Abstract
Shallow lakes are highly sensitive to respond wind dynamics. The phytoplankton biomass production and distribution scenarios vary annually and seasonally in shallow lakes. The present study investigated the impact of long-term wind dynamics to phytoplankton biomass at Lake Taihu. The response of phytoplankton taxonomic groups to annual and seasonal wind dynamics using generalized additive model showed significant correlation. The critical annual and seasonal wind speed was 2.2 m/s showing the highest correlation with phytoplankton biomass whereas, wind speed declined by 0.02 m/s annually from Jan 2000 to Dec 2016 at Lake Taihu. The lower to medium wind speeds with frequent directional switching toward shore areas developed higher mixing scenarios which helped in reduction of water temperatures, distribution and colony formation of phytoplankton near the Meiliang Bay. The onshore winds were most frequent winds with low wind speeds whereas, offshore winds have been observed with medium to higher wind speeds. The results of generalized additive model well described the wind impacts on phytoplankton biomass and successfully modeled the response of phytoplankton to wind speed. Therefore, it has been concluded that long-term wind dynamic control the movement, mixing and colony formation processes of phytoplankton at Lake Taihu. The fundamental mechanism found in the present study was the long-term higher variations (annual and seasonal) of the wind-induced phytoplankton biomass changes which has been considered as the major physical factor regulating the phytoplankton blooms formation in the large shallow lake Taihu.
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Acknowledgements
The current study was supported by National science foundation of China (NSFC) (41772378), a National basic research program of China (NBRPC) (2017YFA0605200) and “One hundred Talent Program” of Chinese Academy of Science (Y6SL011001).
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Jalil, A., Zhang, K., Qi, L. et al. Phytoplankton response to long-term wind dynamics at large shallow Lake Taihu (shallow lake phytoplankton response to long-term wind dynamics). Int. J. Environ. Sci. Technol. 18, 341–352 (2021). https://doi.org/10.1007/s13762-020-02827-5
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DOI: https://doi.org/10.1007/s13762-020-02827-5