While inverse parameter estimation techniques for determining key parameters affecting water flow and solutetransport are becoming increasingly common in saturated and unsaturated zone studies, their application to practicalproblems, such as irrigation, have received relatively little attention. In this article, we used the LevenbergMarquardtoptimization algorithm in combination with the HYDRUS2D numerical code to estimate soil hydraulic and solute transportparameters of several soil horizons below experimental furrows. Three experiments were carried out, each of the sameduration but with different amounts of water and solutes resulting from 6, 10, and 14 cm water depths in the furrows. Two moreexperiments 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 soilhydraulic properties, and then simultaneously estimated the saturated hydraulic conductivity (Ks) and the longitudinaldispersivity (DL) for the different horizons. Model predictions showed only minor improvements over those previouslyobtained assuming homogeneous soil profiles. In an effort to improve the predictions, we also carried out a twostep,sequential optimization in which we first estimated the soil hydraulic parameters followed by estimation of the solutetransport parameters. This approach allowed us to include additional parameters in the optimization process. A sensitivityanalysis was performed to determine the most sensitive hydraulic and solute transport parameters. Soil water contents werefound to be most sensitive to the n parameter in van Genuchtens 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 varioussoil horizons of the sequential optimization process during the first step, and only DL during the second step. Sequentialestimation somewhat improved predictions of the cumulative infiltration rates during the first irrigation event. It alsosignificantly improved descriptions of the soil water content, particularly of the upper horizons, as compared to those obtainedusing simultaneous estimation, whereas deep percolation rates of water did not improve. Solute concentrations in the soilprofiles were predicted equally well with both optimization approaches.