Preprints
https://doi.org/10.5194/hess-2021-271
https://doi.org/10.5194/hess-2021-271

  13 Jul 2021

13 Jul 2021

Review status: a revised version of this preprint is currently under review for the journal HESS.

Quantifying the Regional Water Balance of the Ethiopian Rift Valley Lake Basin Using an Uncertainty Estimation Framework

Tesfalem Abraham1,2, Yan Liu2, Sirak Tekleab1, and Andreas Hartmann2,3 Tesfalem Abraham et al.
  • 1Department of Water Resources and Irrigation Engineering, Institute of Technology, Hawassa University, Hawassa, Ethiopia
  • 2Faculty of Environment and Natural Resources, University of Freiburg, Freiburg, Germany
  • 3Department of Civil Engineering, University of Bristol, Bristol, UK

Abstract. In Ethiopia more than 80 % of big freshwater lakes are located in the Rift Valley Lake Basin (RVLB), serving over 15 million people a multipurpose water supply. The basin covers an area of 53,035 km2, and most of the catchments recharging these lakes are ungauged and their water balance is not well quantified, hence limiting the development of appropriate water resource management strategies. Prediction for ungauged basins (PUB) has demonstrated its effectiveness in hydro-climatic data-rich regions. However, these approaches are not well evaluated in climatic data-limited conditions and the consequent uncertainty is not adequately quantified. In this study we use the Hydrologiska Byråns Vattenbalansavdelning (HBV) model to simulate streamflow at a regional scale using global precipitation and potential evapotranspiration products as forcings. We develop and apply a Monte-Carlo scheme to estimate model parameters and quantify uncertainty at 16 catchments in the basin where gauging stations are available. Out of these 16, we use the 14 most reliable catchments to derive the best regional regression model. We use three different strategies to extract possible parameter sets for regionalization by correlating the best calibration parameters, the best validation parameters, and parameters that give the most stable predictions with catchment properties that are available throughout the basin. A weighting scheme in the regional regression accounts for parameter uncertainty in the calibration. A spatial cross-validation is applied multiple times to test the quality of the regionalization and to estimate the regionalization uncertainty. Our results show that, other than the commonly used best-calibrated parameters, the best parameter sets of the validation period provide the most robust estimates of regionalized parameters. We then apply the regionalized parameter sets to the remaining 35 ungauged catchments in the RVLB to provide regional water balance estimations, including quantifications of regionalization uncertainty. The uncertainties of elasticities from the regionalization in the ungauged catchments are higher than those obtained from the simulations in the gauged catchments. With these results, our study provides a new procedure to use global precipitation and evapotranspiration products to predict and evaluate streamflow simulation for hydro-climatically data-scarce regions considering uncertainty. This procedure enhances the confidence to understand the water balance of under-represented regions like ours and supports the planning and development of water resources.

Tesfalem Abraham et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-271', Anonymous Referee #1, 30 Jul 2021
    • AC1: 'Reply on RC1', Tesfalem Abraham, 06 Oct 2021
  • RC2: 'Comment on hess-2021-271', Anonymous Referee #2, 26 Aug 2021
    • AC2: 'Reply on RC2', Tesfalem Abraham, 06 Oct 2021

Tesfalem Abraham et al.

Tesfalem Abraham et al.

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Short summary
In this study we demonstrate the use of global data products for the regionalization of model parameters. We combine three steps of uncertainty quantification from the parameter sampling, best parameter sets identification, and spatial cross-validation. Our results show the best validation parameters provide the most robust regionalization results, and the uncertainties from the regionalization in the ungauged catchments are higher than those obtained from simulations in the gauged catchments.