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Understanding the detectability of potential changes to the 100-year peak storm surge

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  • Robert L. Ceres

    (The Pennsylvania State University)

  • Chris E. Forest

    (The Pennsylvania State University
    The Pennsylvania State University
    The Pennsylvania State University)

  • Klaus Keller

    (The Pennsylvania State University
    The Pennsylvania State University
    Carnegie Mellon University)

Abstract

In many coastal communities, the risks driven by storm surges are motivating substantial investments in flood risk management. The design of adaptive risk management strategies, however, hinges on the ability to detect future changes in storm surge statistics. Previous studies have used observations to identify changes in past storm surge statistics. Here, we focus on the simple and decision-relevant question: How fast can we learn from past and potential future storm surge observations about changes in future statistics? Using Observing System Simulation Experiments, we quantify the time required to detect changes in the probability of extreme storm surge events. We estimate low probabilities of detection when substantial but gradual changes to the 100-year storm surge occur. As a result, policy makers may underestimate considerable increases in storm surge risk over the typically long lifespans of major infrastructure projects.

Suggested Citation

  • Robert L. Ceres & Chris E. Forest & Klaus Keller, 2017. "Understanding the detectability of potential changes to the 100-year peak storm surge," Climatic Change, Springer, vol. 145(1), pages 221-235, November.
    • Handle: RePEc:spr:climat:v:145:y:2017:i:1:d:10.1007_s10584-017-2075-0
    DOI: 10.1007/s10584-017-2075-0
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    References listed on IDEAS

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    Cited by:

    1. Jung-A Yang & Sooyoul Kim & Sangyoung Son & Nobuhito Mori & Hajime Mase, 2020. "Assessment of uncertainties in projecting future changes to extreme storm surge height depending on future SST and greenhouse gas concentration scenarios," Climatic Change, Springer, vol. 162(2), pages 425-442, September.
    2. Xue Jin & U. Rashid Sumaila & Kedong Yin, 2020. "Direct and Indirect Loss Evaluation of Storm Surge Disaster Based on Static and Dynamic Input-Output Models," Sustainability, MDPI, vol. 12(18), pages 1-25, September.
    3. Robert L. Ceres & Chris E. Forest & Klaus Keller, 2020. "Trade-offs and synergies in managing coastal flood risk: A case study for New York City," Papers 2010.12043, arXiv.org.
    4. Luciano Raso & Jan Kwakkel & Jos Timmermans & Geremy Panthou, 2019. "How to evaluate a monitoring system for adaptive policies: criteria for signposts selection and their model-based evaluation," Climatic Change, Springer, vol. 153(1), pages 267-283, March.
    5. Luciano Raso & Jan Kwakkel & Jos Timmermans, 2019. "Assessing the Capacity of Adaptive Policy Pathways to Adapt on Time by Mapping Trigger Values to Their Outcomes," Sustainability, MDPI, vol. 11(6), pages 1-16, March.

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