A multi-distribution approach to POT methods for determining extreme wave heights

Mazas, Franck; Hamm, Luc

doi:10.1016/j.coastaleng.2010.12.003

Cited by 108 publications

(62 citation statements)

References 12 publications

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“…Ailliot et al, 2011) and the GPD for maxima exceeding thresholds have found the most attraction in environmental extreme value analysis (e.g. MacKay et al, 2011;Moloney and Davidsen, 2011;Mazas and Hamm, 2011). For the practical implementation in R several packages are available, like "ismev", "evir" and "POT" (Ribatet, 2006), which has been used in this study.…”

Section: Extreme Valuesmentioning

confidence: 99%

Technical Note: The normal quantile transformation and its application in a flood forecasting system

Bogner

Pappenberger

Cloke

2012

Hydrol. Earth Syst. Sci.

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Abstract. The Normal Quantile Transform (NQT) has been used in many hydrological and meteorological applications in order to make the Cumulated Distribution Function (CDF) of the observed, simulated and forecast river discharge, water level or precipitation data Gaussian. It is also the heart of the meta-Gaussian model for assessing the total predictive uncertainty of the Hydrological Uncertainty Processor (HUP) developed by Krzysztofowicz. In the field of geo-statistics this transformation is better known as the Normal-Score Transform. In this paper some possible problems caused by small sample sizes when applying the NQT in flood forecasting systems will be discussed and a novel way to solve the problem will be outlined by combining extreme value analysis and non-parametric regression methods. The method will be illustrated by examples of hydrological stream-flow forecasts.

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Section: Extreme Valuesmentioning

confidence: 99%

Technical Note: The normal quantile transformation and its application in a flood forecasting system

Bogner

Pappenberger

Cloke

2012

Hydrol. Earth Syst. Sci.

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“…Par exemple, les présents auteurs [13] La densité de probabilité de la marée astronomique s'obtient aujourd'hui facilement pour la plupart des ports. Il suffit de disposer d'un cycle long de 18,6 ans de mesures pour obtenir la quasi-intégralité de la densité de probabilité recherchée.…”

Section: Iii26 Autres Modèlesunclassified

“…Les présents auteurs [13] ont rappelé l'importance de séparer les échantillons de vagues en populations homogènes distinctes pour une bonne analyse statistique destinée à déterminer les vagues extrêmes. Or, cette même recommandation peut s'appliquer aux surcotes / décotes météorologiques.…”

Section: V2 Homogénéisation Des Surcotesunclassified

Niveaux marins extrêmes en France : état des lieux

Mazas

Hamm

2011

La Houille Blanche

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“…It is indeed important to clarify which, among the numerous parameters to be defined for an OTM analysis, often largely arbitrary (Takvor and Panagiota, 2001), are involved in the physical declustering and which ones are involved in the optimization procedure. Even when in the past the two steps were performed separately (Dupuis, 1998;Egozcue et al, 2005;Bernardara et al, 2008Bernardara et al, , 2011Garavaglia et al, 2010;Bardet et al, 2011;Mazas and Hamm, 2011;Wahl et al, 2011), or when approaching the two steps at the same time (i.e. the extremal index approach), the underlying concepts were not clearly exposed.…”

Section: P Bernardara Et Al: a Two-step Framework For Over-thresholmentioning

confidence: 99%

“…Garavaglia et al (2010) introduced a compound distribution for extreme rainfalls taking into account seasonality and different physical genesis. For wave heights, probability distribution and even time series autocorrelation may depend on direction, fetch, water depth and other covariates (Mathiesen et al, 1994;Jonathan and Ewans, 2007;Taylor et al, 2009;MacKay et al, 2010;Mazas and Hamm, 2011). Many possibilities exist for getting time series of homogeneous physical phenomena (clipping, decomposition, cleaning, etc.)…”

Section: P Bernardara Et Al: a Two-step Framework For Over-thresholmentioning

confidence: 99%

A two-step framework for over-threshold modelling of environmental extremes

Bernardara

Mazas

Kergadallan

et al. 2014

Nat. Hazards Earth Syst. Sci.

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Abstract. The evaluation of the probability of occurrence of extreme natural events is important for the protection of urban areas, industrial facilities and others. Traditionally, the extreme value theory (EVT) offers a valid theoretical framework on this topic. In an over-threshold modelling (OTM) approach, Pickands' theorem, (Pickands, 1975) states that, for a sample composed by independent and identically distributed (i.i.d.) values, the distribution of the data exceeding a given threshold converges through a generalized Pareto distribution (GPD). Following this theoretical result, the analysis of realizations of environmental variables exceeding a threshold spread widely in the literature. However, applying this theorem to an auto-correlated time series logically involves two successive and complementary steps: the first one is required to build a sample of i.i.d. values from the available information, as required by the EVT; the second to set the threshold for the optimal convergence toward the GPD. In the past, the same threshold was often employed both for sampling observations and for meeting the hypothesis of extreme value convergence. This confusion can lead to an erroneous understanding of methodologies and tools available in the literature. This paper aims at clarifying the conceptual framework involved in threshold selection, reviewing the available methods for the application of both steps and illustrating it with a double threshold approach.

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A multi-distribution approach to POT methods for determining extreme wave heights

Cited by 108 publications

References 12 publications

Technical Note: The normal quantile transformation and its application in a flood forecasting system

Technical Note: The normal quantile transformation and its application in a flood forecasting system

Niveaux marins extrêmes en France : état des lieux

A two-step framework for over-threshold modelling of environmental extremes

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