Water tanks as traditional rainwater harvesting systems for agriculture are widely distributed in South India. They have a strong impact on hydrological processes, affecting streamflow in rivers as well as evapotranspiration. This study aims at an accurate representation of water harvesting systems in a hydrologic model to improve model performance and assessment of the catchment water balance. To this end, spatio-temporal variations of water bodies between the years 2016 and 2018 and the months of January and May 2017 were derived from Sentinel-2 satellite data to parameterize the water tanks (reservoir) parameters in the Soil and Water Assessment Tool (SWAT+) model of the Adyar basin, Chennai, India. Approximately 16% of the basin is covered by water tanks. The initial model performance was evaluated for two model setups, with and without water tanks. The best model run was selected with a multi-metric approach comparing observed and modelled monthly streamflow for 5000 model runs. The final model evaluation was carried out by comparing estimated water body areas by the model and remote sensing observations for January to May 2017. The results showed that representing water tanks in the hydrologic model led to an improvement in the representation of the seasonal variations of streamflow for the whole simulation period (2004–2018). The model performance was classified as good and very good for the calibration (2004–2011) and validation (2012–2018) periods as NSE varies between 0.67 and 0.85, KGE varies between 0.65 and 0.72, PBIAS varies between −24.1 and −23.6, and RSR varies between 0.57 and 0.39. The best fit was shown for the high and middle flow segments of the hydrograph where the coefficient of determination (R2) ranges from 0.81 to 0.97 and 0.75 to 0.81, respectively. The monthly variation of water body areas in 2017 estimated by the hydrologic model was consistent with changes observed in remote sensing surveys. In summary, the water tank parametrization using remote sensing techniques enhanced the hydrologic model's efficiency and applicability for future studies.
