A comparison of the main methods for estimating probabilities of extreme still water levels. Haigh, I. D., Nicholls, R., & Wells, N. 57(9):838–849. Number: 9
A comparison of the main methods for estimating probabilities of extreme still water levels [link]Paper  doi  abstract   bibtex   
Sea-level return periods are estimated at 18 sites around the English Channel using: (i) the annual maxima method; (ii) the r-largest method; (iii) the joint probability method; and (iv) the revised joint probability method. Tests are undertaken to determine how sensitive these four methods are to three factors which may significantly influence the results; (a) the treatment of the long-term trends in extreme sea level; (b) the relative magnitudes of the tidal and non-tidal components of sea level; and (c) the frequency, length and completeness of the available data. Results show that unless sea-level records with lengths of at least 50years are used, the way in which the long-term trends is handled in the different methods can lead to significant differences in the estimated return levels. The direct methods (i.e. methods i and ii) underestimate the long (\textgreater20years) period return levels when the astronomical tidal variations of sea level (relative to a mean of zero) are about twice that of the non-tidal variations. The performance of each of the four methods is assessed using prediction errors (the difference between the return periods of the observed maximum level at each site and the corresponding data range). Finally, return periods, estimated using the four methods, are compared with estimates from the spatial revised joint probability method along the UK south coast and are found to be significantly larger at most sites along this coast, due to the comparatively short records originally used to calibrate the model in this area. The revised joint probability method is found to have the lowest prediction errors at most sites analysed and this method is recommended for application wherever possible. However, no method can compensate for poor data.
@article{haigh_comparison_2010,
	title = {A comparison of the main methods for estimating probabilities of extreme still water levels},
	volume = {57},
	issn = {0378-3839},
	url = {http://www.sciencedirect.com/science/article/pii/S0378383910000499},
	doi = {10.1016/j.coastaleng.2010.04.002},
	abstract = {Sea-level return periods are estimated at 18 sites around the English Channel using: (i) the annual maxima method; (ii) the r-largest method; (iii) the joint probability method; and (iv) the revised joint probability method. Tests are undertaken to determine how sensitive these four methods are to three factors which may significantly influence the results; (a) the treatment of the long-term trends in extreme sea level; (b) the relative magnitudes of the tidal and non-tidal components of sea level; and (c) the frequency, length and completeness of the available data. Results show that unless sea-level records with lengths of at least 50years are used, the way in which the long-term trends is handled in the different methods can lead to significant differences in the estimated return levels. The direct methods (i.e. methods i and ii) underestimate the long ({\textgreater}20years) period return levels when the astronomical tidal variations of sea level (relative to a mean of zero) are about twice that of the non-tidal variations. The performance of each of the four methods is assessed using prediction errors (the difference between the return periods of the observed maximum level at each site and the corresponding data range). Finally, return periods, estimated using the four methods, are compared with estimates from the spatial revised joint probability method along the {UK} south coast and are found to be significantly larger at most sites along this coast, due to the comparatively short records originally used to calibrate the model in this area. The revised joint probability method is found to have the lowest prediction errors at most sites analysed and this method is recommended for application wherever possible. However, no method can compensate for poor data.},
	pages = {838--849},
	number = {9},
	journaltitle = {Coastal Engineering},
	shortjournal = {Coastal Engineering},
	author = {Haigh, Ivan D. and Nicholls, Robert and Wells, Neil},
	urldate = {2020-01-27},
	date = {2010-09-01},
	langid = {english},
	note = {Number: 9},
	keywords = {Climate change, English Channel, Floods, Mean sea level, Extreme value theory}
}
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