Studying model uncertainty and identifying the parameter uncertainty in the modeling of water flow through the soil is useful to improve water and soil management. This research aimed to assess the uncertainty of tshe parameters soil water retention curve (SWRC) models using an efficient hybrid of the Monte Carlo technique e.g. generalized likelihood uncertainty estimation (GLUE). GLUE estimates the parameters of vanGenuchten, vanGenuchten-Mualem, and vanGenuchten-Burdine models for four soil classes. Also, to evaluate the relative importance of the model parameters, generalized sensitivity analysis (GSA) was performed. The results of the uncertainty analysis showed that among the studied models, the vanGenuchten-Mualem model with the indices of S = 0.05, T = 0.4, d-factor = 0.25 and, P_CI = 100 was considered as the most accurate model with the least uncertainty. Also, the results of GSA were demonstrated that alpha and n parameters were sensitive parameters in the models. Consequently, identifying the uncertainty of the SWRC model structure and its parameters, relevant models with higher accuracy can be used in the study of soil water processes, and better water resource allocation.

在通过土壤的水流建模中，研究模型不确定性并识别参数不确定性对于改善水和土壤管理非常有用。本研究旨在使用蒙特卡罗技术的有效混合方法（例如广义似然不确定性估计（GLUE））来评估参数土壤保水曲线（SWRC）模型的不确定性。GLUE估算了四种土壤类别的vanGenuchten，vanGenuchten-Mualem和vanGenuchten-Burdine模型的参数。另外，为了评估模型参数的相对重要性，进行了广义灵敏度分析（GSA）。不确定性分析的结果表明，在所研究的模型中，vanGenuchten-Mualem模型的指标为S = 0.05，T = 0.4，d因子 = 0.25和P_CI  = 100被认为是最准确的模型，不确定性最小。此外，GSA的结果证明了alpha和n参数是模型中的敏感参数。因此，确定SWRC模型结构及其参数的不确定性，可以在研究土壤水过程和更好地分配水资源方面使用精度更高的相关模型。