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Open AccessArticle

Performance Evaluation of Wave Input Reduction Techniques for Modeling Inter-Annual Sandbar Dynamics

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Deltares, P.O. Box 177, 2600 MH Delft, The Netherlands
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Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, The Netherlands
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PwC Strategy&, 1066 JR Amsterdam, The Netherlands
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Korean Institute of Ocean Science and Technology (KIOST), Yeongdo Haeyangro 385, Busan 49111, Korea
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Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2019, 7(5), 148; https://doi.org/10.3390/jmse7050148
Received: 8 April 2019 / Revised: 5 May 2019 / Accepted: 8 May 2019 / Published: 15 May 2019
(This article belongs to the Section Coastal Engineering)
In process-based numerical models, reducing the amount of input parameters, known as input reduction (IR), is often required to reduce the computational effort of these models and to enable long-term, ensemble predictions. Currently, a comprehensive performance assessment of IR-methods is lacking, which hampers guidance on selecting suitable methods and settings in practice. In this study, we investigated the performance of 10 IR-methods and 36 subvariants for wave climate reduction to model the inter-annual evolution of nearshore bars. The performance of reduced wave climates is evaluated by means of a brute force simulation based on the full climate. Additionally, we tested how the performance is affected by the number of wave conditions, sequencing, and duration of the reduced wave climate. We found that the Sediment Transport Bins method is the most promising method. Furthermore, we found that the resolution in directional space is more important for the performance than the resolution in wave height. The results show that a reduced wave climate with fewer conditions applied on a smaller timescale performs better in terms of morphology than a climate with more conditions applied on a longer timescale. The findings of this study can be applied as initial guidelines for selecting input reduction methods at other locations, in other models, or for other domains. View Full-Text
Keywords: input reduction; morphodynamics; sandbars; process-based modeling; wave climate; sequencing; Markov Chain; Monte Carlo input reduction; morphodynamics; sandbars; process-based modeling; wave climate; sequencing; Markov Chain; Monte Carlo
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de Queiroz, B.; Scheel, F.; Caires, S.; Walstra, D.-J.; Olij, D.; Yoo, J.; Reniers, A.; de Boer, W. Performance Evaluation of Wave Input Reduction Techniques for Modeling Inter-Annual Sandbar Dynamics. J. Mar. Sci. Eng. 2019, 7, 148.

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