Articles | Volume 14, issue 10
Hydrol. Earth Syst. Sci., 14, 2153–2165, 2010
https://doi.org/10.5194/hess-14-2153-2010
Hydrol. Earth Syst. Sci., 14, 2153–2165, 2010
https://doi.org/10.5194/hess-14-2153-2010

  29 Oct 2010

29 Oct 2010

Analyzing catchment behavior through catchment modeling in the Gilgel Abay, Upper Blue Nile River Basin, Ethiopia

S. Uhlenbrook1,2, Y. Mohamed1,2,3, and A. S. Gragne4 S. Uhlenbrook et al.
  • 1UNESCO-IHE, Department of Water Engineering, P.O. Box 3015, 2601 DA Delft, The Netherlands
  • 2Delft University of Technology, Department of Water Resources, P.O. Box 5048, 2600 GA Delft, The Netherlands
  • 3Hydraulic Research Station, P.O. Box 318, Wad Medani, Sudan
  • 4Jimma University, Department of Water Resources and Environmental Engineering, Jimma, Ethiopia

Abstract. Understanding catchment hydrological processes is essential for water resources management, in particular in data scarce regions. The Gilgel Abay catchment (a major tributary into Lake Tana, source of the Blue Nile) is undergoing intensive plans for water management, which is part of larger development plans in the Blue Nile basin in Ethiopia. To obtain a better understanding of the water balance dynamics and runoff generation mechanisms and to evaluate model transferability, catchment modeling has been conducted using the conceptual hydrological model HBV. Accordingly, the catchment of the Gilgel Abay has been divided into two gauged sub-catchments (Upper Gilgel Abay and Koga) and the un-gauged part of the catchment. All available data sets were tested for stationarity, consistency and homogeneity and the data limitations (quality and quantity) are discussed. Manual calibration of the daily models for three different catchment representations, i.e. (i) lumped, (ii) lumped with multiple vegetation zones, and (iii) semi-distributed with multiple vegetation and elevation zones, showed good to satisfactory model performances with Nash-Sutcliffe efficiencies Reff > 0.75 and > 0.6 for the Upper Gilgel Abay and Koga sub-catchments, respectively. Better model results could not be obtained with manual calibration, very likely due to the limited data quality and model insufficiencies. Increasing the computation time step to 15 and 30 days improved the model performance in both sub-catchments to Reff > 0.8. Model parameter transferability tests have been conducted by interchanging parameters sets between the two gauged sub-catchments. Results showed poor performances for the daily models (0.30 < Reff < 0.67), but better performances for the 15 and 30 days models, Reff > 0.80. The transferability tests together with a sensitivity analysis using Monte Carlo simulations (more than 1 million model runs per catchment representation) explained the different hydrologic responses of the two sub-catchments, which seems to be mainly caused by the presence of dambos in Koga sub-catchment. It is concluded that daily model transferability is not feasible, while it can produce acceptable results for the 15 and 30 days models. This is very useful for water resources planning and management, but not sufficient to capture detailed hydrological processes in an ungauged area.

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