Journal
COASTAL ENGINEERING
Volume 88, Issue -, Pages 15-26Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.coastaleng.2014.01.012
Keywords
Joint probability; Multivariate extremes; Flood risk; Meta-model; Probabilistic design
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Funding
- Spanish Ministry of Science and Innovation [BES-2009-027228]
- Spanish Ministry MICINN within the Ramon y Cajal programme
- European Commission through the project Innovative Technologies for safer European coasts in a changing climate (THESEUS) [244104, FP7.2009-1]
- Project IMAR21 from the Spanish Government [IMAR21 (CTM2010-15009)]
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Extreme sea conditions in the nearshore zone are required for coastal flood risk analysis and structural design. Many multivariate extreme value methods that have been applied in the past have been limited by assumptions relating to the dependence structure in the extremes. A conditional extremes statistical model overcomes a number of these previous limitations. To apply the method in practice, a Monte Carlo sampling procedure is required whereby large samples of synthetically generated events are simulated. The use of Monte Carlo approaches, in combination with computationally intensive physical process models, can raise significant practical challenges in terms of computation. To overcome these challenges there has been extensive research into the use of meta-models. Meta-models are approximations of computationally intensive physical process models (simulators). They are derived by fitting functions to the outputs from simulators. Due to their simplified representation they are computationally more efficient than the simulators they approximate. Here, a methodology for deriving a large Monte Carlo sample of extreme nearshore sea states is described. The methodology comprises the generation of a large sample of offshore sea conditions using the conditional extremes model. A meta-model of the wave transformation process is then constructed. A clustering algorithm is used to aid the development of the meta-model. The large sample of offshore data is then transformed through to the nearshore using the meta-model. The resulting nearshore sea states can be used for the probabilistic design of structures or flood risk analysis. The application of the methodology to a case study site on the North Coast of Spain is described. (C) 2014 Elsevier B.V. All rights reserved.
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