【Objective】Computer programs, such as CXTFIT, are commonly used to calibrate soil hydraulic and transport parameters, such as dispersion coefficient and retardation factor. CXTFIT can be used to fit observations quite well, which leave researchers in this aspect such an impression that the “optimum” parameter sets simulated with this program can be used directly for modeling prediction. However, in the process of parameter simulation, inherent uncertainties do exist and are often underestimated. The objectives of this study were to assess and even quantify the uncertainties that may occur in parameter estimation using the convection-dispersion equation (CDE) and in adoption of the parameters in modeling prediction with the non-linear least squares (NLLS) and generalized likelihood uncertainty estimation (GLUE) methods. 【Method】 In this study, with the aid of CXTFIT, NLLS and GLUE coupled with the Latin hypercube sampling strategy was used to fit concentrations of bromide and copper nitrate in transport through three oil columns different in texture (i.e. Sandy loam, loamy sand and sandy clay loam), separately. And the parameters were optimized and analyzed to quantify the uncertainties that may occur in these processes by means of three quantitative metrics, that is, MNS (maximum coefficient determination coefficient ), P_95CI (the percentage of observations included within the 95% confidence intervals) and ARIL (average relative interval length).【Result】Results show that the only “optimum” parameter set obtained with the NLLS technique fits the curve of solute outflow quite well with determination coefficients (R² ) all > 0.98 for fitting Br- transport and > 0.937 for fitting Cu²⁺ transport, and with root mean square error lingering at the magnitude of 10^-2, but it fails to cope with a large number of equivalent parameters. R² being high in value only indicates the “optimum” parameter set is a proper fit of observation, but it does not mean the “optimum” parameter set is the true characterization of solute transport. The parameter set corresponding to the MNS of solute outflow fitted with can be used to simulate the observation as well as NLLS (R² >0.937). But the value range of acceptable parameters determined by GLUE are much wider than that of NLLS (the length of 95% confidence intervals of GLUE is about more than 5 times as high as that of NLLS), which means that a large number of parameter sets that are high in likelihood value fall outside of the 95% confidence intervals determined by NLLS. The 95% confidence intervals of outflow concentration determined by NLLS covered 28.13%, 64.00%, and 46.01% of the data observed separately in the soil columns different in texture, leaving almost half uncovered on average, whereas those determined by GLUE did 87.62%, 80.93%, and 84.3%, separately, which indicates that it is not a good choice to use NLLS to optimize parameters and uncertainties of the model output. 【Conclusion】 To put all into a nutshell, GLUE performs better than NLLS in both parameter and response surface uncertainty analysis, for NLLS underestimates significantly the uncertainties in estimation of major transport parameters. GLUE has a much wider acceptable parameter valuation range and 95% confidence intervals for outflow concentration, which indicates that the “optimum” solution acquired by NLLS does not show any robustness as the solution acquired by CXTFIT does. So the usage of only “optimum” parameter sets to predict solute transport has to face high risk and high uncertainty.