Large scale modelling of bankfull flow: An example for Europe

SummaryBankfull flow is a relevant parameter in the field of large scale modelling especially for the analysis of environmental flows and flood related hydrological processes. In our case, bankfull flow data were required within the SCENES project in order to analyse ecological important inundation events at selected grid cells of a European raster. In practise, the determination of bankfull flow is a complex task even on local scale. Subsequent to a literature survey of bankfull flow studies, this paper describes a method which can be applied to estimate bankfull flow on a global or continental grid cell raster. The method is based on the partial duration series approach taking into account a 40-years time series of daily discharge data modelled by the global water model WaterGAP. An increasing threshold censoring procedure, a declustering scheme and the generalised Pareto distribution are applied. Modelled bankfull flow values are then validated by different efficiency criteria against bankfull flows observed at gauging stations in Europe. Thereby, the impact of (i) the applied distribution function, (ii) the threshold setting in the partial duration series, (iii) the climate input data and (iv) applying the annual maxima series are evaluated and compared to the proposed approach. The results show that bankfull flow can be reasonably estimated with a high model efficiency (E1 = 0.71) and weighted correlation (ωr2 = 0.90) as well as a systematic overestimation of 22.8%. Finally it turned out that in our study focusing on hydrological extremes, the appliance of the daily climate input data is a basic requirement. While the choice of the distribution function had no significant impact on the final results, the threshold setting in the partial duration series was crucial.

► Bankfull flow was modelled on continental scale applying the partial duration series.
► An increasing threshold censoring procedure was applied.
► The setting of an appropriate threshold is more crucial than the distribution function.
► The partial duration series provided slightly better results than annual maxima.
► Bias corrected, daily climate input data led to more accurate results.