Determining the parameters of the soil water retention curve (SWRC) is essential to model the water flow in soil and investigate the water and nutrients transport around the roots of plants and ensure optimal management of irrigation. The unweighted least squares regression (ULS) is the most common approach applied to fit the SWRC functions to the observed data-points to optimize their parameters. The main purpose of this study was to evaluate the ULS and the weighted least squares regression (WLS) fitting process of SWRC and their impact on simulations of soil water flow. In this regard, the measured SWRC data in six parallel samples from given depth were fitted to the SWRC equations to optimize their parameters, through either the WLS or the ULS. In the WLS approach, the weights were calculated as the inverse of the variance in each pressure head of different iterations (six repetitions in this study). The results showed that although WLS generally resulted in an increased error in estimating the SWRC in the van Genuchten-Mualem model (RMSE = 0.027 and 0.043 cm³ cm⁻³ in the ULS and WLS methods, respectively), it improved the accuracy of the estimations at lower water contents (dry-end), as compared to the ULS. However, the efficiency of the ULS and WLS in estimating the SWRC was different from that in simulating the soil water flow. The water flow in the soil was more accurately simulated using the hydraulic parameters obtained by the WLS (with RMSE = 0.029 and 0.026 cm³ cm^-3 at x = 0 and x = 20 cm, respectively, in the WLS, and RMSE = 0.062 and 0.065 cm³ cm^-3 at x = 0 and x = 20 cm, respectively, in the ULS), especially at the low water range. While there was an important difference between the ULS and WLS in terms of estimating the SWRC and water flow simulations, other dispersion scenarios should be evaluated in future studies to compute the error of the fitting processes at low-pressure heads.

确定土壤保水曲线（SWRC）的参数对于模拟土壤中的水流以及调查植物根部周围的水分和养分迁移并确保最佳灌溉管理至关重要。不加权最小二乘回归（ULS）是用于将SWRC函数拟合到观察到的数据点以优化其参数的最常用方法。这项研究的主要目的是评估SWRC的ULS和加权最小二乘回归（WLS）拟合过程及其对土壤水流模拟的影响。在这方面，通过WLS或ULS将来自给定深度的六个平行样本中的测得的SWRC数据拟合到SWRC方程中，以优化其参数。在WLS方法中，权重计算为不同迭代的每个压头中方差的倒数（本研究中重复六次）。结果表明，尽管在Ww Genuchten-Mualem模型中，WLS通常导致估计SWRC的误差增加（在ULS和WLS方法中，RMSE分别为0.027和0.043 cm ³ cm ^-3），与ULS相比，它在较低含水量（干端）下提高了估算的准确性。但是，ULS和WLS估算SWRC的效率与模拟土壤水流的效率不同。使用WLS获得的水力参数可以更准确地模拟土壤中的水流（在WLS中，RMSE = 0.029和0.026 cm ³ cm ^-3，在x = 0和x = 20 cm时，RMSE = 0.062和0.065厘米³厘米^-3在ULS中分别位于x = 0和x = 20 cm处），特别是在低水位范围内。尽管在估算SWRC和水流模拟方面ULS和WLS之间存在重要差异，但在以后的研究中应评估其他分散情况，以计算低压头处的拟合过程误差。