Image_2_Evaluating Uncertainties in Reconstructing the Pre-eutrophic State of the North Sea.TIF (1.15 MB)
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posted on 04.06.2021, 05:33 by Christoph Stegert, Hermann-Josef Lenhart, Anouk Blauw, René Friedland, Wera Leujak, Onur Kerimoglu

The North Sea is affected by eutrophication problems despite the decreasing riverine nutrient fluxes since the late 1980s. Formally, assessment of the eutrophication state of European marine environments is based on their historical state. Model estimates are increasingly used to support monitoring data that often do not encompass such pre-eutrophic conditions. However, various sources of uncertainties emerge when producing these estimates. In this study, we systematically quantify various sources of uncertainties in terms of variability, and assess their importance for the North Sea. For the reconstruction of the historical state, we use two coupled physical-biogeochemical model systems: ECOHAM on a 20-km grid for the European shelf and GPM on a high-resolution (1.5–4.5 km) grid for the Southern North Sea. To gain insights into the impacts due to the uncertainty in riverine loadings, we consider the historical nutrient inputs from two alternative watershed-models (MONERIS and E-HYPE). Overall, the modeled historic state based on E-HYPE shows higher nutrient concentrations compared to the state based on MONERIS, especially in the coastal regions. Assessing the degree of methodological uncertainties by an inter-comparison of different sources and against natural variabilities provides insight into the reliability of the model-based reconstruction of the historical state. We find that in regions influenced by freshwater from major rivers uncertainties owed to riverine loading scenarios exceed the natural sources of variability. For the offshore regions, natural sources of variability dominate over those caused by model- and scenario-related uncertainties. These findings are expected to assist decision makers and researchers in gaining insight into the degree of confidence in evaluating the model results, and prioritizing the need for refinement of models and scenarios for the production of reliable projections.

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