Abstract: The SCS-CN method is one of the most widely used hydrological models to predict surface runoff from watershed for a given rainfall event. However, runoff generation is governed by spatially and temporally heterogeneous factors including topography, landform, soil, climate, vegetation and land use, and using standard SCS-CN method to predict surface runoff, could thus result in large errors. Therefore, it is an effective way to modify the original model for particular region, specific watershed for improving the accuracy. The measured event rainfall-runoff datasets from three watersheds located in Caijiachuan watershed on the Loess Plateau of China during 2004 and 2011 were used for calibrating (2004 to 2009) and validating (2010—2011) the original and five modified SCS-CN models. The selected three watersheds are dominated by farmland and grassland, plantation forests, and secondary forests, respectively. We found that the standard SCS-CN method poorly estimated the event runoff for all three watersheds (model efficiency coefficients E less than 0). The performance of revised SCS-CN based on rainfall amount was better than the standard one even though overestimation for small runoff events and underestimation for large ones were observed across the watersheds. The optimized SCS-CN model by rainfall intensity revised and initial abstraction coefficient improved the prediction accuracy most among the five modified models for watersheds dominated by farmland and grassland and plantation forest. Interestingly, revised SCS-CN by rainfall amount only improved significantly the predicting accuracy for secondary forest dominated watershed (E=0.79). In addition, the initial abstraction coefficient (λ) was 0.069, 0.189, and 0.200 for watersheds dominated by plantation forest, farmland and grassland, and secondary forest, respectively, indicating that the water storage capacity was affected by the vegetation type.