alexa Climate Change Impact on Probability Analysis of Hurricanes | OMICS International
ISSN: 2157-7617

Journal of Earth Science & Climatic Change
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Research Article

Climate Change Impact on Probability Analysis of Hurricanes

Keshtpoor M* and Mark Osler
Michael Baker International, Alexandria, VA 22304, USA
Corresponding Author : Keshtpoor M
Michael Baker International
Alexandria, VA 22304, USA
Tel: (571) 357-6002
E-mail: [email protected]
Received: January 03, 2016; Accepted: January 20, 2016; Published: January 26, 2016
Citation: Keshtpoor M, Osler M (2016) Climate Change Impact on Probability Analysis of Hurricanes. J Earth Sci Clim Change. 6:317. doi:10.4172/2157-7617.1000317
Copyright: © 2016 Keshtpoor M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Coastal flood risk due to cyclonic storms is a significant topic of concern for coastal communities. Planning and engineering efforts within these communities often require estimates of water surface elevations associated with specific return periods. In order to generate the surge elevations for prospective return periods, Joint Probability Method (JPM) techniques are often used [1,2]. Within a JPM approach, statistical representations of cyclonic storm characteristics (i.e. storm frequency, intensity, and radius to maximum wind) are parameterized, along with an associated probability distribution for each parameter. The probability distributions for each of the major hurricane characteristics are based on local historical climatology. A key assumption in the development of the probability distribution for the storm parameters is that each is statistically stationary. Global climate models suggest that characteristics of cyclonic storms may be impacted by climate change. Such changes would challenge the assumption of statistical stationary within the traditional JPM approach. Here, a straightforward windowing approach is proposed to account for possible variation of characteristics. This approach results in more recent storm events having a larger impact on the probability distribution of storm parameters, should such an adjustment be judged necessary by the JPM practitioner. The application of the proposed approach is demonstrated by applying on sample data sets of hurricanes at the mid-Atlantic region and South Florida.

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