A 45-year hydrological and planktonic time series in the South Bight of the North Sea

Abstract. This article presents a 45-year data series (from 1978 to 2023) acquired under the IGA (Impact des Grands Aménagements in French, Impacts of Major Developments in English) program conducted by IFREMER for EDF (Électricité de France, the French multinational electricity utility company). The IGA program was established to monitor the ecological and environmental quality of the coastal area surrounding the Gravelines Nuclear Power Plant (GNPP) located in the South Bight of the North Sea. The main objective of this program is to assess medium- and long-term environmental evolution by providing the means to identify possible changes in local marine habitats. Since 1978, the IGA program has measured key parameters, including temperature, salinity, nutrient concentrations, oxygen levels, chlorophyll-a concentrations, and the abundance of phytoplankton and zooplankton species. These measurements have been taken at different sampling stations around the GNPP, including the Canal d'amenée sampling station, for which hydrological and biological characteristics are considered representative of the broader coastal area of the South Bight of the North Sea. This data paper provides an overview of the main statistical characteristics of the time series (available at https://doi.org/10.17882/102656, Lefebvre et al., 2024), including long-term trends and shift analysis. Despite the importance and length of this dataset, one of the longest available for this region, its application in advancing knowledge of hydrological and biological processes has been surprisingly limited. The aim of this paper is to make this valuable dataset available to the scientific community, stakeholders, and society to help decipher the local and global influences of anthropogenic activities in a world increasingly affected by climate change. Since all the main statistics and patterns are still available thanks to our analysis, users should be able to use these data and combine them with other sources (in situ, satellite, and modeling) in order to dive into deeper analyses and investigate new key scientific challenges and more specific ones.