Wind storm risk management: sensitivity of return period calculations and spread on the territory

Mornet, Alexandre; Opitz, Thomas; Luzi, Michel; Loisel, Stéphane; Bailleul, Bernard

doi:10.1007/s00477-016-1367-7

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…In this latter case, recent studies have underlined that classical correlation measures are often inadequate to capture the actual dependence structure between individual risk factors, especially in a financial and environmental context (see, among others, Embrechts et al, 2002;Poulin et al, 2007;Salvadori and De Michele, 2010;Salvadori and De Michele, 2011). As such, several investigations have been carried out exploiting tools from extremevalue analysis (see, for instance, De Luca and Zuccolotto, 2011;Durante et al, 2014Durante et al, , 2015Mornet et al, 2016). Within the class of model-based clustering methods, copula-based algorithms (see, e.g., Di Lascio et al, 2017, and references therein) use the copula information to derive the specific criterion that determines the clustering composition.…”

Section: The Methodologymentioning

confidence: 99%

Clustering of concurrent flood risks via Hazard Scenarios

et al. 2018

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Section: The Methodologymentioning

confidence: 99%

Clustering of concurrent flood risks via Hazard Scenarios

et al. 2018

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“…All these methods generate storms whose tail behaviour cannot be extrapolated to still rarer events. Extreme value theory was applied to the problem by Della Marta and Mathis (2008) and by Mornet et al (2017), who performed a POT analysis on univariate summaries characterising extreme windstorms, but did not model spatial dependence. Ferreira and de Haan (2014) suggest how historical windstorm records might be up-scaled to higher intensities using Pareto processes, but their approach cannot generate new storms.…”

Section: Risk Estimation For Extreme Windstormsmentioning

confidence: 99%

Functional Peaks-Over-Threshold Analysis

Fondeville

Davison

2022

Journal of the Royal Statistical Society Series B: Statistical Methodology

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Peaks‐over‐threshold analysis using the generalised Pareto distribution is widely applied in modelling tails of univariate random variables, but much information may be lost when complex extreme events are studied using univariate results. In this paper, we extend peaks‐over‐threshold analysis to extremes of functional data. Threshold exceedances defined using a functional r are modelled by the generalised r‐Pareto process, a functional generalisation of the generalised Pareto distribution that covers the three classical regimes for the decay of tail probabilities, and that is the only possible continuous limit for r‐exceedances of a properly rescaled process. We give construction rules, simulation algorithms and inference procedures for generalised r‐Pareto processes, discuss model validation and apply the new methodology to extreme European windstorms and heavy spatial rainfall.

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“…It is worth mentioning that the 50-year return period was adopted throughout the whole study. According to Sundaraj [14] and Mornet et al [15], the value of the basic wind speed is significantly affected by the length of the historical wind speed data, and the longer the period, the better the assessment. Furthermore, the height of 10 m is the standardized reference height all over the world [16].…”

Section: Basic Wind Speed Mapmentioning

confidence: 99%

Development of Site-Specific Wind Hazard Map for Peninsular Malaysia via Spatial Modeling

et al. 2020

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The commonly used approach to predict and evaluate the wind risk in Peninsular Malaysia is to employ the basic wind speed (Vs) hazard map, which underestimates the real damage due to the limitation of factors considered by the current map. This paper aimed to develop a new wind hazard map for Peninsular Malaysia based on the site wind speed (Vsite) rather than the Vs using the Geographical Information System. The development of the Vsite map considered the effects of the Land Use Land Cover (LULC) and the topography conditions that were not taken into consideration by the Malaysian Standard during the development of the Vs map. The statistical analysis proved that the wind hazard in Peninsular Malaysia is directly proportional to the LULC and inversely proportional to the Hill Shape Multiplier. In addition, the results showed that the existing Vs map underrated the wind hazard in Peninsular Malaysia by almost 9.02% to 17.79% compared to the developed Vsite map. Therefore, the use of the newly developed map to evaluate the wind hazards will significantly enhance the assessment, and the new map has the potential to be incorporated into the Malaysian Standard for this purpose.

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Wind storm risk management: sensitivity of return period calculations and spread on the territory

Cited by 6 publications

References 18 publications

Clustering of concurrent flood risks via Hazard Scenarios

Clustering of concurrent flood risks via Hazard Scenarios

Functional Peaks-Over-Threshold Analysis

Development of Site-Specific Wind Hazard Map for Peninsular Malaysia via Spatial Modeling

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