A Bayesian method for missing rainfall estimation using a conceptual rainfall–runoff model. Issue 15 (18th November 2017)

• Record Type: Journal Article
• Authors: Sun, Siao; Leonhardt, Günther; Sandoval, Santiago; Bertrand-Krajewski, Jean-Luc; Rauch, Wolfgang
• Is Part Of: Hydrological sciences journal. Volume 62:Issue 15(2017)
• Page Start: 2456
• Page End: 2468
• Publication Date: 2017-11-18
• Subjects: Bayesian method -- conceptual rainfall–runoff model -- missing rainfall data -- rainfall estimates -- uncertainty; Hydrology -- Periodicals; 551.4805
• DOI: 10.1080/02626667.2017.1390317
• Languages: English
• ISSNs: 0262-6667

Abstract

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.