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Cited by 555

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Ignorance is bliss: Or seven reasons not to use uncertainty analysis

Pappenberger, F., Beven, K. J.·2006·Water Resources Research

Abstract

Uncertainty analysis of models has received increasing attention over the last two decades in water resources research. However, a significant part of the community is still reluctant to embrace the estimation of uncertainty in hydrological and hydraulic modeling. In this paper, we summarize and explore seven common arguments: uncertainty analysis is not necessary given physically realistic models; uncertainty analysis cannot be used in hydrological and hydraulic hypothesis testing; uncertainty (probability) distributions cannot be understood by policy makers and the public; uncertainty analysis cannot be incorporated into the decision‐making process; uncertainty analysis is too subjective; uncertainty analysis is too difficult to perform; uncertainty does not really matter in making the final decision. We will argue that none of the arguments against uncertainty analysis rehearsed are, in the end, tenable. Moreover, we suggest that one reason why the application of uncertainty analysis is not normal and expected part of modeling practice is that mature guidance on methods and applications does not exist. The paper concludes with suggesting that a Code of Practice is needed as a way of formalizing such guidance.

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Metadata

Authors
Pappenberger, F., & Beven, K. J.
Venue
Water Resources Research
Published
2006-05
Volume
42
Issue
5
ISSN
  • 0043-1397 (ISSN-L)
  • 1944-7973
OpenAlex
W1594703822
Cited by
555
References
2
FWCI
33.54
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Ignorance is bliss: Or seven reasons not to use uncertainty analysis. https://app.cochat.ai/papers/10.1029/2005wr004820
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Pappenberger, F., & Beven, K. J. (2006). Ignorance is bliss: Or seven reasons not to use uncertainty analysis. Water Resources Research. https://doi.org/10.1029/2005wr004820

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.