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Temporal and spatial characteristics of potential energy anomaly in Lake Taihu

Research Article
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Abstract

In this study, water temperature and meteorological data in Lake Taihu from June 11 to July 6, 2013, are collected to calibrate and verify the unstructured grid finite-volume community ocean model (FVCOM) coupled with a heat exchange module. The spatial and temporal variations of potential energy anomalies (PEA) in the lake, simulated by the calibrated FVCOM, are analyzed to explore the stratification and de-stratification processes in water body. The temporal variation of PEA primarily follows the diurnal cycles of solar radiation, while the spatial heterogeneity of PEA is jointly determined by solar radiation and vertical shear of horizontal velocity coupled with the topography of the lake. The maxima of PEA in the lake are not greater than 4 J/m3 in the summer and even smaller along shore regions and near the Pingtaishan station. This study is helpful to improve understanding of the effect of physical processes on the algae bloom in Lake Taihu.

Keywords

Potential energy anomaly (PEA) Water stratification/de-stratification Lake Taihu FVCOM 

Notes

Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant No. 41371222), the National Water Pollution Control and Treatment Science and Technology Major Project (Grant No. 2012ZX07101-010), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PARD), and the Program for Changjiang Scholars and Innovative Research Team (PCSIRT). The source code for the model used in this study, the FVCOM, is freely available at http://fvcom.smast.umassd.edu/fvcom/. Both the field experiments data and input files necessary to reproduce the experiments with FVCOM, are available from the leading author upon request (qhzhao@nuist.edu.cn). The data are archived at the College of Hydrometeorology, Nanjing University of Information Science and Technology (NUIST).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of HydrometeorologyNanjing University of Information Science and TechnologyNanjingChina
  2. 2.Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologyNanjingChina
  3. 3.Air Resources LaboratoryNational Oceanic and Atmospheric AdministrationCollege ParkUSA

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