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32 results for “Flood impacts”
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.
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.
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>
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>
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>
India Flood Inventory-Impacts (IFI-Impacts) [1967-2023]: A multi-source national geospatial database to facilitate comprehensive flood research
<p>This repository hosts the India Flood Inventory with Impacts (IFI-Impacts) database. It contains flood event data sourced from the Indian Meteorological Department from 1967-2023. It has undergone extensive manual digitization, cleaning, and includes new information to make it suitable for computational research in hydroclimate.</p> <p>v4.0: Development of District Flood Severity Index (DFSI)</p> <p>v3.0: India Flood Inventory (IFI) 1967-2023. Updated with local government codes (LGD) for state and district. </p> <p>v1.0: India Flood Inventory (IFI) 1967-2016.</p> <p>v2.0: India Flood Inventory (IFI) 1967-2023. With impacts and district flooded area.</p> <p><strong>REFERENCES</strong></p> <p>Saharia, M., Jain, A., Baishya, R.R., Haobam, S., Sreejith, O.P., Pai, D.S., Rafieeinasab, A., 2021. India flood inventory: creation of a multi-source national geospatial database to facilitate comprehensive flood research. Nat Hazards. <a href="https://doi.org/10.1007/s11069-021-04698-6">https://doi.org/10.1007/s11069-021-04698-6</a></p> <div> <div>Saharia, M., Jain, S.K., Prakash, V., Malik, H., Sreejith, O.P., Joshi, D., 2025. A district-level flood severity index for flood management in India. Nat Hazards. <a href="https://doi.org/10.1007/s11069-025-07493-9">https://doi.org/10.1007/s11069-025-07493-9</a></div> </div>
Future water level, discharge, and flood maps under climate change and infrastructure impacts along the Cambodian Mekong.
<p>Baseline and future (2036-2065) river water levels and discharges at 4 gauging stations along the Cambodian Mekong (Kratie, Kampong Cham, Chrouy Changva, and Neak Loeung) under different scenarios of climate change (RCP 4.5 and 8.5) and infrastructural developments. Average depth and duration flood maps are also included for each scenario.</p> <p> </p> <p>A full description of the methods and results can be found in the article: </p> <p>Alexander J. Horton, Nguyen V. K. Triet, Long P. Hoang, Sokchhay Heng, Panha Hok, Sarit Chung, Jorma Koponen, and Matti Kummu. (2022). The Cambodian Mekong floodplain under future development plans and climate change. <em>Nat. Hazards Earth Syst. Sci.</em></p>
Large Language Model-Based Classification of Flash Flood Impacts Across the United States
<p>This repository contains the data sets used for the publication of the journal article titled <em>Large Language Model-Based Classification of Flash Flood Impacts Across the United States</em>.</p> <p>This is the first release of the data with a Zenodo DOI attached to the README.md file. </p> <p>Further information about the data can be found in the GitHub repository's README.md file.</p>
Impact attribution of European floods, 1950-2020
<p>This collection contains impact attribution of 1729 riverine, flash, coastal and compound floods that affected Europe between 1950 and 2020. Each event was reconstructed with a modelling chain combining hazard, exposure, and vulnerability. A total of six drivers are included in two versions: factual (historical) being the best estimate of the drivers per each event, and counterfactual, being an estimate of the driver under static, 1950 conditions. All combinations of possible factual and counterfactual drivers results in 64 scenarios. A list of all scenarios is contained in 'Attribution_scenarios_list.csv', where the columns are as follows:</p> <ul> <li>Scenario - scenario number used in attribution results </li> <li>Climate_change - effect of changes in river discharges, storm surge heights, wave run-up, and long-term sea level rise</li> <li>Catchment_alteration - effect of changes in land use, reservoir capacity and water demand on the hydrological cycle</li> <li>Exposure_growth - effect of change in population stock and gross domestic product (GDP) at subnational region level (NUTS3)</li> <li>Exposure_local - effect of local-scale exposure changes: land use structure, GDP composition by sector, asset-to-GDP ratio, building of new infrastructure</li> <li>Flood_protection - effect of change in probability that a hydrological flood will cause significant socioeconomic impacts, primarily through the breach of structural defenses</li> <li>Vulnerability - effect of changing relative loss due to local factors, e.g. private precaution, building material, early warning, emergency measures, etc.</li> </ul> <p>'Event_data.zip' contains six files per each flood event identified by their HANZE ID number (https://zenodo.org/records/12635205):</p> <ul> <li>'Attribution_mean_ID.csv' - mean estimate of impact per scenario (fatalities, persons affected, economic loss in 2020 euros)</li> <li>'Attribution_params_ID.csv' - additional impact data per scenario: chance of flood protection failure (fraction), change of fatalities (fraction), relative fatalities (%), relative population affected (%), relative economic loss (%), number of impacted NUTS3 regions, potential absolute flood exposure (fatalities, population affected, economic loss)</li> <li>'Attribution_eco_ID.csv' - mean estimate of economic loss in 2020 euros per scenario per NUTS3 region</li> <li>'Attribution_fat_ID.csv' - mean estimate of fatalities in 2020 euros per scenario per NUTS3 region</li> <li>'Attribution_pop_ID.csv' - mean estimate of population affected in 2020 euros per scenario per NUTS3 region</li> <li>'Attribution_unc_ID.npy' - impact per scenario (fatalities, persons affected, economic loss in 2020 euros), 1000 samples of the uncertainty distribution of impact.</li> </ul> <p>'Aggregated_data.zip' contains six files of aggregated impacts (fatalities, persons affected, economic loss in 2020 euros) per scenario:</p> <ul> <li>'Attribution_aggregate_event_IMPACT.csv' - all events aggregated in one file per impact type, displaying: <ul> <li>HANZE_ID - unique ID of the event in HANZE database</li> <li>Year - year when the event started</li> <li>Code - country two-letter code</li> <li>Type - type of flood</li> <li>scen_X - scenario number X</li> </ul> </li> <li>'Attribution_aggregate_region_IMPACT.csv' - estimated NUTS3-level impacts of all events that affected a given region between 1950 and 2020 (see NUTS3 region map in https://zenodo.org/records/12635205).</li> </ul> <p>Graphs of attribution per event, and maps of aggregated attribution can be viewed online: <a href="https://naturalhazards.eu">https://naturalhazards.eu</a></p> <p>Results can be reproduced using code (HANZE model v2.4) and input data also available from Zenodo.</p>
Supporting data for "A service to help insurers understand the financial impacts of changing flood risk in Europe, based on PESETA IV"
<p>Supporting data for the paper "A service to help insurers understand the financial impacts of changing flood risk in Europe, based on PESETA IV".</p> <p><a href="https://doi.org/10.1016/j.cliser.2023.100395">https://doi.org/10.1016/j.cliser.2023.100395</a></p>
A dynamic von Mises-based model to evaluate the impact of urbanization and climate change on flood timing in Yangtze and Huaihe River Basins, China
<p>The daily streamflow data extracted from 8 selected stations from the Huaihe and Yangtze River Basins, China.</p>
HANZE v2.4 flood impact model input data
<p>This dataset provides input data needed to run HANZE v2.4 model. The ZIP files need to be downloaded and unpacked in the same directory, which has to be defined in "get_file.py" of the HANZE model (variable "repo_path" at the beginning of the file).</p>
HANZE v2.2 flood impact model input data
<p>This dataset provides input data needed to run HANZE v2.2 model. The ZIP files need to be downloaded and unpacked in the same directory, which has to be defined in "get_file.py" of the HANZE model (variable "repo_path" at the beginning of the file).</p>
HANZE v2.3 flood impact model input data
<p>This dataset provides input data needed to run HANZE v2.3 model. The ZIP files need to be downloaded and unpacked in the same directory, which has to be defined in "get_file.py" of the HANZE model (variable "repo_path" at the beginning of the file).</p>
Deciphering the impacts of main inflowing rivers on dissolved organic matter in Lake Daye using isotopes, optical spectroscopy, and FT-ICR-MS during non-flood season
<p>The uploaded data include water quality data, isotopes, DOM fluorescence index and FT ICR MS data of Daye Lake and its inflowing rivers.</p>
Quantitative assessment of the reservoir-induced and urbanization-induced impact on multivariate flood risk via a nonstationary vine Copula model
<p>Here we show the results of the characteristics of the floods at Huayuankou, Lanzhou and Toudaoguai gauges selected by AMS and POT mentod, respectively. Besides that, the inormation about the reservoirs and the imprevious layer in the control catchment of each station is also uploaded.</p>
Data for Tropical Cyclones flood hazards and impacts in Beira for study "Exploring coastal climate adaptation through storylines: Insights from Cyclone Idai in Beira, Mozambique"
<p>Data for Tropical Cyclones flood hazards and impacts in Beira for study "Exploring coastal climate adaptation through storylines: Insights from Cyclone Idai in Beira, Mozambique"<br><br><span><a href="../api/records/12664900/draft/files/hmax_idai_ifs_rebuild_bc_hist_rain_surge_noadapt.tiff/content" target="_blank" rel="noopener noreferrer">hmax_idai_ifs_rebuild*</a> -> Flood maps<br><a href="../api/records/12664900/draft/files/spatial_idai_ifs_rebuild_bc_3c-hightide_rain_surge_retreat.gpkg/content" target="_blank" rel="noopener noreferrer">spatial_idai_ifs_rebuild*</a> -> Impacts<br></span></p>
Supporting Datasets for 'SWOT Satellite Reveals Devastating Flood Impact in Rio Grande do Sul, Brazil'
<p>This dataset contains multiple sources of geospatial and environmental data used for flood extension and volume analysis in the study of the May 2023 extreme flood that hit south Brazil. The data integrates satellite remote sensing, atmospheric reanalysis, hydrological measurements, terrain data, and socioeconomic indicators to evaluate flooding impacts and their connection to climate variability.</p> <p>The dataset include:</p> <ol> <li> <p><strong>Sentinel-2 Satellite Data</strong></p> </li> <li> <p><strong>SWOT Mission High-Density Pixel Cloud Data</strong></p> </li> <li> <p><strong>HIDROWEB Water Level Data</strong></p> </li> <li> <p><strong>Forest And Buildings Removed Copernicus Digital Elevation Model (FABDEM)</strong></p> </li> <li> <p><strong>Modern-Era Retrospective Analysis for Research and Applications 2 (MERRA-2)</strong></p> </li> <li> <p><strong>Integrated Multi-satellite Retrievals for Global Precipitation Measurements (IMERG)</strong></p> </li> <li> <p><strong>Brazilian Meteorological Database (BDMEP)</strong></p> </li> <li> <p><strong>Institute of Hydraulic Research (IPH) Flood Extent Map</strong></p> </li> <li> <p><strong>Socioeconomic Data from the Social Vulnerability Atlas</strong></p> </li> </ol> <p> </p> <p><strong>Disclaimer:</strong></p> <p>This dataset is a compilation of various data sources, each of which may be subject to its own specific licensing terms and conditions. Users of this dataset are strongly advised to review the license associated with each individual sub-dataset before using, modifying, or redistributing the data. While we have provided the dataset under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, certain sub-datasets may have additional restrictions or requirements, such as attribution, non-commercial use, or limitations on derivative works. It is the responsibility of the user to ensure compliance with all applicable licenses. Please refer to the original data sources and their respective licenses for detailed information.</p>
Timing of hydrologic anomalies direct impacts on migration traits in a flood pulse fishery system
<p>1. Understanding adaptive reservoir management strategies that balance ecological outcomes with other objectives necessitates properly articulated environmental objectives. Aside from flood pulse extent-related metrics, residual-based descriptors provide robust descriptions of fish assemblage structure and harvest.</p> <p>2. We proposed a model framework based on spectral analysis of hydrologic variation and the Multivariate AutoRegressive State Space (MARSS) model to statistically quantify the effect sizes of hydrologic variation impacts on total fish catch and functional group (migration types) fish harvest and applied it to 17 years of fish harvest data from the Lower Mekong River Basin (LMB).</p> <p>3. Our findings suggest that duration and timing of hydrologic anomalies matter as much as their magnitude. Anomaly droughts coupled with strong pulse can benefit species if timed correctly. Longitudinal migrators were more sensitive to anomalous floods and droughts than other migratory species. Fish catch projections using effect sizes derived from historical data revealed that a well-timed and protracted flood followed by a powerful flood pulse would be advantageous to the fishery, but a flood delay could negate such benefits.</p> <p>4. Our results add to a growing body of research that suggests ecological flows can be engineered. For most dams, the rule curve describing reservoir releases and resulting downstream hydrograph is designed in an ecological vacuum in which the objectives are to maximize human services—power production, flood control or navigation. Our work demonstrates that hydrograph can be designed to manage aspects of functional biodiversity directly. Though the exact shape of our hydrograph may not apply to other engineered river systems, we suggest that the approach can be applied generally, and globally both to developed and developing river basins. Specifically, a functional biodiversity rule curve could be optimized as an additional objective function in a multi-objective optimization framework. Our methodology provides a quantitative method for deriving an ecological flow for this larger tradeoff analysis.</p>
Impact of a workshop with visualization and ethics discussion on awareness of flood risk and intent to protect
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