Inverse estimation of soil hydraulic and solute transport parameters from transient field experiments: Heterogeneous soil

While inverse parameter estimation techniques for determining key parameters affecting water flow and solute transport are becoming increasingly common in saturated and unsaturated zone studies, their application to practical problems, such as irrigation, have received relatively little attention. In this article, we used the Levenberg–Marquardt optimization algorithm in combination with the HYDRUS–2D numerical code to estimate soil hydraulic and solute transport parameters of several soil horizons below experimental furrows. Three experiments were carried out, each of the same duration but with different amounts of water and solutes resulting from 6, 10, and 14 cm water depths in the furrows. Two more experiments were performed with the same amounts of applied water and solute and, consequently, for different durations, on furrows with depths of 6 and 10 cm of water. We first used a scaling method to characterize spatial variability in the soil hydraulic properties, and then simultaneously estimated the saturated hydraulic conductivity (Ks) and the longitudinal dispersivity (DL) for the different horizons. Model predictions showed only minor improvements over those previously obtained assuming homogeneous soil profiles. In an effort to improve the predictions, we also carried out a two–step, sequential optimization in which we first estimated the soil hydraulic parameters followed by estimation of the solute transport parameters. This approach allowed us to include additional parameters in the optimization process. A sensitivity analysis was performed to determine the most sensitive hydraulic and solute transport parameters. Soil water contents were found to be most sensitive to the n parameter in van Genuchten’s soil hydraulic model, followed by the saturated water content (.s), while solute concentrations were most affected by .s and DL. For these reasons, we estimated .s and n for the various soil horizons of the sequential optimization process during the first step, and only DL during the second step. Sequential estimation somewhat improved predictions of the cumulative infiltration rates during the first irrigation event. It also significantly improved descriptions of the soil water content, particularly of the upper horizons, as compared to those obtained using simultaneous estimation, whereas deep percolation rates of water did not improve. Solute concentrations in the soil profiles were predicted equally well with both optimization approaches.