Impacts of human activities on the supply of marine ecosystem services: A conceptual model for offshore wind farms to aid quantitative assessments

Pol, Lennert van de; Biest, K. Van der; Taelman, Sue Ellen; Peña, Laura De Luca; Everaert, Gert; Hernández, Santiago Cortés; Culhane, Fiona; Borja, Ángel; Heymans, Johanna J.; Hoey, Gert Van; Vanaverbeke, Jan; Meire, Patrick

doi:10.1016/j.heliyon.2023.e13589

Cited by 5 publications

(2 citation statements)

References 58 publications

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“…Spatially explicit frameworks to analyze the integrated effects of wind farms on the marine environment aiming to evaluate how wind farms can contribute to the protection of the marine environment through strategic and economically viable location choices are developed and applied for quite a long time (i.e., [119]). Systematic methods for mapping how increased pressures from human activities may cause cumulative ecological effects on marine ecosystems are developed [120]. Those frameworks aim to provide answers regarding the integrated effect.…”

Section: Existing Modeling Solutions For Ecological Impactsmentioning

confidence: 99%

Fit-for-Purpose Information for Offshore Wind Farming Applications—Part-II: Gap Analysis and Recommendations

Schulz-Stellenfleth,

Blauw,

Laakso

et al. 2023

JMSE

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Offshore wind energy installations in coastal areas have grown massively over the last decade. This development comes with a large number of technological, environmental, economic, and scientific challenges, which need to be addressed to make the use of offshore wind energy sustainable. One important component in these optimization activities is suitable information from observations and numerical models. The purpose of this study is to analyze the gaps that exist in the present monitoring systems and their respective integration with models. This paper is the second part of two manuscripts and uses results from the first part about the requirements for different application fields. The present solutions to provide measurements for the required information products are described for several European countries with growing offshore wind operations. The gaps are then identified and discussed in different contexts, like technology evolution, trans-European monitoring and modeling initiatives, legal aspects, and cooperation between industry and science. The monitoring gaps are further quantified in terms of missing observed quantities, spatial coverage, accuracy, and continuity. Strategies to fill the gaps are discussed, and respective recommendations are provided. The study shows that there are significant information deficiencies that need to be addressed to ensure the economical and environmentally friendly growth of the offshore wind farm sector. It was also found that many of these gaps are related to insufficient information about connectivities, e.g., concerning the interactions of wind farms from different countries or the coupling between physical and biological processes.

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Section: Existing Modeling Solutions For Ecological Impactsmentioning

confidence: 99%

Fit-for-Purpose Information for Offshore Wind Farming Applications—Part-II: Gap Analysis and Recommendations

Schulz-Stellenfleth,

Blauw,

Laakso

et al. 2023

JMSE

View full text Add to dashboard Cite

show abstract

“…Moreover, such a modelling approach will highlight which MSFD descriptors are more ecologically significant when assessing overall GES and CIA (Elliott et al, 2015b). The larger ecosystem modelling will also contribute to a better understanding of ecosystem services and their socio-economic implications on other activities, such as fisheries likely to be displaced under MREDs and climate change scenarios (Kafas, 2018;Trifonova et al, 2022;Van de Pol et al, 2023). However, our proposed holistic approach integrating the MSFD into the SEA and EIA processes requires using an open-source national scheme for data sharing of ecosystem modelling outputs and needs to link MEDIN and MDE (grey bubbles and yellow arrows in Fig.…”

Section: Tool 2: Ecosystem Modelling Frameworkmentioning

confidence: 99%