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766 results for “flooding”
HANZE database of historical flood impacts in Europe, 1870-2025
<p>The HANZE dataset covers riverine, pluvial, coastal and compound floods that have occurred in 42 European countries between 1870 and 31 March 2025. The data was collected by extensive data-collection from more than 1000 sources ranging from news reports through government databases to scientific papers. The dataset includes 2687 events characterized by at least one impact statistic: area inundated, fatalities, persons affected or economic loss. Economic losses are presented both in the original currencies and price levels as well as inflation and exchange-rate adjusted to 2024 value of the euro. The spatial footprint of affected areas is consistently recorded using more than 1400 subnational units corresponding, with minor exceptions, to the European Union’s Nomenclature of Territorial Units for Statistics (NUTS), level 3. Daily start and end dates, information on causes of the event, notes on data quality issues or associated non-flood impacts, and full bibliography of each record supplement the dataset. Apart from the possibility to download the data, the database can be viewed, filtered and visualized online: <a href="https://naturalhazards.eu">https://naturalhazards.eu</a>. The dataset is designed to be complimentary to HANZE-Exposure, a high-resolution model of historical exposure changes (such as population and asset value), and be easily usable in statistical and spatial analyses.</p> <p><strong>This is a preliminary update of HANZE v2.1, adding 169 floods for years 2021-2025 (until 31 March 2025). It makes only minor revisions to previous data (adds 11 pre-2021 events, revises 17 records and removes 3 events that were newly reassessed as non-flood events). A more extensive revision of the data is planned for 2026.</strong></p> <p>The dataset contains the following files (CSV comma-delimited, UTF8, and ESRI shapefiles in zipped folders)</p> <p><strong>HANZE flood events database </strong></p> <p>HANZE_events.csv - Flood event data</p> <p>HANZE_references.csv - List of all references</p> <p>HANZE3_events_regions_2010.zip - Flood event data as GIS file (regions v2010)</p> <p>HANZE3_events_regions_2021.zip - Flood event data as GIS file (regions v2021)</p> <p>HANZE3_events_regions_2021.zip - Flood event data as GIS file (regions v2021)</p> <p><strong>Supplementary data </strong></p> <p>S1_countries_codes_and_names.csv - Country codes/names</p> <p>S2_regions_codes_and_names_v2010.csv - Region codes/names, v2010</p> <p>S3_regions_codes_and_names_v2021.csv - Region codes/names, v2021</p> <p>S3a_regions_codes_and_names_v2024.csv - Region codes/names, v2024</p> <p>S4_list_of_all_currencies_by_country.csv - Data on all currencies used in the study area since 1870</p> <p>S5_currency_conversion_rates.csv - Conversion rates applied to compute losses in 2024 euros</p> <p>S6_GDP_deflators_by_country.csv - Gross domestic product deflator by country, 1870-2025</p> <p>S7_floods_removed_from_HANZE.csv - Flood events in HANZE v1 and v2, which were excluded from v3</p> <p>Regions_v2010_simplified.zip - Map of subnational regions used in the database, v2010</p> <p>Regions_v2021_simplified.zip - Map of subnational regions used in the database, v2021</p> <p>Regions_v2024_simplified.zip - Map of subnational regions used in the database, v2024</p>
Surface water and flooding dynamics based on seasonally continuous Landsat data (1986-2011) in a dryland river basin (monthly, seasonally, and yearly animations)
<p>The animations provided here are part of the following publication:<br> 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>Please refer to the above mentioned publication for a description of the data and interpretation of the patterns.</p> <p>The animations are based on statistically validated surface water and flooding extent dynamics data derived from seasonally continous Landsat TM/ETM+ and random forest models from 1986 to 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 and available here: https://www.sciencedirect.com/science/article/pii/S0034425716300621 </p>
Macroinvertebrate collections following floods in Sycamore Creek, Arizona, USA 1985-1999
The primary objective of this project is to understand how long-term climate variability influences the structure and function of desert streams. Climate and hydrology are intimately linked in arid landscapes; for this reason, desert streams are particularly well suited for both observing and understanding the consequences of climate variability and directional change. Arid regions are characterized by high interannual variation in precipitation, and these climate patterns drive the overall disturbance regime (in terms of flooding and drying) and nutrient status of desert stream ecosystems. At long time scales, the number and size of floods in a given year or cluster of years dictate nutrient delivery to streams from the surrounding catchment, and also influence the biogeomorphic structure of the stream-riparian corridor. Embedded within this decadal-scale hydrologic regime, flash floods scour stream channels and initiate a series of rapid successional changes by benthic algae and macroinvertebrates at short time scales (i.e., within a year). An important goal of this research is to determine how recovery following discrete events is influenced by both stream nutrient status and channel structure and to thus better understand how long-term climate variability and change guide the interactions among slow (biogeomorphic change) and fast (post-flood succession) features and processes characteristic of desert stream ecosystems.
FCE Redlands Flood Zones, Miami-Dade County, South Florida
Urban growth models have increasingly been used by planners and policy makers to visualize, organize, understand, and predict urban growth. However, these models reveal a wide disparity in their attention to policy factors. Some urban growth models capture few if any specific policy effects (e.g.,as model variables), while others integrate certain policies but not others. Since zoning policies are the most widely used form of land use control in the United States, their conspicuous absence from so many urban growth models is surprising. This research investigated the impacts of zoning on urban growth by calibrating and simulating a cellular automaton urban growth model, SLEUTH, under two conditions in a South Florida location. The first condition integrated restrictive agricultural zoning into SLEUTH, while the other ignored zoning data. Goodness of fit metrics indicate that including the agricultural zoning data improved model performance. The results further suggest that agricultural zoning has been somewhat successful in retarding urban growth in South Florida. Ignoring zoning information is detrimental to SLEUTH performance in particular, and urban growth modeling in general.
Flooded forest plot sampling in Ecuador
In order to better understand how flooding and gap formation affect Amazonian rainforests, I set up plots both in three major forest types that differed by flooding duration (referred to here as dry, wet, very wet) and in their respective gaps. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Measurements of tidal creek discharge measurements 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 volume of water during lateral exchange measurements in tidal creek systems draining predominantly low-elevation marsh dominated by Spartina alterniflora (LM1 and LM2) and high-elevation marsh dominated by Spartina patens (West, Nelson, HM1). Creeks are located in Rowley, MA, PIE LTER.
Water-column conductivity, salinity, dissolved oxygen, turbidity, and pH by deployed sonde during tidal creek lateral exchange measurements approximately every 5 minutes from beginning of flood tide to the following low tide, Rowley, MA, PIE LTER.
Measurement of water-column conductivity, salinity, temperature, dissolved oxygen, turbidity, and pH logged by deployed sonde in tidal creek systems draining predominantly low-elevation marsh dominated by Spartina alterniflora (LM1 and LM2) and high-elevation marsh dominated by Spartina patens (West, Nelson, HM1). Creeks are located in Rowley, MA, PIE LTER.
Datasets for manuscript: Global River Discharge and Floods in the Warmer Climate of the Last Interglacial
<p>This datasets contains results of the global hydrological and hydrodynamic modeling presented in the paper referenced in the title (doi: 10.1029/2020GL089375). The dataset comprises results for one set of simulations, based on Global Climate Model CESM1.2, out of the eight sets of simulations for eight GCMs included in the paper. The corresponding results for the other seven sets of simulations (based on GCMs CESM2, EC‐EARTH3.2, HadGEM3‐GC3.1, IPSL‐CM6‐LR, MPI‐ESM 1.2.01p1‐LR, NorESM1‐F, and NUIST‐CSM) can be obtained by writing to the corresponding author at paolo.scussolini@vu.nl.</p> <p>Files description:</p> <p>fldare_yearmean_timmean_CEM1.2_LIG.nc : Annual average flood area for the Last Interglacial simulation with GCM CESM1.2, hydrological model PCR-GLOBWB and hydrodynamic model CaMa-Flood.<br> <br> fldare_yearmean_timmean_CESM1.2_PI.nc 4 Mb : Annual average flood area for the Pre-Industrial simulation with GCM CESM1.2, hydrological model PCR-GLOBWB and hydrodynamic model CaMa-Flood.<br> <br> fldsto_yearmean_timmean_CESM1.2_LIG.nc 4 Mb : Annual average flood volume for the Last Interglacial simulation with GCM CESM1.2, hydrological model PCR-GLOBWB and hydrodynamic model CaMa-Flood.<br> <br> fldsto_yearmean_timmean_CESM1.2_PI.nc 4 Mb : Annual average flood volume for the Pre-Industrial simulation with GCM CESM1.2, hydrological model PCR-GLOBWB and hydrodynamic model CaMa-Flood.<br> <br> outflw_yearmean_timmean_CESM1.2_LIG.nc 4 Mb : Annual average river discharge for the Last Interglacial simulation with GCM CESM1.2, hydrological model PCR-GLOBWB and hydrodynamic model CaMa-Flood.<br> <br> outflw_yearmean_timmean_CESM1.2_PI.nc 4 Mb : Annual average river discharge for the Pre-Industrial simulation with GCM CESM1.2, hydrological model PCR-GLOBWB and hydrodynamic model CaMa-Flood.<br> <br> runoff_annuaTot_output_mergetime_timmean_CESM1.2_LIG.nc : Annual average runoff for the Last Interglacial simulation with GCM CESM1.2 and hydrological model PCR-GLOBWB.<br> <br> runoff_annuaTot_output_mergetime_timmean_CESM1.2_PI.nc : Annual average runoff for the Pre-Industrial simulation with GCM CESM1.2 and hydrological model PCR-GLOBWB.</p> <p> </p>
Collection of global datasets for the study of floods, droughts and their interactions with human societies
<p>This is a collection of 134 global and free datasets allowing for spatial (and temporal) analyses of floods, droughts and their interactions with human societies. We have structured the datasets into seven categories: hydrographic baseline, hydrological dynamics, hydrological extremes, land cover & agriculture, human presence, water management, and vulnerability. Please refer to <a href="https://doi.org/10.1002/wat2.1424">Lindersson et al. (2020)</a> for further information about review methodology.</p> <p>The collection is a descriptive list, holding the following information for each dataset: </p> <ul> <li>Category<em> - as structured in Lindersson et al. (2020).</em></li> <li>Sub-category<em>- as structured in Lindersson et al. (2020).</em></li> <li>Abbreviation - <em>official or as specified in Lindersson et al. (2020).</em></li> <li>Title <em>- full title of dataset.</em></li> <li>Product(s)<em> - type of product(s) offered by the dataset.</em></li> <li>Period<em> - time period covered by the dataset, not defined for all datasets.</em></li> <li>Temporal resolution<em> - not defined for static datasets.</em></li> <li>Angular spatial resolution<em> - only defined for gridded datasets.</em></li> <li>Metric spatial resolution <em>- only defined for gridded datasets.</em></li> <li>Map scale</li> <li>Extent<em> - geographic coverage of dataset given in latitude limits.</em></li> <li>Description</li> <li>Creating institute(s)</li> <li>Data type<em> - raster, vector or tabular.</em></li> <li>File format</li> <li>Primary EO type<em> - specifies if the product primarily is based on remote sensing, ground-based data, or a hybrid between remote sensing and ground-based data.</em></li> <li>Data sources<em> - lists the data sources behind the dataset, to the extent this is feasible.</em></li> <li>Data sources also in this table<em> - data sources that are also included as datasets in this collection.</em></li> <li>Intentionally compatible with<em> - defines other datasets in this collection that the dataset is intentinoally compatible with.</em></li> <li>Citation<em> - dataset reference or credit.</em></li> <li>Documentation <em>- dataset documentation.</em></li> <li>Web address<em> - dataset access link.</em></li> </ul> <p>NOTE: Carefully consult the data usage licenses as given by the data providers, to assure that the exact permissions and restrictions are followed.</p>
NYU FloodSense street sign mounted flood depth sensor
<p>Water depth level in mm from a sensor mounted on a street sign post at the corner of 5th Street and Hoyt, Brooklyn, NY (40.676640, -73.994595). The sensor is designed to detect flood water that fills the street and blocks vehicle and pedestrian traffic, as well as depositing micro-organisms on the street. Ultrasonic technology is used to detect flood water depth.</p> <p>The sensor transmits its data via LoRaWAN and is equipped with a solar panel for continuous operation.</p> <p>Depth data is collected at ~5min intervals. Time fields are in local time (New York). Date format is: 2020-10-04 20:11:45.742594232-04:00</p> <p>Two flood events have been observed in this dataset between these date ranges:</p> <ol> <li> <p>"2020-11-15 19:37:00.000000000-05:00" to "2020-11-16 00:30:00.000000000-05:00"</p> </li> <li> <p>"2020-11-30 10:20:00.000000000-05:00" to "2020-11-30 13:30:00.000000000-05:00"</p> </li> </ol> <p>Erroneous data has been observed:</p> <ul> <li>There are ~1% decreases in depth measures on days with sun which suggests that the distance sensor is affected by direct sunlight</li> </ul> <p>This data is preliminary and is for prototyping purposes. </p> <p>This dataset will be updated when more data is collected.</p> <p>Please see our github org for sensor information and build instructions: <a href="https://github.com/floodsense">github.com/floodsense</a></p>
Data: Cutting the costs of coastal protection by integrating vegetation in flood defences.
<p>File: levee_crest_height_reduction_per_country_version_July2021.nc<br>Fields: (1) Crest height reduction m per km along the populated coastline susceptible to flooding (return period = 100 years)<br> (2) Crest height reduction cost saving per country in million USD<sub>2005</sub> PPP along the populated coastline susceptible to flooding (return period = 100 years)<br> (3) Cost savings as percentage of GDP<sub>2005</sub> along the urban populated coastline susceptible to flooding (return period = 100 years)</p> <p>File: transectdata_version_July2021.nc<br> Transectdata of vegetated transects within the study area.<br>Fields: <br>(1) rps = return period <br>(2) fid = id of the transects<br>(3) centroids = coordinates of the transects<br>(4) inun = (1) in area susceptible to flooding<br>(5) urban = (1) in urban area, (0) not in urban area<br>(6) veg_width = derived coastal vegetation belt width along the foreshore<br>(7) veg_type = derived coastal vegetation type along the foreshore (1: salt marshes, 2: mangroves)<br>(8) hsig = Offshore significant wave heights (multiple return periods) corresponding to the transects<br>(9) wave period = Offshore peak wave period (multiple return periods) corresponding to the transects<br>(10) surge = Extreme water level combination of surge and tide (m +MSL) (multiple return periods)<br>(11) veg_z0 = elevation at the start of the vegetated zone (m +MSL)<br>(12) hrms_end_noveg = root mean square wave height at the end of the foreshore (without vegetation) (multiple return periods)<br>(13) hrms_endveg = root mean square wave height at the end of the foreshore (with vegetation) (multiple return periods) <br>(14) pdens_15km = population density derived using buffer of 15 kilometre radius</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>
First Street Foundation Property Level Flood Risk Statistics V1.3
<p>The property level flood risk statistics generated by the First Street Foundation Flood Model Version 1.3 come in CSV format. The data that is included in the CSV includes:</p> <ul> <li> <p>An FSID; a First Street ID (FSID) is a unique identifier assigned to each location.</p> </li> <li> <p>The latitude and longitude of a parcel as well as the zip code, census block group, census tract, county, congressional district, and state of a given parcel.</p> </li> <li> <p>The property’s Flood Factor as well as data on economic loss.</p> </li> <li> <p>The flood depth in centimeters at the low, medium, and high CMIP 4.5 climate scenarios for the 2, 5, 20, 100, and 500 year storms in 2021, 2036, and 2051.</p> </li> <li> <p>Data on the cumulative probability of a flood event exceeding the 0cm, 15cm, and 30cm threshold depth is provided at the low, medium, and high climate scenarios for years 2021, 2036, and 2051.</p> </li> <li> <p>Information on historical events and flood adaptation, such as ID and name.</p> </li> </ul> <p>You can download a sample of the property level flood risk statistics generated by First Street's Flood Model on this page. You can purchase the property level data for areas within the contiguous United States on the First Street website <a href="https://firststreet.org/data-access/paid-access/?utm_source=Property_Statistics&utm_medium=Purchase_Data&utm_campaign=Zenodo#pricing-component">here</a>. You can find the data dictionary which breaks down the data that is available with each property-level data purchase <a href="https://firststreet.org/data-access/getting-started-with-first-street-data/data-dictionary/?utm_source=Property_Statistics&utm_medium=Data_Dictionary&utm_campaign=Zenodo">here</a>. If you are also interested in the hazard layers, you can find more information <a href="https://firststreet.org/data-access/getting-started-with-first-street-data/documentation-hazard-dictionary/?utm_source=Property_Statistics&utm_medium=Hazard_Dictionary&utm_campaign=Zenodo">here</a>.</p>
First Street Foundation Property Level Flood Risk Statistics V2.0
<p>The property level flood risk statistics generated by the First Street Foundation Flood Model Version 2.0 come in CSV format. </p> <p>The data that is included in the CSV includes:</p> <ul> <li> <p>An FSID; a First Street ID (FSID) is a unique identifier assigned to each location.</p> </li> <li> <p>The latitude and longitude of a parcel as well as the zip code, census block group, census tract, county, congressional district, and state of a given parcel.</p> </li> <li> <p>The property’s Flood Factor as well as data on economic loss.</p> </li> <li> <p>The flood depth in centimeters at the low, medium, and high CMIP 4.5 climate scenarios for the 2, 5, 20, 100, and 500 year storms this year and in 30 years.</p> </li> <li> <p>Data on the cumulative probability of a flood event exceeding the 0cm, 15cm, and 30cm threshold depth is provided at the low, medium, and high climate scenarios for this year and in 30 years.</p> </li> <li> <p>Information on historical events and flood adaptation, such as ID and name.</p> </li> </ul> <p> </p> <p>This dataset includes <a href="https://firststreet.org/">First Street</a>'s aggregated flood risk summary statistics. The data is available in CSV format and is aggregated at the congressional district, county, and zip code level. The data allows you to compare FSF data with FEMA data. You can also view aggregated flood risk statistics for various modeled return periods (5-, 100-, and 500-year) and see how risk changes due to climate change (compare FSF 2020 and 2050 data). There are various <a href="https://floodfactor.com/">Flood Factor</a> risk score aggregations available including the average risk score for all properties (flood factor risk scores 1-10) and the average risk score for properties with risk (i.e. flood factor risk scores of 2 or greater). This is version 2.0 of the data and it covers the 50 United States and Puerto Rico. There will be updated versions to follow.</p> <p>If you are interested in acquiring First Street flood data, you can request to access the data <a href="https://firststreet.org/data-access/paid-access/?utm_source=Summary_Statistics_v1.3&utm_medium=Purchase_Data&utm_campaign=Zenodo#pricing-component">here</a>. More information on First Street's flood risk statistics can be found <a href="https://firststreet.org/data-access/getting-started-with-first-street-data/documentation-data-dictionaryv2/">here</a> and information on First Street's hazards can be found <a href="https://firststreet.org/data-access/getting-started-with-first-street-data/documentation-hazard-dictionary/?utm_source=Summary_Statistics_v1.3&utm_medium=Hazard_Dictionary&utm_campaign=Zenodo">here</a>.</p> <p>The data dictionary for the parcel-level data is below.</p> <table> <tbody> <tr> <td> <p><strong>Field Name</strong></p> </td> <td> <p><strong>Type</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>fsid</p> </td> <td> <p>int</p> </td> <td> <p>First Street ID (FSID) is a unique identifier assigned to each location</p> </td> </tr> <tr> <td> <p>long</p> </td> <td> <p>float</p> </td> <td> <p>Longitude</p> </td> </tr> <tr> <td> <p>lat</p> </td> <td> <p>float</p> </td> <td> <p>Latitude</p> </td> </tr> <tr> <td> <p>zcta</p> </td> <td> <p>int</p> </td> <td> <p>ZIP code tabulation area as provided by the US Census Bureau</p> </td> </tr> <tr> <td> <p>blkgrp_fips</p> </td> <td> <p>int</p> </td> <td> <p>US Census Block Group FIPS Code</p> </td> </tr> <tr> <td> <p>tract_fips</p> </td> <td> <p>int</p> </td> <td> <p>US Census Tract FIPS Code</p> </td> </tr> <tr> <td> <p>county_fips</p> </td> <td> <p>int</p> </td> <td> <p>County FIPS Code</p> </td> </tr> <tr> <td> <p>cd_fips</p> </td> <td> <p>int</p> </td> <td> <p>Congressional District FIPS Code for the 116th Congress</p> </td> </tr> <tr> <td> <p>state_fips</p> </td> <td> <p>int</p> </td> <td> <p>State FIPS Code</p> </td> </tr> <tr> <td> <p>floodfactor</p> </td> <td> <p>int</p> </td> <td> <p>The property's Flood Factor, a numeric integer from 1-10 (where 1 = minimal and 10 = extreme) based on flooding risk to the building footprint. Flood risk is defined as a combination of cumulative risk over 30 years and flood depth. Flood depth is calculated at the lowest elevation of the building footprint (largest if more than 1 exists, or property centroid where footprint does not exist)</p> </td> </tr> <tr> <td> <p>CS_depth_RP_YY</p> </td> <td> <p>int</p> </td> <td> <p>Climate Scenario (low, medium or high) by Flood depth (in cm) for the Return Period (2, 5, 20, 100 or 500) and Year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_depth_002_year00</p> </td> </tr> <tr> <td> <p>CS_chance_flood_YY</p> </td> <td> <p>float</p> </td> <td> <p>Climate Scenario (low, medium or high) by Cumulative probability (percent) of at least one flooding event that exceeds the threshold at a threshold flooding depth in cm (0, 15, 30) for the year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_chance_00_year00</p> </td> </tr> <tr> <td> <p>aal_YY_CS</p> </td> <td> <p>int</p> </td> <td> <p>The annualized economic damage estimate to the building structure from flooding by Year (today or 30 years in the future) by Climate Scenario (low, medium, high). Today as year00 and 30 years as year30. ex: aal_year00_low</p> </td> </tr> <tr> <td> <p>hist1_id</p> </td> <td> <p>int</p> </td> <td> <p>A unique First Street identifier assigned to a historic storm event modeled by First Street</p> </td> </tr> <tr> <td> <p>hist1_event</p> </td> <td> <p>string</p> </td> <td> <p>Short name of the modeled historic event</p> </td> </tr> <tr> <td> <p>hist1_year</p> </td> <td> <p>int</p> </td> <td> <p>Year the modeled historic event occurred</p> </td> </tr> <tr> <td> <p>hist1_depth</p> </td> <td> <p>int</p> </td> <td> <p>Depth (in cm) of flooding to the building from this historic event</p> </td> </tr> <tr> <td> <p>hist2_id</p> </td> <td> <p>int</p> </td> <td> <p>A unique First Street identifier assigned to a historic storm event modeled by First Street</p> </td> </tr> <tr> <td> <p>hist2_event</p> </td> <td> <p>string</p> </td> <td> <p>Short name of the modeled historic event</p> </td> </tr> <tr> <td> <p>hist2_year</p> </td> <td> <p>int</p> </td> <td> <p>Year the modeled historic event occurred</p> </td> </tr> <tr> <td> <p>hist2_depth</p> </td> <td> <p>int</p> </td> <td> <p>Depth (in cm) of flooding to the building from this historic event</p> </td> </tr> <tr> <td> <p>adapt_id</p> </td> <td> <p>int</p> </td> <td> <p>A unique First Street identifier assigned to each adaptation project</p> </td> </tr> <tr> <td> <p>adapt_name</p> </td> <td> <p>string</p> </td> <td> <p>Name of adaptation project</p> </td> </tr> <tr> <td> <p>adapt_rp</p> </td> <td> <p>int</p> </td> <td> <p>Return period of flood event structure provides protection for when applicable</p> </td> </tr> <tr> <td> <p>adapt_type</p> </td> <td> <p>string</p> </td> <td> <p>Specific flood adaptation structure type (can be one of many structures associated with a project)</p> </td> </tr> <tr> <td> <p>fema_zone</p> </td> <td> <p>string</p> </td> <td> <p>Specific FEMA zone categorization of the property ex: A, AE, V. Zones beginning with "A" or "V" are inside the Special Flood Hazard Area which indicates high risk and flood insurance is required for structures with mortgages from federally regulated or insured lenders</p> </td> </tr> <tr> <td> <p>footprint_flag</p> </td> <td> <p>int</p> </td> <td> <p>Statistics for the property are calculated at the centroid of the building footprint (1) or at the centroid of the parcel (0)</p> </td> </tr> </tbody> </table> <p> </p>
Raw data for the journal article "Cracks as efficient tools to mitigate flooding in gas diffusion electrodes used for the electrochemical reduction of carbon dioxide"
<p>This data set corresponds to the article by Kong et al. entitled "Cracks as efficient tools to mitigate flooding in gas diffusion electrodes used for the electrochemical reduction of carbon dioxide", published in Small Methods</p>
Codes and dataset of the publication "Effectiveness of Sentinel-1 and Sentinel-2 for Flood Detection Assessment in Europe"
<p>The folder contains the codes, input and output of the analysis carried out for supporting the publication of the paper:</p> <p>Tarpanelli A., Mondini A., Camici S.:Effectiveness of Sentinel-1 and Sentinel-2 for Flood Detection Assessment in Europe, Natural Hazards and Earth System Sciences, https://doi.org/10.5194/nhess-2022-63, 2022.</p> <p> </p> <p>The codes should be run in order A1-A7 to generate all the figures of the paper.</p> <p>For details please send an email to:</p> <p>angelica.tarpanelli@irpi.cnr.it</p> <p> </p>
FloodSense street sign mounted flood depth sensor
<p><strong>Flood Depth Data (FDD)</strong> collected by a fleet of sensors deployed across 5 boroughs of New York City with a resolution of half an inch or less. The metadata for the sensors is included in the metadata.csv to identify the deployment coordinates of sensors, each with a unique <strong><em>deployment_id</em></strong>. </p> <p>The depth data is collected at least every five minutes and every minute in some locations depending on the ability to harvest solar energy at that deployment location. </p> <p>The final depth data field is <strong><em>depth_proc_mm</em></strong>, and the raw data is <strong><em>dist_mm</em></strong>. </p> <p>The raw measurement values received from the sensor are distance measurements (dist_mm), which are simply distance measurements collected from a ranging ultrasonic-based sensor. These distance measurements are converted to depths using <strong><em>night_median_dist_mm</em></strong> which is a daily calculated median of nighttime sensor readings. Direct sunlight affects ranging measurements due to high variance in the air column between the sensor and the concrete surface that it is mounted over. Additionally, the housing internally heats up when under direct sunlight, which affects the sensor readings and appears as if the surface dips with the daily increase and decrease in temperature during the daytime.</p> <p>After converting to raw depth values, a simple range filter is applied to the data removing any anomalies that lie below 10 millimeters and above unrealistic depth values (for example a person - between 5ft to 6ft), which is named <strong><em>depth_filt_mm</em></strong>.</p> <p>Further, this filtered depth value is processed through data filters eliminating blips, any pulse chains, or a flat line due to garbage or a car parked underneath the sensor. The output of these filters is labeled <strong><em>depth_proc_mm</em></strong>. </p> <p>This data is intended for use by communities, researchers, and New York City government agencies to better understand the frequency, severity, and impacts of flooding in New York City. </p> <p>Here is the live dashboard for these sensors deployed: <a href="https://dataviz.floodnet.nyc/">FloodNet Data Dashboard</a></p> <p>More about this project at <a href="https://www.floodnet.nyc/">FloodNet.NYC</a></p> <p>This is an open-source project and for more information on the sensors and build manuals see the <a href="https://github.com/floodnet-nyc/flood-sensor">FloodNet FloodSensor GitHub page</a></p>
floodX Flooding Videos
<p>This package contains archives of videos of the flooding taken with surveillance cameras. The videos are grouped by camera and by recording sessions. The estimated temporal offset of each camera for each recording session is provided in the file "temporal_offsets_of cameras.txt".</p> <p> </p> <p>This package belongs to a collection of packages containing data collected from the floodX experiments.</p> <p> - floodX Raw Data, Metadata, and Preprocessing Code (doi: 10.5281/zenodo.830505)<br> - floodX Preprocessed Monitoring Data (doi: 10.5281/zenodo.830511)<br> - floodX Preprocessed Calibration Data (doi: 10.5281/zenodo.830513)<br> - floodX Flooding Videos (doi: 10.5281/zenodo.830451)<br> - floodX Flooding Images (doi: 10.5281/zenodo.830501)<br> - floodX Data Logger Images (doi: 10.5281/zenodo.830507)<br> - floodX Data Logger Videos (doi: 10.5281/zenodo.830504)<br> - floodX Documentation (doi: 10.5281/zenodo.830506)</p>
FloodSformer: River Flood datasets&checkpoints
<div> <div>Data used for the paper: Pianforini et al. (2025). FloodSformer: A transformer-based data-driven model for predicting the 2-D dynamics of fluvial floods. <em>Environmental Modelling & Software</em>. <a href="https://doi.org/10.1016/j.envsoft.2025.106599">https://doi.org/10.1016/j.envsoft.2025.106599</a></div> <div> </div> <div>The repository contains the training/testing datasets and the checkpoints for the Toce River case study.</div> <div>The data for the Po River test case are unavailable due to restrictions on data permissions.</div> <div> </div> <div> <div> <div>The python code of the FloodSformer model is available at the <a href="https://github.com/mpianforini/FloodSformer">GitHub repository</a>.</div> </div> </div> </div> <p>More detailed information on the repository content are provided in the README_dataset.md file.</p> <div> </div> <div><strong>Acknowledgements</strong></div> <div> <div>This research was granted by <span>University of Parma</span> through the action “Bando di Ateneo 2024 per la ricerca”. RV and SD acknowledge financial support from the <span>PNRR MUR project</span> <span><span>ECS_00000033_ECOSISTER</span></span>. This research also benefits from the HPC facility of the University of Parma. Finally, the Authors acknowledge the CINECA award under the ISCRA initiative, for the availability of high-performance computing resources and support (projects AMNERIS and MOZART).</div> <div> </div> </div>
HANZE catalogue of modelled and historical floods in Europe, 1950-2020
<p>The HANZE dataset covers riverine, pluvial, coastal and compound floods that have occurred in 42 European countries. It contains:</p> <ul> <li>2521 historical floods with impact data (1870-2020);</li> <li>237 further historical floods with significant impacts, but without precise impact data (1950-2020)</li> <li>Nearly 15,000 modelled floods with a potential to cause significant impacts, classified by actual historical occurrence or non-occurrence impacts (1950-2020).</li> </ul> <p>Historical floods and the classification of modelled floods was completed by extensive data-collection from more than 900 sources ranging from news reports through government databases to scientific papers. Impact data collected or modelled include area inundated, fatalities, persons affected or economic loss. Economic losses were inflation- and exchange-rate adjusted to 2020 value of the euro. The historical catalogue (lsit A) also includes losses in the original currencies and price levels. The spatial footprint of affected areas is consistently recorded using more than 1400 subnational units corresponding, with minor exceptions, to the European Union’s Nomenclature of Territorial Units for Statistics (NUTS), level 3. Apart from the possibility to download the data, the database can be viewed, filtered and visualized online: <a href="https://naturalhazards.eu">https://naturalhazards.eu</a>. </p> <p>The dataset contains the following files (CSV comma-delimited, UTF8, and ESRI shapefiles in zipped folders):</p> <p>HANZE_historical_floods_catalogue_listA.csv - historical floods with impact data (1870-2020)</p> <p>HANZE_historical_floods_catalogue_listB.csv - historical floods without impact data (1950-2020)</p> <p>HANZE_potential_flood_catalogue_all.csv - modelled potential floods (1950-2020)</p> <p>HANZE_list_of_references.csv - List of all references used in the catalogues</p> <p>HANZE_model_completness_analysis.csv - Comparison between modelled and reported footprints of historical floods</p> <p>Regions_v2010_simplified.zip - Map of subnational regions (v2010)</p> <p>Regions_v2021_simplified.zip - Map of subnational regions (regions v2021)</p> <p> </p> <p>v1.2: corrected NUTS regions v2021 for a few events, which were accidently coded with v2010 regions.</p> <p>v1.1: errors in two records in "HANZE_historical_floods_catalogue_listB.csv" (wrong country code in event ID 8227 and wrong start date in event ID 8237) were corrected.</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)
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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.