Articles | Volume 26, issue 6
Hydrol. Earth Syst. Sci., 26, 1695–1711, 2022
Hydrol. Earth Syst. Sci., 26, 1695–1711, 2022
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 et al.

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Cited articles

Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313,, 2017. a, b, c
Addor, N., Newman, A., Mizukami, M., and Clark, M. P.: Catchment attributes for large-sample studies, Boulder, CO, UCAR/NCAR [data set],, 2017b. a
Aguilera, P. A., Fernández, A., Fernández, R., Rumí, R., and Salmerón, A.: Bayesian networks in environmental modelling, Environ. Modell. Softw., 26, 1376–1388,, 2011. a, b, c
Anmala, J., Zhang, B., and Govindaraju, R. S.: Comparison of ANNs and Empirical Approaches for Predicting Watershed Runoff, J. Water Res. Pl., 126, 156–166, 2000. a
Barbarossa, V., Huijbregts, M. A., Hendriks, A. J., Beusen, A. H., Clavreul, J., King, H., and Schipper, A. M.: Developing and testing a global-scale regression model to quantify mean annual streamflow, J. Hydrol., 544, 479–487,, 2017. a, b
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.