Abstract
A few areas in a large watershed might be more critical and responsible for high amount of runoff and soil losses. For an effective and efficient implementation of watershed management practices, identification of these critical areas is vital. In this study, we used the Soil and Water Assessment Tool (SWAT, 2009) to identify and prioritize the critical sub-basins in a highly mountainous watershed with imprecise and uncertain data (Bazoft watershed, southwestern Iran). Three different SWAT models were first developed using different climate input data sets. The first data set (denoted as CRU) was derived from the climate research unit data set developed by the British Atmosphere Data Center (BADC). The second data set (denoted as CDW) was included the climate data obtained from the precipitation and air temperature stations in the study area. The third set (denoted as COM) was a combination of CRU and CDW climate data. The Generalized Likelihood Uncertainty Estimation (GLUE) program was used for calibrating and validating the SWAT model. Daily rainfall, temperature, and runoff data of 20 years (1989-2008) were used in this study. In results, the constructed SWAT model using COM data set simulated the runoff more satisfactorily than the two other developed SWAT models according to the statistical evaluation criteria. The correlation coefficient and Nash-Sutcliff values for the constructed SWAT model using COM data set were 0.40 and 0.38, respectively. The model simulated the runoff satisfactorily; however, the predicted runoff values were much more in agreement with the measured data for the calibration period than those for the validation period. Sub-basins S10, S12, and S13 were assigned as the most top critical sub-basins in runoff production in the watershed. The study revealed that the SWAT model could successfully be used for identifying the critical sub-basins in a watershed with imprecise and uncertain data for management purposes.

Keywords: Runoff, watershed management, SWAT model, GLUE algorithm, uncertainty analysis

References

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