Comment on "Hydrological forecasting uncertainty assessment: incoherence of the GLUE methodology" by Pietro Mantovan and Ezio Todini
Comment on "Hydrological forecasting uncertainty assessment: incoherence of the GLUE methodology" by Pietro Mantovan and Ezio Todini
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DOI：
10.1016/j.jhydrol.2007.02.023
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发表时间：
2007-05
影响因子：
6.4
通讯作者：
K. Beven;Paul Smith;J. Freer
中科院分区：
文献类型：
--
作者：
K. Beven;Paul Smith;J. Freer
这一评论是对[Mantovan，P.，Todini，E.，2006.水文预报不确定性评估：GLUE方法的不一致性，J.水文学，2006年]。在这篇评论中，它表明，正式的贝叶斯识别模型是一个特殊的情况下，可以使用的胶，其中建模者准备作出非常强的假设建模误差的性质。对于Mantovan和Todini的假设性研究，假设正式贝叶斯识别的确切假设是已知的，但在将GLUE应用于相同数据时被忽略。我们发现，一个更合理的应用GLUE这个问题，使用类似的先验知识，给出了同样连贯的结果，正式的贝叶斯识别。在真实的应用中，受输入和模型结构误差的影响，建议MT 06的一致性条件不能保持在单观测水平，并且正式贝叶斯似然函数的选择可能不一致。在这些（更有趣的）情况下，GLUE可以在基于数据块的似然度量的应用中是一致的，但是度量和块的不同选择有效地表示了关于具有输入和模型结构错误的真实的应用中的数据的信息内容的不同信念。
This comment is a response to the criticisms of the GLUE methodology by [Mantovan, P., Todini, E., 2006. Hydrological forecasting uncertainty assessment: Incoherence of the GLUE methodology, J. Hydrology, 2006]. In this comment it is shown that the formal Bayesian identification of models is a special case of GLUE that can be used where the modeller is prepared to make very strong assumptions about the nature of the modelling errors. For the hypothetical study of Mantovan and Todini, exact assumptions were assumed known for the formal Bayesian identification, but were then ignored in the application of GLUE to the same data. We show that a more reasonable application of GLUE to this problem using similar prior knowledge shows that gives equally coherent results to the formal Bayes identification. In real applications, subject to input and model structural error it is suggested that the coherency condition of MT06 cannot hold at the single observation level and that the choice of a formal Bayesian likelihood function may then be incoherent. In these (more interesting) cases, GLUE can be coherent in the application of likelihood measures based on blocks of data, but different choices of measures and blocks effectively represent different beliefs about the information content of data in real applications with input and model structural errors.