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Advances in Storm Tide and Wave Simulations and Assessment
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Advances in Storm Tide and Wave Simulations and Assessment

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Physical Oceanography".

Deadline for manuscript submissions: 25 August 2025 | Viewed by 2237

Special Issue Editors

Dr. Wei-Che Huang

E-Mail Website
Guest Editor
Department of Civil and Disaster Prevention Engineering, National United University, Miaoli 360302, Taiwan
Interests: storm surge and wave simulation; large-scale particle image velocity (LSPIV); river hydrological observation (flow, velocity, and water level); applying deep learning to hydrological observations
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wen Cheng Liu

E-Mail Website
Guest Editor
Department of Civil and Disaster Prevention Engineering, National United University, Miaoli 36063, Taiwan
Interests: hydrology and hydraulics; environmental fluid mechanics; disaster prevention and ecological engineering; contaminant transport modeling; environmental monitoring; climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Chih-Chieh Young

E-Mail Website
Guest Editor
Department of Marine Environmental Informatics, National Taiwan Ocean University, Keelung, Taiwan
Interests: nonlinear wave dynamics; coastal oceanography; computational fluid dynamics; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Storm surges and waves pose significant risks to coastal areas, increasingly exacerbated by climate change and sea-level rise. The accurate simulation and assessment of these phenomena are essential for understanding their impacts on coastal infrastructure and communities. However, predicting storm tides and wave behavior remains challenging due to complex environmental interactions. This Special Issue seeks to highlight advancements in storm tide and wave simulations, including new modeling techniques, data assimilation strategies, and observational methods. We aim to improve the understanding of these phenomena under current and future climate scenarios and promote innovative approaches to risk reduction and adaptation. We encourage submissions that focus on the development of new numerical models, integration of novel data sources, and advanced forecasting techniques using artificial intelligence and machine learning. Papers that present real-world applications and innovative solutions for mitigating risks are particularly welcome. We invite original research, reviews, and case studies that advance knowledge in storm tide and wave simulations and assessments, particularly those enhancing prediction accuracy, understanding physical mechanisms, and proposing innovative risk management solutions.

Dr. Wei-Che Huang
Prof. Dr. Wen Cheng Liu
Dr. Chih-Chieh Young
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • storm surge and waves
  • model simulation
  • climate change

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

22 pages, 5757 KiB  
Article
Uncertainty Analysis of Overflow Due to Sea Dike Failure During Typhoon Events
by Wei-Che Huang, Wen-Cheng Liu and Hong-Ming Liu
J. Mar. Sci. Eng. 2025, 13(3), 573; https://doi.org/10.3390/jmse13030573 - 14 Mar 2025
Viewed by 481
Abstract
Taiwan is frequently affected by typhoons, which cause storm surges and wave impacts that damage sea dikes, resulting in overflow and subsequent flooding. Therefore, it is essential to analyze the damage to sea dikes caused by storm surges and wave impacts, leading to [...] Read more.
Taiwan is frequently affected by typhoons, which cause storm surges and wave impacts that damage sea dikes, resulting in overflow and subsequent flooding. Therefore, it is essential to analyze the damage to sea dikes caused by storm surges and wave impacts, leading to overflow, for effective coastal protection. This study employs the ADCIRC model coupled with the SWAN model to simulate storm surges and waves around Taiwan and develops a sea dike failure model that incorporates mechanisms for impact damage, run-up damage, and overflow calculation. To ensure model accuracy, three historical typhoon events were used for calibration and validation of the ADCIRC+SWAN model. The results show that the ADCIRC coupled with SWAN model can effectively simulate storm surges and waves during typhoons. Typhoon Soulik (2013) was simulated to examine a breach in the Tamsui Youchekou sea dike in northern Taiwan, and an uncertainty analysis was conducted using the Monte Carlo method and Bayesian theorem. The results indicate that when the compressive strength of the sea dike is reduced to 5% of its original strength, impact and run-up damage occur, leading to overflow. In the case of impact damage, the overflow volume due to the breach falls within a 95% confidence interval of 0.16 × 106 m3 to 130 × 106 m3. For run-up damage, the 95% confidence interval for the overflow volume ranges from 0.16 × 106 m3 to 639 × 106 m3. The ADCIRC+SWAN model is used to simulate storm surge and waves, incorporating impact damage and run-up damage mechanisms to represent concrete sea dike failure. This approach effectively models dike failure and calculates the resulting overflow. Full article
(This article belongs to the Special Issue Advances in Storm Tide and Wave Simulations and Assessment)
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17 pages, 5280 KiB  
Article
The Optimization of Four Key Parameters in the XBeach Model by GLUE Method: Taking Chudao South Beach as an Example
by Yunyun Gai, Longsheng Li, Zikang Li and Hongyuan Shi
J. Mar. Sci. Eng. 2025, 13(3), 555; https://doi.org/10.3390/jmse13030555 - 13 Mar 2025
Viewed by 406
Abstract
When the XBeach model is used to simulate beach profiles, the selection of four sensitive parameters—facua, gammax, eps, and gamma—is crucial. Among these, the two key parameters, facua and gamma, are particularly sensitive. However, the XBeach model does not specify the exact choice [...] Read more.
When the XBeach model is used to simulate beach profiles, the selection of four sensitive parameters—facua, gammax, eps, and gamma—is crucial. Among these, the two key parameters, facua and gamma, are particularly sensitive. However, the XBeach model does not specify the exact choice of these four key parameters, offering only a broad range for each one. In this paper, we investigate the applicability of tuning these four parameters within the XBeach model. We employ Generalized Likelihood Uncertainty Estimation (GLUE) to optimize the model settings. The Brier Skill Score (BSS) for each parameter combination is calculated to quantify the likelihood probability distribution of each parameter. The optimal parameter set (facua = 0.20, gamma = 0.50) was ultimately determined. Here, the facua parameter represents the degree of influence of wave skewness and asymmetry on the direction of sediment transport, while the gamma parameter represents the equivalent random wave in the wave dissipation model and is used to calculate the probability of wave breaking. Six profiles of the southern beach on Chudao Island are selected to validate the results, establishing the XBeach model based on profile measurement data before and after Typhoon “Lekima”. The results indicate that after parameter optimization, the simulation accuracy of XBeach is significantly improved, with the BSS increasing from 0.3 and 0.17 to 0.68 and 0.79 in P1 and P6 profiles, respectively. This paper provides a recommended range for parameter values for future research. Full article
(This article belongs to the Special Issue Advances in Storm Tide and Wave Simulations and Assessment)
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25 pages, 9826 KiB  
Article
Parametric Estimation of Directional Wave Spectra from Moored FPSO Motion Data Using Optimized Artificial Neural Networks
by Do-Soo Kwon, Sung-Jae Kim, Chungkuk Jin and MooHyun Kim
J. Mar. Sci. Eng. 2025, 13(1), 69; https://doi.org/10.3390/jmse13010069 - 3 Jan 2025
Cited by 2 | Viewed by 970
Abstract
This paper introduces a comprehensive, data-driven framework for parametrically estimating directional ocean wave spectra from numerically simulated FPSO (Floating Production Storage and Offloading) vessel motions. Leveraging a mid-fidelity digital twin of a spread-moored FPSO vessel in the Guyana Sea, this approach integrates a [...] Read more.
This paper introduces a comprehensive, data-driven framework for parametrically estimating directional ocean wave spectra from numerically simulated FPSO (Floating Production Storage and Offloading) vessel motions. Leveraging a mid-fidelity digital twin of a spread-moored FPSO vessel in the Guyana Sea, this approach integrates a wide range of statistical values calculated from the time histories of vessel responses—displacements, angular velocities, and translational accelerations. Artificial neural networks (ANNs), trained and optimized through hyperparameter tuning and feature selection, are employed to estimate wave parameters including the significant wave height, peak period, main wave direction, enhancement parameter, and directional-spreading factor. A systematic correlation analysis ensures that informative input features are retained, while extensive sensitivity tests confirm that richer input sets notably improve predictive accuracy. In addition, comparisons against other machine learning (ML) methods—such as Support Vector Machines, Random Forest, Gradient Boosting, and Ridge Regression—demonstrate the present ANN model’s superior ability to capture intricate nonlinear interdependencies between vessel motions and environmental conditions. Full article
(This article belongs to the Special Issue Advances in Storm Tide and Wave Simulations and Assessment)
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