3.7.4 Gaps, limitations and opportunities

This impact and risk analysis allows governments, industry and the community to focus on areas that are potentially impacted when making regulatory, water management and planning decisions. Due to the conservative nature of the modelling, the greatest confidence in results is for those areas that are very unlikely to be impacted (that is, outside the zone of potential hydrological change). Areas that identify impacts indicate where further work may be required to obtain better predictions of the potential magnitude of impacts to ecosystems and individual assets.

Each of the companion products identifies key knowledge gaps for the Namoi subregion. The following sections are a summary of the key knowledge gaps where further work could improve the understanding of the potential impacts of coal resource development.

3.7.4.1 Overall

The probabilistic approach to modelling undertaken in the assessment is ideally suited to deal with data and knowledge gaps. The Assessment team focused on integrating data and information that were quality-assured and relevant for this regional-scale analysis. However, this meant that some data and information about the Namoi subregion were not used to inform the modelling – for instance, because they were localised and ad hoc in their coverage; lacked reliable metadata to quality assure the data; not available to the Assessment team at the time of analysis; or because operational constraints prevented collating and scrutinising the data to the standards set out in the BA.

A wide array of parameterisations to represent the possibility of highly conductive, highly connected landscapes through to low-conductivity, poorly connected landscapes provided result sets that are intended to span the hydrological changes that may occur, help ensure that the zone of potential hydrological change is conservative, and allow BAs to make strong statements about non-impact for landscape classes or assets that are outside the zone. In flagging gaps and identifying opportunities for improvement in the following sections, it is important to be aware that more and better data will not necessarily improve the model predictions from the regional-scale model, but could potentially contribute to constraining model results for local-scale application.

This Assessment did not incorporate water quality into the modelling and this remains an area for further investigation.

3.7.4.2 Geological model

The geological model constructed to underpin the groundwater modelling, described in Section 2.1.2 of companion product 2.1-2.2 for the Namoi subregion (Aryal et al., 2018) is an improvement of the existing CDM Smith conceptualisation, with areas modified where more updated geological knowledge was available, most notably in the Surat Basin and the alluvium. However, it does not include faults, which can act as barriers or conduits to groundwater flow. A more detailed understanding of fault locations and depths would improve the geological model and provide an improved precision to the model results if included into the groundwater modelling. Including faults into the current modelling is unlikely to change the spatial extent of the zone of potential hydrological change at the current resolution of 1 km². However, probabilistic groundwater modelling accuracy and precision at the local scale is likely to benefit from fault inclusion if faults are present. Fault relevance for the modelling could also be determined, for example, through the analysis of well completion reports.

Further improvements to the geological model accuracy would be possible with additional spatially explicit data on the thickness of the stratigraphic layers, including well completion reports and structural mapping from the companies operating in the Namoi subregion that were not available to the Assessment team at the time. This would improve the accuracy of the modelling, particularly in the Maules Creek sub-basin.

3.7.4.3 Groundwater information

Groundwater data are often patchy and poorly documented, particularly for regional applications. This was an issue in the Namoi subregion, particularly for depth to watertable, recharge and contributions to baseflow from groundwater.

Groundwater data from state databases primarily include monitoring data for shallow groundwater systems and aquifers used for irrigation, stock and domestic purposes. These data are usually in the form of water level measurements and major ion analyses, which support knowledge of groundwater recharge processes and interactions between rivers and groundwater. However, it provides limited understanding of deeper groundwater systems. Local monitoring of the effect on groundwater levels by proponents of their mining activities is less relevant. Such data, rather than constraining the predictions, can bias the predictions if the historical stresses and local geological conditions of these monitoring data are not well understood and represented in the regional model. This has been factored into the assessment’s uncertainty analysis and modelling. Future assessments would be assisted by improved information on deeper groundwater systems.

Depth to groundwater is a determinant for groundwater dependency of vegetation. For example, in areas where groundwater is sufficiently close to the landsurface for tree root access, the tree communities at these locations are using the groundwater (Anderson et al., 2016). Improved mapping of depth to groundwater, and its spatial and temporal variation, not only have potential to constrain hydrological change predictions, they provide much needed context for the interpretation of the ecological impacts due to hydrological change. Interactions between changes in groundwater availability and the health and persistence of terrestrial groundwater-dependent vegetation remain uncertain due, in part, to sparse mapping of groundwater depths outside of alluvial layers.

The groundwater model results are relevant for a BA purpose and for the specific regional-scale resolution only. Using the model and its results for other purposes, especially at a more local-scale resolution is not advisable. Use for any other purpose requires a formal re-evaluation of the suitability of the conceptual model and model assumptions, in line with the Australian groundwater modelling guidelines (Barnett et al., 2012).

3.7.4.4 Integrated hydrological modelling

Interaction between the groundwater and surface water, the flux of water through the hyporheic zone, is important for estimating hydrological response variables, especially those relating to low- or no-flow conditions. The companion product 2.1-2.2 (Aryal et al., 2018) provides a regional overview and improved treatment of these interactions for the major streams at the subregion level. However, assessment of the impacts of hydrological changes from coal resource development on ecological assets, ecosystems and ecoclines (ecosystem boundary zones) generally requires a finer-scale approach in areas where local populations are under investigation. Hence, impact assessments need to develop integrated hydrological models that provide predictions suitable to the local ecological assets.

At the BA model scale, the depth of incision of the streambed below topography is a very influential parameter for the simulation of groundwater level in the regional watertable aquifer and the surface water – groundwater flux. Improvements in these parameters would enable an increased precision for results of the surface water hydrological modelling.

In the Namoi modelled stream network, the distribution of model nodes was too sparse to enable a comprehensive extrapolation to network reaches, resulting in many ‘potentially impacted’ reaches, where hydrological changes could not be quantified. A higher density of model nodes and gauging information, located immediately upstream of major stream confluences and upstream and downstream of mine operations, would allow the point-scale information to be interpolated to a greater proportion of the stream network. A more extensive quantification of hydrological changes along the stream network would enable better spatial coverage of the results of the receptor impact modelling.

3.7.4.5 Assessing ecological impacts

Receptor impact modelling included experts’ input, which resulted in receptor impact models for specific landscape classes in the ‘Floodplain or lowland riverine’ and the ‘Non-floodplain or upland riverine’ landscape groups. Predictions of receptor impact variables from these models provide indicators of potential ecosystem risk for landscape classes in the Namoi subregion, but are constrained to those areas where surface water modelling could provide the hydrological response variables. Where there is limited or no surface water modelling, it is not possible to quantify the risk, though the underlying groundwater drawdown and the proximity to the coal resource development relative to other areas that are modelled are typically informative. In these areas, a simple spatial overlay of landscape classes over the zone of potential hydrological change identifies the locations (and parts of landscape classes) where additional work may be needed to quantify the risk.

The receptor impact modelling relied heavily on expert opinion. Thus, the number of experts and their expertise will influence the choice of ecosystem indicators used in the qualitative model development and the choice of receptor variables. It would be prudent to test the predictions against (regional) verifications of the ecological responses.

A receptor impact model quantifies the range or distribution of responses for a receptor impact variable given a specific change in hydrology. Predictions of receptor impact variables at specific locations (assessment units) then incorporate the hydrological uncertainty at that location and the uncertainty the experts have in the response across the landscape class or ecosystem for that change in hydrology. These are not predictions of the receptor impact variable at the specific location but rather predictions of the receptor impact variable response across the landscape class for the change in hydrology at the specific location. The predicted receptor impact variable may indicate where the hydrological change is of such a magnitude that it is commensurate with potential ecosystem change, even if the receptor impact variable is not found at that location. The usefulness of these models is in identifying the areas where the potential risk is greater and where further investigation should focus. These priority areas require consideration in conjunction with those where a lack of hydrological modelling prevented receptor impact modelling outputs.

Improving the knowledge of the existing qualitative models and the associated receptor impact models could improve the ecosystem response predictions. Revisiting the qualitative models for the landscape classes specifically and adjusting these for the purpose of prioritising future (ecological) research may be an effective way directing additional research resources (see e.g. Herr et al. (2016)).

Not all landscape classes have receptor impact modelling associated with them. For those landscape classes, the overlay with the zone of potential hydrological change identifies the areas or parts of landscape classes where there are potentially unquantified impacts that may warrant further investigation. For example, groundwater drawdown has the largest impact on the ‘Temporary upland stream’ landscape class in the Pilliga. While the surface water modelling in the Pilliga reporting area does not identify any changes in Bohena Creek for this landscape class, there are many other temporary upland streams where there is no surface water modelling output and thus, no receptor impact modelling for this landscape class. It would be prudent to clarify if the non-modelled streams experience any surface water-related changes before focusing solely on areas identified as higher risk from receptor impact modelling.

A similar argument holds for the assessment of ecological assets within the zone of hydrological change. The assessment identified assets ‘more at risk of hydrological changes’ based on hydrological modelling thresholds. This means that, while the assessment identified specific assets ‘more at risk’ based on their intersection with the hydrological modelling output, there are many locations where there is no quantified surface water change. This means the assets that are outside these areas, and are within the zone of potential hydrological change, do not have a quantified risk in this assessment. This will need consideration in dealing with these assets for the purpose of addressing priorities and impacts.

3.7.4.6 Sociocultural assets

Many sociocultural assets in the Namoi subregion from the Register of the National Estate are built infrastructure, such as historic buildings or bridges. The Programme does not have the expertise to comment on potential impacts of changes in hydrological regimes to built infrastructure.

At the time of this Assessment the locations of many of the Indigenous cultural assets of the Namoi subregion were not explicitly known. This prevented an assessment of potential water dependency. Cultural sensitivities often attach to Indigenous assets, and the Indigenous communities may prefer that details of their location and value are retained with their Elders or within their communities. The Programme does not have the expertise to comment on potential impacts on Indigenous assets. Thus, it is not clear what opportunity there might be to undertake a risk assessment of Indigenous assets in a culturally appropriate way.

3.7.4.7 Climate change and land use

The implications of climate change and changes in land use did not have any consideration in the modelling. A more complete picture of the potential impacts due to additional coal resource development could be obtained by considering these changes in the context of a warming climate and changing demands for water, particularly from agriculture in the Namoi subregion. Future work could include the role of interactions between coal resource development and agricultural land uses to identify the magnitude and influence of these interactions on changes in hydrology. This Assessment identified the risk to water-dependent landscape classes and assets from additional coal resource development, but how this information is used could differ if, for example, more area was set aside for strategic agricultural uses, or if the water demands of the urban populations changed.