Understanding the soil and hydrologic processes in agricultural watersheds are vital for reliable assessments of water quantity and quality to support integrated river basin management. However, deriving hydrology-relevant information is complicated in flat data-scarce agricultural watersheds due to constraints in watershed delineation, flat topography, poor natural drainage, and irregular irrigation schedules by human intervention. The study aimed to improve the applicability of the Soil and Water Assessment Tool (SWAT) model to simulate daily flow and NO3 concentrations in a flat data-scarce agricultural watershed in the Lower Seyhan Plain (LSP) in Turkey. Refined digitized stream networks, discharge data derived from fully equipped gauging stations, and satellite data (Landsat 7 ETM+, Aster GDEM, etc.) had to be integrated into the modeling process to improve the simulation quality. The model was calibrated using a 2-year (2011-2012) dataset of streamflow and NO3 using the Sequential Uncertainty Fitting (SUFI-2) approach and validated from 2013 to 2018. Daily water yields were predicted with a reasonable simulation accuracy (E values ranging from 0.53 to 0.82 and percent bias (PBIAS) from 0 to +4.1). The results proved that integrating redefined stream networks to SWAT within a Geographic Information System (GIS) environment increases the simulation capability of flow and nitrate dynamics efficiently. Automated delineation of these networks and sub-basins at low topographic transitions limits the SWAT accuracy.