Assessing the cumulative impact of mining …...Assessing the cumulative impact of mining scenarios on bioregional assets in the Namoi Catchment Development and trial of a GIS tool

Assessing the cumulative impact of mining scenarios on bioregional assets in the Namoi Catchment Development and trial of a GIS tool – NCRAT Version 1 Final Report Version 1 prepared for Namoi Catchment Management Authority

23 November 2012

S p a t i a l T o o l f o r A s s e s s i n g t h e C um ul a t i ve R is k o f M i n i n g t o N R M A s s e t s i n t h e N a m o i C a t c hm e n t

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DOCUMENT TRACKING

ITEM DETAIL

Project Name Assessing the cumulative impact of mining scenarios on bioregional assets in the Namoi Catchment: Development and trial of an interactive GIS tool – NCRAT Version 1

Project Number 11COFNRM-0013

File location H:/synergy/projects/11cofnrm/11cofnrm-0013/report

Project Manager Dr Julian Wall

Prepared by Dr Julian Wall

Approved by Rob Mezzatesta

Status Final

Version Number 1

Last saved on 31 August 2012

Cover photo Blakely’s Red Gum woodland, east of Manilla

This report should be cited as ‘Eco Logical Australia 2012. Assessing the cumulative risk of mining scenarios on bioregional assets in the Namoi Catchment: Development and trial of an interactive GIS tool. Prepared for Namoi Catchment Management Authority’.

ACKNOWLEDGEMENTS

This document has been prepared by Eco Logical Australia Pty Ltd with support from Bruce Brown and Francesca Andreoni, Namoi CMA. We acknowledge Fiona McCallum, PhiR Pty Ltd, for her valued contribution to NCRAT.

Disclaimer This document may only be used for the purpose for which it was commissioned and in accordance with the contract between Eco Logical Australia Pty Ltd and Namoi CMA. The scope of services was defined in consultation with Namoi CMA, by time and budgetary constraints imposed by the client, and the availability of reports and other data on the subject area. Changes to available information, legislation and schedules are made on an ongoing basis and readers should obtain up to date information. Eco Logical Australia Pty Ltd accepts no liability or responsibility whatsoever for or in respect of any use of or reliance upon this report and its supporting material by any third party. Information provided is not intended to be a substitute for site specific assessment or legal advice in relation to any matter. Unauthorised use of this report in any form is prohibited.


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Executive Summary The coal mining industry is experiencing a period of rapid expansion in NSW. Forward estimates of global demand for coal have resulted in expansion of the mining sector in the Gunnedah Basin which lies immediately north of the Hunter Valley within the central parts of the Namoi Catchment in northern NSW. Some existing mines in the Gunnedah Basin are up scaling, and several new mines are seeking approval or are otherwise planned for the region.

Concern about the cumulative impacts of multiple mines on natural resources assets in the Namoi Catchment have evoked concerns in the Namoi from both environment and farming interest groups and the broader Catchment Community. The Namoi Catchment Management Authority has responded by commissioning this study to develop a tool for quantifying the risk of cumulative impacts across ten natural resource assets in the Catchment, namely land use; soils; carbon; surface water; groundwater; vegetation extent; vegetation type; vegetation condition (intactness); vegetation connectivity and threatened species.

The Namoi Cumulative Risk Assessment Tool (NCRAT) is a spatial tool that is ArcGIS compatible and interfaces with ArcGIS Version 10. It has been designed to report the cumulative risk of any mining scenario constituting a combination of one or more mines including open cut mines, long wall mines, and coal seam gas operations. NCRAT is designed to:

- analyse the cumulative impact of a scenario (input by the user) across a number of asset sensitivity surfaces;

- call on respective risk tables that associate sensitivity and likeliness/magnitude with risk; and

- produce a risk report that includes maps, area statistics, single and cumulative risk diagrams, and statements about specific assets impacted.

The following assumptions apply to NCRAT:

- it is designed to establish the level of impact and risk at the strategic landscape scale, but is not designed for site or project scale risk assessment;

- it considers relative not absolute risk, thus is most useful for comparing risk between scenarios;

- it considers unmitigated risk to establish a level of baseline risk, where initiatives such as biodiversity offsets and water buyback are assumed not to have been undertaken (Version 2 of NCRAT, currently being developed, will incorporate opportunities for risk mitigation); and

- it is sector-specific in that it considers cumulative risk of the mining sector only.

A number of new spatial layers have been developed for this project; specifically landscape corridors, local links, local catchments, vegetation intactness and threatened species hotspots, These layers are embedded in NCRAT, but like other natural resource layers available in the Namoi Catchment, they may be used to support strategic planning work and on-ground initiatives.


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List of Contents Executive Summary ................................................................................................................................ ii

List of Contents ...................................................................................................................................... iii

1 Introduction ................................................................................................................................. 1

1.1 Setting ........................................................................................................................................... 1

1.2 Context ......................................................................................................................................... 2

1.3 Definitions and assumptions......................................................................................................... 2

1.3.1 Mining .............................................................................................................................. 2 1.3.2 Interpretation of risk ......................................................................................................... 2 1.3.3 Framework ....................................................................................................................... 5

2 Methodology ............................................................................................................................... 6

2.1 Context ......................................................................................................................................... 6

2.2 Data compilation ........................................................................................................................... 6

2.3 Preparation of spatial data ............................................................................................................ 6

2.3.1 Base case mining layer.................................................................................................... 6 2.3.2 Vegetation cover .............................................................................................................. 7 2.3.3 Vegetation type ................................................................................................................ 8 2.3.4 Intactness ........................................................................................................................ 9 2.3.5 Habitat links and viable habitat area .............................................................................. 12 2.3.6 Landscape corridors ...................................................................................................... 14 2.3.7 Threatened species models .......................................................................................... 14 2.3.8 Land use ........................................................................................................................ 16 2.3.9 Soil productivity .............................................................................................................. 16 2.3.10 Carbon store .................................................................................................................. 17 2.3.11 Surface water flow ......................................................................................................... 19 2.3.12 Surface water quality ..................................................................................................... 19 2.3.13 Groundwater drawdown................................................................................................. 20 2.3.14 Groundwater quality ....................................................................................................... 24

2.4 Development of input ‘sensitivity’ layers ..................................................................................... 25

2.4.1 Context........................................................................................................................... 25 2.4.2 Percent cover (sub-catchment) ..................................................................................... 26 2.4.3 Percent cover (local) ...................................................................................................... 26 2.4.4 Vegetation type percent cover threshold ....................................................................... 27 2.4.5 Vegetation type EEC likelihood ..................................................................................... 27 2.4.6 Intactness index ............................................................................................................. 27


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2.4.7 Connectivity index .......................................................................................................... 28 2.4.8 Corridor index ................................................................................................................ 29 2.4.9 Threatened species richness ......................................................................................... 30 2.4.10 Land use classes ........................................................................................................... 30 2.4.11 Soil productivity class .................................................................................................... 30 2.4.12 Carbon store .................................................................................................................. 31 2.4.13 Long term surface water flow ........................................................................................ 31 2.4.14 Surface water pollution index ........................................................................................ 32 2.4.15 Groundwater drawdown................................................................................................. 33 2.4.16 Groundwater quality ....................................................................................................... 34

2.5 Development of a risk matrix for each input layer ...................................................................... 36

2.5.1 Preamble ....................................................................................................................... 36 2.5.2 Vegetation cover ............................................................................................................ 36 2.5.3 Vegetation types ............................................................................................................ 37 2.5.4 Intactness ...................................................................................................................... 37 2.5.5 Connectivity ................................................................................................................... 38 2.5.6 Landscape corridors ...................................................................................................... 38 2.5.7 Threatened species ....................................................................................................... 38 2.5.8 Land use ........................................................................................................................ 39 2.5.9 Soil productivity .............................................................................................................. 39 2.5.10 Carbon store .................................................................................................................. 40 2.5.11 Long-term surface flow .................................................................................................. 40 2.5.12 Surface water quality ..................................................................................................... 40 2.5.13 Groundwater drawdown................................................................................................. 41 2.5.14 Groundwater quality ....................................................................................................... 41

2.6 Development of cumulative risk tool ........................................................................................... 42

2.6.1 Context........................................................................................................................... 42 2.6.2 Structure ........................................................................................................................ 42 2.6.3 Input mining scenario ..................................................................................................... 42 2.6.4 Indices and algorithms ................................................................................................... 42

2.7 Development of user guide ......................................................................................................... 43

3 Results ....................................................................................................................................... 44

3.1 Preparation of datasets .............................................................................................................. 44

3.1.1 Base case mining layer.................................................................................................. 44 3.1.2 Vegetation extent ........................................................................................................... 45 3.1.3 Vegetation type .............................................................................................................. 49 3.1.4 Intactness ...................................................................................................................... 49 3.1.5 Local links ...................................................................................................................... 54 3.1.6 Landscape corridors ...................................................................................................... 56 3.1.7 Threatened species ....................................................................................................... 56


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3.1.8 Land use ........................................................................................................................ 56 3.1.9 Soils ............................................................................................................................... 59 3.1.10 Carbon store .................................................................................................................. 59 3.1.11 Surface water flow ......................................................................................................... 60 3.1.12 Surface water quality ..................................................................................................... 62 3.1.13 Groundwater drawdown and groundwater quality ......................................................... 66

3.2 Sensitivity layers ......................................................................................................................... 67

3.3 Model indices and assumptions ................................................................................................. 75

3.3.1 CSGMs .......................................................................................................................... 75 3.3.2 OCM/LWM ..................................................................................................................... 78

3.4 cumulative risk tool ..................................................................................................................... 80

3.4.1 Broad framework ........................................................................................................... 80 3.4.2 Modules ......................................................................................................................... 82 3.4.3 System requirements ..................................................................................................... 87

4 Recommendations ................................................................................................................... 89

References ............................................................................................................................................. 90

Appendix I. Summary of CSG operations in Australia ...................................................................... 93

Appendix II. Rules for assigning a priority to each link .................................................................. 101

Appendix III: Threatened species known or predicted to occur in the Namoi Catchment .......... 103

Appendix IV: Spatial data categories used for modelling threatened species ............................. 107

Appendix V: Assignment of sensitivity classes to land use categories ....................................... 116

Appendix VI: Examples of sub-module flow charts informing structure of NCRAT .................... 121

Appendix VII: NCRAT Output (template only) .................................................................................. 127


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List of Figures Figure 1. Influence of % clearing and patchiness on intactness .............................................................. 12

Figure 2. Coal and CSG potential in the Namoi Catchment .................................................................... 22

Figure 3. Groundwater aquifers in the Namoi Catchment ........................................................................ 23

Figure 4. Groundwater depth within Upper and Lower Namoi Alluvia (source: NOW 2009) ................... 23

Figure 5. GDE potential in the Namoi Catchment (data from SKM 2010a) ............................................. 24

Figure 6. Base case mining footprint in the Namoi Catchment ................................................................ 45

Figure 7. Native vegetation cover gains and losses in the Namoi Catchment ......................................... 46

Figure 8. Native vegetation cover in the Namoi Catchment (not including derived types) ...................... 46

Figure 9. ‘Smoothed’ vegetation cover in the Namoi Catchment ............................................................. 47

Figure 10. Vegetation intactness index in the Namoi Catchment ............................................................ 54

Figure 11. Distribution of local links in the Namoi Catchment .................................................................. 55

Figure 12. Example of an unweighted threatened species model – Glossy Black Cockatoo .................. 57

Figure 13. Threatened species composite layer for the Namoi Catchment ............................................. 57

Figure 14. Landscape corridors in the Namoi Catchment ........................................................................ 58

Figure 15. Distribution of land use in the Namoi Catchment .................................................................... 58

Figure 16. Distribution of land suitability classes in the Namoi Catchment ............................................. 59

Figure 17. Carbon store of RVCs in the Namoi Catchment (vegetation and soils) .................................. 60

Figure 18. Local catchments within the Namoi Catchment ...................................................................... 62

Figure 19. Possible errors with the sub-catchment coverage .................................................................. 64

Figure 20. Distribution of streams in the Namoi Catchment .................................................................... 65

Figure 21. Priority wetlands and storages, and their contributing flow buffers ........................................ 66

Figure 22. Sensitivity surface for vegetation cover .................................................................................. 68

Figure 23. Sensitivity layer for RVC cover and EECs .............................................................................. 69

Figure 24. Sensitivity layer for landscape corridors and local links .......................................................... 70

Figure 25. Sensitivity layer for intactness and threatened species .......................................................... 71

Figure 26. Sensitivity layer for land use and soil productivity .................................................................. 72

Figure 27. Sensitivity layer for surface water ........................................................................................... 73

Figure 28. Sensitivity layers for groundwater ........................................................................................... 74

Figure 29. Sensitivity layers for carbon .................................................................................................... 75

Figure 30. CSGM footprint, Dalby. ........................................................................................................... 76

Figure 31. Broad framework of the Cumulative Risk Tool ....................................................................... 80

Figure 32. Module structure of the risk assessment framework .............................................................. 83


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List of Tables Table 1. Example of a generic matrix used to assign classes of relative risk ............................................ 3

Table 2. Maximum canopy separation ratios for major vegetation formations in the Namoi Catchment .. 7

Table 3. Threatened/Endangered Ecological Communities of the Namoi Catchment. ............................ 10

Table 4. Criteria for classifying mapped links ........................................................................................... 13

Table 5. Criteria for assigning a ‘magnitude’ class to mapped links ........................................................ 14

Table 6. Input surfaces used to inform threatened species models ......................................................... 16

Table 7. Threatened species weighting factors ........................................................................................ 16

Table 8. LSC classification used for the Namoi Catchment. .................................................................... 17

Table 9. Site quality classes ..................................................................................................................... 18

Table 10. Input surfaces used to inform surface contamination potential ................................................ 19

Table 11. Characteristic of groundwater management areas in the Namoi Catchment .......................... 21

Table 12. Spatial data used for groundwater quality ................................................................................ 24

Table 13: Impacts of mining on bioregional assets .................................................................................. 25

Table 14. Sensitivity to clearing of vegetation extent (based on Namoi CAP thresholds) ....................... 26

Table 15. Sensitivity of clearing of vegetation extent (based on linear relationship) ............................... 26

Table 16. Sensitivity of native vegetation types (based on Namoi CAP threshold) ................................. 27

Table 17. Sensitivity of native vegetation based on EEC candidacy ....................................................... 27

Table 18. Proposed classes for vegetation condition .............................................................................. 28

Table 19. Sensitivity classes for local links .............................................................................................. 28

Table 20. Sensitivity classes for landscape corridors .............................................................................. 29

Table 21. Threatened species sensitivity thresholds ............................................................................... 30

Table 22. Land use sensitivity classes ..................................................................................................... 30

Table 23. Sensitivity classes used for soil productivity ............................................................................ 31

Table 24. Sensitivity classes used for carbon store (includes soil carbon) .............................................. 31

Table 25. Sensitivity classes used for surface water flow ........................................................................ 31

Table 26. Surface water quality sensitivity criteria ................................................................................... 32

Table 27. Rule set for assigning groundwater drawdown sensitivity to asset layers ............................... 34

Table 28. Sensitivity classes for groundwater quality .............................................................................. 35

Table 29. Matrix for assessing relative risk associated with reduction in vegetation extent .................... 36

Table 30. Matrix for assessing relative risk associated with clearing vegetation types ........................... 37

Table 31. Matrix for assessing relative risk of mining to vegetation intactness ....................................... 37


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Table 32. Matrix for assessing relative risk of mining to functional connectivity ...................................... 38

Table 33. Matrix for assessing relative risk of mining to landscape corridors .......................................... 38

Table 34. Matrix for assessing relative risk of mining to persistence of threatened species ................... 39

Table 35. Matrix for assessing relative risk of mining to current land use ............................................... 39

Table 36. Matrix for assessing relative risk of mining to agricultural productivity .................................... 39

Table 37. Matrix for assessing relative risk of mining to carbon stored in vegetation and soils .............. 40

Table 38. Matrix for assessing relative risk of mining to surface flow ...................................................... 40

Table 39. Matrix for assessing relative risk of mining to surface water quality ........................................ 41

Table 40. Matrix for assessing relative risk of mining to groundwater drawdown .................................... 41

Table 41. Matrix for assessing relative risk of mining to groundwater quality .......................................... 41

Table 42. Mine types contributing to the base case mining layer ............................................................ 44

Table 43. Revised vegetation cover statistics for Namoi sub-catchments ............................................... 47

Table 44. Forest and woodland patch size distribution in the Namoi Catchment .................................... 48

Table 45. Change in RVC area statistics in the Namoi Catchment ......................................................... 50

Table 46. Local link statistics .................................................................................................................... 54

Table 47. Patch class statistics with and without functional links ............................................................ 56

Table 48. Surface flow thresholds for Namoi sub-catchments ................................................................. 61

Table 49. Major rivers of the Namoi Catchment ....................................................................................... 63

Table 50. Local catchments containing existing/approved mines in the Namoi region ........................... 64

Table 51. Summary data for each baseline sensitivity surface (area values in km2) ............................... 67

Table 52. CSGM indices .......................................................................................................................... 77

Table 53. Estimated water use of major coals mines in the Namoi Catchment (from SWS 2011) .......... 78

Table 54. Main components of NCRAT ................................................................................................... 81

Table 55. Type of cumulative impact associated with each receptor ....................................................... 84

Table 56. Spatial layer nomenclature used in the Cumulative Risk Tool ................................................. 85

Table 57: Hardware requirements ............................................................................................................ 87


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Abbreviations ABBREVIATION DESCRIPTION

ADS Airborne Digital Sensor

CAP Catchment Action Plan

CMA Catchment Management Authority

CSG Coal seam gas

CSGM Coal seam gas mine

DEC Department of Environment and Conservation (now OEH)

DECC Department of Environment and Climate Change (now OEH)

DECCW Department of Environment, Climate Change and Water (now OEH)

DEM Digital elevation model

DERM Queensland Department of Environment and Resource Management

DPI Department of Primary Industries

EEC Endangered Ecological Community

ELA Eco Logical Australia

EPBC Environmental Protection and Conservation

GAB Great Artesian Basin

GDE Groundwater Dependent Ecosystem

GMA Groundwater Management Area

IIESC Interim Independent Expert Scientific Committee

IUCN International Union for the Conservation of Nature

LWM Long wall mine/mining

LSC Land suitability class

MAR Mean annual rainfall (mm.yr-1)

NCRAT Namoi Cumulative Risk Assessment Tool

NOW NSW Office of Water

NRM Natural Resource Management

NWC National Water Commission

OCM Open cut mine/mining

OEH NSW Office of Environment and Heritage (previously DECCW, DECC and DEC)

RVC Regional Vegetation Community

SPOT Système Pour l'Observation de la Terre (French satellite)

TEC Threatened Ecological Community

TSC Threatened Species Conservation


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1 Introduction 1.1 SETTING

The Namoi Catchment in northern NSW supports a rich diversity of natural resources including a broad diversity of native flora and fauna, extensive floodplain regions enriched with basaltic and alluvial soils, a network of permanent streams and rivers and an extensive groundwater resource fed from surrounding hills in the east and south and the Great Artesian Basin (GAB) in the west. The catchment has supported agriculture (mainly grazing and cropping) and its farming communities for over 150 years.

The Namoi Catchment also possesses rich coal deposits within the Gunnedah Basin and mining activity has escalated over the past decade with six major coal mines in operation with more being planned, including coal seam gas developments (Schlumberger Water Services - SWS 2012). While mining operations in many areas of Australia have co-existed with the broader community without conflict (e.g. in the Pilbara Region of Western Australia) and has contributed substantially to the establishment of sustainable economies (e.g. the Victorian Goldfields) (SKM 2010b), the Namoi Catchment and other regions in NSW are experiencing conflict between the agriculture and mining sectors over limited natural resources. The agricultural sector is concerned about the potential cumulative risks of open cut mines (OCMs), long wall mines (LWMs)1 and coal seam gas mines (CSGMs) to assets such as productive soils, surface water and groundwater. Conversely, the mining industry recognises opportunities to stimulate regional economies and to restore health to terrestrial ecosystems through appropriate stewardship (e.g. offsets) associated with their operations.

In response to the issue, the Commonwealth Government established in 2012 the Interim Independent Expert Scientific Committee (IIESC) on Coal Seam Gas and Coal Mining2. The main charter of the IIESC is to ensure that future decisions about potential water-related impacts of coal seam gas and large coal mining activities are informed by substantially improved science and independent expert advice. To achieve this, the IIESC will scope and advise on bioregional assessments in areas where coal seam gas and/or large coal mining developments are underway or planned. These assessments will involve scientific analysis of the ecology, hydrology and geology of an area for the purpose of assessing the potential risks to water resources in the area as a result of the direct and indirect impacts of coal seam gas development or large coal mining development. The Namoi Catchment is one of a number of priority regions identified for bioregional assessment.

The NSW Government’s response to these issues has been in the form of a Strategic Regional Land Use Policy . Key elements of the policy include Strategic Regional Land Use Plans (one of which has been developed for the North West Region of NSW which encompasses the Gunnedah Basin), an Aquifer Interference Policy, a requirement for Agricultural Impact Statements, two new Codes of Practice for the CSG industry and the creation of a new Land and Water Commissioner.

1 Also referred to as underground mines.

2 Pending the formal establishment of the Independent Expert Scientific Committee on Coal Seam Gas and Coal Mining (the Committee) under legislation.



1.2 CONTEXT

Eco Logical Australia (ELA) was engaged by the Namoi Catchment Management Authority (Namoi CMA) in 2011 to build a spatial tool that is able to report the cumulative risk of potential mining scenarios to key NRM assets (land, water, biodiversity) in the Namoi Catchment (Stage 3)3. Strategic review of project scope in early 2012 resulted in submission of a Stage 4 proposal to Namoi CMA that plans to incorporate ‘opportunities’ into the tool as well as risks. While Stage 4 has yet to commence, the spatial tool is henceforth referred to as the Namoi Cumulative Risks Assessment Tool (NCRAT).

This report identifies an approach to undertaking a regional bioregional assessment as it identifies, prepares and integrates a number of key NRM datasets. The approach also provides a mechanism to report risk, through generation of spatial sensitivity layers (from asset layers), construction of risk tables, and configuration and operation of an assessment tool. Definitions, parameters and assumptions are stated in this report, and examples of input and outputs provided.

The Stage 2 report (ELA 2011) provides background information on OCM, LWM and CSG operation and potential environmental impacts. The summary of CSG extraction provided in Section 3.4 of ELA (2011) is updated in Appendix I of this report to account for a more recent review of literature which places more emphasis on approaches used in Australia.

1.3 DEFINITIONS AND ASSUMPTIONS

1.3.1 Mining

For the purposes of this project, ‘mining’ refers to all activities associated with the extraction and on-site processing of mineral ores or coal seam gas that take place on a mine lease, and that is therefore captured within a spatial mine footprint. These activities include ore recovery, tailings management, gas processing, water supply, wastewater retention, coal washery and beneficiation. Off-site mine-related activities such as loading, ports, and transport and pipeline infrastructure are not included.

1.3.2 Interpretation of risk

Relative and absolute

There is an important distinction to be made on how the level of risk reported for any mining scenario is interpreted. For mine-specific assessments, risk will generally be measured in absolute terms. For example, ‘what is the real risk of groundwater contamination associated with a particular CSGM?’ In contrast, for projects that consider risk at the strategic landscape scale such as NCRAT (this project); risk is generally considered in relative terms. For example, what is the relative risk to groundwater contamination of a CSGM at locality ‘A’ compared to a similar CSGM at locality ‘B’. There is an important distinction to be made here – that reporting a level of risk in relative terms (in the context of NCRAT and other strategic assessments) cannot be interpreted as the same level of risk in absolute terms (in the context of the real risk of impacts of the mine).

3 This project follows conceptualisation and expert scientific review of the framework (ELA 2011 – Stages 1, 2).



Reporting the relative risk of mining scenarios augers with the new resilience approach adopted by the Namoi Catchment Action Plan (CAP)4, where landscape-scale thresholds effectively define tipping points for NRM assets (i.e. assets close to tipping points are at relatively high risk in the catchment). Reporting absolute risk of mining scenarios at a strategic level is probably not possible.

As outlined in ELA (2011), the risk approach underpinning this framework is based on the Risk Management Principles and Guidelines (AS/NZ ISO 31:000:2009) (Standards Australia and New Zealand 2004). These international guidelines have been developed to assist organisations in dealing with internal/external risk factors in accordance with International Standards.

Table 1 presents an example of a risk matrix that supports the ‘risk analysis’ component of the Standard in the context of relative risk at a landscape scale. In Table 1 one of five categories of relative risk (very low, low, moderate, high, extreme) is assigned to each category of impact likelihood/magnitude and sensitivity. This is used as the basis for a proposed risk assessment for the Namoi Catchment.

In summary, an important assumption is that risk is reported from the assessment tool relative to other parts of the catchment, but does not necessarily translate to absolute risk, which would need to be assessed in more detail using specific site-scale data.

Table 1. Example of a generic matrix used to assign classes of relative risk

SENSITIVITY TO POTENTAL IMPACTS

LIKELIHOOD/MAGNITUDE VERY LOW LOW MODERATE HIGH VERY HIGH

ALMOST CERTAIN M M H E E

LIKELY L M H H E

POSSIBLE L L M H E

UNLIKELY VL L M M H

RARE VL VL L M H

DESCRIPTION OF RELATIVE RISK CLASSES

E Extreme – NRM asset is most susceptible to mining using Namoi CAP and other criteria, relative to other parts of the catchment. Avoid if possible.

H High – NRM asset is susceptible to mining using Namoi CAP and other criteria relative to other parts of the catchment. Consider other locations in the absence of strategic offsets.

M Moderate – NRM asset is somewhat susceptible to mining using Namoi CAP and other criteria relative to other parts of the catchment. Proceed with adequate offsets.

L Low – NRM asset has low susceptibility to mining using Namoi CAP and other criteria relative to other parts of the catchment. Offsets generally not required.

VL Very low - NRM asset is least susceptible to mining using Namoi CAP and other relative to other parts of the catchment. Best option if possible.

4 “Resilience thinking” approach appreciates the capacity of the system to absorb disturbance with a certain level of tolerance and still maintain its basic function and structure.



Mitigated and unmitigated

The cumulative risk framework outlined in the ELA (2011) report proposes that only unmitigated risks of mining activities to bioregional assets of the Namoi Catchment are considered. The report acknowledges that mining companies routinely mitigate or offset impacts as part of planning approval so that overall impacts are reduced or negated. However, the report states that for the requirements of the Namoi CMA, only unmitigated (worse-case) risks are considered.

For the purpose of this report, and development of the spatial tool, use of the term ‘unmitigated’ is replaced by the term ‘baseline’, and is clarified as follows:

- Baseline risk applies to NCRAT to the extent that any activity undertaken by a mining company is assumed not to be mitigated beyond standard mitigation (e.g. routine water, dust and soil management). Non-standard measures include offsetting vegetation cover, offsetting threatened species habitat, re-injection of treated backwater into depressurised aquifers, and long-term site remediation. These measures are non-standard because they vary considerably from mine to mine in terms of their comprehensiveness and effectiveness. In some cases they may not be undertaken.

- Baseline risk assessment provides scope to compare the risks of mining with other industries and sectors (e.g. agricultural development, urban expansion).

Contribution of other sectors to risk

This report acknowledges that cumulative impacts to NRM assets such as groundwater and native vegetation cover are occurring in response to the activities of other sectors as well as mining (e.g. SKM 2011b). However, NCRAT Version 1 has been configured to predict cumulative risks to NRM assets of the mining sector only. This will enable Namoi CMA (and the mining industry) to quantify mine-exclusive risk, recognising that overall risk is likely to be higher given the ongoing effects of other sectors.

It is also acknowledged that early Government policy inadvertently resulted in the mismanagement of natural resource assets in the Namoi Catchment through encouragement of broad scale land clearing, inappropriate soil works, liberal use of toxic chemicals, introduction of invasive species, unregulated use of surface water and groundwater, as well as substandard mine tailings management. It is further acknowledged that historical impact to NRM assets in the Namoi Catchment has been mostly the result of agricultural development under the original guidance of the early policy framework that resulted in major ecological change (e.g. over half the catchment has been cleared of its original vegetation, and supports over 100 invasive plant and animal species). It follows that the level of cumulative risk imposed by new mine and mine scenarios will be largely (and unavoidably) dictated by past impacts imposed by other sectors, mainly agriculture. As these factors are beyond our control, it is not reasonable to compare past agricultural impacts with future mining impacts, at least not before introduction of land clearing laws in NSW (around 1990).

It is reasonable, however, to compare future agricultural (and other) impacts with future mining impact, although NCRAT Version 1 will not allow us to do that directly. One of the key recommendations of this report is that future land cover change data be hard-wired into a scenario template that is run as a pre-cursor to any future scenario, so that cumulative risk can continue to be assessed from the 2012 baseline, in terms of both mining and agriculture.



1.3.3 Framework

The framework is divided into 3 broad modules:

- Module 1 is the input module. It allows the user to input one or more potential mines. Each mine is classified as CSGM, LWM or OCM.

- Module 2 is the analysis module. It superimposes each mine against sensitivity surfaces generated for each asset. Some of the sensitivity layers are updated after addition of each mine, providing a ‘compounding’ cumulative result. Those that are not updated provide a ‘linear’ cumulative result.

- Module 3 is the reporting module. It links risk tables to sensitivity outputs and provides a final risk statement for the input scenario. The report includes charts, maps and summary statements about the impact of the scenario to underlying assets.

The following assumptions are important:

1. The tool is designed to assess cumulative risk at the landscape scale, and uses broad regional NRM data to support the assessment. The tool is not designed to assess cumulative risk at the mine-site scale. This will be undertaken through normal approval channels using a mine-scale framework (e.g. SKM 2011b) and will require application of site-specific data.

2. The tool is designed to measure relative rather than absolute risk (see above).



2 Methodology 2.1 CONTEXT

The methodology for this project comprised of 4 distinctive tasks.

1. Compilation and preparation of spatial NRM data

2. Development of sensitivity layers and risk tables

3. Establishment of assumptions and indices for NCRAT

4. Construction of NCRAT and accompanying user guide

2.2 DATA COMPILATION

A number of key spatial datasets were requested and sourced from Government agencies through November and December 2011. Data licence agreements were prepared and signed for each dataset. Data were stored into project folders and used as the basis for establishment of asset sensitivity layers.

2.3 PREPARATION OF SPATIAL DATA

2.3.1 Base case mining layer

The spatial footprint of the zone of impact5 of all past and present mines in the Namoi Catchment was sourced from the draft NSW Land Use Mapping dataset (Emery et al. in prep). The spatial orientation of these was checked and if necessary modified on-screen against SPOT5 remote sensing imagery using ArcGIS software. Topographic maps were systematically checked across the extent of the Namoi Catchment to locate past and present mines and quarries. Any that were not included in the NSW Office of Environment and Heritage (OEH) data were also digitised on-screen.

Approved future mines or mine extensions (but not exploration areas) were added to the layer by digitising proposed mining footprints. The mapped location of areas approved for future mining activity was sourced from assessment reports publically available on the NSW Department of Planning website6. These included reports for Ardglen Quarry Project near Murrurundi, as well as Boggabri, Narrabri Coal, RocGlen, Sunnyside, Tarrawonga and Werris Creek Coal Projects.

Data captured in the base case mining layer included name of mine; owner/operator; status (derelict, operating, approved); type (CSGM, LWM or OCM (including quarries)); mine area (ha); and resource (e.g. coal, tin, rock).

5 The zone of impact includes all areas where vegetation is cleared for open pits, drill pads, storage of overburden, and construction of buildings, roads, storage ponds and other infrastructure. It also includes the footprint of any LWM panels.

6 http://majorprojects.planning.nsw.gov.au/page/development-categories/mining--petroleum---extractive-industries/.

http://majorprojects.planning.nsw.gov.au/page/development-categories/mining--petroleum---extractive-industries/



2.3.2 Vegetation cover

Context

Spatial data that show the distribution of native vegetation cover (woody and non-woody) also indicate the contiguity (or conversely the patchiness) of landscapes, and are necessary for deriving secondary spatial data including connectivity and intactness.

The most recent data on vegetation extent in the Namoi Catchment is embodied in the RVC map (ELA 2009a). As this product was assembled from a number of prior mapping datasets, each captured at different scales and using different methodologies, an update of the cover data was required.

Removal of non-woody areas from woody RVCs

Because the original RVC layer was constructed from a composite of existing spatial datasets that were captured from the 1970s to the early 2000s, some areas of the Namoi that are currently mapped as forest or woodland have since been cleared for pasture grazing or cropping. To address this, the RVC map was overlaid across 2009 SPOT5 satellite imagery, and on-screen modification was undertaken to identify and separate parts of ‘woody’ polygons that appeared to have been cleared. All cleared areas detectible at a scale of 1:20,000 were included.

Addition of woody areas

Any area of native woody vegetation in the Namoi Catchment that was ≥ 1 ha in area and not part of the existing RVC map was delineated and integrated into the RVC map. An RVC tag was assigned to each polygon by intersecting it with the pre-European RVC layer.

Mapping involved delineation of areas which exhibited an average canopy separation ratio less than a threshold level shown in Table 2 (depending on vegetation formation and bioregion). Data in Table 2 were compiled using expert knowledge about RVCs in combination with reported crown separation ratios for major vegetation formations (McDonald et al 1990).

Table 2. Maximum canopy separation ratios [minimum projected crown cover values] for major vegetation formations within major bioregions in the Namoi Catchment

Formation New England Tablelands Nandewar Brigalow Belt

South Darling Riverine

Plains

Tall forest 0.5 [50] - - -

Forest 1.0 [25] 1.0 [25] 1.0 [25] -

Woodland 2.0 [10] 3.0 [6] 4.0 [4] 5.0 [3]

Derivation of a forest/woodland patch surface

Each cluster of adjoining forest and woodland RVC polygons was merged into a single patch, and its total area calculated and assigned to one of four categories:

1. Small patch (4 to 20 ha)

2. Local patch (>20 to 200 ha)

3. Regional patch (>200 to 10,000 ha)

4. Continental patch (> 10,000 ha).



Capture of clumps of trees

Each unmapped remnant patch 1- 4 ha was digitised and tagged as a ‘clump’. Clumps of trees are not considered to be large enough to support breeding pairs of many native fauna species, but are known to provide temporary refuge for some, and play a key role in their movement and dispersal.

Derivation of ‘smoothed’ cover surface

On completion of the vegetation cover layer, a ‘smoothed’ cover surface was generated (as a 25 m x 25 m raster) by assigning to each gridcell the average cover within a 5 km radius. This layer formed the basis for generation of two input sensitivity layers (Sections 2.4.2 and 2.4.3).

2.3.3 Vegetation type Background

Identification of areas of high conservation value within a CMA and other jurisdictions is a necessary part of conservation planning and management in NSW and spatial distribution of vegetation types (current and predicted) is often used as the key driver of conservation value assessment. Regional Vegetation Communities (RVCs)7 have been described and mapped in the Namoi Catchment. RVCs underpin various targets in the Namoi CAP, and will contribute to development of a key input layer for the cumulative risk assessment that provides a measure of conservation status at a state and/or national scale:

Threatened Ecological Communities (TECs) listed under the Commonwealth Environmental Protection and Conservation Act 1999 (EPBC Act) and Endangered Ecological Communities (EECs) listed under the NSW Threatened Species Conservation Act 1995 (TSC Act).

International Union for the Conservation of Nature (IUCN) status was also reported by ELA (2011) as a potential layer for the tool. However, initial comparison of the EEC and IUCN surfaces showed a high degree of overlap in sensitivity, thus IUCN was not considered further.

EEC mapping

A total of 19 EECs occur (or possibly occur) in the Namoi Catchment, nine (9) of which are equivalent or partly equivalent to TECs listed under Commonwealth legislation (Table 3). As the relationship between RVCs and TECs/EECs is often not one-to-one8, each RVC was assigned one of the following five categories, based on the mapped proportion of that RVC that is likely to constitute TEC/EEC:

- > 75% - 50 – 75% - 25 – 50% - 5 – 25% - < 5%

7 See Appendix III for a list of RVCs

8 Some TECs/EECs may be represented in more than one RVC, while some RVCs may include one or more TECs/EECs and other non-listed communities



Percent cover threshold (for RVCs)

A percent cleared estimate was calculated for each RVC following spatial revision of woody vegetation cover in section 2.3.2. This was estimated as:

Percent cover (%) = 100 x Remaining area (ha)

Pre-European area (ha)

2.3.4 Intactness Context

The intactness of a landscape is its ‘naturalness’ and is influenced by the proportion of native vegetation remaining and its patchiness9. Intact landscapes have little or no degree of disturbance and exhibit high connectivity and a low degree of modification (McIntyre and Hobbs 1999). Intactness is a reasonable (but not absolute) measure of vegetation condition as roads and other easements that bisect contiguous areas of native vegetation can act as vectors for movement of alien species (thus increasing disturbance), and may also increase the risk of wildfire.

An intactness input layer was developed for this project by first dissecting all extant native vegetation patches with existing infrastructure easements (road, rail and powerlines), then applying the following equation to a 25 m gridcell layer at every point in the landscape that considered all surrounding vegetation within a 5 km buffer.

Intactness = [[(Native vegetation)Area ] / [(Total)Area] ] [1 + (0.01 * (no. patches)]

Where:

(Native vegetation)Area = combined area of all true10 native vegetation within the 5 km buffer

(Total)Area = area of a circle of 5 km radius

No. patches = number of patches in the 5 km radius (including those divided by easements)

The power factor increases with the total number of patches and is used to account for the impact of edge effects. Thus, the more the landscape has been cleared and the greater the number of remnant patches, the greater the relative loss of intactness in the landscape. This is demonstrated in Figure 1, which replaces Figure 2 in ELA (2011; page 14).

9 Patchiness refers to the number of patches per unit area. 10 “True’ refers to a native vegetation type found in situ that is likely to have been in situ at the time of European settlement.



Table 3. Threatened/Endangered Ecological Communities of the Namoi Catchment.

(bolded communities are those most commonly occurring within the catchment)

EEC (TSC-listed) TEC (EPBC-listed)

Artesian Springs Ecological Community

Ben Halls Gap National Park Sphagnum Moss Cool Temperate Rainforest

Brigalow within the Brigalow Belt South, Nandewar and Darling Riverine Plains Bioregions Brigalow (Acacia harpophylla dominant and co-dominant)

Brigalow-Gidgee woodland/shrubland in the Mulga Lands and Darling Riverine Plains Bioregions

Carbeen Open Forest community in the Darling Riverine Plains and Brigalow Belt South Bioregions

Cadellia pentastylis (Ooline) community in the Nandewar and Brigalow Belt South bioregion

Coolibah-Black Box Woodland of the northern riverine plains in the Darling Riverine Plains and Brigalow Belt South bioregions

Coolibah-Black Box Woodland of the Brigalow Belt South and Darling Riverine Plains Bioregions

Fuzzy Box Woodland on alluvial soils of the South Western Slopes, Darling Riverine Plains and Brigalow Belt South Bioregions

Howell Shrublands in the New England Tablelands and Nandewar Bioregions

Inland Grey Box Woodland in the Riverina, NSW South Western Slopes, Cobar Peneplain, Nandewar and Brigalow Belt South Bioregions

Grey Box (Eucalyptus microcarpa) Grassy Woodlands and Derived Native Grasslands of South-eastern Australia

McKies Stringybark/Blackbutt Open Forest in the Nandewar and New England Tableland Bioregions

Montane Peatlands and Swamps of the New England Tableland, NSW North Coast, Sydney Basin, South East Corner, South Eastern Highlands and Australian Alps Bioregions

Myall Woodland in the Darling Riverine Plains, Brigalow Belt South, Cobar Peneplain, Murray-Darling Depression, Riverina and NSW South Western Slopes bioregions

Weeping Myall Woodlands



EEC (TSC-listed) TEC (EPBC-listed)

Native Vegetation on Cracking Clay Soils of the Liverpool Plains Natural grasslands on basalt and fine-textured alluvial plains of northern New South Wales and southern Queensland

New England Peppermint (Eucalyptus nova-anglica) Woodland on Basalts and Sediments in the New England Tableland Bioregion New England Peppermint Grassy Woodlands

Ribbon Gum-Mountain Gum-Snow Gum Grassy Forest/Woodland of the New England Tableland Bioregion

Semi-evergreen Vine Thicket in the Brigalow Belt South and Nandewar Bioregions Semi-evergreen vine thickets of the Brigalow Belt (North and South) and Nandewar Bioregions

Upland Wetlands of the Drainage Divide of the New England Tableland Bioregion Upland Wetlands of the New England Tablelands and the Monaro Plateau

White Box Yellow Box Blakely’s Red Gum Woodland White Box-Yellow Box-Blakely's Red Gum Grassy Woodland and Derived Native Grassland



Figure 1. Influence of % clearing and patchiness on intactness

2.3.5 Habitat links and viable habitat area Context

ELA (2011) provides a detailed review of the importance of functional connectivity for maintaining the viability of fauna populations in variegated and fragmented rural landscapes such as in the Namoi Catchment. The role of paddock trees and small clumps of trees as ‘stepping stones’ between larger patches is discussed and the importance of the aggregated patch size (i.e. the combined area of all functionally linked patches) is also highlighted in the context of effective habitat areas and meta-populations.

Capture of scattered paddock trees

Mature native paddock trees that were observed to be outside the extent of native woody vegetation were each captured as a single point in ArcGIS11 where they were located between two vegetation patches that were separated by no more than 1.5 km. Each scattered paddock tree was assigned an RVC tag (based on coincidence with pre-European vegetation), and all paddock trees were assigned a unique number.

11 Use of object-recognition software such as ENVI was considered for this process; however the significant volume of data, disk space and analysis memory space rendered such use unachievable in the timeframe.



Delineation of links

For all pairs of adjacent patches of native woody vegetation (including clumps) in the Namoi Catchment that were no more than 1.5 km apart and were linked by a linear sequence of paddock trees with no gaps greater than about 170 m (based on Doerr et al. 2010), connectivity was mapped via a single line linking the most direct sequence of structural stepping stones (i.e. paddock trees) between the patches.

Classification of links into ‘link integrity’ classes

Each link was classified according to its length, width, tree density and groundcover (categories shown in Table 4). Each link was then allocated a link integrity category (very high, high, moderate, low, very low) using the rule set outlined in Appendix II. This rule set assumes that links that are of shorter distance between patches, of broader width, of higher tree density and that comprise a native understorey, are likely to provide better connectivity than links that are longer, thinner, of low tree density and comprise a cropped understorey.

Table 4. Criteria for classifying mapped links

Length (m) Width (m) Average tree density (no/ha) Groundcover

< 300 Single crown < 1 Native grass/shrubs

300 - 499 10 - 50 1 - 5 Exotic pasture/crops

500 - 699 > 50 - 100 6 - 10

700 - 999 > 100 > 10

1000 - 1500 Grouping and classifying patch clusters

Each group of patches that were identified as functionally connected (i.e. linked by paddock trees) were amalgamated into a single patch cluster, and the total habitat area was calculated for each cluster (as the sum of areas of all individual patches contributing to the cluster).

Each patch and patch cluster was then assigned into one of five viable habitat classes based on the following:

Clump 1 – 4 ha Regional patch 200 – 10,000 ha

Small patch 4 – 20 ha Continental patch > 10,000 ha

Local patch 20 – 200 ha

Classification of links into ‘magnitude’ classes

Following classification of patch clusters into the five area classes above, links were further prioritised into ‘magnitude’ classes based on the likely loss of viable habitat area should they be removed (Table 5).



Table 5. Criteria for assigning a ‘magnitude’ class to mapped links

Link magnitude Description

Cornerstone Link that maintains the ‘continental’ status of a patch or connects two continental patches.

Major Link that maintains the ‘regional’ status of a patch, connects two regional patches, or connects a regional patch to a continental patch.

Moderate Link that maintains the ‘local’ status of a patch, connects two local patches, or connects a local patch to a regional or continental patch.

Minor Link that maintains the ‘small’ status of a patch, connects two small patches, or connects a small patch to a local, regional or continental patch.

Insignificant Link that connects a clump to any other patch.

Note: the term patch in this table may refer to ‘patch cluster’, where all patches in the cluster are functionally linked

2.3.6 Landscape corridors

Context

The capacity for species to migrate and disperse throughout landscapes is influenced by the availability (and transmissivity) of major vegetated corridors, which are often connected by smaller cornerstone links (see above). Thus spatial delineation of major landscape corridors is an important consideration in strategic risk assessments.

Delineation of corridors

Landscape corridors were delineated as lines that followed the centre of contiguous reaches of native woody vegetation linking areas of at least 10,000 ha. A standard 2 km buffer was generated around the landscape corridor layer (1 km on each side), before dividing it into 2 km segments and clipping it to the extent of the Namoi Catchment. Total vegetation cover (%) was then calculated for each segment.

2.3.7 Threatened species models

Context

A total of 120 terrestrial species listed as threatened under the Commonwealth Environmental Protection and Conservation Act 1999 (EPBC Act)12 and/or the NSW Threatened Species Conservation Act 1995 (TSC Act) are known or predicted to occur in the Namoi Catchment, including 80 fauna species and 40 flora species. Two (2) threatened fish species are also likely to occur in the Namoi Catchment, one listed under the NSW Fisheries Management Act 1994 (FM Act) and one under the EPBC Act. One endangered fauna population (Australian Brush-turkey population in the Nandewar and Brigalow Belt South bioregions) is listed under the TSC Act and one endangered fish population (Olive Perchlet) is listed under the FM Act. All threatened species and populations (124 in total) are listed in Appendix III.

12 Not including species listed as ‘migratory’ under the EPBC Act.



A predicted distribution model was derived for each species (and the two endangered populations) using existing locality data, environmental layers and expert knowledge. Each model was weighted according to its listing status, then all models were merged into a threatened species composite layer for input into NCRAT.

Threatened species locality data

A digital database of all threatened species records in and surrounding the Namoi Catchment13 was sourced from the NSW Wildlife Atlas in late November 2011 via a request to OEH. This was supplemented by vouchered plant records held by herbaria at the University of New England and the Sydney Botanic Gardens. All data were scrutinised and any erroneous or dubious records were quarantined and played no further part in analysis.

Derivation of individual models

Point locality data for each threatened species were intersected with the mapped classes of each of nine (9) input surfaces (Table 6) to ascertain the density of records per class. Reported density values were then appended into the attribute table of each spatial layer, before all density data across all layers were tallied to derive a preliminary model for each threatened species (developed as a 25 m gridcell layer). All models other than those representing highly mobile fauna species were masked by the extent of native vegetation14 before each was scrutinised by expert ecologists, and revised as necessary.

Models for threatened species that were not represented by spatial records were derived using expert rule sets based on literature and/or knowledge about their known broad distribution. For example, information about distribution across bioregional subregions, or elevation ranges, was used to guide broad models. An expert model was prepared for each of the five listed riverine species using spatial river data developed by ELA (2009c).

A final unweighted model was produced for each species that comprised three categories: 2 (species highly likely to be present); 1 (species possibly present); and 0 (species highly unlikely to be present).

Weighting factor for individual models

A weighting factor was applied to each model based on the species’ listing status under the TSC and/or EPBC Acts (Table 7). This factor was multiplied to each of the numerical classes (0, 1 and 2) above to derive a set of weighted species models.

Threatened species composite layer

A threatened species composite was produced by adding all 124 weighted species models, where the value of any gridcell in the composite was the sum of the values of corresponding gridcells in the individual species models (weighted). For example, if 110 models returned a value of ‘0’, eight models returned a value of ‘1’, three models returned a value of ‘2’, two models returned a value of ‘3’, and one model returned a value of ‘6’ for a common gridcell, then the value of that gridcell in the composite would be 26. 13 Records were obtained within a rectangular area encompassing the Namoi Catchment 14 Assumes that most fauna species and all flora species are highly unlikely to occur in areas where native vegetation has been removed (e.g. cropland, urban areas)



As a final step, the composite layer was transformed linearly from 0% (no overlapping species) to 100% (maximum score for overlapping species).

Table 6. Input surfaces used to inform threatened species models

Spatial layer Source Number of classes 1

Namoi sub-catchments Namoi CMA 40

RVCs (pre-European) ELA (2009b) 66

Soil landscapes Namoi CMA 300

Major geomorphology ELA (2009c) 3

Latitude Topographic maps 5

Longitude Topographic maps 10

Mean annual rainfall DEC layer 8

Elevation DEM 14

Slope DEM 9

1. Refer to Appendix IV for a list of classes for each asset

Table 7. Threatened species weighting factors

TSC Status

EPBC status Critically Endangered Endangered Vulnerable Not listed

Critically Endangered 7 6 5 4

Endangered 6 5 4 3

Vulnerable 5 4 3 2

Not listed (or Migratory) 3 2 1

2.3.8 Land use Land use mapping was extracted from DECCW’s draft NSW Land Use Mapping Program (Emery et al. in prep), the latest dataset compiled in 2009. This dataset partitions the landscape into various categories of cropping, horticulture, intensive animal production, grazing, mining and quarrying, tree and shrub cover (native and exotic), urban, energy, transport, wetlands, rivers and drainage systems, conservation areas and other special categories (Appendix V). The most recent mining footprint (Section 2.3.1) was included.

2.3.9 Soil productivity Land suitability class (LSC) mapping was sourced from Namoi CMA. This classifies land into eight (8) broad classes based on limitation of soils for agriculture, and takes into account soil type and productivity. Thus it is a reasonable surrogate for soil productivity and was used for this study. Table 8 lists and describes LSCs in the Namoi Catchment. Geology mapping was used check and potentially amend productivity mapping.



Table 8. LSC classification used for the Namoi Catchment.

LSC Description

1 No special land management practices needed very slight to negligible limitations. None mapped in Namoi.

2 Soils exhibit slight limitations that can be managed through appropriate land use practices. Land uses include intensive cropping with cultivation, grazing etc.). Soil conservation practises such as strip cropping, conservation tillage and crop rotation are routinely used. Land suitable for regular cultivation.

3

Soils exhibit moderate limitations that can be managed by more intensive land use practices such as reducing tillage and stubble retention. Structural soil conservation works such as division banks, graded banks and waterways are used, together with soil conservation practises as in class 2. Land suitable for regular cultivation.

4

Soils exhibit moderate to severe limitations that require specialised management practices, such as cropping with minimal or no cultivation. Soil conservation practices include pasture improvement, stock control, application of fertiliser and minimal cultivation for establishment or re-establishment of pasture. Land suitable for grazing with or without occasional cultivation.

5

Soils exhibit severe limitations for high impact uses (cropping) that include lower fertility, sloping land (10-25%), land subject to wind erosion, acidity or salinity. Structural soil conservation works are used such as absorption banks, diversion banks, and contour ripping, together with the practices as in class 4. Land suitable for grazing with or without occasional cultivation.

6 Soils exhibit very severe limitations for cropping, moderate to high intensity grazing, and horticulture) which cannot be overcome by land management practices. Soil conservation practices include limitation of stock, broadcasting of seed and fertiliser, and prevention of fire and destruction of vermin. Suitable for grazing only.

7 Soils exhibit extremely severe limitations and are best protected by retention of native vegetation. Generally stock should be excluded.

8 Land suited only for conservation management (e.g. cliffs, wetlands, lakes) and other lands incapable of sustaining agricultural or pastoral production; including all soil landscape disturbed terrain.

2.3.10 Carbon store A carbon store layer for the Namoi Catchment was generated for this project by deriving a site-quality surface based on mean annual temperature, mean annual rainfall and soil fertility (derived from the land suitability classes in Table 8 15). Table 9 shows the ten (10) site quality classes and the above-ground carbon store estimates and potential sequestration rates associated with forest and woodland types that occupy them (from Wall 2001).

The site quality surface was subsequently intersected with the extant RVC map to derive categories of site quality index for each forest and woodland RVC in the Namoi Catchment, to which an estimated level of carbon store was expertly assigned, using Table 9 as a guide.

15 High soil fertility = LSC 1,2,3; medium soil quality = LSC 4,5,6; low soil fertility = LSC 7,8 (LSC description provided in Table 8).



Biomass results in Table 9 were readily converted to CO2-equivalents (CO2-e) by imposing the following assumptions:

- the average carbon content of above ground biomass is about 50% (Gifford 2000); and

- the ratio of CO2 to stored carbon is 11:3 (i.e. decomposition of 3 tonnes of carbon stored in a forest releases 11 tonnes of CO2).

Soil carbon also represents a significant carbon pool within vegetated systems. The following assumptions provided by the National Greenhouse Gas Inventory Committee (NGGIC 1996) were used to derive some broad soil carbon estimates for RVCs:

- average soil carbon for temperate open forest is 85 t.ha-1

- average soil carbon for woodland/scrub is 70 t.ha-1

- 50% of soil carbon is forfeited to the atmosphere on clearing of forest for agriculture.

It is also assumed that below-ground woody biomass (tree roots and decomposing wood) is included in the soil carbon pool.

Table 9. Site quality classes according to combinations of mean annual rainfall, mean annual temperature, and soil fertility, and above-ground standing biomass/growth rate estimates associated with each class, for native forest and woodland (from Wall 2001).

Mean Annual Temperature (°C) Soil Fertility Class ≥ 18.0 15 – 17.9 12 – 14.9 < 12 High 1 1 3 6 ≥ 1200 Medium 2 3 5 7 Low 4 5 6 7 High 2 2 4 7 800 - 1199 Medium 3 4 6 8 Low 5 6 7 8

Mean Annual High 5 6 7 8 Rainfall 600-799 Medium 6 7 7 9 (mm/yr) Low 7 8 8 9

High 7 7 8 9 500-599 Medium 7 8 9 10 Low 8 8 9 10 High 8 9 9 9 < 500 Medium 9 9 10 10 Low 10 10 10 10

1. growth rate ≥ 17.0 t.ha-1.yr-1 1. standing biomass ≥ 400 t.ha-1 2. growth rate 15.0 – 16.9 t.ha-1.yr-1 2. standing biomass 300 - 399 t.ha-1 3. growth rate 13.0 – 14.9 t.ha-1.yr-1 3. standing biomass 250 - 299 t.ha-1 4. growth rate 11.0 – 12.9 t.ha-1.yr-1 4. standing biomass 200 – 249 t.ha-1 5. growth rate 9.5 – 10.9 t.ha-1.yr-1 5. standing biomass 150 – 199 t.ha-1 6. growth rate 8.0 – 9.4 t.ha-1.yr-1 6. standing biomass 125 – 149 t.ha-1 7. growth rate 6.5 – 7.9 t.ha-1.yr-1 7. standing biomass 100 – 124 t.ha-1 8. growth rate 5.0 – 6.4 t.ha-1.yr-1 8. standing biomass 75 - 99 t.ha-1 9. growth rate 3.5 – 4.9 t.ha-1.yr-1 9. standing biomass 50 – 74 t.ha-1 10. growth rate 2.0 – 3.4 t.ha-1.yr-1 10. standing biomass 0 – 49 t.ha-1



2.3.11 Surface water flow The Namoi River system includes over 8,000 km of major streams and rivers within 40 sub-catchments (ELA 2009c) and accounts for approximately 3.8% of the area of the Murray Darling Basin (Hyder Consulting 2008). There are three key water supply storages in the Namoi, including Chaffey Dam on the Peel River, Keepit Dam on the upper reaches of the Namoi River, and Split Rock Dam on the Manilla River. All are used for provision of irrigation water and regulation of flooding. Chaffey is also used to augment water supply to the city of Tamworth.

Recognising its important role in aquatic and floodplain diversity, alluvial soil health and domestic and commercial use; the Namoi CAP has set a critical threshold for surface water flow of 66% of natural (pre-development) flow for each of the 40 sub-catchments in the Namoi Catchment, with a sensitivity to natural frequency and duration. Median flow and annual water entitlement data were provided by NOW (2012 unpubl. data) for this project.

2.3.12 Surface water quality

The major sources of contaminants from the mining industry are reviewed in Chapter 3 of ELA (2011) and include contamination from leaks or spills from produced waters of fraccing fluids, as well as sedimentation from development of mine infrastructure (roads, pipelines, well pads). Potential contamination of surface water is highest where mines are located closer to natural assets such as wetlands, rivers and streams (e.g. Entrekin et al. 2011), as this increases the probability that contaminants will enter a stream network increases, should a breach or accidental spillage occur. Other criteria such as topography, climate, pedology and geomorphology are also important (ELA 2011).

Several spatial layers were acquired to support development of a sensitivity layer for surface water contamination. Table 10 lists the layers, why they were selected, and how they were classed for this project.

Table 10. Input surfaces used to inform surface contamination potential

Spatial layer Rationale (assuming an incident)

Stream network Mines closer to streams more likely to pollute them

River styles Higher condition riparian vegetation at more risk than lower condition riparian vegetation

Local catchment The greater the proportion of a local catchment occupied by mines, the greater the risk of contamination to the outflow of that catchment

Water storages and significant wetlands

Mines located on and immediately upstream of water storage and wetland assets more likely to pollute them (assets ≥ 1 ha)

Surface water infrastructure

Mines located in areas that contain a high level of infrastructure (irrigation canals and farm dams) likely to result in higher economic cost

All of the above datasets were available for this project other than local catchments and significant wetlands and storages. These new datasets were captured as follows:



Local catchments

Local catchments were captured using on-screen digitising, guided by a topographic map mosaic and 25m DEM (1m height resolution). Local catchments were generally captured at 1:20,000 scale in the eastern part of the catchment, and 1:30,000 scale in the western part of the catchment. Each local catchment possessed a minimum area of 1,000 ha. Each local catchment was linked to its existing sub-catchment and any discrepancies were mapped. The area (and proportion) of existing mines in each sub-catchment was calculated by intersecting the local catchment layer with the base case mining layer (section 2.3.1).

Significant wetlands and storages

A significant wetland layer was revised from prior wetland mapping using more recent SPOT and ADS40 imagery, cross-referenced against wetlands identified from the topographic mosaic. Significant storages captured from prior datasets were also checked against the latest SPOT and ADS40 imagery, and new data digitised as necessary. Wetlands and storages of less than 1 ha in size were not included.

2.3.13 Groundwater drawdown Context

SWS (2011) provide a characterisation of the hydrogeology of the Namoi Catchment that is summarised as follows:

- Groundwater resources are highly developed, with extraction concentrated in the alluvial aquifers of the Namoi and its major tributaries, within the Narrabri, Gunnedah and Cubbaroo Formations.

- Jurassic sandstones and Permian Gunnedah Basin sediments underlie and surround the alluvial deposits within the centre of the catchment, west of the Hunter-Mooki Fault.

- The Lower Namoi alluvium is underlain by Jurassic shales, sandstones and mudstones that form the edge of the Great Artesian Basin.

- Part of the New England Fold Belt occurs to the east of the Hunter-Mooki Fault. This is characterised by highly deformed Silurian to Permian rocks overlain by Carboniferous sediments that provide limited groundwater.

SWS (2011) and Badenhop et al. (2012) divided the Namoi Catchment into 12 groundwater management areas (GMAs) for assessing long-term drawdown characteristics, the characteristics of which are summarised in Table 11. Of these 12 GMAs, the Upper Namoi Alluvium and the Lower Namoi Alluvium are most highly developed and stressed (Badenhop et al. 2012).

Observed groundwater levels reported from bore data in the major alluvial zones the Namoi Catchment (SWS 2012; Figures 6.19 to 6.25) suggest that groundwater levels were at or near their long-term maximum drawdown levels around 2007-2009, after a prolonged episode of below-average rainfall from 2000. More recent analysis of hydrograph data from monitoring bores (to June 2011) suggests that groundwater levels in some aquifers have recovered to some degree, the result of elevated recharge from three years of above average rainfall and several significant floods, as well as the associated reduction in seasonal pumping drawdown (Badenhop et al. 2012). However, areas of continued groundwater stress have been observed near Curlewis on the Breeza Plain, on Cox’s Creek, between Burren Junction and Walgett, and north-east and far north of Wee Waa (Badenhop et al. 2012).



The level of aquifer connectivity in the Upper Namoi Alluvium and Lower Namoi Alluvium was also assessed by Badenhop et al. (2012), who found that Zones 3 and 8 of the Upper Namoi Alluvium, as well as the lower Namoi between Narrabri and Wee Waa and north of Pian Creek between Wee Waa and Burren Junction, exhibited poor connectivity to recharge and were thus more susceptible to drawdown stress.

Table 11. Characteristic of groundwater management areas in the Namoi Catchment

Management Area Area (km2) Coal mine potential? Characteristics

Barwon Region Alluvium 210 - Small aquifers around Werris Creek and Quirindi.

Galarganbone Tertiary Basalt 80 - Basaltic region of the Warrumbungles.

Great Artesian Basin Alluvium 4,100 CSG

Aquifer up to 60 m depth that overlays the Great Artesian Basin Surat Groundwater Source. Recharged by rain infiltration, river infiltration, and upward leakage from the GAB.

Great Artesian Basin 7,010 CSG Large area coincident with the Pilliga, overlain with Pilliga sandstones.

Gunnedah Basin 3,530 LWM, OCM Permo-Triassic sediments. Extensively hydraulically connected with Oxley Basin.

Liverpool Ranges Basalt 1,720 - Basalt of the Barrington Tops Plateau to the south.

Lower Namoi alluvium 5,870 CSG Unconsolidated sediments extending approximately 160 km west from Narrabri

New England Fold Belt 7,840 - Ancient eroded mountain range of the Silurian. Occurs to the east of the Hunter-Mooki Fault

Oxley Basin 3,230 CSG, LWM, OCM

Jurassic and Cretaceous rocks that overlay sediments of the Gunnedah Basin, with which it extensively hydraulically connected.

Peel Valley Alluvium 190 -

Thin aquifer of unconsolidated alluvial deposits in the Peel Valley (15 - 20 m thick; 1.5 - 3.0 km wide). Used for stock, domestic, irrigation and town water supply purposes.

Peel Valley Fractured Rock 4,490 -

Sedimentary and metamorphosed rocks of the Peel catchment. Groundwater used extensively for stock water.

Upper Namoi alluvium 3,790 OCM/LWM Unconsolidated sediments associated with the Namoi River and a number of its tributaries including the Mooki River, Maules Creek, and Cox’s Creek.



Data

Acquiring relevant data for groundwater drawdown sensitivity was undertaken in the context of the distribution of coal and groundwater assets in the catchment (GDEs and bores) and the resilience threshold stated in the Namoi CAP: “Alluvial aquifers are not drawn down below long term historical maximum drawdown level” (Namoi CMA 2010). Four data layers were sourced accordingly:

1. Coal and gas potential, supplied by DPI (Figure 2).

2. Distribution of major groundwater aquifers in the Namoi Catchment (Figure 3), including status and connectivity data modelled across the Upper and Lower Namoi Alluvium zones and Peel Alluvium Valley by Badenhop et al. (2012), using data from about 830 bores. A spatial dataset was developed for this project by mapping groundwater bodies in terms groundwater status (recovering, stable, declining) and groundwater connectivity (connected, transition, disconnected) as reported by Badenhop et al. (2012).

3. Groundwater depth data collected by NOW (2009) that portioned groundwater into the following categories: < 5m; 5 – 10m; 10 – 15m; 15 – 20m; 20 – 25m; 25 – 30m; > 30m; expected to be < 30m (refer to Figure 4).

4. GDE potential, mapped by Sinclair Knight Merz (SKM 2010a) into four broad classes: high; moderate; low; very low (refer to Figure 5).

Figure 2. Coal and CSG potential in the Namoi Catchment



Figure 3. Groundwater aquifers in the Namoi Catchment

Figure 4. Groundwater depth within Upper and Lower Namoi Alluvia (source: NOW 2009)



Figure 5. GDE potential in the Namoi Catchment (data from SKM 2010a)

2.3.14 Groundwater quality

Context

The groundwater resources contained in the major aquifers of the Namoi Catchment are arguably the most intensively developed in the state if not Australia (SKM 2010a). Surface and groundwater resources support substantial cotton, lucerne and other cropping industries, including supply water for stock, domestic, and various industrial and town water supply uses.

There are various ways in which groundwater can become contaminated as a result of coal mining (ELA 2011 Chapter 3). Open cut mines that intersect groundwater bodies can lead to rapid oxidation of elements in the coal measures (e.g. pyrites) that can lead to hyper-salinisation of groundwater. Use of fraccing fluids associated with CSG operations may leave residual contaminants in groundwater bodies, and both LWM and CSG projects have the potential to fracture and mix groundwater bodies.

Data

A number of spatial asset data were sourced to inform a sensitivity layer for groundwater quality. Table 12 lists each NRM dataset used for groundwater quality, and why it was selected.

Table 12. Spatial data used for groundwater quality

Data Rationale

Coal resource potential Areas where OCM, LWM and CSGM are feasible

Distribution of alluvial aquifers Higher risk where alluvial aquifers overlay accessible coal beds.

Density of agricultural bores Indicator of demand for groundwater use by agriculture



2.4 DEVELOPMENT OF INPUT ‘SENSITIVITY’ LAYERS

2.4.1 Context The impacts of mining on NRM assets are reviewed by ELA (2011)16 and summarised in Table 13. The latest spatial data for each of the nine assets (column 1; Table 13) were sourced from Namoi CMA and reclassified into one of 12 input layers (column 3; Table 13), each comprising one of five standard ‘sensitivity’ classes (very low, low, moderate, high, and very high) that relate to the likely impact (and associated risk) of unmitigated mining operations (OCM, LWM and CSG) on the NRM asset.

Table 13: Impacts of mining on bioregional assets

Bioregional asset Primary Impact Type of cumulative impact Input layer(s)

Vegetation cover Reduction in vegetation extent Loss of native habitat

Percent cover threshold (sub-catchment) 1

Percent cover (smoothed)

Vegetation type Reduction in extent of vegetation types Potential reduction in biodiversity

Percent cover threshold 1

Endangered ecological communities

Vegetation condition (intactness)

Roads through intact areas

Incursion of invasive species and other edge effects into intact areas (potential reduction in ecosystem function)

Intactness index

Connectivity Fragmentation of viable habitat

Impedance to species movement and dispersal, and potential reduction in sub-population viability

Connectivity index

Threatened species Removal of threatened species habitat

Possible contraction in range of threatened species

Threatened species richness

Land use Land clearing Disruption to current land use Land use class

Soil Productivity Land clearing plus overburden

Sterilisation of prime agricultural land that would otherwise be farmed to produce food and fibre

Productivity class

Surface water

Surface water abstraction

Reduced water for agriculture and wetlands/riverine systems

Long term % surface flow (sub-catchment) 1

Surface water contamination

Contamination of drinking water and wetlands/river systems

Surface water pollution index

Groundwater

Groundwater drawdown

Reduced water for agriculture and GDEs Groundwater structure 2

Groundwater contamination

Contamination of bore water, wetlands/river systems, and groundwater fauna

Groundwater pollution index

1. Based on thresholds identified in the Namoi CAP 2. Derived from data supplied for The Namoi Water Study

16 Refer to section 9.2 of ELA (2011).



2.4.2 Percent cover (sub-catchment) The percentage of native vegetation cover remaining in each sub-catchment in the Namoi Catchment was calculated using the revised vegetation cover layer derived in Section 2.3.2. A percent cover (sub-catchment) sensitivity layer was then constructed by assigning to each sub-catchment (in its entirety) a single sensitivity class based on the rule set outlined in Table 14 (from ELA 2011). This rule set considers critical clearing thresholds of woody vegetation cover that are proposed in the Namoi CAP. A higher sensitivity to the impact of clearing (and thus a higher risk) was imposed on sub-catchments in which vegetation cover was close to either of the thresholds (30% cleared or 70% cleared), and a lower sensitivity was assigned to sub-catchments where cover was further from these thresholds.17

Table 14. Sensitivity to clearing of vegetation extent (based on Namoi CAP thresholds)

Percent cover (Namoi CAP threshold) Sensitivity

25 – 35; 65 – 75 Very high

20 – 25; 35 – 45; 60 – 65; 75 – 85 High

15 – 20; 45 – 50; 55 – 60; 85 – 90 Moderate

10 – 15; 50 – 55; 90 – 95 Low

< 10; > 95 Very low

2.4.3 Percent cover (local)

A percent cover (local) sensitivity layer was constructed by assigning a sensitivity class to each gridcell in the ‘smoothed’ cover layer (Section 2.3.2), based on the linear rule set outlined in Table 15. This layer assigned the highest sensitivity to areas of most native vegetation cover and lowest sensitivity to areas of least native vegetation cover, based on expert advice provided during peer review of the framework (ELA 2011).

Table 15. Sensitivity of clearing of vegetation extent (based on linear relationship)

Percent cover Sensitivity

80 - 100 Very high

60 - 80 High

40 - 60 Moderate

20 - 40 Low

0 - 20 Very low

17 For the purposes of the Namoi CAP, vegetation extent has been assessed on a sub-catchment level by comparing extant and

pre-European areas in each sub-catchment, and reporting which sub-catchments are close to thresholds.



2.4.4 Vegetation type percent cover threshold A vegetation type percent cover sensitivity layer was constructed by assigning to each RVC mapped in the Namoi Catchment a sensitivity class based on the rule set outlined in Table 16 (from ELA 2011). This rule set considers critical clearing thresholds of RVCs that are stated in the Namoi CAP. A higher sensitivity to the impact of clearing (and thus a higher risk) is imposed on RVCs whose remaining extent is close to the 70% cleared threshold.

Table 16. Sensitivity of native vegetation types (based on Namoi CAP threshold)

Percent RVC cover (Namoi CAP threshold) Sensitivity

25 – 35 Very high

20 – 25; 35 – 45 High

15 – 20; 45 – 55 Moderate

10 – 15; 55 – 65 Low

< 10; > 65 Very low

2.4.5 Vegetation type EEC likelihood EEC likelihood classes assigned to RVCs in section 2.3.3 were converted to sensitivity classes based on the relationship shown in Table 17.

Table 17. Sensitivity of native vegetation based on EEC candidacy

Likelihood of occurrence of EEC Sensitivity

> 75% Very high

50 – 75% High

25 – 50% Moderate

5 – 25% Low

< 5% Very low

2.4.6 Intactness index The condition of native vegetation is an important factor in risk assessment, as higher impacts and risks will always be afforded to native vegetation that is in better condition (i.e. vegetation that is more capable of supporting ecological processes in the landscape). By mapping the intactness of native vegetation cover across the Namoi Catchment, a spatial impact layer can be developed using criteria outlined in Table 18.



Table 18. Proposed classes for vegetation condition

Vegetation condition Intactness index Sensitivity

Excellent A 0.9 – 1.0 Very high

Good 0.7 – 0.9 High

Moderate 0.3 – 0.7 Moderate

Poor 0.1 – 0.3 Low

Very poor B 0 – 0.1 Very low

A. High % vegetation extent, large contiguous patches. B. Low % vegetation extent, many small isolated patches.

2.4.7 Connectivity index Each link was assigned a sensitivity class by applying rules listed in Table 19, in which sensitivity increases with link magnitude and link integrity (section 2.3.5). Each was then buffered by 50 metres to provide a polygon layer of functional links across the landscape, each assigned with a sensitivity class. Areas outside the links polygon were assigned a sensitivity of 0.

Table 19. Sensitivity classes for local links

Link magnitude (Table 5) Link integrity (Appendix II) Sensitivity

Cornerstone Very high, high, moderate, low Very high

Cornerstone Very low High

Major Very high, high, moderate

Major Low, very low Moderate

Moderate Very high, high

Moderate Moderate, low Low

Minor Very high, high

Moderate Very low

Very low Minor Moderate, low, very low

Insignificant ALL

Non-link Nil



2.4.8 Corridor index Each corridor segment was assigned a sensitivity class by applying rules listed in Table 20 in which sensitivity decrease with total vegetation cover within and laterally adjacent to the segment. That is, the narrower or more diffuse the strip of native vegetation that contributes to the landscape corridor (i.e. the greater the proportion of non-vegetation that may act as a barrier to movement) and the lower the opportunity for movement parallel to the corridor, then the greater its sensitivity to development.

Table 20. Sensitivity classes for landscape corridors

Vegetation cover of segment (%) Vegetation cover laterally adjacent to segment (%) Sensitivity

0 - 20

0 - 20 Very high

20 - 40 Very high

40 - 60 High

60 - 80 Moderate

80 - 100 Moderate

20 - 40

0 - 20 Very high

20 - 40 High

40 - 60 Moderate

60 - 80 Moderate

80 - 100 Low

40 - 60

0 - 20 Very high

20 - 40 High

40 - 60 Moderate

60 - 80 Low

80 - 100 Low

60 - 80

0 - 20 High

20 - 40 Moderate

40 - 60 Low

60 - 80 Low

80 - 100 Very low

80 - 100

0 - 20 High

20 - 40 Moderate

40 - 60 Low

60 - 80 Very low

80 - 100 Very low

Non-corridor na Very low



2.4.9 Threatened species richness The final threatened species composite layer (Section 2.3.7) was categorised into a sensitivity map incorporating five classes – very high, high, moderate, low, and very low – based on the rule set outlined in Table 21.

Table 21. Threatened species sensitivity thresholds

Description Sensitivity

Threatened species score ≥ (80% of range + minimum score) Very high

Threatened species score = (61-80% of range + minimum score) High

Threatened species score = (41-60% of range + minimum score) Moderate

Threatened species score = (21-40% of range + minimum score) Low

Threatened species score ≤ (20% of range + minimum score) Very low

2.4.10 Land use classes The land use layer developed in section 2.3.8 was reclassified into sensitivity zones by applying broad rules listed in Table 22. Mining footprints that coincide with land uses for which compensation is likely to be relatively high (e.g. urban zones, infrastructure, irrigated cropping) are assigned a relatively high sensitivity; those that coincide with land uses for which compensation is likely to be relatively low (e.g. marginal grazing land, native forestry) are assigned a relatively low sensitivity. Appendix V provides a list of all land use allocations.

Table 22. Land use sensitivity classes

Broad land use Sensitivity

Urban; major infrastructure Very high

Cropping (irrigated); intensive agriculture; nature conservation High

Cropping (dryland) Moderate

Open grazing land (incl. improved pasture) Low

Other grazing (forest and woodland); forestry Very low

2.4.11 Soil productivity class Sensitivity was assigned to soil productivity based on the Namoi LSC mapping (Section 2.3.9). The rule set is shown in Table 23 (refer to Table 8 for LSC descriptions). The mapped extent of Pilliga Sandstone (code JPS) was extracted from geology mapping and assigned a sensitivity of ‘very low’. This footprint was overlaid on LSC-derived sensitivity surface, which appeared to over-estimate productivity across much of the extent of the Pilliga Sandstone18.

18 Soil productivity associated with Pilliga Sandstones is known to be very poor (e.g. Goldrik et al. 2001).



Table 23. Sensitivity classes used for soil productivity

LSC Sensitivity LSC Sensitivity

1 Very high 5 Moderate 2 Very high 6 Low 3 High 7 Very low 4 High 8 Very low

2.4.12 Carbon store A carbon sensitivity layer was developed by partitioning carbon store data as shown in Table 24. As a rule of thumb, sensitivity increases with site quality, as a greater volume of CO2 would be released to the atmosphere upon clearing of ecosystems that contain more standing and below ground biomass.

Table 24. Sensitivity classes used for carbon store (includes soil carbon)

Carbon (t/ha) CO2-e (t/ha) Sensitivity

> 250 > 460 Very high

200 - 250 370 - 460 High

140 - 200 250 - 370 Moderate

50 - 140 90 – 250 Low

< 50 < 90 Very low

2.4.13 Long term surface water flow Spatial mapping of current relative and pre-development surface flow (based on Namoi sub-catchments) was used to map sensitivity classes across the Namoi Catchment by applying the rule set in Table 25. This rule sets assumes that new mines established in sub-catchments that are closer to (or less than) the critical 66% flow threshold have a potentially greater risk to security of surface water (for a range of purposes) than mines established in sub-catchments that are more distant from the critical threshold (i.e. where current flow is closer to pre-development flow).

Table 25. Sensitivity classes used for surface water flow

Current flow as % of pre-development flow (sub-catchment) Sensitivity

≤ 66 Very high

66.1 – 75 High

75.1 – 85 Moderate

85.1 – 95 Low

> 95 Very low



2.4.14 Surface water pollution index A spatial index of the sensitivity to surface water pollution was constructed by building a separate raster surface for each asset listed in Table 10, then merging all rasters using the ‘maximum’ command, in which the highest sensitivity class across all assets took precedence. Table 26 lists the criteria for each asset.

Table 26. Surface water quality sensitivity criteria

Spatial layer Sensitivity Rules

Stream network and river styles

Very high 0 – 100 m from any permanent stream/river in good condition.

High

0 – 20 m from any minor stream (1st and 2nd order)

0 – 100 m from any permanent stream/river in poor to moderate condition, or any ephemeral stream (3rd order or higher) in good condition.

100 – 300 m from any permanent stream/river in good condition

Moderate

20 - 50 m from any minor stream (1st and 2nd order)

0 – 100 m from any ephemeral stream (3rd order or higher) in poor to moderate condition.


300 – 500 m from any permanent stream/river in good condition;

Low

50 - 100 m from any minor stream (1st and 2nd order)



500 – 1000 m from any permanent stream/river in good condition;

Very low

> 100 m from any minor stream (1st and 2nd order)


> 500 m from any permanent stream/river in poor to moderate condition, or any ephemeral stream (3rd order or higher) in good condition.

> 1000 m from any permanent stream/river in good condition.

Local catchment 1

Very high > 15% of catchment mined

High 5 – 15% of catchment mined

Moderate 1 – 5% of catchment mined

Low 0.1 – 1% of catchment mined

Very low < 0.1% of catchment mined



Spatial layer Sensitivity Rules

Water storages and significant wetlands

Very high Asset plus contributing catchment 0 – 1 km upstream of asset

High Contributing catchment 1 – 2 km upstream of asset

Moderate Contributing catchment 2 – 5 km upstream of asset

Low Contributing catchment 5 – 10 km upstream of asset

Very low >10 km upstream of asset

Surface water infrastructure

(canals and farm dams) 2

Very high

Irrigation canals > 10 km/km2

Farm dams > 10 ha/km2

Irrigation canals > 5 km/km2 and farm dams > 5 ha/km2 ...........................................1

High

Irrigation canals 5 - 10 km/km2

Farm dams 5 - 10 ha/km2

Irrigation canals > 1 km/km2 and farm dams > 1 ha/km2 (not including 1) ................ 2

Moderate

Irrigation canals 1 - 5 km/km2

Farm dams 1 - 5 ha/km2

Irrigation canals > 0.1 km/km2 and farm dams > 0.1 ha/km2 (not including 1 or 2) ... 3

Low

Irrigation canals 0.1 - 1 km/km2

Farm dams 0.1 - 1 ha/km2

Irrigation canals > 0 km/km2 and farm dams > 0 ha/km2 (not including 1,2 or 3) ...... 4

Very low Irrigation canals < 0.1 km/km2

Farm dams < 0.1 ha/km2

1. Includes existing (base case) mines

2. Densities for irrigation canal (km/ha) and farm dams (area/ha) were reported within each cell of a 500 x 500 m grid across the Namoi Catchment

2.4.15 Groundwater drawdown The key question in developing a sensitivity layer for groundwater volume is “what are the circumstances in which potential groundwater drawdown will have a relatively high impact across stated NRM assets, and a relatively low impact?” It is assumed that all forms of mine will have some drawdown effect: OCMs through pit inflow; LWMs though inflow to the panel void; and CSGMs through direct abstraction, and that the magnitude of impact will be dependent on proposed mine size and underlying asset data.

For this project, more sensitive areas are those that:

- exhibit relatively shallow water tables; - contain groundwater that is poorly connected to surface flow; - do not appear to be recovering following good seasons; - are mapped as recharge areas; and - coincide with mapped distribution of GDEs.



Each of the five asset layers above was partitioned into scores using rules shown in Table 27. The five layers were then intersected and an average score calculated. A final sensitivity surface was developed using the following cut-offs:

Very high ≥ 1.50 High 1.25 – 1.49 Moderate 1.00 – 1.24 Low 0.50 – 0.99 Very low < 0.50

Table 27. Rule set for assigning groundwater drawdown sensitivity to asset layers

Asset Score = 3 Score = 2 Score = 1 Score = 0

Depth to Groundwater (from NOW 2009) < 10m 10 – 20m 20 – 30m

Potentially < 30m > 30m

Groundwater connectivity Badenhop et al. (2012) Disconnected Transitional Connected -

Groundwater status Badenhop et al. (2012) Stressed Stable Recovering -

Major recharge area Badenhop et al. (2012) Yes - - No

GDE potential (from SKM 2010a) High Moderate Low Very low or none

2.4.16 Groundwater quality The main risks to groundwater quality in the Gunnedah and Great Artesian Basins are:

- aquifer fracture that leads to mixing of groundwater aquifers; and

- chemical additives entering groundwater from fraccing fluids.

While these risks are low in absolute terms, in relative terms they are linked to coincidence of coal seams with alluvial aquifers and the Great Artesian Basin and the current level of groundwater use for other purposes. Table 28 shows the sensitivity rules for groundwater quality.



Table 28. Sensitivity classes for groundwater quality, based on resource potential, presence of aquifers and density of agricultural bores

i. CSG mines

CSG potential

Part of the GAB (non

alluvia)

Overlaid by alluvial aquifer (incl. GAB alluvia) Not overlaid by alluvial

aquifer or part of GAB

Bores < 1/km2

Bores 1 - 2/km2

Bores > 2/km2

high High Moderate High Very high Moderate

moderate Moderate Low Moderate High Low

low Low Low Moderate Moderate Low

none Very low Very low Very low Low Very low

ii. OCMs

OCM (coal) potential


alluvia)

Overlaid by alluvial aquifer (incl. GAB alluvia) Not overlaid by

alluvial aquifer or

part of GAB Bores

< 1/km2 Bores

1 - 2/km2 Bores

> 2/km2

yes Moderate Moderate High Very high Low

possible Low Low Moderate High Low

no Very low Very low Very low Very low Very low

iii. LWMs

LWM potential


alluvia)

Overlaid by alluvial aquifer (incl. GAB alluvia) Not overlaid by alluvial

aquifer or part of GAB

Bores < 1/km2

Bores 1 - 2/km2

Bores > 2/km2

yes Low Low Moderate HIgh Very low

no Very low Very low Very low Very low Very low



2.5 DEVELOPMENT OF A RISK MATRIX FOR EACH INPUT LAYER

2.5.1 Preamble A risk matrix was also constructed for each type of impact based on the original matrix developed by ELA (2011), modified to account for new information and/or advice. The cumulative risk framework operates by explicitly linking a ‘sensitivity’ class (where it is intersected by a mining scenario) to one or more cells in the appropriate risk matrix, thus providing a statement about the relative (not absolute) risk of the scenario to the underlying assets. The assets, sensitivity classes and risk categories were informed from literature reviewed for the project and from feedback from independent experts (ELA 2011). However, the risk tables are not final and may be modified as improved data comes to hand.

2.5.2 Vegetation cover The risk matrix for removal of native vegetation cover is shown as Table 29. The impact area associated with a mining scenario19 will be intersected with the vegetation cover sensitivity layer to determine the appropriate ‘cell’ in Table 29 from which a risk class is extracted and reported. The area thresholds in Table 29 are based on the species–area relationship20. Application of this matrix enables relative risk to be assessed in the context of both mine size and sensitivity to clearing (where sensitivity is represented by either proximity to the Namoi CAP threshold, or by %-cover within the local landscape (Sections 2.4.2 and 2.4.3).

Table 29. Matrix for assessing relative risk associated with reduction in vegetation extent

Sensitivity (from Table 14 and Table 15)

Reduction in Vegetation Extent21 Very low Low Moderate High Very high

> 10,000 ha M H E E E

625 – 10,000 ha M M H H E

40 – 625 ha L L M M H

2 – 40 ha VL L L L M

< 2 ha VL VL VL L L

19 All for OCMs, the template CSGM footprint for CSGMs, and no area for LWM panels.

20 The species-area curve is a relationship between the area of a habitat and the number of species found within that area. Larger areas contain larger numbers of species, and empirically, the relative numbers seem to follow systematic mathematical relationships. We use the relationship S = A0.25 (Hugh Possingham pers comm).

21 The Namoi Catchment supports about 1.7 million hectares of native vegetation.



2.5.3 Vegetation types The risk matrix for reduction in the extent of native vegetation types is shown in Table 30. The matrix is relevant to reduction of types against both the Namoi CAP threshold table (Table 16) and the EEC candidacy table (Table 17). Relative risk to vegetation types associated with a mining scenario will be readily calculated by imposing the spatial footprint of the scenario across the vegetation type sensitivity layers to determine the appropriate ‘cell’ in Table 30 from which a risk class is extracted and reported. The area thresholds in Table 30 are also based on the species–area relationship, but are much less than those in Table 29 given that there are 70 RVCs in the Namoi Catchment, each contributing to total vegetation cover. Application of this matrix enables relative risk to be assessed in the context of both mine size and sensitivity to loss of vegetation types (where sensitivity is represented by either proximity to the Namoi CAP threshold for RVCs, or by EEC candidacy (sections 2.4.4 and 2.4.5)).

Table 30. Matrix for assessing relative risk associated with clearing vegetation types


Reduction in extent of RVC/EEC Very low Low Moderate High Very high

> 125 ha L L M H E

25 – 125 ha VL L L M H

5 – 25 ha VL VL L L M

1 – 5 ha VL VL VL L L

< 1 ha VL VL VL VL L

2.5.4 Intactness The risk matrix associated with intactness is shown in Table 31. Relative risk will be highest where sensitivity to loss of intactness is high (i.e. in areas of contiguous vegetation), and where size of the OCM or CSG footprint is large. LWM underground panels will pose little risk to intactness.

Table 31. Matrix for assessing relative risk of mining to vegetation intactness

Sensitivity (from Table 18)

Mine characteristics Very low Low Moderate High Very high

OCM (> 10,000 ha); CSG (> 50,000 ha) L M H E E

OCM (625 - 10,000 ha); CSG (5,000 – 50,000 ha) L L M H E

OCM (40 - 625 ha); CSG (500 – 5,000 ha) VL L L M H

OCM (< 40 ha); CSG (< 500 ha) VL VL L L M

LWM VL VL VL VL L



2.5.5 Connectivity The risk matrix associated with connectivity is shown in Table 32. Relative risk increases with increased number of links impacted, and link sensitivity. Only OCMs will impact on functional connectivity in the context of movement of species between forest and woodland patches. The impact of CSGMs on connectivity within patches will be manifest in the intactness algorithm. LWMs will have no tangible impact on functional connectivity.

Table 32. Matrix for assessing relative risk of mining to functional connectivity


Number of links impacted Very low Low Moderate High Very high

> 50 M H E E E

11 – 50 L M H E E

6 – 10 L M H H E

2 – 5 VL L M H E

1 VL VL L M H

2.5.6 Landscape corridors The risk matrix associated with landscape corridors is shown in Table 33. Relative risk in this case is not dependent on the number of times a landscape corridor is intersected (once is enough to threaten dispersal and migration of species). As for local links, only OCMs will impact on corridors in the context of movement of species.

Table 33. Matrix for assessing relative risk of mining to landscape corridors


Number of corridors impacted Very low Low Moderate High Very high

≥ 1 VL L M H E

2.5.7 Threatened species The proposed risk matrix for assigning risk to threatened species habitat is shown in Table 34. The impact area associated with a mining scenario22 will be intersected with the threatened species sensitivity layer to determine the appropriate ‘cell’ in Table 34 from which a risk class is extracted and reported. The table is the same as that for vegetation extent (Table 29), using area thresholds based on the species–area relationship. Application of this matrix enables relative risk to be assessed in the context of both mine size and sensitivity to clearing.

22 All for OCMs, the template CSGM footprint for CSGMs, and no area for LWM panels.



Table 34. Matrix for assessing relative risk of mining to persistence of threatened species


Area impacted Very low Low Moderate High Very high

> 10,000 ha M H E E E

625 – 10,000 ha M M H H E

40 – 625 ha L L M M H

2 – 40 ha VL L L L M

< 2 ha VL VL VL L L

2.5.8 Land use The risk matrix for land use is shown in Table 35. Relative risk of the clearing footprint of any OCM and CSGM scenario is directly equivalent to the level of mapped sensitivity. LWM panels will also contribute to cumulative risk. In this table, the level of risk is not dependent on the area of the mine footprint.

Table 35. Matrix for assessing relative risk of mining to current land use


Impact zone Insignificant Minor Moderate Major Severe

Clearing zone (OCMs and CSGMs) VL L M H E

Subsidence zone of LWMs VL VL L M H

2.5.9 Soil productivity The risk matrix for soil productivity is simple and is shown in Table 36. In this table relative risk of the footprint of any mine is directly equivalent to the underlying level of mapped sensitivity. Assignment of relative risk of a mining footprint to classes of soil productivity is undertaken in the context of ‘community perception’ of mining on high productivity agricultural lands compared with lower productivity or non-productive lands. Like land use above, risk is not area-dependent.

Table 36. Matrix for assessing relative risk of mining to agricultural productivity


Mine footprint Very low Low Moderate High Very high

All mines (including LWM panels) VL L M H E



2.5.10 Carbon store The risk matrix for carbon store is simple and is shown in Table 37. Relative risk of the clearing footprint of any OCM and CSGM scenario is directly equivalent to the level of mapped sensitivity. LWM panels will also contribute little to cumulative risk, as significant quantities of carbon are not likely to be lost from forest and woodland systems as a result of subsidence.

Table 37. Matrix for assessing relative risk of mining to carbon stored in vegetation and soils


Impact zone Insignificant Minor Moderate Major Severe

Clearing zone (OCMs and CSGMs) VL L M H E

Subsidence zone of LWMs VL VL VL L L

2.5.11 Long-term surface flow The risk matrix for long-term flow of surface water is shown in Table 38, where the level of risk is related to mine type and mine size. Open cut mines pose a relatively greater risk to surface water flow than CSG and LWMs as much of the rainfall at the mine site is intercepted and retained (most rainfall that intercepts LWM and CSG operations continues to contribute to surface flow). CSG mines pose a relatively low risk to surface flow given that water abstraction for drilling and gas liberation (typically 1 ML/well) may be offset by liberation of treated groundwater permeate to the surface water system. In contrast, LWMs pose a higher risk as more surface water for underground operations than is released, and also as a result of potential for subsidence-related seepage of surface flow to groundwater.

Table 38. Matrix for assessing relative risk of mining to surface flow


Mine type and size class Very low Low Moderate High Very high

OCM (≥ 1000 ha) VL L M H E

OCM (< 1000 ha) VL VL L M H

LWM (≥ 500 ha) VL VL VL L M

LWM (200 – 500 ha); CSG (≥ 10000 ha) VL VL VL VL L

LWM (< 200 ha); CSG (< 10000 ha) VL VL VL VL VL

2.5.12 Surface water quality The relative risk to the quality of surface water assets, including rivers, streams and wetlands, varies with mine type and size, as shown in Table 39. CSG operations are highest risk as they require impoundment and management of toxic brine at many locations throughout the mining footprint. OCMs also run the risk of surface water contamination (albeit lower) through flow of tailing leachates into local watercourses, and breach of containment ponds. LWMs pose a relatively low risk to the quality of surface water.



Table 39. Matrix for assessing relative risk of mining to surface water quality


Mine type and size class Very low Low Moderate High Very high

CSG (≥ 10000 ha) VL L M H E

CSG (2000 ha - 10000 ha); OCM (≥ 1000 ha) VL VL L M H

CSG (< 2000 ha); OCM (< 1000 ha); LWM (≥ 500 ha) VL VL VL L M

LWM (200 - 500 ha) VL VL VL VL L

LWM (< 200 ha) VL VL VL VL VL

2.5.13 Groundwater drawdown The relative risk of different mine types and sizes is shown in Table 40. The highest risk to groundwater drawdown relates to CSG operations, particularly extensive ones involving hundreds of wells. Subsidence associated with panels of large LWMs may lead to subsurface fracturing that poses risk in more sensitive areas. The risk of groundwater loss from open cut mines is generally less than that from CSG and LWM operations.

Table 40. Matrix for assessing relative risk of mining to groundwater drawdown

Sensitivity

Impact Very low Low Moderate High Very high


CSG (2000 - 10000 ha), LWM (≥ 500 ha) VL VL L M H

CSG (< 2000 ha); LWM (< 500 ha), OCM (≥ 1000 ha) VL VL VL L M

OCM (< 1000 ha) VL VL VL VL L

2.5.14 Groundwater quality The relative risk of different mine types and sizes is shown in Table 41 (it is identical to the risk profile for groundwater drawdown - Table 40). The highest risk to groundwater contamination relates to CSG operations, particularly extensive ones that hundreds of wells and holding ponds. Subsidence associated with panels of large LWMs may lead to subsurface fracturing that poses higher risk of groundwater mixing in more sensitive areas (e.g. high productivity floodplain areas). The risk of groundwater contamination from open cut mines is generally less than that from CSG and LWM operations.

Table 41. Matrix for assessing relative risk of mining to groundwater quality

Sensitivity

Impact Very low Low Moderate High Very high


CSG (2000 - 10000 ha), LWM (≥ 500 ha) VL VL L M H

CSG (< 2000 ha); LWM (< 500 ha), OCM (≥ 1000 ha) VL VL VL L M

OCM (< 1000 ha) VL VL VL VL L



2.6 DEVELOPMENT OF CUMULATIVE RISK TOOL

2.6.1 Context NCRAT was developed to provide users with a simple menu-driven interface that enabled testing of the iterative risk of a hypothetic mining scenario (a sequence of hypothetical future mines) to NRM assets. The main considerations in developing the cumulative risk tool were that it be:

1. User-friendly

2. Menu-driven;

3. Interactive (i.e. contains prompts and output reports);

4. Logical (i.e. simple in structure); and

5. Underpinned by best available science.

2.6.2 Structure The structure of the cumulative risk tool was developed following a number of internal meetings. Flow charts of major modules were constructed for ease of interpretation.

2.6.3 Input mining scenario Following work by ELA (2011), NCRAT was designed to enable users to define the clearing zone of OCMs (this would assume to include the pit, overburden, ponds and infrastructure), the zone of underground panel(s) of LWMs, and the overall footprint of CSGMs (incorporating all pads, roads and infrastructure).

2.6.4 Indices and algorithms

Water use

Environmental Assessment and other documents relating to coal and coal seam gas projects in the Gunnedah Basin were reviewed to determine an indicative level of water use for mines, reported as:

- ML/yr/well foe CSG operations; and

- ML/yr/ha for OCMs and LWMs.

CSGM spatial indices

CSGMs typically comprise a grid of small cleared areas (gas well clearings, water treatment plants, compressor stations) connected by a network of service roads (Figure 30). As it would be impractical in terms of cost and time to digitise the lattice-work of cleared areas of each CSGM that contributes to a mining scenario, a relationship between CSGM footprint (i.e. area bounded by the mine perimeter) and total cleared area (ha) was established by digitising the current footprint of roads and well pads (and associated infrastructure) of five operating or exploration CSGMs identified from Google Earth images in eastern Australia. These included Pilliga in NSW, and Dalby, Durham Downs, Fairview and Tipton in Queensland. For each CSGM footprint, the following statistics were generated:



- Number of drill pads;

- Area of drill pads (ha);

- Length of road (km) (not including access roads);

- Area of road (ha) (assumed a standard 6 m width);

- Area of mine (ha) (total extent, that links the pads on the periphery of the mine); and

- Number of land segments enclosed by the latticework of mine roads.

- Ratio of CSGM area to the combined cleared area (cleared for roads and well pads); and

- Ratio of CSGM area to number of land segments (i.e. level of fragmentation).

The CSGM indices were used to guide development a ‘hypothetical’ CSGM template for the entire extent of the catchment that will be used to populate any area that is selected as part of a mining scenario. This footprint was developed to be a uniformly-patterned mosaic of equi-spaced well pads and latticework of roads, delineated to satisfy the average length of roads, number and size of well pads, and average number of land segments that are evident within real CSGM footprints. The spatial template was converted to the following raster surfaces:

- CSGM: a 25m raster of the CSGM footprint template across Namoi Catchment for updating vegetation cover and associated layers (does not include footprint of roads)

- CSGMr: a 25m raster of CSGM vegetation clearing footprint template across Namoi Catchment for updating vegetation intactness (includes roads).

2.7 DEVELOPMENT OF USER GUIDE

A user guide was developed to assist with operation of NCRAT. Contents included:

- Hardware and software requirements

- Program installation

- Scenario input; and

- Interpretation of output report.



3 Results 3.1 PREPARATION OF DATASETS

3.1.1 Base case mining layer The total base case mining footprint in the Namoi Catchment comprises 468 mines and occupied a collective area of 8,820 ha. It includes 429 quarries (0.3 – 36.0 ha) and 39 other mines (Table 42) of which nine (9) are greater than 100 ha (all coal mines). Two (2) mines are in excess of 1,000 ha, including the Narrabri North LWM (3,240 ha) and the Boggabri OCM (1,175 ha), both approved.

The total area of the mining footprint as a proportion of the Namoi Catchment is 0.2%. In contrast, the total area of the Namoi Catchment cleared or highly modified for agriculture is about 55%.

About 80% of the current mining footprint is within three (3) Namoi sub-catchments: Bluevale; Maules Creek; and Eulah Creek. These sub-catchments are coincident with major coal beds of the Gunnedah Basin, and are likely to be subject to more development in the future.

Figure 6 shows the location of existing mines in the Namoi Catchment. Sub-catchments are included.

Table 42. Mine types contributing to the base case mining layer

Mine type Status Number Combined Area (ha) Comments

LWM Operating/Approved 1 3240 Narrabri North LWM

OCM (coal) Operating/Approved 7 2570 Includes Boggabri, Canyon, Rocglen, Sunnyside, Tarrawonga and Werris Creek OCMs

OCM (coal) Closed 11 530 Includes Gunnedah, Hobsons and Vickery OCMs

OCM (other) Operating/Approved 12 240 Includes Ardglen, Attunga and Bell Mountain

OCM (other) Closed 6 380 Includes Woodsreef (300 ha)

CSG - 0 0 None yet approved in Namoi

Quarry Operating/closed 429 1240 Most < 10 ha; Largest 36 ha

Storage facility Operating 2 20 Coal rail storage facility

ALL 468 8220



Figure 6. Base case mining footprint in the Namoi Catchment

3.1.2 Vegetation extent

Cover change

Following spatial revision of the vegetation mapping layer, it is evident that about 203,000 ha of the Namoi catchment (about 5%) has been cleared of native vegetation since the original mapping was undertaken (Table 43). This results from the replacement of about 247,000 ha of native vegetation with derived grassland, cropland or other non-native vegetation, and the addition of about 44,000 ha that was predominantly mapped as derived grassland. Figure 7 shows the spatial extent of vegetation gains and losses following the spatial revision.

The total area of all native vegetation types (excluding derived native grasslands) is estimated to be 1,706,300 ha, or just over 40% of the area of Namoi Catchment. Figure 8 shows its present distribution, Figure 9 shows its ‘smoothed’ distribution (the average cover within 5 km) and Table 43 lists the vegetation cover statistics for each sub-catchment in the Namoi.

Patch size distribution

The total area of forest and woodland communities is estimated to be 1,686,300 ha. This occurs as 12,100 individual patches that range from 1 ha to 752,600 ha in area (within the Namoi Catchment). About 98% of all patches are < 200 ha in area, but constitute just 10% of the area of the catchment’s remaining forest and woodland. About 2% of all patches are each at least 200 ha in area (381 in total), and make up a total of 90% of the catchment’s forest and woodland. These include 10 ‘continental’ patches that are ≥ 10,000 ha (Table 44).



Figure 7. Native vegetation cover gains and losses in the Namoi Catchment

Figure 8. Native vegetation cover in the Namoi Catchment (not including derived types)



Figure 9. ‘Smoothed’ vegetation cover in the Namoi Catchment

Table 43. Revised vegetation cover statistics for Namoi sub-catchments

Area of native vegetation (ha)

Sub-catchment Area (ha) Before After Reduction % reduction

Baradine 178510 132240 127290 4950 3.7

Bluevale 124600 29540 23130 6410 21.7

Bobbiwaa 56020 15820 14160 1660 10.5

Bohena 83230 70620 70240 380 0.5

Borah 139710 127380 124050 3330 2.6

Box Creek 169650 71500 56860 14640 20.5

Brigalow 32380 17570 16430 1140 6.5

Bugilbone 236580 68700 48050 20650 30.1

Bundella Creek 249950 118440 96420 22020 18.6

Bundock 54920 21180 18650 2530 11.9

Carroll 18660 7150 4310 2840 39.7

Chaffey 42030 15500 15520 -20 -0.1

Cockburn River 112650 61570 60630 940 1.5

Coghill 79390 76830 75770 1060 1.4

Cox's Creek 135810 39620 33720 5900 14.9

Etoo 102370 84640 81200 3440 4.1

Eulah Creek 158140 73150 73360 -210 -0.3



Area of native vegetation (ha)

Sub-catchment Area (ha) Before After Reduction % reduction

Ginudgera 99140 32710 25150 7560 23.1

Goonoo Goonoo 66340 18930 14820 4110 21.7

Keepit 60580 15400 13240 2160 14

Lake Goran 187100 65920 53270 12650 19.2

Lower Manilla 42950 22000 18100 3900 17.7

Lower Peel 159770 39400 36920 2480 6.3

Lower Pian 225480 98040 72970 25070 25.6

Maules 115540 70930 66430 4500 6.3

Mid Macdonald 91290 36270 36310 -40 -0.1

Mooki 84880 17230 13020 4210 24.4

Phillips 52930 19740 19050 690 3.5

Quirindi 83970 35600 32720 2880 8.1

Rangira 32050 8400 8150 250 3

Split Rock 25390 11580 10320 1260 10.9

Spring Creek 27410 8210 8340 -130 -1.6

Tallaba 68790 53760 49410 4350 8.1

Upper Macdonald 84430 14760 16110 -1350 -9.1

Upper Manilla 138590 70060 60190 9870 14.1

Upper Namoi 130370 82210 71960 10250 12.5

Upper Peel River 85680 42830 42490 340 0.8

Upper Pian 115090 34230 21760 12470 36.4

Warrah 153120 42120 41210 910 2.2

Werris Creek 100650 38060 34570 3490 9.2

ALL 4206140 1909840 1706300 203540 10.7

Table 44. Forest and woodland patch size distribution in the Namoi Catchment

Patch Type Patch Area (ha) No. Patches Total Area (ha)

Clump 1 - 4 4,589 10,600

Small 4 - 20 5,085 46,400

Local 20 - 200 2,134 116,000

Regional 200 – 10,000 281 261,500

Continental ≥ 10,000 10 1,251,800

ALL 12,100 1,686,300



3.1.3 Vegetation type The reduction in vegetation extent described in section 3.1.2 is reported in Table 45 in the context of change to individual RVC areas in the Namoi Catchment. The greatest loss of native vegetation appears to have occurred in the central and western parts of the catchment where RVCs such as ‘White Box grassy woodland, Brigalow Belt South and Nandewar (RVC 18)’, ‘Coolibah - Poplar Box - Belah woodlands on floodplains, mainly Darling Riverine Plains and Brigalow Belt South (RVC 76)’ and ‘Black Box woodland on floodplains, mainly Darling Riverine Plains (RVC 77)’ have been replaced by croplands and derived grasslands.

Table 45 also lists the EEC likelihood of each RVC, from which the total area and proportion of EEC likelihood classes in the Namoi Catchment is summarised as follows:

< 5% likelihood = 2,128,500 ha (50.6%)

5 – 25% likelihood = 1,682,400 ha (40.0%)

25 – 50% likelihood = 92,300 ha (2.2%)

50 – 75% likelihood = 4,500 ha (0.1%)

> 75% likelihood = 298,100 ha (7.1%)

3.1.4 Intactness The final intactness surface is shown in Figure 10 and the total area of mapped intactness categories is shown below. In summary, over 75% of the catchment exhibits an intactness index less than 0.5 and over 50% has an intactness of 0.2, illustrating a generally high level of fragmentation. The Pilliga forests, Kaputar National Park, and parts of the eastern granite belt maintain a high level of intactness.

0.0000 – 0.1000 1,383,800 ha (32.9%) 0.5001 – 0.6000 188,200 ha ( 4.5%)

0.1001 – 0.2000 791,700 ha (18.8%) 0.6001 – 0.7000 152,900 ha ( 3.6%)

0.2001 – 0.3000 471,900 ha (11.2%) 0.7001 – 0.8000 136,700 ha ( 3.2%)

0.3001 – 0.4000 304,515 ha ( 7.2%) 0.8001 – 0.9000 115,600 ha ( 2.7%)

0.4001 – 0.5000 236,600 ha ( 5.6%) 0.9001 – 1,0000 424,000 ha (10.1%)

S p a t i a l T o o l f o r A s s e s s i n g t h e C um ul a t i ve R is k o f M i n i n g t o N R M A s s e t s i n t h e N a m o i


Table 45. Change in RVC area statistics in the Namoi Catchment based on vegetation map revision (mapped RVCs only)

Area (ha)

RVC RVC Name Original mapping

Nett change

Revised mapping

Pre-European

Percent remaining

EEC likelihood (%)

Non-RVCs and derived types

0 Cropping 911437 62906 974343 0 - < 5

0 Forestry Plantation 7457 -104 7353 0 - < 5

0 Infrastructure 7285 629 7914 0 - < 5

0 Urban 10019 -88 9931 0 - < 5

0 Water Storage 17646 493 18139 0 - < 5

0 Other non-vegetation 15590 -628 14962 0 - < 5

26d Dry grasslands of alluvial plains, Darling Riverine Plains and Brigalow Belt South - derived occurrence 184965 53439 238404 0 - < 5

27 Derived grasslands, New England Tablelands 185279 -2114 183165 0 - 5 - 25

28 Derived grasslands, Brigalow Belt South and Nandewar 946691 86784 1033475 0 - 5 - 25

29d Plains Grass - Blue Grass grasslands, Brigalow Belt South and Nandewar - derived occurrence 9856 1997 11853 0 - 5 - 25

All non-RVCs and derived types 2296225 203314 2499539 0 -

True RVCs

1 Giant Stinging Tree - Fig dry subtropical rainforest, mainly NSW North Coast 109 0 109 110 99.1 < 5

2 Rusty Fig - Wild Quince - Native Olive dry rainforest of rocky areas, Nandewar and New England Tablelands 714 -43 671 753 89.1 50 - 75

4 Wilga - Western Rosewood shrubland, Darling Riverine Plains and Brigalow Belt South 1168 -828 340 36620 0.9 < 5

5 Ooline forests, Brigalow Belt South and Nandewar 806 -27 779 1266 61.5 > 75

6 Semi-evergreen vine thicket of basalt hills, Brigalow Belt South and Nandewar 451 138 589 2823 20.9 > 75

9 Messmate - gum moist forests of the escarpment ranges, eastern New England Tablelands and NSW North Coast 4257 -9 4248 8896 47.8 < 5

11 Silvertop Stringybark - Nandewar Box open forests in the Kaputar area, Nandewar 8232 1 8233 8671 94.9 < 5



Area (ha)


Nett change

Revised mapping

Pre-European

Percent remaining

EEC likelihood (%)

12 Snow Gum - Black Sallee grassy woodlands, New England Tablelands 766 11 777 3558 21.8 > 75

13 Gum grassy open forests, New England Tablelands 1298 -20 1278 2998 42.6 > 75

14 New England Peppermint grassy woodlands, New England Tablelands 451 164 615 11672 5.3 > 75

15 Bendemeer White Gum grassy woodland, southern New England Tablelands 1847 123 1970 12928 15.2 5 - 25

16 Box - gum grassy woodlands, New England Tablelands 8435 -146 8289 48000 17.3 > 75

17 Box - gum grassy woodlands, Brigalow Belt South and Nandewar 37269 -11471 25798 221930 11.6 > 75

18 White Box grassy woodland, Brigalow Belt South and Nandewar 228440 -57592 170848 844294 20.2 > 75

19 White Cypress Pine - Silver-leaved Ironbark grassy woodland, Nandewar 1862 -488 1374 7104 19.3 < 5

20 Rough-barked Apple - Blakely's Red Gum riparian grassy woodlands, Brigalow Belt South and Nandewar 64700 -1728 62972 76514 82.3 5 - 25

21 Inland Grey Box tall grassy woodland on clay soils, Brigalow Belt South and Nandewar 1634 -339 1295 14725 8.8 > 75

22 Poplar Box - Belah woodlands, mainly Darling Riverine Plains and Brigalow Belt South 15739 -3637 12102 57756 21 5 - 25

23 Wet tussock grasslands of cold air drainage areas, New England Tablelands 128 0 128 128 100 < 5

25 Mitchell Grass grassland of alluvial floodplains, mainly Darling Riverine Plains 768 -424 344 2738 12.6 > 75

26t Dry grasslands of alluvial plains, Darling Riverine Plains and Brigalow Belt South - natural occurrence 2884 -87 2797 44914 6.2 > 75

29t Plains Grass - Blue Grass grasslands, Brigalow Belt South and Nandewar - natural occurrence 5977 -22 5955 138110 4.3 > 75

31 Broombush shrubland of the sand plains of the Pilliga region, Brigalow Belt South 17764 0 17764 17764 100 < 5

32 Pilliga Box - Poplar Box- White Cypress Pine grassy open woodland on alluvial loams, Darling Riverine Plains and Brigalow Belt South 64638 -4937 59701 177335 33.7 < 5

33 Ironbark shrubby woodlands of the Pilliga area, Brigalow Belt South 315031 -8259 306772 357589 85.8 < 5

35 Mountain Gum - Snow Gum open forests, New England Tablelands and NSW North Coast 1879 21 1900 3496 54.3 > 75

36 Stringybark - gum - peppermint open forests, eastern New England Tablelands 19094 655 19749 58647 33.7 5 – 25

38 Silvertop Stringybark - gum open forest on basalts of the Liverpool Range, Brigalow Belt South and Nandewar 11562 -81 11481 13144 87.3 < 5

39 Silvertop Stringybark grassy open forests, eastern Nandewar and New England Tablelands 51138 12 51150 84467 60.6 < 5



Area (ha)


Nett change

Revised mapping

Pre-European

Percent remaining

EEC likelihood (%)

40 Stringybark - Blakely's Red Gum open forests, New England Tablelands 2901 -103 2798 8475 33 50 – 75

41 White Box - stringybark shrubby woodlands, Brigalow Belt South and Nandewar 88952 -2167 86785 140058 62 5 – 25

43 Mugga Ironbark open forests, Nandewar and western New England Tablelands 7503 -82 7421 10217 72.6 < 5

44 White Box - pine - Silver-leaved Ironbark shrubby open forests, Brigalow Belt South and Nandewar 187890 -10242 177648 232471 76.4 5 – 25

45 Stringybark - spinifex woodland, Nandewar 1234 -36 1198 1786 67.1 < 5

47 Narrow-leaved Peppermint - Wattle-leaved Peppermint open forest, eastern New England Tablelands 79 -3 76 79 96.2 < 5

49 Black Cypress Pine - Orange Gum - Tumbledown Red Gum shrubby woodlands, Nandewar and western New England Tablelands 20160 -24 20136 20630 97.6 < 5

50 Stringybark - Blakely's Red Gum - Rough-barked Apple open forests, Nandewar and western New England Tablelands 41744 334 42078 75988 55.4 5 – 25

51 New England Blackbutt - stringybark open forests, Nandewar and western New England Tablelands 28274 -978 27296 32810 83.2 5 – 25

56 Ironbark - Brown Bloodwood - Black Cypress Pine heathy woodlands, Brigalow Belt South 215781 -3250 212531 222202 95.6 < 5

58 Shrubby woodlands or mallee woodlands on stoney soils, Brigalow Belt South and Nandewar 1223 -13 1210 1223 98.9 < 5

59 Narrow-leaved Ironbark - pine - box woodlands and open forests, Brigalow Belt South and Nandewar 82254 -2439 79815 86710 92 < 5

62 Shrublands of rocky areas, Nandewar and western New England Tablelands 2622 5 2627 2642 99.4 5 – 25

63 Tea-tree shrubland in drainage lines, Nandewar and New England Tablelands 127 -34 93 127 73.2 < 5

64 Fens and wet heaths, Nandewar and New England Tablelands 1226 -155 1071 1259 85.1 50 – 75

67 Eurah shrubland of inland floodplains, Darling Riverine Plains 210 -11 199 266 74.8 5 – 25

68 Lignum - River Coobah shrublands on floodplains, Darling Riverine Plains and Brigalow Belt South 1911 -437 1474 3854 38.2 25 – 50

70 Wetlands and marshes, inland NSW 14837 -6104 8733 11162 78.2 5 – 25

71 River Oak riparian woodland, eastern NSW 10820 -255 10565 17494 60.4 < 5

72 Bracteate Honey Myrtle riparian shrubland, Brigalow Belt South 991 -40 951 1478 64.3 < 5

73 River Red Gum riverine woodlands and forests, Darling Riverine Plains, Brigalow Belt South and Nandewar 33264 -2838 30426 65536 46.4 < 5

75 Weeping Myall open woodland, Darling Riverine Plains, Brigalow Belt South and Nandewar 5850 -2692 3158 51631 6.1 > 75



Area (ha)


Nett change

Revised mapping

Pre-European

Percent remaining

EEC likelihood (%)

76 Coolibah - Poplar Box - Belah woodlands on floodplains, mainly Darling Riverine Plains and Brigalow Belt South 114133 -39337 74796 424131 17.6 25 – 50

77 Black Box woodland on floodplains, mainly Darling Riverine Plains 72448 -23645 48803 157351 31 > 75

78 Coolibah - River Coobah - Lignum woodland of frequently flooded channels, mainly Darling Riverine Plains 18737 -2243 16494 23269 70.9 > 75

79 Brigalow - Belah woodland on alluvial clay soil, mainly Brigalow Belt South 10445 -2222 8223 36610 22.5 > 75

80 Poplar Box grassy woodland on alluvial clay soils, Brigalow Belt South and Nandewar 18189 -6436 11753 220466 5.3 5 – 25

81 Leopardwood woodland of alluvial plains, Darling Riverine Plains and Brigalow Belt South 234 -69 165 546 30.2 < 5

82 Poplar Box low woodlands, western NSW 31406 -5042 26364 55733 47.3 < 5

84 Whitewood open woodland, mainly eastern Darling Riverine Plains 684 -565 119 2910 4.1 < 5

85 Carbeen woodland on alluvial soils, Darling Riverine Plains and Brigalow Belt South 431 -230 201 971 20.7 > 75

86 Dirty Gum tall woodland on sand monkeys, Darling Riverine Plains and Brigalow Belt South 2855 -11 2844 3398 83.7 < 5

87 Silver-leaved Ironbark - White Cypress Pine on alluvial sandy loam, Darling Riverine Plains 128 9 137 780 17.6 < 5

88 Saltbush chenopod shrublands, mainly Darling Riverine Plains 830 -390 440 6508 6.8 < 5

89 Copperburr chenopod shrubland, Darling Riverine Plains and Brigalow Belt South 3473 -1836 1637 7805 21 < 5

90 Ephemeral forblands on playas and scalds, Darling Riverine Plains and Cobar Peneplain Not mapped ? < 5

96 Blakely's Red Gum riparian woodland of the Pilliga Outwash, Brigalow Belt South Bioregion 16733 -720 16013 18319 87.4 25 – 50

All true RVCs 1909620 -203314 1706306 4205845 40.6

TOTAL 4205845 0 4205845 4205845


© E C O L O G I C AL AU S T R AL I A P T Y L T D

Figure 10. Vegetation intactness index in the Namoi Catchment

3.1.5 Local links A total of 12,323 local links were delineated in the Namoi Catchment. From summary statistics presented in Table 46, the majority of links cross derived native grassland rather than cropland, have an average density of 1 - 5 trees per hectare, are single crown width, and are less than 300 ha in length. These structural characteristics result in well over 60% of links being classed as ‘high’ to ‘very high’ integrity. In terms of magnitude, the majority of links connect smaller rather than larger patch clusters.

Figure 11 shows the distribution of all local links in the Namoi Catchment.

Table 46. Local link statistics

Category Classes Number of Links % of links

Groundcover Cropped 743 6.0

Native 11580 94.0

Avg Tree Density (no/ha)

< 1 2086 16.9

1 - 5 5409 43.9

6 - 10 3803 30.9

> 10 1025 8.3

Width (m)

Single crown 7119 57.8

10 - 49 3503 28.4

50 - 99 994 8.1

> 100 707 5.7



Category Classes Number of Links % of links

Length (m)

1000 - 1500 302 2.5

700 - 999 575 4.7

500 - 699 1004 8.1

300 - 499 2478 20.1

< 300 7964 64.6

Integrity

Very low 468 3.8

Low 2486 20.1

Moderate 1241 10.1

High 5963 48.4

Very high 2165 17.6

Magnitude

Insignificant 3544 28.8

Minor 4567 37.1

Moderate 2850 23.1

Major 1123 9.1

Cornerstone 239 1.9

Figure 11. Distribution of local links in the Namoi Catchment



Table 47 summarises the results of linking patches in the Namoi Catchment. Over 8,000 pre-linked patches classed as either clump, small, local or regional patches were reclassified to ‘continental’ following delineation of links. There was a consequent increase of more than 330,000 ha in the total area of continental patch in the Namoi and a concurrent reduction in the number and area of regional, local and small patches and clumps. This suggests that for the meta-populations of more mobile species at least, a considerable number of smaller patches may contribute to breeding habitat through the persistence of functional connectivity.

Table 47. Patch class statistics with and without functional links

Unlinked patches

(from Table 44)

Linked patch clusters

Patch Type Patch Area (ha) # Patches Area (ha) # Individual

patches # Patch clusters Area (ha)

Clump 1 - 4 4,589 10,600 641 625 1,400

Small 4 - 20 5,085 46,400 1,138 859 7,800

Local 20 - 200 2,134 116,000 1,126 430 24,400

Regional 200 – 10,000 281 261,400 978 81 63,900

Continental ≥ 10,000 10 1,251,800 8,216 3 1,588,700

ALL 12,099 1,686,200 12,099 1,998 1,686,200

3.1.6 Landscape corridors A total of 2,197 km of landscape corridor was mapped in the Namoi Catchment (Figure 14), partitioned into a total of 1,101 segments for analysis. The Pilliga forest represents a major core habitat from which seven (7) landscape corridors originate. The Namoi River itself is also a major corridor that links the catchment from east to west. The New England granite belt and the Liverpool Range represent two other landscape corridors.

3.1.7 Threatened species A total of 124 individual models were generated for this project (e.g. Figure 12), each weighted then added to derive a threatened species composite layer (Figure 13). Data in the composite layer ranges from 21 (lowest coincidence of potential threatened species) to 129 (highest coincidence of potential threatened species). The Pilliga forest, Kaputar region and high elevation areas along the Nandewar Range possess a relatively high score, while floodplains of the Darling Riverine Plains to the west of the Pilliga and Liverpool Plains to the east of Pilliga possess a relatively low score.

3.1.8 Land use

The distribution of land use classes is shown in Figure 15. The majority of the catchment is used for grazing livestock, while much of the valley floodplain is cropped. National Parks and other reserves are largely confined to the Pilliga and Mount Kaputar, while water storage areas occur around Spilt Rock Dam and Lake Keepit.



Figure 12. Example of an unweighted threatened species model – Glossy Black Cockatoo

Figure 13. Threatened species composite layer for the Namoi Catchment



Figure 14. Landscape corridors in the Namoi Catchment

Figure 15. Distribution of land use in the Namoi Catchment



3.1.9 Soils

The distribution of land suitability classes, used as a broad surrogate of soil productivity and agricultural value, is shown in Figure 16. As expected the better quality soils are those associated with the major floodplains and undulating slopes and plains, while poorer soils are those on steep and inaccessible terrain. The LSC associated with Pilliga Sandstone is erroneous - it should be lower. This was addressed in assigning sensitivity classes based in LSC (see Section 2.4.11).

Figure 16. Distribution of land suitability classes in the Namoi Catchment

3.1.10 Carbon store

The distribution of carbon store across different vegetation types in the Namoi Catchment is illustrated in Figure 17. The highest level of CO2-e is associated with high-productivity RVCs that occur in the ranges to the north, east and south, with values over 500 t/ha for RVCs such as Messmate - Gum Moist Forest (RVC 9) and Silvertop Stringybark – Gum Forest (RVC 36). The majority of RVCs hold 300 – 400 t/ha of CO2-e and include the grassy and shrubby woodlands that are widespread through the catchment and open shrubby forests of the Pilliga. Areas that have been cleared of native forest and woodland (i.e. derived grasslands, exotic grasslands and croplands) have a relatively low carbon store, the majority of vegetation carbon having been removed during deforestation and some soil carbon lost through cultivation and application of fertiliser.



Figure 17. Carbon store of RVCs in the Namoi Catchment (vegetation and soils)

3.1.11 Surface water flow

Pre-development median surface flow and current surface flow entitlement estimates for each sub-catchment (OEH 2012 unpublished data) are shown in Table 48. A total of 25 sub-catchments have a median flow value that is above the 66% threshold, while 15 sub-catchments have a median flow below the 66% threshold (assuming all entitlements are used).



Table 48. Surface flow thresholds for Namoi sub-catchments

Pre-development annual flow estimates (ML/yr)

Sub-Catchment Mean Median Cumulative upstream entitlement (ML/yr)

Regulated flow as a % of pre-development

median flow Baradine 14500 1800 280 84.4 Bluevale 773000 467800 171100 63.4 Bobbiwaa 793300 475700 313600 34.1 Bohena 3800 2000 240 88.0 Borah 2500 1300 0 100.0 Box Creek 890 530 * 0 100.0 Brigalow 1400 830 * 0 100.0 Bugilbone 853900 417800 566000 0 Bundella Creek 60400 25100 5400 78.5 Bundock 520 310 * 1000 0 Carroll 739200 476900 144900 69.6 Chaffey 53200 40900 390 99.0 Cockburn River 86000 69500 4000 94.2 Coghill 3800 2250 * 0 100.0 Coxs Creek 57900 18100 16500 8.8 Etoo 4000 2400 * 0 100.0 Eulah Creek 80 40 670 0 Ginudgera 813200 449800 382400 15.0 Goonoo Goonoo 30500 16600 1200 92.8 Keepit 363100 268300 27000 89.9 Lake Goran 34000 13800 18800 0 Lower Manilla 82800 52400 9300 82.3 Lower Peel 259300 181900 65600 63.9 Lower Pian 8600 60 130400 0 Maules 793200 478500 178800 62.6 Mid Macdonald 188700 144800 4800 96.7 Mooki 146200 59800 47600 20.4 Phillips 16700 6800 430 93.7 Quirindi 20900 8700 2500 71.3 Rangira # 35000 1500 Split Rock 73700 39300 2100 94.7 Spring Creek 800300 482300 194300 59.7 Tallaba 2700 1600 * 0 100.0 Upper Macdonald 134100 119000 660 99.4 Upper Manilla 68200 31500 2100 93.3 Upper Namoi 267900 196000 14500 92.6 Upper Peel 112300 86900 19700 77.3 Upper Pian 7500 2500 109800 0 Warrah 51500 24600 270 98.9 Werris Creek 16800 6800 1300 80.9

* no data available, so values calculated from mean-median relationship derived from other sub-catchments (median = 0.592 * mean; r2 = 0.98) # no data provided by OEH – 35,000 ML/yr used based on similar area and location to Split Rock sub-catchment



3.1.12 Surface water quality

Local catchments

The local catchment layer that was developed specifically for this project is illustrated in Figure 18. A total of 646 local catchments were delineated, including 551 named rivers and creeks (or reaches thereof), 82 unnamed creeks,12 drainage plains, and one (1) inland lake (Lake Goran). Areas ranged from 1,009 ha (unnamed creek 12 in Lower Manilla subcatchment) to 70,515 ha (Lake Goran drainage plain).

A total of 630 local catchments contribute surface water flow to the outlet of the Namoi River at Walgett. The total area of the Namoi River catchment is 4,029,650 ha. The remaining 16 local catchments drain into the Castlereagh-Barwon system, and are not hydrologically part of the Namoi Catchment (although they all occur entirely or partly within the Namoi CMA region).

Of the 646 local catchments, 418 are ‘1st order’ catchments that do not receive surface flow from upstream. The other 228 local catchments receive surface flow from local catchments upstream, and include most reaches of the major creeks and rivers in the Namoi Catchment. In addition to the Namoi River itself, major creeks and rivers to a minimum 50,000 ha are shown in Table 49. This does not include Lake Goran (183,190 ha), Dead Bullock Warrambool (140,010 ha) and Bugilbone drainage plain (61,410 ha).

Figure 18. Local catchments within the Namoi Catchment



Table 49. Major rivers of the Namoi Catchment

River/Creek Number of local catchments * Contributing area (ha)

Namoi River 630 (15) 4,029,650 Mooki River 82 (4) 660.760 Peel River 109 (8) 466,281 Pian Creek 12 (3) 394,570 Coxs Creek 63 (6) 386,950 Baradine Creek 32 (5) 299,180 Manilla River 47 (5) 207,580 McDonald River 44 (7) 183,750 Turragulla Creek 26 (2) 160,600 Warrah Creek 19 (3) 153,730 Yaminba Creek 27 (3) 125,530 Cockburn River 27 (1) 112,710 Box Creek 4 (1) 98,060 Bohena Creek 19 (2) 91,260 Etoo Creek 13 (3) 83,550 Quirindi Creek 17 (3) 80,410 Maules Creek 18 (3) 78,590 Coghill Creek 13 (2) 70,400 Werris Creek 13 (2) 69,220 Goonoo Goonoo Creek 14 (3) 66,370 Ironbark Creek 14 (2) 59,280 Bomero Creek 12 (1) 54,190 Garrawilla Creek 13 (2) 50,660 * number in () represents number of reaches of the named river/creek The local catchments were found to be reasonably well nested within Namoi sub-catchments that were delineated by Namoi CMA several years ago. However, some areas of refute were found between the sub-catchments (as guided by delineation of local catchments) and the original sub-catchments. These areas are shown in Figure 19. The main areas of contention are:

1. Large section of Werris Creek sub-catchment (29,600 ha) flows directly into the Mooki sub-catchment;

2. Large section of Box Creek sub-catchment (93,500 ha) flows into the Castlereagh River;

3. Part of Bundella Creek sub-catchment (11,100 ha) is part of Coxs Creek sub-catchment;

4. Part of Borah sub-catchment (13,900 ha) is part of Bohena Creek sub-catchment;

5. Part of Bobbiwaa sub-catchment (16,700 ha) flows into other sub-catchments, or is outside the Namoi Catchment.

6. Part of the Ginudgera sub-catchment (14,900 ha) is within Bugilbone sub-catchment

7. Most of the far western sub-catchment drains south-west into the Castlereagh. This includes a number of local catchments that are essentially divided in half by the Namoi Catchment boundary.



Figure 19. Possible errors with the sub-catchment coverage

Of the 646 local catchments, 457 (just over 70%) do not appear to have been mined in the past. Of the other 189 that have been mined (or have been approved to be mined in the near future), a total of 14 have had (or will have) greater than 1% of their area affected. These are listed in Table 50.

Table 50. Local catchments containing existing/approved mines in the Namoi region

Subcatchment Local Catchment Area (ha) Area mined (ha) Area mined (%) Mine(s) Eulah Creek Kurrajong Creek 5964 1519 25.5 Narrabri North LWM Eulah Creek Pine Creek 5277 1310 24.8 Narrabri North LWM Maules Cooboobindi Creek 7475 1342 18.0 Boggabri/Tarrawonga Eulah Creek Namoi River (reach 09) 8370 419 5.0 Narrabri North LWM Maules Ballol Creek 5946 276 4.6 Tarrawonga Bluevale Driggle Draggle Creek 15283 564 3.7 Vickery/Canyon/Rocglen Bluevale Unnamed creek 40 2947 103 3.5 Black Jack Mountain Upper Manilla Ironbark Creek (lower) 8806 291 3.3 Woodsreef (asbestos) Bluevale Unnamed creek 41 11340 282 2.5 Rocglen Warrah Doughboy Hollow Creek 1425 30 2.1 Ardglen (hardrock) Bluevale Coocooboonah Creek 4178 87 2.1 Gunnedah Quirindi Quipolly Creek 16330 262 1.6 Werris Creek Bohema Sandy Creek 1122 13 1.2 quarry Werris Creek Killen Creek 1425 17 1.2 quarry



Stream network

The location of larger and smaller streams in the Namoi Catchment is shown in Figure 20. Over 8,000 km of larger streams and 64,000 km of smaller streams are mapped in the catchment.

Figure 20. Distribution of streams in the Namoi Catchment

Wetlands and storages

The distribution of priority wetlands and water storages, along with critical buffer distances (i.e. contributing catchments upstream of each asset - Table 26), is shown in Figure 21. The total area of the 270 priority wetlands is 12,300 ha, while that of the 36 water storages is 7,100 ha. The total area of contributing catchments around priority wetlands is 530,000 ha, while the total area of contributing catchments around major storages is 136,500 ha.



Figure 21. Priority wetlands and storages, and their contributing flow buffers

3.1.13 Groundwater drawdown and groundwater quality

Groundwater asset data are presented in Sections 2.3.13 and 2.3.14 as part of the methodology.



3.2 SENSITIVITY LAYERS

A total of 15 baseline sensitivity layers were developed for NCRAT, five (5) of which will update upon the addition of each mine in the scenario (not including the first mine). Table 51 provides summary data for each baseline sensitivity layer while Figure 22 to Figure 29 show each sensitivity layer.

Table 51. Summary data for each baseline sensitivity surface (area values in km2)

Sensitivity class

Very low Low Moderate High Very high

Sensitivity layer Area % Area % Area % Area % Area % Iterative?

Sub-catchment cover 793 1.9 1453 3.5 10391 24.7 15906 37.8 13507 32.1

Local cover 12992 30.9 12074 28.7 6881 16.4 4141 9.8 5971 14.2

RVC cover 28764 68.4 4251 10.1 2230 5.3 5324 12.7 1566 3.7

EECs 21284 50.6 16824 40.0 923 2.2 45 0.1 2981 7.1

Landscape corridors 1 39175 93.1 634 1.5 425 1.0 574 1.4 1251 3.0

Local links 1 4205516 100.0 174 0.0 112 0.0 41 0.0 8 0.0

Intactness 13838 32.9 12636 30.0 8822 21.0 2523 6.0 4240 10.1

Threatened species 12422 29.5 13851 32.9 8101 19.3 6507 15.5 1158 2.8

Lands use 26020 62.0 2747 6.5 8254 19.6 3925 9.3 1072 2.5

Soil productivity 5811 13.8 6194 14.7 6951 16.5 19998 47.6 3098 7.4

Carbon 20945 49.8 4478 10.6 12950 30.8 2994 7.1 682 1.6

Surface water flow 17104 40.7 1888 4.5 5717 13.6 6713 15.9 10628 25.3

Surface water quality 16286 38.7 11029 26.2 7143 17.0 4006 9.5 3594 8.5

Groundwater drawdown 32179 75.4 3931 9.2 5961 14.0 262 0.6 326 0.8

GW quality (CSG) 23290 55.4 7220 17.2 7637 18.2 3758 8.9 154 0.4

GW quality (LWM) 32509 77.3 4813 11.4 3990 9.5 640 1.5 107 0.3

GW quality (OCM) 40417 96.1 1059 2.5 1 0.0 564 1.3 17 0.0 1. The majority of Namoi Catchment does not comprise corridor or link



Figure 22. Sensitivity surface for vegetation cover



Figure 23. Sensitivity layer for RVC cover and EECs



Figure 24. Sensitivity layer for landscape corridors and local links

Note: width of links magnified



Figure 25. Sensitivity layer for intactness and threatened species



Figure 26. Sensitivity layer for land use and soil productivity



Figure 27. Sensitivity layer for surface water



Figure 28. Sensitivity layers for groundwater



Figure 29. Sensitivity layers for carbon

3.3 MODEL INDICES AND ASSUMPTIONS

3.3.1 CSGMs

Clearing footprint

Delineation of roads and drill pads was completed for five (5) mines. Figure 30 shows the result of mapping for the Dalby mine in Queensland. The left hand image shows the Google image of the mine. The right hand image shows the digitised mine footprint, well pads and service roads. This footprint is assumed to include central processing plant, water treatment plants, compressor stations, pipelines, and supporting infrastructure.



Figure 30. CSGM footprint, Dalby.

Following delineation of all roads and drill pads associated with the five CSG operations as exemplified in Figure 30, a number of indices were calculated from the spatial data, listed in Table 52. In summary, the development of well pads and service roads results in an average 1.6% of clearing within the mine footprint, from construction of an average 1.1 well pads and 1.6 km of road per 1.0 km2 of CSGM mine extent. Development of the road footprint also results in fragmentation of the land, with an average of 61 ‘islands’ (i.e. parcels of land encompassed by roads) produced per 100 km2 of CSGM footprint.



Table 52. CSGM indices

CSGM

Index Dalby Durham Downs Fairview Pilliga Tipton ALL

Total area of mine extent (km2) 36.58 132.22 276.52 26.29 84.94 556.55

Number of wells 90 86 174 16 140 506

Density of wells (wells/km2) 2.5 0.7 1.0 0.6 1.6 1.1

Total area of well pads (ha) 66.2 60.2 150.3 10.5 64.5 351.7

Average area of well pad (ha) 0.74 0.70 0.86 0.66 0.46 0.70

Length of service road (km) 104.8 159.3 447.3 24.0 179.3 914.6

Length of service road per area mine (km/km2) 2.9 1.2 1.6 0.9 2.1 1.6

Total area of service roads (ha) 62.5 95.4 267.5 14.4 107.2 547.0

Total area of service roads and well pads (ha) 128.7 155.6 417.8 24.9 171.7 898.7

% clearing for service roads and well pads 3.5 1.2 1.5 0.9 2.0 1.6

Number of land segments isolated by roads 76 42 115 8 101 342

Density of segments (no/100 km2) 208 32 42 30 119 61

Average size of segment (ha) 46.4 311.1 236.8 325.5 82.4 160.1

Based on an approximate 441 well pads in a 20 km x 20 km area, a square grid of 441 points was derived in ArcGIS, each representing a well pad (i.e. 21 x 21 points). Points were spaced in a square lattice 952 metres from north to south, and from east to west. A hypothetic system of roads was included that satisfied the total length and number of segments (i.e. 640 km of road and 244 segments within a 20 km x 20 km footprint), based on statistics in Table 52.

Water use

The Narrabri CSG project is the only pilot CSG project in the Namoi although there are many other exploration areas. It is owned by Santos and includes two areas of development – the Bohena CSG pilot and the Bibblewindi CSG pilot. A total of 31 wells have been drilled to date, including 25 vertical wells and 6 lateral wells (Schlumberger Water Services; SWS 2011). The proven resource is 115 PJ, and the additional probable resource is 1405 PJ (SWS 2011). The original proponents of the Narrabri CSG project, Eastern Star Gas (ESG), have estimated groundwater abstraction at 160 m3/day (5.8 ML/yr) from each CSG well in the pilot project (SWS 2011).

In the ‘Bando’ area of interest west of Werris Creek, Santos has projected total groundwater abstraction to be 105 GL from 600 wells over the life of a gasfield (175 ML/well) and in the ‘Narrabri’ area of interest, ESG projected total groundwater abstraction to be 41.4 GL from 500 wells over the life of a gasfield (83 ML/well) (SWS 2012).

CSG groundwater consumption data for the Surat and Bowen Basins in central Queensland, cited by SWS (2012; Table 7.3), indicate a total water use of between 100 and 200 ML/well, with project lifespans typically 25 to 35 years.

From the assembled data, our model applies the following groundwater consumption for CSG operations: 5 ML/well/yr. The model also assumes that there is no net loss to surface flow.



3.3.2 OCM/LWM

Clearing footprint

For OCMs the model assumes that the entire area of the input layer constitutes either the pit, the overburden, or associated infrastructure and storage. Any native vegetation within the footprint is assumed to have been removed, and any soil is assumed to have been taken out of production. For LWMs, the model assumes that no surface vegetation or soil is removed within the footprint of the underlying panels. Any cleared area associated with LWM would be input separately as an OCM (LWMs are often commenced from the pit of existing OCMs).

Water use

SWS (2011) calculated from corporate reporting that approximately 0.2 m3 of fresh water is consumed for every tonne of coal produced form OCMs and LWMs. As the estimated marketable tonnage of coal from a sub-set of major operating coal mines (Narrabri, Rocglen, Sunnyside, Tarrawonga, and Werris Creek) is forecast to be about 300 Mt (from SWS 2011; Table 6.1), it follows that about 60 GL of freshwater will be required for operating these six mines over their combined mine-life of about 110 years (an average 540 ML/yr).

Further review of literature associated with mines in the Gunnedah Basin established that an average 1.0 ML/yr is used for every 1.0 ha of OCM or LWM footprint. While there is considerable variation around this mean value, it provides the best available estimate for the purpose of reporting model outputs.

Further information about water use of coal mines in the Namoi Catchment is provided in Table 53. From this table we infer that the average level of groundwater inflow into OCMs and LWMs is 0.4 ML/yr for every 1.0 ha of OCM or LWM.

Table 53. Estimated water use of major coals mines in the Namoi Catchment (from SWS 2011)

Mine Total area A (ha)

Coal extraction rate (Mt/yr)

Daily water use (m3/d)

Groundwater inflow (m3/d)

Boggabri OCM 1175 2.8 – 4.3 600 - 3000 1200

Canyon OCM (closed) 280 1.0 110 - 230 -

Rocglen OCM 297 1.5 280 -

Sunnyside OCM 128 1.0 205 - 274 175 - 290

Tarrawonga OCM 443 1.5 240 500 - 700

Werris Creek OCM 242 1.5 300 -

Narrabri LWM 3240 B 2.5 – 8.0 3000 200 – 3900 C A. Area of pit and overburden, and associated infrastructure and roading adjacent to the site – calculated from delineation

of base case mining layer (section 3.1.1). B. Narrabri North area only C. Dewatering of Narrabri North LWM is projected to increase from 200 m3/d in 2011 to 3,900 in 2028, before falling after

2028.



There are five (5) main sources of water for use in mine operations:

- captured rainfall;

- licensed abstraction from nearby surface water sources;

- licensed abstraction from nearby groundwater sources;

- use of intercepted groundwater inflow; and

- onsite treated and reused water.

The demand on the surface water flow and groundwater drawdown is unique for each mine as it is dependent on the relative contribution of the above sources to mine operation. Some mines may need to discharge water into streams at different times, and others may also reinject treated water into the aquifer.

For the purpose of this study, the following broad assumptions apply for OCMs:

- all rainfall captured on site is used in mine operation;

- no mine water is released to surface water (i.e. retained water is either used or evaporated);

- sub-catchment outflow reduces linearly with total mine area as a result of rainfall intersection and/or surface water abstraction;

- 0.4 ML/yr of groundwater inflows to each hectare of the open void; and

- open cut void represents 50% of the total mine area.

For the purpose of this study, the following broad assumptions apply for LWMs:

- no rainfall is captured above panels;

- sub-catchment outflow reduces linearly with total mine area as a result of surface water abstraction; and

- 0.4 ML/yr of groundwater inflows to each hectare of the LW panel(s)



3.4 CUMULATIVE RISK TOOL

3.4.1 Broad framework The broad framework of the cumulative risk tool is shown in Figure 31. In summary, a mining scenario is initiated by defining the type and spatial footprint of the first mine and intersecting it with each original sensitivity layer. The output sensitivity is reported against its appropriate risk matrix to derive a risk statement for each asset, for mine 1. Delineation of additional mines (optional) requires that the original asset and associated sensitivity layers are updated as temporary layers (enabling a synergistic analysis of ‘cumulative’ impacts). These updated sensitivity layers are reported against equivalent risk matrices to derive a risk statement for each asset, for each additional mine. Risk statements for individual mines are combined into a final risk statement for the scenario.

Figure 31. Broad framework of the Cumulative Risk Tool



The main components of NCRAT are listed in Table 54. Each component is embedded with one of three modules. The modules are outlined in the following section.

Table 54. Main components of NCRAT

NCRAT component Component type Module Description

NCRAT ArcGIS Model All User interface and script controller.

Field Check Python script 1 Checks input shape files for required fields.

NCRAT A_Model: Scenario Pre-processing Python script 1 Checks input shape files for required format, correct

projection and converts it to a raster based on mine type.

NCRAT B Model ArcGIS Model 2 Clips sensitivity grids and calculates area values.

NCRAT B Model: Calculate Precent Risk Python script 2 Calculates percent risk based on clipped sensitivity grids.

NCRAT B Model: Calculate Risk Python script 2 Calculates risk based on clipped sensitivity grids.

NCRAT B Model: Local Links ArcGIS Model 2 Counts links within clipped local links sensitivity grid

NCRAT B Model: Local Links Risk Python script 2 Calculates risk based on clipped local links sensitivity grid

NCRAT Iteration: Cleanup1 ArcGIS Model 2 Copies output grids from iteration tools to original files name

in preparation for next iteration of NCRAT

NCRAT Iteration: Intact ArcGIS Model 2 Re-calculates the intactness grid based on the current mining scenario

NCRAT Iteration: RVC ArcGIS Model 2 Re-calculates the Regional Vegetation Community (% remaining) grid based on the current mining scenario

NCRAT Iteration: VC_NC_Update ArcGIS Model 2 Re-calculates the Vegetation Cover Native Cover grid based

on the current mining scenario

NCRAT Iteration: VC_NSC ArcGIS Model 2 Re-calculates the Vegetation Cover Namoi Sub-catchment

grid based on the current mining scenario

NCRAT Iteration: VCSM ArcGIS Model 2 Re-calculates the Vegetation Cover Smooth grid based on the current mining scenario

NCRAT Iteration: LCs ArcGIS Model 2 Re-calculates the Surface Water Quality grid based on updated mine density for local catchments

NCRAT Iteration: SWF ArcGIS Model 2 Re-calculates the surface water flow grid based on calculation of hydrological impact of mine

NCRAT Iteration: GW ArcGIS Model 2 Re-calculates the Groundwater Quality grid based on updated mine density for mapped aquifers

NCRAT Reporting : Show Report Python script 3 Opens the NCRAT html report when all processing is

complete

NCRAT Reporting : Write Asset Risk Python script 3 Writes risk tables and pie charts to html report

NCRAT Reporting : Write Footer Python script 3 Writes footer to html report

NCRAT Reporting : Write Header Python script 3 Creates NCRAT html report and writes header information

NCRAT Reporting : Write Asset Risk Python script 3 Writes risk tables and pie charts to html report

NCRAT: Process Mine Shapefile Python script 3 Overarching script that calls the different components of

NCRAT (listed above) as required



3.4.2 Modules Figure 32 presents a more detailed illustration in which the framework is divided into three interacting modules. Module 1 is scenario setting, in which users select one or more mines that constitute a mining scenario. Module 2 is asset analysis, in which each asset layer and associated sensitivity layer is analysed spatially in response to a new mine in the scenario (sub-catchment vegetation cover is shown as an example). Module 3 is reporting, in which risk matrix for each asset (or receptor) is applied to the results of spatial analyses for each corresponding asset to generate a risk statement.

Module 1

Module 1 is configured to allow the users to input the footprint of a mine as an ArcGIS shapefile, and to classify the mine type as either OCM, LWM or CSGM. The user may input proposed and/or mines or hypothetical mines. A spatial layer of the Gunnedah Coal Basin and the extent of petroleum exploration tenements in the Namoi Catchment will be available to guide mine selection. However NCRAT will permit the user to locate mines anywhere in the catchment including parts of the catchment where coal and coal seam gas resources are not known to occur.

Module 2

The way in which asset and sensitivity data are manipulated in Module 2 is dependent on the nature of the asset. Some assets such as land use are not unduly affected outside the immediate mining footprint, so analysis is limited to that part of the surface that is directly forfeited as a result of proposed mines. These assets are subject to additive cumulative impacts. Conversely, some assets such as sub-catchment vegetation cover, vegetation type cover and surface and groundwater quality are affected outside as well as within the mining footprint, so asset and baseline data need to be updated for every additional mine in the scenario. For these assets, the impact of a mine on an asset will be influenced by the prior impacts of other mines on the asset. These assets are subject to compounding (or synergistic/antagonistic) cumulative impacts. Table 55 lists the nature of the cumulative impact associated with each receptor, as it is built into the tool. Appendix VI presents a flow diagram for some of the receptors in Module 2.

The smooth operation of Module 2 requires appropriate file structure and nomenclature. The full list of spatial files is shown in Table 56 and the NCRAT data file structure (i.e. directories and sub-directories) is as follows:

C:/NCRAT/readme.txt C:/NCRAT/data/basecase/asset C:/NCRAT/data/basecase/sensitivity C:/NCRAT/data/tmp/asset C:/NCRAT/data/tmp/sensitivity C:/NCRAT/scripts C:/NCRAT/reports C:/NCRAT/outputs/grid C:/NCRAT/outputs/tables C:/NCRAT/documents

Module 3

Module 3 writes out NCRAT input and output information into a report template that includes, and introduction and colour legend, an overview map of the location of each mine in the scenario, a set of single and cumulative risk tables and diagrams, and set of overview risk maps and statements about the assets likely to be impacted. The risk report template is shown in Appendix VII.



Figure 32. Module structure of the risk assessment framework



Table 55. Type of cumulative impact associated with each receptor

Receptor Type of cumulative impact

Percent vegetation cover (sub-catchment) Compounding

Percent vegetation cover (local) Compounding

RVC cover Compounding

Endangered ecological communities Linear

Intactness Compounding

Connectivity Compounding

Threatened species richness Linear

Land use Linear

Soil productivity Linear

Long term % surface flow (sub-catchment) Linear

Surface water pollution index Compounding

Groundwater structure Linear

Groundwater pollution index Compounding



Table 56. Spatial layer nomenclature used in the Cumulative Risk Tool

i. Scenario setting

Characteristics of data layer

Name Type Description

MINE_n Input raster 25m raster of proposed mine number n in the scenario, generated from associated shapefile uploaded by user. Labelled with mine type (CSGM, LWM or OCM).

MINBUF10_tmp Input raster Temporary 25 m raster constituting a 10 km buffer around MINE_n that also includes the footprint of MINE_n. Replaced with each new mine.

MINBUF05_tmp Input raster Temporary 25 m raster constituting a 5 km buffer around MINE_n that also includes the footprint of MINE_n. Replaced with each new mine.

CSGM Input raster 25m raster of CSGM vegetation clearing footprint template across Namoi Catchment (well pad footprint only)

CSGMr Input raster 25m raster of CSGM vegetation clearing footprint template across Namoi Catchment (well pad and road footprints)

CSGM_tmp Input raster Temporary 25m raster of CSGM vegetation clearing footprint template within the selected footprint MINE_n.

OCM_tmp Input raster Temporary 25m raster of OCM vegetation clearing footprint template within the selected footprint MINE_n.

ii. Asset analysis



VC_NC Input raster 25m raster of native vegetation cover in the Namoi Catchment (excludes derived native grassland).

VC_NC_tmp Input/output raster

Temporary 25m raster of native vegetation cover in the Namoi Catchment. It is generated from the VC_NC grid on submission of the first mine in the scenario. It is updated on submission of each additional mine through intersection with the CSGM, LWM or OCM grid. It informs revision of both VC_NSC_tmp and VCsm_tmp.

VC_NSC Input raster 25m raster of Namoi Sub-catchments, populated with %-native cover estimate and sensitivity classes. The %-cover values are derived from the VC_NC grid.

VC_NSC_tmp Input/output raster

Temporary 25m raster of total native vegetation cover in each Namoi Sub-catchment. It is generated from the VC_NSC grid on submission of the first mine in the scenario, and it is updated from the VC_NC_tmp grid on submission of each additional mine.

VCsm Input raster 25m raster of ‘smoothed’ native vegetation cover raster, derived for each gridcell by averaging cover (from VC_NC) within a 5 km radius.

VCsm_tmp Input/output raster

Temporary 25m raster of smoothed native vegetation cover in the Namoi Catchment. It is generated from the VCsm grid on submission of the first mine in the scenario, and it is updated on submission of each additional mine.





RVC Input raster 25m raster of current RVC distribution populated with pre-European extent and current extent fields.

RVC_tmp Input/output raster

Temporary 25m raster of RVC distribution, populated with pre-European extent and current extent fields, as well as a sensitivity field. It is generated from the RVC grid on submission of the first mine in the scenario, and is updated on submission of each additional mine.

EEC_tmp Input/output raster

Temporary 25m raster of EEC distribution derived from RVC grid, and populated with EEC candidacy ranking and associated sensitivity class. The layer is updated on addition of each mine, but addition does not affect the sensitivity of the next (i.e. this raster provides a linear cumulative impact rather than a compounding cumulative impact).

INTACT Input raster A 25 m raster layer derived from spatial application of the intactness equation (Section 2.3.4) to each cell in the VC_NC layer.

INTACT_tmp Input/output raster

Temporary 25m raster of intactness initially derived from INTACT, then revised by overlaying INTACT05_tmp after each mine iteration.

INTACT10_tmp Input/output raster

Temporary 25m raster of intactness derived from spatial application of the intactness equation (Section 2.3.4) to MINBUF10_tmp.

INTACT05_tmp Input raster Result of intersection of INTACT10_tmp with MINBUF05_tmp. This file is used to update INTACT_tmp after each mine iteration.

MINBUF05_tmp Output raster Temporary 5 km buffer around (and including) mine ‘n’ in the scenario, within which INTACT05_tmp is calculated.

MINBUF10_tmp Output raster Temporary 10 km buffer around (and including) mine ‘n’ in the scenario, within which INTACT10_tmp is calculated.

TS Input raster Threatened species composite layer, including sensitivity classes.

TS_tmp Input /output raster

Temporary threatened species composite layer, including sensitivity classes, generated on commencement of scenario.

LU Input raster Land use layer, including sensitivity classes.

LU_tmp Input /output raster

Temporary land use layer, including sensitivity classes, generated on commencement of scenario.

SOIL Input raster Soil productivity including sensitivity classes.

SOIL_tmp Input raster Temporary soil productivity generated on commencement of scenario (includes sensitivity classes).

LCON Input raster Landscape connectivity including sensitivity classes.

LCON_tmp Input raster Temporary landscape connectivity generated on commencement of scenario (including sensitivity classes).

SWF Input raster Surface water flow including sensitivity classes.

SWF_tmp Input raster Temporary surface water flow, generated on commencement of scenario and updated with each new mine (including sensitivity classes).

SWQ Input raster Surface water quality including sensitivity classes.

SWQ_tmp Input raster Temporary surface water quality generated on commencement of scenario and updated with each new mine (including sensitivity classes).





GWD Input raster Groundwater drawdown including sensitivity classes.

GWD_tmp Input raster Temporary groundwater drawdown surface, generated on commencement of scenario and updated with each new mine (including sensitivity classes).

GWQ Input raster Groundwater quality including sensitivity classes.

GWQ_tmp Input raster Temporary groundwater quality generated on commencement of scenario (including sensitivity classes).

3.4.3 System requirements Hardware

The hardware specifications listed in Table 57: Hardware requirements are recommended as a minimum when running NCRAT.

Table 57: Hardware requirements

CPU Speed 2.2 GHz minimum or higher; Hyper-threading (HHT) or Multi-core recommended

Processor Intel Pentium 4, Intel Core Duo, or Xeon Processors; SSE2 (or greater)

Memory/RAM 2 GB or higher

Display Properties 24 bit colour depth

Screen Resolution 1024 x 768 recommended or higher at Normal size (96dpi)

Swap Space Determined by the operating system, 500 MB minimum.

Disk Space

2.4 GB In addition, up to 50 MB of disk space maybe needed in the Windows System directory (typically C:\Windows\System32). You can view the disk space requirement for each of the 10.0 components in the Setup program.

Video/Graphics Adapter

64 MB RAM minimum, 256 MB RAM or higher recommended. NVIDIA, ATI and INTEL chipsets supported 24 bit capable graphics accelerator OpenGL version 2.0 runtime or higher is required, and Shader Model 3.0 or higher is recommended. Be sure to use the latest available driver.



Software

NCRAT operates on ArcGIS 10.0 (Service Pack 3) with the Spatial Analysis extension. NCRAT has been successfully tested on both Windows XP and Windows 7 operating systems. Modifications to NCRAT may be required for new releases of ArcGIS and or Windows.

The reporting element of NCRAT utilises HTML which can be viewed using internet browsing software such as Fire Fox or Google Chrome. If using Internet Explorer, the Adobe SVG viewer plug-in is also required. http://www.adobe.com/svg/viewer/install/

Installation

NCRAT is provided as a self-extracting zip file. Double click the NCRAT.zip file to execute the installation. NCRAT is installed in the folder 'C:\NCRAT'.

http://www.adobe.com/svg/viewer/install/



4 Recommendations This section outlines 4 (four) recommendations for future work.

Recommendation 1. Addition of a spatial optimisation module

The capacity of NCRAT to report mitigated as well as baseline risk has been identified as an important function for consideration in the future. Reporting mitigated risk requires that a separate component be built into the Tool that enables delineation of an optimal spatial footprint for offsetting (mitigating) risk associated with a mining scenario. This will enable users to gauge the magnitude of cumulative risk associated with a given scenario and to identify opportunities in the landscape where such risk might be offset by strategic restorative NRM projects.

It is thus proposed that an interactive spatial tool (Version 2) be developed that not only addresses risk, but also optimises co-benefits (opportunities) in terms of spatial overlap of biodiversity, land, water and carbon outcomes.

Recommendation 2. Contribution of other sectors to cumulative risk

As per the project brief, NCRAT was designed to assess cumulative risk associated exclusively with the mining sector. Thus a clear knowledge gap is how other sectors, particularly agriculture, contribute to cumulative risk over time. Consistent with most literature on approaches to cumulative risk assessment, Namoi CMA should consider expanding NCRAT to include other sectors in the risk assessment, as this would provide a more balanced and complete picture.

Inclusion of other sectors is also important in developing a scenario template (Recommendation 3).

Recommendation 3. Building a scenario template

As new mines will invariably be approved in the Namoi Catchment and land cover will be modified for other purposes, it is important that a scenario template be established that reflects real iterative change to land cover in the catchment. The sequence of land cover changes that will occur in the future will need to be embedded or ‘locked in’ to future scenario runs.

The most efficient way to build the scenario template would be to obtain land cover change data from the NSW Government, delineate areas of land cover change (say greater than 10 ha) and lock them into a scenario template that is run as a prelude to future mining scenarios.

Recommendation 4. Socio-economic values

NCRAT is structured so that asset classes other than water, land and biodiversity can be incorporated, through appropriate preparation and iteration of sensitivity layers. It is recommended that Namoi CMA explore the potential for incorporation of socio-economic values that may include, for example, Aboriginal and European cultural heritage, farm size/value, tourism potential and visual amenity.


E C O L O G I C AL AU S T R AL I A P T Y L T D 90

References APLNG (2010). Australia Pacific Liquefied Natural Gas Project – Environmental Impact Statement. March 2010.

Atkinson, C.M., (2002). Environmental Hazards of Oil and Gas Exploration. Report to NPA, August 2002.

Badenhop, A.M., Wasko, C.D. and Timms, W.A. (2012). Namoi Groundwater Mapping and Transition Zones. Water Resources Lab Technical Report 2012/01. University of NSW. May 2012.

Carter, R. H., Holditch, S. A., and Wolhart, S. L. (1996). Results of a 1995 hydraulic fracturing survey and a comparison of 1995 and 1990 industry practices. Presented at the Society of Petroleum Engineers Annual Technical Conference, Denver, CO.

Dart Energy (undated). The development of a coal seam gas industry in New South Wales. http://www.dartenergy.com.au/content/Document/The%20Development%20of%20a%20CSG%20Industry%20in%20NSW_Robbert%20de%20Weijer_July%202011.pdf

DEEDI (2012). Queensland Coal Seam Gas Overview. Queensland Government. February 2012.

DERM (2012) Image downloaded on 10 May 2012. http://www.derm.qld.gov.au/environmental_ management/ucg/images/csg-diagram.gif

DERM (2010) Guideline: Preparing an Environmental Management Plan for Coal Seam Gas Activities. March 2010.

Doerr, V.A.J., Doerr, E.D. and Davies, M.J. (2010). Does structural connectivity facilitate dispersal of native species in Australia’s fragmented terrestrial landscapes? Systematic Review No. 44, Collaboration of Environmental Evidence. CSIRO. Canberra.

Eco Logical Australia (2011). Cumulative Risk of Mining to the Natural Resource Assets of the Namoi Catchment: Final Scoping Study. Project 11COFNRM-0006 prepared for the Namoi CMA. September 2011.

Eco Logical Australia (2008). Namoi Wetland Assessment and Prioritisation Project. Project 125-005 prepared for Namoi CMA. September 2008.

Eco Logical Australia (2009a). A Vegetation Map for the Namoi Catchment Management Authority. Project 125-004. Prepared for Namoi CMA. June 2009.

Eco Logical Australia (2009b). A Pre-European Vegetation Map for the Namoi Catchment Management Authority. Project 125-009. Prepared for Namoi CMA. October 2009.

Eco Logical Australia (2009c). Riverine Vegetation in the Namoi Catchment. An Assessment of Type and Condition. Project 222-001 prepared for the Cotton Catchment Communities CRC and Namoi CMA. May 2009.

Emery, K. et al. (in-prep). NSW Land Use Mapping Program (draft data and report). NSW DECC.



Entrekin, S., Evans-White, M., Johnson, B. and Hagenbunch, E. (2011). Rapid expansion of natural gas development poses a threat to surface waters. Frontiers in Ecology and the Environment. 9: 503-511.

Geoscience Australia (2012) Website Information obtained on 10 May 2012. http://www.ga.gov.au/ energy/petroleum-resources/coal-seam-gas.html.

Gifford, R.M. (2000). Carbon Content of Above-Ground Tissues of Forest and Woodland Trees. NCAS Tech. Rep. No. 22. AGO. 17 pp.

GISERA (2012). What is Hydraulic Fracturing? April 2012.

Golder Associates (2010). Coal Seam Hydraulic Fracturing Fluid Risk Assessment. Response to the Coordinator General Requirements for Coal Seam Gas Operations in the Surat and Bowen Basins, Queensland. Report Prepared for Santos Limited.

Goldrick, G., Chapman, G.A., Simons, N.A., Milford, H.B., Murphy, C.L., McGaw, A.J.E., Edye, J.A., and Macleod, A.P. (2001). New technology and soil landscape mapping in N.S.W. In: Proceedings of the Geospatial Information and Agriculture Symposium, Sydney 2001.

Herczeg, A. L. (2008). Background Report on the Great Artesian Basin. A report to the Australian Government from the CSIRO Murray-Darling Basin sustainable yields project. CSIRO, Australia, pp. 18.

Hyder Consulting (2008). Namoi Regional State of the Environment Report. Report to the Namoi CMA. November 2008.

McDonald, R. C., Isbell, R. F., Speight, J. G., Walker, J. and Hopkins, M. S. (1990). Australian Soil and Land Survey – Field Handbook (Second Edition). Australian Collaborative Land Evaluation Program.

Mitchell, G. W. (2012). Longwall Mining. In: Monograph 26 Australian Coal Mining Practice. Pp. 340-375.

McIntyre, S. and Hobbs, R. (1999). A framework for conceptualizing human effects on landscapes and its relevance to management and research models. Conservation Biology. 13: 1282-1292.

Namoi CMA (2011). Namoi Catchment Action Plan 2010 – 2020 (Pending Ministerial Approval). Version 3. September 2011.

NGGIC (1996). Workbook for Carbon Dioxide from the Biosphere. Workbook 4.2. Australian methodology for the estimation of greenhouse gas emissions and sinks. AGO.

NOW (2009). Namoi Catchment – 2008 Depth to Water Table Map Report. September 2009.

NOW (2012). Unpublished flow data for the Namoi Catchment.

NWC (2011). Onshore Co-Produced Water: Extent and Management. RPS Australia East Pty Ltd. Waterlines Report Series No 54. September 2011.

Santos (2011). Senate Rural Affairs and Transport References Committee Inquiry into management of the Murray Darling Basin – impact of coal seam gas.

SKM (2010a). Mapping Groundwater Dependent Ecosystems in the Namoi Catchment. Final report to Namoi CMA. May 2010.

SKM (2010b). Framework for Assessing Potential Local and Cumulative Effects of Mining on Groundwater Resources. Report 3. Final report to the National Water Commission. May 2010.



Standards Australia/Standards New Zealand (2004). Australian/New Zealand Standard, Risk Management – Principles and Guidelines AS/NZS ISO 31000:2009.

SWS (2011). Namoi Catchment Water Study Phase 2 Report. Prepared for the NSW Department of Trade and Investment, Regional Infrastructure and Services. August 2011.

SWS (2012). Namoi Catchment Water Study Phase 3 Report. Prepared for the NSW Department of Trade and Investment, Regional Infrastructure and Services. April 2012.

Taylor, R.J, Christian, N. Drielsma, M.D., Mazzer, L. and Bollard, K. (2011). Biodiversity Management Plan for the Namoi Catchment (Draft). Office of Environment and Heritage.

URS (2009). Gladstone Liquefied Natural Gas Project – Environmental Impact Statement. Prepared for Santos Limited.

USEPA (2011). Draft Plan to Study the Potential Impacts of Hydraulic Fracturing on Drinking Water Resources. Office of Research and Development, U.S. Environmental Protection Agency, Washington D.C. February 2011.

Wall, J.P. (2001). Carbon Sinks for Ecosystem Reconstruction: Identification and Classification of Potential Reforestation Areas on NPWS-Managed Land in NSW. Strategic Policy Division, NPWS. Aug 2001. 129 pp.



Appendix I. Summary of CSG operations in Australia

Background

Coal seam gas (CSG) is naturally occurring methane gas (CH4) found in underground coal seams (Geoscience Australia 2012). Methane gas is trapped by molecular bonding (adsorption) on the internal surfaces of micropores (cleats) within the coal and water generated pressure captures it within the coal seam. In essence, the coal seam acts as the source; reservoir and seal for this type of gas deposit (Atkinson 2002).

Current Coal Seam Gas Production in Australia

Reserves of CSG in Australia are known from the Bowen and Surat Basins in Queensland, and the Sydney, Gunnedah, Clarence-Moreton and Gloucester Basins in New South Wales. Exploration is proposed or currently occurring in other coal basins including the Galilee, Arckaringa, Perth and Pedirka Basins. (Geoscience Australia 2012) CSG has been produced in Queensland from the Bowen Basin since 1997 and in the Surat Basin since 2005. Queensland’s industry has grown rapidly over the past 15 years - the annual number of wells drilled increasing from 10 in the early 1990s to almost 600 in 2010 - 2011. Many Queensland basins are highly prospective for CSG and production in the Bowen (Permian coal measures) and Surat (Jurassic Walloon Coal Measures) Basins represents more than 79% of the total gas produced in the state (QLD Department of Employment, Economic, Development and Innovation, DEEDI 2012)

In NSW, AGL currently produces gas to the domestic market from its Camden Gas Project in the Sydney Basin. The Camden Gas Project provides approximately 5% of NSW gas supply. CSG exploration and appraisal activities in NSW are currently occurring in the Gunnedah, Gloucester and Clarence Morton Basin.

Differences between coal seam gas and conventional gas

Natural gas in Australia has traditionally been extracted from conventional gas fields. In a conventional gas field, the gas has been generated over geologic time from organic material trapped in a source rock which has then migrated into a trapping reservoir which typically has high porosity and permeability. Compared to CSG, conventional gas reservoirs are generally at greater depth, the gas flows to surface at higher pressure and there is very little water associated with the gas production. Conventional gas reservoirs are generally discrete structures compared to the regionally extensive coal seams and typically fewer wells are required to develop a conventional gas resource (Australia Pacific Liquefied Natural Gas, APLNG 2010).

CSG development differs significantly from developing conventional natural gas. To produce gas from a coal seam, normally the water associated with the gas in the reservoir must first be withdrawn using artificial lift (pump) installed in the well at the depth of the coal seam being targeted. This reduces the pressure within the coal seam and liberates the adsorbed gas from the coal (APLNG, 2010).

CSG extraction

Target coal seams for the CSG production are typically 200 m to 1,000 m below the ground surface, where the gas is held to the coal surface under water pressure. CSG is extracted by drilling wells and pumping the formation water from the coal seam, enabling the gas to be released (desorbed) from the coal micropores and cleats, and allowing the gas and ‘produced water’ to be carried to the surface. This



reduces the pressure in the coal seam and allows the gas to flow from the surfaces of the coal. Water and gas are separated by gravity in a ‘seperator’ (a cylinder of varying sizes depending on the water production rate of the coal seam) installed at the processing plant. From here the gas is piped to a gathering network, then to the consumer. Figure 1 shows a schematic of coal seam gas extraction, and Figure 2 shows a typical CSG wellhead in the field.

Figure 1. Coal Seam Gas Extraction Schematic (Source: DERM 2012)



Figure 2. Typical Coals Seam Gas Wellhead (Source: APLNG 2010)

CSG Field Development

Development of a CSG field typically includes the following process of activities (e.g. URS 2009):

• Exploration – including geophysical surveys and drilling of exploration wells

• Appraisal – drilling and testing of appraisal wells (also called pilot wells).

• Development, including:

• Roading and establishment of drill pads

• Drilling and completion of wells (wells drilled to enable gas production), and

• Construction of centralised compression and water treatment facilities, gas and water gathering networks and other related infrastructure.

• Production and operation; and

• Rehabilitation and decommissioning. A CSG production field typically includes the following:

• CSG wells and associated infrastructure (e.g. telemetry, generator, water transfer tank).

• Water and gas gathering pipe networks.

• Water treatment facilities (e.g. storage ponds, reverse osmosis plants, brine storage and injection).

• Gas treatment and compression facilities including filtration, compression, cooling and dehydration process items.

• Power supply networks (above and below ground).

• Field infrastructure such as access roads and tracks, storage warehouses, workers accommodation camps, offices and telecommunications.



Water Production and Management

As shown in Figure 3, water production is higher in the initial stages of CSG appraisal and production, and decreases as the gas production increases. This is the opposite in conventional gas production, where gas production starts high and gradually reduces as water production increases.

Figure 3. Conceptual Coal Seam Gas and Water Production Curve

Actual production rates and times within and between coal measures vary considerably with the structure and depth of seams and aquifer characteristics. Queensland Gas Company Pty Ltd (QGC) indicated that initial water quantities extracted from a well in the Surat Basin ranged from 0.4 ML/day to 0.8 ML/day, decreasing to about 0.1 ML/day over a period of six months to a few years (National Water Commission, NWC 2011). At the Fairview field in the Bowen Basin, Santos reported an average initial daily water production rate of 0.2 ML/day/well, which decreased to 0.02 ML/day/well after 12 years (NWC 2011). Exploration and appraisal activities in the Gunnedah Basin are ongoing; however current water production estimates reported by Santos appear to be similar to the water production rates in the Bowen Basin (Santos 2011).

Historically, the produced water was either directly discharged to surface or streams or stored in evaporation ponds. More recently the CSG industry has developed water treatment facilities so the produced water can potentially be re-used. CSG projects in Queensland now typically store the produced water in transfer ponds prior to being treated (e.g. amended and / or desalinated). Following water treatment, a permeate (clean water) and a brine (salty water) stream is produced. Current management of permeate in Australia includes trails for irrigation of fodder and hardwood, reinjection into town water supply groundwater system, discharge to surface waters and for operational activities such as dust suppression. Current management of brine in Australia includes reinjected into a deep isolated rock formation or temporary storage and disposal to a licenced facility. Trials are currently underway in Queensland for the crystalisation and commercial production of salt from the CSG brine.



The Queensland Department of Environment and Resource Management (DERM) Guideline ‘Preparing an Environmental Management Plan for Coal Seam Gas Activities’ (DERM 2010) sets out preferred and non-preferred management options for CSG water:

• Category 1 – preferred management options include: - injection where detrimental impact unlikely; - untreated use where detrimental impact unlikely; and - treatment to an agreed standard for agricultural, industrial and potable uses.

• Category 2 – non-preferred management options include:

- disposal via evaporation dams; - disposal via injection where a detrimental impact is likely; - disposal to surface waters; and - disposal to land.

NSW does not currently have a CSG water management policy.

Hydraulic Fracturing, Cavitation and Multi-Lateral Drilling

In some cases coal permeability is low and gas production is small (sub-economic). In these cases, hydraulic fracturing or cavitation (commonly referred to as fraccing) can be used to further assist the flow of gas through the coal to the producing well (Gas Industry Social and Environmental Research Alliance, GISERA 2012). A new technology is currently being developed in the Gunnedah Basin called ‘multi-lateral drilling’, which serves as an alternative to hydraulic fracturing and cavitation. This method may increase gas flow and reduce the surface infrastructure requirements for CSG production. These methods are discussed below.

Hydraulic Fracturing

Hydraulic fracturing involves pumping treated fluid (usually water) containing sand grains into coal cleats at a high rate and pressure to form and extend a fracture in the coal reservoir. This creates a high conductivity pathway to the well bore and increases the production capability of the well (APLNG 2010).

Hydraulic fracturing of production wells is technology that has been used for more than 50 years in conventional oil and gas production in the United States, and about 25 years in Australia. It works to enhance recovery by enlarging fractures through which oil and gas, including coal bed methane, can be drawn to a well and pumped to the surface. It involves pressurised injection of water, chemical additives, and proppants into a geologic formation, inducing fractures in the formation that stimulate the flow of natural gas or oil, thus increasing the volume of gas or oil that can be recovered from coal beds, shales, and tight sands - the so-called “unconventional” reservoirs (USEPA 2011).

Most hydraulic fracturing fluids are water-based fluids that are designed to create pressure to propagate the fracture, and to carry the proppant into the fracture. Proppants are solid materials that are used to keep the fractures open after pressure is reduced in the well, the most common proppant being sand (Carter et al. 1996). Water-based fluids containing sand have become the predominant type of fracturing fluids, although fluids can also be based on oil, methanol, or a combination of water and methanol. After fluids are injected to expand fractures within a coal seam, large quantities of ground water and some of the injecting fracturing fluids are pumped back out of the well to facilitate the production of the gas.



In addition to proppants and water, hydraulic fracturing fluids often contain chemical additives. The types and concentrations of chemical additives vary depending on the conditions of the specific well being fractured, and are selected to create a fracturing fluid tailored to the properties of the formation and the needs of the project.

The process of coal seam hydraulic fracturing typically involves the implementation of the following a series of tasks (Golder Associates 2010):

• Well casing perforation (access hole in the steal well casing pipe are created to allow access to the coal seam groundwater and CSG);

• Acid Injection (to open up the coal seam cleats where they are filled with natural calcite); • ‘Pad Volume’ injection (hydraulic fracturing fluid) which comprises a mix of water, guar gum, sand

and stabiliser chemicals injected to fracture the seam; • ‘Slurry Volume’ injection (hydraulic fracturing slurry plus beach sand) which includes the addition

of sand to prop open the fracture followed by the addition of a breaker; • ‘Flush Volume’ injection (water only) to force the remaining hydraulic fracturing fluids, contained

in the well casing, into the coal seam to complete the hydraulic fracturing process; • ‘Flow-Back’ pumping involves the extractive pumping of a volume of fluids equivalent to around

110% of the total volume of hydraulic fracturing fluids previously injected (as described above) and aims at recovering the majority of the hydraulic fracturing fluids injected. The remaining mobile components will largely be recovered and treated as part of the production pumping of CSG; and

• Well stabilisation dosing to preserve the hydraulic fracturing job for the period between well

completion and operational gas production.

Cavitation

Cavitation is an alternative technology for well completion that may be utilised when other methodologies are not suitable. Cavitation uses air pumped at high pressure to penetrate the coal cleats within the formation. The pressure is held on the well bore for a given amount of time then released suddenly, causing the coal to fail and slump into the well bore. The failed coal is flowed to the surface, leaving a cavity in the coal reservoir sections and a zone of increased permeability around the cavity within the coal formation (APLNG 2010).

Multi-Lateral Drilling

Multi-lateral wells target several coal seams through a single well bore at the surface, with a horizontal leg drilled laterally within each seam for a distance of several kilometres (Figure 4). Implementation of multi-lateral wells enables a large reservoir area to be drained of gas with fewer surface installations. This drilling technology is well established and Australia is a global technical leader in its execution.

Multi-lateral drilling can have the added benefit of minimising or eliminating the need for hydraulic fracturing as lateral wells drilled along the natural fracture system of the bed. Multi-lateral drilling is



currently being considered in the Gunnedah Basin although geological variables will ultimately dictate the CSG extraction method used throughout.

Figure 4. Multi-Lateral Drilling (Source: Dart Energy, undated)

Typical Chemicals Used in Drilling, Completions and Stimulation

Generally, drilling of a gas well utilises approximately 200 m3 (0.2 ML) of drilling mud comprised mainly of water and bentonite. Water from the drilling mud is separated from the drill cuttings and stored for treatment. The rill cuttings brought to the surface are rehabilitated in-situ (APLNG 2010).

Minor quantities of additional chemical additives are typically blended into the drilling and completion fluids to assist the drilling process. Biocides are used to limit the growth and spread of bacteria that may cause fouling. Corrosion inhibitors limit potential for corrosion and failure of well completions, thus maintaining the integrity of the wells (APLNG 2010).

Well completion that involves hydraulic fracturing will typically utilise about 1,600 m3 (1.6 ML) of fraccing fluid, predominately water, containing inert proppant solids (typically glass beads, sand and/or silica in composition) and additives. This fluid remains in-situ to assist in maintaining the flow of CSG. The well completion fluids used for fracturing the coal seam will be pumped from the well during development, returned to the surface, and treated through a water treatment plant (APLNG 2010).

The additives and use of typical components of hydraulic fracturing fluid are (DERM website):

• Acid (such as hydrochloric acid) – removes cement and drilling mud from casing perforations prior to hydraulic fracturing fluid injection.

• Activator (such as 2-butoxyethanol) – used to initiate foaming.



• Gelling agents and binders (such as guar gum) – these are used to increase the viscosity of the hydraulic fracturing fluid and allow more sand to be carried into the fractures.

• Cross linker (such as boric acid) – used to change the viscous fluid into a pseudo-plastic fluid enabling more proppant to be carried.

• Proppant (such as sand or quartz) – to hold the fracture faces apart. • Breakers (such as ammonium persulphate) – these are used to break down the fraccing gel and

enable release of the proppant into fractures; they also enhance the recovery of the fraccing fluid. • Buffers, stabilizers and solvents (such as potassium carbonate) – maintains the stability of the

fracturing fluid, immobilises clays and enhances pre-fracture. • Microbial control (such as sodium hypochlorite) – inhibits growth of organisms which could

contaminate the gas resources and the hydraulic fracturing fluid. • Surfactants (such as orange oil) – reduces the surface tension thereby aiding fluid recovery. • Clay management (such as choline chloride) – used to minimise clay swelling in the vicinity of the

well and in the formation. • Corrosion inhibitor and oxygen scavenger (such as fumaric acid) – used to prevent corrosion of

well equipment.

References

Refer to reference list in main report.



Appendix II. Rules for assigning a priority to each link Link Integrity Groundcover Average tree density (no/ha) Width (m) Length (m)

Very high Native

> 10 ≥ 100 < 1000

50 - 99 < 700 10 - 49 < 500

6 - 10 ≥ 100 < 700

50 - 99 < 500 10 - 49 < 300

1 - 5 ≥ 100 < 500

50 - 99 < 300

High

Native

> 10

≥ 100 1000 - 1500 50 - 99 700 - 1500 10 - 49 500 - 999

Single crown < 700

6 - 10

≥ 100 700 - 1500 50 - 99 500 - 999 10 - 49 300 - 699

Single crown < 500

1 - 5

≥ 100 500 - 999 50 - 99 300 - 699 10 - 49 < 500

Single crown < 300

< 1 ≥ 100 < 700

50 - 99 < 500 10 - 49 < 300

Cropped

> 10 ≥ 100 < 1000

50 - 99 < 700 10 - 49 < 500

6 - 10 ≥ 100 < 700

50 - 99 < 500 10 - 49 < 300

1 - 5 ≥ 100 < 500

50 - 99 < 300

Moderate

Native

> 10 10 - 49 1000 - 1500

Single crown 700 - 999

6 - 10 50 - 99 1000 - 1500 10 - 49 700 - 999


1 - 5

≥ 100 1000 - 1500 50 - 99 700 - 999 10 - 49 500 - 699


< 1 ≥ 100 700 - 999

50 - 99 500 - 699 10 - 49 300 - 499

Cropped

> 10 50 - 99 700 - 999 10 - 49 500 - 699

Single crown < 300

6 - 10 ≥ 100 700 - 999

50 - 99 500 - 699 10 - 49 300 - 499

1 - 5 ≥ 100 500 - 699

50 - 99 300 - 499 < 1 ≥ 100 < 300



Link Integrity Groundcover Average tree density (no/ha) Width (m) Length (m)

Low

Native

> 10 Single crown 1000 - 1500

6 - 10 10 - 49 1000 - 1500


1 - 5 50 - 99 1000 - 1500 10 - 49 700 - 1500


< 1

≥ 100 1000 - 1500 50 - 99 700 - 999 10 - 49 500 - 999

Single crown < 500

Cropped

> 10 ≥ 50 1000 - 1500

10 - 49 700 - 999 Single crown 300 - 699

6 - 10

≥ 100 1000 - 1500 50 - 99 700 - 999 10 - 49 500 - 999

Single crown < 500

1 - 5

≥ 100 700 - 999 50 - 99 500 - 999 10 - 49 < 700

Single crown < 300

< 1 ≥ 100 < 700

50 - 99 < 500 10 - 49 < 300

Very low

Native 1 - 10 Single crown 1000 - 1500

< 1 10 - 99 1000 - 1500


Cropped

> 10 10 - 49 1000 - 1500


6 - 10 10 - 99 1000 - 1500


1 - 5 ≥ 50 1000 - 1500

10 - 49 700 - 1500 Single crown 300 - 1500

< 1

≥ 100 700 - 1500 50 - 99 500 - 1500 10 - 49 300 - 1500

Single crown all



Appendix III: Threatened species known or predicted to occur in the Namoi Catchment

Fauna

Listing Status A

Common name Scientific name TSC Act EPBC Act Weighting B

Birds

Regent Honeyeater Anthochaera phrygia CE E 6

Red Goshawk Erythrotriorchis radiatus CE V 5

Australasian Bittern Botaurus poiciloptilus E E 5

Swift Parrot Lathamus discolor E E 5

Malleefowl Leipoa ocellata E V 4

Australian Painted Snipe Rostratula australis E V 4

Superb Parrot Polytelis swainsonii V V 3

Australian Brush-turkey population in Nandewar and BBS bioregions Alectura lathami E - 2

Australian Bustard Ardeotis australis E - 2

Black-necked Stork Ephippiorhynchus asiaticus E - 2

Bush Stone-curlew Burhinus grallarius E - 2

Grey Falcon Falco hypoleucos E - 2

Barking Owl Ninox connivens V - 1

Black-breasted Buzzard Hamirostra melanosternon V - 1

Black-chinned Honeyeater (eastern subspecies) Melithreptus gularis gularis V - 1

Black-tailed Godwit Limosa limosa V - 1

Blue-billed Duck Oxyura australis V - 1

Brolga Grus rubicunda V - 1

Brown Treecreeper (eastern subspecies) Climacteris picumnus victoriae V - 1

Comb-crested Jacana Irediparra gallinacea V - 1

Diamond Firetail Stagonopleura guttata V - 1

Eastern Grass Owl Tylo longimembris V - 1 Eastern Osprey Pandion cristatis V - 1 Flame Robin Petroica phoenicea V - 1 Freckled Duck Stictonetta naevosa V - 1 Gang-gang Cockatoo Callocephalon fimbriatum V - 1 Gilbert's Whistler Pachycephala inornata V - 1 Glossy Black-cockatoo Calyptorhynchus lathami V - 1

Grey-crowned Babbler (eastern subspecies) Pomatostomus temporalis temporalis V - 1

Hooded Robin (south-eastern form) Melanodryas cucullata cucullata V - 1 Little Eagle Hieraaetus morphnoides V - 1 Little Lorikeet Glossopsitta pusilla V - 1 Magpie Goose Anseranas semipalmata V - 1 Masked Owl Tyto novaehollandiae V - 1 Olive Whistler Pachycephala olivacea V - 1 Painted Honeyeater Grantiella picta V - 1



Listing Status A


Pied Honeyeater Certhionyx variegatus V - 1 Pink Cockatoo Lophochroa leadbeateri V - 1 Powerful Owl Ninox strenua V - 1

Red-tailed Black Cockatoo (Inland subspecies) Calyptorhynchus banksii samueli V - 1

Scarlet Robin Petroica boodang V - 1

Sooty Owl Tyto tenebricosa V - 1

Speckled Warbler Chthonicola sagittata V - 1

Spotted Harrier Circus assimilis V - 1

Square-tailed Kite Lophoictinia isura V - 1

Turquoise Parrot Neophema pulchella V - 1

Varied sittella Daphoenositta chrysoptera V - 1

White-fronted Chat Epthianura albifrons V - 1

Frogs

Booroolong Frog Litoria booroolongensis E E 5

Davies' Tree Frog Litoria daviesae V - 1

Glandular Frog Litoria subglandulosa V - 1

Sloane's Froglet Crinia sloanei V - 1

Mammals

Hastings River Mouse Pseudomys oralis E E 5

Brush-tailed Rock-wallaby Petrogale penicillata E V 4

Spotted-tailed Quoll Dasyurus maculatus V E 4

Greater Long-eared Bat Nyctophilus timoriensis (N. corbeni) V V 3

Grey-headed Flying-fox Pteropus poliocephalus V V 3

Large-eared Pied Bat Chalinolobus dwyeri V V 3

Pilliga Mouse Pseudomys pilligaensis V V 3

Black-striped Wallaby Macropus dorsalis E - 2

Silky Mouse Pseudomys apodemoides E - 2

Brush-tailed Phascogale Phascogale tapoatafa V - 1

Eastern Bentwing-bat Miniopterus schreibersii oceanensis V - 1

Eastern Cave Bat Vespadelus troughtoni V - 1

Eastern False Pipistrelle Falsistrellus tasmaniensis V - 1

Eastern Pygmy-possum Cercartetus nanus V - 1

Greater Broad-Nosed Bat Scoteanax rueppellii V - 1

Inland Forest Bat Vespadelus baverstocki V - 1

Koala Phascolarctos cinereus V - 1

Little Bentwing Bat Miniopterus australis V - 1

Little Pied Bat Chalinolobus picatus V - 1

Rufous Bettong Aepyprymnus rufescens V - 1

Squirrel Glider Petaurus norfolcensis V - 1

Stripe-faced Dunnart Sminthopsis macroura V - 1

Yellow-bellied Glider Petaurus australis V - 1

Yellow-bellied Sheathtail-bat Saccolaimus flaviventris V - 1



Listing Status A


Reptiles

Five-clawed Worm-skink Anomalopus mackayi E V 4

Bell's Turtle Elseya belli V V 3

Border Thick-tailed Gecko Underwoodisaurus sphyrurus V V 3

Pink-tailed Worm-lizard Aprasia parapulchella V V 3

Pale-headed Snake Hoplocephalus bitorquatus V - 1

Aquatic species

Olive perchlet endangered population Ambassis agassizii E C - 2

Purple spotted gudgeon Mogurnda adspersa E C - 2

Murray Cod Maccullochella peelii peelii - V 2 A. CE = Critically Endangered; E = Endangered; V = Vulnerable B. applied to preliminary models C. listed under the FM Act

Flora

Listing Status A


Grasses and sedges

Finger Panic Grass Digitaria porrecta E E 5

Belson’s Panic Homopholis belsonii V V 3

Bluegrass Dichanthium setosum V V 3

Lobed Blue-grass Bothriochloa biloba - V 2

Cyperus conicus E - 2

Herbs

Euphrasia arguta CE C CE 7

Winged Peppercress Lepidium monoplocoides E E 5

Austral Toadflax Thesium australe V V 3

Southern Pipewort Eriocaulon australasicum E E 5

Hankweed Picris evae V V 3

Slender Darling Pea Swainsona murrayana V V 3

Spiny Peppercress Lepidium aschersonii V V 3

Myriophyllum implicatum CE - 3

Large-leafed Monotaxis Monotaxis macrophylla E - 2

Native Milkwort Polygala linariifolia E - 2

Orchids

Small Snake Orchid Diuris pedunculata E E 5

Prasophyllum sp. Wybong - CE 4

Greenhood Orchid Pterostylis cobarensis V V 3

Barrington Tops Ant Orchid Chiloglottis platyptera V - 1

Pine Donkey Orchid Diuris tricolor V - 1



Listing Status A


Shrubs Lake Keepit Hakea Hakea pulvinifera E E 5 Velvet Wattle Acacia pubifolia E V 4 Broad-leaved Pepperbush Tasmannia purpurascens V V 3 Coolabah Bertya Bertya opponens V V 3 Granite Homoranthus Homoranthus prolixus V V 3 Tall Velvet Sea-berry Haloragis exalata subsp. velutina V V 3 Rulingia procumbens V V 3 Dungowan Starbush Asterolasia sp. "Dungowan Creek" E - 2 Rupp's Boronia Boronia ruppii E - 2 Scant Pomaderris Pomaderris queenslandica E - 2 Shrub Sida Sida rohlenae E - 2 Phyllanthus maderaspatensis E - 2 Prickly Bottlebrush Callistemon pungens - V 2 Square Raspwort Haloragis exalata subsp. exalata - V 2 Philotheca ericifolia - V 2

Trees McKie's Stringybark Eucalyptus mckieana V V 3 Narrow-leaved Black Peppermint Eucalyptus nicholii V V 3 Ooline Cadellia pentastylis V V 3 Small-fruited Mountain Gum Eucalyptus oresbia V - 1

Vines Tylophora linearis V E 4

A. CE = Critically Endangered; E = Endangered; V = Vulnerable B. applied to preliminary models C. proposed to be listed as CE



Appendix IV: Spatial data categories used for modelling threatened species

Namoi Sub-catchments – 40 categories

Sub-catchment Name Area (ha)

Baradine 178070

Bluevale 124660

Bobbiwaa 55970

Bohena 83150

Borah 139520

Box Creek 168940

Brigalow 32330

Bugilbone 235650

Bundella Creek 249840

Bundock 54850

Carroll 18680

Chaffey 42160

Cockburn River 113020

Coghill 79250

Cox's Creek 135750

Etoo 102130

Eulah Creek 158110

Ginudgera 98890

Goonoo Goonoo 66510

Keepit 60670

Lake Goran 187180

Lower Manilla 43020

Lower Peel 160110

Lower Pian 224480

Maules 115600

Mid Macdonald 91590

Mooki 84960

Phillips 52970

Quirindi 84140

Rangira 32090

Split Rock 25440

Spring Creek 27400

Tallaba 68640

Upper Macdonald 84770

Upper Manilla 138840

Upper Namoi 130700

Upper Peel River 85960

Upper Pian 114840

Warrah 153360

Werris Creek 100810

ALL 4205050



Pre-European Regional Vegetation Communities (RVCs) – 66 categories

RVC No RVC Name Area (ha)

1 Giant Stinging Tree - Fig dry subtropical rainforest, mainly NSW North Coast 110

2 Rusty Fig - Wild Quince - Native Olive dry rainforest of rocky areas, Nandewar and New England Tablelands 753

4 Wilga - Western Rosewood shrubland, Darling Riverine Plains and Brigalow Belt South 37411

5 Ooline forests, Brigalow Belt South and Nandewar 1291

6 Semi-evergreen vine thicket of basalt hills, Brigalow Belt South and Nandewar 3051

9 Messmate - gum moist forests of the escarpment ranges, eastern New England Tablelands and NSW North Coast 8896

11 Silvertop Stringybark - Nandewar Box open forests in the Kaputar area, Nandewar 8671

12 Snow Gum - Black Sallee grassy woodlands, New England Tablelands 3558

13 Gum grassy woodlands, New England Tablelands 2998

14 New England Peppermint grassy woodlands, New England Tablelands 11672

15 Bendemeer White Gum grassy woodland, southern New England Tablelands 12928

16 Box - gum grassy woodlands, New England Tablelands 48000

17 Box - gum grassy woodlands, Brigalow Belt South and Nandewar 221536

18 White Box grassy woodland, Brigalow Belt South and Nandewar 845885

19 White Cypress Pine - Silver-leaved Ironbark grassy woodland, Nandewar 7190

20 Rough-barked Apple - Blakely's Red Gum riparian grassy woodlands, Brigalow Belt South and Nandewar 76514

21 Inland Grey Box tall grassy woodland on clay soils, Brigalow Belt South and Nandewar 19077

22 Poplar Box - Belah woodlands, mainly Darling Riverine Plains and Brigalow Belt South 43213

23 Wet tussock grasslands of cold air drainage areas, New England Tablelands 128

26t Dry grasslands of alluvial plains, Darling Riverine Plains and Brigalow Belt South - Natural occurrence 23518

29t Plains Grass - Blue Grass grasslands, Brigalow Belt South and Nandewar - Natural occurrence 133714

31 Broombush shrubland of the sand plains of the Pilliga region, Brigalow Belt South 17764

32 Pilliga Box - Poplar Box- White Cypress Pine grassy open woodland on alluvial loams, Darling Riverine Plains and Brigalow Belt South 178431

33 Ironbark shrubby woodlands of the Pilliga area, Brigalow Belt South 357589

35 Mountain Gum - Snow Gum open forests, New England Tablelands and NSW North Coast 3496

36 Stringybark - gum - peppermint open forests, eastern New England Tablelands 58647

38 Silvertop Stringybark - gum open forest on basalts of the Liverpool Range, Brigalow Belt South and Nandewar 13144




39 Silvertop Stringybark grassy open forests, eastern Nandewar and New England Tablelands 84467

40 Stringybark - Blakely's Red Gum open forests, New England Tablelands 8475

41 White Box - stringybark shrubby woodlands, Brigalow Belt South and Nandewar 140058

43 Mugga Ironbark open forests, Nandewar and western New England Tablelands 10217

44 White Box - pine - Silver-leaved Ironbark shrubby open forests, Brigalow Belt South and Nandewar 232346

45 Stringybark - spinifex woodland, Nandewar 1786

47 Narrow-leaved Peppermint - Wattle-leaved Peppermint open forest, eastern New England Tablelands 79

49 Black Cypress Pine - Orange Gum - Tumbledown Red Gum shrubby woodlands, Nandewar and western New England Tablelands 20630

50 Stringybark - Blakely's Red Gum - Rough-barked Apple open forests, Nandewar and western New England Tablelands 75988

51 New England Blackbutt - stringybark open forests, Nandewar and western New England Tablelands 32809

56 Ironbark - Brown Bloodwood - Black Cypress Pine heathy woodlands, Brigalow Belt South 222202

58 Shrubby woodlands or mallee woodlands on stoney soils, Brigalow Belt South and Nandewar 1223

59 Narrow-leaved Ironbark - pine - box woodlands and open forests, Brigalow Belt South and Nandewar 86824

62 Shrublands of rocky areas, Nandewar and western New England Tablelands 2642

63 Tea-tree shrubland in drainage lines, Nandewar and New England Tablelands 127

64 Fens and wet heaths, Nandewar and New England Tablelands 1259

67 Eurah shrubland of inland floodplains, Darling Riverine Plains 242

68 Lignum - River Coobah shrublands on floodplains, Darling Riverine Plains and Brigalow Belt South 2037

70 Wetlands and marshes, inland NSW 11135

71 River Oak riparian woodland, eastern NSW 17494

72 Bracteate Honey Myrtle riparian shrubland, Brigalow Belt South 1478

73 River Red Gum riverine woodlands and forests, Darling Riverine Plains, Brigalow Belt South and Nandewar 66310

75 Weeping Myall open woodland, Darling Riverine Plains, Brigalow Belt South and Nandewar 45974

76 Coolibah - Poplar Box - Belah woodlands on floodplains, mainly Darling Riverine Plains and Brigalow Belt South 447874

77 Black Box woodland on floodplains, mainly Darling Riverine Plains 167982

78 Coolibah - River Coobah - Lignum woodland of frequently flooded channels, mainly Darling Riverine Plains 22186

79 Brigalow - Belah woodland on alluvial clay soil, mainly Brigalow Belt South 36610




80 Poplar Box grassy woodland on alluvial clay soils, Brigalow Belt South and Nandewar 236456

81 Leopardwood woodland of alluvial plains, Darling Riverine Plains and Brigalow Belt South 309

82 Poplar Box low woodlands, western NSW 55733

84 Whitewood open woodland, mainly eastern Darling Riverine Plains 2449

85 Carbeen woodland on alluvial soils, Darling Riverine Plains and Brigalow Belt South 906

86 Dirty Gum tall woodland on sand monkeys, Darling Riverine Plains and Brigalow Belt South 3398

87 Silver-leaved Ironbark - White Cypress Pine on alluvial sandy loam, Darling Riverine Plains 738

88 Saltbush chenopod shrublands, mainly Darling Riverine Plains 2656

89 Copperburr chenopod shrubland, Darling Riverine Plains and Brigalow Belt South 5051

90 Ephemeral forblands on playas and scalds, Darling Riverine Plains and Cobar Peneplain 206

96 Blakely's Red Gum riparian woodland of the Pilliga Outwash, Brigalow Belt South Bioregion 18302

ALL 4205843



Namoi soil landscapes – 300 categories

SL_NAME Area SL_NAME Area SL_NAME Area

Aberlady 12740 Clear View 5960 Fullwoods Hill 12230

Ant Hill 110 Collygra Creek 12280 Fullwoods Hill variant a 350

Ant Hill Variant a 1830 Come By Chance Backplain 80460 Fullwoods Road 13890

Arizona 6110 Come by Chance Playa Plain 2900 Galla Gilla 30

Babinboon 1650 Conadilly 70720 Garawilla Creek 8950

Bando 11270 Conadilly, variant a 320 Gaspard Road 2370

Banoon 12310 Conglomerate Creek 1390 Ghoolendaadi 2090

Banoon Variant a 21950 Coober-Bulga 2830 Gins Leap 2160

Barraba Plain 5250 Cooee Mountain Plateaux 10240 Girds Hill 1110

Barradine Creek Alluvials 73420 Coogal Plain 5730 Glen Oak 24600

Basin Gully 6230 Coronation Flat 2170 Glendower Mountain 141300

Battery Hill 1470 Coronation Flat variant a 220 Glenmore 12080

Bingle 1430 Crow Mountain 29980 Goally 21890

Bingle, variant a 70 Cryon Plain 34470 Goally variant a 240

Black Jack 1360 Cubbaroo Plains 64350 Goonoo Goonoo 4320

Blue Vale 10840 Cubbo Creek Terraces 31420 Goran Lake 8020

Bobbiwaa 17320 Curlewis Swamp 510 Goscombes Road 12900

Boggabri Trig 330 Currububula Creek 4180 Granny's Armchair 15440

Booloocooroo 19220 Currububula Creek variant a 180 Green Island 60

Borah 66320 Cuttabri Alluvials 310310 Gunnembene 5250

Borah, variant a 20 Daruka 48010 Halls Creek Valley 17430

Borambil Creek 3870 Dead Horse 17510 Hanging Rock Nundle 790

Borambil Creek, variant a 290 Dead Horse variant a 1330 Hangmans Hill 20920

Borambil Creek, variant b 140 Denison 11790 Hangmans Hill variant a 4550

Braefield 1060 Deriah Mountain 770 Hardwick Range 4270

Branga Hills 7000 Disturbed Terrain 2180 Hartfell 3000

Brentry 15140 Disturbed Terrain Asbestos Mine 350 Hidden Valley 710

Brentry variant a 120 Doreen Plain 47120 Hurricane Ridge 2480

Brewon Plain 48220 Driggle Draggle 34530 Indiana 9060

Brigalows 7880 Driggle Draggle Variant b 3540 Inverkip Road 6740

Brigalows variant a 1410 Driggle Draggle variant a 1170 Kamilaroi 2430

Bugilbone Rises 2160 Duff's Gully 10370 Kamilaroi, variant a 20

Bullum Bulla 4570 Dunnadie 1930 Kangaroo Hills 12930

Burburgate 61930 Dunover 7210 Kelvin Forest 26560

Burma Hills 126370 Dunover variant a 3520 Killarney State Forest 7010

Burren Backplains 134630 Duri 89560 Killphysics Road 1550

Camels Hump Mountain 700 Duri variant a 560 Langs Neck 49180

Campions Hill 1590 East Lynne 3250 Leard 6800

Campions Hill, variant a 250 Escott 370 Leard Varaiant a 2890

Canna Gap 3100 Eurunderee 6410 Lelsies Road variant b 340

Carinya 4430 Eurunderee variant a 20 Leslies Road 28720

Carroll Creek 13670 Fig Tree 110 Leslies Road variant a 5870

Claremont Swamp 30 Fitzroy 23240 Leslies Road variant c 70




Lever Gully 71490 Niangala Hills 38280 Round Hill Variant a 810

Lever Gully, variant a 1440 Ningadoo 8750 Rowan Leigh 17370

Linton North Plateau 6220 Nombi 1280 Rowena Rise 600

Linton South 24090 Nombi Variant c 180 Saltwater Creek 930

Little Plain 250 Nombi, variant a 80 Sand Monkey 12750

Lochaber 1630 Nombi, variant b 20 Scratch Road 8100

Long Mountain 1030 Noojee 43840 Sleigholmes Road 1460

Lower Coxs 20050 Noojee variant c 6120 Slippery Rock 7020

Lower Coxs variant b 7120 Noojee, variant a 3170 Slippery Rock, variant a 2480

Lower Coxs variant c 340 Noojee, variant b 370 Spion Kop Plateaux 1990

Lower Manilla River 1880 Norfolk 160 Spitzbergen Hills 4390

Maryland 1020 North Nowley Plains 41460 Spring Creek 1350

Maules Creek 15950 Nundle Forest Hills 12010 Spring Creek Variant a 90

McDonald River Floodplain 2040 Oakleigh 490 Spring Valley Swamp 1520

Mecathloan Downs 200 Oodnadatta 13050 St Leonards 6360

Melville 13880 Orange Grove 27800 St Mervins 3980

Melville variant a 1780 Orchard Creek 16820 St Mervins variant a 70

Mercadool Plain 22830 Oxley Road 1250 Stafford Gap 37040

Mid Barwon 5670 Paleshades 730 Stafford Gap, variant a 130

Mihi 1890 Parnel 29180 Stafford Gap, variant b 380

Millie Creek 570 Peel 11960 Standbye 690

Moan 54970 Peel Terraces 520 Sunny Side 580

Mooki River 1820 Phillips Creek 5340 Taggarts Mountain 18700

Moongabah 5900 Phillips Creek variant a 40 Taggarts Mountain variant a 30

Moongabah variant a 630 Pian Plain 31770 Tally Ho 13130

Moore Creek 350 Pigeon Box 7120 Tally Ho Variant b 2040

Mount Elija 7710 Pilliga Floodbasins 37870 Tambar Springs 11040

Mount Milbulla 30100 Pine Knob 3000 Terrible 5360

Mount Tamarang 12590 Pistol 4640 The Forest 22850

Mount Wilga 200 Pitt Hill 5150 The Forest tablelands variant 520

Mount Winton 2100 Ponderosa 7890 The Siphon 11590

Mount Winton Variant a 1360 Porcupine 1320 Thounderbolts Mountain variant b 220

Mount Winton Variant b 2370 Powells Gap 1490 Thunderbolts Mountain 6130

Mountain Forest Road 5770 Quipolly 11700 Thunderbolts Mountain variant a 260

Myuna 48280 Quipolly, variant a 830 Thunderbolts Mountain variant d 820

Namoi Bugwah Meander Plain - Central 73000 Quirindi Creek 23470 Thunderbolts Mountain, variant

c 740

Namoi Bugwah Meander Plain - North 33950 Quirindi Creek variant b 1050 Timor Mt 2920

Namoi Bugwah Meander Plain - South 36950 Quirindi Creek, variant a 520 Timor Rock 2110

Namoi Inset Floodplain 44820 Rangarai Creek 5160 Top Rock 12810

Nandewar Plateau 4130 Rangarai Outwash 8870 Trinkey Forest 202550

Nany Rock 170 Red Hill Plateau 1130 Tulcumba 19070

Narrawolga 200 Round Hill 36980 Turkey Range 107750




Turkey Range variant a 120 Warrabah Station 38670 Willow Tree 140

Uplands 2180 Warrah 1830 Windy Creek 39680

Uplands, variant a 10 Warrah, variant a 350 Windy Creek, variant a 380

Upper Coxs 10630 Warral Station 11600 Windy Creek, variant b 220

Upper Coxs, variant a 890 Warral Station variant a 1370 Wirrawee Plateau 5690

Upper Manilla River 1440 Warung 5590 Wongo 32330

Upper Namoi Plains 2230 Water 6940 Woodsreef Serpentinites 3350

Velyama 10500 Watermark 2120 Woolbrook 34210

Walcha Road Basalt 6570 Weabonga Valley 15980 Yarraman 38160

Walgett Braided Stream 31110 Weaners Retreat 1720 Yarramine 190

Walgett Braided Stream Variant a 3590 Whites Sugarloaf Swamp 1310 Yarrie Lake Gilgais 8410

Walla Walla 48740 Whyalla Mountain 7890 Yetman Cemetery 6430

Walla Walla variant a 20 Whyalla Mountain variant a 10 Yilgarn 2290

Wangarang 7740 Wilga Farm 3170 Yuggel 5210

Warrabah Gorge 32770 Wilga Farm, variant a 1150 Yuggel, variant a 170

ALL 4206310

Major geomorphology – 3 categories

No Category Area (ha)

1 Main channels 102170

2 Major floodplain 926040

3 Non channel/floodplain 3177650

ALL 4205860



Latitude – 5 categories


1 29.5 to 30.0 degrees south 176280

2 30.0 to 30.5 degrees south 1214800

3 30.5 to 31.0 degrees south 1292600

4 31.0 to 31.5 degrees south 1156400

5 31.5 to 32.0 degrees south 365760

ALL 4205840

Longitude – 10 categories


1 147.0 to 147.5 degrees east 4460

2 147.5 to 148.0 degrees east 40330

3 148.0 to 148.5 degrees east 260440

4 148.5 to 149.0 degrees east 433610

5 149.0 to 149.5 degrees east 646280

6 149.5 to 150.0 degrees east 744960

7 150.0 to 150.5 degrees east 816200

8 150.5 to 151.0 degrees east 792740

9 151.0 to 151.5 degrees east 426260

10 151.5 to 152.0 degrees east 40570

ALL 4205850

Mean annual rainfall – 8 categories


1 300 – 399 mm 460060

2 400 – 499 mm 1762820

3 500 – 599 mm 1169910

4 600 – 699 mm 636340

5 700 – 799 mm 119020

6 800 – 899 mm 41340

7 900 – 999 mm 10430

8 1000 – 1199 mm 6960

ALL 4206880



Elevation – 14 categories


1 100 – 199 m 948290

2 200 – 299 m 759930

3 300 – 399 m 831860

4 400 – 499 m 535250

5 500 – 599 m 350620

6 600 – 699 m 213200

7 700 – 799 m 155310

8 800 – 899 m 139040

9 900 – 999 m 103430

10 1000 – 1099 m 71330

11 1100 – 1199 m 60580

12 1200 – 1299 m 31120

13 1300 – 1399 m 5580

14 1400 – 1499 m 310

ALL 4205850

Slope – 9 categories


1 0 to 4.9 degrees 3348500

2 5 to 9.9 degrees 384060

3 10 to 14.9 degrees 199720

4 15 to 19.9 degrees 127890

5 20 to 24.9 degrees 78810

6 25 to 29.9 degrees 41680

7 30 to 34.9 degrees 17520

8 35 to 39.9 degrees 5670

9 40 to 44.9 degrees 1480

ALL 4205330



Appendix V: Assignment of sensitivity classes to land use categories

Sensitivity = very high

Abattoir Aerodrome/airport Agricultural industry in a rural location e.g. cotton gin Airstrip (local/farmer, grass or bare surface, not sealed) Ancillary (saddle) wall to reservoir Aquaculture - fish, prawn, yabby or beach worm farm Bore drain (active) Caravan park or mobile home village Cemetery Communications facility Cultural heritage site - aboriginal or European Drainage channel (from irrigation system or a channel draining a swamp; base of channel is lined) Drainage or water supply channel - base of channel is not lined Effluent ponds from intensive animal industries Electricity substation Energy corridor Farm Infrastructure - house, machinery & storage sheds and garden areas Flood or irrigation structure Foreshores land to State Water dam Govt and private facilities - gaol, training centre, school, religious institutions & training centres, religious retreats Industrial/commercial Intensive animal production -dairy shed Irrigation dam Irrigation farm infrastructure; misc. lands within farms including access roads, bund walls, bldgs and services Irrigation supply channel Landfill (garbage) Levee bank for urban area Levee for flood protection around house and farm infrastructure Private conservation agreement Railway Research facility Reservoir Residential Road or road reserve Rural residential Saleyard Sawmill Sewage disposal ponds Shade house or glass house (includes hydroponic use) Small to medium forested or wilderness blocks with isolated residential buildings Tourist development Urban recreation Water supply pressure reservoir including water filtration plant



Sensitivity = high

Areas irrigated with effluent from sewage disposal ponds

Cotton - irrigated

Cotton - irrigated; irrigation practice - laser levelled with tail water reticulation and on-farm storage of tail water

Cropping - continuous or rotation - irrigated

Cropping - continuous or rotation - irrigated; irrigation practice - laser levelled with tail water reticulation and on-farm storage of tail water

Cropping - with a fixed irrigation system not used at the time of mapping

Cropping - with a fixed irrigation system not used at the time of mapping; irrigation practice - laser levelled with tail water reticulation and on-farm storage of tail water

Cropping within an ephemeral wetland (does not include cropping within an ephemeral lake

Disposal dam, depression or lake bed for irrigation tail water

Eucalyptus oil plantation

Farm dam

Flood refuge (constructed features located within flood prone areas)

Fodder crop - irrigated

Hobby farm

Horse stud and/or horse breeding facilities

Intensive animal production

Intensive animal production - beef feedlot

Intensive animal production - deer

Intensive animal production - horse

Intensive animal production - ostriches

Intensive animal production - piggery

Intensive animal production - poultry

National park

Nature reserve

Nursery

Olives

Olives - irrigated

Orchard - tree fruits

Orchard - tree fruits - irrigated

Pecan, macadamia and other nuts

Pecan, macadamia and other nuts - irrigated

Turf farming

Turf farming - irrigated

Vegetables - irrigated

Vineyard - grape and other vine fruits

Vineyard - grape and other vine fruits - irrigated



Sensitivity = moderate

Constructed grass waterway for water disposal

Cropping - continuous or rotation

Cropping - continuous or rotation - with a woody vegetation cover of woodland

Dog kennel, dog run for greyhounds

Drainage depression in cropping paddock

Drainage depression in cropping paddock - with more than 30% of ground area having regeneration of native trees

Fodder crop

Irrigated pastures

Jojoba planting

Native forest - filter strips in softwood plantation

Rural recreation (blocks are isolated and not associated with an urban area)

Saltbush plantings (for grazing purposes and not as part of a salinity control program)

Sown, improved pastures - with fixed irrigation system not used at the time of mapping

Sown, improved perennial pastures

State recreation area Storage site for agricultural chemicals and products (e.g. fertiliser dumps, cotton bunkers and temporary grain storages) Wide road reserve or TSR, currently used for intensive grazing

Windbreak or tree corridor

Sensitivity = low

Abandoned urban or industrial area and site is locked up e.g. Glen Alice

Crown reserve with public access

Grazing within an ephemeral wetland - with a woody vegetation cover of open forest

Grazing within an ephemeral wetland - with a woody vegetation cover of woodland

Grazing within an ephemeral wetland - with more than 30% of ground area having regeneration of native trees

Grazing within an ephemeral wetland (does not include cropping within an ephemeral lake)

Grazing within bed of an ephemeral lake or watercourse

Grazing of areas with water ponding treatments

Recently cleared land (cleared of forest vegetation as yet not covered by crop or pasture)

Recently cleared land (cleared of forest vegetation as yet not covered by crop or pasture) - with more than 30% of ground area having regeneration of native tree species

Softwood plantation

Softwood plantation and within a State Forest

Tree lot

Volunteer, naturalised, native or improved pastures - with fixed irrigation system not used at the time of mapping

Volunteer, naturalised, native or improved pastures, with previous evidence of cultivation

Wide road reserve or TSR, heavily timbered but with some grazing

Wide road reserve or TSR, with some grazing

Wide road reserve or TSR, with some grazing - with a woody vegetation cover of woodland Wide road reserve or TSR, with some grazing - with more than 30% of ground area having regeneration of native tree species



Sensitivity = very low

Abandoned orchard and vine lands; regrowth of native shrubs and trees is occurring

Areas of dense standing dead trees with the ground cover consisting of volunteer species such as bracken, blady grass and tea tree. Bore drain (no longer used)

Cliff/rock outcrop

Crown reserve

Crown reserve - with a woody vegetation cover of woodland

Degraded land (salt site, eroded area)

Dense shrub growth - limited to nil grazing capacity

Derelict mining land

Farm dam within a State Forest

Firebreak

Flood chute (flood runners that are filled with water during and after floods) and designated floodway in irrigation districts, localities

Flood chute and designated floodway in irrigation districts, localities - with a woody vegetation cover of woodland

Flood runners in western NSW - with a woody vegetation cover of woodland

Flood runners in western NSW - with more than 30% of ground area having regeneration of native tree species

Flood runners in western NSW. (Vegetation is indicative of a more prolonged period of inundation or wetness.)

Floodplain swamp

Floodplain swamp - back swamp

Floodplain swamp - billabong

Fly ash dam/spoil dump

Grassland within mining lease

Grassland within mining lease with previous evidence of cultivation

Grazing - Residual strips (block or linear feature) of native grassland within cultivated paddock - with a woody vegetation cover of open forest

Grazing - Residual strips (block or linear feature) of native grassland within cultivated paddock - with a woody vegetation cover of woodland

Grazing - Residual strips (block or linear feature) of native grassland within cultivated paddock. Strips contain scattered to isolated trees only - with more than 30% of ground area having native shrub regeneration.

Grazing - Residual strips (block or linear feature) of native grassland within cultivated paddock. Strips contain scattered to isolated trees only.

Grazing of native vegetation. Grazing of domestic stock on essentially unmodified native vegetation

Grazing of native vegetation. Grazing of domestic stock on essentially unmodified native vegetation - with a woody vegetation cover of open forest Grazing of native vegetation. Grazing of domestic stock on essentially unmodified native vegetation - with a woody vegetation cover of woodland

Grazing of native vegetation. Grazing of domestic stock on essentially unmodified native vegetation - with more than 30% of ground area having native shrub regeneration

Grazing of native vegetation. Grazing of domestic stock on essentially unmodified native vegetation - with more than 30% of ground area having regeneration of native tree species

Grazing of native vegetation. Grazing of domestic stock on essentially unmodified native vegetation, with previous evidence of cultivation

Lagoon or inland lake

Mine site

Native forest



Native forest - regeneration

Native forest and within a State Forest

Native woody shrub

Prior stream

Quarry

Quarry - within a State Forest

Railway - track no longer used

Restored mining lands

River gravel deposit

River, creek or other incised drainage feature; includes cowals in western NSW River, creek or other incised drainage feature; includes cowals in western NSW - with a woody vegetation cover of woodland State forest

Stock pile of mined material, located remotely from mine site. Often situated next to railway lines or at ports

Swamp

Temporary water storage area (e.g. rice farming - opportunistic storage of water in natural depressions

Volunteer, naturalised or improved pastures - with a woody vegetation cover of closed shrubland

Volunteer, naturalised or improved pastures - with a woody vegetation cover of open forest

Volunteer, naturalised or improved pastures - with a woody vegetation cover of woodland

Volunteer, naturalised, native or improved pastures

Volunteer, naturalised, native or improved pastures - with more than 30% of ground area having exotic weeds

Volunteer, naturalised, native or improved pastures - with more than 30% of ground area having native shrub regeneration Volunteer, naturalised, native or improved pastures - with more than 30% of ground area having regeneration of native tree species Waste dump from sawmill site

Woodland (unmodified native vegetation)



Appendix VI: Examples of sub-module flow charts informing structure of NCRAT

Vegetation cover (sub-catchment)



RVC Cover



EECs



Intactness



Threatened species



Land use



Appendix VII: NCRAT Output (template only)

Namoi Cumulative Risk Assessment Tool

This report provides a statement about the individual and cumulative risk of one or more mines in a mining scenario, to natural resource assets in the Namoi Catchment.

The risk statement records relative risk, not real or absolute risk.

Risk is classified into five categories, consistent with the Australia Risk Standard. Risk is presented in this report in tabular and graphic format. Graphic presentation uses pie charts with the following colour key.

Mine areas (ha)

5,000 10,000 25,000 50,000 100,000

Disclaimer This document may only be used for the purpose for which it was commissioned and in accordance with the contract between Eco Logical Australia Pty Ltd and Namoi CMA. The scope of services was defined in consultation with Namoi CMA, by time and budgetary constraints imposed by the client, and the availability of reports and other data on the subject area. Changes to spatial information are made on an ongoing basis and users should be aware that there may be data limitations. Eco Logical Australia Pty Ltd accepts no liability or responsibility whatsoever for or in respect of any use of or reliance upon this report and its supporting material by any third party. Information provided is not intended to be a substitute for site specific assessment or legal advice in relation to any matter. Unauthorised use of this report in any form is prohibited.



MINE SCENARIO NAME: TEST01

OVERVIEW MAP



RISK PROFILE

Mine 1: Type: OCM Area: _____ ha

Input file: C:\11COFNRM0013\NamoiTestScenario\OCM01.shp Risk of single mine

Biodiversity Assets Vegetation Cover Namoi Sub-catchment

REL RISK Area (ha) % area Extreme

High Medium

Low Very low

Vegetation Cover Neighbouthood


High Medium

Low Very low

Regional Vegetation Community


High Medium

Low Very low

Endangered Ecological Community


High Medium

Low Very low

Local Links

REL RISK Count Extreme

High Medium

Low Very low



Landscape corridors


High Medium

Low Very low

Intactness


High Medium

Low Very low

Threatened Species


High Medium

Low Very low

Carbon store


High Medium

Low Very low

Land Assets Landuse


High Medium

Low Very low



Soil Fertility


High Medium

Low Very low

Water Assets Surface water flow


High Medium

Low Very low

Surface water quality


High Medium

Low Very low



High Medium

Low Very low

Groundwater quality


High Medium

Low Very low



Mine 2: Type: CSGM Area: _____ ha Input file: C:\11COFNRM0013\NamoiTestScenario\OCM01.shp Risk of single mine Cumulative risk of all mines Biodiversity Assets Vegetation Cover Namoi Sub-catchment

REL RISK Area (ha) % area Extreme High Medium Low Very low

Vegetation Cover Namoi Sub-catchment














Local Links

REL RISK Count Extreme High Medium Low Very low

Local Links

REL RISK Count Extreme High Medium Low Very low



Landscape corridors

REL RISK Area % area Extreme High Medium Low Very low

Landscape corridors

REL RISK Area % area Extreme High Medium Low Very low

Intactness


Intactness


Threatened Species


Threatened Species


Carbon store


Carbon store


Land Assets Landuse


Landuse




Soil Fertility


Soil Fertility




Surface water flow










Groundwater quality


Groundwater quality




RELATIVE RISK MAPS Biodiversity Assets

Vegetation Cover Namoi Sub-catchment




Local Links

Landscape Corridors

Intactness

Threatened Species

Carbon store



Land Assets Landuse

Soil Fertility



Ground water drawdown

Ground water quality



Assets Impacted Biodiversity Assets Vegetation Cover Area of vegetation removed = _____ ha. Vegetation Cover Namoi Sub-catchment Critical vegetation cover thresholds = 70% and 30% Sub-catchment in orange font within 5% of threshold Sub-catchments in red font have crossed threshold

% cover % cover

Sub-catchment Pre-scenario Post scenario Sub-catchment Pre-scenario Post scenario

Baradine 71.3 Lake Goran 28.5

Bluevale 18.6 Lower Manilla 42.1

Bobbiwaa 25.3 Lower Peel 23.1

Bohena 84.4 Lower Pian 32.4

Borah 88.8 Maules 57.5

Box Creek 33.5 Mid Macdonald 39.8

Brigalow 50.7 Mooki 15.3

Bugilbone 20.3 Phillips 36.0

Bundella Creek 38.6 Quirindi 39.0

Bundock 34.0 Rangira 25.4

Carroll 23.1 Split Rock 40.6

Chaffey 36.9 Spring Creek 30.4

Cockburn River 53.8 Tallaba 71.8

Coghill 95.4 Upper Macdonald 19.1

Cox's Creek 24.8 Upper Manilla 43.4

Etoo 79.3 Upper Namoi 55.2

Eulah Creek 46.4 Upper Peel River 49.6

Ginudgera 25.4 Upper Pian 18.9

Goonoo Goonoo 22.3 Warrah 26.9

Keepit 21.9 Werris Creek 34.3



Regional Vegetation Community Critical RVC cover threshold = 30% remaining RVCs in orange font within 5% of threshold RVCs in red font have crossed threshold

% cover

RVC RVC Name Pre-scenario

Post- scenario

1 Giant Stinging Tree - Fig dry subtropical rainforest, mainly NSW North Coast 99.1

2 Rusty Fig - Wild Quince - Native Olive dry rainforest of rocky areas, Nandewar and New England Tablelands 89.1

4 Wilga - Western Rosewood shrubland, Darling Riverine Plains and Brigalow Belt South 0.9

5 Ooline forests, Brigalow Belt South and Nandewar 61.5

6 Semi-evergreen vine thicket of basalt hills, Brigalow Belt South and Nandewar 20.9

9 Messmate - gum moist forests of the escarpment ranges, eastern New England Tablelands and NSW North Coast 47.8

11 Silvertop Stringybark - Nandewar Box open forests in the Kaputar area, Nandewar 94.9

12 Snow Gum - Black Sallee grassy woodlands, New England Tablelands 21.8

13 Gum grassy open forests, New England Tablelands 42.6

14 New England Peppermint grassy woodlands, New England Tablelands 5.3

15 Bendemeer White Gum grassy woodland, southern New England Tablelands 15.2

16 Box - gum grassy woodlands, New England Tablelands 17.3

17 Box - gum grassy woodlands, Brigalow Belt South and Nandewar 11.6

18 White Box grassy woodland, Brigalow Belt South and Nandewar 20.2

19 White Cypress Pine - Silver-leaved Ironbark grassy woodland, Nandewar 19.3

20 Rough-barked Apple - Blakely's Red Gum riparian grassy woodlands, Brigalow Belt South and Nandewar 82.3

21 Inland Grey Box tall grassy woodland on clay soils, Brigalow Belt South and Nandewar 8.8

22 Poplar Box - Belah woodlands, mainly Darling Riverine Plains and Brigalow Belt South 21.0

23 Wet tussock grasslands of cold air drainage areas, New England Tablelands 100.0

25 Mitchell Grass grassland of alluvial floodplains, mainly Darling Riverine Plains 12.6

26t Dry grasslands of alluvial plains, Darling Riverine Plains and Brigalow Belt South - natural occurrence 6.2

29t Plains Grass - Blue Grass grasslands, Brigalow Belt South and Nandewar - natural occurrence 4.3

31 Broombush shrubland of the sand plains of the Pilliga region, Brigalow Belt South 100.0

32 Pilliga Box - Poplar Box- White Cypress Pine grassy open woodland on alluvial loams, Darling Riverine Plains and Brigalow Belt South 33.7

33 Ironbark shrubby woodlands of the Pilliga area, Brigalow Belt South 85.8

35 Mountain Gum - Snow Gum open forests, New England Tablelands and NSW North Coast 54.3

36 Stringybark - gum - peppermint open forests, eastern New England Tablelands 33.7

38 Silvertop Stringybark - gum open forest on basalts of the Liverpool Range, Brigalow Belt South and Nandewar 87.3



% cover

RVC RVC Name Pre-scenario

Post- scenario

39 Silvertop Stringybark grassy open forests, eastern Nandewar and New England Tablelands 60.6

40 Stringybark - Blakely's Red Gum open forests, New England Tablelands 33.0 41 White Box - stringybark shrubby woodlands, Brigalow Belt South and Nandewar 62.0

43 Mugga Ironbark open forests, Nandewar and western New England Tablelands 72.6

44 White Box - pine - Silver-leaved Ironbark shrubby open forests, Brigalow Belt South and Nandewar 76.4

45 Stringybark - spinifex woodland, Nandewar 67.1

47 Narrow-leaved Peppermint - Wattle-leaved Peppermint open forest, eastern New England Tablelands 96.2

49 Black Cypress Pine - Orange Gum - Tumbledown Red Gum shrubby woodlands, Nandewar and western New England Tablelands 97.6

50 Stringybark - Blakely's Red Gum - Rough-barked Apple open forests, Nandewar and western New England Tablelands 55.4

51 New England Blackbutt - stringybark open forests, Nandewar and western New England Tablelands 83.2

56 Ironbark - Brown Bloodwood - Black Cypress Pine heathy woodlands, Brigalow Belt South 95.6

58 Shrubby woodlands or mallee woodlands on stoney soils, Brigalow Belt South and Nandewar 98.9

59 Narrow-leaved Ironbark - pine - box woodlands and open forests, Brigalow Belt South and Nandewar 92.0

62 Shrublands of rocky areas, Nandewar and western New England Tablelands 99.4

63 Tea-tree shrubland in drainage lines, Nandewar and New England Tablelands 73.2

64 Fens and wet heaths, Nandewar and New England Tablelands 85.1

67 Eurah shrubland of inland floodplains, Darling Riverine Plains 74.8

68 Lignum - River Coobah shrublands on floodplains, Darling Riverine Plains and Brigalow Belt South 38.2

70 Wetlands and marshes, inland NSW 78.2

71 River Oak riparian woodland, eastern NSW 60.4

72 Bracteate Honey Myrtle riparian shrubland, Brigalow Belt South 64.3

73 River Red Gum riverine woodlands and forests, Darling Riverine Plains, Brigalow Belt South and Nandewar 46.4

75 Weeping Myall open woodland, Darling Riverine Plains, Brigalow Belt South and Nandewar 6.1

76 Coolibah - Poplar Box - Belah woodlands on floodplains, mainly Darling Riverine Plains and Brigalow Belt South 17.6

77 Black Box woodland on floodplains, mainly Darling Riverine Plains 31.0

78 Coolibah - River Coobah - Lignum woodland of frequently flooded channels, mainly Darling Riverine Plains 70.9

79 Brigalow - Belah woodland on alluvial clay soil, mainly Brigalow Belt South 22.5

80 Poplar Box grassy woodland on alluvial clay soils, Brigalow Belt South and Nandewar 5.3

81 Leopardwood woodland of alluvial plains, Darling Riverine Plains and Brigalow Belt South 30.2

82 Poplar Box low woodlands, western NSW 47.3

84 Whitewood open woodland, mainly eastern Darling Riverine Plains 4.1

85 Carbeen woodland on alluvial soils, Darling Riverine Plains and Brigalow Belt South 20.7

86 Dirty Gum tall woodland on sand monkeys, Darling Riverine Plains and Brigalow Belt South 83.7

87 Silver-leaved Ironbark - White Cypress Pine on alluvial sandy loam, Darling Riverine Plains 17.6

88 Saltbush chenopod shrublands, mainly Darling Riverine Plains 6.8

89 Copperburr chenopod shrubland, Darling Riverine Plains and Brigalow Belt South 21.0

96 Blakely's Red Gum riparian woodland of the Pilliga Outwash, Brigalow Belt South Bioregion 87.4



Endangered Ecological Community (RVC equivalents)

RVC RVC Name Area cleared (ha)

5 Ooline forests, Brigalow Belt South and Nandewar

6 Semi-evergreen vine thicket of basalt hills, Brigalow Belt South and Nandewar

12 Snow Gum - Black Sallee grassy woodlands, New England Tablelands

13 Gum grassy open forests, New England Tablelands

14 New England Peppermint grassy woodlands, New England Tablelands

16 Box - gum grassy woodlands, New England Tablelands

17 Box - gum grassy woodlands, Brigalow Belt South and Nandewar

18 White Box grassy woodland, Brigalow Belt South and Nandewar

21 Inland Grey Box tall grassy woodland on clay soils, Brigalow Belt South and Nandewar

25 Mitchell Grass grassland of alluvial floodplains, mainly Darling Riverine Plains

26t Dry grasslands of alluvial plains, Darling Riverine Plains and Brigalow Belt South - natural occurrence

29t Plains Grass - Blue Grass grasslands, Brigalow Belt South and Nandewar - natural occurrence

35 Mountain Gum - Snow Gum open forests, New England Tablelands and NSW North Coast

75 Weeping Myall open woodland, Darling Riverine Plains, Brigalow Belt South and Nandewar

77 Black Box woodland on floodplains, mainly Darling Riverine Plains

78 Coolibah - River Coobah - Lignum woodland of frequently flooded channels, mainly Darling Riverine Plains

79 Brigalow - Belah woodland on alluvial clay soil, mainly Brigalow Belt South

85 Carbeen woodland on alluvial soils, Darling Riverine Plains and Brigalow Belt South

Local Links

Link type Number impacted

1 – cornerstone

2 - major

3 - moderate

4 - minor

5 - insignificant



Landscape corridors Area of landscape corridor impacted = _____ ha Intactness

Calculated for the scenario footprint and 5 km buffer

Pre-scenario Post-scenario

% vegetation remaining

Number of patches

Intactness

Threatened Species Table to be inserted that will list all threatened plants and animals likely to be impacted by the scenario. Land Assets Carbon store Approx. mass of CO2 liberated, associated with vegetation loss = ____ t.CO2-e Landuse

Landuse Area impacted (ha)

Infrastructure

Mining Irrigated cropland/horticulture

Non-irrigated cropland/horticulture Intensive agriculture (other)

Grazing land (native grasslands) Grazing land (native forests and woodlands)

Water storage National Park and other reserves

Other Soil fertility (based on Land suitability class)

Land suitability class Area impacted (ha)

1,2 (very high production)

3,4 (high production potential)



Water Assets Surface water flow Total estimated reduction in surface flow = _______ ML/yr Critical surface flow threshold = 66% (sub-catchment basis) Sub-catchment in orange font within 5% of threshold Sub-catchments in red font have crossed threshold

% threshold % threshold

Sub-catchment Pre-scenario Post scenario Sub-catchment Pre-scenario Post scenario

Baradine Lake Goran

Bluevale Lower Manilla

Bobbiwaa Lower Peel

Bohena Lower Pian

Borah Maules

Box Creek Mid Macdonald

Brigalow Mooki

Bugilbone Phillips

Bundella Creek Quirindi

Bundock Rangira

Carroll Split Rock

Chaffey Spring Creek

Cockburn River Tallaba

Coghill Upper Macdonald

Cox's Creek Upper Manilla

Etoo Upper Namoi

Eulah Creek Upper Peel River

Ginudgera Upper Pian

Goonoo Goonoo Warrah

Keepit Werris Creek


Parameter Area (ha)

2 km of a significant wetland or major water storage

‘High’ to ‘very high’ sensitivity local catchment

‘High’ to ‘very high’ sensitivity stream buffer

‘High’ to ‘very high’ surface water infrastructure



Groundwater drawdown and quality Indicative volume of groundwater used = ______ ML/yr

Parameter Area (ha)

High use alluvia within area

High GDE potential

Shallow groundwater (< 10 m)

Diconnected groundwater

Stressed aquifer

Recharge area

I n t e r a c t i ve S p a t i a l T o o l f o r A s s e ss i n g t h e C um u la t i ve R is k o f M i n i n g t o N R M A s s e ts i n t h e N am o i


HEAD OFFICE Suite 4, Level 1 2-4 Merton Street Sutherland NSW 2232 T 02 8536 8600 F 02 9542 5622

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NAROOMA 5/20 Canty Street Narooma NSW 2546 T 02 4476 1151 F 02 4476 1161

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ARMIDALE 92 Taylor Street Armidale NSW 2350 T 02 8081 2681 F 02 6772 1279

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Documents

Assessing the cumulative impact of mining …...Assessing the cumulative impact of mining scenarios on bioregional assets in the Namoi Catchment Development and trial of a GIS tool