This data collection contains Hydrodynamic Model output data produced by the Sydney Harbour Hydrodynamic Model.

The Sydney Harbour (real-time) model collates observations from the Bureau of Meteorology, Macquarie University, Sydney Ports Authority and the Manly Hydraulics Laboratory offshore buoy. The Sydney Harbour Model is contained within the Sydney Harbour Observatory (SHO) system.

The Sydney Harbour Hydrodynamic Model divides the Harbour water into a number of boxes or voxels. Each voxel is less than 60m x 60m x 1m in depth. In narrow parts of the Harbour, or in shallower regions, the voxels are smaller. Layers are numbered - so the sea floor is number 1 and the surface is number 24.

The model is driven by the conditions on the boundaries. It uses rainfall rates at 13 sites in the Sydney catchment, the wind speed, tide height, the solar radiation and astronomical tides. Every hour the display is refreshed.

The model utilizes the following environmental data inputs;

• Dr Serena Lee provide the following: 24 layer grid of the Sydney Harbour Estuary, bathymetry inputs, and the run-off coefficient formula used to convert rainfall readings provided by the Bureau of Meteorology into boundary input data.
• The Bureau of Meteorology provides the following model inputs; rainfall from 13 individual rain gauges, air temperature, humidity, barometric pressure, cloud cover, evaporation, wind speed, wind direction and forecast data
• Sydney Ports Authority provides tidal input data.
• The Office of Environment and Heritage, and the Manly Hydraulics Laboratory provides ocean boundary temperature input data.
• Macquarie University provides solar radiation input data.

The hydrodynamic modeling system models the following environmental variables:

• Salinity
• Temperature
• Depth average salinity
• Horizontal water velocity
• Vertical water velocity
• Depth average north velocity
• Depth average east velocity
• Water elevation

This dataset is available in Network Common Data Form – Climate and Forecast (NetCDF-CF) format.

Household Travel Survey (HTS) is the most comprehensive source of personal travel data for the Sydney Greater Metropolitan Area (GMA). This data explores average weekday travel patterns for residents in Sydney GMA. The Household Travel Survey (HTS) collects information on personal travel behaviour. The study area for the survey is the Sydney Greater Metropolitan Area (GMA) which includes Sydney Greater Capital City Statistical Area (GCCSA), parts of Illawarra and Hunter regions. All residents of occupied private dwellings within the Sydney GMA are considered within scope of the survey and are randomly selected to participate. The HTS has been running continuously since 1997/981 and collects data for all days through the year – including during school and public holidays. Typically, approximately 2,000-3,000 households participate in the survey annually. Data is collected on all trips made over a 24-hour period by all members of the participating households. Annual estimates from the HTS are usually produced on a rolling basis using multiple years of pooled data for each reporting year2. All estimates are weighted to the Australian Bureau of Statistics’ Estimated Resident Population, corresponding to the year of collection3. Unless otherwise stated, all reported estimates are for an average weekday. Due to disruptions in data collection resulting from the lockdowns during the COVID-19 pandemic, post-COVID releases of HTS data are based on a lower sample size than previous HTS releases. To ensure integrity of the results and mitigate risk of sampling errors some post-COVID results have been reported differently to previous years. Please see below for more information on changes to HTS post-COVID (2020/21 onwards). Data collection for the HTS was suspended during lock-down periods announced by the NSW Government due to COVID-19. Exceptions apply to the estimates for 2020/21 which are based on a single year of sample as it was decided not to pool the sample with data collected pre-COVID-19. HTS population estimates are also slightly lower than those reported in the ABS census as the survey excludes overseas visitors and those in non-private dwellings. Changes to HTS post-COVID (2020/21 onwards) HTS was suspended from late March 2020 to early October 2020 due to the impact and restrictions of COVID-19, and again from July 2021 to October 2021 following the Delta wave of COVID-19. Consequently, both the 2020/21 and 2021/22 releases are based on a reduced data collection period and smaller samples. Due to the impact of changed travel behaviours resulting from COVID-19 breaking previous trends, HTS releases since 2020/21 have been separated from pre-COVID-19 samples when pooled. As a result, HTS 2020/21 was based on a single wave of data collection which limited the breadth of geography available for release. Subsequent releases are based on pooled post-COVID samples to expand the geographies included with reliable estimates. Disruption to the data collection during, and post-COVID has led to some adjustments being made to the HTS estimates released post-COVID: SA3 level data has not been released for 2020/21 and 2021/22 due to low sample collection. LGA level data for 2021/22 has been released for selected LGAs when robust Relative Standard Error (RSE) for total trips are achieved Mode categories for all geographies are aggregated differently to the pre-COVID categories Purpose categories for some geographies are aggregated differently across 2020/21 and 2021/22. A new data release – for six cities as defined by the Greater Sydney Commission - is included since 2021/22. Please refer to the Data Document for 2022/23 (PDF, 262.54 KB) for further details. RELEASE NOTE The latest release of HTS data is 15 May 2025. This release includes Region, LGA, SA3 and Six Cities data for 2023/24. Please see 2023/24 Data Document for details. A revised dataset for LGAs and Six Cities for HTS 2022/23 data has also been included in this release on 15 May 2025. If you have downloaded HTS 2022/23 data by LGA and/or Six Cities from this link prior to 15/05/2025, we advise you replace it with the revised tables. If you have been supplied bespoke data tables for 2022/23 LGAs and/or Six Cities, please request updated tables. Revisions to HTS data may be made on previously published data as new sample data is appended to improve reliability of results. Please check this page for release dates to ensure you are using the most current version or create a subscription (https://opendata.transport.nsw.gov.au/subscriptions) to be notified of revisions and future releases.

The stochastic climate data include 10,000 replicates of 130-yr daily data sets of rainfall and potential evapotranspiration generated using observed data sets without and with combined climate data. This work has been undertaken by researchers at the University of Newcastle and used in modelling for Greater Sydney Water Strategy. This particular Asset (070101-070330) houses Silo Station IDs: 070105 - MOUNT FAIRY (MERIGAN) 070119 - BIG HILL (GLEN DUSK) 070124 - RICHLANDS (BOUVERIE) 070131 - WOODHOUSELEE (LEESTON) 070135 - MUMMELL (KANGAROOBIE) 070143 - BRAYTON (LONGREACH) 070144 - TARALGA (CIRCLE C) 070147 - GOULBURN (HILLWOOD) 070183 - WINDELLIMA (BUDJONG) 070261 - JERRABATTGULLA (GILSTON) 070263 - GOULBURN TAFE 070330 - GOULBURN AIRPORT Note: If you would like to ask a question, make any suggestions, or tell us how you are using this dataset, please visit the NSW Water Hub which has an online forum you can join.

# 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
- version: v1.0
- institution: "ARC Centre of Excellence for Climate Extremes, UNSW Sydney, Australia
- 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/
- 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
- author: Mathew Lipson

## Description

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.

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,
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/
- additional land surface tiles are included where previously set to nan
- land surface tiles which did not sum to 1.0 were excluded (14 tiles)
- building height mean and standard deviation is still calculated from the average of Geoscape roof (max) and eave (min) heights.
An optional processing step is applied to account for buildings covering multiple grids where building vector information
is burnt to a raster and then area weighted to calculate grid-level statistics. This requires much longer processing time,
as well as additional modules (GeoCube), however avoids the previous issue of the statistics of buildings over multiple grids
being applied to one grid only.
- building height maximum is now based only on Geoscape roof height (i.e. the maximum measured height). Again, an optional
processing step is included based on rasterised data.

## Inputs:

- Geoscape Surface cover V1.6 (tiff)
- Geoscape Trees v1.6 (tiff)
- Geoscape Buildings v2.0 (shp)
- template file for grid (here based on CCI)

## Outputs:

- cell_area: plan area of grid cell (m2)
- building_height: mean building height in grid cell (avg. of geoscape roof and eave height)
- building_height_max: maximum building height in grid cell (avg. of geoscape roof and eave height)
- building_height_std: standard deviation of building height in grid cell (avg. of geoscape roof and eave height)
- wall_density: sum of building wall area as fraction of grid area
- frontal_density: sum of cardinally averaged building frontal area as fraction of grid area
- tree_height: average vegetation canopy height in grid
- tree_height_std: standard deviation in vegetation canopy height in grid
- building_fraction: building footprint area as fraction of grid area, corrected for cloud and shadow fractions
- tree_fraction: tree canopy plan area as fraction of grid area, corrected for cloud and shadow fractions
- lowveg_fraction: low vegetation (grass, shrubs, other vegetation) as fraction of grid area, corrected for cloud and shadow fractions
- water_fraction: all open water (ocean, lakes, pools) as fraction of grid area, corrected for cloud and shadow fractions
- bareearth_fraction: bare earth including construction sites, rock, sand and sparsely vegetated areas as fraction of grid area, corrected for cloud and shadow fractions
- 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
- total_built: all impervious surfaces including buildings, roads, paths and other hard surfaces, corrected for cloud and shadow fractions
- total_pervious: all pervious surfaces including vegetation, water and bare earth, corrected for cloud and shadow fractions
- 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
- 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
- displacement_mac: zero-plane displacement height, Eq. 23 from Macdonald et al., 1998: https://doi.org/10.1016/S1352-2310(97)00403-2
- roughness_mac: roughness length for staggered arrays. Eq. 26 from Macdonald et al., 1998: https://doi.org/10.1016/S1352-2310(97)00403-2
- displacement_kanda: zero-plane displacement height, Eq. 5 from Kanda et al., 2013: https://doi.org/10.1007/s10546-013-9818-x
- 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
providing the datasets necessary for this study, drawing on Geoscape Buildings, Surface Cover and Trees datasets,
© Geoscape Australia, 2020: https://geoscape.com.au/legal/data-copyright-and-disclaimer/.
This research was supported by the Australian Research Council (ARC) Centre of Excellence for Climate System Science
(grant CE110001028), the ARC Centre of Excellence for Climate Extremes (grant CE170100023).

The fundamental input data of work undertaken by Water Modelling Team is climate data in the form of daily rainfall and potential evapotranspiration. This data is input to water models of varying types, purposes, and complexity. The water models transform this input data to produce a range of water related modelled data.

The stochastic climate data and palaeo stochastic climate data include 10,000 replicates of 130-yr daily data sets of rainfall and potential evapotranspiration generated using observed data sets without and with combined palaeo climate data. This work has been undertaken by researchers at the University of Newcastle and used in modelling for Greater Sydney Water Strategy.

Stochastic Climate data and palaeo stochastic climate data are available to download for Greater Sydney region from the Related Datasets section below.

Note: If you would like to ask a question, make any suggestions, or tell us how you are using this dataset, please visit the NSW Water Hub which has an online forum you can join.

This data is provided by the City of Sydney and provides bus shelter locations. There are 40 public bus shelters controlled by the City of Sydney. The API provides data in GeoJSON format, for more information visit City of Sydney.

Abstract

This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied.

The metadata was not provided by the data supplier and has been compiled by the programme based on known details at the time of acquisition.

This is a special data request to Sydney Catchment Authority for daily total flow data fom every gauge for as long as possible for all SCA/SW guages to calibrate AWRA-L for the Sydney Bioregional Assessment.

Daily total discharge for each gauge, with a day = 0900 day_j to 0900 day_j+1

Please note that river level height for the same gauges can be found in the following file which was aquired on 27th July 2015 and requested from Sydney Catchment Authority:

231df909-3185-4345-926d-0c6d13bfea70 River Level Daily Mean 9am - 9am M Sydney Catchment Authority 20150727

Purpose

This is a special data request to Sydney Catchment Authority for daily total flow data to calibrate AWRA-L for the Sydney Bioregional Assessment.

"Variable 151.01 is a total daily flow volume which was manually computed years ago and stored in Hydstra as a flow value (someone had to type these in ... and convert from gallons or acre feet or whatever the original values were).

Variable 151 is a total daily flow volume computed by Hydstra from the recorded level values and the applicable rating curve for the time. Where the two sets of values overlap, they will be slightly different due to the different calculation methods, the simplifications made in the manual calculation, perhaps some information available at the time of the manual calculation that has not been digitised, or that has simply been lost in the mists of time."

Dataset History

Special Data request to Sydney Catchment Authority by the Bureau of Meteorology through the Water Act. Data created by querying SCA's internal systems/Databases.

Data provided in excel files in CSV format.

"Variable 151.01 is a total daily flow volume which was manually computed years ago and stored in Hydstra as a flow value (someone had to type these in ... and convert from gallons or acre feet or whatever the original values were).

Variable 151 is a total daily flow volume computed by Hydstra from the recorded level values and the applicable rating curve for the time. Where the two sets of values overlap, they will be slightly different due to the different calculation methods, the simplifications made in the manual calculation, perhaps some information available at the time of the manual calculation that has not been digitised, or that has simply been lost in the mists of time."

Dataset Citation

Sydney Catchment Authority (2014) Total Daily Stream flow Sydney Catchment Authority 20141211. Bioregional Assessment Source Dataset. Viewed 14 June 2018, http://data.bioregionalassessments.gov.au/dataset/019128a1-d747-4ac2-842d-c0b30a4f8627.

Hourly ambient sulphur dioxide (SO2) data in parts per billion from provincial ambient air quality monitoring stations across Nova Scotia up to the end of 2023.

This dataset represents the daily transfer to BOM in accordance with the legislative requirements of the Water Act 2007. The dataset includes surface water information from storages (level and volume), rivers (level and flow) , operational data (release volumes), water quality data (in-field instrumentation) and meteorological data (rainfall, windspeed, temperature). The data is primarily derived from instrumentation and delivered unverified. The data represents all storages within WaterNSW Greater Sydney network and key meteorological and river stations.

Hourly ambient ground-level ozone (O3) data in parts per billion from provincial ambient air quality monitoring stations across Nova Scotia up to the end of 2023.

The stochastic climate data and palaeo stochastic climate data include 10,000 replicates of 130-yr daily data sets of rainfall and potential evapotranspiration generated using observed data sets without and with combined palaeo climate data. This work has been undertaken by researchers at the University of Newcastle and used in modelling for Greater Sydney Water Strategy.\r \r -----------------------------------\r \r Note: If you would like to ask a question, make any suggestions, or tell us how you are using this dataset, please visit the NSW Water Hub which has an online forum you can join.

Existing, rideable bicycle routes through the City of Sydney local government area for bicycle commuters. For more information visit Cycling - City of Sydney. Existing, rideable bicycle routes through the City of Sydney local government area for bicycle commuters. For more information visit Cycling - City of Sydney.

Your one-stop shop for all things active transport.\r \r \r Active transport provides tangible benefits by increasing daily physical activity levels and reducing greenhouse gas emissions through a reduction in cars on the road. Other benefits include improved social well-being and a greater sense of community.\r \r \r This data set contains links to the various data sets available on the Open Data Hub that relate to Active Transport.\r \r \r * Pop Up Cycleway \r * Cycling Propensity \r * Cycling Count \r * Cycle Network - City of Sydney \r * Cycleway Data \r * Sydney Spring Cycle 2017 - Road Closures \r * Smart Pedestrian Project \r * Active Transport: Walking \r * Smart Cities Macquarie Park \r * Walking Count Sites \r * Eurobodalla Shire Council Cycleway \r * UNSW Bicycling Dashboards \r \r

This dataset provides monthly measures for on-time running, service cancellations, customer complaints, and customer experience metrics across all Greater Sydney Bus Contract (GSBC) and Outer Sydney Metropolitan Bus Contract (OSMBSC) contracts. Data Description : Monthly % Bus Service Cancellations : Percentage of timetabled services that were cancelled at the First Transit Stop of a trip. Monthly % Bus Service Untracked Trips : Percentage of timetabled services that were not tracked in real time at the First Transit Stop of a trip. Monthly Bus Driver Vacancies : Number of driver vacancies. Monthly Bus Related Complaints : The level of bus related customer complaints per 100,000 boardings.

A Heat Vulnerability Index was built with Open Data for Metropolitan Sydney, for the years 2011 and 2016. Vulnerability is defined as the propensity of a population to be adversely affected by extreme heat and depends on 3 components: the exposure, sensitivity and adaptive capacity of the population. These 3 sub-indexes were calculated with various indicators that you can find as attributes to this layer. The scale of the study is the Statistical Areas 2 (SA2) of the Australian Bureau of Statistics. Bodilis, Carole ; Yenneti, Komali; Hawken, Scott (2018): Heat Vulnerability Index for Sydney. Faculty of Built Environment, UNSW Sydney.

City of Sydney being the first local council in Australia to be certified as carbon neutral under the National Carbon Offset Standard. It has a table that shows our emissions reduction progress since 2005/06 for our operations.

Hourly ambient nitrogen oxides (NOx, NO2, NO) data in parts per billion from provincial ambient air quality monitoring stations across Nova Scotia up to the end of 2023.

These visualisations feature Opal Trips for all available modes of Public Transport in Sydney CBD. View weekly, monthly and yearly trips from July 2016 onwards.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Live machine readable feed of Sydney Trains information about service interruptions.