Hydrological models must be carefully calibrated to ensure that predictions are scientifically sound and reliable. As a goodness-of-fit measure, an ideal threshold of the Nash-Sutcliffe efficiency coefficient (Ens) has been reported to be 0.65, based on yearly and monthly simulation results with the soil and water assessment tool (SWAT). To further explore the use of Ens as a goodness-of-fit measure for daily runoff simulation with SWAT, the authors ran the model with five different parameter values, using Jinjiang River Basin on the southeast coast of China as the study area. In addition, to find how the model versions varied in predicting changes in flood and drought with changes in land use, each of the five model versions were run two times separately: once for the land use condition in 1985, and then again for the land use condition in 2006. The authors investigated the relationships between Ens variation and model accuracy and discussed the differences in predicted runoff due to land use change as caused by the five model versions. The results show that the coefficient of determination (R2) was highly associated with Ens, with a high R2 corresponding to a high Ens; the Ens value was quite sensitive to simulation results of the maximum one-day (1d) runoff; 0.75; the Ens value was not sensitive to simulation results of the minimum 1d and seven-day (7d) runoff, and unlike the flood runoff, the errors in drought flow increased adversely with increased Ens, indicating the poor performance of the SWAT model regarding drought flows; and the variations in runoff simulations, due to the land use change, were sensitive to model version switching and were fundamentally similar to the changes in the error indicators said above. The five SWAT versions varied in performance (Ens) in daily runoff simulation and had different measures of goodness-of-fit for various characteristics of the flood and drought processes. As a result, an Ens of 0.75 is suggested as a threshold for satisfactory simulation of maximum 1d discharge in the study watershed.