Accurate fitting of soil water retention curve (SWRC) parameters is crucial in the modeling of soil water flow and the assessment of soil quality. The un-weighted least squares regression (ULS) is the most common approach applied for fitting the SWRC functions to the observed data-points in order to optimize their parameters. However, the variance of SWRC data varies in different water contents; therefore, unlike the wet-end of the SWRC, the ULS method may not be sufficiently effective in estimating its dry-end. This study examined the differences between parameter approximations achieved by the ULS and the weighted least-squares (WLS) in the SWRC. Then, an analysis of both approaches in the simulation of water redistribution and the related soil physical quality indicators (SPQIs) was done. Accordingly, the measured SWRC data in six replications were fitted to the SWRC equations to optimize their parameters, through either WLS or ULS. The results showed that despite the increase of error in the SWRC estimation by the WLS method (RMSE=0.027 and 0.043 cm3 cm-3 in the ULS and WLS, respectively), WLS increased the accuracy of the estimations at the lower water contents (dry-end), when compared to the ULS. The WLS regression resulted in different values of SPQIs (e.g., S-index = 0.033 and 0.042 or RFC (relative field capacity) = 0.57 and 0.62 in the ULS and WLS methods, respectively). Furthermore, the simulated soil water movement in either wet or dry water conditions was different for the SWRC parameter estimated by WLS and ULS regressions.