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Volume 19, issue 3 | Copyright
Hydrol. Earth Syst. Sci., 19, 1469-1485, 2015
https://doi.org/10.5194/hess-19-1469-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 23 Mar 2015

Research article | 23 Mar 2015

Operational river discharge forecasting in poorly gauged basins: the Kavango River basin case study

P. Bauer-Gottwein1, I. H. Jensen1, R. Guzinski2, G. K. T. Bredtoft1, S. Hansen1, and C. I. Michailovsky3,1 P. Bauer-Gottwein et al.
  • 1Department of Environmental Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
  • 2DHI GRAS, 2970 Hørsholm, Denmark
  • 3now at: Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA

Abstract. Operational probabilistic forecasts of river discharge are essential for effective water resources management. Many studies have addressed this topic using different approaches ranging from purely statistical black-box approaches to physically based and distributed modeling schemes employing data assimilation techniques. However, few studies have attempted to develop operational probabilistic forecasting approaches for large and poorly gauged river basins. The objective of this study is to develop open-source software tools to support hydrologic forecasting and integrated water resources management in Africa. We present an operational probabilistic forecasting approach which uses public-domain climate forcing data and a hydrologic–hydrodynamic model which is entirely based on open-source software. Data assimilation techniques are used to inform the forecasts with the latest available observations. Forecasts are produced in real time for lead times of 0–7 days. The operational probabilistic forecasts are evaluated using a selection of performance statistics and indicators and the performance is compared to persistence and climatology benchmarks. The forecasting system delivers useful forecasts for the Kavango River, which are reliable and sharp. Results indicate that the value of the forecasts is greatest for intermediate lead times between 4 and 7 days.

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