Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
doi:10.5194/hess-2016-250
© Author(s) 2016. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
20 Jun 2016
Review status
A revision of this discussion paper was accepted for the journal Hydrology and Earth System Sciences (HESS) and is expected to appear here in due course.
Estimating extreme river discharges in Europe through a Bayesian Network
Dominik Paprotny and Oswaldo Morales Nápoles Department of Hydraulic Engineering, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, 2628 CN Delft, The Netherlands
Abstract. Large-scale hydrological modelling of flood hazard requires adequate extreme discharge data. Models based on physics are applied alongside those utilizing only statistical analysis. The former requires enormous computation power, while the latter are most limited in accuracy and spatial coverage. In this paper we introduce an alternate, statistical approach based on Bayesian Networks (BN), a graphical model for dependent random variables. We use a non-parametric BN to describe the joint distribution of extreme discharges in European rivers and variables describing the geographical characteristics of their catchments. Data on annual maxima of daily discharges from more than 1800 river gauge stations were collected, together with information on terrain, land use and climate of catchments that drain to those locations. The (conditional) correlations between the variables are modelled through copulas, with the dependency structure defined in the network. The results show that using this method, mean annual maxima and return periods of discharges could be estimated with an accuracy similar to existing studies using physical models for Europe, and better than a comparable global statistical method. Performance of the model varies slightly between regions of Europe, but is consistent between different time periods, and is not affected by a split-sample validation. The BN was applied to a large domain covering all sizes of rivers in the continent, both for present and future climate, showing large variation in influence of climate change on river discharges, as well as large differences between emission scenarios. The method could be used to provide quick estimates of extreme discharges at any location for the purpose of obtaining input information for hydraulic modelling.

Citation: Paprotny, D. and Morales Nápoles, O.: Estimating extreme river discharges in Europe through a Bayesian Network, Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-250, in review, 2016.
Dominik Paprotny and Oswaldo Morales Nápoles

Data sets

Pan-European data sets of river flood probability of occurrence under present and future climate
Paprotny, D. and Morales Nápoles, O.
doi:10.4121/uuid:968098ce-afe1-4b21-a509-dedaf9bf4bd5
Dominik Paprotny and Oswaldo Morales Nápoles

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