Luciana das Neves

Co-locating floating offshore wind turbines with wave energy converters can reduce costs, enhance power stability, and improve the spatial efficiency of offshore renewable energy developments. However, most existing studies assess hybrid systems at single sites or without accounting for long-term variability, spatial constraints or techno-economic interactions across broad marine regions. This work introduces a spatially explicit techno-economic assessment framework for co-located floating wind–wave energy farms that integrates multi-decadal metocean hindcasts, multi-criteria site selection, device-scale operational modelling, hybrid variability metrics, and cost modelling. The framework is demonstrated using a 510 MW hybrid farm evaluated across the mainland Portuguese offshore region, drawing on 45 years of SWAN- and ERA5-derived wave and wind conditions. At each feasible location, the method computes annual energy production, capacity factor, non-operational time, complementarity indicators, levelized cost of energy (LCoE), and a composite variability index. These metrics are synthesized using K-means clustering to identify spatial regimes of hybrid performance, variability reduction and economic feasibility. Results show that wave energy provides nearly continuous availability, substantially reducing zero-generation events, while wind contributes most of the variability and downtime. Offshore areas off central and northern Portugal achieve the strongest hybrid performance, with high combined capacity factors and the lowest LCoE values (≈60–90 €/MWh). The clustering analysis identifies three techno-economic hybrid classes that capture the interplay among resource quality, complementarity and cost. Beyond the case study, the methodology offers a transferable framework for early-stage planning of hybrid offshore renewable energy systems, supporting marine spatial planning, technology selection and strategic investment.

The European coasts are among the most densely populated of the world, with natural sand dune barriers urbanised and replaced by traditional hard coastal protection structures. Without the needed measures to adapt, the number of people exposed to floods is anticipated to increase 187 million worldwide by the end of the 21st century. In Europe, the coasts of The North Sea, the Baltic Sea, and the Atlantic are anticipated to experience substantial flood risks from sea-level rise, and climate extremes are also expected to impact southern Europe Mediterranean coasts (Vousdoukas et al., 2017). Future coastal management surpasses the current fixed and non-adaptive flood coastal protection setup: Hybrid Nature-based-Solutions (abbreviated as NbS) that can efficiently integrate static hard infrastructure with dynamic aeolian, and vegetated sediments are currently developed along urbanized areas of most of the European sandy coasts, yet still at small scales.

The integration of machine learning (ML) techniques in coastal engineering marks a paradigm shift in how coastal processes are modeled and understood. While traditional empirical and numerical models have been stalwarts in simulating coastal phenomena, the burgeoning complexity and computational demands have paved the way for data-driven approaches to take center stage. This review underscores the increasing preference for ML methods in coastal engineering, particularly in predictive tasks like wave pattern prediction, water level fluctuation, and morphology change. Although the scope of this review is not exhaustive, it aims to spotlight recent advancements and the capacity of ML techniques to harness vast datasets for more efficient and cost-effective simulations of coastal dynamics. However, challenges persist, including issues related to data availability and quality, algorithm selection, and model generalization. This entails addressing fundamental questions about data quantity and quality, determining optimal methodologies for specific problems, and refining techniques for model training and validation. The reviewed literature paints a promising picture of a future where ML not only complements but significantly enhances our ability to predict and manage the intricate dynamics of coastal environments.

Earth observation can provide managers with valuable information on ongoing coastal processes and major trends in coastline evolution, especially in data-poor regions. This paper examines the use of optical satellite images in the mapping of the changes in shoreline position before, during, and after the implementation of a protection scheme. The aim of this paper is twofold: (i) to demonstrate the potential of satellite imagery as an effective, robust, and low-cost tool to remotely monitor the effectiveness of protective structures based on a large-scale case study in West Africa; and (ii) to compile lessons learned from this case study that can be used in the design of future interventions. The analysis shows that before the implementation of the protection scheme, the coastal sector was retreating at a rate of −1.6 m/year, which is in line with the average retreat rates reported in other studies for the region. After project implementation, this trend reversed into shoreline accretion at a rate of +1.0 m/year, locally experiencing positive and negative oscillations in the short term. Furthermore, the shoreline-extracted positions proved useful in assessing the impact of differences in the groynes’ permeability with respect to temporary leeside erosion. Finally, it is recommended to continue this monitoring to assess long-term trends.