Non-stationary extreme value models to account for trends and shifts in the extreme wave climate due to climate change

Vanem, Erik

doi:10.1016/j.apor.2015.06.010

Cited by 50 publications

(19 citation statements)

References 26 publications

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“…Previous studies indicate that Climate-Change also has a non-negligible effect on extremes (Trenberth and Shepherd, 2015;Hemer and Trenham, 2016;Du et al, 2015), so assumptions such as a stationary stormthreshold cannot be adopted. This is a first indication that non-stationarity needs to be addressed (Vanem, 2015).…”

Section: Non-stationary Modelmentioning

confidence: 91%

Multivariate statistical modelling of future marine storms

Lin-Ye¹,

Garcı́a-León²,

Gràcia³

et al. 2017

Applied Ocean Research

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Extreme events, such as wave-storms, need to be characterized for coastal infrastructure design purposes. Such description should contain information on both the univariate behaviour and the joint-dependence of storm-variables. These two aspects have been here addressed through generalized Pareto distributions and hierarchical Archimedean copulas. A non-stationary model has been used to highlight the relationship between these extreme events and non-stationary climate. It has been applied to a Representative Concentration Pathway 8.5 Climate-Change scenario, for a fetch-limited environment (Catalan Coast). In the non-stationary model, all considered variables decrease in time, except for storm-duration at the northern part of the Catalan Coast. The joint distribution of storm variables presents cyclical fluctuations, with a stronger influence of climate dynamics than of climate itself.Peer ReviewedPostprint (author's final draft

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Section: Non-stationary Modelmentioning

confidence: 91%

Multivariate statistical modelling of future marine storms

Lin-Ye¹,

Garcı́a-León²,

Gràcia³

et al. 2017

Applied Ocean Research

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“…While the sample data are large, the largest or smallest value from a sample has a degradation problem. However, the extremal type theorem can effectively solve this problem ("as discussed by Vanem [15]"). The extremal type theorem is presented as follows.…”

Section: The Study Of the Evtmentioning

confidence: 99%

Using EVT for Geological Anomaly Design and Its Application in Identifying Anomalies in Mining Areas

Qin¹,

Liu²,

Guo³

2016

Mathematical Problems in Engineering

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A geological anomaly is the basis of mineral deposit prediction. Through the study of the knowledge and characteristics of geological anomalies, the category of extreme value theory (EVT) to which a geological anomaly belongs can be determined. Associating the principle of the EVT and ensuring the methods of the shape parameter and scale parameter for the generalized Pareto distribution (GPD), the methods to select the threshold of the GPD can be studied. This paper designs a new algorithm called the EVT model of geological anomaly. These study data on Cu and Au originate from 26 exploration lines of the Jiguanzui Cu-Au mining area in Hubei, China. The proposed EVT model of the geological anomaly is applied to identify anomalies in the Jiguanzui Cu-Au mining area. The results show that the model can effectively identify the geological anomaly region of Cu and Au. The anomaly region of Cu and Au is consistent with the range of ore bodies of actual engineering exploration. Therefore, the EVT model of the geological anomaly can effectively identify anomalies, and it has a high indicating function with respect to ore prospecting.

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“…This method (i.e. the POT-GPD method) makes the most of the samples, extends the return period when the threshold is suitable (Alves and Young, 2003;You, 2011;Vanem, 2015a;Samayam et al, 2017;Shao et al, 2017) and, due to this method, extracts all high samples.…”

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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Non-stationary extreme value models to account for trends and shifts in the extreme wave climate due to climate change

Cited by 50 publications

References 26 publications

Multivariate statistical modelling of future marine storms

Multivariate statistical modelling of future marine storms

Using EVT for Geological Anomaly Design and Its Application in Identifying Anomalies in Mining Areas

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

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