In 2024, the annual mean rainfall in Australia was ***** millimeters. Over the last twenty years, the mean area-average rainfall has fluctuated in Australia, with the lowest value recorded in 2019.

In 2021, Tasmania received the highest annual rainfall of any state or territory in Australia at an average of 1378 millimeters. South Australia was the driest state with *** millimeters of rainfall on average.

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.

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

In 2024, the mean rainfall in Australia was 128 millimeters higher than the reference value, indicating a positive anomaly. Over the course of the last century, mean rainfall anomaly measurements in Australia have fluctuated.

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.

In 2024, the mean temperature deviation in Australia was 1.46 degrees Celsius higher than the reference value for that year, indicating a positive anomaly. Over the course of the last century, mean temperature anomaly measurements in Australia have exhibited an overall increasing trend. Temperature trending upwards Global land temperature anomalies have been fluctuating since the start of their measurement but show an overall upward tendency. Australian mean temperatures have followed this trend and continued to rise as well. Considered the driest inhabited continent on earth, this has severe consequences for the country. In particular, the south of Australia is predicted to become susceptible to drought, which could lead to an increase in bushfires as well. The highest temperatures recorded in Australia as of 2022 were measured in South Australia and Western Australia, both exceeding 50 degrees. The 2019/2020 bushfire season Already prone to wildfires due to its dry climate, the change in temperature has made Australia even more vulnerable to an increase in bushfires. One of the worst wildfires in Australia, and on a global level as well, happened during the 2019/2020 bushfire season. The combination of the hottest days and the lowest annual mean rainfall in 20 years resulted in a destruction of 12.5 million acres. New South Wales was the region with the largest area burned by bushfires in that year, a major part of which was conservation land.

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

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.

The Australian Rainfall Trend Explorer is an interactive web application and a simple tool to query rainfall data for selected weather stations and selected agricultural production areas in Australia. The tool assists identification of statistically significant long-term trends in annual, seasonal and extreme rainfall between 1907 and 2018. The selected agricultural production areas are the Western Australia Wheat Belt, the Northern Murray Darling Basin and the coastal areas of southern Queensland and northern New South Wales. They span across important cropping, horticulture, and livestock production zones and different climate zones. Gross value of production in these three areas together accounts for approximately 30% of the national total. The selected weather stations represent different rainfall zones in each study area. The objective of the Australian Rainfall Trend Explorer is to compare year-to-year variability in precipitation with any potential long-term trend and to understand if recent experience of a drying trend in parts of Australia are part of a longer-term trend.

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

In 2023, the observed annual average mean temperature in Australia reached 22.32 degrees Celsius. Overall, the annual average temperature had increased compared to the temperature reported for 1901. Impact of climate change The rising temperatures in Australia are a prime example of global climate change. As a dry country, peak temperatures and drought pose significant environmental threats to Australia, leading to water shortages and an increase in bushfires. Western and South Australia reported the highest temperatures measured in the country, with record high temperatures of over 50°C in 2022. Australia’s emission sources While Australia has pledged its commitment to the Paris Climate Agreement, it still relies economically on a few high greenhouse gas emitting sectors, such as the mining and energy sectors. Australia’s current leading source of greenhouse gas emissions is the generation of electricity, and black coal is still a dominant source for its total energy production. One of the future challenges of the country will thus be to find a balance between economic security and the mitigation of environmental impact.

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

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

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)

(From http://www.worldclim.org/methods) - For a complete description, see:

Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.

The data layers were generated through interpolation of average monthly climate data from weather stations on a 30 arc-second resolution grid (often referred to as 1 km2 resolution). Variables included are monthly total precipitation, and monthly mean, minimum and maximum temperature, and 19 derived bioclimatic variables.

The WorldClim interpolated climate layers were made using: * Major climate databases compiled by the Global Historical Climatology Network (GHCN), the FAO, the WMO, the International Center for Tropical Agriculture (CIAT), R-HYdronet, and a number of additional minor databases for Australia, New Zealand, the Nordic European Countries, Ecuador, Peru, Bolivia, among others. * The SRTM elevation database (aggregeated to 30 arc-seconds, 1 km) * The ANUSPLIN software. ANUSPLIN is a program for interpolating noisy multi-variate data using thin plate smoothing splines. We used latitude, longitude, and elevation as independent variables.

This report analyses the level of annual rainfall in Australia. This is an average rate over the whole country, including desert areas. The data for this report is sourced from the Bureau of Meteorology (BOM) and is measured in millimetres per financial year.

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.

Mean annual gridded water balance estimates (2001-2010) derived from mean annual AET and mean annual rainfall data for the Lower South East of South Australia. The data is made available as ArcInfo …Show full descriptionMean annual gridded water balance estimates (2001-2010) derived from mean annual AET and mean annual rainfall data for the Lower South East of South Australia. The data is made available as ArcInfo ASCII Grid format. Although the associated metadata is public, the files (if any) have not been approved for general release. Please phone or email the contact person for this collection to discuss access to the files.