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

Data sets

Catchment attributes for large-sample studies N. Addor, A. Newman, M. Mizukami, and M. P. Clark

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.