In this study, a two-dimensional hydrodynamic water-quality model is proposed for river-connected lakes in an effort to improve calibration accuracy and reduce computational burden. To achieve this, the sensitivity of parameters involved in the hydrodynamic model is analyzed using stepwise rank regression and Latin hypercube sampling (LHS), and the roughness coefficient, wind drag coefficient and wind resistance coefficient are identified as the most important parameters affecting the hydrodynamics of the Hongze Lake. Then, ensemble Kalman filter (EnKF) is used to assimilate observations to the proposed hydrodynamic and water quality model. It is found that assimilation of both state variables and model parameters results in a significant improvement of the simulation of the water level, flow velocity and pollutant concentration in the Hongze Lake.

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