Hydraulic properties

The groundwater model needs to define horizontal and vertical hydraulic conductivity and specific storage for every model cell. These properties vary depending on the composition and architecture of the rocks and sediments. An analysis of 463 hydraulic conductivity measurements from the Namoi subregion found a correlation with depth (see Figure 17 in Aryal et al., 2018). Companion submethodology M07 for groundwater modelling (Crosbie et al., 2016) proposes the use of a simple parameterisation of hydrostratigraphy in bioregional assessment (BA) groundwater model layers that treats the layers as homogeneous, but varies hydraulic properties with depth. In the absence of a good basis for varying hydraulic properties by lithology or geology, the parameterisation of the Namoi subregion groundwater model adopts this approach. Setting up of distinct model layers as aquifers and interburden sequences and parameterising them independently based on a depth relationship enabled parameterisation of the layers based on the characteristics of the formation. For example, a deeper aquifer/coal seam formation can be parameterised with higher hydraulic conductivity values than an overlying interburden.

As described in Section, every point in the model domain can be defined in terms of a layer number, node number and a depth, d, below the model surface topography. The horizontal hydraulic conductivity, Kh, and the specific storage, SS, are assumed to be of the form:

K open parenthesis d close parenthesis equals open parenthesis 1 plus 10 to the power of we end exponent asterisk times EXP open parenthesis negative 0.06 asterisk times we to the power of 0.5 end exponent asterisk times d close parenthesis close parenthesis asterisk times open parenthesis K 0 asterisk times EXP open parenthesis negative alpha subscript Kk end subscript asterisk times d close parenthesis close parenthesis


S subscript S end subscript open parenthesis d close parenthesis equals S subscript S end subscript 0 asterisk times EXP open parenthesis negative alpha subscript S end subscript asterisk times d close parenthesis


where K(d) is the hydraulic conductivity (K, m/day) at a certain depth d, (m), we is the enhancement due to weathering (orders of magnitude), K0 is the hydraulic conductivity of fresh material at the surface, αK is the decay constant, SS(d) is the specific storage (SS, m–1) at a certain depth (d, m), SS0 is the specific storage at the surface and αS is the decay constant. A constant storage coefficient is assumed throughout the simulation using the MODFLOW layer type 0. This means that the model is unable to switch from confined to unconfined conditions during the model simulation. This assumption is used primarily to increase the model stability and achieve a robust model that is required for the comprehensive uncertainty analysis. The areas that will be effected by this assumpation are only where the top layer of the model drys out, with generally thick model layers this only occurs in small areas around open-cut mines. The effect on predictions is an overestimate of the drawdown. The effect of this simplification on the model predictions is minimised by using storage values based on specific yield in areas where layers are outcropping. The specific yield parameters used for this are also included in the uncertainty analysis to explore prediction uncertainty caused by uncertainty of the specific yield parameters.

It is not appropriate to directly use the raw conductivity data presented in companion product 2.1-2.2 (Aryal et al., 2018) in the groundwater model. This is because the measured data pertain to samples on the spatial scale of centimetres (for lab measurements) to a few tens of metres (in-situ measurements). The regional groundwater model has a best resolution of 300 m, so some ‘upscaling’ of the raw data is necessary. Consider the problem of prescribing a suitable conductivity to a 300 m zone of the groundwater model. Typically there will be some regions of low conductivity within that region, but there will also be regions of high conductivity, and water will flow preferentially through those highly conductive regions, almost entirely bypassing the regions of low conductivity. As the true hydraulic properties of the model domain are unknown, these properties will be determined probabilistically; representative samples of the distribution of the hydraulic properties for the coal-bearing units (Hoskissons Coal, Maules Creek Formation) and the interburden are shown in Figure 19.

Figure 19

Figure 19 Parameter space explored in the numerical modelling for the hydraulic conductivity and specific storage for the interburden and coal layers (from Figure 21 in companion product 2.1-2.2 for the Namoi subregion (Aryal et al., 2018))

The orange line is a least squares fit of the measured hydraulic conductivity data (red dots) for the interburden and coal-bearing layers. The black lines are 64 random realisations of the parameter space that is used in the sensitivity analysis.

Data: Bioregional Assessment Programme (Dataset 1)

Less data are available to constrain vertical conductivity. Aquitards typically occur as roughly horizontal layers of low conductivity in stratified geological systems such as the Namoi subregion. Such aquitards have little effect on horizontal water flow, since it flows quickly through the surrounding aquifers, but have a greater effect on vertical flow, since the water must pass through the aquitard. This means that groundwater models typically use a vertical conductivity that is a small multiple of horizontal conductivity. A multiplying factor (Kv to Kh ratio) is chosen in the Namoi subregion groundwater model (and this is varied in the uncertainty analysis).

The productive aquifers of the alluvium are assumed to be more porous and conduct water more rapidly than the interburden layers, reflecting their grain size and arrangement (see companion product 2.1-2.2 (Aryal et al., 2018)). The alluvium model layers do not have a decay in the hydraulic conductivity with depth as there was no evidence seen in the field measurements (see companion product 2.1-2.2 (Aryal et al., 2018)).

Last updated:
6 December 2018
Thumbnail of the Namoi subregion

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