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Modeling non-stationarity in significant wave height over the Northern Indian Ocean.

Authors :
Dhanyamol, P.
Agilan, V.
KV, Anand
Source :
Stochastic Environmental Research & Risk Assessment. Oct2024, Vol. 38 Issue 10, p3823-3836. 14p.
Publication Year :
2024

Abstract

Statistical descriptions of extreme met-ocean conditions are essential for the safe and reliable design and operation of structures in marine environments. The significant wave height ( H S ) is one of the most essential wave parameters for coastal and offshore structural design. Recent studies have reported that a time-varying component exists globally in the H S . Therefore, the non-stationary behavior of an annual maximum series of H S is important for various ocean engineering applications. This study aims to analyze the frequency of H S over the northern Indian Ocean by modeling the non-stationarity in the H S series using a non-stationary Generalized Extreme Value (GEV) distribution. The hourly maximum H S data (with a spatial resolution of 0.5° longitude × 0.5° latitude) collected from the global atmospheric reanalysis dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF) is used for the study. To model the annual maximum series of H S using a non-stationary GEV distribution, two physical covariates (El-Ni n ~ o Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD)) and time covariates are introduced into the location and scale parameters of the GEV distribution. The return levels of various frequencies of H S are estimated under non-stationary conditions. From the results, average increases of 13.46%, 13.66%, 13.85%, and 14.02% are observed over the study area for the 25-year, 50-year, 100-year, and 200-year return periods, respectively. A maximum percentage decrease of 33.3% and a percentage increase of 167% are observed in the return levels of various return periods. The changes in the non-stationary return levels over time highlight the importance of modeling the non-stationarity in H S . [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14363240
Volume :
38
Issue :
10
Database :
Academic Search Index
Journal :
Stochastic Environmental Research & Risk Assessment
Publication Type :
Academic Journal
Accession number :
179970860
Full Text :
https://doi.org/10.1007/s00477-024-02775-3