Extreme Wave Loads

2008 ◽  
Author(s):  
Sofia Caires ◽  
Marcel R. A. van Gent
Keyword(s):  
Wave Height ◽  
Ad Hoc ◽  
Value Theory ◽  
Extreme Value ◽  
Wave Period ◽  
Extreme Value Analysis ◽  
Wave Loads ◽  
Extreme Wave ◽  
Value Analysis ◽  
Return Value

This paper compares three main methods for estimating extreme wave loads with a view towards determining the sensitivity of estimates to the particular approach chosen. The approaches considered include: a) The generally used ad-hoc procedure of performing an extreme value analysis of the Hs data, trying to find a relationship between wave height and period at the storm peaks and then, once the return values of extreme wave heights are estimated, estimating the associated return value of the wave period by means of the relationship found. b) The ‘structure variable method’ in which the pairs wave height and period observations are converted into univariate loads to which univariate extreme value theory is applied to estimate the return value of the structural load. c) The multivariate extreme value approach suggested by [1] in which a ‘multivariate return value’, namely the most probable value of the wave period conditional on a return value of the wave height, is estimated. Our study is based on a 44-yr long timeseries of wave conditions created using the shallow water wave model SWAN and calibrated ERA-40 data. The results suggest that the three approaches yield similar estimates. However, the ad-hoc procedure a gives the least conservative estimates. Approach c provides results that apply to any choice of load function and which to a certain extent are independent of the location in which the estimates are obtained, for which reason it may generally be the preferred one.

scholarly journals RELATION BETWEEN SHAPE OF EXTREME WAVE HEIGHT DISTRIBUTIONS AND WEATHER SYSTEM

2018 ◽  
pp. 73
Author(s):  
Maki Chiwata ◽  
Tomoya Shimura ◽  
Nobuhito Mori
Keyword(s):  
Wave Height ◽  
Extreme Value ◽  
Structure Design ◽  
Extreme Value Analysis ◽  
Observation Data ◽  
Height Distribution ◽  
Extreme Wave ◽  
Engineering Applications ◽  
Value Analysis

The extreme value analysis of wave height has been used to estimate design value of coastal structure design. The procedure of extreme value analysis is standardized but distributions change highly depending on target locations. A long-term atmospheric reanalysis is useful for engineering applications to complement observation data. Although the reanalysis dataset was insufficient for coastal engineering applications due to shorter length of period and sparse spatial resolution of modeling, recent reanalysis (e.g. CFSR) give reasonable performance for engineering applications as wave climate both accuracy and length of periods (e.g. Menendez and Losada, 2017). This study analyzes characteristics of extreme wave heights and understands relation between extreme wave height distribution and its dependence on weather systems based on long-term analysis and observed data.

scholarly journals Projected 21st Century changes in extreme wind-wave events

2020 ◽  
Author(s):  
Alberto Meucci
Keyword(s):  
Wave Height ◽  
21St Century ◽  
Wind Wave ◽  
Ocean Waves ◽  
Extreme Value Analysis ◽  
High Latitudes ◽  
Extreme Wave ◽  
Value Analysis ◽  
The North ◽  
Uncertainty Estimates

<p>Extreme ocean waves shape world coastlines and significantly impact offshore operations. Climate change may further exacerbate these effects increasing losses in human lives and economic activities. Studies generally agree on the trends in the mean values, yet there is no consensus on the extreme events, and whether their magnitude and/or frequency are changing. The present work applies an innovative extreme value analysis approach to a multi-model ensemble wind-wave climate dataset, derived from seven global climate models, to evaluate projected extreme wave height changes towards the end of the 21st century. Under two greenhouse gas emission scenarios, we find that at the end of the 21st century, the one in 100-year wave height event increases across the scenarios by 5 to 15 % over the Southern Ocean. The North Atlantic shows a decrease at low to mid-latitudes (5 to 15 %) and an increase at the high latitudes (10 %). The extreme wave heights in the North Pacific increase at the high latitudes by 5 to 10 %. The present work suggests that pooling an ensemble of future projected ocean storms from different GCMs might significantly improve uncertainty estimates connected to future coastal and offshore wave extremes, thereby improving climate adaptation strategies.</p>

A New Generation of Earthquake Recurrence Models Based on The Extreme Value Theory and Impact on Probabilistic Seismic Hazard Assessments

2021 ◽  
Author(s):  
Anne Dutfoy ◽  
Gloria Senfaute
Keyword(s):  
Seismic Hazard ◽  
Extreme Value Theory ◽  
Generalized Pareto Distribution ◽  
Random Variable ◽  
Value Theory ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Probabilistic Seismic Hazard ◽  
Universal Method ◽  
Value Analysis

Abstract Probabilistic Seismic Hazard Analysis (PSHA) procedures require that at least the mean activity rate be known, as well as the distribution of magnitudes. Within the Gutenberg-Richter assumption, that distribution is an Exponential distribution, upperly truncated to a maximum possible magnitude denoted $m_{max}$. This parameter is often fixed from expert judgement under tectonics considerations, due to a lack of universal method. In this paper, we propose two innovative alternatives to the Gutenberg-Richter model, based on the Extreme Value Theory and that don't require to fix a priori the value of $m_{max}$: the first one models the tail distribution magnitudes with a Generalized Pareto Distribution; the second one is a variation on the usual Gutenberg-Richter model where $m_{max}$ is a random variable that follows a distribution defined from an extreme value analysis. We use the maximum likelihood estimators taking into account the unequal observation spans depending on magnitude, the incompleteness threshold of the catalog and the uncertainty in the magnitude value itself. We apply these new recurrence models on the data observed in the Alps region, in the south of France and we integrate them into a probabilistic seismic hazard calculation to evaluate their impact on the seismic hazard levels. The proposed new recurrence models introduce a reduction of the seismic hazard level compared to the common Gutenberg-Richter model conventionally used for PSHA calculations. This decrease is significant for all frequencies below 10 Hz, mainly at the lowest frequencies and for very long return periods. To our knowledge, both new models have never been used in a probabilistic seismic hazard calculation and constitute a new promising generation of recurrence models.

Robustness of precipitation and river discharge extremes in the surrogate world of seasonal forecasts

2021 ◽  
Author(s):  
Katharina Klehmet ◽  
Peter Berg ◽  
Denica Bozhinova ◽  
Louise Crochemore ◽  
Ilias Pechlivanidis ◽  
...  
Keyword(s):  
Extreme Precipitation ◽  
River Discharge ◽  
Value Theory ◽  
Extreme Value ◽  
Generalized Extreme Value Distribution ◽  
Extreme Value Analysis ◽  
Seasonal Forecasts ◽  
Adaptation Measures ◽  
Value Analysis ◽  
Mitigation And Adaptation

<p>Robust information of hydrometeorological extremes is important for effective risk management, mitigation and adaptation measures by public authorities, civil and engineers dealing for example with water management. Typically, return values of certain variables, such as extreme precipitation and river discharge, are of particular interest and are modelled statistically using Extreme Value Theory (EVT). However, the estimation of these rare events based on extreme value analysis are affected by short observational data records leading to large uncertainties.</p><p>In order to overcome this limitation, we propose to use the latest seasonal meteorological prediction system of the European Centre for Medium-Range Weather Forecasts (ECMWF SEAS5) and seasonal hydrological forecasts generated with the pan-European E-HYPE model of the original period 1993-2015 and to extend the dataset to longer synthetic time series by pooling single forecast months to surrogate years. To ensure an independent dataset, the seasonal forecast skill is assessed in advance and months (and lead months) with positive skill are excluded. In this study, we simplify the method and work with samples of 6- and 4-month forecasts (instead of the full 7-month forecasts) depending on the statistical independency of the variables. It enables the record to be extended from the original 23 years to 3450 and 2300 surrogate years for the 6- and 4-month forecasts respectively.</p><p>Furthermore, we investigate the robustness of estimated 50- and 100-year return values for extreme precipitation and river discharge using 1-year block maxima that are fitted to the Generalized Extreme Value distribution. Surrogate sets of pooled years are randomly constructed using the Monte-Carlo approach and different sample sizes are chosen. This analysis reveals a considerable reduction in the uncertainty of all return period estimations for both variables for selected locations across Europe using a sample size of 500 years. This highlights the potential in using the ensembles of meteorological and hydrological seasonal forecasts to obtain timeseries of sufficient length and minimize the uncertainty in the extreme value analysis.</p>

Frequentist and Bayesian extreme value analysis on the wildfire events in Greece

2020 ◽  
Author(s):  
Nikos Koutsias ◽  
Frank A. Coutelieris
Keyword(s):  
Extreme Value Theory ◽  
Extreme Values ◽  
Value Theory ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Burned Area ◽  
Accurate Assessment ◽  
Peaks Over Threshold ◽  
Bayesian Approaches ◽  
Value Analysis

<p>A statistical analysis on the wildfire events, that have taken place in Greece during the period 1985-2007, for the assessment of the extremes has been performed. The total burned area of each fire was considered here as a key variable to express the significance of a given event. The data have been analyzed through the extreme value theory, which has been in general proved a powerful tool for the accurate assessment of the return period of extreme events. Both frequentist and Bayesian approaches have been used for comparison and evaluation purposes. Precisely, the Generalized Extreme Value (GEV) distribution along with Peaks over Threshold (POT) have been compared with the Bayesian Extreme Value modelling. Furthermore, the correlation of the burned area with the potential extreme values for other key parameters (e.g. wind, temperature, humidity, etc.) has been also investigated.</p>

scholarly journals Comparison on the Analysis on PM10 Data based on Average and Extreme Series

2018 ◽  
Vol 150 ◽  
pp. 05025
Author(s):  
Nor Azrita Mohd Amin ◽  
Siti Aisyah Zakaria
Keyword(s):  
Extreme Events ◽  
Value Theory ◽  
Extreme Value ◽  
Return Level ◽  
Extreme Value Analysis ◽  
Main Concern ◽  
Value Analysis ◽  
Average Data ◽  
Average Value ◽  
High Level

The main concern in environmental issue is on extreme phenomena (catastrophic) instead of common events. However, most statistical approaches are concerned primarily with the centre of a distribution or on the average value rather than the tail of the distribution which contains the extreme observations. The concept of extreme value theory affords attention to the tails of distribution where standard models are proved unreliable to analyse extreme series. High level of particulate matter (PM10) is a common environmental problem which causes various impacts to human health and material damages. If the main concern is on extreme events, then extreme value analysis provides the best result with significant evidence. The monthly average and monthly maxima PM10 data for Perlis from 2003 to 2014 were analysed. Forecasting for average data is made by Holt-Winters method while return level determine the predicted value of extreme events that occur on average once in a certain period. The forecasting from January 2015 to December 2016 for average data found that the highest forecasted value is 58.18 (standard deviation 18.45) on February 2016 while return level achieved 253.76 units for 24 months (2015-2016) return periods.

Bias and Uncertainty in the Estimation of Extreme Wave Heights and Crests

2009 ◽  
Author(s):  
Richard Gibson ◽  
Colin Grant ◽  
George Z. Forristall ◽  
Rory Smyth ◽  
Peter Owrid ◽  
...  
Keyword(s):  
North Sea ◽  
Extreme Value ◽  
Accurate Prediction ◽  
Design Parameters ◽  
Extreme Value Analysis ◽  
Extreme Wave ◽  
Data Set ◽  
Value Analysis ◽  
Design Values ◽  
Wave Heights

The accurate prediction of extreme wave heights and crests is important to the design of offshore structures. For example, knowledge of the extreme crest elevation is required to set the deck elevation of the topside of a jacket structure. However, methods of extreme value analysis have an inherent bias, and the manner in which they are applied affects this bias. Furthermore, there is uncertainty in the design parameters at the time of design and the possibility that the predictions will change during the life of the structure. This paper is concerned with the accurate prediction of design values that incorporate uncertainty. In the first part of this paper the details of commonly applied extreme value analysis techniques are examined. This is achieved through analysis of simulated data of known distribution. In particular it is the application of least squares minimisation routines that is investigated; however, comparisons are made with maximum likelihood estimation. From this, preferred approaches to the analysis are recommended and their advantages and disadvantages discussed. The methods are applied to the analysis of a North Sea data set and the implications for the design values ascertained. In the second part of the paper Bayesian inference is used to consider the effect of uncertainty in the predicted wave heights and crest elevations. The practical implications are determined by the analysis of a measured North Sea data set.

scholarly journals Wind and Wave Extremes from Atmosphere and Wave Model Ensembles

2018 ◽  
Vol 31 (21) ◽  
pp. 8819-8842 ◽  
Author(s):  
Alberto Meucci ◽  
Ian R. Young ◽  
Øyvind Breivik
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wave Height ◽  
Significant Wave Height ◽  
Wave Model ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Ensemble Forecasts ◽  
Value Analysis ◽  
Model Ensembles

The present work develops an innovative approach to wind speed and significant wave height extreme value analysis. The approach is based on global atmosphere–wave model ensembles, the members of which are propagated in time from the best estimate of the initial state, with slight perturbations to the initial conditions, to estimate the uncertainties connected to model representations of reality. The low correlation of individual ensemble member forecasts at advanced lead times guarantees their independence and allows us to perform inference statistics. The advantage of ensemble probabilistic forecasts is that it is possible to synthesize an equivalent dataset of duration far longer than the simulation period. This allows the use of direct inference statistics to obtain extreme value estimates. A short time series of six years (from 2010 to 2016) of ensemble forecasts is selected to avoid major changes to the model physics and resolution and thus ensure stationarity. This time series is used to undertake extreme value analysis. The study estimates global wind speed and wave height return periods by selecting peaks from ensemble forecasts from +216- to +240-h lead time from the operational ensemble forecast dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF). The results are compared with extreme value analyses performed on a commonly used reanalysis dataset, ERA-Interim, and buoy data. The comparison with traditional methods demonstrates the potential of this novel approach for statistical analysis of significant wave height and wind speed ocean extremes at the global scale.

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