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766 results for “flooding”
Water samples collected for dissolved inorganic carbon and nutrient analysis during tidal creek lateral exchange measurements approximately every 15 minutes from beginning of flood tide to the following low tide, Rowley, MA, PIE LTER.
Measurement of the lateral exchange of nutrients, sediment, and carbon in tidal creek systems draining predominantly low-elevation marsh dominated by Spartina alterniflora and high-elevation marsh dominated by Spartina patens located in Rowley, MA.
Data associated with the FLooded Upland Dynamics EXperiment (FLUDEX), conducted at the IISD Experimental Lakes Area 1997 to 2003, investigating reservoir flooding impacts on ecosystems, particularly the release of mercury and greenhouse gases.
The data included in this repository were collected over the course of the FLooded Upland Dynamics Experiment (FLUDEX) conducted at the IISD Experimental Lakes Area (IISD-ELA) from 1997 to 2003. A plethora of data was collected over five years of flooding three upland reservoir sites, in order to examine the relationship between the amount of flooded, and thus decomposed, terrestrial organic matter and the production of methylmercury (MeHg), total mercury (THg), and greenhouse gases (GHGs) in the reservoirs. Findings from this experiment suggest that the amount of organic carbon stored in a flooded site does not directly influence the amount of THg, MeHg, and GHGs produced, but it does affect the persistence of mercury in the reservoir and food web. This version of the repository contains data collected on water chemistry, benthic invertebrate (chironomid) emergence, mercury and methylmercury concentrations in the water and food web, stable isotopes of carbon and nitrogen in emerging insects and zooplankton, and abundance and biomass of zooplankton, phytoplankton, and bacteria. This data package contains only some of the data from the FLUDEX project. IISD-ELA hopes to add more data in subsequent versions.
Characterisation of Social Vulnerability to the environmental hazard of flooding in Cork City and County, Ireland, derived from national census and EU Copernicus datasets.
<p>This dataset includes all input information for indicators that were used to derive social vulnerability and the overall processed data of the social vulnerability index score for the region of Cork, Ireland. The input variables used in this dataset come from the national census data for Ireland and EU Copernicus data.</p> <div> <p>The social vulnerability indicators used in these datasets are based on research including a review of existing literature and the interpretation of factors affecting social vulnerability. Interpretation of some indicators are contestable and open to debate.</p> </div>
Surface water and flooding dynamics data set based on seasonally continuous Landsat data (1986-2011) in a dryland river basin
<p>Animations of the data are available here: <a href="https://doi.org/10.5281/zenodo.2438110">https://doi.org/10.5281/zenodo.2438110</a></p> <p>If you are using this data set, please cite the following publication:</p> <p>Tulbure, M.G. and M. Broich (2018). Spatiotemporal patterns and effects of climate and land use on surface water extent dynamics in a dryland region with three decades of Landsat satellite data. Science of the Total Environment. https://www.sciencedirect.com/science/article/pii/S0048969718347466 </p> <p>The data represent statistically validated surface water and flooding extent dynamics derived from seasonally continous Landsat TM/ETM+ data and random forest models, and summarised to the maximum extent of surface water per season between 1986-2011 over Australia's Murray-Darling Basin. The overall accuracy was over 99% and producer's accuracy for water 87% +/- 3%. </p> <p>The method is described in the following publication: <br> Tulbure, M.G., M. Broich, S.V. Stehman, A. Kommareddy. (2016). Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region. Remote Sensing of Environment. 178: 142-157</p> <p>URL: https://www.sciencedirect.com/science/article/pii/S0034425716300621 </p> <p>Data are provided in GeoTIFF format per season per year. File naming convention is as follows:<br> yy_inund_freq_season_SamplingMethod. For example, "99_inund_freq_winter_max" will represent inundation frequency for winter 1999 resampled using a maximum resampling method. </p> <p>Inundation frequency represents the number of times a pixel has been flagged as flooded out of the times that pixel had valid observations * 100. Valid observation exclude no data values and clouds. The valid range of inundation frequency is 0-100 [%], with 255 indicating no data values. Data type is eight bit unsigned integer (uint8). </p> <p>The data were resampled to 120m resolution to reduce file size. The resampling methods used include max (e.g. selects the max value of all non-NODATA contributing 30m pixels) and mean (median and min can be provided upon request). If you are unsure which resampling to use, you may want to start with the mean. </p>
CFMDG: a Coastal Flood Modelling Dataset in Gâvres (France) to support risk prevention and metamodels development
<p>Along most of the coastal areas, detailed coastal flood observations (e.g. inland water depths) are scarce, and when they are available, this for a limited number of events. Given recent scientific advances, <strong>coastal flooding</strong> events can be properly modelled, even in complex environments and under the action of wave overtopping, and thus provide detailed information. However, such models are computationally expensive, which prevents their use for instance for forecasting and warning. At the same time, metamodelling techniques have been explored for coastal hydrodynamics and have shown promising results. Metamodels are functions that aim to reproduce the behaviour of a “true” model (e.g., a numerical hydrodynamic model) for given input variables (for instance, offshore conditions). Within the RISCOPE research project (<a href="http://perso.math.univ-toulouse.fr/riscope">https://perso.math.univ-toulouse.fr/riscope</a>/) aiming at exploring to which extent such metamodelling techniques may allow to forecast coastal floods with a good accuracy, a <strong>simulated flood database</strong> has been built for the site of Gâvres (France), characterised by a significant effect of wave overtopping processes.</p> <p>The <strong>CFMDG dataset </strong>compiles a set of post-processed coastal flood simulations on the site of Gâvres. The dataset includes 250 scenarios. Each scenarios is defined by 6h time series centered on high tide, with one time series per forcing variables. The forcing variables (called X) are: local relative mean sea-level, tide, atmospheric storm surge, the offshore wave characteristics and the offshore wind. These scenarios combine past real (flood and no flood) events in the 1900-2021 time span with extreme statistics based events, and some complementary fictive events. The post-processed outputs (called Y) includes, for each scenario, the maximal flooded area (m²) and the maximal water depth (m) in each of the 64 618 inland model grid points.</p> <p>The modelling chain that allowed building this dataset relies on the joint use of a spectral wave model (WW3) to propagate the waves to the coast, and a non-hydrostatic wave-flow model (SWASH) to simulate the nearshore hydrodynamics and the flooding. The spatial and temporal resolution of the SWASH configuration validated on the Gâvres site are respectively 3 m and more than 10Hz. All the results are obtained for a Digital Elevation Model corresponding to the 2018 configuration of the site. </p> <p>Such type of dataset is of use for local knowledge, risk prevention, metamodel testing/training, and local coastal flood forecast. </p> <p>Part of this dataset has already been used in (<a href="http://www.mdpi.com/2077-1312/9/11/1191">Idier et al., 2021</a>; <a href="http://www.sciencedirect.com/science/article/pii/S0951832021006293?via%3Dihub">López-Lopera et al., 2021</a>; <a href="https://hal.science/hal-02536624">Betancourt et al., 2022</a>), to develop metamodels and set up a coastal flood forecast and early warning prototype.</p> <p>We hope and expect that making this dataset accessible will trigger further developments/investigations for improving risk knowledge on the considered site as well as methodological developments on machine-learning/metamodel-based techniques to support flood forecast.</p> <p>The table below summarizes the variables contained in the dataset, for each scenario.</p> <table> <tbody> <tr> <td> <p><strong>Variable name</strong></p> </td> <td> <p><strong>Description and unit </strong></p> </td> <td> <p><strong>Comment</strong></p> </td> </tr> <tr> <td> <p>Scenario n°</p> </td> <td> <p>Number of the scenario.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>INPUTS (X)</strong></p> </td> </tr> <tr> <td> <p>NM</p> </td> <td> <p>Relative mean sea level, referenced to the French vertical datum (m, IGN69)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>T</p> </td> <td> <p>Tidal water level (m), referenced to the relative mean sea level</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>S</p> </td> <td> <p>Atmospheric storm surge (m)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>Hs</p> </td> <td> <p>Significant wave height (m)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>Tp</p> </td> <td> <p>Wave peak period (s)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>Dp</p> </td> <td> <p>Wave peak direction (° in nautical convention)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>U</p> </td> <td> <p>Wind speed (m/s)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>DU</p> </td> <td> <p>Wind direction (° in nautical convention)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>t</p> </td> <td> <p>Relative time centered on the high tide of each event (min)</p> </td> <td> <p>Not Concerned</p> </td> </tr> <tr> <td> <p>High Tide date</p> </td> <td> <p>UTC date for scenarios corresponding to past real events</p> </td> <td> <p>Not Concerned</p> </td> </tr> <tr> <td> <p><strong>OUTPUTS (Y)</strong></p> </td> </tr> <tr> <td> <p>Smax</p> </td> <td> <p>Maximum flooded area during the event (m²)</p> </td> <td> <p>Post-processed scalar output</p> </td> </tr> <tr> <td> <p>Hmax</p> </td> <td> <p>Maximum water depth reached during the event (m), provided for each inland location</p> </td> <td> <p>Post-processed functional (map) output</p> </td> </tr> <tr> <td> <p>longitude</p> </td> <td> <p>Longitude (°, WGS84)</p> </td> <td> <p>For each inland location point</p> </td> </tr> <tr> <td> <p>latitude</p> </td> <td> <p>Latitude (°, WGS84)</p> </td> <td> <p>For each inland location point</p> </td> </tr> <tr> <td> <p>XL93</p> </td> <td> <p>Longitude (m, Lambert 93)</p> </td> <td> <p>For each inland location point</p> </td> </tr> <tr> <td> <p>YL93</p> </td> <td> <p>Latitude (m, Lambert 93)</p> </td> <td> <p>For each inland location point</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><br> </p>
Stream Restoration and Flood Impacts in the Kickapoo River Watershed, Wisconsin, 2019
Data were collected from May to November 2019 at five sites on two stream reaches in the Kickapoo River Watershed. Sites include Billings Creek restoration site (BRES), Billings Creek reference site (BREF), Warner Creek upstream site (WUP), Warner Creek middle site (WMD), and Warner Creek downstream (WDN) site. Data were collected by Dr. Caroline Gottschalk Druschke as part of research into the impacts of stream restoration and flooding on Kickapoo River Watershed (WI, USA) streams. Data were collected on methane and carbon dioxide fluxes on all reaches, as well as cross sectional area and soft sediment depth on the restored reach on Billings Creek before and after restoration in July 2019.
Data and Code for Lowman et al. 2024, Macroscale controls determine the recovery of river ecosystem productivity following flood disturbances
<p>Data and code for analyses in Lowman et al. 2024, Macroscale controls determine the recovery of river ecosystem productivity following flood disturbances.</p> <p>See publication and ReadMe file for analysis description and further details. </p>
Data needed to reproduce the flood hazard modeling of Pollack et al., 2024
<p>This repository contains some of the data needed (Data_Flood_Modeling) to reproduce the flood hazard modeling of Pollack et al., 2024 "[Funding rules that promote equity in climate adaptation outcomes](https://osf.io/preprints/osf/6ewmu)." which are required to run the codes https://github.com/CoRE-Lab-UCF/Pollack_et_al_2024.git </p> <p>Specifically, this repository contains:</p> <ol> <li>dem_subgrid_1m_nbd.asc (DEM at 1m resolution in ascii format. Source DEM is CoNED, see Supporting material of Pollack et al., 2024)</li> <li>Gloucester_street_light_utm.tif (Basemap in UTM coordinates, UTM18N with EPSGcode=26918)</li> <li>sfincs.inp (Model file of SFINCS)</li> <li>Unique_Land_Classes_CN.xls (Table including the land cover classes and corresponding Manning coefficients used for surface roughness)</li> </ol>
Raw data for the article "Visualisation and quantification of flooding phenomena in gas diffusion electrodes used for electrochemical CO2 reduction: A combined EDX/ICP–MS approach"
<p>Raw data for the article "Visualisation and quantification of flooding phenomena in gas diffusion electrodes used for electrochemical CO2 reduction: A combined EDX/ICP–MS approach", published in Journal of Catalysis 2022 408:1–8, doi: <a href="https://doi.org/10.1016/j.jcat.2022.02.014">10.1016/j.jcat.2022.02.014</a></p> <p>Folder names describe the type of data content.</p>
Twitter dataset of flood-related images for September 2021, Thailand and June/July 2021, Nepal floods
<p>Twitter dataset related to flood events onsets in Thailand and Nepal, focused on September 26/27, 2022, June 16/17 2021 and July 01/02 2021. The dataset has been processed with a VisualCit pipeline in order to automatically filter a relevant subset of posts through automated image analysis, using deep learning techniques. The posts were then geolocated using the CIME algorithm. Additional information about the data collection and data processing are described in <a href="http://arxiv.org/abs/2202.12014">http://arxiv.org/abs/2202.12014</a></p>
Modelling NBSs for coastal erosion and marine flooding: the Emilia-Romagna case studies
<p>The study was conducted in the context of the OPEn-air laboRAtories for Nature baseD solUtions to Manage environmental risks (OPERANDUM) project which is an H2020 project which aims at providing tools and methodologies for the assessment of NBS efficiency around the world. Two NBs were tested via modelling simulations on the Bellocchio Beach at Lido di Spina (Italy) located in the northern part of the Emilia-Romagna coast (northern Adriatic Sea): an artificial dune built with natural materials and a marine seagrass meadow.</p> <p>The artificial dune is an engineered structure that will mimic the functioning of natural dunes. Its aims are reducing both natural dune erosion and flooding in adjacent coastal lowlands. It consists of a barrier between the sea and land, in a similar way to a seawall. Unlike the latter, the NBS are ‘dynamic’, i.e. the dune/beach system interacts a great deal and is constantly undergoing small adjustments in response to changes in wind and wave climate or sea level. Its construction involves the placement of sediment from dredged sources on the beach and it will be reinforced with a structure composed of biodegradable material. Different typologies of experimental solutions are foreseen.</p> <p>The second NBS consists of an alongshore seagrass belt located in front of the coastal area. It was investigated as a potential mechanism for wave amplitude reduction. Among the few species that can live in the northern Adriatic Sea, Zostera Marina was chosen due to its ability to live in a marine environment influenced by freshwaters. A more detailed description can be found in (Pillai et al., 2021).</p> <p>The numerical model chain, specifically developed for the study, consists of an Ocean Circulation model, so-called SHYFEM (Umgiesser et al., 2004), a wave model, so-called WWIII (Alves and Ardhuin, 2016), and a morphological model, so-called XBeach (Roelvink et al., 2009). Ten years of XBeach simulations have been executed to simulate the morphological impacts on the coastal strip for the present (2010-19) and future climate (2040-49). For each 10 years period, four scenarios were simulated: the baseline scenario without NBS (baseline_run), the scenario with the dune (dune_run), the scenario with the seagrass effect (seagrass_run) and the scenario with the two NBS integration (dune_seagrass_run).XBeach was forced with sea level and wave time series predicted by the SHYFEM and WWIII models respectively.</p> <p>The model domain consists in a curvilinear structured grid of about 3.2 km (longshore) x 2.8 km (cross-shore) covering the coastal stretch of Bellocchio beach at Lido di Spina (Italy) and extends seaward up to about 10 m depth.</p> <p>The performance of the NBSs and their impact on coastal erosion and marine flooding were investigated. For both present and future scenarios (201-2019 and 2040-2049), the reduction in wave intensity obtained with the seagrass provided greater benefits in terms of erosion mitigation and flood reduction. The analysis highlighted the limited scale of the dune intervention, in particular under present conditions, highlighting that the longer the artificial dune implemented, the larger the beach and dune area protected. For the future scenarios, the results are still significant and even small projects are expected to help in mitigating coastal erosion and marine flooding.</p> <p>For long-period simulations, no relevant improvements in reducing beach erosion was observed when the artificial dune was combined with the seagrass meadows with respect to the seagrass effects only. Instead, a dominant increase in sea levels will probably highlight the dune functions in hindering the marine ingression into the lagoon area behind and the consequent sediment redistribution.</p> <p>This dataset consists of XBeach model results, mainly:</p> <ul> <li>Morphological evolution of the coastal bottom at Bellocchio beach (Lido di Spina, Italy) in terms of initial and final bed levels, for the current (201-2019) and the future (2040-2049) scenarios. Results are available for the four NBS scenarios described above (and detailed in the Presentation.pdf)</li> <li>Maximum flood depth, defined as the non-simultaneous maximum water depth on the beach domain of Bellocchio (Lido di Spina, Italy) for the current (201-2019) and the future (2040-2049) scenarios. Results are available for the four NBS scenarios described above (and detailed in the Presentation.pdf) </li> <li>Erosion-deposition maps for the current (201-2019) and the future (2040-2049) scenarios. Results are available for the four NBS scenarios described above (and detailed in the Presentation.pdf).</li> </ul>
Regional Flood Frequency Analysis of the Sava River in South-Eastern Europe
<p>Journal: Sustainability</p> <p>Abstract: Regional flood frequency analysis (RFFA) is a powerful method for interrogating hydrological series since it combines observational time series from several sites within a region to estimate risk-relevant statistical parameters with higher accuracy than from single-site series. Since RFFA extreme value estimates depend on the shape of the selected distribution of the data-generating stochastic process, there is need for a suitable goodness-of-distributional-fit measure in order to optimally utilize given data. Here we present a novel, least-squares-based measure to select the optimal fit from a set of five distributions, namely Generalized Extreme Value (GEV), Generalized Logistic, Gumbel, Log-Normal Type III and Log-Pearson Type III. The fit metric is applied to annual maximum discharge series from six hydrological stations along the Sava River in South-eastern Europe, spanning the years 1961 to 2020. Results reveal that (1) the Sava River basin can be assessed as hydrologically homogeneous and (2) the GEV distribution provides typically the best fit. We offer hydrological‒meteorological insights into the differences among the six stations. For the period studied, almost all stations exhibit statistically insignificant trends, which renders the conclusions about flood risk as relevant for hydrological sciences and the design of regional flood protection infrastructure.</p> <p>URL: https://www.mdpi.com/2071-1050/14/15/9282</p> <p>The uploaded datasets are the Annual Maximum Series of Sava River runoff for the six analysed hydrological stations: Radovljica, Čatež, Zagreb, Jasenovac, Županja and S. Mitrovica.</p>
Flash Flood Severity Index (Flashiness) dataset for the United States
<p>(Saharia et al., 2017)</p> <p>Flash floods, a subset of floods, are a particularly damaging natural hazard worldwide because of their multidisciplinary nature, difficulty in forecasting, and fast onset that limits emergency responses. In this study, a new variable called “flashiness” is introduced as a measure of flood severity. This work utilizes a representative and long archive of flooding events spanning 78 years to map flash flood severity, as quantified by the flashiness variable. Flood severity is then modeled as a function of a large number of geomorphological and climatological variables, which is then used to extend and regionalize the flashiness variable from gauged basins to a high-resolution grid covering the conterminous United States. Six flash flood “hotspots” are identified and additional analysis is presented on the seasonality of flash flooding. The findings from this study are then compared to other related datasets in the United States, including National Weather Service storm reports and a historical flood fatalities database.</p>
Database - Bridge clogging and debris - July 2021 flood
<p><span>This dataset documents 71 floating debris accumulations at bridges following an extreme hydrological event that hit Belgium and Germany in July 2021. Data were collected from various sources including public authorities’ documents, public online database, post event pictures and field visits. The dataset covers bridge geometry, flood conditions and debris accumulation. In particular, it systematically details deposits dimensions and classifies deposits components, which contain a significant portion of man-made objects, in addition to driftwood. </span></p> <p><span>The dataset is stored in a single CSV file, with semicolon separator. The file contains 72 lines and 63 columns. First line contains the label of the columns parameters. Each of the 71 following lines contains the data of one bridge and corresponding accumulation. </span></p> <p><span>A data descriptor is under review in Nature Scientfic Data: <br>Erpicum S., Poppema D., Burghardt L., Benet L., Wüthrich D., Klopries E., Dewals B., (submitted) A dataset of floating debris accumulation at bridges after July 2021 Flood in Germany and Belgium, Nature Scientific Data<br></span></p>
Dataset of Floods in Germany 1950-2010
<p>This is a data set comprising 29,248 flood events which were sampled from time-series of daily streamflow of 374 catchments across Germany over the period 1950-2010. The flood events were sampled by applying a Peak-Over-Threshold}approach on each catchment, separately. For each of the events, a set of preconditions was collected: First, preconditions in catchment averages of daily precipitation sum [mm/d], daily mean soil moisture [\%] and daily mean temperature of each flood event were sampled: Going back in time from the day of the flood event, the means of predictor values over multiple time periods, "delta t" = [0, 1, 3, 5, 7], were extracted. Note that P is the cumulative sum of rainfall and snowmelt. Both P and SM are model output from the "Mesoscale Hydrological Model" (see references below). Second, data on 10 catchment attributes of various types, i.e. static predictors, are included for each of the catchments. These include data on the average climatic conditions (aridity index, mean annual precipitation), topography (slope, elevation), geomorphology (channel slope, drainage density).</p>
STURM-Flood
<p>This repository hosts the STURM-Flood dataset, an open-access resource designed for flood extent mapping using Sentinel-1 and Sentinel-2 satellite imagery. The dataset comprises 21,602 Sentinel-1 tiles and 2,675 Sentinel-2 tiles, each of size 128 × 128 pixels at a resolution of 10 meters, along with corresponding water masks covering 60 flood events globally. This curated dataset is optimized for deep learning applications and provides ground-truth data from the Copernicus Emergency Management Service to facilitate robust model development. We invite researchers and developers to utilize this resource for advancing flood mapping techniques in disaster management. For further details on the methodology, results, and implementation, please refer to our study published in <em>Big Earth Data (2096-4471)</em>: <a title="STURM-Flood: a curated dataset for deep learning-based flood extent mapping leveraging Sentinel-1 and Sentinel-2 imagery." href="https://doi.org/10.1080/20964471.2025.2458714" target="_blank" rel="noopener">https://doi.org/10.1080/20964471.2025.2458714 .</a></p>
RGB orthophoto mosaic, DSM, 3d point cloud and LIDAR LAZ of the flash flood damages in Karavelovo and Bogdan vilages, Bulgaria- September 2, 2022
<p>The present dataset contains geospatial resources aimed at investigating and assessing the consequences of a flash flood of debris flow character, relatively significant in extent and magnitude of damage, in the area of two villages in the Municipality of Karlovo, located in central Bulgaria, which happened on September 2, 2022. For this purpose, an integrated approach based on the combination of digital photogrammetry with high spatial resolution and spatial accuracy, based on a fixed wing unmanned aerial system, and laser altimetry (LIDAR), based on a multirotor unmanned platform, was used. The data collection was carried out 2 days after the occurrence of the disaster, resulting in the generation of valuable information resources that allow not only to spatially and quantitatively determine the damage of the disaster, but also to reveal the mechanism of occurrence of the phenomenon: 1) orthophoto mosaic, Digital surface model-DSM and 3D point cloud (from photogrammetry) 2) Classified 3D point cloud- from LIDAR survey.</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 (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>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.