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| Titel |
Considering rating curve uncertainty in water level predictions |
| VerfasserIn |
A. E. Sikorska, A. Scheidegger, K. Banasik, J. Rieckermann |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 17, no. 11 ; Nr. 17, no. 11 (2013-11-08), S.4415-4427 |
| Datensatznummer |
250085988
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| Publikation (Nr.) |
 copernicus.org/hess-17-4415-2013.pdf |
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| Zusammenfassung |
| 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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