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81 results for “urban climate”
Satellite remote sensing dataset for urban climate in Bergen and Prague (TURBAN-D09)
<p><span>Shared dataset contains remote sensing data necessary for a land surface temperature (LST) calculation. Layers were processed for two cities; Bergen (Norway) and Prague (Czech Republic). Original data were downloaded from the U.S. Geological Survey (https://doi.org/10.5066/P975CC9B). For a LST calculation, a land surface emissivity (LSE) algorithm was used.</span></p> <h3><span>Processing of LANDSAT-8 and LANDSAT-9 data</span></h3> <ol> <li><span>Reading metadata file for each scene (*MTL.txt)</span></li> <li><span>Reprojection of scene (note: Bergen scenes have two UTM Zones; 31N and 32N)</span></li> <li><span>Cloud cover raster (see folder 01_CloudCover)</span></li> <li><span>Calculation Top-Of-Atmosphere (TOA) reflectance for bands 10 and 11 (TB_10 and TB_11), saving to folder 02_TOA-reflectance</span></li> <li><span>Calculating of NDVI and Fractional Vegetation Cover (FVC), saving to folder 03_FVC-NDVI</span></li> <li><span>Calculating of LSE for both bands, same as different and mean LSE (folder 04_LSE)</span></li> <li><span>Calculating of LST</span></li> <li><span>Saving of metadata file (see *metadata.txt)</span></li> </ol>
Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets
<p><strong>Sydney morphology and land surface dataset</strong></p> <p>This dataset for Sydney, Australia, represents land cover, building morphology, vegetation morphology and other parameters appropriate for input into local or mesoscale urban climate models.</p> <p>The dataset is provided in netCDF4 and GeoTiff formats.</p> <p>Associated manuscript:</p> <blockquote> <p><a href="https://doi.org/10.3389/fenvs.2022.866398">A transformation in city-descriptive input data for urban climate models</a></p> </blockquote> <p>Citation for the open dataset:<br> - Lipson, M., Nazarian, N., Hart, M. A., Nice, K. A., and Conroy, B.: Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets (v1.01), <a href="https://doi.org/10.5281/zenodo.6579061">https://doi.org/10.5281/zenodo.6579061</a>, 2022.</p> <p>Citation for the associated manuscript:<br> - Lipson, M. J., Nazarian, N., Hart, M. A., Nice, K. A., and Conroy, B.: A Transformation in City-Descriptive Input Data for Urban Climate Models, Frontiers in Environmental Science, 10, <a href="https://doi.org/10.3389/fenvs.2022.866398">https://doi.org/10.3389/fenvs.2022.866398</a>, 2022.</p> <p>Location of associated processing code:<br> - <a href="https://github.com/matlipson/geoscape_processing_public.git">https://github.com/matlipson/geoscape_processing_public.git</a></p> <p><strong>Acknowledgments</strong></p> <p>We gratefully acknowledge the Australian Urban Research Infrastructure Network (AURIN) and Geoscape Australia for <br> providing the datasets necessary for this study, drawing on Geoscape Buildings, Surface Cover and Trees datasets, <br> © Geoscape Australia, 2020: https://geoscape.com.au/legal/data-copyright-and-disclaimer/. <br> This research was supported by the Australian Research Council (ARC) Centre of Excellence for Climate System Science <br> (grant CE110001028), the ARC Centre of Excellence for Climate Extremes (grant CE170100023). </p> <p> </p>
Database of indicators to evaluate the contribution of urban nature-based solutions to climate change adaptation, biodiversity conservation, and social justice
<p>Supplementary data used within the publication: Goodwin, S., Olazabal, M., Castro, A. J., & Pascual, U. (2024). Measuring the contribution of nature-based solutions beyond climate adaptation in cities. <em>Global Environmental Change</em>, <em>89</em>, 102939. <a href="https://doi.org/10.1016/j.gloenvcha.2024.102939">https://doi.org/10.1016/j.gloenvcha.2024.102939</a>. Please also cite this paper when citing this database.</p> <div> <div>Within this database, you can find a list of indicators used to evaluate the contribution of a collection of 74 nature-based solutions (NbS) to climate change adaptation and related biodiversity and social justice challenges in cities. This list of indicators may be useful to those working in cities to provide inspiration for similar indicators they may wish to use to evaluate NbS in their city. This collection of NbS was drawn from previous work published in <em>Nature Sustainability</em> <a href="https://rdcu.be/c4tjk">here</a>.</div> <div> </div> </div> <p><em>The project that gave rise to these results received the support of a fellowship from the “la Caixa” Foundation (ID 100010434). The fellowship code is “LCF/BQ/DI20/11780006”. Marta Olazabal’s research is funded by the European Union (ERC, IMAGINE adaptation, 101039429). This research is further supported by María de Maeztu Excellence Unit 2023-2027 (ref. CEX2021-001201-M), funded by the Ministerio de Ciencia, Innovación y Universidades/Agencia Estatal de Investigación (AEI) (Spain) (MCIN/AEI/10.13039/501100011033/); and by the Basque Government through the BERC 2022-2025 program. </em></p> <p><em>Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.</em></p>
Dataset of Proportion of non-native plants in urban parks correlates with climate, socioeconomic factors and plant traits
<p>Full datasets for the research entitled 'Proportion of non-native plants in urban parks correlates with climate, socioeconomic factors and plant traits'.</p>
Modelled urban climate island during the record-breaking 2022 heatwave in London
<p>This record is created as a data supplement for the manuscript "Estimated mortality attributable to the urban heat island during the record-breaking 2022 heatwave in London".</p> <p>These data were produced using the Weather Research Forecasting model with BEP-BEM. The model setup is described in Brousse et al (2023) <a href="doi.org/10.1175/JAMC-D-22-0142.1">10.1175/JAMC-D-22-0142.1</a>. These data cover the period 2022-07-10 to 2022-07-25, during which temperatures exceding<strong> </strong>40 °C were recorded in London for the first time.</p> <p>The data comprise two NetCDF files. One is labelled "Urb" one "Nourb". In the "Nourb" file, the urban tile is removed from the model and the land surface replaced by the nearest natural tile. This can be used to estimate the influence of the urban tile on the local climate.</p> <p>Variables included in the file are T2 (temperature at 2 m elevation in Kelvin), V10 and U10 (winds at 10 m elevation in metres per second), PSFC (surface level pressure in Pascal), RAINNC (rain in mm), TH2 (potential temperature at 2m elevation in Kelvin), and Q2 (specific humidity at 2 m elevation, which is dimensionless). All variables are provided at hourly timestep.</p> <p>Queries about this dataset can be directed to o.brousse@ucl.ac.uk</p>
Data from: Complex climate-mediated effects of urbanization on plant reproductive phenology and frost risk
<p>This dataset comprises crowdsourced data using digitized herbarium specimen images from two comprehensively digitized regional floras; the Consortium of Northeastern Herbaria (CNH; <a href="http://portal.neherbaria.org/portal/">http://portal.neherbaria.org/portal/</a>) and Southeast Regional Network of Expertise and Collections (SERNEC; <a href="http://sernecportal.org/portal/index.php">http://sernecportal.org/portal/index.php</a>) for 200 plant species in the eastern United States, and four reproductive phenophases (i.e., flowering, peak flowering, fruiting, and peak fruiting) extracted from the herbarium specimens with associated climate data from PRISM and human population density from US Census Bureau.</p>
HeatResilientCity II - work package 2.2: Influence of regional and urban climate on indoor overheating - Results of building performance simulation
<p>This repository contains the <strong>results of the building performance simulations</strong> carried out in the working package 2.2 Influence of regional and urban climate on indoor overheating of the project <a href="http://heatresilientcity.de/">HeatResilientCity II</a>. The buildings under consideration are a multi-residential so-called ‘Gründerzeithaus’ (GZH) and a large-panel construction (LPC) building. The results were extracted for two rooms on the top floor/attic of each building and follow a consistent name convention. Each file contains hourly resolved values for the outdoor air temperature, the indoor air temperature, the indoor operative temperature and the relative humidity indoors and outdoors. Further information can be found in the README of this repository. The simulations were performed for five <strong>different</strong> <strong>regions</strong> in Germany (Dresden, Hamburg, Köln, Stuttgart and Potsdam) for <strong>average present</strong> and <strong>future</strong> <strong>summers</strong> based on meteorological measurement data and under consideration of <strong>urban</strong> <strong>climate</strong>.</p> <p>In addition to the ‘plain’ simulation results, some <strong>heat-indicator variables</strong> were calculated and listed in the files <em>Calculated_Variables.txt</em>. The calculated quantities include temperature-weighted exceedance hours (TWEH) for the limits of 25, 26 and 27 °C (defined in DIN 4108-2:2013 as ‘Übertemperaturgradstunden’) and the maximum operative temperature calculated for the period from April to September.</p> <p>The used <strong>input data</strong> and <strong>building models</strong> can be found in the related repository.</p>
Comparison of Urban-Rural Tree Growth Response to Climate in the Eastern U.S. for the years 1990-2014
Climate is an important driver of tree growth in forests of the eastern U.S. Urbanization can augment the growing conditions of trees in ways that could change the sensitivity of radial growth to climate stressors such as excessive heat and water stress. This dataset include tree ring chronologies (1990-2014) in the form of basal area increment for canopy oak and maples trees from paired urban and nearby rural reference forest sites in Baltimore, Maryland, New York City, New York, and Boston, Massachusetts. Also included are metrics of heat stress and water stress from 1990-2014.
Urban heat island: temperature climate trends in central Arizona-Phoenix: period 1948 to 2007
The question was to what degree are summer minimum temperature climate trends in the latter half of the 20th and early part of the 21st century attributed to local urban development as opposed to global climate change? The approach was to select a range of towns/cities in CA, NV, and AZ for which a pairing of sites from a town/city and a site outside that town/city was possible. Climate records for the period 1948 to 2007 were accessed, and statistical time trends determined for the urban vs. rural locations for towns/cities over a considerable range of population (i.e., from 3.5K to 3.2M). The urban heat island effect increased with the natural log of the population, ranging from a total change in minimum monthly temperatures of ca. 1.5F to over 12F over the population range of 3.5K to 3.2M. These rates of change in the 1948-2007 period overwhelm any background global climate change, with the exception of the rural sites and smaller towns. This study for the first time identified the temperature trends of a range of towns and cities in the Sonoran and Mojave deserts to unravel the impact of urban warming from that of global warming in the contemporary global warming era sometimes called the Anthropocene era. Previous literature investigatin these sites were only up to 1984 or did not address the urban warming contribution. The impact depends on land cover and extent of population development over time.
Reply to comment on "High-resolution, multi-layer modelling of Singapore's urban climate incorporating local climate zones"
<p>This data is for the publication submitted to the Journal of Geophysical Research Atmospheres</p>
Global urban tree LAI/SAI dataset for urban climate modeling
<p>This dataset is the first global urban tree LAI/SAI product at a 500-meter resolution, specifically designed for urban climate modeling to simulate the tree's effects in urban environments. It covers the period from 2000 to 2022 and was developed by using a reprocessed MODIS LAI product with a Random Forest model, demonstrating high accuracy.</p> <p>The original product is a netCDF4 file that has been compressed into three tar.gz files: global_15s.tar.gz, global_0.05.tar.gz, and global_0.5.tar.gz, with resolutions of 500 m, 0.05°, and 0.5°, respectively. Each netCDF4 file in the compressed archive is named Global_UrbanTree_LAI_XX_YYYY.nc, where XX represents the resolution and YYYY represents the year. Each file contains monthly LAI/SAI data for that year, with data dimensions of mon x lat x lon.</p> <p>For version 3 of the LAI data, we replaced the meteorological data from WorldClim v2 with WorldClim v2.1 during model training. This version of the dataset has undergone peer review.</p>
Data for "COSMO-BEP-Tree v1.0: a coupled urban climate model with explicit representation of street trees"
<p>In order to represent the interactions between street trees, urban elements and the atmosphere in realistic regional weather and climate simulations, we coupled the vegetated urban canopy model BEPTree and the mesoscale weather and climate model COSMO.</p> <p>The performance and applicability of the coupled model, named COSMO-BEP-Tree, are demonstrated over the urban area of Basel, Switzerland, during the heatwave event of June-July 2015.</p> <p>The data includes:</p> <p>1. <em>datasets</em><br> Datasets of building geometries (Shapefile, WGS84), trees (GeoTiff, WGS84), Landsat 7 scene (GeoTIFF, WGS84) and imperviousness (GeoTIFF, WGS84).</p> <p>2. <em>model outputs</em><br> The processed model outputs (.npy files, generated with Python v3) are provided for all the simulations, in terms of time series at the observation sites and spatial distributions. The full 3D model outputs, 1 TB) can be provided by request by contacting the author (<a href="mailto:mussetti.gianluca@gmail.com">mussetti.gianluca@gmail.com</a>).</p> <p>3. <em>model inputs</em><br> Input namelists for the COSMO-BEP-Tree model and initial/static conditions. The full 3D boundary conditions (60 GB) can be provided by request (<a href="mailto:mussetti.gianluca@gmail.com">mussetti.gianluca@gmail.com</a>).</p> <p>4. <em>observations</em><br> Measurement data (.txt).</p> <p>5. <em>post-processing scripts</em><br> Jupyter (Python 3) Notebook files used to generate the figures and to analyse model results. Tested in Python 3.6.5.</p>
Fig. 3 in Effect of urbanization on zoonotic gastrointestinal parasite prevalence in endemic toque macaque (Macaca sinica) from different climatic zones in Sri Lanka
Fig. 3. GI parasite genera types identified from fecal samples of toque macaques. I. Protozoan types: (A) Balantidium cyst, (B) Balantidium trophozoite, (C) Endolimax cyst, (D) Entamoeba cyst, (E) Isospora cyst. (F) Unidentified protozoan cyst; II. Cestode types: (G) Bertiella ova, (H) Diphyllobothrium ova, (I) Hymenolepis ova; III. Trematode types: (J–K) Unidentified trematode ova; IV. Acanthocephalan type: (L) Moniliformis ova; V. Nematode types: (M) Oesophagostomum ova, (N) Strongyloides ova, (O) Ascaris ova, (P) Trichuris ova, (Q) Strongyle/ Hookworm ova, (R) Enterobius ova, (S)Trichostrongylus ova, (T) Unidentified nematode ova.
Fig. 2 in Effect of urbanization on zoonotic gastrointestinal parasite prevalence in endemic toque macaque (Macaca sinica) from different climatic zones in Sri Lanka
Fig. 2. Map of Sri Lanka with sampling localities in the dry and the wet zones and the montane region.
Fig. 4 in Effect of urbanization on zoonotic gastrointestinal parasite prevalence in endemic toque macaque (Macaca sinica) from different climatic zones in Sri Lanka
Fig. 4. Number of parasite genera types (species richness) infecting M. s. aurifrons, M. s. sinica and M. s. opisthomelas in urban, suburban, and wild habitats.
Fig. 1 in Effect of urbanization on zoonotic gastrointestinal parasite prevalence in endemic toque macaque (Macaca sinica) from different climatic zones in Sri Lanka
Fig. 1. The three subspecies of macaque's endemic to Sri Lanka. (A) Common macaque (Macaca sinica sinica), (B) dusky or pale-fronted macaque (M. s. aurifrons), and (C) hill-zone macaque (M. s. opisthomelas) (image courtesy: Madura De Silva).
Urban nature-based solutions to climate change adaptation database
<p>This dataset is the result of a systematic mapping of the application of nature-based solutions (NbS) to climate change adaptation in urban areas across the world. We screened 823 potential urban NbS to climate adaptation, which resulted in the inclusion of 216 interventions worldwide from 130 cities in 55 countries within our dataset. We analysed each of the NbS according to key characteristics in terms of how these interventions are helping cities confront the grave climate change, biodiversity, and related social challenges they are facing. We further analyse the capacity for each NbS to affect change in the city it is implemented within, which ranges from incremental (shallow) change to reformistic (middle ground) and finally transformative (deep) change. The full range of climate, biodiversity, and social challenges, as well as further discussion on the meaning of these different levels of change, is described in the attached file in the coding template tab. </p> <p>The results and analysis of this database (v 1.0.0) appear in the following article:</p> <p>Goodwin, S., M. Olazabal, A. Castro, U. Pascual. "Global mapping of urban nature-based solutions for climate change adaptation". <em>Nature Sustainability. doi: <a href="http://doi.org/10.1038/s41893-022-01036-x">10.1038/s41893-022-01036-x </a></em></p> <p><strong>A read-only version of this article can be found online for free <a href="https://rdcu.be/c4tjk">here</a>.</strong></p> <p>For any use of this dataset, please cite this dataset along with the associated publication in Nature Sustainability. Please report any errors or omissions to Sean Goodwin.</p>
Urban resilience to extreme natural events and climate change: from Brasil to Europe
<p>Originally published at this link: <a href="https://www.iai.it/en/eventi/urban-resilience-extreme-natural-events-and-climate-change-brasil-europe">Urban resilience to extreme natural events and climate change: from Brasil to Europe | IAI Istituto Affari Internazionali</a></p> <p>The workshop will discuss best practices and innovative methodologies for urban resilience building to extreme natural events and climate change. The focus will be on participatory processes and citizen engagement practices, looking in particular at their relevance for physically and socially vulnerable urban areas. The event is designed as a knowledge-sharing opportunity for cities and will include the participation of researchers and experts working on the field with municipalities.</p> <p>The project “Waterproofing Data: Engaging Stakeholders in Sustainable Flood Risk Governance for Urban Resilience” will be discussed as a successful case study, focusing on data co-production practices and the role of digital tools. The project has been implemented in several Brazilian cities to build resilience to flooding in vulnerable communities by engaging citizens in data generation processes critical to design effective early-warning systems and strategies to reduce the risk of flood-related events. Professor João Porto De Albuquerque (University of Glasgow, UK) and Professor Maria Alexandra Viegas da Cunha (Fundação Getulio Vargas, São Paolo, Brasil) will illustrate the results of Waterproofing Data project and its innovative methodology, discussing opportunities for its implementation in cities both in Brasil and Europe and application to a broad spectrum of extreme events, from flooding to heatwaves and drought.</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>
Urban Heat: Forward-Looking Climate Modeling for Nis, Serbia
<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 <strong>present-day and future conditions</strong> under selected climate scenarios (<strong>present, SSP1-1.9, SSP3-7.0</strong>). The study domain focuses on Nis, Serbia.</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>150 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>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_Nis.docx</li> </ul>
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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.