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DRAINMOD is one of the widely used computer simulation models for drainage water management. Direct and indirect methods are available to feed DRAINMOD with the required inputs. Direct measurements of soil parameters are time consuming and costly compared with indirect methods such as the use of pedotransfer functions (PTFs). The goal of this study is to assess the feasibility of running DRAINMOD using saturated hydraulic conductivity (Ksat) and soil water characteristics (SWC) data estimated by PTFs. In previous research, we have identified the best performing PTFs for estimating the Ksat and SWC data for US soils of different textural classes. Data from Four U.S. agricultural drained sites, having different soils, crops, drainage systems, and climatological conditions, were used in the analysis. For each of the four sites Ksat and SWC data have been estimated using the best performing PTFs according to the soil textural class. The model was run using both calibrated and PTF-estimated Ksat and SWC data. Measured annual drainage was compared to predicted drainage using estimated and calibrated soil hydraulic properties. The Normalized Root Mean Square Error and Modeling Efficiency were used to assess the model performance. As expected, predicted annual drainage using the calibrated soil parameters (NRMSE=9-24%, EF=0.62-0.91) was more accurate than predicted drainage using PTF-estimated soil parameters (NRMSE=21-33%, EF=0.29-0.70). The errors in DRAINMOD predictions induced by using PTF-estimated soil parameters appear to have a small impact on DRAINMOD predictions of the systems response (annual drainage rate) to different management scenarios (e.g. conventional and controlled drainage). This finding has important implications regarding the large scale application of DRAINMOD to quantify the hydrologic and water quality impacts of controlled drainage across the U.S. Midwest.