In recent years, the importance of continental-scale hazard maps for riverine floods has grown. Nowadays, such maps are used for a variety of research and commercial activities, such as evaluating present and future risk scenarios and adaptation strategies, as well as supporting management plans for national and local flood risk. In this paper we present a new set of high-resolution (100 m) hazard maps for river flooding that covers most European countries, as well as all of the river basins entering the Mediterranean and Black Sea in the Caucasus, the Middle East and northern Africa. The new river flood hazard maps represent inundation along 329 000 km of the river network, for six different flood return periods, expanding on the datasets previously available for the region. The input river flow data for the new maps are produced by means of the hydrological model LISFLOOD using new calibration and meteorological data, while inundation simulations are performed with the hydrodynamic model LISFLOOD-FP. In addition, we present here a detailed validation exercise using official hazard maps for Hungary, Italy, Norway, Spain and the UK, which provides a more detailed evaluation of the new dataset compared with previous works in the region. We find that the modelled maps can identify on average two-thirds of reference flood extent, but they also overestimate flood-prone areas with below 1-in-100-year flood probabilities, while for return periods equal to or above 500 years, the maps can correctly identify more than half of flooded areas. Further verification is required in the northern African and eastern Mediterranean regions, in order to better understand the performance of the flood maps in arid areas outside Europe. We attribute the observed skill to a number of shortcomings of the modelling framework, such as the absence of flood protections and rivers with an upstream area below 500 km² and the limitations in representing river channels and the topography of lowland areas. In addition, the different designs of reference maps (e.g. extent of areas included) affect the correct identification of the areas for the validation, thus penalizing the scores. However, modelled maps achieve comparable results to existing large-scale flood models when using similar parameters for the validation. We conclude that recently released high-resolution elevation datasets, combined with reliable data of river channel geometry, may greatly contribute to improving future versions of continental-scale river flood hazard maps. The new high-resolution database of river flood hazard maps is available for download at https://doi.org/10.2905/1D128B6C-A4EE-4858-9E34-6210707F3C81 (Dottori et al., 2020a).

In this paper we present EMO-5 (“European Meteorological Observations”, spatial resolution of 5 km), a European high-resolution, (sub-)daily, multi-variable meteorological dataset built on historical and real-time observations obtained by integrating data from 18 964 ground weather stations, four high-resolution regional observational grids (i.e. CombiPrecip, ZAMG – INCA, EURO4M-APGD, and CarpatClim), and one global reanalysis (ERA-Interim/Land). EMO-5 includes the following at daily resolution: total precipitation, temperatures (minimum and maximum), wind speed, solar radiation, and water vapour pressure. In addition, EMO-5 also makes available 6-hourly precipitation and mean temperature data. The raw observations from the ground weather stations underwent a set of quality controls before SPHEREMAP and Yamamoto interpolation methods were applied in order to estimate for each 5×5 km grid cell the variable value and its affiliated uncertainty, respectively. The quality of the EMO-5 precipitation data was evaluated through (1) comparison with two regional high-resolution datasets (i.e. seNorge2 and seNorge2018), (2) analysis of 15 heavy precipitation events, and (3) examination of the interpolation uncertainty. Results show that EMO-5 successfully captured 80 % of the heavy precipitation events, and that it is of comparable quality to a regional high-resolution dataset. The availability of the uncertainty fields increases the transparency of the dataset and hence the possible usage. EMO-5 (version 1) covers the time period from 1990 to 2019, with a near real-time release of the latest gridded observations foreseen with version 2. As a product of Copernicus, the EU's Earth Observation Programme, the EMO-5 dataset is free and open, and can be accessed at https://doi.org/10.2905/0BD84BE4-CEC8-4180-97A6-8B3ADAAC4D26 (Thiemig et al., 2020).

Flow–duration curves (FDCs) are essential to support decisions on water resources management, and their regionalization is fundamental for the assessment of ungauged basins. In comparison with calibrated rainfall–runoff models, statistical methods provide data-driven estimates representing a useful benchmark. The objective of this work is the interpolation of FDCs from ~500 discharge gauging stations in the Danube. To this aim we use total negative deviation top-kriging (TNDTK), as multi-regression models are shown to be unsuitable for representing FDCs across all durations and sites. TNDTK shows a high accuracy for the entire Danube region, with overall Nash-Sutcliffe efficiency values computed in a leave-p-out cross-validation scheme (p equal to one site, one-third and half of the sites), all above 0.88. A reliability measure based on kriging variance is attached to each interpolated FDC at ~4000 prediction nodes. The GIS layer of regionalized FDCs is made available for broader use in the region.

For many years, meteorological models have been run with perturbated initial conditions or parameters to produce ensemble forecasts that are used as a proxy of the uncertainty of the forecasts. However, the ensembles are usually both biased (the mean is systematically too high or too low, compared with the observed weather), and has dispersion errors (the ensemble variance indicates a too low or too high confidence in the forecast, compared with the observed weather). The ensembles are therefore commonly post-processed to correct for these shortcomings. Here we look at one of these techniques, referred to as Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). Originally, the post-processing parameters were identified as a fixed set of parameters for a region. The application of our work is the European Flood Awareness System (http://www.efas.eu), where a distributed model is run with meteorological ensembles as input. We are therefore dealing with a considerably larger data set than previous analyses. We also want to regionalize the parameters themselves for other locations than the calibration gauges. The post-processing parameters are therefore estimated for each calibration station, but with a spatial penalty for deviations from neighbouring stations, depending on the expected semivariance between the calibration catchment and these stations. The estimated post-processed parameters can then be used for regionalization of the postprocessing parameters also for uncalibrated locations using top-kriging in the rtop-package (Skøien et al., 2006, 2014). We will show results from cross-validation of the methodology and although our interest is mainly in identifying exceedance probabilities for certain return levels, we will also show how the rtop package can be used for creating a set of post-processed ensembles through simulations.

Continental scale hazard maps for riverine floods have grown in importance in the last years. Nowadays, they are used for a variety of research and commercial activities, such as evaluating present and future risk scenarios and adaptation strategies, as well as a support of national and local flood risk management plans. Here, we present a new set of hazard maps for river flooding that covers most of the geographical Europe and all the river basins entering the Mediterranean and Black Seas in the Caucasus, Middle East and Northern Africa countries. Maps represent inundation along 329’000 km of river network at 100 m resolution, for six different flood return periods. The input river flow data is produced by the hydrological model LISFLOOD, while inundation simulations are performed with the 2D hydrodynamic modelling LISFLOOD-FP. To provide an overview of the skill of the new maps, we undertake a detailed validation exercise of the new maps using official hazard maps for Hungary, Italy, Norway, Spain and the United Kingdom. We find that modelled maps can identify on average two-thirds of reference flood extent, however they also overestimate flood-prone areas for flood probabilities below 1-in-100-year, while for return periods equal or above 500 years the maps can correctly identify more than half of flooded areas. We attribute the observed skill to a number of shortcomings of the modelling framework, such as the absence of flood protections and rivers with upstream area below 500 km², and the limitations in representing river channels and topography of low land areas. In addition, the large variability of reference maps affects the correct identification of the areas for the validation, thus penalizing scores. However, modelled maps achieve comparable results to existing large-scale flood models when using similar parameters for the validation. We conclude that recently released high-resolution elevation datasets combined with reliable data of river channel geometry may greatly contribute to improve future versions of continental-scale flood hazard maps. The database is available for download at https://data.jrc.ec.europa.eu/dataset/1d128b6c-a4ee-4858-9e34-6210707f3c81 (Dottori et al., 2020a).