Parametrization and Uncertainty analysis of SWAT model for streamflow prediction in the Palar sub-basin of Tamil Nadu

For efficient agriculture and flood risk reduction, the Palar sub-basin requires strong water asset management. For hydrological demonstration, A semi-circulated based model, SWAT, was chosen and created in the Palar sub-basin. As part of the validation and calibration process, the Sequential Uncertainty Fitting (SUFI-2) approach was applied to the SWAT CUP model (SWAT-Calibration and Uncertainty Programs).The seven-year observed daily series of inflow data was used to calibrate and validate the SWAT model (2003-2012). The initial three years (2003-2005) were used to set the warm-up period in the SWAT model. The subsequent four years (2006-2009) were used for calibration and the remaining three years (2010-2012) for validation. Two variables, the p-factor, and the r-factor, were used to assess the competence of model validation and calibration. The calculated P-factor and r-factor for validation were 0.84 and 0.24, respectively. The P-factor with high value (0.84) expressed that 84 percent of observed inflow fell within the model uncertainty range (95 PPU). As the r-factor describes the thickness of the uncertainty band, less r-factor of 0.24 indicated the lower predictive uncertainty of the model during the validation stage. The further predictive capability of the model was quantified by PBIAS, RSR, and R2. The presence of high agreement between the observed and simulated stream flow was shown by R2 values of more than 0.8 for calibration (0.81) and validation (0.85). The NS value above 0.7 during calibration (0.75) and validation (0.8) also stated the good performance of the model. PBIAS demonstrated a smaller deviation of simulated values from the observed values by 13.3 and 11.5 % for the calibration and validation phase respectively. The discoveries would be valuable to the hydrological local area, water resource administrators working in Agriculture and soil water protection, policymakers and managing catastrophes like dry spells and floods.