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Coastal Disaster Assessment and Response
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Coastal Disaster Assessment and Response

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 7371

Special Issue Editor

Dr. Deniz Velioglu Sogut

E-Mail Website
Guest Editor
Ocean Engineering and Marine Sciences, Florida Institute of Technology, 150 W University Blvd, Melbourne, FL 32901, USA
Interests: nearshore hydrodynamics; coastal hydrodynamics; coastal morphodynamics; wave-induced scour; wave–current–structure interactions; coastal resilience; nature-based solutions (NBS); tsunami; storm surge; wave energy; fluid mechanics; computational fluid dynamics (CFD); high-performance computing (HPC); data analysis and processing

Special Issue Information

Dear Colleagues,

There has been a substantial increase in the intensity, frequency, and duration of extreme natural events (e.g., hurricanes, storms, tsunamis, and landslides) over the past four decades. Given the high population concentration in low-elevation coastal zones, the most pressing challenge for coastal communities is to strengthen their resilience to future coastal disasters. Understanding risk and planning actions ahead of time is critical for improving the ability to adapt to changing conditions and rapidly recovering from disruption caused by these threats. A proper disaster response will reduce disaster-related fatalities, ensure the economic sustainability of coastal communities, and help to maintain coastal ecosystems. This Special Issue aims to address the impact of extreme natural events on coastal communities and promote coastal disaster preparedness. Original research articles and reviews are all welcome. Research areas may include (but are not limited to) pre-field, field, post-field surveys, as well as analytical, numerical, and experimental approaches, to assess coastal disaster impacts and responses.

We look forward to receiving your contributions.

Dr. Deniz Velioglu Sogut
Guest Editor

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

  • hurricane
  • storm surge
  • tsunami
  • scour
  • flooding
  • nature-based solutions
  • vegetation
  • artificial reef
  • coastal resilience
  • field survey

Published Papers (5 papers)

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Research

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16 pages, 4659 KiB  
Article
Morphological Changes in Storm Hinnamnor and the Numerical Modeling of Overwash
by Bohyeon Hwang, Kideok Do and Sungyeol Chang
J. Mar. Sci. Eng. 2024, 12(1), 196; https://doi.org/10.3390/jmse12010196 - 22 Jan 2024
Viewed by 656
Abstract
Constant changes occur in coastal areas over different timescales, requiring observation and modeling. Specifically, modeling morphological changes resulting from short-term events, such as storms, is of great importance in coastal management. Parameter calibration is necessary to achieve more accurate simulations of process-based models [...] Read more.
Constant changes occur in coastal areas over different timescales, requiring observation and modeling. Specifically, modeling morphological changes resulting from short-term events, such as storms, is of great importance in coastal management. Parameter calibration is necessary to achieve more accurate simulations of process-based models that focus on specific locations and event characteristics. In this study, the XBeach depth-averaged model was adopted to simulate subaerial data pre- and post-storms, and overwash phenomena were observed using the data acquired through unmanned aerial vehicles. The parameters used for the model calibration included those proposed in previous studies. However, an emphasis was placed on calibrating the parameters related to sediment transport that were directly associated with overwash and deposition. Specifically, the parameters corresponding to the waveform parameters, wave skewness, and wave asymmetry were either integrated or separated to enable an adequate representation of the deposition resulting from overwash events. The performance and sensitivity of the model to changes in volume were assessed. Overall, the waveform parameters exhibit significant sensitivity to volume changes, forming the basis for calibrating the deposition effects caused by overwashing. These results are expected to assist in the more effective selection and calibration of parameters for simulating sediment deposition due to overwash events. Full article
(This article belongs to the Special Issue Coastal Disaster Assessment and Response)
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19 pages, 8091 KiB  
Article
Applicability Evaluation of the Global Synthetic Tropical Cyclone Hazard Dataset in Coastal China
by Xiaomin Li, Qi Hou, Jie Zhang, Suming Zhang, Xuexue Du and Tangqi Zhao
J. Mar. Sci. Eng. 2024, 12(1), 73; https://doi.org/10.3390/jmse12010073 - 27 Dec 2023
Viewed by 615
Abstract
A tropical cyclone dataset is an important data source for tropical cyclone disaster research, and the evaluation of its applicability is a necessary prerequisite. The Global Synthetic Tropical Cyclone Hazard (GSTCH) dataset is a dataset of global tropical cyclone activity for 10,000 years [...] Read more.
A tropical cyclone dataset is an important data source for tropical cyclone disaster research, and the evaluation of its applicability is a necessary prerequisite. The Global Synthetic Tropical Cyclone Hazard (GSTCH) dataset is a dataset of global tropical cyclone activity for 10,000 years from 2018, and has become accepted as a major data source for the study of global tropical cyclone hazards. On the basis of the authoritative Tropical Cyclone Best Track (TCBT) dataset proposed by the China Meteorological Administration, this study evaluated the applicability of the GSTCH dataset in relation to two regions: the Northwest Pacific and China’s coastal provinces. For the Northwest Pacific, the results show no significant differences in the means and standard deviations of landfall wind speed, landfall pressure, and annual occurrence number between the two datasets at the 95% confidence level. They also show the cumulative distributions of central minimum pressure and central maximum wind speed along the track passed the Kolmogorov–Smirnov (K-S) test at the 95% confidence level, thereby verifying that the GSTCH dataset is consistent with the TCBT dataset at sea-area scale. For China’s coastal provinces, the results show that the means or standard deviations of tropical cyclone characteristics between the two datasets were not significantly different in provinces other than Guangdong and Hainan, and further analysis revealed that the cumulative distributions of the tropical cyclone characteristics in Guangdong and Hainan provinces passed the K-S test at the 95% confidence level, thereby verifying that the GSTCH dataset is consistent with the TCBT dataset at province scale. The applicability evaluation revealed that no significant differences exist between most of the tropical cyclone characteristics in the TCBT and GSTCH datasets, and that the GSTCH dataset is an available and reliable data source for tropical cyclone hazard studies in China’s coastal areas. Full article
(This article belongs to the Special Issue Coastal Disaster Assessment and Response)
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15 pages, 21250 KiB  
Article
Assessing Coastal Vulnerability to Storms: A Case Study on the Coast of Thrace, Greece
by Iason A. Chalmoukis
J. Mar. Sci. Eng. 2023, 11(8), 1490; https://doi.org/10.3390/jmse11081490 - 26 Jul 2023
Cited by 1 | Viewed by 720
Abstract
Climate change is expected to increase the risks of coastal hazards (erosion and inundation). To effectively cope with these emerging problems, littoral countries are advised to assess their coastal vulnerabilities. In this study, coastal vulnerability is first assessed by considering two basic storm-induced [...] Read more.
Climate change is expected to increase the risks of coastal hazards (erosion and inundation). To effectively cope with these emerging problems, littoral countries are advised to assess their coastal vulnerabilities. In this study, coastal vulnerability is first assessed by considering two basic storm-induced phenomena, i.e., erosion and inundation. First, the erosion is computed using the numerical model for Storm-induced BEAch CHange (SBEACH), whereas the inundation is estimated using two different empirical equations for comparison. Then, the integration of the vulnerabilities of both storm-induced impacts associated with the same return period permits the identification of the most hazardous regions. The methodology is applied to the coast of Thrace (Greece). The majority of the coastline is not vulnerable to erosion, except for some steep and narrow beaches and the coast along the city of Alexandroupolis. Beaches with very low heights are highly vulnerable to inundation. Half of the studied coastline is considered highly or very highly vulnerable, whereas the other half is relatively safe. The above results will help decision-makers choose how to invest their resources for preventing damage. Full article
(This article belongs to the Special Issue Coastal Disaster Assessment and Response)
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21 pages, 9815 KiB  
Article
Evaluation of Reliquefaction Behavior of Coastal Embankment Due to Successive Earthquakes Based on Shaking Table Tests
by Mintaek Yoo and Sun Yong Kwon
J. Mar. Sci. Eng. 2023, 11(5), 1002; https://doi.org/10.3390/jmse11051002 - 08 May 2023
Viewed by 1067
Abstract
Liquefaction caused by long-term cyclic loads in loose saturated soil can lead to ground subsidence and superstructure failures. To address this issue, this study aimed to emulate the liquefaction phenomenon based on a shaking table test while especially focusing on the soil behavior [...] Read more.
Liquefaction caused by long-term cyclic loads in loose saturated soil can lead to ground subsidence and superstructure failures. To address this issue, this study aimed to emulate the liquefaction phenomenon based on a shaking table test while especially focusing on the soil behavior mechanism due to the reliquefaction effect. Liquefaction and reliquefaction behaviors were analyzed by ground conditions where an embankment was located on the coastal ground. Silica sand was used for the experiment for various thickness and liquefiable conditions, and the embankment model was constructed above the model ground. For seismic waves, sine wave excitation was applied, and a total of five excitations (cases) were conducted. When the upper ground layer consisted of a non-liquefiable layer, liquefaction did not occur due to the first excitations but occurred by the third excitation. The results indicated that as the earthquake was applied, the water level in the liquefiable layer increased to the height of the non-liquefiable layer and liquefaction could occur. It was identified that even if liquefaction did not occur for the main earthquake, liquefaction could occur due to aftershocks caused by a rise in the groundwater level due to a series of earthquakes. In a general seismic design code, liquefaction assessment is performed only for soil layers below the groundwater level; however, when successive earthquakes occur, unexpected liquefaction damage could occur. Therefore, to mitigate the earthquake risk of liquefaction for coastal embankments, it is necessary to evaluate the liquefaction by aftershocks even when the groundwater level of the ground layer under an embankment is low. Full article
(This article belongs to the Special Issue Coastal Disaster Assessment and Response)
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Review

Jump to: Research

30 pages, 4130 KiB  
Review
Ensemble Neural Networks for the Development of Storm Surge Flood Modeling: A Comprehensive Review
by Saeid Khaksari Nezhad, Mohammad Barooni, Deniz Velioglu Sogut and Robert J. Weaver
J. Mar. Sci. Eng. 2023, 11(11), 2154; https://doi.org/10.3390/jmse11112154 - 11 Nov 2023
Cited by 1 | Viewed by 1371
Abstract
This review paper focuses on the use of ensemble neural networks (ENN) in the development of storm surge flood models. Storm surges are a major concern in coastal regions, and accurate flood modeling is essential for effective disaster management. Neural network (NN) ensembles [...] Read more.
This review paper focuses on the use of ensemble neural networks (ENN) in the development of storm surge flood models. Storm surges are a major concern in coastal regions, and accurate flood modeling is essential for effective disaster management. Neural network (NN) ensembles have shown great potential in improving the accuracy and reliability of such models. This paper presents an overview of the latest research on the application of NNs in storm surge flood modeling and covers the principles and concepts of ENNs, various ensemble architectures, the main challenges associated with NN ensemble algorithms, and their potential benefits in improving flood forecasting accuracy. The main part of this paper pertains to the techniques used to combine a mixed set of predictions from multiple NN models. The combination of these models can lead to improved accuracy, robustness, and generalization performance compared to using a single model. However, generating neural network ensembles also requires careful consideration of the trade-offs between model diversity, model complexity, and computational resources. The ensemble must balance these factors to achieve the best performance. The insights presented in this review paper are particularly relevant for researchers and practitioners working in coastal regions where accurate storm surge flood modeling is critical. Full article
(This article belongs to the Special Issue Coastal Disaster Assessment and Response)
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