Abstract
Abstract. Large-scale hydrological modelling of flood hazards requires adequate extreme discharge data. In practise, models based on physics are applied alongside those utilizing only statistical analysis. The former require enormous computational power, while the latter are mostly limited in accuracy and spatial coverage. In this paper we introduce an alternate, statistical approach based on Bayesian networks (BNs), 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 representing the geographical characteristics of their catchments. Annual maxima of daily discharges from more than 1800 river gauges (stations with catchment areas ranging from 1.4 to 807 000 km2) were collected, together with information on terrain, land use and local climate. 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 model. Performance of the model varies slightly between regions of Europe, but is consistent between different time periods, and remains the same in a split-sample validation. Though discharge prediction under climate change is not the main scope of this paper, the BN was applied to a large domain covering all sizes of rivers in the continent both for present and future climate, as an example. Results show substantial variation in the influence of climate change on river discharges. The model can be used to provide quick estimates of extreme discharges at any location for the purpose of obtaining input information for hydraulic modelling.
Highlights
There is currently substantial concern in Europe about increasing flood risk linked mainly to climate change
Extreme river discharges calculated using the Bayesian networks (BNs) are compared with observed river discharges
In this paper we presented a first attempt to model extreme river discharges in Europe using BNs
Summary
There is currently substantial concern in Europe about increasing flood risk linked mainly to climate change. The amount of hydrological observations at our disposal is far from sufficient for comprehensive assessments of flood hazard This is the result of the uneven distribution of measurement stations, and of the limited dissemination of data by national or local bodies responsible for their collection. High-resolution historical measurements are critical for calculating hydrological event scenarios for the purpose of delineating flood zones Those scenarios are typically values of extreme river discharge or water level with a certain return period, i.e. the average interval of time between the occurrences of an event with the same magnitude. Such calculation requires long data series, further narrowing the number of locations were such analysis can be performed. There are two primary approaches used to obtain discharge values in ungauged catchments, i.e. catchments for which no discharge measurements are available
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