Quantifying the Catastrophe Exposures from Cyclones, Floods and Earthquakes in 4 Indian States

1. Quantifying the Catastrophe Exposures from Cyclones, Floods and Earthquakes in 4 Indian States Adityam Krovvidi Head & General Manager Risk Management Group, RMSI June 3, 2003 Delivering a world of solutions

2. Modeling Framework • Stochastic Module • Hazard Module • Vulnerability Module • Financial Module • Results & Discussion • Limitations • Introduction Application Software Development Presentation outline

3. 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

4. Definition of Block • Average AP block • 242 sq. km (95 sq. mi) • 1.5 times Florida zip Andhra Pradesh district map (23) Block map (1134) Introduction

5. Introduction • Modeling Framework • Stochastic Module • Hazard Module • Vulnerability Module • Financial Module • Results & Discussion • Limitations • Modeling Framework Application Software Development Presentation outline

6. Modeling Framework • Probabilistic analysis for loss estimation Modeling framework

7. Introduction • Modeling Framework • Stochastic Module • Hazard Module • Vulnerability Module • Financial Module • Results & Discussion • Limitations • Stochastic Module Application Software Development Presentation outline

8. 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

9. 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

10. 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

11. Introduction • Modeling Framework • Stochastic Module • Hazard Module • Vulnerability Module • Financial Module • Results & Discussion • Limitations • Hazard Module Application Software Development Presentation outline

12. Site § (, P, VT, Rmax) R § § Cyclone Track § 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

13. 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

14. 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

15. Cyclone Hazard Mapping • Wind speed • Rainfall • Storm surge Hazard module

16. Introduction • Modeling Framework • Stochastic Module • Hazard Module • Vulnerability Module • Financial Module • Results & Discussion • Limitations • Vulnerability Module Application Software Development Presentation outline

17. Assets Inventory • Buildings: residential, education & medical • Source: Census 1991 projected to 2001 • Roads & bridges: NH, SH, MDR, ODR, VR • Source: Remote sensing images Housing inventory in Andhra Pradesh Vulnerability module

18. Domestic Published Research Inventory Vulnerability Functions Base vulnerability Function (composite) + + Engineering review (Vul. Atlas, IS codes) Damage data from Event recon. + + + Reported loss data Benchmark curves (Intl. experience) MDR (%) Peakgust Vulnerability Functions – Overall Approach Vulnerability module

19. 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 Vulnerability module

20. Introduction • Modeling Framework • Stochastic Module • Hazard Module • Vulnerability Module • Financial Module • Results & Discussion • Limitations • Financial Module Application Software Development Presentation outline

21. Exposure Exposure is the total value or replacement cost of assets that is at risk • Modeled assets • Housing • Public infrastructure • Roads & bridges • Educational institutions • Medical facilities • Valuation at 2001 prices Financial module

22. Model Validation - Detailed AAL at district level in Andhra Pradesh Financial module

23. Model Validation - Aggregated Financial module

24. Average Annual Loss (AAL) AAL is the expected loss per year when averaged over a very long period Financial module

25. Exceeding Probability (EP) Curve EP curves are cumulative distributions that show the probability that losses will exceed a certain amount Financial module

26. Introduction • Modeling Framework • Stochastic Module • Hazard Module • Vulnerability Module • Financial Module • Results & Discussion • Limitations • Results & Discussion Application Software Development Presentation outline

27. 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 Million $ Results & Discussion

28. 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 Results & Discussion

29. 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 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. • 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 Results & Discussion

30. EP Curves Summary • 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) Results & Discussion

31. 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 • 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 Results & Discussion

32. State AAL (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% 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 Results & Discussion

33. Introduction • Modeling Framework • Stochastic Module • Hazard Module • Vulnerability Module • Financial Module • Results & Discussion • Limitations Application Software Development • Limitations Presentation outline

34. Limitations of the Study • 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 Limitations

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