Similar to the streamflow parameter calibration in SWAT-CUP, the sensitivity of sediment parameters was assessed by using GSA in SWAT-CUP. Then, the calibration and validation were performed by optimizing the six most sensitive sediment parameters (listed in Table 5) using SUFI-2 in SWAT-CUP. A summary of the statistical performance of SSL simulation for calibration and validation is given in Table 10.

Table 10

Model performance using the GPM P dataset for the prediction of SSL

ModelR2
NSE
PBIAS (%)
RMSE (tons/day)
RSR
TrainTestTrainTestTrainTestTrainTestTrainTest
SWAT-CUP 0.54 0.35 0.52 0.32 −10.35 −11.85 82,243.94 1,70,556.42 0.69 0.82 
SWAT-RF 0.67 0.63 0.67 0.63 1.73 4.12 83,790.21 1,04,196.61 0.57 0.60 
SWAT-ANN 0.74 0.66 0.74 0.65 3.17 3.74 81,808.82 82,729.57 0.50 0.60 
SWAT-SVR 0.51 0.52 0.51 0.52 1.80 1.5 1,05,069.12 1,11,570.7 0.69 0.70 
ModelR2
NSE
PBIAS (%)
RMSE (tons/day)
RSR
TrainTestTrainTestTrainTestTrainTestTrainTest
SWAT-CUP 0.54 0.35 0.52 0.32 −10.35 −11.85 82,243.94 1,70,556.42 0.69 0.82 
SWAT-RF 0.67 0.63 0.67 0.63 1.73 4.12 83,790.21 1,04,196.61 0.57 0.60 
SWAT-ANN 0.74 0.66 0.74 0.65 3.17 3.74 81,808.82 82,729.57 0.50 0.60 
SWAT-SVR 0.51 0.52 0.51 0.52 1.80 1.5 1,05,069.12 1,11,570.7 0.69 0.70 
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