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Journal Article

Citation

Rowan E, Evans C, Riley-Gilbert M, Hyman R, Kafalenos R, Beucler B, Rodehorst B, Choate A, Schultz P. Transp. Res. Rec. 2013; 2326: 16-23.

Copyright

(Copyright © 2013, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.3141/2326-03

PMID

unavailable

Abstract

Transportation officials are increasingly faced with challenging decisions about how to design, plan, and manage infrastructure to confront changes in climate and extreme weather events. An understanding of which impacts affect infrastructure and at what point damage begins to occur is a critical step toward assessing overall vulnerability and risk. However, few resources exist to help managers and designers identify key thresholds and indicators of sensitivity to weather and climate impacts. This paper introduces a sensitivity matrix, a tool developed for the U.S. Department of Transportation's Gulf Coast Study, Phase 2, adaptation pilot project in Mobile, Alabama. This matrix is an important step toward a more comprehensive understanding of relationships between climate and transportation. Transportation planners can use this matrix to screen for assets that are particularly sensitive and, therefore, potentially vulnerable to climate change. Where possible, the matrix includes key thresholds at which damage may be observed. This resource can assist the transportation community in conducting climate vulnerability and risk assessments. This sensitivity matrix reveals three main conclusions about the sensitivity of the transportation system to climate stressors. First, transportation assets tend to be more sensitive to extreme events than to incremental changes in the mean of climate variables. Second, services such as maintenance, traffic conveyance, and safety often are more sensitive to climate stressors than are physical assets. Finally, an asset is often sensitive to stressors whose occurrence is relatively unlikely in comparison with typical weather variability.

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