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Application of a fast stochastic storm surge model on estimating the high water level frequency in the Lower Rhine Delta

Author

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  • H. Zhong
  • P. van Gelder
  • P. van Overloop
  • W. Wang

Abstract

In the Lower Rhine Delta of the Netherlands, the high water level is driven by a joint impact of the downstream storm surge and the upstream fluvial discharge, and affected by the operation of existing man-made structures. In scenario-based risk assessment, a large number of stochastic scenarios of storm surges are required for estimating the high water level frequency. In this article, a fast computing stochastic storm surge model is applied to the gauge station of Hook of Holland in the west of the Netherlands. A fixed number of tides are considered in this model based on the information of historical storm surge events. Based on this model, a large number of stochastic storm surge scenarios are derived and forced into a one-dimensional hydrodynamic model of the Netherlands, resulting in peak water levels in Rotterdam, the most vulnerable city in the delta. These peak water levels are statistically analyzed and converted to the high water level frequency curve in Rotterdam. The high water level frequency curve in Rotterdam tends to a much lower design water level compared to the official design water level that is used to design the dikes and structures for protection of the city. Moreover, there is a significant difference in the high water level frequency curves due to the fact that the stochastic storm surge model considers different numbers of tides. This highlights the critical impact of the storm surge duration on the high water level frequency in the Lower Rhine Delta. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • H. Zhong & P. van Gelder & P. van Overloop & W. Wang, 2014. "Application of a fast stochastic storm surge model on estimating the high water level frequency in the Lower Rhine Delta," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 73(2), pages 743-759, September.
    • Handle: RePEc:spr:nathaz:v:73:y:2014:i:2:p:743-759
    DOI: 10.1007/s11069-014-1104-9
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    References listed on IDEAS

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    1. Peter W. Glynn & Donald L. Iglehart, 1989. "Importance Sampling for Stochastic Simulations," Management Science, INFORMS, vol. 35(11), pages 1367-1392, November.
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