Struggling with Epistemic Uncertainties in Environmental Modelling of Natural Hazards

Beven, Keith; Younger, P. M.; Freer, Jim

doi:10.1061/9780784413609.002

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Several recent papers discuss the issue of "deep uncertainty" in environmental modelling and risk assessment (Spiegelhalter and Riesch 2011;Beven et al 2014. This issue resembles the concept of "vague uncertainties" of Budescu and Wallsten (1987) and similar concepts dating back to the 1920s (Knight 1921), and extends the notion of epistemic uncertainty in the model itself to include problems such as unknown inadequacies in the modelling process, and possible disagreements about the framing of the problem.…”

Section: Types and Sources Of Uncertaintymentioning

confidence: 99%

Grappling with uncertainties in physical climate impact projections of water resources

Dankers

Kundzewicz

2020

Climatic Change

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This paper reviews the sources of uncertainty in physical climate impact assessments. It draws on examples from related fields such as climate modelling and numerical weather prediction in discussing how to interpret the results of multi-model ensembles and the role of model evaluation. Using large-scale, multi-model simulations of hydrological extremes as an example, we demonstrate how large uncertainty at the local scale does not preclude more robust conclusions at the global scale. Finally, some recommendations are made: climate impact studies should be clear about the questions they want to address, transparent about the uncertainties involved, and honest about the assumptions being made.

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Section: Types and Sources Of Uncertaintymentioning

confidence: 99%

Grappling with uncertainties in physical climate impact projections of water resources

Dankers

Kundzewicz

2020

Climatic Change

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Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores

Knoben

Freer

Woods

2019

Hydrol. Earth Syst. Sci.

830

388

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Abstract. A traditional metric used in hydrology to summarize model performance is the Nash–Sutcliffe efficiency (NSE). Increasingly an alternative metric, the Kling–Gupta efficiency (KGE), is used instead. When NSE is used, NSE = 0 corresponds to using the mean flow as a benchmark predictor. The same reasoning is applied in various studies that use KGE as a metric: negative KGE values are viewed as bad model performance, and only positive values are seen as good model performance. Here we show that using the mean flow as a predictor does not result in KGE = 0, but instead KGE =1-√2≈-0.41. Thus, KGE values greater than −0.41 indicate that a model improves upon the mean flow benchmark – even if the model's KGE value is negative. NSE and KGE values cannot be directly compared, because their relationship is non-unique and depends in part on the coefficient of variation of the observed time series. Therefore, modellers who use the KGE metric should not let their understanding of NSE values guide them in interpreting KGE values and instead develop new understanding based on the constitutive parts of the KGE metric and the explicit use of benchmark values to compare KGE scores against. More generally, a strong case can be made for moving away from ad hoc use of aggregated efficiency metrics and towards a framework based on purpose-dependent evaluation metrics and benchmarks that allows for more robust model adequacy assessment.

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Struggling with Epistemic Uncertainties in Environmental Modelling of Natural Hazards

Cited by 2 publications

References 14 publications

Grappling with uncertainties in physical climate impact projections of water resources

Grappling with uncertainties in physical climate impact projections of water resources

Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores

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