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Climatic Change

, Volume 145, Issue 3–4, pp 397–412 | Cite as

Fail-safe and safe-to-fail adaptation: decision-making for urban flooding under climate change

  • Yeowon Kim
  • Daniel A. Eisenberg
  • Emily N. Bondank
  • Mikhail V. Chester
  • Giuseppe Mascaro
  • B. Shane Underwood
Article

Abstract

As climate change affects precipitation patterns, urban infrastructure may become more vulnerable to flooding. Flooding mitigation strategies must be developed such that the failure of infrastructure does not compromise people, activities, or other infrastructure. “Safe-to-fail” is an emerging paradigm that broadly describes adaptation scenarios that allow infrastructure to fail but control or minimize the consequences of the failure. Traditionally, infrastructure is designed as “fail-safe” where they provide robust protection when the risks are accurately predicted within a designed safety factor. However, the risks and uncertainties faced by urban infrastructure are becoming so great due to climate change that the “fail-safe” paradigm should be questioned. We propose a framework to assess potential flooding solutions based on multiple infrastructure resilience characteristics using a multi-criteria decision analysis (MCDA) analytic hierarchy process algorithm to prioritize “safe-to-fail” and “fail-safe” strategies depending on stakeholder preferences. Using urban flooding in Phoenix, Arizona, as a case study, we first estimate flooding intensity and evaluate roadway vulnerability using the Storm Water Management Model for a series of downpours that occurred on September 8, 2014. Results show the roadway types and locations that are vulnerable. Next, we identify a suite of adaptation strategies and characteristics of these strategies and attempt to more explicitly categorize flooding solutions as “safe-to-fail” and “fail-safe” with these characteristics. Lastly, we use MCDA to show how adaptation strategy rankings change when stakeholders have different preferences for particular adaptation characteristics.

Notes

Acknowledgements

This research is supported by several National Science Foundation awards from the IMEE (Nos. 1335556, 1335640, and 1635490), SRN (No. 1444755), TUES (No. 1245205), WSC (No. 1360509), and RIPS (No. 1441352) programs and by the CHI University Grant Program (software package PCSWMM).

Supplementary material

10584_2017_2090_MOESM1_ESM.docx (2.1 mb)
ESM 1 (DOCX 2173 kb)

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Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.School of SustainabilityArizona State UniversityTempeUSA
  2. 2.Civil, Environmental, and Sustainable EngineeringArizona State UniversityTempeUSA
  3. 3.Civil, Construction, and Environmental EngineeringNorth Carolina State UniversityRaleighUSA

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