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766 results for “flood”
Human and economic losses from river flooding with anthropogenic warming
<p>This repository contains the data set derived from a modelling framework developed to evaluate present and future impacts from river flooding and described in the scientific paper "Increased Human and economic losses from river flooding with anthropogenic warming", published in Nature Climate Change. The repository contains the following data, for more details please refer to the "readme.doc" file.</p> <ul> <li>Static and dynamic exposure maps (land use, economic variables, population distribution)</li> <li>global distributed maps of economic damage from river flooding for the present and future periods. Maps are provided for each model realization (i.e. combination of a global climate model and a global hydrological model)</li> <li>global maps of population affected by river flooding for the present and future periods, computed with current socioeconomic conditions and with SSP3 and SSP5 population projections. Maps are provided for each model realization</li> <li>tables of socio-economic impacts (fatalities, mortality rates, economic losses, population exposed) aggregated per continents, macro-regions and countries. Tables are provided for each model realization</li> <li>Tables of the variables used as input data in the impact assessment procedure</li> </ul>
Flood Masks Doñana 1984/2019
<p>Time Series of flooded areas derived from Landsat TM, ETM+ & OLI in the Path 202 Row 34 (Doñana). Also, these products and its metadata are freely available to consult or downloaded in the LAST-EBD Cartography Server: http://mercurio.ebd.csic.es/imgs/</p> <p>Methodology is described in this paper: Remote Sensing 8(9):775 · September 2016. DOI: 10.3390/rs8090775</p>
A Global Database of Historic and Real-time Flood Events based on Social Media
<p>Early event detection and response can significantly reduce the societal impact of floods. Currently, early warning systems rely on gauges, radar data, models and informal local sources. However, the scope and reliability of these systems are limited. Recently, the use of social media for detecting disasters has shown promising results, especially for earthquakes. Here, we present a new database for detecting floods in real-time on a global scale using Twitter. The method was developed using 88 million tweets, from which we derived over 10.000 flood events (i.e., flooding occurring in a country or first order administrative subdivision) across 176 countries in 11 languages in just over four years. Using strict parameters, validation shows that approximately 90% of the events were correctly detected. In countries where the first official language is included, our algorithm detected 63% of events in NatCatSERVICE disaster database at admin 1 level. Moreover, a large number of flood events not included in NatCatSERVICE are detected. All results are publicly available on <a href="http://www.globalfloodmonitor.org">www.globalfloodmonitor.org</a>.</p>
3D-Flood Dataset
<p>The Aristotle University of Thessaloniki (hereinafter, <strong>AUTH</strong>) created the following <strong>dataset, entitled ‘3D-Flood’</strong>, within the context of the project TEMA that was funded by the European Commission-European Union.</p> <p>The dataset will be used for the construction of a 3D model regarding the district of Agios Thomas in Larisa, Greece, after the flood events of 2023. It is comprised of 795 UAV video frames, taken from 4 YouTube videos.</p> <p>We provide the links for each YouTube video, along with the frame numbers that we kept for each video.</p> <p>Details on acquiring the dataset can be found <strong><a href="https://aiia.csd.auth.gr/3d-flood-dataset/" target="_blank" rel="noopener">here</a></strong>.</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>
Dataset for DoS and DDoS Attacks on Digital Meter SICAM via GOOSE Protocol Flooding
<p>This dataset presents network traffic data from simulated Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks on a Digital Meter SICAM device using the GOOSE protocol. An unauthorized attacker floods the SICAM meter's communication by initially sending 100 GOOSE packets at 1 ms intervals, followed by an intensified attack of 500 GOOSE packets. These actions render the meter unreachable by the legitimate Control Station, disrupting normal operations and data retrieval processes.</p>
Song capturing lived-experiences of flooding and climate resilience with St. Eugenes Choir Newtownstewart (BluePrint project)
<p>This audio piece represents one of the creative risk communication outputs co-created within the BluePrint project. Between March and October 2024, socially engaged artist Sara Walmsley worked creatively with flood-affected community representatives in Newtownstewart, Co. Tyrone and Eglinton, Co. Derry-Londonderry exploring their lived-experiences of flooding and need for climate adaptation and resilience. </p> <p>In the audio piece, you will hear the melodic, polyphonic harmonies of St. Eugene’s Church choir (Newtownstewart) as they give music to the words of members of their community whose homes were destroyed and lives endangered by flood water. The piece captures the voices of those striving to adapt to our changing climate, those who are responding to the urgency by finding solace, hope, strength and courage in the unending and unsurprising resilience and creativity of our communities. </p> <p>The BluePrint project is led by the MaREI Centre, University College Cork, with partners the Playhouse, Derry City and Strabane District Council, and Mayo County Council. The BluePrint project is a recipient of the Creative Climate Action fund, an initiative from the Creative Ireland Programme. It is funded by the Department of Tourism, Culture, Arts, Gaeltacht, Sport and Media in collaboration with the Department of the Environment, Climate and Communications. </p> <p>Find out more: <a href="https://www.marei.ie/project/blueprint/">https://www.marei.ie/project/blueprint/</a></p>
Benchmarking (multi)wavelet-based dynamic and static non-uniform grid solvers for flood inundation modelling (Simulation results)
<p>Simulation result data for Environment Agency benchmark test 5, Thamesmead hypothetical flood, and Carlisle 2005 case studies, using uniform DG2, adaptive MWDG2, adaptive HWFV1, non-uniform DG2, non-uniform FV1 and non-uniform ACC solvers. </p> <p>Model results are archived in 3 zip files:</p> <ul> <li>EA5.zip contains results of Environment Agency test 5 (Néelz and Pender, 2013)</li> <li>Thamesmead.zip contains results of Thamesmead hypothetical flood (Liang et al., 2008)</li> <li>Carlisle.zip contains results of Carlisle 2005 flooding (Neal et al., 2009)</li> </ul> <p>The results are stored with the following file extensions:</p> <ul> <li>".wd" for 2D flood inundation maps in ESRI ASCII format</li> <li>".stage" for water depth or water level time-series at staging points in tabulated text format</li> <li>".velocity" for velocity time-series at staging points in tabulated text format</li> </ul> <p>Model outputs are stored under directories named for each solver.</p> <p><strong>References</strong></p> <p>Néelz, S., & Pender, G. (2013). Benchmarking the latest generation of 2D hydraulic modelling packages. <em>Environment Agency: Bristol, UK</em>.</p> <p>Liang, Q., Du, G., Hall, J. W., & Borthwick, A. G. (2008). Flood Inundation Modeling with an Adaptive Quadtree Grid Shallow Water Equation Solver. <em>Journal of Hydraulic Engineering</em>, <em>134</em>(11), 1603–1610. https://doi.org/10.1061/(ASCE)0733-9429(2008)134:11(1603)</p> <p>Neal, J. C., Bates, P. D., Fewtrell, T. J., Hunter, N. M., Wilson, M. D., & Horritt, M. S. (2009). Distributed whole city water level measurements from the Carlisle 2005 urban flood event and comparison with hydraulic model simulations. <em>Journal of Hydrology</em>, <em>368</em>(1–4), 42–55. https://doi.org/10.1016/j.jhydrol.2009.01.026</p> <p> </p>
Longitudinal data to explore changes in flood risk awareness and preparedness
<p>This upload includes two different longitudinal datasets. The Panel datasets includes two rounds of surveys where the same individuals were interviewed. The Repeated Cross-Sectional includes two rounds of surveys where different individuals were interviewed in each round. The first survey round is the same in the two datasets. Data were collected in the municipality of Negrar (VR), in North-eastern Italy in February 2019 and in February 2020, following a flash flood which occurred in September 2018. </p>
River flooding impacts using CLIMRISK-RIVER
<p>Direct impacts of river flooding using the CLIMRISK-RIVER model. This includes direct impacts to built environment and infrastructure.</p> <p>The damage is expressed as a change in expected annual damage (EAD) w.r.t. 2010. </p> <p>Files include two adaptation assumptions: no additional adaptation and optimal adaptation (using CBA estimates).</p> <p>Units: millions EUR (2015) PPP.</p>
Riverine Flood Insurance assessment indicators under climate and socio-economic change
<p>Expected annual river flood damages, flood insurance premiums, and insurance penetration rates, for EU-regions (NUTS2) and under future climatic and socio-economic conditions (RCP-SSP combinations).</p>
Tracing and visualisation of contributing water sources in a model of flood inundation: video supplement
<p>These are video supplement files to Wilson & Coulthard (2021), produced using version 1.8f-WS of CAESAR-Lisflood software, <a href="https://doi.org/10.5281/zenodo.5541122">available on Zenodo here</a>. For a full description of the methodology and case studies, please refer to the paper which is available here: <a href="https://doi.org/10.5194/gmd-2021-340">https://doi.org/10.5194/gmd-2021-340</a>.</p> <p>Video animations (no audio) for the following case studies are included:</p> <p>1. <strong>Carlisle, United Kingdom</strong> (carlisleanimation-sourcetracing.avi and carlisleanimation-depthonly.avi):</p> <ul> <li>Simulation of the January 2005 flood event at the confluence of the Rivers Caldew, Petteril and Eden, using a 5 m grid.</li> <li>Both water source tracing and depth only versions are provided.</li> <li>In the water tracing version, blue colours represent flows from the River Eden, reds are from the River Petteril and greens are from the River Caldew; darker shades represent deeper water. Available on YouTube here: <a href="https://youtu.be/xOtOi06cXvA">https://youtu.be/xOtOi06cXvA</a></li> <li>In the depth only version, darker shades of blue represent deeper water, with no information about the water source in a grid cell. Available on YouTube here: <a href="https://youtu.be/aFz-sPRGHVE">https://youtu.be/aFz-sPRGHVE</a></li> </ul> <p>2. <strong>Avon-Heathcote estuary in Christchurch, New Zealand</strong> (avonheathcoteanimation.avi):</p> <ul> <li>Simulation for July 2017, which included a high flow event on 22 July, using a model grid of 10 m.</li> <li>Blue colours represent flows from tide, reds are from the River Avon and greens are from the Heathcote River; darker shades represent deeper water.</li> <li>Available on YouTube here: <a href="https://youtu.be/Fczr5tczzXU">https://youtu.be/Fczr5tczzXU</a></li> </ul> <p>3. <strong>Amazon </strong>(amazonanimation.avi):</p> <ul> <li>Simulation at the confluence of the Solimões (mainstem Amazon) and Purus rivers in the central Amazon, Brazil, for the period of 1 October 2013 through December 2014, using a ~270 m model grid.</li> <li>Red colours are from the Solimões, green colours are from the Purus; darker shades represent deeper water.</li> <li>Available on YouTube here: <a href="https://youtu.be/PknAL_8fd1I">https://youtu.be/PknAL_8fd1I</a></li> </ul> <p>4. <strong>Planar slope</strong> (planaranimation.avi):</p> <ul> <li>A simple test case consisting of a 2000 x 1000 m planar slope (0.001 m/m), with walls added at 250 m intervals across the slope, each of which has several gaps through which water can flow. Model grid was 5 m.</li> <li>Eight water sources were traced in total, with three visualised in the animation: red = source 2, green = source 4, blue = source 6. Depths are shown in the middle plot.</li> <li>Available on YouTube here: <a href="https://youtu.be/DTw8ysJtx8o">https://youtu.be/DTw8ysJtx8o</a></li> </ul> <p>Please feel free to use these animations, under the terms of the CC-BY-4.0 license. Please provide a link back to this site and a citation to Wilson & Coulthard (2021).</p> <p>Reference:</p> <p>Wilson, M. D. and Coulthard, T. J.: Tracing and visualisation of contributing water sources in the LISFLOOD-FP model of flood inundation, Geosci. Model Dev. Discuss. [preprint], <a href="https://doi.org/10.5194/gmd-2021-340">https://doi.org/10.5194/gmd-2021-340</a>, in review, 2021</p>
Using Hydroclimate Modeling and Social Science to Enhance Flood Resilience on Lake Ontario through the Climate Smart Communities Program
<p>This repository contains several data products associated with the New York Sea Grant project R/CHD-15 entitled <em>Using Hydroclimate Modeling and Social Science to Enhance Flood Resilience on Lake Ontario through the Climate Smart Communities Program.</em><strong><em> </em></strong>These products include:</p> <p>1. Estimates of the 25-year, 50-year, and 100-year flood across the New York coastline of Lake Ontario. These design events (reported in feet) are for still water levels that take into account both average water levels across the lake as well as local variations in water level due to storm surge. Wave setup and wave run-up are not considered in these design events. The design events incorporate the effects of water level regulation and the potential impacts of climate change on water supplies to Lake Ontario, and they are tailored for 79 unique locations along the shoreline (identified based on longitude and latitude). These flood levels are presented in an online flood risk assessment tool at: https://kts48.users.earthengine.app/view/lake-ontario-water-level-scenarios</p> <p>2. Protocols and summary of results for a series of focus groups and structured telephone interviews with local officials from communities along the Lake Ontario shoreline to assess barriers to participation in the New York State Climate Smart Communities Program.</p> <p>3. A Crosswalk between activities and administrative requirements of the New York State Climate Smart Communities Program and other federal and state flood resiliency programs. </p> <p>4. A final report summarizing the products above. </p>
Monitoring NBS for coastal erosion and marine flooding: the Emilia-Romagna case study
<p>The study was conducted in the context of the OPERANDUM project which is an H2020 project which aims at providing tools and methodologies for the assessment of NBS efficiency around the world. As NBS will be tested an artificial dune built with natural materials. </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 Bellocchio Beach at Lido di Spina (Italy) was initially chosen for the study, however the Volano beach was selected as the new study area because of the strong erosion caused by an intense storm event in December 2020 at Bellocchio. The dune was built on the Volano beach and monitoring surveys were carried out on this new site. </p> <p>A morphological monitoring aimed to assess the beach evolution and the performance of the NBS were performed. Monitoring of morphology evolution of shoreline and inland area provide information about impact of the NBS on coastal erosion. Furthermore, the changes in the form of the work give information about the resistance of the NBS to wave attacks. Sedimentological campaigns have been planned in order to provide information regarding the texture of the sediments present in the area detected and possibly highlight changes after the construction of the dune.</p> <p>Three monitoring campaigns were carried out before, immediately after and six months later the construction of the dune (January, May and October 2022). All data were analysed to assess local coastal dynamics and NBS evolution. </p> <p>The monitoring consisted of: </p> <ul> <li> <p>topographic and bathymetric surveys (GNSS and multibeam/singlebeam echosounder) to generate DTMs of the entire area (10 m cell size); </p> </li> <li> <p>aerial photogrammetric surveys by UAV for the production of orthophotos and high resolutions DTMs of the emerged beach (1m cell size) and of the dune area (0.2 m cell size); </p> </li> <li> <p>sediment sampling and grain size analysis. </p> </li> </ul> <p>Surveys show that morphological and sedimentological changes are determined mostly by anthropic actions to the beach and seabed maintenance (artificial winter banks and Sacca di Goro channel). </p> <p>Regarding the dune area no significant changes in morphology were observed due to the limited period between the surveys. Appreciable signals were detected, such as the natural recolonization by pioneer plant species and the slight sand accumulation on the dune foot.</p> <p>This dataset consists of data related to monitoring activities. </p>
United States Flood Database
<p>This dataset is a merged and unified one from seven individual datasets, making it the longest records ever and wide coverage in the US for flood studies. All individual databases and a unified database are provided to accommodate different user needs. It is anticipated that this database can support a variety of flood-related research, such as a validation resource for hydrologic or hydraulic simulations, climatic studies concerning spatiotemporal patterns of floods given this long-term and U.S.-wide coverage, and flood susceptibility analysis for vulnerable geophysical locations.</p> <p>Description of filenames:</p> <p>1. cyberFlood_1104.csv – web-based crowdsourced flood database, developed at the University of Oklahoma (Wan et al., 2014). 203 flood events from 1998 to 2008 are retrieved with the latest version. Data accessed on 11/04/2020.</p> <p>Data attributes: ID, Year, Month, Day, Duration, fatality, Severity, Cause, Lat, Long, Country Code, Continent Code</p> <p>2. DFO.xlsx – the Dartmouth Flood Observatory flood database. It is a tabular form of global flood database, collected from news, government agencies, stream gauges, and remote sensing instruments from 1985 to the present. Data accessed on 10/27/2020.</p> <p>Data attributes: ID, GlodeNumber, Country, OtherCountry, long, lat, Area, Began, Ended, Validation, Dead, Displaced, MainCause, Severity</p> <p>3. <a href="https://zenodo.org/api/files/cc1f4627-ccd7-46e2-9d9f-b7d58b456cd2/emdat_public_2020_11_01_query_uid-MSWGVQ.xlsx?versionId=b7c2ba8d-1aad-4268-9f0b-cdadd95b38c3">emdat_public_2020_11_01_query_uid-MSWGVQ.xlsx</a> – Emergency Events Database (EM-DAT). This flood report is managed by the Centre for Research on the Epidemiology of Disasters in Belgium, which contains all types of global natural disasters from 1900 to the present. Data accessed on 11/01/2020.</p> <p>Data attributes: Dis No, Year, Seq, Disaster Group, Disaster Subgroup, Disaster Type, Disaster Subtype, Disaster Subsubtype, Event Nane, Entity Criteria, Country, ISO, Region, Continent, Location, Origin, Associated Disaster, Associated Disaster2, OFDA Response, Appeal, Declaration, Aid Contribution, Disaster Magnitude, Latitude, Longitude, Local Time, River Basin, Start Year, Start Month, Start Day, End Year, End Month, End Day, Total Death, No. Injured, No. Affected, No. Homeless, Total Affected, Reconstruction, Insured Damages, Total Damages, CPI</p> <p>4. <a href="https://zenodo.org/api/files/cc1f4627-ccd7-46e2-9d9f-b7d58b456cd2/extracted_events_NOAA.csv?versionId=3c47db6f-f908-4c04-93c4-62afb5f8a68f">extracted_events_NOAA.csv</a> – The national weather service storm reports. The NOAA NWS team collects weather-related natural hazards from 1950 to the present. Data accessed on 10/27/2020.</p> <p>Data attributes: BEGIN_YEARMONTH, BEGIN_DAY, BEGIN_TIME, END_YEARMONTH, END_DAY, END_TIME, EPISODE_ID, EVENT_ID, STATE, STATE_FIPS, YEAR, MONTH_NAME, EVENT_TYPE, CZ_TYPE, CZ_FIPS, CZ_NAME, WFO, BEGIN_DATETIME, CZ_TIMEZONE, END_DATE_TIME, INJURIES_DIRECT, INJURIES_INDIRECT, DEATHS_DIRECT, DEATHS_INDIRECT, DAMAGE_PROPERTY, DAMAGE_CROPS, SOURCE, MAGNITUDE, MAGNITUDE_TYPE, FLOOD CAUSE, CATEGORY, TOR_F_SCALE< TOR_LENGTH, TOR_WIDTH, TOR_OTHER_WFO, TOR_OTHER_CZ_STATE, TOR_OTHER_CZ_FIPS, BEGIN_RANGE, BEGIN_AZIMUTH, BEGIN_LOCATION, END_RANGE, END_AZIMUTH, END_LOCATION, BEGIN_LAT, BEGIN_LON, END_LAT, END_LON, EPISODE_NARRATIVE, EVENT_NARRATIVE, DATA_SOURCE<strong> </strong></p> <p>5. FEDB_1118.csv – The University of Connecticut Flood Events Database. Floods retrieved from 6,301 stream gauges in the U.S. after flow separation from 2002 to 2013 (Shen et al., 2017). Data accessed on 11/18/2020.</p> <p>Data attributes: STCD, StartTimeP, EndTimeP, StartTimeF, EndTimeF, Perc, Peak, RunoffCoef, IBF, Vp, Vb, Vt, Pmean, ETr, ELs, VarTr, VarLs, EQ, Q2, CovTrLs, Category, Geometry</p> <p>6. GFM_events.csv – Global Flood Monitoring dataset. It is a crowdsourcing flood database derived from Twitter tweets over the globe since 2014. Data accessed on 11/9/2020.</p> <p>Data attributes: event_id, location_ID, location_ID_url, name, type, country_location_ID, country_ISO3, start, end, time of detection</p> <p>7. mPing_1030.csv – meteorological Phenomena Identification Near the Ground (mPing). The mPing app is a crowdsourcing, weather-reporting software jointly developed by NOAA National Severe Storms Laboratory (NSSL) and the University of Oklahoma (Elmore et al., 2014). Data accessed on 10/30/2020.</p> <p>Data attributes: id, obtime, category, description, description_id, lon, lat</p> <p>8. USFD_v1.1.csv – A merged United States Flood Database from 1900 to the present (UPDATED)</p> <p>Data attributes: DATE_BEGIN, DATE_END, DURATION, LON, LAT, COUNTRY, STATE, AREA, FATALITY, DAMAGE, SEVERITY, SOURCE, CAUSE, SOURCE_DB, SOURCE_ID, DESCRIPTION, SLOPE, DEM, LULC, DISTANCE_RIVER, CONT_AREA, DEPTH, YEAR.</p> <p>Details of attributes:</p> <p>DATE_BEGIN: begin datetime of an event. yyyymmddHHMMSS</p> <p>DATE_END: end datetime of an event. yyyymmddHHMMSS</p> <p>DURATION: duration of an event in hours</p> <p>LON: longitude in degrees</p> <p>LAT: latitude in degrees</p> <p>COUNTRY: United States of America</p> <p>STATE: US state name</p> <p>AREA: affected areas in km^2</p> <p>FATALITY: number of fatalities</p> <p>DAMAGE: economic damages in US dollars</p> <p>SEVERITY: event severity, (1/1.5/2) according to DFO.</p> <p>SOURCE: flood information source.</p> <p>CAUSE: flood cause.</p> <p>SOURCE_DB: source database from item 1-7.</p> <p>SOURCE_ID: original ID in the source database.</p> <p>DESCRIPTION: event description</p> <p>SLOPE: calculated slope based on SRTM DEM 90m</p> <p>DEM: Digital Elevation Model</p> <p>LULC: Land Use Land Cover</p> <p>DISTANCE_RIVER: distance to major river network in km,</p> <p>CONT_AREA: contributing area (km^2), from MERIT Hydro</p> <p>DEPTH: 500-yr flood depth</p> <p>YEAR: year of the event.</p> <p>9. attribution_table.xlsx – description of each database, and URLs are provided to retrieve these databases.</p> <p>The script to merge all sources and figure plots can be found in https://github.com/chrimerss/USFD.</p> <p>If you intend to use this dataset, please cite our description paper:</p> <p>Li, Z., Chen, M., Gao, S., Gourley, J. J., Yang, T., Shen, X., Kolar, R., and Hong, Y.: A multi-source 120-year US flood database with a unified common format and public access, Earth Syst. Sci. Data, 13, 3755–3766, https://doi.org/10.5194/essd-13-3755-2021, 2021.</p> <p> </p>
Observational rainfall data of the 2021 mid-July flood event in Belgium – Part 1. Rain gauges observations
<p>From July 13th to 16th 2021, a long period of sustained and heavy rainfall affected Central Europe producing extreme rainfall amounts in western Germany, eastern Belgium, Luxembourg and The Netherlands. In Belgium, this unusual event induced massive flooding on a large part of the country and was responsible for 39 fatalities and strong damages to buildings and infrastructures.</p><p>Such extremely rare event needs to be documented as much as possible and data must be made available for further studies in hydrology, in urban planning and, more generally, in all multi-disciplinary studies aiming at identifying and understanding all factors leading to such disaster.</p><p>The observational rainfall data available for Belgium during the period from July 13th to July 16th 2021 are here shared with the scientific community. These data are twofold and provided in 2 parts:</p><p><br><strong>Part 1. </strong><a href="https://doi.org/10.5281/zenodo.7739983"><strong>Observations from high-quality rain gauges</strong></a></p><p>The dataset includes daily precipitation accumulation recorded by 323 weighing and manual rain gauges in Belgium as well as 5-min precipitation data recorded by 168 weighing rain gauges. These data were checked for possible errors and inconsistencies.</p><p>The rain gauges observations are provided in csv format in 2 files:</p><ul><li>RainGaugesData_FLOOD21_daily.csv</li><li>RainGaugesData_FLOOD21_5min.csv</li></ul><p><br><strong>Part 2. </strong><a href="https://doi.org/10.5281/zenodo.7740059"><strong>Radar-based quantitative precipitation estimation (RADFLOOD21)</strong></a></p><p>This product provides a quantitative precipitation estimation of the event at high spatial (i.e., 1 km) and temporal (i.e., 5 min and hourly) resolutions. It is obtained after a careful processing of the weather radar measurements and a merging with rain gauge measurements. The data is provided in hdf5 format. In addition, an animation of the 5-min RADFLOOD21 data is also made available.</p><p> </p><p>These data are exposed and discussed in <a href="https://hess.copernicus.org/articles/27/3169/2023/">https://hess.copernicus.org/articles/27/3169/2023/</a>. In particular, several analyses of these data are performed to describe the spatial and temporal distribution of rainfall during the event and to illustrate its exceptional character.</p><p> </p>
Dataset for the article "Development of an integrated socio-hydrological modeling framework for assessing the impacts of shelter location arrangement and human behaviors on flood evacuation processes"
<p>This dataset include the data needed to create the socio-hydrological model to simulate human evacuation processes via a transportation network before a flood hits the residential area. Source code, in JAVA, for generating households in the agent-based model are also provided. </p>
Joint series underlying the paper " Compound coastal-riverine flooding of St. Lawrence River coasts under sea level rise conditions"
<p>Compound coastal-riverine flooding, known as flooding events caused by the co-occurrence of high streamflow and coast water levels, can have substantial economic and social implications in low-lying coastal regions. Recent studies over Canada’s coasts have shown that neglecting the interdependency between flood drivers can underestimate the risk of flooding by up to 50%. However, to date, such interdependency and its effect on the frequency of compound riverine-coastal flooding has not been investigated for the coasts of the St. Lawrence River, Estuary, and Gulf system (StL), where Sea Level Rise (SLR), along with intensified river peaks, are already threatening communities. In this study, a copula-based bivariate frequency analysis (AND hazard scenario) was applied to quantify the differences between joint return periods computed under dependent and independent assumptions, for 26 sites along the StL. Furthermore, design pairs for 100-year joint events in the historical period (1986-2020) were compared with the 2100 horizon, where the SLR associated with the RCP8.5 emission scenario was incorporated into the water level time series. Results show that 1) the independence assumption can underestimate the frequency of compound flooding in the Fluvial Section of the StL by up to 30 times and 2) the SLR can increase the frequency of compound flooding by up to 50 times in the Estuary and the Gulf and by up to 5 times in the Fluvial Section of the StL. This study highlights the need for explicit consideration of the dependence between flood drivers and of SLR in the delineation of flood maps along all of the coasts of the St. Lawrence.</p>
Data to support the publication "Unknown risk: assessing refugee camp flood risk in Ethiopia"
<p>This dataset supports the publication "Unknown risk: assessing refugee camp flood risk in Ethiopia". This dataset contains the delineated boundaries for 24 refugee camps in Ethiopia. Also included are refugee camp building footprint data (where available). All datasets are in shapefile format.</p>
Transitions in flooding intensity on an experimental delta
<p>Information about past environments is stored in sedimentary rocks via biogeochemical markers stored in the sediments. Using these markers, the signal of paleoclimate and other environmental factors can be reconstructed from the strata. However, because sediment accumulation occurs stochastically, the stratigraphic record is often difficult to reconstruct with confidence. It is generally thought that with a sufficient sample size though, noise averages out, and the true signal can be reconstructed. This assumption is valid when the statistics of erosion and deposition remain steady throughout the interval of interest. In fact, it is known that changes in climate can alter the statistics of erosion and deposition, but the impact of this effect on paleoclimate reconstructions remains poorly understood. </p> <p>This dataset describes a set of physical delta experiments conducted at the Tulane University Sediment Dynamics and Stratigraphy Laboratory. Throughout the experiment, the level of flooding intensity that the delta was exposed to alternated between two end-member values, with transitions of varying durations. We monitored channel dynamics, and reconstructed synthetic climate records from the strata to see how the changing statistics of sediment accumulation impacted the preservation of environmental signals in the strata. </p> <p>This dataset is an HDF5 dataset, which is a general format. The data largely consist of a set of 3D arrays that contain 2D topography and imagery data, where the third dimension is time. Each data object is paired with a 1D vector that links datasets across the time dimension, since data were collected at different intervals. The appropriate linking datasets are also included as CSVs.</p>
ScienceDex guides
Understand access before you commit
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.