# 2.6.2.1.1 Background and context

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The numerical groundwater modelling has a very specific objective: to probabilistically evaluate potential hydrological change in the coal resource development pathway (CRDP) relative to the baseline at specified locations in the landscape to inform the impact and risk analysis reported in product 3-4 (impact and risk analysis).

In confined groundwater systems, and to an extent in unconfined systems, the response in groundwater level or flux is linear with respect to the change in stress – that is, a doubling of the pumping rate will result in a doubling of drawdown (Reilly et al., 1987; Rassam et al., 2004). If a system behaves linearly, it means that changes are additive, which is known as the principle of superposition (Reilly et al., 1987). The biggest implication of this is that the change to the system due to a change in stress is largely independent of current or initial conditions. The most well-known example is the interpretation of a pumping test; the drawdown is only a function of the hydraulic properties of the aquifer, not of the initial conditions.

While the validity of the principle of superposition will be evaluated, it does enable the modelling to focus on the change in hydrogeological stress and the hydraulic properties, rather than on reproducing historical conditions or predicting future-state variables of the system, such as groundwater levels or fluxes.

The probabilistic aspect of the analysis implies that modelling does not provide a single best estimate of the change, but rather an ensemble of estimates. This ensemble enables statements such as:

• ‘In 95% of the simulations, the change at location x,y does not exceed z.’
• ‘The probability of exceeding a drawdown of 5 m at location x,y is p%.’

To generate these ensembles of predictions, a large number of model parameter sets will be evaluated for the numerical modelling. The range of parameters reflects both the natural variability of the system and the uncertainty in the understanding of the system as of July 2015. During the uncertainty analysis, these parameter combinations are filtered in such a way that only those that are consistent with the available observations and the understanding of the system are used to generate the ensemble of predictions. The details are documented in companion submethodology M09 (as listed in Table 1) for propagating uncertainty through models (Peeters et al., 2016).

It is not possible to capture all uncertainty of the understanding of the system in the parameterisation of the numerical models, so it is inevitable that there will be a number of assumptions and model choices necessary to create the models. These assumptions are introduced and briefly discussed in Section 2.6.2.3 about model development. The qualitative uncertainty analysis in Section 2.6.2.8.1 further provides a systematic and comprehensive discussion of these assumptions. This discussion focuses on the rationale behind the assumptions and the effect on the predictions.

The latter is crucial in justifying assumptions. In the numerical modelling the precautionary principle is adopted: impacts are overestimated rather than underestimated. As long as it can be shown that an assumption overestimates – not underestimates – impacts, the assumption is considered valid for the specific purpose of this modelling.

However, an overly conservative estimate of impact is not desirable either. If there are sound reasons to believe that predicted impacts are deemed unrealistically high (e.g. in comparison to earlier modelling efforts in the bioregion) or in excess of legally defined thresholds (such as the specified drawdown thresholds in the NSW aquifer interference policy), the assumptions may need to be revisited.

Another advantage of this stochastic modelling approach is that it enables a comprehensive sensitivity analysis to identify the model parameters or aspects of the system that are most influential on the predictions – and others that have little or no effect on the predictions. This information can guide future data collection and model development or inform the regulatory process.

This product starts with an overview of the methods as applied to the Maranoa-Balonne-Condamine subregion (Section 2.6.2.1.2), focusing on the numerical modelling requirements for BA and the groundwater modelling approach used for this Assessment, followed by a review of the existing groundwater models (Section 2.6.2.2). Section 2.6.2.3 to Section 2.6.2.7 describe the development of the model, its parameterisation, the available observations, required predictions and sensitivity of the model parameters to observations and predictions. Next is the uncertainty analysis (Section 2.6.2.8), which contains the justification of assumptions and the resulting ensembles of predicted impacts. The product concludes by describing the limitations and conclusions (Section 2.6.2.9).

Last updated:
17 October 2018

2016

## ASSESSMENT

ASSESSMENT COMPONENT