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75 results for “urban heat”
Heat Stress Exposure Maps - Urban Planning Scenario 2026 - 2045: Antwerp, Belgium
<p><strong>Antwerp heat stress exposure map: average number of heatwave days per year versus socio economic data - urban planning scenario (2026-2045)</strong></p> <p>Heat stress exposure maps for Antwerp representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modeled over the reference period 2026-2045 using the present land use / cover situation for the city but combined with urban planning projects information until 2030. Hence, the urban morphology has been updated accordingly.</p> <p><strong>Exposure mapping variable include:</strong><br /> * Total population 2030<br /> * Population density inhabitants per hectare 2030</p>
Heat Stress Exposure Maps - Urban Planning Scenario 2026 - 2045: Almada, Portugal
<p><strong>Almada heat stress exposure map: average number of heatwave days per year versus socio economic data - urban planning scenario (2026-2045)</strong></p> <p>Heat stress exposure maps for the city of Almada representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modeled over the reference period 2026-2045 using the present land use / cover situation for the city but combined with urban planning projects information until 2030. Hence, the urban morphology has been updated accordingly.</p> <p>Exposure mapping variable include:<br /> * Total population 2011<br /> * Population density inhabitants per hectare 2011</p>
Model simulations utilizing the latest urban underlying surface and anthropogenic heat data
<p>Based on numerical simulations utilizing the latest urban underlying surface and anthropogenic heat data over the Yangtze River Delta urban agglomeration, we find that LU change and AH emission can result in opposite effects on summer precipitation. The related model simulations are included in this dataset.</p>
Heat knock-down times of urban and rural Chiasmia clathrata moth larvae and adults
<p>Urbanization and its urban‐heat‐island effect (UHI) have expanding footprints worldwide. The UHI means that urban habitats experience a higher mean and more frequent extreme high temperatures than rural habitats, impacting the ontogeny and resilience of urban biodiversity. However, many organisms occupy different microhabitats during different life stages and thus may experience the UHI differently across their development. While evolutionary changes in heat tolerance in line with the UHI have been demonstrated, it is unknown whether such evolutionary responses can vary across development. Here, using common‐garden‐reared <em>Chiasmia clathrata</em> moths from urban and rural populations from three European countries, we tested for urban evolution of heat shock tolerance in two life stages: larvae and adults. Our results indicate widespread urban evolution of increased heat tolerance in the adult stage only, suggesting that the UHI may be a stronger selective agent in adults. We also found that the difference in heat tolerance between urban and rural populations was similar to the difference between Mid‐ and North‐European regions, suggesting similarity between adaptation to the UHI and natural, latitudinal temperature variation. Our observations incentivize further research to quantify the impact of these UHI adaptations on fitness during urbanization and climate change, and to check whether life‐stage‐specific adaptations in heat tolerance are typical of other ectothermic species that manage to survive in urbanized settings.</p>
Data from: Contrasting heat tolerance of evergreen and deciduous urban woody species during heat waves
<p>The increasing frequency and intensity of heat waves caused significant damages to urban woody species, and the different leaf structures between evergreen and deciduous species may be closely related to leaf heat tolerance. However, whether the different leaf structural traits of evergreen and deciduous plants contribute to their different responses under heat waves is still unclear.</p> <p>During the record-breaking and long-lasting 2022 summer heat waves in China, we investigated the relationships between leaf thermal indices and leaf structural traits of 36 urban woody species in four cities along the Yangtze River.</p> <p>We found that all the four thermal indices were significantly but weakly related with leaf damage status. The critical temperature that causes the initial 15% damage to photosystem II (Tcrit) may serve as a sensitive measure of heat tolerance. Evergreen species suffered less leaf damage during the heat waves and exhibited higher leaf heat tolerance, thicker leaves than deciduous species. Tcrit was significantly correlated with leaf mass per area, leaf thickness and thickness of spongy tissue.</p> <p><em>Synthesis</em>:<em> </em>Urban woody species with high Tcrit, leaf mass per area, and leaf thickness tend to be more tolerant to heat stress. This study provides insights for predicting leaf heat tolerance of urban woody plants in subtropical China and their physiological and ecological responses to severe heat waves.</p>
Strategic Integration of Urban Excess Heat Sources in District Heating: A Spatio-Temporal Optimization Paradigm
<p>This data repository supports the techno-economic and geospatial optimization analysis of district heating systems (DHS) in Stockholm, with a particular focus on integrating Urban Excess Heat (UEH) sources. The dataset includes both input data and results, organized by the key analytical tools employed in the study: GIS, OSeMOSYS, and the Hotmaps Dispatch Model.</p> <p>The <strong>folder structure</strong> is organized as follows:</p> <p>/Data<br>│<br>├── Input_Data/<br>│ ├── GIS/<br>│ ├── OSeMOSYS/<br>│ ├── Hotmaps_Dispatch/<br>│<br>└── Results/<br> ├── GIS/<br> │ ├── Optimized DHS network extensions for UEH sources<br> │ ├── Long-term capacity and emissions projections for DHS<br> ├── Dispatch outputs for different electricity price scenarios<br><br></p> <h2>Dataset Details</h2> <p>1. Input Data Folder: This folder includes all data used as inputs across the three primary tools:<br> - GIS: Contains geospatial data on the DHS network paths, source locations, and spatial configurations for network optimization.<br> - OSeMOSYS: Provides techno-economic parameters for heat generation technologies, including capital and operational costs, fuel price projections, and emissions factors.<br> - Hotmaps Dispatch: Includes detailed demand profiles, projected electricity prices, and the supply temperature requirements essential for dispatch simulations.</p> <p>2. Results Folder: This folder contains the results generated by each tool after processing the input data:<br> - GIS: Outputs optimized paths for network expansion and geospatial mapping of UEH sources.<br> -OSeMOSYS: Contains projections for DHS technology adoption, emissions reduction, and cost optimization over a 23-year model period.<br> - Hotmaps Dispatch: Stores dispatch model results under various electricity pricing and temperature scenarios, capturing the optimal deployment of DHS resources.</p> <h2>Summary of Tools Used</h2> <p>- GIS (Geospatial Information System): Used to locate UEH sources and map their proximity to the existing DHS network, guiding the optimization of network expansions.<br>- OSeMOSYS (Open Source Energy Modelling System): Performs long-term optimization for the DHS, factoring in the addition of UEH sources, electricity price scenarios, and emissions constraints.<br>- Hotmaps Dispatch Model: Simulates operational dispatch of heat generation technologies in response to varying electricity prices and network temperatures, providing insights into real-time heat supply optimization.</p> <p>This repository thus provides a comprehensive dataset to support the techno-economic and spatial analysis of DHS, facilitating the integration of UEH sources in urban heating systems through scenario-based optimizations.</p> <p><br><br></p>
Variations of the urban PM2.5 chemical components and corresponding light extinction for three heating seasons in the Guanzhong Plain, China
<p>The basic data of the thesis “Variations of the urban PM2.5 chemical components and corresponding light extinction for three heating seasons in the Guanzhong Plain, China”.</p>
Urban heat hazard in the Global South
<p>This dataset includes various estimates of fine-grained outdoor heat hazard and vegetation metrics for cities in the Global South, and also for U.S. cities.<br><br>Extent of urban clusters based on the Global Human Settlement Layer (GHSL; RWI_ALL_v2.geojson), European Space Agency Climate Change Initiative (ESACCI; RWI_ALL_GLOB_v2.geojson) land cover data, and U.S. urbanized areas (GLOB_distance_US.geojson) in geojson format.</p> <p>The geojsons and data tables have several environmental and socioeconomic variables by grid and census tract (for the U.S.).<br><br>The variables are:</p> <p>AT_max: 10-year (2010-2019) average maximum annual air temperature<br>AT_max_summ: 10-year (2010-2019) average maximum summer air temperature<br>AT_min: 10-year (2010-2019) average minimum annual air temperature<br>AT_min_summ: 10-year (2010-2019) average minimum summer air temperature<br>All_area: Area of the grid or census tract<br>EVI: Mean 10-year (2010-2019) Enhanced Vegetation Index<br>Grass_area: Area of grassland for ~2020 from the ESA WorldCover dataset<br>ID: ID of urban cluster (U.S. urbanized areas havr a 'NAME' for the urbanized area instead)<br>LST_day: 10-year (2010-2019) average daytime annual land surface temperature from MODIS Aqua<br>LST_day_summ: 10-year (2010-2019) average daytime summer land surface temperature from MODIS Aqua<br>LST_night: 10-year (2010-2019) average nighttime annual land surface temperature from MODIS Aqua<br>LST_night_summ: 10-year (2010-2019) average nighttime summer land surface temperature from MODIS Aqua<br>PM25: 10-year (2010-2019) average particulate matter below 2.5 micron (not used in paper)<br>Pop: Population of grid or census tract<br>REGION_WB: World Bank region<br>SUBREGION: World Bank subregion (not used in paper)<br>Tree: 10-year (2010-2019) average tree percentage from MODIS continuous vegetation fields<br>Tree_area: Area of trees for ~2020 from the ESA WorldCover dataset<br>Veg: 10-year (2010-2019) average non-tree vegetation percentage from MODIS continuous vegetation fields<br>error: Error in Relative Wealth Index<br>rwi: relative Wealth Index</p> <p> </p> <p>In addition to these, the 'distance' files include a column for distance of the grids or census tract from the centroid of the cluster in belongs to.</p>
Ottawa climate data for building simulations with urban heat island effects and nature-based solutions
<p>As cities face rising temperatures, increased frequency of extreme weather events, and altered precipitation patterns, buildings are subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under changing climate conditions is essential for building sustainable and resilient communities. Unique climate characteristics of cities, such as the urban heat island effect, are not well simulated by global or regional climate models, and is therefore often not included in typical building analyses. Consequently, a computationally efficient approach is used to generate “urbanized” climate data, derived from regional climate models, to prepare building simulation climate data that incorporate urban effects. We demonstrate this process using existing climate data for Ottawa airport’s weather station and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. We find significant improvements in the representation of the urban heat island and subsequent cooling effects of nature-based solutions in the urbanized climate data. This dataset allows building practitioners to evaluate building performance under historical and potential future changes in climate, considering the complex interactions within the urban canopy and the implementation of mitigation efforts such as nature-based solutions.</p> <p>This dataset contains hourly historical and future weather files for use in building simulations for the city of Ottawa, Canada. While similar weather files are usually based on measurements taken at a city's nearby airport, the current dataset utilizes a novel statistical-dynamical downscaling technique which involves the use of the dynamical Weather Research and Forecasting (WRF) model combined with a statistical approach and climate projections from an ensemble of 15 Canadian Regional Climate Model 4 (CanRCM4) to generate urban climate data which includes the effects of the urban heat island and different nature-based solutions (NBS) as mitigation strategies (such as increasing surface albedo and greenery). Additionally, different levels of implementation of these mitigation strategies were produced, for example, when the albedo is increased to 0.40 (ALBD40) and 0.80 (ALBD80), and similarly for the green and combined scenarios, GRN40, GRN80, COMB40, and COMB80. The URBAN scenario is considered the control case where the urban heat island effects are accounted for in the data, but the NBS scenarios are not yet implemtned. </p> <p>The data are stored in large CSV files, where the rows consists of all 15 realizations of the CanRCM4 ensemble and the variables make up the columns. For example, each 31-year period is repeated 15 times, once for each of the RCM realizations. Therefore, there are 4,073,400 (15x31x8760) rows in each file. We recommend viewing the data using packages from Python or R. </p> <p> </p> <p>The historical and future global warming thresholds and their corresponding time periods are as follows:</p> <table> <tbody> <tr> <td> <p><strong>Global Warming Scenario</strong></p> </td> <td> <p><strong>Time Period</strong></p> </td> </tr> <tr> <td> <p><strong>Historical</strong></p> </td> <td> <p>1991-2021</p> </td> </tr> <tr> <td> <p><strong>Global Warming 0.5ºC</strong></p> </td> <td> <p>2003-2033</p> </td> </tr> <tr> <td> <p><strong>Global Warming 1.0ºC</strong></p> </td> <td> <p>2014-2044</p> </td> </tr> <tr> <td> <p><strong>Global Warming 1.5ºC</strong></p> </td> <td> <p>2024-2054</p> </td> </tr> <tr> <td> <p><strong>Global Warming 2.0ºC</strong></p> </td> <td> <p>2034-2064</p> </td> </tr> <tr> <td> <p><strong>Global Warming 2.5ºC</strong></p> </td> <td> <p>2042-2072</p> </td> </tr> <tr> <td> <p><strong>Global Warming 3.0ºC</strong></p> </td> <td> <p>2051-2081</p> </td> </tr> <tr> <td> <p><strong>Global Warming 3.5ºC</strong></p> </td> <td> <p>2064-2094</p> </td> </tr> </tbody> </table> <p> </p> <p>The following variables are included in the files:</p> <table> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><strong>RUN</strong></td> <td>Run number (R1-R15) of Canadian Regional Climate Model, CanRCM4 large ensemble associated with the selected reference year data</td> </tr> <tr> <td><strong>YEAR</strong></td> <td>Year associated with the record</td> </tr> <tr> <td><strong>MONTH</strong></td> <td>Month associated with the record</td> </tr> <tr> <td><strong>DAY</strong></td> <td>Day of the month associated with the record</td> </tr> <tr> <td><strong>HOUR</strong></td> <td>Hour associated with the record</td> </tr> <tr> <td><strong>YDAY</strong></td> <td>Day of the year associated with the record</td> </tr> <tr> <td><strong>DRI_kJPerM2</strong></td> <td>Direct horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>DHI_kJperM2</strong></td> <td>Diffused horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>DNI_kJperM2</strong></td> <td>Direct normal irradiance in kJ/m2 (total from previous HOUR to the <em>HOUR</em> indicated)</td> </tr> <tr> <td><strong>GHI_kJperM2</strong></td> <td>Global horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>TCC_Percent</strong></td> <td>Instantaneous total cloud cover at the HOUR in % (range: 0-100)</td> </tr> <tr> <td><strong>RAIN_Mm</strong></td> <td>Total rainfall in mm (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>WDIR_ClockwiseDegFromNorth</strong></td> <td>Instantaneous wind direction at the HOUR in degrees (measured clockwise from the North)</td> </tr> <tr> <td><strong>WSP_MPerSec</strong></td> <td>Instantaneous wind speed at the HOUR in meters/sec</td> </tr> <tr> <td><strong>RHUM_Percent</strong></td> <td>Instantaneous relative humidity at the HOUR in %</td> </tr> <tr> <td><strong>TEMP_K</strong></td> <td>Instantaneous temperature at the HOUR in Kelvin</td> </tr> <tr> <td><strong>ATMPR_Pa</strong></td> <td>Instantaneous atmospheric pressure at the HOUR in Pascal</td> </tr> <tr> <td><strong>SnowC_Yes1No0 </strong></td> <td>Instantaneous snow-cover at the HOUR (1 - snow; 0 - no snow)</td> </tr> <tr> <td><strong>SNWD_Cm</strong></td> <td>Instantaneous snow depth at the HOUR in cm</td> </tr> </tbody> </table>
Mitigating urban heat island through neighboring rural land cover: Dataset
<p>A dataset of the manuscript "Mitigating urban heat island through neighboring rural land cover". Includes: Land surface temperature acquisition code (Google Earth Engine) - <strong><em>Average_LST_GEE.sh</em></strong>; Codes for calculating urban development intensity - <em><strong>UrbanDevelopmentIntensity.py</strong></em>; Regression, Machine Learning, Interpretable Machine Learning Code - <em><strong>All</strong><strong><em>R</em>egression.py, SHAP.py, ALE.py</strong></em>; a zip file containing the data used in the calculations - <em><strong>OperationalData</strong></em>.<em><strong>rar.</strong></em></p>
Data for irrigation impacts on urban heat stress in North America
<p>Includes all model simulation results, summaries by urban clusters, and evaluation results of the study. <br><br>The netcdf files are for various model variables and simulations (noURB for no urban simulation, IRR for irrigation simulation, and CTRL for the urban with no irrigation simulation, which is treated as the baseline), the csv files are the summaries for various cases (by urban cluster, and by world and climate zone for model evaluations), and the geotiff files are the raster images for key variables shown in the manuscript.<br><br>The python notebook has all the scripts to estimate the heat stress metrics from the netcdf files.</p>
Urban heat island in Geneva city
<p>As an indicator of urbanization impact, urban heat island (UHI) effect brings us the thermal discomfort and detrimental effects on human health. Considering NDVI and population density in this dataset, we aim to find the most influenced UHI area in Geneva city.</p>
Comparing the Influence of Global Warming and Urban Anthropogenic Heat on Extreme Precipitation in Urbanized Pearl River Delta Area Based on WRF Dynamical Downscaling
<p>The simulation outputs from the Weather Research and Forecasting (WRF) v3.8.1 coupled with single layer urban canopy model from three experiments (HIST_AH300, HIST_AH0, and RCP85_AH300).</p> <p>Variables include hourly precipitation, wind, specific humidity, relative humidity, convective available potential energy, convective inhibition, temperature, model height, land use land cover, and topography.</p> <p> </p>
Time Series Analysis of a Daytime Urban Heat Island in Dar es Salaam Metropolitan Areas.
<p>The study aimed to determine the existing linear influence levels of various causative factors of Urban Heat Island (UHI) by using Geographically Weighted Regression Model (GWR) that determines non-stationarity by generating a new equation for each sample size. Urban heat island occurs when higher temperatures reside in urban areas compared to surrounding areas due to the replacement of natural vegetation with construction materials during development purposes. Time Series with Linear Regression was used to determine the linearity between dependent and independent variables. Moderate Resolution Imaging Spectroradiometer (MODIS) products such as MOD11A1, MCD43A1, MOD09A1 and MOD13A1 were used to acquire Land Surface Temperature (LST), Albedo, Indexed-Based Built-Up Index (IBI) and Enhanced Vegetation Index (EVI) respectively. In-situ datasets of the wind speed were acquired from Tanzania Meteorological Agency (TMA). UHI was observed to have a non-linear trend with IBI and wind speed and a linear trend with Albedo and EVI. The strongest values of UHI were observed at the city center, IBI was observed as a leading causative factor by having a non-lineality influence of 0.023 followed by Albedo, wind speed and EVI with an influence of 0.019, 0.016 and -0.015 respectively. Since IBI and Albedo contribute more to the development of heat island, urban residents should be encouraged to use construction materials with a lower absorption rate of solar energy.</p>
Climatologial analysis of urban heat island effects in Swiss cities
<p>Dataset, namelists and python scripts for the paper "Climatologial analysis of urban heat island effects in Swiss cities".</p>
Urban Heat: Forward-Looking Climate Modeling for Skopje, North Macedonia
<p>We produced actionable data on heat stress in cities to inform analysis and client dialogue on the part of World Bank teams. We applied an urban-scale climate modeling framework to generate datasets describing modeled heat stress exposure for present-day and future conditions under selected climate scenarios. The study domain focuses on Skopje, North Macedonia.</p> <p>More details about the dataset: </p> <ul> <li>The dataset includes calculations for each indicator across three scenarios (<strong>present, SSP1-1.9, SSP3-7.0</strong>) and three twenty-year periods (<strong>2001-2020, 2021-2040, and 2041-2060</strong>). The present period refers to 2001-2020, while the other two periods correspond to the two SSP scenarios.</li> <li>All indicators are available in both <strong>NetCDF</strong> and <strong>GeoTiff</strong> formats.</li> <li>The indicators are calculated at a resolution of <strong>100 m</strong>, consistent with the UrbClim and WBGT simulations. Additionally, downscaled versions of the indicators are provided at a resolution of <strong>30 m</strong>.</li> <li>The UrbClim and WBGT simulations, as well as the postprocessing, are conducted using the regional projection <strong>E</strong><strong>PSG 32634</strong>. The NetCDF and GeoTiff data also adopt this projection. Furthermore, a GeoTiff data file with <strong>EPSG 4326</strong> projection is included.</li> <li>All indicators are calculated as <strong>yearly averages</strong>. Some indicators also have additional calculations for <strong>seasonal averages</strong>, including Spring (MAM), Summer (JJA), Autumn (SON), and Winter (DJF).</li> <li>Ten representative locations within the study domain have been selected to retrieve the WBGT profile on a chosen date (2017-07-11). The results and the locations are stored in wbgt_profile.xlsx.</li> <li>Images for <strong>quick viewing</strong> <strong>in</strong> <strong>png</strong> format visualizing the results for each indicator. Present denotes the period 2001-2020; 2030 denotes the period 2021-2040; & 2050 denotes the period 2041-2060.</li> <li>The NetCDF and GeoTiff data can be found in the data.zip; The png files for quick viewing can be found in quickview.zip; more information about the dataset, including the methodology, all available data list, contact information, etc. can be found in the Technical_Annex_Skopje.docx</li> </ul>
From green to red: Urban heat stress drives leaf color evolution
<p><span>Urban environments, occupying approximately 1% of total land area, often impose novel biotic and abiotic selective pressures on organisms and provide valuable opportunities to understand the eco-evolutionary dynamics between nature and human societies. Prevalence of impervious surface and resulting higher temperatures in urban areas, known as urban heat islands, comprises prominent characteristics in global cities. However, it is not known whether and how urban plants adapt to such heat stress. This study focused on <em>Oxalis</em> <em>corniculata</em>, which has intraspecific polymorphism in leaf color (green, red), and examined whether the leaf color variation is associated with urban heat stress. Field observations revealed consistent associations between leaf color and habitat types (green vs. urban) at local (< 500m), landscape (< 50km), and global scales. Green-leaved plants were dominant in green habitats, and red-leaved individuals had increased in number in urban habitats. Growth and photosynthesis experiments indicated the adaptive benefit and cost of red/green leaves associated with heat stresses. Red-leaved individuals had higher growth rates and photosynthetic efficiency under heat stress, while green-leaved individuals displayed higher growth rates and photosynthetic efficiency under non-stressful conditions. Genome-wide SNP analysis suggests that the red leaf trait may have evolved multiple times from the ancestral green leaf, rather than spreading from a single origin of red leaf evolution. Overall, the results suggested that the dominance of red leaves of <em>O. corniculata</em> seen in cities worldwide would be evidence of plant adaptative evolution due to urban heat islands.</span></p>
Heat knock-down times of urban and rural Chiasmia clathrata moth larvae and adults
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Data from: Contrasting heat tolerance of evergreen and deciduous urban woody species during heat waves
Open the record for dataset details and reuse information.
From green to red: Urban heat stress drives leaf color evolution
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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