A Bayesian Network model to identify suitable areas for offshore wave energy farms, in the framework of ecosystem approach to marine spatial planning.

The production of energy from waves is gaining attention. In its expansion strategy, technical, environmental and socioeconomic aspects should be taken into account to identify suitable areas for development of wave energy projects. In this research we provide a novel approach for suitable site identification for wave energy farms. To achieve this objective, we (i) developed a conceptual framework, considering technical, environmental and conflicts for space aspects that play a role on the development of those projects, and (ii) it was operationalized in a Bayesian Network, by building a spatially explicit model adopting the Spanish and Portuguese Economic Exclusive Zones as case study. The model results indicate that 1723 km2 and 17,409 km2 are highly suitable or suitable for the development of wave energy projects (i.e. low potential conflicts with other activities and low ecological risk). Suitable areas account for a total of 2.5 TWh∙m-1 energy resource. These areas are placed between 82 and 111 m water depth, 18-30 km to the nearest port, 21-29 km to the nearest electrical substation onshore, with 143-170 MWh m-1 mean annual energy resource and having 124-150 of good weather windows per year for construction and maintenance work. The approach proposed supports scientists, managers and industry, reducing uncertainties during the consenting process, by identifying the most relevant technical, environmental and socioeconomic factors when authorising wave energy projects. The model and the suitability maps produced can be used during site identification processes, informing Strategic Environmental Assessment and ecosystem approach to marine spatial planning.

Climate regime shifts and biodiversity redistribution in the Bay of Biscay.

Global ocean warming, wave extreme events, and accelerating sea-level rise are challenges that coastal communities must address to anticipate damages in coming decades. The objective of this study is to undertake a time-series analysis of climate change (CC) indicators within the Bay of Biscay, including the Basque coast. We used an integrated and flexible methodology, based on Generalized Additive Mixed Models, to detect trends on 19 indicators (including marine physics, chemistry, atmosphere, hydrology, geomorphology, biodiversity, and commercial species). The results of 87 long-term time series analysed (~512,000 observations), in the last four decades, indicate four groups of climate regime shifts: 1) A gradual shift associated with CC starting in the 1980s, with a warming of the sea surface down to 100 m depth in the bay (0.10-0.25 °C per decade), increase in air temperature and insolation. This warming may have impacted on benthic community redistribution in the Basque coast, favouring warm-water species relative to cold-water species. Weight at age for anchovy and sardine decreased in the last two decades. 2) Deepening of the winter mixed layer depth in the south-eastern bay that probably led to increases in nutrients, surface oxygen, and chlorophyll concentration. Current increases on chlorophyll and zooplankton (i.e., copepods) biomass are contrary to those expected under CC scenarios in the region. 3) Sea-level rise (1.5-3.5 cm per decade since 1990s), associated with CC. 4) Increase of extreme wave height events of 16.8 cm per decade in the south-eastern bay, probably related to stormy conditions in the last decade, with impacts on beach erosion. Estimating accurate rates of sea warming, sea-level rise, extreme events, and foreseeing the future pathways of marine productivity, are key to define the best adaptation measures to minimize negative CC impacts in the region.

Beach carrying capacity management under Covid-19 era on the Basque Coast by means of automated coastal videometry.

This paper describes the methodology followed to implement social distancing recommendations in the COVID-19 context along the beaches of the coast of Gipuzkoa (Basque Country, Northern Spain) by means of automated coastal videometry. The coastal videometry network of Gipuzkoa, based on the KostaSystem technology, covers 14 beaches, with 12 stations, along 50 km of coastline. A beach user detection algorithm based on a machine learning approach has been developed allowing for automatic assessment of beach attendance in real time at regional scale. For each beach, a simple classification of occupancy (low, medium, high, and full) was estimated as a function of the beach user density (BUD), obtained in real time from the images and the maximum beach carrying capacity (BCC), estimated based on the minimal social distance recommended by the authorities. This information was displayed in real time via a web/mobile app and was simultaneously sent to beach managers who controlled the beach access. The results showed a strong receptivity from beach users (more than 50.000 app downloads) and that real time information of beach occupation can help in short-term/daily beach management. In the longer term, the analysis of this information provides the necessary data for beach carrying capacity management and can help the authorities in controlling and in determining their maximum capacity.