Modelling of Streamflow and Water Balance in the Kuttiyadi River Basin Using SWAT and Remote Sensing/GIS Tools

The current study uses remote sensing derived products, high resolution gridded rainfall and temperature, and SWAT inside a Geographic Information System (GIS) to assess the Kuttiyadi River's hydrological response and water balance components. As Kuttiyadi basin lacks a rainfall monitoring station, making hydrological studies difficult. Thus, we used satellite-based rainfall data as a solution to data shortages in the basin. The basin has separated into 104 numbers of hydrological response units (HRUs) based on unique land use, soil, and slope. The available streamflow data was divided for the calibration (2004–2013) and validation (2014–2017) for the modelling of both daily and monthly streamflow. The simulation of the streamflow was observed to be good on the daily time step ( R 2 = 0.65, NSE = 0.62 and R 2 = 0.62, NSE = 0.60 for the calibration and validation respectively) which is further improved for the monthly time step ( R 2 = 0.90, NSE = 0.80 and R 2 = 0.88, NSE = 0.85 for the calibration and validation respectively). During the monsoon, PBIAS value for the daily validations exceeded from the permissible limit due to the higher fluctuations in the daily streamflow. Our modelling results found that NE monsoon has a greater influence than the SW monsoon, generating almost 75% of total surface runoff in the basin. Study of the basin's water balance indicates that surface runoff is more prevalent, and contributes 35% to annual precipitation. The curve number, hydraulic conductivity of a channel and soil water capacity are highly sensitive parameters which showed rapid changes in land-use and hydraulic conductivity of the mainstream channel owing to the bi-directional interaction of the groundwater with the streamflow. The current study found that the PET and ET were fairly high, and that ET accounted for 24% of the total precipitation. Article Highlights SWAT model was used to calculate streamflow & water balance components. Parameters CN 2 , ALPHA_BF, GWQMN, GE_REVAP, REVAPM, and ESCO were found most sensitive to peak flows. During the monsoon, daily PBIAS values exceeded the permissible limit. SWAT modelling proved potentially robust to understand stream hydrology for long-term management.