2.6.1.1.1 Background and context

The numerical surface water modelling in a bioregional assessment 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 subregion to inform the impact and risk analysis reported in product 3-4. Outputs from the surface water modelling are also used as inputs to product 2.7 (receptor impact modelling) to facilitate evaluation of the cumulative impacts of mining on water-dependent ecological and economic assets.

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.’

To generate these ensembles of predictions, a large number of model parameter sets are evaluated for the surface water and groundwater models. The range of parameters reflects both the natural variability of the system and the uncertainty in the understanding of the system. 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 2.6.1.3 on model development. The uncertainty analysis in Section 2.6.1.5 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 over estimated rather than under estimated. As long as it can be shown that an assumption over estimates – not under estimates – 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 subregion), the assumptions may need to be revisited.

Another advantage of this probabilistic 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 Hunter subregion (Section 2.6.1.1.2), focusing on the interaction between the surface water and groundwater model, followed with a review of the existing surface water models (Section 2.6.1.2). Section 2.6.1.3 and Section 2.6.1.4 describe the development of the model and its calibration. Next is the uncertainty analysis (Section 2.6.1.5), which contains the justification of assumptions and the resulting ensembles of predicted impacts. The product concludes by describing the predictions arising from the surface water model (Section 2.6.1.6).