2.6.2.8.2 Factors not included in formal uncertainty analysis


The major assumptions and model choices underpinning the Hunter subregion groundwater model are listed in Table 9. Many of these are not included in the formal uncertainty analysis. Each assumption is rated against four attributes: data, resources, technical and effect on predictions. These ratings have been assigned based on expert opinion of the Assessment team. A more detailed discussion of each assumption, including the rationale for the rating, follows.

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 rating indicates the assumption is not influenced by data availability – that is, the same assumption would be made with more or different data; a high rating indicates the assumption would be revisited if more data were available.

The resources rating reflects the extent to which resources available for the modelling, such as computing resources, personnel and time, influenced the assumption or model choice. A low rating indicates the same assumption would have been made with unlimited resources; a high rating indicates the assumption is driven by resource constraints.

The technical rating reflects the extent to which the assumption is influenced by technical issues. A high rating is assigned to assumptions and model choices that are driven by computational or technical limitations of the model code. These include issues related to spatial and temporal resolution of the models.

The most important rating summarises the effect of the assumption or model choice on model predictions. These ratings reflect a qualitative assessment by the Assessment team. The following discussion of each assumption confirms that those having a medium or high effect on predictions are consistent with the precautionary principle – that is, that the effect on predictions is towards overestimation rather than underestimation of the impact.

Table 9 Qualitative uncertainty analysis of the groundwater model of the Hunter subregion


Number

Assumption/model choice

Data

Resources

Technical

Effect on predictions

1

Selection of parameters for sensitivity and uncertainty analysis

Low

High

Low

Medium

2

Specification of prior parameter distributions

High

Low

Low

Medium

3

Spatially uniform hydraulic properties

Medium

Medium

Medium

Medium

4

Depth dependence of hydraulic properties

High

High

Low

High

5

Hydraulic enhancement after longwall mine collapse

High

Low

Low

Medium

6

Mine footprints represented as time invariant polygons

Low

Medium

Medium

Medium

7

Representation of surface water – groundwater interactions

High

Medium

Medium

Medium

8

Distance-based weighting of observations

Medium

Medium

Low

Low

9

Constraining parameters with groundwater level observations

High

Medium

Low

Medium

10

Constraining parameters with streamflow observations

High

High

Medium

Medium

11

Zonal recharge from chloride mass balance

High

Low

Low

Low

12

Lateral boundaries

High

Medium

Low

Medium

13

Simulation period from 2012 to 2102

Low

High

Medium

Low

14

Resolution and geometry of mesh

High

Medium

Low

Medium

15

Horizontal and vertical extent of alluvium

Low

Medium

Low

Low

16

Evapotranspiration extinction depth

High

Low

Low

Medium

17

Van Genuchten parameters

High

Low

Low

Low

18

No representation of faults

High

Medium

Low

Medium

19

No historical mines

High

High

Low

Low

20

Non-mining groundwater extraction rates

Low

Low

Low

Low

2.6.2.8.2.1 Selection of parameters for sensitivity and uncertainty analysis

The Hunter subregion groundwater model has many parameters (Table 7) of which ten were considered in the uncertainty analysis (Table 8). This selection was based on initial runs of the groundwater model, the experience of the BA groundwater modelling team and recognition that it is possible to fix some parameters and vary others to obtain a satisfactory characterisation of the range of possible outcomes (see Section 2.6.2.7.3).

Selection was not based on the availability of data as a parameter range can be defined for each parameter from local information or the literature. The data attribute therefore scores low. The resource attribute is, however, scored high. Every additional parameter included in the uncertainty analysis requires additional model runs to adequately sample the parameter space for development of robust emulators (Sahama and Diamond, 2001). A pragmatic choice was made to limit the analysis to the ten selected parameters to ensure sufficient resolution with the available computing resources. The resources attribute is therefore scored high. There are no technical issues with including more parameters, hence the attribute is scored low.

The initial, exploratory runs indicated that the parameters predictions are most sensitive to include the ten selected parameters. However, due to non-linearities between parameter values and predictions, the sensitivity of predictions to a parameter can depend on the value of the parameter (Hill and Tiedeman, 2007). Therefore the possibility that the excluded parameters could have an effect on predictions in other parts of their feasible parameter range cannot be ruled out. The effect on predictions is therefore scored medium. The number of model runs required to include all parameters in the uncertainty analysis are, however, too large to be evaluated within the timeframe allowed for modelling within this project.

2.6.2.8.2.2 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 the Hunter subregion and equivalent analogue basins in Australia and the world.

Additional data will allow adjustment of these prior distributions to agree more closely with the conditions in the Hunter 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 the uncertainty analysis 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, as to ensure the predictive uncertainty is overestimated rather than underestimated.

2.6.2.8.2.3 Spatially uniform hydraulic properties

The hydraulic properties are implemented as spatially uniform horizontally, although they are varied with depth (see next section).

Insufficient data are available to characterise spatial variability at a regional scale, although in the vicinity of existing and proposed mines some 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 from point measurements to wider areas for use in numerical modelling (Renard and de Marsily, 1997). 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 the uncertainty in 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 drawdown and change of flux.

For groundwater quantity predictions at the regional scale, Australian groundwater modelling guidelines state that the representative elementary volume is valid and can be applied to capture spatial variability in hydraulic properties by using equivalent values (guiding principle 7.3; Barnett et al., 2012).

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 larger scales the effect is minimal (see companion submethodology M07 (as listed in Table 1) for groundwater modelling (Crosbie et al., 2016)).

2.6.2.8.2.4 Depth dependence of hydraulic properties

The hydraulic properties of the groundwater system are varied with depth rather than by lithology.

The data attribute is rated high. Analysis of the available hydraulic property data indicated no systematic variations of hydraulic conductivity with lithology or stratigraphy (see Section 2.6.2.6.1 and Section 2.1.3.2 in companion product 2.1-2.2 for the Hunter subregion (Herron et al., 2018b)). This is likely due to high spatial variability in hydraulic conductivity measurements generally, but can also be due to the uncertainty in the lithology within the geological model, and the coarseness of the geological model. In the vicinity of some mines, local geological information indicates that local aquitards exist, which could be introduced into the model in a similar way to the seam discs. Although local information is available, considerable resources are required to integrate this in a consistent manner in a regional model. Section 2.6.2.8.1.4 does, however, provide an example of how local-scale information can be integrated for selected predictions. The resources attribute is rated high. The technical attribute is rated low as it is relatively straightforward to implement different parameterisations.

The effect on predictions is rated high as the predictions of drawdown are very sensitive to the hydraulic properties and the observation data have limited potential to constrain the hydraulic property parameters. The current parameterisation, without regionally extensive aquitards, will overestimate the vertical propagation of drawdown to the watertable, while underestimating the lateral extent of drawdown in the coal seams and adjacent confined aquifers. The assumption is nevertheless deemed conservative, as the prime groundwater related variable of interest for receptor impact modelling is the drawdown at the watertable, not the drawdown in the coal seams and adjacent aquifers.

2.6.2.8.2.5 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 mine polygon immediately upon commencement of mining. The actual enhancement from each mine working is dynamic, advancing with the mining face and consolidating in the goaf region. If the phasing of each mine working were included, a more accurate representation of the groundwater 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 in recharge in areas affected by subsidence and longwall mine collapse. This feature is not implemented; however, an increase in recharge would likely counter the drawdown due to the additional coal resource development. By not including this enhanced recharge, drawdown due to the additional coal resource development is likely to be overestimated.

2.6.2.8.2.6 Mine footprints represented as time invariant polygons

The underground mines are represented as time invariant polygons, as discussed in Section 2.6.2.5. These polygons are an approximation of the maximum mine footprint and the progression of mining is not captured.

The data attribute is rated low, whereas the resource and technical attributes are rated medium. This reflects that this method of representing the mines is driven by the low spatial resolution of the regional modelling. Detailed information on mine footprints is available and, especially for the historical developments, the timing of mining is also often available, but extracting the data is time and resource consuming. The spatial resolution of the regional groundwater model is not sufficient to capture this detail. Similarly, the regional geological model does not represent individual coal seams.

The effect on predictions is rated medium as the regional drawdown due to additional coal resource development is controlled more by the mine pumping rate and the hydraulic properties than the exact outline of the mined area.

2.6.2.8.2.7 Representation of surface water – groundwater interactions

Surface watergroundwater interactions are implemented through a boundary condition in the groundwater model (Section 2.6.2.3). The parameter d_riv controls the depth of the riverbed below the surface elevation of the model mesh element, hence the drainage level. River stage is kept constant throughout the simulation period, so d_riv also contains river stage height information. The parameter C_riv describes the riverbed conductance, which is assumed to be uniform throughout the Hunter subregion.

The data attribute is rated high. Surveyed information of riverbed elevation is not available for most of the river network. Although there is a reasonable network of river gauges measuring river stage, these need to be interpolated between gauge locations and extrapolated upstream of headwater gauges to regionalise the point information. Although the available high-resolution digital elevation models partly alleviate the need for surveyed riverbed elevations, a river channel depth still needs to be assumed. Similarly, very few estimates of riverbed conductance are available, and measuring this parameter throughout the Hunter subregion to enable realistic spatial variation in the model is impractical.

The resources attribute is rated medium as a detailed analysis of the currently available stream geometry information would enable better representation of the spatial variation in drainage level. Introducing spatially varying C_riv would be fairly simple. The technical attribute is also rated medium as the resolution at which the stream can be represented depends on the mesh resolution.

The effect on the predictions is rated medium. The sensitivity analysis (Section 2.6.2.7.3) found that the simulated groundwater levels and surface water – groundwater fluxes are sensitive to the drainage level, but the predictions of the dmax are much less sensitive to these parameters. For model nodes close to rivers, dmax can be sensitive to C_riv but for other points, C_riv is unimportant. An improved representation of the river boundary will therefore not directly result in a greatly reduced uncertainty in the predicted drawdowns. However, a more accurate, independently specified boundary condition will reduce the conceptual model uncertainty which can improve the potential for the groundwater level and streamflow observations to constrain other model components.

2.6.2.8.2.8 Distance-based weighting of observations

Related to the previous assumption is the weight assigned to each groundwater level observation. The weight of an observation in constraining parameters for a particular prediction is based on the distance between observation and prediction and also distance of the observation to the nearest blue line network (i.e. mapped river network).

With the available data density and operational constraints, development of a tailored weighting for each observation based on the aquifer it is situated in and local hydrogeological conditions is not possible. Therefore the data and resources column is 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 the groundwater level 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.

2.6.2.8.2.9 Constraining parameters with groundwater level observations

Groundwater level observations are often the only data used to constrain the parameters and conceptualisation of a groundwater model. In the groundwater model for the Hunter subregion groundwater level observations are used to constrain the model parameters as well as streamflow observations.

In Section 2.6.2.7.1 the available groundwater observation data from the NSW Department of Primary Industries, Office of Water (DPI Water, Dataset 3) 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 completing a groundwater bore. 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 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 piezometric surface. However, uncertainties arising from poorly specified point (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 dataset 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 publically 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 a surface water feature. 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 reduce uncertainty in 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 probability distributions. The sensitivity analysis (Section 2.6.2.7.3) indicated that groundwater levels are most sensitive to the drainage level of the river model nodes, while the change in groundwater level predictions are 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.

2.6.2.8.2.10 Constraining parameters with streamflow observations

Percentiles of the total observed historical streamflow are used to constrain the surface watergroundwater flux. By specifying that the average simulated historical surface water – groundwater flux needs to be between the negative of the 20th percentile of long-term total streamflow and the 70th percentile of long-term total streamflow, only the most extreme unrealistic simulations are excluded from the posterior parameter combinations.

If more robust, regional-scale estimates of surface water – groundwater fluxes are available, there is enormous potential to constrain this variable. Although some local-scale, detailed information is available (Lamontagne et al., 2003), long-term, regional-scale estimates are not available. The data attribute is therefore rated high.

Estimating surface water – groundwater fluxes at a regional scale is not trivial. The various methods available, such as baseflow separation with digital filters, salt balance or environmental tracers, each have their shortcomings. To obtain consistent, robust estimates of the surface water – groundwater flux, these methods need to be applied in combination. This analysis was deemed beyond the scope and operational constraints of the BAs.

The effect on predictions is deemed to be medium. The surface water – groundwater flux is most sensitive to the drainage level (d_riv) and, to a lesser extent, the hydraulic properties of the groundwater system, while drawdown predictions, however, are most sensitive to hydraulic properties, hydraulic enhancement after longwall mine collapse and mine pumping rates.

As with the groundwater level observation, narrowing the bounds on the surface water – groundwater flux can better constrain the river drainage level. 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 drawdown due to the additional coal resource development (difference in drawdown between CRDP and baseline).

2.6.2.8.2.11 Zonal recharge from chloride mass balance

Groundwater recharge is implemented spatially using a spatially varying correction factor to the temporal recharge signal obtained from the surface water model output. The correction factor is based on measurements of chloride in groundwater and rainfall with the chloride mass balance method (see Section 2.1.3 of companion product 2.1-2.2 for the Hunter subregion (Herron et al., 2018b)). The chloride deposition in rainfall was from a continental scale chloride deposition surface which used all available measured values within the subregion. The spatial coverage of bedrock groundwater chloride measurements in the Hunter subregion is variable. Other reliable and representative measurements of diffuse recharge 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 zonation and parameterisation of groundwater recharge, the chloride in groundwater measurements are likely to contribute more to recharge uncertainty than the chloride deposition due to rainfall. 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 reduced uncertainty will 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 groundwater level predictions. The effect on the predictions attribute is therefore rated low.

2.6.2.8.2.12 Lateral boundaries

The model’s north-eastern boundary has been assumed to be impermeable (coinciding with the extent of the geological basin), while general-head boundary conditions have been applied to its other lateral boundaries. The specified groundwater level is obtained from a quasi-steady-state pre-development simulation to ensure the groundwater level is consistent with the parameterisation.

The data attribute is rated high as there is very limited piezometric information available to independently specify the groundwater levels. The resources attribute is rated medium as it would require considerable additional resources in model development to expand the model to coincide with natural boundaries. The technical column is rated low as it is trivial to change the boundary condition in the model.

The effect on predictions is rated medium. Determining whether these boundary conditions are correct would help reduce boundary effects. Most of the mines are sufficiently far from the lateral boundary that the zone of hydrological change does not extend to the lateral boundary. Predictions around mines close to a general-head boundary, such as Ulan, Moolarben and Mount Owen, may be improved through reducing boundary effects.

2.6.2.8.2.13 Simulation period from 2012 to 2102

The simulation period for all BAs is 2012 to 2102 (see companion submethodology M06 (as listed in Table 1) for surface water modelling (Viney, 2016) and companion submethodology M07 (as listed in Table 1) for groundwater modelling (Crosbie et al., 2016)). For some parameter combinations and some model nodes this means that the dmax is not realised within the simulation period, as shown in Figure 40.

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, it 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 the groundwater model. The climate scaling factors used to specify future rainfall and therefore recharge 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 tmax, presented in submethodology M07 (as listed in Table 1) for groundwater modelling (Crosbie et al., 2016), shows that any dmax realised after 2102 will always be smaller than the dmax realised before 2102. This is in line with the precautionary principle as it means that by limiting the simulation period, the hydrological change will not be underestimated.

2.6.2.8.2.14 Resolution and geometry of the mesh

The geometry of the mesh is taken from the geological model developed in Section 2.1.2 of companion product 2.1-2.2 for the Hunter subregion (Herron et al., 2018b), although the horizontal resolution is variable, with high resolution close to rivers and mines.

The data and resources attribute are both scored medium, while the technical attribute is scored low. The latter reflects that it is straightforward to change the mesh resolution and geometry in the design of the model. For higher resolution meshes, with more elements, the computation time will increase. The resource attribute is thus scored medium to reflect that increased resolution requires an increase in computational load. The accuracy of the underlying geological model ultimately depends on the data availability and interpretation. This attribute is scored high. Note that model horizontal resolution is not constrained by data availability.

In the current parameterisation, the effect on predictions is low, as hydraulic properties do not vary with stratigraphy but with depth. Higher resolution geological models with improved representation of coal seams and local aquitards, however, may warrant revisiting this parameterisation scheme, which at least locally, may affect predictions. The overall score of this attribute is medium.

It is noted that NSW Geological Survey has developed a detailed three-dimensional geological model of part of the subregion. Project-timing issues prevented this recent geological model being integrated into the groundwater model.

2.6.2.8.2.15 Horizontal and vertical extent of alluvium

The horizontal and vertical extent of the alluvium is well defined and available from geological maps and shallow bores, especially compared to the understanding of the deeper sedimentary rocks. The data attribute is therefore scored low.

The variations in lateral extent and thickness of the alluvium are, however, often at a scale that is smaller than the model mesh resolution. A more accurate representation would therefore require a finer mesh, which in turn increases the computational load, hence a medium score for the resources attribute. There are no technical issues with implementing a finer mesh, so the technical score is low.

The overall impact on predictions is scored low. An improved accuracy in the representation of the alluvium may reduce the predictive uncertainty of additional drawdown locally in alluvial aquifers. The predicted change in surface watergroundwater exchange flux is deemed to be minimally affected by an increased resolution of the alluvium as this flux is mostly controlled by the river bed conductance and the river stage.

2.6.2.8.2.16 Evapotranspiration extinction depth

Potential evapotranspiration is obtained from AWRA-L and the evapotranspiration extinction depth is assumed to be proportional to vegetation height, and varies between 0 m to 10 m. These extinction depths are consistent with depths used in other groundwater modelling studies (Canadell et al., 1996; Doble et al., 2016).

The data attribute is scored high as there are very limited data available on rooting depth for different vegetation types in the Hunter subregion, let alone evapotranspiration rate as a function of groundwater level, rooting depth, vegetation, soil/rock type, etc. The resources and technical attributes are scored low as it is relatively straightforward to update the evapotranspiration extinction depth if new information is available.

Evaluating the impact on predictions of changing extinction depth is not straightforward and warrants a more formal evaluation. This was done using a local one-at-a-time sensitivity analysis in which each parameter is varied using a small increment, while keeping other parameters at their base value (Hill and Tiedeman, 2007). This analysis is the most frugal sensitivity analysis method because it requires only one or two model runs per parameter (Hill et al., 2015), but it can produce misleading results (Saltelli and Annoni, 2010). Four groundwater model parameter were used in the analysis: the extinction depth of evapotranspiration (ET) expressed as a fraction of the vegetation height, the effective porosity (ne), the horizontal hydraulic conductivity (Kh) and the hydraulic conductivity ramp function (K_ramp). The latter three were chosen as the reference parameters because the global sensitivity analysis showed that drawdown predictions were sensitive to them.

Results from the analysis indicated that the extinction depth does affect the predictions and the uncertainty in the predictions of dmax, but the effect is much smaller than for ne and Kh. While results from this local one-at-a-time sensitivity analysis should be viewed as indicative only (due to possibility of misleading results), they suggest that including extinction depth in the uncertainty analysis will only have a minimal effect on the posterior predictive distributions. The effect on predictions is therefore scored low.

2.6.2.8.2.17 Van Genuchten parameters

The Van Genuchten parameters control the hydraulic behaviour of the unsaturated zone in the groundwater model.

In the absence of local data, these parameters are based on literature values. The data attribute is thus scored high. Different values for the Van Genuchten parameters are easy to implement and will have a limited effect on the model runtime. Both the resource and technical attributes are scored low.

The effect on predictions is rated low. The unsaturated flow parameters control how much water is stored in regions above the watertable, which may affect the dynamics of the unsaturated region, in particular in the regions around rivers where baseflow and leakage occur.

2.6.2.8.2.18 No representation of faults

Faults are not incorporated in the geological model as insufficient information is available about their three-dimensional structure, dip, throw and displacement (see Section 2.1.2.2.4 in companion product 2.1-2.2 for the Hunter subregion (Herron et al., 2018b)).

Faults are not included in the groundwater model either as there is insufficient information on the location and flow behaviour of faults and fractures in the subregion. The data attribute is therefore scored high. The resource attribute is scored medium as incorporating faults and fractures will require a refinement of the mesh and an update of the parameterisation. The technical attribute is scored low as there is no technical issue in implementing fault-related flow in the Multiphysics Object-Oriented Simulation Environment (MOOSE).

Effects on predictions is rated medium as faults and fractures can locally alter predictions.

2.6.2.8.2.19 No historical mines

Historical mines are those coal mining operations that had ceased operations prior to the end of 2012. These mines therefore were not included in the baseline, as outlined in the introduction.

Including such historical coal mining operations in the groundwater model requires extensive data on historical mine footprints and pumping rates. The data attribute is therefore scored high. Some of that information is available, especially for the more recent mines. It requires a considerable investment to collate that information, carry out a quality assurance and transform into a format suitable for incorporation in the groundwater model. The resources attribute is therefore scored high. The technical attribute is scored low as it is straightforward to add mines to the model if the information is available.

The effects on predictions is scored low, however. The effects of historical coal mining operations are the same between the baseline and CRDP and so cancel out in estimating the drawdown due to the additional coal resource development.

It is recognised that accurate representation of historical coal mining operations and their legacy effects may improve the representation of historical groundwater levels and surface water – groundwater flux, which in turn may reduce the uncertainty in the posterior parameter distributions.

2.6.2.8.2.20 Non-mining groundwater extraction rates

Groundwater extractions 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 bores. 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 both baseline and CRDP and their impacts largely cancel out in calculating the difference between the modelled results under baseline and CRDP. As previously stated, in relation to including more historical mines in the baseline, time series of actual water extraction rates would help to constrain the model parameters.

Last updated:
7 November 2018
Thumbnail of the Hunter subregion

Product Finalisation date

2018
PRODUCT CONTENTS

ASSESSMENT