addressing climate change adaptation in regional transportation plans
addressing climate change adaptation in regional transportation plans addressing climate change adaptation in regional transportation plans
Addressing Climate Change Adaptation in Regional Transportation PlansA Guide for California MPOs and RTPAswill consider only potential, not actual, risk the estimate of likelihood can beperformed solely for the climate stressor.Determining stressor likelihood varies widely in degree of difficulty, although, aswith other uncertainties treated in this module, the preference is for a quick,sketch level methodology rather than a rigorous but time consuming approach.There are three primary perspectives for considering stressor likelihood, averageannual frequency of occurrence (frequency), average annual exceedanceprobability (probability), and average recurrence interval, which express thesame phenomenon using different terminology.Average annual frequency of occurrence. Events that are described by thenumber of days (or other time periods) meeting or exceeding thresholdvalues, such as days ≤95°F temperatures, ≤1” rainfall, or the upper onepercentrainfall event 21 , can be considered in terms of their average annualfrequencies. These events are often associated with maintenance andoperational impacts or asset deterioration, rather than major damage,although with each event there may be a remote, and potentially increasing,likelihood of more significant impacts (for example a high-heat inducedconcrete blow-up that causes a motorcycle fatality). Historical averageannual frequencies can be derived from weather station records collected bythe National Weather Service, with detailed information available on-linefrom the National Climatic Data Center.Average annual exceedance probability (AEP). When events are describedby their annual likelihood of occurrence, they are referred to in terms of theirexceedance probabilities. These stressors typically include flood events,runoff volumes, and significant rainfall events. The FEMA floodplains, forexample, represent estimated flood coverage areas for (commonly) 1-percentand 0.2-percent chance flooding events (although common convention, theterms 100-year and 500-year to describe floodplains are misleading, insteadreferring to average recurrence interval).Average recurrence interval (ARI). The NOAA Atlas 14 provides estimatesof rainfall intensity and depth ranges (associated with 90-percent confidenceintervals) for a matrix of event durations (in minutes, hours, and days) andaverage recurrence intervals from 1 to 1,000 years (periods betweenexceedance events are random). For the “24-hour” rainfall event, forexample, the user can view the range (upper and lower bounds of theconfidence interval) of absolute rainfall in inches expected to recur every100 years, on average (e.g., 5.4 inches, with a range of 4.56 to 6.52 inches, forSacramento). The average recurrence interval can be adjusted through21 In this case, the one-percent precipitation event denotes the values that fall into the topone percent of all precipitation events; not to be confused with the one-percent chancerainfall event.11-10 Cambridge Systematics, Inc.
Addressing Climate Change Adaptation in Regional Transportation PlansA Guide for California MPOs and RTPAsclimate stressor downscaling to derive estimates for the future recurrence of aspecific event. In other words, if the 10-year, 24-hour event is associated witha specific set of asset impacts, the assessment could consider how often thissame threshold event (e.g., 3.44 inches in Sacramento) might be expected tooccur in the analysis timeframe.Table 11.1ARI to AEP Conversion TableCommon ValuesARI(Years)AEP(Percentage)1 63.22 39.35 18.110 9.520 4.950 2.0100 1.0Source: Australian Bureau of Meteorology, 2012.All three expressions of potential likelihood may be projected for future timeperiods though climate stressor downscaling techniques, which adjust currentvalues based on potential climate futures. The 100-year ARI may become the 80-to 90 year ARI, for example, or the region may expect to experience an average of25 days annually ≤95°F, instead of 10. While it is important to considerprojections as potential climate futures, instead of predictions, responsible use ofestimates can support decision-making in most cases (the possible exceptionbeing significant disagreement among projections).Where specific projections are not readily available or are not reliable, it may stillbe possible to characterize the trend direction qualitatively, either based on otherstatewide guidance, such as Reports from the Third Assessment from theCalifornia Climate Change Center, or based on the observations of infrastructuremanagers (again, while not a scientifically rigorous technique, it may serve theneeds of some users).Characterize Risk and Prioritize Assets for Module 4The final stage of Module 3 is to consider potential consequences and estimatedlikelihoods in integration. Ideally, the outcome of this exercise will be amanageable selection of assets (or asset types) suitable for advancement to themore detailed and resource intensive approach explained in Module 4.For many users, the appropriate (and potentially familiar) vehicle forcharacterizing risk will be the risk matrix, which arrays magnitude ofCambridge Systematics, Inc. 11-11
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Address<strong>in</strong>g Climate Change Adaptation <strong>in</strong> Regional Transportation PlansA Guide for California MPOs and RTPAs<strong>climate</strong> stressor downscal<strong>in</strong>g to derive estimates for the future recurrence of aspecific event. In other words, if the 10-year, 24-hour event is associated witha specific set of asset impacts, the assessment could consider how often thissame threshold event (e.g., 3.44 <strong>in</strong>ches <strong>in</strong> Sacramento) might be expected tooccur <strong>in</strong> the analysis timeframe.Table 11.1ARI to AEP Conversion TableCommon ValuesARI(Years)AEP(Percentage)1 63.22 39.35 18.110 9.520 4.950 2.0100 1.0Source: Australian Bureau of Meteorology, 2012.All three expressions of potential likelihood may be projected for future timeperiods though <strong>climate</strong> stressor downscal<strong>in</strong>g techniques, which adjust currentvalues based on potential <strong>climate</strong> futures. The 100-year ARI may become the 80-to 90 year ARI, for example, or the region may expect to experience an average of25 days annually ≤95°F, <strong>in</strong>stead of 10. While it is important to considerprojections as potential <strong>climate</strong> futures, <strong>in</strong>stead of predictions, responsible use ofestimates can support decision-mak<strong>in</strong>g <strong>in</strong> most cases (the possible exceptionbe<strong>in</strong>g significant disagreement among projections).Where specific projections are not readily available or are not reliable, it may stillbe possible to characterize the trend direction qualitatively, either based on otherstatewide guidance, such as Reports from the Third Assessment from theCalifornia Climate Change Center, or based on the observations of <strong>in</strong>frastructuremanagers (aga<strong>in</strong>, while not a scientifically rigorous technique, it may serve theneeds of some users).Characterize Risk and Prioritize Assets for Module 4The f<strong>in</strong>al stage of Module 3 is to consider potential consequences and estimatedlikelihoods <strong>in</strong> <strong>in</strong>tegration. Ideally, the outcome of this exercise will be amanageable selection of assets (or asset types) suitable for advancement to themore detailed and resource <strong>in</strong>tensive approach expla<strong>in</strong>ed <strong>in</strong> Module 4.For many users, the appropriate (and potentially familiar) vehicle forcharacteriz<strong>in</strong>g risk will be the risk matrix, which arrays magnitude ofCambridge Systematics, Inc. 11-11
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