Extreme storm surge modelling in the North Sea View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-01-30

AUTHORS

Nina Ridder, Hylke de Vries, Sybren Drijfhout, Henk van den Brink, Erik van Meijgaard, Hans de Vries

ABSTRACT

This study shows that storm surge model performance in the North Sea is mostly unaffected by the application of temporal variations of surface drag due to changes in sea state provided the choice of a suitable constant Charnock parameter in the sea-state-independent case. Including essential meteorological features on smaller scales and minimising interpolation errors by increasing forcing data resolution are shown to be more important for the improvement of model performance particularly at the high tail of the probability distribution. This is found in a modelling study using WAQUA/DCSMv5 by evaluating the influence of a realistic air-sea momentum transfer parameterization and comparing it to the influence of changes in the spatial and temporal resolution of the applied forcing fields in an effort to support the improvement of impact and climate analysis studies. Particular attention is given to the representation of extreme water levels over the past decades based on the example of the Netherlands. For this, WAQUA/DCSMv5 is forced with ERA-Interim reanalysis data. Model results are obtained from a set of different forcing fields, which either (i) include a wave-state-dependent Charnock parameter or (ii) apply a constant Charnock parameter (αCh = 0.032) tuned for young sea states in the North Sea, but differ in their spatial and/or temporal resolution. Increasing forcing field resolution from roughly 79 to 12 km through dynamically downscaling can reduce the modelled low bias, depending on coastal station, by up to 0.25 m for the modelled extreme water levels with a 1-year return period and between 0.1 m and 0.5 m for extreme surge heights. More... »

PAGES

255-272

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10236-018-1133-0

DOI

http://dx.doi.org/10.1007/s10236-018-1133-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1100693892


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