Abstract. The Generalized Likelihood Uncertainty Estimation (GLUE) method is one of the popular methods for parameter estimation and uncertainty analysis, although it has been criticized for some drawbacks in numerous studies. In this study, we performed an uncertainty analysis for the ORYZA_V3 model using the GLUE method integrated with Latin hypercube sampling (LHS). Different likelihood measures were examined to understand the differences in derived posterior parameter distributions and uncertainty estimates of the model predictions based on a variety of observations from field experiments. The results indicated that the parameter posterior distributions and 95% confidence intervals (95CI) of model outputs were very sensitive to the choice of likelihood measure, as well as the weights assigned to observations at different dates and to different observation types within a likelihood measure. Performance of the likelihood measure with a proper likelihood function based on normal distribution of model errors and the combining method based on mathematical multiplication was the best, with respect to the effectiveness of reducing the uncertainties of parameter values and model predictions. Moreover, only the means and standard deviations of observation replicates were enough to construct an effective likelihood function in the GLUE method. This study highlighted the importance of using appropriate likelihood measures integrated with multiple observation types in the GLUE method. Keywords: GLUE, Likelihood measures, Model uncertainty, Crop model.
Parameter estimation and uncertainty analysis in hydrological modeling
