e-f-82-tem-0026 1 (report template 2010)mer-web.s3.amazonaws.com/ceip/mlp appendix m - mine...

Central Eyre Iron Project Mining Lease Proposal

APPENDIX M MINE WATER MANAGEMENT – NUMERICAL

GROUNDWATER FLOW MODEL

COPYRIGHT Copyright © IRD Mining Operations Pty Ltd and Iron Road Limited, 2015 All rights reserved

This document and any related documentation is protected by copyright owned by IRD Mining Operations Pty Ltd and Iron Road Limited. The content of this document and any related documentation may only be copied and distributed for purposes of section 35A of the Mining Act, 1971 (SA) and otherwise with the prior written consent of IRD Mining Operations Pty Ltd and Iron Road Limited.

DISCLAIMER A declaration has been made on behalf of IRD Mining Operations Pty Ltd by its Managing Director that he has taken reasonable steps to review the information contained in this document and to ensure its accuracy as at 5 November 2015.

Subject to that declaration:

(a) in writing this document, Iron Road Limited has relied on information provided by specialist consultants, government agencies, and other third parties. Iron Road Limited has reviewed all information to the best of its ability but does not take responsibility for the accuracy or completeness; and

(b) this document has been prepared for information purposes only and, to the full extent permitted by law, Iron Road Limited, in respect of all persons other than the relevant government departments, makes no representation and gives no warranty or undertaking, express or implied, in respect to the information contained herein, and does not accept responsibility and is not liable for any loss or liability whatsoever arising as a result of any person acting or refraining from acting on any information contained within it..

REPORT

CENTRAL EYRE IRON PROJECT MINE WATER MANAGEMENT – NUMERICAL GROUNDWATER

FLOW MODEL

E-F-16-RPT-0017

Revision Issue Date Revision Description Document Author Checked By Approved By

A 20/12/2013 IRD review Vincent Puech Paul Howe Robert James

B 17/02/2014 IRD review Vincent Puech Paul Howe Robert James

C 03/03/2014 Final Vincent Puech Paul Howe Robert James

D 25/07/2014 Final with Addendum A and B Vincent Puech Dougal Currie

Brian Barnett Rohan Baird

0 21/10/2014 Approved with updated seepage modelling integrated into report

Dougal Currie Brian Barnett Rohan Baird

0 11/12/2014 Amended with minor comments from client

Leighton Randell Greg Hoxley Greg Hoxley

1 13/10/2015 Amended with comments from DEWNR

Brian Barnett Johan du Plooy Johan du Plooy

M - Mine Water Management - Numerical Groundwater Flow Model.docx 4/11/2015 Page 2 of 42

Contents List of Figures ............................................................................................................................................................ 4

List of Tables ............................................................................................................................................................. 5

1 Introduction ...................................................................................................................................................... 6

1.1 Modelling objectives ................................................................................................................................ 6

1.2 Model confidence level classification ....................................................................................................... 6

2 Model design and construction ........................................................................................................................ 7

2.1 Numerical method .................................................................................................................................... 7

2.2 Extent ........................................................................................................................................................ 7

2.3 Topography ............................................................................................................................................... 9

2.4 Geology and hydrostratigraphy ..............................................................................................................10

2.4.1 Overview .........................................................................................................................................10

2.4.2 Quaternary and Tertiary strata .......................................................................................................10

2.4.3 Basement strata ..............................................................................................................................11

2.5 Model layers ...........................................................................................................................................11

2.5.1 Overview .........................................................................................................................................11

2.5.2 Layer 1 (Quaternary).......................................................................................................................11

2.5.3 Layer 2 and 3 (Tertiary)...................................................................................................................12

2.5.4 Layer 4 (saprolite) ...........................................................................................................................16

2.5.5 Layer 5 and 6 (basement) ...............................................................................................................16

2.6 Starting model parameters .....................................................................................................................18

2.6.1 Hydraulic properties .......................................................................................................................18

2.6.2 Recharge .........................................................................................................................................19

2.6.3 Evapotranspiration .........................................................................................................................20

2.7 Boundary conditions ...............................................................................................................................20

2.8 Calibration ..............................................................................................................................................23

3 Operational mine water management ...........................................................................................................29

3.1 Dewatering simulations ..........................................................................................................................29

3.2 CEIP model predictions ...........................................................................................................................31

3.2.1 Dewatering wells ............................................................................................................................31

3.2.2 Pumping from pit sumps ................................................................................................................33

4 Groundwater impact assessment modelling ..................................................................................................35

4.1 Overview .................................................................................................................................................35

4.2 Modification to the model ......................................................................................................................35

4.2.1 Integrated landform .......................................................................................................................35


4.3 Model predictions...................................................................................................................................36

4.3.1 During mining operation.................................................................................................................36

4.3.2 Post closure predictions .................................................................................................................38

4.3.3 Model sensitivity.............................................................................................................................42

5 Conclusions .....................................................................................................................................................45

6 Limitation statement ......................................................................................................................................46

6.1 General ...................................................................................................................................................46

6.2 Model predictive uncertainty .................................................................................................................46

6.3 Management scenarios for pit inflows ...................................................................................................46

7 References ......................................................................................................................................................47

Appendix A Unsaturated zone modelling used to predict recharge under the integrated landform ...............48

Introduction ........................................................................................................................................................48

Method ...............................................................................................................................................................48

Overview .........................................................................................................................................................48

Model platform...............................................................................................................................................48

Conceptual model ...........................................................................................................................................48

Material properties .........................................................................................................................................51

Model runs ......................................................................................................................................................53

Results ................................................................................................................................................................53

Background recharge ......................................................................................................................................53

Integrated landform recharge during construction .......................................................................................54

Closure recharge .............................................................................................................................................56

Summary of key assumptions .............................................................................................................................56

References ..........................................................................................................................................................57

Appendix B Location of observation bores used as calibration targets for each layer .....................................58

Appendix C Model calibration data ...................................................................................................................61

Appendix D Model sensitivity analysis parameters ...........................................................................................64


List of Figures Figure 1: Model grid extend ..................................................................................................................................... 8 Figure 2: DEM of ground surface elevation for the model domain (numbered locations represent depth to groundwater measurements for the water table aquifer) ....................................................................................... 9 Figure 3: Distribution of Tertiary strata with lease superimposed (SKM 2013; adapted from Hou et. al., 2012) 10 Figure 4: Layer 1 (Quaternary) thickness................................................................................................................12 Figure 5: Conceptualisation of the Tertiary sediment profile (see Figure 3 for alignment of section A-A’- viewed from northwest) .....................................................................................................................................................13 Figure 6: Layer 2 (Neogene) thickness ....................................................................................................................13 Figure 7: Layer 3 (Paleogene) thickness .................................................................................................................14 Figure 8: Location of extended cross sections .......................................................................................................15 Figure 9: SW – NE cross section to include Polda Basin and highlands NE of proposed mine lease .....................15 Figure 10: NW – SE Cross Sectionto include proposed mine lease and Polda Basin ..............................................16 Figure 11: Layer 4 (saprolite) thickness ..................................................................................................................17 Figure 12: Broken ground records per drillhole (number/drillhole/interval) ........................................................18 Figure 13: Zones of inferred higher fracturing in shallow basement (Layer 5) ......................................................18 Figure 14: Integrated landform layout ...................................................................................................................19 Figure 15: Model boundary conditions (in blue the constant heads boundary conditions). ................................22 Figure 16: Relationship between natural surface elevation and depth to water table. ........................................23 Figure 17: Predicted direction of vertical groundwater flow (SKM, 2014) ............................................................23 Figure 18: Observed versus calculated heads ........................................................................................................26 Figure 19: Modified field of hydraulic conductivity for layer 1 ..............................................................................26 Figure 20: Recharge zone defined in the model (purple represents zones of higher recharge in elevated areas, the inset shows how recharge was represented under the integrated landform) ................................................27 Figure 21: Pre-mining head distributions in model Layer 1 (Watertable) and in Layer 5 (Basement) ...................28 Figure 22: Locations of simulated dewatering wells ..............................................................................................29 Figure 23: Representation of the drain cells (shown in yellow) within the model domain ...................................31 Figure 24: Predicted groundwater pit dewatering during the 25 years of mining operation ................................32 Figure 25. Dry cells (purple) in the quaternary layer at the beginning of mining ..................................................35 Figure 26. Drawdown in the Tertiary aquifer (Layer 3) at the end of mining (year 25) .........................................36 Figure 27. Drawdown in the fractured bedrock aquifer (Layer 5) at the end of mining (year 25).........................37 Figure 28: Model Water Budget during Mining .....................................................................................................38 Figure 29. Residual drawdown in the Tertiary aquifer (Layer 3) at steady state (1000 years) ..............................40 Figure 30. Residual drawdown in the fractured bedrock aquifer (Layer 5) at steady state (1000 years) ..............40 Figure 31: Predicted pit inflow and outflow 1000 years post closure ....................................................................41 Figure 32: Predicted evolution of pit lake water level post closure .......................................................................42 Figure 33. . Model uncertainty – extent of drawdown (1 m contour) in the Tertiary aquifer (Layer 3) at the end of mining (year 25) and 1000 years after closure...................................................................................................43 Figure 34. Model uncertainty – extent of drawdown (1 m contour) in the fractured bedrock aquifer (Layer 5) at the end of mining (year 25) and 1000 years after closure .....................................................................................43 Figure 35. Model sensitivity – predicted water levels in the pit post mine closure. ..............................................44 Figure 36 Conceptual model for background scenario ..........................................................................................49 Figure 37 Conceptual model for integrated landform scenario .............................................................................50 Figure 38 Conceptual model for closure scenario ..................................................................................................51 Figure 39 Particle size distribution (PSD) curves based on available data .............................................................52 Figure 40 Water retention curves for the materials modelled ..............................................................................53 Figure 41 Tornado plot of base case sensitivity analysis ........................................................................................54


Figure 42 Tornado plot of waste rock sensitivity analysis ......................................................................................55 Figure 43 Comparison of historic rainfall trend (BOM site Warramboo 18090) to modelled recharge ................55 Figure 44 Tornado plot of topsoil sensitivity analysis ............................................................................................56 List of Tables Table 2: Model layers .............................................................................................................................................20 Table 3: Calibrated hydraulic conductivities (m/day) .............................................................................................25 Table 4: Lowest simulated pit floor elevation for selected years of the proposed mine operation (m AHD) .......30 Table 5: Simulated dewatering rates from dedicated dewatering wells ...............................................................32 Table 6: Predicted average mine pit groundwater inflow rates for each year of operation (ML/day) ..................33 Table 7: Total predicted seepage per model layer for each pit (ML) .....................................................................33 Table 8: Total predicted seepage per model layer for each pit as percentage of total seepage volume ..............34 Table 9: Stress periods used in post mining predictive simulations. ......................................................................39 Table 10: Model Sensitivity – estimated areas of drawdown as defined by the 1 m contour. ..............................43 Table 11 Assumed material properties ..................................................................................................................52 Table 12 Predicted average annual water balance components for the three primary scenarios ........................53


1 Introduction

1.1 Modelling objectives A numerical groundwater flow model has been developed for the proposed Central Eyre Iron Project (CEIP) to represent and simulate the groundwater system around the proposed pit and the waste rock integrated landform. The numerical model aims to provide predictions of future groundwater behaviour in response to groundwater affecting activities associated with the project, namely the mine pit and development of the waste rock integrated landform. The purpose of the modelling is twofold:

1) to address the project’s groundwater engineering objectives, by providing predictions of groundwater inflow to the pit and the necessary infrastructure required to control inflow and achieve safe mining conditions for the life of the mine; and

2) to inform the groundwater impact assessment component of the project Environmental Impact Statement (EIS) for which the effect of mining and associated activities on the groundwater regime can be simulated for the life of mine and post-closure.

1.2 Model confidence level classification The Australian Groundwater Modelling Guidelines (Barnett et. al., 2012) provide a means of classifying numerical groundwater flow models according to the confidence with which they can be used as a predictive tool. The classification depends on a number of factors including:

n The amount and quality of data on which the conceptualisation and model calibration are based. n The manner in which the model is calibrated and the accuracy of the calibration. n The objectives and requirements of the investigation. n The manner in which the predictions are formulated.

The confidence level classification reflects the expected modelling procedures and outcomes, and takes account of the project requirements. Calibration of the CEIP has been undertaken in steady state, and as a consequence provides limited constraints on the hydrogeological parameters that control the storage and movement of groundwater. The model confidence level classification as described by Barnett et. al. (2012) is targeted at Class 2 (medium confidence). Additional confidence in model predictions (and an increase in confidence level classification) will follow model upgrade to a transient calibration after recording the evolution of groundwater levels during the first few years of mining and dewatering. The inclusion of additional groundwater data would allow a more rigorous calibration of the CEIP mine site model.


2 Model design and construction

2.1 Numerical method The model has been developed in the MODFLOW numerical code using Groundwater Vistas (v6) graphical user interface (GUI). MODFLOW has long been accepted as the industry standard groundwater flow modelling package. The MODFLOW-SURFACT code was used to improve numerical stability and accuracy for modelling the drying and rewetting of model cells, which is particularly useful when simulating operational and closure phases of mining pits.

2.2 Extent The model size and spatial discretisation have been chosen to ensure that the model domain extends beyond hydrogeological features of significance, including the expected extent of groundwater depressurisation associated with pit dewatering, which was estimated prior to model development using analytical solutions (SKM, 2013). This work suggested that a radius of influence of up to 20 km is possible in response to mine pit development. The model grid covers an area of 2561 km2 (48.6 km in the north-south direction, and 52.7 km in the east-west direction) centred approximately on the mine pit location. Model cell dimensions range from 100 x 100 m at the mine pits to 1,000 x 1,000 m at the edge of the model domain. The outer extent of the model grid is represented in Figure 1 along with the proposed mine lease, mine pits and integrated landform. The model grid is oriented with one axis (east west) parallel to strike to enable the effect of basement anisotropy to be incorporated in the model design. The total model thickness is on average 690 m to account for the planned mine pit depth of 620 m below ground level. The hydraulic conductivity and storativity in the basement at a depth greater than 620 m is assumed to be negligible due to limited open fracturing and weathering below that depth.


Figure 1: Model grid extend


2.3 Topography Data from a 50 m regional Digital Elevation Model (DEM) covering the model domain and a more detailed LIDAR DEM with a 20 m resolution covering the proposed mine lease area (18.6 x 12 km) have been merged to generate the numerical model ground surface elevation (Figure 2). The LIDAR data provides accurate vertical resolution across the mine area (0.2 m), allowing adequate representation of the ground surface elevation around the zone of interest (mine pits and integrated landform). The locations of all available depth to groundwater measurements for the water table aquifer are also indicated on Figure 2 (discussed further in Section 2.7).

Figure 2: DEM of ground surface elevation for the model domain (numbered locations represent depth to groundwater measurements for the water table aquifer)


2.4 Geology and hydrostratigraphy

2.4.1 Overview

The current conceptual hydrogeological model is discussed in detail in SKM (2014a). The following provides a summary of the information pertinent to the development of the numerical groundwater flow model.

2.4.2 Quaternary and Tertiary strata

Examination of the 1:250,000 geological map (Kimba sheet) indicates the thickness of Quaternary sediments is typically 1 to 14 m. These sediments are unlikely to be of hydrogeological significance in relation to mine water management, as they generally occur above the water table. Tertiary sediments in the project area consist broadly of two sequences, Palaeogene (mostly Eocene) deposits and younger Neogene (Miocene to early Pliocene). Palaeogene sediments, as depicted on the available palaeodrainage map (Hou et. al., 2012), do not extend to the pit location, and only Neogene (mostly Miocene) sediments are expected at the pit area and the immediate surroundings. To the southwest of the pit the Neogene sediments are underlain by older Palaeogene sediments (Figure 3). Small areas exist where no Palaeogene or Neogene sediments have been mapped (Hou et. al., 2012). These areas appear to coincide with basement and topographic highs. One such area lies at the western end of the mine pits (Murphy’s and Boo-loo; Figure 3). The lithology of the Neogene in the area of the pits is very heterogeneous, and is predominantly argillaceous (clays and silts). However, the base of the Neogene sediments are often characterised by a sandier facies resulting from reworking of older Palaeogene sediments, which contained coarser fluvial and marine sandy facies (Hou et. al., 2003).

Figure 3: Distribution of Tertiary strata with lease superimposed (SKM 2013; adapted from Hou et. al., 2012)

Basement/topographic high – mapped as no Tertiary but modelled as low k layers to replicate bore log data

1

Palaeogene sediments largely overlain by Neogene sediments Extent of Neogene sediments


2.4.3 Basement strata

Basement strata are comprised of three distinct units having different hydrogeological characteristics:

n The uppermost unit is composed of weathered gneiss (saprolite), which is typically of low permeability and storage.

n The permeability in the underlying un-weathered basement strata (gneiss) is expected (based on prior and recent drilling) to be dominated by secondary porosity (fracturing) which is typically of moderate permeability.

n Below this it is expected that basement is largely unfractured or, where fracturing exists, the fractures are closed resulting in low permeability.

2.5 Model layers

2.5.1 Overview The model layer structure reflects the sequence of stratigraphic units described in Section 2.4. The model is made of 6 layers representing:

n Quaternary deposits (Layer 1); n Tertiary Neogene argillaceous sediments (Layer 2); n Tertiary Neogene sandy zones combined with Paleogene sediments (Layer 3); n Weathered basement / saprolite (Layer 4); n Fractured basement (Layer 5); and n Basement with very limited open fracturing (Layer 6).

The surfaces that delimit each layer have been derived sequentially by adding (or subtracting) the interpreted thickness of the layer from the previous surface. With the exception of Layer 2, layer thicknesses were derived from bore logs sourced from publicly available databases and recent CEIP mineral resource and hydrogeological drilling. Broadly, each layer was assigned an average thickness based on data reported in bore logs, however within approximately 1.5 km of bore locations thicknesses were interpolated to match the information interpreted from borelogs. Similarly, thicknesses were reduced when the proximity of the basement to the surface did not allow each unit to be set at their assumed constant thickness (e.g. where basement outcrops or is close to outcropping), in which case the thicknesses were proportionally reduced. Layer 2 thickness was assigned as the difference between the bottom of Layer 1 and the top of Layer 3. The layers thicknesses are detailed in the following sections.

2.5.2 Layer 1 (Quaternary) The Quaternary layer was assigned an average thickness of 10 metres over the majority of the model domain. Where basement outcrops/subcrops the thickness was reduced while in areas with reliable hydrogeological borelogs, the thickness was increased to represent the available data. Figure 4 presents an image of the model domain showing the thickness of Quaternary sediments.


Figure 4: Layer 1 (Quaternary) thickness

2.5.3 Layer 2 and 3 (Tertiary) As reported in the data review (SKM, 2013), the presence of sand at the base of the Neogene is not consistent across the study area. In fact, the distribution and thickness is too variable to be represented in detail in the model. It is considered important, however, that a broad scale representation of a permeable sandy facies is incorporated in the model. As a result, the Tertiary is represented by two layers, with the lower layer (Layer 3) representing the Palaeogene and the upper layer (Layer 2) the Neogene. In areas where the Palaeogene is absent, such as around the pit (Figure 3), Layer 3 is still incorporated in the model as a thin permeable layer to represent the sandy facies at the base of the Neogene. Where both the Neogene and the Palaeogene sediments are mapped as absent (such as in the west of the Lease and beyond, see Figure 3), both Layer 2 and Layer 3 have been assigned a low permeability to represent saprolite, which is seen in both the bore database and during recent drilling. A schematic profile of the conceptualisation of Tertiary sediments (as described above) is presented in Figure 5. Figure 6 presents an image of the model domain showing the thickness of the Neogene sediments, and Figure 7 presents an image of the model domain showing the thickness of the Paleogene sediments.


Figure 5: Conceptualisation of the Tertiary sediment profile (see Figure 3 for alignment of section A-A’- viewed from northwest)

Figure 6: Layer 2 (Neogene) thickness

Layer 4 - saprolite Layer 3 - Palaeogene/

Neogene

Sandy – ‘high’ k

Clayey – ‘low’ k

Basement/topographic high

Extent of Palaeogene

A’ A

Only N

eogene

Approximate Pit Location

Only Neogene

Layer 2 - Neogene

Layer 1 - Quaternary


Figure 7: Layer 3 (Paleogene) thickness

Figure 8, Figure 9 and Figure 10 below portrays two extended cross sections through the study area (inclusive of the model domain)


Figure 8: Location of extended cross sections

Figure 9: SW – NE cross section to include Polda Basin and highlands NE of proposed mine lease


Figure 10: NW – SE Cross Sectionto include proposed mine lease and Polda Basin

2.5.4 Layer 4 (saprolite) Layer 4, which represents saprolite, has been assigned a constant thickness of 30 m, corrected at existing bore locations and basement sub and outcropping areas. Figure 11 presents an image of the model domain showing the thickness of the saprolite.

2.5.5 Layer 5 and 6 (basement) Unweathered basement in the model is represented by two layers; the top 180 m (fractured basement, in addition to saprock) is represented as Layer 5, and the bottom 450 m (largely un-fractured rock) is represented as Layer 6. A histogram of the number of records of broken ground (recorded in the IRD resource drilling database) displayed over uniform depth intervals is shown in Figure 12. The histogram illustrates a higher degree of fracturing up to a depth of approximately 180 m below ground level. Accordingly, a thickness for Layer 5 of 180 m was adopted. Drill logs and aquifer testing results from recent field investigations (SKM, 2014d) suggest the presence of increased fracturing aligned with the mineralisation of the Murphy South and Boo Loo pits (around bores SKM1, SKM3, SKM 4 and SKM7, Figure 13). To account for these observations, two zones of increased higher permeability fracturing were defined in Layer 5 of the model (Figure 13) to represent this.


Figure 11: Layer 4 (saprolite) thickness


Figure 12: Broken ground records per drillhole (number/drillhole/interval)

Figure 13: Zones of inferred higher fracturing in shallow basement (Layer 5)

2.6 Starting model parameters

2.6.1 Hydraulic properties

A summary of the hydraulic conductivity estimates of the geological units in the study area based on earlier work completed by Coffey (Coffey, 2012 and 2013) and from recent field investigations conducted by SKM (2014d) are provided in Table 2. These estimates were used as a basis for model development and adjusted during the calibration process.

0.000 0.050 0.100 0.150 0.200 0.250 0.300 0.350 0.400 0.450 0.500

8325781

105129154178202226251275299324348372396421445469494518542566

Broken ground records per drillhole

Dept

h (m

bgl)

Histogram


2.6.2 Recharge

Studies of recharge in arid areas of Australia consistently show values of less than 1 mm/year under natural vegetation conditions (Dawes et al., 2002), however higher recharge can be expected where land clearance has occurred. In addition to rainfall recharge, construction of the waste rock integrated landform may also alter recharge to the local groundwater system. It is proposed that the integrated landform will be developed using a dry stacking technique in which a combination of waste rock from in-pit crushing and from the ore processing facility is distributed over the natural land surface. Given the disturbance of the natural surface, it is likely that the creation of the integrated landform will alter groundwater recharge over the footprint of the landform. Recharge under the integrated landform has been estimated before, during and after mining using an unsaturated zone model. A detailed account of the unsaturated zone modelling is provided in Appendix A. The integrated landform has been modelled as 25 segments (Figure 14), each representing 1 year of development. The integrated landform will be developed to its full height of 135 m in a progressive manner using three mobile stackers working in a clock-wise direction.

Figure 14: Integrated landform layout


Table 1: Model layers

Age Unit name Model layer

Initial estimate of K (m/d)[1]

Starting model K (m/d)

Specific yield Sy

Specific storage Ss

Quaternary Quaternary Layer 1 0.02 to 0.004[1] 0.01 0.1 5x10-6

Tertiary Neogene (Miocene / Pliocene)

Layer 2 0.5 to 3.0[2] 0.01 (clayey) 0.1 (sandy)

0.05 5x10-6

Palaeogene (Eocene - Poelpena)

Layer 3 0.2[3] 0.1 0.1 5x10-6

Achaean Upper Saprolite Layer 4 0.01[3] 0.01 0.05 5x10-6

Lower Saprolite

Saprock Layer 5 0.025 to 2.25[2] 0.1 (fractured) 0.001 (un-fractured)

0.001 5x10-6

Gneiss (Sleaford Complex)

Potentially fractured

Largely un-fractured

Layer 6 0.001[4] 0.001 0.0001 5x10-6

Notes [1] Data from Coffey (2012) tailings storage facility geotechnical investigation bores. [2] Data from SKM (2014a) drilling, construction and testing completion report. [3] Data from Coffey (2013) hydrogeological investigations groundwater monitoring bore installation and sampling program. [4] Upper estimate of unfractured rock hydraulic conductivity (Todd and Mays, 2005).

2.6.3 Evapotranspiration

Groundwater is discharged from areas of shallow water table through transpiration in areas where vegetation can access the water table and evaporation from shallow groundwater at or near the ground surface (typically in local ground surface depressions). The rate of each is likely to be significantly influenced by water table depth and groundwater salinity. Where the groundwater level comes close to the surface, evapotranspiration needs to be represented by the model. This does not necessarily require the groundwater level to reach the surface, as capillary rise to the surface and plant transpiration will extract water from the sub-surface. Evapotranspiration (ET) has been simulated using the EVT package of MODFLOW (head dependent flux). The initial maximum ET has been assigned as 1,000 mm/yr, with an extinction depth of between 1 and 5 metres, which allows the model to adjust ET depending on the depth to the water table. Calibration was achieved with a uniform ET extinction depth of 2 m applied across the entire model domain. The maximum ET rate of 1000 mm/yr has been adopted as a compromise between the potential evapotranspiration rate (which is substantially higher than this rate) and the linear decay of ET with depth as assumed in the EVT Package. In reality a typical plant water use with water depth relationship is not linear and it is necessary to adopt a maximum ET rate in the model that is somewhat lower than the potential ET rate to better match the plant water use characteristic within the depth of interest (in this case 0 to 2 m). Surface run-off into salt pans has not been modelled, although based on CEIP hydrological studies (RPS, 2013) it is expected to be minimal.


2.7 Boundary conditions The boundary conditions at the edges of the model domain reflect the conceptualisation of the regional hydrogeology, noting the model domain does not define the complete groundwater flow system as it is a portion of a larger regional flow system (i.e. the model boundaries do not coincide with the actual groundwater basin boundaries). The boundary conditions at the outer extent of the domain must account for water exchange and pressure transmission (groundwater inflow and outflow) between the model domain and the broader groundwater flow system. MODFLOW’s Constant Head Boundary package (CHD) has been used to assign heads to the outer model domain extent to allow groundwater to enter or exit the model (Figure 15). The following head conditions were assigned to the model constant head boundaries: Tertiary Aquifer

· Upper left corner – 108 mAHD · Lower left corner – 41 mAHD · Upper right corner – 112 m AHD · Lower right corner – 106 mAHD

Basement Aquifer

· Upper left corner – 79 mAHD · Lower left corner – 43 mAHD · Upper right corner – 51 m AHD · Lower right corner – 88 mAHD

The model extent has been made large enough so that groundwater stresses from mine water affecting activities will not be significantly constrained by boundary conditions (i.e. no unreasonably large induced flow across the boundaries that cannot be explained by the regional water balance).


Figure 15: Model boundary conditions (in blue the constant heads boundary conditions).

Two sets of CHDs were defined. The first set for the water table aquifer (Layers 1, 2 and 3) and the second for the fractured basement aquifer (Layers 5 and 6). For the water table aquifer, heads to be assigned at the model boundaries were estimated using the relationship between the natural surface elevation and the depth to water table at observation bores with available groundwater level data (Figure 12). Although the available data do not provide a strong correlation, the model domain has been defined at a large enough scale to keep the impacts of mining activities away from the edge of the model domain. The depth to water data presented in Figure 16 has been plotted on the DEM in Figure 2 for reference. For the basement aquifer, constant heads simulated at the model boundaries were defined by accounting for the observed and interpolated vertical gradient between the sedimentary and basement systems. The vertical gradient between the sedimentary and basement systems were assessed using a method outlined by Post et al (2007) (SKM, 2014). The calculated gradients are presented in Figure 17 below.


Figure 16: Relationship between natural surface elevation and depth to water table.

Figure 17: Predicted direction of vertical groundwater flow (SKM, 2014)

2.8 Calibration The calibration model was run in steady state. The available dataset, which comprises water levels gauged at different times, was utilised to represent an average potentiometry in the groundwater system and is assumed to represent stable equilibrium conditions. There were insufficient time-varying head data to enable a transient calibration to be undertaken. The calibration procedure involved matching predicted steady state groundwater heads with observed groundwater heads. The purpose of calibration is to help constrain boundary conditions and aquifer parameters and to increase confidence in model outcomes by illustrating the model’s ability to replicate observed groundwater heads.


Targets for the calibration were sourced from recent field investigations (SKM, 2014d) and from publicly available water level records (SA Government WaterConnect database). Most recent data were used where available, but historic data were also used in preference to using no data at all. Screen depth (where available) or constructed bore depth data were used to identify which model layer each water level reading should be assigned to. As the model was calibrated in steady state, a single head value (water level) for each location was used to characterise the aquifer potentiometric surface. These heads were recorded in observation wells over a period extending from 1960 to the present. The targeted heads, therefore, do not represent a snapshot at a given time but are a mosaic of observations made over time. The locations of observation bores for each layer are provided in Appendix B. The values of the observed heads are provided in Appendix C. The match between observed and modelled heads can be evaluated statistically through the calculation of the Root Mean Square (RMS) error, which is calculated by taking the square root of the average of the squared differences between observed and simulated heads. The RMS error of the calibration model is 4.6 m and the scaled RMS (SRMS) (which defines the ratio of RMS to the range of head measurements) is 4.3% indicating a satisfactory steady state calibration. In general the level of constraint over model parameters achieved in calibration is inadequate to generate a high level of confidence in model predictions. This lack of confidence arises from the fact that the model has been calibrated in steady state to a non-stressed aquifer condition whereas predictions are made in transient mode with significant external stresses associated with mine dewatering and TSF seepage. The model is probably best described as a Class 1 to 2 Confidence Level Classification (as defined by the Australian Modelling Guidelines; Barnett et. al., 2012). A high level of detailed geological data in the region of the mine and the availability of long term pumping tests that have been used to estimate the hydrogeological parameters used in the model are relevant features of the model that elevate it above a Class 1 Model. A Class 2 model is described by the guidelines as suitable for "providing estimates of dewatering requirements for mines and excavations and the associated impacts". Improvement in Confidence Level Classification can be expected once the mining to shallow levels is commenced and the model is verified against observed groundwater responses. The observed versus calculated heads are illustrated graphically in Figure 18 with the residuals (difference between observed and calculated heads) presented in Appendix C. Calibrated hydraulic conductivity values are summarised in Table 2. Assessment of the residuals indicates that within the vicinity of the mine pit, groundwater levels are generally over predicted (with the exception of SKM7, 4 and 2) whilst in the far northeast of the model domain water levels are generally under predicted (with the exception of 6031-300). Given the scale of the model which has been developed to assess mine specific engineering strategies as well as being used to assess possible regional impacts and the paucity of the input data, such variations in observed versus measured groundwater levels can be expected. Further to this, the noise (or uncertainty) surrounding the calibration data is large as data spans several years and seasons. To overcome numerical stability issues related to the largely dry Quaternary sediments (Layer 1) and the partially dry Tertiary aquifer, the structure of the top layers was redefined so that the water table is positioned in Layer 1. The bottom of the layer was lowered and the hydraulic conductivity values assigned were altered to match that of the unit in which the water table would normally lie. The illustration of the resulting hydraulic conductivity field is presented in Figure 19. A new zone of hydraulic conductivity was defined in Layer 1 for the purpose of calibrating to observations from bores that are located on areas of higher topography.


In relation to recharge and model calibration, the model domain was assigned a recharge rate of 1 mm/year except in areas of higher surface elevation (over 130 m AHD) where a recharge rate of 14.6 mm/year was applied to match observed heads. Increased recharge at higher elevations is expected as a result of orographic effects and due to the conceptual hydrogeological model that includes outcropping and sub-cropping fractured basement rocks at higher elevations that encourage relatively high recharge rates. The recharge zones are illustrated in Figure 20. Calibration has relied on available data and the current interpretation of the hydrogeological system. As further data become available the uncertainty in the model predictions will be reduced. Table 2: Calibrated hydraulic conductivities (m/day)

Kh (x-plane)

Kh (y-plane)

Kv (z-plane)

Layer 1 Quaternary – Zone 1 0.5 0.5 0.05

Quaternary – Zone 2 0.8 0.8 0.08

Quaternary – Zone 3 3 3 0.3

Quaternary – Zone 5 0.025 0.025 0.0025

Quaternary – Zone 7 0.007 0.007 0.0007

Quaternary – Zone 8 0.001 0.001 0.001

Layer 2 Tertiary (Neogene) 0.8 0.8 0.08

Layer 3 Tertiary (Paleogene) 3 3 0.3

Layer 4 Saprolite 0.007 0.007 0.0007

Layer 5 Fractured basement 0.025 0.025 0.025

Layer 5 Fractured zone 1[1] 0.5 0.5 0.5

Layer 5 Fractured zone 1[2] 1.5 1.5 1.5

Layer 6 Un-fractured basement 0.001 0.0005[3] 0.001 Notes: 1. Murphy South pit 2. Boo Loo pit 3. Kh reduced in un-fractured basement


Figure 18: Observed versus calculated heads

Figure 19: Modified field of hydraulic conductivity for layer 1


Figure 20: Recharge zone defined in the model (purple represents zones of higher recharge in elevated areas, the inset shows how recharge was represented under the integrated landform)

Steady state, pre-mining heads predicted by the model are presented in Figure21. The figure shows model predicted head distributions in Layer 1, representing the watertable and in Layer 5 representing the basement aquifer.

Watertable


Figure 21: Pre-mining head distributions in model Layer 1 (Watertable) and in Layer 5 (Basement)

Basement


3 Operational mine water management

3.1 Dewatering simulations Mining simulations were formulated to provide predictions during the operational mining phase. Predictive scenarios were constructed with an initial steady state period to stabilise all model heads and fluxes at appropriate pre-mining levels. Scenarios included a subsequent 27 stress periods each of 365 days. Each stress period is subdivided into ten calculation time steps of increasing length using a multiplier of 1.2 (time step length is 1.2 times that of the previous time step). Dewatering via groundwater wells is preferable to in-pit pumping for operational reasons including, reduced sediment load to the process water pond and improved pit wall stability. Despite this, the use of dewatering wells alone will not ultimately achieve dry mining conditions and in-pit sump pumps will be required to control groundwater which is not intercepted by dewatering wells. Various dewatering system configurations were tested to assess the number of dewatering wells required to reduce the volume of water reporting to in-pit sumps. Figure 22 presents a locality plan showing the location of dewatering wells, which have been simulated using MODFLOW’s WEL (well) package. Wells are specified by assigning a pumping rate to a selected cell at the location of each well. Individual well abstraction rates have been constrained to realistic estimates based on the field testing program, ranging from 5 to 20 L/s. In pit wells SKM1, 4, 6, and 7 are located to align with existing wells installed during the 2013 drilling, construction and testing program (SKM, 2014d). Ex-pit wells 1, 2, 3, 4, 5, 6, and 7 have been positioned to align with zones of inferred higher fracture frequency, which have the potential to contribute higher dewatering pumping yields. The scenario presented removes ‘in-pit’ wells from the dewatering system at the commencement of mining (year 0), however it may be feasible to continue pumping from these wells if their integrity can be sustained during mining operations.

Figure 22: Locations of simulated dewatering wells


The extent of the mine pit is based on that provided in the mine layout at the time the numerical model was developed. Groundwater inflow volumes and rates to the Murphy South and Boo Loo mine pits have been assessed using MODFLOW’s DRN (drains) package. Drains are a head dependent boundary condition defined by assigning a drain invert elevation and a conductance term that constrains flow to the drain cell. The drain cells cease to function once the potentiometric surface reaches or declines below the invert elevation. The drain cell conductance term is set uniformly at 10 000 m2/d for all drain cells thereby representing a relatively unconstrained flow of groundwater into the boundary condition. Drain cell invert elevations were assigned to the base of model cells that are above the mine floor and to the elevation of the mine pit floor for those cells in which the floor is present. Drain cells are varied for each year of operation in accordance with the mine plan (refer Table 3 and Figure 23). Table 3: Lowest simulated pit floor elevation for selected years of the proposed mine operation (m AHD)

Year of Mining Pit floor elevation

Murphy South Boo Loo 1 39 - 2 -9 - 3 -57 - 4 -93 - 5 -141 - 6 -201 - 7 -237 - 8 -309 - 9 -309 -

10 -309 - 15 -417 68 18 -477 -52 21 -537 -220 22 -537 -220 23 -537 -220 24 -537 -220 25 -537 -220


Figure 23: Representation of the drain cells (shown in yellow) within the model domain

3.2 CEIP model predictions

3.2.1 Dewatering wells The predicted average daily rates of abstraction from dewatering wells over the life of the mine (including two years of advanced dewatering) are presented in Table 4 and Figure 24. The predicted total volume of water abstracted by the dewatering wells during the 27 years of operation including the two years of advanced dewatering approximates 58 GL, around 61% of the total volumes of groundwater managed during mine operation (including water pumped from in-pit sumps; see Section 3.2.2).


Table 4: Simulated dewatering rates from dedicated dewatering wells

Well ID Coordinates Abstraction rate Start pumping

[mining year] End pumping

[mining year]1 Easting Northing (ML/day) (L/sec)

1 558400 6321800 1.73 20 -2 25

2 560500 6322600 0.43 5 -2 25

3 561600 6322500 1.73 20 -2 25

4 565400 6321800 0.86 10 -2 15

5 563800 6320900 0.43 5 -2 16

6 561100 6320300 0.43 5 -2 25

7 559300 6320800 0.86 10 -2 8

SKM1 2 563900 6321200 1.73 20 -2 0

SKM4 2 560300 6320900 1.73 20 -2 0

SKM6 2 562300 6321100 0.43 5 -2 0

SKM7 2 560200 6322100 1.73 20 -2 0

Notes: 1. The last year of pumping was dictated in the model when the pumping cells became dry for wells 1 to 7 2. Existing wells installed during field investigations reported in SKM (2014d)

Figure 24: Predicted groundwater pit dewatering during the 25 years of mining operation


3.2.2 Pumping from pit sumps The predicted average annual pit inflow rates to both Murphy South and Boo Loo mine pits are presented in Table 5 (total combined) and Figure 24 (broken down by each of the lithological units intersected by the mine pits). The assumption has been made that all of the water will report to in-pit sumps from where it will be recovered and pumped to the mine process water pond. Table 5: Predicted average mine pit groundwater inflow rates for each year of operation (ML/day)

Year of Mining Predicted inflows to mine pits (ML/d)

Murphy South Boo Loo

1 3.7 -

2 10.3 -

3 13.2 -

4 14.3 -

5 16.8 -

6 14.9 -

7 15.5 -

8 14.4 -

9 13.4 -

10 12.5 -

11 13.1 -

12 10.6 -

13 9.5 -

14 8.7 -

15 8.1 -

16 7.8 1.1

17 7.6 0.1

18 7.3 0.0

19 6.6 6.2

20 6.2 3.3

21 5.9 2.2

22 5.7 1.5

23 5.5 1.1

24 5.4 0.9

25 5.4 0.8

The predicted contribution of each stratigraphic unit to mine pit inflow is summarised in Table 6 and Table 7, showing that, over the life of the mine:

n around 93% of the total predicted groundwater inflow occurs to the Murphy South pit, which is the deeper pit and developed in advance of the Boo Loo pit

n fractured basement contributes around 82% of the total volume of groundwater predicted to report to the pits

n un-fractured basement accounts for around 11% of the predicted pit inflows n Paleogene sediments are predicted to contribute less than 5% to pit inflows n other lithological layers are predicted to contribute insignificant volumes of water to the pits


Table 6: Total predicted seepage per model layer for each pit (ML)

Murphy’s South Boo Loo Total

Tertiary (Neogene) 296 0 296

Tertiary (Paleogene) 4,150 0 4150

Saprolite 539 0 539

Fractured Basement 72,236 6,264 78500

Low-Fractured-Basement 11,317 649 11966

88538 6913 95451

Table 7: Total predicted seepage per model layer for each pit as percentage of total seepage volume

Murphy’s South Boo Loo Total

Tertiary (Neogene) 0.3% 0.0% 0.3%

Tertiary (Paleogene) 4.3% 0.0% 4.3%

Saprolite 0.6% 0.0% 0.6%

Fractured Basement 75.7% 6.6% 82.2%

Low-Fractured-Basement 11.9% 0.7% 12.5%

92.8% 7.2% 100% Prior to development of the Boo Loo pit, operation of the dewatering wells and dewatering of the Murphy South pit using in-pit sumps is predicted to reduce pit in-flows to Boo Loo during the first few years of its development to very small amounts.


4 Groundwater impact assessment modelling

4.1 Overview Two model scenarios were prepared to inform the groundwater impact assessment being undertaken for the CEIP mine site:

1) Mine operation with progressive development of the integrated landform facility up to year 25; and 2) Post-mining (closure) simulation with an altered recharge rate under the footprint of the integrated

landform until steady state conditions are achieved.

4.2 Modification to the model

4.2.1 Integrated landform The integrated landform is represented in the model by applying recharge to the landform footprint. An unsaturated zone modelling assessment (Appendix A) was used to estimate a recharge rate for the integrated landform during its construction (50 mm/y) and post-rehabilitation (6 mm/y). In both cases, a conservative estimate of recharge has been chosen to provide a robust assessment of the potential impacts related to groundwater. In the model, the transfer of water through the unsaturated zone is not represented and recharge is applied directly to the uppermost saturated model layer. At places where the Quaternary layer (model layer 1) is dry, the recharge is applied directly to the underlying layer (Figure 25). A time lag of 20 years has been assumed for recharge to reach the groundwater table from the top of the integrated landform. The rationale for this is discussed in Appendix A.

Figure 25. Dry cells (purple) in the quaternary layer at the beginning of mining

After rehabilitation, recharge under the integrated landform is altered to 6 mm/yr, which provides for a conservative estimate of recharge under the rehabilitation options being considered by Iron Road (see Appendix A). Again, the purpose of the conservatism applied in the estimate of recharge is to provide for a robust assessment of potential groundwater-related impacts.


4.3 Model predictions

4.3.1 During mining operation The predicted drawdown in the Tertiary aquifer (Model Layer 3) at the completion of mining (year 25) is presented in Figure 26. Drawdown as defined by the 1 m drawdown contour is not predicted to extend more than seven kilometres from the mine pits. Similarly, drawdown in the fractured rock aquifer (Model Layer 5) at the completion of mining is not predicted to extend more than seven kilometres from the mine pits (Figure 27). Although enhanced recharge has been applied to the model, there is no observed increase in groundwater levels beneath the integrated landform during the operational phase. Any potential mounding is controlled by the extent of the cone of depression caused by pit dewatering.

Figure 26. Drawdown in the Tertiary aquifer (Layer 3) at the end of mining (year 25)


Figure 27. Drawdown in the fractured bedrock aquifer (Layer 5) at the end of mining (year 25)

The model predicted water budget for the whole model is shown in Figure 28. It shows relatively small fluxes in and out of the constant head boundary conditions defined on the edges of the model. A net storage change (reflecting drawdown at the pit) is balanced by the loss of groundwater through pit inflows and well pumping.


Figure 28: Model Water Budget during Mining

4.3.2 Post closure predictions The post closure predictions assume a permanent 6 mm/year of recharge below the entire integrated landform as well as decommissioning of the dewatering system and ongoing net evaporation from the mine pits. The closure simulation was run for 10,000 years until a new steady state was achieved. Post mining simulations start at the completion of mining and pit dewatering operations. Because of numerical instability caused by the steep hydraulic gradients and rapid recovery rates immediately after cessation of mining, simulations included small initial stress periods with progressive expansion in stress period length through to 10 000 years. Each stress period is subdivided into ten calculation time steps of increasing length using a multiplier of 1.2 (time step length is 1.2 times that of the previous time step). The stress period s are summarised in Table 8. Note that although simulations continued for a total of 10 000 years post mining, most results are reported after 1 000 years.


Table 8: Stress periods used in post mining predictive simulations.

Period_Days Years_Post_Mining Time_Steps Multiplier 1 0.00 10 1.2 2 0.01 10 1.2 3 0.02 10 1.2 6 0.03 10 1.2 12 0.07 10 1.2 23 0.13 10 1.2 42 0.24 10 1.2 79 0.46 10 1.2 147 0.86 10 1.2 274 1.61 10 1.2 511 3.01 10 1.2 953 5.62 10 1.2 1778 10.50 10 1.2 3317 19.58 10 1.2 6189 36.54 10 1.2 11547 68.18 10 1.2 21542 127.19 10 1.2 40190 237.30 10 1.2 74981 442.73 10 1.2 203763 1000.99 10 1.2 260982 1716.01 10 1.2 486902 3049.98 10 1.2 908390 5538.72 10 1.2 1628675 10000.85 10 1.2 The model predicts no residual mounding below the integrated landform. The predicted change in groundwater head 1000 years after mining (steady state) for the Tertiary aquifer is shown in Figure 29. This figure shows a net reduction in groundwater level between 2 and 12 m below the integrated landform (from the pre-mining groundwater level). In this case the drawdown has been estimated as the difference in groundwater head between the pre-mining and steady state post mining conditions. The long term residual drawdown prediction of Figure 29 shows that on-going evaporation from the pit lake creates a cone of drawdown extending approximately seven kilometres from the mine pits in the Tertiary aquifer. Similarly, drawdown in the fractured bedrock aquifer is predicted to extend approximately seven kilometres from the mine pits (Figure 31). The model predicted drawdowns are asymmetrical and the distances discussed above are the maximum predicted extents.


Figure 29. Residual drawdown in the Tertiary aquifer (Layer 3) at steady state (1000 years)

Figure 30. Residual drawdown in the fractured bedrock aquifer (Layer 5) at steady state (1000 years)


The predicted rates of groundwater inflow, incident rainfall and evaporative losses from the pit from the end of mining to 1000 years post closure are presented in Figure 31. The model predicts groundwater inflow to the pit will stabilise at approximately 7 ML/d 20 years after closure. Incident rainfall is predicted to contribute approximately 6 ML/d while evaporative losses are predicted to rise (as a result of the increasing pit surface area) from less than 1 ML/day at 1 year after closure to more than 13 ML/day at 1000 years after closure.

Figure 31: Predicted pit inflow and outflow 1000 years post closure

Predicted pit lake water level recovery over the 1,000 year simulation period is presented in Figure 32. The pit water level is predicted to recover to approximately -275 m AHD 1000 years post closure which is approximately 335 m below the pre-mine groundwater table, and as such a cone of groundwater depression is predicted to form around the pit.

-30

-20

-10

0

10

20

30

0.2

0.7 2 6 16 45 126

355

1000

Pit I

nflo

w (>

0) a

nd o

utflo

w (<

0)

[ML/

day]

Years post closure

Groundwater inflowIncident rainfallEvaporation


Figure 32: Predicted evolution of pit lake water level post closure

4.3.3 Model sensitivity Model uncertainty analysis was conducted to assess the potential range in the extent and magnitude of drawdown that could be expected in response to mine operation. The analysis was conducted by varying aquifer transmissivity and storage parameters within credible ranges. For a given pumping duration, the radius of the cone of drawdown is a function of the square root of transmissivity divided by the storage coefficient, a term called aquifer diffusivity. A high aquifer diffusivity (high transmissivity and low storage) will generally produce an extensive relatively flat cone of drawdown while a low aquifer diffusivity (low transmissivity and high storage) will produce a less extensive relatively steep cone of drawdown. Hydraulic conductivity and storage parameters were doubled and halved accordingly to assess model sensitivity. A summary of the aquifer parameters used in the uncertainty analysis is provided in Appendix D. The results of the numerical modelling are presented in Figure 33 and Figure 34 for Layer 3 (Tertiary aquifer) and Layer 5 (fractured bedrock aquifer), respectively. The results indicate that there is only a modest difference in the extent of the drawdown cone in the three different model scenarios. The areas contained within the 1 m drawdown contour have been estimated and are detailed in Table 9. The results illustrate that the extent of the 1 m drawdown contour is directly proportional to the diffusivity at all times. Results suggest that at steady state (1000 years post closure) the 1 m drawdown contours for the different diffusivity cases are almost coincident in some locations. This observation can be explained by:

a. Aquifer storage parameters included in the diffusivity adjustment do not influence the steady state result,

b. Changes in evapotranspiration fluxes impact on the shape and extent of the drawdown cone. Since evapotranspiration is a head dependent flux and the modelled heads vary with diffusivity, then the drawdown cone is affected differently for each of the three diffusivity assumptions.


c. The hydraulic conductivity distribution also impacts on the shape and areal extent of the drawdown cone.

Figure 33. . Model uncertainty – extent of drawdown (1 m contour) in the Tertiary aquifer (Layer 3) at the end of mining (year 25) and 1000 years after closure

Figure 34. Model uncertainty – extent of drawdown (1 m contour) in the fractured bedrock aquifer (Layer 5) at the end of mining (year 25) and 1000 years after closure

Table 9: Model Sensitivity – estimated areas of drawdown as defined by the 1 m contour.

Layer 3 Maximum Diffusivity Best Estimate Minimum Diffusivity25 years 291.0 148.6 89.345 years 374.4 184.1 115.81000 years 413.6 267 235.6Layer 525 years 293.0 151.7 94.445 years 376.2 185.7 1161000 years 414.9 270.6 237

1m Drawdown Contour Area (km2)

Year 25 Year 1000

Year 25 Year 1000


The predicted recovery of the pit water levels following mine closure is also impacted by the diffusivity included in the model parameters. The sensitivity of the simulated water level recovery in the mining pit is illustrated in Figure 35. The results indicate pit lake water levels that range between -300 and -225 m AHD with the best estimate model simulating a level of -275 m AHD after 1000 years.

Figure 35. Model sensitivity – predicted water levels in the pit post mine closure.


5 Conclusions A numerical groundwater flow model has been developed to predict the volumes of groundwater that are required to be abstracted and managed to create suitable mining conditions for the Boo Loo and Murphy South mine pits. A groundwater dewatering system comprising seven permanent ex-pit wells, four temporary in-pit wells and in-pit sumps that hold the groundwater level at the base of the active mine pits has been simulated. The numerical groundwater flow model predicts that abstractions from the dewatering wellfield will range from approximately 12 ML/d two years prior to the commencement of mining to approximately 4 ML/d at the end of mining (year 25). Total combined volumes of groundwater that are predicted to report to the mine pits range from 3.7 ML/d to 16.8 ML/d over the life of the mine, with peak inflows predicted in year 5. The predicted mine pit inflows are expected to be at the upper range of inflows based on the aquifer parameters used to simulate the hydrogeological system. Groundwater drawdown in response to pit dewatering is not predicted to extend beyond seven kilometres from the mine pits in the Tertiary aquifer during mining. At the completion of mining when the dewatering system is decommissioned, a pit lake is predicted to form in the mine void with the water level predicted to stabilise at approximately 335 m below the pre-mining groundwater level creating a cone of depression surrounding the pit. The pit lake is therefore predicted to act as a regional groundwater sink reducing the potential for water held within the pit to migrate off site. Evaporation is predicted to account for a loss of 7 ML/d from the regional groundwater system. The cone of depression caused by ongoing evaporation from the pit lake is not predicted to extend beyond ten kilometres from the mine pits. Modelling undertaken to inform the groundwater impact assessment found:

n During the operational phase there is no increase in groundwater level beneath the integrated landform.

n In the long term there is no mounding predicted to occur below the integrated landform. n The long term drawdown shows that on-going evaporation from the pit lake creates a cone of

drawdown extending approximately ten kilometres from the mine pits. The conservative nature of the assumptions made for the groundwater impact assessment modelling provides confidence that potential impacts will not exceed those predicted by the model.


6 Limitation statement

6.1 General The sole purpose of this report and the associated services performed by Jacobs is to assess the potential mine groundwater management requirements for the CEIP and the associated groundwater impacts. The work has been undertaken in accordance with the scope of services set out in the contract between Jacobs and IRD, and the available information and data. In preparing this report, Jacobs has relied upon, and presumed accurate, certain information (or absence thereof) provided by IRD and other sources. It is anticipated that the information and data used as a basis for this assessment may be revised at a future date, when more information becomes available. In the event that this happens, it is recommended that this report also be reviewed. Jacobs derived the data in this report from a variety of sources, which are identified at the time of writing. The passage of time, manifestation of latent conditions, impacts of future events or newly acquired information may require further examination of the project and subsequent data analysis, and re-evaluation of the data, findings, observations and conclusions expressed in this report. Jacobs has prepared this report in accordance with the usual care and thoroughness of the consulting profession, for the sole purpose of the project and by reference to applicable standards, procedures and practices at the date of issue of this report. This report should be read in full and no excerpts are to be taken as representative of the findings. For the reasons outlined above, however, no other warranty or guarantee, whether expressed or implied, is made as to the data, observations and findings expressed in this report.

6.2 Model predictive uncertainty Fractured rock aquifers, such as the fractured gneiss aquifer, are typically heterogeneous and the level of certainty that can be assigned to estimates of hydraulic parameters derived from field testing is constrained by the scale of field testing programs (numbers of test locations and the period of time over which pumping tests are undertaken). Improved modelling accuracy can be expected once the model has been tested or validated against early observations of groundwater response to mine dewatering.

6.3 Management scenarios for pit inflows The model described is based on the assumption that all groundwater inflow to the mine pits is collected by pit floor sumps. No allowance has been made for different management approaches (such as collection of water seeping from the overburden sedimentary units by perimeter drains) or water that may be lost via evaporation once water is expressed at the pit walls or from the sumps (which may be considerable during summer months but much reduced during winter months).


7 References Barnett et al (2012) Australian groundwater modelling guidelines, Waterlines report, National Water Commission, Canberra Coffey (2013) Groundwater Monitoring Bore Installation and Sampling Program Summary, prepared for Iron Road Limited (report number E-F-24-RPT-0001). Coffey (2012) Geotechnical Investigation, Central Eyre Iron Project, Tailings Storage Facility. Report by Coffey Mining Pty Ltd for Iron Road Ltd. (IRD Reference E-F-24-RPT-0001). Hou et al (2012) Palaeodrainage and Cenozoic Coastal Barriers of South Australia. DMITRE August 2012. Hou, B., Frakes, L.A. & Alley, N.F. (2003) Palaeochannel evolution, northwestern Gawler Craton, South Australia. CRC LEME Volume, Perth RPS, (2013) CEIP Hydrological Studies, prepared for Iron Road Limited (report reference no. E-F-34-RPT-0026_B) SKM (2014a). CEIP Mine Site Hydrogeological Studies, prepared for Iron Road Limited (report reference no. E-F-16-RPT-0005). SKM (2014d). CEIP Groundwater Drilling and Aquifer Testing Completion Report, prepared for Iron Road Limited (report reference no. E-F-16-RPT-0015). SKM, (2013) CEIP Hydrogeological Numerical Model Plan, prepared for Iron Road Limited (IRD reference E-F-16-MMO-003_B) Todd DK, Mays LW (2005) Groundwater Hydrology. 3rd ed., Hoboken: John Wiley & Sons.

M - Mine Water Management - Numerical Groundwater Flow Model.docx 4/11/2015

Appendix A Unsaturated zone modelling used to predict recharge under the integrated landform

Introduction

The creation of an integrated landform for the CEIP is an activity which may lead to changes in the groundwater regime. It is proposed that the integrated landform will be developed using a dry stacking technique in which a combination of waste rock from in-pit crushing and from the ore processing facility is distributed progressively using mobile spreaders over the natural land surface. Given the disturbance of the natural surface, it is likely that the creation of this integrated landform will alter groundwater recharge over the footprint of the landform. An unsaturated zone modelling study was undertaken to estimate any changes to groundwater recharge caused by the creation of an integrated landform. This appendix documents the methods used, the assumptions applied, and the results obtained from the unsaturated zone modelling.

Method

Overview The purpose of the unsaturated modelling is to estimate predicted changes to groundwater recharge as a result of the development of the integrated landform. The unsaturated zone modelling aims to predict recharge rates under three scenarios:

· A background scenario representing existing (pre-mining) conditions. · An integrated landform scenario in which the natural surface is covered by the integrated landform and

remains unvegetated during the course of the construction. · A closure scenario in which the integrated landform is overlain by a growth medium (comprised of the

pre-existing topsoil), and the closure option of dryland agriculture is simulated. Of the closure options being considered, dryland agriculture would result in the highest amount of recharge due to lower annual evapotranspiration compared to perennial native vegetation cover. The selection of dryland agriculture as a closure option has been made solely for the purpose of providing a robust groundwater impact assessment, and its selection for this modelling should not imply that it is the only closure option being considered for the CEIP.

Model platform The Soil-Water-Atmosphere-Plant (SWAP) was selected as the modelling platform. SWAP is a 1D model that employs the Richards Equation to simulate soil moisture movement in the vadose zone in interaction with vegetation development. Its domain extends from a plane just above the vegetation canopy to a plane in the shallow groundwater. The theoretical background for SWAP is documented by Kroes et. al., (2008).

Conceptual model Figure 36 shows the conceptual model used to represent the background scenario. The features of the background scenario are as follows:


· Transient climate data using a 100 year dataset (1914–2013) of daily observations for Warramboo. This dataset was constructed using daily rainfall records from the Bureau of Meteorology (site 18090), in-filled with a synthetic climate data sequence obtained from SILO2 for the same location.

· A surface vegetated by an annual wheat crop to represent current land use practices. It is assumed that the wheat is sown in mid-May and harvested in mid-November, with the soil surface remaining bare at other times of the year. The wheat crop is assumed to have a peak Leaf Area Index (LAI) of 1.0 and a rooting depth of 100 cm.

· A soil profile comprising sandy loam surface horizons to a depth of 1.5 m and a clay loam subsoil layer to the base of the model, which is based on available bore logs for the designated footprint of the integrated landform.

· A lower boundary defined as a free drainage condition. Seepage calculated by the numerical model through the free drainage condition is used as the estimate of groundwater recharge.

Figure 36 Conceptual model for background scenario

Figure 37 shows the conceptual model used to represent the integrated landform scenario, which is assumed to be bare during construction. The major changes from the base case scenario are as follows:

· A 135 m thick layer of waste rock is added to the existing soil surface. · The surface of the waste rock remains unvegetated (i.e. there will be no transpiration losses).

2 http://www.longpaddock.qld.gov.au/silo/

Rainfall as per historic data

Evaporation from surface

Transpiration from winter

crop

1.5 m

11 m

Free drainage

Upper boundary condition

Unsaturated Zone Profile

Lower boundary condition

Existing subsoil (clay loam)

Existing topsoil (sandy loam)

http://www.longpaddock.qld.gov.au/silo/


Figure 37 Conceptual model for integrated landform scenario

Figure 38 shows the conceptual model used to represent the closure scenario. The major changes from the bare integrated landform scenario are as follows:

· A 1.65 m thick layer of growth medium is added to the integrated landform surface to represent the dryland agriculture closure option being considered. The growth medium is comprised of the same sandy loam surface soil used in the background scenario.

· The upper boundary condition is simulated as per the background scenario to represent dryland agriculture.



135 m

1.5 m

11 m

Free drainage


Lower boundary condition

No Transpiration (unvegetated

surface)



Integrated landform

Unsaturated zone profile


Figure 38 Conceptual model for closure scenario

Material properties The three materials used in the numerical modelling assessment are as follows:

· Sandy loam topsoil of the existing soil profile and the growth medium of the cover system for the integrated landform.

· Clay loam subsoil of the existing soil profile. · Integrated landform, which is comprised of a mixture of waste rock from in-pit crushing and a

combination of coarse and fine tails from the ore processing facility. Figure 39 shows the Particle Size Distribution (PSD) for each material that was assumed for the modelling assessment. The assumed PSDs for the existing soil materials were derived according to baseline geotechnical data collected by Coffey (2012). The assumed PSD of the waste rock material was derived by modelled rock



Transpiration from winter

crop

1.65 m

135 m

1.5 m

11 m

Free drainageLower

boundary condition

Top growth medium (sandy loam)

Unsaturated zone profile


Integrated landform




crushing outputs and waste rock stream proportions supplied by Iron Road. Both the existing soil materials and integrated landform were assumed to be spatially homogenous.

Figure 39 Particle size distribution (PSD) curves based on available data

Table 10 lists the assumed properties for the materials considered. The hydraulic properties were derived from examples in the SoilVision3 database which lists the hydraulic properties of materials with similar particle size distributions. Water retention curves are shown in Figure 40. Table 10 Assumed material properties

Sandy Loam

Clay Loam

Integrated Landform

SoilVision Counter ID 11308 11463 18750

Dry density (g/cm3) 1.48 1.52 2.00 Sat. water content (cm3/cm3)* 0.44 0.42 0.24

Residual water content (cm3/cm3)* 0.002 0.015 0.01

Air-entry value (cm) -5 -10 -15

alpha (1/kPa)* 0.42 0.60 0.30

n* 1.30 1.12 1.20

m* 0.23 0.11 0.17

Ks (m/s) 5 x 10-6 1 x 10-7 1 x 10-7

*van Genuchten (1980) parameter

3 SoilVision is a commercially available dataset representing a comprehensive compilation of unsaturated soils information, compiled from over 6,000 soils. For more information see: http://www.soilvision.com/subdomains/soildatabase.com/databases.shtml

0

10

20

30

40

50

60

70

80

90

100

0.001 0.010 0.100 1.000 10.000 100.000 1,000.000

Perc

enta

ge P

assi

ng (%

)

Particle Size (mm)

Integrated landform

Sandy loam

Clay loam

http://www.soilvision.com/subdomains/soildatabase.com/databases.shtml


Figure 40 Water retention curves for the materials modelled

Model runs The models for each scenario were run over the full 100 year climatic sequence and recycled through this sequence until a new dynamic equilibrium was established. The objective of recycling the model in this manner is to remove any influence of the initial conditions (which are unknown and have to be assumed). Sensitivity analyses were also performed to evaluate the sensitivity of recharge to key parameter inputs.

Results

Table 11 shows the predicted water balance components as derived by the numerical modelling. Water input to the unsaturated profile occurs as infiltration of rainfall. Water outputs include evaporation and transpiration to the atmosphere, and recharge of underlying groundwater through the base of the model. Table 11 Predicted average annual water balance components for the three primary scenarios

Scenario Rainfall (mm)

Evaporation (mm)

Transpiration (mm)

Recharge (mm)

Background 338 238 99 1

Integrated Landform 338 288 0 50

Closure 338 237 99 2

Background recharge In the background scenario most of the rainfall is offset by evapotranspiration, with there being little in the way of excess water that results in recharge (1 mm/y). In the integrated landform scenario evaporation is higher (due to there being no vegetation to shade the surface and the lower permeability of the surface material) but there is no transpiration, resulting in a lower amount of overall evapotranspiration and a higher recharge rate

0

5

10

15

20

25

30

35

40

45

50

0.10 1.00 10.00 100.00 1000.00 10000.00

Volu

met

ric W

ater

Con

tent

(%)

Matric Suction (kPa)

Sandy Loam 11308

Clay loam 11463

Integrated landform18750


(50 mm/y). In the closure scenario total evapotranspiration is slightly lower which results in a slightly higher recharge rate (2 mm/y) compared to background. Sensitivity analyses were conducted for each of the primary scenarios. The analysis included increasing and decreasing the hydraulic conductivity (Ksat) by one order of magnitude, doubling and halving the air-entry value (AEV), increasing and decreasing the maximum leaf area index (LAI) by 50% and increasing and decreasing the maximum root depth (RD) by 50%. The parameters used in the sensitivity analysis reflect the ranges of possible values that could be reasonably expected for these parameters. Figure 41 shows a tornado plot of a sensitivity analysis of the background scenario which suggests recharge could range between 0.25 and 4.5 mm/y. Recharge is most sensitive to changes to vegetation parameters, as this would impact on evaporation and transpiration from the surface.

Figure 41 Tornado plot of base case sensitivity analysis

Integrated landform recharge during construction Figure 42 shows a tornado plot of a sensitivity analysis of the integrated landform scenario. The analysis was the same as for the background scenario, but without vegetation parameters as the surface is assumed to be bare. The sensitivity analysis conducted with respect to hydraulic conductivity was somewhat unexpected in that the base case scenario resulted in a higher recharge rate than the two sensitivity analyses in which the hydraulic conductivity was increased and decreased by an order of magnitude. Intuitively one would anticipate that the higher Ksat scenario would result in less recharge than the base case scenario, however the result merely emphasises the dynamism and non-linearity that occurs in the unsaturated zone, particularly at the surface. In this case, less recharge for the higher Ksat scenario can be explained by a higher Ksat leading to a higher sustainable evaporation rate from the bare soil surface.

0 1 2 3 4 5

Base case

Average Annual Recharge (mm)

Ksat

AEV

LAI

RD


Figure 42 Tornado plot of waste rock sensitivity analysis

The thickness of the integrated landform (approximately 135 m) is such that a significant transit time could be expected for water infiltrating below the surface of the integrated landform to reach the water table. The magnitude of this time lag can be explored by examining the relationship between historic rainfall trends and modelled recharge. Figure 43 shows a plot which compares the cumulative deviation in mean annual rainfall (based on historic data) to the recharge calculated by the model – noting that this recharge trend is plotted after the model has been recycled and a dynamic equilibrium is reached.

Figure 43 Comparison of historic rainfall trend (BOM site Warramboo 18090) to modelled recharge

Recharge varies about a mean of 50 mm/y in response to differences in rainfall, and other climatic drivers, and follows a similar trend to rainfall albeit offset by a period of approximately 20 years. It is, therefore, reasonable to assume a 20 year time lag would apply to recharge after the development of the integrated landform

30 35 40 45 50 55 60

Base case


Ksat

AEV

0

10

20

30

40

50

60

70

80

-1000

-800

-600

-400

-200

0

200

400

600

1900 1920 1940 1960 1980 2000 2020

Rech

arge

(mm

)

Cum

ulat

ive

Devi

atio

n fr

om m

ean

annu

al

rain

fall

(mm

)

Year

Cumulative Deviation from mean annualrainfall

Integrated landform recharge duringconstruction


construction. This assumption is also a conservative one, because the 20 year lag time is based on an unsaturated profile that has been wet up and equilibrated to the new conditions. Much longer time lags would be associated with the drier initial conditions that are likely to occur during the dry-stacking process of the integrated landform construction.

Closure recharge Figure 44 shows a tornado plot of a sensitivity analysis of the closure scenario. The analysis included a ‘high recharge’ scenario in which each parameter was varied in unison towards the edge of their expected range to encourage recharge. The non-linearity of unsaturated zone processes is evident again with the ‘high recharge’ scenario resulting in less recharge than the scenario in which only LAI was varied. This result is probably due to the dynamic nature of plant water availability, which controls transpiration, varies with soil moisture and is dependent on all of the parameters adjusted as part of the sensitivity analysis. The sensitivity analysis shows potential recharge under the closure option to vary between 0.15 and 7 mm/y.

Figure 44 Tornado plot of topsoil sensitivity analysis

Summary of key assumptions

The unsaturated modelling assessment relied on several key assumptions, which are important to bear in mind when interpreting the results. The following assumptions were applied:

· The integrated landform surface remains un-vegetated for a period of one year after which progressive rehabilitation is scheduled to occur in accordance with the closure plan.

· Soil hydraulic properties are assumed. While they are based on real world examples of similar material from geodatabases, significant variability in soil hydraulic properties is not uncommon. Therefore, the range of results provided by the sensitivity analyses should be considered as opposed to the single recharge rates listed in Table 11.

· The soil and integrated landform profiles are assumed to be spatially homogenous. Some uncertainty in the modelling results is acknowledged given the unknowns. Uncertainty in this case has been addressed through the use of modelling assumptions that have been applied from a conservative

0 1 2 3 4 5 6 7 8

Base case


Ksat

AEV

LAI

RD

High Recharge


perspective so as to inform the groundwater impact assessment for the CEIP. By applying this conservatism the assessment of potential impacts can be made with confidence.

References

Coffey Environments (2013). Groundwater Monitoring Bore Installation and Sampling Program Summary, prepared for Iron Road Limited (IRD Reference E-F-16-RPT-0001). Kroes, J.G., J.C. Van Dam, P. Groenendijk, R.F.A. Hendriks, C.M.J. Jacobs, (2008) SWAP version 3.2. Theory description and user manual. Wageningen, Alterra, Alterra Report1649(02). Van Genuchten, M Th (1980) A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal, 44, 892-8.


Appendix B Location of observation bores used as calibration targets for each layer4

4 Previous waste rock landform depicted in these figures


Appendix C Model calibration data

Bore_ID East North Layer Observed

RWL [m AHD]

Calculated water Level

[m AHD]

Residual [m]

5931-41 542,880.8 6,313,792.2 1 62.91 56.31 6.60 6031-300 579,238.8 6,330,171.2 1 148.13 152.02 -3.89 6031-160 579,180.7 6,320,935.4 1 136.18 132.97 3.21 5931-54 537,816.6 6,302,889.3 1 44.91 48.12 -3.21 5931-84 539,907.7 6,299,317.1 1 47.04 49.66 -2.62 5931-53 537,027.7 6,303,518.1 1 44.37 46.33 -1.96 5931-11 544,597.6 6,322,726.4 1 53.47 55.05 -1.58 5931-42 539,688.7 6,314,219.2 1 55.88 54.45 1.43 5931-60 537,222.6 6,298,505.1 1 43.46 44.68 -1.23 5931-39 543,848.8 6,314,001.3 1 55.76 56.94 -1.19 5931-10 542,876.6 6,322,440.3 1 54.77 53.58 1.18 5931-57 538,462.8 6,300,062.4 1 46.34 47.44 -1.10

5931-140 536,701.8 6,299,220.1 1 45.18 44.17 1.01 5931-66 541,414.7 6,300,217.2 1 49.06 50.03 -0.97 5931-40 544,018.7 6,314,000.2 1 57.79 57.06 0.73 5931-48 535,605.6 6,299,612.3 1 42.46 42.79 -0.34

6031-497 567,677.0 6,315,278.0 2 58.03 67.31 -9.28 5931-73 543,800.6 6,304,309.2 2 45.05 53.73 -8.68 5931-75 542,617.6 6,297,506.3 2 45.27 52.82 -7.54 5931-45 543,643.6 6,307,360.1 2 48.16 54.62 -6.46 5931-43 539,095.8 6,314,363.3 2 57.98 53.71 4.27

5931-126 537,379.6 6,297,518.1 2 41.53 44.90 -3.38 5931-51 537,867.0 6,299,454.0 2 43.61 46.91 -3.30 5931-69 544,963.6 6,302,068.2 2 50.56 53.81 -3.25 5931-59 538,582.8 6,298,688.3 2 44.41 47.62 -3.21 5931-50 536,391.8 6,299,117.2 2 42.47 44.59 -2.13

5931-572 543,324.8 6,342,654.2 2 69.17 67.75 1.41 5931-52 539,250.6 6,299,757.1 2 49.15 48.14 1.01

6031-129 558,912.7 6,326,555.3 2 63.10 63.20 -0.10 SKM9 573,257.0 6,315,322.0 3 58.47 68.13 -9.66 US2 567,674.0 6,315,278.0 3 57.80 67.10 -9.30

SKM10 562,903.0 6,315,406.0 3 57.82 65.80 -7.98 SKM8 567,542.0 6,320,502.0 3 61.63 68.64 -7.01 US1 550,385.0 6,325,201.0 3 53.60 58.65 -5.05

6031-39 547,098.7 6,338,904.4 3 59.89 58.60 1.29 5931-448 536,428.7 6,298,981.2 3 44.35 45.52 -1.16


Bore_ID East North Layer Observed

RWL [m AHD]

Calculated water Level

[m AHD]

Residual [m]

6031-42 551,834.8 6,333,622.4 3 60.12 59.63 0.49 SKM02 563,919.0 6,321,253.0 3 62.69 62.23 0.46

5931-447 541,048.7 6,300,021.3 3 50.11 49.81 0.30 LS2 567,674.0 6,315,278.0 4 59.00 66.61 -7.61 LS4 563,155.0 6,321,103.0 4 58.10 65.54 -7.44 LS1 550,385.0 6,325,201.0 4 51.80 58.19 -6.39

5931-7 543,101.7 6,343,089.2 4 66.21 69.23 -3.02 6031-372 546,578.6 6,305,171.2 4 57.44 55.00 2.44

5931-9 540,955.9 6,339,548.2 4 51.58 52.11 -0.54 6031-8 584,910.9 6,324,781.2 4 102.64 102.60 0.05

5931-707 543,391.6 6,342,603.6 4 67.01 66.98 0.03 G2 567,674.0 6,315,278.0 5 54.90 66.14 -11.24

SKM03 563,922.0 6,321,275.0 5 59.82 67.64 -7.82 SKM01 563,919.0 6,321,241.0 5 60.67 67.63 -6.96 5931-8 543,058.6 6,343,385.3 5 64.40 70.40 -5.99 SKM6 562,250.0 6,321,145.0 5 60.44 66.35 -5.91

G1 550,385.0 6,325,201.0 5 52.90 58.08 -5.18 6031-162 575,994.9 6,342,199.4 5 76.39 71.56 4.83 6031-364 575,703.8 6,341,539.3 5 73.88 71.33 2.55 5931-442 541,648.6 6,307,501.3 5 53.47 51.53 1.94 5931-444 542,353.7 6,309,971.2 5 54.26 52.89 1.37

SKM7 560,179.0 6,322,056.0 5 63.87 65.15 -1.28 5931-445 536,503.6 6,303,371.1 5 45.14 43.88 1.26

SKM4 560,321.0 6,320,939.0 5 64.21 65.21 -1.00 G3 573,082.0 6,325,796.0 5 82.30 82.13 0.17


Figure B.1: Residual groundwater heads (m)


Appendix D Model sensitivity analysis parameters


Model Layer Description

Base Case Model Low Diffusivity Model High Diffusivity Model

Kh Specific yield

Specific storage Kh Specific

yield Specific storage Kh Specific

yield Specific storage

(x-plane) Sy Ss (x-plane) Sy Ss (x-plane) Sy Ss

Layer 1

Quaternary – Zone 1 0.5 0.1 5x10-6 0.25 0.15 5x10-6 1 0.067 5x10-6

Quaternary – Zone 2 0.8 0.1 5x10-6 0.4 0.15 5x10-6 1.6 0.067 5x10-6

Quaternary – Zone 3 3 0.1 5x10-6 1.5 0.15 5x10-6 6 0.067 5x10-6




Layer 2 Tertiary (Neogene) 0.8 0.05 5x10-6 0.4 0.075 5x10-6 1.6 0.033 5x10-6

Layer 3 Tertiary (Paleogene) 3 0.1 5x10-6 1.5 0.15 5x10-6 6 0.067 5x10-6

Layer 4 Saprolite 0.007 0.05 5x10-6 0.0035 0.075 5x10-6 0.014 0.033 5x10-6

Layer 5 Fractured basement 0.025 0.001 5x10-6 0.0125 0.0015 5x10-6 0.05 6.67E-04 5x10-6

Layer 5 Fractured zone 1[1] 0.5 0.001 5x10-6 0.25 0.0015 5x10-6 1 6.67E-04 5x10-6

Layer 5 Fractured zone 1[2] 1.5 0.001 5x10-6 0.75 0.0015 5x10-6 3 6.67E-04 5x10-6

Layer 6 Un-fractured basement 0.001 0.0001 5x10-6 0.0005 0.00015 5x10-6 0.002 6.67E-05 5x10-6

Notes: K: Hydraulic Conductivity (m/day) Sy: Specific Yield (unit less) Ss: Specific Storage (m-1)

e-f-82-tem-0026 1 (report template 2010)mer-web.s3.amazonaws.com/ceip/mlp appendix m - mine...

Documents