Abstract

The dataset was derived by the Bioregional Assessment Programme from another derived data set. The source dataset is the 'Node catchment for Galilee surface water modelling' and is identified in the Lineage field in this metadata statement.

The processes undertaken to produce this dataset are described in the History field.

This dataset represents catchment contributing areas for 11 tributaries included in the surface water modelling in the Galilee subregion.

Purpose

This data set was created to show the catchment area above and below the 7 proposed mines and the catchment area under each of 11 tributary zones in Galilee surface water modelling.

Dataset History

Catchment contributing area for each tributary was identified by merging node catchments (residual) that drains to a tributary using ArcGIS merging tool. This data set was created from the 'Node catchment for Galilee surface water modelling' data set.

Dataset Citation

Bioregional Assessment Programme (2016) Galilee tributary catchments. Bioregional Assessment Derived Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/76da964a-9ac7-412f-9ee4-27168c4c0da3.

Dataset Ancestors

• Derived From Onsite and offsite mine infrastructure for the Carmichael Coal Mine and Rail Project, Adani Mining Pty Ltd 2012

• Derived From Alpha Coal Project Environmental Impact Statement

• Derived From QLD Exploration and Production Tenements (20140728)

• Derived From China Stone Coal Project initial advice statement

• Derived From Kevin's Corner Project Environmental Impact Statement

• Derived From Galilee surface water modelling nodes

• Derived From Geoscience Australia GEODATA TOPO series - 1:1 Million to 1:10 Million scale

• Derived From China First Galilee Coal Project Environmental Impact Assessment

• Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

• Derived From Node catchment for Galilee surface water modelling

• Derived From Seven coal mines included in Galilee surface water modelling

The University of Geosciences in Wuhan is developing the computer systems to help the provincial surveys re-map the geology of China at 1:250 000 and 50 000 scales in just 12 years. With a land area 25% larger than Australia's, China has about 15 000 1:50 000 map sheets! The maps are really just by-products, though, as the ultimate goal is to build a computer database of the geology and mineral resources of the whole of China. LIU Songfa and I went to Wuhan in late 1999 to talk to Professor WU and his colleagues about techniques of field-data acquisition and geoscience database design.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement.

The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

The dataset contains grids with the interpolated quantiles of maximum drawdown for weathered zone, outside the alluvium, for the Galilee subregion. It has grid of every 5th percentile of drawdown between the 5th and 95th percentile for baseline, coal resource development pathway and additional coal resource development futures.

Dataset History

Selected ACRD future HRV normalized data from the source data, extracted and cross-tabulated in MS Access. Then exported as a table to be appended to the node features attribute table.

Dataset Citation

Bioregional Assessment Programme (2017) GAL Eco HRV SW Quantiles Interpolation for IMIA Database. Bioregional Assessment Derived Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/6b605ef6-305a-44ba-a970-d900f0d94492.

Dataset Ancestors

• Derived From Onsite and offsite mine infrastructure for the Carmichael Coal Mine and Rail Project, Adani Mining Pty Ltd 2012

• Derived From Alpha Coal Project Environmental Impact Statement

• Derived From QLD Exploration and Production Tenements (20140728)

• Derived From China Stone Coal Project initial advice statement

• Derived From GAL AWRA-L Model v01

• Derived From National Surface Water sites Hydstra

• Derived From Kevin's Corner Project Environmental Impact Statement

• Derived From Galilee tributary catchments

• Derived From Galilee gauge contributing area

• Derived From Galilee surface water modelling nodes

• Derived From Geoscience Australia GEODATA TOPO series - 1:1 Million to 1:10 Million scale

• Derived From Stream gauges for the Galilee surface water model calibration

• Derived From China First Galilee Coal Project Environmental Impact Assessment

• Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

• Derived From Node catchment for Galilee surface water modelling

• Derived From Seven coal mines included in Galilee surface water modelling

• Derived From Stream gauges within Galilee surface water modelling domain

This dataset contains information from Study 1 and Study 2 for this project including interviews, journal transcriptions, audio recordings, Excel & SPSS Output. Study 1 relates to the first part of this mixed-methods study, i.e. quantitative data analysis component including audio recordings, Excel and SPSS output, which investigated the cross-cultural adaptation (CCA) experiences of Chinese international students (CIS) studying in Australia. Data collection for the quantitative component took place during the Autumn semester (February to April) of 2015, whereby 133 CIS from the same university in the Sydney metropolitan area participated in this study (whereby the was 30 partial completions and 103 fully completed responses). The dataset comprised of SPSS Data (with corresponding pdf printoout) regarding Chinese students' L2 motivation, identity change, academic & sociocultural adjustment obtained from main Excel dataset. There was also audio recordings as well as a an excel spreadsheet of a modified Myers-Briggs responses from Study 2, i.e., the qualitative study, which was added as raw data, whereby the interview transcriptions of the audio recordings is found in the dataset for Study 2. Attached data is from Study 2, i.e., the qualitative component, of the mixed methods study investigating the cross-cultural adaptation (CCA) experiences of Chinese international students (CIS) in Australia. The data collection for this component of the study was conducted between 2017 to 2019, and involved 15 CIS who resided in mainland China who embarked on their first year residing and studying in Australia. These participants came from the same university in the Sydney metropolitan area. The dataset comprises of the interviews (derived from audio recordings) and diary journal entries of their CCA experiences as part of this short-term (3-month) longitudinal study. The dataset contains sensitive data that cannot be published. To discuss the data, please contact Dennis Lam 11165141@student.westernsydney.edu.au ORCID 0000-0002-7199-4378

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets.

The source datasets are identified in the Lineage field in this metadata statement.

The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This dataset contains water balance information taken from EIS documents for proposed coal developments in the Galilee subregion. The water balances outline expected dewatering volumes associated with mining, as well as water needs for coal washing and processing. The proposed developments for which an EIS was available at the time of writing are: China Stone, Alpha, Carmichael, China First (Galilee), Kevin's Corner, and South Galilee

Purpose

This dataset was prepared to inform the water balance calculations made by the BA programme for the Galilee subregion.

Dataset History

Data were compiled from the mine water balance sections of all available EIS or SEIS documentation on proposed coal developments in the Galilee subregion. Data collected included: pumping rates for underground mine dewatering, pumping rates for open cut mine dewatering, runoff over the project area, water needs for mine activities, evaporative losses from storage, expected volumes of controlled release from the project site, and any external water needs expected.

Dataset Citation

Bioregional Assessment Programme (2016) Galilee mine water balances. Bioregional Assessment Derived Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/e72caa0f-f206-482f-941d-e13cab8675db.

Dataset Ancestors

• Derived From Alpha Coal Project Environmental Impact Statement

• Derived From Kevin's Corner Project Environmental Impact Statement

• Derived From China First Galilee Coal Project Environmental Impact Assessment

• Derived From China Stone coal project draft EIS

• Derived From South Galilee Coal Project Environmental Impact Statement

• Derived From Carmichael Coal Mine and Rail Project Environmental Impact Statement

Scientists and engineers from a number of institutions such as the Chinese National Bureau of Oceanography, the Second Institute of Oceanography, the National Ocean Technology Centre, Tongi University and the Institute of Acoustics travelled to Sydney and Canberra in the first of the Australia-China meetings on Ocean Science and Technology. The meeting was successful at making parties from each country aware of the latest research and development in each other country.

The papers presented at the conference include:

** Underwater Acoustic Imaging: Effects of One-bit Digitisation (David G Blair, Ian S F Jones, Andrew Madry)

** Seabed Equivalent Geoacoustic Parameters Inversion from the South China Sea Experiment (Wei Chen, Li Ma and Yaoming Chen)

** Coastal Impact of Climate Change: Stochastic Simulation and Risk Management (Peter Cowell)

** Ocean Power Conversion (Tim Finnigan)

** The Application of Remote Sensing to Locate Shipwrecks (Jeremy Green)

** Classifying Cumulative Grain Size Curves with Program Clara (Les J Hamilton)

** Seabed Mapping and Characterization of Australia's EEZ: Recent Developments at Geoscience Australia (Peter T Harris, Andrew Heap, James Daniell, Mark Hemer, Alix Post, Alison Hancock, Alan Hinde, Laura Sbaff, Kriton Glenn, Emma Mathews, Lana Twyford)

** Satellite and Airborne Sea Surface Salinity Mapping with Microwave Radiometers (M.L. Heron, D.M. Burrage, A. Prytz, J.Wesson and P.V. Ridd)

** Modelling of Coastal Hydrodynamics (Dong-Sheng Jeng)

** Surveying for the Census of Marine Life (Ian S F Jones and David G Blair)

** The Underwater Nonlinear Beams and Their Applications in Acoustic Imaging and Bottom Profiling (Songwen Li)
Comparison on Different Methods for Estimating Doppler Shift (Jie Liang)

** Influence of Variations of Water-sediment Process on the Coastal Line in the Yellow River Delta (Liu Shuguang and Zhang Yu)

** Comparison of Geoacoustic Models for the Seafloor Properties (Li Ma)

** Studies on the Sulu Sea (Phillip J Mulhearn)
** Strange Tales From a Diurnal Estuary (Charitha Pattiaratchi and Joanne O¿Callaghan)

** Research at the Centre for Marine Science and Technology (John Penrose)

** Relationship Between Integrated Bottom Scattering Strength and Modal Back-scattering Matrix (J.R. Wu and E.C. Shang)

** Coastal Erosion and Protection Measures in China (Yincan Ye, Zhenye Zhuang, Dujuan Liu and Xiaoling Chen)

** Study on Beach Erosion at a Sandy Coast of Qinghuangdao (Zhang Yu and Liu Shuguang)

Abstract copyright UK Data Service and data collection copyright owner.

The OECD International Trade in Services provides annual data and is presented in the following tables:

Trade in services - EBOPS 2010
This dataset aims to assemble and disseminate balance of payments data on trade in services at the most detailed partner-country and service-category level available. To the extent that countries report them, data are also broken down by type of service according to the EBOPS classification. These data cover international trade in services between residents and non-residents of countries and are reported within the framework of the sixth edition of the IMF's Balance of Payments Manual and the Extended Balance of Payments Services Classification (EBOPS2010), which is consistent with the balance of payments classification but is more detailed. Statistics by partner country and service category on international trade in services such as transportation, communication services, financial services, government services are recorded for Australia and Chile from 1999 onwards and shown in US dollars.

Trade in services - EBOPS 2002
This dataset provides statistics on international trade in services by service category and partner country for 34 OECD countries plus the European Union (EU27), the euro area (EA17), the Russian Federation and Hong Kong, China as well as definitions and methodological notes. These data cover international trade in services between residents and non-residents of countries and are reported within the framework of the fifth edition of the IMF's Balance of Payments Manual and the Extended Balance of Payments Services Classification (EBOPS 2002), which is consistent with the balance of payments classification but is more detailed. To the extent that countries report them, data are also broken down by type of service according to the EBOPS classification. Series are shown in national currency, euros and US dollars and are recorded from 1970 onwards.

Trade in services: national classification items
This dataset contains additional national data on international trade in services for Australia, Canada, New Zealand, Turkey and the United States. The data are reported within the framework of the fifth and sixth editions of the IMF's Balance of Payments Manual and the Extended Balance of Payments Services Classification (EBOPS 2002 and EBOPS 2010), which is consistent with the balance of payments classification but is more detailed. Series are shown in US dollars and are recorded from 1970 onwards.

These data were first provided by the UK Data Service in November 2004.

Main Topics:

The database covers:

• trade in services
• financial services
• business services
• maintenance services
• insurance services
• travel services
• balance of payments
• imports
• exports

The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs (https://earthdata.nasa.gov/about/competitive-programs/measures)) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Australia, New Zealand, China, and Mongolia for nominal year 2015 at 30 meter resolution (GFSAD30AUNZCNMOCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30AUNZCNMOCE data product uses the pixel-based supervised classifier, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data. Each GFSAD30AUNZCNMOCE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10 degree by 10 degree area. Known Issues Note overlapping tiles: The following tile also covers part of another tile in GFSAD30SEACE (Indonesia). Please ignore the Indonesian data in the following tile: GFSAD30AUNZCNMOCE_2015_S20E120_001_2017286154500.tif Additional known issues including constraints and limitations are provided on page 22 of the ATBD.

Abstract

This dataset was derived by the Bioregional Assessment Programme. The parent datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this dataset are described in the History field in this metadata statement.

This dataset contains ratios of absolute maximum change and variability range (5th, 50th and 95th percentiles) for a HRV.

Dataset History

i) For 5th percentile:

=(Absolute value of 5th percentile of maximum change for a HRV)/(Variability for that HRV at 95th percentile- Variability at 5th Percentile)

ii) For 50th percentile:

=(Absolute value of 50th percentile of maximum change for a HRV)/(Variability for that HRV at 95th percentile- Variability at 5th Percentile)

iii) For 95th percentile:

=(Absolute value of 95th percentile of maximum change for a HRV)/(Variability for that HRV at 95th percentile- Variability at 5th Percentile

Dataset Citation

Bioregional Assessment Programme (2017) Galilee HRV ratios. Bioregional Assessment Derived Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/0778db8b-6201-476e-a812-d339a27c46cc.

Dataset Ancestors

• Derived From Surface Geology of Australia, 1:2 500 000 scale, 2012 edition

• Derived From Onsite and offsite mine infrastructure for the Carmichael Coal Mine and Rail Project, Adani Mining Pty Ltd 2012

• Derived From Alpha Coal Project Environmental Impact Statement

• Derived From Galilee model HRV receptors gdb

• Derived From QLD Exploration and Production Tenements (20140728)

• Derived From Galilee groundwater numerical modelling AEM models

• Derived From National Surface Water sites Hydstra

• Derived From GAL AWRA-L Model v01

• Derived From China Stone Coal Project initial advice statement

• Derived From Kevin's Corner Project Environmental Impact Statement

• Derived From Galilee tributary catchments

• Derived From Galilee Hydrological Response Variable (HRV) model

• Derived From Galilee gauge contributing area

• Derived From Galilee surface water modelling nodes

• Derived From Geoscience Australia GEODATA TOPO series - 1:1 Million to 1:10 Million scale

• Derived From Stream gauges for the Galilee surface water model calibration

• Derived From China First Galilee Coal Project Environmental Impact Assessment

• Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

• Derived From Node catchment for Galilee surface water modelling

• Derived From Seven coal mines included in Galilee surface water modelling

• Derived From Stream gauges within Galilee surface water modelling domain

CRISI-ADAPT II project had as one of its main purposes to develop coherent, reliable and usable downscaled climate projections from the last CMIP6 in order to construct the basis for efficient support to climate adaptation and decision making of the related stakeholders. These projections were obtained with also the purpose to be freely available for further use in subsequent studies and, hence, foster adaptation to climate change in more areas.

For further details, find here a brief of the methodology followed:

Methodology

Information provided by 10 models belonging to CMIP6 have been included. Each model has a historical archive, from 01/01/1950 to 31/12/2014 and 4 future scenarios (ssp126, ssp245, ssp370 and ssp585) ranging from 01/01/2015 to 31/12/2100. The relation of the selected models is detailed in the next Table:

Table. Information about the ten climate models belonging to the 6 Coupled Model Intercomparison Project (CMIP6) corresponding to the sixth report of the IPCC. Models were supplied by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) archives.

| CMPI6 MODELS | Resolution | Responsible Centre | References | | BCC-CSM2-MR | 1,125º x 1,121º | Beijing Climate Center (BCC), China Meteorological Administration, China. | Wu, T. et al. (2019) | | CanESM5 | 2,812º x 2,790º | Canadian Centre for Climate Modeling and Analysis (CC-CMA), Canadá. | Swart, N.C. et al. (2019) | | CNRM-ESM2-1 | 1,406º x 1,401º | CNRM (Centre National de Recherches Meteorologiques), Meteo-France, Francia. | Seferian, R. (2019) | | EC-EARTH3 | 0,703º x 0,702º | EC-EARTH Consortium | EC-Earth Consortium. (2019) | | GFDL-ESM4 | 1,250º x 1,000º | National Oceanic and Atmospheric Administration (NOAA), E.E.U.U. | Krasting, J.P. et al. (2018) | | MPI-ESM1-2-HR | 0,938º x 0,935º | Max-Planck Institute for Meteorology (MPI-M), Germany. | Von Storch, J. et al. (2017) | | MRI-ESM2-0 | 1,125º x 1,121º | Meteorological Research Institute (MRI), Japan. | Yukimoto, S. et al. (2019) | | UKESM1-0-LL | 1,875º x 1,250º | Uk Met Office, Hadley Centre, United Kingdom | Good, P. et al. (2019) | | NorESM2-MM | 1,250º x 0,942º | Norwegian Climate Centre (NCC), Norway. | Bentsen, M. et al. (2019) | | ACCESS-ESM1-5 | 1,875º x 1,250º | Australian Community Climate and Earth System Simulator (ACCESS), Australia | Ziehn, T. et al. (2019) |

Since the case studies are distributed among Portugal, Spain, Italy, Malta and Cyprus, a grid covering the whole Mediterranean area, between latitudes 30°N and 50°N and longitudes between 15°W and 40°E, has been chosen for the study. The atmospheric variables available from CMIP6 are wind, temperature, humidity and rainfall at a daily timescale and sea level rise at a monthly timescale. However, it is possible simulate sub-daily rainfall (e.g. for the sector of Flooding and Emergency Response) thanks to the index-n method (Monjo et al. 2016). Other variables such as fog and wave height requires to be obtained from model post-processing.

In addition to these models, information has also been combined to the ERA5-LAND, which has a resolution of 0.07°×0.07°. For each climate variable simulated by the CMIP6 models, a statistical downscaling was applied according to seven steps:

1. Firstly, as a reference field, a purely geo-statistical downscaling of the original Era5-Land grid (0.07°×0.07°) was performed for each variable to a 1km×1km grid, using linear stepwise regression with topological and geographical parameters (altitude, latitude, longitude and distance to the Atlantic Ocean and Mediterranean Sea), and bilinear model for the residual errors.
2. For all models and their corresponding scenarios, the average values for the study area have been calculated for the periods 1981-2010, 2021-2050 and 2071-2100 and their rate of variation between the periods 2071-2100 and 2021-2050.
3. The model scenario with the highest rate of variation and the model scenario with the lowest rate of variation have been chosen to range future variations of the variables. Quantiles 90th, 50th and 10th scenarios have been called Upper, Medium and Lower, respectively.
4. For these scenarios, Upper, Medium and Lower, the empirical values corresponding to the return periods of 5, 10, 20 and 30 years for the periods 1981-2010, 2021-2050, 2046-2075 and 2071-2100 have been calculated for each grid point in the model.
5. Once the above results were obtained, an interpolation to a grid of 1km×1km was performed using the bilinear method.
6. Then, the increment or difference with respect to the same return periods of the period 1981-2010 has been calculated for each period of 30 years (2021-2050, 2046-2075 and 2071-2100) and for each return period. Relative increment (instead of absolute increment) was considered for some variable such as precipitation and wind.
7. Finally, the absolute o relative increment of each scenario and return period (step 6) was added to the reference values of each variable (step 1), obtaining climate scenarios in a 1km×1km grid (see for instance Figure 8). This entire process, applied to return-period values, is an empirical quantile mapping by increment from reanalysis (Monjo et al. 2013).

Exports in Australia increased to 44027 AUD Million in December from 43556 AUD Million in November of 2024. This dataset provides the latest reported value for - Australia Exports - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.