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

Ana D Maldonado; Ibon Galparsoro; Gotzon Mandiola; Iñaki de Santiago; Roland Garnier; Sarai Pouso; Ángel Borja; Iratxe Menchaca; Dorleta Marina; Laura Zubiate; Juan Bald

doi:10.1016/j.scitotenv.2022.156037

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

Sci Total Environ. 2022 Sep 10;838(Pt 2):156037. doi: 10.1016/j.scitotenv.2022.156037. Epub 2022 May 20.

Authors

Affiliations

¹ AZTI, Marine Research Division, Basque Research and Technology Alliance (BRTA), Herrera Kaia Portualdea z/g, 20110 Pasaia, Spain; Department of Mathematics, University of Almería, Carretera Sacramento s/n, 04120 La Cañada, Almería, Spain.
² AZTI, Marine Research Division, Basque Research and Technology Alliance (BRTA), Herrera Kaia Portualdea z/g, 20110 Pasaia, Spain. Electronic address: igalparsoro@azti.es.
³ AZTI, Marine Research Division, Basque Research and Technology Alliance (BRTA), Herrera Kaia Portualdea z/g, 20110 Pasaia, Spain.
⁴ AZTI, Marine Research Division, Basque Research and Technology Alliance (BRTA), Herrera Kaia Portualdea z/g, 20110 Pasaia, Spain; King Abdulaziz University, Faculty of Marine Sciences, Jeddah, Saudi Arabia.
⁵ BiMEP, Biscay Marine Energy Platform, C/Atalaia n°2, bajo, 48620, Bizkaia, Spain.

PMID: 35598669
DOI: 10.1016/j.scitotenv.2022.156037

Abstract

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.

Keywords: Decision support tool; Ecological risk; MSP; Ocean energy; Renewable energy; Wave energy converter.

MeSH terms

Bayes Theorem
Ecosystem*
Farms