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) 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 (NPP), located in the southern bight of the North Sea. The main objective of this program is to assess medium- and long-term environmental evolution by providing means to identify possible changes in the local marine habitats. Since 1978, the IGA program has measured key parameters, including temperature, salinity, nutrient concentrations, oxygen levels, chlorophyll-a concentrations, as well as the abundance of phytoplankton and zooplankton species. These measurements have been taken at different sampling stations around the NPP, including the “Canal d’amenée” sampling station, for which hydrological and biological characteristics are considered as representative of broader coastal area of the southern bight of the North Sea. This data paper provides an overview of the main statistical characteristics of the time series, including long-term trends and shifts 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 influence 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, the user should be able to use this data and combine it with other sources (in situ, satellite, modelling), in order to dive into deeper analyses, and to investigate new key scientific challenges as well as more specific ones.