| Summary: | The hydrological model is the primary tool for regional water resources management, allocation, and prediction. However, these models always suffer from large uncertainties from multiple sources. Therefore, it is necessary to conduct an uncertainty analysis before performing hydrological simulation. Sequential Uncertainty Fitting (SUFI-2), Parameter Solution (ParaSol), Generalized Likelihood Uncertainty Estimation (GLUE), and Particle Swarm Optimization (PSO) integrated with the SWAT-CUP software were used to calibrate the Soil and Water Assessment Tool (SWAT) model and quantify the parameter sensitivity and prediction uncertainty of the SWAT in the Lancang River (LR) Basin, which is located in the southwest of China. This model was calibrated and validated using the four algorithms both at the daily scale, and the optimal simulation results derived by the four methods showed that the SWAT model performed well over the Yunjinghong station with Nash–Sutcliffe efficiency coefficient (NSE) and coefficient of determination (R<sup>2</sup>) values greater than 0.8 both in the calibration (1975 to 1989) and validation (1990 to 2004) periods. Among the four algorithms, the ParaSol algorithm produced the best simulation result at the daily scale with NSE values of 0.89 and 0.90 for the calibration and validation periods, respectively. Furthermore, the ParaSol algorithm has the greatest proportion of simulations (94%) with an NSE greater than 0.5. Parameter sensitivity analysis results demonstrated that the four methods all can be used for parameter sensitivity analysis in streamflow simulation, and they all identified that the base flow factor for bank storage (ALPHA_BNK) and effective hydraulic conductivity in the main channel alluvium (CH_K2) were more sensitive. The uncertainty analysis of model parameters showed that the parameter 95PPU (95% prediction uncertainty) width yielded by the ParaSol algorithm was the smallest compared with that of the other methods, followed by PSO, SUFI-2, and GLUE. The uncertainty analysis of the model simulation indicated that the SUFI-2 and PSO methods can achieve satisfactory results (with P-factor > 0.7 and R-factor < 1.5) at the daily scale; among them, SUFI-2 (P-factor = 0.93, R-factor = 1.17) performed much better than PSO (P-factor = 0.78, R-factor = 1.14). In general, by comparing its evaluation criteria (NSE, R<sup>2</sup>, RE, P-factor, and R-factor) to other methods, ParaSol stood out as the most efficient tool for model calibration. However, SUFI-2 remains the most robust method to perform uncertainty analysis considering its uncertainties of model structure, model inputs, and parameters. This study provides insight into hydrological simulation of the LR Basin using the appropriate algorithm to calibrate the model and implement the uncertainty analysis.
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