In Korea, a Total Maximum Daily Load(TMDL) management policy has been enforced since 2004 to improve river water quality. This policy requires pollutant load assessment from both point and nonpoint sources from watershed to meet water quality target at control points in rivers. Because the control of nonpoint source pollutants are realized as a critical part of TMDL, distributed watershed models(i.e., SWAT, HSPF, SWMM) are increasingly being used to support the TMDL implementation and find alternative watershed management strategies. However, these models demand a great amount of input data, process parameters, a proper calibration, and sometimes result in significant uncertainty in the simulation results due to lack of information. For this reason, it is important that these models need a careful calibration and uncertainty analysis to minimize the risk in decision making. To satisfy this demand, in recent years, scientists have come up with various uncertainty analysis techniques for watershed models. The objectives of this study were to evaluate three different uncertainty analysis algorithms(SUFI-2: Sequential Uncertainty Fitting-Ver.2, GLUE: Generalized Likelihood Uncertainty Estimation, ParaSol: Parameter Solution) that used to analyze sensitivity of SWAT parameters and auto-calibration in Guryangcheon watershed(172.38km2) located in Geum river basin in Korea, evaluate uncertainty on the simulations of runoff and sediment load, and suggest alternatives to reduce the uncertainty of the model. The results confirmed that the parameters which are most sensitive to runoff and sediment simulations were consistent in three algorithms although the order of importance is slightly different. In addition, there was no significant difference in the performance of auto-calibration results for runoff simulations and all algorithms shown satisfactory modeling efficiency(NSE:　0.71∼0.82, R2: 0.74∼0.83). On the other hand, sediment calibration results showed significant biases and less efficiency(NSE: 0.40∼0.52, R2: 0.40∼0.52), which is probably due to the lack of measurement data. The p-factor representing the percentage of observations covered by 95PPU(95 Percent Prediction Uncertainty) and r-factor representing the average thickness of the 95PPU band were used to assess the uncertainty. In the runoff simulations, the p-factor and r-factor values were in the range of 0.11∼0.75 and 0.07∼0.30 respectively, meanwhile the values were 0.11∼0.51 and 0.24∼7.71, respectively in sediment simulations. It is obvious that the parameter uncertainty in the sediment simulation is much grater than that in the runoff simulation. To decrease uncertainty of SWAT simulations, it is required to estimate a feasible ranges of model parameters, and obtain sufficient and reliable measurement data for the study site.