scholarly journals Deriving the 100-Year Total Water Level around the Coast of Corsica by Combining Trivariate Extreme Value Analysis and Coastal Hydrodynamic Models

2021 ◽  
Vol 9 (12) ◽  
pp. 1347
Author(s):  
Jessie Louisor ◽  
Jérémy Rohmer ◽  
Thomas Bulteau ◽  
Faïza Boulahya ◽  
Rodrigo Pedreros ◽  
...  
Keyword(s):  
Water Level ◽  
Return Period ◽  
Extreme Value ◽  
Coastal Areas ◽  
Extreme Value Analysis ◽  
Total Water ◽  
Hydrodynamic Models ◽  
Value Analysis ◽  
Multivariate Extreme Value ◽  
Total Water Level

As low-lying coastal areas can be impacted by flooding caused by dynamic components that are dependent on each other (wind, waves, water levels—tide, atmospheric surge, currents), the analysis of the return period of a single component is not representative of the return period of the total water level at the coast. It is important to assess a joint return period of all the components. Based on a semiparametric multivariate extreme value analysis, we determined the joint probabilities that significant wave heights (Hs), wind intensity at 10 m above the ground (U), and still water level (SWL) exceeded jointly imposed thresholds all along the Corsica Island coasts (Mediterranean Sea). We also considered the covariate peak direction (Dp), the peak period (Tp), and the wind direction (Du). Here, we focus on providing extreme scenarios to populate coastal hydrodynamic models, SWAN and SWASH-2DH, in order to compute the 100-year total water level (100y-TWL) all along the coasts. We show how the proposed multivariate extreme value analysis can help to more accurately define low-lying zones potentially exposed to coastal flooding, especially in Corsica where a unique value of 2 m was taken into account in previous studies. The computed 100y-TWL values are between 1 m along the eastern coasts and a maximum of 1.8 m on the western coast. The calculated values are also below the 2.4 m threshold recommended when considering the sea level rise (SLR). This highlights the added value of performing a full integration of extreme offshore conditions, together with their dependence on hydrodynamic simulations for screening out the coastal areas potentially exposed to flooding.

scholarly journals Bitcoin and Cryptocurrencies—Not for the Faint-Hearted

2017 ◽  
Vol 4 (1) ◽  
pp. 56 ◽  
Author(s):  
Joerg Osterrieder ◽  
Martin Strika ◽  
Julian Lorenz
Keyword(s):  
Financial Engineering ◽  
Heavy Tails ◽  
Tail Dependence ◽  
Extreme Value ◽  
Point Of View ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Risk Characteristics ◽  
Asset Class ◽  
Multivariate Extreme Value

Cryptocurrencies became popular with the emergence of Bitcoin and have shown an unprecedented growth over the last few years. As of November 2016, more than 720 cryptocurrencies exist, with Bitcoin still being the most popular one. We provide both a statistical analysis as well as an extreme value analysis of the returns of the most important cryptocurrencies. A particular focus is on the tail risk characteristics and we will provide an in-depth univariate and multivariate extreme value analysis. The tail dependence of cryptocurrencies is investigated (using both empirical and Gaussian copulas). For investors—especially institutional ones—as well as regulators, an understanding of the risk and tail characteristics are of utmost importance. For cryptocurrencies to become a mainstream investable asset class, studying these properties is necessary. Our findings show that cryptocurrencies exhibit strong non-normal characteristics, large tail dependencies, depending on the particular cryptocurrencies and heavy tails. Statistical similarities can be observed for cryptocurrencies that share the same underlying technology. This has implications for risk management, financial engineering (such as derivatives on cryptocurrencies)—both from an investor’s as well as from a regulator’s point of view. To our knowledge, this is the first detailed study looking at the extreme value behaviour of cryptocurrencies, their correlations and tail dependencies as well as their statistical properties.

scholarly journals The Influence of Hindcast Modeling Uncertainty on the Prediction of High Return Period Wave Conditions

2004 ◽  
Author(s):  
Daniel C. Brooker ◽  
Geoffrey K. Cole ◽  
Jason D. McConochie
Keyword(s):  
Return Period ◽  
Extreme Value Distribution ◽  
Likelihood Estimation ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Random Errors ◽  
Value Analysis ◽  
Simulation Techniques ◽  
Modeling Uncertainties ◽  
Wave Conditions

Extreme value analysis for the prediction of long return period met-ocean conditions is often based upon hindcast studies of wind and wave conditions. The random errors associated with hindcast modeling are not usually incorporated when fitting an extreme value distribution to hindcast data. In this paper, a modified probability distribution function is derived so that modeling uncertainties can be explicitly included in extreme value analysis. Maximum likelihood estimation is then used to incorporate hindcast uncertainty into return value estimates and confidence intervals. The method presented here is compared against simulation techniques for accounting for hindcast errors. The influence of random errors within modeled datasets on predicted 100 year return wave estimates is discussed.

Analysing the Return Period and Risk under the Influence of Physical Covariates on Hydrological Extreme

2021 ◽  
Author(s):  
Jew Das ◽  
Nanduri Umamahesh ◽  
Srinidhi Jha
Keyword(s):  
Water Resources ◽  
Return Period ◽  
Large Scale ◽  
Location Parameter ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Change Scenario ◽  
Stationary Condition ◽  
Value Analysis ◽  
Monsoon Index

<p>For sustainable water resources planning and management, it is necessary to redefine the concept of return period, risk, and reliability of hydrologic extreme under non-stationary condition. Thus, the present study aims to examine the return period, risk introducing physical based covariates in the location parameter of the generalised extreme value (GEV) distribution. The study is performed over the Godavari River basin, India. The expected waiting time (EWT) approach is used to make comparison of return period, risk between stationary and non-stationary approaches. From the analysis, it is found that 50% of the gauging stations are impacted by large scale modes/oscillations and regional hydrological variability, primarily by Indian Summer Monsoon Index (ISMI) and precipitation. The EWT interpretation estimates that the non-stationary return period, risk, and reliability are significantly different from stationary condition. Hence, it is concluded that return period analysis and risk assessment using non-stationary approach can be beneficial to water managers and policy makers in order to devise sustainable and resilient water resources infrastructure under climate change scenario.</p><p><strong>Keywords:</strong> Extreme value analysis; Return period; Risk; Non-stationarity; Uncertainty</p>

How to Carry Out Metocean Studies

2011 ◽  
Author(s):  
Judith van Os ◽  
Sofia Caires
Keyword(s):  
Water Level ◽  
Practice Guidelines ◽  
Best Practice ◽  
Extreme Value ◽  
Statistical Analyses ◽  
Extreme Value Analysis ◽  
Data Validation ◽  
Value Analysis ◽  
Best Practice Guidelines

Metocean studies involve quite a lot of statistical analyses. The detail and extent of the required metocean conditions (waves, water level, currents and wind) is study dependent. Nevertheless, most of the studies involve data validation, determination of normal and extreme metocean conditions and offshore to nearshore transformation of data. All of the above aspects of a metocean study can be performed in numerous different ways, which will in a way depend on the quality and amount of the available data. Data validation, for example, can be done visually and numerically and there are several methods to perform an extreme value analysis. It is therefore needed to have best practice guidelines to execute metocean studies in an efficient and standardized way so that accurate and verifiable results can be obtained. Deltares carries out a lot of metocean studies for the industry and is presently working on a method to standardize the execution of metocean studies, by developing guidelines along with a MATLAB tool, called ORCA, which integrates the main aspects of analyzing metocean data.

scholarly journals A Climatology and Extreme Value Analysis of Large Hail in China

2020 ◽  
Vol 148 (4) ◽  
pp. 1431-1447
Author(s):  
Xiang Ni ◽  
Andreas Muehlbauer ◽  
John T. Allen ◽  
Qinghong Zhang ◽  
Jiwen Fan
Keyword(s):  
Spatial Distribution ◽  
Return Period ◽  
Eastern China ◽  
Northern China ◽  
Extreme Value ◽  
Western China ◽  
Extreme Value Analysis ◽  
Annual Maximum ◽  
Return Periods ◽  
Value Analysis

Abstract Hail size records are analyzed at 2254 stations in China and a hail size climatology is developed based on gridded hail observations for the period 1960–2015. It is found that the annual percentiles of hail size records changed sharply and national-wide after 1980, therefore two periods, 1960–79 and 1980–2015, are studied. There are some similarities between the two periods in terms of the characteristics of hail size such as the spatial distribution patterns of mean annual maximum hail size and occurrence week of annual maximum hail size. The 1980–2015 period had higher observation density than the 1960–79 period, but showed smaller mean annual maximum hail size, especially in northern China. In the majority of grid boxes, the annual maximum hail size experienced a decreasing trend during the 1980–2015 period. A Gumbel extreme value model is fitted to each grid box to estimate the return periods of maximum hail size. The scale and location parameter of the fitted Gumbel distributions are higher in eastern China than in western China, thereby reflecting a greater likelihood of large hail in eastern China. In southern China, the maximum hail size exceeds 127 mm for a 10-yr return period, whereas in northern China maximum hail size exceeds this threshold for a 50-yr return period. The Gumbel model is found to potentially underestimate the maximum hail size for certain return periods, but provides a more informed picture of the spatial distribution of extreme hail size and the regional differences.

Multivariate extreme value analysis of a moored semi-submersible

1997 ◽  
Vol 10 (6) ◽  
pp. 443-463
Author(s):  
John Bowers ◽  
Ian Morton ◽  
Gill Mould
Keyword(s):  
Extreme Value ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Multivariate Extreme Value

scholarly journals Determining the dependence structure of multivariate extremes

Biometrika ◽  
2020 ◽  
Vol 107 (3) ◽  
pp. 513-532
Author(s):  
E S Simpson ◽  
J L Wadsworth ◽  
J A Tawn
Keyword(s):  
Statistical Models ◽  
Regular Variation ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Dependence Structure ◽  
Value Analysis ◽  
Extremal Dependence ◽  
Measures Of Dependence ◽  
Hidden Regular Variation ◽  
Multivariate Extreme Value

Summary In multivariate extreme value analysis, the nature of the extremal dependence between variables should be considered when selecting appropriate statistical models. Interest often lies in determining which subsets of variables can take their largest values simultaneously while the others are of smaller order. Our approach to this problem exploits hidden regular variation properties on a collection of nonstandard cones, and provides a new set of indices that reveal aspects of the extremal dependence structure not available through existing measures of dependence. We derive theoretical properties of these indices, demonstrate their utility through a series of examples, and develop methods of inference that also estimate the proportion of extremal mass associated with each cone. We apply the methods to river flows in the U.K., estimating the probabilities of different subsets of sites being large simultaneously.

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