Search Results (379)

Landscapes and heritage sites hold significant historical, scientific, and social value but face increasing threats from climate change and human activities. Coastal and maritime heritage are at risk from sea-level rise, storms, erosion, ocean acidification, and pressures such as urbanization, construction, and industrial development. Assessing vulnerability involves considering physical, geomorphological, and socioeconomic factors, including land use, population density, tourism, and ecosystem sensitivity. Long-term monitoring, interdisciplinary research, and holistic approaches are essential for effective risk assessment and planning. This study focuses on the coastal landscapes of northern Portugal, where climate change adaptation is urgent. These areas contain important historical heritage, especially fortified military structures that reflect regional identity and maritime history shared with other coastal nations. The research highlights significant risks to these monuments because of their proximity to the sea and expanding urban areas, providing insights to guide policymakers and support localized adaptation strategies. A two-phase methodology was employed, beginning with a comprehensive literature review to identify key indicators that informed field observations, surveys, and archival research, resulting in a detailed inventory of coastal and estuarine fortifications. The second phase assessed their vulnerability to sea-level rise, coastal flooding, and shoreline retreat. The study presents a methodological approach that provides local decision-makers with strategic guidance to enhance the protection and sustainable management of coastal heritage. Full article

Coral reefs are integral components of tropical coastal marine ecosystems that have considerable capacity to mitigate extreme flows and marine floods caused by storms and tsunamis. However, limited studies on coral reef efficacy in reducing such flows, coupled with variable roughness coefficient characteristics, hinder their broader utilization in sustainable engineering applications for societal benefit. In this study, we conducted comprehensive experimental investigations to examine flow–coral interactions and the flow energy reduction capabilities of coral reefs. Three-dimensional-printed coral reefs were used to simulate actual coral reefs, providing a scalable and environmentally responsible approach for studying nature-based coastal protection systems. Flow characteristics within the coral reef were investigated through flow depth and velocity measurements taken at the front of, over, and behind the reef. Analysis was performed considering nondimensional parameters, i.e., the Froude number (Fr), the depth effect (DE; ratio of flow depth to coral height), and the size effect (SE; ratio of coral length to coral height), to assess the flow energy reduction under different coral combinations and flow conditions. Spatial variations in flow depth over the reef showed that fast and shallow flows exhibited a reduction gradient toward the back of the reef. The findings revealed a substantial reduction in flow depth and velocity, reaching up to 27.5% and 25%, respectively, at the back boundary of the coral. Two-layered velocity analyses showed that the velocity over the top of corals could be six times higher than that through the coral reef structure for deep flows. Manning’s roughness coefficient varied considerably from 0.03 to 0.26. Overall, this study contributes to sustainable coastal engineering by demonstrating how bio-inspired coral reef structures can be applied to reduce flow energy and enhance coastal resilience in an environmentally adaptive manner. Full article

Dynamic and vulnerable, coastal zones face multiple hazards such as storms, flooding, and erosion, posing serious risks to populations and ecosystems. Continuous observation of coastal processes, particularly shoreline evolution, is therefore essential. Over the past three decades, coastal video-monitoring systems have proven valuable and cost-effective for studying coastal dynamics. Several approaches have been proposed to determine shoreline position, but each presents limitations, often depending on local conditions or illumination. This study proposes a method based on semantic segmentation using deep neural networks, specifically U-Net and DeepLabv3+ architectures. Both models were trained using time-exposure images from a coastal video-monitoring system, with DeepLabv3+ further evaluated using four convolutional neural network (CNN) backbones (ResNet-18, ResNet-50, MobileNetV2, and Xception). Unlike previous satellite- or UAV-based studies, this work applies deep learning to fixed coastal video systems, enabling continuous and high-frequency shoreline monitoring. Both architectures achieved high performance, with Global Accuracy of 0.98, Mean IoU between 0.95 and 0.97, and Mean Boundary F1 Score up to 0.99. These findings highlight the effectiveness and flexibility of the proposed approach, which provides a robust, transferable, and easily deployable solution for diverse coastal settings. Full article

Accurate water-level prediction is a critical component for ensuring safe maritime navigation, optimizing port operations, and mitigating coastal flooding risks. However, the complex, non-linear spatiotemporal dynamics of water systems pose significant challenges for current forecasting models. The proposed framework introduces three key innovations. First, a dual-weight graph construction mechanism integrates geographical proximity with Dynamic Time Warping (DTW)-derived temporal similarity to better represent hydrodynamic connectivity in coastal and estuarine environments. Second, a state-aware weighted loss function is designed to enhance predictive accuracy during critical hydrological events, such as storm surges and extreme tides, by prioritizing the reduction in errors in these high-risk periods. Third, the WS-STGTN architecture combines graph attention with temporal self-attention to capture long-range dependencies in both space and time. Extensive experiments are conducted using water-level data from five stations in the tidal-influenced lower Yangtze River, a vital artery for shipping and a region susceptible to coastal hydrological extremes. The results demonstrate that the model consistently surpasses a range of baseline methods. Notably, the WS-STGTN achieves an average reduction in Mean Squared Error (MSE) of 27.6% compared to the standard Transformer model, along with the highest coefficient of determination ($R^2\approx 0.96$) across all datasets, indicating its stronger explanatory power for observed water-level variability. This work provides a powerful tool that can be directly applied to improve coastal risk management, marine navigation safety, and the operational planning of port and coastal engineering projects. Full article

This paper presents the hydrological and meteorological parameters of the Wolfgang storm surge on the southern Baltic Sea coast and the storm’s impact on coastal areas with highly urbanised and developed zones. The surge emerged during a rare cyclonic system that was located over Western Europe in October 2023. A high difference in air pressure between the western and eastern parts of the Baltic coast led to the high-velocity wind blowing from the eastern direction to the centre of the cyclone located over Denmark. It caused high sea levels in the western part of the Baltic Sea. On the German and Danish coasts, the inflow of water at a high wind velocity perpendicular to the coast caused a very high surge of the sea and strong undulation. In this part of the Baltic Sea, the storm caused an increase in the sea level ranging from 1.5 to 2.2 m above average. It was lower on the eastern part of the Polish coast, exceeding 0.9 m above average sea level. The erosion of the base of cliffs ranged from 2 to 7 m, depending on the sea level. The dune erosion was larger but more varied, which resulted from different heights of the beach, at a maximum of up to 18 m. The water run-up reached 5.2 m above mean sea level (AMSL). The run-up parameter is a more accurate indicator of the potential threat than the sea level height. As a result of water run-up on the coast, lowlands situated even as far as 300 m from the shore were flooded. The storm caused significant damage to the coastal infrastructure and harbours. Research was conducted based on field studies and the analysis of digital documentation from websites, with the records of water run-up and the effects of the storm. Field studies were based on measures of coast retreat. Sea levels and wind were studied based on collected data. Full article

Urban coastal areas are increasingly vulnerable to compound flooding due to the convergence of extreme rainfall, storm surges, and infrastructure aging, especially in high-density settings. This study proposes and empirically validates a multi-scale strategy for enhancing urban flood resilience in the Macau Peninsula, a densely built coastal city with complex flood exposure patterns. Building on a previously developed network-based resilience assessment framework, the study integrates hydrodynamic simulation and complex network analysis to evaluate the effectiveness of targeted interventions, including segmented storm surge defense barriers, drainage infrastructure upgrades, and spatially optimized low-impact development (LID) measures. The Macau Peninsula was partitioned into multiple shoreline defense zones, each guided by context-specific design principles and functional zoning. Based on our previously developed flood simulation framework covering extreme rainfall, storm surge, and compound events in high-density coastal zones, this study validates resilience strategies that achieve significant reductions in inundation extent, water depth, and recession time. Additionally, the network-based resilience index showed marked improvement in system connectivity and recovery efficiency, particularly under compound hazard conditions. The findings highlight the value of integrating spatial planning, ecological infrastructure, and systemic modeling to inform adaptive flood resilience strategies in compact coastal cities. The framework developed offers transferable insights for other urban regions confronting escalating hydrometeorological risks under climate change. Full article

Coastal wetlands provide crucial ecosystem services, including habitats for wildlife, carbon storage, greenhouse gas emission reduction, storm surge and flood protection, water purification, recreation, and nature-based tourism. Their protection and restoration are therefore of growing importance to conservationists, scientists, local communities, and policymakers. This paper analyses the European Union’s (EU) policy framework, alongside international and regional agreements relevant to wetland conservation and restoration, focusing on coastal ecosystems. Drawing on policy content analysis, it assesses how 36 EU policies and multilateral agreements support or limit coastal wetland restoration and conservation efforts in Europe. The findings reveal two key gaps: first, an absence of a consistent definition of coastal wetlands within the EU policy framework; and second, the limited number of explicit policy references to these ecosystems. These shortcomings restrict opportunities for their effective inclusion in action plans and undermine coordinated conservation and restoration efforts. Most binding targets and objectives addressing coastal wetlands stem from EU policies and multilateral agreements on nature conservation, including regional sea conventions. This reliance risks overlooking opportunities within other policy sectors. While EU climate policies increasingly recognise the importance of wetland restoration, they often do so through non-binding provisions and voluntary action. To unlock the full potential of coastal wetlands for biodiversity and climate benefits, it is essential to embed coastal wetlands more explicitly within policy targets and to leverage emerging opportunities within the EU policy framework to further upscale coastal wetland restoration. Full article

This study examines the impacts of climate change on fisheries and aquaculture in Bangladesh, one of the most climate-vulnerable countries in the world. The fisheries and aquaculture sectors contribute significantly to the national GDP and support the livelihoods of 12% of the total population. Using a Critical Literature Review (CLR) approach, peer-reviewed articles, government reports, and official datasets published between 2006 and 2025 were reviewed across databases such as Scopus, Web of Science, FAO, and the Bangladesh Department of Fisheries (DoF). The analysis identifies major climate drivers, including rising temperature, erratic rainfall, salinity intrusion, sea-level rise, floods, droughts, cyclones, and extreme events, and reviews their differentiated impacts on key components of the sector: inland capture fisheries, marine fisheries, and aquaculture systems. For inland capture fisheries, the review highlights habitat degradation, biodiversity loss, and disrupted fish migration and breeding cycles. In aquaculture, particularly in coastal systems, this study reviews the challenges posed by disease outbreaks, water quality deterioration, and disruptions in seed supply, affecting species such as carp, tilapia, pangasius, and shrimp. Coastal aquaculture is also particularly vulnerable to cyclones, tidal surges, and saline water intrusion, with documented economic losses from events such as Cyclones Yaas, Bulbul, Amphan, and Remal. The study synthesizes key findings related to climate-resilient aquaculture practices, monitoring frameworks, ecosystem-based approaches, and community-based adaptation strategies. It underscores the need for targeted interventions, especially in coastal areas facing increasing salinity levels and frequent storms. This study calls for collective action through policy interventions, research and development, and the promotion of climate-smart technologies to enhance resilience and sustain fisheries and aquaculture in the context of a rapidly changing climate. Full article

The real-time forecast estimates of total water levels (TWL) associated with coastal storms by the Boston Office of the National Weather Service (NWS), and the analysis, identification, and field mapping of storm tide pathways (STP) by the Center for Coastal Studies (CCS) within the forecast region, has led to improved model forecasts, enhanced allocation of resources prior to storm impact (e.g., placement of flood control measures, identification of evacuation routes, development of applications to visualize and communicate threats, etc.), and increased public awareness of the practical implications of sea level rise and storm-related coastal flooding. Both NWS modeling and STP mapping are discussed here. The coupling of these methods began in 2016–2017 in Provincetown, MA, and its utility was highlighted during the new storm of record for most of southern New England, a nor’easter in January 2018. The use of this information by managers, stakeholders, and the public has increased since combining the TWL modeling and STP mapping in an online portal in 2021, and it continues to be used by emergency managers and the public to plan for approaching coastal storms. Full article

Coastal regions are among the areas most affected by climate change, facing rising sea levels, frequent flooding, and accelerated erosion that place renewable energy infrastructures under serious threat. Solar farms, which are often built along shorelines to maximize sunlight, are particularly vulnerable to salt-induced corrosion, storm surges, and wind damage. These challenges call for monitoring solutions that are not only accurate but also scalable and privacy-preserving. To address this need, Q-MobiGraphNet, a quantum-inspired multimodal classification framework, is proposed for federated coastal vulnerability analysis and solar infrastructure assessment. The framework integrates IoT sensor telemetry, UAV imagery, and geospatial metadata through a Multimodal Feature Harmonization Suite (MFHS), which reduces heterogeneity and ensures consistency across diverse data sources. A quantum sinusoidal encoding layer enriches feature representations, while lightweight MobileNet-based convolution and graph convolutional reasoning capture both local patterns and structural dependencies. For interpretability, the Q-SHAPE module extends Shapley value analysis with quantum-weighted sampling, and a Hybrid Jellyfish–Sailfish Optimization (HJFSO) strategy enables efficient hyperparameter tuning in federated environments. Extensive experiments on datasets from Norwegian coastal solar farms show that Q-MobiGraphNet achieves 98.6% accuracy, and 97.2% F1-score, and 90.8% Prediction Agreement Consistency (PAC), outperforming state-of-the-art multimodal fusion models. With only 16.2 M parameters and an inference time of 46 ms, the framework is lightweight enough for real-time deployment. By combining accuracy, interpretability, and fairness across distributed clients, Q-MobiGraphNet offers actionable insights to enhance the resilience of coastal renewable energy systems. Full article

This study investigates the multifaceted factors influencing the success of government-funded construction projects and addresses the challenges posed by climate-induced flooding, proposing integrated solutions encompassing structural vulnerability, system capacity, and organizational preparedness. By examining the challenges faced by coastal infrastructure, such as aging infrastructure, sea-level rise, and extreme weather events, this research seeks to identify strategies that enhance resilience and minimize the impact of flooding on coastal communities. The study presents a systematic review of 80 scholarly articles integrating quantitative and qualitative findings. Utilizing the PRISMA guidelines, the review highlights structural analysis, hydraulic modeling, and organizational surveys, to assess the resilience of coastal infrastructure systems. The results of this study offer actionable insights for policymakers, infrastructure managers, and coastal communities, facilitating informed decision-making and promoting climate-resilient development. Coastal regions around the world are increasingly vulnerable to climate-induced hazards such as sea level rise, storm surges, and intense flooding events. Among the most at-risk assets are transport infrastructure and buildings, which serve as the backbone of urban and regional functionality. This research paper presents a multidimensional assessment framework that integrates structural vulnerability, system capacity, and organizational preparedness to evaluate the resilience of coastal infrastructure. Drawing upon principles of resilience such as robustness, redundancy, safe-to-fail design, and change-readiness, the study critically reviews and synthesizes existing literature, identifies gaps in current assessment models, and proposes a comprehensive methodology for resilience evaluation. By focusing on both transport systems and building infrastructure, the research aims to inform adaptive strategies and policy interventions that enhance infrastructure performance and continuity under future climate stressors. Full article

Coastal vegetated ecosystems, including tidal marshes, vegetated dunes, and shrub- and forest-dominated wetlands, can mitigate hurricane impacts such as coastal flooding and erosion by increasing surface roughness and reducing wave energy. Land cover maps can be used as input to improve simulations of surface roughness in advanced hydro-morphological models. Consequently, there is a need for efficient tools to develop up-to-date land cover maps that include the accurate distribution of vegetation types prior to an extreme storm. In response, we developed the RUSH tool (Rapid remote sensing Updates of land cover for Storm and Hurricane forecast models). RUSH delivers high-resolution maps of coastal vegetation for near-real-time or historical conditions via a Jupyter Notebook application and a graphical user interface (GUI). The application generates 3 m spatial resolution land cover maps with classes relevant to coastal settings, especially along mainland beaches, headlands, and barrier islands, as follows: (1) open water; (2) emergent wetlands; (3) dune grass; (4) woody wetlands; and (5) bare ground. These maps are developed by applying one of two seasonal random-forest machine learning models to Planet Labs SuperDove multispectral imagery. Cool Season and Warm Season Models were trained on 665 and 594 reference points, respectively, located across study regions in the North Carolina Outer Banks, the Mississippi Delta in Louisiana, and a portion of the Florida Gulf Coast near Apalachicola. Cool Season and Warm Season Models were tested with 666 and 595 independent points, with an overall accuracy of 93% and 94%, respectively. The Jupyter Notebook application provides users with a flexible platform for customization for advanced users, whereas the GUI, designed with user-experience feedback, provides non-experts access to remote sensing capabilities. This application can also be used for long-term coastal geomorphic and ecosystem change assessments. Full article

Understanding the morphodynamic evolution of muddy coasts under complex wave–tidal forcing is crucial for effective coastal management, particularly under the unstable hydrodynamic conditions associated with global climate change. This study employs a one-dimensional Delft3D model to investigate a tidal flat north of Dafeng Port, Jiangsu Province, China, validated with multi-year profile measurements. Under typical conditions, the profile consistently exhibits upper-flat accretion and lower-flat erosion, with threshold values of Hs ≈ 1.2 m and Tp ≈ 4.5 s triggering nonlinear bed-level changes. During storm tides, the profile displays a distinct upper flood-tide and lower ebb-tide response. Long-term simulations suggest that the profile will likely reach dynamic equilibrium by 2026. Overall, this study demonstrates the capability of one-dimensional modeling to capture nonlinear tidal flat evolution and provides valuable insights into the spatially variable morphodynamics of muddy coasts for adaptive management. Full article

Climate change is elevating temperatures, shifting weather patterns, and increasing frequency and severity of extreme weather events. Despite the urgency with which solutions are needed, relatively few studies comprehensively investigate the effectiveness of alternative flood risk management options under different climate conditions. Specifically, we are interested in a comparison of the effectiveness of resistance, nature-based, and managed retreat strategies. Using an integrated 1D-2D PCSWMM model, this paper presents a comprehensive investigation into the effectiveness of alternative adaptation strategies in reducing flood risks in Eastwick, a community of Philadelphia, PA, subject to fluvial, pluvial, and coastal flood hazards. While addressing the urgent public need to develop local solutions to this community’s flood problems, the research also presents transferable insights into the limitations and opportunities of different flood risk reduction strategies, manifested here by a levee, watershed-scale green stormwater infrastructure (GSI) program, and a land swap. The effectiveness of these options is compared, respectively, under compound climate change conditions, with the spatiotemporal patterns of precipitation and Delaware river tidal conditions based on Tropical Storm Isaias (2020). The hypothesis was that the GSI and managed retreat approaches would be superior to the levee, due to their intrinsic ability to address the compound climate hazards faced by this community. Indeed, the findings illustrate significant differences in the predicted flood extents, depths, and duration of flooding of the various options under both current and future climate scenarios. However, the ideal remedy to flooding in Eastwick is more likely to require an integrated approach, based on more work to evaluate cost-effectiveness, stakeholder preferences, and various logistical factors. The paper concludes with a call for integrating multiple strategies into multifunctional flood risk management. Full article

This paper is aimed at landscape managers and designers. It looks at 123 real-world coastal landscape projects and organizes them into clear design categories, i.e., wetland restoration, hybrid infrastructure, or urban green spaces. We looked at how these projects were framed (whether they focused on climate adaptation, flood protection, or other goals) and how they tracked performance. We are hoping to bring some clarity to a very scattered field, helping us to see patterns in what is actually being carried out in terms of landscape interventions and increasing sea levels. We are hoping to provide a practical reference for making better, more climate-responsive design decisions. Coastal cities face escalating climate-driven threats from increasing sea levels and storm surges to urban heat islands. These threats are driving increased interest in nature-based solutions (NbSs) as green adaptive alternatives to traditional gray infrastructure. Despite an abundance of individual case studies, there have been few systematic syntheses aimed at landscape designers and managers linking design typologies, project framing, and performance outcomes. This study addresses this gap through a meta-synthesis of 123 implemented coastal landscape interventions aimed directly at landscape-oriented research and professions. Flood risk reduction was the dominant framing strategy (30.9%), followed by climate resilience (24.4%). Critical evidence gaps emerged—only 1.6% employed integrated monitoring approaches, 30.1% provided ambiguous performance documentation, and mean monitoring quality scored 0.89 out of 5.0. While 95.9% of the projects acknowledged SLR as a driver, only 4.1% explicitly integrated climate projections into design parameters. Community monitoring approaches demonstrated significantly higher ecosystem service integration, particularly cultural services (36.4% vs. 6.9%, $p<0.001$), and enhanced monitoring quality (mean score 1.64 vs. 0.76, $p<0.001$). Implementation barriers spanned technical constraints, institutional fragmentation, and data limitations, each affecting 20.3% of projects. Geographic analysis revealed evidence generation inequities, with systematic underrepresentation of high-risk regions (Africa: 4.1%; Latin America: 2.4%) versus concentration in well-resourced areas (North America: 27.6%; Europe: 17.1%). Full article