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34 results for “flood maps”
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 (Pal J et al., 2024 - <a href="https://doi.org/10.5281/zenodo.14035736" target="_blank" rel="noopener">10.5281/zenodo.14035736</a>). 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>Short description of the datase:</p> <p>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>
Indicative distribution map for Ecosystem Functional Group TF1.1 Tropical flooded forests and peat forests
<p>This archive contains indicative distribution maps and profiles for <strong>TF1.1 Tropical flooded forests and peat forests</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Indicative distribution map for Ecosystem Functional Group SF2.2 Flooded mines and other voids
<p>This archive contains indicative distribution maps and profiles for <strong>SF2.2 Flooded mines and other voids</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Flood Hazard Maps and Associated Data for Case Study: Funding rules that promote equity in climate adaptation outcomes
<p>Inundation grids for multiple return periods and multiple scenarios. Please see the underlying study for more details about the methods. The data here can be reproduced following the code and instructions at this repository: https://github.com/CoRE-Lab-UCF/Pollack_et_al_2024/tree/main. Also available here: https://doi.org/10.5281/zenodo.14515896. </p>
Mapping a novel metric for Flash Flood Recovery using Interpretable Machine Learning
<p>This data is supplementary to the paper titled "Mapping a novel metric for Flash Flood Recovery using Interpretable Machine Learning". The file contains the main results.<br><br>For any queries, please visit <a href="https://hydrosense.iitd.ac.in" target="_blank" rel="noopener">Hydrosense Lab (IIT Delhi)</a>.</p>
Flash Flood Risk Map Hallstatt / Gosau / Bad Goisern: Risk Map
Damage Risk Map of FFRM catchment Hallstatt / Gosau / Bad Goisern based on max. water depth in all timesteps, zoning plan, building density and specific damage function
An empirical social vulnerability map for flood risk assessment at global scale ('GlobE-SoVI')
<p>These data were produced as part of the study "An empirical social vulnerability map for flood risk assessment at global scale ('GlobE-SoVI')" (in press in Earth's Future, https://doi.org/10.1029/2023EF003895). We provide raster data at 30 arc seconds spatial resolution (folder 'raster') and vector and table data per administrative unit (folder 'admin') of five social vulnerability variables and the final Global Empirical Social Vulnerability Index (GlobE-SoVI) calculated from the five variables. Please see 'overview_table.pdf' for names and units.</p> <p>The code for data processing and analysis is available at https://github.com/lena-reimann/GlobE-SoVI (https://doi.org/10.5281/zenodo.10671539).</p>
Future water level, discharge, and flood maps under climate change and infrastructure impacts along the Cambodian Mekong.
<p>Baseline and future (2036-2065) river water levels and discharges at 4 gauging stations along the Cambodian Mekong (Kratie, Kampong Cham, Chrouy Changva, and Neak Loeung) under different scenarios of climate change (RCP 4.5 and 8.5) and infrastructural developments. Average depth and duration flood maps are also included for each scenario.</p> <p> </p> <p>A full description of the methods and results can be found in the article: </p> <p>Alexander J. Horton, Nguyen V. K. Triet, Long P. Hoang, Sokchhay Heng, Panha Hok, Sarit Chung, Jorma Koponen, and Matti Kummu. (2022). The Cambodian Mekong floodplain under future development plans and climate change. <em>Nat. Hazards Earth Syst. Sci.</em></p>
Redistribution of the map with the flood prone areas in Flanders (status 2017-07-13)
<p>This is a redistribution of the data source '<a href="http://www.geopunt.be/catalogus/datasetfolder/f5b2c84c-0d78-4efa-a97d-7cd172726572">Overstromingsgevoelige gebieden 2017 - (Watertoets), correctie 13/07/2017</a>', originally published by 'Vlaamse Milieumaatschappij - afdeling Operationeel Waterbeheer' and ‘Departement Mobiliteit en Openbare Werken, afdeling Waterbouwkundig Laboratorium’, and distributed by 'Informatie Vlaanderen' under a CC-BY compatible license. It is redistributed for reproducible, analytical workflows on Flemish Natura 2000 habitats and regionally important biotopes.</p> <p>In the context of the Flemish Water Assay (Watertoets), a fourth version of a map has been created that shows flood prone areas up to the plot level for the entire Flemish Region. The map contains the effectively flood prone areas (‘effectief overstromingsgevoelig’) and the potentially flood prone areas (‘mogelijk overstromingsgevoelig’). </p> <p>In this new version, the effectively flood prone areas were processed with information from new and updated modeled flood areas, in addition to the registered local floods between 2006 and now. These modifications honour the changes to the implementing decision that the Flemish Government approved on 15 May 2017. Unlike previous versions that were raster files, the 2017 version is a vector file.</p> <p>The data source is a coproduction of the Hydraulic Engineering Laboratory of the Department of Mobility and Public Works (Departement Mobiliteit en Openbare Werken, afdeling Waterbouwkundig Laboratorium) and the division Operational Water Management of the Flemish Environmental Agency (Vlaamse Milieumaatschappij - VMM, afdeling Operationeel Waterbeheer), and is owned and administered by the latter.</p>
Fig. 2 in (Hem.: Pentatomidae) in flooded rice crop in Southern Brazil Mapping of spatiotemporal distribution of Tibraca limbativentris Stal
Fig. 2. Interpolation maps by multiquadric equations of spatiotemporal distribution of occurrence categories of Tibraca limbativentris [I = no insect (green), II = adult (red), III = nymphs (pink), IV = adult + nymphs (blue)] in flooded rice crop in Southern Brazil, 2011/2012 crop season. *Thematic maps: (A) 11/19/11 [V4]; (B) 12/03/11 [V6]; (C) 12/17/11 [V8/V9]; (D) 01/07/12 [V11]; (E) 01/21/12 [R1]; (F) 02/02/12 [R5]; (G) 02/15/12 [R9]; (H) 02/29/12 [post-harvest = crop residues destroyed]. Phenological stage according to Counce et al. (2000).
Coastal flood maps and extreme sea levels for the German Baltic Sea coast
<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 also be accessible via the preprint given below):</p> <p>Kiesel, J., Lorenz, M., König, M., Gräwe, U., and Vafeidis, A. T.: A new modelling framework for regional<br> assessment of extreme sea levels and associated coastal flooding along the German Baltic Sea coast,<br> Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2022-275, in review, 2023.</p> <p>The dataset contains:</p> <p>- the location and names of flood boundary stations</p> <p>- the boundary conditions provided by the coastal ocean model at each of the flood boundary stations for all storm surge events simulated in the study cited above</p> <p>- the flood maps containing both the maximum flood extent and maximum inundation depth at every grid cell of the coastal inundation model</p> <p>- the spatially explicit results of the extreme value analysis for every grid cell in the coastal ocean model</p> <p>- the modelled monthly peak water levels between 1961 and 2018 for every grid cell of the coastal ocean model</p> <p>- the modelled timeseries of water levels during the storm surge from January 2nd 2019 and the entire hindcast period (1961-2018) for all tide gauges along the German Baltic Sea coast</p> <p>For further information, we refer the reader to the readme file in this dataset or the publication itself.</p> <p> </p> <p> </p> <p> </p>
Current and Future Flood maps for Flood risk assessment under the Shared Socioeconomic Pathways in the Greater Accra region, Ghana
<p>The study used 15 flood conditioning factors in simulating current and future flood conditions under the SSP scenarios using the Frequency Ratio (FR) model </p>
Flood maps of Rainfall Event of November 2023 in Tuscany, Italy
<p>To support the evaluation of the Digital Twin system of the SCORE project, we defined an improved flood mapping procedure based on high-resolution SAR data to obtain flood maps of hystorical events, which will be used as validation reference for the Digital Twin outputs maps. We analyzed a recent significant flood event occurred in the densely populated Florence-Prato-Pistoia plain area (Tuscany, Italy) between 2 and 5 November, 2023. The estimated total losses for the entire region are at 1890 million euros.<br>The available images used for the the flood event analysis were acquired by COSMO-SkyMed Second Generation on 5 November 2023 in dual polarization HH-HV and by COSMO-SkyMed First Generation on 6 November 2023 in HH polarization. The flood mapping procedure, based on clustering and fuzzy logic approach, has been applied to Florence-Prato-Pistoia plain study area in rural areas and, when cross-polarization data are available, in urban areas. The dataset includes two images: the first one (named flood_05.11.2023_compressed.tif) is a map of the flood extent in rural areas of the region of interest and urban areas of Pistoia, Prato, Sesto Fiorentino and Firenze on 5 November; the second one (named flood_06.11.2023_compressed.tif) is a map of the flood extent in rural areas of the region of interest on 6 November.</p> <div> <div> <div> <div> </div> </div> </div> </div> <p> </p>
Inundation map of Mandra's flood event (Athens, Greece) 2017
<p>In this dataset we manually reproduced and we share as a shape file the flood extent derived by the National Observatory of Athens BEYOND team, through the processing of a WordlView-4 satellite high resolution (31 cm) image on 21/11/2017 and with ground observations, of the flood event which hit Mandra, Athens, Greece in 15th of November 2017 (Bellos et al., 2020).</p> <p>It is available online by BEYOND team at <a href="http://beyond">http://beyond</a> <a href="http://eocenter.eu/index.php/web-services/floodhub">eocenter.eu/index.php/web-services/floodhub</a>). </p> <p>Another one flood dataset is provided in:</p> <p>https://zenodo.org/record/7140750</p> <p>References</p> <p>Bellos, V., Papageorgaki, I., Kourtis, I., Vangelis, H., Kalogiros, I., Tsakiris, G. (2020).<br> Reconstruction of a flash flood event using a 2D hydrodynamic model under spatial and temporal variability of storm.<br> Natural Hazards, 101(3), 711-726.</p> <p>The dataset is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-sa/4.0/): CC BY-NC-SA) You are free to: Share — copy and redistribute the material in any medium or format; Adapt — remix, transform, and build upon the material, for not commercial use, under the following terms:</p> <p>1) Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made.</p> <p>2) You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.</p> <p>3) ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.</p>
Mapping of spatiotemporal distribution of Tibraca limbativentris Stal in (Hem.: Pentatomidae) in flooded rice crop in Southern Brazil Mapping of spatiotemporal distribution of Tibraca limbativentris Stal
Mapping of spatiotemporal distribution of Tibraca limbativentris Stal
Data collection of article research tittle "Online GIS and Remote Sensing-Based Mapping of Flood Vulnerability in Samarinda Seberang Subdistrict"
<p>This dataset contains the definition and name of the data used in the study. It also contains rows of data for all flood parameters applied to the creation of flood vulnerability maps, namely rainfall data, landsat-8 files, DEM, DSMW and drainage survey data.</p>
Data testing of article research tittle "Online GIS and Remote Sensing-Based Mapping of Flood Vulnerability in Samarinda Seberang Subdistrict"
<p>This dataset explains validation testing in a study of the Samarinda Seberang flood vulnerability map. There are two test methods, namely the Kappa accuracy test and the 3D simulation visualization test. The Kappa accuracy test tab displays a table of Kappa calculation results, and the second tab contains a 3D simulation scenario image.</p>
100-Year Flood Hazard Map with Municipal Boundaries Overlay
<p>100-Year Flood Hazard Map with Municipal Boundaries Overlay</p>
Leveraging the Power of Unsupervised Learning for Flood Mapping
Open the record for dataset details and reuse information.
Subspecies and Distribution. T.n.napuF.Cuvier,1822—SMyanmar,Thai/MalayPeninsula,islandsoffWMalayPeninsula(Langkawi&Pangkor),Borneo,SSumatra,BangkaI,islandsoffBorneo(Laut&Serasan). T.n.bangue:Chasen&Kloss,1931—BanggiIandBalembanganI,offNBorneo. T.n.bunguranensisMiller,1901—NatunaIs(=Bunguran),oftWBorneo. T.n.neubronneriSody,1931—NSumatra. T.n.nmiasisLyon,1916—NiasI,offWSumatra. T.n.rufulusMiller,1900—TiomanI,offEMalayPeninsula,RiauandLinggaArchipelagos. T. n. terutus Thomas & Wroughton, 1909 — Terutau I, off W Malay Peninsula. The species was recently reconfirmed for Singapore. Maps that include Vietnam, Cambodia, and Laos in the distribution range are based on the earlier assumption that 7. versicolor was a subspecies of 1. napu. Subsequent studies have indicated that 7. versicolor is a distinct species, and that the range of 1. napu therefore does not extend into Cambodia, Laos, and Vietnam. The northern limit on the Thai-Malay peninsula is not well defined. Specimens of 1. napu have been collected from as far north as Bankachon in southern Myanmar (10° 08" N), but despite fairly intensive camera-trapping in Kui Buri National Park, Thailand (12° N), 7. napu has not been photographed there. At the northern margin ofits range, it is generally rare. It has been reported, for example, that during the flooding of the Chiew Larn Reservoir (Surat Thani Province; about 9° N, 98° 45' E), only six 7. napu were rescued compared with 172 71. kanchil. This area is the transition zone from wetter evergreen forest to drier deciduous types, and it might be that 7° napu is not well adapted to the drier forest types towards the northern limit ofits range. There are unconfirmed reports of the species on Java, where it may have been confused with one of the two color morphs of 7. javanicus. As explained in the Taxonomy section, the subspecific status of the populations of several islands remains unclear. in Tragulidae
Subspecies and Distribution. T.n.napuF.Cuvier,1822—SMyanmar,Thai/MalayPeninsula,islandsoffWMalayPeninsula(Langkawi&Pangkor),Borneo,SSumatra,BangkaI,islandsoffBorneo(Laut&Serasan). T.n.bangue:Chasen&Kloss,1931—BanggiIandBalembanganI,offNBorneo. T.n.bunguranensisMiller,1901—NatunaIs(=Bunguran),oftWBorneo. T.n.neubronneriSody,1931—NSumatra. T.n.nmiasisLyon,1916—NiasI,offWSumatra. T.n.rufulusMiller,1900—TiomanI,offEMalayPeninsula,RiauandLinggaArchipelagos. T. n. terutus Thomas & Wroughton, 1909 — Terutau I, off W Malay Peninsula. The species was recently reconfirmed for Singapore. Maps that include Vietnam, Cambodia, and Laos in the distribution range are based on the earlier assumption that 7. versicolor was a subspecies of 1. napu. Subsequent studies have indicated that 7. versicolor is a distinct species, and that the range of 1. napu therefore does not extend into Cambodia, Laos, and Vietnam. The northern limit on the Thai-Malay peninsula is not well defined. Specimens of 1. napu have been collected from as far north as Bankachon in southern Myanmar (10° 08" N), but despite fairly intensive camera-trapping in Kui Buri National Park, Thailand (12° N), 7. napu has not been photographed there. At the northern margin ofits range, it is generally rare. It has been reported, for example, that during the flooding of the Chiew Larn Reservoir (Surat Thani Province; about 9° N, 98° 45' E), only six 7. napu were rescued compared with 172 71. kanchil. This area is the transition zone from wetter evergreen forest to drier deciduous types, and it might be that 7° napu is not well adapted to the drier forest types towards the northern limit ofits range. There are unconfirmed reports of the species on Java, where it may have been confused with one of the two color morphs of 7. javanicus. As explained in the Taxonomy section, the subspecific status of the populations of several islands remains unclear.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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