Uncertainties in extreme value modelling of wave data in a climate change perspective

Abstract

The extreme values of wave climate data are of great interest in a number of different applications, including the design and operation of ships and offshore structures, marine energy generation, aquaculture and coastal installations. Typically, the return values of certain met-ocean parameters such as significant wave height are of particular importance. In a climate change perspective, projections of such return values to a future climate are of great importance for risk management and adaptation purposes. However, there are various ways of estimating the required return values, which introduce additional uncertainties in extreme weather and climate variables pertaining to both current and future climates. Many of these approaches are investigated in this paper by applying different methods to particular data sets of significant wave height, corresponding to the historic climate and two future projections of the climate assuming different forcing scenarios. In this way, the uncertainty due to the extreme value analysis can also be compared to the uncertainty due to a changing climate. The different approaches that are considered in this paper are the initial distribution approach, the block maxima approach, the peak over threshold approach and the average conditional exceedance rate method. Furthermore, the effect of different modelling choices within each of the approaches will be explored. Thus, a range of different return value estimates for the different data sets is obtained. This exercise reveals that the uncertainty due to the extreme value analysis method is notable and, as expected, the variability of the estimates increases for higher return periods. Moreover, even though the variability due to the extreme value analysis is greater than the climate variability, a shift towards higher extremes in a future wave climate

Non-stationary extreme value models to account for trends and shifts in the extreme wave climate due to climate change

“…It is acknowledged that other fitting methods than maximum likelihood are available, some of which might be more appropriate and result in smaller uncertainties (see, e.g. [9]) for a discussion of uncertainties due to fitting method), but only maximum likelihood is used in this study. In order to use this approach, one must tacitly assume that the extremes can be considered stationary within each of the time intervals, i.e.…”

“…It is acknowledged that this method may be sensitive to the block size (see, e.g. [9]), but only annual maxima are used in this study, corresponding to a block size of 2920 for the 3-hourly data and a total of 30 block maxima for fitting the models within each subset of data. The estimated model parameters and associated return value estimates for 20-and 100-year return periods are presented in Table 2.…”

“…The uncertainty of extreme value analysis of significant wave height due to, inter alia, the choice of extreme value model, the fitting procedure and the block size is explored in Ref. [9]. In this paper, the block maxima approach will be used and various stationary and non-stationary GEV-models are fitted to annual maximum significant wave height data for current and future wave climates in the North Atlantic.…”

Extreme significant wave height of tropical cyclone waves in the South China Sea

“…To select a suitable threshold, many methods have been proposed, such as graphical diagnostics (Coles, 2001;Sánchez-Arcilla et al, 2017;Bernardara et al, 2014), empirical methods (Ferreira et al, 2003;Neves and Alves, 2004;Reiss and Thomas, 2007), probabilistically based techniques (Hill, 1975;Beirlant et al, 2006;Goegebeur et al, 2008), computational approaches (Danielsson et al, 2001;Beguería, 2005; and mixture models (Carreau and Bengio, 2009;Eastoe and Tawn, 2010;MacDonald et al, 2011). Among these methods, a graphical diagnostic referred to as the sensitivity of the return-significant wave height to the threshold (Scarrott and MacDonald, 2012) is commonly accepted (Petrov et al, 2013;Northrop and Coleman, 2014;Vanem, 2015b;Northrop et al, 2017;Sulis et al, 2017).…”

An Experimental Assessment of Extreme Wave Evaluation by Integrating Model and Wave Buoy Data

“…Joint distribution of the extreme wave height and wind speed are considered in [24].Another important aspect, which has been tackled by many researchers, and which has significant connection with the present paper, is the influence of various factors on the accuracy of EVPD estimates, especially when synthetic rather than experimental data are used: You et al [25] found that the uncertainty is caused mainly by short wave record, missing storm wave data, different methods, high or too low thresholds. The effect of threshold values on the estimates of extreme wave heights is considered in [26][27][28][29]. It is also interesting in this context to note that Beyá et al [30], in carrying out a 35-year wave hindcast and calibration, found that the accuracy is lower for the highest wave heights.Since the use of synthetic data is the only available possibility in locations where no wave meters are available, it is clear that there is ample room for improvement; the present paper seeks to provide…”