Increasing flood losses over the last decades emphasize the need towards significantly improved and more efficient flood risk management. One key requirement is reliable risk assessment in conjunction with consistent flood loss modeling. Current risk assessments and flood loss estimations for Europe are until now based on regional approaches using deterministic depth-damage function and do rarely report associated uncertainties. To reduce these shortcomings, we present the results of a novel, consistent approach based on the Bayesian Network Flood Loss Estimation MOdel for the private sector (BN-FLEMOps).The dataset is consistent in terms of the input data used to drive the model and because we use the same vulnerability model to derive the flood loss estimation. Essential inputs for any flood loss estimation are hazard (usually water depth), asset (value of objects at risk) and flood experience parameters. The hazard input was given by a European inundation scenario for a continent-wide flood with 100 years return period (Alfieri et al., 2014). Asset values were computed following the the approach by Huizinga et al. (2017) and the flood experience was derived using the database of the Dartmouth Flood Observatory (DFO) (Brakenridge, 2018).The provided dataset comprises a flood loss estimation covering the European continent, spatially aggregated on level three of the standard territorial units for statistics NUTS-3 (https://ec.europa.eu/eurostat/web/nuts/background). The data set reports the summary statistics as a flood loss distribution per NUTS-3 region in 10 per cent quantile steps. The flood loss estimations are given in Million Euro. In addition, the NUTS-3 code, the underlying version of the standard territorial unit and the associated NUTS level are provided. This data publication includes the exact dataset as reported in Lüdtke et al (2019) [filename_1], which is single model application. Supplementary, we provide the summary statistics from an ensemble of 1000 model runs to account for the inherent variability of the probabilistic model [filename_2]. The ensemble model application reports the same statistical measures as the single model application (flood loss distribution per NUTS-3 region in 10 per cent quantile steps), but the given numbers show the median of 1000 model runs for each quantile step (10%, 20%, … 90%).The dateset is provided as a multi-polygon vector. All polygons that belong to the same standard territorial unit share the same attributes. The spatial reference system is defined by EPSG:4326. We provide two formats, (I) an ESRI shape file and (ii) a GEOjson representation. For more information please refer to the associated data description.
This dataset comprises time series of 6-hourly surges and the daily streamflow records simulated from hydrodynamic-hydrologic modelling to quantify the compound effects of surges and peak river discharge over northwestern Europe. We simulate the surge height (m) and river discharge (m3/s) at the vicinity of the coast in the reference (1981–2005) and projected (2040–2069) periods using time series of high-resolution (0.11⁰, which is about 12 km) regional dynamically downscaled meteorological forcings from the World Climate Research Program CORDEX (COordinated Regional Climate Downscaling EXperiment) framework (Nikulin et al., 2011) (https://esg-dn1.nsc.liu.se/search/esgf-liu/) for Europe, forced by five host (or parent)-GCMs from the CMIP5 project. Given data availability, we use meteorological forcing dataset from SHMI’s Rossby Centre regional atmospheric model (RCA4; Strandberg et al., 2015) driven by five host GCMs participating in CMIP5, i.e., CNRM-CERFACS-CNRM-CM5, ICHEC-EC-EARTH, IPSL-IPSL-CM5A-MR, MOHC-HadGEM2-ES, and MPI-M-MPI-ESM-LR. For each host GCM, the first ensemble member (r1i1p1) of climate realization has been used except the ICHEC-EC-EARTH model, r12i1p1 realization has been used. All simulations have the same physical version (p1) and initialization method (i1) but differ in initial states (i.e., r1 and r12). After 2005, the future scenarios diverge, and we investigate projected change in compound flood climatology during 2040 – 2069 using business as usual RCP8.5 scenario to cover extremes. While we simulate surge at 33 tide gauges using hydrodynamic model Delft3D (Delft3D-FLOW, 2014), the simulation of discharge from 39 stream gauges is performed using the global hydrological and water use model, WaterGAP 2.2d (Müller Schmied et al., 2014). Since we are mostly interested in the meteorological phenomena that drive the compound flood mechanism, we focus on modeling of surges and do not simulate tides. The individual datasets of the surge and discharge time series for each host GCMs in the GCM-RCM chains are available in the sub-folders ‘Discharge’ and ‘Surge’ under the zip-file ‘CF_drivers’.