Improved understanding of nitrate trends, eutrophication indicators, and risk areas using machine learning

Abstract

Abstract. Nitrate is an essential inorganic nutrient limiting phytoplankton growth in many marine environments. Eutrophication, often caused by nitrogen deposition, is a reoccurring problem in coastal regions, including the North-West European Shelf (NWES). Despite their importance, nitrate observations on the NWES are costly to obtain and thus sparse in both time and space. We demonstrate that machine learning (ML) can generate, from sparse observations, a skilled, gap-free, bi-decadal (1998–2020) surface nitrate dataset. We demonstrate that the effective resolution (scales on which the dataset is skilled) is slightly coarser than the 7 km and daily resolution of the product but still completely sufficient to analyse nitrate dynamics on a monthly scale. With such a dataset we are able to (i) highlight the coastal regions that show strong summer nutrient limitation, covering eutrophication problem areas identified by monitoring bodies (i.e. OSPAR), but also other regions, such as the southern Irish coastline and parts of the Irish Sea. Our results could indicate greater potential for eutrophication events in regions subject to high-riverine-nutrient-discharge scenarios. (ii) We demonstrate that bi-decadal 1998–2020 trends in coastal nitrate, responding to long-term policy-driven reduction in riverine discharge, are mostly modest, with a notable exception of the Bay of Biscay. (iii) We show that winter nitrate plays a relatively minor direct role in the intensity of the phytoplankton bloom the following spring, which can have some implications for using winter inorganic nitrogen as an indicator of eutrophication (as often included by OSPAR). The last two results are consistent with recent findings in the literature (Axe et al., 2022; Devlin et al., 2023; Van Leeuwen et al., 2023). We propose using the nitrate dataset for data assimilation and hypothesise that it has the potential to substantially improve phytoplankton forecasts in operational runs.