Pacific Catastrophe Risk Assessment and Financing Initiative

1. Pacific Catastrophe Risk Assessment and Financing Initiative Bishwa Pandey GIS Product Manager AIR Worldwide Corporation Suva, Fiji. Nov 24, 2010

2. PCRFI Project • Lead by the World Bank in partnership with • the Global Facility for Disaster Reduction and Recovery (GFDRR) • the Japan Policy and Human Resources Development Fund (PHDRD), • the Australia Overseas Aid Program (AUSAID), • the Asian Development Bank (ADB), • the Pacific Islands Applied Geoscience Commission (SOPAC), and the Pacific Islands Forum Secretariat • AIR Worldwide provides the risk modeling work

3. PCRFI Considers 15 Pacific Island Countries (PICs) • Cook Islands (CK) • Fiji (FJ) • Federated States of Micronesia (FM) • Kiribati (KI) • Republic of Marshall Islands (MH) • Niue (NU) • Nauru (NR) • Palau (PW) • Papua New Guinea (PG) • Samoa (WS) • Solomon Islands (SB) • Timor-Leste (TP) • Tonga (TO) • Tuvalu (TV) • Vanuatu (VU)

4. Risk Assessment and modeling Hazard Assessment Tropical Cyclone Earthquake Exposure Database Population Buildings Infrastructure Crops Engineering Vulnerability Damage Risk Model – probabilistic model

5. Components of a Catastrophe Risk Model LOSS MITIGATION Risk Transfer Vehicle

6. Components of a Catastrophe Risk Model Where? How big? How frequent? LOSS MITIGATION Risk Transfer Vehicle Risk Transfer Vehicle

7. Components of a Catastrophe Risk Model LOSS MITIGATION Risk Transfer Vehicle Risk Transfer Vehicle

8. Components of a Catastrophe Risk Model Population Buildings Infrastructure Crops LOSS MITIGATION Risk Transfer Vehicle Risk Transfer Vehicle

9. Components of a Catastrophe Risk Model LOSS MITIGATION Risk Transfer Vehicle Risk Transfer Vehicle

10. Component 1: Hazard data and loss data collection management

11. Component 1a: Hazard Literature Review (Completed) • Hundreds of articles and reports collected related to • Regional Earthquake Hazard • Regional Tropical Cyclone Hazard • Consequences generated by Earthquakes and Tropical Cyclones in the region • List of selected references included in Component 1 report • 650Mb of references (mostly pdf files) ready to be delivered

12. Component 1b: Database of Historical Earthquakes and Tropical Cyclones (Completed) • Raw data collected from multiple sources (e.g., global and regional databases, articles and reports) • Data cleaned, processed, and augmented • Final geo-referenced catalogs • Earthquakes (1768-2009): 114,131 events (32,569 with M≥5) • Tropical Cyclones (1948-2008) 2,422 events in total (1,438 in the Northern Hemisphere and 984 in the Southern) with central pressure below 1,020mb

13. Map of Historical Eatrhquake Events by Magnitude

14. Map of All Historical Tropical Cyclones (2,422 Events)

15. Component 1c: Consequence Database (Completed) • Raw data collected from multiple sources (e.g., global and regional, public and proprietary databases, articles and reports) • Data cleaned and processed • Sources acknowledged and inconsistent data preserved • Events linked to those in the historical database, when possible • About 450 disaster entries from 1831 - 2009 with quantitative data for economic loss and life loss reported for 50% of entries • Key data fields include – • Total Number of People Affected • Estimated Total Economic Loss • Time-of-event loss • Trended loss to current values via a macro-economic approach considering exposure growth • Total Life Loss • Total Injured • Number of Buildings Damaged or Destroyed • Evidence of Crop Damage

16. Number of Database Entries (≈ 450) Peril Type Country

17. Map of 120 Known Historical Earthquakes That Caused Consequences in PICs M8.1 9/29/2009 Samoa and Tonga 192 fatalities 3,000 homeless 130M-220M USD in losses

18. Map of Historical Tropical Cyclones with Known Consequences in PICs (Southern Hemisphere Only)

19. Seismogenic Sources in the AIR model

20. Component 1e: Data Collection, Processing and Development • Administrative Boundaries • Population Census Data • Agricultural Census Data • Surface Geology Maps • Topographic maps • Land cover / land use maps • Surface soil maps • Bathymetry maps • Infrastructure maps (e.g., roads, bridges, Utilities, etc.) • Geodetic and Fault Data • …

21. Data Collection Progress – Samoa

22. Component 2: Exposure data collection and management

23. Component 2: Exposure Database Exposure database contains • Population Extracted from census data, processed and trended for growth • Buildings GIS database of counts and attribute data of varying resolution • Infrastructure GIS database of counts and attribute data of varying resolution • Crops GIS database of crop types

24. Component 2a: Population Database (Completed) • Data Collected from multiple sources (e.g., SPC – PopGIS and PRISM --, SOPAC, DIGO, UNPG, GMI, etc.) • Process • Use PopGIS where available • Use country specific census when PopGIS is not available • Reconcile the country total with boundary files at all available resolutions • Project population to 2010 for all available resolutions

25. Population Database – Fiji by Province

26. Population Database – Fiji by Tikina

27. Population by Enumeration Area - Fiji

28. Component 2b: Building Exposure Database (in Progress) It includes the following four cases: • Buildings digitized and field verified building footprints (~80,000 by GNS/SOPAC/PDC) • Buildings digitized building footprints with attribute data but not field verified (~375,000 SOPAC and AIR) • Buildings counts in 39 selected contiguous urban areas (estimated by AIR using satellite imagery and population database) • Buildings counts in all rural settlements identified by AIR from low- and moderate-resolution satellite imagery (~370,000 100 m x 100 m cells with settlements) ~ 1,000,000

29. Example of Moderate-Resolution SPOT Imagery in Papua New Guinea EO-1 (Cyan), LANDSAT (Yellow)

30. Settlement Boundaries Identification and Building Count Estimation • Settlements located from low- and moderate resolution imagery using proprietary algorithms • Number of dwellings per settlement estimated based on population census • Number of residential buildings estimated based on average number of dwellings per building and total dwelling count • Number of commercial and public buildings estimated using statistical techniques based on country-specific data

31. Example: Samoa Village level population census data for Samoa

32. Component 2c: Infrastructure Database (in Progress) • Most of the infrastructure database are secondary databases collected from third party sources and supplemented with data digitized by AIR and SOPAC and with field data collected by GNS/SOPAC/PDC • Major infrastructure are digitized as they are seen in high resolution imagery

33. Example of Digitized Infrastructure Asset Zoomed Portion showing digitized Infrastructures overlaid on Satellite Imagery Above figure showing the Infrastructures facilities in CD5 (Sub-station) digitized in Timor Leste) for validation.

34. Component 2d: Crop Classification (Almost Completed) Pre Processing Supervised classification of crops using LandSAT ETM or EO-1 and SPOT Post processing Multiple iteration of QA/QC using high resolution imagery, field survey data, Google Earth Imagery, DIGO vector and aerial photo third party maps/data/report and SPOT Imagery QA Ongoing Local Experts Reclassification Based on post- processing, major crops digitized directly on high-resolution imagery and another iteration of QA/QC is done

35. Crop Classification – Limitations • Vintage of the satellite imagery Mostly 2001/2003 • Resolution of the imagery - Most of the imagery scenes used had a resolution of 10m or 30m. Some high resolution imagery was used in selected areas • Cloud cover in the imagery, which is non-negligible in some areas of these countries • Limited ground truthing – Most of the truthing was done via Virtual truthing using high resolution satellite imagery where available • Difficulty in categorizing particular crop when there is mixed land use. • Seasonality of crops vis-à-vis no continuous coverage of satellite imagery . The signature on satellite imagery of some crops (e.g., sugar cane) may be different in different seasons of the year. For example, certain crops, such as sugar cane, are in a fallow state in some parts of the year. • Unavailability of existing maps and other information there may be in possession of local governments and organizations.

36. Component 3: Country Catastrophe Hazard and risk models

37. Component 3a: EQ and TC Hazard Model • Earthquake (ground shaking + tsunami) and Tropical Cyclone Hazard Models thoroughly Reviewed by Geoscience Australia • Main GA scientists involved • David Robinson and Alanna Simpson (POCs) • Trevor Allen (EQ ground shaking) • Nick Horspool (Tsunami) • Craig Arthur (TC) • “It is my pleasure to inform you that we have reviewed AIR’s work in the SW pacific. We are pleased to confirm that the work is of a high standard, thorough and representative of best practice”

38. Statistics in the simulated 10,000yr Stochastic Catalog

39. Simulated Events in 10,000yr Catalog: Magnitude 6 to 7

40. Simulated Events in 10,000yr Catalog: Magnitude 7 to 8

41. Simulated Events in 10,000yr Catalog: Magnitude ≥ 8

42. Seismic Hazard Maps for 15 PICs • Seismic Hazard maps for 15 PICs available for the first time • Earlier maps available only for some countries (e.g., Fiji, PNG and Vanuatu) did not include local soil conditions • Maps developed for three ground motion parameters • Horizontal Peak Ground Acceleration (PGA) • Elastic 5%-damped Spectral Acceleration at 1.0s – Sa(1.0s) • Elastic 5%-damped Spectral Acceleration at 0.3s – Sa(0.3s) • Maps available for mean return periods (MRPs) ranging from 72yrs (50%PE/50rs) to 2,500yrs (2%PE/50rs)

43. Fiji: PGA hazard maps for 100yr, 500yr, and 2,500yr MRPs

44. Component 3a: Tropical Cyclone Model • Statistics in the simulated 10,000yr Stochastic Catalog

45. Tropical Cyclone Historical Catalog over last 25 years for Category 4 and 5 Storms Only

46. Tropical Cyclone Historical Catalog over last 25 years

47. What’s Next? • Building vulnerability • Identification building classes in each country and their frequency in urban and rural settings • Identification of vulnerability classes from building classes • Development of Damage functions for wind, flood, ground shaking and tsunami waves • Infrastructure Vulnerability • Crop vulnerability • Impact on Population • Country risk profile

48. The 2007 Solomon Islands Tsunami (example from Phase I)

49. Country Risk Profiles: Tropical Cyclone and Earthquake Risk Combined (Example from Phase I)