Impact of model complexity and precipitation data products on modeled streamflow

With the proliferation of remote sensing platforms as well as numerous ground products based on weather radar estimation, there are now multiple options for precipitation data beyond traditional rain gauges for which most hydrologic models were originally designed. This study evaluates four precipitation products as input for generating streamflow simulations using two hydrologic models that significantly vary in complexity. The four precipitation products include two ground products from the National Weather Service: the Multi-sensor Precipitation Estimator (MPE) and rain gauge data. The two satellite products come from NASA9s Tropical Rainfall Measurement Mission (TRMM) and include the TRMM 3B42 Research Version 6, which has a built-in ground bias correction, and the real-time TRMM Multi-Satellite Precipitation Analysis. The two hydrologic models utilized include the Soil and Water Assessment Tool (SWAT) and Gridded Surface and Subsurface Hydrologic Analysis (GSSHA). Simulations were conducted in three, moderate- to large-sized basins across the southern United States, the San Casimiro (South Texas), Skuna (northern Mississippi), Alapaha (southern Georgia), and were run for over 2 years. This study affirms the realization that input precipitation is at least as important as the choice of hydrologic model.