Building on this assessment

If new coal resource developments emerge in the future, the data, information, analytical results and models from this assessment would provide a comprehensive basis for bioregion-scale re-assessment of potential impacts under an updated coal resource development pathway. For example, new coal resource developments could be incorporated in the groundwater model. Components such as the water-dependent asset register (Bioregional Assessment Programme, 2017; Dataset 12) remain relevant for future assessments. The information and approach may also be applicable for other types of resource development, such as agriculture or shale gas.

Informing local-scale assessments

There are opportunities to tailor the bioregional assessment modelling results for more local analyses (e.g. local-scale environmental impact assessments of new coal resource developments) by combining more detailed local geological and hydrogeological information with the groundwater model emulators developed through bioregional assessments.

Causal pathways

There are limited long-term consistent surface water quality and quantity data for the Namoi subregion, which are required for developing models that can predict water quality into the future. There is a lack of detailed understanding of the interaction between the surface water and groundwater systems, particularly at the local level.

The lack of knowledge about the location of subterranean faults means it is difficult to incorporate their effects on groundwater in the ‘Subsurface depressurisation and dewatering’ and ‘Subsurface physical flow paths’ causal pathway groups directly. However, the uncertainty analysis undertaken for the numerical groundwater modelling enables a probabilistic estimate of maximum groundwater level decline, as described in the groundwater numerical modelling (Janardhanan et al., 2018) for the subregion.

The geological model underpinning the groundwater modelling did not include fault locations and depths. Inclusion of faults would increase the precision of groundwater modelling but, given the regional scale of this assessment, it would not change the extent of the zone of potential hydrological change. However, probabilistic groundwater modelling accuracy and precision at the local scale is likely to benefit from fault inclusion.

Hydrological modelling

A higher density of surface water 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 and improve the extent of surface water modelling, as well as a more extensive assessment of relative risks to water-dependent assets and ecosystems.

Improved mapping of depth to groundwater, and its spatial and temporal variation, not only has potential to constrain hydrological change predictions, it also provides 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.

Quantifying the interaction between groundwater and surface water, the flux of water through the hyporheic zone (the zone beneath a streambed where groundwater and surface water mix), is important for estimating the hydrological response, especially those relating to low- or no-flow conditions. A finer-scale understanding and representation of this interaction may improve the assessment of impact, particularly where local populations are under investigation.


The separation between groundwater-dependent and surface water – dependent wetlands may not always be accurate. In many areas there is little knowledge of surface water – groundwater interactions. There is also a data gap in the understanding of water thresholds for ecosystems associated with springs and other water-dependent ecological assets.

There is limited knowledge on the actual water requirements of different plant communities. Future work could include the identification of suitable indicators of ecosystem condition and alternative methods of assessing the condition of water-dependent ecosystems, which would improve the quantification of risk.

Improving the qualitative models and receptor impact models would better predict the ecological changes in ecosystems in response to hydrological changes. Revisiting the qualitative models and adjusting these specifically for the purpose of prioritising future (ecological) research may be an effective way of directing additional research resources (see, for example, Herr et al. (2016)).

Climate change and land use

In comparing results under two different futures in this assessment, factors such as climate change or land use were held constant. Future assessment iterations could look to include these and other stressors to more fully predict cumulative impacts at a regional scale. This would particularly be informative for the Namoi subregion, due to the extensive agriculture in this subregion.

Indigenous assets

The Bioregional Assessment Programme does not have the expertise to comment on potential impacts on Indigenous assets.

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. However, a report is available that outlines an approach to engage with Indigenous communities and collect information on Indigenous water assets in the subregions and bioregions within NSW (DPI, 2016).

Identifying water-dependent assets valued by local Indigenous communities would provide a more comprehensive account of sociocultural assets, even if many of those assets are already in the water-dependent asset register through other sources, such as a wetland that may have both ecological and Indigenous value.

Future monitoring

At the highest level, monitoring efforts should reflect the risk predictions, and focus the effort where the changes are expected to be the largest or where there was an inability to quantify the risk. However, it is important to place some monitoring effort at locations with no or lower risk predictions so as to confirm the range of potential impacts and identify unexpected outcomes.

Existing monitoring of instream water quality is sparse in terms of spatial and temporal coverage. Where water quality impacts of coal resource development is of concern, it is necessary to separate these changes from the location-specific background water quality, which may include impacts from agricultural activities and infrastructure. An improved monitoring approach with improved spatial and temporal data for surface water and groundwater may be appropriate.

Given the many temporary streams in the subregion, biological sampling along with measuring changes in the hyporheic zone and in groundwater quality and quantity would fill a crucial gap in the understanding of the hyporheic biota (the organisms found beneath a streambed where groundwater and surface water mix) and subterranean biota (the organisms found underground, mostly in groundwater between sediment particles and rocks).

Establishing an understanding on the wider extent, compositions, structures and hydrological habitat requirements of the subterranean biota of the Namoi subregion is necessary when attempting to address risk from developments. Monitoring subterranean biota will help address risks to this barely understood part of Australia’s ecology.


See sections titled ‘Gaps’ in:

Description of water-dependent asset register, product 1.3 (O’Grady et al., 2015)

Current water accounts and water quality, product 1.5 (Peña-Arancibia et al., 2016)

Conceptual modelling, product 2.3 (Herr et al., 2018b)

Surface water numerical modelling, product 2.6.1 (Aryal et al., 2018a)

Groundwater numerical modelling, product 2.6.2 (Janardhanan et al., 2018)

Impact and risk analysis, product 3-4 (Herr et al. (2018a)

See for links to information about all datasets used or created, most of which can be downloaded from

Last updated:
6 December 2018