A multi-criteria approach for improving streamflow prediction in a rapidly urbanizing data scarce catchment

This study advocates a multi-criteria approach to improve the streamflow predictions in a data-scarce catchment of Chennai metropolitan city of India using the Soil Water and Assessment Tool (SWAT). The remotely sensed evapotranspiration (ET) data, groundwater recharge estimation, and parameter regionalization were used to improve model prediction. Dynamic change of Land Use and Land Cover (LULC) was accounted for along with multi-parameter calibration for reducing the uncertainty in model parameters. The results revealed an improved streamflow prediction accuracy by 10%, especially in the prediction of medium and high flows with the Nash-Sutcliffe efficiency of 0.60. The enhanced parameters were regionalized to ungauged sub-basins and validated using a measured flow event downstream of regionalization with 15% prediction uncertainty. This semi-arid catchment is dominated by ET (58%) and runoff (27%) in the region's hydrology. The finding of this study can be applied to improve the hydrological modelling and predictions in data-scarce regions.