Www.rmsi.com Delivering a world of solutions Quantifying the Catastrophe Exposures from Cyclones, Floods and Earthquakes in 4 Indian States Adityam Krovvidi

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Delivering a world of solutions

Quantifying the Catastrophe Exposures from

Cyclones, Floods and Earthquakes in 4 Indian States

Adityam KrovvidiHead & General ManagerRisk Management Group, RMSI

June 3, 2003


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Application Software Development

Modeling Framework

Stochastic Module

Hazard Module

Vulnerability Module

Financial Module

Results & Discussion

Limitations

Introduction

Presentation outline

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Introduction

A World Bank initiative

RMSI study objectives – Risk assessment

– Inputs for decision-making

Scope

– Four states: AP, OR, GJ, MH

– Three perils: Cyclone, Earthquake, Flood

– Assets: Housing, Educational & Medical Buildings, Roads & Bridges

Model resolution: Block

Results

– Exposure databases

– Hazard & risk mapping

– Potential costs of disasters

Deliverables: A detailed report

Introduction

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Definition of Block

Introduction

Andhra Pradesh Andhra Pradesh district mapdistrict map

(23)(23)

Block map Block map (1134)(1134)

Average AP block

– 242 sq. km (95 sq. mi)

– 1.5 times Florida zip

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Introduction

Modeling Framework

Stochastic Module

Hazard Module


Financial Module


Limitations

Modeling Framework


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Modeling Framework

Probabilistic analysis for loss estimation

Modeling framework

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Introduction

Modeling Framework

Stochastic Module

Hazard Module


Financial Module


Limitations

Stochastic Module


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Historical Cyclones Catalog

Past in-house study

Catalog compilation

– Major source: IMD

– Track data: 1891-2000

– Parametric data: 1950-2000» Central pressure

» Forward velocity

» Radius to max wind

Intensity scale

– Modified Saffir-Simpson

– Categories 0 to 5

Stochastic module

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Cyclone Activity in Andhra Pradesh

Landfall rate = 73 in 110 years

Catastrophic events

– 1977 Chirala - CAT5

– 1979 Ongole - CAT4

– 1984 Srihari Kota - CAT1

– 1989 Kavali - CAT4

– 1990 Machalipatnam - CAT3

– 1996 Kakinada - CAT1

Stochastic module

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Stochastic Events Generation

Coastline segmentation

– The 50 nmi gates capture the complex orientations

Simulation of events on each gate

– Develop CDFs for cyclone parameters» Central pressure

» Forward velocity

» Angle of landfall

– Stratified sampling of CDFs

– Events defined by random matching of parameters

– Pattern matching with historical tracks

4800

Stochastic events

Stochastic module

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Introduction

Modeling Framework

Stochastic Module

Hazard Module


Financial Module


Limitations

Hazard Module


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Windfield modeling

Georgiou’s (1985) model adopted

Model parameters

– Pressure drop

– Forward velocity

– Track angle with site

– Radius to max wind

– Distance to site

Calibration of coefficients

– Historical storms reconstruction

Directional roughness

Peak gust wind speed at site

Validation

Hazard module

Site

R

Cyclone Track

(, P, VT, Rmax)

§

§

§

§

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Rainfall modeling

Model parameters

– Hourly precipitation rate

– Translational speed

– Size of cyclone

Hourly precipitation rate

– Jayanti (1987)

– Depends on:» Intensity of storm

» Sector & radius to site

– Modified for higher CATs

Significant size = 300 km radius

Integration at block centroid

Validation

Hazard module

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Storm Surge modeling

Nomogram based model

– Ghosh (1977, 1983)

Model parameters

– Central pressure

– Radius of max wind

– Forward velocity

– Angle of track to coastline

– Bathymetry

Profiles along and across the coast

Flood depth computations

– 100 m x 100 m DEM used

Surge tide at important towns on coast

Validation

Hazard module

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Cyclone Hazard Mapping

Hazard module

Wind speed

Rainfall

Storm surge

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Introduction

Modeling Framework

Stochastic Module

Hazard Module


Financial Module


Limitations



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Assets Inventory

Housing inventory in Andhra Pradesh

Vulnerability module

Buildings: residential, education & medical

– Source: Census 1991 projected to 2001

Roads & bridges: NH, SH, MDR, ODR, VR

– Source: Remote sensing images

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Vulnerability Functions – Overall Approach


Benchmark Benchmark curvescurves

(Intl. experience)(Intl. experience)

Reported loss Reported loss datadata

Damage data Damage data fromfrom

Event recon.Event recon.

Domestic Domestic Published Published Research Research

Base Base vulnerability vulnerability

Function Function (composite)(composite)

Engineering Engineering reviewreview

(Vul. Atlas, IS (Vul. Atlas, IS codes)codes)

InventoryInventory

++

+

Vulnerability Vulnerability FunctionsFunctions

Peakgust

MD

R (

%)

+

+

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Vulnerability Functions – Key Features

Considered

– Independent effects of wind, rainfall & storm surge

– Occupancy type: residential, educational & medical

– Building type based on wall+roof material

– Road type » Major- NH, SH, & MDR

» Minor- ODR & VR

Explicitly not considered

– Building age & height


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Introduction

Modeling Framework

Stochastic Module

Hazard Module


Financial Module


Limitations

Financial Module


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Exposure

Modeled assets

– Housing

– Public infrastructure» Roads & bridges

» Educational institutions

» Medical facilities

Valuation at 2001 prices

Financial module

Exposure is the total value or replacement cost of assets that is at risk

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AAL at district level in Andhra Pradesh

Model Validation - Detailed

Financial module

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-

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

1977 Chirala 1979 Ongole 1990Machilipatinam

1999 Orissa

Modeled Loss (Crore Rs.)

Observed Loss (Crore Rs.)

Model Validation - Aggregated

Financial module

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Average Annual Loss (AAL)

Financial module

AAL is the expected loss per year when averaged over a very long period

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Exceeding Probability (EP) Curve

Financial module

EP curves are cumulative distributions that show the probability that losses will exceed a certain amount

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Introduction

Modeling Framework

Stochastic Module

Hazard Module


Financial Module


Limitations



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Exposure Summary

45% of housing exposure in coastal districts of AP & OR

OR has lowest exposure due to poor economic status

Housing contributes about two-thirds in total exposure


0

10,000

20,000

30,000

40,000

50,000

60,000

ANDHRA PRADESH GUJARAT MAHARASHTRA ORISSA

Housing

Roads &Bridges

Education

Medical

Million $

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AAL Summary

AAL as % of exposure

– AP = 0.2%

– GJ = 0.1%

– MR = 0%

– OR = 0.3%

The long-term average losses are driven by Cyclones in AP, GJ & OR

AP has highest Cat risk and MR has least

0

10

20

30

40

50

60

70

80

90

Millions

AP GJ MR OR

EQ

FL

CY


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Loss Cost Summary

Risk modelers consider loss cost as AAL per thousand dollars of exposed value. The major advantage of loss cost over AAL is that it can be compared across perils, coverages, geographies, etc.

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

AP GJ MR OR

CY

EQ

FL

FL JP AP GJ OR

Cyclone 1.56 0.27 1.55 0.76 3.22

CA JP GJ MR

Earthquake 2.51 1.67 0.52 0.05 Housing damage potential

compared globally

OR cyclones have damage potential double that of Florida hurricanes

AP cyclones have the same potential as Florida and GJ’s potential is one-half of AP

GJ earthquakes have damage potential 10 times more than that of Maharashtra. However, it is 3 and 5 times lower than Japan and California


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EP Curves Summary

0.0%

5.0%

10.0%

15.0%

20.0%

- 500 1,000 1,500 2,000 2,500 3,000

Aggregate Loss (Million USD)

An

nu

al E

xcee

din

g P

rob

abili

ty

Andhra Pradesh Gujarat

Orissa Maharashtra

Loss return periods of historical events

– 2001 GJ earthquake, M7.9: 195 years ($1183 mn)

– 1999 OR cyclone, CAT5: 123 years ($1074 mn)

– 1993 MR earthquake, M6.3: 474 years ($127 mn)

– 1990 AP cyclone, CAT4: 37 years ($502 mn)


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Probable Maximum Loss (PML) Summary

There is no common approach or unified definition to evaluate PML. Since developing economies cannot afford to plan for a high risk tolerance a 150 year PML is suggested

0

200

400

600

800

1,000

1,200

Millions

AP GJ MR OR

Combined

Housing

Infrastructure

PML as % of exposure

– AP = 2.1%

– GJ = 2.1%

– MR = 0.1%

– OR = 3.2%

GJ needs $1 billion for Cat risk preparedness, closely followed by AP


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Financial Impact on States

Maharashtra is comfortably placed

Cat losses are unbearable given the debt and fiscal deficit position

PML has a significant impact on state GDP

States have no capacity to absorb PML shock

Orissa is the worst affected

StateAAL

(million $) %GDP %Tax rev%Fiscaldeficit

PML(million $) %GDP %Tax rev

%Fiscaldeficit

AP 83 0.3% 2.6% 5.5% 921 3.3% 28.7% 61.5%

GJ 65 0.3% 2.8% 2.1% 1,009 4.4% 43.7% 32.8%

MR 3 0.0% 0.1% 0.1% 59 0.1% 1.1% 2.7%

OR 43 0.6% 3.8% 1.8% 479 6.5% 41.9% 19.9%


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Introduction

Modeling Framework

Stochastic Module

Hazard Module


Financial Module


Limitations Limitations


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Limitations of the Study

Limitations

Limitations are driven by the resource constraints and objectives

Census 2001 data was not available at the time of study

No detailed inventory – floor area, age, height, etc.

No boundary data below the block, say post code

Uncertainty not quantified

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[email protected]

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Www.rmsi.com Delivering a world of solutions Quantifying the Catastrophe Exposures from Cyclones, Floods and Earthquakes in 4 Indian States Adityam Krovvidi