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34 results for “flood mapping”
Aqueduct Floods Hazard Maps extract for Tanzania
<p>This contains an extract from the <a href="https://www.wri.org/resources/data-sets/aqueduct-floods-hazard-maps">WRI Aqueduct global open flood dataset</a>,<br> Version 2 (updated October 20, 2020), showing flood depth in metres for<br> coastal and river flooding under both current baseline conditions and future<br> projections in 2030, 2050, and 2080.</p> <p>The data has been clipped to a bounding box around Tanzania (29.321032<br> -11.731272 40.449392 -0.98583).</p> <p>Please see the <a href="https://www.wri.org/publication/aqueduct-floods-methodology">Technical Note</a> for further details of the full dataset and<br> methodology used to create it.</p> <p>License: Creative Commons Attribution 4.0 International License. Full license<br> text available at <a href="http://www.wri.org/publications/permissions-licensing">Creative Commons Attribution 4.0</a></p> <p> </p>
Rapid mapping of flood inundation by deep learning-based image super-resolution
<div> <div># Rapid mapping of flood inundation by deep learning-based image super-resolution</div> <div># Developer: Wenke Song</div> <div># The University of Hong Kong</div> <div># Contact email: songwk@connect.hku.hk</div> <div># MIT License</div> <div># Copyright (c) 2024 songwk0924</div> <div> </div> <div>There are two folders in the compressed file: Trained_model and Test_cases:</div> <div>(1) Trained_model</div> <div> model_d_DenseUnet.pth, for predicting the maximum water depth;</div> <div> model_v_DenseUnet.pth, for predicting the maximum velocity.</div> <div> </div> <div>(2) Test_cases</div> <div> Test_d_r1.npy, Test_d_r2.npy, Test_d_r3.npy: Input features for predicting maximum water depth of rainfall events r1-r3;</div> <div> Test_v_r1.npy, Test_v_r2.npy, Test_v_r3.npy: Input features for predicting maximum velocity of rainfall events r1-r3; <div> </div> </div> <div> bathy_mat_5m_0p.csv: Elevation data to create mask layer;</div> <div> Fine_grid_flood_maps (2DSWEs):</div> <div> hmax_r1.asc, hmax_r2.asc, hmax_r3.asc, maximum water depth simulated by 2DSWEs of rainfall events r1-r3;</div> <div> velmax_r1.asc, velmax_r2.asc, velmax_r3.asc, maximum velocity simulated by 2DSWEs of rainfall events r1-r3;</div> <div> </div> <div><span>The aforementioned data will be used as input for model prediction (Prediction.py). </span></div> <div><a name="OLE_LINK902"></a><a name="OLE_LINK909"></a><a href="https://github.com/songwk0924/Flood-inundation-mapping"><span>https://github.com/songwk0924/Flood-inundation-mapping</span></a></div> </div>
Data analysis of article research tittle "Online GIS and Remote Sensing-Based Mapping of Flood Vulnerability in Samarinda Seberang Subdistrict"
<p>This dataset contains an explanation of data analysis for creating a flood vulnerability map of Samarinda Seberang District. The dataset contains sub-criteria for each flood parameter and its score value. In addition, this dataset contains the weight value of each parameter, flood vulnerability level and its coloring, and the results of calculating the area of each vulnerability level.</p>
Flood Hazard Maps using Google Earth Engine: Thrace and Thessaly River Basin Districts (Greece)
<p>This dataset contains three raster files with a spatial resolution of 10 m, derived by the Google Earth Engine:</p> <p> </p> <p>1) DynamicWorld_Floods_2015_2023.tif: Number of days flooded for the River Basin District of Thrace (Greece) starting from 2015 until 2023</p> <p>2) Thessaly_2015_August2023.tiff: Number of days flooded for the River Basin District of Thessaly (Greece) starting from 2015 until August 2023</p> <p>3) Thessaly_2015_now.tiff: Number of days flooded for the River Basin District of Thessaly (Greece) starting from 2015 until January 2024</p> <p> </p>
Interactive maps: Unveiling hidden risks in healthcare from flood-induced transportation disruption
<p>We provide interactive maps in HTML format, viewable in standard web browsers, to illustrate the impact of flood events (indicated by the index in the title) on specific regions, focusing on hospitals at risk. This collection includes 321 maps, each representing a hospital where the change in service population exceeds 0.3. Hovering over the hospital symbols (marked with a plus) reveals meta information, including service population details before and after a flood event. Additionally, hovering over the polygons provides further insights into the hospital in focus, offering a detailed view of each facility’s status and impact zone.</p>
Urban pluvial flood maps under different green cover scenarios
<p>This dataset provides pluvial flood water depth maps for the cities of Logroño, Spain; Gdynia, Poland; Milan Italy; and Athens Greece as a part of the REACHOUT project. The maps are generated using a Pluvial Flood Tool for different return periods estimated based on observations and EURO-CORDEX future climate change scenarios (Logroño only) under different nature-based green cover scenarios, depending on the city.</p> <p>Technical Info</p> <p>The pluvial flood hazard maps are generated for each event using rainfall intensity as input for the hydrostatic inundation model SaferRAIN (Samela et al., 2020). This is a simplified raster-based model based on a hierarchical filling and spilling algorithm, identifying inundated areas on the basis of high-resolution digital elevation model. It accounts for spatially distributed rainfall input and infiltration, building upon the pixel-based Green-Ampt model (Green and Ampt, 1911). It is suitable for applications over large urban areas.</p> <p>Rainfall input for the pluvial flood model is computed for return periods (RPs) of 2-, 5-, 10-, 25-, 50-, 100-, 200-years based on the historical rainfall data. Different datasets have been utilized in various cities to tailor the analysis to their specific needs. More specifically:</p> <ul> <li> <p>In the city of Gdynia, historical local station data (Climate data IMGW 1960-2021: https://danepubliczne.imgw.pl/) are used to estimate RPs and assess different precipitation events. </p> </li> </ul> <ul> <li> <p>For the cities of Milan and Athens, 2.2-km ERA5 downscaled data are employed to assess historical precipitation events under different RPs (Essenfelder et al., 2021). </p> </li> <li> <p>In the city of Logroño, historical local station data (SOS-Logroño precipitation data 1999-2022: https://www.larioja.org/emergencias-112/es/meteorologia/datos-actuales-rioja/detalle-estacion?homepage=9&cod_muni=89) are used to estimate RPs and assess different precipitation events. Additionally, here, future climate change projections have been analyzed. These projections are based on the precipitation Intensity-Duration-Frequency (IDF) curves computed from the EURO-CORDEX data (REF to zenodo dataset, Pal J et al., 2024). Observations are then scaled according to the changes simulated between future and historical scenarios, using the median and 90th percentile values estimated from the EURO-CORDEX ensemble.</p> </li> </ul> <p>Different urban green cover maps are used as input for the model to simulate the pluvial flood maps under the current land cover conditions and for different nature-based adaptation scenarios for each city to estimate their benefits. Nature-based adaptation scenarios are the result of codesign processes carried out within REACHOUT, involving local stakeholders, experts and representatives of local administrations. Urban green cover scenarios were identified based on areas that could be converted from built-up areas and concrete surfaces (no water infiltration) to green areas allowing for rainwater infiltration. In addition, during this process, local station precipitation, high-resolution digital elevation model and high-resolution land cover data were collected to configure and run the pluvial flood model.</p> <p>Description of the datase: This dataset contains urban pluvial flood maps for return periods of 2-, 5-, 10-, 25-, 50-, 100-, 200-years for hourly and 15-minute events for different urban green cover scenarios and climate change scenarios depending on the city.</p> <p>Format:</p> <p>The format of this dataset is organized in a ZIP file: PluvialFloodMap_{Cityname}.zip. The zip file is organised into sub-folders, one for each urban green cover scenario, including raster (Tiff) files for the rainfall event associated with each return period.</p> <p>Logrono:</p> <ul> <li> <p>Precipitation events historical: 15-minute events – 9.79 mm (RP2), 13.51mm (RP5), 16.27 mm (RP10), 20.15 mm (RP25), 23.33 mm (RP50), 26.77 mm (RP100), 30.50 mm (RP200)</p> </li> <li> <p>Precipitation events climate change: 15-minute events – CC_Q50 (median): 10.49 mm (RP2), 14.91 mm (RP5), 18.32 mm (RP10), 23.18 mm (RP25), 26.61 mm (RP50), 31.25 mm (RP100), 35.40 mm (RP200): CC_Q90 (90th percentile): 11.83 mm (RP2), 16.76 mm (RP5), 20.96 mm (RP10), 26.87 mm (RP25), 32.27 mm (RP50), 38.99 mm (RP100), 46.65 mm (RP200)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS planned: baseline + additional 4 bioswales/ponds (= 29,850 m3) and a green corridor (5.3 km x 5 m) in the southern part of the city.</p> </li> <li> <p>NBS planned plus: NBS planned scenarios + additional small ponds/rain gardens (depth 0.5 m, 13,500 m3)</p> </li> <li> <p>All Green: baseline + all open spaces converted to green</p> </li> </ul> <p>Milan</p> <ul> <li> <p>Precipitation events historical: 1-hour events – 33.36 mm (RP5), 38.52 mm (RP10), 45.04 mm (RP25), 49.88 mm (RP50), 54.68 mm (RP100)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>DMG_Green Buildings*: baseline + establishment of new green roofs, defined to prioritise economic damage reduction</p> </li> <li> <p>DMG_Green Spaces*: baseline + open, ground spaces converted to green, defined to prioritise economic damage reduction</p> </li> <li> <p>DMG_Green City*: combination of Green Buildings and Green Spaces scenarios, defined to prioritise economic damage reduction</p> </li> <li> <p>POP_Green Buildings*: baseline + establishment of new green roofs, defined to prioritise exposed population reduction</p> </li> <li> <p>POP_Green Spaces*: baseline + open, ground spaces converted to green, defined to prioritise exposed population reduction</p> </li> <li> <p>POP_Green City*: combination of Green Buildings and Green Spaces scenarios, defined to prioritise exposed population reduction</p> </li> </ul> <p>* Each green conversion scenario considers four different incremental conversion percentages: 25%, 50%, 75%, and 100% of all potential green areas.</p> <p>Gdynia</p> <ul> <li> <p>Precipitation events historical: 6-hours events – 24.89 mm (RP2), 35.93 mm (RP5), 43.55 mm (RP10), 53.19 mm (RP25), 60.60 mm (RP100), 75.74 mm (RP200) </p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS: baseline + bioswales/ponds (+ 50,000 m3)</p> </li> <li> <p>All green: baseline + all open spaces converted to green</p> </li> <li> <p>NBS All green: all green + NBS</p> </li> </ul> <p>Athens</p> <ul> <li> <p>Precipitation events historical: 1-hour events – 28.05 mm (RP5), 34.08 mm (RP10), 42.28 mm (RP25), 48.83 mm (RP50), 55.74 mm (RP100)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS: baseline + ponds/rain gardens in existing green spaces (depth 1m) in the northern district of the city</p> </li> <li> <p>All green: baseline + all open spaces (>100 m2) converted to green</p> </li> </ul>
ECFAS Pan-EU Flood Catalogue, D5.4 – Pan-EU flood maps catalogue - ECFAS project (GA 101004211), https://www.ecfas.eu/
<p>The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.</p> <p>The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the <strong>European Commission H2020 Framework Programme</strong> within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services. </p> <p><em><strong>Reference literature:</strong></em></p> <p><em><strong>Le Gal, M., Fernández-Montblanc, T., Duo, E., Montes Perez, J., Cabrita, P., Souto Ceccon, P., Gastal, V., Ciavola, P., and Armaroli, C.: A new European coastal flood database for low–medium intensity events, Nat. Hazards Earth Syst. Sci., 23, 3585–3602, <a href="https://doi.org/10.5194/nhess-23-3585-2023">https://doi.org/10.5194/nhess-23-3585-2023</a>, 2023.</strong></em></p> <p><strong>Description of the Dataset</strong></p> <p>The present database gathers flood and velocity maps for the European Union coast as well as their associated forcing parameters. The coast is divided into geographic regions embracing similar oceanographic conditions and subsequently into coastal sectors. The coastal sectors can be identified by its region index RXXX and its own index CSYYY. For each coastal sector, flood models were developed using the LISFLOOD-FP model with a grid resolution of 100 m. The flood model configuration follows the recommendation highlighted in ECFAS Deliverable D5.2 - Validated LISFLOOD-FP model for coastal areas. The flood and velocity maps are associated with synthetic storms that are characterised by a specific extreme water level and storm duration. These parameters were derived from Extreme Value Analyses performed on the ECFAS ANYEU-SSL hindcast (ECFAS D4.1 - Report on the calibration and validation of hindcasts and forecasts of TWL and D4.3 - Report on the identification of local thresholds of TWL for triggering coastal flooding). Five extreme water level values for each coastal point of the hindcast, and three durations (12, 24 and 36 h) were identified leading to 15 scenarios for each coastal sector. The flood and velocity maps are gathered into a NetCDF file for each coastal sector indicating the scenario parameters as attributes. In addition, the extreme water level values used for each coastal sector are contained in a complementary NetCDF file.</p> <p>The shapefile of the polygons defining the coastal sectors as defined for the catalogue implementation is included in the database.</p> <p><strong>- The ECFAS Flood Catalogue was used to produce the associated ECFAS Pan-EU Impact Catalogue:</strong></p> <p><strong>Impact Catalogue in Zenodo</strong>: Duo, E., Montes Pérez, J., Le Gal, M., Souto Ceccon, P.E., Cabrita, P., Fernández Montblanc, T., and Ciavola, P., 2022. ECFAS Pan-EU Impact Catalogue, D5.4 – Pan-EU flood maps catalogue - ECFAS project (GA 101004211). <a href="http://www.ecfas.eu/">www.ecfas.eu</a> [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.6778864">https://doi.org/10.5281/zenodo.677865</a></p> <p><em><strong>Impact Catalogue Reference literature</strong>: Duo, E., Montes, J., Le Gal, M., Fernández-Montblanc, T., Ciavola, P., and Armaroli, C.: Validated probabilistic approach to estimate flood direct impacts on the population and assets on European coastlines, Nat. Hazards Earth Syst. Sci., 25, 13–39, <a href="https://doi.org/10.5194/nhess-25-13-2025">https://doi.org/10.5194/nhess-25-13-2025</a>, 2025.</em></p> <p> </p> <p>The Flood Catalogue is accompanied by a technical document describing methods, datasets, structure, format and content of the ECFAS Flood and Impact Catalogues:</p> <p>Duo, E., Le Gal, M., Souto Ceccon, P.E., Montes Pérez, J., 2022. <a href="https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e5ee287a5d&appId=PPGMS">Technical document</a> on the ECFAS Flood and Impact Catalogue, D5.4 – Pan-EU flood maps catalogue - ECFAS project (GA 101004211). <a href="http://www.ecfas.eu/">www.ecfas.eu</a></p> <p> </p> <p>This ECFAS <strong>Flood Catalogue</strong> is made available under the <strong>Open Database License</strong>: <a href="http://opendatacommons.org/licenses/odbl/1.0/">http://opendatacommons.org/licenses/odbl/1.0/</a>. Any rights in individual contents of the Flood Catalogue are licensed under the <strong>Open Database License</strong>: <a href="http://opendatacommons.org/licenses/dbcl/1.0/">http://opendatacommons.org/licenses/dbcl/1.0/</a>.</p> <p>This <strong>technical document</strong> describing methods, datasets, structure, format and content of the ECFAS Flood and Impact Catalogues is made available under the <strong>Creative Commons Attribution 4.0 International License</strong>.</p> <p>*The size of the uncompressed dataset is 124 GB.</p> <p> </p> <p><em><strong>Disclaimer:</strong></em></p> <p>ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.</p> <p>This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211</p> <p> </p>
Global coastal flood maps
<p>Global coastal flood maps for the present day 100-year event. </p>
Baltic Sea flood maps under the influence of sea-level rise, dike height increases and managed realignment
<p>The provided data was produced as part of the Ecas-Baltic project (2020 - 2023). The project is funded by the Federal Ministry of Education and Research in Germany (BMBF, funding code 03F0860H).</p><p>The dataset contains information supporting the conclusions presented in the following publication (the final, revised version of the article will be accessible via the journal webpage):</p><p>Kiesel, J., Honsel, L.E., Lorenz, M., Gräwe, U., and Vafeidis, A. T.: Raising dikes and managed realignment may be insufficient for maintaining current flood risk along the German Baltic Sea coast, <a href="https://www.nature.com/commsenv/">Communications Earth & Environment</a>, accepted for publication, 2023.</p><p> </p><p>The dataset contains:</p><p>- the flood maps containing both the maximum flood extent and maximum inundation depth at every grid cell of the coastal inundation model. The flood maps cover two sea-level rise (1 m and 1.5 m) and three adaptation scenarios (state dikes plus 1.5 m, all dikes plus 1.5 m and potential managed realignment sites including state dikes plus 1.5 m)</p><p>- the potential for physically plausible managed realignment sites along the German Baltic Sea coast</p><p>- a readme file containing further information on the datasets and related data and publications</p><p> </p><p>For methodological details we refer the reader to the publication cited above and the publication presenting the modelling setup (Kiesel et al., 2023: https://doi.org/10.5194/nhess-23-2961-2023). The previously mentioned article provides inundation maps representing the current state of adaptation in terms of dike lines and associated elevations (https://doi.org/10.5281/zenodo.7886455). The code to detect the potential physically plausible managed realignment sites is publically available from https://gitlab.com/larsenno/sumare.</p>
Water surface elevations of the base flood for the central Arizona Flood Insurance Rate Maps (FIRM)
Water surface elevations of the base flood as approved by the Federal mergency management Agency (FEMA) for the Flood Insurance Rate Maps (FIRM). The base flood elevation, in feet, is in relation to the National Geodetic Vertical Datum of 1929. Profile baselines are for the Flood Insurance Rate Maps (FIRM). The cross section data are used for the production of Flood Insurance Rate Maps. The Flood Insurance Rate Maps (FIRMs) show different floodplains with different zone designations. These are primarily for insurance rating purposes, but the zone differentiation can be very helpful for other floodplain management purposes. The differentiated floodplain zones are used for the production of Flood Insurance Rate Maps. Maricopa County has been subdivided into FIRM panels for the publication and distribution of FIRMs. Profile baselines are for the Flood Insurance Rate Maps (FIRM).
GPM Ground Validation Global Flood Monitoring System (GFMS) Flood Maps IFloodS V1
The GPM Ground Validation Global Flood Monitoring System (GFMS) Flood Maps IFloodS dataset contains global flood estimates on a 0.25 degree spatial resolution every 3 hours, from March 26, 2013 through June 30, 2013. These data are provided in support of the Iowa Flood Studies (IFloodS) experiment conducted in eastern Iowa. The goals of the IFloodS campaign were to collect detailed measurements of precipitation at the Earth’s surface using ground instruments and advanced weather radars and to simultaneously collect data from satellites passing overhead. The data are available in netCDF-4 and ASCII formats. Flood map and rain graph files are available in KMZ, JPG, and GIF formats.
Spatial scale evaluation of forecast flood inundation maps
<p>Spatial scale evaluation of forecast flood inundation maps, data and code</p> <p>Creator: Helen Hooker[1] Publication Year: 2022</p> <p>Organisation(s): 1. Department of Meteorology, University of Reading, U.K</p> <p>Description: This dataset contains:</p> <p>- Python functions for a new scale-selective approach to forecast flood map evaluation.</p> <p>- SAR-derived observed flood maps used in the study.</p> <p>- JBA Consulting Flood Foresight forecast flood maps used in the study. </p> <p>Helen Hooker. (2022). Spatial scale evaluation of forecast flood inundation maps (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6011882</p> <p>Related publications:</p> <p>Spatial scale evaluation of forecast flood inundation maps; 2022; Journal of Hydrology (in preparation) Helen Hooker[1], Sarah L. Dance[1,2,3], David C. Mason[4], John Bevington[5], and Kay Shelton[5]</p> <ol> <li>Department of Meteorology, University of Reading, UK.</li> <li>Department of Mathematics and Statistics, University of Reading, UK.</li> <li>NCEO, University of Reading, UK.</li> <li>Department of Geography and Environmental Science, University of Reading, UK.</li> <li>JBA Consulting, UK.</li> </ol> <p>Correspondence: Helen Hooker (<a href="mailto:h.hooker@pgr.reading.ac.uk">h.hooker@pgr.reading.ac.uk</a>)</p>
Ensemble flood map spatial verification
<p>Assessing the spatial spread-skill of ensemble flood maps with remote sensing observations, data and code.</p> <p>Creator: Helen Hooker[1] Publication Year: 2022</p> <p>Organisation(s): 1. Department of Meteorology, University of Reading, U.K</p> <p>Description: This dataset contains:</p> <p>- Python functions for ensemble flood map spatial spread-skill evaluation.</p> <p>- SAR-derived observed flood maps used in the study.</p> <p>Helen Hooker. (2022). Ensemble flood map spatial verification (v1.0) [Data set]. Zenodo. https://10.5281/zenodo.6603101</p> <p>Related publications:</p> <p>Assessing the spatial spread-skill of ensemble flood maps with remote sensing observations; 2023; NHESS; Helen Hooker[1], Sarah L. Dance[1,2,3], David C. Mason[4], John Bevington[5], and Kay Shelton[5]</p> <ol> <li>Department of Meteorology, University of Reading, UK.</li> <li>Department of Mathematics and Statistics, University of Reading, UK.</li> <li>NCEO, University of Reading, UK.</li> <li>Department of Geography and Environmental Science, University of Reading, UK.</li> <li>JBA Consulting, UK.</li> </ol> <p>Correspondence: Helen Hooker (<a href="mailto:h.hooker@pgr.reading.ac.uk">h.hooker@pgr.reading.ac.uk</a>)</p>
ECFAS Pan-EU Impact Catalogue, D5.4 – Pan-EU flood maps catalogue - ECFAS project (GA 101004211), www.ecfas.eu
<p>The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.</p> <p>The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the <strong>European Commission H2020 Framework Programme</strong> within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services. </p> <p><em><strong>Reference literature:</strong></em></p> <p><strong><em>Duo, E., Montes, J., Le Gal, M., Fernández-Montblanc, T., Ciavola, P., and Armaroli, C.: Validated probabilistic approach to estimate flood direct impacts on the population and assets on European coastlines, Nat. Hazards Earth Syst. Sci., 25, 13–39, <a href="https://doi.org/10.5194/nhess-25-13-2025">https://doi.org/10.5194/nhess-25-13-2025</a>, 2025.</em></strong></p> <p><em>Montes, J., Duo, E., Souto, P., Gastal, V., Grigoriadis, D., Le Gal, M., Fernández-Montblanc, T., Delbour, S., Ieronymidi, E., Armaroli, C., and Ciavola, P.: Evaluating coastal flood impacts at the EU-scale: the ECFAS approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11295, <a href="https://doi.org/10.5194/egusphere-egu22-11295">https://doi.org/10.5194/egusphere-egu22-11295</a>, 2022.</em></p> <p><strong>Description of the files contained in the Dataset</strong></p> <p>The ECFAS Pan-EU Impact Catalogue collects impact layers associated to the flood scenarios contained in the ECFAS Pan-EU Flood Catalogue. To produce the Flood Catalogue, the coast was divided into geographic regions embracing similar oceanographic conditions, and subsequently into coastal sectors. The coastal sectors can be identified by its region index RXXX and its own index CSYYY. Impacts associated to the flood maps were calculated following the approach described in the technical document of the ECFAS Deliverable 5.3 Algorithms for Impact Assessment (ECFAS Impact Tool; Duo et al., 2021). The ECFAS Impact Tool was adapted to assess the affected population, the damage to buildings, roads and railways and the exposure of a variety of other assets (e.g. agriculture, points of interest, etc.) for the flood scenarios included in the ECFAS Flood Catalogue.</p> <p>The shapefile of the polygons defining the coastal sectors as defined for the catalogue implementation is included in the database.</p> <p><strong>The Pan-EU Impact Catalogue is associated with the following additional ECFAS products: </strong></p> <p><strong>- The ECFAS Pan-EU Flood Catalogue</strong></p> <p><strong>Flood Catalogue in Zenodo</strong>: Le Gal, M., Fernández Montblanc, T., Montes Pérez, J., Duo, E., Souto Ceccon, P.E., Cabrita, P., & Ciavola, P. (2022). ECFAS Pan-EU Flood Catalogue, D5.4 – Pan-EU flood maps catalogue - ECFAS project (GA 101004211), https://www.ecfas.eu/ (1.2) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.6778807">https://doi.org/10.5281/zenodo.6778807</a></p> <p><em><strong>Flood Catalogue Reference literature</strong>: Le Gal, M., Fernández-Montblanc, T., Duo, E., Montes Perez, J., Cabrita, P., Souto Ceccon, P., Gastal, V., Ciavola, P., and Armaroli, C.: A new European coastal flood database for low–medium intensity events, Nat. Hazards Earth Syst. Sci., 23, 3585–3602, <a href="https://doi.org/10.5194/nhess-23-3585-2023">https://doi.org/10.5194/nhess-23-3585-2023</a>, 2023.</em></p> <p><strong>- The ECFAS Impact Tool</strong></p> <p><strong>Impact Tool in Zenodo</strong>: Duo, E., Montes Pérez, J., and Souto-Ceccon, P.E. (2021). ECFAS Impact Tool, D5.3 – Algorithms for impact assessment - ECFAS project (GA 101004211), <a href="http://www.ecfas.eu/">www.ecfas.eu</a>, link: <a href="https://doi.org/10.5281/zenodo.5809296">https://doi.org/10.5281/zenodo.5809296</a></p> <p> </p> <p>The Impact Catalogue is accompanied by a technical document describing methods, datasets, structure, format and content of the ECFAS Flood and Impact Catalogues:</p> <p>- Duo, E., Le Gal, M., Souto Ceccon, P.E., Montes Pérez, J., 2022. <a href="https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e5ee287a5d&appId=PPGMS">Technical document</a> on the ECFAS Flood and Impact Catalogue, D5.4 – Pan-EU flood maps catalogue - ECFAS project (GA 101004211). <a href="http://www.ecfas.eu/">www.ecfas.eu</a></p> <p> </p> <p>This ECFAS <strong>Impact Catalogue</strong> is made available under the <strong>Open Database License</strong>: <a href="http://opendatacommons.org/licenses/odbl/1.0/">http://opendatacommons.org/licenses/odbl/1.0/</a>. Any rights in individual contents of the Impact Catalogue are licensed under the <strong>Open Database License</strong>: <a href="http://opendatacommons.org/licenses/dbcl/1.0/">http://opendatacommons.org/licenses/dbcl/1.0/</a>.</p> <p>The <strong>technical document</strong> describing methods, datasets, structure, format and content of the ECFAS Flood and Impact Catalogues is made available under the <strong>Creative Commons Attribution 4.0 International License</strong>.</p> <p>*The size of the uncompressed dataset is 211 GB.</p> <p><em><strong>Disclaimer:</strong></em></p> <p>ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.</p> <p>This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211</p> <p> </p>
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