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Beven, K.J. and Binley, A. (1992) The Future of Distributed Models: Model Calibration and Uncertainty Prediction. Hydrology Processes, 6, 279-298.
https://doi.org/10.1002/hyp.3360060305

has been cited by the following article:

  • TITLE: Parameter Uncertainty Estimation by Using the Concept of Ideal Data in GLUE Approach

    AUTHORS: Junjun Zhu, Hong Du

    KEYWORDS: Ideal Data, GLUE Methodology, Likelihood Measure, NASH Model, Yanduhe Catchment, Uncertainty Principles

    JOURNAL NAME: Journal of Water Resource and Protection, Vol.9 No.1, January 19, 2017

    ABSTRACT: The hydrological uncertainty about NASH model parameters is investigated and addressed in the paper through “ideal data” concept by using the Generalized Likelihood Uncertainty Estimation (GLUE) methodology in an application to the small Yanduhe research catchment in Yangtze River, China. And a suitable likelihood measure is assured here to reduce the uncertainty coming from the parameters relationship. “Ideal data” is assumed to be no error for the input-output data and model structure. The relationship between parameters k and n of NASH model is clearly quantitatively demonstrated based on the real data and it shows the existence of uncertainty factors different from the parameter one. Ideal data research results show that the accuracy of data and model structure are the two important preconditions for parameter estimation. And with suitable likelihood measure, the parameter uncertainty could be decreased or even disappeared. Moreover it is shown how distributions of predicted discharge errors are non-Gaussian and vary in shape with time and discharge under the single existence of parameter uncertainty or under the existence of all uncertainties.