Application of Data Assimilation with the Root Zone Water Quality Model for Soil Moisture Profile Estimation

The Ensemble Kalman Filter (EnKF), a popular data assimilation technique for non-linear systems was applied to the Root Zone Water Quality Model. Measured soil moisture data at four different depths (5cm, 20cm, 40cm and 60cm) from two agricultural fields (AS1 and AS2) in northeastern Indiana were used for assimilation and validation purposes. Through daily update, the EnKF improved all statistical results (correlation coefficient and Root Mean Square Error and Mean Bias Error) compared to the direct insertion method and model results without assimilation for 5cm and 20cm depth. Soil moisture estimates for deeper layers (40cm and 60cm) did not show significant improvement from assimilating surface soil moisture depending on the initial soil moisture simulation results. It is also demonstrated that update intervals longer than three or four days do not improve the statistical results significantly. In addition, various ensemble sizes make little difference in the results for the upper layers.