A copula-based approach for the estimation of wave height records through spatial correlation

Jane, Robert; Valle, Luciana Dalla; Simmonds, David; Raby, Alison

doi:10.1016/j.coastaleng.2016.06.008

Cited by 39 publications

(30 citation statements)

References 84 publications

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“…The values obtained in table 3 are similar to the ones presented in the literature for other locations; e.g. [6], [7], [11], [15] and [22] reported values of τK for several locations, ranging from 0.21 to 0.8.…”

Section: P 11supporting

confidence: 85%

“…The use of copula-based models to build the joint distribution function of the significant wave height and mean up-crossing period, or the spectral peak period, has been newly applied, e.g. [6], [7]. The use of copulas is mainly due to their simplicity of calculation and due to their ability to describe the dependence structure between random variables, regardless of the assumptions made for the marginal distributions [19].Moreover, copula-based models present a straightforward and computationally fast method to simulate the random variables [20].…”

Section: Introductionmentioning

confidence: 99%

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Asymmetric copula–based distribution models for met-ocean data in offshore wind engineering applications

et al. 2018

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Joint statistical models for long-term wave climate are a key aspect of offshore wind engineering design. However, to find a joint model for sea-state characteristics is often difficult due to the complex nature of the wave climate and the physical constraints of seastate phenomena. The available records of wave heights and periods are often very asymmetric in their nature. This paper presents a copula-based approach to obtain the joint cumulative distribution function of significant wave heights and the mean up-crossing periods. This study is based on 124 months hindcast data concerning Horns Rev 3 offshore wind farm. The extra-parametrization technique of symmetric copulas is implemented to account for the asymmetry present in the data. The analysis of the total sea, the wind-sea and primary swell components is performed separately. The results show that the extraparametrization technique with pairwise copulas consistently provided a better goodnessof-fit when compared to symmetric copulas. Moreover, it is demonstrated that the separation of the total sea into its components does not always improve the extraparametrized copula's performance. Furthermore, this paper also discusses copula application to offshore wind engineering.

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Section: P 11supporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Asymmetric copula–based distribution models for met-ocean data in offshore wind engineering applications

et al. 2018

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“…Further, hindcast wave data could be used to lengthen wave buoy data, though the accuracy of those data relies on an appropriate validation. Past research, e.g., [3,48,53], as well as our own assessment highlight the necessity of high quality and longer wave data to better understand the wave climate. Similarly, spatial consistency in the way wave data are recorded is crucial to improve our understanding of coastal flood risk and planning [47].…”

Section: Discussionmentioning

confidence: 86%

Spatial and Temporal Clustering Analysis of Extreme Wave Events around the UK Coastline

Santos

Haigh

Wahl

2017

JMSE

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Densely populated coastal regions are vulnerable to extreme wave events, which can cause loss of life and considerable damage to coastal infrastructure and ecological assets. Here, an event-based analysis approach, across multiple sites, has been used to assess the spatial footprint and temporal clustering of extreme storm-wave events around the coast of the United Kingdom (UK). The correlated spatial and temporal characteristics of wave events are often ignored even though they amplify flood consequences. Waves that exceeded the 1 in 1-year return level were analysed from 18 different buoy records and declustered into distinct storm events. In total, 92 extreme wave events are identified for the period from 2002 (when buoys began to record) to mid-2016. The tracks of the storms of these events were also captured. Six main spatial footprints were identified in terms of extreme wave events occurrence along stretches of coastline. The majority of events were observed between November and March, with large inter-annual differences in the number of events per season associated with the West Europe Pressure Anomaly (WEPA). The 2013/14 storm season was an outlier regarding the number of wave events, their temporal clustering and return levels. The presented spatial and temporal analysis framework for extreme wave events can be applied to any coastal region with sufficient observational data and highlights the importance of developing statistical tools to accurately predict such processes.

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“…In a similar application in the Weihe River basin (China), Ma et al (2010) reported that the trivariate Gaussian copula gave a better fit than the Student's t copula, with both outperforming six Archimedean copulas, half of which possessed (radial) asymmetry. In other environmental applications, the Student's t copula has been shown to offer a superior fit to the Gaussian copula in the presence of tail dependence (e.g., Jane et al, 2016;Wahl et al, 2016). In this study two elliptical copulas and three Archimedean copulas (Gumbel, Clayton, and Frank) are considered.…”

Section: Standard Higher Dimensional Copulasmentioning

confidence: 99%

Multivariate statistical modelling of the drivers of compound flood events in South Florida

Jane¹,

Cadavid²,

Obeysekera³

et al. 2020

Preprint

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Abstract. Miami-Dade County (south-east Florida) is among the most vulnerable regions to sea-level rise in the United States, due to a variety of natural and human factors. The co-occurrence of multiple, often statistically dependent flooding drivers – termed compound events – typically exacerbates impacts compared with their isolated occurrence. Ignoring dependencies between the drivers will potentially lead to underestimation of flood risk and under-design of flood defence structures. At present, design assessments of flood defence structures in Miami-Dade County assume rainfall and Ocean-side Water Level (O-sWL) are fully dependent, a conservative assumption inducing large safety factors. Here, an analysis of the dependence between the principal flooding drivers over a range of lags at three locations across the county is carried out. The conservative nature of the existing structural design assessment is subsequently explored, by combining a two-dimensional analysis of rainfall and O-sWL with regional sea-level rise projections. Finally, the vine copula and Heffernan and Tawn (2004) models are shown to outperform five standard higher dimensional copulas in capturing the dependence between the principal drivers of compound flooding: rainfall, O-sWL, and groundwater level. This leads to recommendations for revised future design frameworks able to capture and represent dependencies between different flood drivers.

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A copula-based approach for the estimation of wave height records through spatial correlation

Cited by 39 publications

References 84 publications

Asymmetric copula–based distribution models for met-ocean data in offshore wind engineering applications

Asymmetric copula–based distribution models for met-ocean data in offshore wind engineering applications

Spatial and Temporal Clustering Analysis of Extreme Wave Events around the UK Coastline

Multivariate statistical modelling of the drivers of compound flood events in South Florida

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