Comment on "Hydrological forecasting uncertainty assessment: incoherence of the GLUE methodology" by Pietro Mantovan and Ezio Todini

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On the colour and spin of epistemic error (and what we might do about it) (2011, methodology): “GLUE is consistent with hierarchical Bayesian methods in that if an error model component is added and the associated likelihood based on formal statistical assumptions is used, then the results should be the same (Beven et al., 2007, 2008).”
Confronting the Challenge of Modeling Cloud and Precipitation Microphysics (2020, methodology): “…in atmospheric models, Bayesian methods have been used to estimate parameters and quantify uncertainty in land surface and hydrology models (e.g., Beven et al., 2007; Franks & Beven, 1997; Kavetski et al., 2006; Knorr & Kattge, 2005; Raoult et al., 2016; Shi et al., 2015; Smith & Marshall, 2008;…”
Parameter and modeling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological model (2010, background): “Subsequently, Beven et al. (2007) responded that GLUE was not developed to deal with cases for which every observation added information to the conditioning process.”
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;…”