Statistical analysis on extreme wave height

Teena, N. V.; Kumar, V. Sanil; Sudheesh, K.; Sajeev, R.

doi:10.1007/s11069-012-0229-y

Cited by 44 publications

(31 citation statements)

References 31 publications

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“…The annual maxima series resulted in a value of 5.66 m as the 100-year return level for the total H s (Fig. 8), which is comparable to Teena et al (2012) estimation for the location off the central west coast of India.…”

Section: Long-term Statistical Analysis Of Wind Seas and Swellssupporting

confidence: 74%

“…Various marine activities such as the design of coastal and offshore facilities, planning of harbor operations and ship design require detailed assessment of wave characteristics with certain return periods (Caires and Sterl, 2005;Menéndez et al, 2009;Goda et al, 2010). Generally, extreme value theory (EVT) is used for the determination of return levels by adopting a statistical analysis of historic time series of wave heights obtained from various sources such as in situ buoy measurements (e.g., Soares and Scotto, 2004;Méndez et al, 2008;Viselli et al, 2015), satellite data (e.g., Alves et al, 2003;Izaguirre et al, 2010), and hindcasted or reanalysis data by numerical models (e.g., Goda et al, 1993;Caires and Sterl, 2005;Teena et al, 2012;Jonathan et al, 2014). EVT consists of two types of distributions, viz.…”

Section: Introductionmentioning

confidence: 99%

“…GPD is another class of distribution introduced by Pickands (1975) and has been used by several authors such as Caires and Sterl (2005) and Thevasiyani et al (2014). Teena et al (2012) and Samayam et al (2017) have carried out the EVT analysis of ocean surface waves in the northern Indian Ocean based on wave hindcast data and ERA-Interim reanalysis data.…”

Section: Introductionmentioning

confidence: 99%

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

Naseef

2017

Nat. Hazards Earth Syst. Sci.

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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 (H s ) 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 H s 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 5.10 m for the monsoon season (JJAS), 2.66 m for the premonsoon season (FMAM) and 4.28 m 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 7.20 m in the r-largest series followed by monthly maxima (6.02 m) and annual maxima (AM) (5.66 m) 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 H s using the GEV method are comparable for short-term (2008 to 2016) buoy data (4.18 m) and long-term (1979 to 2016) ERA-Interim shallow data (4.39 m). The 6 h interval data tend to miss high values of H s , and hence there is a significant difference in the 100-year return level H s obtained using 6 h interval data compared to data at 0.5 h interval. The study shows that a single storm can cause a large difference in the 100-year H s value.

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Section: Long-term Statistical Analysis Of Wind Seas and Swellssupporting

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Naseef

2017

Nat. Hazards Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

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“…It is recommended by Caires [3] that if the dataset is relatively small (a few years) then a POT approach should be taken. Additionally, if multiple storm events occur within relatively small time-frames, it is recommended in Teena et al [4] to use this POT method to include all the extreme events in the analysis. As this is the case for the dataset available a POT approach will be used in this work.…”

Section: Significant Wave Heightmentioning

confidence: 99%

“…When defining extreme wave conditions, it is common to extrapolate extreme significant wave height, H m0 , values as detailed in Coles [2], Caires [3], Teena et al [4]. To define and simulate sea states, however, appropriate wave period values are also required.…”

Section: Introductionmentioning

confidence: 99%

Simulating Extreme Directional Wave Conditions

2017

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Wave tank tests often involve simulating extreme wave conditions as they enable the maximum expected loads to be inferred: a vital parameter for structural design. The definition, and simulation of, extreme conditions are often fairly simplistic, which can result in conditions and associated loads that are not representative of those that would be observed at the deployment location. Here we present a method of defining, simulating at scale, and validating realistic site-specific extreme wave conditions for survival testing of wave energy converters. Bivariate inverse-first order reliability method (I-FORM) environmental contours define extreme pairs of significant wave height and energy period (H m0 -T E ), while observed extreme conditions are used to define realistic frequency and directional distributions. These sea states are scaled, simulated and validated at the FloWave Ocean Energy Research Facility to demonstrate that the site-specific extreme wave conditions can be re-created with accuracy. The presented approach enables greater realism to be incorporated into tank testing with survival sea states. The techniques outlined and explored here can provide further and more realistic insight into the response of offshore structures and devices, and can help make important design decisions prior to full-scale deployment.

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Storm overtopping of a marine terrace at the penultimate stage of evolution

Horton,

Dickson,

Stephenson

et al. 2024

Earth Surf Processes Landf

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The youngest uplifted marine terrace at Kahutara Point on Māhia Peninsula, New Zealand, is undergoing rapid retreat (backwear) despite being fronted by a wide contemporary shore platform that attenuates wave energy. In this paper, wave energy and water level were recorded across the shore platform, and these data were used to model (a) how frequently waves reach the terrace riser and potentially erode it and (b) how frequently waves overtop the terrace. Analyses of wave data across shore normal transects show that under quiescent and storm conditions >90% of the energy delivered to the back beach and terrace riser is at infragravity frequencies (i.e., <0.05 Hz). Significant wave heights are reduced in a landward direction for both gravity (Hm0H) and infragravity (Hm0L) wave frequencies, with 50%–80% of Hm0H and 20%–50% of Hm0L reduced between seaward and landward sensors. Wave energy during quiescent conditions is strongly attenuated at the seaward margin whereas under storm conditions, proportionally more energy is delivered to the marine terrace riser. The development of a simple inundation model at Māhia reveals that the northern flank of Kahutara Point is more vulnerable to wave inundation, with 17 storms overtopping the youngest marine terrace between 1980 and 2020. Furthermore, despite there being no evidence of a 1‐in‐100 year storm event occurring at Māhia since 1980, there has been an increase in storminess since 2013. Changes in storm frequency may have offset decreases in wave energy (from energy attenuation) with increased platform width associated with marine terrace retreat.

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Statistical analysis on extreme wave height

Cited by 44 publications

References 31 publications

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

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

Simulating Extreme Directional Wave Conditions

Storm overtopping of a marine terrace at the penultimate stage of evolution

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