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A Bayesian method for missing rainfall estimation using a conceptual rainfall–runoff model
Key laboratory of Regional Sustainable Development Modeling, Institute of Geographical Sciences and Natural Resource Research, Chinese Academy of Sciences, Beijing, People’s Republic of China.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Architecture and Water. Unit of Environmental Engineering, University of Innsbruck.ORCID iD: 0000-0003-0367-3449
Université de Lyon, INSA Lyon, DEEP, Villeurbanne, France.
Université de Lyon, INSA Lyon, DEEP, Villeurbanne, France.
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2017 (English)In: Hydrological Sciences Journal, ISSN 0262-6667, E-ISSN 2150-3435, Vol. 62, no 15, 2456-2468 p.Article in journal (Refereed) Published
Abstract [en]

The estimation of missing rainfall data is an important problem for data analysis and modelling studies in hydrology. This paper develops a Bayesian method to address missing rainfall estimation from runoff measurements based on a pre-calibrated conceptual rainfall–runoff model. The Bayesian method assigns posterior probability of rainfall estimates proportional to the likelihood function of measured runoff flows and prior rainfall information, which is presented by uniform distributions in the absence of rainfall data. The likelihood function of measured runoff can be determined via the test of different residual error models in the calibration phase. The application of this method to a French urban catchment indicates that the proposed Bayesian method is able to assess missing rainfall and its uncertainty based only on runoff measurements, which provides an alternative to the reverse model for missing rainfall estimates.

Place, publisher, year, edition, pages
Taylor & Francis, 2017. Vol. 62, no 15, 2456-2468 p.
Keyword [en]
Bayesian method; conceptual ra infall–runoff model; missing rainfall data; rainfall estimates; uncertainty
National Category
Other Civil Engineering Water Engineering Other Environmental Engineering
Research subject
Urban Water Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-66031DOI: 10.1080/02626667.2017.1390317OAI: oai:DiVA.org:ltu-66031DiVA: diva2:1148277
Projects
Assessment and modeling of green infrastructure for urban catchments (2016-2018)
Funder
Swedish Research Council Formas, 2015-778
Note

Validerad;2017;Nivå 2;2017-11-28 (rokbeg)

Available from: 2017-10-10 Created: 2017-10-10 Last updated: 2017-11-28Bibliographically approved

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Publisher's full texthttp://dx.doi.org/10.1080/02626667.2017.1390317

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