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Volume 17, issue 10 | Copyright
Nat. Hazards Earth Syst. Sci., 17, 1763-1778, 2017
https://doi.org/10.5194/nhess-17-1763-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 19 Oct 2017

Research article | 19 Oct 2017

Variations in return value estimate of ocean surface waves – a study based on measured buoy data and ERA-Interim reanalysis data

T. Muhammed Naseef and V. Sanil Kumar T. Muhammed Naseef and V. Sanil Kumar
  • Ocean Engineering Division, Council of Scientific and Industrial Research (CSIR)–National Institute of Oceanography, Dona Paula 403 004, India

Abstract. An assessment of extreme wave characteristics during the design of marine facilities not only helps to ensure their safety but also assess the economic aspects. In this study, return levels of significant wave height (Hs) for different periods are estimated using the generalized extreme value distribution (GEV) and generalized Pareto distribution (GPD) based on the Waverider buoy data spanning 8 years and the ERA-Interim reanalysis data spanning 38 years. The analysis is carried out for wind-sea, swell and total Hs separately for buoy data. Seasonality of the prevailing wave climate is also considered in the analysis to provide return levels for short-term activities in the location. The study shows that the initial distribution method (IDM) underestimates return levels compared to GPD. The maximum return levels estimated by the GPD corresponding to 100 years are 5.10m for the monsoon season (JJAS), 2.66m for the pre-monsoon season (FMAM) and 4.28m for the post-monsoon season (ONDJ). The intercomparison of return levels by block maxima (annual, seasonal and monthly maxima) and the r-largest method for GEV theory shows that the maximum return level for 100 years is 7.20m in the r-largest series followed by monthly maxima (6.02m) and annual maxima (AM) (5.66m) series. The analysis is also carried out to understand the sensitivity of the number of observations for the GEV annual maxima estimates. It indicates that the variations in the standard deviation of the series caused by changes in the number of observations are positively correlated with the return level estimates. The 100-year return level results of Hs using the GEV method are comparable for short-term (2008 to 2016) buoy data (4.18m) and long-term (1979 to 2016) ERA-Interim shallow data (4.39m). The 6h interval data tend to miss high values of Hs, and hence there is a significant difference in the 100-year return level Hs obtained using 6h interval data compared to data at 0.5h interval. The study shows that a single storm can cause a large difference in the 100-year Hs value.

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Assessment of design waves is performed using generalized extreme value (GEV) and generalized Pareto distribution (GPD) based on buoy data for 8 years and ERA-Interim reanalysis data for 38 years. The initial distribution method underestimates return levels compared to GPD. Intercomparison of return levels by block maxima and r-largest method for GEV theory shows that return level for 100 years is 7.24 m by r-largest series. A single storm can cause a large difference in the 100-year Hs value.
Assessment of design waves is performed using generalized extreme value (GEV) and generalized...
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