Facets of uncertainty: epistemic uncertainty, non-stationarity, likelihood, hypothesis testing, and communication

By Keith Beven. 2016

297 citations recorded by the source corpus.

ABSTRACT This paper presents a discussion of some of the issues associated with the multiple sources of uncertainty and non-stationarity in the analysis and modelling of hydrological systems. Different forms of aleatory, epistemic, semantic, and ontological uncertainty are defined. The potential for epistemic uncertainties to induce disinformation in calibration data and arbitrary non-stationarities in model error characteristics, and surprises in predicting the future, are discussed in the context of other forms of non-stationarity. It is suggested that a condition tree is used to be explicit about the assumptions that underlie any assessment of uncertainty. This also provides an audit trail for providing evidence to decision makers. Editor D. Koutsoyiannis; Associate editor S. Weijs

Subfields: Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/8b1ebf3f1a141373e72e6743cd74a15412801f54

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

In Water Science and Technology

Citations in the corpus

Groundwater sustainability: a review of the interactions between science and policy (2020, background): “Thus, a fundamental question in water resources decision support is how to present scientific knowledge, which has stirred ample discussion in the literature (Kitanidis 2015, Wheater and Gober 2015, Nearing et al 2016, Beven 2016).”
A philosophical basis for hydrological uncertainty (2016, background): “…and phenomenological uncertainty is not analogous to the distinction between epistemic and aleatory uncertainties defined by either Gong et al. (2013) or Beven (2015), nor is it analogous to the distinction between contextual and phenomenological uncertainties proposed by Kreye et al. (2011).”
Uncertainty in climate change impacts on water resources (2018, background): “In summary, as put by Beven (2016), estimates of uncertainty are conditioned on the assumptions made.”
Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches (2018, methodology): “Blazkova and Beven, 2009; Liu et al., 2009) as well as using different model realization for different periods of a hydrological year (e.g. Choi and Beven (2007) are some of the measures taken to minimize the risk of making Type II errors.”