Analysis of soluble chemical transfer from soil to surface runoff and incomplete mixing parameter identification

A two-layer mathematical model proposed by Tong et al. （2010） was used to predict soluble chemical transfer from soil into surface runoff with ponded water on the soil surface. Infiltration-related incomplete mixing parameter γ and runoff-related incomplete mixing parameter a in the analytical solution of the Tong et al. （2010） model were assumed to be constant. In this study, different laboratory experimental data of soluble chemical concentration in surface runoff from initially unsaturated and saturated soils were used to identify the variables γ and a based on the analytical solution of the model. The values of γ and a without occurrence of surface runoff were constant and equal to their values at the moment when the surface runoff started. It was determined from the results that γ decreases with the increase of the ponded water depth, and when the initial volumetric water content is closer to the saturated water content, there is less variation of parameter γ after the occurrence of surface runoff. As infiltration increases, the soluble chemical concentration in surface runoff decreases. The values of parameter a range from 0 to 1 for the fine loam and sand under the controlled infiltration conditions, while it can increase to a very large value, greater than 1, for the sand under the restrained infiltration conditions, and the analytical solution of the model is not valid for experimental soil without any infiltration if a is expected to be less than or equal to 1. The soluble chemical concentrations predicted from the model with variable incomplete mixing parameters γ and α are more accurate than those from the model with constant γ and α values.

Comparison between gradient based UCODE2005 and the ensemble Kalman Filter for transient groundwater flow inverse modeling

Gradient based UCODE_2005 and data assimilation based on the Ensemble Kalman Filter(EnKF) are two different inverse methods. A synthetic two-dimensional flow case with four no-flow boundaries is used to compare the UCODE_2005 with the Ensemble Kalman Filter(EnKF) for their efficiency to inversely calculate and calibrate a hydraulic conductivity field based on hydraulic head data. A zonal, random heterogeneous conductivity field is calibrated by assimilating the time series of heads observed in monitoring wells. The study results indicate that the two inverse methods, UCODE_2005 and EnKF, could be used to calibrate the hydraulic conductivity field to a certain degree. More available observations and information about the conductivity field, more accurate inverse results will be obtained for the UCODE_2005. On the other hand, for a realistic zonal heterogeneous hydraulic conductivity field, EnKF can only efficiently determine the hydraulic conductivity field at the first several assimilated time steps. The results obtained by the UCODE_2005 look better than those by the EnKF. This is possibly due to the fact that the UCODE_2005 uses observed head data at every time step, while EnKF can only use observed heads at first several steps due to the filter divergence problem.