Direct Joint Probability Method for Estimating Extreme Sea Levels
Publication: Journal of Waterway, Port, Coastal, and Ocean Engineering
Volume 136, Issue 1
Abstract
A key design element in coastal structures is the crest elevation which protects against damages due to overflowing and overtopping. In order to avoid overflowing, the design crest elevation should be above the extreme flood level, which is usually composed of tides and storm surges but could also include tsunami, El Niño, and other climatologic and geologic effects. The extreme flood level may be determined with the annual maxima, simple addition, or joint probability methods (JPM). These methods have various limitations in terms of the amount of required data, the representation of factors contributing to sea level fluctuations, the ability to assess the joint probability of these factors, and the degree of data independence required. To minimize overtopping, the design crest elevation should be above the extreme sea level which is evaluated considering wave runup and the extreme flood level. Wave runup estimates are based on selected extreme flood levels and the extreme wave climate, data for which are often dependent. A modification of the JPM, the direct JPM (DJPM), is developed for estimating extreme flood and sea levels. This method may be applied to consider any number of dependent contributing factors. Data for the City of Richmond, B.C., Canada, are used to demonstrate the DJPM. The DJPM provides an estimate of the extreme flood level for Richmond that is within the same range as those obtained using traditional estimation methods. The results indicate a large difference between extreme flood and sea level estimates. The sea levels at Richmond are also increasing due to climate and tectonic effects. A hybrid direct joint probability-simple addition method is applied to consider these effects.
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Acknowledgments
The writers are grateful to Mr. Yaroslav Shumuk, from UMA Engineering Consultants, for his insights which were the inspiration for this research, and Mr. T. Crowe, City Planner, and Mr. D. Brownlee, Planner-Special Projects, from the City of Richmond for their support and guidance throughout this project and for providing the data for the City of Richmond case study. The advice of Dr. Sheng Li, Research Associate, University of British Columbia, and Mr. Scott Tomlinson from the Marine Environment Data Service of the Canadian Department of Fisheries and Oceans is also gratefully acknowledged. Financial support for this research was provided by the Natural Science and Engineering Research Council of Canada (NSERC) under Discovery Research Grant Awards to the second and third writers and the Vancouver Institute through a Flood Fund Endowment Grant to the Department of Civil Engineering at the University of British Columbia.
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© 2010 ASCE.
History
Received: May 20, 2005
Accepted: Aug 22, 2009
Published online: Dec 15, 2009
Published in print: Jan 2010
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