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Coastal Flood Hazard Risk Assessment and Mitigation Strategies
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Coastal Flood Hazard Risk Assessment and Mitigation Strategies

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water and Climate Change".

Deadline for manuscript submissions: 25 July 2026 | Viewed by 3539

Special Issue Editors

Dr. Quan Van Dau

E-Mail Website
Guest Editor
Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, St. Peters Bay, Charlottetown, PE C0A 2A0, Canada
Interests: hydrological cycle; water resources planning and management; remote sensing; artificial intelligence; climate change adaptation; irrigation water management
Prof. Dr. Xander Wang

E-Mail Website
Guest Editor
Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, 100 Pownal Street, St. Peters Bay, Charlottetown, PE C0A 2A0, Canada
Interests: climate-smart agriculture; high-resolution climate modeling; climate change adaptation; hydroclimate risk assessment; geospatial and big data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rising sea levels and increasingly frequent extreme weather events, combined with the rapid growth of coastal populations, emphasize the urgent need for advanced risk assessment and mitigation in coastal and low-lying areas.

For this Special Issue, we welcome high-quality, collaborative research on the impacts of climate change and coastal processes, aimed at strengthening the resilience of vulnerable coastal regions. Original research, case studies, and reviews that advance knowledge in climate risk, vulnerability, and adaptation are encouraged for submission.

Topics of interest include, but are not limited to, the following:

  • Coastal flooding under present and future climate scenarios;
  • Hydrological and hydrodynamical modeling;
  • GIS and remote sensing-based hazard assessment and mapping;
  • Nature-based and engineered coastal protection measures;
  • Socioeconomic impacts and community-level adaptation strategies;
  • Integrated risk management and policy frameworks for resilience building.

We welcome submissions in the form of research articles, reviews, brief communications, case studies, commentaries, registered reports, opinions, and data reports.

Dr. Quan Van Dau
Prof. Dr. Xander Wang
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 250 words) can be sent to the Editorial Office for assessment.

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. Water is an international peer-reviewed open access semimonthly 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

  • climate change
  • water security
  • hydrological modeling
  • coastal erosion
  • climate adaptation
  • compound flooding
  • sea level rise
  • extreme weather event

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Published Papers (3 papers)

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Research

21 pages, 8050 KB  
Article
Projections of Temperature-Driven Changes in Seasonal Ice Coverage Around Prince Edward Island, Canada
by Genevieve Keefe and Xiuquan Wang
Water 2026, 18(7), 777; https://doi.org/10.3390/w18070777 - 25 Mar 2026
Viewed by 517
Abstract
Seasonal ice is typically present in the southern Gulf of Saint Lawrence from December through March; however, climate change is predicted to reduce this season and alter local ecosystems, geomorphologies, and infrastructure. This impact assessment ascertains the influence of climate change on the [...] Read more.
Seasonal ice is typically present in the southern Gulf of Saint Lawrence from December through March; however, climate change is predicted to reduce this season and alter local ecosystems, geomorphologies, and infrastructure. This impact assessment ascertains the influence of climate change on the ice coverage along Prince Edward Island’s coast. Ice concentration data from 50 study sites were logarithmically correlated with cumulative freezing degree days (FDDs). Correlations were generally good (mean R2 = 0.63), although poorer values were observed in areas with greater exposure to wind and waves. An ensemble of the CMIP6 models’ forecasts of future temperatures showed that FDD will drop from an average of 487 °C days during the historical period (1981–2025) to less than 164 °C days in the 2090s under a low-emission scenario, SSP1-2.6. For the same study period, a high-emission scenario (SSP5-8.5) projects FDD to drop to 28 °C days by the end of the century, while a moderate-emission scenario (SSP2-4.5) forecasts 97 °C days annually. Seasonal ice indices demonstrated a similarly substantial decrease, from an average historical value of 11.1 to 3.8, 3.2, and 0.8 for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. The length of the ice season was also analyzed, with mean season lengths for the 2090s ranging from 3 to 24 days, depending on the emission scenario, representing a 70–96% reduction in season length from the baseline observation. Mild variations were measured in the rate of ice loss throughout the province; however, significant differences in the ice coverage’s baseline values, due to local currents and wave exposure, led to a broad range in the relative proportions of ice loss, with areas along the eastern coastline projecting zero ice winters. Over the next 80 years, projections point to a considerable decline in ice coverage around Prince Edward Island. Full article
(This article belongs to the Special Issue Coastal Flood Hazard Risk Assessment and Mitigation Strategies)
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30 pages, 48641 KB  
Article
Coastal Flooding Analysis in the Presence of REWEC1 Farms: A Case Study in Southern Italy
by Francesco Aristodemo, Giuseppe Tripepi and Pasquale Giuseppe Fabio Filianoti
Water 2026, 18(4), 524; https://doi.org/10.3390/w18040524 - 22 Feb 2026
Viewed by 454
Abstract
Resonant Wave Energy Converter 1 (REWEC1) is a submerged caisson breakwater integrating a device designed to absorb incoming wave energy. Although the wave energy-extraction performance of this system and its hydraulic characteristics have been extensively investigated, its potential role in reducing coastal inundation, [...] Read more.
Resonant Wave Energy Converter 1 (REWEC1) is a submerged caisson breakwater integrating a device designed to absorb incoming wave energy. Although the wave energy-extraction performance of this system and its hydraulic characteristics have been extensively investigated, its potential role in reducing coastal inundation, as an alternative to traditional rubble-mound breakwaters, has not yet been examined. In this context, the present study analyzes the mitigation effects on coastal flooding induced by the installation of REWEC1 barriers. The analysis focuses on the coast of Cetraro, located along the Tyrrhenian Sea in the province of Cosenza (Calabria, Southern Italy). The effectiveness of REWEC1 farms in reducing coastal flooding was assessed by considering fixed-air and no-air operation modes, as well as different spatial configurations. The input wave conditions were propagated in the nearshore using the SWAN model to simulate wave–structure interactions, while the XBeach model was employed to investigate coastal inundation processes based on the wave field behind the caissons, also accounting for Sea Level Rise (SLR). The results were evaluated in terms of maximum flooded areas and water penetration lengths along the emerged coast, as well as wave run-up and set-up along selected transects. To assess the robustness of the results, a sensitivity analysis was carried out by varying the transmission coefficients of the REWEC1 units within a plausible uncertainty range, and the corresponding variability in flooding indicators was quantified. The numerical results indicate a progressive reduction in these hydrodynamic response indicators as the spacing between adjacent REWEC1 devices decreases, and show that the relative mitigation performance of REWEC1 remains consistent when accounting for uncertainties in wave–structure interaction parameters. Further analyses were conducted to compare the effectiveness of REWEC1 farms with that of conventional rubble-mound breakwaters in reducing coastal flooding. Full article
(This article belongs to the Special Issue Coastal Flood Hazard Risk Assessment and Mitigation Strategies)
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21 pages, 979 KB  
Article
AI-Enhanced Coastal Flood Risk Assessment: A Real-Time Web Platform with Multi-Source Integration and Chesapeake Bay Case Study
by Paul Magoulick
Water 2025, 17(15), 2231; https://doi.org/10.3390/w17152231 - 26 Jul 2025
Cited by 2 | Viewed by 2074
Abstract
A critical gap exists between coastal communities’ need for accessible flood risk assessment tools and the availability of sophisticated modeling, which remains limited by technical barriers and computational demands. This study introduces three key innovations through Coastal Defense Pro: (1) the first operational [...] Read more.
A critical gap exists between coastal communities’ need for accessible flood risk assessment tools and the availability of sophisticated modeling, which remains limited by technical barriers and computational demands. This study introduces three key innovations through Coastal Defense Pro: (1) the first operational web-based AI ensemble for coastal flood risk assessment integrating real-time multi-agency data, (2) an automated regional calibration system that corrects systematic model biases through machine learning, and (3) browser-accessible implementation of research-grade modeling previously requiring specialized computational resources. The system combines Bayesian neural networks with optional LSTM and attention-based models, implementing automatic regional calibration and multi-source elevation consensus through a modular Python architecture. Real-time API integration achieves >99% system uptime with sub-3-second response times via intelligent caching. Validation against Hurricane Isabel (2003) demonstrates correction from 197% overprediction (6.92 m predicted vs. 2.33 m observed) to accurate prediction through automated identification of a Chesapeake Bay-specific reduction factor of 0.337. Comprehensive validation against 15 major storms (1992–2024) shows substantial improvement over standard methods (RMSE = 0.436 m vs. 2.267 m; R2 = 0.934 vs. −0.786). Economic assessment using NACCS fragility curves demonstrates 12.7-year payback periods for flood protection investments. The open-source Streamlit implementation democratizes access to research-grade risk assessment, transforming months-long specialist analyses into immediate browser-based tools without compromising scientific rigor. Full article
(This article belongs to the Special Issue Coastal Flood Hazard Risk Assessment and Mitigation Strategies)
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