Technical Abstract: The integration of geographical information systems (GIS) and hydrologic models provides the user the ability to simulate watershed scale processes within a spatially digitized computer based environment. Such model simulations have become increasingly popular within the scientific community for several reasons, most notably: 1) the increased availability of input data sets via the internet, 2) modeling is usually more cost effective and less labor intensive than conducting field research and, 3) the use of spatially distributed data rather than point-scale measurements. Thus, the application of GIS and hydrologic modeling has become widely used for watershed environmental assessment studies, as well as providing decision support for many aspects of water resource management. Fundamental to optimal model performance is the quality, consistency, and structure of the spatial data sets used as model input. In particular, soil type and land use information are essential data elements in hydrologic modeling. This investigation was conducted to evaluate the use of SSURGO and STATSGO soil classification schemes and the National Agricultural Statistics Survey (NASS) and GAP Analysis Project (GAP) land use data sets in the watershed scale model referred to as the Soil and Water Assessment Tool (SWAT). Performance of the model was tested on the Cedar Creek Experimental Watershed (CCEW) in northeastern Indiana, one of twelve benchmark watersheds in the U.S. Department of Agriculture, Agricultural Research Service (USDA ARS) national Conservation Effects Assessment Project (CEAP). Model performance of the annual and monthly streamflow response in SWAT was assessed using the Root Mean Square Error (RMSE), coefficient of determination (R2) and the Nash-Suttcliffe Efficiency coefficient (ENS). Results show that for the seven year streamflow record a combination of the STATSGO and NASS data sets gave the lowest RMSE (0.7067) and highest ENS value (0.7245). However, on a monthly basis, a combination of the GAP and STATSGO data sets resulted in the lowest RMSE (3.912) and highest ENS value (0.775). Results of this study show that the estimation of streamflow by the SWAT model is sensitive to soil and land cover input data sets. Taking into consideration the extensive use of SWAT in the CEAP project, as well as several other models, a set of guidelines for certain input data layers may be necessary to ensure consistency when comparing modeling results for assessing the benefits of conservation practices in different regions across the country.