A multivariate model of sea storms using copulas

Michele, Carlo De; Salvadori, Gianfausto; Passoni, Giuseppe; Vezzoli, Renata

doi:10.1016/j.coastaleng.2007.05.007

Cited by 198 publications

(111 citation statements)

References 34 publications

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“…This resulted in an extensive expert discussion (Genest and Remillard, 2006;de Vries and Zhou, 2006;Segers, 2006). However, as outlined in the following sections, Copula functions represent a powerful alternative to deal with various multivariate problems in coastal engineering.…”

Section: T Wahl Et Al: Assessing the Hydrodynamic Boundary Conditiomentioning

confidence: 99%

Assessing the hydrodynamic boundary conditions for risk analyses in coastal areas: a multivariate statistical approach based on Copula functions

Wahl

Mudersbach

Jensen

2012

Nat. Hazards Earth Syst. Sci.

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Abstract. This paper presents an advanced approach to statistically analyse storm surge events. In former studies the highest water level during a storm surge event usually was the only parameter that was used for the statistical assessment. This is not always sufficient, especially when statistically analysing storm surge scenarios for event-based risk analyses. Here, Archimedean Copula functions are applied and allow for the consideration of further important parameters in addition to the highest storm surge water levels. First, a bivariate model is presented and used to estimate exceedance probabilities of storm surges (for two tide gauges in the German Bight) by jointly analysing the important storm surge parameters "highest turning point" and "intensity". Second, another dimension is added and a trivariate fully nested Archimedean Copula model is applied to additionally incorporate the significant wave height as an important wave parameter. With the presented methodology, reliable and realistic exceedance probabilities are derived and can be considered (among others) for integrated flood risk analyses contributing to improve the overall results. It is highlighted that the concept of Copulas represents a promising alternative for facing multivariate problems in coastal engineering.

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Section: T Wahl Et Al: Assessing the Hydrodynamic Boundary Conditiomentioning

confidence: 99%

Assessing the hydrodynamic boundary conditions for risk analyses in coastal areas: a multivariate statistical approach based on Copula functions

Wahl

Mudersbach

Jensen

2012

Nat. Hazards Earth Syst. Sci.

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“…To highlight the limitations of the current JPC method, seven example alternative multivariate contouring definitions have been applied (Serinaldi, 2014) and include those developed and explored by a range of authors (Corbella and Stretch, 2013;De Michele et al, 2007;Hawkes et al, 2002;Jonathan et al, 2013;Salvadori and De Michele, 2004;Salvadori et al, 2011;Volpi and Fiori, 2014). While these alternatives are not routinely applied in coastal engineering practice within the UK, they have been introduced here to raise awareness of the limitations of multivariate contouring approaches.…”

Section: Multivariate Return Periodsmentioning

confidence: 99%

Multivariate extreme value modelling of sea conditions around the coast of England

Gouldby

Wyncoll

Panzeri

et al. 2017

Proceedings of the Institution of Civil Engineers - Maritime En

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It is widely recognised that coastal flood events can arise from combinations of extreme waves and sea levels. For flood risk analysis and the design of coastal structures it is therefore necessary to assess the joint probability of the occurrence of these variables. Traditional methods have involved the application of joint probability contours, defined in terms of extremes of sea conditions that can, if applied without correction factors, lead to the underestimation of flood risk and under-design of coastal structures. This paper describes the application of a robust multivariate statistical model to analyse extreme offshore waves, wind and sea levels around the coast of England. The approach described here is risk based in that it seeks to define extremes of response variables directly, rather than the joint extremes of sea conditions. The output of the statistical model comprises a Monte Carlo simulation of extreme events. These distributions of extreme events have been transformed from offshore to nearshore using a statistical emulator of a wave transformation model. The resulting nearshore extreme sea condition distributions have the potential to be applied for a range of purposes. The application is demonstrated using two structures located on the south coast of England. Notation

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“…In recent years, it has been widely applied to various aspects of hydrological studies [13]. The main applications of the Copula Function include the analysis of: precipitation characteristics [12], the correlation between flood peak and flood volume [14,15], the frequency and recurrence interval of floods [16][17][18], characteristics of storms [19,20], the frequency and recurrence intervals of droughts [21], drought assessment [22], risk assessment [23], and an assessment of environmental hydrological model performance [24]. Due to the specialty of the water table, the establishment of the Copula Function for this purpose is relatively difficult; therefore, no research that uses the Copula Function to analyze the water table has been reported.…”

Section: Introductionmentioning

confidence: 99%

Probability Analysis of the Water Table and Driving Factors Using a Multidimensional Copula Function

You

Liu

2018

Water

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Abstract:The relationship between the water table and driving factors is a reliable theoretical reference for the reasonable planning of surface water resources and the water table. Previous research has neglected the distribution and probabilities of the water table. However, this paper analyzes the relationship between the water table and driving factors from a statistical perspective by correcting the variables and introducing the Kernel Distribution Estimation and the Copula Function. The average data of the buried depth of the phreatic water, annual irrigation volume of the surface water, and precipitation in the Jinghui Irrigation District in China from 1977 to 2013 were adopted. We precisely obtained the two-dimensional (2D) and three-dimensional (3D) Joint Distribution Function of each driving factor and the marginal distribution of the water table, calculate the conditional probability in different ranges, and exactly predict the design value of surface water irrigation giving set conditions. Eventually, we emphasize the importance of probability analysis and prediction in groundwater planning.

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A multivariate model of sea storms using copulas

Cited by 198 publications

References 34 publications

Assessing the hydrodynamic boundary conditions for risk analyses in coastal areas: a multivariate statistical approach based on Copula functions

Assessing the hydrodynamic boundary conditions for risk analyses in coastal areas: a multivariate statistical approach based on Copula functions

Multivariate extreme value modelling of sea conditions around the coast of England

Probability Analysis of the Water Table and Driving Factors Using a Multidimensional Copula Function

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