umwelt.info: Search results

Discharge data for flood events at 516 river gauges across Germany (1954–2021), generated using the perfect storm method

This dataset comprises discharge values for all flood events at 516 river gauges across Germany, generated using our perfect storm method. The discharge data were simulated with the mesoscale hydrological model (mHM). In total, the dataset includes 120 scenarios: 110 recombined events and 10 original flood events.

floodsimilarity - a python module to compute the similarity between multiple flood events

floodsimilarity provides classes and methods to conduct a similarity analysis between multiple flood events. The library mainly consists of two parts: (1) algorithms to compute indices and other statistics based on pandas and xarray (2) well-defined data structures for data exchange (e.g. through the Similarity Backend Module) floodsimilarity is used by the Digital Earth Similarity Backend Module (Eggert, 2021) as part of the Digital Earth Flood Event Explorer. It is developed at the GFZ German Research Centre for Geosciences and funded by the Initiative and Networking Fund of the Helmholtz Association through the Digital Earth project.

Risk Estimates from Process-based Regional Flood Model for Germany

This dataset provides risk estimates from the long-term (5000-year) simulations of the process-based Regional Flood Model chain (RFM) developed for Germany (Falter et al. 2015). The 5000-year simulation is run as an ensemble of 50 100-year simulations. Each of those 100-year simulations is referred to as a scenario. The risk estimates are derived in Euros adjusted to prices as of 2018 for all major catchments in Germany – Elbe, Danube, Rhine, Weser and Ems. The dataset consists of the risk estimates for every simulated event at the catchment-level classified according to the sector – private sector (ps), commercial (com) and agriculture (agr). Losses to buildings and contents are estimated for private and commercial sectors. Crop losses are estimated for the agriculture sector. The full description of the RFM along with the derivation of the risk estimates and uncertainty measurement is provided in Sairam et al. (2021).

Flood event and catchment characteristics in Germany and Austria

The dataset comprises a range of variables describing characteristics of flood events and river catchments for 480 gauging stations in Germany and Austria. The event characteristics are asscoiated with annual maximum flood events in the period from 1951 to 2010. They include variables on event precipitation, antecedent catchment state, event catchment response, event timing, and event types. The catchment characteristics include variables on catchment area, catchment wetness, tail heaviness of rainfall, nonlinearity of catchment response, and synchronicity of precipitation and catchment state. The variables were compiled as potential predictors of heavy tail behaviour of flood peak distributions. They are based on gauge observations of discharge, E-OBS meteorological data (Haylock et al. 2008), mHM hydrological model simulations (Samaniego et al., 2010), 4DAS climate reanalysis data (Primo et al., 2019), and the 25x25 m resolution EU-DEM v1.1. A short description of the data processing is included in the file inventory and more details can be found in Macdonald et al. (2022).

Pan-European probabilistic flood loss data for residential buildings

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.

Data collection for a damage assessment after the flash flood in Braunsbach (Germany) in May 2016

A severe flash flood event hit the town of Braunsbach (Baden-Württemberg, Germany) on the evening of May 29, 2016, heavily damaging and destroying several dozens of buildings. It was only one of several disastrous events in Central Europe caused by the low-pressure system “Elvira”. The DFG Research Training Group “Natural hazards and risks in a changing world” (NatRiskChange, GRK 2043/1) at the University of Potsdam investigated the Braunsbach flash flood. In this context damage data for 94 affected buildings, describing building characteristics, the degree of impact and the caused damage, were collected ten days after the flood event and provide the basis for damage assessment studies (Agarwal et al., 2017; Laudan et al., 2017, Vogel et al., 2017).