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.
[1] Bergstrom, S.: Development and application of a conceptual runoff model for Scandinavian catchments. SHMI RHO 7, Norrkoping, Sweden, 1976.
[2] Bergstrom, S.: The HBV model - its structure and applications. Report RH No.4, Swedish Meteorological and Hydrological Institute (SHMI) Hydrology, Norrkoping, Sweden, 1992.
[3] Beven, K.J. and Freer, J.: A dynamic TOPMODEL. Hydrol. Process. No. 15, 1993-2011,2001.
[4] Beven, K.J.: Rainfall-runoff modelling: the primer. John Wiley and Sons, 2004, p. 372.
[5] Geem, Z.W.: Music-inspired harmony search algorithm: theory and applications. Springer, Berlin, 2009.
[6] Gupta, H.V., Beven, K.J. and Wagener, T.: Model Calibration and Uncertainty Estimation. In: Anderson, MG. (Ed): Encyclopedia of Hydrological Sciences, John Wiley & Sons, Ltd., Chichester, 2005, p. 2015 - 2031.
[7] Kavetski, D., Franks, S.W. and Kuczera, G.: Confronting input uncertainty in environmental modelling. In Calibration of Watershed Models, Water Science and Application Series, 6, Duan, Q., Gupta, H.V., Sorooshian, S., Rousseau, A.N. and Turcotte, R. (Eds.), American Geophysical Union: Washington, 2003, p. 49-68.
[8] Kavetski, D., Kuczera, G. and Franks, S.W.: Calibration of conceptual hydrological models revisited: 1. Overcoming numerical artefacts. Journal of Hydrology, Vol. 320, No. 1-2, 2006a, p. 173-186.
[9] Kavetski, D., Kuczera, G. and Franks, S.W.: Calibration of conceptual hydrological models revisited: 2. Improving optimisation and analysis. Journal of Hydrology, Vol. 320, No. 1-2, 2006b, p. 187-201.
[10] Klemeš, V: Operational testing on hydrological simulation models. Hydrological Sciences Journal. Vol. 31, 1986, p. 13-24.
[11] Mitchell, M.: An Introduction to Genetic Algorithm, The MIT Press, London, 1996.
[12] Nash, JE. and Sutcliffe, J.V.: River flow forecasting through conceptual models, part 1 - a discussion of principles. Journal of Hydrology, No. 10, 1970, p. 282-290.
[13] Onwubolu, G.C. and Babu, B.V.: Optimization techniques in engineering. Springer, 2004, p. 712.
[14] Rao, S.S.: Engineering Optimization: Theory and Practice (4th ed). Wiley and Sons. Hoboken, New Jersey, 2009, p. 840.
[15] Sekaj, L: Evolučné výpočty a ich využitie v praxi (Evolutionary calculations and their use in practice) (in Slovak). IRIS, Bratislava, 2005.
[16] Sorooshian, S. and Dracup, JA.: Stochastic parameter estimation procedures for hydrológie rainfall-runoff models: correlated and heteroscedastic error cases. Water Resources Research, 16(2), 1980, p. 430^142.
[17] Sorooshian, S., Gupta, VK. and Fulton, J.L.: Evaluation of maximum likelihood parameter estimation techniques for conceptual rainfall-runoff models - influence of calibration data variability and length on model credibility. Water Resources Research, 19(1), 1983, p. 251-259.
[18] Turčan, J.: Empirical regressive model for runoff forecasting in the Bodrog River system (in Slovak). Vodohospodársky časopis, 30 (3), 1982.
[19] Vieux, BE.: Distributed Hydrologie Modeling Using GIS (2nd ed). Louisiana State University, Baton Rouge, LA, USA, 2004.
[20] Wagener, T., Wheater, H., Gupta, H.V: Rainfall-runoff modelling in gauged and ungauged catchments. Imperial College Press, 2004, p. 306.
[21] Weise, T.: Global Optimization Algorithms: Theory and Application (2nd ed). Accessed on: 17.2.2012, Available at: http://www.it-weise.de/projects/book.pdf, 2009.
