An automated threshold selection method based on the characteristic of extrapolated significant wave heights

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

doi:10.1016/j.coastaleng.2018.12.001

Cited by 43 publications

(38 citation statements)

References 45 publications

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“…In the POT method, the peak significant wave height is extracted over the threshold, u, as the extreme sample for extrapolation. Considering the significant role of the threshold for this method, this value should be prudently selected [31].…”

Section: Pot/gpd Methodsmentioning

confidence: 99%

“…To select a suitable threshold, the GPD parameter plot [39] is employed. By fitting the GPD over a range of candidate thresholds, the corresponding shape parameter and modified scale parameter ( 1 ku   ) [31] can be estimated. By identifying the stability of these parameters, a suitable threshold can be determined.…”

Section: Return Significant Wave Heightmentioning

confidence: 99%

“…In fact, the estimated parameters cannot be exactly constant due to the sampling variability. However, these parameters should be stable after allowing for the sampling error [31]. When the estimated parameters remain nearly constant, the suitable threshold can be defined as the lowest threshold within the stable threshold range.…”

Section: Return Significant Wave Heightmentioning

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: Pot/gpd Methodsmentioning

confidence: 99%

Section: Return Significant Wave Heightmentioning

confidence: 99%

Section: Return Significant Wave Heightmentioning

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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“…In the South China Sea (SCS), time series wave parameters have been simulated (Zheng et al, 2012;Mirzaei et al, 2015;Yaakob et al, 2016) and extreme waves have been investigated based on long-term continuous data (Zheng et al, 2015;Chen et al, 2017;Wang et al, 2018). In addition, Shao et al (2018a) and Liang et al (2019) analysed the extreme significant wave height in a tropical cyclone. Shao et al (2018a) compared the annual maxima (AM) method (Tawn, 1988) with the POT method.…”

Section: Introductionmentioning

confidence: 99%

“…However, the threshold selection process is relatively complex. Shao et al (2018a) and Liang et al (2019) analysed the sensitivity of the return-significant wave height to the threshold. The researchers found that the suitable threshold should be determined within the stable threshold range.…”

Section: Introductionmentioning

confidence: 99%

Extreme significant wave height of tropical cyclone waves in the South China Sea

Shao

Liang

et al. 2019

Nat. Hazards Earth Syst. Sci.

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Abstract. Extreme significant wave heights are assessed in the South China Sea (SCS), as assessments of wave heights are crucial for coastal and offshore engineering. Two significant factors include the initial database and assessment method. The initial database is a basis for assessment, and the assessment method is used to extrapolate appropriate return-significant wave heights during a given period. In this study, a 40-year (1975–2014) hindcast of tropical cyclone waves is used to analyse the extreme significant wave height, employing the peak over threshold (POT) method with the generalized Pareto distribution (GPD) model. The peak exceedances over a sufficiently large value (i.e. threshold) are fitted; thus, the return-significant wave heights are highly dependent on the threshold. To determine a suitable threshold, the sensitivity of return-significant wave heights and the characteristics of tropical cyclone waves are studied. The sample distribution presents a separation that distinguishes the high sample from the low sample, and this separation is within the stable threshold range. Because the variation in return-significant wave heights in this range is generally small and the separation is objectively determined by the track and intensity of the tropical cyclone, the separation is selected as a suitable threshold for extracting the extreme sample in the tropical cyclone wave. The asymptotic tail approximation and estimation uncertainty show that the selection is reasonable.

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Peaks-over-threshold model in flood frequency analysis: a scoping review

Pan

Rahman

Haddad

et al. 2022

Stoch Environ Res Risk Assess

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In flood frequency analysis (FFA), annual maximum (AM) model is widely adopted in practice due to its straightforward sampling process. However, AM model has been criticized for its limited flexibility. FFA using peaks-over-threshold (POT) model is an alternative to AM model, which offers several theoretical advantages; however, this model is currently underemployed internationally. This study aims to bridge the current knowledge gap by conducting a scoping review covering several aspects of the POT approach including model assumptions, independence criteria, threshold selection, parameter estimation, probability distribution, regionalization and stationarity. We have reviewed the previously published articles on POT model to investigate: (a) possible reasons for underemployment of the POT model in FFA; and (b) challenges in applying the POT model. It is highlighted that the POT model offers a greater flexibility compared to the AM model due to the nature of sampling process associated with the POT model. The POT is more capable of providing less biased flood estimates for frequent floods. The underemployment of POT model in FFA is mainly due to the complexity in selecting a threshold (e.g., physical threshold to satisfy independence criteria and statistical threshold for Generalized Pareto distribution – the most commonly applied distribution in POT modelling). It is also found that the uncertainty due to individual variable and combined effects of the variables are not well assessed in previous research, and there is a lack of established guideline to apply POT model in FFA.

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An automated threshold selection method based on the characteristic of extrapolated significant wave heights

Cited by 43 publications

References 45 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

Extreme significant wave height of tropical cyclone waves in the South China Sea

Peaks-over-threshold model in flood frequency analysis: a scoping review

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