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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 17, issue 11
Hydrol. Earth Syst. Sci., 17, 4415–4427, 2013
https://doi.org/10.5194/hess-17-4415-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 17, 4415–4427, 2013
https://doi.org/10.5194/hess-17-4415-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 08 Nov 2013

Research article | 08 Nov 2013

Considering rating curve uncertainty in water level predictions

A. E. Sikorska1,2, A. Scheidegger1, K. Banasik2, and J. Rieckermann1 A. E. Sikorska et al.
  • 1Swiss Federal Institute of Aquatic Science and Technology (Eawag), Dept. of Urban Water Management, Dübendorf, Switzerland
  • 2Warsaw University of Life Sciences – SGGW, Dept. of Hydraulic Engineering, Warsaw, Poland

Abstract. Streamflow cannot be measured directly and is typically derived with a rating curve model. Unfortunately, this causes uncertainties in the streamflow data and also influences the calibration of rainfall-runoff models if they are conditioned on such data. However, it is currently unknown to what extent these uncertainties propagate to rainfall-runoff predictions. This study therefore presents a quantitative approach to rigorously consider the impact of the rating curve on the prediction uncertainty of water levels. The uncertainty analysis is performed within a formal Bayesian framework and the contributions of rating curve versus rainfall-runoff model parameters to the total predictive uncertainty are addressed. A major benefit of the approach is its independence from the applied rainfall-runoff model and rating curve. In addition, it only requires already existing hydrometric data. The approach was successfully demonstrated on a small catchment in Poland, where a dedicated monitoring campaign was performed in 2011. The results of our case study indicate that the uncertainty in calibration data derived by the rating curve method may be of the same relevance as rainfall-runoff model parameters themselves. A conceptual limitation of the approach presented is that it is limited to water level predictions. Nevertheless, regarding flood level predictions, the Bayesian framework seems very promising because it (i) enables the modeler to incorporate informal knowledge from easily accessible information and (ii) better assesses the individual error contributions. Especially the latter is important to improve the predictive capability of hydrological models.

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