Using Data Assimilation Method Via an Ensemble Kalman Filter to Predict Adsorptive Solute Cr(Ⅵ) Transfer from Soil into Surface Runoff

Abstract:

With the development of modern agriculture, large amount of fertilizer and pesticide outflow from farming land causes great wastes and contributes to serious pollution of surface water and groundwater, which threatens ecological environment and human life. In this paper, laboratory experiments are conducted to simulate adsorbed Cr(VI) transfer from soil into runoff. A two-layer in-mixing analytical model is developed to to analyze laboratory experimental results. A data assimilation (DA) method via the ensemble Kalman filter (EnKF) is used to update parameters and improve predictions. In comparison with the observed data, DA results are much better than forward model predictions. Based on the used rainfall and relevant physical principles, the updated value of the incomplete mixing coefficient is about 7.4 times of the value of the incomplete mixing coefficient in experiment 1 and about 14.0 times in experiment 2, which indicates the loss of Cr(VI) in soil solute is mainly due to infiltration, rather than surface runoff. With the increase of soil adsorption ability and the mixing layer depth, the loss of soil solute will decrease. These results provide information for preventing and reducing the agricultural nonpoint source pollution.