Abstract. This study highlights the importance of tides in controlling the spatial and temporal distributions of phytoplankton and other factors related to growth, such as nutrients and light availability. To quantify the responses of net primary production (NPP) to tidal forcing, we conducted scenario model simulations considering M2 and S2 tidal constituents using the physical–biogeochemical coupled model ECOSMO (ECOSystem MOdel). The results were analyzed with respect to a reference simulation without tidal forcing, with particular focus on the spatial scale of the tidally induced changes. Tidal forcing regulates the mixing–stratification processes in shelf seas such as the North Sea and hence also influences ecosystem dynamics. In principle, the results suggest three different response types with respect to primary production: (i) in southern shallow areas with strong tidal energy dissipation, tidal mixing dilutes phytoplankton concentrations in the upper water layers and thereby decreases NPP. Additionally, tides increase turbidity in near-coastal shallow areas, which has the potential to further hamper NPP. (ii) In the frontal region of the southern North Sea, which is a transition zone between stratified and mixed areas, tidal mixing infuses nutrients into the surface mixed layer and resolves summer nutrient depletion, thus sustaining the NPP during the summer season after spring bloom nutrient depletion. (iii) In the northern North Sea, the NPP response to tidal forcing is limited. Additionally, our simulations indicate that spring bloom phenology is impacted by tidal forcing, leading to a later onset of the spring bloom in large parts of the North Sea and to generally higher spring bloom peak phytoplankton biomasses. By testing the related changes in stratification, light conditions and grazing pressure, we found that all three factors potentially contribute to the change in spring bloom phenology with clear local differences. Finally, we also analyzed the impact of the spring–neap tidal cycle on NPP. The annual mean impact of spring–neap tidal forcing on NPP is limited. However, locally, we found substantial differences in NPP either in phase or anti-phase with the spring–neap tidal cycle. These differences could be attributed to locally different dominant factors such as light or nutrient availability during spring tides. In general, we conclude that in shallow shelf seas such as the North Sea, intensified vertical mixing induced by tidal forcing could either promote NPP by counteracting nutrient depletion or hinder NPP by deteriorating the light environment because of the resuspension and mixing of suspended matter into the euphotic zone.
Outliers are often present in large datasets of water quality monitoring time series data. A method of combining the sliding window technique with Dixon detection criterion for the automatic detection of outliers in time series data is limited by the empirical determination of sliding window sizes. The scientific determination of the optimal sliding window size is very meaningful research work. This paper presents a new Monte Carlo Search Method (MCSM) based on random sampling to optimize the size of the sliding window, which fully takes advantage of computers and statistics. The MCSM was applied in a case study to automatic monitoring data of water quality factors in order to test its validity and usefulness. The results of comparing the accuracy and efficiency of the MCSM show that the new method in this paper is scientific and effective. The experimental results show that, at different sample sizes, the average accuracy is between 58.70% and 75.75%, and the average computation time increase is between 17.09% and 45.53%. In the era of big data in environmental monitoring, the proposed new methods can meet the required accuracy of outlier detection and improve the efficiency of calculation.
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