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

Chapter 8: Probabilistic Risk Modeling: Basic Principles and ApplicationsFigure 8.4: Probability density functions (PDFs)Economic parametersGrowthPopulationClimate parametersSLRStormsImpactsHRHRHRHR• Infrastructuredamage• Land Flooding• People displacedLLLLWithin the Monte Carlo simulation, using PDFs (e.g. of sea-level rise) as an input produces PDFs as anoutput. This approach can be used to calculate the outcomes of many parameter sets. With definedPDFs for important parameters, these parameters are correlated (e.g. high population growth with higheconomic growth). A model with samples of the input PDFs help to create hundreds of thousands of simulationruns (e.g. by brute force or Latin hypercube sampling).Characterizing a PDFAcademic literature, statistical or empirical evidence, or expert elicitation can provide the basis for characterizingPDFs. Scientific literature supports parameters of sea-level rise, for example, as long as publishedSLR trends are adjusted for local conditions. Empirical data supports parameters of economic growthand population trends. PDFs for low-frequency, high-consequence events (e.g. tropical storms), however,are more difficult to characterize. Science does indicate that storms will become more intense and morefrequent, particularly in the Atlantic basin (Kossin, 2007), but this trend is still uncertain and difficult toquantify in a particular area. While historical tracks (e.g. from National Hurricane Center, 2005) show that ElSalvador and Central America are particularly prone to these problems, it is still necessary to quantify risksof these storms in El Salvador.Models can help to estimate storm trends for a specific location. The physics-based Couple HurricaneIntensity Prediction System (CHIPS) model (MIT), for example, can help determine which and how manyof thousands of storms seeded will persist and hit El Salvador. By simulating thousands of storms, thefrequency of storms passing near Central America and the distribution of storm intensities can be defined.With this model, given physics of ocean, winds and the atmosphere, it is possible to gain a better idea ofwhat types of storms and frequency will hit a particular area.The CHIPS model has been used to create PDFs of storms for St. Mary’s Parish (Louisiana) and illustratesfrequency of tropical depressions and category 1 – 5 storms. In Figure 8.5, the four lines in this graphrepresenting four different climates (two present-day and two future climates) indicate variance amongthe four models but also a shift in storm distribution toward stronger storms.100Paving the Way for Climate-Resilient Infrastructure: Conference Proceedings