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20 January 2016 Assessing the Resilience of Coastal Systems: A Probabilistic Approach
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Schultz, M.T. and Smith, E.R., 2016. Assessing the resilience of coastal systems: A probabilistic approach.Resilience is the ability to anticipate, prepare for, and adapt to changing conditions and withstand, respond to, and recover rapidly from disruptions. Methods and tools to quantify resilience are needed to provide actionable intelligence to plan, design, construct, and manage coastal systems. This paper describes how a probabilistic measure of resilience can be assessed for a coastal community using a Bayesian network. The measure of resilience is the joint probability of meeting two management objectives, one with respect to the level of system performance and the other with respect to the length of time required to restore system performance. This paper describes a pilot study to demonstrate the approach in Jamaica Bay, New York, a dense, urban, residential community located on the southern coast of Long Island. Results of the pilot study illustrate how practical information can be developed to support decisions about managing coastal systems. The pilot study provides insights into data and information requirements; the advantages, challenges, and limitations of the approach; and the feasibility of implementing this approach for operations. This approach to resilience assessment is well suited for coastal planning contexts because it explicitly incorporates information about uncertainty in the severity of coastal storm events, as well as uncertainty in how the system will perform when exposed to storm loads. The method challenges the community to establish explicit objectives for coastal resilience, identifies what data are needed to monitor progress toward objectives, and provides a platform from which to explore how those objectives might be achieved in practice.
Martin T. Schultz and Ernest R. Smith "Assessing the Resilience of Coastal Systems: A Probabilistic Approach," Journal of Coastal Research (JCR) 32(5), (20 January 2016).
Received: 5 September 2015; Accepted: 16 October 2015; Published: 20 January 2016

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