Disaster Risk Assessment: Disaster Risk Modeling Dr. Jianping Yan Disaster Risk Assessment Specialist
Session Outline Overview of Risk Modeling For insurance For public policy Conceptual Model Modeling Approaches Modeling Frameworks Scenario Building Uncertainty Analysis
Overview of Risk Modeling History and Key Modelers
Catastrophe Modeling Framework (Swiss Re) Grossi & Kunreuther (2005)
Catastrophe Modeling Framework (Muenich Re)
Catastrophe Modeling Framework (ABS consulting)
Key Risk Model Developers
Key Risk Model Developers (cont.)
Hazard risk analysis for public policy
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
Disaster Risk Modeling For Public Policy and Decision Making
Conceptual Risk Model Exposure Vulnerability Risk Hazard Risk = Hazard x Exposure x Vulnerability
How to Build Numeric Model? Trigger(s) Hazard Frequency Probability Time frame Risk Consequence Hazard Intensity Vulnerability Exposure
Conceptual Disaster Risk Model Exposure Hazard Disaster Risk Model Vulnerability Disaster Loss & Impact
Hazard Module Hazard categorization Historic event catalogues Hazard- and disaster-prone areas Event chains Scenarios Hazard/event intensity mapping Hazard zonation
Event Chains Hierarchy of event chains Primary, most natural hazard events Secondary, most natural hazard events Tertiary, environmental & technological events Quaternary, socio-economic events
Exposure Module Characterization of the target system Categorization of elements at risk Spatial resolution (analysis unit) Representation (geo-referencing & gridded) Spatio-Temporal evolution
Vulnerability Module Vulnerability/fragility functions Empirical class Index Function Damage assessment Monetary value of damage (replacement prices) Normalization Aggregation
Vulnerability Functions DR (%) 100 80 60 40 20 DR (%) 100 80 60 40 20 IV V VI VII VIII IV V VI VII VIII MMI Intensity MMI Intensity
Vulnerability Functions Vulnerability curves for typical Costa Rican building types (Sauter and Shah, 1978)
Loss/Impact Module Recovery functions Damage-Functioning Loss-Impact relations Loss/impact assessment Monetary value of loss/impact Normalization Aggregation
Recovery Functions Damage functions for Water Treatment Plants (ATC-25) Recovery functions for Water Treatment Plants (ATC-25)
Differences in Damage, Loss, Impact Ecuadorian Oil pipeline 1987 Earthquake Damage to the oil pipe Environmental Impact
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
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
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
Generic Loss/Impact Model
HazUS Loss Model
ECLAC Loss Model
Disaster Module Characterization of the functioning of the target society Sectoral Importance-dependence Cascading effects Disaster scenarios
Dimensions of Disaster
Interdependence Analysis
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
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)
Index-based risk modeling Hazard type Intensity Probability Exposure Vulnerability Preparedn ess Earthquake 3 2 5 1 4 15 Tropical cyclone Storm tide Total score 4 5 1 1 1 12 2 5 5 5 1 18 Tsunami 5 1 5 5 3 18 Flood 2 5 5 1 5 18
Scenario Building Framework
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.
Uncertainty Analysis Uncertainty type Uncertainty sources Analysis methods
Uncertainty Variability Knowledge Temporal Heterogeneity Spatial Model Parameter Scenario
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
Analysis methods Sensitivity analysis Logic tree Simulation techniques Deterministic approach Probabilistic approach (Monte Carlo simulation)
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.
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