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GLO AEM dmax v01

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 uses the results of the design of experiment runs of the analytic element groundwater model of the Gloucester to train emulators to (a) constrain the prior parameter ensembles into the posterior parameter ensembles and to (b) generate the predictive posterior ensembles of maximum drawdown and time to maximum drawdown. This is described in product 2.6.2 Groundwater numerical modelling (Peeters et al. 2016).

Peeters L, Dawes W, Rachakonda P, Pagendam D, Singh R, Pickett T, Frery E, Marvanek S, and McVicar T (2016) Groundwater numerical modelling for the Gloucester subregion. Product 2.6.2 for the Gloucester subregion from the Northern Sydney Basin Bioregional Assessment. Department of the Environment, Bureau of Meteorology, CSIRO and Geoscience Australia, Australia. http://data.bioregionalassessments.gov.au/product/NSB/GLO/2.6.2.

Dataset History

This dataset contains all the scripts to carry out the uncertainty analysis for the maximum drawdown and time to maximum drawdown at the groundwater receptors used in the Gloucester groundwater analytical element model and all the resulting posterior predictions. This is described in product 2.6.2 Groundwater numerical modelling (Peeters et al. 2016).

The file 'GLO_AEM_dmax_UA_workflow.pptx' shows a diagram of the workflow of the uncertainty analysis.

The post-processed results from the numerical modelling (dataset GLO AEM Model v01) are summarised in file 'GLO_AEM_10kruns.csv'. Python script 'GLO_AEM_dmax_Predictions.py' creates four input files for the uncertainty analysis:

  • 'GLO_AEM_dmax_Predictions.csv': summary of the maximum drawdown (dmax) predictions at the receptors - Name, Transform, Emulate, Min, Max, Median

  • 'GLO_AEM_tmax_Predictions.csv': summary of the time to maximum drawdown (tmax) predictions at the receptors - Name, Transform, Emulate, Min, Max, Median

  • 'GLO_AEM_dmax_DoE_Predictions.csv': simulated maximum drawdown values at receptor locations corresponding to the parameter combinations in 'GLO_DoE_Parameters.csv'

  • 'GLO_AEM_tmax_DoE_Predictions.csv': simulated time to maximum drawdown values at receptor locations corresponding to the parameter combinations in 'GLO_DoE_Parameters.csv'

File 'GLO_DoE_Parameters.csv' contains the 10.000 parameter combinations used in the design of experiment, generated with the maximin latin hypercube sampling.

File 'GLO_AEM_dmax_Parameters.csv' contains information on the parameters of the Gloucester AEM model that are varied in the uncertainty analysis. This is the name, transform, minimum, maximum, description, the type of prior distribution, the first and second moments of the prior distribution and the covariance. These values are specified by the modelling team and described in section 2.6.2.6 in Peeters et al. (2016).

The script 'GLO_AEM_SensitivityAnalysis.py' calculates the Plischke et al (2013) sensitivity index for all HRV-receptor-parameter combination, which is stored in files 'GLO_AEM_dmax_SI.csv' and 'GLO_AEM_tmax_SI.csv'.

For each prediction of tmax and dmax an emulator is created with R-scripts 'GLO_AEM_dmax_Emulate_pred_i.R' and 'GLO_AEM_dmax_Emulate_pred_i.R'. The script uses the R-scripts in dataset 'R-scripts for uncertainty analysis v01' and stores an R data object with extension .Rdata in subdirectory 'emulators/predictionname'.

The R script 'GLO_AEM_dmax_CreatePosteriorParameters.R' performs the Approximate Bayesian Computation Markov Chain Monte Carlo to constrain the prior parameter distributions with an acceptance criterion based on the maximum coal seam gas water production rate. This model result is summarised in file 'GLO_AEM_Qcsg.csv' and all results of the design of experiment are stored in 'GLO_AEM_Qcsg_DoE.csv'. The script results in an emulator object (file '\emulator\GLO_Qcsg\GLO_Qcsg.RData') and a spreadsheet with 10.000 posterior parameter combinations (file 'GLO_AEM_dmax_Posterior_Parameters.csv'). The first 200 of these parameter combinations form file 'GLO_AEM_Posterior_interpol.csv' that is used to generate the output for interpolation of exceedance probabilities.

The R scripts 'GLO_AEM_tmax_MCsampler_pred_i.R' and 'GLO_AEM_dmax_MCsampler_pred_i.R' use the emulators for each prediction to sample the posterior parameters to create the posterior predictive ensembles, which are stored for each prediction separately in '\emulators\predname_prediction.csv'

Post-processing script 'GLO_AEM_dmax_Postprocess_Predictions.py' combines these individual files into two posterior prediction files: 'GLO_AEM_dmax_Posterior_Predictions.csv' and 'GLO_AEM_tmax_Posterior_Predictions.csv'.

These files are further summarised in exceedance probabilities with script 'GLO_AEM_dmax_probabilities.py', which outputs files 'GLO_AEM_dmax_ExceedanceProbabilities.csv' and 'GLO_AEM_tmax_ExceedanceProbabilities.csv'

References

Peeters L, Dawes W, Rachakonda P, Pagendam D, Singh R, Pickett T, Frery E, Marvanek S, and McVicar T (2016) Groundwater numerical modelling for the Gloucester subregion. Product 2.6.2 for the Gloucester subregion from the Northern Sydney Basin Bioregional Assessment. Department of the Environment, Bureau of Meteorology, CSIRO and Geoscience Australia, Australia. http://data.bioregionalassessments.gov.au/product/NSB/GLO/2.6.2.

Plischke E, Borgonovo E, and Smith CL (2013) Global sensitivity measures from given data European Journal of Operational Research 226, 536-550

Dataset Citation

Bioregional Assessment Programme (XXXX) GLO AEM dmax v01. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/c54640db-ca88-4ed2-8e58-6b1a198293c5.

Dataset Ancestors

Data and Resources

Additional Info

Field Value
Title GLO AEM dmax v01
Type Dataset
Language eng
Licence Creative Commons Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/, (c) Commonwealth of Australia (Bioregional Assessment Programme http://www.bioregionalassessments.gov.au), (c) R.W. Corkery & Co Pty Limited., (c) Yancoal Australia Ltd, (c) State of New South Wales (Office of Water), (c) Commonwealth of Australia (Geoscience Australia) 2014, (c) Commonwealth of Australia (Department of the Environment) 2014, (c) Commonwealth of Australia (Bureau of Meteorology), (c) Australian Heritage Council, Australian Government Department of the Environment, (c) Commonwealth of Australia (Bureau of Meteorology) 2012, (c) NSW Department of Planning and Infrastructure, (c) Commonwealth of Australia (Department of the Environment), (c) AGL Energy Ltd, (c) State of New South Wales (Department of Trade and Investment, Regional Infrastructure and Services), (c) NSW Department of Planning and Environment
Data Status NONE
Update Frequency NONE
Landing Page https://data.gov.au/data/dataset/9c5e6f60-797b-49ae-976e-2c835377d6aa
Date Published 2018-07-10
Date Updated 2019-11-20
Contact Point
Bioregional Assessment Program
bioregionalassessments@environment.gov.au
Temporal Coverage N/A - N/A
Geospatial Coverage POLYGON ((0 0, 0 0, 0 0, 0 0))
Jurisdiction New South Wales
Data Portal data.gov.au
Publisher/Agency Bioregional Assessment Program