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.
Highlights
Premier researches about modeling uncertainty are mainly about model parameters but with the existence of other uncertainties, such as the Generalized Likelihood Uncertainty Estimation (GLUE) methodology [4] [6], the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA) [7], and the Markov Chain Monte Carlo (MCMC) method [8]
The error coming from XAJ model is assumed as input error of NASH model
In the application of GLUE methodology, it is done by Monte Carlo simulation, using uniform sampling in the specified parameter range
Summary
It is well known that the hydrological processes are very complicated and influenced by climate, weather, geographic and geomorphic conditions, underlying surface conditions and that it is very difficult to obtain the hydrographic features (precipitation, evaporation, discharge etc.) as well as the spatial and temporal. Premier researches about modeling uncertainty are mainly about model parameters but with the existence of other uncertainties, such as the Generalized Likelihood Uncertainty Estimation (GLUE) methodology [4] [6], the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA) [7], and the Markov Chain Monte Carlo (MCMC) method [8]. All these methods were aimed to represent the parameter uncertainty but ignored the influence of other uncertainty factors. The proposed approach is applied to the well-known NASH runoff model considering as case study in Yanduhe basin of Yangtze River, China
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