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Development and comparison in uncertainty assessment based Bayesian modularization method in hydrological modeling
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL.
2013 (English)In: Journal of Hydrology, ISSN 0022-1694, E-ISSN 1879-2707, Vol. 486, 384-394 p.Article in journal (Refereed) Published
Abstract [en]

With respect to model calibration, parameter estimation and analysis of uncertainty sources, various regression and probabilistic approaches are used in hydrological modeling. A family of Bayesian methods, which incorporates different sources of information into a single analysis through Bayes' theorem, is widely used for uncertainty assessment. However, none of these approaches can well treat the impact of high flows in hydrological modeling. This study proposes a Bayesian modularization uncertainty assessment approach in which the highest streamflow observations are treated as suspect information that should not influence the inference of the main bulk of the model parameters. This study includes a comprehensive comparison and evaluation of uncertainty assessments by our new Bayesian modularization method and standard Bayesian methods using the Metropolis-Hastings (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions were used in combination with standard Bayesian method: the AR(1) plus Normal model independent of time (Model 1), the AR(1) plus Normal model dependent on time (Model 2) and the AR(1) plus Multi-normal model (Model 3). The results reveal that the Bayesian modularization method provides the most accurate streamflow estimates measured by the Nash-Sutcliffe efficiency and provide the best in uncertainty estimates for low, medium and entire flows compared to standard Bayesian methods. The study thus provides a new approach for reducing the impact of high flows on the discharge uncertainty assessment of hydrological models via Bayesian method.

Place, publisher, year, edition, pages
2013. Vol. 486, 384-394 p.
Keyword [en]
High flows, Uncertainty assessment, Modularization Bayesian, Hydrological model, WASMOD
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:uu:diva-200349DOI: 10.1016/j.jhydrol.2013.02.002ISI: 000317537400032OAI: oai:DiVA.org:uu-200349DiVA: diva2:623612
Available from: 2013-05-28 Created: 2013-05-27 Last updated: 2017-12-06Bibliographically approved

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Xu, Chong-Yu

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CiteExportLink to record
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  • apa
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