Topic
GLUE
About: GLUE is a research topic. Over the lifetime, 2702 publications have been published within this topic receiving 21124 citations.
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TL;DR: The GLUE procedure works with multiple sets of parameter values and allows that, within the limitations of a given model structure and errors in boundary conditions and field observations, different sets of values may be equally likely as simulators of a catchment.
Abstract: This paper describes a methodology for calibration and uncertainty estimation of distributed models based on generalized likelihood measures. The GLUE procedure works with multiple sets of parameter values and allows that, within the limitations of a given model structure and errors in boundary conditions and field observations, different sets of values may be equally likely as simulators of a catchment. Procedures for incorporating different types of observations into the calibration; Bayesian updating of likelihood values and evaluating the value of additional observations to the calibration process are described. The procedure is computationally intensive but has been implemented on a local parallel processing computer.
4,146 citations
TL;DR: The generalised likelihood uncertainty estimation (GLUE) methodology for model identification allowing for equifinality is described, and an example application to rainfall-runoff modelling is used to illustrate the methodology, including the updating of likelihood measures.
1,977 citations
TL;DR: This paper addresses the problem of evaluating the predictive uncertainty of TOPMODEL using the Bayesian Generalised Likelihood Uncertainty Estimation (GLUE) methodology in an application to the small Ringelbach research catchment in the Vosges, France.
Abstract: This paper addresses the problem of evaluating the predictive uncertainty of TOPMODEL using the Bayesian Generalised Likelihood Uncertainty Estimation (GLUE) methodology in an application to the small Ringelbach research catchment in the Vosges, France. The wide range of parameter sets giving acceptable simulations is demonstrated, and uncertainty bands are presented based on different likelihood measures. It is shown how the distributions of predicted discharges are non-Gaussian and vary in shape through time and with discharge. Updating of the likelihood weights using Bayes equation is demonstrated after each year of record and it is shown how the additional data can be evaluated in terms of the way they constrain the uncertainty bands.
807 citations
TL;DR: In this article, an uncertainty analysis of the unsteady flow component of the one-dimensional model HEC-RAS within the generalised likelihood uncertainty estimation (GLUE) is presented.
447 citations
TL;DR: The notions of coherence for learning and prediction processes as well as the value of a statistical experiment are introduced and are useful in showing that the GLUE methodology defines a statistical inference process, which is inconsistent and incoherent.
418 citations