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.

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.

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.

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.

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.

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.

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 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

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. 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

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.

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.

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.

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.

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)

Australia Average Precipitation in Depth data was reported at 534.000 mm/Year in 2020. This stayed constant from the previous number of 534.000 mm/Year for 2019. Australia Average Precipitation in Depth data is updated yearly, averaging 534.000 mm/Year from Dec 1961 (Median) to 2020, with 60 observations. The data reached an all-time high of 534.000 mm/Year in 2020 and a record low of 534.000 mm/Year in 2020. Australia Average Precipitation in Depth data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Environmental: Land Use, Protected Areas and National Wealth. Average precipitation is the long-term average in depth (over space and time) of annual precipitation in the country. Precipitation is defined as any kind of water that falls from clouds as a liquid or a solid.;Food and Agriculture Organization, electronic files and web site.;;

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.

A selection of 9sec gridded National climate change variables for biodiversity modelling. This collection represents 30-year averages centred on each of 1990, 2050, 2070, 2090. Projected future climates were generated by applying within-model changes for two circulation model outputs: GFDL and ACCESS1.0; and for two representative concentration pathways (RCP 4.5, 8.5), calculated at the native general circulation model grid resolution to these current surfaces, using ANUCLIM 6.1 prior to radiative adjustment. That the maximum temperature variables have been adjusted for topographic slope/aspect and shading effects. A short methods summary is provided in the file 9sClimateMethodsSummary.pdf for further information, including a nomenclature for files. The selected climate variables provided in this collection are: TNM - mean annual minimum temperature TXM - mean annual maximum temperature TXX - mean maximum monthly maximum temperature TXI - mean minimum monthly maximum temperature TNI - mean minimum monthly minimum temperature TNX - mean maximum monthly minimum temperature PTA - Average total annual rainfall PTX - mean maximum monthly rainfall PTI - mean minimum monthly rainfall Other variables (evaporation and water balance, temperature range, and seasonality, etc) are available upon application. The data are provided in ESRI binary float grid format (*.hdr, *.flt), Projection is geographic GDA94. Lineage: Climate surfaces for the present were based on the ANUCLIM 6.1 (Xu and Hutchinson, 2011) 30 year average climate surfaces for Australia (1976-2005), with elevational lapse rate correction applied over the 9s GEODATA digital elevation model (Hutchinson et al , 2008). Radiative correction derived from the same DEM was applied to radiation and maximum temperature before calculation of evaporation, using the CSIRO TerraFormer software. Summary statistics for each variable were then calculated including variables described in Williams et al (2012: Which environmental variables should I use in my biodiversity model? International Journal of Geographic Information Sciences 26(11), 2009-2047. DOI: 10.1080/13658816.2012.698015.). Details are given in the short summary report by Tom Harwood, Noboru Ota, Justin Perry, Kristen Williams, Ian Harman, Simon Ferrier (2014) gridded continental climate variables for Australia: November 2014. CSIRO Land and Water, Canberra. Attached with the collection. Key published references: Reside AE, VanDerWal J, Phillips B, Shoo L, Rosauer D, Anderson BA, Welbergen J, Moritz C, Ferrier S, Harwood TD, Williams KJ, Mackey B, Hugh S and Williams SE (2013) Climate change refugia for terrestrial biodiversity: Defining areas that promote species persistence and ecosystem resilience in the face of global climate change. National Climate Change Adaptation Research Facility, Griffith University, Gold Coast, Qld. Xu T and Hutchinson MF (2013) New developments and applications in the ANUCLIM spatial climatic and bioclimatic modelling package. Environmental Modelling & Software 40(0), 267-279. DOI: http://dx.doi.org/10.1016/j.envsoft.2012.10.003. ACCESS: Bi D, Dix M, Marsland SJ, O’Farrell S, Rashid HA, Uotila P, Hirst AC, Kowalczyk E, Golebiewski M, Sullivan A, Yan H, Hannah N, Franklin C, Sun Z, Vohralik P, Watterson I, Zhou X, Fiedler R, Collier M, Ma Y, Noonan J, Stevens L, Uhe P, Zhu H, Griffies SM, Hill R, Harris C and Puri K (2013) The ACCESS coupled model: description, control climate and evaluation. Australian Meteorological and Oceanographic Journal 63(1), 41-64. GFDL: Dunne JP, John JG, Shevliakova E, Stouffer RJ, Krasting JP, Malyshev SL, Milly PCD, Sentman LT, Adcroft AJ, Cooke W, Dunne KA, Griffies SM, Hallberg RW, Harrison MJ, Levy H, Wittenberg AT, Phillips PJ and Zadeh N (2013) GFDL’s ESM2 global coupled climate–carbon earth system models. Part II: Carbon system formulation and baseline simulation characteristics. Journal of Climate 26(7), 2247-2267. DOI: 10.1175/JCLI-D-12-00150.1.

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 long term average diffuse recharge rate for the Clarence-Morton Basin was determined using upscaled point estimates from chloride mass balance for the porous rock areas and empirical relationships for the alluvial areas. The chloride mass balance is a simple method of estimating recharge because it only relies upon knowing the chloride deposition from rainfall and the chloride concentration of the groundwater. It can be used if all sources of chloride can be accounted for (in this case assumed to come from rainfall). Where the source of chloride could also be from interactions from the stream network or upward flow from deeper layers the empirical equations developed by Wohling et al (2012, HESS) were used for estimating recharge.

Point estimates of recharge were made using the chloride mass balance and split into three groups, Tertiary Volcanics, Walloon Coal Measures and everything else. The data were sparse and did not have a complete geographical coverage of the area of interest (374 point estimates), so the recharge estimates had to be upscaled using annual average rainfall as a co-variate rather than by interpolation. To achieve this, relationships were developed between the average annual rainfall and log transformed average annual recharge.

The empirical equations developed by Wohling et al (2012, HESS) are based on field estimates of recharge from across Australia, they relate average annual recharge to average annual rainfall, vegetation type and soil type.

Purpose

The purpose of this recharge data set is as an input into the numerical groundwater modelling for the Clarence-Morton Basin.

Dataset History

The chloride in groudwater data was sourced from the 'CLM - Bore water quality NSW' and 'CLM - Bore water quality QLD' datasets, this data was accepted for further processing if it was in an outcrop area (i.e. the stratigaphic layer that the screen was in was the same as the surface geology). Where there were multiple chloride analyses in the same bore over time, the geometric mean of the the samples was taken. The chloride deposition data was sourced from the 'Australian 0.05º gridded chloride deposition' dataset, the chloride deposition was extracted from this raster for each of the locations with chloride in groundwater data. The point scale recharge is calculated as R = D / Cg where R is the recharge, D is the chloride deposition and Cg is the chloride in groundwater. The point scale recharge estimates were split into three groups based upon the surface geology, these groups were the Tertiary Volcanics, Walloon Coal Measures and everything else.

As the point estimates of recharge were too sparse to be interpolated directly, they were upscaled using average annual rainfall as a co-variate in each of the three surface geology groups. The rainfall data was sourced from 'BOM, Australian Average Rainfall Data from 1961 to 1990'. The upscaling was achieved using the equation log(R) = a.P + b where log(R) is the log transform of the average annual recharge, P is the average annual rainfall and a and b are fitting parameters that were fitted using a least squares regression. The surface geology and rainfall were transformed to a regular 100 m grid and with the regression equation for each surface geology group a regular 100 m grid of average annual recharge was created.

Dataset Citation

Bioregional Assessment Programme (2015) CLM - Groundwater Recharge Estimates - Chloride Mass Balance technique v01. Bioregional Assessment Derived Dataset. Viewed 09 October 2017, http://data.bioregionalassessments.gov.au/dataset/41ad36ae-9399-439d-9e1c-ec55fa2058c4.

Dataset Ancestors

• Derived From CLM - Bore water quality QLD

• 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)

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