Comparing different extreme wave analysis models for wave climate assessment along the Italian coast

Sartini, Ludovica; Mentaschi, Lorenzo; Besio, Giovanni

doi:10.1016/j.coastaleng.2015.03.006

Cited by 40 publications

(26 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the following, the peak over threshold (POT) method in the form described for instance in [29][30][31][32][33] was followed to produce a set of extreme significant wave height values. There is here a choice to be made as to which extreme value distribution should be fitted to the data.…”

Section: Methodsmentioning

confidence: 99%

Extreme Wave Analysis by Integrating Model and Wave Buoy Data

Dentale

Furcolo

Carratelli

et al. 2018

Water

View full text Add to dashboard Cite

Abstract:Estimating the extreme values of significant wave height (H S ), generally described by the H S return period T R function H S (T R ) and by its confidence intervals, is a necessity in many branches of coastal science and engineering. The availability of indirect wave data generated by global and regional wind and wave model chains have brought radical changes to the estimation procedures of such probability distribution-weather and wave modeling systems are routinely run all over the world, and H S time series for each grid point are produced and published after assimilation (analysis) of the ground truth. However, while the sources of such indirect data are numerous, and generally of good quality, many aspects of their procedures are hidden to the users, who cannot evaluate the reliability and the limits of the H S (T R ) deriving from such data. In order to provide a simple engineering tool to evaluate the probability of extreme sea-states as well as the quality of such estimates, we propose here a procedure based on integrating H S time series generated by model chains with those recorded by wave buoys in the same area.

show abstract

Section: Methodsmentioning

confidence: 99%

Extreme Wave Analysis by Integrating Model and Wave Buoy Data

Dentale

Furcolo

Carratelli

et al. 2018

Water

View full text Add to dashboard Cite

show abstract

“…The Peak Over Threshold (POT) method was applied: a standard procedure, as described for instance by Sartini et al (2015b), was followed to produce the Weibull distribution (Eq. 1).…”

Section: Methodsmentioning

confidence: 99%

Sampling Bias in the Estimation of Significant Wave Height Extreme Values

Dentale

Reale

D’Alessandro

et al. 2017

Int. Conf. Coastal. Eng.

View full text Add to dashboard Cite

It has been shown before, and it is intuitively evident, that in a Significant Wave Height (SWH) time series, the longer the sampling interval, the lower is the number of events which are above a given threshold value. As a consequence, the use of data with a low time resolution (such as a 3 h sampling, for instance) causes a considerable undervaluation of the extreme SWH values for a given return time RT. In this paper an example of such a bias is provided, and a method is suggested to estimate it on a regional basis. Results may help to improve the use of historical wave meters data which were often collected with a low time resolution, and may also provide a tool to improve the application of Numerical Meteo-Wave models to the evaluation of extremes.

show abstract

“…Thus, the selected extreme events should occur randomly in time according to the Poisson process, with annual mean νT , where ν is the event rate (yr −1 ) and T = 1 yr. Events should be sufficiently far apart to be independent, and the exceedances to u should have an approximate generalized Pareto distribution (GPD) G(y,σ,ξ)={1−(1+ξyσ)−(1/ξ);ξ≠01−exp(−bold-italicyσ);ξ=0, where y = y | y > u − u is the exceedance by y of the threshold u , ξ is the shape parameter and σ > 0 is the scale parameter. The combination of the Poisson model for frequency and the GPD model for intensity can be expressed in a form compatible with the generalized extreme value distribution (GEV) for annual maxima provided the following equations are respected ([19,27,28]) σ=ψ+ξfalse(u−μfalse) and ν=(ξfalse(u−μfalse)ψ)−1/ξ, where ψ > 0 and μ are the GEV's scale and location parameters, respectively.…”

Section: Wave Loadingmentioning

confidence: 99%

Wolf Rock lighthouse: past developments and future survivability under wave loading

Raby

Antonini

Pappas

et al. 2019

Phil. Trans. R. Soc. A.

View full text Add to dashboard Cite

Lighthouses situated on exposed rocky outcrops warn mariners of the dangers that lurk beneath the waves. They were first constructed when approaches to wave loading and structural response were relatively unsophisticated, essentially learning from previous failures. Here, we chart the evolution of lighthouses on the Wolf Rock, situated between Land's End and the Isles of Scilly in the UK. The first empirical approaches are described, followed by design aspects of the present tower, informed by innovations developed on other rocky outcrops. We focus on a particular development associated with the automation of lighthouses: the helideck platform. The design concept is described and the structure then scrutinized for future survivability, using the latest structural modelling techniques of the entire lighthouse and helideck. Model validation data were obtained through a complex logistical field operation and experimental modal analysis. Extreme wave loading for the model required the identification of the 250-year return period wave using a Bayesian method with informative prior distributions, for two different scenarios (2017 and 2067). The structural models predict responses of the helideck to wave loading which is characterized by differential displacements of 0.093 m (2017) and 0.115 m (2067) with associated high tension forces and plastic strain. This article is part of the theme issue ‘Environmental loading of heritage structures’.

show abstract

Comparing different extreme wave analysis models for wave climate assessment along the Italian coast

Cited by 40 publications

References 45 publications

Extreme Wave Analysis by Integrating Model and Wave Buoy Data

Extreme Wave Analysis by Integrating Model and Wave Buoy Data

Sampling Bias in the Estimation of Significant Wave Height Extreme Values

Wolf Rock lighthouse: past developments and future survivability under wave loading

Contact Info

Product

Resources

About