Using an Ensemble Kalman Filter Method to Calibrate Parameters and Update Soluble Chemical Transfer From Soil to Surface Runoff

A data assimilation method, an ensemble Kalman filter (EnKF), is applied to simultaneously calibrate parameters and update prediction for soluble chemical transfer from soil into surface runoff. The soluble chemical transfer is calculated using a two-layer analytical model with constant parameters, h _mix (water depth of the soil-mixing layer), α and γ (surface runoff and infiltration-related incomplete mixing parameters). The model is presented as the forward model. Based on laboratory experimental results, the measured chemical concentrations in the surface runoff are assimilated into the calculation through the developed EnKF method to calibrate the parameters and update chemical concentration in the runoff. In comparison with the calculation without data assimilation method, the updated solute concentration results are much closer to the experimental observed data and the calibrated parameters, h _mix, α and γ, are no longer constants, but time dependent, which are physically reasonable. The study results indicate that the EnKF method significantly improves the prediction for solute chemical transfer from soil into surface runoff, whereas the extended Kalman filter will not, and the ensemble size of 100 will be suitable for the chemical concentration calculation based on our trial.