Estimation of Parameter Uncertainty in the HBV Model

Abstract

Usually the HBV model is calibrated by seeking one optimal parameter set that represents the catchment. From experience we know, however, that it is hardly possible to find an unique parameter set. This is because of errors in both the model structure and the observed variables and because of interactions between the different model parameters. Therefore, there may be many sets of parameters which give similar good results during a calibration period, but their predictions may differ when simulating runoff in the future. In this study a Monte Carlo procedure was used to assess the uncertainty of the parameter estimation and to describe differences in this uncertainty for the various parameters. A fuzzy measure of model goodness was introduced to allow combination of different objective functions. Only a few of the parameters were well-defined, whereas for most parameters good results could be obtained over large ranges. Tentatively an indication of the uncertainty in model predictions arising from the uncertainty in the parameterization was given by viewing the predictions of runoff during two periods.