Development of a Groundwater Model for the Western Davenport Plains
Knapton, Anthony; CloudGMS Pty Ltd
Northern Territory. Department of Environment, Parks and Water Security
E-Publications; E-Books; PublicationNT; WRD Technical Report 27/2017
Western Davenport Water Control District
CloudGMS has been commissioned by DENR to develop a numerical groundwater model of the aquifers within the central area of the WDWCD to improve confidence in the sustainability of the groundwater resources, as this is the area within the WCD with greatest potential for intensive development.
Made available by via Publications (Legal Deposit) Act 2004 (NT); Prepared for Dept Environment and Natural resources
Executive summary -- 1 Background -- 2 Physical -- 3 Available data -- 4 Conceptual model -- 5 Model design & construction -- 6 Parameter estimation -- 7 Water balances -- 8 Sensitivity analysis -- 9 Predictive scenarios -- 10 Conclusions -- 11 Reference -- 12 Document history and version control -- Appendix A - Groundwater level hydrographs - Appendix B - Alek range horticultural farm sub-regional modelling
Groundwater; Northern Territory; Western Davenport Water Control District; Conceptual mode
Northern Territory Governmnet
WRD Technical Report 27/2017
ix, 127 pages : colour illustration and maps ; 30 cm
Attribution International 4.0 (CC BY 4.0)
Northern Territory Government
https://hdl.handle.net/10070/842058 [LANT E-Publications: Development of a Groundwater Model for the Western Davenport Plains, version 1.1]
Western Davenport WCD Groundwater Model (v2.0) Parameter Estimation CloudGMS 87 Table 23 Recommended groundwater model performance measures (after Barnett, 2012) Performance measure Criterion Model convergence The model must converge in the sense that the maximum change in heads between iterations is acceptably small. The iteration convergence criterion should be one or two orders of magnitude smaller than the level of accuracy required in head predictions, typically, of the order of centimetres or millimetres. Water balance The model must demonstrate an accurate water balance, at all times and in steady state. The water balance error is the difference between total predicted inflow and total predicted outflow, including changes in storage, divided by either total inflow or outflow and expressed as a percentage. A value less than 1% should be achieved and reported at all times and cumulatively over the whole simulation. Ideally the error should be much less. An error of >5% would be unacceptable, and usually indicates some kind of error in the way the model has been set up. Qualitative measures The model results must make sense and be consistent with the conceptual model. Contours of heads, hydrographs and flow patterns must be reasonable, and similar to those anticipated, based either on measurements or intuition. Estimated parameters must make sense and be consistent with the conceptual model and with expectations based on similar hydrogeological systems. Qualitative measures apply during calibration, when comparisons can be made with historical measurements, but also during predictions, when there is still a need for consistency with expectations. There is no specific measure of success. A subjective assessment is required as to the reasonableness of model results, relative to observations and expectations. The modeller should report on relevant qualitative measures and discuss the reasons for consistency and inconsistency with expectations. Quantitative measures The goodness of fit between the model and historical measurements can be quantified, using statistics such as RMS, SRMS, MSR and SMSR for trial-anderror calibration and the objective function in automated calibration. Quantitative measures only apply during calibration. Statistics of goodness of fit are useful descriptors but should not necessarily be used to define targets. Targets such as SRMS < 5% or SRMS < 10% may be useful if a model is similar to other existing models and there is good reason to believe that the target is achievable. Even if a formal target is not set, these measures may provide useful guides. 6.7.1. Model convergence Section 5.2.6 documents that the iteration convergence criterion (maximum head change per iteration) was set at 0.0001 m (or 0.1 mm). This criterion is two orders of magnitude smaller than the estimated accuracy of the observed head measurements. On completion of the transient model runs, the model log was queried to ensure all iterations converged. 6.7.2. Qualitative measures The final estimated parameters are considered to be consistent with the conceptual model and with expectations based on similar hydrogeological systems. The modelled water budget is also considered to be consistent with the conceptual model, although the volume in storage is considerably greater than the previous estimates as discussed further below. The contours of heads, hydrographs and flow patterns are reasonable, and similar to those anticipated, based on observed measurements. Generally, the absolute modelled groundwater levels are in good agreement with the observed values. Long term trends in the groundwater levels are generally reproduced.