Numerical simulation of unsaturated soil hydrology relies on calibrated soil hydraulic parameters, which are subject to uncertainty due to imperfect information during the inverse modelling. This study investigates the effectiveness of reducing parameter uncertainty using the recently developed Rosetta 3 pedotransfer function. The GLUE method was employed for numerical modeling using the Darcy-Richards equation under two strategies for sampling Mualem-van Genuchten (MvG) parameters: the first uses conventional random generation of MvG parameters (GLUE-random), while the second adopts Rosetta 3 to transfer soil particle composition to MvG parameter (GLUE-Rosetta). Both approaches were used for inverse modeling of 9 typical soils, each with a recommended parameter set defined as true values and associated soil moisture dynamics as observations. The posterior parameters selected with both GLUE-random and GLUE-Rosetta show an equifinality phenomenon. GLUE-random fails to provide well-constrained posterior parameters to recover the pre-defined true values, and its posterior results of soil water characteristic curve (SWCC) and soil hydraulic conductivity function (HCF) are poorly constrained. In contrast, GLUE-Rosetta significantly improves the accuracy of the inversely-estimated soil hydraulic parameters, and the ensemble of posterior SWCC and HCF also encompasses the predefined true curves. The results demonstrate the effectiveness of using Rosetta 3 to reduce the dimensionality of the optimization problem, which results in reliable estimation of soil hydraulic parameters and soil particle compositions. Moreover, GLUE-Rosetta outperforms GLUE-random in predicting soil moisture dynamics under different rainfall intensities. Overall, it is recommended to integrate Rosetta 3 with existing optimization tools to reduce the uncertainty of soil parameters and support more reliable modeling of unsaturated soil hydrology.

非饱和土壤水文的数值模拟依赖于校准的土壤水力参数，由于在反演建模过程中信息不完善，这些参数容易受到不确定性的影响。本研究调查了使用最近开发的 Rosetta 3 pedotransfer 函数减少参数不确定性的有效性。采用 GLUE 方法在两种采样 Mualem-van Genuchten (MvG) 参数的策略下使用 Darcy-Richards 方程进行数值建模：第一种使用常规随机生成 MvG 参数（GLUE-随机），而第二种采用 Rosetta 3 来将土壤颗粒成分转移到 MvG 参数 (GLUE-Rosetta)。这两种方法都用于 9 种典型土壤的逆向建模，每种土壤都将推荐的参数集定义为真实值，并将相关的土壤水分动态作为观测值。使用 GLUE-random 和 GLUE-Rosetta 选择的后验参数都表现出 equifinality 现象。GLUE-random 未能提供约束良好的后验参数来恢复预定义的真实值，其土壤水分特征曲线（SWCC）和土壤导水率函数（HCF）的后验结果约束不佳。相比之下，GLUE-Rosetta 显着提高了反向估计土壤水力参数的准确性，后 SWCC 和 HCF 的集合也包含预定义的真实曲线。结果证明了使用 Rosetta 3 降低优化问题维数的有效性，从而可以可靠地估计土壤水力参数和土壤颗粒成分。而且，GLUE-Rosetta 在预测不同降雨强度下的土壤水分动态方面优于 GLUE-random。总体而言，建议将 Rosetta 3 与现有优化工具集成，以减少土壤参数的不确定性，并支持更可靠的非饱和土壤水文建模。