This paper presents a recently developed Operational Forecast Platform (OFP) for prevailing sea conditions at very important ports worldwide (Accu-Waves). The OFP produces reliable high-resolution predictions of wave characteristics in and around ocean ports. Its goal is to support safer navigation, predict possible port downtime, assist vessel approaching, enhance management of towing services, and bolster secure ship maneuvering in busy ports around the globe. Accu-Waves OFP is based on integrated, high-resolution wave modelling over the continental shelf and in coastal areas that incorporates data from global- and regional-scale, open-sea wave and ocean circulation forecasts as boundary conditions. The coupling, nesting, calibration, and implementation of the models are reported and discussed in this paper, concerning 50 selected areas near and inside significant port basins. The detailed setup of the Accu-Waves OFP and its sub-system services is also provided regarding three-day forecasts at three-hourly intervals. The validation of the wave forecast system against in situ observations from wave buoys in coastal areas of the USA, Belgium, and Spain, as well as other model predictions by established OFPs, seems very promising, with performance skill scores ranging from adequate to very good. An exceptional case of stormy seas under severe marine weather conditions with very high wave maxima (>10 m) in the port of Algeciras is further discussed, confirming the good performance of the Accu-Waves OFP.

COVID-19 pandemic has raised concerns for a number of strategical social interactions. From quarantines and social distancing to vaccination and geopolitic

In this paper, an integrated methodology is developed to determine optimum areas for Photovoltaic (PV) installations that minimize the relevant visual disturbance and satisfy spatial constraints associated with land use, as well as environmental and techno-economic siting factors. The visual disturbance due to PV installations is quantified by introducing and calculating the “Social Disturbance” (SDIS) indicator, whereas optimum locations are determined for predefined values of two siting preferences (maximum allowable PV locations—grid station distance and minimum allowable total coverage area of PV installations). Thematic maps of appropriate selected exclusion criteria are produced, followed by a cumulative weighted viewshed analysis, where the SDIS indicator is calculated. Optimum solutions are then determined by developing and employing a Genetic Algorithms (GAs) optimization process. The methodology is applied for the municipality of La Palma Del Condado in Spain for 100 different combinations of the two siting preferences. The optimization results are also employed to create a flexible and easy-to-use web-GIS application, facilitating policy-makers to choose the set of solutions that better fulfils their preferences. The GAs algorithm offers the ability to determine distinguishable, but compact, regions of optimum locations in the region, whereas the results indicate the strong dependence of the optimum areas upon the two siting preferences.

Spatial conservation prioritization is traditionally focusing on ensuring the representation of species populations and habitats within protected areas. Recently there has been an increased interest in incorporating connectivity into planning, with higher priority given to areas exhibiting strong ecological linkages. We introduce three metrics (s‐core, Louvain clustering, walktrap clustering) that allow us to improve the spatial prioritization process by protecting areas that present high connectivity values. Instead of prioritizing unique planning units (PUs), by incorporating these metrics into spatial prioritization process we manage to identify clusters of PUs that collectively exhibit high connectivity values. This way we account for properties of connectivity structure (i.e. densely connected sites) into final detection of areas of high conservation interest. We evaluated the efficacy of these metrics in safeguarding ecological connectivity. The proposed metrics result in up to 25% higher connectivity values compared with the scenario in which no connectivity metrics are used. The results of the connectivity metrics were compared to the results obtained from other classic graph‐theoretic centrality metrics (degree, betweenness centrality, Eigenvector centrality, page rank) highlighting their potential to enhance performance across various spatial contexts. The proposed metrics can utilize existing connectivity data, such as edge lists, and their application can be tailored to address diverse conservation priorities. Overall, by illustrating the clustering properties of the connectivity datasets, the proposed metrics can introduce new approaches to improve the integration of ecological connectivity in conservation prioritization.