A Python Tool for Multi-Gage Calibration of SWAT Models using the NSGA-II Algorithm

Calibration of large watershed models requires multi-gage calibration, however there are limited tools available for performing these calibrations of SWAT models. Non-Dominated Sorting Genetic Algorithm II (NSGA-II) has been shown to be an effective and efficient multi-objective calibration algorithm in various disciplines. Although NSGA-II has been used with SWAT before, there is no publically available software tool for easily applying the calibration approach for SWAT models. Therefore, the objective of this study was to create an open source tool for multi-gage calibration of SWAT models using the Python programming language. This tool is demonstrated through an application for the Upper Neuse Watershed in North Carolina, USA. The objective functions used for the calibration were Nash-Sutcliffe (E) and Percent Bias (PB), and the objective sites were the Flat, Little, and Eno watershed outlets. Results from the chosen parameter set in the Pareto front were E values ranging between 0.65 and 0.75 and PB values ranging between 0.02 and 0.08 for the objective sites. Similar to previous studies, the results show that the use of multi-objective calibration algorithms for SWAT calibration improved model performance.