Please use this identifier to cite or link to this item: http://nopr.niscpr.res.in/handle/123456789/54738
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dc.contributor.authorUpadhyaya, S K-
dc.contributor.authorRao, Subba-
dc.contributor.authorManu-
dc.date.accessioned2020-07-23T08:51:04Z-
dc.date.available2020-07-23T08:51:04Z-
dc.date.issued2020-05-
dc.identifier.issn0975-1033 (Online); 0379-5136 (Print)-
dc.identifier.urihttp://nopr.niscair.res.in/handle/123456789/54738-
dc.description717-723en_US
dc.description.abstractA competent coastal engineer should have adequate knowledge with regard to wave analysis. Long-term analysis is being effectively used worldwide to predict wave heights based on extreme wave statistics. The objective here is to analyze the ocean waves off Mangaluru coast, India. Long-term analysis is initially performed based on probability distributions like Log-normal distribution, Gumbel distribution, Fretchet distribution, Exponential distribution, and Weibull distributions on the in-situ wave data recorded at coastal waters off Mangaluru coast. The Extreme wave analysis is performed based on Peak Over Threshold method from which the best-fit probability distribution is obtained. Further, the best performing distribution is applied on the simulated wave heights obtained from MIKE 21 numerical model forced with ERA-interim wind speed data of 38-years. From the analysis, amongst the five different distributions considered Weibull distribution with α = 1.3 gave the best-fit for the wave dataset considered. Long-term analysis is then performed on 38-years hindcast data for 10, 50 and 100 year return periods for the Mangaluru coast region.en_US
dc.language.isoen_USen_US
dc.publisherNISCAIR-CSIR, Indiaen_US
dc.rights CC Attribution-Noncommercial-No Derivative Works 2.5 Indiaen_US
dc.sourceIJMS Vol.49(05) [May 2020]en_US
dc.subjectDesign wave heighten_US
dc.subjectERA-interimen_US
dc.subjectLong-term wave analysisen_US
dc.subjectMIKE 21en_US
dc.subjectProbability distribution functionsen_US
dc.titleLong-term analysis of waves off Mangaluru coasten_US
dc.typeArticleen_US
Appears in Collections:IJMS Vol.49(05) [May 2020]

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