A Bayesian method for missing rainfall estimation using a conceptual rainfall–runoff model

Abstract : 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.
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Journal articles
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https://hal.archives-ouvertes.fr/hal-01758992
Contributor : Jean-Luc Bertrand-Krajewski <>
Submitted on : Thursday, April 5, 2018 - 8:09:52 AM
Last modification on : Thursday, June 13, 2019 - 11:39:20 AM

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Siao Sun, Günther Leonhardt, Santiago Sandoval, Jean-Luc Bertrand-Krajewski, Wolfgang Rauch. A Bayesian method for missing rainfall estimation using a conceptual rainfall–runoff model. Hydrological Sciences Journal, Taylor & Francis, 2017, 62 (15), pp.2456 - 2468. ⟨10.1080/02626667.2017.1390317⟩. ⟨hal-01758992⟩

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