SWAT model application and prediction uncertainty analysis in the Lake Tana Basin,Ethiopia |
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Authors: | Shimelis G. Setegn Ragahavan Srinivasan Assefa M. Melesse Bijan Dargahi |
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Affiliation: | 1. Hydraulic Engineering Division, Department of Land & Water Resources Engineering, The Royal Institute of Technology (KTH), Stockholm, Sweden;2. Spatial Science Laboratory, Texas A & M University, College Station, TX, USA;3. Department of Environmental Studies, Florida International University, Miami, FL, USA |
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Abstract: | Lake Tana Basin is of significant importance to Ethiopia concerning water resources aspects and the ecological balance of the area. Many years of mismanagement, wetland losses due to urban encroachment and population growth, and droughts are causing its rapid deterioration. The main objective of this study was to assess the performance and applicability of the soil water assessment tool (SWAT) model for prediction of streamflow in the Lake Tana Basin, so that the influence of topography, land use, soil and climatic condition on the hydrology of Lake Tana Basin can be well examined. The physically based SWAT model was calibrated and validated for four tributaries of Lake Tana. Sequential uncertainty fitting (SUFI‐2), parameter solution (ParaSol) and generalized likelihood uncertainty estimation (GLUE) calibration and uncertainty analysis methods were compared and used for the set‐up of the SWAT model. The model evaluation statistics for streamflows prediction shows that there is a good agreement between the measured and simulated flows that was verified by coefficients of determination and Nash Sutcliffe efficiency greater than 0·5. The hydrological water balance analysis of the basin indicated that baseflow is an important component of the total discharge within the study area that contributes more than the surface runoff. More than 60% of losses in the watershed are through evapotranspiration. Copyright © 2009 John Wiley & Sons, Ltd. |
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Keywords: | SWAT Lake Tana hydrological modelling SUFI‐2 GLUE ParaSol |
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