Automatic calibration and predictive uncertainty analysis of a semi-distributed watershed model
Radcliffe, David E.
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A two-stage routine has been developed for automatic calibration of the Soil Water Assessment Tool (SWAT, a semi-distributed watershed model) that finds the best values for the model parameters, preserves the spatial variability in essential parameters, and leads to a measure of the model prediction uncertainty. We calibrated the stream flow in the Etowah River measured at Canton, GA (a watershed area of 1,580 km2) for the years 1983-1992 and used the years 1993-2001 for validation. Calibration for daily and monthly flow produced a very good fit to the measured data. Nash-Sutcliffe coefficients for daily and monthly flow over the calibration period were 0.60 and 0.86, respectively; they were 0.61 and 0.87 respectively over the validation period. Regardless of the level of model-to-measurement fit, non-uniqueness of the optimal parameter values necessitates uncertainty analysis for model prediction. The nonlinear prediction uncertainty analysis showed that caution must be exercised when using the SWAT model to predict short-term (7-day average) flows, especially under low and high flow conditions.