This dissertation describes the modeling efforts on the Upper Mississippi River Basin (UMRB) using the Soil and Water Assessment Tool (SWAT) model. The main goal of this study is to apply the SWAT model to the UMRB to evaluate the model as a tool for agricultural policy analysis and climate change impact analysis. A sensitivity analysis using influence coefficient method was conducted for eight selected hydrologic input parameters to identify the most to the least sensitive parameters. Calibration and validation of SWAT were performed for the Maquoketa River Watershed for streamflow on annual and monthly basis. The model was then validated for the entire UMRB streamflow and evaluated for a climate change impact analysis. The results indicate that the UMRB hydrology is very sensitve to potential future climate changes. The impact of future climate change was then explored for the streamflow by using two 10-year scenario periods (1990 and 2040s) generated by introducing a regional climate model (RegCM2) to dynamically downscale global model (HadCM2) results. The combined GCM-RCM-SWAT model system produced an increase in future scenario climate precipitation of 21% with a resulting 50% increase in total water yield in the UMRB. Furthermore, evaluation of model-introduced uncertainties due to use of SWAT, GCM, and RCM models yielded the highest percentage bias (18%) for the GCM downscaling error. Building upon the above SWAT validation, a SWAT modeling framework was constructed for the entire UMRB, which incorporates more detailed input data and is designed to assess the effects of land use, climate, and soil conditions on streamflow and water quality. An application of SWAT is presented for the Iowa and Des Moines River watersheds within the modeling framework constructed for the UMRB. A scenario run where conservation tillage adoption increased to 100% found a small sediment reduction of 5.8% for Iowa River Watershed and 5.7% for Des Moines River Watershed. On per-acre basis, sediment reduction for Iowa and Des Moines River Watersheds was found to be 1.86 and 1.18 metric tons respectively. Furthermore an attempt to validate the model for the entire UMRB yielded strong annual results.