Australian Bureau of Meteorology assembled this dataset of 191 Australian rainfall stations for the purpose of climate change monitoring and assessment. These stations were selected because they are believed to be the highest quality and most reliable long-term rainfall stations in Australia. The longest period of record is August 1840 to December 1990, but the actual periods vary by individual station. Each data record in the dataset contains at least a monthly precipitation total, and most records also have daily data as well.

Precipitation in Australia increased to 517.75 mm in 2024 from 480.06 mm in 2023. This dataset includes a chart with historical data for Australia Average Precipitation.

This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/

The goal of this dataset is to predict the total daily rainfall using 24 hours of temperature measurements. This is useful as temperature sensors are much cheaper and easy to maintain as compared to rain gauges. This dataset contains 160,267 time series obtained from a dataset released by the Australian Bureau of Meteorology (BOM).The time series has 3 dimensions, measuring the average hourly temperature, minimum hourly temperature and maximum hourly temperature from 518 weather stations throughout all of Australia.

Please refer to https://data.gov.au/data/dataset/weather-forecasting-verification-data-2015-05-to-2016-04 for more details

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Mean monthly and mean annual rainfall grids. The grids show the rainfall values across Australia in the form of two-dimensional array data. The mean data are based on the standard 30-year period 1961-1990.

Purpose

To demonstrate distribution of rainfall depths over Arckaringa subregion.

Dataset History

Gridded data were generated using the ANU (Australian National University) 3-D Spline (surface fitting algorithm). The resolution of the data is 0.025 degrees (approximately 2.5km) - as part of the 3-D analysis process a 0.025 degree resolution digital elevation model (DEM) was used. Approximately 6300 stations were used in the analysis over Australia. All input station data underwent a high degree of quality control before analysis, and conform to WMO (World Meteorological Organisation) standards for data quality.

Dataset Citation

SA Department of Environment, Water and Natural Resources (2015) Mean monthly and mean annual rainfall data (base climatological data sets) - ARC. Bioregional Assessment Source Dataset. Viewed 26 May 2016, http://data.bioregionalassessments.gov.au/dataset/05feced7-5fe0-442c-9ee1-d703654e2486.

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 is the same as the source data "BOM, Australian Average Rainfall Data from 1961 to 1990" but clipped to the combined extent of the Hunter subregion and Sydney Basin bioregion.

Purpose

Report map production.

Dataset History

The source Aust wide rainfall raster rainann was clipped to the Hunter subregion + sydney Basin bioregion using ArcMap Spatial Analyst Extract by Mask tool

Dataset Citation

Bioregional Assessment Programme (2015) SYD Mean Annual Rainfall v01. Bioregional Assessment Derived Dataset. Viewed 22 June 2018, http://data.bioregionalassessments.gov.au/dataset/81593e61-cada-44e1-a8e9-1710cdf2fcf2.

Dataset Ancestors

• Derived From Bioregional Assessment areas v02

• Derived From Gippsland Project boundary

• Derived From Bioregional Assessment areas v04

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From Victoria - Seamless Geology 2014

• Derived From Bioregional Assessment areas v05

• Derived From BOM, Australian Average Rainfall Data from 1961 to 1990

• Derived From Bioregional Assessment areas v01

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From GEODATA TOPO 250K Series 3

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From Bioregional Assessment areas v06

Daily (1981-2019), monthly (1981-2019) and monthly mean (1981-2010) surfaces of precipitation across Victoria at a spatial resolution of 9 seconds (approx. 250 m). Lineage: A) Data modelling: 1. Weather station observations collected by the Australian Bureau of Meteorology were obtained via the SILO patched point dataset (https://data.qld.gov.au/dataset/silo-patched-point-datasets-for-queensland), followed by the removal of all interpolated records. 2. Climate normals representing the 1981-2010 reference period were calculated for each weather station. A regression patching procedure (Hopkinson et al. 2012) was used to correct for biases arising due to differences in record length where possible. 3. Climate normals for each month were interpolated using trivariate splines (latitude, longitude and elevation as spline variables) using a DEM smoothed (Gaussian filter with a standard deviation of 10 and a search radius of 0.0825°, optimised using cross validation) to account for the lack of strong correlation between elevation and precipitation at short distances (Hutchinson 1998; Sharples et al. 2005). All data was interpolated using ANUSPLIN 4.4 (Hutchinson & Xu 2013). 4. Monthly surfaces were interpolated directly from monthly station records using the methods described in step 3. 5. Daily anomalies were calculated as a proportion of monthly precipitation, and interpolated with full spline dependence on latitude and longitude. 6. Interpolated anomalies (constrained to values between 0 and 1) were multiplied by monthly precipitation to obtain the final daily surfaces. B) Spatial data inputs: 1. Fenner School of Environment and Society and Geoscience Australia. 2008. GEODATA 9 Second Digital Elevation Model (DEM-9S) Version 3. C) Model performance: Accuracy assessment was conducted with leave-one-out cross validation. Mean monthly precipitation: RMSE = 7.65 mm (14.0% relative to mean) Monthly precipitation: RMSE = 13.12 mm (24.7% relative to mean) Daily precipitation: RMSE = 2.21 mm (26.3% relative to mean)

Three datasets containing climate data, compiled in April 2011, have been purchased from the Bureau of Meteorology. These datasets include observations from stations in all Australian States and Territories. Each dataset includes a file which gives details of the stations where observations were made and a file describing the data. AWS Hourly Data contains hourly records of precipitation, air temperature, wet bulb temperature, dew point temperature, relative humidity, vapour pressure, saturated vapour pressure, wind speed, wind direction, maximum wind gust, mean sea level pressure, station level pressure. Each record for each parameter is also flagged to indicate the quality of the value.Synoptic Data contains records of air temperature, dew point temperature, wet bulb temperature, relative humidity, wind speed, wind direction, mean sea level pressure, station level pressure, QNH pressure, vapour pressure and saturated vapour pressure. Each record for each parameter is also flagged to indicate the quality of the value.Daily Rainfall Data contains records precipitation in the 24 hours before 9 am, number of days of rain within the days of accumulation and the accumulated number of days over which the precipitation was measured. Each precipitation record is flagged to indicate the quality of the value.

This average precipitation grid are current as at 10/3/2011 and is version 3 of the Australian Water Availability Project. It is the average precipitation for all months from January 1900 until December 2010.

Abstract

The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from multiple datasets. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

This dataset includes the following parameters clipped to BA_SYD extent.

1) Mean annual BAWAP (Bureau of Meteorology Australian Water Availability Project) rainfall of year 1981 - 2013

2) Mean annual penman PET (potential evapotranspiration) of year 1981 - 2013

3) Mean annual runoff using the 'Budyko-framework' implementation of Choudhury

Dataset History

Lineage is as per the BA All mean climate data for Australia except the national data has been clipped to BA SYD extent.

The mean annual rainfall data is created from monthly BAWAP grids which is created from daily BILO rainfall.

Jones, D. A., W. Wang and R. Fawcett (2009). "High-quality spatial climate data-sets for Australia." Australian Meteorological and Oceanographic Journal 58(4): 233-248.

The Mean annual penman PET is created as per the Donohue et al (2010) paper using the fully physically based Penman formulation of potential evapotranspiration, exept that daily wind speed grids used here were generated with a spline (i.e., ANUSPLIN) as per McVicar et al (2008), not the TIN as per Donohue et al (2010). For comprehensive details regarding the generation of some of these datasets (i.e., net radiation, Rn) see the details provided in Donohue et al (2009).

Donohue, R.J., McVicar, T.R. and Roderick, M.L. (2010) Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology. 386(1-4), 186-197. doi:10.1016/j.jhydrol.2010.03.020

Donohue, R.J., McVicar, T.R. and Roderick, M.L., (2009) Generating Australian potential evaporation data suitable for assessing the dynamics in evaporative demand within a changing climate. CSIRO: Water for a Healthy Country Flagship, pp 43. http://www.clw.csiro.au/publications/waterforahealthycountry/2009/wfhc-evaporative-demand-dynamics.pdf

McVicar, T.R., Van Niel, T.G., Li, L.T., Roderick, M.L., Rayner, D.P., Ricciardulli, L. and Donohue, R.J. (2008) Wind speed climatology and trends for Australia, 1975-2006: Capturing the stilling phenomenon and comparison with near-surface reanalysis output. Geophysical Research Letters. 35, L20403, doi:10.1029/2008GL035627

The Mean annual runoff was created as per the Donohue et al (2010) paper. The data represent the runoff expected from the steady-state 'Budyko curve' longterm mean annual water-energy limit approach using BAWAP precipitation and the Penman potential ET described above.

Choudhury BJ (1999) Evaluation of an empirical equation for annual evaporation using field observations and results from a biophysical model. Journal of Hydrology 216, 99-110.

Donohue, R.J., McVicar, T.R. and Roderick, M.L. (2010) Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology. 386(1-4), 186-197. doi:10.1016/j.jhydrol.2010.03.020

Donohue, R.J., McVicar, T.R. and Roderick, M.L., (2009) Generating Australian potential evaporation data suitable for assessing the dynamics in evaporative demand within a changing climate. CSIRO: Water for a Healthy Country Flagship, pp 43. http://www.clw.csiro.au/publications/waterforahealthycountry/2009/wfhc-evaporative-demand-dynamics.pdf

McVicar, T.R., Van Niel, T.G., Li, L.T., Roderick, M.L., Rayner, D.P., Ricciardulli, L. and Donohue, R.J. (2008) Wind speed climatology and trends for Australia, 1975-2006: Capturing the stilling phenomenon and comparison with near-surface reanalysis output. Geophysical Research Letters. 35, L20403, doi:10.1029/2008GL035627

Dataset Citation

Bioregional Assessment Programme (2014) Mean annual climate data clipped to BA_SYD extent. Bioregional Assessment Derived Dataset. Viewed 18 June 2018, http://data.bioregionalassessments.gov.au/dataset/a8393a45-5e86-431b-b504-c0b2953296f4.

Dataset Ancestors

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

• Derived From Mean Annual Climate Data of Australia 1981 to 2012

Abstract

The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from multiple datasets provided by the Bureau of Meteorology. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

This dataset includes the following parameters for the whole of Australia:

1) Mean annual BAWAP (Bureau of Meteorology Australian Water Availability Project) rainfall of year 1981 - 2013

2) Mean annual penman PET (potential evapotranspiration) of year 1981 - 2013

3) Mean annual runoff using the 'Budyko-framework' implementation of Choudhury

Purpose

Provide long term (last 30 years) average annual grids of rainfall, penman PET and runoff for whole Australia.

Dataset History

The mean annual rainfall data is created from monthly BAWAP grids (Dataset ID: 7aaf0621-a0e5-4b01-9333-53ebcb1f1c14) which is created from daily BILO rainfall.

Jones, D. A., W. Wang and R. Fawcett (2009). "High-quality spatial climate data-sets for Australia." Australian Meteorological and Oceanographic Journal 58(4): 233-248.

The Mean annual penman PET is created by Randall Donohue, as per the Donohue et al (2010) paper using the fully physically based Penman formulation of potential evapotranspiration, except that daily wind speed grids used here were generated with a spline (i.e., ANUSPLIN) as per McVicar et al (2008), not the TIN as per Donohue et al (2010). For comprehensive details regarding the generation of some of these datasets (i.e., net radiation, Rn) see the details provided in Donohue et al (2009).

Donohue, R.J., McVicar, T.R. and Roderick, M.L. (2010) Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology. 386(1-4), 186-197. doi:10.1016/j.jhydrol.2010.03.020

Donohue, R.J., McVicar, T.R. and Roderick, M.L., (2009) Generating Australian potential evaporation data suitable for assessing the dynamics in evaporative demand within a changing climate. CSIRO: Water for a Healthy Country Flagship, pp 43. http://www.clw.csiro.au/publications/waterforahealthycountry/2009/wfhc-evaporative-demand-dynamics.pdf

McVicar, T.R., Van Niel, T.G., Li, L.T., Roderick, M.L., Rayner, D.P., Ricciardulli, L. and Donohue, R.J. (2008) Wind speed climatology and trends for Australia, 1975-2006: Capturing the stilling phenomenon and comparison with near-surface reanalysis output. Geophysical Research Letters. 35, L20403, doi:10.1029/2008GL035627

The Mean annual runoff was created by Randall Donohue, as per the Donohue et al (2010) paper. The data represent the runoff expected from the steady-state 'Budyko curve' longterm mean annual water-energy limit approach using BAWAP precipitation and the Penman potential ET described above.

Choudhury BJ (1999) Evaluation of an empirical equation for annual evaporation using field observations and results from a biophysical model. Journal of Hydrology 216, 99-110.

Donohue, R.J., McVicar, T.R. and Roderick, M.L. (2010) Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology. 386(1-4), 186-197. doi:10.1016/j.jhydrol.2010.03.020

Donohue, R.J., McVicar, T.R. and Roderick, M.L., (2009) Generating Australian potential evaporation data suitable for assessing the dynamics in evaporative demand within a changing climate. CSIRO: Water for a Healthy Country Flagship, pp 43. http://www.clw.csiro.au/publications/waterforahealthycountry/2009/wfhc-evaporative-demand-dynamics.pdf

McVicar, T.R., Van Niel, T.G., Li, L.T., Roderick, M.L., Rayner, D.P., Ricciardulli, L. and Donohue, R.J. (2008) Wind speed climatology and trends for Australia, 1975-2006: Capturing the stilling phenomenon and comparison with near-surface reanalysis output. Geophysical Research Letters. 35, L20403, doi:10.1029/2008GL035627

Dataset Citation

Bioregional Assessment Programme (2014) Mean Annual Climate Data of Australia 1981 to 2012. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/02418c67-f8bb-48a8-88a3-2a5c6b485f78.

Dataset Ancestors

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

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 …Show full descriptionAbstract 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 the 30-year totals of modelled rainfall predictions, accumulated up to the catchment areas, above each of the model nodes in the Richmond river basin. This summary is derived from the rainfall modelling predictions for CLM. Purpose To provide rainfall totals at each model node for a water balance calculation. Dataset History 30-year totals of modelled rainfall from the BILO gridded dataset have been edited. This dataset accumulates this gridded data to provide rainfall totals from the catchment areas above each of the 16 surface water modelling nodes in the Richmond river basin. Dataset Citation Bioregional Assessment Programme (2016) CLM Modelled rainfall predictions. Bioregional Assessment Derived Dataset. Viewed 09 October 2017, http://data.bioregionalassessments.gov.au/dataset/75ab5801-cb68-4d90-a13b-9df1cdba10cc. Dataset Ancestors Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012 Derived From Mean Annual Climate Data of Australia 1981 to 2012 Derived From CLM Richmond River basin surface water modelling domain Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008 Derived From Climate model 0.05x0.05 cells and cell centroids

This NetCDF4 dataset contains gridded rainfall estimates created from a blend of Global Satellite Mapping of Precipitation (GSMaP) satellite rainfall and Australian Gridded Climate Dataset (AGCD) rain gauge analysis data. The blending process consisted of a two-step method. The first step involved correcting the data through the use of multiplicative ratio grids. For each month, the ratio of the satellite data to the rain gauge data was found at each station. These ratios were then converted into a grid using Ordinary Kriging. The ratio grid was then applied onto the original GSMaP data to form the corrected GSMaP data. The second step involved blending the corrected GSMaP data and AGCD data. The blend is formed from the weighted average of the two datasets using weights derived from their error variances. The weights were inversely proportional to the error variances of the respective datasets. The error variances were calculated on a seasonal basis using the Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset as truth. The weighted average is the final blended product. The temporal coverage of the data spans a total of 20 years from January 2001 to December 2020, on a monthly basis. The spatial domain of the data is a rectangular domain centred over Australia. The latitude ranges from 108 to 156 degrees east while the longitude ranges from -45 to -9 degrees north. The resolution is 0.1 degrees. The data was created in an attempt to provide better representation of rainfall away from rain gauges whilst retaining strong correlations to rain gauges where they exist. The algorithm described earlier was performed using Python 3. This is version 1 of the data. Refinements are planned in the future.

Average annual rainfall for the Australian continent based on a standard 30-year climatology (1961-1990). Source: Bureau of Meteorology - see http://www.bom.gov.au/jsp/ncc/climate_averages/rainfall/index.jsp

Map prepared by the Department of Environment and Energy in order to produce Figure WAT2 in the Inland Waters theme of the 2016 State of the Environment Report, available at http://www.soe.environment.gov.au

The map service can be viewed at http://soe.terria.io/#share=s-ruqkk2hcl1aejMkbavYvrDSH1T7

Downloadable data also available below.

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 four rasters of groundwater recharge over the Sydney Basin. There is a deterministic "best" estimate of the recharge and there are 3 percentiles (5th, 50th and 95th) from a probabilistic estimate of the recharge over the Sydney Basin.

Purpose

This dataset was created as an input into the numerical groundwater modelling.

Dataset History

This dataset was created using regression equations developed from the point estimates of recharge using the chloride mass balance. The regression equations were created from a relationship between the annual average rainfall and average annual recharge for different surface geology groupings.

Dataset Citation

Bioregional Assessment Programme (XXXX) SYD Dryland Diffuse Groundwater Recharge v01. Bioregional Assessment Derived Dataset. Viewed 22 June 2018, http://data.bioregionalassessments.gov.au/dataset/c73b2181-b2eb-4272-939c-98823f4e7ce1.

Dataset Ancestors

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

• Derived From Gippsland Project boundary

• Derived From Geological Provinces - Full Extent

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From Bioregional Assessment areas v06

• Derived From Bioregional Assessment areas v05

• Derived From BOM, Australian Average Rainfall Data from 1961 to 1990

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From SYD Chloride Deposition in Rainfall v01

• Derived From SYD Point Recharge Esitmates from Chloride Mass Balance v01

• Derived From Australian 0.05º gridded chloride deposition v2

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From GEODATA TOPO 250K Series 3

• Derived From Victoria - Seamless Geology 2014

• Derived From NSW Office of Water - Groundwater quality extract

• Derived From Bioregional Assessment areas v04

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

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 shapefile contains the point estimates of recharge for the Sydney Basin. This includes the chloride deposition rate, the chloride concentration of the groundwater and some contextual information such as surface geology, annual average rainfall.

Purpose

This is an intermediary step in estimating groundwater recharge spatially across the Sydney Basin.

Dataset History

Chloride deposition data and chloride concentration of groundwater data were used to estimate groundwater recharge using the Chloride Mass Balance method.

Dataset Citation

Bioregional Assessment Programme (XXXX) SYD Point Recharge Esitmates from Chloride Mass Balance v01. Bioregional Assessment Derived Dataset. Viewed 22 June 2018, http://data.bioregionalassessments.gov.au/dataset/2de56d51-44d0-4217-a98a-10ae1dedcf8b.

Dataset Ancestors

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

• Derived From Bioregional Assessment areas v06

• Derived From Bioregional Assessment areas v04

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From NSW Office of Water - Groundwater quality extract

• Derived From BOM, Australian Average Rainfall Data from 1961 to 1990

• Derived From GEODATA TOPO 250K Series 3

• Derived From SYD Chloride Deposition in Rainfall v01

• Derived From Australian 0.05º gridded chloride deposition v2

• Derived From Bioregional Assessment areas v05

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Victoria - Seamless Geology 2014

• Derived From Gippsland Project boundary

• Derived From Geological Provinces - Full Extent

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

Mean monthly, seasonal and annual rainfall grids. The grids show the 30-year averages (for 1961-1990) of mean monthly, seasonal and annual rainfall across Australia. Averages have been derived …Show full descriptionMean monthly, seasonal and annual rainfall grids. The grids show the 30-year averages (for 1961-1990) of mean monthly, seasonal and annual rainfall across Australia. Averages have been derived from daily rainfall totals (see DataQuality/Lineage section, for details)

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 is the estimation of groundwater recharge in the Namoi. It is uses the chloride mass balance method in the consolidated rock area and an empirical relationship betwenn soils, land use and climate in the alluvial areas.

Purpose

This dataset provides a long term average recharge rate for input to the groundwater modelling.

Dataset History

This dataset is the estimation of groundwater recharge in the Namoi. It is uses the chloride mass balance method in the consolidated rock area and an empirical relationship betwenn soils, land use and climate in the alluvial areas. The methods used are described in NAM213

Dataset Citation

Bioregional Assessment Programme (2016) Namoi dryland diffuse groundwater recharge. Bioregional Assessment Derived Dataset. Viewed 12 March 2019, http://data.bioregionalassessments.gov.au/dataset/858327d1-a558-4edc-9315-8779924f3632.

Dataset Ancestors

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

• Derived From BOM, Australian Average Rainfall Data from 1961 to 1990

• Derived From Soil and Landscape Grid National Soil Attribute Maps - Clay 3 resolution - Release 1

• Derived From Bioregional_Assessment_Programme_Catchment Scale Land Use of Australia - 2014

• Derived From Australian 0.05º gridded chloride deposition v2

Abstract

The dataset was derived by the Bioregional Assessment Programme from a source dataset. The source dataset is 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.

Groundwater recharge estimates shapefile for the Clarence-Moreton bioregion.

Dataset History

A shapefile was created from the dataset 'Clarence-Morton Groundwater Recharge Estimates - Chloride Mass Balance technique v01' (http://data.bioregionalassessments.gov.au/dataset/41ad36ae-9399-439d-9e1c-ec55fa2058c4) using the XY locations and the groundwater recharge estimates column.

Dataset Citation

Bioregional Assessment Programme (2016) CLM - groundwater recharge estimates shapefile v1. Bioregional Assessment Derived Dataset. Viewed 09 October 2017, http://data.bioregionalassessments.gov.au/dataset/c6a331b5-b28d-4c39-9cbb-b40926fa2201.

Dataset Ancestors

• Derived From CLM - Bore water quality QLD

• Derived From CLM - Groundwater Recharge Estimates - Chloride Mass Balance technique v01

• Derived From CLM - Bore water quality NSW

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From BOM, Australian Average Rainfall Data from 1961 to 1990

• Derived From Australian 0.05º gridded chloride deposition v2

• Derived From QLD Department of Natural Resources and Mines Groundwater Database Extract 20142808

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From GEODATA TOPO 250K Series 3

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From Hydstra Groundwater Measurement Update - NSW Office of Water, Nov2013

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

This dataset contains the precipitation, mean maximum temperature and mean minimum temperature data used in the study Application of Machine Learning to Attribution and Prediction of Seasonal Precipitation and Temperature Trends in Canberra, Australia. This data was originally from the Australian Bureau of Meteorology Climate Data Online (http://www.bom.gov.au/climate/data/index.shtml), but has been updated to have missing values (1% of data) filled using a moving average centred on the year for which the data is missing.

Below is the abstract for the paper.

Southeast Australia is frequently impacted by drought, requiring monitoring of how the various factors influencing drought change over time. Precipitation and temperature trends were analysed for Canberra, Australia, revealing decreasing autumn precipitation. However, annual precipitation remains stable as summer precipitation increased and the other seasons show no trend. Further, mean temperature increases in all seasons. These results suggest that Canberra is increasingly vulnerable to drought. Wavelet analysis suggests that the El-Niño Southern Oscillation (ENSO) influences precipitation and temperature in Canberra, although its impact on precipitation has decreased since the 2000s. Linear regression (LR) and support vector regression (SVR) were applied to attribute climate drivers of annual precipitation and mean maximum temperature (TMax). Important attributes of precipitation include ENSO, the southern annular mode (SAM), Indian Ocean Dipole (DMI) and Tasman Sea SST anomalies. Drivers of TMax included DMI and global warming attributes. The SVR models achieved high correlations of 0.737 and 0.531 on prediction of precipitation and TMax, respectively, outperforming the LR models which obtained correlations of 0.516 and 0.415 for prediction of precipitation and TMax on the testing data. This highlights the importance of continued research utilising machine learning methods for prediction of atmospheric variables and weather pattens on multiple time scales.

This dataset contains time series for monthly precipitation over six sites (Blackheath, Braidwood, Darkes Forest, Goulburn, Lithgow and Moss Vale) in the Sydney Catchment Area (SCA) and monthly mean maximum and mean minimum temperature for three sites (Goulburn, Lithgow, and Moss Vale) in the SCA. This data was used in the study Attribution and Prediction of Precipitation and Temperature Trends within the Sydney Catchment Using Machine Learning. The data was originally from the Australian Bureau of Meteorology Climate Data Online (http://www.bom.gov.au/climate/data/index.shtml), but has been updated to have missing values (8% of data) filled using a moving average centred on the year for which the data is missing.

Below is the abstract for the paper:

Droughts in southeastern Australia can profoundly affect the water supply to Sydney, Australia's largest city. Increasing population, a warming climate, land surface changes, and expanded agricultural use increase water demand and reduce catchment runoff. Studying Sydney's water supply is necessary to manage water resources and lower the risk of severe water shortages. This study aims at understanding Sydney water supply by analysing precipitation and temperature trends across the catchment. A decreasing trend in annual precipitation was found across the Sydney catchment area. Annual precipitation also is significantly less variable, due to fewer years above the 80th percentile. These trends result from significant reductions in precipitation during spring and autumn, especially over the last 20 years. Wavelet analysis is applied to assess how the influence of climate drivers has changed over time. Attribute selection was carried out using linear regression and machine learning techniques including random forests and support vector regression. Drivers of annual precipitation included Niño3.4, SAM, DMI and measures of global warming such as the Tasman Sea Sea Surface temperature anomalies. The support vector regression model with a polynomial kernel achieved correlations of 0.921 and a skill score compared to climatology of 0.721. The linear regression model also performed well with a correlation of 0.815 and skill score of 0.567, highlighting the importance of considering both linear and non-linear methods when developing statistical models. Models were also developed on autumn and winter precipitation but performed worse than annual precipitation on prediction. For example, the best performing model on autumn precipitation, which accounts for approximately one quarter of annual precipitation, achieved an RMSE of 418.036 mm2 on the testing data while annual precipitation achieved an RMSE of 613.704 mm2. However, the seasonal models provided valuable insight into whether the season would be wet or dry compared to the climatology.