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135 results for “urban modelling”
Mapping and Modeling Clandestine Drivers of Urban Expansion in Mexico City (2016-2019)
This dataset incorporates Mexico City related essential data files associated with Beth Tellman's dissertation: Mapping and Modeling Illicit and Clandestine Drivers of Land Use Change: Urban Expansion in Mexico City and Deforestation in Central America. It contains spatio-temporal datasets covering three domains; i) urban expansion from 1992-2015, ii) district and section electoral records for 6 elections from 2000-2015, iii) land titling (regularization) data for informal settlements from 1997-2012 on private and ejido land. The urban expansion data includes 30m resolution urban land cover for 1992 and 2013 (methods published in Goldblatt et al 2018), and a shapefile of digitized urban informal expansion in conservation land from 2000-2015 using the Worldview-2 satellite. The electoral records include shapefiles with the geospatial boundaries of electoral districts and sections for each election, and .csv files of the number of votes per party for mayoral, delegate, and legislature candidates. The private land titling data includes the approximate (in coordinates) location and date of titles given by the city government (DGRT) extracted from public records (Diario Oficial) from 1997-2012. The titling data on ejido land includes a shapefile of georeferenced polygons taken from photos in the CORETT office or ejido land that has been expropriated by the government, and including an accompany .csv from the National Agrarian Registry detailing the date and reason for expropriation from 1987-2007. Further details are provided in the dissertation and subsequent article publication (Tellman et al 2021). The Mexico City portion of these data were generated via a National Science Foundation sponsored project (No. 1657773, DDRI: Mapping and Modeling Clandestine Drivers of Urban Expansion in Mexico City). The project P.I. is Beth Tellman with collaborators at ASU (B.L Turner II and Hallie Eakin). Other collaborators include the National Autonomous University of Mexico (UNAM),
Modeling the effects of lake morphology on chloride retention and salt-driven stratification in two urban lakes in St. Paul, MN
Road salt inputs have caused widespread salinization of urban lakes in northern temperate regions. Watershed characteristics are known to be important drivers of lake chloride concentrations, but there has been less focus on how lake morphometry influences seasonal and interannual dynamics in lake chloride, and how these chloride levels may alter mixing in the water column. We analyzed chloride retention for two urban lakes (Como Lake and Lake McCarrons) in Saint Paul, Minnesota, that are in adjacent watersheds and have similar surface areas, but differ in depth and water residence time. Summer chloride concentrations were negatively related to total summer precipitation for Como Lake (maximum depth 2.2 m), but the relationship was less strong for Lake McCarrons (maximum depth 7.6 m). We used a zero-dimensional model to simulate chloride dynamics in both lakes and tracked the fate of chloride over time. In Como Lake, the mass of chloride in the lake turns over within three years, whereas chloride inputs are retained for >10 years in Lake McCarrons. We then used a one-dimensional hydrodynamic lake model (GLM-AED) to examine how lake depth affects how current chloride loading rates alter lake mixing. Salt inputs significantly extended the duration of summer stratification for simulated lakes with depths of 8 m or more, and salt inputs increased the number of days of hypoxia and anoxia across all depths. These results underscore the importance of considering lake morphometry in understanding the effects of salt inputs on lake ecosystems.
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>
The data that support the findings of a review paper "From urban data to city-scale models: A review of traffic simulation case studies"
<p>This dataset contains the data that were used in a review paper "From urban data to city-scale models: A review of traffic simulation case studies". It contains the following files:</p> <ul> <li>keywords with counts.txt - list of keywords and their counts in the considered corpus of traffic simulation case studies. The data were used to produce Figure 2 and Figure 3 in the paper.</li> <li>Papers analysis.xlsx - Excel file containing the data on the reviewed studies. The document has the following sheets: <ul> <li> Appendix A - contains a table short reference, location, simulation period, spatial scale, simulated units and marked categories for a paper;</li> <li>Geography - contains data on geographical distribution of simulated areas between world regions and countries, these data were used to produce Figure 4 in the paper;</li> <li>Software tools - contains data on simulation tools used in the studies. </li> <li>Journals and conferences - contains data on where the reviewed papers were published.</li> </ul> </li> </ul>
Air mass back-trajectory modeling output along an urban-rural transect in central Ohio, 2021
This data package contains modeled air parcel back-trajectories generated using the Stochastic Time-Inverted Lagrangian Transport model (STILT) via the R interface. The purpose of the study was to characterize the geochemical and isotopic signatures of dust in relation to different land uses, and to connect the geochemistry of deposited dust to air mass trajectories. Back-trajectories are three-dimensional paths of air parcels from a receptor site (the dust collection site) backwards in time and space for the duration of the tracking interval, calculated iteratively using wind fields from high-resolution gridded meteorological data. To calculate a probability of potential pathways, rather than a single back-trajectory, STILT introduces small random perturbations into the wind fields during each time step. For four sites along an urban-rural transect in central Ohio for June-July 2021, we generated weekly footprints of potential sources for the dust deposited at each site. These back-trajectories can be paired with geochemical data to establish a connection between land use and anthropogenic dust composition. This dataset is complete and will not be updated.
Impact of urban and shipping emissions on NASA-Unified Weather Research and Forecasting model results
<p>This dataset supports Huang et al. (2019, JGR-Atmospheres): "Impact of aerosols from urban and shipping emission sources on terrestrial carbon uptake and evapotranspiration: a case study in East Asia". The file named "NUWRFout.tar.gz" contains NUWRF base and sensitivity simulation results on 31 May 2016. The file named "LIS_soil_LAI.zip" contains model grid information, soil conditions and leaf area index (LAI) at NUWRF initialization times in late May 2016.</p>
Data: "Using butterfly survey data to model habitat associations in urban developments", JEJ Cooper et al., (2023)
<p>This data package has been used to examine the responses of UK butterfly species </p> <p>to different features of the urban environment. 'JC_WCBSmodel.Rdata' presents the</p> <p>butterfly abundance data, and supporting information about </p> <p>species and sites. This data can be fed through the script '04_model_builder.R', to </p> <p>produce the models reported in the research article. '00_functions.R' is a script </p> <p>containing functions which support the modelling process, which is loaded as part of </p> <p>the 04_model_builder script. </p> <p> </p> <p>Summaries of the resulting models are an output of that script - </p> <p>'Butterfly_GAM_Outputs.xlsx'. These are represented graphically in the manuscript, </p> <p>using scripts '06_01_Map'.R:'06_03_Cross_Validation'. '06_04_Model_Metric.R' </p> <p>is a further summary of the .xlsx file, found in the Supplementary Materials. </p> <p>'06_05_graphic_4_twitter.R' produces a condensed version of the figure resulting </p> <p>from the script '06_02_Metric_Summary.R'</p> <p> </p> <p>Dataset descriptions are found in the attached readme.txt</p> <p>........................................................................................</p> <p>We would also greatly appreciate if you could fill out <a href="https://forms.gle/DCc58VXpdmqnTmTk8" target="_blank" rel="noopener">this very short form</a> to tell us how you intend to use these data. Thanks in advance!</p>
Challenges of high-fidelity air quality modeling in urban environments - PALM sensitivity study during stable conditions (TURBAN)
<h3>Introduction</h3> <p>This dataset contains the PALM model inputs and the source code used to create the simulations for Prague-Legerova scenarios performed in the scope of the <strong>TURBAN</strong> project (<a href="https://www.project-turban.eu/">https://www.project-turban.eu/</a>). Detailed description of the simulations is provided in the referencing scientific paper.</p> <h3>List of simulations</h3> <table> <tbody> <tr> <td><strong>Scenario name</strong></td> <td><strong>Days simulated</strong></td> <td><strong>IBC</strong></td> <td><strong>Configuration changes</strong></td> </tr> <tr> <td>legerovas_s6_sens_base</td> <td>13–15 February 2023</td> <td>ICON</td> <td>-</td> </tr> <tr> <td>legerovas_s6_sens_dtmax</td> <td>13 February 2023</td> <td>ICON</td> <td>dt_max=0.2</td> </tr> <tr> <td>legerovas_s6_sens_heat</td> <td>13 February 2023</td> <td>ICON</td> <td>car anthropogenic heat (custom code)</td> </tr> <tr> <td>legerovas_s6_sens_sgs</td> <td>13 February 2023</td> <td>ICON</td> <td>e_min=0.02</td> </tr> <tr> <td>legerovas_s6_sens_stg</td> <td>13 February 2023</td> <td>ICON</td> <td>STG_PROFILES added</td> </tr> <tr> <td>legerovas_s6_sens_alad</td> <td>13–15 February 2023</td> <td>ALADIN</td> <td>-</td> </tr> <tr> <td>legerovas_s6_sens_alad_heat</td> <td>13 February 2023</td> <td>ALADIN</td> <td>car anthropogenic heat (custom code)</td> </tr> <tr> <td>legerovas_s6_sens_alad_sgs</td> <td>13 February 2023</td> <td>ALADIN</td> <td>e_min=0.02</td> </tr> <tr> <td>legerovas_s6_sens_alad_stg</td> <td>13 February 2023</td> <td>ALADIN</td> <td>STG_PROFILES added</td> </tr> <tr> <td>legerovas_s6_sens_wrf</td> <td>13–15 February 2023</td> <td>WRF</td> <td>-</td> </tr> <tr> <td>legerovas_s6_sens_wrf_heat</td> <td>13 February 2023</td> <td>WRF</td> <td>car anthropogenic heat (custom code)</td> </tr> <tr> <td>legerovas_s6_sens_wrf_sgs</td> <td>13 February 2023</td> <td>WRF</td> <td>e_min=0.02</td> </tr> <tr> <td>legerovas_s6_sens_wrf_stg</td> <td>13 February 2023</td> <td>WRF</td> <td>STG_PROFILES added</td> </tr> </tbody> </table> <h3>Directory structure</h3> <p>The directory inputs contains the model inputs and it is further divided into these subdirectories:</p> <p>- inputs/common: The PALM static driver and the emission drivers for the parent and child domains. These files are common to all simulations</p> <p>- inputs/dynamic/*: These directories contain the dynamic drivers for the parent and child domanis, which contain the initial and boundary conditions (IBC) as well as external radiation data. The three subdirectories aladin, icon and wrf contain IBCs created from the respective mesoscale model outputs. </p> <p>- inputs/legerovas_s6_sens_*: These directories contain the PALM model configuration (p3d) for both domains for each simulation.</p> <p>- inputs/build_config: The included .palm.iofiles configuration file ensures that the files STG_PROFILES are correctly copied from the input directory.</p> <p>The directory palm_sources contains the exact model source used for the simulations. It is derived from the PALM model release 23.04 with additional bugfixes. There are two source archives:</p> <p>- heat.tar.gz: PALM source further modified to include anthropogenic heat from cars, used for the simulations legerovas_s6_sens_*_heat</p> <p>- standard.tar.gz: PALM source used for all other included simulations.</p> <h3>Reproducing the simulations</h3> <p>In order to reproduce the simulations, unpack the respective source code archive and follow the standard installation, configuration and build procedures described in the README.md file within the archive and on the PALM model website http://www.palm-model.org/. Then copy the input files for the respective simulation in the JOBS directory. The common files and the dynamic driver files need to be renamed so that they match the prefix given by the name of the simulation, as is described in the PALM model documentation.</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>
Dataset for the preprint: "Intercomparison of biogenic CO2 flux models in four urban parks in the city of Zurich"
<p>Dataset supporting the submission of the manuscript titled "Intercomparison of biogenic CO2 flux models in four urban parks in the city of Zurich" to the to the international journal "Biogeosciences".</p> <p><strong>Meteorological data</strong></p> <p>Hourly aggregated meteorological dataset for the urban area of Zurich, originating from two urban stations: Kaserne (8°32'/47°23'), which is a station of the Swiss national air pollution monitoring network NABEL, and Hardau II (8°30'/47°23'), which is a station established for the ICOS-Cities project. Zurich Kaserne is located in a large courtyard. Wind and global radiation are measured on top of a four-storey building. Wind is measured at 35 m and global radiation at 27 m above ground. Hardau II station is established on the top of a high-rise building (110 m a.g.l.). Meteorological observation gaps were filled using Copernicus ERA5-Land data.</p> <p>Data format: comma separated values (csv)</p> <p>Time step: 60 min (aggregated)</p> <p>Time stamp: yyyy-MM-dd HH:mm, UTC, end of aggregated period</p> <p>Period: 01/2022–09/2023</p> <p>Monthly mean atmospheric CO2 concentration data derived from the ICOS-Cities Hardau II station (07/2022–09/2023) and the Beromunster station (11/2012–02/2022). Observation gaps were filled using Copernicus ERA5-Land data.</p> <p>Further information on the dataset can be found in the submitted manuscript. </p> <p>Data format: comma separated values (csv)</p> <p>Time step: Monthly (mean)</p> <p>Time stamp: yyyy-MM-dd </p> <p>Period: 11/2012–09/2023</p> <table> <tbody> <tr> <td> <p><strong>Variable</strong></p> </td> <td> <p><strong>Acronym</strong></p> </td> <td> <p><strong>Units</strong></p> </td> <td> <p><strong>Height above ground</strong></p> </td> <td> <p><strong>Location</strong></p> </td> <td> <p><strong>Geographic location</strong></p> </td> </tr> <tr> <td> <p>Global radiation</p> </td> <td> <p>G</p> </td> <td> <p>W m-2</p> </td> <td> <p>27 m</p> </td> <td> <p>Kaserne</p> </td> <td> <p>8°32'/47°23'</p> </td> </tr> <tr> <td> <p>Air temperature</p> </td> <td> <p>Tair</p> </td> <td> <p>Degrees Celsius</p> </td> <td> <p>2 m</p> </td> <td> <p>Kaserne</p> </td> <td> <p>8°32'/47°23'</p> </td> </tr> <tr> <td> <p>Relative humidity</p> </td> <td> <p>RH</p> </td> <td> <p>%</p> </td> <td> <p>2 m</p> </td> <td> <p>Kaserne</p> </td> <td> <p>8°32'/47°23'</p> </td> </tr> <tr> <td> <p>Air pressure</p> </td> <td> <p>P</p> </td> <td> <p>hPa</p> </td> <td> <p>2 m</p> </td> <td> <p>Kaserne</p> </td> <td> <p>8°32'/47°23'</p> </td> </tr> <tr> <td> <p>Wind speed</p> </td> <td> <p>u</p> </td> <td> <p>m s-1</p> </td> <td> <p>35 m</p> </td> <td> <p>Kaserne</p> </td> <td> <p>8°32'/47°23'</p> </td> </tr> <tr> <td> <p>Precipitation</p> </td> <td> <p>R</p> </td> <td> <p>mm</p> </td> <td> <p>2 m</p> </td> <td> <p>Kaserne</p> </td> <td> <p>8°32'/47°23'</p> </td> </tr> <tr> <td> <p>Downward longwave radiation</p> </td> <td> <p>LW</p> </td> <td> <p>W m-2</p> </td> <td> <p>110 m</p> </td> <td> <p>Hardau II, ERA-5</p> </td> <td> <p>8°30'/47°23'</p> </td> </tr> <tr> <td> <p>Soil temperature</p> </td> <td> <p>Tsoil</p> </td> <td> <p>Degrees Celsius</p> </td> <td> <p>-0.15 m</p> </td> <td> <p>Parks</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Soil water content</p> </td> <td> <p>SWC</p> </td> <td> <p>m3 m-3</p> </td> <td> <p>-0.15 m</p> </td> <td> <p>Parks</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Atmospheric CO2 concentration</p> </td> <td> <p>CO2</p> </td> <td> <p>ppmv</p> </td> <td> <p>2 m</p> </td> <td> <p>Hardau II, Beromunster, ERA-5</p> </td> <td> <p>8°30'/47°23', 8°10'/47°11</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>In-situ ecophysiological data</strong></p> <p>In-situ ecophysiology measurements performed on park trees and lawns in the city of Zurich during the ICOS-Cities project.</p> <p>LAI (leaf area index) was measured in dense <em>Platanus</em> sp. tree stands, found only in Bullingerhof and Hardaupark, during sunny conditions using a ceptometer (SS1 SunScan, Delta-T Devices).</p> <p>Sap flow was measured at six trees (<em>Platanus</em> sp., <em>Tilia</em> sp.), at Bullingerhof, Hardaupark and Fritschiwiese, with heat pulse sap flow sensors (3 x 3 cm probes, Implexx Sense), providing continuous measurements at 10-min sampling intervals. Daily aggregated sap flux densities (cm3 cm−2 d−1) were calculated from the 10-min data using the sensor inner thermistors, averaged for the six sampled trees.</p> <p>Soil and grass respiration were measured using a portable CO2 soil efflux system equipped with a 20 cm diameter survey chamber (LI-8200-01S, LI-COR Biosciences) and a CO2/H2O analyser (LI-870, LI-COR Biosciences). The observations originate from a total of 10 soil collars (Bullingerhof, Hardaupark, Fritschiwiese, Heiligfeld) separated to undisturbed grass collars (Reco, μmol CO2 m-2 s-1) and collars where the aboveground grass was clipped (Rsoil, μmol CO2 m-2 s-1).</p> <p>Further information on the dataset can be found in the submitted manuscript.</p> <p>Data format: comma separated values (csv)</p> <p>Time stamp: yyyy-MM-dd</p> <p>Period: 04/2022–09/2023</p> <p> </p> <p><strong>Land cover map</strong></p> <p>Land cover map of part of Zurich urban area. Datasets used to derive this map:</p> <ul> <li>· Land Use Cadastre of the Canton of Zurich (https://www.geolion.zh.ch/geodatensatz/show?gdsid=443)</li> <li>· Urban Atlas (https://doi.org/10.2909/fb4dffa1-6ceb-4cc0-8372-1ed354c285e6)</li> <li>· Vegetation Height Model (VHM) from the Swiss federal forest inventory (https://opendata.swiss/de/dataset/vegetationshohenmodell-lfi)</li> <li>· Forest Mixture from the Swiss Federal Forest Inventory (https://opendata.swiss/de/dataset/waldmischungsgrad-lfi)</li> </ul> <p>Further information on the dataset can be found in the submitted manuscript.</p> <p> </p> <p>Data specifications:</p> <p>CRS: EPSG:32632 - WGS 84 / UTM zone 32N - Projected</p> <p>Spatial Extent: 461972.1, 5246490.4 : 463972.1, 5248490.4</p> <p>Temporal Extent: 2023</p> <p>Units: meters</p> <p>Width: 2000</p> <p>Height: 2000</p> <p>Bands: 1</p> <p>Pixel Size: 1,-1</p> <p>Data type: Float32 - Thirty two bit floating point</p> <p>GDAL Driver Description: GTiff</p> <p>GDAL Driver Metadata: GeoTIFF</p> <p> </p> <p>Legend:</p> <p>30 Grass</p> <p>40 Crops</p> <p>50 Paved</p> <p>60 Buildings</p> <p>70 Deciduous trees</p> <p>80 Water</p> <p> </p> <p><strong>CO2 fluxes</strong></p> <p>Hourly mean CO2 fluxes estimated by the models diFUME, JSBACH, SUEWS and VPRM for the trees and lawns of the Zurich urban parks: Bullingerhof, Hardaupark, Fritschiwiese and Heiligfeld. GPP stands for gross primary productivity, Reco for ecosystem respiration and NEE for net ecosystem exchange. All fluxes are in units: μmol CO2 m-2 s-1.</p> <p>The parameter sets used by each model are presented in the Tables below. Further information on the dataset can be found in the submitted manuscript.</p> <p>Parameters used by diFUME model</p> <table> <tbody> <tr> <td> <p><strong>Parameter</strong></p> </td> <td> <p><strong>Value</strong></p> </td> <td> <p><strong>Units</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>A_max</p> </td> <td> <p>15</p> </td> <td> <p>μmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></p> </td> <td> <p>maximum leaf gross photosynthetic rate</p> </td> </tr> <tr> <td> <p>a</p> </td> <td> <p>0.045</p> </td> <td> <p>mol CO<sub>2</sub> mol<sup>-1</sup> PAR</p> </td> <td> <p>quantum yield for CO2 assimilation</p> </td> </tr> <tr> <td> <p>a_1</p> </td> <td> <p>25</p> </td> <td> <p>N/A</p> </td> <td> <p>empirical coefficient in Leuning (1995) model</p> </td> </tr> <tr> <td> <p>b_</p> </td> <td> <p>0.65</p> </td> <td> <p>N/A</p> </td> <td> <p>empirical coefficient in β-factor formula</p> </td> </tr> <tr> <td> <p>b_1</p> </td> <td> <p>5</p> </td> <td> <p>N/A</p> </td> <td> <p>empirical coefficient</p> </td> </tr> <tr> <td> <p>D_o</p> </td> <td> <p>0.3</p> </td> <td> <p>kPa</p> </td> <td> <p>empirically determined coefficient for the VPD scalar inside Leuning (1995) model</p> </td> </tr> <tr> <td> <p>D_sc</p> </td> <td> <p>1</p> </td> <td> <p>N/A</p> </td> <td> <p>daylight scalar for dark respiration inhibition during day (1: no inhibition)</p> </td> </tr> <tr> <td> <p>E_0</p> </td> <td> <p>487.75</p> </td> <td> <p>K</p> </td> <td> <p>temperature sensitivity parameter for soil respiration</p> </td> </tr> <tr> <td> <p>g_o</p> </td> <td> <p>0.01</p> </td> <td> <p>mol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></p> </td> <td> <p>residual stomatal conductance for CO2 (g_s when Anet = 0, PAR = 0).</p> </td> </tr> <tr> <td> <p>Q_10</p> </td> <td> <p>1.85</p> </td> <td> <p>N/A</p> </td> <td> <p>temperature sensitivity of leaf respiration</p> </td> </tr> <tr> <td> <p>R_(l,ref)</p> </td> <td> <p>1.53</p> </td> <td> <p>μmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></p> </td> <td> <p>reference leaf respiration at Tair = 25 °C</p> </td> </tr> <tr> <td> <p>R_(S,ref)</p> </td> <td> <p>2.49</p> </td> <td> <p>μmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></p> </td> <td> <p>reference soil respiration at Tsoil = 10 °C</p> </td> </tr> <tr> <td> <p>T_opt</p> </td> <td> <p>23</p> </td> <td> <p>°C</p> </td> <td> <p>optimum air temperature for gross photosynthesis</p> </td> </tr> <tr> <td> <p>T_0</p> </td> <td> <p>-46</p> </td> <td> <p>°C</p> </td> <td> <p>low-temperature limit for soil respiration</p> </td> </tr> <tr> <td> <p>T_(ref,S)</p> </td> <td> <p>10</p> </td> <td> <p>°C</p> </td> <td> <p>reference soil temperature for soil respiration</p> </td> </tr> <tr> <td> <p>T_(ref,l)</p> </td> <td> <p>25</p> </td> <td> <p>°C</p> </td> <td> <p>reference air temperature for leaf respiration</p> </td> </tr> <tr> <td> <p>W</p> </td> <td> <p>10</p> </td> <td> <p>°C</p> </td> <td> <p>width of the bell-shape curve at f(T_air ) = 0.5</p> </td> </tr> <tr> <td> <p>θ_ref</p> </td> <td> <p>0.4</p> </td> <td> <p>m<sup>3</sup> m<sup>-3</sup></p> </td> <td> <p>saturated soil volumetric water content </p> </td> </tr> <tr> <td> <p>θ_g</p> </td> <td> <p>0.1</p> </td> <td> <p>m<sup>3</sup> m<sup>-3</sup></p> </td> <td> <p>minimum soil volumetric water content, limit to stomatal conductance</p> </td> </tr> <tr> <td> <p>θ_0</p> </td> <td> <p>0.04</p> </td> <td> <p>m<sup>3</sup> m<sup>-3</sup></p> </td> <td> <p>minimum soil volumetric water content, limit to soil respiration</p> </td> </tr> </tbody> </table> <p> </p> <p>Parameters used by JSBACH model</p> <table> <tbody> <tr> <td> <p><strong>Parameter</strong></p> </td> <td> <p><strong>Trees</strong></p> </td> <td> <p><strong>Lawn</strong></p> </td> <td> <p><strong>Units</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>J_max</p> </td> <td> <p>104.5</p> </td> <td> <p>148.6</p> </td> <td> <p>μmol(CO<sub>2</sub>) m<sup>-2</sup>(leaf) s<sup>-1</sup></p> </td> <td> <p>Maximum electron transport rate at 25 °C</p> </td> </tr> <tr> <td> <p>T_alt</p> </td> <td> <p>4.0–4.5</p> </td> <td> <p> </p> </td> <td> <p>°C</p> </td> <td> <p>Alternation temperature</p> </td> </tr> <tr> <td> <p>θ_cap</p> </td> <td> <p>0.32– 0.39</p> </td> <td> <p>0.32– 0.34</p> </td> <td> <p>m m<sup>-1</sup></p> </td> <td> <p>Volumetric soil field capacity</p> </td> </tr> <tr> <td> <p>θ_pwp</p> </td> <td> <p>0.13– 0.21</p> </td> <td> <p>0.135–0.165</p> </td> <td> <p>m m<sup>-1</sup></p> </td> <td> <p>Volumetric wilting point</p> </td> </tr> <tr> <td> <p>V_max</p> </td> <td> <p>55.0</p> </td> <td> <p>78.2</p> </td> <td> <p>μmol(CO<sub>2</sub>) m<sup>-2</sup>(leaf) s<sup>-1</sup></p> </td> <td> <p>Maximum carboxylation rate at 25 °C</p> </td> </tr> <tr> <td> <p>z_root</p> </td> <td> <p>0.5</p> </td> <td> <p>0.12</p> </td> <td> <p>m</p> </td> <td> <p>Root depth</p> </td> </tr> <tr> <td> <p>CC</p> </td> <td> <p>1.25</p> </td> <td> <p>1.25</p> </td> <td> <p>N/A</p> </td> <td> <p>Relative cost to produce one carbon</p> </td> </tr> <tr> <td> <p>f_faeces</p> </td> <td> <p>0.3</p> </td> <td> <p>0.3</p> </td> <td> <p>N/A</p> </td> <td> <p>Fraction of carbon from herbivore faeces that goes into the green litter pool</p> </td> </tr> <tr> <td> <p>f_leaf</p> </td> <td> <p>0.4</p> </td> <td> <p>0.4</p> </td> <td> <p>N/A</p> </td> <td> <p>A fixed fraction of canopy maintenance respiration that makes up the dark respiration</p> </td> </tr> <tr> <td> <p>k</p> </td> <td> <p>0.1</p> </td> <td> <p>0.09</p> </td> <td> <p>N/A</p> </td> <td> <p>LAI growth rate during growth phase</p> </td> </tr> <tr> <td> <p>LAI_max</p> </td> <td> <p>3.6–4.1</p> </td> <td> <p>3.0</p> </td> <td> <p>m2 m-2</p> </td> <td> <p>Maximum leaf area index</p> </td> </tr> <tr> <td> <p>p</p> </td> <td> <p>veg:</p> <p>0.004</p> <p>rest:</p> <p>0.1</p> </td> <td> <p>growth:</p> <p>0.1</p> <p>dry:</p> <p>0.015</p> </td> <td> <p>N/A</p> </td> <td> <p>LAI shedding rate (trees: vegetative and rest phase; grass: growth and dry season)</p> </td> </tr> <tr> <td> <p>r_d</p> </td> <td> <p>0.605</p> </td> <td> <p>0.8602</p> </td> <td> <p>μmol(CO2) m-2(leaf) s-1</p> </td> <td> <p>Dark respiration at 25 °C, fraction of Vmax</p> </td> </tr> </tbody> </table> <p> </p> <p>Parameters used by SUEWS model</p> <table> <tbody> <tr> <td> <p><strong>Parameter</strong></p> </td> <td> <p><strong>Trees</strong></p> </td> <td> <p><strong>Lawn</strong></p> </td> <td> <p><strong>Units</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>f_i</p> </td> <td> <p>0.21</p> </td> <td> <p>0.18</p> </td> <td> <p>N/A</p> </td> <td> <p>Fraction of each vegetation type i</p> </td> </tr> <tr> <td> <p>F_(pho,max,i)</p> </td> <td> <p>8.3</p> </td> <td> <p>8.92</p> </td> <td> <p>μmol m<sup>-2</sup> s<sup>-1</sup></p> </td> <td> <p>Maximum potential photosynthesis</p> </td> </tr> <tr> <td> <p>LAI_(max,i)</p> </td> <td> <p>4.8</p> </td> <td> <p>3</p> </td> <td> <p>m<sup>2</sup> m<sup>-2</sup></p> </td> <td> <p>Full leaf-on summertime value</p> </td> </tr> <tr> <td> <p>LAI_(min,i)</p> </td> <td> <p>0.66</p> </td> <td> <p>1.6</p> </td> <td> <p>m<sup>2</sup> m<sup>-2</sup></p> </td> <td> <p>Leaf-off wintertime value</p> </td> </tr> <tr> <td> <p>T_L</p> </td> <td> <p>-10</p> </td> <td> <p>-10</p> </td> <td> <p>°C</p> </td> <td> <p>Lower air temperature limit</p> </td> </tr> <tr> <td> <p>T_H</p> </td> <td> <p>55</p> </td> <td> <p>55</p> </td> <td> <p>°C</p> </td> <td> <p>Upper air temperature limit</p> </td> </tr> <tr> <td> <p>G_5</p> </td> <td> <p>30</p> </td> <td> <p>30</p> </td> <td> <p>°C</p> </td> <td> <p>Parameter related to temperature dependence</p> </td> </tr> <tr> <td> <p>G_3</p> </td> <td> <p>0.66</p> </td> <td> <p>0.538</p> </td> <td> <p>N/A</p> </td> <td> <p>Parameter related to VPD dependence</p> </td> </tr> <tr> <td> <p>G_4</p> </td> <td> <p>0.89</p> </td> <td> <p>0.87</p> </td> <td> <p>N/A</p> </td> <td> <p>Parameter related to VPD dependence</p> </td> </tr> <tr> <td> <p>G_6</p> </td> <td> <p>0.36</p> </td> <td> <p>0.55</p> </td> <td> <p>mm<sup>-1</sup></p> </td> <td> <p>Parameter related to soil moisture dependence</p> </td> </tr> <tr> <td> <p>G_2</p> </td> <td> <p>477</p> </td> <td> <p>263.5</p> </td> <td> <p>W m<sup>-2</sup></p> </td> <td> <p>Parameter related to dependence</p> </td> </tr> <tr> <td> <p>Δθ_WP</p> </td> <td> <p>132.5</p> </td> <td> <p>143</p> </td> <td> <p>mm</p> </td> <td> <p>Wilting point deficit</p> </td> </tr> <tr> <td> <p>K_(↓max)</p> </td> <td> <p>1200</p> </td> <td> <p>1200</p> </td> <td> <p>W m<sup>-2</sup></p> </td> <td> <p>Maximum incoming shortwave radiation</p> </td> </tr> <tr> <td> <p>a_i</p> </td> <td> <p>0.78</p> </td> <td> <p>1.7</p> </td> <td> <p>N/A</p> </td> <td> <p>Empirical soil and vegetation respiration coefficient</p> </td> </tr> <tr> <td> <p>b_i</p> </td> <td> <p>0.08</p> </td> <td> <p>0.06</p> </td> <td> <p>N/A</p> </td> <td> <p>Empirical soil and vegetation respiration coefficient</p> </td> </tr> <tr> <td> <p>ω_(1,GDD,i)</p> </td> <td> <p>0.04</p> </td> <td> <p>0.04</p> </td> <td> <p>N/A</p> </td> <td> <p>Parameter for LAI calculation</p> </td> </tr> <tr> <td> <p>ω_(2,GDD,i)</p> </td> <td> <p>0.0005</p> </td> <td> <p>0.0005</p> </td> <td> <p>N/A</p> </td> <td> <p>Parameter for LAI calculation</p> </td> </tr> <tr> <td> <p>ω_(1,SDD,i)</p> </td> <td> <p>-1.5</p> </td> <td> <p>-1.5</p> </td> <td> <p>N/A</p> </td> <td> <p>Parameter for LAI calculation</p> </td> </tr> <tr> <td> <p>ω_(1,SDD,i)</p> </td> <td> <p>0.0025</p> </td> <td> <p>0.0025</p> </td> <td> <p>N/A</p> </td> <td> <p>Parameter for LAI calculation</p> </td> </tr> <tr> <td> <p>GDD</p> </td> <td> <p>300</p> </td> <td> <p>300</p> </td> <td> <p>days</p> </td> <td> <p>The growing degree days (GDD) needed for full capacity of the leaf area index</p> </td> </tr> <tr> <td> <p>SDD</p> </td> <td> <p>-300</p> </td> <td> <p>-300</p> </td> <td> <p>days</p> </td> <td> <p>The senescence degree days (SDD) needed to initiate leaf off</p> </td> </tr> <tr> <td> <p>T_(base,GDD)</p> </td> <td> <p>5</p> </td> <td> <p>5</p> </td> <td> <p>°C</p> </td> <td> <p>Base Temperature for initiating growing degree days (GDD) for leaf growth</p> </td> </tr> <tr> <td> <p>T_(base,SDD)</p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>°C</p> </td> <td> <p>Base temperature for initiating senescence degree days (SDD) for leaf off</p> </td> </tr> </tbody> </table> <p> </p> <p><span>Parameters used by VPRM model</span></p> <table> <tbody> <tr> <td> <p><strong><span>Parameter</span></strong></p> </td> <td> <p><strong><span>Trees</span></strong></p> </td> <td> <p><strong><span>Lawn</span></strong></p> </td> <td> <p><strong><span>Units</span></strong></p> </td> <td> <p><strong><span>Description</span></strong></p> </td> </tr> <tr> <td> <p><span>λ</span></p> </td> <td> <div> <p><span>-0.16</span></p> </div> </td> <td> <div> <p><span>-0.13</span></p> </div> </td> <td> <div> <p><span>μmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></span></p> </div> </td> <td> <div> <p><span>light use efficiency</span></p> </div> </td> </tr> <tr> <td> <p><span>PAR_0</span></p> </td> <td> <div> <p><span>356.99</span></p> </div> </td> <td> <div> <p><span>545.61</span></p> </div> </td> <td> <div> <p><span>μmol m<sup>-2</sup> s<sup>-1</sup></span></p> </div> </td> <td> <div> <p><span>half-saturation value</span></p> </div> </td> </tr> <tr> <td> <p><span>α</span></p> </td> <td> <div> <p><span>0.22</span></p> </div> </td> <td> <div> <p><span>0.40</span></p> </div> </td> <td> <div> <p><span>μmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup> /<sup>0</sup>C</span></p> </div> </td> <td> <div> <p><span>empirical coefficient</span></p> </div> </td> </tr> <tr> <td> <p><span>β</span></p> </td> <td> <div> <p><span>1.09</span></p> </div> </td> <td> <div> <p><span>0.42</span></p> </div> </td> <td> <div> <p><span>μmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></span></p> </div> </td> <td> <div> <p><span>empirical coefficient</span></p> </div> </td> </tr> <tr> <td> <p><span>T_max</span></p> </td> <td> <div> <p><span>40</span></p> </div> </td> <td> <div> <p><span>40</span></p> </div> </td> <td> <div> <p><span>°C </span></p> </div> </td> <td> <div> <p><span>maximum temperature for photosynthesis</span></p> </div> </td> </tr> <tr> <td> <p><span>T_min</span></p> </td> <td> <div> <p><span>0</span></p> </div> </td> <td> <div> <p><span>2</span></p> </div> </td> <td> <div> <p><span>°C </span></p> </div> </td> <td> <div> <p><span>minimum temperature for photosynthesis</span></p> </div> </td> </tr> <tr> <td> <p><span>T_opt</span></p> </td> <td> <div> <p><span>20</span></p> </div> </td> <td> <div> <p><span>18</span></p> </div> </td> <td> <div> <p><span>°C </span></p> </div> </td> <td> <div> <p><span>optimal temperature for photosynthesis</span></p> </div> </td> </tr> <tr> <td> <p><span>T_low</span></p> </td> <td> <div> <p><span>0</span></p> </div> </td> <td> <div> <p><span>0</span></p> </div> </td> <td> <div> <p><span>°C </span></p> </div> </td> <td> <div> <p><span>to account for the persistence of soil respiration in winter</span></p> </div> </td> </tr> </tbody> </table> <p><span> </span></p> <p>Data format: comma separated values (csv)</p> <p>Time step: 60 min (aggregated)</p> <p>Time stamp: yyyy-MM-dd HH:mm, UTC, end of aggregated period</p> <p>Period: 01/2022–09/2023</p>
Urban Vegetation Data - Canopy Height Model (Brussels Capital Region, 2021)
<p>This GIS dataset was created for the following scientific publication, as part of the EU-funded <a href="https://coolschools.eu/">Cool Schools</a> research project (under Grant Agreement No. 101003758) : Gallez, E., Canters, F., Gadeyne, S., & Baró, F. (2024). <a href="https://www.sciencedirect.com/science/article/pii/S2212041624000846?via%3Dihub">A multi-indicator distributive justice approach to assess school-related green infrastructure benefits in Brussels - ScienceDirect</a>. Ecosystem Services, 70, 101677. https://doi.org/10.1016/j.ecoser.2024.101677. </p> <p><em>Very-High Resolution Canopy Height Model (resolution : 25cm), distinguishing between 4 vegetation types (trees, high shrubs, low shrubs and grass) in the Brussels Capital Region.</em></p> <p><em>Coordinate system : Lambert_Belge_72.</em></p> <p><em>The CHM was built on </em><em>:</em></p> <ul> <li><em>VHR aerial orthophotos (visible RGB and NIR) (“UrbIS-Ortho N-S, 2021”) for the Brussels Capital Region, of 5x5cm resolution Source: Paradigm. (2021). UrbIS-Ortho N-S. Paradigm.Brussels. <a href="https://datastore.brussels/web/data/dataset/fec72767-d6b6-41b9-a767-616df2779aae#access">https://datastore.brussels/web/urbisdownload</a>. and;</em></li> <li><em>digital terrain models (DSM and DTM) of 50x50cm, captured on 22/09/2021. Paradigm.Brussels. </em><em>Source: Paradigm. (2021). DSM / DTM. Paradigm.Brussels. <a href="https://datastore.brussels/web/data/dataset/1d7bd49d-fe83-4388-af85-6f5dc8ec7909#access">https://datastore.brussels/web/urbisdownload.</a></em></li> </ul> <p><em>Both the orthophotos and digital terrain models were resampled to a 25x25cm resolution, using a bilinear interpolation method. </em></p> <p><em>The Canopy Height Model was then created by selecting NDVI values of 0.2 and higher, - a commonly used threshold value to distinguish vegetated land from built land (Hashim et al., 2019) -, </em><em>and vegetation height thresholds of < 0.5m (for grass), 0.5 - 2m (for low shrubs), 2 - 5m (for high shrubs), and > 5m (for trees) (Derkzen et al., 2015; Sankey et al., 2018). </em><em>Green roofs were excluded.The CHM raster was then converted to polygon features. </em></p> <p><em>Classification :</em></p> <ul> <li><em>From 0 to 0.5 m (nDSM value) : gridcode 1 = </em><em>grass</em></li> <li><em>From 0.5 to 2 m (nDSM value): gridcode 2 = </em><em>low shrubs</em></li> <li><em>From 2 to 5 m (nDSM value): gridcode 3 =</em><em> high shrubs</em></li> <li><em>From 5 to 113.96 m (nDSM value): gridcode 4 = </em><em>trees</em></li> </ul>
Dataset for simulation of a low-carbon urban energy system using the Backbone model
<p>The dataset contains the input data for cost optimization of an urban energy system. The case study has been described in the article "Impact of power-to-gas on the cost and design of the future low-carbon urban energy system" of Applied Energy.</p> <p>The dataset is in Microsoft Excel format. To make it available for GAMS, one should use e.g. the attached shell script (requires GAMS installation) to convert it to *.gdx file. The generation expansion model is available in the Git repository https://gitlab.vtt.fi/backbone/backbone (under branch projik/planet).</p>
A High-Resolution Dataset of Global Urban Fraction for Mesoscale Urban Modelling
<p>Coupled urban-atmospheric models are extensively used to understand the urban environment and its impact on atmospheric processes. A common requirement of these models is information about the “urban fraction” (fraction of model grid covered by impervious surface area (ISA)). The European Space Agency (ESA) WorldCover product provides a global land cover map for the base year of 2020 and 2021 at a spatial resolution of 10 m. The dataset is based on Sentinel-1 and Sentinel-2 data with an overall accuracy of 74.4% (2020) and 76.7% (2021). In this study we process the WorldCover dataset and provide a ready-to-use “urban fraction” that can be incorporated in urban modelling systems. The dataset contains GeoTIFF and Weather Research and Forecasting Pre-processing System (WRF-WPS) format files for 1, 0.5, 0.25, 0.009 (~1 km), 0.0027 (~300 m), and 0.0009 (~100 m) degree spatial resolutions. The GeoTIFF files can be converted to other urban mesoscale modelling systems. Please check the README.txt for more information on using the dataset.</p> <p>Note: version 2.0.0 uses WorldCover 2021 v200 dataset for processing of urban fractions, while version 1.0.0 uses WorldCover 2020 v100 dataset.</p> <p>For more information please see here: <a href="https://1drv.ms/w/s!Ai5IcIuv5U4DioElr0E8CEq0DE3CSw?e=Wdp9i4">https://1drv.ms/w/s!Ai5IcIuv5U4DioElr0E8CEq0DE3CSw?e=Wdp9i4</a></p>
Datasets for figures in Implementation and evaluation of Wet Bulb Globe Temperature within non-urban environments in the Community Land Model version 5
<p>The files contain 4 scripts and 6 netcdf files. </p> <p>"laborCap_200400.ncl" uses "Lancet_LRF.nc" to create Figure 1.</p> <p>Script "world_plot_ensemble_Avg.I2000.csh", drives a NCL script, "plot_modern.I2000.WBGT.ncl" to make figures 3 and 4, using the netcdf files, "I2000_PR_22_x1_60_5.exceed.WBGT.20yrs.75_99.nc," "I2000_PR_22_x1_60_5.exceed.WBGT_BG_R.20yrs.75_99.nc," "I2000_PR_22_x1_60_5.exceed.WBGT_BC_R.20yrs.75_99.nc," and "I2000_PR_22_x1_60_5.exceed.WBGT_AC_R.20yrs.75_99.nc."</p> <p>"heatmap.wbgt.v4.org.ncl" uses netcdf "I2000_PR_23_Chicago_x1_60_1.11-17.Chicago.allvars.nc" to create figures 5-7. </p>
Model results and configuration files for "Large modeling uncertainty in projecting decadal surface ozone changes over urban and industrial regions of China"
<p>This repository includes files as described below:</p> <p><strong>1. namelist_CBMZ09_example.input, namelist_MOZART202_example.input:</strong></p> <p>Two WRF-chem namelist files for CBMZ and MOZART simulation.</p> <p>They are modified according to the namelist from <a href="https://github.com/wrfchem-leeds/WRFotron">https://github.com/wrfchem-leeds/WRFotron</a>.</p> <p><strong>2. wps_namelist_example.wps:</strong></p> <p>namelist for WRF Preprocessing System (WPS)</p> <p><strong>3. temporal_hourly_scale_factor_emission.csv:</strong></p> <p>Hourly scale factors for emissions.</p> <p>Hourly allocation is applied to all emission data (i.e., emissions for 2017, 2030 and perturbated emissions of NOx, VOCs).</p> <p><strong>4. vertical_emission_ratio.csv</strong></p> <p>Vertical shares (ratios) of emissions.</p> <p>Emissions from sectors of power and industry are vertically allocated based on this file. Vertical allocation is conducted for all emission data.</p> <p>These shares are suggested by MICS-ASIA III intercomparison framework.</p> <p><strong>5. 01_2030_2017_simulations.zip: </strong></p> <p>Simulated MDA8 ozone under future (2030) and 2017 emission scenarios by the two chemical mechanisms (i.e., CBMZ, MOZART).</p> <p><strong>6. 02_perturbations_of_NOxVOCs.zip:</strong></p> <p>Simulated MDA8 ozone given perturbations of NOx and VOCs emissions by the two chemical mechanisms.</p> <p><strong>7. 03_hourly_diff_O3_NOx_OH_HNO3.zip: </strong></p> <p>Differences of hourly simulated concentrations of O3, NOx, OH and HNO3 during July in the Base-2017 scenario between CBMZ and MOZART (CBMZ - MOZART).</p>
TOM.D: Taking Advantage of Microclimate Data for Urban Building Energy Modeling
<p>Data required to rebuild the study: "TOM.D: Taking Advantage of Microclimate Data for Urban Building Energy Modeling". In this dataset of New York City, one can find building footprints, monthly energy consumption data for each of these buildings, and matching / cleaned microclimate data from a variety of data sources which are referenced in the work. Among them, thermal infrared measurements may be found, climate models from NOAA and ERA5 may be found, and preprocessed vision systems from Google are used.</p>
R code and data to reproduce figures from the "Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg" paper
<p>This repository contains the R code and data to reproduce figures from the "Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg" paper.</p>
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>
TURDATA: a database of low-cost air quality and remote sensing measurements for the validation of micro-scale models in the real Prague urban environments
<p><strong>README</strong></p> <p>TURDATA is a supplementary data set for the TURBAN project Prague observation campaign described in the manuscript Bauerová et al. 2024 (submitted for publication). The measurement campaign was focused on air pollution and meteorological measurement, including vertical profiles in selected part of Prague city centre called here as Legerova domain. Within this area, one professional meteorological station (MS) Prague Karlov and one reference traffic air quality monitoring (AQM) station Prague 2-Legerova (classified as traffic hotspot) are located. To gain high spatial and temporal resolution data, the supplementary measurement network was established, which consisted of:</p> <p>- 20 combined low-cost sensor (LCS) stations for monitoring of PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations (using Plantower PMS7003 particle counters and Envea Cairsense electrochemical sensors) placed in different sites and different height levels AGL (higher = H, lower = L),</p> <p>- 1 mobile telescopic meteorological mast for measuring temperature, relative humidity, wind velocity and direction and air pressure (using 2D ultrasonic anemometer Gill WindSonic 60 and weather station Gill MetConnect THP),</p> <p>- 1 MTP-5-He microwave radiometer (MWR; Attex) for temperature vertical profile,</p> <p>- 1 StreamLine XR Doppler LIDAR (HALO Photonics) for wind vertical profile. </p> <p>The main Legerova campaign lasted from 30 May 2022 to 28 March 2023 with some exceptions (see <em>TURDATA_metadata.xlsx</em> with all details). Because LCSs are known for their highly variable measurement quality, before their deployment the Legerova campaign, a sufficiently long-term initial field comparative measurement of all LCSs at RM Prague 4-Libuš was carried out (lasting from 16/12/2021 to 30/5/2022). The results showed that most of the LCSs were in raw measurement differently zero-shifted against each other and against gaseous reference or aerosol optical equivalent monitors (RMs or EMs). Therefore, the Multivariate Adaptive Regression Splines (MARS) method was applied to calculate corrected LCS concentrations based on initial field comparative measurement complemented by meteorological data from MS Prague Libuš. To check the quality of raw and MARS corrected LCS concentrations at the end of the measurement campaign, the final comparative field measurement of all LCSs at Prague 4-Libuš RM station was performed.</p> <p>Therefore, in case of LCSs measurement (both raw and corrected) the important columns of location (measurement placement: RM_Prague_4-Libus and Legerova_domain) and measurement_program (Initial_comparative_measurement, Legerova_campaign and Final_comparative_measurement) were added.</p> <p>In case of PM<sub>10</sub> and PM<sub>2.5</sub> measurement the maximum raw and MARS-corrected concentrations were influenced by temporary pollution episode on 26 July 2022 around 4 a.m. and 9 p.m. (both UTC) caused by aerosol pollution transported from large forest fire in Hřensko (the northern part of the Czech Republic). </p> <p> </p> <p>TURDATA includes the following files:</p> <p>1. <strong>TURDATA_metadata_and_photos.zip</strong> containing:</p> <p>- "<em>TURDATA_metadata.xlsx</em>" with the important list of metadata about devices placement, locations parameters and measurement periods</p> <p>- Folder "<em>Photos_from_Legerova_campaign</em>" with photos from Legerova measurement campaign</p> <p>2. <strong>AQ_LCSs_raw_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>NO2_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of NO<sub>2</sub> raw measured concentrations by all LCSs</p> <p>- "<em>O3_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of O<sub>3</sub> raw measured concentrations by all LCSs</p> <p>- "<em>PM10_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>10</sub> raw measured concentrations by all LCSs</p> <p>- "<em>PM2_5_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>2.5</sub> raw measured concentrations by all LCSs</p> <p>- "<em>AQ_LCSs_raw_measurement_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>3. <strong>AQ_data_RM_stations_Prague_TURDATA.zip</strong> containing:</p> <p>- "<em>AQ_data_Prague_RM_stations_TURDATA_12-2021_06-2023.xlsx</em>" with air quality data measured by reference AQM stations in Prague</p> <p>- "<em>AQ_data_RM_stations_Prague_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>4. <strong>Meteo_data_Prague_MS_TURDATA.zip</strong> containing:</p> <p>- "<em>Meteo_data_Prague_MS_TURDATA_12-2021_06-2023.xlsx</em>" with meteorological data measured by professional meteorological stations in Prague</p> <p>- "<em>Meteo_data_Prague_MS_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>5. <strong>AQ_LCSs_MARS-corrected_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>NO2_COR_LCSs_TURDATA.xlsx</em>" with complete data set of NO<sub>2</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>O3_COR_LCSs_TURDATA.xlsx</em>" with complete data set of O<sub>3</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>PM10_COR_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>10</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>PM2_5_COR_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>2.5</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>AQ_LCSs_MARS-corrected_measurement_TURDATA_readme.txt</em>" with all necessary information for correct data use and brief description of MARS correction method</p> <p>6. <strong>Meteo-mast_PVK_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>Meteo-mast_PVK_TURDATA_06-2022_06_2023.xlsx</em>“ with non-referential meteorological data measured by mobile meteo-mast</p> <p>- "<em>Meteo-mast_data_PVK_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>7. <strong>MWR_temperature_profile_TURDATA.zip</strong> containing:</p> <p>- "<em>MWR_5min_temperature_TURDATA_02-2022_03-2023.xlsx</em>" with raw temperature vertical profile measurement from microwave radiometer</p> <p>- "<em>MWR_1hour_temperature_TURDATA.xlsx</em>" with 1-hour averaged temperature vertical profile from microwave radiometer</p> <p>- "<em>MWR_1hour_TMP_gradient_TURDATA.xlsx</em>" with 1hour temperature gradient calculated from raw temperature profiles measured by microwave radiometer</p> <p>- "<em>MWR_temperature_profile_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>8. <strong>LIDAR_wind_profile_TURDATA.zip</strong> contains:</p> <p>- Individual folders "yyyymm“ -> "yyyymmdd"</p> <p>- Each daily folder "yyyymmdd" contains files:</p> <p>a) "<em>Processed_Wind_Profile_188_yyyymmdd_hhmmss.hpl</em>" with processed WV and WS data</p> <p>b) "<em>Wind_Profile_188_yyyymmdd_hhmmss.hpl</em>" with non-processed Doppler wind profile data</p> <p>- "<em>LIDAR_wind_profile_TURADATA_readme.txt</em>" with all necessary information for correct data use</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>
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.