Aikaterini Karditsa

The 174 beach profiles investigated along the Greek coast are mainly from small, fine-grained, gently sloping, and predominantly south-facing beaches. Dunes are the most common backshore setting (39%), while 30% of profiles are backed by anthropogenic features such as roads or buildings, and 29% have beachrock. For their morphodynamic classification, Multiple Factor Analysis (MFA) and Hierarchical Clustering on Principal Components (HCPC) with k-means consolidation were applied. The first two MFA dimensions summarized exposure/wave conditions and sediment–backshore character, and the clustering results were consistent with overlapping environmental gradients within the surveyed sample. Consolidated HCPC yielded three main operational types (k = 3): exposed high-energy beaches (n = 83), sheltered fine-grained beaches (n = 64), and a smaller sheltered coarse-grained, beachrock-associated group (n = 27). Mean silhouette was 0.370; fixed-plane bootstrap Jaccard values of 0.765, 0.688, and 0.563 indicated unequal stability across Types I–III. Finer partitions (k = 5 and k = 8) provided higher-resolution views that cross-cut the three-type partition. The typology summarizes differences in wave exposure, sediment texture, and coastal setting and provides a basis for targeted monitoring; it does not quantify erosion rates or predict overwash events.

Adaptation to Climate Change (CC) impacts, Sea Level Rise (SLR) in particular, necessitates the application of Ecosystem Management Tools such as environmental risk assessments, impact assessments and impact management tools, that directly operate in a cause-consequence-response framework and suggest actions at various levels. In this contribution, an effort to support SLR adaptation planning in response to CC impacts on coastal tourism is made, through the integration of the IPCC Sixth Assessment Report (AR6) assessment framework and the DAPSI(W)R(M) framework, as well as the practical integration of the Impact Chain (IC) Tool and Bow-Tie Analysis. The proposed approach uses the Bow-Tie Analysis as a policy tool to fine-tune adaptation management plans in order to address the CC impacts on the coastal tourism sector, as they have been identified by the prior application of IC Tool, following IPCC AR6. The integration of the two approaches forms a framework able to support a concrete schema to address SLR induced beach retreat. The Bow-Tie analysis acts complementarily to the IC tool to structure potential mitigation and adaptation actions addressing the raised issues and respond to CC. The interrelation of the two frameworks enables climatic risk assessment and allows the identification of appropriate management measures addressing the specific components and facilitating the effective preparation for the forthcoming climatic conditions.

Mapping coastal landforms is critical for the sustainable management of ecosystems influenced by both natural dynamics and human activity. This study investigates the application of Transformer-based semantic segmentation models for pixel-level classification of key surface types such as water, sandy shores, rocky areas, vegetation, and built structures. We utilize a diverse, multi-resolution dataset that includes NAIP (1 m), Quadrangle (6 m), Sentinel-2 (10 m), and Landsat-8 (15 m) imagery from U.S. coastlines, along with high-resolution aerial images of the Greek coastline provided by the Hellenic Land Registry. Due to the lack of labeled Greek data, models were pre-trained on U.S. datasets and fine-tuned using a manually annotated subset of Greek images. We evaluate the performance of three advanced Transformer architectures, with Mask2Former achieving the most robust results, further improved 11 through a coastal-class weighted focal loss to enhance boundary precision. The findings demonstrate that Transformer-based models offer an effective, scalable, and cost-efficient solution for automated coastal monitoring. This work highlights the potential of AI-driven remote sensing to replace or complement traditional in-situ surveys, and lays the foundation for future research in multimodal data integration and regional adaptation for environmental analysis.

Accurate wind information is essential for safe and efficient port operations, yet many small and medium-sized coastal ports lack dense meteorological instrumentation. This paper presents a data-driven framework for wind speed prediction at such ports by leveraging long-term historical measurements from nearby reference stations. Focusing on a real-world case study at the Chalkida port in Greece, the framework integrates both deterministic and Machine Learning (ML) models trained on historical wind patterns of archived wind data from four surrounding locations. We examine both short- and long-horizon prediction periods, using recently acquired wind measurements at the target port for model validation. Deterministic baselines include simple and weighted averaging schemes, while supervised ML methods, such as Multiple Linear Regression, Decision Trees, Support Vector Regression, Random Forests, and Gradient Boosting, are trained to capture complex spatiotemporal patterns. Experimental results highlight that ensemble-based ML models, particularly Gradient Boosting, achieve superior accuracy in short-term forecasting, while the optimal predictor varies with the forecast horizon. The proposed approach enables the deployment of virtual wind stations in data-scarce ports and can be periodically updated to dynamically select the most suitable model, thereby supporting climate adaptation strategies, localized wind monitoring, and operational planning without requiring dense local instrumentation.

Ports are crucial nodes in global supply chains, and are critical infrastructures for the execution of global trade and components of the Blue Economy; however, they are highly vulnerable to climate change implications, especially for insular countries like Greece. This study investigates port stakeholder perceptions, priorities, and preparedness for climate risks through field research with the use of questionnaires and in-depth interviews. The findings reveal a strong recognition of climate change threats—especially extreme weather conditions—but limited institutional capacity for adaptation. Key needs include targeted funding, regulatory clarity, and specialized environmental units. National coordination remains weak, with climate change often framed in economic rather than systemic terms. Smaller ports face greater exposure yet fewer resources. The results highlight governance gaps, emphasizing the need for integrated, stakeholder-informed strategies to enhance port resilience and ensure alignment with EU climate directives. This research provides evidence-based insights to guide policy development and foster adaptive capacity in the Greek port sector.

This study examines the evolution of a beach formed along the coastline of a semi-enclosed, essentially tideless, embayment in the eastern Mediterranean Sea. The analysis revealed that the primary factors influencing its recent evolution are the terrestrial sediment influxes, current nearshore oceanographic conditions, and the existence of coastal constructions. The beach zone is exposed to waves approaching from the south with extreme values of height and period of 7 m and 4.3 s, respectively. Associated morphodynamic characteristics include a closure depth of 7 m, a breaking depth of 4.3 m, and a maximum run-up of 2.4 m. Since the mid-1900s, the shoreline has evolved through an accretional phase from 1960 to 1988, followed by a retreating phase from 1989 to 1997, except in the central part, where progradation has continued. The most recent period (1998–2017) has been relatively stable, though with a slight retreating trend. During storm events, changes to the beach are not uniform along-shore. Gross estimates of beach retreat due to sea level rise induced by climate change threaten the existence of the entire beach (for moderate and extreme IPCC Special Report Emissions Scenarios); however, this does not seem to be the case if riverine sediment influx continues.

The objective of this study is to compare the most common methods for forecasting wave characteristics (height and period) for the maximum in height 1/3, 1/10, and 1/100 of the waves, using available wind and wave datasets. The testing marine region is the Central Aegean Sea and, in particular, the east coast of Evia Island that is exposed to N, NE, E, and SE incoming offshore waves. The estimated values of wave characteristics resulting from three different wind datasets and the two wave datasets present differences of 2–60%. These variations are attributed to the different methodological approaches followed, to the different types of raw data in terms of space and time periods, and to the fetch limitations. The proposed methodology for the calculation of wave characteristics for the highest (1/10) and (1/100) waves appears to yield reasonable outcomes when compared to the corresponding significant (highest 1/3) wave heights and periods. Finally, mean values of wave height and period calculated from the five available datasets, considering their standard deviation, seem to be more representative than the values calculated separately from each dataset.

The maritime industry has a significant influence on the global economy, underscoring the need for operational availability and safety through effective maintenance practices. Predictive maintenance emerges as a promising solution compared to conventional maintenance schemes currently employed by the industry, offering proactive failure predictions, reduced downtime events, and extended machinery lifespan. This paper addresses a critical gap in the existing literature by providing a comprehensive overview of the main data-driven PdM systems. Specifically, the review explores common issues found in vessel components (i.e., propulsion, auxiliary, electric, hull), examining how different state-of-the-art PdM architectures, ranging from basic machine learning models to advanced deep learning techniques aim to address them. Additionally, the concepts of centralized machine learning, federated, and transfer learning are also discussed, demonstrating their potential to enhance PdM systems as well as their limitations. Finally, the current challenges hindering adoption are discussed, together with the future directions to advance implementation in the field.

Beaches play a pivotal role in supporting the socio-economic sector, particularly within the 'Sun, Sea, and Sand' (3S) tourism model. Valued for their ecosystem services, these coastal landforms are among the most dynamic and vulnerable environments, facing significant pressure from various climate challenges, which is further intensified by extensive anthropic exploitation. Their sustainable use is intrinsically linked to balancing the 3Ps: Planet, People, and Profit. To address the need for protection and sustainable utilization of coastal areas, the European Union has introduced the Protocol on Integrated Coastal Zone Management (ICZM) in the Mediterranean, specifically recommending in Article 8(2) the establishment of a setback buffer zone where permanent constructions are prohibited. This paper explores the application of risk mapping in the context of coastal setback policies, focusing on their effectiveness in managing beach retreat and adapting to climate change, particularly rising sea levels. The study examines selected beach areas in Peloponnese, Greece, influenced by various socio-economic factors. Through this analysis, the paper aims to contribute to the discourse on coastal management strategies that balance environmental sustainability with socio-economic benefits.

Rising sea levels pose a significant threat to coastal infrastructure, particularly seaports, which are vital to global maritime trade. This study assesses the impact of sea level rise (SLR) on the Port of Heraklion, Greece, contributing to the broader discourse on climate change in the port sector while providing a framework for evaluating and quantifying economic consequences. Using Digital Elevation Model (DEM) analysis, historical sea level trends, and projections under the high-emission SSP5-8.5 scenario, the study examines the port’s vulnerability to operational disruptions. Findings indicate that lower-elevation docks, particularly those at 1.8m, are highly susceptible to water level exceedances, affecting passenger ferries, cruise ships, and dry bulk cargo operations. Historical analysis (1998–2023) reveals a mean sea level of 0.63m, with extreme values reaching 1.14m, while operational thresholds at the port range from 0.91m to 1.31m, depending on dock height. The frequency of threshold exceedances has increased from fewer than one event per year before 2005 to approximately four events per year since then. By 2030, these events are projected to occur on ~44 days annually, escalating to ~186 days by 2050, rendering many docks inoperable. However, docks above 2.1m remain largely functional, underscoring elevation as a critical resilience factor. Financially, cumulative revenue losses are estimated at €3.1 million by 2030, rising to €32.2 million by 2050. These findings highlight the urgent need for adaptation measures to mitigate economic and operational risks. Practical implications emphasize the necessity of integrating climate resilience strategies into port planning, particularly for insular regions where maritime connectivity is essential.