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NRM PLANNING FOR CLIMATE CHANGE

Final Project Report 1 – Impact and Vulnerability

Assessment Process and Spatial Outputs

Prepared for Victorian Catchment Management Authorities (on behalf of seven Victorian CMAs)

Final Report

July 2014

NRM Planning for Climate Change - Victorian Catchment Management Authorities Final Project Report 1 – Impact and Vulnerability Assessment Process and Spatial Outputs

Spatial Vision Innovations Pty Ltd Level 4 575 Bourke Street

Melbourne 3000

Victoria Australia

Tel +61 3 9691 3000

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About this Document

Project Number SV004025

Project Name NRM Planning for Climate Change

Document File Name NRM_Climate_Final_Report_1_Impact_Assessment_25_07_14_Final

Project Client Victorian Catchment Management Authorities

Date of Issue 21/07/14

Version Number 2.0

Document Type Final Project Report 1 – Impact and Vulnerability Assessment Process and Spatial Outputs

Document Status Final

Contact Details

SV Contact Person Stephen Farrell

Direct Dial Telephone (03) 9691 3028

Mobile Telephone 0419 185 452

E-mail [email protected]

Revision History

Version No. Date Author Status Revision Notes

0.1 09 July 2014 Stephen Farrell Draft for initial CMA circulation

0.2 21 July 2014 Stephen Farrell Draft for final CMA circulation Incorporates internal team feedback

1.0 25 July 2014 Stephen Farrell Final Draft

Authorisation

Author Date Signature

Prepared by Stephen Farrell 09 July 2014 NA

Reviewed by Geoff Park 14 July 2014 NA

Authorised by Stephen Farrell 25 July 2014 NA

Disclaimer

It is Spatial Vision’s understanding that this report provided to the client is to be used for the purpose agreed between the parties. This purpose was a significant factor in determining the scope and level of the Services being offered to the Client. Should the purpose for which the report is to be used change, the report may no longer be valid or appropriate and any further use of or reliance upon the report in those circumstances by the Client without Spatial Vision's review and advice shall be at the Client's own or sole risk.

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http://www.spatialvision.com.au/

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Ref: SV004025 29/07/14 Commercial-in-Confidence of 220 Spatial Vision

Contents

1. Introduction .................................................................................................................... 13

1.1 Document Purpose ....................................................................................................... 13

1.2 Companion Documents ................................................................................................. 13

1.3 NRM Planning for Climate Change – Stage 1 Project .................................................. 13

1.4 Objectives...................................................................................................................... 14

1.5 Deliverables .................................................................................................................. 16

1.6 Expert Panel .................................................................................................................. 16

1.7 Project Control Group ................................................................................................... 16

2. Vulnerability Method and Concepts ............................................................................. 18

2.1 Overview of Vulnerability Assessment Method ............................................................. 18

2.2 Definition of terms ......................................................................................................... 19

2.3 Application of Method - Overview ................................................................................. 20

2.4 Application of Spatial Data to Support Assessment ..................................................... 21

3. Natural Asset Types and Delineation ........................................................................... 24

3.1 Assets identified in Regional Catchment Strategies ..................................................... 24

3.2 Spatial Delineation of Natural Asset Types................................................................... 27

3.3 Asset types .................................................................................................................... 27

4. Climate Change Scenarios ............................................................................................ 31

4.1 Available Climate Data .................................................................................................. 31

4.2 Climate Scenarios ......................................................................................................... 32

4.3 Direct Climate Stressors ............................................................................................... 33

4.4 Climate Change Time Frames ...................................................................................... 33

4.5 Climate Related Events ................................................................................................. 33

5. Application of Vulnerability Assessment Method ...................................................... 35

5.1 Introduction.................................................................................................................... 35

5.2 Calculation of Impact and Vulnerability ratings ............................................................. 35

5.3 Climate Stressors Applied to Asset Types .................................................................... 40

5.4 Direct Climate Stressors Classes ................................................................................. 40

5.5 Indirect Climate Stressors Classes ............................................................................... 42

5.6 Summary of Asset Climate Stressors, Sensitivity and Adaptive Capacity .................... 43

5.7 Sensitivity – Criteria Applied ......................................................................................... 46

5.8 Adaptive Capacity – Criteria Applied ............................................................................ 51

6. Vulnerability Assessment Implementation Issues ..................................................... 54

6.1 Native Vegetation .......................................................................................................... 54

6.2 Wetlands ....................................................................................................................... 54

6.3 Estuaries ....................................................................................................................... 55

6.4 Rivers and Streams ....................................................................................................... 55

6.5 Soils and Land .............................................................................................................. 55

6.6 Coastal Wetlands .......................................................................................................... 57



7. Treatment of Coastal Assets ......................................................................................... 58

7.1 Key Exposures .............................................................................................................. 58

7.2 Coastal Asset Sensitivities ............................................................................................ 59

7.3 Likely Impacts ............................................................................................................... 60

8. Delivery of Project Outputs ........................................................................................... 63

8.1 CMA Data Pack of Project Outputs ............................................................................... 63

8.2 User Interaction with Spatial Outputs ............................................................................ 66

8.3 Web Delivery of outputs ................................................................................................ 66

8.4 Summary of Project Outputs ......................................................................................... 67

9. Data Collation and Organisation .................................................................................. 69

9.1 Data Collation ................................................................................................................ 69

9.2 Spatial Data Library ....................................................................................................... 69

9.3 Data Volumes ................................................................................................................ 71

10. References ...................................................................................................................... 74

APPENDICES

Appendix 1: Terms and Definitions ............................................................................................. 75

Appendix 2: Anticipated Sea Level Rise for 2040, 2070 and 2100 ............................................. 77

Appendix 3: Anticipated Climate Change for key Climate Stressors RCP 4.5 and RCP 8.5 ...... 81

Appendix 4: Climate Change Sensitivity Rating Assigned to Assets .......................................... 97

Appendix 5: Adaptive Capacity Datasets and Criteria .............................................................. 105

Appendix 6: Native Vegetation – RCP 8.5 Vulnerability Assessment Outputs ......................... 109

Appendix 7: Wetlands – RCP 8.5 Vulnerability Assessment Outputs ....................................... 119

Appendix 8: Estuaries – RCP 8.5 Vulnerability Assessment Outputs ....................................... 129

Appendix 9: Rivers – RCP 8.5 Vulnerability Assessment Method Outputs .............................. 141

Appendix 10: Soils and Land – RCP 8.5 Vulnerability Assessment Outputs ............................ 151

Appendix 11: Coastal Wetlands – RCP 8.5 Vulnerability Assessment Outputs ....................... 161

Appendix 12: RCP 4.5 Selected Vulnerability Assessment Outputs ......................................... 171

Appendix 13: Source Datasets .................................................................................................. 207

Appendix 14: Final Project Datasets Generated – Project Outputs .......................................... 215

Appendix 15: Acronyms ............................................................................................................. 219



List of Tables

Table 1. Natural assets identified in various CMA strategies ................................................ 25

Table 2. Application of the asset classification hierarchy to support assignment of sensitivity to climate stressors ................................................................................. 28

Table 3. Explanation of the sensitivity ratings assigned to an Asset Class for each of the 5 change classes identified for each climate stressor ....................................... 36

Table 4. Relationship between the likely sensitivity ratings for a given Asset Class and the 5 change classes assigned to each climate stressor assigned on the basis of the six response types (A to F)used to depicting the likely relationship .............. 36

Table 5. Basis for adjusting the initial Vulnerability rating to assign Vulnerability SLR rating ........................................................................................................................ 39

Table 6. Direct and Indirect Climate Stressors applied in the Vulnerability Assessment Method ..................................................................................................................... 40

Table 7. Direct and Indirect Climate Stressors Classes applied in the Vulnerability Assessment Method based on change observed in 2050 (RCP8.5) data ............... 41

Table 8. Summary of the Climate Stressors (Exposures), Climate Stressor Sensitivity considerations and Adaptive Capacity inputs applied to each Asset Types ........... 44

Table 9. Criteria to be used to assign Sensitivity to assets in relation to Climate Change Stressors .................................................................................................... 47

Table 10. Criteria to be used to assign Adaptive Capacity to assets in relation to Climate Change Stressors .................................................................................................... 52

Table 11. Anticipated change for in three indirect climate stressors over time for key coastal areas, sourced from Klemke and Arundel (editors, 2013) ........................... 59

Table 12. Volume of spatial data residing in State wide Project Data Library broken down by Data Types ................................................................................................ 72

Table 13. Volume of spatial data residing in CMA Based Project Data Libraries broken down by Data Format Type ...................................................................................... 73



List of Figures

Figure 1. Victorian CMAs involved in this Project ................................................................... 14

Figure 2. Relationship between the three core elements of the project approach.................. 17

Figure 3. Conceptual framework for assessing vulnerability to climate change, showing relationships between exposure, sensitivity, impacts, adaptive capacity and vulnerability. ............................................................................................................. 18

Figure 4. Climate change impact and vulnerability assessment framework ........................... 20

Figure 5. Relationship between key spatial data repositories used in the management of project data. ......................................................................................................... 23

Figure 6. Asset classification hierarchy applied in project. ..................................................... 27

Figure 7. Native Vegetation Asset Classes, represented by Ecological Vegetation Class Sub-groups. .............................................................................................................. 30

Figure 8. Assessed sensitivity of Changes in Total Annual Rainfall, where the result shown assumes a uniform change in excess of 110mm across the state (based on greatest change identified under carton emission scenario RCP 8.5 for 2090). .................................................................................................................. 30

Figure 9. Relationship between four new scenarios, denoted Representative Concentration Pathways (RCPs), where RCPs provide standardised greenhouse gas concentration inputs for running climate models. ......................... 32

Figure 11. View of additional coastal asset classes, including rocky reefs and shore, sea grass and tidal flats used to assess the impact of anticipated change in key indirect climate stressors ......................................................................................... 61

Figure 12. View of the potential impact of climate change on rocky, sea grass and tidal flats coastal asset classes based on the anticipated change in key indirect climate stressors ...................................................................................................... 62

Figure 13. Screen view of the GIS Cloud web-site that provides users with selected project outputs.......................................................................................................... 66

Figure 14. Diagrammatical representation of the project outputs in terms of the key Asset Types, and components of the Climate Change Vulnerability Assessment Method. .................................................................................................................... 68

Figure 15. Diagrammatic representation of the spatial data library .......................................... 70

Figure 16. Screen views of the source data repository. ............................................................ 71

Figure 17. Anticipated Sea Level Rise (SLR) and Storm Surge - 2040 .................................... 79



Figure 20. RCP 8.5 - Total Annual Rainfall Anticipated Change - 2030 ................................... 83




Figure 24. RCP 8.5 - Total Rainfall March to November Anticipated Change - 2030............... 85




Figure 28. RCP 8.5 – Mean Daily Maximum Temperature Anticipated Change - 2030 ........... 87

Figure 29. RCP 8.5 - Mean Daily Maximum Temperature Anticipated Change - 2050 ............ 87

















Figure 44. Native Vegetation Asset Classes, represented by Ecological Vegetation Class Sub-groups ............................................................................................................. 110

Figure 45. Native Vegetation - Adaptive Capacity .................................................................. 110

Figure 46. Native Vegetation - Sensitivity rating to highest anticipated change in Total Annual Rainfall (of greater than 46mm) uniformly across state ............................. 111

Figure 47. Native Vegetation - Sensitivity rating to highest anticipated change in Mean Daily Max Temperature November to March (of greater than 4C) uniformly across state ............................................................................................................ 111

Figure 48. RCP 8.5 - Total Annual Rainfall Anticipated Change - 2050 ................................. 112

Figure 49. Native Vegetation RCP 8.5 - Potential Impact Total Annual Rainfall - 2050 ......... 112

Figure 50. RCP 8.5 - Mean Daily Max Temperature (Nov to May) Anticipated Change - 2050 ....................................................................................................................... 113

Figure 51. Native Vegetation RCP 8.5 - Potential Impact Mean Daily Max Temperature (Nov to Mar) - 2050 ............................................................................................... 113

Figure 52. Native Vegetation RCP 8.5 - Worst Impact for a combination of climate stressors (Annual Rainfall & Mean Max Daily Temperature) - 2030 ..................... 114


Figure 54. Native Vegetation RCP 8.5 - Worst Impact for a combination of climate stressors (Annual Rainfall & Max Daily Temperature) - 2070 ............................... 115


Figure 56. Native Vegetation RCP 8.5 - Potential Vulnerability - 2030 ................................... 116




Figure 60. Wetlands Asset Classes, represented by Wetland Types ..................................... 120

Figure 61. Wetlands - Adaptive Capacity ................................................................................ 120

Figure 62. Wetlands - Sensitivity rating to highest anticipated change in Total Rainfall March to November (of greater than 31 mm) uniformly across state .................... 121



Figure 63. Wetlands - Sensitivity rating to highest anticipated change in Average Daily Max Temperature November to April (of greater than 4C) uniformly across state ....................................................................................................................... 121

Figure 64. RCP 8.5 - Anticipated change in Total Rainfall March to November – 2050 ........ 122

Figure 65. Wetlands RCP 8.5 - Potential Impact Total Rainfall March to November - 2050 .. 122

Figure 66. RCP 8.5 - Anticipated change in Av Daily Max Temperature (Nov to Apr) – 2050 ....................................................................................................................... 123

Figure 67. Wetlands RCP 8.5 - Potential Impact Av Max Daily Temperature (Nov to Apr) – 2050 .................................................................................................................... 123

Figure 68. Wetlands RCP 8.5 - Worst Impact for a combination of climate stressors (Rainfall & Max Daily Temperature) – 2030 ........................................................... 124

Figure 69. Wetlands RCP 8.5 - Worst Impact for a combination of climate stressors (Rainfall & Max Daily Temperature) - 2050 ........................................................... 124



Figure 72. Wetlands RCP 8.5 - Potential Vulnerability – 2030 ............................................... 126




Figure 76. Estuaries Asset Classes. ....................................................................................... 131

Figure 77. Estuaries – Adaptive Capacity ............................................................................... 131

Figure 78. Estuaries - Sensitivity to highest anticipated change in Total Rainfall March to November (of greater than 31mm) uniformly across state. ................................... 132

Figure 79. Anticipated Sea Level Rise and Storm Surge in 2100, used to assign Final Vulnerability rating ................................................................................................. 132

Figure 80. RCP 8.5 - Anticipated change in Total Rainfall March to November - 2050.......... 133

Figure 81. Estuaries RCP 8.5 - Potential Impact Total Rainfall March to November – 2050 ....................................................................................................................... 133

Figure 82. Estuaries RCP 8.5 - Worst Impact for Total Rainfall March to November – 2050 ....................................................................................................................... 134




Figure 86. Estuaries RCP 8.5 - Potential Vulnerability (Rainfall Only) - 2030 ........................ 136


Figure 88. Estuaries RCP 8.5 - Potential Vulnerability (Rainfall Only) – 2070 ....................... 137


Figure 90. Estuaries RCP 8.5 - Potential Vulnerability (Rainfall and Sea Level Rise) – 2030 ....................................................................................................................... 138






Figure 94. Rivers & Streams, represented by River Asset Classes........................................ 142

Figure 95. Rivers & Streams – Adaptive Capacity .................................................................. 142

Figure 96. Rivers & Streams – Sensitivity rating to highest anticipated change in Total Rainfall March to November (of greater than 31 mm) uniformly across state. ...... 143

Figure 97. Rivers & Streams – Sensitivity rating to highest anticipated change in Average Daily Max Temperature November to April (of greater than 4C) uniformly across state. ........................................................................................................... 143

Figure 98. RCP 8.5 - Anticipated change in Total Rainfall March to November – 2050 ........ 144

Figure 99. Rivers & Streams RCP 8.5 – Potential Impact Total Rainfall March to November - 2050 ................................................................................................... 144

Figure 100. RCP 8.5 – Anticipated change in Av Daily Max Temperature (Nov to Apr) – 2050 ....................................................................................................................... 145

Figure 101. Rivers & Streams RCP 8.5 – Anticipated change in Av Daily Max Temperature (Nov to Apr) – 2050 ......................................................................... 145

Figure 102. Rivers & Streams RCP 8.5 – Worst Impact for a combination of climate stressors (Rainfall & Max Daily Temperature) – 2030 ........................................... 146

Figure 103. Rivers & Streams RCP 8.5 – Worst Impact for a combination of climate stressors (Rainfall & Max Daily Temperature) - 2050 ............................................ 146



Figure 106. Rivers & Streams RCP 8.5 – Potential Vulnerability – 2030 ................................ 148




Figure 110. Soils & Land Asset Classes ................................................................................... 152

Figure 111. Soils & Land - Adaptive Capacity........................................................................... 152

Figure 112. Soils & Land - Sensitivity rating to highest anticipated change in Total Annual Rainfall (of greater than 46mm) uniformly across state ......................................... 153

Figure 113. Soils & Land - Sensitivity rating to highest anticipated change in Mean Daily Max Temperature November to March (of greater than 4C) uniformly across state. ...................................................................................................................... 153

Figure 114. RCP 8.5 - Total Annual Rainfall Anticipated Change - 2050 ................................. 154

Figure 115. Soils & Land RCP 8.5 - Potential Impact Total Annual Rainfall - 2050 ................. 154

Figure 116. RCP 8.5 - Mean Daily Max Temperature (Nov to May) Anticipated Change - 2050 ....................................................................................................................... 155

Figure 117. Soils & Land RCP 8.5 - Potential Impact Mean Daily Max Temperature (Nov to Mar) - 2050........................................................................................................ 155

Figure 118. Soils & Land RCP 8.5 - Worst Impact for a combination of climate stressors (Annual Rainfall & Mean Max Daily Temperature) - 2030 ..................................... 156

Figure 119. Soils & Land RCP 8.5 - Worst Impact for a combination of climate stressors (Annual Rainfall & Mean Max Daily Temperature) - 2050 ..................................... 157



Figure 120. Soils & Land RCP 8.5 - Worst Impact for a combination of climate stressors (Annual Rainfall & Max Daily Temperature) - 2070 ............................................... 157

Figure 121. Soils & Land RCP 8.5 - Worst Impact for a combination of climate stressors (Annual Rainfall & Max Daily Temperature) - 2090 ............................................... 158

Figure 122. Soils & Land RCP 8.5 - Potential Vulnerability - 2030 ........................................... 158




Figure 126. Coastal Wetland Asset Classes ............................................................................. 163

Figure 127. Coastal Wetlands - Adaptive Capacity .................................................................. 163

Figure 128. Coastal Wetlands - Sensitivity to highest anticipated change in Total Rainfall March to November (of greater than 31mm) uniformly across state. .................... 164

Figure 129. Anticipated Sea Level Rise and Storm Surge in 2100, used to assign Final Vulnerability rating ................................................................................................. 164

Figure 130. RCP 8.5 - Anticipated change in Total Rainfall March to November - 2050.......... 165

Figure 131. Coastal Wetlands RCP 8.5 – Potential Impact Total Rainfall March to November – 2050................................................................................................... 165

Figure 132. Coastal Wetlands RCP 8.5 – Worst Impact for Total Rainfall March to November – 2050................................................................................................... 166



Figure 135. Coastal Wetlands RCP 8.5 - Worst Impact for Total Rainfall March to November – 2090................................................................................................... 167

Figure 136. Coastal Wetlands RCP 8.5 - Potential Vulnerability (Rainfall and Sea Level Rise) – 2030 ........................................................................................................... 168












Figure 148. Wetlands RCP 4.5 - Worst Impact for a combination of climate stressors (Annual Rainfall & Mean Daily Max Temperature) - 2030 ..................................... 178






Figure 152. Wetlands RCP 4.5 - Potential Vulnerability - 2030 ................................................ 180




Figure 156. Estuaries RCP 4.5 – Potential Impact Total Rainfall March to November – 2030 ....................................................................................................................... 184




Figure 160. Estuaries RCP 4.5 - Potential Vulnerability - 2030 ................................................ 186




Figure 164. Rivers & Streams RCP 4.5 - Worst Impact for a combination of climate stressors (Annual Rainfall & Mean Daily Max Temperature) - 2030 ..................... 190




Figure 168. Rivers & Streams RCP 4.5 - Potential Vulnerability - 2030 ................................... 192




Figure 172. Soils & Land RCP 4.5 - Worst Impact for a combination of climate stressors (Annual Rainfall & Mean Daily Max Temperature) - 2030 ..................................... 196














Figure 184. Coastal Wetlands RCP 4.5 - Potential Vulnerability - 2030 ................................... 204






1. Introduction

1.1 Document Purpose

This document outlines the findings and approach used to assess the potential impact of climate change on a range of natural asset types and values in seven Catchment Management Authority (CMA) regions of the State.

This document also provides an overview of the spatial data outputs generated by the project for use by CMAs to plan for likely climate change related impacts on natural assets and land resources within their respective areas of operation.

It also contains a description of the process applied including the key terms and definitions applied in this process, in addition to the key inputs and the sequence of steps applied.

This report is a key output of the NRM Planning for Climate Change – Stage 1: Spatial Identification of Climate Change Impacts project.

This constitutes Report 1 of the project.

The seven participating CMAs are Corangamite, North Central, Wimmera, Mallee, East Gippsland, Port Phillip and Western Port and Glenelg-Hopkins.

1.2 Companion Documents

This document should be read in conjunction with a second project report (Report 2) that supports the integration of project impact assessment outputs with CMA decision making processes to identify priority landscapes for climate change adaptation and mitigation.

This second report is titled Final Report 2 – Decision Making Frameworks and Integration of Socio-economic Data.

1.3 NRM Planning for Climate Change – Stage 1 Project

The NRM Planning for Climate Change – Stage 1: Spatial Identification of Climate Change Impacts project involved assisting seven Victorian Catchment Management Authorities to:

Undertake a comprehensive spatial climate change impact assessment that considers multiple asset classes and values, where the assessment will include the use of currently available data to reflect the outcomes being pursued, coupled with the addition of new data resulting from research; and

Develop recommendations on an appropriate framework and decision support process aimed at assisting CMAs in identifying priority locations in the landscape for adaptation and mitigation activities.

The focus for this project was to spatially depict the potential impacts of climate change for NRM assets within the seven participating CMAs to assist the CMAs accommodate these impacts in regional planning processes. The project reviewed the current state of knowledge regarding the potential impact and vulnerability of NRM assets to anticipated climate change, and then brought together the best available data to assess and identify which NRM assets are likely to be impacted the greatest and which are the most vulnerable to likely climate change.

To achieve this the project delivers of framework that can be readily built on as new information, knowledge concerning sensitivity, and the resolution of likely exposure, and the attributes that depict longer term adaptive capacity to climate stressors, become available.

The area management by each of the seven participating CMAs is presented in Figure 1. The numbers refer to: 1- Mallee, 2 -Wimmera, 3 - North Central, 4 - Glenelg-Hopkins, 5 - Corangamite, 6 - Western Port and Port Phillip, and 7 - East Gippsland.



Figure 1. Victorian CMAs involved in this Project

1.4 Objectives

The three key objectives of the project (as outlined in the CMA project brief document) were to:

Objective 1 - Identify areas in the landscape that will be most impacted by climate change across multiple asset classes and values

This involved undertaking a comprehensive spatial climate change impact assessment with the consideration of multiple asset classes and values). The assessment was to include the use of currently available biophysical data to reflect the outcomes being pursued, coupled with the addition of new data resulting from research.

Existing data to be used included, but was not be limited to:

Modelled vegetation type / ecological vegetation class (EVC) and therefore conservation status;

Modelled vegetation condition (proxy for current resilience and ecological function);

Landscape context modelling (provides continuous GIS model for connectivity and fragmentation, and thus may inform biolink / corridor potential); and

The DEPI NaturePrint version two model (assigns a continuous habitat value for a significant number of listed threatened fauna).

Waterway datasets for rivers/streams, estuaries and wetlands including outputs from AVIRA and Index of Stream Condition.

This objective was to be achieved through two key phases:

Phase 1: Biophysical Data Assessment

An analysis of available biophysical data to highlight areas of greater potential impact from climate change through assessment of the exposure and sensitivity of environmental and agricultural assets to projected future changes in climate, for each of the seven CMAs.



The assessment was to incorporate opportunity and risk parameters. The assessment was to include climate change projection scenarios to support planning and implementation of adaptation and mitigation activities for a range of time scale projections. These scenarios were to be based on selected exposure surfaces available from Stream 2 research and development activities (through the Southern Slopes Climate Change Adaptation Research Partnership (SCARP) and Murray CMA Cluster).

It was envisaged that the outputs of the vulnerability assessment would be used by CMAs to inform planning for adaptation and mitigation measures at a range of scales (farm paddock to regional).

The output(s) were therefore required to be capable of supporting this work.

Phase 2: Socio-economic Data

To further support the future work of CMAs in adaptation and mitigation, the project was to:

Collate and assemble into layers, all available and relevant socio-economic data; and,

Develop a process by which this socio-economic data can interact with the biophysical data to best inform adaptation and mitigation planning in each CMA region.

Objective 2 – Adopt/propose a planning process that is logical, comprehensive and transparent.

During the development of this project it was anticipated that the suitable incorporation of previous regional planning, including the recently renewed Regional Catchment Strategies (RCSs) and associated Strategies and Action Plans from each CMA would occur. In addition, relevant national and Victorian policies, agreements and strategies will be incorporated.

The product of this project was to be spatial, was to be able to be web-based, and designed to be dynamic. The maintenance of the system was to enable new information, policies, best practices etc, to be updated as appropriate.

Throughout the development of previous regional NRM Planning considerable engagement was noted to have occurred with a range of key regional stakeholders to better understand regional aspirations. Engagement with stakeholders was to be limited for this project, and focus on obtaining or clarifying critical gaps in data. Stream 2 research activities including interviews and focus group discussions was anticipated to complement any engagement activities.

It was noted it was desirable that there is consistency in the presentation of the project state-wide although CMAs believed it important to still allow for different regional approaches to information development to reflect the unique nature of each catchment’s resources, communities, partnerships and organisational structure.

The assessment was anticipated to be dynamic in response to the knowledge that new information continues to become available over time and the most useful systems are able to make suitable use of this information. Information provided from the stream two research was to be a significant source of data to be incorporated. The ability to modify sections of the plan in response to an improvement in our knowledge base was to be made easier by the fact that the data and plan were required to be web-based.

Objective 3 - Develop recommendations for identifying priority locations in the landscape for adaption and mitigation, as well as determining a best recommended practice for developing appropriate management actions.

Developing a recommended approach for CMAs to develop a decision planning tool that identifies priority landscapes for climate change adaptation and mitigation in the context of improving landscape resilience was also a required outcome of this project.

Determining a best recommended practice for developing appropriate management actions that will lead to improved landscape resilience was also required.

This objective aimed to provide guidance on appropriate tools, but did not require development of these tools as part of this project. It was noted that all CMAs would consider how the planning stage of the project will be delivered once the Impact assessment is completed.



1.5 Deliverables

Key project deliverables were:

A final spatial climate change impact assessment, based on biophysical data, which considers multiple asset classes and values (parameters).

Recommendations on approaches to identify priority locations in the landscape for adaption and mitigation and developing appropriate management actions. (Note that this deliverable provides guidance on appropriate tool, but does not develop these tools as part of the consultancy. All CMAs will consider how the planning stage of the project will be delivered once the impact assessment is completed).

The study area included the CMA regions of Mallee, Wimmera, North Central, Glenelg- Hopkins, Corangamite, Port Phillip Westernport and East Gippsland.

The relationship between the three project objectives and project deliverables is summarised in Figure 2.

1.6 Expert Panel

The project team was supported by an internal Expert Panel that reviewed draft project outputs from a conceptual and methodological viewpoint, prior to their broader distribution and application. This Panel comprised two natural resource management and climate experts: Dr Roger Jones (Victoria University) and Professor Ted Lefroy (University of Tasmania); and a lead social researcher into landholder adaptation to climate change; Allan Curtis (Charles Sturt University. The Panel also included a lead agricultural consultant, Jim Shovelton (from Mike Stephens and Associates).

The appointment and role played by this Expert Panel was considered critical to success of the project and the rigour of the conceptual framework, criteria and rule development applied.

1.7 Project Control Group

The Project Control Group for the project appointed on behalf of the seven participating CMAs comprised:

Chris Pitfield Corangamite Catchment Management Authority

Tony Baker Wimmera CMA

Rex Candy East Gippsland CMA

Rohan Hogan North Central CMA



Figure 2. Relationship between the three core elements of the project approach



2. Vulnerability Method and Concepts

2.1 Overview of Vulnerability Assessment Method

This project applied an overall vulnerability assessment method that is largely consistent with that outlined and adopted in the: Guidelines For Developing a Climate Change Adaptation Plan and Undertaking an Integrated Climate Change Vulnerability Assessment; November 2012; Local Government Association of South Australia. This method describes how likely exposure to climate scenarios, and sensitivity and adaptive capacity of assets to these climate changes, are used to assess the potential impact and vulnerability of assets to these changes. This process was developed by the Allen Consulting Group, 2005, and is based on that developed by the IPCC, 2007.

The conceptual framework on which this vulnerability assessment process is generally based is presented in Figure 3 (Adapted from: Capon et al., 2013). The key variation to this general framework in the approach proposed in this project is that the human adaptation strategies (e.g. projects aimed at protecting assets) that may influence adaptive capacity and hence the impact of climate change considered after asset vulnerability is determined. As will be identified later in this document, adaptive capacity for the purposes of this project was defined as the properties of the natural asset based on its current state.

Figure 3. Conceptual framework for assessing vulnerability to climate change, showing relationships between exposure, sensitivity, impacts, adaptive capacity and vulnerability.

Solid lines indicate direct affective relationships between biophysical parameters (such as the impact of climate change on direct climate stressors, or of non-climate stressors on exposure to climatic stimuli). Dashed lines indicate the effects of human activity, including the impacts of climate change and adaptation and mitigation activities. (Adapted from: Riparian Ecosystems in the 21st Century: Hotspots for Climate Change Adaptation; Samantha J. Capon et al; Ecosystems (2013) 16: 359–381)



2.2 Definition of terms

Vulnerability

The term ‘vulnerability’ is used in many different ways by various research communities, such as those concerned with secure livelihoods, food security, natural hazards, disaster risk management, public health, global environmental change, and climate change (Fussel and Klein, 2006). The glossary of the 2001 IPCC Assessment Report (McCarthy et al., 2001) defines vulnerability (to climate change) as follows:

Vulnerability: The degree to which a system is susceptible to, or unable to cope with, adverse effects of climate change, including climate variability and extremes. Vulnerability is a function of the character, magnitude, and rate of climate variation to which a system is exposed, its sensitivity, and its adaptive capacity.

The IPCC describes vulnerability as a function of impact and adaptive capacity and “the degree to which a system is susceptible to, or unable to cope with, adverse effects of climate change, including climate variability and extremes. Vulnerability is a function of the character, magnitude and rate of climate variation to which a system is exposed, its sensitivity and its adaptive capacity” (IPCC 2007). The components of exposure, sensitivity and adaptive capacity and their relationship to vulnerability are illustrated in Figure 3 (Allen Consulting Group 2005).

Again, in the context of this project, vulnerability (and hence the scope of the assessment) was defined as a “measure of possible harm” (Hinkel 2011). In this case harm to the environment includes such things as a loss of habitat or species diversity, disruption to food webs, reduction in ecosystem services or loss of ecosystem resilience and the capacity to bounce back from stresses, reduced water quantity or quality or an increase in habitat fragmentation.

Other terms

The project adopted the following definitions of exposure, sensitivity and adaptive capacity in an effort to achieve a consistent understanding and interpretation of the proposed framework for this project. These definitions are based on those provided in “Guidelines For Developing a Climate Change Adaptation Plan and Undertaking an Integrated Climate Change Vulnerability Assessment; November 2012; Local Government Association of South Australia.”

Exposure: relates to the influences or stimuli that impact on a system. Exposure is a measure of the predicted changes in the climate for the future scenario assessed. It includes both direct stressors (such as increased temperature), and indirect stressors or related events (such as increased frequency of wildfire).

Sensitivity: reflects the responsiveness of a system to climatic stressors or influences, and the degree to which changes in climate might affect that system in its current form. Sensitive systems are highly responsive to climate and can be significantly affected by small climate changes.

For the purposes of this project, assessment of sensitivity will be made in reference to a benchmark example or state of the particular asset.

Adaptive capacity: is the ability of a system to adjust to climate change (including climate variability and extremes) to moderate potential damages, to take advantage of opportunities, or to cope with the consequences. The adaptive capacity of a system or society describes its ability to modify its characteristics or behaviour so as to cope better with changes in external conditions. The more adaptive a system, the less vulnerable it is. It is also defined as the property of a system to adjust its characteristics or behaviour in order to expand its coping range under existing climate variability or future climate conditions. For the purposes of this project, adaptive capacity will be assigned in terms of the ability of an asset to adjust to climate stressors based on its current state, which may vary from pristine to degraded.

This project is focused on the vulnerability of environmental assets to anticipated climate change stressors or influences, as defined in the various Regional Catchment Strategies.



2.3 Application of Method - Overview

The approach used in this project to assess potential impacts and vulnerability of natural assets to climate change required consideration of the sensitivity and adaptive capacity of the relevant asset.

The climate change impact assessment was required to incorporate multiple climate change projection scenarios, over different time-frames and considered the potential climate change impact and vulnerability using the assessment framework presented in Figure 4.

Figure 4. Climate change impact and vulnerability assessment framework

The Spatial Vision/Natural Decisions team developed and applied an NRM asset vulnerability assessment method based on this impact assessment process to each of the asset types described in this report.

The process involved identifying the sensitivity of an asset type to two different climate exposures (or climatic stressors under a particular climate scenario), and adaptive capacity, and using this information to determine the potential impact, and assessed vulnerability rating.

While the climate stressors vary based on the climate scenario chosen in terms of the climate model used and particular timeframe, the sensitivity relationship or profile for an asset type is applicable for any anticipated climate scenario and timeframe under consideration.

The general steps undertaken for each natural asset type in application of the vulnerability assessment process were as follow:

1. Identify two key Climate Stressors/Exposures (and potential changes)

2. Identify Asset Classes relevant to Stressors

3. Assign likely Sensitivity to Climate Stressors (likely response to change)

4. Calculate Potential Impact for each Climate Stressor (Exposure) for the change anticipated for a given climate scenario and time frame

5. Calculate the worst Potential Impact for each combination of Climate Stressors (Exposure) for a given climate scenario and time frame

6. Develop a likely Adaptive Capacity measure (based on current condition) for NRM asset

7. Calculate Vulnerability based on potential impact and intrinsic adaptive capacity based on current state for a given climate scenario and time frame

Application of the vulnerability assessment process is described in further detail in Section 5.



2.4 Application of Spatial Data to Support Assessment

Assignment of State-wide Sensitivity and Adaptive Capacity

Application of the vulnerability assessment process involved utilising spatial datasets available from CMAs, DEPI and other stakeholders where possible. Given the project involved seven of the 10 CMAs in Victoria, it was decided that the project should take an entire state view. Hence, a key requirement of the project was that the spatial data used would have state-wide coverage.

A series of statewide sensitivity and adaptive capacity rating datasets for each asset type were generated based on the appllcation of statewide spatial data.

Assignment of Regional Ratings

While regional information was not applied in the process, it was noted that the final project outputs should be suitable for regional refinement based on local information concerning adaptive capacity in particular. To support this requirement a second version of the final vulnerability datasets for each asset type was prepared that contained adaptive capacity and asset attributes that could be refined on a local basis to recalculate the assigned vulnerability rating.

It is anticipated the regional rating would be either used to replace or supercede the statewide (universally assessed rating), or alternatively, the regional rating would be used in combination with the statewide assigned ratings.

Spatial Resolution

The spatial resolution and feature representations contained in the source datasets was used to represent the natural assets used in the vulnerability assessment process.

A series of rules were developed to support each stage of the process, and while source data was in a range of formats (eg. points, lines, polygons, or raster1), natural assets and hence their assigned sensitivity to exposure, and adaptive capacity datasets, were generated as 100m state-wide raster datasets.

Use of the 100m raster grids allowed:

continuous variables (such as terrain) to be used as an input into classifications where required

multiple datasets depicting a similar data theme, such as adaptive capacity inputs, to be combined; and

data themes represented in multiple ways (ie. as points, lines or polygons) to be combined.

The 100m by 100m raster unit was termed a landscape unit for the purposes of this project. This landscape unit was the fundamental unit of a common geography used to integrate relevant information to support the vulnerability aseessment process. Hence, a key output of the project was the creation of a set of 100m by 100m grids depicting the key final and interim project outputs, these being:

Asset Information

o Asset Classes

o Asset Sensitivity to 2 exposures (ie. Annual Rainfall, Maximum Average Daily Temperature)

o Adaptive Capacity

1 Raster data consists of rows and columns of cells (or pixels) where a single value is stored against each cell and the term raster implies a regularly spaced grid.

http://en.wikipedia.org/wiki/Bit_array



Asset Impact and Vulnerability ratings for each climate scenario (for climate emission scenarios rcp8.5 and for rcp4.5 for four periods: 2030, 2050, 2070, 2090)

o Potential Impact to 2 exposures (ie. Rainfall, Temperature)

o Worst Potential Impact (based on 2 exposure results)

o Vulnerability

o Vulnerability with all attributes attached to support data updates

The conversion of vector data to a 100m by 100m raster dataset involved a majority classification approach.

Categorisation of Spatial Data

Project data was organised in terms of the natural asset types identified and other broad categories that supported the overall vulnerability assessment process.

The broad Data Groups adopted were:

Natural Assets

Climate Exposures

Biophysical

Land Management and Use

Socio-Economic

Miscellaneous

Miscellaneous information primarily included base map information.

Natural asset data was further grouped into the broad asset types adopted in the project. These broad asset types were:

1. Native Vegetation

2. Significant Flora and Fauna

3. Wetlands

4. Estuaries

5. Rivers and Streams

6. Aquifers

7. Soils and Land

8. Coastal Wetlands

9. Coasts & Marine

Key parameters identified to support an evaluation of sensitivity and adaptive capacity ratings for the natural asset types were managed in these groups. These parameters were identified by the project team together with the project Expert Panel and participating CMA representatives.



Data Library Structure

To support the translation of source datasets into these categories and the assignment of related parameters, a project library was implemented that comprised three key components:

Source attribute data (as provided by data custodians)

Criteria (or value added) data generated by the project team to support the vulnerability assessment process. This includes sensitivity and adaptive capacity ratings profile information (parameters); and

Final Potential Impact and Vulnerability Assessment Outputs for each climate model and period.

This approach (as shown below in Figure 5) involved translating source attribute data into value added criteria data, and subsequently processing this into the key Derived Potential Impact and Vulnerability Assessment Outputs.

.

Figure 5. Relationship between key spatial data repositories used in the management of project data.

Source Data

Source attribute data

eg. EVC, Wetland, land use, or AVIRA river threat information

Criteria Data

Value added derived data

eg. Land use ratings used to assign an Adaptive Capacity Rating for wetlands (100m x 100m grid) dataset.

Derived datasets include Asset Class datasets used as the basis to assign sensitivity, Adaptive Capacity datasets, and key inputs in to their development, such as upper, intermediate and lower region of a catchment.

Derived Potential Impact and

Vulnerability Assessment Outputs

Key project outputs (grid and vector based)

eg. Vulnerability Rating for native vegetation under RCP carbon emissions scenario 8.5, for the 2070 time period (100m x 100m grid) dataset.



3. Natural Asset Types and Delineation

3.1 Assets identified in Regional Catchment Strategies

Victorian CMAs have adopted an asset-based approach to Natural Resource Management. This approach aims to prioritise where investment and action is applied. Many Victorian government strategies and policies relating to natural resource management and integrated catchment management, including the Regional Catchment Strategies (RCS), have also adopted an asset-based approach.

This approach recognises assets as tangible, physical elements of the environment, which are valued by people for a variety of reasons. More specifically, the environment is made up of natural resources or assets, which society uses or values in a variety of ways. These uses and values are increasingly termed ‘services’ or ‘ecosystem services’ in NRM planning. The asset-based approach to NRM planning focuses on protecting or maintaining biophysical items that are of most value to people.

The asset-based approach combines information about asset values, threats to assets and the risks of not addressing these threats. This information can then be used to assist regions to decide priority actions and investments, and for the State to have a view of priorities across Victoria.

For this project natural assets needed to be defined at a level that is meaningful from the viewpoint of assessing potential climate change impacts.

As part of this project a review was made of the land-based assets identified within Regional Catchment Strategies. The level at which assets are identified across the CMAs varies. Some CMAs identify assets only at a very broad level while others at a detailed level.

Based on a review of the Regional Catchment Strategies (and relevant sub-strategies) for the seven participating CMAs in this study, the nine Asset Types identified in Table 1 were identified.

This analysis revealed a degree of commonality of approach, but also some significant differences, largely related to scale, between the strategies.

Based on the outcomes of this comparative analysis the following asset types were used for the purposes of undertaking the vulnerability assessment process described earlier in this report:



3. Wetlands

4. Estuaries


6. Aquifers

7. Soils and Land

8. Coastal Wetlands

9. Coasts & Marine

Table 1 illustrates the way in which scale was manifested in the treatment of natural assets in the various CMA strategies. The table presents a selection of different assets (assigned according to the above typology). Selected examples based on this review of CMA strategies were considered in developing and applying the vulnerability assessment process implemented and used to explore the suitability of the approach and identify issues that needed to be considered.



Table 1. Natural assets identified in various CMA strategies

Asset Types

CMA Region Native Vegetation

Threatened /significant species

Wetlands Estuaries River or streams

Land and soils Coasts Marine habitat Aquifers

Corangamite Freshwater meadows or marshes

Barwon River High production value soils in SW of region

Cape Otway reefs

East Gippsland Alpine peatlands

Mitchell River Tambo Valley

Gippsland Lakes

Glenelg-Hopkins Native vegetation of the Dundas tablelands

Grampians High value Natureprint area

Hopkins estuary

Hopkins Basin - H11 & H12 subcatchments

Mallee Mallee woodlands and shrublands

Murray River

Calcarosols (suitable for cereal cropping)

Murrayville Groundwater Protection Area

North Central Lower Avoca Grasslands

Threatened Woodland birds

Kerang Ramsar wetlands

Lower Campaspe River

Port Phillip & Western Port

Fish in the Moorabool, Melton Wyndham & Greater Geelong area

None nominated

Upper Yarra system

Corio Bay south

Wimmera Grey Box Grassy Woodlands and derived native grasslands

Threatened Orchid species

Wimmera Heritage River

Upper catchment acid soils

NRM Planning for Climate Change - Victorian Catchment Management Authorities Discussion paper 1 – Impact and Vulnerability Assessment Process


The asset matrix in Table 1 highlights some issues with application of the vulnerability assessment approach described. These issues include:

1. Differences in scale – there is a great range in the scale of assets, from large contiguous features such as the entire Murray River, to river segments (e.g. Lower Campaspe River), then to aggregations of smaller waterways and tributaries (e.g. Upper Yarra system).

2. Ambiguity of assignment to asset category – for example, the Gippsland Lakes in East Gippsland, has been assigned to coastal asset category whereas it could also fit neatly into the wetland category. It general it appears that there is some confusion on the classification of coastal assets – many are better thought of as hybrids of other asset classes, such as wetlands, terrestrial habitat and estuaries.

3. Aggregated assets – for example the Grampians High Value Natureprint area (GHCMA) contains an aggregation of different threatened species, no doubt with different sensitivities and adaptive capacity.

These issues present some of the challenges addressed in the vulnerability assessment process adopted. To address these and other challenges the following principles were applied to select and assess the vulnerability of natural assets:

1. Assessment of natural assets at a resolution at 100m by 100m assists address the issue of large scale assets, such that the application of sensitivity and adaptive capacity criteria and descriptors are applied at this level and hence a subunit of larger assets. By way of illustration, with the example provided of the Murray River, the assessment of sensitivity is applied at a nominated river reach level, while the impact and vulnerability is assessed at and assigned to individual 100m by 100m landscape units.

2. Where there is ambiguity of asset assignment, the assessment was made according to the asset theme that is dominant within the overall asset. In some cases the assessment has been made for more than one asset type or theme for the one individual 100m by 100m landscape units. For example, a wetland may be assessed from the viewpoint of its native vegetation type, and also in terms of its wetland type. In this situation a general hierarchy has been implied in the treatment of asset types (e.g. in this case the assessed vulnerability assigned based on the wetland class would be viewed as more important and of value than the assessed vulnerability in relation to the broad native vegetation class). However, it is proposed that land managers have access to the vulnerability assessment outputs for different asset themes or types for any given are to best import the decision making process.

3. The same approach applied when there is ambiguity of asset assignment was applied for aggregated assets. Hence, for aggregated assets the vulnerability assessment is applied to different asset types contained within the given asset unit (eg. native vegetation, wetlands, and soils/land for the one 100m by 100m landscape).



3.2 Spatial Delineation of Natural Asset Types

Asset Classification

Natural assets needed to be spatially delineated and meaningful for the purposes of assigning sensitivity and adaptive capacity to climate stressors (based on possible climate change).

The level at which asset sensitivity was assigned was termed the Asset Class level.

It follows that an Asset Class was the level at which an asset was spatially delineated for the purposes of assigning potential climate change impacts and vulnerability.

An Asset Class was viewed in terms of an asset classification system or hierarchy where the concept of Asset Types, Asset Classes, and Asset Sub-classes were identified.

An example of this classification hierarchy is presented in Figure 6.

Figure 6. Asset classification hierarchy applied in project.

3.3 Asset types

As previously identified, for the purposes of this project nine broad categories of natural asset types were recognised. These were:



3. Wetlands

4. Estuaries


6. Aquifers

7. Soils and Land

8. Coastal Wetlands

9. Coasts & Marine



In relation to the asset classification system identified, Asset Types were broken down into Asset Classes on the basis of the general characteristics identified in Table 2. Hence, suitable spatial datasets were identified and used to differentiate each Asset Type into sub-units to support the assignment of a sensitivity rating of an Asset Type to climate stressors.

For example, in the case of rivers and streams that are represented by river reaches, Victorian Bioregions were used to better differentiate sections of the river reach into upper, intermediate and lower sections of a given catchment to assign a classification that was suited to the assignment of sensitivity to potential climate change.

Table 2. Application of the asset classification hierarchy to support assignment of sensitivity to climate stressors

Asset Type Asset Class Description

1. Native Vegetation Ecological Vegetation Class Sub-Groups

2. Threatened Flora and Fauna (or related critical habitat)

Not pursued on the basis that critical habitat or species distribution was viewed to be significantly covered by native vegetation, and that structural vegetation components were of a temporal nature, and hence not particularly meaningful from the climate scenario timeframes identified.

The application of the vulnerability assessment process to a number of indicator species was initially considered, in addition to consideration of its application to areas of Old-growth Forest.

In conclusion, it was decided that critical habitat was best treated like asset value or significance information, and hence used in combination with Native Vegetation, Wetland, or other asset information.

3. Wetlands Wetland categories based on Corrick wetland type and wetland water source (groundwater, river or neither). Alpine wetlands were also identified.

All wetlands that were found to have a tidal water source, or were viewed to be potentially impacted by anticipated sea level rise by the year 2100 were removed from the Asset Type and identified as Coastal Wetlands.

4. Estuaries Estuaries were classified on the basis of whether they were permanently or intermittently open, whether they were on the coast or within a bay, and whether the waterways within their catchment were regulated or unregulated.

5. Rivers and Streams Rivers were classified on the basis of whether they were regulated or unregulated, whether they were perennial or non-perennial and the section of the catchment within which they occurred (based on upper, intermediate and lower sections of a given catchment).

6. Soils and Land Soils were classified on the basis of the inherent susceptibility to water and wind erosion on the basis or land system units, and a general terrain ruggedness rating based on relative change in slope.

7. Aquifers Not pursued on the basis that there was insufficient information on which to apply the vulnerability assessment process with the project timelines and resources.



Asset Type Asset Class Description

8. Coastal Wetlands Coastal wetlands were those with a tidal water source, or were viewed to be potentially impacted by anticipated sea level rise by the year 2100. As with other wetlands these wetland categories were based on Corrick wetland type and wetland water source (groundwater, river or neither), and whether the tidal regime was supra-tidal.

9. Coasts and Marine Coastal assets were not fully assessed using the vulnerability assessment process applied to other natural assets. This is because the sensitivities and adaptive capacity elements were less well developed. Coasts were also viewed to be largely addressed through the assessment applied to estuaries, soils, native vegetation and coastal wetlands which included a range of wetland types

A simplified climate change impact assessment was undertaken for rock outcrops and areas of sea grass.

Asset Classes

An Asset Class represents the level to which assets were defined in the NRM Climate Change Impact Assessment Project for the purposes of assigning sensitivity and hence assign potential impact and vulnerability to climate scenarios.

This level of asset differentiation is in part determined by available spatial data to support this process. The approach taken has been to classify Asset Types into 20 to 40 unique classes so as to provide a suitable differentiation and level of resolution for which potential impact and vulnerability to climate scenarios will be meaningfully and practically assessed.

An example of how the asset classification hierarchy may be applied in relation to Native Vegetation is provided in Figures 7 and 8. Figure 7 presents Native Vegetation represented by Ecological Vegetation Class Sub-groups, which is the identified level, or Asset Class, at which sensitivity is to be assigned, and hence assign potential impact and vulnerability to climate scenarios is to be assessed.

Figure 8 presents the assessed sensitivity of these Asset Classes to the climate change stressor of decreased in Annual Rainfall. The Figures shows the sensitivity level identified to the largest reduction in rainfall identified under climate scenarios (based on the RCP8.5 model for the year 2090).



Figure 7. Native Vegetation Asset Classes, represented by Ecological Vegetation Class Sub-groups.

Figure 8. Assessed sensitivity of Changes in Total Annual Rainfall, where the result shown assumes a uniform change in excess of 110mm across the state (based on greatest change identified under carton emission scenario RCP 8.5 for 2090).



4. Climate Change Scenarios

4.1 Available Climate Data

Available Climate Models and Time frames

Preliminary updated climate projections, termed CMIP5 projections, were released by CSIRO in March 2014 and made available for use in this project.

The ‘Draft Projections for Australia’s NRM Regional Data Delivery Brochure’ prepared by CSIRO which accompanied this information outlines the background to the climate scenario data provided. This draft brochure (Webb, 2014) states that:

CSIRO, in partnership with the Bureau of Meteorology (BoM), have developed climate change projections for Australia’s natural resource management (NRM) sector. The projections have been developed to assist in sustainably managing Australia’s natural resources in a changing climate.

Climate change projection data are usually based on output from climate models driven by various scenarios of greenhouse gas and aerosol emissions (IPCC, 2013).

The climate projections team from CSIRO and BoM have undertaken an assessment of the latest projections to give users access to the results, with accompanying guidance on which products are fit for purpose.

For the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC, 2013), the scientific community defined a set of four new scenarios, denoted Representative Concentration Pathways (RCPs). The RCPs provide standardised greenhouse gas concentration inputs for running climate models. Climate projections are available from model simulations using our RCPs: RCP8.5, RCP6, RCP4.5 and RCP2.6 (Moss et al., 2010, Van Vuuren et al., 2011) (Figure 9 in this report). These are named in accordance with the range of radiative forcing values (in watts per square metre), which are a measure of the level of influence these gases have on the Earth’s energy balance. Each RCP is representative of a range of economic, technological, demographic, policy and institutional futures. RCP4.5 could be considered as a trajectory with moderate emission reductions, consistent with the lowest (B1) scenario of the IPCC SRES suite developed in 2000. RCP8.5 is similar to the highest (A1FI) SRES scenario. RCP2.6 is lower than the lowest SRES scenario. Therefore, the range of climate projections based on RCPs is broader than those based on the SRES scenarios

For the NRM climate projections, data from 16 to 40 climate models1 have been analysed from the Climate Model Intercomparison Project (CMIP5) (Taylor et al., 2012).

Webb (2014) also note that:

Users of climate projections are strongly advised to represent a range of climate model results in their studies and reports. CSIRO’s Climate Futures approach has been developed to help capture the range of projection results relevant to their region (Whetton et al., 2012).



Figure 9. Relationship between four new scenarios, denoted Representative Concentration Pathways (RCPs), where RCPs provide standardised greenhouse gas concentration inputs for running climate models.

Climate data used in this project

In relation to the specific climate data provided by CSIRO for this project, the data provided includes projected climate changes (relative to the IPCC reference period 1986–2005), based on CMIP5 global climate models judged to perform well over Australia.

These interim projections are derived from global climate models from the Climate Model Intercomparison Project Phase 5 (CMIP5). They take the form of projected 20-year average changes relative to the 1986-2005 model averages. The data are underpinned by a Technical Report which has not passed the peer-review process yet. CSIRO note that these data would not normally be released until the review process is complete, but CSIRO recognises the value of providing interim projections at an early stage to selected stakeholders.

The data made available has been provided with its native grid resolution of ~135km. The data includes:

Historical climatology: 1986-2005)

Future climatology’s: 2021-2040, 2041-2060, 2061-2080, 2080-2099

Change(absolute): future climate relative to 1986-2005

Climate variables: Temperature minimums (Tmin), Temperature maximums (Tmax,), precipitation

4.2 Climate Scenarios

Climate scenarios considered in this project in terms of carbon emission projections based on the CMIP5 climate model results provided by CSIRO were:

RCP 4.5 - Moderate scenario (in terms of future carbon emissions)

RCP 8.5 - Extreme scenario (in terms of future carbon emissions)

In processing the CMIP5 Rainfall and Max Temp data provided by CSIRO, the project team expressed some concerns to the CSIRO with the RCP 4.5 emission scenario data. These issues related to the 2041-2060 model, where it was observed that in comparing the 2021 – 2040 model and the 2041-2060 model, the annual rainfall actually increased in the 2041-2060 model for a large area of the state. This is also observed in the RCP8.5 2041-2060 model rainfall but to a lesser extent.



In following up this issue with Leanne Webb, CSIRO, the team was advised the observations are likely to be variability in the model response, where there will be wetter and drier decades, though with an overall trend to drying.

4.3 Direct Climate Stressors

The direct climate stressors provided in the CSIRO data used in this project were:

Mean Daily Maximum Temperatures (Tmax) for each season

Mean Daily Rainfall (Precipitation) for each season

These direct climate stressors were considered in relation to their relative importance on each of the natural asset types identified.

4.4 Climate Change Time Frames

The base line climate data for the CMIP5 climate projections is based on average climate variables for the period 1986-2005. For the purposes of this project the baseline year was identified as 1990. Hence, the 2090 time period was viewed to represent the 100 year timeframe scenario.

The years at which potential impacts were assessed in this project based on RCP 4.5 and RCP 8.5 emission scenario information provided in the CMIP5 climate model results from CSIRO were:

2030 (40 years from the baseline year)




4.5 Climate Related Events

Wildfire and Intense Weather Events

While climate changes will have an impact on indirect climate stressors such as wildfire frequency and intensity, it was decided that exposure surfaces or surrogate information related to these events was generally not available or in a suitable format for use in this project.

Similarly the increased frequency of extreme events while very important in relation to natural assets was viewed as too difficult to represent in terms of an exposure surface.

However, it was felt by the project team that direct climate stressors relating to seasonal rainfall, and maximum temperatures were a good indicator of the likely increased frequency of climate related events such as wildfire frequency and intensity. These same stressors were viewed as important when determining the likely impact of indirect climate change stressors relating to intense storm events on soils and land. It was felt that the likely impact of such events which involve intense wind and rainfall, on soils and land assets were the health of ground or vegetation cover.

Sea Level Rise and Storm Surge

Anticipated Sea Level Rise (SLR) and Storm Surge (SS) information is available for four time periods: 2009, 2040, 2070 and 2100. The relationship between these coastal changes and anticipated carbon emissions was not clear. Hence, the same SLR and SS information was applied under RCP 4.5 and RCP 8.5 emission scenario outputs.



Using a conservative approach SLR and SS information was applied to the following four climate timeframes on the basis that SLR and SS would be applied ahead but not after any year for which impacts were assessed. Hence the following relationship was applied in the application of this information to assessing impacts:

Year for which Anticipated Sea Level Rise (SLR) and Storm Surge (SS)

information is available

Years for which potential climate change impacts were assessed

2040 2030

2070 2050

2070 2070

2100 2090

Appendix 2 provides map views of the areas identified to be subject to one of three anticipated sea level rise related change classes in three time periods: 2040, 2070 and 2100. These maps identify the:

Area of land subject to anticipated storm surge

Area of land subject to anticipated sea level rise

Area of sea or coastal waters that is anticipated to be deeper

The same sea level rise related change classes were applied to both the RCP 4.5 and RCP 8.5 carbon emissions scenarios.



5. Application of Vulnerability Assessment Method

5.1 Introduction

This section outlines the approach taken to applying the climate change vulnerability assessment method to natural assets.

It outlines the values and ratings assigned, the rationale and criteria on which these are based, and the calculations applied. The results obtained in apply the criteria and algorithms identified are presented in Section 6 of this report.

As previously indicated the climate change vulnerability assessment method applied to natural assets in this project involved the following seven steps:

1. Identify two key Climate Stressors/Exposures (and potential changes)

2. Identify Asset Classes relevant to Stressors

3. Assign likely Sensitivity to Climate Stressors (likely response to change)

4. Calculate Potential Impact for each Climate Stressor (Exposure) for the change anticipated for a given climate scenario and time frame

5. Calculate the Worst Potential Impact for each combination of Climate Stressors (Exposure) for a given climate scenario and time frame

6. Develop a likely Adaptive Capacity measure (based on current condition) for NRM asset

7. Calculate Vulnerability based on potential impact and intrinsic adaptive capacity based on current state for a given climate scenario and time frame

This section provides information developed and applied in the assessment process in each of these seven steps.

A summary of the Climate Stressors (Exposures), Climate Stressor Sensitivity considerations and Adaptive Capacity inputs applied to each of the key Asset Types to which the vulnerability assessment method was applied is provided in Table 8 (in Section 5.6).

5.2 Calculation of Impact and Vulnerability ratings

Step 1: Identify two key Climate Stressors/Exposures (and potential changes)

Step1 involved assigning 5 classes to the level of change identified for each direct climate stressor (which included rainfall or maximum temperature) identified to be relevant for a particular natural asset. For example, changes in annual rainfall were classified into 5 classes where 1 was the smallest change and 5 the largest change. The classes for each direct climate stressor were assigned on the basis of estimated change in the relevant climate stressor under the RCP 8.5 emission scenario for the year 2050.

Two key direct climate stressors were applied to each of the Asset Types identified with the exception of Coastal Wetlands and Estuaries for which Sea Level Rise and Storm Surge is used as the second climate stressor.

Step 2: Identify Asset Classes relevant to Stressors

This step involved identifying attributes that were likely to influence the sensitivity rating assigned to assets. This involved categorising each Asset Type into a set of Asset Classes. This process attempted to differentiate each Asset Type into approximately 20 to 40 sub-units, or Asset Classes. For native vegetation this involved one spatial dataset and using the ecological vegetation class sub-group attribute within this dataset. In the case of rivers it involved three spatial datasets that identified the extent of the asset (which was based on the Vicmap watercourse dataset, and selecting where hierarchy was H or M), a modified rivers dataset (used to identify regulated and unregulated rivers) , and a catchment position dataset (for which Victorian Bioregions was used to classify rivers into upper, intermediate and lower sections of a catchment).



Step 3: Assign likely Sensitivity to Climate Stressors (likely response to change)

This step required assigning a sensitivity rating to each Asset Class identified in Step 2, based on the 5 change classes assigned to each climate stressor in Step 1.

The sensitivity ratings were assigned on the basis of an anticipated response type by a given Asset Class to each climate stressor. In the case of native vegetation, the estimated distribution of native vegetation Asset Classes prior to European settlement (based on the pre-1750 version of ecological vegetation classes) was used as a reference for assigning the likely sensitivity to annual rainfall and maximum temperature over the spring and summer period.

Sensitivity ratings were assigned on the basis of expert opinion. Six sensitivity ratings were assigned ranging from 1 – very low, to 5 – very high, and 6 – catastrophic (where catastrophic was viewed to represent where expert opinion felt an Asset Type would no longer be present in its current form (see Table 3).

Table 3. Explanation of the sensitivity ratings assigned to an Asset Class for each of the 5 change classes identified for each climate stressor

Sensitivity to Climate Stressor

Sensitivity Rating Description

1 Very Low

2 Low

3 Medium

4 High

5 Very High

6 Catastrophic

The sensitivity ratings were assigned by identifying which of six response types were viewed to be the most appropriate in depicting the likely relationship between incremental changes in the climate stressor over time and the sensitivity of an Asset Class to that change.

Three linear (low, medium and high), two exponential (low and high), and one stepped response type were identified and applied.

The relationship between the six sensitivity ratings assigned to each of the 5 change classes assigned to each climate stressor (in Step 1) on the basis of the six response types used to depicting the likely relationship, is presented in Table 4.

Table 4. Relationship between the likely sensitivity ratings for a given Asset Class and the 5 change classes assigned to each climate stressor assigned on the basis of the six response types (A to F)used to depicting the likely relationship

Type No

Type Code

Response Type Description

Sensitivity to Climate Variation - for each climate stressor change class

1 2 3 4 5

1 A Low linear 1 1 1 2 2 2 B Medium linear 1 2 2 3 3 3 C High linear 1 2 3 4 5 4 D Low exponential 1 2 4 5 6 5 E High exponential 1 3 5 6 6 6 F Low step 1 1 3 4 4



Asset Classes identified for each Asset Type and the sensitivity assigned to the direct climate stressors identified to be the key factors influencing likely potential impacts on this natural Asset Type are presented in Appendix 4. The key spatial datasets used to delineate the Asset Classes identified are provided in Appendix 13 of this report.

Step 4: Calculate Potential Impact for each Climate Stressor (Exposure) for the change anticipated for a given climate scenario and time frame

The Potential Impacts of climate change are related to the interaction of asset exposure (the magnitude of change an asset will face due to modifications to the climate) and asset sensitivity (how much the asset will be affected by those changes).

This step involves calculating the Potential Impact on an Asset Class of anticipated changes in a given climate stressor (within a given time period and under a given climate emissions scenario), based on the following algorithm:

Impact (I) = Exposure rating (E) multiplied by Sensitivity rating (S)

Where:

Exposure rating: is the climate stressor change class to which the natural asset or portion of an asset will be subjected within a given time period (eg. 2030, 2050, 2070, 2090), under a given climate emissions scenario (RCP 8.5 or RCP 4.5).

Sensitivity rating: is the sensitivity rating to the climate stressor assigned on the basis of the change classes to which the natural asset or portion of an asset will be subjected within a given time period, under a given climate emissions scenario, on the basis of the six response types used to depicting the likely relationship.

(It is noted that the approach to calculating the impacts of climate change on assets in the methods described by the LGASA Guidelines, apply an additive relationship where: I = E + S.

However, to provide a greater differentiation in the values attribute to impact an interactive (or multiplicative) relationship i.e. I = E x S, has been applied in this project). This approach was also judged to be more theoretically robust than the additive approach (David Pannell, pers. comm).

Step 5: Calculate the Worst Potential Impact for each combination of Climate Stressors (Exposure) for a given climate scenario and time frame

This step involves comparing the Potential Impact rating calculated for each of two climate stressors, and assigning the highest assessed Potential Impact to the natural asset or portion of an asset. This is termed the Worst Potential Impact rating.

As previously indicated, two key direct climate stressors were applied to each of the Asset Types and hence two Potential Impact rating calculated with the exception of Coastal Wetlands and Estuaries for which only one Potential Impact rating relating to rainfall was calculated. In this situation the one Potential Impact rating becomes the Worst Potential Impact rating.

For these Asset Types Sea Level Rise and Storm Surge was directly incorporated into a revised Vulnerability rating form these Asset Types on the basis that Adaptive Capacity, as defined, was not viewed to represent an intrinsic ability to adjust to this indirect climate change stressor.



Step 6: Develop a likely Adaptive Capacity measure (based on current condition) for NRM asset

This step involved identifying attributes that were likely to reflect the current condition or state of a natural asset based on its intrinsic ability to adjust to likely climate change, and using these to assign an Adaptive Capacity rating to each natural asset. Criteria used to assign Adaptive Capacity vary based on the Asset Type, and were developed and assigned on the basis of expert opinion.

Adaptive Capacity has been assigned a rating of between 1 – Very Low and 5 – Very High for each of the Asset Types identified for this project and for which the full vulnerability assessment method described here has been applied. An explanation of how these rating were assigned is provided in Appendix 4.

As previously indicated, coastal assets were not fully assessed using the vulnerability assessment process applied to other natural assets. This is because the sensitivities and adaptive capacity elements were less well developed. Coasts were also viewed to be largely addressed through the assessment applied to estuaries, soils, native vegetation and coastal wetlands which included a range of wetland types. A simplified climate change impact assessment was undertaken for rock outcrops and areas of sea grass

Step 7: Calculate Vulnerability based on potential impact and intrinsic adaptive capacity based on current state for a given climate scenario and time frame

Vulnerability is defined as the measure of possible harm which could affect an asset or system, arising from climate change. This harm may include habitat contraction or degradation, loss of species diversity and ecosystem function, local extinction, reduction in water flows and availability, and loss of ecosystem resilience.

This step involves calculating the Vulnerability of an Asset Class, to anticipated changes in a given climate stressor (within a given time period and under a given climate emissions scenario), based the Worst Potential Impact and Adaptive Capacity, based on the following algorithm:

Vulnerability (V) = [Worst Impact (W) less 2 times Adaptive Capacity (A)] plus 10

(It is noted that the approach to calculating the vulnerability of assets to climate change in the methods described by the LGASA Guidelines, apply the following relationship: V = (I – A) + 10.

While the multiplication approach applied in calculation Potential Impact increases the total rating for Vulnerability, negative values can still be generated, hence the need to add 10. Assigning Adaptive Capacity a score from 1 to 5 also required the rating to be doubled to align it with other approaches, such as that outlined in the LGASA Guidelines).

As previously indicated, for Coastal Wetlands and Estuaries at second Vulnerability rating is assigned based on Sea Level Rise and Storm Surge.

This second Vulnerability rating is termed Vulnerability SLR rating. Vulnerability SLR ratings are assigned to Coastal Wetlands and Estuaries based on areas identified to be subject to one of three anticipated change classes within a given timeframe identified in the Sea Level Rise and Storm Surge exposure data. These three anticipated change classes are:




On the basis of these three classes the initial Vulnerability rating was adjusted as identified in Table 6.



Table 5. Basis for adjusting the initial Vulnerability rating to assign Vulnerability SLR rating

Coastal area description Vulnerability rating

Vulnerability SLR rating

Land area subject to no change Rating 1 to 38 No change


Rating 1 to 38 40

Area of land subject to anticipated storm surge Rating 1 to 38 45

Area of land subject to anticipated sea level rise Rating 1 to 38 50



5.3 Climate Stressors Applied to Asset Types

The Direct and Indirect Climate Stressors applied in the Vulnerability Assessment Method described in the previous section are outlined in Table 7.

Table 6. Direct and Indirect Climate Stressors applied in the Vulnerability Assessment Method

Asset Type Climate Stressors

(Used to assess Impact and Vulnerability)

Indirect Climate Stressors

(Used to refine Vulnerability to generate Vulnerability SLR)

Native Vegetation

excludes: wetlands

Total Annual Rainfall

Nov to April - daily Max Temp

None

Wetlands

excludes: tidal wetlands and wetlands within anticipated 2100 SLR and storm surge extent

Mar to Nov - Rainfall


None

Estuaries Mar to Nov - Rainfall

Sea Level Rise & Storm Surge

Rivers and Streams

includes: Levels high and moderate in watercourse dataset



None

Soils and Land Total Annual Rainfall


None

Coastal Wetlands

includes: tidal wetlands and wetlands within anticipated 2100 SLR and storm surge extent



5.4 Direct Climate Stressors Classes

The five classes identified for each of the three direct climate stressors applied in the Vulnerability Assessment Method are presented in Table 8. As previously indicated, the classes were assigned on the basis of estimated change in the relevant climate stressor under the RCP 8.5 emission scenario for the year 2050. This estimated change was based on estimated change observed within Victoria, including a 90km buffer on the basis of the resolution of the climate data used in the assessment process.

Appendix 2 provides map views of the anticipated relative change for the direct climate stressors applied in this project. This appendix presents the anticipated change for both the RCP 4.5 and RCP 8.5 carbon emissions scenarios for each of the four time intervals identified.

The sensitivity assigned to Asset Classes for each Asset Type to relevant direct climate stressors is presented in Appendix 4.



Table 7. Direct and Indirect Climate Stressors Classes applied in the Vulnerability Assessment Method based on change observed in 2050 (RCP8.5) data

Total Annual Rainfall (Change in mm per year)

Change_Class Description

1 >= -5

2 -6 to -15

3 -16 to -34

4 -35 to -45

5 <= -46

Total Rainfall – Mar to Nov (mm) (Change in Autumn/Winter/Spring)


1 >= -5

2 -6 to -14

3 -15 to -22

4 -23 to -30

5 <= -31

Average Daily Max Temp – Nov to Apr (Celsius) (Increase in Summer/Autumn)


1 0 - 0.99 degree increase




5 >= 4 degree increase

Consideration was given to the impact of relative change in climate stressors such the relative change in total annual rainfall. This approach was viewed to require significant additional effort and was not pursued due to time constraints. Figure 10 presents the relative change in total annual rainfall for the period 1990 to 2090 under RCP8.5.



Figure 10. Relative change expressed as a % n total annual rainfall for the period 1990 to 2090.

5.5 Indirect Climate Stressors Classes

The five classes identified for each of the three direct climate stressors applied in the Vulnerability Assessment Method are presented in Table 8. As previously indicated, the classes were assigned on the basis of estimated change in the relevant climate stressor under the RCP 8.5 emission scenario for the year 2050. This estimated change was based on estimated change observed within Victoria, including a 90km buffer on the basis of the resolution of the climate data used in the assessment process.

As previously indicated, for Coastal Wetlands and Estuaries at second Vulnerability rating is assigned based on Sea Level Rise and Storm Surge.

This second Vulnerability rating is termed Vulnerability SLR rating. Vulnerability SLR ratings are assigned to Coastal Wetlands and Estuaries based on areas identified to be subject to one of three anticipated change classes within a given timeframe identified in the Sea Level Rise and Storm Surge exposure data. These three anticipated change classes are:




On the basis of these three classes the initial Vulnerability rating was adjusted as identified in Table 6. Appendix 2 provides map views of the anticipated relative change for the direct climate stressors applied in this project. This appendix presents the anticipated change for both the RCP 4.5 and RCP 8.5 carbon emissions scenarios for each of the four time intervals identified.



5.6 Summary of Asset Climate Stressors, Sensitivity and Adaptive Capacity

The following table (Table 8) summarises the Climate Stressors (Exposures), Climate Stressor Sensitivity considerations and Adaptive Capacity inputs applied to each of the key Asset Types to which the vulnerability assessment method was applied. A list of the spatial datasets used to delineate these criteria and apply them in the relevant steps of the vulnerability assessment method is provided in Appendices 4 and 5.



Table 8. Summary of the Climate Stressors (Exposures), Climate Stressor Sensitivity considerations and Adaptive Capacity inputs applied to each Asset Types

Asset Type Climate Stressors Sensitivity inputs Adaptive Capacity inputs

Native Vegetation

excludes: wetlands

Total Rainfall


• EVC sub-groups • Site condition

• Landscape connectivity

Wetlands

excludes: tidal wetlands and wetlands within anticipated 2100 SLR and storm surge extent



• Wetland type (FW meadows, marshes etc)

• Water Source (river, groundwater)

• Alpine/non-alpine

• Within 2100 SLR and storm surge extent

• %native veg presence within 100m

• Dominant native veg quality within 100m

• Dominant land use within 100m

• Presence of drain, levee or cropping

Estuaries Mar to Nov - Rainfall


• Open – Permanent & Intermittent

• Regulated catchment or not

• Mouth type – bay / coast

• %native veg within catchment

• Quality of native veg within catchment

• Population & pop density within catchment

Rivers and Streams

includes: Levels high and moderate in watercourse dataset



• Regulated or not

• Perennial / permanent

• Terrain category – plains, intermediate, upper


• Quality of native veg within 100m

• AVIRA – reduction in high flow mag

• AVIRA – increase in prop of low flow

• AVIRA – change in monthly flow variability

Soils and Land Total Rainfall


• Land system based soils

• Susceptibility to wind erosion

• Susceptibility to water erosion & terrain type

• Native vegetation cover

• Site condition & landscape context?

• Proposed - Current land degradation (salinity, erosion) (appears to be insufficient data)



Asset Type Climate Stressors Sensitivity inputs Adaptive Capacity inputs

Coastal Wetlands

includes: tidal wetlands and wetlands within anticipated 2100 SLR and storm surge extent



• Wetland type (Freshwater meadows, marshes etc)

• Wetlands Regime - Supratidal

• Water Source (river, groundwater)

• Within 2100 SLR and storm surge extent




• Presence of drain, levee or cropping



5.7 Sensitivity – Criteria Applied

Sensitivity refers to the degree to which assets respond to the climate stressors they face. Some assets will have a proportionately large reaction compared to others, even of the same type. For example, some plant species may have a very narrow physiological tolerance to changes in temperature, while other more generalist species are sensitive only to very extreme changes in temperature.

It is proposed that when assessing Sensitivity this is done in relation to an agreed benchmark state.

Table 9 provides a list of Sensitivity criteria by which the likely impact of climate change stressors on the natural assets has been assessed in this project. This list of criteria has been used as the basis for sourcing and utilising spatial data to assign a statewide rating in relation to these criteria whereever practical.

It is proposed that this list of Sensitivity indicators be considered and applied by land managers at the regional or local level to review and refine the criteria presented in Appendix 4, and hence the outcomes generataed in the statewide vulnerability asessment method process.



Table 9. Criteria to be used to assign Sensitivity to assets in relation to Climate Change Stressors

Asset Type Sensitivity 1 2 3 4 5

Indicator No.

Indicator Very low Low Moderate High Very high

Terrestrial habitat

1 Condition and intactness

No loss of habitat quality and key ecosystem processes

Some loss of habitat quality and minor degradation of ecosystem processes

Significant loss of habitat quality but ecological processes remain largely intact

Major loss of habitat quality and long-term impact on ecological processes

Irreversible loss of habitat quality and ecological processes

2 Landscape context Extent and connectivity unaltered

Minor loss of habitat extent and some increased fragmentation of habitat - short-term recovery expected

Moderate loss of habitat extent with significant increase in fragmentation

Major loss of habitat quality and increased fragmentation with long-term effects

Irretrievable loss of habitat extent

3 Biodiversity Nil/minimal loss of ecosystem components and species

Loss of some ecosystem elements and/or species but repair and recolonisation within short time-frame

Some ecosystem components and species lost

Significant damage to key ecosystem components and long-term loss of species

Irretrievable loss of key ecosystem components and species

Threatened/ significant species

1 % of range and distribution

Less than 5% of species overall range and number of populations

Less than 25 % of overall range and number of populations

25-50% of overall range and number of populations

50-75% of overall range and number of populations

Represents the entire range of species distribution

2 Viability trend Improving Stable Shows some decline Suffers significant decline Critically endangered and suffering irreversible decline

3 Habitat dependency Species found across a wide range of habitats of varying quality

Species occurs across multiple habitat types of acceptable quality

Species occurs across multiple habitat types of varying quality

Species is dependent on a restricted number of habitat types in good condition

Species is highly dependent on restricted habitat type of high quality

Wetlands 1 Hydrological processes

Processes unaffected Minor and short-term loss of hydrological function

Significant but recoverable loss of hydrological function

Major loss of hydrological function

Irretrievable loss of hydrological function

2 Catchment and buffer integrity

Nil/minimal impact Minor damage to contributing catchment and buffer

Moderate damage to catchment/buffer with some long-term impacts

Major alteration to catchment and buffer with long-term effects

Irreversible damage to catchment and buffer integrity









Indicator No.


Estuaries 1 Hydrological character Processes unaffected Minor and short-term change

Moderate change recoverable in medium term

Major change but recoverable in the long-term


2 Catchment and buffer integrity

Nil/minimal impact Minor damage to contributing catchment and buffer

Moderate damage to catchment/buffer with some long-term impacts

Major alteration to catchment and buffer with long-term effects

Irreversible damage to catchment and buffer integrity






Rivers/ streams

1 Streamside zone - condition and intactness

No change to habitat quality and connectivity

Habitat quality suffers minor degradation but key ecological processes remain intact

Significant loss of habitat quality and connectivity but ecological processes remain largely intact

Major loss of habitat quality and increased fragmentation with long-term effects

Irretrievable loss of streamside zone extent

2 In-stream habitat In-stream habitat remains in near pristine condition

Minor alteration to quality and composition of in-steam habitat, natural repair processes intact

Moderate alteration to habitat quality and composition, with some long-term effects

In-stream habitat substantially degraded

Irreversible damage to in-stream habitat values

3 Ecosystem water quality

No discernible human impact

Minor damage to water dependent ecosystems, where full recovery could be expected

Moderate damage to water dependent ecosystems, where recovery would have minor long term effects

Major damage to water dependent ecosystems, where recovery would have significant long term effects

Irreversible damage to water dependent ecosystems

Aquifers 1 Aquifer character Key characteristics (depth, water quality and volume) unaffected

Minor and short-term change

Moderate change recoverable in medium term

Major change but recoverable in the long-term

Irretrievable change in character

Marine habitat 1 Biodiversity Nil/minimal loss of ecosystem components and species





2 Condition and intactness

No loss of habitat quality and key ecosystem processes

Some loss of habitat quality and minor degradation of ecosystem processes

Significant loss of habitat quality but ecological processes remain largely intact

Major loss of habitat quality and long-term impact on ecological processes

Irreversible loss of habitat quality and ecological processes




Indicator No.


Land/soils 1 Hydrological processes

Processes unaffected Minor and short-term loss of hydrological function

Significant but recoverable loss of hydrological function

Major loss of hydrological function


2 Change in soil structure/properties

Soil physico-chemical properties unaltered

Some alteration to physico-chemical properties but capability and productivity largely unaffected

Significant changes to soil structure and properties reducing productivity in the short to medium term

Major alteration of soil phyiso-chemical properties with long-term impact on productivity

Irreversible changes to soil structure/properties

3 Hazard - salinity, sodicity etc

No change in area of affected land

Minor increase in extent of land affected but no off-site impacts

Significant increase in extent of land affected with some off-site impacts

Major increase in land affected with significant off-site impacts

Irreversible and catastrophic effects



5.8 Adaptive Capacity – Criteria Applied

Adaptive Capacity is the ability of an asset or system to adjust or adapt to climate change stressors (including variability and extremes, and direct and indirect variables). Examples of natural systems with low adaptive capacity include species with limited genetic variablity, or with very specialist requirements for breeding habitat or food sources, or habitats that are already degraded through the impacts of clearing, invasive species or excessive water extraction.

It is important to note that assessing the adaptive capacity of assets has been made with reference to their current state (which may be anywhere between pristine and degraded), rather than the benchmark state which was used when assessing sensitivity.

Judgement has been made, all things considered, in assigning an Adapative Capacity as Very Low (1), Low (2), Moderate (3), High (4) or Very High (5).

Table 10 provides a list of Adaptive Capacity criteria by which the likely impact of climate change stressors on the natural assets has been assessed in this project. As with sensitivity, this list of criteria has been used as the basis for sourcing and utilising spatial data to assign a statewide rating in relation to these criteria whereever practical. For example, the size and shape of an asset may be viewed as an important indicator of Adaptive Capacity. Further, in relation to Adaptive Capacity a broader range of contextual parameters that can be derived from spatial data has also been considered. For example, the fragmentation or an asset, or its place in a catchment.

As with sensitivity, it is proposed that this list of Adaptive Capacity indicators be considered and applied by land managers at the regional or local level to review and refine the statewide vulnerability asessment method outputs. The spatial criteria used to assign a statewide rating for of Adaptive Capacityispresented in Appendix 5.



Table 10. Criteria to be used to assign Adaptive Capacity to assets in relation to Climate Change Stressors

Asset type Criteria for consideration in assigning adaptive capacity

Terrestrial habitat Current condition Landscape context – patch size, fragmentation and connectivity

Level of other threats (if known)

Threatened/significant species

Number of populations/individuals

Species mobility Breadth of habitat niches Level of other threats (if known)

Wetlands % of native vegetation within 100 metres

Quality of dominant native vegetation within 100 metres

Dominant land use within 100 metres

Presence of a drain, levee or cropping within wetland

Estuaries % tree cover within estuary catchment

Population and population density within catchment

AVIRA catchment ratings for:

Reduction in high flow magnitude

Increase in proportion of low flow

Change in monthly flow variability


Rivers/streams % native vegetation within 100 metres

Quality of native vegetation within 100 metres

AVIRA catchment ratings for:

Reduction in high flow magnitude

Increase in proportion of low flow

Change in monthly flow variability


Aquifers Current condition – quantity and quality

Recharge ability Level of other threats – e.g. extraction, pollution

Marine habitat Current condition relative to benchmark

Current extent relative to benchmark

Proximity to coast Level of other threats (if known)



Asset type Criteria for consideration in assigning adaptive capacity

Land/soils Native vegetation cover Level of degradation – salinity, erosion, acid sulphate soils where applicable

Ground cover



6. Vulnerability Assessment Implementation Issues

This section describes key issues identified and addressed in the implementation of the vulnerability assessment method to natural asset types.

A conservative approach was taken in the assignment of Asset Class sensitivity to climate stressors where there was no available information. For example, where no river regulation information was available it was assumed rivers were unregulated since this class of river was assigned a higher level of sensitivity.

As previously indicated, the sensitivity ratings assigned to Asset Classes for each Asset Type to relevant direct climate stressors are presented in Appendix 4.

The Adaptive Capacity ratings assigned to each Asset Type are presented in Appendix 5.

Table 8 in Section 5 summarises the Climate Stressors (Exposures), Climate Stressor Sensitivity considerations and Adaptive Capacity inputs applied to each of the key Asset Types to which the vulnerability assessment method was applied

6.1 Native Vegetation

Sensitivity

Consideration was given to the estimated pre-1750 EVC distribution to assign likely sensitivity of Asset Classes. Where an EVC sub-group (or Native Vegetation Asset Class) was known to occur, or have occurred, across a broad climate envelope, a less sensitive rating was assigned than if a more restricted climate range was observed.

Adaptive Capacity

Use of the State-wide vegetation quality dataset was viewed as the most suitable and defensible dataset to provide suitable native vegetation condition and landscape context information. This dataset included a site condition component that comprises 70% of the assigned vegetation quality rating with and landscape context comprising the other 30%.

6.2 Wetlands

Sensitivity

Consideration was given to using the flood extent and other water source related information in the assignment of wetland sensitivity, but it was felt that the Wetland Current dataset provided suitable information on water regime.

It was noted that the native vegetation asset classes include a version of wetlands, but that these areas should be replaced with the sensitivity and hence the impact and vulnerability ratings assigned on the basis of Wetland Asset Classes.

The wetlands impact and vulnerability datasets generated in this project included tidal wetlands and other wetlands on the coast that will be potentially impacted by anticipated sea level rise. As with the wetlands identified in the Native Vegetation Asset Classes, these areas should be replaced with the sensitivity and hence the impact and vulnerability ratings assigned on the basis of the Coastal Wetland Asset Class.

Adaptive Capacity

Consideration was given to using Index of Wetland Condition data, or related information in the AVIRA system, in the assignment of an Adaptive Capacity rating for wetlands. However, it was felt this information was not suitable at the time the project was implemented due to its level of completeness or accuracy.



6.3 Estuaries

Sensitivity

The treatment of Estuaries and Coastal Wetlands in relation to anticipated sea level rise differed from the approach taken with other asset types. Given that Adaptive Capacity, as defined, was not viewed to represent an intrinsic ability of these assets to adjust to sea level rise, the concept of sensitivity was not applied to this indirect climate change stressor in the same way as it was to direct climate stressors, such as rainfall.

Hence, only the one sensitivity rating and one potential impact rating was assigned and calculated for these assets.

Adaptive Capacity

Consideration was given to using a newly developed Index of Estuary Condition data in the assignment of an adaptive capacity rating for estuaries. However, this data was not made available in time for use in the project.

The approach with assigning adaptive capacity for estuaries involved assigning a rating based on attributes of the estuary catchment area.

6.4 Rivers and Streams

Sensitivity

Consideration was given to excluding river regulation information in the assignment of sensitivity ratings. However, it was felt that by including this as a differentiator in the assignment of sensitivity, users would have the option to adjust the sensitivity level if required, whereas if it was not included it would be more effort to add in at a later stage.

Inclusion of river regulation information in the adaptive capacity was also considered by the general consensus was that it was more appropriately applied in the assignment of sensitivity. It should also be noted that a dataset identifying modified rivers on the basis of impoundments or diversions was used in the absence of a more readily available dataset depicting regulated and unregulated river systems.

Rivers and streams were defined on the basis of the watercourses depicted in the State-wide Vicmap Digital watercourse dataset (VM_HYDRO_WATERCOURSE), where the Hierarchy item in this dataset was assigned a value of ‘H’ or ‘M’, that identified high and medium order rivers and streams.

Adaptive Capacity

AVIRA data relating to the hydrological properties of a watercourse was used to assign components of an Adaptive Capacity rating. Melbourne Water data was obtained to provide the relevant information for watercourses with the Port Philip and Westernport CMA.

6.5 Soils and Land

Sensitivity

Anticipated climate change is likely to include an increased occurrence of high intensity storm and wind events. These events will place significant pressure on soil and land assets, such that areas with poor land or vegetation cover are likely to experience significant wind and water erosion.

Several soils datasets were identified as part of this project, although given the project timeframe, soil classifications, data reliability, and strategic nature of the study, land system information prepared by Jim Rowan in the 1980s that identifies the inherent susceptibility of largely homogenous land units to key land degradation processes was used in this project. Other soils datasets identified, but not pursued in detail, included:



National Soil Texture dataset – generated as part of the National Land and Water Audit

National Soil Thickness dataset – generated as part of the National Land and Water Audit; and

1:25K and 1:100K DPI soils dataset managed on a Catchment Management Authority basis.

The land systems dataset used was the enhanced Land Systems (version 3) mapping and information framework (1:250 000) for Victoria finalised in 2000 by David Rees.

Notes on this datasets from DEPI in relation to this dataset are as follow:

Land System surveys have generally resulted in maps at 1:100 000 to 1:250 000 scale being produced. Many of these published studies describe (but do not map) the individual components of each Land System. A consistent statewide coverage of Land Systems (at 1:250 000 scale) was developed by Jim Rowan (1990) and has subsequently been updated with new information

The enhanced Land Systems (version 3) mapping and information framework builds on the existing Land Systems of Victoria (version 2) by Rowan (1990). Revision was achieved by using more recently acquired soil and land information (e.g. West Wimmera), and utilising techniques such as radiometrics interpretation and digital elevation modelling. Key activities included redefinition of the Land System Key and nomenclature.

Additional details can be found at:

http://www.dpi.vic.gov.au/dpi/vro/vrosite.nsf/pages/landform_systems

http://www.dpi.vic.gov.au/dpi/vro/vrosite.nsf/pages/landform_land_systems_vic

While the relative exposure of areas across the state to an increased occurrence of high intensity storm and wind events is not possible, it was felt that the key indicators of areas likely to be exposed to greater stress are the same as those used to assign the sensitivity of climate change on native vegetation – Total Annual Rainfall and Mean Monthly Maximum Temperatures (in Summer and Autumn). This was because these stressors directly impact on the quality and type of vegetation cover.

Hence, these two direct climate stressors were used to assign a sensitivity rating to soils and land asset classes based on their inherent susceptibility to wind and water erosion degradation.

The land system data was used in combination with a Ruggedness dataset generated from a 100m resolution digital terrain model for the state.

Adaptive Capacity

Consideration was given to whether land management practices could be used to assign an Adaptive Capacity rating. It was agreed that while areas of native vegetation could be assigned a greater Adaptive Capacity than cleared areas, there was no basis to differentiate the Adaptive Capacity of areas under agricultural land management.

It was viewed that land degradation impacts on the adaptive capacity of the soil and land asset, and hence spatial datasets that identify areas currently or recently subject to significant land degradation were reviewed.

Soil Salinity

No recent state wide spatial datasets were identified that depict areas currently impacted by soil salinity and soil erosion. To demonstrate the application of this information, a version of dryland salinity discharge mapping that was compiled in 2000 was used. This set of state wide datasets comprising line, point, and polygon features depicted the extent

http://www.dpi.vic.gov.au/dpi/vro/vrosite.nsf/pages/landform_systems

http://www.dpi.vic.gov.au/dpi/vro/vrosite.nsf/pages/landform_land_systems_vic



of dryland salt-affected soil mapped across the state of Victoria by regional staff, co-ordinated by the Centre for Land Protection Research (CLPR). Sites with a rating of medium or high severity were assigned as impacted by dryland salinity.

It was noted that a coordinated, comprehensive mapping of dryland soil salinity has never been funded and so the database is not able to provide a complete picture of soil salinity across Victoria at one time. The Victorian dryland soil salinity data base held on the CSDL is a compilation of almost all Victorian soil salinity surveys and provides the most current record of the mapped extents of dryland soil salinity. (Clark, 2008). It is also noted that earlier assessments will become 'dated' and therefore lose accuracy (with most site boundaries are expected to expand with time), in addition to seasonal (and climatic) fluctuations at time of assessment that can skew the site severity rating assigned.

Soil erosion

No suitable statewide dataset depicting areas subject to current soil erosion was identified.

Acid Sulphate soils

Coastal acid sulphate soils were also identified to significantly impact on adaptive capacity. The spatial data representing Victorian coastal lands which have the potential to contain coastal acid sulphate soil was utilised for this purpose. Hence, this spatial data is used for triggering investigations of a site where proposed activities risk disturbing acid sulphate soils.

6.6 Coastal Wetlands

Sensitivity

Coastal Wetlands were treated in a similar manner to Estuaries in relation to anticipated sea level rise, although the sensitivity ratings assigned for anticipated changes in rainfall were similar to those applied to other Wetlands

Hence, only the one sensitivity rating and one potential impact rating were also assigned and calculated for these assets.

Coastal Wetlands included tidal and non-tidal wetlands, where the non-tidal wetlands included those located within areas anticipated to be impacted by future sea level rise.

Adaptive Capacity

The approach applied in assigning Adaptive Capacity for Coastal Wetlands was the same as that used for other Wetlands.



7. Treatment of Coastal Assets

This section describes the approach undertaken with coastal assets.

As previously mentioned, significant coastal assets have been considered in the assessment of:

• Estuaries

• Soils

• Native Vegetation, and

• Coastal Wetlands

However, it was felt that some effort should be applied in considering the potential impact of climate change on other significant coastal assets such as rocky reefs, rocky shores, mud flats and sea grass areas, if possible.

While the likely changes associated with anticipated climate change that will impact on these assets has been identified, it was viewed that the available information did not support the same level of vulnerability assessment method that was applied to other natural asset types. This was largely because the sensitivities and adaptive capacity elements were less well developed. For this reason a simplified climate change impact assessment was undertaken for the following additional coastal assets:

• rocky reefs

• rock shores,

• mud flats, and

• sea grass.

The rationale and information applied in the treatment of these assets draws on the ‘Implications of Future Climate for Victoria’s Marine Environment’ report prepared by Jo Klemke and Helen Arundel (editors) for the Glenelg Hopkins Catchment Management Authority (2013).

This report (Klemke and Arundel, editors, 2013) outlines a very similar vulnerability assessment method to that applied in this NRM Planning for Climate Change project, although the assessment presented in the report is undertaken at a much higher level.

7.1 Key Exposures

For the purposes of identifying likely climate change stressors on rocky reefs, rock shores, mud flats and sea grass areas, the Victorian coast was divided into the following four key sectors identified by Klemke and Arundel (editors, 2013):

• Western Coast

• Central Coast

• Eastern Coast

• Embayments (that includes Port Phillip Bay, Westernport, and Corner Inlet)

Based on the work by Klemke and Arundel (editors, 2013) the likely indirect climate stressors in relation to the additional coastal/marine assets were identified to be:

• Sea Surface Temperature

• Run-off Volume

• Waves



The anticipated change in these three indirect climate stressors over time is presented in the Table 11.

Table 11. Anticipated change for in three indirect climate stressors over time for key coastal areas, sourced from Klemke and Arundel (editors, 2013)

Summary of anticipated change in indirect climate stressors used to assign Potential Impact

Indirect Climate Stressor

Western Coast Central Coast Embayments Eastern Coast

Sea Surface Temperature Low Low High Medium

Waves Medium Medium Very Low Low

Run-off Volume High Medium Low Low

(Map and top section sourced from: Klemke and Arundel, editors, 2013)

7.2 Coastal Asset Sensitivities

Smartline dataset

For the purposes of this project the National Smartline dataset was used to identify intertidal and subtidal coastal asset classes of potential interest to natural resource managers.

The Smartline dataset, referred to as the Coastal Geomorphic Map of Australia, is a map of the coastal landform types – or geomorphology – of continental Australia and most adjacent islands (excluding the Great Barrier Reef). This National Smartline dataset has been generated for OzCoasts by the University of Tasmania.

As a 'geomorphic' map, it represents not just the topography of the coast but also indicates what the differing coastal landforms are made of – varying rock types, laterite, coral, sand, mud, laterite, boulders, beachrock, and so on. The map classifies coastal landforms into differing combinations of form (generalised shape) and constituents (or fabric) which in turn are indicative of the differing natural processes by which each coastal landform has developed.

The Smartline dataset represents this information in the form of a single line map representing the coastline, which is split into segments where ever the coastal landform types change. Each distinctive segment is tagged or attributed with multiple attribute fields (data records) describing the landform types of that segment of the coast. The coastal characteristics recorded refer not only to those at the precise location of the line itself (typically High Water Mark), but to a coastal zone nominally extending up 500m inland and offshore of the HWM itself. The line can be divided into long or short segments



representing different coastal landforms, allowing the Smartline to record alongshore variations in coastal type to a high degree of detail.

The above description of the National Smartline dataset has been sourced from the following web-site, which contains details on Smartline attributes and the processes by which the dataset was generated:

http://www.ozcoasts.gov.au/coastal/introduction.jsp

Sensitivity of Assets to Climate Change Stressors

Klemke and Arundel (editors, 2013) provide information on the likely sensitivity and vulnerability of coastal and marine assets to climate stressors. The below figure from this report presents the vulnerabilities assigned to sea grass and soft sediments for each climate stressor identified in the report.

(Sourced from: Klemke and Arundel, editors, 2013)

The Smartline dataset has been used to identify sections of the coast where the following asset classes occur:

Intertidal

o Rocky shore

o Hard bedrock shore

o Hard rocky shore platform

o Sand beach with bedrock protruding

o Tidal flats

Subtidal

o Rocky reefs

o Seagrass

7.3 Likely Impacts

The potential impact of climate change on the additional coastal asset classes identified in the previous section has been assigned based on anticipated levels of change in the indirect climate stressors identified in Table 11.

This assignment of potential impact has taken the simplistic approach of identifying sensitive assets that are likely to be exposed to either a medium or high level change to

http://www.ozcoasts.gov.au/coastal/introduction.jsp



the indirect climate stressors identified to be associated with climate change, to be potentially subject to a significant impact.

It has been assumed that:

rocky, sea grass and tidal flats coastal asset classes, will both be significantly impacted by a medium or high level change to waves; and

sea grass and tidal flats, will be significantly impacted by a medium or high level change to sea surface temperature and runoff volume.

A map view of these additional coastal assets is identified in Figure 11. The potential impact of climate change on these additional coastal asset classes based on the anticipated change in key climate stressors is presented in Figure 12.

Figure 11. View of additional coastal asset classes, including rocky reefs and shore, sea grass and tidal flats used to assess the impact of anticipated change in key indirect climate stressors

A composite dataset was produced as part of the NRM Planning for Climate Change Project that contains the additional coastal asset classes, and anticipated climate stressor information presented in this section in addition to the assigned level of potential impact.



Figure 12. View of the potential impact of climate change on rocky, sea grass and tidal flats coastal asset classes based on the anticipated change in key indirect climate stressors



8. Delivery of Project Outputs

8.1 CMA Data Pack of Project Outputs

Content and Structure

This section describes the spatial data pack provided to CMAs as a key output of this project. The Data Pack comprised digital versions of all final spatial datasets, Statewide maps and supporting project documentation.

The Data Pack contained a copy of all final spatial datasets generated by the project in two formats:

ESRI Geodatabases (raster and vector datasets)

MapInfo TAB files.

Maps views of key data outputs were also provided as part of the Data Pack to assist with an interpretation of data outputs.

Two key datasets contained in the Data Pack were:

Vulnerability_ … _All_Classes Raster and vector datasets that contain attributes to support a recalculation of the Vulnerability rating assigned (Vulner); based on:

o Worst Impact (W_impact)

o Adaptive Capacity (Ad_Cap); where

o Vulnerability (Vulner) = (W_impact – 2 x Ad_Cap) +10

{Asset Type}_Classes Contains a description of all Asset Classes assigned to an Asset Type.

As indicated in the file naming convention, the final Vulnerability spatial datasets that contain attributes to support a reassessment of the Vulnerability rating assigned, include the suffix ‘_All_Classes ’ in the file name.

The Data Pack comprised the following directory structure:

Data (contains Geodatabase and MapInfo files for a given CMA)

Maps (contains all RCP 4.5 and RCP 8.5 carbon emission scenario state-wide maps and Asset Type maps)

Docs (contains key documents to support users of the spatial outputs).

ESRI Geodatabases (raster and vector datasets)

The Data Pack comprised a series of raster and vector geodatabases that were grouped on the basis of the key Asset Types, and components of the Climate Change Vulnerability Assessment Method.

The ESRI raster and vector geodatabases were generated using ArcGIS 10.x, and are compatible for use in ArcGIS10.x

The final vector datasets comprised polygon datasets generated from 100m raster grids where individual polygons were identified through a disaggregation process. For the Vulnerability datasets that contained all relevant attributes to support regional manipulation of the input and final Vulnerability ratings, additional attributes were linked back on to individual polygons to support spatial queries and editing.

For Rivers and Streams, all vector datasets were converted into a linear datasets through an additional step in which the original line features representing the asset were aligned the attributes generated through the grid to polygon process applied for other non-linear assets.



Hence, for a given CMA, spatial data was organised into the following geodatabases:

(Example provided is for the Corangamite CMA).

Raster and vector geodatabases for a given Asset Type were grouped on the basis of the key components of the Climate Change Vulnerability Assessment Method, and were organised into the following geodatabases:

(Example provided is for the Native Vegetation Asset Type).



MapInfo (vector datasets)

The Data Pack comprised a series of MapInfo Tab vector datasets that were also grouped on the basis of the key Asset Types, and components of the Climate Change Vulnerability Assessment Method.

Hence, for a given CMA, MapInfo spatial data was organised into the following geodatabases:

MapInfo Tab vector datasets for a given Asset Type were also grouped on the basis of the key components of the Climate Change Vulnerability Assessment Method. These spatial datasets were organised into the following structure:



8.2 User Interaction with Spatial Outputs

As previously noted, users were provided with a set of vulnerability datasets that allowed users to modify the Vulnerability rating based on the two key input variables:

o Worst Impact (W_impact), and

o Adaptive Capacity (Ad_Cap)

where:

Vulnerability (Vulner) = (W_impact – 2 x Ad_Cap) +10

8.3 Web Delivery of outputs

Selected project outputs have been loaded into a web-based GIS environment (GIS Cloud) to support users of the project outputs.

This GIS Cloud web-site contains views of asset classes, sensitivity, impact, adaptive capacity and vulnerability for the Native Vegetation and Wetlands Asset Types. The site presents images of project outputs which allows users to pan and zoom to review the outputs. The map views incorporate a separate map legend window.

The URL for the site is:

http://spatialvision.com.au/html/NRM/

A view of the GIS Cloud web-site is provided in Figure 13.

Figure 13. Screen view of the GIS Cloud web-site that provides users with selected project outputs.

http://spatialvision.com.au/html/NRM/



8.4 Summary of Project Outputs

A summary of the project outputs in terms of the key Asset Types, and components of the Climate Change Vulnerability Assessment Method is presented in Figure 14. This figure shows that for the one Asset Type, after adopting the relevant carbon emissions scenario (RCP rating), the method involves:

Defining numerous Asset Classes that are subjected to up to two Climate Change Exposures, over four Timeframes, to generate up to two Potential Impact datasets; and

That these Potential Impacts are used to generate a Worst Impact dataset, that in turn is combined with an Adaptive Capacity dataset to generate the final Potential Vulnerability dataset for a given timeframe.

Map views provided in Appendices 6 to 12, and the spatial datasets described earlier in this section, are effectively the outputs generated at each key step in the process, and are therefore represented by the coloured dots in the figure.

These Appendices Identify:

Appendix 6 Native Vegetation – RCP 8.5 Vulnerability Impacts

Appendix 7 Wetlands – RCP 8.5 Vulnerability Impacts

Appendix 8 Estuaries – RCP 8.5 Vulnerability Impacts

Appendix 9 Rivers – RCP 8.5 Vulnerability Impacts

Appendix 10 Soils and Land – RCP 8.5 Vulnerability Impacts

Appendix 11 Coastal Wetlands – RCP 8.5 Vulnerability Impacts

Appendix 12 All Assets – RCP 8.5 Vulnerability Impacts



Figure 14. Diagrammatical representation of the project outputs in terms of the key Asset Types, and components of the Climate Change Vulnerability Assessment Method.



9. Data Collation and Organisation

This section provides an overview of the approach undertaken to identify, collate and organise key datasets of interest to support an the assessment of the likely impacts of climate change. It includes an outline of the data structure adopted.

9.1 Data Collation

A list of source datasets collated in relation to the key asset types and features relevant in the assessment process identified early in the project, including custodian details, is provided in Appendix 13.

The process of sourcing spatial data for use in the project generally involved:

requesting the required data from the known data custodian; or

directly accessing the data from its source location, as in the case of datasets directly available from the internet.

9.2 Spatial Data Library

A process was required to take source data from agencies and transfer it into a data library.

The final repository for spatial data obtained during this project was termed the ‘Project Data Library’. This library was structured in line with the data types previously identified, comprising:

Source attribute data

Criteria (or value added) data generated by the project team to support the assessment process

Final Potential Impact and Vulnerability Outputs for each climate scenario and time frame

The library also included an ‘Agency Source Data’ repository which contained all data supplied by an individual agency in individual agency directories. This allowed easy cross referencing of any data related issues.

Data in the Project Data Library was also organised into the broad Data Groups and Classes identified earlier in this report. This included groupings related to Asset Types in addition to Climate Stressors and other broad data types. This information framework and supporting library can be conceptualised as a matrix that consists of:

Data Types (Source, Criteria, and Output data); and

Broad Data Groups

A diagrammatic representation of the spatial data library developed, and the flow of raw data obtained from agencies and organisations into this library is provided in Figure 15.

This figure illustrates how data obtained from agencies or organisations, termed ‘Raw Data’ was transferred into an Agency Source Data repository structured on the basis of the source from which data was obtained, prior to being transferred into the final Project Data Library for the project.

A key aspect of the source data repository was that it contained data in a consistent and standardised spatial format (in ESRI geodatabase file format), and consistent data projection and datum (the VicGrid 94 projection and datum).

The source data repository provided an easy way of clarifying who had contributed what data at any point during the project.



Figure 15. Diagrammatic representation of the spatial data library

In addition to the separate directories being prepared for each Asset Type, spatial data for each Asset Type in the master library was organised on the basis of the Criteria Data and Final Output Data Classes. Hence a separate geodatabase was created for Adaptive Capacity, Sensitivity (to Climate Stressors), Impact and Vulnerability for each Asset Type.

Individual spatial datasets were assigned to an Asset Type, Data Type, carbon emissions scenario, and time frame. Examples of the grouping and naming convensions are as follow::

Data Type / Geodatabase

Data Format Asset Type Dataset Name

Sensitivity Grid Native_Veg Native_Veg_Classes

Sensitivity Grid Native_Veg Native_Veg_Sensitivity_Rainfall

Sensitivity Grid Native_Veg Native_Veg_Sensitivity_MaxTemp

Adaptiveness Grid Native_Veg Native_Veg_Adaptive_capacity_Composite

Impact Grid Native_Veg Impact_01_Native_Veg_Rainfall_rcp45_2030_Annual

Impact Grid Native_Veg Impact_01_Native_Veg_Rainfall_rcp85_2030_Annual

Impact Grid Native_Veg Impact_01_Native_Veg_MaxTemp_rcp85_2030_Nov_Apr

Impact Grid Native_Veg Impact_01_Native_Veg_Worst_Case_rcp85_2030

Vulnerability Grid Native_Veg Vulnerability_01_Native_Veg_rcp45_2030

Figure 16 illustrates an example of how spatial data was organised in the Project Data Library. The Figure shows how source datasets were translated into criteria datasets used to generate an Adaptive Capacity rating inputs that were then used to produce the composite Adaptive Capacity dataset (that contributed to the final Vulnerability rating for an Asset Type). The example provided identifies the inputs to the Rivers and Streams Asset Type Adaptive Capacity Rating.



Figure 16. Screen views of the source data repository.

9.3 Data Volumes

The volume of spatial data residing in the Project Data Library broken down by Data Types is presented in Tables 12 and 13.



Table 12. Volume of spatial data residing in State wide Project Data Library broken down by Data Types

Asset Type Data Volume (GB) Total Volume (GB)

Native Veg Source 0.99 3.29

Sensitivity 1.12

Impact 0.61

Adaptive Capacity 0.09

Vulnerability 0.39

Wetlands Source 0.11 2.23

Sensitivity 0.77

Impact 0.15


Vulnerability 0.08

Estuaries Source 0.01 0.13

Sensitivity 0.08

Impact 0.01


Vulnerability 0.01

Rivers Source 0.14 0.58

Sensitivity 0.16

Impact 0.05


Vulnerability 0.04

Soils Source 0.04 1.01

Sensitivity 0.41

Impact 0.12


Vulnerability 0.20

Coastal Wetlands Source 0.01 0.14

Sensitivity 0.02

Impact 0.01


Vulnerability 0.02



Table 13. Volume of spatial data residing in CMA Based Project Data Libraries broken down by Data Format Type

CMA Pack Data Volume Total Volume

Corangamite ESRI File GDB 0.68 GB 1.03 GB

MapInfo TAB 0.35 GB

North Central ESRI File GDB 1.45 GB 2.25 GB

MapInfo TAB 0.80 GB

East Gippsland ESRI File GDB 1.02 GB 1.61 GB

MapInfo TAB 0.59GB

Wimmera ESRI File GDB 1.15 GB 1.78 GB

MapInfo TAB 0.63 GB

Mallee ESRI File GDB 1.19 GB 1.85 GB

MapInfo TAB 0.66 GB

Port Phillip & Westernport ESRI File GDB 0.94 GB 1.43 GB

MapInfo TAB 0.49 GB

Glenelg Hopkins ESRI File GDB 1.35 GB 2.08 GB

MapInfo TAB 0.73 GB



10. References

Allen Consulting Group (2005) Climate Change Risk and Vulnerability. Canberra, Allen Consulting Group.

Capon,S. J., Chambers, L. E., MacNally, R., Naiman, R. J., Davies, P., Marshall, N., Pittock, J., Reid, M., Capon, T., Douglas M. (2013) Riparian Ecosystems in the 21st Century: Hotspots for Climate Change Adaptation; Ecosystems (2013) 16: 359–381).

Clark, R. M. (2008) Dryland salinity in Victoria in 2007: an analysis of data from the soil salinity database and Victorian discharge monitoring network; Bendigo, Vic. : Department of Primary Industries.

Clarke, J.M., Whetton, P.H. and Hennessy, K.J. (2011) Providing application-specific climate projections datasets: CSIRO’s Climate Futures Framework; CSIRO Marine and Atmospheric Research, Victoria.

Department of Sustainability and Environment (2005) The Asset-Based Approach for Natural Resource Management in Victoria: Discussion Paper.

Fussel, H.M., & Klein, R.J.T. (2006). Climate change vulnerability assessments: an evolution of conceptual thinking. Climatic Change, 75, 301-329

Local Government Association of South Australia (2012) Guidelines For Developing a Climate Change Adaptation Plan and Undertaking an Integrated Climate Change Vulnerability Assessment; November 2012.

Hinkel, J. (2011) Indicators of vulnerability and adaptive capacity: Towards a clarification of the science - policy interface. Global Environmental Change 21: 198-208.

IPCC (2007) Climate change 2007: The physical science basis. Geneva, Switzerland, Intergovernmental Panel on Climate Change: 21.

IPCC (2013) Climate Change 2013: The Physical Science Basis; Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Geneva, Switzerland

IPCC (2000) - Nebojsa Nakicenovic and Rob Swart (Eds.) Cambridge University Press, UK. pp 570.

Rowan, J. (1990) Land Systems of Victoria, Department of Conservation and Environment and the Land Conservation Council, Victoria.

Klemke and Arundel (Editors, 2013) ‘Implications of Future Climate for Victoria’s Marine Environment’ report prepared by Jo Klemke and Helen Arundel (editors) for the Glenelg Hopkins Catchment Management Authority (2013).

Local Government Association of South Australia (2012) Guidelines for Developing a Climate Change Adaptation Plan and Undertaking an Integrated Climate Change Vulnerability Assessment; November 2012; Local Government Association of South Australia.

McCarthy J.J, Canziani O.F, Leary N.A, Dokken D.J, White K.S (2001) Climate change 2001: impacts, adaptation, and vulnerability. In Cambridge University Press Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change2001 Cambridge, UK: Cambridge University Press.

Moss, R.H., Edmonds, J. A., Hibbard, K. A., Manning; M. R., Rose; S. K., van Vuuren, D.P., Carter, T. R., Emori, S, Kainuma, M., Kram, T., Meehl, G. A., Mitchell, J. F. B., Nakicenovic, N. 10, Riahi, K., Smith, S. J., Stouffer, R. J., Thomson, A. M., Weyant, J. P. and Wilbanks, T. J. (2010) The next generation of scenarios for climate change research and assessment; Nature 463, 747-756.

Taylor, K.E., Stouffer, R.J. Meehl, G.A. (2012) An Overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485-498.

Webb, L., Clarke J., Hennessy, K., Heady C. (2014) Draft Projections for Australia’s NRM Regions Data Delivery Brochure, February 2014, CSIRO Marine and Atmospheric



Appendix 1: Terms and Definitions



Vulnerability Assessment Terms and Definitions

Exposure Exposure refers to the type and magnitude of local and regional biophysical stressors that assets will likely face as a result of climate change, including direct climatic variables (such as temperature, rainfall, seasonality, frost days) and indirect climatic impacts, such as flooding and bushfire frequency. Exposure is specifically related to the amount of a factor to which an asset is exposed to

Sensitivity Sensitivity is the degree to which a system is affected, either adversely or beneficially, by climate variability or change. The effect may be direct or indirect. Sensitivity is refers to the “dose-response relationship” between a system or asset’s exposure and the potential for that to result in impacts. Differences in sensitivity between assets relate to the differences in responses between them to the same amount of climate variability or change.

Impacts Potential Impacts of climate change involve the interaction of asset exposure (the magnitude of change an asset will face due to modifications to the climate) and asset sensitivity (how much the asset will be affected by those changes).

Adaptive Capacity

Adaptive Capacity relates to the ability of a system via intrinsic mechanisms to adjust to changes in climate parameters, including climate variability and extremes, to moderate potential damages, to take advantage of opportunities or to cope with the consequences. Sensitivity and Adaptive Capacity are theoretically similar and interrelated concepts. For example, a system which has been degraded by the impacts of invasive weed species may have a high sensitivity to increases in temperature (i.e. a large-dose dependent response), compared to a habitat not impacted by invasive species. In addition, reduced seed dispersal rates due to changes in population age structures of plant species due to the impact of weed species can reduce their adaptive capacity. Care must be taken to clearly delineate between these concepts, to ensure that the final spatial model is not impacted by “double-counting” of various factors

Vulnerability Vulnerability is the degree to which a system is susceptible to, and unable to cope with, adverse effects of climate change including climate variability and extremes. Vulnerability is a function of the character, magnitude and rate of climate change and variation to which a system is exposed, its sensitivity and its adaptive capacity.

Mitigation Mitigation is an anthropogenic (human-caused) intervention to reduce the anthropogenic forcing of the climate system; it includes strategies to reduce greenhouse gas sources and emissions, and enhancing greenhouse gas sinks

Adaptation Adaptation is an adjustment in natural or human systems in response to actual or expected climatic stimuli or their effects, which moderates harm or exploits beneficial opportunities. Various types of adaptation can be distinguished, including anticipatory (takes place before impacts of climate change are observed), autonomous (non-conscious or planned responses triggered by ecological changes in natural systems) or planned (the result of a deliberate policy decision to achieve a desired state).



Appendix 2: Anticipated Sea Level Rise for 2040, 2070 and 2100



Figure 17. Anticipated Sea Level Rise (SLR) and Storm Surge - 2040




Appendix 3: Anticipated Climate Change for key Climate Stressors RCP 4.5 and RCP 8.5



RCP 8.5 Carbon Emissions Scenario – Anticipated Changes in Climate



Figure 20. RCP 8.5 - Total Annual Rainfall Anticipated Change - 2030




Figure 24. RCP 8.5 - Total Rainfall March to November Anticipated Change - 2030




Figure 28. RCP 8.5 – Mean Daily Maximum Temperature Anticipated Change - 2030

Figure 29. RCP 8.5 - Mean Daily Maximum Temperature Anticipated Change - 2050



RCP 4.5 Carbon Emissions Scenario – Anticipated Changes in Climate



Appendix 4: Climate Change Sensitivity Rating Assigned to Assets



Native Vegetation Asset Class – Sensitivity Rating



Wetlands Asset Class – Sensitivity Rating



Estuaries Asset Class – Sensitivity Rating



Rivers Asset Class – Sensitivity Rating



Soils / Land Asset Class – Sensitivity Rating



Coastal Wetlands Asset Class – Sensitivity Rating



Appendix 5: Adaptive Capacity Datasets and Criteria



Adaptive Capacity Inputs Dataset Dataset Description

Contribution to Adaptive Capacity Rating

Native Vegetation

• Site condition


NV2005_QUAL

Modelled native vegetation quality - Includes Site Condition and Landscape context

100%

Wetlands


NV2005_EVCBCS Derived Native Vegetation extent, combining Bioregions, pre-1750 EVC's and modelled current extent

25%


NV2005_QUAL Modelled native vegetation quality - Includes Site Condition and Landscape context

25%


VLUIS Describes what land tenure, land use and land cover is present

25%

• Presence of drain, levee or cropping in wetland

Wetland_Current Wetlands of Victoria (2013 ) 25%

Estuaries

• %native veg within catchment

NV2005_EVCBCS

Derived Native Vegetation extent, combining Bioregions, pre-1750 EVC's and modelled current extent, includes bioregional conservation status and geographic occurrence

20%

• AVIRA threat attribute – reduction in high flow magnitude

• AVIRA threat attribute – increase in proportion of low flow

• AVIRA threat attribute – change in monthly flow variability

River_Reach with AVIRA and Melbourne Water attributes

Water quality and threat information assigned to river reaches in the DEPI and Melbourne Water river asset databases

60%

• Population and Pop Density within Estuary catchment

WATER_EST_ CATCH

Areas of estuarine catchment derived by Deakin University as part of the project: "Linking catchments to the sea: Understanding how human activities impact on Victorian estuaries"

20%



Adaptive Capacity Inputs Dataset Dataset Description

Contribution to Adaptive Capacity Rating

Rivers and Streams



20%

• Quality of native veg within 100m

NV2005_QUAL


20%






60%

Soil and Land

• Native vegetation cover NV2005_EVCBCS Derived Native Vegetation extent, combining Bioregions, pre-1750 EVC's and modelled current extent

25%

• Site condition & landscape context


25%

• Area impacted by land degradation (soil salinity), best available

SOILSAL25_ (arc, point, poly)

Dryland Salt affected areas mapped across Victoria between 1989 and 2000.

25%

• Area impacted by Coastal Acid Sulphate Soils

COASTAL_ACID_ SULPHATE_SOILS

Coastal Acid Sulphate Soils areas mapped across Victoria between 2001and 2011.

25%

Coastal Wetlands



25%



25%


VLUIS Describes what land tenure, land use and land cover is present

25%


Wetland_Current Wetlands of Victoria (2013 ) 25%



Appendix 6: Native Vegetation – RCP 8.5 Vulnerability Assessment Outputs



Figure 44. Native Vegetation Asset Classes, represented by Ecological Vegetation Class Sub-groups

Figure 45. Native Vegetation - Adaptive Capacity



Figure 46. Native Vegetation - Sensitivity rating to highest anticipated change in Total Annual Rainfall (of greater than 46mm) uniformly across state

Figure 47. Native Vegetation - Sensitivity rating to highest anticipated change in Mean Daily Max Temperature November to March (of greater than 4C) uniformly across state




Figure 49. Native Vegetation RCP 8.5 - Potential Impact Total Annual Rainfall - 2050



Figure 50. RCP 8.5 - Mean Daily Max Temperature (Nov to May) Anticipated Change - 2050

Figure 51. Native Vegetation RCP 8.5 - Potential Impact Mean Daily Max Temperature (Nov to Mar) - 2050



Figure 52. Native Vegetation RCP 8.5 - Worst Impact for a combination of climate stressors (Annual Rainfall & Mean Max Daily Temperature) - 2030




Figure 54. Native Vegetation RCP 8.5 - Worst Impact for a combination of climate stressors (Annual Rainfall & Max Daily Temperature) - 2070




Figure 56. Native Vegetation RCP 8.5 - Potential Vulnerability - 2030




Appendix 7: Wetlands – RCP 8.5 Vulnerability Assessment Outputs



Figure 60. Wetlands Asset Classes, represented by Wetland Types

Figure 61. Wetlands - Adaptive Capacity



Figure 62. Wetlands - Sensitivity rating to highest anticipated change in Total Rainfall March to November (of greater than 31 mm) uniformly across state

Figure 63. Wetlands - Sensitivity rating to highest anticipated change in Average Daily Max Temperature November to April (of greater than 4C) uniformly across state



Figure 64. RCP 8.5 - Anticipated change in Total Rainfall March to November – 2050

Figure 65. Wetlands RCP 8.5 - Potential Impact Total Rainfall March to November - 2050



Figure 66. RCP 8.5 - Anticipated change in Av Daily Max Temperature (Nov to Apr) – 2050

Figure 67. Wetlands RCP 8.5 - Potential Impact Av Max Daily Temperature (Nov to Apr) – 2050



Figure 68. Wetlands RCP 8.5 - Worst Impact for a combination of climate stressors (Rainfall & Max Daily Temperature) – 2030

Figure 69. Wetlands RCP 8.5 - Worst Impact for a combination of climate stressors (Rainfall & Max Daily Temperature) - 2050



Figure 72. Wetlands RCP 8.5 - Potential Vulnerability – 2030




Appendix 8: Estuaries – RCP 8.5 Vulnerability Assessment Outputs



Figure 76. Estuaries Asset Classes.

Figure 77. Estuaries – Adaptive Capacity



Figure 78. Estuaries - Sensitivity to highest anticipated change in Total Rainfall March to November (of greater than 31mm) uniformly across state.

Figure 79. Anticipated Sea Level Rise and Storm Surge in 2100, used to assign Final Vulnerability rating



Figure 80. RCP 8.5 - Anticipated change in Total Rainfall March to November - 2050

Figure 81. Estuaries RCP 8.5 - Potential Impact Total Rainfall March to November – 2050



Figure 82. Estuaries RCP 8.5 - Worst Impact for Total Rainfall March to November – 2050




Figure 86. Estuaries RCP 8.5 - Potential Vulnerability (Rainfall Only) - 2030




Figure 88. Estuaries RCP 8.5 - Potential Vulnerability (Rainfall Only) – 2070




Figure 90. Estuaries RCP 8.5 - Potential Vulnerability (Rainfall and Sea Level Rise) – 2030




Appendix 9: Rivers – RCP 8.5 Vulnerability Assessment Method Outputs



Figure 94. Rivers & Streams, represented by River Asset Classes

Figure 95. Rivers & Streams – Adaptive Capacity



Figure 96. Rivers & Streams – Sensitivity rating to highest anticipated change in Total Rainfall March to November (of greater than 31 mm) uniformly across state.

Figure 97. Rivers & Streams – Sensitivity rating to highest anticipated change in Average Daily Max Temperature November to April (of greater than 4C) uniformly across state.



Figure 98. RCP 8.5 - Anticipated change in Total Rainfall March to November – 2050

Figure 99. Rivers & Streams RCP 8.5 – Potential Impact Total Rainfall March to November - 2050



Figure 100. RCP 8.5 – Anticipated change in Av Daily Max Temperature (Nov to Apr) – 2050

Figure 101. Rivers & Streams RCP 8.5 – Anticipated change in Av Daily Max Temperature (Nov to Apr) – 2050



Figure 102. Rivers & Streams RCP 8.5 – Worst Impact for a combination of climate stressors (Rainfall & Max Daily Temperature) – 2030

Figure 103. Rivers & Streams RCP 8.5 – Worst Impact for a combination of climate stressors (Rainfall & Max Daily Temperature) - 2050



Figure 106. Rivers & Streams RCP 8.5 – Potential Vulnerability – 2030




Appendix 10: Soils and Land – RCP 8.5 Vulnerability Assessment Outputs



Figure 110. Soils & Land Asset Classes

Figure 111. Soils & Land - Adaptive Capacity



Figure 112. Soils & Land - Sensitivity rating to highest anticipated change in Total Annual Rainfall (of greater than 46mm) uniformly across state

Figure 113. Soils & Land - Sensitivity rating to highest anticipated change in Mean Daily Max Temperature November to March (of greater than 4C) uniformly across state.




Figure 115. Soils & Land RCP 8.5 - Potential Impact Total Annual Rainfall - 2050



Figure 116. RCP 8.5 - Mean Daily Max Temperature (Nov to May) Anticipated Change - 2050

Figure 117. Soils & Land RCP 8.5 - Potential Impact Mean Daily Max Temperature (Nov to Mar) - 2050



Figure 118. Soils & Land RCP 8.5 - Worst Impact for a combination of climate stressors (Annual Rainfall & Mean Max Daily Temperature) - 2030



Figure 119. Soils & Land RCP 8.5 - Worst Impact for a combination of climate stressors (Annual Rainfall & Mean Max Daily Temperature) - 2050

Figure 120. Soils & Land RCP 8.5 - Worst Impact for a combination of climate stressors (Annual Rainfall & Max Daily Temperature) - 2070



Figure 121. Soils & Land RCP 8.5 - Worst Impact for a combination of climate stressors (Annual Rainfall & Max Daily Temperature) - 2090

Figure 122. Soils & Land RCP 8.5 - Potential Vulnerability - 2030




Appendix 11: Coastal Wetlands – RCP 8.5 Vulnerability Assessment Outputs



Figure 126. Coastal Wetland Asset Classes

Figure 127. Coastal Wetlands - Adaptive Capacity



Figure 128. Coastal Wetlands - Sensitivity to highest anticipated change in Total Rainfall March to November (of greater than 31mm) uniformly across state.

Figure 129. Anticipated Sea Level Rise and Storm Surge in 2100, used to assign Final Vulnerability rating



Figure 130. RCP 8.5 - Anticipated change in Total Rainfall March to November - 2050

Figure 131. Coastal Wetlands RCP 8.5 – Potential Impact Total Rainfall March to November – 2050



Figure 132. Coastal Wetlands RCP 8.5 – Worst Impact for Total Rainfall March to November – 2050





Figure 135. Coastal Wetlands RCP 8.5 - Worst Impact for Total Rainfall March to November – 2090



Figure 136. Coastal Wetlands RCP 8.5 - Potential Vulnerability (Rainfall and Sea Level Rise) – 2030




Appendix 12: RCP 4.5 Selected Vulnerability Assessment Outputs



Native Vegetation – RCP 4.5



Wetlands



Figure 148. Wetlands RCP 4.5 - Worst Impact for a combination of climate stressors (Annual Rainfall & Mean Daily Max Temperature) - 2030




Figure 152. Wetlands RCP 4.5 - Potential Vulnerability - 2030




Estuaries



Figure 156. Estuaries RCP 4.5 – Potential Impact Total Rainfall March to November – 2030




Figure 160. Estuaries RCP 4.5 - Potential Vulnerability - 2030




Rivers & Streams



Figure 164. Rivers & Streams RCP 4.5 - Worst Impact for a combination of climate stressors (Annual Rainfall & Mean Daily Max Temperature) - 2030




Figure 168. Rivers & Streams RCP 4.5 - Potential Vulnerability - 2030




Soils & Land



Figure 172. Soils & Land RCP 4.5 - Worst Impact for a combination of climate stressors (Annual Rainfall & Mean Daily Max Temperature) - 2030




Coastal Wetlands



Figure 184. Coastal Wetlands RCP 4.5 - Potential Vulnerability - 2030




Appendix 13: Source Datasets



Source datasets

Asset Type Inputs Dataset Used Dataset Description Agency Derived or Original Data

Native Vegetation excludes: wetlands

Sensitivity inputs

• EVC sub-groups NV2005_EVCBCS Derived Native Vegetation extent, combining Bioregions, pre-1750 EVC's and modelled current extent

DEPI Original Data

Adaptive Capacity inputs

• Site condition


NV2005_QUAL


DEPI

Original Data

Wetlands excludes: tidal wetlands and wetlands within anticipated 2100 SLR and storm surge extent

Sensitivity inputs

• Wetland type (Freshwater meadows, marshes etc)

Wetland_Current Wetlands of Victoria (2013 ) DEPI Original Data

• Alpine/non-alpine areas VBIOREG100 Bioregions of Victoria capturing the ecological characteristics in landscapes

DEPI Original Data

• Anticipated area impacted by 2100 sea level rise and storm surge extent

slr82cm_st_2100 Victorian Coastal Inundation sea level rise and storm surge extent by year 2100 - anticipated

DEPI Original Data


• %native veg presence within 100m NV2005_EVCBCS Derived Native Vegetation extent, combining Bioregions, pre-1750 EVC's and modelled current extent

DEPI Original Data



DEPI Original Data

• Dominant land use within 100m VLUIS Describes what land tenure, land use and land cover is present

DEPI Original Data






Estuaries

Sensitivity inputs

• Mouth opening – Permanent or Intermittent

• Mouth type – Bay or Coast

Water_Estuaries

Areas of estuarine waters derived by Deakin University as part of the project: "Linking catchments to the sea: Understanding how human activities impact on Victorian estuaries"

DEPI Original Data

• Modified (Regulated) river catchment or not

Modified_Rivers

'modified' and 'unmodified' waterways for Victoria based on whether a water storage of significant volume resides on waterway

DEPI

Original Data


• %native veg within catchment NV2005_EVCBCS

Derived Native Vegetation extent, combining Bioregions, pre-1750 EVC's and modelled current extent, includes bioregional conservation status and geographic occurrence

DEPI Original Data






DEPI and Melbourne Water

Original Data

• Population and Pop Density within Estuary catchment

WATER_EST_CATCH

Areas of estuarine catchment derived by Deakin University as part of the project: "Linking catchments to the sea: Understanding how human activities impact on Victorian estuaries"

DEPI Original Data




Rivers and Streams includes: Levels high and moderate in watercourse dataset

Sensitivity inputs

• Modified (Regulated) river catchment or not

• Perennial or non-perennial waterway

Modified_Rivers

'modified' and 'unmodified' waterways for Victoria based on whether a water storage of significant volume resides on waterway, and whether the waterway is 'perennial' or 'non-perennial'

DEPI

Original Data

• Used to define spatial representation of rivers and streams

Vicmap Hydro Watercourse

Major Rivers and Streams of Victoria (For project, Rivers defined as those with ‘Hierarchy’ value of "Medium" and "High"

DEPI Original Data

• Used to assign sensitivity attributes to rivers and streams

sdl_catch

Sustainable Diversion Limits (SDL) catchment s represented in dataset showing upper limit on winterfall diversions, beyond which additional extractions may degrade environment

DEPI

Original Data

• Used to assign sensitivity

attributes to rivers and streams

basin100 River Basins (used to supplement SDL_CATCH to complete Victoria)

DEPI Original Data

• Terrain category – plains, intermediate, upper

VBIOREG100

Bioregions of Victoria capturing the ecological characteristics in landscapes were assigned a terrain category

DEPI Original Data



DEPI Original Data

• Quality of native veg within 100m NV2005_QUAL Modelled native vegetation quality - Includes Site Condition and Landscape context

DEPI Original Data






DEPI and Melbourne Water

Original Data




Soils and Land

Sensitivity inputs

• Land systems with assigned: o Susceptibility to wind erosion o Susceptibility to water erosion &

terrain type

LANDSYS250

Land systems of Victoria, as described and delineated by Rowan in 1990, assigned an inherent susceptibility based on soil texture and depth.

DEPI Original Data

• Ruggedness classes indicating areas of greatest and least terrain variation

RUGGEDNESS Ruggedness classes derived from 100m digital terrain model

SV Derived Data


• Native vegetation cover NV2005_EVCBCS Derived Native Vegetation extent, combining Bioregions, pre-1750 EVC's and modelled current extent

DEPI Original Data

• Site condition & landscape context NV2005_QUAL Modelled native vegetation quality - Includes Site Condition and Landscape context

DEPI Original Data

• Area impacted by land degradation (soil salinity), best available

SOILSAL25_ (arc, point, poly)

Dryland Salt affected areas mapped across Victoria between 1989 and 2000.

DEPI Original Data

• Area impacted by Coastal Acid Sulphate Soils

COASTAL_ACID_ SULPHATE_SOILS

Coastal Acid Sulphate Soils areas mapped across Victoria between 2001and 2011.

DEPI Original Data

Coastal Wetlands includes: tidal wetlands and wetlands within anticipated 2100 SLR and storm surge extent

Sensitivity inputs

• Wetland type (eg. Freshwater meadows, marshes etc)

• Wetlands Regime – Supratidal

• Water Source (eg. river, groundwater)

Wetland_Current

Wetlands of Victoria (2013 ), where attributes identify the water regime type, and relationship type to range of water sources

DEPI Original Data

• Anticipated area impacted by 2100 sea level rise and storm surge extent

slr82cm_st_2100 Victorian Coastal Inundation sea level rise and storm surge extent by year 2100 - anticipated

DEPI Original Data




Coastal Wetlands includes: tidal wetlands

and wetlands within anticipated 2100 SLR and storm surge extent



DEPI Original Data



DEPI Original Data

• Dominant land use within 100m VLUIS

Describes what land tenure, land use and land cover is present

DEPI Original Data





Appendix 14: Final Project Datasets Generated – Project Outputs



Final Project Datasets Generated – Native Vegetation (Example)

Asset Data Asset Type Dataset Name

Sensitivity Native_Veg Native_Veg_Classes

Sensitivity Native_Veg Native_Veg_Sensitivity_Rainfall

Sensitivity Native_Veg Native_Veg_Sensitivity_MaxTemp

Adaptiveness Native_Veg Native_Veg_Adaptive_capacity_Composite

Impact Native_Veg Impact_01_Native_Veg_Rainfall_rcp45_2030_Annual




Impact Native_Veg Impact_01_Native_Veg_MaxTemp_rcp45_2030_Nov_Apr












Impact Native_Veg Impact_01_Native_Veg_Worst_Case_rcp45_2030








Vulnerability Native_Veg Vulnerability_01_Native_Veg_rcp45_2030








Vulnerability Native_Veg Vulnerability_01_Native_Veg_rcp45_2030_All_Classes










Appendix 15: Acronyms



Acronyms

AVIRA Aquatic Value Identification and risk Assessment

BOM Bureau of Meteorology

CMA Catchment Management Authority

DEPI Department of Sustainability and Environment

EVC ecological vegetation class

GIS Geographic Information System

IPCC Intergovernmental Panel on Climate Change

LGA Local Government Area

NRM natural resource management

PCG Project Control Group

PV Parks Victoria

RCS Regional Catchment Strategy

SCARP Southern Slopes Climate Change Adaptation Research Partnership

SV Spatial Vision