This data publication contains seismic catalog developed by the analysis of seismicity recorded during hydraulic stimulation campaign performed in May 2020 in the 5.8-km deep OTN-2 well near Helsinki, Finland as part of the St1 Deep Heat project (Kwiatek et al., 2022). The original seismic data to develop the seismic catalog were acquired with the high-resolution seismic network composed of 22 geophones surrounding the project site. The centerpiece of the network was a 10-level borehole array of Geospace OMNI-2400 geophones (3C/15 Hz) sampled at 2 kHz placed in the OTN-3 well adjacent to the OTN-2 injection well, and located at 1.93 - 2.55 km depth, approx. 3km from injection intervals. Additional 12 stations at distances <10 km from project site formed the satellite network that was equipped with short-period 3C 4.5 Hz Sunfull PSH geophones, completing the seismic network. Near-real-time processing of induced seismicity data started on Jan 26, 2020, i.e. about 3 months prior to the onset of the injection, covering entire period of the stimulation campaign in May 2020. The monitoring stopped end of June 2020, about one month after the stimulation finished. The monitoring campaign resulted in initial industrial seismicity catalog containing 6,243 events that was refined and further extended (cf. Kwiatek et al., 2022). The final catalog associated with this data publication contains 6,318 earthquakes, including 197, 5427 and 694 events recorded before, during, and after stimulation campaign. The core catalog data contains origin time, local magnitude, (re)location and focal mechanism data.
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 data set provides a stochastic event set of flood inundation depth maps (fluvial flood hazard footprints) for the German part of the Danube catchment for current and future climate in GEOTIFF format..
The maps provide inundation depth information in cm above ground level on a 100 m grid along the major rivers (4150 km) based on 2D hydro-numeric simulations. Flood event sets are derived for the historical period (1970-1990) and two RCPs (4.5 and 8.5) for the near future (2020-2049) and far future (2070-2099) for four CORDEX models. These flood event sets are created within continuous long-term simulations of a coupled model chain including the IMAGE stochastic multi-variable, multi-site weather generator, the eco-hydrological model SWIM and 1D river network coupled with 2D hydro-numeric hinterland inundation model.
10,000 years of continuous daily simulation of meteorological fields are available for each time period, rcp and climate model. The current version of the flood inundation data sets includes 100 years of simulations. 1D model cross section geometries are based on 10m DEM (BKG), adjustment of dike heights in model calibration. 2D hinterland simulation using LISFLOOD-FP inertia model on a 100m grid resampled from 10 m DEM.
Key usages of the data are large-scale flood risk assessment, future flood risk assessment and flood risk management with long-term perspective. The data have been produced within the OASIS+ demonstrator project 'Future Danube Multi Hazard and Risk Model' funded by Climate-KIC in the period from January 2016 to December 2017.
Climate change manifests in terms of changing frequency and magnitude of extreme hydro-meteorological events and thus drives changes in urban flood hazard. Flood risk oriented urban planning is key to derive smart adaptation strategies, strengthen resilience and achieve sustainable development. 3D city models offer detailed spatial information which is useful to describe the exposure and to characterize the susceptibility of buildings at risk.
This web-based application presents the 3d-city flood damage module (3DCFD) prototype which has been developed and implemented within a pathfinder projected funded by Climate-KIC during 2015-2016. The presentation illustrates the results of the 3DCFD-module exemplarily for the demonstration case in the City of Dresden. Relative damage to residential buildings which results from various flooding scenarios is shown for the focus area Pieschen in Dresden.
The application allows the user to browse through the virtual city model and to colour the residential buildings regarding their relative damage values caused by different flooding scenarios. To do so click on 'Content', then on the brush-icon next to 'Buildings' and select a certain style from the drop-down menu. A style represents a specific combination of loss model and flooding scenario. Flooding scenarios provide spatially detailed inundation depth information according to different water stages at the gauge Dresden. Currently two flood loss models are implemented: a simple stage-damage-function (sdf) which related inundation depth to relative loss and the 3DCFD-module which uses additional information about building characteristics available from the virtual city model. A click on a coloured building will display additional information. The loss estimation module has been developed by the German Research Centre for Geosciences (GFZ), Section Hydrology. The web-application has been developed by virtualcitySYSTEMS GmbH. The data consisting of flood scenarios, a virtual 3D city model, and a terrain model were provided by the City of Dresden.