Probabilistic catastrophe models for disaster risk reduction

Transcription

1 Probabilistic catastrophe models for disaster risk reduction Robert Muir-Wood Chief Research Officer, RMS Disaster Risk Reduction for Natural Hazards Putting Research into Practice UCL Nov 5 th 2009

2 The function of insurance Insurance is based on the ability to transfer potential unforeseen accidental costs to a counterparty in exchange for an ex-ante fee Counterparty requires less capital gearing to support a payout if the risk can be diversified Diversification assumption breaks down under risk correlation = catastrophe which is why insurers buy insurance (from reinsurers) The insurance food-chain is driven by the need to transfer risk onto an entity who can achieve further diversification Insured to Insurer to Reinsurer (to Capital Markets) 2

3 The motivation of Catastrophe Modeling Before the early 1990s insurers and reinsurers would: Use the concept of the probable maximum loss PML Employ (recent) historical scenarios for looking at expected losses Measure their aggregate accumulations in zones Catastrophe Modelling Replaced the PML with the exceedance probability EP curve Recognised that the next catastrophe will not be a repeat of a historical catastrophe Recognised that aggregates can only be managed probabilistically Emerged through the 1990s to become mainstream for how insurers and reinsurers managed catastrophe risk 3

4 Catastrophe Modeling tools 4

5 Catastrophe Loss Modeling of Hurricane 90% $ Loss Define Hurricane Assess Wind Speed Calculate Damage Quantify Financial Loss Stochastic Hazard Vulnerability Financial 100,000 years of simulation of physical parameters of each storm Wind and flood footprints Damage as a function of hazard value and exposure Loss distribution after contract structures

6 Annual Probability of Exceedence (%) Exceedance Probability (EP) Output year loss = $131 million M 100M 150M 200M 250M 300M Loss Amount

7 RiskLink v4.3: Geography of Risk ME VT NH NY MA CT RI PA NJ DC WV DE VA NC SC MS TX AL GA LA FL HI Confidential Residential Woodframe Losscost ($/1,000) > to to to to to to 0.05 <

8 A specific Scenario the windfield of 2008 Hurricane Ike Stochastic events may be modelled to have less random complexity than real events However may need to capture localized correlation in hazard field As a new smaller eyewall develops, high winds also move to the left of the track

9 Perspectives on Catastrophe risk Alternative perspectives of: Hazard maps The EP curve Expected Loss at key return period Average Annualised Loss Mapped risk costs Scenario events Each perspective is useful No single perspective provides all the information needed to manage catastrophe risk One needs to scroll through the perspectives 9

10 Hazard Mapping (Step 1 in Risk Quantification) Examples of Best Practice 10

11 Best Practices Key to tables Hazard Map Producing Authority Type of map Parameters Risk Boundaries Information Retrieval Method Access Coverage Either risk based (probabilistic/historical) or forecast Damaging agent A, damaging agent B, damaging agent C Boundaries for damaging agent A, boundaries for damaging agents B and C Preparedness, evacuation or response information How the data is retrieved by the user (e.g. address search or coordinates) How the user initially locates the map or the search form Stars approximately represent the coverage of the risk area e.g. US hurricane maps achieve coverage despite only including the East/Gulf Coast Key to colours Very Good Good Average Poor Very difficult to improve Sufficient but minor improvements possible Functional but requires some improvement Very limited requires substantial improvement

12 Tsunamis Maximum wave heights along the WA coast for return periods of a) 100 years, b) 500 years, c) 1000 years, d) 2000 years Tsunami Threat Map FESA Western Australia Type of map Probabilistic Parameters Wave height Risk Boundaries 100/500/1000/2000 year RP Information None Retrieval Method Not searchable Access Map available on FESA website, full report linked from GA website Coverage Covers entire area at risk from tsunamis

13 Earthquakes Screenshot of the information page showing the risk level of hazards, the checklist of suggested steps to prepare and the tabs to view flood and fire risk My Hazards CALEMA Type of map Probabilistic Parameters Shaking, liquefaction, landslide, fault rupture Risk Boundaries High/medium, at risk areas Information Preparedness information tailored to each risk level, multi-format Retrieval Method Search by zip/address/coordinates Access Prominent banner link from California state and emergency website Coverage Covers all areas of the state of California

14 Tropical Storms NHC map of the Gulf Coast showing return periods for Category 5 hurricanes Hurricane Preparedness NHC/NOAA Type of map Probabilistic Parameters Intensity (Saffir-Simpson scale) Risk Boundaries Exact return periods for 75nm areas Information None (available in other sections of NHC website) Retrieval Method Selectable by three areas of coast (Gulf, Southeast, Northeast) Access Link from NHC website Coverage Covers all of East and Gulf coast but only at discrete points

15 Storm Surges Example of 1% flood heights for the French Quarter, New Orleans New Orleans Risk US Army Corps of Engineers Type of map Probabilistic Parameters Height of inundation Risk Boundaries 2%/1%/0.2% annual probabilities on separate maps (50/100/500 year RP) Information None (available in other sections of New Orleans Risk website) Retrieval Method Selectable by city area Access Whilst this is easily available on NOR website, links should be available from more obvious sites such as Louisiana or New Orleans disaster planning sites Coverage Covers New Orleans and Plaquemines but not entire NO metro area

16 Floods Screenshot of map showing regions of likely flooding, extreme flooding and protected zones. Data on specific location risk is available by clicking on the map Flood Map England and Wales EA Type of map Probabilistic Parameters No information Risk Boundaries 1%/0.5%/0.1% annual chance of flooding (100/200/1000 year RP) Information Tailored information for each flood risk category Retrieval Method Search by postcode or browse map Access Prominent link from Environment Agency website Coverage Covers all of England and Wales

17 Volcanoes Extract from hazard map for Mt. Rainier, Washington Mt. Rainier USGS Type of map Parameters Risk Boundaries Information Retrieval Method Access Coverage Probabilistic Lahars, all other volcanic hazards 1-100/ / year RP, year RP None (available on other sections of USGS volcano site) Viewed by volcano Easily located from main USGS hazards page Detailed mapping only covers areas surrounding major volcanoes

18 Landslides Extract from the IFFI GIS map. Dark green areas are at risk from superficial landslides, Light green areas have COLAMENTO RAPIDO, Purple hatching represents urban development and Red areas have experienced collapse or tipping. Progetto IFFI ISPRA Type of map Probabilistic/Historical Parameters Superficial landslides, subsidence, collapse, expansion Risk Boundaries Only given as at risk areas Information None Retrieval Method By address or area Access Theoretically easily accessed but site frequently goes down Coverage All of Italy covered CHECK THIS

19 Indonesia Earthquake, 27 th May 2006 Casualties: 5,800 Losses: US$3100million Magnitude 6.2 event some 25km South- West of Yogyakarta Despite relatively low intensity, extremely shallow depth meant it was very damaging Most casualties in the Bantul district Indonesia Eq, Fl, Ls, Ts, Vo Earthquakes Floods Landslides Tsunamis Volcanoes Probabilistic Ground shaking 475/2500 year RP Well reported on the BMG website, some Linked from the PVMBG website. More No search function information on hazards detailed map from USGS P/F Flooding High/Medium/Low risk Forecast for up to 2 months available, Maps linked from BMG website, but Selectable by area information available information harder to find Probabilistic Landslide High/Medium/Low risk Plenty of information available on PVMBG Selectable by area Linked from the PVMBG website website Historical Damaged area - Preparedness road shows/online Not directly available on Indonesian Selectable by area information and early warning websites Debris avalanches, pyroclastic flows, Worst case limits, 200/300/500/700 m Worst Case 3 /s lateral blasts, tephra, lahars volume flux Hazard levels and information for major PVMBG information easily available. PVMBG information selectable by volcano volcanoes is on PVMBG. Detailed Merabi map in a paper.

20 Myanmar Cyclone Nargis, 2 nd May 2008 Casualties: Estimates range from 100,000 upwards Losses: Estimated US$4billion-10billion Most damage and casualties caused by storm surge No accurate loss/casualty figures available Several million left homeless Myanmar Eq, Fl, Tr/Ss, Ts Earthquakes Floods Tropical Storm Storm Surge Tsunami Probabilistic Ground shaking 475 year RP Reports of significant seismic activity on DMH website. No information No search function Not linked from any Myanmar government website None DMH provides flood warnings. - Information links on DMH website do not function None DMH provides tropical storm and storm surge warnings. - Information links on DMH website do not function None None - None

21 Implications of the hazard information deficit Cyclone Nargis in May 2007 highlighted the implications of a situation where hazard data is unavailable No flood hazard maps in the Irrawaddy Delta People did not know they lived in a storm surge flood zone No evacuation plans No monitoring of cyclone forecasts As a result c 100,000 died through an information deficit Economic and health consequences will endure for years Accessible information on its own can provide powerful disaster risk reduction Mapped hazard information should be a 21 st Century universal right 21

22 Probabilistic Catastrophe risk modeling with application for improved disaster resistance 22

23 The megacities of South America Quito 1.8M RMS Research Trip Santiago 5.9M Bogotá 8.5M Caracas 7.0M RMS Research Trip Lima 8.9M 23 RMS Research Trip

24 Why Capital Cities? ~ 80% of South America s population lives in urban areas Capital cities are major economic centers and act as magnets for rural poor and refugees Significant fraction of entire nation s population resides in capitals Santiago, Chile 5.9 M 36% Lima, Peru 8.9 M 30% Bogota, Colombia 8.6 M 19% La Paz, Bolivia 1.8 M 19% Caracas, Venezuela 4.9 M 19% Quito, Ecuador 1.7M 12% 24

25 With their dense, highly vulnerable neighborhoods Caracas, Venezuela RMS Research Trip La Paz, Bolivia Myplanetaustralia.com Lima, Peru RMS Research Trip Bogotá, Colombia 25 RMS Research Trip

26 Probabilistic Hazard Completed as Part of v9.0 EQ Model Caracas, VZ Bogota, CO Quito, EC Lima, PE La Paz, BO Santiago, CL Loss Cost - SFD RiskLink

27 Economic RES Loss Cost (AAL/$1000 exposure value) -- Capital Cities 27

28 Project Objectives Phase I Quantify risk Extend v9.0 Americas EQ model to quantify humanitarian impacts as well as economic losses for 6 seismically at-risk, South American capital cities Potential fatalities & injuries Displaced households Phase II Implement mitigation solutions Use analyses to raise risk awareness and forge partnerships with NGOs and other local stakeholders to design mitigation strategies and programs to reduce future losses and suffering 28

29 Typical Barrios Housing Conditions House millions of people Informal construction using cheap, readily available materials Often built on landslide-prone hillsides on city margins Many are squatters Shared walls Unplanned additions over time Few roads through housing hampering emergency response Images of some homes 29

30 Not Just a Barrios Problem Superbloques of Caracas, Venezuela 24,000 people live in these 40-story MFDs 30

31 Not Just a Barrios Problem More 20-story superbloques of Caracas Residents often remove interior walls to create space 31

32 Individual EQ Scenarios: Quito + Caracas Pichincha Volcano Quito Caracas Quito fault an active reverse fault Well-determined 1 mm/year slip rate From geology No such event recorded in historic time ~500 yrs M6.5 eq expected based on fault length and elapsed time M 7.1 March 25, 1812 earthquake near Caracas 26,000-40,000 dead, city nearly destroyed San Sebastian strike-slip fault offshore, slip rate 10 mm/yr ~ 2 m slip accumulated => M7+ earthquake M 6.5 Coast of Venezuela, 30 mi W of Caracas. 240 dead, hundreds injured $100 M property damage in Caracas ~ 80,000 persons homeless. 4 major apt, buildings, stories high, collapsed. 32

33 Hazard : EQ-induced Landslides Will Compound the Looming Catastrophe Challenge to adequately model landslide susceptibility and vulnerability 33

34 A window into future impacts: cat models as a tool to explore impacts and benefits of adaptation Modify hazard to explore physical climate change impacts Modify vulnerability to explore impacts of individual adaptation / hard defences Quantify future loss scenarios Storm Surge Flood Vulnerability $ Loss 90% 500,000 tracks Exposure Financial Stochastic Windfield Wind Vulnerability e.g. future coastal flood risk Modify inventory to explore impacts of socio-economic growth w/wo land use policy

35 Modelling Benefits ( ) of Adaptation to Climate Change Model Changes to Hazard Hazard Model Changes to the Vulnerability of Buildings Vulnerability Model Changes to Inventory Inventory e.g. Flood Defences e.g. Flood Resistance & Resilience Measures (changes to primary and secondary modifiers) e.g. Risk Averse Land Planning 35

36 Temporary flood wall Raised ground floor (0-0.5m) Dry flood proofing Moving vulnerable contents Engineered foundations Protected external equipment Reinforced cladding Comparing individual measures Individual measures can substantially reduce losses (both in terms of average Reduction in annual losses and losses from extreme events). 40% average loss (individual) % AAL reduction 35% 28% Flood loss reduction by adaptation method 25% 22% 15% 10% 0% Temporary Flood Wall Raised Ground Floor (0-0.5m) Dry Flood Proofing Moving Vulnerable Contents Engineered Foundations Protected External Equipment Reinforced Cladding No adaptation 36

37 Examples of spontaneous adaptation (Bahamas) Queen s Cove canal estate North Grand Bahama Flooded by surges 3 times in 6 years No longer insurable The response 37

38 Disaster Risk reduction and Cat Modelling Cannot identify where best to invest in risk reduction, without first understanding the peaks in the landscape of risk Cannot identify the best value for money in risk reduction (adaptation) without exploring alternative mitigative options Disaster Risk reduction may also have an insurance component to spread risk example of the Caribbean Catastrophe Risk Insurance Facility Commercial Cat models are expanding beyond the developed world Open Source Cat models coming online in particular for EQ New models for drought, heatwave etc The future of disaster risk reduction will be probabilistic! 38