A Python Tool for Multi-Gage Calibration
Use of multiple streamflow observation stations in calibration algorithms increases
hydrologic model performance. SWAT is a comprehensive, semi-distributed river basin
model that can benefit from multi-gage calibration. Non-Dominated Sorting Genetic
Algorithm II (NSGA-II) has been shown to be an effective and efficient multi-objective
calibration algorithm in hydrologic modeling community including for SWAT models. Although
NSGA-II has been used with SWAT before, there is no publicly available software tool for
easily applying the calibration approach for SWAT models. Thus, I created a tool for applying
NSGA-II method for multi-gage calibration of SWAT models using open source Python programing
language. Then, I demonstrated the tool with a case study for Upper Neuse watershed in
North Carolina with three observation gages and two fitness coefficients. Similar to
previous studies, the NSGA-II method with multi-gage calibration improved SWAT model accuracy.
Figure: NSGA-II SWAT Calibration Architecture*
*Ercan, M. B. and J. L. Goodall (2016),
Design and implementation of a general software library for using NSGA-II with SWAT for multi-objective model calibration.,
Environmental Modelling & Software, 84, 112-120.