department of civil construction and environmental engineering[土木工程建筑環(huán)境工程系](-47)

1、1Department of Civil, Construction, and Environmental EngineeringNorth Carolina State UniversityRaleigh, NC 27695Prepared for: 2nd Workshop on Impacts of Global Climate ChangeOn Hydraulics and Hydrology and TransportationCenter for Transportation and the EnvironmentWashington, DCMarch 29, 2006 H. Ch

2、ristopher Frey, Ph.D.ProfessorIncorporating Risk and Uncertainty into the Assessment of Impacts of Global Climate Change on Transportation Systems 2Outline Risk and Uncertainty Overview of impacts of climate change on transportation systems Risk assessment methodologies Uncertainty analysis methodol

3、ogies Qualitative assessments Recommendations 3Definitions Risk: Probability and severity of an adverse outcome Uncertainty: Lack of knowledge regarding the true value of a quantity 4POSSIBLE IMPACTS OF GLOBAL CLIMATE CHANGE ON TRANSPORTATION SYSTEMS All modes: highway, rail, air, shipping, pipeline

4、, pedestrianPassenger and freight Possible climate impacts (natural processes)Sea-level riseIncreased frequency and severity of stormsHigher average temperatures (location-specific) 5Implications of Possible Climate Change (Effects Processes) Loss of coastal land area Damage to infrastructure via st

5、orms (e.g., winds, flooding) Damage to infrastructure because of temperature extremes (e.g., rail kinks, pavement damage) Impede operations and safety Design, construction, operation, maintenance, repair, decommissioning 6METHODOLOGICAL FRAMEWORKS FOR DEALING WITH RISK Vulnerability or hazard assess

6、ment Exposure assessment Effects processes Quantification of risk Risk management 7Vulnerability Assessment Physical, social, political, economic, cultural, and psychological harms to which individuals and modern societies are susceptible (Slovic, 2002). Identify valuable targets at risk Conceptuali

7、ze various ways in which they are vulnerable to such an attack by defining various scenarios. Clearly state the scale and the scope of the analysis (e.g., the world, a country, or specific region) considering that the risk assessment process will become easier as the scope narrows down. Does not inc

8、lude assessment of the likelihood of such an event. For example, coastal cities are vulnerable to the effects of sea level rise. 8Paradigm for Human Health Risk Assessment (NRC, 1983)RiskCharacterizationLaboratory andField WorkExtrapolationMethodsHazardIdentificationDose-ReponseAssessmentExposureAss

9、essmentRegulatoryOptionsEvaluations ofOptionsDecisions andActionsResearchRisk AssessmentFieldMeasurements,Modeling 9An Alternative View of Human Health Risk Assessment (PCRARM, 1997)StakeholderCollaborationProblem/ContextRisksOptionsDecisionsActionsEvaluation 10Example of A General Risk Assessment F

10、ramework (Morgan)ExposureProcessesEffectsProcessesHumanPerceptionProcessesHumanEvaluationProcessesNaturalNaturalProcessesProcessesHumanHumanActivitiesActivitiesExposure of objects Exposure of objects and processes in and processes in natural and human natural and human environment to the environment

11、 to the possibility of changepossibility of changeEffects on objects Effects on objects and processes in and processes in the natural and the natural and human human environmentenvironmentHuman Human Perceptions of Perceptions of exposures exposures and of effectsand of effectsCosts and Costs and Be

12、nefitsBenefitsHuman EnvironmentHuman EnvironmentNatural EnvironmentNatural Environment 11Risk Analysis and Risk Management Analysis should be free of policy-motivated assumptions Yet, analysis should include scenarios relevant to decision-making Some argue for analysts and decision makers to be kept

13、 apart to avoid biases in the analysis Others argue that they must interact in order to define the assessment objective A practical, useful analysis needs to balance both concerns 12Realities of Decision-Making Decision-making regarding response to the impacts of climate change will involve: multipl

14、e parties; a local context; considerations beyond just the science and technology (such as equity, justice, culture, and others); and implications for potentially large transfers of resources among different societal stakeholders. Such decision-making may not produce an “optimal” outcome when viewed

15、 from a particular (e.g., national, analytical) perspective. Based on Morgan (2003) 13METHODOLOGICAL FRAMEWORKS FOR DEALING WITH UNCERTAINTY Role of uncertainty in decision making Scenarios Models Model inputsEmpirically-basedExpert judgment-based Model outputs Other quantitative approaches Qualitat

16、ive approaches 14Uncertainty and Decision Making How well do we know these numbers? What is the precision of the estimates?Is there a systematic error (bias) in the estimates? Are the estimates based upon measurements, modeling, or expert judgment? How significant are differences between two alterna

17、tives? How significant are apparent trends over time? How effective are proposed control or management strategies? What is the key source of uncertainty in these numbers? How can uncertainty be reduced? 15Implications of Uncertainty in Decision Making Risk preference Risk averseRisk neutralRisk seek

18、ing Utility theory Benefits of quantifying uncertainty: Expected Value of Including Uncertainty Benefits of reducing uncertainty: Expected Value of Perfect Information 16Framing the Problem: Objectives and Scenarios Need a well-formulated study objective that is relevant to decision making A scenari

19、o is a set of structural assumptions about the situation to be analyzed:spatial and temporal dimensionsspecific hazards, exposures, and adverse outcomes Typical errors: description, aggregation, expert judgment, incompleteness Failure to properly specify scenario(s) leads to bias in the analysis, ev

20、en if all other elements are perfect. 17Model Uncertainty A model is a hypothesis regarding how a system works. Ideally, the model should be tested by comparing its predictions with observations from the real world system, under specified conditions. Difficult for unique or future events. In practic

21、e, validation is often incomplete. Extrapolation. Other factors: simplifications, aggregation, exclusion, structure, resolution, model boundaries, boundary conditions, and calibration. 18Examples of Alternative ModelsSublinearLinearSuperlinearThresholdStateChange?Explanatory VariableSystem Response

22、19Model Uncertainty Climate Change Impacts Enumeration of a set of plausible or possible alternative models, Comparisons of their predictions or development of a weighting scheme to combine the predictions of multiple models into one estimate It seems inappropriate to increase the complexity of the

23、analysis in situations where less is known (Casman et al., 1999) 20Model UncertaintyWeighted CombinationOf Model OutputsModel 2Model 3w1w2w3Model 1 21The Role of Models When Structural Uncertainties are Large Assessment of climate change impacts involves many component models Some are better than ot

24、hers, and they “degrade” at different rates as one goes farther into the future. For problem areas in which there is little relevant data, theory, or experience, a simpler “order-of-magnitude” model may be adequate. For problem areas in which little is known, very simple bounding analyses may be all

25、 that can be justified. For poorly supported models, it is no longer possible to search for optimal decision strategies. Instead, one can attempt to find feasible or robust strategies 22Quantification of Uncertainty in Inputs and Outputs of ModelsInput UncertaintiesOutputUncertaintyModel 23Statistic

26、al MethodsBased Upon Empirical Data Frequentist, classical Statistical inference from sample dataParametric approachesParameter estimationGoodness-of-fitNonparametric approachesMixture distributionsCensored dataDependencies, correlations, deconvolutionTime series, autocorrelation 24Statistical Metho

27、ds Based on Empirical Data Need a random, representative sample Not always available when predicting events into the future 25Example of an Empirical Data Set Regarding VariabilityEmpirical Quantity 26Fitted Lognormal DistributionEmpirical Quantity 27Bootstrap Simulation to Quantify Uncertainty90 pe

28、rcent0.00.81.0Cumulative Probability10-310-210-1100Benzene Emission Factor (ton/yr/tank)50 percent90 percent95 percentData SetConfidence IntervalFitted DistributionEmpirical Quantity 28Results of Bootstrap Simulation: Uncertainty in the MeanUncertainty in mean -73% to +200%0.06Empirical Qua

29、ntity 29Estimating Uncertainties Based on Expert Judgment Probability can be used to quantify the state of knowledge (or ignorance) regarding a quantity. Bayesian methods for statistical inference are based upon sample information (e.g., empirical data, when available) and a prior distribution. A pr

30、ior distribution is a quantitative statement of the degree of belief a person has that a particular outcome will occur. Methods for eliciting subjective probability distributions are intended to produce estimates that accurately reflect the true state of knowledge and that are free of significant co

31、gnitive and motivational biases Useful when random, representative data, or models, are not available, but when there is some “epistemic status” upon which to base a judgment 30Heuristics and Possible Biases in Expert Judgment Heuristics and BiasesAvailabilityAnchoring and AdjustmentRepresentativene

32、ssOthers (e.g., Motivational, Expert, etc.) Consider motivational bias when choosing experts Deal with cognitive heuristics via an appropriate elicitation protocol 31An Example of an Elicitation Protocol:Stanford/SRI ProtocolMotivating (Establish Rapport)Structuring (Identify Variables)Conditioning

33、(Get Expert to Think About Evidence)Encoding (Quantify Judgment About Uncertainty)Verify (Test the Judgment) 32Frequently Asked Questions Regarding Expert Elicitation How to choose the experts How many experts are needed Whether to perform elicitation individually or with groups of experts Elicitati

34、on of correlated uncertainties What to do if experts disagree Whether and how to combine judgments from multiple experts What resources are needed for expert elicitation 33Propagating Uncertainties Through Models Analytical solutions exact but of limited applicability Approximate solutions more broa

35、dly applicable but increase in complexity or error as model and inputs become more complex (e.g., Taylor series expansion) Numerical methods flexible and popular (e.g., Monte Carlo simulation) 34Monte Carlo Simulation and Similar MethodsValue of Random Variable, xProbability Densityf(x)PROBABILITY D

36、ENSITY FUNCTIONCumulative Probability, uValue of Random Variable, x10F(x) = P(xX)CUMULATIVE DISTRIBUTION FUNCTIONMONTE CARLO SIMULATION Generate a random number uU(0,1) Calculate F (u) for each value of u LATIN HYPERCUBE SAMPLING Divide u into N equal intervals Select median of each interval Calcula

37、te F (u) for each interval Rank each sample based on U(0,1) (or restricted pairing technique)-1-1Cumulative Probability, uValue of Random Variable, x01F (u)-1INVERSE CUMULATIVE DISTRIBUTION FUNCTIONF(x) = Pr(xX) 35Sensitivity Analysis: Which Model Inputs Contribute Most to Uncertainty in Output? Lin

38、earized sensitivity coefficients Statistical methods:CorrelationRegressionAdvanced methodszx= sx,bb(xa,ya)xyzzy= sy,bbzx= sx,aazy= sy,aa(xb,yb)Example from Sobols MethodBW, 8%Main Effect of Others, 30%Interactions, 24%AM, 6%WB, 6%DR, 1%FTR, 25% 36Other Quantitative Methods Interval Methods: Provide

39、bounds, but not very informative Fuzzy Sets: represents vagueness, rather than uncertainty 37Qualitative Methods Principles of Rationality Lines of Reasoning Weight of Evidence 38Principles of Rationality Conceptual clarity: well-defined terminology Logical consistency: inferences should follow from

40、 assumptions and data Ontological realism: free of scientific error Epistemological reflection: evidential support Methodological rigor: use of proven techniques Practicality Valuational selection: focus on what matters the most 39Lines of Reasoning Direct empirical evidence Semi-empirical evidence

41、(surrogate data) Empirical correlations (relationships between known processes and the unknown process of interest) Theory-based inference causal mechanisms Existential insight expert judgment 40Judgment of Epistemic Status The result of an analysis of epistemic status is a judgment regarding the qu

42、ality of each premise or alternative e.g., no basis for using a premise in decision-making. partial or high confidence basis for using a particular premise as the basis for decision making. 41Weight of Evidence Legal context - whether the proof for one premise is greater than for another. Often used

43、 when a categorical judgment is needed. However, tends to be less formal than the analysis of epistemic status, less transparent than properly documented analyses of epistemic status 42Qualitative Statements Regarding Uncertainty Qualitative approaches for describing uncertainty are best with fundam

44、ental problems of ambiguity. The same words mean:different things to different people, different things to the same person in different contexts Based on Wallsten et al., 1986:“Probable” was associated with quantitative probabilities of approximately 0.5 to 1.0“Possible” was associated with probabil

45、ities of approximately 0.0 to 1.0. Qualitative schemes for dealing with uncertainty are typically not useful 43CONCLUSIONS - 1 There is growing recognition that climate change has the potential to impact transportation systems. The available literature on the impacts of climate change on transportat

46、ion systems appears to be a vulnerability assessment, rather than a risk analysis. 44CONCLUSIONS - 2 The commitment of large resources should be based on, as thoroughly as necessary or possible, a well-founded analysis. There are many alternative forms of analysis that differ in their “epistemic status,” depending on what type of information is available. Thus, the key question is what kind of analysis is appropriate here? It