Abstract
This dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.
The dataset contains the predictions of maximum drawdown and time to maximum drawdown at all groundwater model nodes in the Namoi subregion, constrained by the observations of groundwater level, river flux and mine water production rates. The dataset also contains the scripts required for and the results of the sensitivity analysis. The dataset contains all the scripts to generate these results from the outputs of the groundwater model (Namoi groundwater model dataset) and all the spreadsheets with the results. The methodology and results are described in Janardhanan et al. (2017)
References
Janardhanan S, Crosbie R, Pickett T, Cui T, Peeters L, Slatter E, Northey J, Merrin LE, Davies P, Miotlinski K, Schmid W and Herr A (2017) Groundwater numerical modelling for the Namoi subregion. Product 2.6.2 for the Namoi subregion from the Northern Inland Catchments Bioregional Assessment. Department of the Environment and Energy, Bureau of Meteorology, CSIRO and Geoscience Australia, Australia., http://data.bioregionalassessments.gov.au/product/NIC/NAM/2.6.2.
Dataset History
The workflow that underpins this dataset is captured in 'NAM_MF_UA_workflow.png'.
Spreadsheet NAM_MF_dmax_Predictions_all.csv is sourced from dataset 'Namoi groundwater model' and contains the name, coordinates, Bore_ID in the model, layer number, the name of the objective function and the minimum, maximum, median, 5th percentile and 95th percentile of the design of experiment runs of maximum drawdown (dmax) for each groundwater model node. The individual results for each node for each run of the design of experiment is stored in spreadsheet 'NAM_MF_dmax_DoE_Predictions_all.csv' The equivalent files for time to maximum drawdown (tmax) are 'NAM_MF_tmax_Predictions_all.csv' and 'NAM_MF_tmax_DoE_Predictions_all.csv'.
These files are combined with the file 'NAM_MF_Observations_all.csv', which contains the observed values for groundwater levels, mine dewatering rates and river flux, and the files NAM_MF_dist_hobs.csv, NAM_MF_dist_rivers.csv, NAM_MF_dist_mines.csv, which contain the distances of the predictions to each mine, groundwater level observation and river, in python script 'NAM_MF_datawranling.csv'. This script selects only those predictions where the 95th percentile of dmax is less than 1 cm for further analysis. The subset of predictions is stored in 'NAM_MF_dmax_Predictions.csv','NAM_MF_tmax_Predictions.csv', 'NAM_MF_dmax_DoE_Predictions.csv','NAM_MF_tmax_DoE_Predictions.csv'. The output spreadsheet 'NAM_MF_Observations.csv' has the observations and the distances to the selected predictions.
As the simulated equivalents to the observations are part of the predictions dataset, these files are combined in python script NAM_MF_OFs.py to generate the objective function values for each run and each prediction. The objective function values are weighted sums of the residuals, stored in NAM_MF_DoE_hres.csv, NAM_MF_DoE_mres.csv, NAM_MF_DoE_rres.csv, according to the distance to the predictions and the results are stored in NAM_MF_DoE_OFh.csv, NAM_MF_DoE_OFm.csv, NAM_MF_DoE_OFr.csv. The threshold values for each objective function and prediction are stored in NAM_MF_OF_thresholds.csv. Python script NAM_MF_OF_wrangling.py further post-processes this information to generate the acceptance rates, saved in spreadsheet NAM_MF_dmax_Predictions_ARs.csv
Python script NAM_MF_CreatePosterior.py selects the results from the design of experiment run that satisfy the acceptance criteria. The results form the posterior predictive distributions stored in NAM_MF_dmax_Posterior.csv and NAM_MF_tmax_Posterior.csv. These are further summarised in NAM_MF_Predictions_summary.csv.
The sensitivity analysis is done with script NAM_MF_SI.py, which uses the results of the design of experiment together with the parameter values, stored in NAM_MF_DoE_Parameters.csv and their description (name, range, transform) in NAM_MF_Parameters.csv. The resulting sensitivity indices for dmax, tmax and river, head and minewater flow observations are stored in NAM_MF_SI_dmax.csv, NAM_MF_SI_tmax.csv, NAM_MF_SI_river.csv, NAM_MF_SI_mine.csv and NAM_MF_SI_head.csv. The intermediate files, ending in xxxx, are the results grouped per 100 predictions. The scripts NAM_MF_SI_collate.py and NAM_MF_SI_collate.slurm collate these.
Dataset Citation
Bioregional Assessment Programme (2017) Namoi groundwater uncertainty analysis. Bioregional Assessment Derived Dataset. Viewed 11 December 2018, http://data.bioregionalassessments.gov.au/dataset/36bd27e9-58d2-4bf2-8e4a-54b22ac98cfb.
Dataset Ancestors
JSON
RDF
ISO19115/ISO19139 XML