Multi-objective autocalibration of SWAT model for improved low flow performance for a small snowfed catchment

ABSTRACT

A reliable modelling framework needs to ensure that the model is simulating reality with limited uncertainty, thus enhancing its predictive ability. In the literature, hydrological model assessment using one or more metrics is reported to be inadequate when the river flow regime is required to be reproduced comprehensively. This research is aimed to: (a) calibrate the Soil and Water Assessment Tool (SWAT) based on the concept of multi-objective optimization by applying the Borg multi-objective evolutionary algorithm (MOEA); (b) apply hydrological signatures as objective functions; and (c) adopt a multi-metric approach for model evaluation. The SWAT model was coupled with a relatively newer and powerful Borg MOEA. The inclusion of hydrological signatures as objective functions along with the conventional statistical functions assisted in improving the performance for low flows by 135% in terms of volume efficiency and 65% for flow time series simulation.