A synthetic groundwater modelling study of the accuracy of GLUE uncertainty intervals

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Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling (2008, methodology): “[3,49,11,42]) as measures that quantify the closeness between model simulations and observations.”
Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling? (2009, methodology): “…the hydrologic literature have criticized GLUE for not being formally Bayesian, resulting in parameter and predictive distributions that are statistically incoherent, unreliable, and that should therefore not be used (Christensen 2004; Montanari 2005; Mantovan and Todini 2006; Vogel et al. 2008).”
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).”
Appraisal of the generalized likelihood uncertainty estimation (GLUE) method (2008, background): “…and Wallach, 2004], water quality models [Smith et al., 2005], acid deposition models [Page et al., 2004], geochemical models [Zak et al., 1997], offshore marine sediment models [Ruessink, 2005], groundwater modeling [Christensen, 2004], wildfire prediction [Bianchini et al., 2006] and others.”