Applying non-parametric Bayesian networks to estimate maximum daily river discharge: potential and challenges

Ragno, Elisa; Hrachowitz, Markus; Morales-Nápoles, Oswaldo

doi:https://doi.org/10.5194/hess-26-1695-2022

Articles | Volume 26, issue 6

https://doi.org/10.5194/hess-26-1695-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-26-1695-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 26, issue 6

Research article

|

31 Mar 2022

Research article |

| 31 Mar 2022

Applying non-parametric Bayesian networks to estimate maximum daily river discharge: potential and challenges

Elisa Ragno, Markus Hrachowitz, and Oswaldo Morales-Nápoles

Supplement

https://doi.org/10.5194/hess-26-1695-2022-supplement

Data sets

Catchment attributes for large-sample studies N. Addor, A. Newman, M. Mizukami, and M. P. Clark https://doi.org/10.5065/D6G73C3Q

Short summary

We explore the ability of non-parametric Bayesian networks to reproduce maximum daily discharge in a given month in a catchment when the remaining hydro-meteorological and catchment attributes are known. We show that a saturated network evaluated in an individual catchment can reproduce statistical characteristics of discharge in about ~ 40 % of the cases, while challenges remain when a saturated network considering all the catchments together is evaluated.