Assessing Parameter Uncertainty of a Semi‐Distributed Hydrology Model for a Shallow Aquifer Dominated Environmental System

Abstract

AbstractThis paper examines the performance of a semi‐distributed hydrology model (i.e., Soil and Water Assessment Tool [SWAT]) using Sequential Uncertainty FItting (SUFI‐2), generalized likelihood uncertainty estimation (GLUE), parameter solution (ParaSol), and particle swarm optimization (PSO). We applied SWAT to the Waccamaw watershed, a shallow aquifer dominated Coastal Plain watershed in the Southeastern United States (U.S.). The model was calibrated (2003‐2005) and validated (2006‐2007) at two U.S. Geological Survey gaging stations, using significant parameters related to surface hydrology, hydrogeology, hydraulics, and physical properties. SWAT performed best during intervals with wet and normal antecedent conditions with varying sensitivity to effluent channel shape and characteristics. In addition, the calibration of all algorithms depended mostly on Manning's n‐value for the tributary channels as the surface friction resistance factor to generate runoff. SUFI‐2 and PSO simulated the same relative probability distribution tails to those observed at an upstream outlet, while all methods (except ParaSol) exhibited longer tails at a downstream outlet. The ParaSol model exhibited large skewness suggesting a global search algorithm was less capable of characterizing parameter uncertainty. Our findings provide insights regarding parameter sensitivity and uncertainty as well as modeling diagnostic analysis that can improve hydrologic theory and prediction in complex watersheds. Editor's note: This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.