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394 results for “hazard”
Characterisation of Social Vulnerability to the environmental hazard of heat in Logroño, and the surrounding La Rioja region in Spain, 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 Logroño, and the surrounding La Rioja region, Spain. The input variables used in this dataset come from the national census data for Spain 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>
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>
Characterisation of Social Vulnerability to the environmental hazard of heat in Milan, 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 Milan, Italy. The input variables used in this dataset come from the national census data for Italy and EU Copernicus data.</p> <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>
Dataset - Generating reliable estimates of tropical cyclone induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks
<p>This data is complementary to the paper by Leijnse et al. 2022 "Generating reliable estimates of tropical cyclone induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks" <br> https://doi.org/10.5194/nhess-2021-181</p> <p>This data is made available in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE</p> <p>For questions about the data ask: tim.leijnse@deltares.nl</p> <p>For more information about the tool to generate the used synthetic tracks TCWiSE see: <a href="https://www.deltares.nl/en/software/tcwise/">https://www.deltares.nl/en/software/tcwise/</a></p> <p> </p>
Residential exposure to natural hazards in Europe, 2000–2020
<p>This dataset provides average national-level current gross replacement costs of the stock of residential assets (buildings and household contents) per m<sup>2</sup> of useful floor space. The dataset includes annual time series (2000–2020) for 33 European countries, in nominal and real prices. It is intended for application in microscale disaster models by enabling approximation of the monetary value of individual buildings exposed to hazards, especially floods, for which damage functions often distinguish between vulnerability of the building structure and contents inside.</p>
Nairobi and Istanbul Multi-Hazard Interrelationships Database
<p><em><span>Nairobi and Istanbul Multi-Hazard Interrelationships Database</span></em></p> <p><strong><span>10.5281/zenodo.13220740</span></strong></p> <p><span>This </span><em><span>Nairobi and Istanbul Multi-Hazard Interrelationships Database</span></em><span> </span><span>uses a critical review of 135 sources (academic and grey literature, databases, online and social media), to identify <strong>the breadth of natural hazard types</strong> that might influence Nairobi (19 possible natural hazard types) and Istanbul (23 hazard types). We further identified <strong>hazard interrelationship pairs</strong> (e.g., an earthquake triggering landslides) in Nairobi (88 potential hazard interrelationship pairs) and Istanbul (105 hazard pairs) out of a possible 576 interrelationships. This extensive Excel (140 kb) database accompanies the paper Šakić Trogrlić et al. (2024).</span></p> <p><span>The <em>Nairobi and Istanbul Multi-Hazard Interrelationships Database</em> consists of the following eight tabs (in brackets the number of rows [R] × columns [C] of information):</span></p> <ul> <li><span>Excel Tab A. <span> </span>Single Hazard Evidence Nairobi (87R×16C)</span></li> <li><span>Excel Tab B. <span> </span>Single Hazard Evidence Istanbul (68R×11C)</span></li> <li><span>Excel Tab C. <span> </span>Hazard Interrelationships Nairobi (118R×14C)</span></li> <li><span>Excel Tab D. <span> </span>Hazard Interrelationships Istanbul (122R×13C)</span></li> <li><span>Excel Tab E. <span> </span>Definitions (Evidence Types) (7 definitions)</span></li> <li><span>Excel Tab F. <span> </span>Definitions (Hazards) (31R×5C)</span></li> <li><span>Excel Tab G. <span> </span>Definitions (Hazard Relations) (3 definitions)</span></li> <li><span>Excel Tab H. <span> </span>References</span></li> </ul> <p><span> </span><span>For <span>Nairobi (Tab A) and Istanbul (Tab B)</span>, each row in the database presents a source of evidence of a <strong>single hazard type</strong> influencing Nairobi or Istanbul. We compiled multiple evidence sources for many of the hazard types, each on its own row. In columns, we describe the evidence through various qualifiers, including the following: </span></p> <ul> <li><span>identifying the hazard type (24 possible hazard types)</span></li> <li><span>source information and URL link</span></li> <li><span>source content</span></li> <li><span>hazard interrelationships</span></li> <li><span>anthropogenic influences</span></li> <li><span>video evidence</span></li> <li><span>source reflections</span></li> </ul> <p><span> </span><span>For <span>Nairobi (<strong>Tab C</strong>) and Istanbul (<strong>Tab D</strong>)</span>, each row in the databases presents a source of evidence of a <strong>hazard interrelationship</strong> in Nairobi or Istanbul. In columns, we describe the evidence through various qualifiers, including the following: </span></p> <ul> <li><span>primary hazard (24 hazard types)</span></li> <li><span>secondary hazard (where applicable, the same 24 hazards as for the primary hazard)</span></li> <li><span>the generic description of hazard interrelationship mechanisms</span></li> <li><span>whether the relationship is triggered or increased probability or both</span></li> <li><span>source information and URL link</span></li> <li><span>source content (e.g., interrelationship type, description, and hazard sequence)</span></li> </ul> <p><span>The reader is referred to Šakić Trogrlić et al. (2024) for a detailed description of the methodology by which this database was constructed.</span></p> <p><strong><span>References</span></strong></p> <p><a name="_Hlk154927256"></a><span>Šakić Trogrlić, R., Thompson, H. E., Yahya Menteşe, E., Hussain, E., Gill, J. C., Taylor, F. E., Mwangi, E., Öner, E., Bukachi, V. G., & Malamud, B. D. (2024). Multi-hazard interrelationships and risk scenarios in urban areas: A case of Nairobi and Istanbul. <em>Earth’s Future.</em> 12, e2023EF004413. https://doi.org/10.1029/2023EF004413</span></p>
Kathmandu Valley Single Hazards and Multi-Hazard Interrelationships Database
<p>This <em>Kathmandu Valley Single Hazards and Multi-Hazard Interrelationships Database</em> uses a systematic review of blended evidence types (academic literature, grey literature, media, databases, and social media) to compile single hazard and multi-hazard interrelationship exemplars of natural hazards in the context of Kathmandu Valley.</p> <p>We identify 58 sources of evidence for single hazard types and 21 sources of evidence for multi-hazard interrelationships. These sources evidence 21 single hazard types across six hazard groups, and 83 multi-hazard interrelationships that could influence Kathmandu Valley. Of these multi-hazard interrelationships, 12 have direct case study evidence of previous influence in Kathmandu Valley.</p> <p>This Excel database accompanies the paper Thompson et al. (2024).</p> <p>The <em>Kathmandu Valley Single Hazards and Multi-Hazard Interrelationships Database </em>comprises the following sheets: <br>A. Single Hazards Evidence <br>B. Hazard Interrelationships Evidence <br>C. Hazard Interrelationships Matrix <br>D. Matrix Evidence <br>E. Definitions (Source Types) <br>F. Definitions (Hazards) <br>G. Definitions (Interrelationships) <br>H. References </p> <p>In Sheet A, each row in the database describes a separate source of evidence of a single hazard influencing Kathmandu Valley. In each column, we describe the evidence using the qualifiers outlined below:</p> <ul> <li>Hazard type</li> <li>Source information and link</li> <li>Source content</li> <li>Hazard interrelationships and anthropogenic processes</li> <li>Video evidence</li> <li>Source reflections</li> <li>Major event typical frequency reflection</li> <li>Any other reflection on a single hazard</li> <li>Impact</li> </ul> <p>In Sheet B, each row in the database describes a separate source of evidence of a multi-hazard interrelationship influencing Kathmandu Valley. In each column, we describe the evidence using the qualifiers outlined below:</p> <ul> <li>Hazard type</li> <li>Source information and link</li> <li>Source content</li> <li>Hazard sequence</li> <li>Source reflections</li> <li>Impact</li> <li>Input from practitioner stakeholders</li> <li>Input from practitioner stakeholders - prioritisation</li> </ul> <p>We refer the reader to Thompson et al. (2024) for details of the methodology used to populate this database.</p> <p><strong>References</strong></p> <p>Thompson, H. E., Gill, J. C., Šakić Trogrlić, R., Taylor, F. E., and Malamud, B. D.: A methodology to compile multi-hazard interrelationships in a data-scarce setting: an application to Kathmandu Valley, Nepal, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2024-101, in review, 2024.</p>
Malawi probabilistic seismic hazard analysis (PSHA) using the Malawi Seismogenic Source Model (MSSM). Supplementary Files v1.1
<p>Updated (October 2022) version of supplementary files for running probabilistic seismic hazard analysis (PSHA) MATLAB codes for Malawi. The PSHA codes themselves (v1.0) are available at: https://doi.org/10.5281/zenodo.7265781and the most recent version will be available on GitHub at: https://github.com/jack-williams1/Malawi_PSHA. Note the variables stored here are not stored on GitHub due to the file size.</p> <p>Includes both input files for performing PSHA and output ground motions for plotting PSHA results.</p> <p>Files are:</p> <ul> <li>malawi_Vs30_active.txt: Input USGS slope-based Vs30 values for Malawi (Wald and Allen 2007)</li> <li>EQCAT_comb.mat: MSSM Direct catalog for all possible rupture weightings (stored as MATLAB variable)</li> <li>GM_MSSM_em_20221027: Ground motions for plotting PSHA maps (stored as MATLAB variable)</li> <li>GM_MSSM_20221021.mat: Ground motions needed for plotting PSHA-site analysis figures (stored as MATLAB variable)</li> <li>mssm_comb.mat: Matlab file for combined MSSM Direct and Adapted MSSM catalogs (stored as MATLAB variable)</li> <li>MSSM_Catalog_Adapted_em.mat: Adapated MSSM event catalog (stored as MATLAB variable)</li> <li>syncat_bg.mat: Areal source stochastic event catalog (stored as MATLAB variable)</li> </ul> <p>Further descriptions of these files and how to use them are provided on Github. An open-access manuscript describing the PSHA is available at: </p> <p>Williams J. N., Werner M. J., Goda K., Wedmore L. N. J., De Risi R., Biggs J., Mdala H., Dulanya Z., Fagereng Å, Mphepo F., Chindandali P. (2023). Fault-based probabilistic seismic hazard analysis in regions with low strain rates and a thick seismogenic layer: a case study from Malawi, Geophysical Journal International, Volume 233, Issue 3, June 2023, Pages 2172–2206, <a href="https://doi.org/10.1093/gji/ggad060">https://doi.org/10.1093/gji/ggad060</a></p> <p>Please reference this publication along with this repository when using these data.</p> <p>USGS vs30 value compilation described in:</p> <p>Allen, T. I., and Wald, D. J., 2009, On the use of high-resolution topographic data as a proxy for seismic site conditions (Vs30), Bulletin of the Seismological Society of America, 99, no. 2A, 935-943.</p> <p> </p>
Inventory maps of hazardous geological processes_Transcarpathia, Ukraine
<p>Under the ImProDiReT Project running at Regional Transcarpathia level an Inventory maps of the hazardous geological processes’ manifestations for the Transcarpathia (landslides, mudflows, flooding and flash floods, karst) have been created.</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>
Climate Solutions Explorer - hazard, impacts and exposure data
<p><a name="_GoBack"></a>The Climate Solutions Explorer website maps and presents information about mitigation pathways, avoided climate impacts, vulnerabilities and risks arising from development and climate change. <a href="https://www.climate-solutions-explorer.eu"><strong>www.climate-solutions-explorer.eu</strong></a></p> <p>Using the latest data, state-of-the-art models were used to assess the future trends of indicators of development- and climate-induced challenges.</p> <p>Updated gridded global climate and impact model data are based on CMIP6 and CMIP5 projections, using a subset of models from the ISIMIP project that have been consistently downscaled and bias-corrected. The data includes various indicators (~42) relating to extremes of precipitation and temperature (e.g. from Expert Team on Climate Change Detection and Indices), hydrological variables including runoff and discharge, heat stress (from wet bulb temperature) events (multiple statistics and durations), and cooling degree days, as well as further indicators relating to air pollution (PM2.5 from the GAINs model), and crop yields and natural habitat land-use change (biodiversity pressure) from the GLOBIOM model.</p> <p>Indicators were calculated at a spatial resolution of 0.5° (approximately 50km at the equator), and subsequently spatially aggregated to the country level – from which population and land area exposure to the impacts were calculated. This has enabled the country-by-country comparison of national climate impacts and avoided exposure. Impacts were calculated at global mean temperature intervals, i.e. 1.2, 1.5, 2, 2.5, 3, and 3.5 °C, compared to a pre-industrial climate.<br><br></p> <p><strong>The dataset includes: </strong></p> <ul> <li>Global gridded projections (in netCDF format) of all the climate impact indicators at 0.5° spatial resolution, at global warming levels of 1.2, 1.5, 2, 2.5, 3, and 3.5 °C<br><br>For each GWL, maps for the absolute indicator values, the relative difference, and the scores are provided. The naming format is: cse_[short_indicator_name]_[ssp]_[gwl]_[metric].nc4. Please note that the Greenland ice sheet and the desert areas have been masked out for the hydrology indicators for these datasets.<br><br></li> <li>Intermediate output data, including gridded maps of absolute values, relative differences, and scores for all ensemble members, as well as gridded maps of the multi-model ensemble statistics for the global warming levels and the reference period <br><br>For the ensemble member data, the naming format is [gcm]_[ssp/rcp]_[gwl]_[short_indicator_name]_global_[start_year]_[end_year].nc4 or [ghm]_[gcm]_[ssp/rcp]_[gwl]_[soc]_[short_indicator_name]_global_[start_year]_[end_year]_[metric].nc4 for the hydrology indicators. <br><br></li> <li>Tabular data (.csv) aggregating the indicators to country (or region) level, for both hazards and exposure, population and land-area weighted<br><br>The .zip archives ‘table_output_climate_exposure_{aggregation_level}.zip’ contain the tabular data for all indicators. Four different aggregation levels are provided: country level, R10 regions and the EU, IPCC AR6-WGI reference regions, and UN R5 regions. A separate file named ‘table_output_climate_exposure_land_air_pollution.zip’ contains the table data for theland and air pollution indicators. <br><br></li> <li>Tabular data (.csv) for avoided impacts by mitigating to 1.5 °C (land and population exposure)<br><br>The .zip archives ‘table_output_avoided_impacts_{aggregation_level}.zip’ contain the tabular data for all indicators. Four different aggregation levels are provided: country level, R10 regions and the EU, IPCC AR6-WGI reference regions, and UN R5 regions. A separate file named ‘table_output_avoided_impacts_land_air_pollution.zip’ contains the table data for the land and air pollution indicators.</li> </ul> <p> </p> <p>Further details are available on the Data Story page – <a href="http://www.climate-solutions-explorer.eu/story/data">www.climate-solutions-explorer.eu/story/data</a>. A detailed description of the methodology and the calculation of the ISIMIP-derived indicators has been published in <a title="Global warming levels indicators of climate change and hotspots of exposure" href="https://doi.org/10.1088/2752-5295/ad8300" target="_blank" rel="noopener">Werning, M. et al. (2024).</a></p> <p> </p> <p><strong>Release notes (v1.1)</strong></p> <p>Changes in this version:</p> <ul> <li>Only table output data for the land and air pollution indicators have been changed, all other indicator data remain unchanged from v1.0</li> <li>Updated land and air pollution indicators to use scaled population data to match the latest SSP population projections from the Wittgenstein Center from 2023</li> <li>Fixed issue with the region mask for the EU</li> <li>Added table output data for the IPCC AR6-WGI reference regions and the UN R5 regions</li> </ul> <p> </p> <p><strong>Release notes (v1.0)</strong></p> <p>Changes in this version:</p> <ul> <li>Fixed calculation of the indicator “Drought intensity” (both for the version using discharge and run-off)</li> <li>Masked out the Greenland ice sheet and the desert areas for the global gridded projections for the hydrology indicators in the final output files</li> <li>Added table output data for the IPCC AR6-WGI reference regions and the UN R5 regions</li> <li>Used scaled population data to match the latest SSP population projections from the Wittgenstein Center from <a>2023</a></li> <li>Added the indicator ‘Heatwave days’</li> <li>Added intermediate outputs for all ensemble members for energy, hydrology, precipitation, and temperature indicators<br><br></li> </ul> <p><strong>Release Notes (v0.4)</strong></p> <p>Changes in this version:</p> <ul> <li>Removed ssp and metric from variable name in netCDF files</li> <li>Removed obsolete coordinates in netCDF files for 'Drought intensity'</li> <li>Added intermediate outputs for energy, hydrology, precipitation, and temperature indicators</li> </ul> <div> </div>
Integrated Datasets for analyses on potentially hazardous locations for women in Valencia, Dublin, San Francisco, and Toluca
<p>This dataset provides a compilation of the data used to analyze and identify potentially dangerous<br>places for women. Multiple data collection techniques, including official data downloads, web<br>scraping, and participatory mapping, were combined for integration, applying specific processing.<br>The datasets refer to four cities: Valencia (Spain), Dublin (Ireland), San Francisco (United States),<br>and Toluca (Mexico).<br>Depending on the availability and context of each city, the datasets are classified into three<br>categories: DATA, TWT, and MAP. The DATA prefix refers to files containing the results of the<br>analysis of socioeconomic variables downloaded from official sources; for the mapping, the<br>standard territorial unit was a 25x25 m grid for Valencia and 50x50 m for Dublin and San Francisco.<br>The files with the prefix TWT are composed of datasets containing tweets collected through web<br>scraping and analyzed using natural language processing (NLP) algorithms and neural networks;<br>the purpose is to identify and classify tweets related to gender violence, feelings of fear, or<br>perceptions of insecurity. For MAP files, participants gathered them through participatory<br>mapping processes, using specific calls to public space users and a supporting web application<br>designed for this purpose. The files with the prefix POL contain datasets used for crime prediction based on crime density for the city of Valencia.</p>
Predicted and experimental chemical and ecotoxicological properties for the toxic unit based hazard assessment
<p><strong>Description</strong></p> <p>This dataset contains ecotoxicity data of 1585 chemicals of environmental concern (CECs) and chemical identifiers. The ecotoxicity data was retrieved from <a href="https://cfpub.epa.gov/ecotox">US EPA ECOTOX Knowlegdebase</a> in ASCII file format and was aggregated for the ecotoxicity groups algae, crustaceans, and fish based on the ideas of <a href="https://dx.doi.org/10.1002/etc.3460">Busch et al. 2016</a>. The dataset includes the 5-percentile, the mean and the geomean of all retrieved ecotoxicity for each compound. Missing ecotoxicity data was estimated with ECOSAR 1.0 algorithms for green algae, daphnids, and fish using <a href="https://www.ufz.de/index.php?en=34593">ChemProp 6.8</a>. The main purpose of this dataset is the <a href="http://doi.org/10.1016/0043-1354(70)90018-7">toxic unit</a> (TU) based hazard assessment of environmental water samples. Chemical properties were estimated using <a href="https://github.com/kmansouri/OPERA">OPERA 2.7</a>, <a href="https://chemaxon.com/products/instant-jchem">Instant JChem</a>, and ACD Percepta 2015 based on QSAR-ready SMILES derived from OPERA 2.7. All data aggregated from EcoTox Knowledgebase (e.g., raw values, species, etc.) is available in the dataset in the detailed sheets. REcoTox, the processing script written in R is available on <a href="https://github.com/tsufz/REcoTox/releases/latest">GitHub</a>.</p> <p><strong>CAUTION</strong></p> <p>It needs to be emphasized that quantitative-structure activity relationship data is just an estimate, which does not necessarily reflect the real property and behaviour of a modelled compound. The calculated data needs to be reviewed in deep. Especially for non-polar or very polar compounds, the QSAR predictions might fail. If a compound ranks high in the TU ranking, it is required to search for literature or regulative data evidences to underpin the finding to avoid false positive prioritizations.</p> <p><strong>RELEASE NOTE</strong></p> <p>Version 210714_v1 was created with <a href="https://github.com/tsufz/REcoTox/releases/tag/v0.1.0">REcoTox version v0.1.0</a>.</p>
Near Pan-Svalbard cryospheric hazards inventory (SvalCryo)
<p>We present a comprehensive inventory of thaw slumps (TS) and thermo-erosion gullies (TEG) on the Svalbard Archipelago. We used the most recent orthophotos (0.5 x 0.5 m pixel size) acquired in 2009-2011 from the Web Map Services (WMS) of the Norwegian Polar Institute. TS and TEG were identified and digitised on-screen as polygons in the ETRS_1989_UTM_Zone_33N coordinate reference system. <span>TS and TEG were identified based on their morphology, digitised on-screen (maximum zoom was 1:1000) as polygons, and then individually quality checked in the GIS environment. This process was repeated twice, to avoid any bias in feature(s) mapping, first by a geomorphologist (first author) and then by an Arctic geologist (second author). The cryospheric inventory of the 14 regions (Andre<span>é</span> Land, Dickson Land, James I Land, Nordenski<span>ö</span>ld Land, Bünsow Land, Olav V Land, Sabine Land, Nathorst Land, Heer Land, Wedel Jarlsberg Land, Torell Land, S<span>ørkapp Land, </span>Barents<span>øya and Edgeøya) </span>totalises 8491 polygons, out of which 3679 are TS and 4812 are TEG. Within the attribute tables, there are eight columns comprising details about each polygon/feature, as follows: FID (ID showing the total number of polygons), Shape (Polygon), ID (each polygon from each region has associated an ID for both TS and TEG), Area (sq. m), Perimeter (m), MaxDistanc (calculated between two points along the polygon perimeter), Elongation (calculated as the maximum distance divided by the square root of the area), Region (the name of the region that the feature belongs to).</span></p>
Emergency management/Natural Hazards: annotated tweets
<p>A set of annotated tweets related to natural hazards and emergency management.</p> <p>Information available for each tweet:</p> <p>- tweet id</p> <p>- boolean flags about its content: floods;storms;landslides;snow;infrastructures;affected individuals;caution advice;donations & volunteering;emotional support;other info;panic</p> <p> </p>
Solotvyno hazard&risk maps_ImProDiReT-783232_UA
<p>As a result of the analysis of geological natural environment of Solotvyno, several natural and anthropogenic processes those are potentially dangerous for the population have been identified: karst and suffosion (subsidence, sinkholes, collapses), seasonal and flash floods, flooding, slope erosion, landslides.</p> <p>A set of hazards / risk maps has been elaborated based on expert complex assessment of the natural and anthropogenic hazardous processes manifestations:</p> <p>1. Inventory map of hazardous technogenic-geological and engineering-geological processes manifestations and phenomena for Solotvyno</p> <p>2. Zonation of the hazardous technogenic-geological and engineering-geological processes manifestations;</p> <p>3. Specific land use for Solotvyno;</p> <p>4. Category of land for Solotvyno (according to StateGeoCadastre data);</p> <p>5. Risk Map of Natural and Natural-Antropogenic Hazards for Solotvyno;</p> <p>6. Risk Map of Natural and Natural-Antropogenic Hazards for Solotvyno (with critical infrastructure objects).</p>
2-meter Universal Thermal Climate Index (UTCI) and Human Heat Health Index (H3I) hazard for Austin, Texas
<p>Universal Thermal Climate Index (UTCI) is a physiological temperature that is widely used in biometeorological studies to assess the heat stress felt by humans. UTCI considers the shortwave and longwave radiation incident on humans from the six cubical directions as well as air temperature, humidity, wind speed and clothing. As a part of NOAA National Integrated Heat Health Information System (NIHHIS) and NASA Interdisciplinary Research in Earth Science (IDS) project, we have generated the UTCI data for Austin, Texas and surrounding peri-urban area at 2-meters spatial resolution for the year 2017. Details on data generation and methodology can be found in Kamath et al., (2023) but are summarized here. </p> <p><strong>1. Datasets and model used</strong></p> <p>The solar and longwave environmental irradiance geometry (SOLWEIG) model was used to simulate shadows, mean radiant temperature (T<sub>MRT</sub>) and the UTCI (Lindberg et al., 2008). T<sub>MRT</sub> is the equivalent temperature due to exposure to absorbed shortwave and longwave radiation from all directions in a standing position. SOLWEIG was forced using near-surface ERA-5 data available at a spatial resolution of 0.25°x 0.25°. Building, vegetation heights, and digital terrain model were again derived from 3DEP LiDAR point cloud data. SOLWEIG was run using the urban multi-scale environment predictor (UMEP) (Lindberg et al., 2018) plug-in with QGIS. </p> <p><strong>2. Data availability</strong></p> <p>Diurnal UTCI data were calculated for typical meteorological clear sky days corresponding to Summer and Fall. The typical clear sky day was selected using the 10-year Typical meteorological Year (TMY) for Austin, Texas (30.2672° N, 97.7431° W) provided by National Solar Radiation Database (NSRDB). More details on TMY files can be found at: https://nsrdb.nrel.gov/data-sets/tmy</p> <p>Additionally, data is developed for heat hazard for daytime Human Heat Health Index (H3I) calculation as defined by Kamath et al., (2023). Briefly, this heat hazard is defined as the fraction of the day when the UTCI exceeds certain threshold. The threshold used to calculate heat hazard for Summer and Fall were 35° C and 32°C, respectively that imply strong heat stress (Jendritzky et al., 2012). Note that UTCI is on a different scale compared to air temperature, and could yield different heat stress levels.</p> <p><strong>3. Data format</strong></p> <p>The georeferenced UTCI and heat hazard data are available in the geoTIFF file format. The files can be readily visualized using GIS software such as QGIS and ArcGIS, as well as programing languages such as Python.</p> <p> <strong>4. Companion dataset</strong></p> <p>Based on the calculated UTCI here, the potential locations for tree planting were calculated to increase the shade to reduce heat vulnerability for Austin, Texas. [https://doi.org/10.5281/zenodo.6363494]</p> <p><strong>References</strong></p> <ol> <li>Kamath, H. G., Martilli, A., Singh, M., Brooks, T., Lanza, K., Bixler, R. P., ... & Niyogi, D. (2023). Human heat health index (H3I) for holistic assessment of heat hazard and mitigation strategies beyond urban heat islands. Urban Climate, 52, 101675.</li> <li>Lindberg, F., Holmer, B., & Thorsson, S. (2008). SOLWEIG 1.0–Modelling spatial variations of 3D radiant fluxes and mean radiant temperature in complex urban settings. <em>International journal of biometeorology</em>, <em>52</em>, 697-713.</li> <li>Lindberg, F., Grimmond, C. S. B., Gabey, A., Huang, B., Kent, C. W., Sun, T., ... & Zhang, Z. (2018). Urban Multi-scale Environmental Predictor (UMEP): An integrated tool for city-based climate services. <em>Environmental modelling & software</em>, <em>99</em>, 70-87.</li> <li>Jendritzky, G., de Dear, R., & Havenith, G. (2012). UTCI—why another thermal index?. <em>International journal of biometeorology</em>, <em>56</em>, 421-428.</li> <li>Bixler, R. P., Coudert, M., Richter, S. M., Jones, J. M., Llanes Pulido, C., Akhavan, N., ... & Niyogi, D. (2022). Reflexive co-production for urban resilience: Guiding framework and experiences from Austin, Texas. Frontiers in Sustainable Cities, 4, 1015630.</li> <li>Lanza, K., Jones, J., Acuña, F., Coudert, M., Bixler, R. P., Kamath, H., & Niyogi, D. (2023). Heat vulnerability of Latino and Black residents in a low-income community and their recommended adaptation strategies: A qualitative study. <em>Urban Climate</em>, <em>51</em>, 101656.</li> </ol>
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>
Database of geo-hydrological hazards in Apulia (Italy)
<p>Geospatial database containing data on geo-hydrological processes (Landslides, Floods, Sinkholes) and/or related damage, occurred between 2008 and 2019 in the Apulia Region (Italy).</p> <p>We provide a GPKG file containing multiple layers for the different types of geometries (point, line, polygon).<br> Data are extracted from a complex relational database structure described originally here https://doi.org/10.1016/j.jenvman.2017.11.022 but recently updated and improved.<br> For the different damage and phenomena we provide information about type, data and time of occurrence, temporal and spatial accuracy, main predisposing factor, etc..<br> For floods we provides codes and information compliant with the EC Flood Directive.</p> <p>The different phenomena and damages are grouped based on the meteorological event responsible for their occurrence.</p>
Machine learning code and dataset for "Nowcasting thunderstorm hazards using machine learning: the impact of data sources on performance"
<p>This repository contains the code and dataset for the paper:</p> <p>Nowcasting thunderstorm hazards using machine learning: the impact of data sources on performance, Natural Hazards and Earth System Sciences, 2022, <a href="https://doi.org/10.5194/nhess-2021-171">https://doi.org/10.5194/nhess-2021-171</a></p> <p>The GitHub code repository at <a href="https://github.com/meteoswiss-mdr/ts-nowcast-datasources">https://github.com/meteoswiss-mdr/ts-nowcast-datasources</a> may contain a more up-to-date version of the code if bug fixes etc. have been necessary. The file <a href="https://zenodo.org/api/files/41faa1b7-17f6-4a75-be09-7743426ef13c/ts-nowcast-datasources-publication.zip">ts-nowcast-datasources-publication.zip</a> in this Zenodo release contains the status of the GitHub repository at the time of the publication of the paper.</p> <p>For instructions for using the data, please see the <a href="https://github.com/meteoswiss-mdr/ts-nowcast-datasources">code repository</a>.</p> <p> </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.