Estimation of Parameter Uncertainty in the HBV Model

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.

[1]  L. Braun,et al.  Application of a conceptual runoff model in different physiographic regions of Switzerland , 1992 .

[2]  Nicholas Kouwen,et al.  Watershed modeling using land classifications , 1992 .

[3]  Keith Beven,et al.  The future of distributed models: model calibration and uncertainty prediction. , 1992 .

[4]  A. Jakeman,et al.  How much complexity is warranted in a rainfall‐runoff model? , 1993 .

[5]  K. Beven,et al.  Shenandoah Watershed Study: Calibration of a Topography‐Based, Variable Contributing Area Hydrological Model to a Small Forested Catchment , 1985 .

[6]  M. B. Beck,et al.  Water quality modeling: A review of the analysis of uncertainty , 1987 .

[7]  F. T. Sefe,et al.  Variation of model parameter values and sensitivity with type of objective function , 1982 .

[8]  K. Beven,et al.  On constraining the predictions of a distributed model: The incorporation of fuzzy estimates of saturated areas into the calibration process , 1998 .

[9]  George Kuczera,et al.  Effect of rainfall errors on accuracy of design flood estimates , 1992 .

[10]  K. Beven,et al.  Bayesian Estimation of Uncertainty in Runoff Prediction and the Value of Data: An Application of the GLUE Approach , 1996 .

[11]  J. Harlin,et al.  Parameter uncertainty and simulation of design floods in Sweden , 1992 .

[12]  Ben Chie Yen,et al.  A reliability estimation in modeling watershed runoff with uncertainties , 1990 .

[13]  B. Ambroise,et al.  Multicriterion Validation of a Semidistributed Conceptual Model of the Water Cycle in the Fecht Catchment (Vosges Massif, France) , 1995 .

[14]  S. Sorooshian,et al.  Uniqueness and observability of conceptual rainfall‐runoff model parameters: The percolation process examined , 1983 .

[15]  S. Sorooshian,et al.  Effective and efficient global optimization for conceptual rainfall‐runoff models , 1992 .

[16]  V. Singh,et al.  The HBV model. , 1995 .

[17]  S. Sorooshian,et al.  Automatic calibration of conceptual rainfall-runoff models: The question of parameter observability and uniqueness , 1983 .