The major assumptions and model choices underpinning the Table 14. The goal of the table is to provide a non-technical audience with a systematic overview of the model assumptions, their justification and effect on predictions, as judged by the modelling team. This table is aimed to assist in an open and transparent review of the modelling.model are listed in
In the table each assumption is scored on four attributes using three levels: ‘high’, ‘medium’ and ‘low’. Beneath the table, each of the assumptions are discussed in detail, including the rationale for the scoring.
The data column is the degree to which the question ‘if more or different data were available, would this assumption/choice still have been made?’ would be answered positively. A ‘low’ score means that the assumption is not influenced by data availability while a ‘high’ score would indicate that this choice would be revisited if more data were available. Closely related is the resources attribute. This column captures the extent to which resources available for the modelling, such as computing resources, personnel and time, influenced this assumption or model choice. Again, a ‘low’ score indicates the same assumption would have been made with unlimited resources, while a ‘high’ value indicates the assumption is driven by resource constraints. The third attribute deals with the technical and computational issues. ‘High’ is assigned to assumptions and model choices that are dominantly driven by computational or technical limitations of the model code. These include issues related to spatial and temporal resolution of the models.
The final, and most important column, is the effect of the assumption or model choice on the predictions. This is a qualitative assessment by the modelling team of the extent to which a model choice will affect the model predictions, with ‘low’ indicating a minimal effect and ‘high’ a large effect. Especially for the assumptions with a large potential impact on the predictions, it will be discussed that the precautionary principle is applied; that is, the hydrological change is over rather than under estimated.
Table 14 Qualitative uncertainty analysis for the groundwater model of the Namoi subregion
18.104.22.168.2.1 Single geological and conceptual model
Themodel is based on the geological model discussed in companion product 2.1-2.2 ( ) and the discussed in companion product 2.3 ( ). Both products highlight and discuss the associated with the geological and conceptual model.
One of the main sources ofin the geological model is availability of data, especially in the deeper sedimentary basins, such as the Gunnedah Basin. A higher density of with lithological and/or stratigraphic data may allow refinement of the geological model as would additional seismic reflection data. The data density in the Namoi alluvium is much greater but nevertheless there is considerable uncertainty in the vertical and lateral lithological variation within these deposits. This affects the confinement status of especially the deeper sections of the Namoi alluvium. The data attribute is therefore scored ‘high’ as more and seismic data will allow refinement of the geological and conceptual model.
With the currently available data it would be possible to investigate different geological and conceptual models that are consistent with the geological and hydrogeological understanding of the bioregion. Comprehensively formulating these different geological interpretations and conceptualisations in a stochastic manner that is amenable to numerical evaluation within the project timeline, is beyond the available resources. The resources attribute is therefore scored ‘medium’.
Related to this are the technical challenges of implementing and evaluating different geological and conceptual models with the MODFLOW code. The MODFLOW-USG code used is very flexible and is able to accurately represent a wider range of geological conditions than previous versions of MODFLOW. Nevertheless, the level of pre- and post-processing required to stochastically vary the geological model and conceptual model, requires the development of elaborate, custom-made computer scripts. This is technically possible, but far from trivial. The technical attribute is therefore scored ‘medium’.
The overall effect on predictions is, however, scored ‘low’. The NCWS and GBRM models are both regional models with comparable extent to the Namoi subregion. Each of these models is based on a different geological model and various aspects of the conceptualisation are different. Despite these differences, the results of the models are quite consistent, when the differences in stress due to coal resource development are accounted for. While this by no means is a comprehensive analysis of the effect of geological and conceptual uncertainty particularly with regards to geological structures, it does provide a level of confidence that the predictions and conclusions are robust against variations in geological and conceptual model.
22.214.171.124.2.2 Lateral and internal boundary conditions
The interaction of 126.96.36.199. These include the lateral no-flow and general-head boundaries, the spatially variable and evapotranspiration fluxes (and associated evapotranspiration depth) and the localised surface water – groundwater interaction linked to the river network.with and with the surrounding area in the groundwater model is described in Section
The boundary conditions are assigned a ‘medium’ score for all three attributes of data, resources and technical, indicating that no single attribute dominates the choice and implementation of the boundary conditions. Additional data and resources may allow more detailed and complex representations of these boundary conditions. The resulting increased dimensionality, however, will increase the technical challenge of carrying out a comprehensiveanalysis.
To the extent possible within the project timeline, the majority of these boundary conditions are included in the stochastic parameterisation of the model. During the stress testing of the model, it became apparent that aspects such as the general-head boundary had very limited to no impact on the predictions. These parameters were therefore excluded from theand uncertainty analyses. Recharge, evapotranspiration and depth of incision of the riverbed, however, are included in the sensitivity analysis. While the groundwater level and river flux observations are sensitive to some of these parameters, especially the depth of incision of the riverbed, the predictions of and are not sensitive to the parameters associated with boundary conditions. The effect on predictions is therefore scored ‘low’.
188.8.131.52.2.3 Implementation of coal mine developments
Coal mines are implemented through drain boundary conditions. The drain boundary is not specified for individual coal seams but for the model layer that hosts the coal seams at the grid cells contained in the mine footprint.
The data attribute is scored ‘high’. The location and timing of planned coal and CSG developments is generally well known from the proponents’ environmental impact statements. The drain elevation, the level to which thelocally will be drained, is informed by the geological model. Additional local mine development and geological data would allow refinement of these drainage elevations.
The resources and technical columns are both scored ‘low’ to indicate that changing the implementation of the coal mines is not limited by available resources or technical challenges.
The effect on predictions is scored ‘high’ because the predictions are conditioned on the presence and implementation of coal mines. The largest differences between the NCWS andmodel are related to differences in developments included in both models. Likewise, the Namoi subregion groundwater model simulates substantial associated with the Caroona Coal Project in the south of the region. During model development, it transpired that the Caroona Coal Project development is not likely to proceed. This implies that those drawdown predictions no longer reflect the most likely . In addition to this, deviations from the coal mine development plan are very likely due to technical and geological issues during production.
Constraining all parameter combinations with the estimated water production rates – at the very least – ensures that the predictedare, to a degree, consistent with the more detailed local simulations carried out by the various proponents.
184.108.40.206.2.4 Implementation of coal seam gas developments
CSGis implemented as a drainage boundary condition in model layers representing the Hoskissons Coal seam and the Maules Creek Formation and water is sourced from the entire layer, not from individual coal seams.
While non-trivial and challenging (), it is technically possible, and within the resources of the , to implement a more detailed conceptualisation of the CSG .
However, insufficient data are available, both on the physical system and on the dimensions of the planned development, to adequately parameterise the added complexity. This motivates the scoring of ‘high’ on the data column with ‘low’ for resources and technical attributes.
One of the limitations of the current modelling approach is that it is not able to simulate dual-phase flow. Using a single-phase model is, however, likely to over estimateand water volumes ( ), in line with the precautionary principle. The model code does allow specification of pumping rates, but these are not known and, because of the dual-phase aspect, will be unlikely to result in a drawdown that is representative of the depressurisation required for CSG extraction (see submethodology M07 (as listed in Table 1) for groundwater modelling ( )).
The effect of changing the water production rates on the predictions is scored ‘high’, for the same reasons as outlined above; the presence or absence of a development will fundamentally affect the predictions. It has to be noted, however, that for, the drawdown due to CSG development is spatially more extensive than the drawdown due to coal mines, but lower in magnitude.
Like with the coal mines, constraining parameter combinations with the estimated water production rates ensures that the predictedare consistent with the more detailed local simulations and reservoir simulations carried out by the CSG proponent.
220.127.116.11.2.5 Spatial variability of hydraulic properties
The hydraulic properties are implemented as spatially uniform horizontally, although they are varied with depth (see Section 18.104.22.168.2.6).
Insufficient data are available to characterise spatial variability at a regional scale, although in the vicinity of existing and proposed mines more information is available. The data availability attribute therefore receives a ‘medium’ score.
The level of spatial detail that can be accommodated in a numerical model is governed by the horizontal and vertical discretisation, but will always require upscaling. Upscaling is a challenging technical task and there are a wide variety of techniques available to scale measurements at a point-scale into hydraulic properties representative of wider areas for use in numerical modelling (). The technical column is rated ‘medium’.
These technical challenges can be partly overcome through stochastic simulation of spatially variable hydraulic properties within model layers. The time and computational resources required to develop and apply stochastic hydraulic property simulators tailored to the subregion are not available within the operational constraints of the Bioregional Assessment Programme. The resources column is therefore rated ‘medium’ as well.
The effect on the final predictions of thein hydraulic properties is deemed to be moderate and is therefore rated ‘medium’. Any change in the hydraulic properties, especially the hydraulic conductivity parameters, will affect the predictions directly. The wide prior distributions defined for the parameters ensure, however, that this uncertainty is adequately captured in the predictive distributions of and change in – flux.
At the regional scale and for groundwater quantity predictions, guiding principle 7.3 in the Australian groundwater modelling guidelines () highlights that the representative elementary volume is valid and can be applied to capture spatial variability in hydraulic properties by using equivalent values.
Although introducing spatial heterogeneity might have an effect on the extent of predicted changes in groundwater level in the immediate vicinity of the mines, at regional scales the effect is minimal (see companion submethodology M07 (as listed in Table 1) for groundwater modelling ()).
The hydraulic properties, hydraulic conductivity and storage, for interburden and coal-bearing layers are varied with depth rather than with 22.214.171.124)., based on an observed decrease of hydraulic conductivity with depth (Section
The data attribute is scored ‘high’ as the data are sparse and highly variable. A higher resolutionof hydraulic conductivities will allow refinement of the depth-varying parameterisation or enable establishment of a relationship between stratigraphy and hydraulic properties.
The choice of varying hydraulic properties of interburden and coal-bearing layers with depth is not constrained by technical or resource limitations, hence the ‘low’ score for both attributes.
The effect on predictions is rated ‘high’ as the predictions ofare very sensitive to the hydraulic properties and the observation data have limited potential to constrain the hydraulic property parameters.
This is mitigated by including the coefficients of the depth-varying hydraulic conductivity function in theanalysis with prior parameter ranges that ensure that the entire spectrum from no variation with depth to a strong decrease with depth are included in the simulations. Each model run thus has an individual depth-varying hydraulic conductivity function.
126.96.36.199.2.7 Hydraulic enhancement after longwall mine collapse
The hydraulic properties above mined coal seams are changed after mining commences to represent the effects of longwall mine collapse.
The data attribute is rated ‘high’ as the process is well described, but data on hydraulic properties after longwall mine collapse are scarce.
Both the resources and technical attributes are rated ‘low’, as it is trivial to implement hydraulic enhancement differently. The hydraulic conductivity enhancement due to underground mining is modelled by a ramp function. The enhancement is applied to the entire area above and below the active drain cells which increment every 5 years through the operation phases of the mine. The actual enhancement from each mine working will be dynamic, advancing with the mining face and consolidating in theregion. If higher temporal resolution data on the phasing of each mine working were included, a more accurate representation of the in regions close to the mines would be obtained.
The effect on predictions is rated ‘medium’, as hydraulic enhancement is locally important for prediction above or close to the mine footprints. Further away from the mines, the enhancement is less important. Related to the hydraulic enhancement is the potential for an increase inin areas affected by and longwall mine collapse. This feature is not implemented; however, an increase in recharge would likely counter the . By not including this enhanced recharge, drawdown due to additional coal resource development is likely to be over estimated.
188.8.131.52.2.8 Specification of prior parameter distributions
The prior parameter distributions are chosen to be uniform within the ranges selected by the modelling team, based on the information available for theand equivalent analogue sedimentary basins in Australia and the world.
Additional data will allow adjustment of these prior distributions to agree more closely with the conditions in the Namoi subregion. This warrants the ‘high’ score for the data component. Specifying prior distributions is not constrained by resources and there are no technical issues as theanalysis methodology is not prescriptive in the type of prior distribution used in the analysis. Both of these attributes score ‘low’.
The effect of the choice of parameter distributions is potentially important as many of the parameters the predictions are sensitive to are not greatly constrained by the available observations. The posterior parameter distributions for these parameters are very similar to the prior distributions. The effect on predictions is therefore rated ‘medium’.
To mitigate this, the distributions are chosen to be conservative, spanning at least two orders of magnitude for most hydraulic properties, so as to ensure the predictive uncertainty is over estimated rather than under estimated.
184.108.40.206.2.9 Distance-based weighting of observations
The weight of an observation in constraining parameters for a particular prediction is based on the distance between observation and prediction and the distance of the observation to the nearest blue line network – that is, the mapped river network.
With the available data density and operational constraints, development of a tailored weighting for each observation based on theit is situated in and local hydrogeological conditions is not possible. Therefore the data and resources columns are rated ‘medium’. Technically it is trivial to implement a different weighting scheme, so the technical column rates ‘low’.
The overall effect on predictions is small, as the information in thelevel observations is generally not able to constrain the parameters relevant to the groundwater change predictions. Locally, however, the effect on predictions could be important, such as in regions where none of the simulated groundwater levels are in agreement with the relevant observations and the model is not deemed reliable. The extent and shape of these regions is fully governed by the observation weighting function. The overall scoring of the effect on predictions is therefore ‘medium’.
220.127.116.11.2.10 Constraining parameters with groundwater level observations
level observations are often the only data used to constrain the parameters and conceptualisation of a groundwater model. In the groundwater model for the , groundwater level observations are used to constrain the model parameters as well as streamflow observations and mine water production rates.
In Section 18.104.22.168.1 the available groundwater observation data from the NSW Department of Primary Industries (NSW Office of Water, ) in the groundwater model domain are presented and discussed. A large number of these observations date back to the late 1970s or early 1980s and mostly correspond to single water level readings carried out directly after installing a groundwater . Some of these readings are likely to be spurious. The metadata associated with these measurements often indicate that the coordinates of the observation location are not surveyed, but are estimated from a map. The elevation of ground level or the reference points for depth-to-watertable measurements is in most cases not surveyed either, but estimated from maps or digital elevation models.
High quality observation data are essential for building a conceptual understanding of groundwater flow in a region and identifying general trends in. However, arising from poorly specified x, y, z information and the representativeness of groundwater levels measured in bores shortly after their installation undermine the utility of an observation for constraining a groundwater model. Observations that had no surveyed coordinates or were not from groundwater observation bores were excluded from the used to constrain the groundwater model. This greatly reduced the number of observation points to constrain the model.
Mining companies install and maintain groundwater monitoring networks in the vicinity of their developments. These data are not publicly available and a licence to use this data requires individual negotiations with the mining companies. Even when a licence to use the groundwater level observations is granted, the data need to be subjected to a stringent quality assurance as well. The main concern here is not the spatial accuracy of the measurements, but the representativeness of the observation for regional groundwater flow conditions. Mine monitoring networks are usually designed to monitor groundwater level changes in the immediate vicinity of the mine or around areas of potential concern, such as close to afeature. Such local detail is not captured in the regional model and using observations dominated by local hydrogeological conditions in constraining the model can introduce considerable bias in the regional parameter estimates.
Thus, in terms of data available to constrain the groundwater model, this is rated ‘high’. Data from a more extensive, quality-assured regional observation network will provide a stronger basis for constraining groundwater models in this subregion. This issue receives a ‘medium’ score on the resources attribute. The quality control and assurance of the database entries, and their suitability to be included in the observation dataset to constrain the model, is based on a desktop study of the information provided in the database. Access to and more comprehensive analysis of the original records and/or a field campaign to identify and verify spatial coordinates of the database entries have the potential to reducein the observation record. There are no technical issues for collecting, verifying or using groundwater level observations, hence the ‘low’ score for the technical attribute.
Despite the limited data availability and uncertainties in the observation record, the effect on predictions is rated ‘medium’. The assumption is important but not deemed to dominate the predictions. A larger observation database with less observation uncertainty has the potential to locally change the conceptual understanding of the system and change the final posterior parameter 22.214.171.124.3) indicated that groundwater levels are most sensitive to the depth of incision of the riverbed, while the change in groundwater level predictions is most sensitive to the hydraulic properties. A greater density of high quality observations close to the river network will reduce the uncertainty in the drainage level, which in turn will allow for the groundwater level observation to better constrain the hydraulic properties of the system.. The analysis (Section
126.96.36.199.2.11 Constraining parameters with streamflow observations
The 20thof the total observed historical streamflow is used to constrain the – flux. By specifying that the average simulated historical surface water – groundwater flux needs to be less than this threshold, parameter combinations that give rise to large fluxes of groundwater to surface water are excluded, as this is not in accordance with the hydrological understanding of the Namoi river system.
Surface water – groundwater interactions are intensely studied in the(see Section 2.1.5 of companion product 2.1-2.2 for the Namoi subregion ( )). The connection status is, however, time-varying and dependent on the level of pumping for agriculture, river regulation and the occurrence of floods. A more detailed constraining of the surface water – groundwater flux therefore requires not only data with a spatial and temporal resolution, it also requires additional resources to integrate that information in the groundwater model and subsequent analysis. These attributes are therefore scored ‘high’ and ‘medium’, respectively. The technical attribute is scored ‘low’ as there are few technical issues associated with implementing more detailed surface water – groundwater interactions.
The effect on predictions is deemed to be ‘medium’. The surface water – groundwater flux is most sensitive to the depth of incision of the streambed and, to a lesser extent, the hydraulic properties of the, while predictions, however, are most sensitive to hydraulic properties.
As with the groundwater level observation, narrowing the bounds on the surface water – groundwater flux can better constrain the depth of incision of the streambed. When this parameter is better constrained, there is more potential for the groundwater level observations to constrain the hydraulic properties. This in turn will further constrain the predictions of(i.e. the difference in drawdown between and ).
188.8.131.52.2.12 Zonal recharge from chloride mass balance
is implemented using a spatially varying correction factor to the temporal signal obtained from the model output. The correction factor is based on measurements of chloride in and rainfall with the chloride mass balance method (see Section 2.1.3 of companion product 2.1-2.2 for the Namoi subregion ( )). The spatial coverage of bedrock groundwater chloride measurements in the is variable. Other reliable and representative measurements of diffuse recharge outside the alluvium, such as from environmental tracers, are not available in the subregion either.
More evenly distributed chloride measurements in groundwater observations across the outcropping geological units or other estimates of diffuse recharge will undoubtedly improve the parameterisation of groundwater recharge. For this reason, a ‘high’ score is attributed to the data column. It is unlikely that additional resources or different techniques will improve the recharge estimates based on the currently available data. Both these columns are therefore given a ‘low’ score.
Recharge estimates with reducedwill reduce uncertainty in groundwater level predictions; however, as the change in groundwater level is not very sensitive to recharge, it will minimally affect changes in predictions. The effect on the predictions attribute is therefore rated ‘low’.
184.108.40.206.2.13 Simulation period from 2012 to 2102
The simulation period for all Figure 30.is 2012 to 2102 (see companion submethodology M06 (as listed in Table 1) for modelling ( ) and companion submethodology M07 (as listed in Table 1) for groundwater modelling ( )). For some parameter combinations and some this means that the is not realised within the simulation period, as shown in
Extending the simulation period is not limited by data as it is about the future, hence the score is rated ‘low’. The resources attribute, however, is rated ‘high’. To ensure that the dmax is realised at all model nodes for all parameter combinations would require extending the simulation period with hundreds to even thousands of years. This would impose a sizeable increase in the computational demand and therefore compromise the comprehensive probabilistic assessment of predictions. The technical attribute is rated ‘medium’. It is trivial to extend the length of the simulation in themodel. The climate scaling factors used to specify future rainfall and therefore are not available beyond 2100. It is therefore a technical issue in devising a justifiable future climate to assign to the modelling.
The effect on predictions, however, is rated ‘low’. The theoretical assessment of the relationship between dmax and, presented in submethodology M07 (as listed in Table 1) for groundwater modelling ( ), shows that any dmax realised after 2102 will always be smaller than the dmax realised before 2102. Since dmax was reached before 2102 at all points within the , limiting the simulation period does not underestimate the hydrological change.
220.127.116.11.2.14 Non-mining groundwater extraction rates
for non-mining uses across the subregion were based on licensed entitlements.
Historical data are generally not available on a bore-by-bore basis to define historical rates of extraction from licensed. The data attribute is rated ‘low’ since actual extraction rates would only apply to the 1983 to 2012 reporting period and assumptions would still need to be made about rates of extraction into the future. The resources and technical attributes are rated ‘low’ as it is trivial to model different extraction rates.
Effect on predictions is also ‘low’, as the same rates of extraction are used in bothand and their largely cancel out in calculating the difference between the modelled results under baseline and CRDP. It has to be noted, however, that time series of actual water extraction rates would help to constrain the model parameters and that unrealistically high water extraction rates may cause model stability issues.
18.104.22.168.2.15 Aggregating hydrostratigraphic units
In the parameterisation of the subsurface, several hydrostratigraphic units that are present between the target coal seams and the shallower Pilliga Sandstone and alluvium are aggregated in the model. The aggregated unit hydraulic properties no longer represent the hydraulic properties of an individual hydrostratigraphic unit, but are the equivalent hydraulic properties of the entire sedimentary column that is aggregated.
Representing the hydrostratigraphic units as separate units in the model necessitates defining both the geometry of each unit as well as establishing prior parameter distributions for each unit. While geometry information is available, the stratigraphic resolution of the hydraulic information in the sedimentary basin is insufficient to inform prior parameter distributions. The data attribute is therefore scored ‘high’. The resources attribute is also scored ‘high’ as increasing the number of model layers will increase the number of model cells and thus the run-time. The technical attribute is scored ‘low’ as there are no technical impediments to implementing the hydrostratigraphic units individually.
The impact on predictions is scored ‘low’. The change inpressure in these hydrostratigraphic units is not an objective of this modelling exercise. The model therefore does not require the vertical resolution to simulate groundwater levels and fluxes in these units. In the model, these units do separate the potentially stressed coal seams from the for which the potential hydrological is of interest. To estimate the propagation of , it is sufficient to know the equivalent hydraulic properties of the aggregated units. In such up-scaling, the equivalent properties will be bounded by harmonic and geometric mean of the units ( ). The wide range specified for these equivalent hydraulic properties means that a very large range of combinations of individual hydrostratigraphic units is implicitly captured in the parameterisation. The extreme low end of the prior parameters would represent a situation with most units being continuous and having a very low hydraulic conductivity. The extreme high end of the prior parameters would represent a situation where aquitards have higher and are not continuous.
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 Boundary and initial conditions
- 220.127.116.11 Implementation of the coal resource development pathway
- 18.104.22.168 Parameterisation
- 22.214.171.124 Observations and predictions
- 126.96.36.199 Uncertainty analysis
- 188.8.131.52 Limitations
- Currency of scientific results
- Contributors to the Technical Programme
- About this technical product