Uncertainty assessment of integrated distributed hydrological models using GLUE with Markov chain Monte Carlo sampling

JOURNAL OF HYDROLOGY

卷:353

Article

Blasone, Roberta-Serena¹  Madsen, Henrik²  Rosbjerg, Dan¹ 

[1] Tech Univ Denmark, Dept Environm Engn, DK-2800 Lyngby, Denmark

[2] DHI Water Environm Hlth, DK-2970 Horsholm, Denmark

关键词: uncertainty assessment;    integrated distributed;    hydrological model;    multi-objective calibration;    generalized likelihood;    uncertainty estimation;    MIKE-SHE;    Markov chain Monte Carlo;   

DOI  :  10.1016/j.jhydrol.2007.12.026

来源: Elsevier

【 摘 要 】

In recent years, there has been an increase in the application of distributed, physically-based and integrated hydrological models. Many questions regarding how to property calibrate and validate distributed models and assess the uncertainty of the estimated parameters and the spatially-distributed responses are, however, still quite unexplored. Especially for complex models, rigorous parameterization, reduction of the parameter space and use of efficient and effective algorithms are essential to facilitate the calibration process and make it more robust. Moreover, for these models multi-site validation must complement the usual time validation. In this study, we develop, through an application, a comprehensive framework for multi-criteria calibration and uncertainty assessment of distributed physically-based, integrated hydrological, models. A revised version of the generalized likelihood uncertainty estimation (GLUE) procedure based on Markov chain Monte Carlo sampling is applied in order to improve the performance of the methodology in estimating parameters and posterior output distributions. The description of the spatial variations of the hydrological. processes is accounted for by defining a measure of model performance that includes multiple criteria and spatially-distributed information. An initial sensitivity analysis is conducted on the model to avoid overparameterisation and to increase the robustness of the approach. It is demonstrated that the employed methodology increases the identifiability of the parameters and results in satisfactory multi-variable simulations and uncertainty estimates. However, the parameter uncertainty atone cannot explain the total uncertainty at all the sites, due to limitations in the distributed data included in the model. calibration. The study also indicates that property distributed information of discharge is particularly crucial in model calibration and validation. (C) 2008 Elsevier B.V. All rights reserved.

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