Paving the Way for Climate-Resilient Infrastructure - UN CC:Learn

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Chapter 8: Probabilistic Risk Modeling: Basic Principles and ApplicationsAdaptation to address potential impacts of both SLR and storms in coastal areas have includedseveral approaches:(1) Zoning requirements: Zoning requirements in coastal areas can prohibit building very close to thebeach or shoreline. This prevents the affect of long-term SLR on buildings and provides a buffer tostorm surges during tropical storms. Zoning can also prevent rebuilding in vulnerable areas in orderto avert further exposure.(2) Protective publicly financed structures: Protective structures, such as sea walls built in Louisiana,can reduce impacts of storm surges.(3) Elevated houses: Elevated houses can also prevent impacts of flooding, such as Mississippi Riverflooding, on homes.(4) Insurance options: Soft policy options, including disinvesting along the coastline, can help protectand build robustness of coastal communities.Risk of climate change in coastal communities is a function of the economic activity, population growthand trends, amount of sea-level rise, frequency and intensity of hurricanes, and the social response (i.e.how people respond to disaster, perceive risk themselves, and how this affects their daily lives). All of thesefactors remain uncertain and thus necessitate probabilistic risk assessment.Probabilistic Risk Assessment: Definition and methodsPRA is a systematic and comprehensive methodology to evaluate risks associated with a complex problemand can be carried out through three primary methods: scenarios, probability trees and Monte Carlo simulations.All of these methods require a model to evaluate the probability.The Feedback-Rich Adaptation to Climate Change (FRACC) Model (developed at The MassachusettsInstitute of Technology [MIT] for coastal communities) pulls together interactions in the PRA related toclimate change, specifically for coastal communities. The arrows in the diagram of this model (see Figure8.1) show the interconnectedness and feedback of factors. Climatic drivers (sea-level rise and tropicalstorms) can be parameterized, as it is necessary to understand risks associated with these drivers. Thesedrivers affect economic and population trends. Large economic growth and high population can leadto exposure and vulnerability by increasing the amount of capital that might be at risk. Adaptation decisionsalso affect risks, as adaptation protection choices resulting from risk assessment influence people’sperception of risk; if people are well prepared, they tend to perceive less risk. All of these factors contributeto further adaptation options and cost of adaptation.96Paving the Way for Climate-Resilient Infrastructure: Conference Proceedings
Chapter 8: Probabilistic Risk Modeling: Basic Principles and ApplicationsPART IITechnicalPresentation 1Figure 8.1: The Feedback-Rich Adaptation to Climate Change (FRACC) ModelWhat a Country ShouldThink About andThen Do to AddressClimate Change andInfrastructure RisksTechnicalPresentation 2Internalization ofClimate Risks in theContext of Planningand Urban DevelopmentTechnicalPresentation 3This framework suggests the need for a systematic and holistic approach to probabilistic risk assessmentto climate change and infrastructure adaptation.Scenarios and decision treesScenarios embody a set of parameters (e.g. economic growth, population, sea-level rise) and allow fora comparison of possible different futures, which can provide structure for development planning.Assigning probabilities to the scenarios provides guidance for decision-making.A Framework forRisk Assessmentand Risk-InformedDecision-Making forInfrastructureDevelopmentTechnicalPresentation 4Probabilistic RiskModeling: BasicPrinciples andApplicationsTable 8.1: Scenario examplesParameter Low Reference (median) HighEconomic growth 0.2% / year 2.0% / year 4.0% / yearPopulation growth 0.1% / year 1.5% / year 4.2% / yearSea-level rise in 2100 0.40 m 0.75 m 1.25 mTechnicalPresentation 5An Economic Frameworkfor Evaluating ClimateProofing Investmentson InfrastructureFor each scenario, important variables (i.e. drivers) of risk are highlighted. For each driver, three differentscenarios can be developed: low, reference (median) and high scenarios. With these three scenarios, wecan examine three possible futures in a model. The minimum amount of exposure is low economic/population growth and low sea-level rise. These scenarios can be used for adaptation planning anddecision-making.TechnicalPresentation 6Supporting ElSalvador to ReduceInfrastructure Riskswithin a Green, Low-Emission and Climate-Resilient Frameworkfor DevelopmentPaving the Way for Climate-Resilient Infrastructure: Conference Proceedings 97
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