An improved Ensemble Kalman Filter for optimizing parameters in a coupled phosphorus model for lowland polders in Lake Taihu Basin, China

- An improved Ensemble Kalman Filter (EnKF) was proposed for parameter optimization.
- The improved EnKF succeeded in updating parameters in a phosphorus model for polders.
- The improved EnKF can be used to investigate parameter trajectories through time.

Ensemble Kalman Filter (EnKF) is potential in optimizing parameters of an environmental model, but may lead to a worse performance of the model in case that improper parameters were updated. To overcome this weakness, EnKF was improved by coupling with a dynamic and multi-objective sensitivity analysis. The improved EnKF was applied to update the parameters of a coupled phosphorus model for simulating phosphorus dynamics of Polder Jian located in Lake Taihu Basin, China. Two parameters that were most sensitive to particulate and dissolved phosphorus were identified at each sub-period, and were then updated using EnKF. To evaluate the performance of the improved EnKF, four simulations with different parameter update strategies were implemented, and compared with measured data. The simulation with the improved EnKF well simulated DP dynamics in Polder Jian with a d value of 0.65 and a RMSE value of 0.015 mg/L. This model fit is better than that of other three simulations with different parameter update strategies, implying a success of the improved EnKF in updating parameters of the coupled phosphorus model. This improved EnKF has the advantage to update several parameters simultaneously, and can be applied in other models with minimal changes.