Bayesian inference of synthetic daily rating curves by coupling Chebyshev Polynomials and the GR4J model

Navas, Rafael; Gamazo, Pablo; Vervoort, R. Willem

doi:https://doi.org/10.5194/piahs-385-399-2024

Articles | Volume 385

https://doi.org/10.5194/piahs-385-399-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/piahs-385-399-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 385

Post-conference publication

|

19 Apr 2024

Post-conference publication |

| 19 Apr 2024

Bayesian inference of synthetic daily rating curves by coupling Chebyshev Polynomials and the GR4J model

Rafael Navas, Pablo Gamazo, and R. Willem Vervoort

Data sets

Water Data Online Bureau of Meteorology http://www.bom.gov.au/waterdata/

SILO - Australian climate data from 1889 to yesterday Queensland Government https://www.longpaddock.qld.gov.au/silo/

Model code and software

Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME Karline Soetaert and Thomas Petzoldt https://doi.org/10.18637/jss.v033.i03

Short summary

It’s difficult to estimate the daily discharge of a river when only one instantaneous level record is available per day. This work proposes to estimate synthetic daily rating curves from nearby gauged locations using a rainfall-runoff model and bayesian inference. The results can help provide a more comprehensive understanding of the hydrological functioning of systems where only one instantaneous stage level per day is available.