The integrity and accuracy of national surface water monitoring data are of great significance for the effectiveness and long-term development of water environment management. Comparative analysis of the applicability and effectiveness of nine methods including two kinds of single imputation (Mean Imputation and KNN) and seven kinds of multiple imputation methods (MF, MICE, blasso, norm, norm.boot, norm.nob, ri) in surface water monitoring data. The imputation performance of 7 surface water indicators in Tumenlou section of Beiyun River, Wuqing District, Tianjin from 2020 to 2022 was evaluated by 9 methods, and the actual missing data of the same indicators were analyzed empirically. The results showed that the Bayesian Lasso multiple imputation method produced superior imputation results. It maximizes the utilization of auxiliary variables and prior information of various indicators to improve imputation accuracy. Additionally, Bayesian Lasso has a fast convergence speed and controllable imputation time.