Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 26, issue 6
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

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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.
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