Panagiota Galiatsatou

Extremes from scarce data: The role of Bayesian and scaling techniques in reducing uncertainty

This paper deals with the analysis of extreme wave heights and their uncertainties. The main purpose is to assess confidence intervals using a conventional extreme value, and a Bayesian approach. It is shown how the introduction of an a priori information helps to bound the upper confidence limit. The analysis is performed with wave-height data recorded off the Spanish Catalan coast (NW Mediterranean) and wave-height data from the Dutch coast (North Sea). An analysis with natural-scale and log-transformed wave-height time series has been performed. This scale selection is proven to be advantageous for naturally bounded variables and also better captures some distribution features. The paper ends with a discussion on how the different techniques can be used to select a statistically robust threshold for an extreme event definition. This affects the evaluation of risk in low-lying coastal areas, associated to variables controlling flooding and erosion risks.

Estimation of Extremes: Conventional versus Bayesian techniques

The Bayesian and Maximum Likelihood (ML) estimators of surges at two stations of the Dutch coast on the North Sea are compared herein. The ML approach is commonly used, while the Bayesian approach allows both for a parameter uncertainty and a randomness inclusion in future observations. In the Bayesian framework, two different ways of constructing prior distributions are examined, namely the near flat distributions for model parameters and the incorporation of information from neighborhood sites of that under consideration through the distribution of quantile differences. The Bayesian framework offers substantial advantages to analyze the extreme values in both cases. The analysis is also performed using log-transformed surge data. This selection is proven to be advantageous for naturally bounded variables and to better capture the “relative” character of extremes.

Climate change effects on the marine characteristics of the Aegean and Ionian Seas

This paper addresses the effects of estimated climate change on the sea-surface dynamics of the Aegean and Ionian Seas (AIS). The main aim is the identification of climate change impacts on the severity and frequency of extreme storm surges and waves in areas of the AIS prone to flooding. An attempt is made to define design levels for future research on coastal protection in Greece. Extreme value analysis is implemented through a nonstationary generalized extreme value distribution function, incorporating time harmonics in its parameters, by means of statistically defined criteria. A 50-year time span analysis is adopted and changes of means and extremes are determined. A Regional Climate Model (RegCM3) is implemented with dynamical downscaling, forced by ECHAM5 fields under 20C3M historical data for the twentieth century and the SRES-A1B scenario for the twenty-first century. Storm surge and wave models (GreCSSM and SWAN, respectively) are used for marine climate simulations. Comparisons of model results with reanalysis and field data of atmospheric and hydrodynamic characteristics, respectively, are in good agreement. Our findings indicate that the dynamically downscaled RegCM3 simulation adequately reproduces the present general circulation patterns over the Mediterranean and Greece. Future changes in sea level pressure and mean wind fields are estimated to be small, yet significant for marine extremes. In general, we estimate a projected intensification of severe wave and storm surge events during the first half of the twenty-first century and a subsequent storminess attenuation leading to the resettlement of milder extreme marine events with increased prediction uncertainty in the second half of the twenty-first century.

Statistical models for bivariate extremal analysis of a spatial process

In the present paper different dependence measures are examined to investigate the structure of a spatial process of storm surges in the Dutch part of the North Sea. Four different dependence measures are implemented to the data: (1) the correlation coefficient as well as the Spearman (rank) correlation of the sample, (2) the transformation of the variables to determine the marginal distributions, (3) the dependence measures (χ, ) and (4) the coefficient of tail dependence η. Two different approaches for modelling the threshold exceedances are examined, namely the implementation of bivariate extreme value models and the use of more general point process characterizations of extremal dependence. The strength of dependence appears to vary with the location as well as with the separation distance. It is also observed that the point process approach estimates are in general lower than those using the bivariate threshold exceedance models.

Bivariate Analysis of Extreme Wave and Storm Surge Events. Determining the Failure Area of Structures

In the present paper a bivariate process of extreme waves and storm surges at a Dutch station on the North Sea is considered. A bivariate logistic model and a sequential estimation procedure are used to extract joint exceedance probabilities of the two variables. The parameters of the margins of the bivariate distribution are defined by three different methods of estimation: a) the Maximum Likelihood Estimation (MLE) approach, b) a Bayesian procedure with flat prior distributions and c) the L-Moments (LM) estimation procedure. Comparison of the results of the three methods is performed and general conclusions are extracted. An approach to estimate the failure area of a particular structure under extreme sea conditions is presented, using the margins resulting from the three different estimation methods.