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64 results for “Urban Scaling”
Evaluation of stormwater urban ecological infrastructure in Phoenix, Arizona (USA): a case study of a small-scale bioretention basin system
In 2017, Arizona State University finished construction on a pedestrian mall central to its Tempe campus, which included a small-scale bioretention basin system for stormwater management. This study analyzed the flood control and water quality improvement performance of the small-scale bioretention basin system in the Phoenix Metropolitan Area, AZ USA. Flood control efficacy was quantified by calculating discharge from the basin system using water level loggers and measuring soil moisture levels using soil moisture probes. Stormwater runoff samples were collected for twenty-one storm events and analyzed for nitrogen and phosphorus constituent concentrations. Nutrient concentrations at the system inflow and outflow were used to determine percent change in concentration. Water quality improvement performance was compared to results from previous studies on bioretention basin system performance. These data were used to create relevant graphical figures. Results were obtained by performing statistical analysis calculations on the data measurements. The results indicated that the bioretention basin system performed adequately for flood control and water quality improvement, supporting the use of stormwater urban infrastructure systems in arid and semi-arid climates. Further research can reveal how these systems may perform during more severe storm events and offer improvements for future designs.
Urban Residential Surface and Subsurface Hydrology: Synergistic Effects of Low-Impact Features at the Parcel Scale
Accurately predicting the hydrologic effects of urbanization requires an understanding of how hydrologic processes are affected by low‐impact development practices. In this study, we explored how growing season surface runoff, deep drainage, and evapotranspiration on a residential parcel are affected by several low‐impact interventions, including three "impervious‐centric" interventions (disconnecting downspouts, disconnecting sidewalks, and adding a transverse slope to the driveway and front walk), two "pervious‐centric" interventions (decompacting soil and adding microtopography), and all possible "holistic" combinations. Results were compared to both a highly and moderately compacted baseline parcel under an average and a dry weather scenario for a temperate climate. We find that under reasonable assumptions for highly compacted soil, pervious areas are a major source of runoff and disconnecting impervious surfaces may be relatively less effective without improving soil conditions. Under both highly and moderately compacted soil conditions, combining efforts to decompact soil with impervious disconnection has a synergistic effect on reducing surface runoff and increasing deep drainage and evapotranspiration. All combinations of interventions enhance infiltration, but the partitioning of additional root zone water between deep drainage and evapotranspiration depends on the weather scenario. Importantly, when all low‐impact interventions are applied together, growing season deep drainage is higher than that from a vacant lot with no impervious surfaces. We infer that ecohydrologic interfaces between impervious and pervious areas are strong controls on urban hydrologic fluxes and that high‐resolution, process‐based models can be used to account for these interfaces and thereby improve predictions of the hydrologic effects of low‐impact interventions.
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>
Urban land expansion in area with decreased urban sprawl at global, national, and city scales during 2000 to 2020
<p>I used calibrated population density thresholds from the year 2000 and 2020 Worldpop population model to measure area and densities for urban and suburban density classes (≥ 250 humans per km<sup>2</sup>) at global and national scales and both broad multi-city agglomerations and fine city cores.</p>
Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer
As cities warm and the need for climate adaptation strategies increases, a more detailed understanding of the cooling effects of land-cover across a continuum of spatial scales will be necessary to guide management decisions. We asked how tree canopy cover and impervious surface cover interact to influence daytime and nighttime summer air temperature, and how effects vary with the spatial scale at which land-cover data are analyzed (10, 30, 60 and 90-m radii). A bicycle-mounted measurement system was used to sample air temperature every 5 m along 10 transects (about 7 km length, sampled 3-12 times each) spanning a range of impervious and tree canopy cover (0 to 100%, each) in a mid-sized city in the Upper Midwest, USA. Variability in daytime air temperature within the urban landscape averaged 3.5 degreeC (range 1.1 to 5.7 degreeC). Temperature decreased nonlinearly with increasing canopy cover, with the greatest cooling when canopy cover exceeded 40%. The magnitude of daytime cooling also increased with spatial scale, and was greatest at the size of a typical city block (60-90 m). Daytime air temperature increased linearly with increasing impervious cover, but the magnitude of warming was less than the cooling associated with increased canopy cover. Variation in nighttime air temperature averaged 2.1C (range 1.2 to 3.0 degreeC), and temperature increased with impervious surface. Effects of canopy were limited at night; thus, reduction of impervious surfaces remains critical for reducing nighttime urban heat. Results suggest strategies for managing urban land-cover patterns to enhance resilience of cities to climate warming.
[Database] Urban Water Consumption at Multiple Spatial and Temporal Scales. A Review of Existing Datasets
<p>This file contains the complete catalog of datasets and publications reviewed in: Di Mauro A., Cominola A., Castelletti A., Di Nardo A.. <em>Urban Water Consumption at Multiple Spatial and Temporal Scales. A Review of Existing Datasets.</em> Water 2021.The <strong>complete catalog</strong> contains:</p> <ul> <li>92 state-of-the-art water demand datasets identified at the district, household, and end use scales;</li> <li>120 related peer-reviewed publications;</li> <li>57 additional datasets with electricity demand data at the end use and household scales.</li> </ul> <p>The following <strong>metadata</strong> are reported, for each <strong>dataset</strong>:</p> <ul> <li>Authors</li> <li>Year</li> <li>Location</li> <li>Dataset Size</li> <li>Time Series Length</li> <li>Time Sampling Resolution</li> <li>Access Policy.</li> </ul> <p>The following <strong>metadata </strong>are reported, for each <strong>publication</strong>:</p> <ul> <li>Authors</li> <li>Year</li> <li>Journal</li> <li>Title</li> <li>Spatial Scale</li> <li>Type of Study: Survey (S) / Dataset (D)</li> <li>Domain: Water (W)/Electricity (E)</li> <li>Time Sampling Resolution</li> <li>Access Policy</li> <li>Dataset Size</li> <li>Time Series Length</li> <li>Location</li> </ul> <p><strong>Authors:</strong><br> Anna Di Mauro - Department of Engineering | Università degli studi della Campania Luigi Vanvitelli (Italy) | <a href="mailto:anna.dimauro@unicampania.it">anna.dimauro@unicampania.it</a>;<br> Andrea Cominola - Chair of Smart Water Networks | Technische Universität Berlin - Einstein Center Digital Future (Germany) | <a href="mailto:andrea.cominola@tu-berlin.de">andrea.cominola@tu-berlin.de</a>; <br> Andrea Castelletti - Department of Electronics, Information and Bioengineering | Politecnico di Milano (Italy) | <a href="mailto:andrea.castelletti@polimi.it">andrea.castelletti@polimi.it</a><br> Armando Di Nardo -Department of Engineering | Università degli studi della Campania Luigi Vanvitelli (Italy) | <a href="mailto:armando.dinardo@unicampania.it">armando.dinardo@unicampania.it</a></p> <p><strong>Citation and reference:</strong></p> <p>If you use this database, please consider citing <a href="https://www.mdpi.com/2073-4441/13/1/36">our paper</a> </p> <p>Di Mauro, A., Cominola, A., Castelletti, A., & Di Nardo, A. (2021). Urban Water Consumption at Multiple Spatial and Temporal Scales. A Review of Existing Datasets. Water, 13(1), 36, https://doi.org/10.3390/w13010036</p> <p><strong>Updates and Contributions:</strong></p> <p>The catalogue stored in this public repository can be collaboratively updated as more datasets become available. The authors will periodically update it to a new version. </p> <p>New requests can be submitted to the authors, so that the dataset collection can be improved by different contributors. Contributors will be cited, step by step, in the updated versions of the dataset catalogue.</p> <p><strong>Updates history:</strong></p> <ol> <li>March 1st, 2021 - Pacheco, C.J.B., Horsburgh, J.S., Tracy, J.R. (Utah State University, Logan, UT - USA) --- The dataset associated with paper <a href="https://doi.org/10.3390/s20133655">Bastidas Pacheco, C.J.; Horsburgh, J.S.; Tracy, R.J.. A Low-Cost, Open Source Monitoring System for Collecting High Temporal Resolution Water Use Data on Magnetically Driven Residential Water Meters. Sensors 2020, 20, 3655.</a> is published in the HydroShare repository, where it is available as an OPEN dataset. Data can be found here: <a href="https://doi.org/10.4211/hs.4de42db6485f47b290bd9e17b017bb51">https://doi.org/10.4211/hs.4de42db6485f47b290bd9e17b017bb51</a></li> </ol>
Patterns in bird and pollinator occupancy and richness in a mosaic of urban office parks across scales and seasons
<p>Urbanization is a leading cause of global biodiversity loss, yet cities can provide resources required by many species throughout the year. In recognition of this, cities around the world are adopting strategies to increase biodiversity. These efforts would benefit from a robust understanding of how natural and enhanced features in urbanized areas influence various taxa. We explored seasonal and spatial patterns in occupancy and taxonomic richness of birds and pollinators among office parks in Santa Clara County, California, USA, where natural features and commercial landscaping have generated variation in conditions across scales. We surveyed birds and insect pollinators, estimated multi-species occupancy and species richness, and found that spatial scale, season, and urban sensitivity were all important for understanding how communities occupied sites. Features at the landscape- and local-scale (i.e., distance to streams or baylands and tree canopy, shrub, or impervious cover, respectively) were the strongest predictors of avian occupancy in all seasons. The pollinator richness index was influenced by local tree canopy and impervious cover in spring, and distance to baylands in early and late summer. We predicted relative contributions of different spatial scales to annual bird species richness by assigning values to simulated sites representing "good" and "poor" quality, based on influential covariates returned by models. Shifting from poor to good quality conditions locally increased annual avian richness by up to 6.8 species with no predicted effect of the quality of the neighborhood. Conversely, sites of poor local- and neighborhood-scale quality in good quality landscapes were predicted to harbor 11.5 more species than sites of good local- and neighborhood-scale quality in poor quality landscapes. Finally, more urban sensitive bird species were gained at good quality sites relative to urban tolerant species, suggesting that urban natural features at the local- and landscape-scales disproportionately benefited them.</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>
Deep Learning based Urban Morphology for City-scale Environmental Modeling
<p>The WRF simulations were performed using the Weather Research and Forecasting (WRF) model, version 4.2.1. The three nested domains are centered over Chicago, USA, with a spatial resolution of 9, 3, and 1 km for the outermost, middle, and innermost domains. The model was implemented with 42 pressure levels, with the first model level located at 21.2 m and the first 1 km vertical height containing 11 model levels. The initial and boundary conditions are taken from the National Centers for Environmental Prediction (NCEP) Final Reanalysis dataset at 1 degree spatial and 6-hourly temporal resolution.</p><p>The physics components include the WRF single moment 6 class for microphysics, Dudhia for shortwave, the Rapid Radiative Transfer Model for longwave radiation parameterizations, Bougeault for the planetary boundary layer, Noah for the land surface model, Building Environment Parametrization (BEP) for the urban model, and Grell for the cumulus scheme (only for the outermost domain of 9 km spatial resolution). The LCZs of Chicago, USA, are generated using the crowd-sourcing method. The training dataset, created manually, is obtained from the WUDAPT portal, and random forest classification is applied to Landsat 8 imagery to derive the LCZs for the desired region. The simulations are performed from 1/Jul/2018 00:00 to 7/Jul/2018 06:00, where the first 6 hours are discarded as spin-up time.</p><p>The Digital Synthetic City (DSC) of Chicago, USA, uses satellite imagery and global-scale population and elevation data as input to the automatic method for producing a statistically similar and synthetic city-scale 3D urban model as output.</p><p>The Control simulations use National Land Cover Database land use/land cover with NUDAPT parameters, the three default WRF urban classes, and corresponding UCPs; the WUDAPT uses the MODIS classes with additional urban LCZs and UCPs from Brousse et al. (2016), and the DSC uses the WUDAPT classes with UCPs generated from DSC method.</p><p>The dataset contains:</p><p>1. Output from DSC in Shapefile.</p><p>2. WRF model output for the third domain (1 km) spatial resolution domain for (a) NUDAPT or Control (b) WUDAPT or LCZs (c) DSC</p>
IuliaMargineanGitHub/Projecting-Heat-Stress-Vulnerable-Populations-at-Intra-Urban-Scales: Projecting heat stress vulnerable populations at intra-urban scales
<p>This repository provides the data and scripts necessary for full reproduction of results, as well as example datasets that were used for data generation and analysis in the manuscript Marginean et al., "High-resolution Modelling and Projecting Local Dynamics of Differential Vulnerability to Urban Heat Stress".</p> <p>The folders in this repository contain the following:</p> <ol> <li>The input datasets: <ul> <li>shares of educational attainment in 2012 and 2020, by sex, age group and census tract</li> <li>internal migration by sex, age group and census tracts</li> <li>decadal mortality and fertility, by sex, age group, scenario, and census tract</li> </ul> </li> <li>Projected decadal data (2012 to 2050) by sex, age group, scenario and census tract</li> <li>Scripts for generating high resolution projections for three Shared Socioeconomic Pathways (SSPs): one script for SSP 2 projections and one scripts for SSP 1 and SSP 3 projections</li> <li>Example input data to reconstruct the projections in SSP 1, 2 and 3 for females ages 25 to 64</li> </ol>
The impact of small-scale green infrastructure on the affective wellbeing associated with urban sites
<p>The database contains participants' reported affective perceptions of 18 images of street images with different levels of green coverage.</p>
Scale insects contribute to spider conservation in urban trees and shrubs
<p>Urbanization filters arthropod communities and selects for species tolerant of urban conditions. Spiders are key generalist predators in urban ecosystems, but certain spider families are rare in cities compared to rural areas. The unique arthropod communities found in different tree species likely affect their ability to conserve spiders by providing different prey resources. If arthropods disperse from trees to plants growing below trees, the conservation benefits of the arthropod communities found in trees may also extend to plants growing beneath them. Certain urban tree species can host high densities of scale-insects and other arthropods that may provide important prey resources for spiders. To assess the conservation value of different arthropod communities in urban trees, we collected spiders from scale-infested and scale-uninfested trees and from shrubs under these trees. We used hanging cup traps to collect spiders that fell from both tree types. Spider abundance was greater within, and in shrubs below, scale-infested compared to scale-uninfested trees. Scale-infested trees hosted more orb web weaving spiders than scale-uninfested trees. Shrubs under scale-infested trees hosted more hunting, orb web weaving, and space web weaving spiders than shrubs under uninfested trees. Our findings suggest that scale-infested urban trees, and the robust arthropod communities they support, conserve certain spider guilds, and these benefits extend to other plants in the landscape.</p> <p><strong>Implications for insect conservation:</strong> The ability of urban trees to conserve spider communities is in part attributable to the abundance of potential prey available within trees. Therefore, tolerating pests such as scale insects in urban trees can conserve spider communities both within trees and in shrubs planted below these trees.</p>
Required data for simulating a typical large-scale urban traffic network
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Patterns in bird and pollinator occupancy and richness in a mosaic of urban office parks across scales and seasons
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Data from: Fine scale phylogeography of urban Western European hedgehog Erinaceus europaeus in south-east England
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Scale insects contribute to spider conservation in urban trees and shrubs
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Data used in manuscript Direct CO2 emissions and uptake at neighbourhood scale over the urban area of Beijing
<p>This dataset provides the data used in the manuscript "<em>Direct CO2 emissions and uptake at neighbourhood scale over the urban area of Beijing</em>".</p> <p>The folders are:</p> <p><strong>1. Modelled_CO2_Flux</strong><br> This folder contains a portion of modelled CO2 fluxes generated by SUEWS. Fc is the net CO2 flux, FcPhoto the CO2 uptake by vegetation, FcRespi the CO2 release from soil and vegetation respiration, FcMetab the CO2 emissions from human metabolism, FcBuild the CO2 emissions from the local fuel combustion in buildings. Longitudes and latitudes denote the centroid of grid.<br> <strong>1.1 Fc_annual_2016_g_C_m-2_yr-1.nc</strong> is the annual CO2 fluxes in g C m-2 year-1.<br> <strong> 1.2 Fc_monthly_2016_g_C_m-2_mon-1.nc</strong> is the monthly CO2 fluxes in g C m-2 month-1.<br> <strong>1.3 Fc_annual_2016_g_C_m-2_yr-1.tiff</strong> is the annual Fc (g C m-2 year-1) provided in GeoTiff format.<br> <strong>1.4 6_ring_EPSG4326</strong> contains the ESRI Shapefile defining the study area (with the 6th Ring Road in Beijing as the boundary).</p> <p><strong>2. ModelRun</strong><br> This folder includes SUEWS source code (Järvi et al., 2011; Ward et al., 2016; Järvi et al., 2019) and a model run sample.<br> <strong>2.1 SUEWS_SourceCode</strong> is a folder including SUEWS V2020b source Fortran codes. For detailed descriptions, readers are referred to SUEWS webpage (https://suews.readthedocs.io/en/latest/). Enter "make" through the command line and a SUEWS executive will be built under ".../ModelRun/Release".<br> <strong>2.2 EvaluationRun</strong> is a folder including the SUEWS run for model performance evaluation. To conduct a quick model run to reproduce the results demonstrated in the manuscript, use command line "./SUEWS_V2020b". </p> <p><strong>3. Observations</strong><br> The unit for CO2 flux (Fc) is μmol m-2 s-1 under this folder.<br> <strong>3.1 co2_flux_140m_2016_rm_QC.csv</strong> is the Fc observations after quality control and resampled to hourly resolution.<br> <strong>3.2 Fc_gapfilled_with_MeanDC.csv</strong> is the Fc time series for the year 2016 gap-filled with the Mean Diurnal Cycle method on a seasonal basis.</p> <p> </p> <p>Contact information: zhengyingqi@mail.iap.ac.cn</p> <p><br><strong>[References]</strong><br>Järvi, L., Grimmond, C. S. B., & Christen, A. (2011). The surface urban energy and water balance scheme (SUEWS): Evaluation in Los Angeles and Vancouver. Journal of Hydrology, 411(3-4), 219-237.<br>Ward, H. C., Kotthaus, S., Järvi, L., & Grimmond, C. S. B. (2016). Surface Urban Energy and Water Balance Scheme (SUEWS): development and evaluation at two UK sites. Urban Climate, 18, 1-32.<br>Järvi, L., Havu, M., Ward, H. C., Bellucco, V., McFadden, J. P., Toivonen, T., ... & Grimmond, C. S. B. (2019). Spatial modeling of local‐scale biogenic and anthropogenic carbon dioxide emissions in Helsinki. Journal of Geophysical Research: Atmospheres, 124(15), 8363-8384.</p>
Urban Stream Environmental and Sequencing Datasets for Exploring the Impacts of Full-Scale Distribution System Orthophosphate Corrosion Control Implementation on the Microbial Ecology of Hydrologically Connected Urban Streams
<p>The dataset of environmental parameters and sequence fastqs used to create figures and do analysis in the paper <strong>Exploring the Impacts of Full-Scale Distribution System Orthophosphate Corrosion Control Implementation on the Microbial Ecology of Hydrologically Connected Urban Streams </strong>submitted to Applied and Environmental Microbiology. </p>
Urban warming signal across scales
<p>Data for urban signal on land surface temperature (LST) and air temperature (AT) at country, climate zone, and select regional scales from different sources.</p> <p>The data are divided into the following directories:</p> <ul> <li>LST: LST data with and without urban pixels at country scale from different LST dproducts and based on different land cover data by year.</li> <li>AT: AT data with and without urban pixels at country scale based on the default ESA CCI land cover product by year.</li> <li>LU: Urban area by country based on different products by country and year.</li> <li>MISC: Other variables calculated with and without urban pixels at country scale based on the default ESA CCI land cover product by year.</li> <li>FUT: Future projections of urban area by country, year, and SSP.</li> <li>REG: LST data with and without urban pixels for select regions from different LST dproducts and based on different land cover data by year.</li> <li>CLIM: LST data with and without urban pixels and urban area for climate zones based on the default ESA CCI land cover product and MODIS LST by year.</li> <li>PREIND: Pre-industrial estimates of urban area by country based on the latest HYDE dataset.</li> </ul> <p>In all cases, data for the European and Asian portions of Russia are separately calculated and adjusted to get continental-scale estimates.</p>
WASHTREET. Runoff velocity data using different Particle Image Velocimetry (PIV) techniques in a full scale urban drainage physical model
<p><strong>WASHTREET - Runoff velocity data using different Particle Image Velocimetry (PIV) techniques in a full scale urban drainage physical model.</strong></p> <p>This dataset contains raw data and runoff velocities results obtained using seeded and unseeded Particle Image Velocimetry (PIV) techniques in an urban drainage physical model, which is placed in the Hydraulic Laboratory of the Centre for Technological Innovation in Construction and Civil Engineering (CITEEC) at the University of A Coruña (Spain). The objective of this work is to obtain an accurate representation of the surface velocity distribution as part of the <a href="https://zenodo.org/communities/washtreet">WASHTREET project</a>, where a series of high-resolution experiments were performed measuring urban surface wash-off and sediment transport through gully pots and pipes under laboratory-controlled conditions. The experimental facility is a 36 m<sup>2</sup> full-scale street section and consists of a rainfall simulator placed over a concrete street surface with two gully pots that drain runoff into an underground pipe system. The dataset was used in the work developed in Naves et al. (2019) (DOI: <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">https://doi.org/10.1016/j.jhydrol.2019.05.003</a>).</p> <p>A detailed description of experimental setup, procedure, postprocessing and results can be consulted in ‘<em>1_TestsDescription.pdf’. </em>4K resolution and 25 fps raw videos from which frames are extracted for the PIV analysis are provided for each experiment performed in separated zip files (named as <em>‘2.</em>(test ID)<em>_RawVideos_</em>(configuration)<em>.zip’</em>). Experiments includes three different steady rainfalls of 30, 50 and 80 mm/h of rain intensity and were recorded with and without added fluorescent traces. Data to orthorectify frames from videos are provided in ‘<em>3_SpatialCalibration.zip</em>’. In addition, 60 seconds of steady conditions are extracted for each test and the frames are processed to obtain velocities from a PIV analysis. ‘<em>4_ProcessedFrames_SteadyFlow.zip’ </em>includes the 1500 rectified and processed frames for each experiment to perform the PIV analysis. Results of runoff velocity distributions are included in ‘<em>5_VelocityResults.zip’</em>.</p> <p>Further details of the rainfall simulator, physical model geometry and more hydraulic and sediment transport results can be consulted in <a href="http://doi.org/10.5281/zenodo.3233918"><em>WASHTREET hydraulic, wash-off and sediment transport experimental data</em></a>. In addition, data regarding the use of photogrammetry to obtain the elevation map of this physical model is included in <a href="http://www.doi.org/10.5281/zenodo.3241337">WASHTREET Structure from Motion data</a>.</p> <p>The WASHTREET project is being developed in the scope of the PhD thesis of the first author, which is in receipt of a Spanish Ministry of Science, Innovation and Universities predoctoral grant [FPU14/01778]. The project also receive funding from the Spanish Ministry of Science, Innovation and Universities under POREDRAIN project RTI2018-094217-B-C33 (MINECO/FEDER-EU)</p> <p>Derived publications:</p> <ul> <li>Naves, J., Anta, J., Puertas, J., Regueiro-Picallo, M., & Suárez, J. (2019). Using a 2D shallow water model to assess Large-Scale Particle Image Velocimetry (LSPIV) and Structure from Motion (SfM) techniques in a street-scale urban drainage physical model. <em>Journal of Hydrology</em>, <em>575</em>, 54-65. <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">https://doi.org/10.1016/j.jhydrol.2019.05.003</a></li> <li>Naves, J., Anta, J., Suárez, J., & Puertas, J. (2020). Hydraulic, wash-off and sediment transport experiments in a full-scale urban drainage physical model. <em>Scientific Data</em>, <em>7</em>(1), 1-13. <a href="https://doi.org/10.1038/s41597-020-0384-z">https://doi.org/10.1038/s41597-020-0384-z</a></li> <li>Naves, J., García, J. T., Puertas, J., & Anta, J. (2021). Assessing different imaging velocimetry techniques to measure shallow runoff velocities during rain events using an urban drainage physical model. <em>Hydrology and Earth System Sciences</em>, <em>25</em>(2), 885-900. <a href="https://doi.org/10.5194/hess-25-885-2021">https://doi.org/10.5194/hess-25-885-2021</a> </li> </ul>
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.
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DANDI Archive for NWB datasets
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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.