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81 results for “urban climate”
Racing amidst change: urbanization and climate alter functional traits and distribution of an Amazonian parthenogenetic lizard
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RESCCUE (RESilience to cope with Climate Change in Urban arEas) EU Project - WP1 Data
<p>These files contain the data generated for the Work Package 1 of the RESCCUE project. RESCCUE project was devised to analyse future urban impacts due to climate change so as to improve resilience of three target cities: Barcelona, Bristol and Lisbon. To achieve that, future climate projections and changes in extreme events were obtained at a local scale for the Work Package 1. Several past studies were analysed to identify all the climate variables and extreme events that could affect urban areas, e.g. heavy rainfall, heat waves and storm surge. All available meteorological observations in the considered areas were collected and filtered through several tests (general consistency, outliers and inhomogeneities) in order to handle datasets long enough and of good quality. As a way to obtain the best input possible, every valid station was extended in time by downscaling process with the ERA-Interim reanalysis.</p> <p>Future climate projections were obtained for ten different global climate models considering two of the main Representative Concentration Pathways (RCP4.5 and RCP8.5) established in the last IPCC report. These models were downscaled through a sophisticated statistical methods (analogous stratification and transfer functions among others) to project local climate according to the identified climate drivers: temperature, precipitation, wind, relative humidity, sea level pressure, potential evapotranspiration, snowfall, wave height and sea level; and for both climate and decadal timescales. Already downscaled models were first validated for the method and afterwards verified, obtaining small errors and good coherent simulations.</p> <p>Extreme events of the main climate drivers were obtained and analysed for both historical and future scenarios through the combination of several statistical methods as well as through the analysis of several teleconnectionpatterns. Derived events such as heat waves, drought, snowstorms, storm surges, wave height among others were afterwards inferred for climate, decadal and seasonal scale.</p> <p><strong>FILES</strong></p> <p>The data generated have been grouped into three different files, one for each studied area: the hydrological basin of the rivers Ter and Llobregat (the area that influences Barcelona), the geographical area between England and South Wales (the area that influences Bristol) and the Lisbon area.</p> <p>Each of the files contains a self-explanatory file detailing the structure of the information contained and the way in which it is provided.</p> <p><strong>ABOUT THE RESCCUE PROJECT</strong></p> <p>The RESCCUE project, Resilience to cope with Climate Change in Urban Areas, –a multisectorial approach focusing on water– aims to provide practical and innovative models and tools to end-users facing climate change challenges to build more resilient cities.</p> <p>The project provides tools to assess urban resilience from a multisectorial approach, for current and future climate scenarios and including multiple hazards. This holistic approach to urban resilience will enable city managers and urban systems operators to decide the optimal investments to cope with future situations.</p> <p>For more information, please visit <a href="http://www.resccue.eu/">www.resccue.eu</a></p>
Integrative assessment of climate change-related impacts and risks on urban land
<p>Shapefile data set estimating trends of mean annual terrestrial surface air temperature (°C) and mean annual total precipitation (mm) and several heat indicators for urban land, characterised by clusters of local spatial autocorrelation in regard to the age of urban area and the coefficient of variation of urban area extent over time.</p>
Reassessing the climate mitigation potential of Chinese ecological restoration: the undiscovered potential of urban areas
<p>The dataset includes urban climate mitigation benefit data for all 371 cities and 1,721 clusters in China, as well as aggregate data of different ecological restoration projects and climate backgrounds. Combining multi-source high-resolution remote sensing data sets and the reanalysis dataset, the absolute value standard deviation method of multiple linear regression is used to extract the largest dominant factor (pixel-by-pixel calculation) of daytime surface temperature in each city except NDVI (as a greening indicator). Then, the time trends of surface temperature, NDVI, and dominant factors were calculated based on the Theil-Sen Median slope estimation method. Finally, based on the linear statistical relationship of the three indicators, the surface temperature trend under the no-greening scenario was constructed, and the difference between the simulated and observed surface temperature trends was used to characterize the urban climate mitigation benefits of the ecological restoration project. The data details are as follows: </p> <p>If you have any questions or comments, please feel free to contact Mr. Dong Xu via <a href="mailto:zhangh573@mail2.sysu.edu.cn">xu.dong@u.nus.edu.</a></p> <table> <tbody> <tr> <td> <p><strong>Data name</strong></p> </td> <td> <p><strong>Spatial resolution</strong></p> </td> <td> <p><strong>Time resolution</strong></p> </td> <td> <p><strong>Source</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> <td> <p><strong>Note</strong></p> </td> </tr> <tr> <td> <p>MOD13A2</p> </td> <td> <p>1000 m</p> </td> <td> <p>16-Day</p> </td> <td> <p>USGS.a</p> </td> <td> <p>/</p> </td> <td> <p>UG index</p> </td> </tr> <tr> <td> <p>MOD11A1</p> </td> <td> <p>1000 m</p> </td> <td> <p>Daily</p> </td> <td> <p>USGS.a</p> </td> <td> <p>Kelvin</p> </td> <td> <p>UST index</p> </td> </tr> <tr> <td> <p>MOD09A1</p> </td> <td> <p>500 m</p> </td> <td> <p>8-Day</p> </td> <td> <p>USGS.a</p> </td> <td> <p>/</p> </td> <td> <p>Calculate IBI index</p> </td> </tr> <tr> <td> <p>ERA5-Land reanalysis dataset</p> </td> <td> <p>10000 m</p> </td> <td> <p>Day</p> </td> <td> <p>ECMWF.b</p> </td> <td> <p>/</p> </td> <td> <p>Sensitivity analysis</p> </td> </tr> <tr> <td> <p>Population density datasets</p> </td> <td> <p>1000 m</p> </td> <td> <p>Annual</p> </td> <td> <p>WorldPop.c</p> </td> <td> <p>/</p> <p> </p> </td> <td> <p>Sensitivity analysis</p> </td> </tr> <tr> <td> <p>Global Urban Boundaries</p> </td> <td> <p>30 m</p> </td> <td> <p>Annual</p> </td> <td> <p>Li et al.</p> </td> <td> <p>/</p> </td> <td> <p>Delineate LUBs</p> </td> </tr> <tr> <td> <p><em>Note:</em> a, United States Geological Survey (https://earthexplorer.usgs.gov/). b, European Centre for Medium-Range Weather Forecasts (https://www.ecmwf.int/). c, WordPop (https://www.worldpop.org/). </p> <p><span>1. </span>Li X, Gong P, Zhou Y, et al. Mapping global urban boundaries from the global artificial impervious area (GAIA) data[J]. Environmental Research Letters, 2020, 15(9): 094044.</p> </td> </tr> </tbody> </table>
GLobAl building MOrphology dataset for URban climate modelling
<p>GLobAl building MOrphology dataset for URban climate modelling (GLAMOUR) offers the building footprint and height files at the resolution of 100 m in global urban centers.</p> <ul> <li>the `BH_100m` contains the building height files where each file is named as `BH_{lon_start}_{lon_end}_{lat_start}_{lat_end}.tif`.</li> <li>the `BF_100m` contains the building footprint files where each file is named as `BF_{lon_start}_{lon_end}_{lat_start}_{lat_end}.tif`.</li> </ul> <p>Here `lon_start`, `lon_end`, `lat_start`, `lat_end` denote the starting and ending positions of the longitude and latitude of target mapping areas.</p> <p>To avoid possible confusion, it should be clarified that the 'building footprint' in GLAMOUR represents the 'building surface fraction', i.e., the ratio of building plan area to total plan area.</p> <p> </p> <p>We also offer the snapshot of source code used for the generation of the GLAMOUR dataset including:</p> <ul> <li>`GC_ROI_def.py` defines regions of interest (ROI) used in the mapping of the GLAMOUR dataset.</li> <li>`GC_user_download.py` retrieves satellite images including Sentinel-1/2, NASADEM and Copernicus DEM from Google Earth Engine and exports them into Google Cloud Storage.</li> <li>`GC_master_pred.py` downloads exported data records from Google Cloud Storage and then performs the estimation of building footprint and height using Tensorflow-based models.</li> <li>`GC_postprocess.py` performs postprocessing on initial estimations by pixel masking with the World Settlement Footprint layer for 2019 (WSF2019).</li> <li>`GC_postprocess_agg.py` aggregates masked patches into larger tiles contained in the GLAMOUR dataset.</li> </ul>
Replication materials for "Effects of Urbanization in China on the East Asian Summer Monsoon as Revealed by Two Global Climate Models"
<p>The datasets are replication materials for the research "Effects of Urbanization in China on the East Asian Summer Monsoon as Revealed by Two Global Climate Models". They show urbanization-induced changes in surface air temperature (SAT), precipitation, and 850hPa atmospheric circulation from two global climate models (NCAR CESM1.2.1 and FGOALS-g3).</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>
Dataset to reproduce the paper "A new framework to evaluate urban design using urban microclimatic modelling in future climatic conditions"
<p>This dataset has been generated with the paper " A new framework to evaluate urban design using urban microclimatic<br> modelling in future climatic conditions" (https://doi.org/10.3390/su10041134). A Python notebook is also included to conduct the analysis.</p> <ol> <li>Data analysis - Sustainability paper.ipynb : Python notebook</li> <li>Geneva_Eur11_TDY_2010_2039 : Climate file for the year 2039 obtained from RCA4</li> <li>Geneva_Eur11_TDY_2010_2039_cim : Climate file for the year 2039 obtained from RCA4-CIM</li> <li>Geneva_Eur11_TDY_2010_2039 : Climate file for the year 2069 obtained from RCA4</li> <li>Geneva_Eur11_TDY_2010_2069_cim : Climate file for the year 2069 obtained from RCA4-CIM</li> <li>Geneva_Eur11_TDY_2010_2099 : Climate file for the year 2099 obtained from RCA4</li> <li>Geneva_Eur11_TDY_2010_2099_cim : Climate file for the year 2099 obtained from RCA4-CIM</li> <li>heating_2039 : Heating demand from CitySim for the year 2039</li> <li>heating_2039_cim : Heating demand from CitySim-CIM for the year 2039</li> <li>heating_2069 : Heating demand from CitySim for the year 2069</li> <li>heating_2069_cim : Heating demand from CitySim-CIM for the year 2069</li> <li>heating_2099 : Heating demand from CitySim for the year 2099</li> <li>heating_2099_cim : Heating demand from CitySim-CIM for the year 2099</li> <li>heating_2099_minP : Heating demand from CitySim for the year 2099 with Minergie-P scenario</li> <li>heating_2099__minP_cim : Heating demand from CitySim-CIM for the year 2099 with Minergie-P scenario</li> <li>cooling_2039 : Cooling demand from CitySim for the year 2039</li> <li>cooling_2039_cim : Cooling demand from CitySim-CIM for the year 2039</li> <li>cooling_2069 : Cooling demand from CitySim for the year 2069</li> <li>cooling_2069_cim : Cooling demand from CitySim-CIM for the year 2069</li> <li>cooling_2099 : Cooling demand from CitySim for the year 2099</li> <li>cooling_2099_cim : Cooling demand from CitySim-CIM for the year 2099</li> <li>cooling_2099_minP : Cooling demand from CitySim for the year 2099 with Minergie-P scenario</li> <li>cooling_2099__minP_cim : Cooling demand from CitySim-CIM for the year 2099 with Minergie-P scenario</li> <li>temp_cim : simulated temperature from CIM using Meteonorm</li> <li>temp_meteonorm : temperature from Meteonorm</li> <li>u_cim : simulated wind speedfrom CIM using Meteonorm</li> <li>u_meteonorm : wind speed from Meteonorm</li> </ol> <p> </p>
Stylized urban landscapes optimized for compactness, climate regulation and vascular plant species richness
<p>The data set provides the output of a genetic algorithm optimizing a stylized urban region with respect to three target functions: urban compactness, climate regulation as an exemplary ecosystem service and vascular plant species richness as a measure of biodiversity.</p> <p>The optimisation varies the spatial allocation of three types of land cover blocks in a stylized urban region: high- and low-density and park blocks which consist of green and/or built-up cells. We systematically vary landscape composition at the block level, but keep city size constant.</p> <p>The data set is related to a publication submitted to Frontiers in Environmental Science.</p>
Urbanization decreases species richness, and increases abundance in dry climates whereas decreases in wet climates: A global meta-analysis
<p>Soil invertebrates have an essential role in decomposition, nutrient turnover and soil structure formation, all of which are strongly threatened by urbanization. Sealing, compaction by trampling and pollution destroy and degrade city soils and potentially damage soil-living invertebrates. The existing literature on how urbanization affects soil invertebrates is inconsistent, presenting both negative and positive effects. Therefore, here we aimed to synthesize the effects of urbanization on soil invertebrates considering their taxonomic (Acari, Annelida, Carabidae, Collembola, Gastropoda, Isopoda, Myriapoda, Nematoda) and functional (soil living vs. soil-related; mobility) identities, as well as to examine how the overall effect is modulated by climatic conditions (total annual precipitation, annual mean ambient temperature), urban heat island effect (based on ambient temperature differences between urban and rural areas) and city population. In a systematic review using hierarchical and categorical meta-analyses, we extracted 158 effect sizes from 75 studies on abundance and 125 effect sizes from 84 studies on species richness. Invertebrate abundance showed an increase, whereas species richness significantly decreased with increasing urbanization. The reason behind this could be that a few generalist species can adapt well to the urban environment and achieve strongly elevated densities. The species richness of annelids, springtails, and snails decreased with advancing urbanization, most probably because these animals are sensitive to soil compaction and pollution, both of which are common consequences of urbanization. The temperature did not modify the effects of urbanization, but precipitation modified the effects on abundance. Abundance increased with advancing urbanization in drier climates, probably because irrigation increased soil moisture, whereas it decreased in wet climates, as urban areas were drier than their surroundings. Making future cities more climate-neutral could better sustain soil biodiversity.</p>
Urbanization can accelerate climate change by increasing soil N2O emission while reducing CH4 uptake
<p><span>Urban land use change has the potential to affect local to </span><span>global biogeochemical carbon (C) and nitrogen (N) cycles and associated greenhouse gas (GHG) fluxes</span><span>. We conducted a meta-analysis to 1) assess the effects of urbanization-induced land-use conversion on soil nitrous oxide (N<sub>2</sub>O) and methane (CH<sub>4</sub>) fluxes, 2) quantify direct </span><span>N<sub>2</sub>O</span><span> emission factors (EF<sub>d</sub>) of fertilized urban soils used e.g., as lawns or forests, and 3) identify the key drivers leading to flux changes associated with urbanization. On average, urbanization increases soil </span><span>N<sub>2</sub>O</span><span> emissions by 153%, to 3.0 kg N ha<sup>-1</sup> yr<sup>-1</sup>, while rates of soil CH4 uptake are reduced by 50%, to 2.0 kg C </span><span>ha<sup>-1</sup> yr<sup>-1</sup></span><span>. The mean annual </span><span>N<sub>2</sub>O</span> <span>EF<sub>d</sub></span><span> of fertilized lawns and urban forests is 1.4%, suggesting that urban soils can be regional hotspots of </span><span>N<sub>2</sub>O</span><span> emissions. On a global basis, conversion of land to urban greenspaces has increased soil </span><span>N<sub>2</sub>O</span><span> emission by 0.46 Tg </span><span>N<sub>2</sub>O</span><span>-N yr<sup>-1</sup> and decreased soil </span><span>CH<sub>4</sub></span><span> uptake by 0.58 Tg </span><span>CH<sub>4</sub></span><span>-C yr<sup>-1</sup>. Urbanization-driven changes in soil </span><span>N<sub>2</sub>O</span><span> emission and CH4 uptake are associated with changes in soil properties (bulk density, pH, total N content and C/N ratio), increased temperature, and management practices, especially fertilizer use. Overall, our meta-analysis shows that urbanization increases soil </span><span>N<sub>2</sub>O</span><span> emissions and reduces the role of soils as a sink for atmospheric </span><span>CH<sub>4</sub></span><span>. These effects can be mitigated by avoiding soil compaction, reducing fertilization of lawns, and restoring native ecosystems in urban landscapes.</span></p>
A secure future? Human urban and agricultural land use benefits a flightless island-endemic rail despite climate change
<p class="MsoNormal"><span>Identifying environmental characteristics that limit species' distributions is important for contemporary conservation and inferring responses to future environmental change. The Tasmanian native hen is an island-endemic flightless rail and a survivor of a prehistoric extirpation event. Little is known about the regional-scale environmental characteristics influencing the distribution of native hens, or how their future distribution might be impacted by environmental shifts (e.g., climate change). </span><span>Using a combination of local fieldwork and species distribution modelling, we assess environmental factors shaping the contemporary distribution of the native hen, and project future distribution changes under predicted climate change. We find 37.2% of Tasmania is currently suitable for the native hens, owing to low summer precipitation, low elevation, human-modified vegetation, and urban areas. <span>Moreover</span>, in unsuitable regions, </span><span>urban areas can create 'oases' of habitat, able to support populations with high breeding activity by providing resources and buffering against environmental constraints. Under climate change predictions, </span><span>native hens were predicted to lose only 5% of their occupied range by 2055. We conclude that the species is resilient to climate change and benefits overall from anthropogenic landscape modifications. As such, this constitutes a rare example of a flightless rail to have adapted to human activity.</span></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>
A secure future? Human urban and agricultural land use benefits a flightless island-endemic rail despite climate change
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Increasing rat numbers in cities are linked to climate warming, urbanization and human population
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Agriculture-urban interfaces, social vulnerability, and climate change shape West Nile virus risk across the United States
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Urbanization can accelerate climate change by increasing soil N2O emission while reducing CH4 uptake
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Spatial-temporal change of climate in relation to urban fringe development in central Arizona-Phoenix
Not many studies have documented climate and air quality changes of settlements at early stages of development. This is because high quality climate and air quality records are deficient for the periods of the early 18th century to mid 20th century when many U.S. cities were formed and grew. Dramatic landscape change induces substantial local climate change during the incipient stage of development. Rapid growth along the urban fringe in Phoenix, coupled with a fine-grained climate monitoring system, provide a unique opportunity to study the climate impacts of urban development as it unfolds. Generally, heat islands form, particularly at night, in proportion to city population size and morphological characteristics. Drier air is produced by replacement of the countryside's moist landscapes with dry, hot urbanized surfaces. Wind is increased due to turbulence induced by the built-up urban fabric and its morphology; although, depending on spatial densities of buildings on the land, wind may also decrease. Air quality conditions are worsened due to increased city emissions and surface disturbances. Depending on the diversity of microclimates in pre-existing rural landscapes and the land-use mosaic in cities, the introduction of settlements over time and space can increase or decrease the variety of microclimates within and near urban regions. These differences in microclimatic conditions can influence variations in health, ecological, architectural, economic, energy and water resources, and quality-of-life conditions in the city. Therefore, studying microclimatic conditions which change in the urban fringe over time and space is at the core of urban ecological goals as part of LTER aims. In analyzing Phoenix and Baltimore long-term rural/urban weather and climate stations, Brazel et al. (In progress) have discovered that long-term (i.e., 100 years) temperature changes do not correlate with populations changes in a linear manner, but rather in a third-order nonlinear re
Evaluation dataset for urban climate simulation
<p>The dataset was used in the following papers:</p> <p>(1) Li, Z., Zhou, Y., Wan, B., Chung, H., Huang, B., & Liu, B. (2019). Model evaluation of high-resolution urban climate simulations: using the WRF/Noah LSM/SLUCM model (Version 3.7. 1) as a case study. <em>Geoscientific Model Development</em>, <em>12</em>(11), 4571-4584.</p> <p>(2) Li, Z., Wan, B., Zhou, Y., & Wong, H. (2020). Incoming data quality control in high-resolution urban climate simulation: Hong Kong-Shenzhen area urban climate simulation as a case study using WRF/Noah LSM/SLUCM model (Version 3.7. 1). <em>Geoscientific Model Development Discussions</em>, 1-13.</p>
Long-term climate trends and urbanization reveal human impacts and decoupling of dissolved organic carbon quantity and composition in streams
<p>This repository contains data, R code, graphical objects, and outputs in Kelley et al. (2025) entitled <strong><span lang="EN-US">Climate Change and Urbanization Decouple Dissolved Organic Carbon Quantity and Composition in Streams</span></strong><span lang="EN-US"> accepted for publication in <em>Global Biogeochemical Cycles</em></span><span lang="EN-US">. </span><a href="https://doi.org/10.1029/2025GB008534">https://doi.org/10.1029/2025GB008534</a></p> <p><span lang="EN-US">In this finalized repository, we provide a detailed meta data file (1_repository_meta_data.xlsx) containing lists and descriptions of all data files, variables, figures, and scripts within.</span> Note that previous versions contained water quality parameters for forms of nitrogen and phosphorus, which have been removed from this final version. These parameters were neither used in any analyses in this work nor underwent QA/QC protocols; thus, we suggest caution in their use from previous versions.</p> <p>Additionally, this repository contains a publication license for a graphical object created using the BioRender platform.</p>
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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.