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1 Disaster Risk Assessment: Disaster Risk Modeling Dr. Jianping Yan Disaster Risk Assessment Specialist

2 Session Outline Overview of Risk Modeling For insurance For public policy Conceptual Model Modeling Approaches Modeling Frameworks Scenario Building Uncertainty Analysis

3 Overview of Risk Modeling History and Key Modelers

4

5 Catastrophe Modeling Framework (Swiss Re) Grossi & Kunreuther (2005)

6 Catastrophe Modeling Framework (Muenich Re)

7 Catastrophe Modeling Framework (ABS consulting)

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9

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14 Key Risk Model Developers

15 Key Risk Model Developers (cont.)

16 Hazard risk analysis for public policy

17

18 HAZUS-MH: Key Features Technical Specification: GIS Technology Nationwide Databases Nationally Standardized Loss Estimation and Risk Assessment Methodology Loss/Impact Estimation: Physical Impacts Economic Impacts Social Impacts

19 Disaster Risk Modeling For Public Policy and Decision Making

20 Conceptual Risk Model Exposure Vulnerability Risk Hazard Risk = Hazard x Exposure x Vulnerability

21 How to Build Numeric Model? Trigger(s) Hazard Frequency Probability Time frame Risk Consequence Hazard Intensity Vulnerability Exposure

22

23 Conceptual Disaster Risk Model Exposure Hazard Disaster Risk Model Vulnerability Disaster Loss & Impact

24 Hazard Module Hazard categorization Historic event catalogues Hazard- and disaster-prone areas Event chains Scenarios Hazard/event intensity mapping Hazard zonation

25 Event Chains Hierarchy of event chains Primary, most natural hazard events Secondary, most natural hazard events Tertiary, environmental & technological events Quaternary, socio-economic events

26 Exposure Module Characterization of the target system Categorization of elements at risk Spatial resolution (analysis unit) Representation (geo-referencing & gridded) Spatio-Temporal evolution

27

28 Vulnerability Module Vulnerability/fragility functions Empirical class Index Function Damage assessment Monetary value of damage (replacement prices) Normalization Aggregation

29 Vulnerability Functions DR (%) DR (%) IV V VI VII VIII IV V VI VII VIII MMI Intensity MMI Intensity

30 Vulnerability Functions Vulnerability curves for typical Costa Rican building types (Sauter and Shah, 1978)

31 Loss/Impact Module Recovery functions Damage-Functioning Loss-Impact relations Loss/impact assessment Monetary value of loss/impact Normalization Aggregation

32 Recovery Functions Damage functions for Water Treatment Plants (ATC-25) Recovery functions for Water Treatment Plants (ATC-25)

33 Differences in Damage, Loss, Impact Ecuadorian Oil pipeline 1987 Earthquake Damage to the oil pipe Environmental Impact

34 Differences in Damage, Loss, Impact Ecuadorian Oil pipeline 1987 Earthquake Physical Damage = 60 km Pipeline, $$? Damage to the oil pipe Functioning Loss = 6 months no oil export ==70% annual revenue, $$$? Macro-Impact = 5 year economic recession, $$$$$$? Environmental Impact

35 by making wrong decision decisions The pipeline crosses seven national parks and protected areas, including a World Bank Global Environment Facility biodiversity reserve The oil exports revenues now play an even larger role in the country s economy Ecuadorian Oil pipe November 2002

36 Estimation of Loss and Impacts The Impacts of the 1975 War on Lebanon s Avaition: - 99 million passengers lost - 500,000 jobs lost The impacts of 2006 war: -? passengers lost -? Job lost

37 Generic Loss/Impact Model

38 HazUS Loss Model

39 ECLAC Loss Model

40 Disaster Module Characterization of the functioning of the target society Sectoral Importance-dependence Cascading effects Disaster scenarios

41 Dimensions of Disaster

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46 Interdependence Analysis

47 Modeling Approaches Index-based approach Index, e.g. Disaster Risk Index (DRI) Scenario-based approach Evidence base, 20 people died with a annual probability of occurrence of 10% Deterministic approach Probabilistic approach

48 Index-based risk models No. 1 Conceptual models Risk = Natural hazard * Elements at risk * Vulnerability Author UNDRO (1991), extended from Fournier d Albe (1979) 2 3 Risk = (Hazard * Vulnerability) Coping capacity Wisner (2001) Risk = (Hazard * Vulnerability) - Mitigation Wisner (2000) 4 5 Risk = Hazard * Exposure * Vulnerability /(*) Preparedness Risk = Hazard * Exposure * Vulnerability * Interconnectivity De La Cruz Reyna, (1996) Yurkovich (2004) 6 Risk = Hazard * Vulnerability / Resilience UN (2002)

49 Index-based risk modeling Hazard type Intensity Probability Exposure Vulnerability Preparedn ess Earthquake Tropical cyclone Storm tide Total score Tsunami Flood

50 Scenario Building Framework

51 What is Scenario? Definition: A plausible (= seems likely to be true or valid) description of how the future may develop, based on a coherent and internally consistent set of assumptions about key relationships and driving forces (e.g., rate of technology changes, prices). Scenarios are neither predictions nor forecasts. The results of scenarios (unlike forecasts) depend on the boundary conditions of the scenario (Green et al., 2004). Recommendation: A plausible description of a situation, based on a coherent and internally consistent set of assumptions. Scenarios are neither predictions nor forecasts. The results of scenarios (unlike forecasts) depend on the boundary conditions of the scenario. Rationale: The definition of Green et al (2004) has an underlying implication of climate change. Within flood system defence reliability analysis, the term scenario is used to define potential combinations of defence failures under specified loading conditions. The definition of Green et al. (2005) has therefore been broadened to encompass the defence reliability aspects. Scenarios of earthquake and tsunami disaster are useful for decision makers to plan disaster mitigation measures and for people to understand the disaster for which they must be prepared.

52 Uncertainty Analysis Uncertainty type Uncertainty sources Analysis methods

53 Uncertainty Variability Knowledge Temporal Heterogeneity Spatial Model Parameter Scenario

54 Sources of uncertainty Sources Hazard assessment Exposure assessment Vulnerability assessment Description -Incomplete knowledge -Lack of historic records -Sampling strategies -Inappropriate scenarios -Inappropriate methods -Lack of precise knowledge -Distributional uncertainty -Selection of indicators -Indicator evaluation -Indicator weighting Damage/impact assessment -Capacity of structural elements -Cost of repair and replacement -Business interruption

55 Analysis methods Sensitivity analysis Logic tree Simulation techniques Deterministic approach Probabilistic approach (Monte Carlo simulation)

56

57 Summary This session briefly overviewed the conceptualization, framework, and existing techniques involved in natural risk modeling, which can serve as a self-learning framework; The three risk modeling techniques presented have their own advantages and disadvantages. The indicator-based risk modeling is simple, whereas the probabilistic modeling most sophisticated; the scenario-based approach is a trade-off between the two, and can meet most requirements for governmental decision and policy making. Uncertainty, another important factor to consider in decision making, should be systematically addressed in risk profiling.

58 Any Questions?

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