Belize benefits from knowledge and experiences from the PPCR
Dr Rashmin Gunasekera Senior Disaster Risk Assessment and Financing Specialist – Consultant World Bank Group
Advances in exposure modelling and implications of using aggregated modelling
for the insurance industry
Photo Credit: http://gov.vc
Outline – Be aware of your exposure model!
2
Public sector and need for exposure models
– Use of catastrophe models
– Global exposure models
Exposure modelling
– Watch out! Sensitivity of exposure models
– Case Study of Antigua
– Future directions and applications to the industry
Implications to national DRFI initiatives
Public Sector: Use of cat. models
Reserve mechanisms, risk transfer, insurance,
budget appropriations
infrastructure retrofitting, education, building codes,
risk mitigation
• GED4GEM
– Multi scale approach
– Residential and Non Residential typology integration
• UN-ISDR Global Assessment Report
– Building type classifications for different size categories of settlements
Publicly available Global Exposure Models
GED4GEM example and web portal:
Source: http://beta.globalquakemodel.org/media/storage/GEMSAMDEC2012_7_ExposureDatabase_Gamba.pdf
• A country level top down methodology to derive exposed asset values (exposure modelling) using openly available information
Being broad-scale in the approach but highly adaptive and flexible → Using highest quality data where available
• Development of an harmonized set of CDRPs for LAC, with a vision of expanding…
• Develop 3 different databases – exposure inventory database (property), disaggregated GDP database and a physical stock infrastructure database.
WB (LAC) - Country Disaster Risk Profiles
$
Physical Location, Size and Shape
Administrative Area
Land Ownership
Land Use
Construction Period
Structural Characteristics
Demographic or Social Characteristics
Replacement value
EXPOSURE DATABASE
The types of exposure information that improves model outputs
Exposure modelling and RS
Top-down approach =
disaggregation
Corine Landcover
ISA
Globcover
Landscan
Gallego method using census data
Urban / rural areas
Bottom-up approach =
building inventory
Crowd-sourcing
Local surveys, building
inventories
Google SketchUp
3D urban models
(e.g.Bing3D)
Remote sensing
(e.g.SPOT)
7
Exposure data requires some modelling to:
– Correctly geo-locate it
– Geocoding
– Disaggregation
– Enhance it – other asset characteristics (e.g. predominant building type in area)
Exposure models can be created by top-down or bottom-up approaches
Identifying built up (≠ urban) areas
Which dataset to use?
GUF 12m + GUF 1km (>15%) + BUREF 1km (>5%)
Satellite Imagery + BUREF 1km (>5%) + GUF 75m + GUF 1km (>15%) No built-up area in MODIS500
Satellite Imagery + BUREF 1km (>5%) + LandScan2012 Explanation for the BUREF detection; LandScan model ‘bug’
Antigua & Barbuda building distribution
Building typology distribution – Antigua (Residential – ext. wall)
PAGER Description PAGER Res. CDRP Res. study
Urban
Res. Rural res. St. John's city Rural A/B Antigua&Barbuda
W1 Wood 44% 44% 41.0% 22.0% 26.8%
W2
Wood&
conc. 33% 33% 18.0% 12.0% 13.5%
C Concrete 23% 23% 41.0% 66.0% 59.6%
Weight 25.4% 74.6% 100.0%
Temporal changes (hazard and exposure)
Current and future applications
• ImageCat – Inhance Exposure data visualization/mgmt. platform
• Project Sensum: ECA Omni-directional exposure collection integrate data from remote-sensing satellites such as the future SENTINEL-2 (left, ESA website) and ground-based camera surveys (right) into multi-spatial scale vulnerability data sets (centre).
Students learning on use of mobile mapping technology in Dominica
Use of Smartphone in capturing real time data
Innovation
community
mapping
World Bank SDN FORUM 2012, February 21, 2012
Cost/ day
($/day)
Time t1 t2
Risk Reduction
Investment (I)
Disaster
Direct Losses
Maintenance
Loss with retrofitting (Lw)
Loss without retrofitting (Lw/o)
tn
Savings
Average Rate of Return
Risk Reduction: Cost Benefit Analysis
Source: Natural Hazards, Un-natural disasters: The economics of effective prevention (2010)
World Bank disaster risk financing instruments
Address immediate liquidity needs Manage and transfer financial risks to the market
Risk Tran
sfer
Risk
Rete
ntio
n
MultiCat Program Insurance-
linked Securities
Facilitates issuance of multi region, multi-peril cat bonds
CCRIF Insurance
Pools
Regional facility pooling risks to cover against natural disasters in 16 Caribbean countries (earthquake & hurricane)
Cat DDO Contingent
Loans
Provides immediate liquidity following a natural disaster
Weather Derivatives
Pro
bab
ility
of
Eve
nt
Seve
rity
of
Imp
act
Minor
Major Low
Insures against weather-related losses, based on an index
High
Conclusions
22
Public sector and need for exposure models
– Use of catastrophe models
– Global exposure models
Exposure modelling
– Watch out! Sensitivity of exposure models
– Case Study of Antigua
– Future directions and applications to the industry
Implications to national DRFI initiatives
• Author wishes to acknowledge contributions to this presentation from Oscar Ishizawa, Christoph Aubrecht and Antonios Pomonis of World Bank Group.
• Results presented are part of the on going Country Disaster Risk Profile (CDRP) study.
• Therefore, presented estimations and results should be considered as preliminary.
• These slides should also be treated confidentially and not to be shared without prior authorization from the authors (contact: [email protected])
• The World Bank Group takes no responsibility for the content of this presentation.
Disclaimer | Work in Progress
