Disaster Management Framework for Preparedness Inderjit Claire Vice President RMSI October, 2007 Delivering a world of solutions Delivering a world of solutions www.rmsi.com.
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Transcript Disaster Management Framework for Preparedness Inderjit Claire Vice President RMSI October, 2007 Delivering a world of solutions Delivering a world of solutions www.rmsi.com.
Disaster Management Framework for Preparedness
Inderjit Claire
Vice President
RMSI
October, 2007
Delivering a world of solutions
Delivering a world of solutions
www.rmsi.com
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 worldwide 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
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
Risk
Assessment
Risk Mitigation Investments
Warning and Monitoring Systems
Hazard Mapping and Land Use Planning
Code Refinement and Enforcement
Hazard Specific Risk Mitigation
Catastrophe Risk Financing
Ex-Ante Funding Arrangements
Catastrophe Insurance Pools
Reserve Funds
Contingent Capital Facility
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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
Calculating the hazard coefficients for
stochastic events generated.
• 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.
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Disaster Risk Modeling Process
Calculating the vulnerability and
exposure of the area against
disasters.
• 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.
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Disaster Risk Modeling Process
Calculating the loss from disasters
• 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.
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Vulnerability
Scale
Indices
National
comparisons of
vulnerability
National
Vulnerability
parameters
Users
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
Ecosystems
Water
Other
sectors
Food
Health
At what scale
theneed
do we
vulnerability
to carry out
mapping
the
needs to be
vulnerability
done
mapping
Settlement
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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
• Physical vulnerability
• Social vulnerability is
refers to the degree to
which an asset would
get damaged or
destroyed in a
hazardous environment
caused by catastrophic
events
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
• Physical vulnerability
can be for residential
and commercial
buildings, critical
facilities, infrastructure
and agriculture
• Population Data
reflecting the age,
gender, ethnicity and
household type
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Exposure Module: Use of Statistical Data
• 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
Exposure Module calculates how much of the population and
buildings are ‘exposed’ to the natural hazard
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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
Residential Exposure
in billion rupees
Results: Various loss results including average
Residential exposure at block level in billion rupees
annual losses (AAL), loss costs and probable
maximum losses (PML)
Alternatives used: Remote Sensing techniques
were used to generate the unavailable data
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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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Case Study: Developing a Disaster Risk Profile for Maldives
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
Exposur
e
Hazard
Assessment
Tsunam
Storm
i
Earthqua
ke
Hazard
zones
SLR
Vulnerability
AnalysisSocial
Physical
Damages/Los
ses
Affected
Population
Risk Profiling
Weights
Individual hazards
hazards and
Individual
multi hazard and
multi hazard
Risk indices by
island
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Case Study: Developing a Disaster Risk Profile for Maldives
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
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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