Www.rmsi.com Delivering a world of solutions Disaster Management Framework for Preparedness...

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Delivering a world of solutions

Disaster Management Framework for Preparedness


Inderjit ClaireVice PresidentRMSI

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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• 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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• 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

StormTsunami

Earthquake

SLR

Exposure

Damages/Losses

Affected Population


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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


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

Shalabh

This is an example to show what this output can tellyou.- The slide first shows the Landuse map and then the ward boundary map. Over that is the final output derived using the method mentioned in the previous slide. In this final map each lansuse polygon represents number of persons residing in it.- An exaple shows the flood affected areas and the population can be estimated by overlaying flood extent over the final map to assess the population affected. This was not possible simply from either land use or Ward map.

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[email protected]

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