Technological and methodological advances of the past few decades have provided hydrologists with advanced and increasingly complex hydrological models. These models improve our ability to simulate hydrological systems, but they also require a lot of detailed input data and, therefore, have a limited applicability in locations with poor data availability. From a case study of Big Creek watershed, a 186.4 km2 urbanizing watershed in Atlanta, GA, for which continuous flow data are available since 1960, this project investigates the relationship between model complexity, data availability and predictive performance in order to provide reliability factors for the use of reduced complexity models in areas with limited data availability, such as small ungaged watersheds in similar environments. My hope is to identify ways to increase model efficiency without sacrificing significant model reliability that will be transferable to ungaged watersheds.