Thursday 2 july 2015
14:50 - 15:05h at Asia (level 0)
Themes: (T) Hydro-environment, (ST) Ecohydraulics and ecohydrology
Parallel session: 12G. Environment - Impact
Data assimilation techniques have been developed for assimilating observations into various models by coupling measured data and model simulations together to enhance the model reliability and reduce forecast uncertainties. The Ensemble Kalman filter (EnKF) which is one of the most widely used data assimilation techniques for complex nonlinear numerical models has been applied to a eutrophication model for predicting dynamics of phytoplankton biomass in Taihu Lake. In this study both of the model parameter and state variable are updated using the observations available. The simulation results show that the fitness between model simulation and observation was improved when the state variable and parameter were updated by measured data. It demonstrates that EnKF is an effective method for improving the simulation accuracy of complex dynamic eutrophication models, which provides a solid foundation for the use of the model predictions.