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Performance evaluation of wave input reduction techniques for modeling inter-annual sandbar dynamics. / de Queiroz, Bruna; Scheel, Freek; Caires, Sofia; Walstra, Dirk Jan; Olij, Derrick; Yoo, Jeseon; Reniers, Ad; de Boer, Wiebe.

In: Journal of Marine Science and Engineering, Vol. 7, No. 5, 148, 2019.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

de Queiroz, B, Scheel, F, Caires, S, Walstra, DJ, Olij, D, Yoo, J, Reniers, A & de Boer, W 2019, 'Performance evaluation of wave input reduction techniques for modeling inter-annual sandbar dynamics', Journal of Marine Science and Engineering, vol. 7, no. 5, 148. https://doi.org/10.3390/jmse7050148

APA

de Queiroz, B., Scheel, F., Caires, S., Walstra, D. J., Olij, D., Yoo, J., Reniers, A., & de Boer, W. (2019). Performance evaluation of wave input reduction techniques for modeling inter-annual sandbar dynamics. Journal of Marine Science and Engineering, 7(5), [148]. https://doi.org/10.3390/jmse7050148

Vancouver

de Queiroz B, Scheel F, Caires S, Walstra DJ, Olij D, Yoo J et al. Performance evaluation of wave input reduction techniques for modeling inter-annual sandbar dynamics. Journal of Marine Science and Engineering. 2019;7(5). 148. https://doi.org/10.3390/jmse7050148

Author

de Queiroz, Bruna ; Scheel, Freek ; Caires, Sofia ; Walstra, Dirk Jan ; Olij, Derrick ; Yoo, Jeseon ; Reniers, Ad ; de Boer, Wiebe. / Performance evaluation of wave input reduction techniques for modeling inter-annual sandbar dynamics. In: Journal of Marine Science and Engineering. 2019 ; Vol. 7, No. 5.

BibTeX

@article{de593a28a2734e73ac0349228fe3af8c,
title = "Performance evaluation of wave input reduction techniques for modeling inter-annual sandbar dynamics",
abstract = "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.",
keywords = "Input reduction, Markov Chain, Monte Carlo, Morphodynamics, Process-based modeling, Sandbars, Sequencing, Wave climate",
author = "{de Queiroz}, Bruna and Freek Scheel and Sofia Caires and Walstra, {Dirk Jan} and Derrick Olij and Jeseon Yoo and Ad Reniers and {de Boer}, Wiebe",
year = "2019",
doi = "10.3390/jmse7050148",
language = "English",
volume = "7",
journal = "Journal of Marine Science and Engineering",
issn = "2077-1312",
publisher = "MDPI",
number = "5",

}

RIS

TY - JOUR

T1 - Performance evaluation of wave input reduction techniques for modeling inter-annual sandbar dynamics

AU - de Queiroz, Bruna

AU - Scheel, Freek

AU - Caires, Sofia

AU - Walstra, Dirk Jan

AU - Olij, Derrick

AU - Yoo, Jeseon

AU - Reniers, Ad

AU - de Boer, Wiebe

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Input reduction

KW - Markov Chain

KW - Monte Carlo

KW - Morphodynamics

KW - Process-based modeling

KW - Sandbars

KW - Sequencing

KW - Wave climate

UR - http://www.scopus.com/inward/record.url?scp=85066451953&partnerID=8YFLogxK

U2 - 10.3390/jmse7050148

DO - 10.3390/jmse7050148

M3 - Article

AN - SCOPUS:85066451953

VL - 7

JO - Journal of Marine Science and Engineering

JF - Journal of Marine Science and Engineering

SN - 2077-1312

IS - 5

M1 - 148

ER -

ID: 54338016