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135 results for “Urban Model”
Sample Input Data and Supporting Files for the SELECT Model of Urbanization
<p>Sample Input Data and Supporting Files for the SELECT Model of Urbanization</p> <p>Code available at: https://github.com/IMMM-SFA/select</p>
Towards transferable data-driven models to predict urban pluvial flood water depth in Berlin, Germany
<p>The attached files include the predictive features and the water depth from 2D hydrodynamic simulations that were used to train data driven models to predict water depth in Berlin.</p>
Dataset of cropping cycles possibilities considered by the MERLIN model for urban microfarms in the London context
<p>This dataset is related to the article “<strong>Reconciling economic viability and socio-ecological aspirations is possible but challenging for London urban microfarms”</strong> submitted to the journal <strong>Agronomy for Sustainable Development.</strong></p> <p>The table presents the 274 cropping possibilities for the 36 crops considered by the MERLIN model for crop planning in London microfarms. For each crop and location (outdoors or in tunnel), the different possibilities of cropping cycles are described with month of planting the crop, the month of first harvest and the month of last harvest (some vegetables can be harvested various times during their production cycle).</p>
Modeling and simulation of a new Urban Lightweight Electric Vehicle concept based on the optimized use of renewable energies and the reduction of CO2 emissions
<p>This work has produced a series of scientifc contributions. This library develops different mathematical expressions and assumptions for the dynamic modelling of an smart-grid located within a solar-powered ULEV are derived. The code was developed using Dymola</p>
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>
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>
Data and Models for Salt Transport in Desert, Urban, and Irrigated Landscapes
<p>Folders contain data and models (SWAT, SWAT-MODFLOW) for analysis and modeling of salt fate and transport in urban, desert, and irrigated landscapes. Study regions include the Arkansas River Basin and the South Platte River Basin (Colorado, USA). The Education folder contains documentation of a STEM kit created at Colorado State University for guiding middle school and high school students through a 2-hour lab on salt pollution, transport, and mitigation in a mixed irrigation-desert watershed landscape.</p>
Permeable pavement hydraulic performance and clogging experiments using a full-scale urban drainage physical model
<p>This dataset contains the results from 15 tests conducted used a physical model in the Hydraulic Laboratory of the Centre for Technological Innovation in Construction and Civil Engineering (CITEEC) at the University of A Coruña (Spain) as part of the POREDRAIN project.</p> <p><br>The objective of the tests is to analyse the hydraulic performance of a porous asphalt layer of the PA-16 type and the impact of clogging on the hydrological behaviour and water quality of the effluent. The porous asphalt was used to retrofit an impervious concrete surface of a 36 m² full-scale street section physical model, which consist of a rainfall simulator placed over the street surface. The behaviour of the porous asphalt layer was assessed by adding surface sediment loads between simulated rainfall events. Stormwater flow discharges were collected from two gully pots and an outlet lateral channel. </p>
Wave-Cascade Nested Model: The Urban-rural Diffusion Model of Suzhou(Essays on Linguistics 2025(1))
<p>This is the high-resolution images (Figure 3) from published paper entitled Wave-Cascade Nested Model: The Urban-rural Diffusion Model of Suzhou (Essays on Linguistics 2025(1)) (in Chinese 波浪-级联嵌套模型”:苏州城乡方言的扩散模式 (《语言学论丛》2025年第1期)) . </p> <p>Given the journal doesn't provide color printing or needs to compress images due to size limitations, it offers readers access to the high-quality and full-color image.</p> <p>If you have any question, please contact me without any hesitation! Thank you very much!</p>
Comparison among three different Digital Surface Models and their respective hydraulic outcomes in the flood-prone urban area of Navaluenga (Ávila, Spain)
<p>Three different Digital Surface Models (DSMs) generated from LiDAR data are presented. The LiDAR information has been considered as raw data (DSM3) and subjected to some transformations to better represent the urban environment (DSM1). DSM2 is an intermediate state between DSM1 and DSM3. </p> <p>On the other hand, a hydraulic model has been run for each DSM and for two return periods (25 and 500 years), obtaining in all cases the graphical outputs of depths, velocities, Froude numbers and hazard. </p> <p>The different DSMs are named DSM1, DSM2 and DSM3, which can be downloaded in TIN format. The hydraulic outputs associated with the different DSMs can be downloaded in raster format and are named as follows: the Digital Surface Model to which it refers, the return period considered and the type of hydraulic output (depth, velocity, Froude number and hazard).</p> <p>DSM1: Digital Surface Model 1 (TIN format).<br> dsm1_25depth: Depths obtained by considering the DSM1 and the flow associated with the 25-years return period (raster format).<br> dsm1_25froud: Froude numbers obtained by considering the DSM1 and the flow associated with the 25-years return period (raster format).<br> dsm1_25haz: Hazard obtained by considering the DSM1 and the flow associated with the 25-years return period (raster format).<br> dsm1_25veloc: Velocities obtained by considering the DSM1 and the flow associated with the 25-years return period (raster format). <br> dsm1_500depth: Depths obtained when considering the DSM1 and the flow associated with the 500-years return period (raster format).<br> dsm1_500froud: Froude numbers obtained by considering the DSM1 and the flow associated with the 500-years return period (raster format).<br> dsm1_500haz: Hazard obtained by considering the DSM1 and the flow associated with the 500-years return period (raster format).<br> dsm1_500veloc: Velocities obtained by considering the DSM1 and the flow associated with the 500-years return period (raster format).</p> <p>DSM2: Digital Surface Model 2 (TIN format).<br> dsm2_25depth: Depths obtained by considering the DSM2 and the flow associated with the 25-years return period (raster format).<br> dsm2_25froud: Froude numbers obtained by considering the DSM2 and the flow associated with the 25-years return period (raster format).<br> dsm2_25haz: Hazard obtained by considering the DSM2 and the flow associated with the 25-years return period (raster format).<br> dsm2_25veloc: Velocities obtained by considering the DSM2 and the flow associated with the 25-years return period (raster format). <br> dsm2_500depth: Depths obtained by considering the DSM2 and the flow associated with the 500-years return period (raster format).<br> dsm2_500froud: Froude numbers obtained by considering the DSM2 and the flow associated with the 500-years return period (raster format).<br> dsm2_500haz: Hazard obtained by considering the DSM2 and the flow associated with the 500-years return period (raster format).<br> dsm2_500veloc: Velocities obtained by considering the DSM2 and the flow associated with the 500-years return period (raster format).</p> <p>DSM3: Digital Surface Model 2 (TIN format).<br> dsm3_25depth: Depths obtained by considering the DSM3 and the flow associated with the 25-years return period (raster format).<br> dsm3_25froud: Froude numbers obtained by considering the DSM3 and the flow associated with the 25-years return period (raster format).<br> dsm3_25haz: Hazard obtained by considering the DSM3 and the flow associated with the 25-years return period (raster format).<br> dsm3_25veloc: Velocities obtained by considering the DSM3 and the flow associated with the 25-years return period (raster format). <br> dsm3_500depth: Depths obtained by considering the DSM3 and the flow associated with the 500-years return period (raster format).<br> dsm3_500froud: Froude numbers obtained by considering the DSM3 and the flow associated with the 500-years return period (raster format).<br> dsm3_500haz: Hazard obtained by considering the DSM3 and the flow associated with the 500-years return period (raster format).<br> dsm3_500veloc: Velocities obtained by considering the DSM3 and the flow associated with the 500-years return period (raster format).</p>
Benchmarks for the Urban-PLUMBER model evaluation project Phase 1 (AU-Preston)
<p><strong>AU-Preston benchmarks for the Urban-PLUMBER project</strong></p><p>These timeseries data are associated with the study:</p><blockquote><p>Evaluation of 30 urban land surface models in the Urban-PLUMBER project: Phase 1 results</p></blockquote><p>doi: <a href="https://doi.org/10.1002/qj.4589">https://doi.org/10.1002/qj.4589</a></p><p>These are empirical models of various complexity used as benchmarks to evaluate land surface models in the project. The benchmarking process follows the methods in the PLUMBER project (Best et al., 2015).</p><p><strong>Benchmarks</strong></p><p>Physically based</p><ol><li><strong>Manabe_1T</strong>: A simple 'slab and bucket' model (Fig. 1a) based on physical principles (i.e. conservation of energy, mass and momentum). The impervious (built) fraction is simulated using a one-tile slab scheme (Best, 2005). For the pervious fraction a simple representation allows precipitation to fill a store which overflows when full, and otherwise freely evaporate (Manabe, 1969). At each timestep, the impervious and pervious tile outputs are calculated and aggregated with a weighted mean.</li></ol><p>Empirical (out-of-sample)</p><ol><li><strong>REG1-SWdown</strong>: Linear regression with one variable (SWdown, e.g. Fig. 1b) is used separately to predict SWup, LWup, Qh, Qle, and Qtau. At night predicted values are constant where SWdown = 0.</li><li><strong>REG2-SWdown-Tair</strong>: Two-variable (SWdown and Tair) linear regression (e.g. Fig. 5c) provides some information at night and more generally for variables closely dependent on temperature (e.g. Lwup and Qh).</li><li><strong>KM3-SWdown-Tair-RH</strong>: Following PLUMBER's conceptual arguments, three predictor variables (SWdown, Tair and RH data) are split into three (low, medium and high) to create 3^3=27 groups, for which independent regressions are trained. K-means clustering is used to determine the training data groups (e.g. Fig. 1d). To use this piecewise regression benchmark, at each time step the input data's proximity to one of the 27 cluster centroids is determined to select the regression to apply. This benchmark equates to PLUMBER's EMP3KM27 (Best et al., 2015), which based on common metrics outperformed all their participating land surface models when predicting sensible and latent heat fluxes across 20 sites.</li></ol><p>Empirical (in-sample)</p><ol><li><strong>KM3-IS-SWdown-Tair-RH</strong>: This follows the KM3-SWdown-Tair-RH method, but trained with in-sample data only (i.e. AU-Preston). This should outperform an out-of-sample model because of the reuse of the forcing data, but performance is expected to degrade if applied to dissimilar conditions (i.e. another site).</li><li><strong>KM4-IS-SWdown-Tair-RH-Wind</strong>: The k-means approach is applied again, but with an additional variable (wind speed), which increases the clusters to 81 (3^4) given the above rationale. Wind speed provides information that helps to predict turbulent heat and momentum fluxes.</li></ol><p><strong>Authors</strong></p><p>Emperical benchmarks were developed by Mathew Lipson: <a href="https://orcid.org/0000-0001-5322-1796">https://orcid.org/0000-0001-5322-1796</a></p><p>The physical benchmark was developed by Martin Best: <a href="https://orcid.org/0000-0003-4468-876X">https://orcid.org/0000-0003-4468-876X</a></p><p>Co-authors for the associated manuscript are:</p><p>Mathew Lipson, Sue Grimmond, Martin Best, Gab Abramowitz, Andrew Coutts, Nigel Tapper, Jong-Jin Baik, Meiring Beyers, Lewis Blunn, Souhail Boussetta, Elie Bou-Zeid, Martin G. De Kauwe, Cécile de Munck, Matthias Demuzere, Simone Fatichi, Krzysztof Fortuniak, Beom-Soon Han, Maggie Hendry, Yukihiro Kikegawa, Hiroaki Kondo, Doo-Il Lee, Sang-Hyun Lee, Aude Lemonsu, Tiago Machado, Gabriele Manoli, Alberto Martilli, Valéry Masson, Joe McNorton, Naika Meili, David Meyer, Kerry A. Nice, Keith W. Oleson, Seung-Bu Park, Michael Roth, Robert Schoetter, Andres Simon, Gert-Jan Steeneveld, Ting Sun, Yuya Takane, Marcus Thatcher, Aristofanis Tsiringakis, Mikhail Varentsov, Chenghao Wang, Zhi-Hua Wang</p><p><strong>References</strong></p><p>Observational data</p><p>Lipson, M., Grimmond, S., Best, M., Chow, W., Christen, A., Chrysoulakis, N., Coutts, A., Crawford, B., Earl, S., Evans, J., Fortuniak, K., Heusinkveld, B. G., Hong, J.-W., Hong, J., Järvi, L., Jo, S., Kim, Y.-H., Kotthaus, S., Lee, K., Masson, V., McFadden, J. P., Michels, O., Pawlak, W., Roth, M., Sugawara, H., Tapper, N., Velasco, E., and Ward, H. C.: Data for "Harmonized gap-filled dataset from 20 urban flux tower sites" for the Urban-PLUMBER project, <a href="https://doi.org/10.5281/zenodo.7104984">https://doi.org/10.5281/zenodo.7104984</a>, 2022.</p><p>Benchmark references</p><p>Best, M. J., Abramowitz, G., Johnson, H. R., Pitman, A. J., Balsamo, G., Boone, A., Cuntz, M., Decharme, B., Dirmeyer, P. A., Dong, J., Ek, M., Guo, Z., Haverd, V., Hurk, B. J. J. van den, Nearing, G. S., Pak, B., Peters-Lidard, C., Santanello, J. A., Stevens, L., and Vuichard, N.: The Plumbing of Land Surface Models: Benchmarking Model Performance, Journal of Hydrometeorology, 16, 1425–1442, <a href="https://doi.org/10.1175/JHM-D-14-0158.1">https://doi.org/10.1175/JHM-D-14-0158.1</a>, 2015.</p><p>Best, M. J.: Representing urban areas within operational numerical weather prediction models, Boundary-Layer Meteorol, 114, 91–109, <a href="https://doi.org/10.1007/s10546-004-4834-5">https://doi.org/10.1007/s10546-004-4834-5</a>, 2005.</p><p>Manabe, S.: CLIMATE AND THE OCEAN CIRCULATION: I. THE ATMOSPHERIC CIRCULATION AND THE HYDROLOGY OF THE EARTH'S SURFACE, Monthly Weather Review, 97, 739–774, <a href="https://doi.org/10.1175/1520-0493(1969)097">https://doi.org/10.1175/1520-0493(1969)097</a><0739:CATOC>2.3.CO;2, 1969.</p>
Surface hourly measurement data of O3, NO2 and PM2.5 for "Large modeling uncertainty in projecting decadal surface ozone changes over urban and industrial regions of China"
<p>Surface hourly measurement data of O3, NO2 and PM2.5 during summer of 2017.</p> <p>In the .csv files, the first column contains the ID for each measurement site. "lon", "lat" are longitude and latitude, respectively.</p> <p>Date format is "YYYYMMDD_hour".</p>
Urban Building Energy Modelling for the Renovation Wave: A Bespoke Approach Based on EPC Databases
<p>Dataset associated to the article: Rodríguez-Álvarez, J.Urban Building Energy Modelling for the Renovation Wave: A Bespoke Approach Based on EPC Databases. <em>Buildings </em><strong>2023</strong>, <em>13</em>, x.</p> <p>It contains filtered EPC datasets as xls and csv and shapefiles with the buildings' geometry and estimated energy loads</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>
Data: Parameter regionalization with donor catchment clustering improves urban flood modeling in ungauged urban catchments
<p>Data: Parameter regionalization with donor catchment clustering improves urban flood modeling in ungauged urban catchments for wrr</p> <p>Chen Hu, Jun Xia, Dunxian She</p> <p>!===<br> This dataset provides flood information for the paper submitted to wrr "Parameter regionalization with donor catchment clustering improves urban flood modeling in ungauged urban catchments",2023WR035071<br> </p>
Strategic housing decisions and the evolution of urban settlements: Optimality modelling and empirical application in Ulaanbaatar, Mongolia
Open the record for dataset details and reuse information.
Urban Forest Effects Model (UFORE) to calculate forest structure and function from sample ground data. Two part set.
Within the City of Baltimore, 195 permanent 1/10 circular plots were established based on a stratified random sample among land uses in 1999. These plots were re-measured in 2004 and 2009 and will be re-measured again in 2014. On each plot, all trees (as defined as woody vegetation with a stem diameter at 4.5 ft (dbh) greater than one-inch) are recorded. For each tree, data are recorded on species, dbh, height, crown width, condition, crown competition, percent canopy missing and distance and direction to nearby residential buildings. These data are analyzed using the i-Tree model (www.itreetools.org) to assess ecosystem services and values. However, more importantly, these plots along with a comparable set of plots established in Syracuse, NY in 1999 are the first and most spatially comprehensive set of long-term urban forest monitoring data within cities globally. These data are being used to understand how urban forest structure and associated ecosystem services are changing through time in the City of Baltimore.
CENTURY modeled urban residential soil and tree carbon
Soils constitute the largest sink of terrestrial carbon (C), and urban soils have the potential to provide significant soil C storage. Soils in urbanized landscapes experience a multitude of human alterations, such as compaction and management subsidies, that impact soil C dynamics. While field studies may provide data on urban soil C storage, modeling soil C dynamics under various human impact scenarios will provide a basis for identifying drivers of urban soil C dynamics and for predicting the potential for these highly altered soils to store C over time intervals not typically amenable to empirical validation. The goal of this study was to model soil C dynamics in residential lawns using CENTURY, a dynamic mechanistic model, to determine whether drivers of soil C dynamics in natural systems (e.g., soil texture) were equally useful for estimating soil C content of highly modified soils in urban residential areas. Without incorporating human impacts, we found no relationship between initial CENTURY model simulations and observed soil C (p > 0.05). Factors that best explained soil C accumulation for the observed soil C (bulk density: r2= 0.30; home age: r2= 0.37; p < 0.01) differed from those found important from the CENTURY model simulations (% sand: r2= 0.72, p < 0.001). Therefore, we conducted a modeling exercise to test whether simulating potential construction disturbance and lawn management practices would improve modeled soil and tree C. We found that incorporating these factors did improve CENTURY�s ability to model soil and tree C (p < 0.001). The results from this analysis suggest that incorporating various human disturbances and management practices that occur in urban landscapes into CENTURY model runs will improve its ability to predict urban soil C dynamics, at least within a 100-year time frame. Thus, enhancing our ability to provide recommendations for management and development practices that result in increasing urban soil C storage.
Supporting dataset for "Numerical insights into the effects of model geometric distortion in laboratory experiments of urban flooding"
<p>The supporting datasets includes:</p> <p>(1) All the Figures in the manuscript in .fig format </p> <p> - Figures in main text</p> <p> - Figures in SI</p> <p>(2) Original matrice data for the results of each run</p> <p> - A file that explains detailed data content in tree structure;</p> <p> - 4 sub-repositories which contain data for models in different scales</p>
Data from: Evaluating the potential for bird-habitat models to support biodiversity-friendly urban planning
<ol> <li>Urban expansion poses a major threat to wildlife populations. Biodiversity-friendly urban landscapes could deliver benefits for both wildlife and people, by incorporating conservation and ecosystem services objectives. Well-designed urban developments could also soften the ecological impacts of urbanisation. However, delivering urban landscapes that integrate biodiversity requirements effectively remains challenging.</li> <li>Ecological models, designed to predict wildlife population responses to alternative urban designs, could prove effective in supporting the creation of biodiversity-friendly urban landscapes. Here, we combine national-scale bird abundance data with high resolution, spatially explicit habitat data to characterise relationships between bird densities and urban landscape form in Britain. From these analyses and cross-validation, we evaluate the potential for well-parameterised, species-specific models to be used to predict bird densities in novel or modified urban areas.</li> <li>Our analyses indicate that responses of bird abundance to urban habitat are species-specific and complex, with few variables consistently affecting a large proportion of species. However, contiguous areas of greenspace within urban sites are preferential for accommodating breeding birds, compared to a more fragmented arrangement of multiple, small greenspace patches. In combination, the bird-habitat relationships identified could successfully predict observed variation in abundance for most bird species considered.</li> <li>Further evaluation of habitat descriptor variables, spatial scales of species' habitat use and analytical modelling approaches may be needed to improve the predictive ability of bird-habitat models for certain species, particularly waterbirds and those observed less frequently in urban areas.</li> <li> <i>Synthesis and applications.</i> We modelled breeding bird abundance in built-up areas with respect to the characteristics and contexts of urban environments. While most variables were important for multiple species, responses overall were species-specific, so simple assemblage metrics, like diversity, will not describe the variation in bird communities well. However, the results illustrate the potential of an evidence-based, spatially explicit evaluation of urban development impacts on biodiversity, by predicting the consequences for bird numbers. Subject to verification of predictive ability, practitioners can apply the models to compare, for example, land-sparing and sharing within developments, or to quantify the biodiversity requirements for effective offsetting. This would be facilitated by incorporation into an online tool allowing user-determined input scenarios.</li> </ol>
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.