Study of sampling methods for assessment of extreme significant wave heights in the South China Sea

Shao, Zhuxiao; Liang, Bingchen; Li, Huajun; Lee, Dongyoung

doi:10.1016/j.oceaneng.2018.09.015

Cited by 29 publications

(33 citation statements)

References 46 publications

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“…The AM method extracts the annual maximal significant wave height as the extreme sample. As a common probabilistic model, the Gumbel model is used to fit this sample, whose distribution function is expressed as follows [30]:…”

Section: Am/gumbel Methodsmentioning

confidence: 99%

“…The peak over threshold (POT) method [13] and annual maxima (AM) method [27] are common methods extracting the extreme sample from the homogenous sample, which select the peak significant wave height over the threshold and the annual maximal significant wave height as the extreme sample, respectively. By fitting these extreme samples, the generalized Pareto distribution (GPD) model [28] and Gumbel model [29], respectively, are widely employed to extrapolate the required return significant wave heights through constructing long-term distributions [30]. In this study, the POT/GPD method and AM/Gumbel method are used to estimate the extreme wave in the Yellow Sea, based on the homogenous sample in the North that is extracted by directional declustering.…”

Section: Introductionmentioning

confidence: 99%

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Study of Directional Declustering for Estimating Extreme Wave Heights in the Yellow Sea

Gao

Shao

et al. 2020

JMSE

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The study of extreme waves is important for the protection of coastal and ocean structures. In this work, a 22-year (1990–2011) wave hindcast in the Yellow Sea is employed to perform the assessment of extreme significant wave heights in this area. To extract the independent sample from this database, the fixed window method is used, which takes the peak significant wave height within five d. With the selected samples, directional declustering is studied to extract the homogenous sample. The results show that most of the independent samples (especially large samples) are observed in the North. In this direction, the peak over threshold (POT) method is used to extract the extreme sample from the homogenous sample, and then the generalized Pareto distribution model is used to extrapolate the extreme significant wave height. In addition to this combination, the annual maxima method with the Gumbel model is also used for estimating extreme values. The comparisons show that the return significant wave heights of the first combination are reliable, resulting from a flexible sampling window in the POT method. With this conclusion, the extreme significant wave height is extrapolated from the Yellow Sea, which can be used to protect the structure in the main directional bin.

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Section: Am/gumbel Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Study of Directional Declustering for Estimating Extreme Wave Heights in the Yellow Sea

Gao

Shao

et al. 2020

JMSE

Self Cite

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“…The use of the extreme value theory has been widely extended in recent years to meteooceanographic variables. In a recent study, [1] used the Gumbel probability density function (PDF) together with maximum annual values to assess the extreme maximum annual significant wave height for projection purposes. [2] used non-stationary GEV models for investigating period trends in extreme waves.…”

Section: Introductionmentioning

confidence: 99%

Single Site Extreme Wave Analysis in the Pacific Ocean Comparing Stationary and Non-stationary GEV Models

Calderón-Vega

Mösso

García–Soto

2019

CJAST

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The adequate knowledge of the weather behavior is very important for the design and management of socioeconomical, environmental and sustainability human interests in the coasts and oceans. In the present study an extreme value analysis of maximum significant waves recorded at a buoy located in the Pacific Ocean was carried out. The analysis was carried out from two perspectives, by considering a Generalized Extreme Value (GEV) model with stationary distribution (i.e., the time variations are not accounted for), and by considering a non-stationary GEV model, which incorporates the monthly seasonality of maximum observed values in time increments; the maximum significant wave behavior was parameterized using harmonic functions for the distribution measures. Both approaches were compared for a single buoy. In the study a seasonality effect was found, which was also present at the Gulf of Mexico in previous studies, and which cannot be captured by a stationary model.

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“…As per the use of model data for the evaluation of wave climate, a recent and very useful contribution is the work by Lin-Ye et al [14] who combine the results from a SWAN model with a non-stationary multivariate statistical approach. An interesting sensitivity study of high return period SWH, based on a SWAN model driven by a combination of the ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis wind and the Holland hurricane model, is also reported in [19]. The previously mentioned work by Niroomandi et al…”

mentioning

confidence: 96%

“…Much work has also gone on the special case of estimating extreme SWH for special applications such as offshore power plants [15][16][17][18].As per the use of model data for the evaluation of wave climate, a recent and very useful contribution is the work by Lin-Ye et al [14] who combine the results from a SWAN model with a non-stationary multivariate statistical approach. An interesting sensitivity study of high return period SWH, based on a SWAN model driven by a combination of the ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis wind and the Holland hurricane model, is also reported in [19]. The previously mentioned work by Niroomandi et al[9] makes use of wave hindcast from the NCEP's Climate Forecast System and a SWAN wave model validated with buoy measurement, to characterize their temporal and spatial variabilities of extreme SWH.…”

mentioning

confidence: 99%

An Experimental Assessment of Extreme Wave Evaluation by Integrating Model and Wave Buoy Data

Reale¹,

Dentale²,

Furcolo³

et al. 2020

Water

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Calculating the significant wave height (SWH) in a given location as a function of the return time is an essential tool of coastal and ocean engineering; such a calculation can be carried out by making use of the now widely available weather and wave model chains, which often lead to underestimating the results, or by means of in situ experimental data (mostly, wave buoys), which are only available in a limited number of sites. A procedure is hereby tested whereby the curves of extreme SWH as a function of the return time deriving from model data are integrated with the similar curves computed from buoy data. A considerable improvement in accuracy is gained by making use of this integrated procedure in all locations where buoy data series are not available or are not long enough for a correct estimation. A useful and general design tool has therefore been provided to derive the extreme value SWH for any point in a given area.data raises however various issues: apart from the obvious problem of reliability of the model chains of both the atmospheric and the sea wave parts, an important aspect is the way through which ground truth wave data are assimilated into the analysis. Most of the assimilation procedures are carried out with satellite altimeter data, which are scattered in time (at many hours' intervals) and wide apart in space (tens or hundreds of kilometers), so extreme SWH values may often be missed. It is also worth noting that the sampling time of the models, i.e., the time interval at which data are stored and released, is often higher than the standard sampling time of buoys, thus causing a negative bias on the estimated extreme values [1][2][3].In order to overcome these problems, an integrated procedure [4] was been proposed by some of the authors of the present paper whereby the curves of extreme SWH as a function of the return time T R (in the following: SWH(T R )) deriving from synthetic data are compared and calibrated with the similar curves computed from buoy data in different locations. This provides a way of deriving SWH(T R ) curves for sites where no experimental data are available.The present paper presents an extension of the same technique and provides an experimental authentication of the methodology based on a new large set of reliable data along the coasts of the USA.The determination of the probability of extreme SWH is one of the main problems of coastal, offshore, and marine engineering, so that the relevant literature is not only extensive, but also increasing with time as the technology improves and the requirements become more stringent. Therefore, in the following, only contributions which are connected to the aims of this paper will be considered.All the procedures are substantially based on fitting Extreme Value Probability Distributions (in the following: EVPD) to buoy recorded time series of SWH; references on the general problem date back to many years ago [5], however Goda's textbook [6] is still the most common reference for maritime engineering, even though many authors...

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Study of sampling methods for assessment of extreme significant wave heights in the South China Sea

Cited by 29 publications

References 46 publications

Study of Directional Declustering for Estimating Extreme Wave Heights in the Yellow Sea

Study of Directional Declustering for Estimating Extreme Wave Heights in the Yellow Sea

Single Site Extreme Wave Analysis in the Pacific Ocean Comparing Stationary and Non-stationary GEV Models

An Experimental Assessment of Extreme Wave Evaluation by Integrating Model and Wave Buoy Data

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