The uncertainty of border irrigation parameters will directly affect the simulation results of border irrigation flow model for surface irrigation management at regional scale. Latin hypercube sampling (LHS), updated Latin hypercube sampling (ULHS) and simple Monte Carlo sampling (SMCS) are studied in sampling convergence, stability and simulation efficiency based on regional scale border irrigation simulation method and soil properties spatial randomness. In terms of the sampling accuracy, ULHS and LHS can satisfactorily represent the statistical characteristics of soil bulk density. In the simulation convergence aspect, ULHS has the faster convergence than LHS and SMCS, which indicates that ULHS can significantly improve the sampling quality and reduce the sampling frequency. In the simulation accuracy, simulation times of ULHS are less than those of LHS and SMCS, simulation accuracy and distribution pattern are better than those of LHS and SMCS. Stability simulation of ULHS is better than those of LHS and SMCS. Computational efficiency of ULHS is increased by 0.23-fold and 1.8-fold than those of LHS and SMCS, respectively. Convergence rate of ULHS is faster than those of LHS and SMCS. In addition，ULHS can improve computational efficiency and sampling stability. ULHS can help to improve the simulation performance of regional scale border irrigation stochastic simulation model.