GLUE: 20 years on

By Keith Beven, A. Binley. 2014

323 citations recorded by the source corpus.

This paper reviews the use of the Generalized Likelihood Uncertainty Estimation (GLUE) methodology in the 20 years since the paper by Beven and Binley in Hydrological Processes in (1992), which is now one of the most highly cited papers in hydrology. The original conception, the on‐going controversy it has generated, the nature of different sources of uncertainty and the meaning of the GLUE prediction uncertainty bounds are discussed. The hydrological, rather than statistical, arguments about the nature of model and data errors and uncertainties that are the basis for GLUE are emphasized. The application of the Institute of Hydrology distributed model to the Gwy catchment at Plynlimon presented in the original paper is revisited, using a larger sample of models, a wider range of likelihood evaluations and new visualization techniques. It is concluded that there are good reasons to reject this model for that data set. This is a positive result in a research environment in that it requires improved models or data to be made available. In practice, there may be ethical issues of using outputs from models for which there is evidence for model rejection in decision making. Finally, some suggestions for what is needed in the next 20 years are provided. © 2013 The Authors. Hydrological Processes published by John Wiley & Sons, Ltd.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/d357450f7bf5594d473796b4277ac64fa965fc68

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Citations in the corpus

Facets of uncertainty: epistemic uncertainty, non-stationarity, likelihood, hypothesis testing, and communication (2016, background): “…so that more informal likelihood measures or limits of acceptability might be justified (e.g. within the GLUE framework of Beven 2006a, 2012, Beven and Binley 1992, 2014, Freer et al. 2004, Smith et al. 2008, Liu et al., 2009; and within approximate Bayesian computation by Nott et al. 2012,…”
What Role Does Hydrological Science Play in the Age of Machine Learning? (2020, background): “There has been an enormous amount of attention paid to this topic in the hydrological sciences (e.g., Beven, 2006a, 2009, 2016; Beven & Binley, 2014; Beven et al., 2007, 2008, 2011, 2012; Clark et al., 2011; Kumar, 2011; Mantovan & Todini, 2006; Montanari, 2007; Montanari & Koutsoyiannis, 2012;…”