Best management practice (BMP) tools that provide site-specific water quality assessment with limited input data are needed to assist soil and water managers as they work to target BMP placement and reduce dissolved and sediment bound pesticide loss from agricultural landscapes. BMP effectiveness largely depends on accurate characterization of dominant regional hydrologic processes. However, most BMP tools are region specific in their characterization of hydrology (e.g., most models capture either infiltration excess runoff or saturation excess runoff but cannot predict a combination of the two). In this study, we develop and couple a pesticide transport module with the Water Erosion Prediction Project (WEPP) model (v2012.8). The WEPP model realistically simulates infiltration excess and saturation excess runoff as well as dissolved pesticide loss from hillslopes in the Goodwater Creek Experimental Watershed (GCEW) in northeastern Missouri. Simulated runoff and dissolved atrazine concentrations were compared to observed data from mulch till (MT) and no till (NT) plots in the GCEW. The timing of runoff predicted by WEPP coincided well with observed events and simulated flow magnitudes were between minimum and maximum observations for replicate plots. Event-based Nash-Sutcliffe Efficiencies (NSE) are 0.84 and 0.79 for MT and NT runoff simulations, respectively. Both simulations and observations showed little difference in total growing season (planting to harvest) runoff between MT and NT. Event-based dissolved atrazine loads are well predicted for MT and NT with NSEs of 0.59 and 0.71, respectively. Consistent with observations, seasonal NT atrazine losses were greater than losses from MT plots. While this study only focuses on dissolved pesticide transport from the base of hillslopes using MT and NT conservation practices, the WEPP pesticide transport module is capable of predicting both dissolved and sediment bound pesticide loss from each section of the hillslope (i.e., top-slope, mid-slope, and toe-slope) for a wide range of BMPs. However, further field data are needed to evaluate these aspects of the model.