A study was undertaken for the prediction of runoff flow from 0.8 ha field-sized agricultural watershed in South Korea using Soil and Water Assessment Tool (SWAT) sub-daily. The SWAT model with sub-daily configuration predicted flow from the watershed within the range of acceptable accuracy. The SWAT sub-daily simulations were carried out for a total of 18 rainfall events, 9 each for calibration and validation. Overall trend and extent of matching simulated flow for the rainfall events in 2007-2008 with measured data during the calibration process were coefficient of determination ( ) value of 0.88 and Nash and Sutcliffe Efficiency ( ) value of 0.88. For validation, and values were 0.9 and 0.84, respectively. Whereas and values for simulation results using daily rainfall data were 0.79 and -0.01, respectively, that were observed to be out of acceptable limits for the model simulation. The importance of higher time resolution (hourly) precipitation records for flow simulation were evaluated by comparing and with 15 min, 2 h, 6 h and 12 h precipitation data, which resulted in lower statistics with increases in time resolution of precipitation data. The SWAT sub-daily sensitivity analysis was performed with the consideration of hydraulic parameter and was found as in the rank order of CN2 (curve number), ESCO (soil evaporation compensation factor), GW_DELAY (ground water delay time), ALPHA_BF ( base flow alpha factor), GWQMN ( a threshold minimum depth of water in the shallow aquifer required for return flow to occur) , REVAPMN (minimum depth of water in shallow aquifer for re-evaporation to occur) , LAT_TIME (lateral flow travel time) respectively. These sensitive parameters were evaluated at 10% higher and lower values of the parameters, corresponding to 70.5% higher and 23.2% lower in simulated flow out from the SWAT model. From the results obtained in this study, hourly precipitation record for SWAT sub-daily with Green-Ampt infiltration method was proven to be efficient for runoff estimation at field sized watershed with higher accuracies that could be efficiently used to develop site-specific Best Management Practices (BMPs) considering rainfall intensity, rather than simply using daily rainfall data.