Modelling of Tubo Sub- Watershed Hydrological Processes Using GIS and SWAT Model

Main Article Content

I. A. Salami
B. K. Adeogun


This study modeled streamflow at the outlet of the gauged Tubo Dan Mari Watershed and also analyzed the associated uncertainty which could affect the accuracy in estimation of the streamflow. The Soil and Water Assessment Tool (SWAT) model was applied to estimate the streamflow of the Tubo Dan Mari catchment and associated uncertainty with the simulated outputs to that effect. The SWAT model was calibrated for the period of 1983 to 1986 and validated for the period of 1987-1988 based on the six parameters identified during sensitivity analysis. The uncertainty analysis was done by using Sequential Uncertainty Fittings Version 2(SUFI-2) and Generalized Likelihood Uncertainty Estimation (GLUE) was used to check parameter uncertainty, SWAT CUP was used to establish the uncertainty bounds of the model. The calibration and validation of the model were found acceptable as performance rating criteria value of coefficient of correlation (R2) and Nash-Sutcliffe simulation efficiency (ENS) was found to be 0.80 and 0.73 for calibration and 0.81 and 0.50 for validation respectively. In the same order from the model uncertainties analysis the percentage of the simulated data within the uncertainty bound was only 33% for calibration and 29% for validation, which showed that there was uncertainty in the process. After that, SWAT CUP parameter uncertainty was tested and found with ENS value of 0.75 for calibration and 0.71 for validation and this showed that the overall associated uncertainty was from either conceptual or input or a combination of both but not from parameter identification. The average annual inflow volume at the watershed outlet was estimated and predicted to be 2.78575MCM which was in line with other predicted parameters during this study.

Article Details



 Abbaspour (2009). SWAT-CUP2: SWAT Calibration and Uncertainty Programs - A User Manual. Department of Systems Analysis, Integrated Assessment and Modeling (SIAM), Eawag, Swiss Federal Institute of Aquatic Science and Technology, Duebendorf, Switzerland, 95pp.
 Arnold, J.G., Sirinivasan, R., R.S Muttiah, J.R. Williams, (1998).Large area hydrologic modelling and assessment, Part 1: Model development. Journal of the American water resources association, 34(1).
 Arnold, J. G., and N. Fohrer (2012). SWAT 2000: Current capabilities and research opportunities in applied watershed modeling. Hydrol. Process. 19(3): 563-572.
 Beven, K.J. and A.M. Binley (1992). The future of distributed models: model calibration and uncertainty prediction, Hydrological Processes, 6, p.279–298
 Beven and Borah, D.K (2002). Watershed-scale hydrologic and nonpoint-source pollution models: Review of applications. Trans. ASAE 47(3): 789-803.
 Boyle R.F and Beven, K.J. (2000). Rainfall-Runoff Modeling: The Primer, John Wiley & Sons, Ltd., New York, New York.
 Lenhart, T., K. Eckhardt, N. Fohrer, H.-G. Frede (2002). Comparison of two different approaches of sensitivity analysis, Physics and Chemistry of the Earth 27 (2002), Elsevier Science Ltd., 645–654pp.
 Neitsch S.L., J.G. Arnold, J.R. Kiniry, J.R. Williams (2005). Soil and Water Assessment Tool (SWAT) Theoretical Documentation, Version 2005, Grassland Soil and Water Research Laboratory, Agricultural Research Service, Blackland Research Center, Texas Agricultural experiment Station.
 Soil Conservation Service (1972). Section 4: Hydrology In National Engineering Handbook.SCS.
 Srinivasan, M.S., P. Gerald-Marchant, T.L. Veith, W.J. Gburek, and T.S. Steenhuis (2005). Watershed scale modeling of critical source areas of runoff generation and phosphorus transport. J. Amer.Water Resour. Assoc. 41(2): 361-375.
 Van Liew, M.W., J.G. Arnold, and J.D. Garbrecht (2003). Hydrologic simulation on agricultural watersheds: choosing between two models. Trans. ASAE 46(6): 1539-1551.
 Veith, T.L., A.N. Sharpley, J.L. Weld, W.J. Gburek (2005). Comparison of measured and simulated phosphorus losses with indexed site vulnerability. Trans. ASAE 48(2): 557-565.