Comparative study of the variability and trends of phytoplankton biomass between spring and winter upwelling systems in the South China Sea

Zeng, Jialing; Liu, Chunli; Li, Xue; Zhao, Hui; Lu, Xiaoling

doi:10.1016/j.jmarsys.2022.103738

Cited by 5 publications

(2 citation statements)

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“…As shown in the distribution of the sea surface current field (Figure 8B), the prevailing southwest wind promotes Cu in the offshore direction, which increases the intensity of Cu in the study area and promotes the transfer of low salinity cold water from the Mekong River to the east, thus indirectly causing significantly low SDD (Van Der Woerd and Pasterkamp, 2008). Cu can lead to the summertime Chl-a jet phenomenon (Chl-a concentrations above 0.13 mg m -3 ) (Zeng et al, 2022), which can have a direct impact on SDD. In addition, Cu driven by wind also has periodic changes due to seasonal changes in WD.…”

Section: Analysis Of the Response Mechanism Of Sdd To Environmental F...mentioning

confidence: 94%

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Analysis of environmental factors impact on water transparency off southeastern Vietnam

Sun

Liu

et al. 2023

Front. Mar. Sci.

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Seawater transparency, one of the important parameters to evaluate the marine ecological environment and functions, can be measured using the Secchi disk depth (SDD). In this study, we use multi-source remote sensing data and other fused data from 2011 to 2020 to study the spatial distribution and variation of SDD off southeastern Vietnam. The monthly average of SDD in the study area has obvious seasonal variation characteristics and shows a double peak characteristic. An important observation is a significant decrease in transparency from July to September each year, which is far lower than other nearby seas. To study this low SDD phenomenon, the generalized additive model (GAM) is used to determine the main environmental factors. The response relationship between SDD and environmental factors on different time scales is explained through empirical mode decomposition (EMD) analysis experiments. The results show that the comprehensive explanation rate of the GAM model is 72.1%, and the main environmental factors affecting SDD all have non-linear response relationships with SDD. The contributions are ranked as sea surface salinity (SSS)> offshore current velocity (Cu)> wind direction (WD)> offshore Ekman transport (ETu)> sea surface temperature (SST)> mean direction of wind waves (MDWW). SDD is positively correlated with SSS and SST, and negatively correlated with Cu and ETu. SSS, Cu, ETu, and SST have a significant effect on SDD at interannual scales. Long-term changes in SDD are driven by SSS, Cu, WD, and SST. Generally, SSS has the most comprehensive impact on SDD. WD indirectly has a non-negligible impact on SDD by changing ocean dynamics processes.

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Section: Analysis Of the Response Mechanism Of Sdd To Environmental F...mentioning

confidence: 94%

“…The coastal upwelling region in the SCS is driven by classic wind-stressed Ekman dynamics (Zeng et al, 2022). In the upwelling region of southeastern Vietnam, Ekman transport along the shore (ETv) and offshore (ETu) and Ekman pumping (EPV) can transport nutrients from the coast or ocean bottom to the upper layer.…”

Section: Sdd and Environmental Factors Datamentioning

confidence: 99%