Uncertainty analysis of runoff and sedimentation in a forested watershed using sequential uncertainty fitting method

Abstract

The Soil and Water Assessment Tool (SWAT) was implemented in a small forested watershed of the Soan River Basin in northern Pakistan through application of the sequential uncertainty fitting (SUFI-2) method to investigate the associated uncertainty in runoff and sediment load estimation. The model was calibrated for a 10-year period (1991-2000) with an initial 4-year warm-up period (1987-1990), and was validated for the subsequent 10-year period (2001-2010). The model evaluation indices R 2 (the coefficient of determination), NS (the Nash-Sutcliffe efficiency), and PBIAS (percent bias) for stream flows simulation indicated that there was a good agreement between the measured and simulated flows. To assess the uncertainty in the model outputs, p -factor (a 95% prediction uncertainty, 95PPU) and r -factors (average wideness width of the 95PPU band divided by the standard deviation of the observed values) were taken into account. The 95PPU band bracketed 72% of the observed data during the calibration and 67% during the validation. The r -factor was 0.81 during the calibration and 0.68 during the validation. For monthly sediment yield, the model evaluation coefficients ( R 2 and NS ) for the calibration were computed as 0.81 and 0.79, respectively; for validation, they were 0.78 and 0.74, respectively. Meanwhile, the 95PPU covered more than 60% of the observed sediment data during calibration and validation. Moreover, improved model prediction and parameter estimation were observed with the increased number of iterations. However, the model performance became worse after the fourth iterations due to an unreasonable parameter estimation. Overall results indicated the applicability of the SWAT model with moderate levels of uncertainty during the calibration and high levels during the validation. Thus, this calibrated SWAT model can be used for assessment of water balance components, climate change studies, and land use management practices.