On the colour and spin of epistemic error (and what we might do about it)

By Keith Beven, Paul Smith, Andy Wood. 2011

149 citations recorded by the source corpus.

Disinformation as a result of epistemic error is an issue in hydrological modelling. In particular the way in which the colour in model residuals resulting from epistemic errors should be expected to be non-stationary means that it is difficult to justify the spin that the structure of residuals can be properly represented by statistical likelihood functions. To do so would be to greatly overestimate the information content in a set of calibration data and increase the possibility of both Type I and Type II errors. Some principles of trying to identify periods of disinformative data prior to evaluation of a model structure of interest, are discussed. An example demonstrates the effect on the estimated parameter values of a hydrological model.

Subfields: Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/4585900f815fb28c196fff63742e320864a307f4

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Citations in the corpus

Facets of uncertainty: epistemic uncertainty, non-stationarity, likelihood, hypothesis testing, and communication (2016, background): “…which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. uncertainties of this type might be disinformative to the model calibration process (Beven et al. 2011, Beven and Westerberg 2011, Kauffeldt et al. 2013; Fig.”
GLUE: 20 years on (2014, methodology): “The Monte Carlo simulations in Binley et al. (1991), however, helped provide a framework for demonstrating GLUE in BB92. Binley et al. (1991) (and subsequently BB92) constrained their Monte Carlo sampling to 500 realizations even though they adopted a relatively simple distributed model [the Institu”
HESS Opinions : The complementary merits of competing modelling philosophies in hydrology (2017, background): “Together with the largely inevitable errors introduced by data uncertainty (e.g. Beven and Westerberg, 2011; Beven et al., 2011; Renard et al., 2011; Beven, 2013; McMillan et al., 2012; Kauffeldt et al., 2013; McMillan and Westerberg, 2015; Coxon et al., 2015) and insufficient model evaluation and…”
Rating curve estimation under epistemic uncertainty (2015, methodology): “Any uncertainty assessment method should represent the character of the epistemic error at the particular gauging site; but epistemic error is, by definition, of unknown and typically time-varying nature (Beven et al., 2011).”