Hydrological forecasting uncertainty assessment: Incoherence of the GLUE methodology

By P. Mantovan, Ezio Todini. 2006

456 citations recorded by the source corpus.

Subfields: Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/7935318227a1327f039d4821750559651f605692

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In Environmental Engineering

In Water Science and Technology

Citations in the corpus

Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors (2010, background): “However, if the likelihood and/or priors are misspecified (which, as discussed in this paper, can be detected using posterior diagnostics), the posterior will be in error [also see Mantovan and Todini, 2006; Beven et al., 2008].”
Comparison of joint versus postprocessor approaches for hydrological uncertainty estimation accounting for error autocorrelation and heteroscedasticity (2014, methodology): “We also note the broader debates on the merits of “formal” versus “informal” uncertainty estimation methods [e.g., Mantovan and Todini, 2006; Beven et al., 2008].”
Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches (2018, methodology): “When dealing with nonlinear systems, the classic hydrological approach of using a single set of model parameters may lead to large predictive biases (e.g., Mantovan and Todini, 2006).”
A philosophical basis for hydrological uncertainty (2016, methodology): “…discussion in hydrology about the role (e.g. Pappenberger and Beven 2006, Sivapalan 2009), nature (e.g. Montanari 2007, Koutsoyiannis 2010), and appropriate handling (e.g. Mantovan and Todini 2006, Beven et al. 2008, 2012, Stedinger et al. 2008, Vrugt et al. 2009, Clark et al. 2012) of uncertainty.”
Facets of uncertainty: epistemic uncertainty, non-stationarity, likelihood, hypothesis testing, and communication (2016, background): “There are those who consider that formal statistics is the only way to have an objective estimate of uncertainty in terms of probabilities (e.g. Mantovan and Todini 2006, Stedinger et al. 2008) or that the only way to deal with the unpredictable is as probabilistic variation (Montanari 2007,…”
Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China (2008, methodology): “To our knowledge only a few papers on comparison of different uncertainty analysis techniques are available and they are limited to applications of simple hydrological models (e.g., Makowski et al., 2002; Vrugt et al., 2003; Mantovan and Todini, 2006).”
Generic error model for calibration and uncertainty estimation of hydrological models (2008, background): “The latter has also been criticized for the adoption of ‘‘less formal likelihoods’’, the subjective choice of ‘‘behavioral’’ parameter sets and the lumping of all sources of uncertainty into a single parameter uncertainty, which leads to very wide confidence bounds [Mantovan and Todini, 2006; Kavets”
Evaluation of the subjective factors of the GLUE method and comparison with the formal Bayesian method in uncertainty assessment of hydrological models (2010, unknown): “On the other hand, other studies point out that prediction limits estimated by GLUE can be very different from prediction limits estimated by the Bayesian methods (Montanari, 2005; Mantovan and Todini, 2006; Stedinger et al., 2008).”
Appraisal of the generalized likelihood uncertainty estimation (GLUE) method (2008, background): “One expects the uncertainty intervals for a regression model to become narrower as more observations are available for calibration [Mantovan and Todini, 2006].”
On the colour and spin of epistemic error (and what we might do about it) (2011, methodology): “This has led to significant criticism of the GLUE method (e.g.Mantovan and Todini, 2006; Stedinger et al., 2008); though both of these critical studies are based on hypothetical examples where the model structure is known to be correct so that there is no epistemic uncertainty (see the extension of…”
Bridging the gap between GLUE and formal statistical approaches: approximate Bayesian computation (2013, background): “…oppose incorrect usage of statistics in favor of coherent probabilistic approaches (Gupta et al., 1998; Beven and Young, 2003; Gupta et al., 2003; Christensen, 2004; Montanari, 2005; Mantovan and Todini, 2006; Stedinger et al., 2008; Beven et al., 2008; Beven, 2009; Vrugt et al., 2008b, c).”
Impact of the numbers of observations and calibration parameters on equifinality, model performance, and output and parameter uncertainty (2015, background): “, 2009) and statistical informality of a generalized likelihood function (Freer et al., 1996; Christensen, 2003; Montanari, 2005, 2007; Mantovan and Todini, 2006; Zheng and Keller, 2007; Jia and Culver, 2008; Stedinger et al., 2008; Sadegh and Vrugt, 2013).”
Parameter and modeling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological model (2010, methodology): “Mantovan and Todini (2006) showed the incoherence of GLUE with Bayesian inference using an experiment, where synthetic input and output and a correct formal likelihood function were applied, and they referred it to as pseudo-Bayes.”
Indices for assessing the prediction bounds of hydrological models and application by generalised likelihood uncertainty estimation / Indices pour évaluer les bornes de prévision de modèles hydrologiques et mise en œuvre pour une estimation d'incertitude par vraisemblance généralisée (2009, background): “…et al., 1999; Aronica et al., 2002; Blazkova & Beven, 2002; Montanari, 2005; Heidari et Lihua Xiong et al. Copyright © 2009 IAHS Press 858 al., 2006; Mantovan & Todini, 2006; McMichael et al., 2006; Beven et al., 2008; Blasone et al., 2008a,b; Xiong & O’Connor, 2008; Stedinger et al., 2008).”
Efficient prediction uncertainty approximation in the calibration of environmental simulation models (2008, background): “For example, Pappenberger et al. [2005] assess the convergence of the cumulative distribution functions (CDFs) of various model predictions by comparing the CDFs constructed with an increasing number of behavioral samples.”
Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework (2007, methodology): “…may not properly apply in GLUE in certain cases, because in GLUE, a certain likelihood measure, or essentially an objective function, is used in place of a formal likelihood function that is consistent within the framework of Bayes’ theorem [e.g., Thiemann et al., 2001; Mantovan and Todini, 2006].”