addressing climate change adaptation in regional transportation plans

Addressing Climate Change Adaptation in Regional Transportation PlansA Guide for California MPOs and RTPAsconsequences and likelihood of occurrence on perpendicular (x/y) axes. Theunits of measurement for each axis will vary by user preference, but are oftenqualitative designations (e.g., “high,” “medium,” “low”) or simple rankings (1 to5). The selected units should have already been established during theconsequences and likelihood steps described previously, but can be refined forbetter compatibility with the risk assessment.The risk matrix suggested for this exercise functions as a screening tool for thesubsequent Module. The intent is for asset/stressor combinations to be allocatedaccording to their integrated risk characteristics. As shown in the examplematrix included below as Figure 11.2, this means that a “high consequence, highlikelihood” asset/stressor grouping would attain the highest rating and almostsurely be advanced to Module 4. If another “high consequence” asset werepaired with a “medium likelihood” stressor, then this asset/stressor combinationmight not make the cut (the cut-off point is, of course, at the discretion of theagency, but should yield a manageable quantity of assets for further assessment).For assets graduating into Module 4 that are linked with multiple stressors,lower likelihood stressors could be footnoted to support a more integratedadaptation assessment.Since the potential consequence is synonymous with an asset’s criticalitycategory, assets themselves may remain in a single tier (e.g., a “high”consequence asset remains “high” for multiple stressors), or, if an agency judgesit to be necessary, a rule of thumb may be developed to facilitate broaddifferentiation between asset-stressor consequences. For example, an agencymay reasonably determine that high heat days do not threaten an asset with thesame magnitude of consequence as heavy rainfall events, and discount theconsequences of high heat accordingly.However, it may be more suitable to factor in the relative threats associated witha particular stressor when ranking the likelihood of each. To continue with theprevious example, whereas each individual high heat day may lead to few, orrelatively minor, marginal consequences, a preponderance of additional extremetemperature events (an increase of 2, 3, or 5 fold, for instance) might have highcumulative consequences related to deterioration or otherwise rare damage anddisruption that could start occurring with greater frequency. When designatingthe ranges that characterize tiers of likelihood, it is not necessary for everystressor to register projections in each tier. To illustrate the concept, the “high”likelihood extreme temperature frequency might be, hypothetically, “over100 days annually,” even if the upper boundary of projections is significantlyless. This way, although consequences for a “high” criticality asset would remainhigh, the likelihood (frequency) of extreme temperature events could not be –meaning that the integrated asset/stressor risk could never be “high/high.”Depending on size of the universe of assets to be assessed, the matrix could bepopulated by GIS, by committee or working group, or a combination of the two.For a systems-level analysis of multiple asset types and/or modes, a GIS couldsupport a comprehensive screening if data is sufficient. For example, by creating11-12 Cambridge Systematics, Inc.