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Cited by 763

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Bayesian Estimation of Uncertainty in Runoff Prediction and the Value of Data: An Application of the GLUE Approach

Freer, Jim, Beven, Keith, Ambroise, Bruno·1996·Water Resources Research

Abstract

This paper addresses the problem of evaluating the predictive uncertainty of TOPMODEL using the Bayesian Generalised Likelihood Uncertainty Estimation (GLUE) methodology in an application to the small Ringelbach research catchment in the Vosges, France. The wide range of parameter sets giving acceptable simulations is demonstrated, and uncertainty bands are presented based on different likelihood measures. It is shown how the distributions of predicted discharges are non‐Gaussian and vary in shape through time and with discharge. Updating of the likelihood weights using Bayes equation is demonstrated after each year of record and it is shown how the additional data can be evaluated in terms of the way they constrain the uncertainty bands.

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Metadata

Authors
Freer, J., Beven, K., & Ambroise, B.
Venue
Water Resources Research
Published
1996-07
Volume
32
Issue
7
Pages
2161-2173
ISSN
  • 0043-1397 (ISSN-L)
  • 1944-7973
OpenAlex
W1987017697
Cited by
763
References
1
FWCI
21.46
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Bayesian Estimation of Uncertainty in Runoff Prediction and the Value of Data: An Application of the GLUE Approach. https://app.cochat.ai/papers/10.1029/95wr03723
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Freer, J., Beven, K., & Ambroise, B. (1996). Bayesian Estimation of Uncertainty in Runoff Prediction and the Value of Data: An Application of the GLUE Approach. Water Resources Research. https://doi.org/10.1029/95wr03723

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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.