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Carlo Riverso  - - - 
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J. Szolgay

31 shared publications

Department of Land and Water Resources Management, Faculty of Civil Engineering STU, Bratislava, Slovakia

Peter Valent

9 shared publications

Department of Land and Water Resources Management, Faculty of Civil Engineering, Slovak University of Technology, Radlinského 11, 810 05 Bratislava, Slovakia

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Article 0 Reads 1 Citation Assessment of The Uncertainties of a Conceptual Hydrologic Model By Using Artificially Generated Flows Peter Valent, Ján Szolgay, Carlo Riverso Published: 01 December 2012
Slovak Journal of Civil Engineering, doi: 10.2478/v10189-012-0020-9
DOI See at publisher website ABS Show/hide abstract
Most of the studies that assess the performance of various calibration techniques have to deal with a certain amount of uncertainty in the calibration data. In this study we tested HBV model calibration procedures in hypothetically ideal conditions under the assumption of no errors in the measured data. This was achieved by creating an artificial time series of the flows created by the HBV model using the parameters obtained from calibrating the measured flows. The artificial flows were then used to replace the original flows in the calibration data, which was then used for testing how calibration procedures can reproduce known model parameters. The results showed that in performing one hundred independent calibration runs of the HBV model, we did not manage to obtain parameters that were almost identical to those used to create the artificial flow data without a certain degree of uncertainty. Although the calibration procedure of the model works properly from a practical point of view, it can be regarded as a demonstration of the equifinality principle, since several parameter sets were obtained which led to equally acceptable or behavioural representations of the observed flows. The study demonstrated that this concept for assessing how uncertain hydrological predictions can be applied in the further development of a model or the choice of calibration method using artificially generated data.