Development of a Groundwater Flow Model - Berry Springs
E-Publications; E-Books; PublicationNT; 17/2016
Made available via the Publications (Legal Deposit) Act 2004 (NT).
Table of Contents -- List of Figures -- List of Tables -- Acknowledgements -- Glossary of Terms -- Executive Summary -- 1 Introduction -- 1.1 Background -- 1.1 Aim of the study -- 2 Site Description -- 2.1 Study area location -- 2.2 Climate -- 2.2.1 Rainfall data -- 2.2.2 Evaporation data -- 2.3 Hydrology -- 2.4 Land use -- 2.5 Groundwater extraction -- 2.6 Water quality -- 3 Hydrogeology -- 3.1 Geological formations -- 3.1.1 Mount Bonnie Formation (Pso) -- 3.1.2 Unnamed Dolostone Unit (Psd): Berry Springs Dolostone -- 3.1.3 Burrell Creek Formation (Pfb) -- 3.1.4 Depot Creek Formation (Ptd) -- 3.1.5 Petrel Formation (JKp) -- 3.1.6 Darwin Member (Kld) -- 3.2 Geological structure -- 3.3 Aquifer characteristics -- 3.3.1 Hydraulic conductivity -- 3.3.2 Storage coefficient -- 4 Groundwater hydrology -- 4.1 Groundwater flow -- 4.2 Recharge -- 4.2.1 Water balance method -- 4.2.2 Water table fluctuation method -- 4.2.3 Spring discharge -- 4.2.4 Evapotranspiration -- 4.3 Rainfall-runoff modelling -- 4.4 Predicted natural conditions compared to recent observed flows -- 4.5 Groundwater chemistry -- 5 Available data -- 5.1 Climate data -- 5.2 SRTM digital terrain model -- 5.3 Geological data -- 5.4 Groundwater level data -- 5.4.1 Steady state groundwater levels -- Berry Springs Groundwater Flow Model -- 5.4.2 Time series groundwater levels -- 5.5 River discharge data -- 5.5.1 Manual gauging data -- 5.5.2 Continuous recorder data -- 5.6 Pumping data -- 5.7 Data gaps -- 6 Groundwater flow model development -- 6.1 What is a groundwater flow model? -- 6.2 Conceptual model -- 6.3 Modelling approach -- 6.4 Model package -- 6.5 Model mesh geometry -- 6.5.1 Mesh design -- 6.5.2 Mesh generation -- 6.6 Material properties -- 6.7 Fracture flow -- 6.8 Boundary conditions -- 6.8.1 Recharge and Areal ET Flux -- 6.8.2 Constant head BC values -- 6.9 Pumping data -- 6.10 FEFLOW settings -- 6.10.1 Problem class -- 6.10.2 Temporal and control data -- 7 Calibration -- 7.1 Steady state finite element model -- 7.1.1 Steady state model results -- 7.2 Transient finite element model -- 8 Scenarios -- 8.1 Water balance assessment -- 8.2 Scenario A – Historic climate without pumping -- 8.2.1 Water balance under historic climate -- 8.3 Scenario B – Historic climate with current pumping estimates -- 8.3.1 Pumping estimate methodology -- 8.3.2 Water balance under historic climate and current pumping -- 8.3.3 Impacts of pumping on groundwater discharge at Berry Springs -- 8.3.4 Flow duration -- 9 Results and discussion -- 9.1 Measurable impacts -- 9.1.1 Reduced dry season flows -- 9.1.2 Recession slope of dry season flows -- 9.1.3 Groundwater levels -- 9.2 Rainfall, recharge & minimum flows analysis -- 9.3 Impacts of pumping based on zones -- 10 Conclusions -- 10.1 Key performance indicators -- 11 References -- Appendix A - Groundwater level hydrographs -- Appendix B - Calibrated transient model results
Berry Springs Dolostone; Berry Springs aquifer System; Groundwater Flow Model
Department of Land Resource Management
72 pages : colour illustration and maps ; 30 cm.
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Berry Springs Groundwater Flow Model Page 68 of 72 Appendix D FEFLOW Model Optimisation Using Parameter ESTimation (PEST) 1.4 Introduction The hydraulic parameters used in the calibrated model were generated using the PEST (Parameter ESTimation software (Doherty, 2004). PEST is a model independent parameter optimizer that uses the Gauss Marquardt Levenberg non linear estimation technique. It is adapted to run the existing model. The purpose of PEST (which is an acronym for Parameter ESTimation) is to assist in data interpretation, model calibration and predictive analysis. PEST will adjust model parameters and/or excitations until the fit between model outputs and laboratory or field observations is optimised in the weighted least squares sense. Where parameter values inferred through this process are nonunique, PEST will analyse the repercussions of this nonuniqueness on predictions made by the model. The universal applicability of PEST lies in its ability to perform these tasks for any model that reads its input data from one or a number of ASCII (ie. text) input files and writes the outcomes of its calculations to one or more ASCII output files. It should be noted that the parameter estimation process does not provide an estimate of systematic error, ie that error associated with an oversimplified conceptual model and model design. If, however, measured values cannot be matched, it is a sign of a wrong conceptual model. For the model calibration, PEST program was used in this study. PEST is a nonlinear parameter estimation and optimization package, and is one of the most recently developed systems offering model independent optimization routines (Doherty and Johnston, 2003). It applies a robust GaussMarquardt Levenberg algorithm, which combines the advantages of the inverse Hessian method and the steep descent method and therefore provides faster and more efficient convergence towards the objective function minimum. The best set of parameters is selected from within reasonable ranges by adjusting the values until the discrepancies between the model generated values and those measured in the field is reduced to a minimum in the weighted least squares sense. Due to its model independent characteristic, PEST can be used easily to estimate parameters in an existing computer model, and can estimate parameters for one or a series of models simultaneously. Since its development, PEST has gained extensive use in many different fields. The specifications of the calibration algorithm include model parameterization, the selection of calibration parameters, defining feasible parameter variation range, assigning prior information to a parameter group, assigning weights to members of the observation groups. 1.5 Parameter estimation process The inputs to PEST are the observed and simulated groundwater levels and discharges and the outputs are the new model parameters. To achieve integration between Parallel PEST and FEFLOW several utility programs were required. Discharge was exported from the model by an IFM DLL module using grouped flux from specified observation point groups along each of the rivers. It should be noted that to correctly report the flux all the nodes on each of the slices with constant head BCs need to be defined in the same observation group. The PEST parallellisation was accomplished using 5 computers. A master PC with 2 xeon 3.0 GHz OS windows xp, 2 slave 2.66 GHz quad-core windows xp x64 OS based PCs and 2 slave 2.4 GHz quad core windows xp x86 OS based PCs. The computers were connected via a TP-LINK fast Ethernet switch. Model runs generally took 120-150 minutes to complete.
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