Improving SWAT Model Calibration Using Soil MERGE (SMERGE)

This study examined eight Great Plains moderate-sized (832 to 4892 km2) watersheds. The Soil and Water Assessment Tool (SWAT) autocalibration routine SUFI-2 was executed using twenty-three model parameters, from 1995 to 2015 in each basin, to identify highly sensitive parameters (HSP). The model was then run on a year-by-year basis, generating optimal parameter values for each year (1995 to 2015). HSP were correlated against annual precipitation (Parameter-elevation Regressions on Independent Slopes Model—PRISM) and root zone soil moisture (Soil MERGE—SMERGE 2.0) anomaly data. HSP with robust correlation (r > 0.5) were used to calibrate the model on an annual basis (2016 to 2018). Results were compared against a baseline simulation, in which optimal parameters were obtained by running the model for the entire period (1992 to 2015). This approach improved performance for annual simulations generated from 2016 to 2018. SMERGE 2.0 produced more robust results compared with the PRISM product. The main virtue of this approach is that it constrains parameter space, minimizesing equifinality and promotesing modeling based on more physically realistic parameter values.