Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets

# Sydney morphology and land surface dataset Associated with the manuscript: "A transformation in city-descriptive input data for urban climate models" - title: Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets<br> - version: v1.0<br> - institution: "ARC Centre of Excellence for Climate Extremes, UNSW Sydney, Australia<br> - source: Developed using Geoscape Buildings v2.0, Trees v1.6 and Surface cover v1.6 (c) Geoscape Australia 2020. https://geoscape.com.au/legal/data-copyright-and-disclaimer/<br> - licence: Data in this file is available under Creative Commons Attribution 4.0 International (CC-BY) with attribution: https://creativecommons.org/licenses/by/4.0/legalcode<br> - author: Mathew Lipson &lt;[e-mail]&gt;<br> - publication: A transformation in city-descriptive input data for urban climate models: Frontiers in Environmental Science 2022<br> - publication_authors: Mathew Lipson, Negin Nazarian, Melissa hart, Kerry Nice, Brooke Conroy ## Description This dataset for Sydney, Australia, represents land cover, building morphology, vegetation morphology and other parameters <br> appropriate for input into local or mesoscale urban climate models. The dataset is provided in netCDF4 and GeoTiff formats. The python code `resample_geoscape_from_template.py` processes Geoscape Australia datasets for Buildings v2.0, <br> Trees v1.6 and Surface cover v1.6 into lower resolution versions. For further details refer to the manuscipt: "A transformation in city-descriptive input data for urban climate models: Frontiers in Environmental Science 2022" The python code `plot_derived_dataset.py` plots select land cover and morphology parameters (figure outputs are included here). ## Version 1.0 This dataset version differs slightly from the one described in the associated paper, with the following changes. - the ~ 300 m grid is based on the global European Space Agency CCI Global Land Cover dataset: https://www.esa-landcover-cci.org/<br> - additional land surface tiles are included where previously set to nan<br> - land surface tiles which did not sum to 1.0 were excluded (14 tiles)<br> - building height mean and standard deviation is still calculated from the average of Geoscape roof (max) and eave (min) heights. <br> An optional processing step is applied to account for buildings covering multiple grids where building vector information <br> is burnt to a raster and then area weighted to calculate grid-level statistics. This requires much longer processing time, <br> as well as additional modules (GeoCube), however avoids the previous issue of the statistics of buildings over multiple grids<br> being applied to one grid only. <br> - building height maximum is now based only on Geoscape roof height (i.e. the maximum measured height). Again, an optional<br> processing step is included based on rasterised data. ## Inputs: - Geoscape Surface cover V1.6 (tiff)<br> - Geoscape Trees v1.6 (tiff) <br> - Geoscape Buildings v2.0 (shp)<br> - template file for grid (here based on CCI) ## Outputs: - cell_area: plan area of grid cell (m2)<br> - building_height: mean building height in grid cell (avg. of geoscape roof and eave height)<br> - building_height_max: maximum building height in grid cell (avg. of geoscape roof and eave height)<br> - building_height_std: standard deviation of building height in grid cell (avg. of geoscape roof and eave height)<br> - wall_density: sum of building wall area as fraction of grid area<br> - frontal_density: sum of cardinally averaged building frontal area as fraction of grid area<br> - tree_height: average vegetation canopy height in grid<br> - tree_height_std: standard deviation in vegetation canopy height in grid<br> - building_fraction: building footprint area as fraction of grid area, corrected for cloud and shadow fractions<br> - tree_fraction: tree canopy plan area as fraction of grid area, corrected for cloud and shadow fractions<br> - lowveg_fraction: low vegetation (grass, shrubs, other vegetation) as fraction of grid area, corrected for cloud and shadow fractions<br> - water_fraction: all open water (ocean, lakes, pools) as fraction of grid area, corrected for cloud and shadow fractions<br> - bareearth_fraction: bare earth including construction sites, rock, sand and sparsely vegetated areas as fraction of grid area, corrected for cloud and shadow fractions<br> - roadpath_fraction: all hard surfaces on ground excluding buildings, defined as "impervious surface fraction" in Stewart and Oke, 2012, corrected for cloud and shadow fractions<br> - total_built: all impervious surfaces including buildings, roads, paths and other hard surfaces, corrected for cloud and shadow fractions<br> - total_pervious: all pervious surfaces including vegetation, water and bare earth, corrected for cloud and shadow fractions<br> - height_to_width: average aspect ratio assuming street canyon geometry using Eq 1 of Masson et al. 2020: https://doi.org/10.1016/j.uclim.2019.100536<br> - skyview_factor: average skyview factor assuming street canyon geometry using Eq 2 of Masson et al. 2020: https://doi.org/10.1016/j.uclim.2019.100536<br> - displacement_mac: zero-plane displacement height, Eq. 23 from Macdonald et al., 1998: https://doi.org/10.1016/S1352-2310(97)00403-2<br> - roughness_mac: roughness length for staggered arrays. Eq. 26 from Macdonald et al., 1998: https://doi.org/10.1016/S1352-2310(97)00403-2<br> - displacement_kanda: zero-plane displacement height, Eq. 5 from Kanda et al., 2013: https://doi.org/10.1007/s10546-013-9818-x<br> - roughness_kanda: roughness length for staggered arrays, Eq. 6 from Kanda et al., 2013: https://doi.org/10.1007/s10546-013-9818-x ## Acknowledgements: 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). <br>