from 1950-2006 (in 2006 $ values)
(Courtesy: NatCatSERVICE, Geo Risks Research, Munich
Re)

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Hazards
 Earthquakes
 Tsunami
 Landslides
 Cyclones
 Floods
 Fire

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Hazard Risk Management Framework
Risk
Assessment
Catastrophe Risk Financing
 Ex-Ante Funding Arrangements
 Catastrophe Insurance Pools
 Reserve Funds
 Contingent Capital Facility
Risk Mitigation Investments
 Warning and Monitoring Systems
 Hazard Mapping and Land Use Planning
 Code Refinement and Enforcement
 Hazard Specific Risk Mitigation
Emergency Preparedness
 Emergency Response Planning
 Exercises
 Public Awareness
 Communication and Information
Management Systems (IMS)
 Technical Emergency Response
Capacity
Institutional Capacity Building
 Community Participation
 Legislative Framework
 Training, Education and knowledge
Sharing
 Decentralized Emergency Management
System
 International Cooperation

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Scenario Based Vulnerability Mapping – Earthquake Example
 Starts with scenarios, then defines the hazard, then estimates the
vulnerability, calculates what is the exposure and finally estimates
probable total damage

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Disaster Risk Modeling Process
• Stochastic Module generates random
events from the characteristics of historical
events that have occurred in the region.
• Hazard Module analyses the hazard
coefficients for each geographic region based
on various identified perils applicable in the
region.
Calculating the hazard coefficients for
stochastic events generated.

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Disaster Risk Modeling Process
• Vulnerability Module focuses on
assessment of physical vulnerability of
buildings and infrastructure to ground shaking
and collateral hazards and social vulnerability
of affected population.
• Exposure Module involves the tasks of
classification and quantification of the
exposures at locality, sector, county,
community and city levels.
Calculating the vulnerability and
exposure of the area against
disasters.

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Disaster Risk Modeling Process
• Damage/Loss Module: Finally, the damage
ratio from the vulnerability module is multiplied
by the value of the exposed risk at a location to
calculate an estimated monetary loss.
Calculating the loss from disasters

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Vulnerability
Scale Indices Users
National
National
comparisons of
vulnerability
Eligibility for
adaptation
funding
Regional Multiple dimension
profiles of regional
vulnerability
Regional
agencies:
Programme
design
Local Profiles of vulnerable
situations or syndromes
Local offices:
Project
evaluation
Eco-
systems
Water
Other
sectors
Food Health
Settle-
ment
Vulnerability
parameters
At what scale
the
vulnerability
mapping
needs to be
done
At what scale
do we need
to carry out
the
vulnerability
mapping

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Vulnerability has a Spatial Component
 Which places are more vulnerable to a hazard?
– Which geographical region, socio-economic class etc.
 Who are the vulnerable people?
– Relative vulnerability among households and
individuals
 What should be done?
– Link to intervention/ adaptation

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Social Vulnerability
 Coping Ability
– Resistance
– Resilience
 Social Environment
– Age
– Gender
– Ethnicity
– Household type
 Economic Environment
– Income and Assets
– Insurance
– Debts
 Overlay environmental hazard maps with vulnerability maps to
determine areas vulnerable to hazards
 Add values, weights, factors for each variable in each layer to
represent “Total Vulnerability”

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Vulnerability Module – Statistical Data Requirements
Physical Vulnerability Social Vulnerability
• Social vulnerability is
the susceptibility of
populations to death and
injuries - the assessment
of which involves
casualty modeling to
compute mortality and
injury rates associated
with various
catastrophic events
• Population Data
reflecting the age,
gender, ethnicity and
household type
• Physical vulnerability
refers to the degree to
which an asset would
get damaged or
destroyed in a
hazardous environment
caused by catastrophic
events
• Physical vulnerability
can be for residential
and commercial
buildings, critical
facilities, infrastructure
and agriculture

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Exposure Module: Use of Statistical Data
Exposure Module calculates how much of the population and
buildings are ‘exposed’ to the natural hazard
•Building Use – Residential, Commercial, Industrial
•Type of Buildings
• Type of Construction – Steel, Concrete, Masonry
• Category/Building class
• Building Height, No. of floors
• Building age
• Built up floor area of the buildings
•Occupancy Details – Population density

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Case Study – India Earthquake Model
Objective of the Project: The risk modeling
involved historical catalog compilation, hazard assessment,
vulnerability evaluation, exposure development, and loss
analysis.
Data Available:
- Census Houses data (Block level)
- Occupancy wise Census data (District level)
- For each block/town total number of residential
census houses is calculated from the total
number of census houses by applying the
percentage of residential census houses
computed at district level
- Building Attribute data available was State level
- Height data was missing for certain areas
Results: Various loss results including average
annual losses (AAL), loss costs and probable
maximum losses (PML)
Alternatives used: Remote Sensing techniques
were used to generate the unavailable data
Residential Exposure
in billion rupees
Residential exposure at block level in billion rupees

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Case Study – Romania Earthquake Model
Objective of the Project: Design and
customization (where appropriate) of a model for
damage computation following an earthquake in
Romania.
Data Available:
- Census Houses data (Commune level)
- Occupancy wise Census data
(Commune level)
- Building Attribute (County level)
- Height data (Commune level)
Results: Various loss results including
average annual losses (AAL), loss costs
and probable maximum losses (PML)
Use of spectral intensity approach which
is different for different heights of the
buildings.

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Case Study: Developing a Disaster Risk Profile for Maldives
 Business need
– Maldives was among the most severely
affected countries hit by the Asian
Tsunami on December 26, 2004
– UNDP initiated a study to analyze
Maldives’ high level of vulnerability and to
avoid the present scale of losses and
damage in the future
– Recovery and development planning to be
based on Disaster Risk Management
(DRM) strategy

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 Solution
– Countrywide study: 200 inhabited
islands out of a total of 1190 islands -
completed in a challenging timeframe of
6 months
– Hazards: Tsunami, Earthquake, Storms,
Floods, and Climate Change
– Vulnerability: Physical and Social
– Exposures: Buildings, infrastructure and
agriculture
– GIS base map developed
– GIS and CAT risk modeling integration
– Hazard and risk maps developed
» Assessments represented on a 5 point
ordinal scale
Historical
data
Hazard
Assessment
Physical Social
Risk Profiling
Individual hazards and
multi hazard
Risk indices by
island
Weights
Historical
data
Hazard
Assessment
Physical Social
Risk Profiling
Individual hazards and
multi hazard
Risk indices by
island
Weights
Hazard
zones
Vulnerability
Analysis
Storm
Tsunam
i
Earthqua
ke
SLR
Exposur
e
Damages/Los
ses
Affected
Population
Case Study: Developing a Disaster Risk Profile for Maldives

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 Benefits
– Comprehensive report and base
maps generated
– Government of Maldives used
the report as a key input for
planning developmental
strategies to mitigate future
disasters
– First GIS base map of Maldives
developed
Case Study: Developing a Disaster Risk Profile for Maldives
3-D view of bathymetry of Maldives (depth
in meters)

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Data Sources
 Public records data
county, city departments
– Census Data
 Other sources
– Satellite imagery, aerial
photos
– Administrative boundary
maps
– Land use/ Land cover maps

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Analysis: Land Use wise Distribution of Population
Flood Extent

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