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Application of the generalized likelihood uncertainty estimation (GLUE) approach for assessing uncertainty in hydrological models: a review

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Abstract

The generalized likelihood uncertainty estimation (GLUE) technique is an innovative uncertainty method that is often employed with environmental simulation models. Over the past years, hydrological literature has seen a large increase in the number of papers dealing with uncertainty. There are now a lot of citations to their original paper which illustrates GLUE tremendous impact. GLUE’s popularity can be attributed to its simplicity and its applicability to nonlinear systems, including those for which a unique calibration is not apparent. The GLUE was introduced for use in uncertainty analysis of watershed models has now been extended well beyond rainfall-runoff watershed models. Given the widespread adoption of GLUE analyses for a broad range or problems, it is appropriate that the validity of the approach be examined with care. In this article, we present an overview of the application of GLUE for assessing uncertainty distribution in hydrological models particularly surface and subsurface hydrology and briefly describe algorithms for sampling of the prior parameter in hydrologic simulation models.

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Acknowledgments

The financial support by the High Impact Research Grant of the University of Malaya and Ministry of Education (UM.C/625/1/HIR/61, account number: H-16001-00-D000061) is gratefully acknowledged.

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Authors and Affiliations

  1. Faculty of Engineering and Science, Universiti Tuanku Abdul Rahman, Kuala Lumpur, Malaysia

    Majid Mirzaei & Yuk Feng Huang

  2. Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia

    Ahmed El-Shafie

  3. Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia

    Akib Shatirah

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  1. Majid Mirzaei
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Correspondence to Majid Mirzaei.

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Mirzaei, M., Huang, Y.F., El-Shafie, A. et al. Application of the generalized likelihood uncertainty estimation (GLUE) approach for assessing uncertainty in hydrological models: a review. Stoch Environ Res Risk Assess 29, 1265–1273 (2015). https://doi.org/10.1007/s00477-014-1000-6

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