Data assimilation is a critical method to improve the performance of complex process-based models. However, the sensitive parameters for lake models are generally changing over time. Therefore, it is challenging to accurately update the sensitive parameters for a specific period, which affects the performance of data assimilation. To address the problem, this study developed a new data assimilation method by coupling the methods of parameter sensitivity analysis and the Ensemble Kalman Filter. The new method aimed to identify the model's sensitive parameters in real time. To evaluate its performance, we collected the high-frequency water quality automatic monitoring data of Lake Chaohu, and investigated the performance improvement of a phytoplankton dynamic model using a new data assimilation method. Our investigation results showed that the developed method was able to identify the sensitive parameters of the model in each simulation period, and updated them based on the measured data to achieve better performance. The simulation accuracy of phytoplankton biomass increased by 55%, i.e., the Nash-Sutcliffe Efficiency (NSE) increased from 0.49 to 0.76. This performance is better than that of the traditional data assimilation method (NSE=0.63). The method can be applied to the data assimilation of other ecological and environmental models, technically supporting an accurate prediction of environmental and ecological factors.