The prediction results show that thein the Namoi subregion has more noticeable effects in in the small tributaries of the Namoi River, where the proportion of contributing area affected by mining is more likely to be high, than at the nodes along the Namoi River itself. This is particularly apparent in streamflow at nodes 22, 25 and 27, which are on tributaries that enter the Namoi River near Boggabri, and at node 34 which is on a tributary of the Mooki River. For instance, the nodes with the three largest footprints due to additional coal resource development are nodes 25, 22 and 34, where the maximum percentage increases in footprint are 26%, 20% and 17%, respectively. The resulting median pmax values for the high-flow flux based variables and P99 are around –23%, –19% and –12%, respectively. Node 25 has the largest changes in each of the three flux-based high-streamflow hydrological response variables (AF, P99 and IQR). Its catchment is relatively small (74 km2), but includes the Boggabri and Tarrawonga additional coal resource developments. The catchments of nodes 22, 27 and 34 include parts of the Maules Creek, Tarrawonga and Watermark mines, respectively. Other nodes with substantial percentage changes in the high-streamflow hydrological response variables are nodes 31 and 54, both of which have parts of additional coal resource development mines in their catchments. All these nodes have relatively small contributing areas. While there are bigger predicted changes in amax at nodes further downstream, the proportional impacts of these changes are diluted by relatively unaffected inflows. The prediction that the biggest changes occur downstream of multiple mine developments highlights the cumulative nature of potential hydrological changes.
The biggest changes (in terms of amax) on the low-streamflow hydrological response variables are also predicted to occur at these small tributary nodes. However, there is a wider geographic spread of changes for the low-streamflow variables that exceed the specified thresholds. For example, for LFD, LFS and LLFS, there are predicted changes at nodes 9 (Bundock Creek), 19 (Jacks Creek) and 20 (unnamed) that exceed the specified thresholds. These nodes are in the vicinity of the Narrabri Gas Project. The coal seam gas (CSG) development is not expected to directly affect runoff as the amount of water the Narrabri Gas Project proposed to release to the surface water courses is minimal at the regional scale (Santos, no date), but may lower watertables to the extent thatcontributions to streamflow may be affected. This would manifest as changes to the low-streamflow hydrological response variables, but would have minimal impact on the high-streamflow variables.
The changes due to the additional coal resource development on the low-streamflow hydrological response variables (P01, LFD, ZFD, LFS and LLFS) appear to be slightly more noticeable than those on the high-streamflow hydrological response variables (AF, P99 and). This especially applies to the streams with near zero or very small baseline low flow (e.g. for nodes 22, 25 and 27). The flux-based variables (AF, IQR, P99 and P01) have similar median pmax values at the most heavily affected nodes. However, of the two frequency-based variables that are most directly comparable – FD and LFD – the increases in median amax values in the latter are typically larger than the decreases in the former. However, the uncertainties in predicted pmax (for the flux-based variables), amax (for the frequency-based variables) and tmax are greater for the low-streamflow variables.
For high-streamflow hydrological response variables, the tmax values at model nodes with noticeable changes occur approximately when the maximum footprint for additional coal resource development occurs. This indicates that the rapid streamflow reduction caused by the mine footprint for additional coal resource development dominates amax and pmax in these hydrological response variables, whereas the changes from the cumulative effect on baseflow over time caused byare minimal. This conclusion is supported by the tightly constrained changes in pmax at most model nodes for high-streamflow hydrological response variables, which suggest that the biggest impact on the high-streamflow hydrological response variables is caused by interception and retention of surface runoff at the mine sites, rather than by reduced baseflow associated with groundwater drawdown.
For the low-streamflow hydrological response variables, the tmax values at nodes with noticeable changes do not coincide consistently with the time of maximum footprint for the additional coal resource development. At many of the most heavily affected nodes, the predicted median tmax values tend to be a little later for two of the low-streamflow hydrological response variables – P01 and LLFS. This indicates that the causes of the changes in the low-streamflow hydrological response variables are controlled by a combination of the instantaneous effect from the additional mine footprints and the cumulative effect on baseflow over time caused by groundwater drawdown. Therefore, it is expected that uncertainty in predicting the changes on low-streamflow hydrological response variables is much larger than that on high-streamflow hydrological response variables. This is also shown by the highly variable pmax for the low-streamflow hydrological response variables.
The predictions of change in AF in this study are not inconsistent with those of the 18.104.22.168). The Schlumberger study predicts reductions in of approximately 0.1% at locations in the Namoi River that are roughly corresponding with nodes 18, 26 and 32. In this study the projected reductions are 0.3%, 0.2% and 0.2%, respectively. Similarly, in the Mooki River at locations approximately corresponding with nodes 33 and 35, the Schlumberger study predicts reductions of 0.2% and 0.0% whereas this study predicts 0.5% and 0.3%. The Schlumberger study therefore predicts slightly smaller changes than this study. This outcome is unsurprising since the Schlumberger study gives changes averaged over a 90-year period, while this study gives the changes in the single year with biggest change over a similar 90-year period.modelling study (see Section
Product Finalisation date
- 22.214.171.124 Methods
- 126.96.36.199 Review of existing models
- 188.8.131.52 Model development
- 184.108.40.206 Calibration
- 220.127.116.11 Uncertainty
- 18.104.22.168 Prediction
- Currency of scientific results
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- About this technical product