Regionalisation for lake level simulation – the case of Lake Tana in the Upper Blue Nile, Ethiopia uri icon


  • A relatively small number of studies are presented on Lake Tana's water balance. In most studies the water balance is solved at monthly time step and the water balance is simply closed by runoff contributions from ungauged catchments. Studies partly relied on simple ad-hoc procedures of area comparison to estimate runoff from ungauged catchments. In this study a regional model is developed that relies on principles of similarity of catchments characteristics. For runoff modelling the HBV-96 model is selected while multi-objective model calibration is by a Monte Carlo procedure. We aim to assess the closure term of Lake Tana's water balance, to assess model parameter uncertainty and to evaluate effectiveness of a multi-objective model calibration approach to make hydrological modeling results more plausible.
  • For the gauged catchments, model performance is assessed by the Nash-Sutcliffe coefficient and Relative Volumetric Error and resulted in satisfactory to good performance for six, large catchments. The regional model is validated and indicated satisfactory to good performance in most cases. Results show that runoff from ungauged catchments is as large as 527 mm per year for the simulation period and amounts to approximately 30% of Lake Tana stream inflow. Results of daily lake level simulation over the simulation period 1994-2003 show a water balance closure term of 85 mm per year that accounts to 2.7% of the total lake inflow. Lake level simulations are assessed by Nash Sutcliffe (0.91) and Relative Volume Error (2.71%) performance measures.
  • In this study lake levels of Lake Tana are simulated at daily time step by solving the water balance for all inflow and outflow processes. Since nearly 62% of the Lake Tana basin area is ungauged a regionalisation procedure is applied to estimate lake inflows from ungauged catchments. The procedure combines automated multi-objective calibration of a simple conceptual model and multiple regression analyses to establish relations between model parameters and catchment characteristics.

publication date

  • 2011
  • 2011