Hydro-climate Knowledge Needs for Climate Change Adaptation · They apply large scale, long term, multidisciplinary science and aim for widespread adoption of solutions. The Flagship

WATER FOR A HEALTHY COUNTRY

Hydro-climate Knowledge

Needs for Climate Change

Adaptation Freshwater Ecosystems and Water Resources Applications

Carolina Casaril, Marie Ekström and Nicky Grigg May 2012

Report to the National Climate Change Adaptation Research Facility

Water for a Healthy Country Flagship Report series ISSN: 1835-095X

Australia is founding its future on science and innovation. Its national science agency, CSIRO, is a powerhouse of ideas, technologies and skills.

CSIRO initiated the National Research Flagships to address Australia’s major research challenges and opportunities. They apply large scale, long term, multidisciplinary science and aim for widespread adoption of solutions. The Flagship Collaboration Fund supports the best and brightest researchers to address these complex challenges through partnerships between CSIRO, universities, research agencies and industry.

The Water for a Healthy Country Flagship aims to provide Australia with solutions for water resource management, creating economic gains of $3 billion per annum by 2030, while protecting or restoring our major water ecosystems. The work contained in this report is collaboration between CSIRO and NCCARF.

For more information about Water for a Healthy Country Flagship or the National Research Flagship Initiative visit www.csiro.au/org/HealthyCountry.html

Citation

Casaril C, Ekström M and Grigg NJ (2012) Hydro-climate Knowledge Needs for Climate Change Adaptation: Freshwater Ecosystems and Water Resources Applications. CSIRO Water for a Healthy Country Flagship, Australia.

Copyright and disclaimer

© 2012 CSIRO To the extent permitted by law, all rights are reserved and no part of this publication covered by copyright may be reproduced or copied in any form or by any means except with the written permission of CSIRO.

Important disclaimer

CSIRO advises that the information contained in this publication comprises general statements based on scientific research. The reader is advised and needs to be aware that such information may be incomplete or unable to be used in any specific situation. No reliance or actions must therefore be made on that information without seeking prior expert professional, scientific and technical advice. To the extent permitted by law, CSIRO (including its employees and consultants) excludes all liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material contained in it.

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Contents

Acknowledgments .............................................................................................................................................. v

Executive summary............................................................................................................................................ vi

Part I Water Scenarios .......................................................................................................................... vi

Part II Aquatic Ecosystems .................................................................................................................. vii

Part III Building Knowledge ................................................................................................................ viii

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

1.1 This report ................................................................................................................................... 1

1.2 Delivering projected hydrometrics ............................................................................................. 1

Part I Water Scenarios 9

2 Historical and future projected hydrological metrics for Australia .................................................... 11

3 Key challenges for accessing water scenario data .............................................................................. 26

3.1 Factors influencing data accessibility in relation to the data provider ..................................... 26

3.2 Factors influencing data accessibility in relation to the data user ........................................... 27

Part II Aquatic Ecosystems 29

4 Linkages between climate change, flow and aquatic ecosystem response ........................................ 31

4.1 Linkages between climate variables and aquatic ecosystems .................................................. 31

4.2 Ecosystem response to hydrological change ............................................................................ 33

4.3 Implications for adaptation strategies ...................................................................................... 36

Part III Building Knowledge 43

5 Identifying cross-sectoral information needs for climate-water adaptation...................................... 45

5.1 Catchment Values ..................................................................................................................... 46

5.2 Vulnerabilities and action triggers ............................................................................................ 48

5.3 Required Ecosystem characteristics ......................................................................................... 50

5.4 Adaptation Options ................................................................................................................... 51

5.5 Cross-sectoral information needs ............................................................................................. 58

5.6 Ranking and sorting .................................................................................................................. 60

6 Modelling Opportunities ..................................................................................................................... 61

6.1 Modelling requirements and opportunities unique to adaptation .......................................... 61

6.2 Issues in climate change adaptation: implications for models ................................................. 62

6.3 Summary ................................................................................................................................... 73

7 Conclusions ......................................................................................................................................... 76

Appendix A Sustainable Yields Projects ....................................................................................................... 80

Appendix B Workshop iMeet downloads .................................................................................................... 98

References ...................................................................................................................................................... 120

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Figures Figure 1 Cascade of uncertainties in climate change projections (adapted from Giorgi 2005). ........................ 6

Figure 2 Areas covered by projects reviewed in this report. ........................................................................... 11

Figure 3 Boundaries for the SEACI region and the 219 gauged catchments used to calibrate the rainfall-runoff models (CSIRO, 2010) ............................................................................................................................ 21

Figure 4 A map of the sub-regions of Queensland identified in the report Climate Q (DERM, 2009) for Regional Summaries. ........................................................................................................................................ 23

Figure 5: Conceptual model of the cascading impacts of changes in the climate drivers on ecological responses in aquatic ecosystems, adapted from Sheldon et al. (2010). .......................................................... 32

Figure 6 Number of citations each year for papers with the topic “environmental flows” (Web of Knowledge search, March 2012, search term “environmental flows”, returning 582 papers with a total of 4523 citations). ................................................................................................................................................. 33

Figure 7: Different components of the natural flow regime are ecologically important over a range of temporal scales. Image from Pusey and Kennard (2009). ................................................................................ 34

Figure 8 Overview of synthesising principles: “Firstly, flow is a major determinant of physical habitat in streams, which in turn is a major determinant of biotic composition; Secondly, aquatic species have evolved life history strategies primarily in direct response to the natural flow regimes; Thirdly, maintenance of natural patterns of longitudinal and lateral connectivity is essential to the viability of populations of many riverine species; Finally, the invasion and success of exotic and introduced species in rivers is facilitated by the alteration of flow regimes.” (Bunn and Arthington, 2002). Image from Figure 1 in Bunn and Arthington, (2002). ......................................................................................................... 34

Figure 9 Structured approach to characterise ecologically significant changes to flow regime (a) classifying rivers and streams by hydrological characteristics; (b) for each class, identify frequency distributions for important flow variables; (c) compare flow-modified streams with reference streams within each class; (d) derive flow-response relationships to characterise health indicators as a function of departure from reference flow condition (Arthington et al., 2006). Image from Arthington et al. (2006) courtesy of Ecological Society of America. ............................................................................................ 38

Figure 10: Climate and hydrology changes identified in workshop discussion (left) that are anticipated to trigger changes to valued aspects of catchment (right). These lists are not comprehensive, but indicative of the range of changes and impacts discussed. (See Apx Table B.2.) ............................................................. 49

Figure 11 Rainfall elasticity of runoff in Australia (Chiew, 2006) Image courtesy of eWater CRC. .................. 64

Figure 12 Percentage change in annual runoff modelled using 15 different global climate models for a 0.9°C increase in global average surface air temperature. Image from Figure 8 in Chiew et al. (2011). ......... 65

Figure 13 Example of the range of model types and purposes used in a multi-model approach to stakeholder engagement (Fulton et al., 2011). ................................................................................................ 68

Figure 14 The importance of a ‘learning loop’ in natural resource governance is well appreciated. In dealing with requirements to transform a system significantly, it is useful to recognise double- and triple-loop learning processes that facilitate substantial change (Pahl-Wostl, 2009). Figure adapted from Pahl-Wostl (2009). ............................................................................................................................................ 71

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Tables Table 1 List of projects reviewed in this report; reference literature, extent of study area, spatial and temporal domain. The final column gives the Appendix that contains a summary of project hydrometrics relevant to this report. ..................................................................................................................................... 13

Table 2 Estimated impact of climate change on urban water demand in 2056 (DERM 2008a,b,c). ............... 24

Table 3 Estimated impact of climate change on rural water demand in 2056 (DERM 2008a,b,c). ................. 25

Table 4: Modified summary table from Poff and Zimmerman (2010) looking at the common ecological responses of aquatic and riparian organisms in relation to flow parameter alteration. ................................. 35

Table 5 Categories of useful indicators for flow alteration–ecological response relationships (adapted from Poff et al (2010)). ..................................................................................................................................... 39

Table 6: Catchment values as identified by workshop participants (Apx Table B.1). Following the workshop, values were loosely categorised into three areas (riparian zones, water, and water bodies) and crosses were added to denote whether related services are supporting, provisioning, regulating and/or cultural, a classification according to the Millennium Ecosystem Assessment (Millennium Ecosystem Assessment, 2005). ......................................................................................................................... 46

Table 7: Summarised responses to discussion on required characteristics for desired outcomes. Summary is derived from full table listed in Appendix B (Apx Table B.4). ....................................................... 51

Table 8 List of adaptation options identified by workshop participants (more details in Appendix B, Apx Table B.3), classified approximately according to three categories of response: knowledge acquisition/research; on-ground action; governance or conceptual framework. ........................................... 52

Table 9 List of information needs identified during the workshop, organised here into three categories: physical, social/economic and system-level information. ............................................................................... 58

Table 10 Table from (Pahl-Wostl, 2007) comparing a prediction and control regime versus an integrated, adaptive regime. ............................................................................................................................................... 75

Apx Table A.1 Average annual surface water balance for the Murray-Darling basin (CSIRO, 2008, p. 30) ..... 80

Apx Table A.2 Effect of climate change by 2030 on water availability (GL/year) for each region and the Murray-Darling Basin as a whole (CSIRO, 2008, p. 35). .................................................................................... 81

Apx Table A.3 Total surface water use (GL/year) by region under different climates (CSIRO, 2008, p. 39). ... 82

Apx Table A.4 Summary of gaps between yield and demand for the entire project area. Positive numbers are surplus of water; negative numbers are deficit (Table 8.2 in CSIRO, 2009a). ............................................ 83

Apx Table A.5 Average annual rainfall, runoff, runoff coefficients, and streamflow volumes under Scenario A (historical) and changes in these for each surface water region in the project area and the area as a whole under scenarios B, Cwet, Cmid and Cdry. (Table 2-4 in CSIRO, 2009a) ................................. 83

Apx Table A.6 Current surface water yields by surface water management area under Scenarios A, B and C (Table 7-2 in CSIRO, 2009a) ........................................................................................................................... 85

Apx Table A.7 Current groundwater yields by groundwater management area under scenarios A, B and C (Table 7-4 in CSIRO, 2009a) .............................................................................................................................. 86

Apx Table A.8 Threshold flow rates associated with the identified ecological functions, flow frequency under Scenario A and the change under scenarios B, Cwet, Cmid, and Cdry relative to Scenario A. (CSIRO, 2009a, p. 222). .................................................................................................................................................. 88

Apx Table A.9 Threshold flow rates associated with the identified ecological functions, flow frequency under Scenario A and the change under scenarios B, Cwet, Cmid, and Cdry relative to Scenario A (CSIRO, 2009a, p.224). ................................................................................................................................................... 89

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Apx Table A.10 Summary table of climate changes for each region within each drainage division and all-of-project area (Table I in CSIRO, 2009c).......................................................................................................... 90

Apx Table A.11 Summary table of changes to surface water for each region within each drainage division and all-of-project area (Table I in CSIRO, 2009c) .............................................................................................. 93

Apx Table A.12 Summary of key findings for the project area and each of the five project regions. Results are presented as absolute number for the historical climate whilst recent climate results are displayed as relative values to the historical climate. Note that results for recent climate are for an 11-year drought and are not directly comparable with the results for future climate, which are based on and 84-year average. Results for future development are presented relative to the future climate where indicated (CSIRO, 2009e, p. 4) .......................................................................................................................... 96

Apx Table B.1 Discussion 1 and 2: Service/Function value............................................................................... 98

Apx Table B.2 Discussion 3: Climate/Hydrology Impacts ............................................................................... 100

Apx Table B.3 Discussion 4: Adaptation Actions ............................................................................................ 104

Apx Table B.4 Discussion 5: A functioning ecosystem .................................................................................... 108

Apx Table B.5 Discussion 6: Resilience and Adaptation ................................................................................. 113

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Acknowledgments

This work was funded through an agreement between the Water Resources and Freshwater Biodiversity Adaptation Research Network, hosted by the Australian Rivers Institute at Griffith University, under the National Climate Change Adaptation Research Facility (NCCARF), and the Commonwealth Scientific and Industrial Research Organisation (CSIRO) through its “Water for a Healthy Country Flagship.

The authors would like to express their gratitude to all participants of the workshops conducted within the Water Resource Modelling project, David Post and Carmel Pollino for their helpful reviews, and Jin Teng for creating Figure 2 in this report.

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Executive summary

The Water Resource Modelling Project (WRMP) is a one year collaborative effort between the Commonwealth Scientific and Industrial Research Organisation (CSIRO) through its Water for a Healthy Country Flagship and the Water Resources and Freshwater Biodiversity National Adaptation Research Network of the National Climate Change Adaptation Research Facility (NCCARF).

Under climate change, current hydrological systems are exposed to potential changes in climatic variables such as temperature and rainfall. However, what these changes may look like, such as their impacts on flow, water quality and aquatic biota, are still largely unknown. The objectives of WRMP involve identifying where metrics quantifying characteristics of hydrological regimes, so called ‘hydrometrics’, can provide guidance on how such impacts can be analysed or quantified and where such metrics can to be integrated into a broader framework that includes ecological and social dimensions. Specifically, the project has compiled a summary of catchment yields and runoff characteristics for historic, current and future climates for a range of regions within Australia and conducted workshops aimed at improving understanding of what information is perceived to be important to stakeholders in the water sector as well as identifying new roles for hydrological models in this context. Project outcomes, as summarised by this report, are organised into three sections, each with a separate summary that follows below:

Part I Water Scenarios

The first part of the report provides an inventory of catchment yields and runoff characteristics for historic, current and future climates for a range of regions within Australia that is currently available for users in the water resources and freshwater ecosystem community. The inventory focuses on work conducted foremost by Commonwealth and State funded projects that aim to make results available for a wider public audience, such as the Commonwealth funded Sustainable Yields projects for the Murray-Darling Basin, Northern Australia, Southwest Western Australia and Tasmania. In addition, results from the South-Eastern Australia Climate Initiative (SEACI), New South Wales Office of Water (NOW) and work done by the Queensland Department of Environment and Resource Management (DERM) are also included in this report. Methodologies and time horizons for model outputs are briefly summarised for each project in sections 2.1.1. to 2.1.7. and key results of selected flow metrics are compiled in appendix A.

KEY FINDINGS

Climate downscaling and hydrological assessments have been done primarily for Australian regions that experience population pressures on water resources.

The temporal windows selected to represent current climate, i.e. the climatology baselines, differ amongst the reviewed projects in this report. The main reasons being data accessibility and different time periods being better suited to represent the regional climate at different geographical locations. In the reviewed studies, baseline climates are referred to as either historical or recent climates; specific details about these are provided in the respective project section. The future scenario data tend to focus on the 2030s, however some studies include 2050 and/or 2070 projection horizons.

The reviewed projects consistently examine the climate parameters: temperature, rainfall, evaporation as well as other climate related events such as cyclone frequency, seasonal patterns and associations with ENSO, and days over 35 degrees Celsius.

Hydrological assessments within the reviewed projects look at aspects of river flow associated with hydrological flow regimes, storage inflows and flow reliability. Water use and water demand are also presented for assessed time horizons projecting potential overall water availability under various climate

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change scenarios. Groundwater assessments are consistently included and where possible interlinked with surface water resources to look at overall water availability.

Environmental flows are assessed in most reviewed projects, particularly changes to flow metrics at specific sites identified as ecological assets. Flow metrics include dry and wet season flows, and number of days below/above low and high flow thresholds. Ecological functions associated with flow thresholds include maintenance of pool habitats, migration and connectivity, inundation of floodplains, benches and channels, and riparian vegetation. Changes in the depth to water table are used as an indication of wetland habitat impacts associated with groundwater.

Data accessibility is complex; there is a will to provide data but technical complexities and licensing sensitivities often create difficulties for data providers. For the majority of projects, climate and rainfall-runoff data can be accessed either via data portals or via designated project contact persons. Access to river flow outputs is more complex due to issues around licensing and may not be available for all projects.

Data providers express concern about communicating the limitations inherent in climate and hydrological model data, particularly the climate change data. Measures taken to address these concerns involve limiting the direct access to the data (to ensure personal contact is first initiated) or providing informative disclosure statements.

Part II Aquatic Ecosystems

The second part of the report looks at the role of hydrological metrics, primarily flow, in characterising, quantifying and monitoring the potential impacts and responses of freshwater biodiversity and aquatic ecosystems to climate change. This information was collated foremost from a selection of peer-reviewed literature reviews and studies.

KEY FINDINGS

There are multiple, interacting ways in which climate change is expected to impact on freshwater ecosystems via a range of drivers (temperature, sea level, precipitation, evaporation and ultra-violet-B (UVB) radiation) and mediated by changes in flow regime, stratification, biogeochemistry, geomorphology, primary production and foodweb interactions.

Ecosystem response to climate change cannot be characterised only with respect to flow regime, but maintaining key components of flow regimes is a vital requirement for protecting and maintaining freshwater biodiversity, ecological processes and societal benefits provided by aquatic ecosystems.

The attributes of the flow regime measured and modelled for managing water quantity are not necessarily the same attributes required by ecologists when characterising ecosystem responses to changes in flow and advising on environmental flow requirements. Extremes, rates of change, predictability, variability, timing, frequency and duration of events are all significant.

Significant syntheses of freshwater biological research in Australia have distilled useful generic principles: principles that describe and organise ecosystem dependencies on flow; and principles for informing environmental flow guidelines.

Such general principles are necessary, but not always sufficient to fully inform on-ground decisions and actions. Informed by these general principles, more detailed assessments have been made:

– classification of important flow-regime characteristics for Australian rivers based on 120 metrics of ecologically-relevant flow-regime characteristics, and classification of Australian rivers into distinct classes based on flow properties;

– comprehensive lists of indicators that are useful for characterising ecological response to alteration to flow.

Syntheses of current literature are not always sufficient to characterise ecological needs in response to flow – ongoing fieldwork aimed at contributing to this synthesising framework is needed, particularly

viii | Climate Change Adaptation for Water Resources and Freshwater Biodiversity

given the spatial heterogeneity, climate variability, rates of change and diversity of unique ecosystems in Australia.

Environmental intelligence efforts at a national level are significant, particularly developments in the Bureau of Meteorology, the National Plan for Environmental Information (NPEI) and investments via the National Collaborative Research Infrastructure Strategy (NCRIS). There is a clear need for such national-level knowledge infrastructure.

There are ongoing challenges associated with managing aquatic ecosystems for prescribed reference conditions (e.g. a natural flow regime). Identifying benefits from ecosystem goods and services and seeking to ensure resilience of those benefits to climate change and other changes is a growing response to that challenge.

Part III Building Knowledge

This part of the report evaluates outcomes from a cross-sectoral workshop (‘Climate change adaptation in the context of freshwater biodiversity and water recourses’) aiming to better understand the links and feedbacks that occur as society responds to changes in climate and subsequent impacts on hydrology and aquatic systems and a subsequent CSIRO ‘in-house’ workshop focusing on the role of hydrological models in this context.

Both workshops were organised as part of the WRMP and run by CSIRO in collaboration with NCCARF with an aim to identify knowledge needs across water sectors when considering climate change adaptation options.

KEY FINDINGS FROM THE CROSS-SECTORAL WORKSHOP

Workshop participants emphasised strong interdependencies between climate, water and flow-on effects of impacts across different sectors. Participants readily identified key climate change vulnerabilities in their sectors, valued catchment goods and services, and the catchment attributes necessary to supply those valued outcomes.

Climate change adaptation options identified by participants can be classified approximately into three kinds: knowledge acquisition or research requirements (e.g. long-term monitoring, risk and vulnerability assessments); on-ground actions (e.g. fencing riparian zones, managed aquifer recharge, changes in storage operations); and changes in governance or conceptual frameworks (e.g. water stewardship frameworks, local level collaborative planning, approaches for avoiding maladaptive short-term adaptation).

The information needs identified by participants were not limited to biophysical data and model output. Rather, participants identified a range of metrics that capture social or economic attributes of importance, and more integrated measures of system-level responses to change.

Benefits of seeking desired outcomes that are resilient to climate change were discussed, and led to discussions on how to enable transformational change in communities and industries alike (particularly in the light of social and political dynamics that favour short-term reactive responses over long-term system-level change).

Adaptation actions are susceptible to unanticipated (and potentially unwanted) consequences due to feedbacks, time delays, nonlinearities and other system attributes. Social processes, not easily understood by working solely with biophysical datasets and models, mediate many of the system links and feedbacks.

Despite their diverse backgrounds and experience, participants were able to find some shared intent among their messages. For example, when seeking commonalities across the group, responses to ‘what we need?’ included not only physical entities (e.g. water supply systems) but non-physical requirements such as knowledge, governance and institutional structures.

Although the workshop involved some ranking and prioritisation of identified catchment values, impact and adaptation options, participants recognised that in general prioritising a limited set of options or information limits opportunities for identifying co-benefits or unanticipated impacts across sectors.

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A high proportion of adaptation options involved general principles, conceptual frameworks or governance requirements and has two implications:

– when working with a cross-sectoral group, the potential for identifying shared options is improved if there is a willingness to include general principles, conceptual frameworks and social processes (e.g. governance arrangements).

– given the ready ability to identify shared generic, framing options, the challenge in climate change adaptation is to build experience of applying those principles and frameworks in different on-ground contexts and sharing those experiences as working examples.

Specific details of values, impacts and adaptation options by participants highlighted that improved knowledge or data alone isn’t sufficient, especially when taken out of context. The knowledge needed to understand the impacts of decisions on the water system are more likely to be appreciated and acted upon when diverse perspectives are brought together for a shared (applied) purpose.

IMPLICATIONS FOR MODELLING AND FUTURE KNOWLEDGE BUILDING

Modelling can serve many purposes, and is not solely for offering predictions about the future. In planning and decision-making for uncertain futures, modelling tools provide a diverse range of approaches that enable exploration of options, organisation and integration of knowledge, and communication and engagement. It is important to be clear about model purpose and where any particular modelling work fits into the broader decision-making context.

Australian and international experience points to particular benefits of using models within adaptive management frameworks. There are fundamental differences between managing for prediction and control, versus managing for integration and adapting, and it is the latter management regime that is required for climate change adaptation.

Knowledge requirements for adaptation are different to those for studying impacts (although necessarily build on knowledge of likely impacts).

Uncertainty need not prevent decision-making, and there are examples of how to apply knowledge so that decision-making benefits from extra information about the uncertainty in that knowledge, and so that decisions are robust to that uncertainty.

Model-derived metrics and estimates underlie many management targets and standards. A continued emphasis on verification, traceability and compliance, combined with a wise balance between practical usability and scientific rigour, is needed as many of the decision-making and governance structures increasingly rely on these systems for timely and equitable management operations.

The development of adaptation options is more about exploration than optimisation, and benefits from modelling approaches that facilitate exploration. Governance structures that make learning and exploration a priority can draw on a range of modelling methods that support these priorities.

Participatory modelling and related approaches offer particular opportunities for stakeholder engagement, and engagement in a way that allows the dynamics of climate-water-society interactions to be explored and better understood.

Scientific researchers and institutions are necessarily acknowledging more explicitly the requirements for science outcomes to be better integrated into societal processes that lead to workable, adaptable decisions that reflect both scientific knowledge and societal values and goals. This recognition is affecting not only the delivery and communication of science, but the actual research questions, methods and models.

Climate Change Adaptation for Water Resources and Freshwater Biodiversity | 1

1 Introduction

1.1 This report

This report is prepared as reference material for the Water Resources and Freshwater Biodiveristy Adaptation Research Network of the National Climate Change Adaptation Research Facility (NCCARF).

In ‘Water scenarios’ (Part I), this report provides an inventory of studies that detail water yields and rainfall-runoff characteristics for historical, present and future time frames in Australian catchments (Section 2). The aim of this inventory is to provide an overview of data that are currently available for developers of climate change scenarios in the water sector, henceforth referred to as ‘water scenarios’. In addition to the inventory, Part I also provides information on key challenges in developing water scenarios, as identified during the preparation of this report (Section 3).

The ‘Aquatic ecosystems’ (Part II), section focuses on the role of hydrological metrics in characterising, quantifying and monitoring the potential impacts and responses of freshwater biodiversity and aquatic ecosystems to climate change (Section 4) and the ‘Building knowledge’ (Part III) section discusses identified knowledge needs across water sectors in the context of climate change adaptation (Section 5-6). The final section provides conclusions drawn from the entire report (Section 7).

Whilst Part I and II rely foremost on previously published material, Part III presents new findings primarily based on two workshops organised within the WRMP. The first workshop, ‘Climate Change Adaptation in the context of Freshwater Biodiversity and Water Resources’, had a cross-sectoral focus, drawing on the knowledge of 25 invited participants representing a wide range of stakeholders in the water and biodiversity communities. The workshop sought to identify commonalities in objectives and collaborative opportunities across different sectors intersecting with water in the context of climate change adaptation. Drawing on issues raised in the workshop discussions, the implications for the role of models and model outputs was then discussed at a follow up CSIRO ‘in house’ workshop involving modellers within the fields of hydrology, climatology and ecology. The purpose of the subsequent workshop was to identify how models can be better utilized for decision making within adaptation work.

1.2 Delivering projected hydrometrics

To facilitate decision making in the water sector under a greenhouse gas (GHG) forced climate, stakeholders increasingly expect the scientific community to provide estimates of local climate and hydrological variables that affect the environment, economy and society. These variables can be used to construct metrics that quantify particular characteristics of hydrological regimes, so called ‘hydrometrics’. However, local scale hydrometrics, such as flow, are associated with large uncertainties and some knowledge about their characteristics and limitations are crucial prior to using the data to inform on possible future climate risks. Nevertheless, despite large uncertainties, there are roles for model-derived hydrometrics within climate adaptation planning. In the following few sections we provide a short justification for why hydrometrics are valuable tools in adaptation work as well as a summary of the modelling steps used to derive projected hydrometrics and those methodologies commonly used to describe and quantify uncertainties in the modelling process.

1.2.1 DOES CLIMATE CHANGE POSE REASONS FOR CONCERN IN THE WATER SECTOR?

Over the past 250 years, human induced emissions of GHG have resulted in increasing global atmospheric temperatures (Le Treut et al., 2007) and due to a strong growth of the world economy up until the 2010s and increase in its carbon intensity in combination with a decreased efficiency of natural carbon sinks, the

2 | Climate Change Adaptation for Water Resources and Freshwater Biodiversity

rate of atmospheric CO2 has increased rapidly since 2000 suggesting a stronger than expected CO2 forcing sooner than anticipated by existing scenarios (Canadell et al., 2007).

There are multiple ways in which GHG forced climate change can alter existing hydrological regimes via changes to precipitation and evaporation rates. For example, climate change can modify the surface and tropospheric moisture and energy budgets (Boer, 1993; Allen and Ingram, 2002) via the Clausius-Clapeyron (CC) expression, defining the relation between temperature and saturation vapour pressure (Held and Soden, 2006). The importance being that as the atmosphere warms it is able to hold larger quantities of water vapour. Changes to hydrological regimes can also occur due to disruptions of current synoptic circulation systems, which impact the horizontal transport of water vapour in the atmosphere and hence regional rainfall patterns (e.g. Giorgi and Lionello, 2008). Recent changes to Australian rainfall patterns have already been attributed to such disruptions of synoptic circulation systems, e.g. in South-west Western Australia both the strengthening of the mid latitude high pressure systems (Hadley Cells) and the depletion of ozone in the stratosphere over Antarctica are thought to be contributing to cold fronts being pushed further south so that they no longer cross the south west corner as high as they once did (Cai et al. 2005; Cai and Cowan 2006; van Ommen et al. 2010) and the strengthening of the Sub-Tropical Ridge was shown to play a role in the Millennium drought in the south-western part of Eastern Australia (Timbal et al. 2010).

If one acknowledges that climate change can influence current hydrological regimes, one also has to acknowledge that climate change poses a risk to freshwater eco-systems. A synthesis of impacts on climate, eco-hydrology and consequential impacts on ecosystems and species is provided by the Water Sector Board of the Sustainable Development Network of the World Bank Group (Le Quesne et al., 2010, their Table 2.1). Their synthesis highlights the multitude of ways in which a changing climate may directly or indirectly impact species and ecosystems. For example, increased precipitation and runoff with more intense rainfall events cause higher and more frequent storm flows, which damage riparian and bottom-dwelling organisms, changes the structure of available habitat (e.g. wider floodplains), and cause less shading from near-channel vegetation that leads to extreme shallow water temperatures.

To better understand potential risks associated with climate change to water resources and biodiversity, hydrometrics derived from model simulations can help investigate what physical processes, such as changes to synoptic scale circulation, are affected by climate change and therefore potential impacts on the local surface environment. Whilst models provide hydrological and climatological estimates of a ‘model world’ they can help to identify possible pathways and sensitivities in the physical systems otherwise difficult to envisage without the support of models.

1.2.2 DESCRIBING FUTURE CLIMATES

The first step towards tackling climate change involves assessing what potential GHG concentrations could occur in different futures. Knowing these concentrations is crucial to climate change modelling as these provide the basis for the climate change forcing, e.g. changing the concentrations of GHGs in the atmosphere alters the radiation and heat balance of the planet. Since ‘knowing’ what concentrations of GHGs may occur in the future is impossible, the climate change community use descriptions of future worlds (scenarios) as a strategy to systematically investigate the climate impact of possible emission futures.

Over the past decades, the Intergovernmental Panel on Climate Change (IPCC) has played an important role in providing emission scenarios for the research community, where each scenario represents a probable future with no attached likelihood. The first set of scenarios, the 1990 IPCC scenario A (SA90, IPCC, 1990), were developed for the IPCCs first assessment report (FAR), the 1992 IPCC Scenarios (IS92; Leggett et al. 1992) used for the third assessment report (TAR), the Special Report of Emissions Scenarios (SRES; Nakidenovid et al., 2000) used for the fourth assessment report (AR4). At each update, the scenarios have evolved to better represent the needs of the research community in terms of portraying current knowledge and considering emerging research priorities. However, for the upcoming fifth assessment report (AR5) the IPCC decided not to release a new set of scenarios but instead handed the process to the research


community. In their response to this challenge, the research community have provided a fundamentally different process in which new scenarios can be delivered to the modelling communities.

Whilst the previous IPCC scenarios were useful at their time, a decade has passed since the release of the SRES, making an update essential to reflect current research agendas. The key points of motivation for why new scenarios were needed by the modelling community are discussed in Moss et al. (2010) and can be summarised as:

The need to reflect new economic data, information about emerging technologies, and observations about environmental factors, such as land use.

The need for scenarios to reflect new needs expressed by policy makers, e.g. overshoot scenarios.

The need for high spatial resolution data for near-term (next 2-3 decades) time periods with improved representation of extreme events for studies of climate change impacts and adaptation planning.

Adaptation work requires development of socioeconomic scenarios that support analysis of vulnerability.

New research interests require modelling time span beyond the previous end point 2100.

As models simulate more physical processes, modellers require more detailed emission scenarios along with consistent land-use and land-cover data and projections.

Due to an increasing overlap between climate, impact and integrated assessment models, there is a need for harmonisation of assumptions and data on some initial conditions, within the limits posed by historical and observational uncertainties.

In addition to the changes listed above, a more time efficient process of delivering the scenarios was also suggested. The previous IPCC scenarios were produced using a sequential approach, i.e. emission scenarios based on different socioeconomic futures were used to estimate emission concentrations that then were used as forcing in climate models and at the final step results were made available to the impact community. The sequential process proved very lengthy and to shorten the time of data delivery to the impacts community, a new parallel approach was proposed for the new scenarios.

In the parallel approach, the first step involves identifying important characteristics for scenarios of radiative forcings, in particular the radiative forcing at the year 2100. These radiative forcing trajectories are not attributed to any particular socioeconomic scenario but could arise due to a range of different combinations of economies, technology developments, demographics etc. (Moss et al., 2010). When these trajectories have been identified, climate change modelling and integrated assessment modelling can take place simultaneously so that climatic scenarios are developed in parallel with socioeconomic and emission scenarios and vulnerability storylines (Moss et al., 2010). The new approach is aimed at facilitating cooperation between the integrated assessment modellers, climate modellers and vulnerability, impact and adaptation research, which in turn will lead to more consistent and comparable research within these research communities (van Vuuren et al., 2012).

To highlight that each trajectory can be generated by a range of many different scenarios and that the trajectory itself is as important as the final concentration, the new scenarios are referred to as representative concentration pathways (RCP) mitigation scenarios (Moss et al. 2010). Based on peer-reviewed literature, four RCPs were chosen to represent the radiative forcing pathways: RPC2.6, RPC4.5, RPC6.0 and RPC8.5 (where the numbers denote the approximate W/m2 forcing by 2100). Together, the scenarios span the radiative forcing scenarios in the published literature at September 2007, however they do not as a group have a consistent internal logic, i.e. they are all associated with different socioeconomic, technology and biophysical assumptions (Moss et al., 2010).

1.2.3 FROM LARGE SCALE ATMOSPHERIC MODELS TO LOCAL SCALE HYDROLOGICAL MODELS

The climate response to emission scenarios is investigated using Coupled Atmosphere-Ocean General Circulation Models AOGCMs. These models are state-of-the art numerical integrations that represent subsystems of the Earth’s climate that simulate the large scale state of the global climate. Each model simulation requires vast computing resources as the AOGCMs are integrated over long time periods. However, as AOGCMs focus on the large scale state of the climate and its outputs is usually on scales of


100-300km, data from AOGCMs are not suitable for most hydrological studies which generally focus on processes that occur on much smaller spatial scales.

To bridge the gap between the AOGCM output scale and that expected by most hydrological models, an additional modelling step, commonly referred to as downscaling, is needed. There are a number of different techniques available, but most can be categorised into either statistical downscaling or dynamical downscaling. Statistical downscaling are methods that use some form of statistical relationships, e.g. correlation, between large scale atmospheric variables and local surface variables. In dynamical downscaling, a climate model, usually only the atmosphere part, is used to simulate climate on a higher spatial resolution than that of the AOGCM. Because the model is usually run for a specific region (model domain), these types of models are commonly referred to as a Limited Area Models (LAMs) or Regional Climate Models (RCMs). The benefit of focusing on a specific region is that the computing power can be used to provide high-resolution outputs over a small area, rather than the case of AOGCMs, which provide low-resolution data over large areas. Some criticism of the dynamical downscaling suggests that the RCM output is still not of high enough resolution to be of benefit to small-scale hydrological applications (Chen et al., 2011). Although other downscale approaches exist, e.g. using AOGCM data re-gridded to a finer resolution, such simple methods are not recommended as they produce false geographic precision to the estimates (IPCC-TGICA, 2007).

Both statistical and dynamical downscaling are data intensive as well as computing intensive, the latter more so than the former. However, the approaches are fundamentally different to each other, as statistical methods largely reproduce the change patterns of the AOGCMs, relying on relationships established between local and large scale variables as defined in observed climates, whilst dynamical models are able to produce local climate changes that are different from the large-scale estimates. However, some argue that whilst the RCM simulates climate on local scale, it doesn’t necessarily mean that this information is more accurate due to (Pielke Sr and Wilby, 2012):

shortcomings of the GCM to simulate first order climate forcings and feedbacks;

shortcomings in accurate simulation of important regional drivers of climate;

propagation of errors in the GCM to the RCM;

the lack of information transfer from RCM to GCM; and

that GCMs fundamentally don’t provide information on regional scale and therefore this information can’t be passed on to the RCM to simulate regional scale processes.

For these reasons, it is argued that RCMs outputs are best used for investigating possible future risks rather than predictions of future climates (Pielke Sr and Wilby, 2012).

Comparisons and reviews of downscaling methods for the purpose of hydrological frameworks have been made by e.g. Fowler et al., 2007, Frost et al., (2009) and Maraun et al. (2010). The general consensus is that there is no optimal method, as the skill of downscaling approach depends on data availability, regional and meso-scale climate as well as on the meteorological variable in question (Fowler et al. 2007). Due to the different strengths and weaknesses of the downscaling methods, the choice of downscaling method could have a large impact on the estimation of local variables. Whilst the driving AOGCM is generally thought to provide the largest source of uncertainty in regional projections, the uncertainty envelope given by using a range of different downscaling methods could be of similar order of magnitude (as demonstrated in Chen et al., 2011).

1.2.4 BASELINE CLIMATES

To understand potential impacts on hydrological regimes in future climates, one must first identify the drivers that underpin the physical characteristics of hydrological regimes under current climatic conditions. Selecting a time period to represent the current climate, often referred to as the baseline climate, is not always straightforward, particularly in regions such as Australia that see large natural variability on decadal scales and has a relatively short historical record. For example, using historical records from the twentieth century as a baseline climate for the southeast of Australia will generate a wetter baseline climate than


using climate data from a shorter period from the 1990s and 2000s, which is dominated by the Millennium drought.

The IPCC recommends that issues to consider when choosing a climatological baseline include what types of data is required, duration of the baseline periods, sources of data and how the data can be applied in an impact assessment (IPCC-TGICA, 2007). The IPCC further recommends that the following criteria are considered when choosing the baseline period (IPCC, 1994):

The data should represent present day or recent average climate in the study region.

The period should be of a sufficient duration to encompass a range of climatic variations, including a number of significant weather anomalies.

The period should cover a period for which data on all major climatological variables are abundant and adequately distributed over space and readily available.

The data covered in the periods should be of sufficiently high quality for use in evaluation impacts.

The period should be consistent or readily comparable with baseline climatologies used in other impact assessments.

The need to consider the regional and temporal characteristics as well as availability of data are all valid reasons for selecting different baseline periods over a large region such as Australia, as reflected in the reviewed projects in section 2. However, in using different baselines the impact results from different regions aren’t necessarily directly comparable, and thus attention to what time periods have been used to construct the baseline is needed when synthesising impact results from different regions/studies.

1.2.5 IDENTIFYING UNCERTAINTIES

In order to generate simulated hydrological metrics, such as runoff or river-flow, several assumptions and modelling steps are required and each is associated with its own type of uncertainties, e.g. statistical, scenario or qualitative uncertainty, which can be of either epistemic and/or stochastic nature. The magnification of uncertainty within a top-down climate change decision framework has long been recognised, at least conceptually. In 1983, Schneider provided a schematic representation of uncertainties associated with the CO2 problem in the form of an inverted pyramid (Schneider, 1983). At the first, or narrow, level in the pyramid, uncertainties are associated with the behavioural assumptions around human activities that can lead to reductions or increases in emissions. Moving upwards, the ever broadening following levels represent the accumulation of uncertainties associated with global climatic response to emissions, then the regional climatic response, physical impacts, economic, social impacts and finally policy responses. This concept, which Schneider (1983) referred to as a cascading pyramid of uncertainties has been developed several times since, e.g. by Henderson-Sellers (1993), Jones (2000), Schneider and Kuntz-Duriseti (2002), Mearns et al. (2001) and Giorgi (2005).

In Giorgi (2005) the concept of cascading uncertainty has evolved into a fuller diagram showing the inter-linkages between the sources of uncertainty (remaining much the same as those in Schneider 1983) and the process of policy responses (referred to as adaptation or mitigation) as well as interactions and feedbacks within the model steps (from AOGCMs down to impact models) (Figure 1).


Figure 1 Cascade of uncertainties in climate change projections (adapted from Giorgi 2005).

Much work derived from water scenarios is intended to quantify the risk of climate change for planning and policy decisions (e.g. Hickox and Nichols, 2003). However, the increase of uncertainty through the framework, as portrayed in Figure 1, pose difficult challenges for planners in the water sector simply due to the large range in possible outcomes. In many situations, it may be preferable to consider the use of model based scenarios as decision support tools instead of decision making tools, i.e. using the scenarios to identify interdependencies of relevant processes which might aid in decision making in an adaptation context, rather than expecting them to provide management solutions (e.g. Barthel et al., 2008).

Whichever the intended role of the impact model data, it is crucial that the end-user is well informed about what type uncertainties are embedded in the scenario, i.e. what uncertainties are represented in the range of outcomes of the impact model - did the scenario consider more than one emission scenario, more than one AOGCM and downscaling method, etc. In an analysis of lessons learnt from the UK Climate Impacts Programme 2002 (UKCIP02), Gawith et al. (2009) found that whilst the climate scenarios had done much to raise awareness on climate change and in engaging organisations in the need to adapt, more work was required to inform users on how to use uncertain information in decision making. Fundamentally, the authors found that scenarios needed to be accompanied by ongoing guidance and support to ensure widespread and appropriate uptake and that a continuous dialogue between providers and user communities were required to ensure that the scenarios both represented user requirements and expectations as well as kept within the limits of what science can currently deliver.

To ensure best practice in terms of deriving water scenarios, the IPCC Task Group on Data and Scenario Support for Impact and Climate Assessment (TGICA) provide guidance on the use of scenario data for climate impact and adaptation assessment (IPCCA-TGICA, 2007). The objectives of the IPCCA-TGICA report is to provide: i) descriptions of the analytical tools that its Data Distribution Centre (DDC) provide, ii) instructions of how to interpret baseline and scenario data held at the DDC and iii) information on the key steps and procedures for using baseline and scenario data for impact and adaptation assessments. The latter outlines issues related to:

Selection of model outputs (as originally proposed by Smith and Hulme (1998)):

– Vintage – try to use the most recent model results as these models are based on the most recent knowledge of physical processes in the climate.

– Resolution – more recent model experiments use models with output on a higher resolution. The higher spatial resolution does, however, not guarantee a superior model performance.

– Validity – Select model outputs that simulate the present day climate most faithfully

Socio-Economic Assumptions

Emission scenarios

Concentration Calculations

Biogeochemical/Chemistry models

Global Climate Change Simulation

AOGCMs

Regional Climate Change Simulation

Downscaling techniques

Impacts

e.g. hydrological and ecosystem

response models

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Emission scenarios

Concentration Calculations

Biogeochemical/Chemistry models

Global Climate Change Simulation

AOGCMs

Regional Climate Change Simulation

Downscaling techniques

Impacts

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– Representativeness of results – if several model outputs are used then select models that represent the full spectra of results for the studied entity.

Construction of scenarios

– Change fields – a scenario for a future time period is derived from adjusting baseline observations to the difference (or ratio) between period-averaged results from the GCM experiment and its control run.

– Downscaling – derive regional scale data from the coarse resolution GCM data. Multiple options of different complexity are suggested, ranging from the use of grid box values (directly or interpolated to a finer grid) to the more sophisticated and data intensive statistical downscaling and the complex high resolution experiments, also known as dynamical downscaling.

Interpreting GCM results and their uncertainties

– Sources of uncertainties – e.g. emission scenario uncertainty, global climate sensitivity uncertainty and uncertainties in regional climate changes (different models but the same mean global warming).

– Approaches to represent uncertainties – use a combination of simple models and GCM models to study the range of uncertainty associated with sources listed above.

– Guided sensitivity analysis – the use of synthetic scenarios (arbitrary adjustments to the baseline climate within a range of plausible changes as identified by simple and complex models).

Changes of means and variability – change in climate variability can be as important as changes to the climate mean for an exposure unit, however, there is still much uncertainty about GCM estimates of future climate variability. Central issues in terms of future climate variability concerns:

– Interannual variability – much remains uncertain about the behaviour of the El Niño-Southern Oscillation (ENSO) phenomenon; the most important source of interannual variability in the tropics and beyond.

– Severe storms and cyclones – GCMs don’t agree on frequency, intensity and track of mid-latitude storms

– Precipitation variability – GCMs project an intensification of the hydrological cycle which can result in more extreme rainfall events. Models also project more frequent or severe drought periods over land areas.

– Diurnal temperature range – observed data indicate narrowing of the diurnal range, impact studies have yet to establish if this trend is continuing in GCM-projected climates.

– Scenarios of changing variability – scenarios that consider impacts both on mean and variability.

Other documents produced by TGICA provide guidance on the use of climate scenarios developed from regional climate model experiments (Mearns et al., 2003) and from statistical downscaling methods, the three TGICA reports can be downloaded from the DDC1.

1.2.6 FACTORS HAMPERING THE IMPLEMENTATION OF PROJECTED HYDROMETRICS IN ADAPTATION WORK

Hydrometrics derived from estimates of future climates variables can play an important role in adaptation planning by contributing to the understanding of exposures and sensitivities to climate change in hydrological systems. However, the implementation of these data in adaptation work is not necessarily straight forward and in the following paragraphs we consider the key reasons for why model derived estimates of future climate and hydrology variables can fail to deliver in adaptation work.

Wilby et al. (2010) proposed three key factors that hindered scenario-led adaptation within the freshwater ecosystem sector: uncertain regional climate change, uncertain response of freshwater ecosystems and uncertain environmental objectives. Of these, the first concerns uncertainties in the model domain whilst

1 http://www.ipcc-data.org/guidelines/index.html


the latter two relates to understanding what information is required by the user, which can be referred to as uncertainties in the user-case domain.

‘Uncertainties in the model domain’, involves any epistemic or stochastic uncertainty associated with the models that produce the climate change scenario data; see details on uncertainty sources in previous sections. Given the ‘cascade of uncertainties’ associated with a top down model-driven adaptation approach, there is some concern that the model approach won’t be able to produce more than a wide range of possible futures (e.g. Dessai et al., 2005; Pielke Sr and Wilby, 2012). ‘Uncertain response of freshwater system’, concerns the lack of understanding of the environmental preferences and limits of aquatic biota (Wilby et al., 2010). Information required for bridging such knowledge gaps typically involve data-dependent information, e.g. more biological, morphological and meteorological data collected simultaneously, at comparable resolution and co-located sites (Wilby et al., 2010). ‘Uncertain environmental objectives’, refers to the lack of consensus on priorities for long term planning, e.g. contrasting priorities posed by conservationists and regulatory bodies (Wilby et al., 2010).

There are, however, pathways one can take to circumvent some of the obstacles posed by these three factors. In terms of uncertainties in the model domain, a practitioner can attempt to quantify some of the uncertainties by adhering to the guidelines issued by the IPCCA-TGICA (2007) on ‘best practice’ for analysing model data for the purpose of adaptation and impact assessment. Other methodologies where scenario data are introduced first at the end of the analysis has also been proposed (and hence limit the propagation of uncertainty through all model steps in a traditional top down approach), see e.g. Brown (n.d.). In terms of identifying responses of freshwater ecosystems to climate change, such information is generally gained from data driven approaches. Whilst long-term monitoring networks exist for hydrological systems, they are generally planned with water regulation in mind and hence the collated data is not directly applicable to other needs, such as adaptation assessments (Wilby et al., 2010). Thus, to improve knowledge about freshwater ecosystem’s exposures and sensitivities to climate change, new long-term research and monitoring networks may be required. Criteria for developing a new monitoring program for management of ecosystems is proposed by Lindenmayer and Likens (2009, p.483): “… (i) address well-defined and tractable questions that are specified before the commencement of a monitoring program; (ii) underpinned by rigorous statistical design; (iii) be based on a conceptual model of how an ecosystem might work or how the components of an ecosystem that are targeted for monitoring (e.g. a population) might function; (iv) be driven by a human need to know about an ecosystem (e.g. the effects of a pollutant or changes in climate) so that they ‘pass the test of management relevance’ …”. These criteria form the basis for ‘adaptive monitoring’, an approach that allows the monitoring program to evolve as new information arises or research questions are modified (Lindenmayer and Likens, 2009). To address inabilities in identifying environmental objectives for a particular environmental asset/community/region, stakeholders are required to find communal ground from which it is possible to define priority areas. Such a process can be facilitated by providing fora that enable different stakeholders to meet and build a common knowledge base. Wilby et al., (2010) propose to establish a thematic programme where researchers of different disciplines can interact with governmental, non-governmental bodies, landowners and the private sector.

Thus, tackling model-domain uncertainties will be difficult for most practitioners in the water resources and freshwater biodiversity field; bar following the guidelines issued by IPCCA-TGICA (2007) or applying methodologies that limit their impacts. However, individual practitioners and/or communities can contribute to the knowledge needs that address uncertainties in the user-case domain by engaging in a discussion with the scientific community and governance organisations on the design of monitoring network programs as well as identifying priority areas for long-term planning.


Part I Water Scenarios


2 Historical and future projected hydrological metrics for Australia

This section focuses on historical and projected future hydrological metrics data available to practitioners in the water resources and freshwater biodiversity fields across Australia. By practitioners we imply those who are engaged in any occupation or way of life that connects with the water resources and freshwater biodiversity fields and by available we imply data that can generally be obtained by any interested practitioner in these fields. Whilst the majority of climate data presented in the reviewed projects can be obtained from web portals or by contacting a project representative (generally most projects have a contact person listed on their respective websites), model licensing issues impose restriction on the availability of some hydrological variables.

Data for this inventory has been collated at Commonwealth and State level, with the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Sustainable Yields Projects2 providing the core material, e.g. the Murray-Darling Basin (MDBSY), the Tasmania (TasSY), the South-west Western Australia (SWWASY), and Northern Australia SY (NASY) projects. The Sustainable Yields (SY) projects provide estimates of climate change impacts on water availability/demand at catchment scale, focusing primarily on surface and groundwater resources. Some projects provide assessments of impacts on storage yields and water entitlement and management options, as well as environmental requirements, specifically identifying freshwater ecosystems that might be vulnerable to change.

Figure 2 Areas covered by projects reviewed in this report.

Other studies complement the Sustainable Yields projects extending the geographical area covered by the report (Figure 2). These include data from the Climate Futures Tasmania (CFT) (Bennett et al., 2010), the

2 http://www.csiro.au/partnerships/SYP.html


South Eastern Australian Climate Initiative (CSIRO, 20103), the New South Wales Office of Water’s Future climate and runoff projections (~2030) (Vaze et al.,2008) and the Queensland State Government’s Department of Environment and Resource Management, through the Queensland Climate Change Centre of Excellence (QCCCE), via Regional Water Supply Strategies for Queensland4 and Climate Q: Towards a Greener Queensland which was released in 2009 (DERM, 2009).

An overview of the projects covered in this report is provided in Table 1, summarising spatial and temporal coverage - please note that although the term scenario usually imply projected data, many of the projects reviewed here also use this term to represent the baseline climate. The hydrological metrics and other catchment details are summarised in the appendices (as identified in Table 1). The following sections give a summary of results and methods for each project.

3 http://www.seaci.org/index.html 4 http://www.derm.qld.gov.au/water/regionalsupply/index.html


Table 1 List of projects reviewed in this report; reference literature, extent of study area, spatial and temporal domain. The final column gives the Appendix that contains a summary of project hydrometrics relevant to this report.

TITLE AND REFERENCE STUDY AREA CLIMATE SCENARIO CHARACTERISTICS APPENDIX

SPATIAL DOMAIN TEMPORAL DOMAIN

Murray-Darling Basin Sustainable Yields Project (MDBSY)(CSIRO, 2008, CSIRO, 2007)

Murray-Darling basin

Catchment and sub-catchment

Time series of 112 years: Scenario A (1895-2006) Scenario B (based on the climate of 1997-2006) Scenario C (based on projected climate of the 2030s)

A.1

South-west Western Australia Sustainable Yields Project (SWWASY)(CSIRO, 2009a)

SW WA Catchment and groundwater aquifer scale

Time series of 33 years: Scenario A (1975-2007) Scenario B (based on the climate of 1997-2007) Scenario C (based on projected climate of the 2030s) Scenario D (median future climate of the 2030s and future development) Current surface water yield (2009)

A.2

Northern Australia Sustainable Yields Project (NASY)(CSIRO, 2009b/c/d)

Northern Australia

Catchment and sub-catchment scale

Time series of 77 years: Scenario A (1930-2007) Scenario B (based on the climate of 1996-2007) Scenario C (projected climate of the 2030s and current development) Scenario D (projected climate of the 2030s and future development)

A.3

Tasmania Sustainable Yields Project (TasSY) (CSIRO, 2009e)

Tasmania Catchment scale (five regions excluding the West Coast)

Time series of 84 years: Scenario A (1924-2007) Scenario B (based on the climate of 1997-2007) Scenario C (based on projected climate of the 2030s)

A.4

Climate Futures for Tasmania (Bennett 2010) Tasmania Whole of Tasmania/catchment scale

Reference period (1961-1990) Bias-adjustment period (1961-2007) Runoff model calibration period (1975-2007) Near Future (2010-2039) Medium-term Future (2040-2069) End-of-Century (2070-2099)

South Eastern Australian Climate Initiative (SEACI) (CSIRO, 2010)

South Eastern Australia

Catchment scale Time series of 112 years: Scenario A (1895-2006) Scenario C (based on projected climate of the 2030s)

Future Climate and runoff projections (~2030) (Vaze et al., 2008)

New South Wales

Gridded data (0.05° grid cells, ~ 5 km x 5 km)

Historical climate (1895-2006) Future climate ( 112 years based on projected climate for the 2030s)

Climate Q: Towards a Greener Queensland and Regional Water Supply Strategies (DERM, 2009, DERM, 2010)

Catchments in QLD

Storage scale Observed baseline climate (1971-2000) Modelled baseline for comparison (1980-1999) Projected futures (2030, 2050, 2070)


2.1.1 MURRAY-DARLING BASIN SUSTAINABLE YIELDS PROJECT

The Murray-Darling Basing Sustainable Yields Project (MDBSY) provided a detailed analysis of water availability within its sub-catchments making it the most comprehensive hydrologic modelling exercise ever undertaken for the region. The modelling conducted within the project includes rainfall-runoff and groundwater recharge, fully linked modelling of all major river systems and their connection with major groundwater systems. If not otherwise stated, the information provided in the following paragraphs is obtained from CSIRO (2008). Appendix A.1 summarises results relevant for the context of this report, full details are found in reports available for download at the CSIRO website5.

The MDBSY project considered two baseline climates and one future scenario, all with a length of 112 years (A, B and C). Scenario A comprises daily climate data for the period 1895-2006 and was used as a baseline for comparison with other scenarios. Scenario B is a time series generated by stochastic replication of climate data for the period 1997-2006. Whilst Scenario A represents the long term baseline climate Scenario B was used to evaluate the consequences of a long-term, continuation of the recent drought episode.

Scenario C considers climate change by the year 2030. The future daily climate series was obtained by pattern scaling, e.g. the scaling of the 1895-2006 historical daily rainfall series was informed by 15 GCMs to three different levels of global warming scenarios from the 4th assessment report (AR4) of the Intergovernmental Panel on Climate Change (IPCC) giving a total of 45 future climates (i.e. 15 GCMs x 3 scenarios). Scaling factors were derived for each meteorological season, GCM and GCM grid cell using linear regression of global average temperature versus the local climate variable. The IPCC Special Report on Emissions Scenarios (SRES; Nakidenovid et al., 2000) A1B emissions scenario was used to scale rainfall percentiles and temperature, whilst a combination of A1B and A2 was used for other variables (Chiew et al., 2008). The absolute change in the climate variable was then converted to a percentage change per degree global warming relative to the model baseline climate of 1975-2005 (except for temperature when the absolute value is used).

Furthermore, the MDBSY makes an assessment of potential future growth in water resource development including growth in farm dam capacity, expansion of plantation forestry, and increased groundwater extractions. Sometimes a ‘without development’ scenario is used, removing the effects of water management infrastructure and consumptive water use from the hydrological model (e.g. inter-basin transfers, irrigation and urban returns). This however does not represent ‘pre-development’ or ‘natural’ conditions and it does not consider the affects of catchment clearing.

After defining the climate scenarios, reporting regions and their composite sub-catchments, rainfall-runoff modelling and rainfall-recharge modelling was conducted for each the three scenario periods. Runoff outputs were propagated through river system models and recharge outputs through groundwater models (or considered in simpler assessments for minor groundwater resources). The connectivity of surface and groundwater was assessed to provide estimates of volume exchange. The model results were compared with monthly water accounting to assess uncertainty levels of the river system models, and used to provide some environmental assessments (CSIRO, 2007). Further details of the methodologies used within the project are available in the report ‘Overview of Project Methods’ (CSIRO, 2007).

The water balance assessment from the Murray-Darling Basin looks at availability as well as potential demand. With historical water availability in the whole region at 23 417 GL/yr, the range of water availability in 2030 is projected to range from 26 047 GL/yr in a wet extreme, to 15 524 GL/yr in a dry extreme with a median at 20 936 GL/yr. In parallel, future water use is projected to decrease from a current 11 327 GL/yr to a median of 10 876 GL/yr with existing water resource management policy.

5 http://www.csiro.au/Organisation-Structure/Flagships/Water-for-a-Healthy-Country-Flagship/Sustainable-Yields-Projects/MDBSY.aspx


Environment and Ecology

The MDBSY project assessed the potential impacts of altered flow regimes on the environment. One of the key drivers for seasonal change to flow regimes is water resource development. For nearly all regions in the MDBSY, results showed that by 2030, impacts on environmentally beneficial flooding due to climate change would be smaller than those already brought about by water resource development. For example, the proportion of years in which lakes in the Narran Lake Nature Reserve received sufficient flood to provide optimal water-bird breeding habitat has more than halved due to water resource development, and the period between environmentally beneficial flooding to the Macquarie Marshes has more than doubled since water resource development began.

Incremental impacts of climate change superimposed on existing impacts from water resource development showed potentially major ecological consequences, as important ecological thresholds may be crossed resulting in possibly largely irreversible changes; for example, when characteristics of the current hydrological regime already cause difficulties to ecological systems. The MDBSY project labelled such changes as potentially catastrophic to the wider ecosystem and the population.

Overall consumptive water use in the Murray-Darling Basin has reduced average annual streamflow at the Murray mouth by 61 % and increased the incidence of cease-to-flow conditions from 1 to 40 % of the time. The median 2030 climate implies worsened conditions for the Lower Lakes, with flows ceasing 47 % of the time. Water sharing arrangements in high water use regions, such as the Murray, Murrumbidgee and Goulburn-Broken, were designed to protect water users from the impact of climate change, transferring the impact disproportionately to the environment. Based on results from the MDBSY-project, it was suggested that without changes to water sharing arrangements in these regions, climate change would likely lead to irreversible ecological degradation.

2.1.2 SOUTH-WEST WESTERN AUSTRALIA SUSTAINABLE YIELDS PROJECT

The South-west Western Australia Sustainable Yields (SWWASY) project considered all fresh, brackish and marginal surface water from Gingin Brook, north of Perth, south to Albany, and groundwater resources between the Perth Basin, north-east of Geraldton, down the west coast and across to Albany in the south-east (CSIRO, 2009a). The project area (62 000 km2) was selected so that it includes all current and anticipated future water resources in SWWA suitable for irrigation, domestic water supplies and industries that require low salinity water; the exceptions being the Gascoyne (Carnarvon) and the Ord (Kimberley), both of which are included in the Northern Australia Sustainable Yields project (see section 2.1.3). Appendix A.2 summarises results relevant for the context of this report, and full details are found in reports available for download at the CSIRO website6. If not otherwise stated, the information provided in the following paragraphs is obtained from CSIRO (2009a).

The project region comprises a surface water area of 34 942 km2. This region has a mean annual rainfall historically (1975-2007) of 837 mm but only about 98 mm (12 %) of runoff, with 739 mm of evaporation or infiltration. Streamflow volume in the region is about 3411 GL. Consumptive water demand is around 1200 GL/yr of which three quarters is met from groundwater resources.

Three climate scenarios (A, B and C) were used for hydrological modelling in the SWWASY project, each comprising 33 year time series of rainfall and evapotranspiration (1975 to 2007); where the relatively short time period reflects the need to focus on a more recent time period due to a climate shift that occurred in SWWA in the mid-1970s, resulting in subsequent rainfalls being 10 to 15% lower than the long term mean. Similar to other Sustainable Yields projects Scenario A represents the historical climate to which other scenarios are compared. For the SWWASY project, Scenario A comprises climate data for the period 1975 to December 2007. Scenario B represents the climate of the period 1997 to 2007 and was used should the future climate be similar to that of the recent past. The 11 year period is replicated three times to give a

6 http://www.csiro.au/Organisation-Structure/Flagships/Water-for-a-Healthy-Country-Flagship/Sustainable-Yields-Projects/SWSY.aspx


time series of 33 years matching the length of Scenario A. The future climate scenario (Scenario C) represents the climate of the 2030s using the same methodology as described for the MDBSY, i.e., by rescaling the Scenario A series according to the relative conditions of 2030s as described by 15 GCMs and three global warming scenarios giving a total of 45 climate series. Using the simulated annual rainfall totals for the region, a dry (10th percentile), median (50th percentile) and wet (90th percentile) were chosen out of the 45 series to represent three different climate futures for the region. For further details on the SWWASY projections please see Charles et al. (2010).

In terms of mean annual rainfall, Scenario B represents a climate with 3% less rainfall compared to Scenario A. In comparison, the three future scenarios: wet, median and dry represent a decrease by 1, 8 and 17 % respectively. When viewed in the light of increasing potential evaporation (2 % for the median scenario) the projections suggest a move into more water-limited state by the 2030s in comparison with the climate represented by Scenario A (Charles et al., 2010).

Runoff in Scenario B (91 mm) is 7 % lower than in Scenario A. Projected changes for future climates (Scenario C) show decreases between 42 and 10 % (57 – 88 mm), with a median of -25 % (74 mm). The runoff coefficient is estimated to remain around 10 to 11 % except under the driest scenario where it drops to 8 %. As a result, streamflow volume could drop from 3411 GL historically to 2574 GL under a median future climate, with flows as low as 1958 GL under the dry scenario.

Surface water yields compare current yield with historical and recent climate as well as future climate. Surface water yields are projected to decrease from a current 425 GL/yr to 323 GL/yr in 2030 under a median climate scenario (a 24 % decrease). Decreases of as little as 4 % to as much as 49 % are possible under wet or dry extremes respectively. By comparison surface water yields in Scenario A and B were 457 GL/yr and 444 GL/yr respectively.

Total groundwater yields are much higher volumetrically than surface water yields at 1556 GL/yr currently. Recent yields dropped by an average of 1 %, the 10 and 90th percentiles being -7 % and 2 % respectively, with a median estimate of -2 % to 1530 GL/yr by 2030. These estimates vary geographically with some uncleared areas (e.g. Gnangara Mound, Albany Area) projected to experience yield decreases of a third to a half under a future dry climate. Greenhouse gases are only able to account for about half of the recorded decrease in rainfall since 1975 in SWWA in GCMs so it is possible that current estimates of drying using these GCMs are conservative. Indeed, since the project ended a very dry winter in 2010 resulted in streamflow ceasing in the Gingin catchment where groundwater was expected to provide baseflow until after 2030.

The gap between current divertible yields and current demand is 825 GL/yr. Median future climate projections for 2030 with current demand decrease the gap to 677 GL/yr whilst a medium future demand scenario would further decrease available yield to 252 GL/yr, amounting to a 30 % decrease. Under a high future demand scenario the decrease in yields would result in a net shortfall of water of 22 GL/yr, or as much as 248 GL/yr under a dry climate extreme. The results suggest that 54 % reduction in available water yields is due to climate whilst 15 % decrease is due to increase in demand, making climate the biggest driver for water supply risk.


Environmental assessments in the SWWASY project focus on environmental water provisions (EWPs). Two main methods were used to estimate the amount of surface water resource that may be diverted without causing an unacceptable risk to the environment. Establishment of site-specific ecological water requirements and regionally estimated sustainable diversion limits were based on statistical analyses of river daily flow hydrographs. Sustainable diversion limits were commonly 10-13 % of the mean annual flow, allowing more than 85 % for environmental demands. Across the project area, the climate conditions showed an important impact on runoff during the winterfill period (15 June to 15 October). Under a median 2030 climate scenario, runoff reduces by 20-30 % relative to the historical scenario.

Environmental assessments in the SWWASY project also include identification of threshold flow rates associated with ecological functions which were assessed for individual water courses in certain regions. Flow thresholds for the ecological functions include (but are not limited to) the following:


Maintain pool habitat in summer

Minimum flow to maintain pool quality

Summer habitat for invertebrates

Upstream migration of small native fish

Winter habitat for invertebrates

Inundate trailing vegetation

Inundate low elevation benches

Riparian vegetation

Upstream migration of large native fish

Inundate medium elevation benches

Inundate active channel

Inundate floodplain

The project looked at projected percentage change in flow frequency from historical conditions and groundwater dependent ecosystem risks using the regional depth to the water table below the ground surface. Based on this depth, 22 % of the study area (the Central and Southern region of Perth Basin) has groundwater levels within 3 m of the soil surface, and a further 14 % between 3-6 m. A comparison of the impact of the climate change scenarios on wetlands (zero to 3 m depth to water table) was mapped out in their study. The areas at high and severe risk were relatively small historically, but increased 4 to 8 % of the current potential habitat area under the median future climate scenario. Although about half of all types of wetlands in the 19 882 km2 area were at risk under extreme dry climate scenario, the risk is assessed to fall within a low risk category. The extent and type of impact for individual environmental assets was not qualified. Results suggest that changes in vegetation species within terrestrial groundwater dependent ecosystems habitats were likely to be less noticeable to the public than the loss of open water in wetlands.

2.1.3 NORTHERN AUSTRALIA SUSTAINABLE YIELDS PROJECT

The Northern Australia Sustainable Yields Project (NASY) provides assessments of current and projected water resources for three drainage divisions in northern Australia: the Timor Sea, and the Gulf of Carpentaria and the Northern North-East Coast (CSIRO, 2009b, c, d respectively). Analyses and results with regard to climate change impacts on runoff in these regions are disseminated in the peer-review literature (Petheram et al., 2012).

In these regions, assessments are performed for the wet and dry season rather than the meteorological seasons (e.g. spring, summer, autumn and winter). For the Gulf of Carpentaria drainage division the dry season runs from April to November and from May to October in the Timor Sea and Northern North-East drainage divisions. Our Appendix A.3 summarises results relevant for the context of this report, full details are found in reports available for download at the CSIRO website 7. If not otherwise stated, the information provided in the following paragraphs is obtained from CSIRO (2009b/c/d).

The NASY project covers 1 246 951 km2 and has relatively sparse data coverage, with monitoring of surface water in only small parts of the region. The entire region has high inter-annual rainfall variability as indicated by a range of 331 mm/yr to 3640 mm/yr between the 10th and 90th percentile; mean annual rainfall for the entire region is 850 mm. Annual evapotranspiration is high with a total mean annual rainfall deficit of -1104 mm. Rainfall is with 94 % of rainfall occurring during the wet season (802 mm)..

Three climate scenarios were generated for NASY: Scenario A, comprising observed data for a 77 year period (1930 to 2007); Scenario B, representing a 11 year subset of Scenario A (1996-2007) and a future climate Scenario C, which is Scenario A scaled to projected climates of the 2030s following three global warming scenarios and 15 GCMs, giving a total of 45 climate variants. See CSIRO (2009b/c/d) and Crosbie et

7 http://www.csiro.au/Organisation-Structure/Flagships/Water-for-a-Healthy-Country-Flagship/Sustainable-Yields-Projects/NASY.aspx


al. (2009) for further details as various subsets have been derived for certain variables, e.g. ground water recharge.

Although water availability is not easy to determine across the whole area due to sparse data availability, mean annual runoff is estimated at 19 % of rainfall or 159 mm. Recent runoff is 41 % higher than historical runoff, while the median future scenario project a decrease in runoff of 1 % compared to historical (due to large similarity between historical and future projected rainfall in terms of magnitude).


Due to a small population size and limited extent of industrial activity, hydrological processes of the drainage division are characterised by a high level of natural integrity from both a national and international perspective (Mackey et al., 2001).

The NASY project considered environmental assets, such as critical wetlands, already identified by State Governments or the Australia Government. The complete set of assets was shortlisted by selecting only those where stream gauging data was available at, or in close proximity to, the asset to ensure that model performance could be tested against gauged data. This list was reduced to cover the range of wetland types and geographic extent identified within the Directory of Important Wetlands8 for the area. The types of wetland assets include lakes, mangroves, area subject to inundation, saline coastal flats, water courses and swamps. All wetlands were deemed important for a variety of ecological reasons or because they bear historical significance or have high cultural value, particularly to Indigenous people.

In deciding whether it was feasible to report on hydrological regime metrics for the shortlisted assets confidence levels were derived for modelled streamflow data. Confidence in results for low flows and high flows were considered separately and only reported where confidence levels were sufficiently high. Standard metrics used, where available, include:

Mean annual flow

Wet season (November to April) flow

Dry season (May to October) flow

Low flow threshold (discharge exceeded 90 % of the time)

Number of days below low flow threshold (mean)

Number of days of zero flow

High flow threshold (discharge exceeded 5 % of the time)

Number of days above high flow threshold.

Annual flow into all assets was dominated by wet season flow which was as much as 29 % higher under recent climate in the eastern most Northern Coral region. High flow threshold exceedance was twice as frequent in the western most Fitzroy (WA) region, when compared to historical climate. In the Northern Coral region, flows were projected to decrease back to historical levels under future climate, although the variability ranged from significant increases to moderate decreases under the wet and dry climate scenarios respectively. In the Fitzroy (WA) region, little change was projected for the high flow threshold exceedance under the median and wet extreme climate scenarios, though under the dry extreme future climate, a moderate decrease was evident.

2.1.4 TASMANIA SUSTAINABLE YIELDS PROJECT AND CLIMATE FUTURES TASMANIA

The Tasmania Sustainable Yields Project (TasSY) provided an assessment of current and likely future characteristics of surface and ground water in five project regions of Tasmania: Arthur-Inglis-Cam, Mersey-Forth, Pipers-Ringarooma, South Esk and Derwent-South East. Our Appendix A.4 summarises results relevant for the context of this report, full details are found in reports available for download at the CSIRO

8 http://www.environment.gov.au/water/publications/environmental/wetlands/directory.html

http://www.environment.gov.au/water/publications/environmental/wetlands/directory.html


website9. If not otherwise stated, the information provided in the following paragraphs is obtained from CSIRO (2009e).

Three climate scenarios were developed for the project, each comprising 84 years of daily data based on historical (1924-2007, Scenario A in TasSY), recent (1997-2007, Scenario B in TasSY) and future (2030, Scenario C in TasSY) climates of Tasmania. The recent scenario (Scenario B) was generated by simply repeating the climatic sequence of the past 11 years seven times, adding the years between 2001 and 2007 to the beginning of the sequence (Post et al., 2009a), whilst a more complex daily scaling approach was used to derive 45 variants of a future scenario (Scenario C). The daily scaling factors (change in climate variable per degree global warming) were derived from 15 GCMs, for rainfall the spatial pattern was fine-tuned using spatial patterns from dynamic downscaling (see Post et al., 2009a for details). The climate scenario data was then applied to hydrological models (Viney et al., 2009) to give assessments of surface and groundwater availability and water yields under current and future development scenarios. In addition, the TasSY project assessed the effects of future development of forestry and major irrigation schemes on water yields, and the impacts of changed water yields on river ecology.

The TasSY project showed that historically (1924-2007) Tasmania has a mean annual rainfall of just over 1000 mm with runoff being approximately half of this amount. The annual surface water extraction was about 636 GL. Ground water extraction sat at 3 % of recharge, and only 1 % of sub-catchments and 13 of the 150 key ecological sites were potentially impacted due to current levels of catchment development. Under recent climate (1997-2007), mean annual rainfall was down by 8 % and runoff by 12 % relative to historical climate (1924-2007). Mean surface water extraction also dropped by 4 %, non-extracted water by 15 % and groundwater extraction was 12 % of recharge.

The future median climate scenario for 2030 showed decreases of 3 % in mean annual rainfall, and 5 % in runoff (relative to historical, not recent). Extraction was set be 2 % lower than historically (or 2 % higher than recent) and groundwater extraction to resume at levels similar to historical at 3 % of recharge.

About 2 % of sub-catchments were estimated to be impacted by changes to flow regimes by 2030, a similar percentage to historically. TasSY also looked at potential changes to future extractions including increases to the area under commercial forests, change in runoff due to land use, and reduction in licensed surface water extractions relative to impacts of future climate change.

The Climate Futures Tasmania (CFT) project (Bennett et al., 2010) covers the whole of Tasmania and builds on TasSY by considering a range of other (non-hydrological) impacts and extending these projections of runoff and river flows to 2100. Furthermore, the CFT project utilises dynamical downscaling in combination with bias-adjustment, rather than daily scaling to generate climate variables for the hydrological models (see Bennett et al., 2010 for further details).

The TasSY project produced runoff projections for the year 2030 which correspond approximately to the period described as the near future (2010-2039) in CFT. Both studies projected state-wide reductions in runoff (by 2 % TasSY and 0.7 % CFT) in the near future. Both studies show marked reductions in runoff in the central highlands, and reduced runoff for a band extending from the central highlands to the north-west. Both studies showed little change in the west coast and south-west regions of the state.

There are some differences between the studies for the east coast; where CFT find little change in the north-east, the Viney et al. (2009) median scenario showed marked drying. Differences between the two studies in the north-east of the state are even more pronounced. Further, CFT data show increases in autumn runoff in the midlands, Derwent Valley and the east coast that are not present in the Viney et al. (2009) median scenario, which projected decreases in the midlands and Derwent Valley and only slight increases along the east coast.

9 http://www.csiro.au/Organisation-Structure/Flagships/Water-for-a-Healthy-Country-Flagship/Sustainable-Yields-Projects/TASSY.aspx


Differences between the TasSY study and CFT are discussed in Bennett et al., (2010, p.52 ) who suggest that

“Differences between the TasSY study and this project are caused by a number of factors. Differences stem from the downscaling methods employed, the different number (and different) GCMs used in both studies, and the different reference periods. We speculate that the most significant differences between the studies are caused by the fundamentally different downscaling techniques and the different GCMs used by the two studies. For example, Post et al’s (2009) method preserves the magnitude of rainfall changes taken directly from GCMs, while our study does not. Unpublished analyses show that the bias-adjustment of sea surface temperatures (SSTs) and fine-resolution downscaling could reverse the sign of rainfall change of a given GCM over the north-east of the state from drying to wetting. In addition, Post et al (2009) used 15 GCMs to produce the TasSY climate scenarios while our study uses six downscaled-GCMs. Importantly, despite using fewer GCMs, our study includes some GCMs not included by the TasSY project. The most notable of these is the UKMO-HadCM3 downscaled-GCM, which consistently produces the wettest future projections in our project.” – the reference to Post et al. (2009) in this citation corresponds to the reference Post et al. (2009a) in this report.


The TasSY project undertook modelling at a sub-catchment level within each of the five project regions. Key sites of concern were identified and assessed to determine their likely condition under the four scenarios of future climate and development and the fifth without-extractions scenario. Impact assessment was based on the degree of change from a baseline period: largely unmodified, slightly modified, moderately modified, substantially modified and severely modified. The baseline condition was defined as what the flow regime might be like, without extractions, but with current levels of infrastructure in place.

The nature and location of likely impacts were determined through an assessment of river condition using an approach based on the Tasmanian River Condition Index (TRCI) method, incorporating the Flow Stress Ranking (FSR) procedure which links flow components to important ecological processes. The TRCI provides a rapid qualitative ‘snapshot’ assessment of river condition based on physical stream form, streamside habitat and hydrological connectivity, while FSR provides a quantitative assessment of the potential stress of a river.

Details of these assessment results are available in reports three to seven prepared for the Australian Government by the CSIRO Tasmania Sustainable Yields Project and focus on the degree of modification to the flow regime, and specific sites potentially impacted, including riverine wetlands, Ramsar wetland and estuaries. All assessments indicate that average future conditions are more similar to historical conditions rather than recent drier conditions. However, when droughts do occur in the future, they may be more severe than those encountered historically.

2.1.5 SOUTH EASTERN AUSTRALIA (SEACI)

The South Eastern Australia Climate Initiative (SEACI) is a collaborative partnership involving the CSIRO, the Australian Government’s Department of Climate Change and Energy Efficiency, the Murray-Darling Basin Authority, the Australian Government Bureau of Meteorology and the Victorian Department of Sustainability and Environment. The work conducted within SEACI focuses on drivers of climate change and climate variability across south-eastern Australia (Figure 3). Results are presented in reports that can be downloaded from the SEACI website10. If not otherwise stated, results summarised here are from Post et al. (2009b).

10 http://www.seaci.org/


Figure 3 Boundaries for the SEACI region and the 219 gauged catchments used to calibrate the rainfall-runoff models (CSIRO, 2010)

It is estimated that the mean annual rainfall and runoff, averaged over 1895 to 2006 over the entire SEACI region, amount to 490 mm and 37 mm respectively. The region itself exhibits strong gradients in both rainfall and runoff, where the former is highest in the southeast (mean annual rainfall of more than 1200 mm) and along the eastern perimeter (800-1000 mm) and lowest in the west (less than 300 mm). For runoff, the higher values are also fund in the southeast corner (mean annual runoff of more than 200 mm) and eastern perimeter (60 to 100 mm) compared to elsewhere in the SEACI region with is less than 10 mm in the western side. In the north of the SEACI region most of the rainfall and runoff occurs in the summer-half of the year, and in the south of the SEACI region, most of the rainfall and runoff occurs in the winter-half of the year.

The methods used to derive future climate change scenario data in SEACI are very similar to those used in the MDBSY Project (see section 2.1.1.). SEACI presents a range of runoff modelling results using climate change projections from 15 GCMs for the IPCC SRES A1B global warming scenario for the SEACI region which encompass much of south-eastern Australia. The projected mean annual runoff for 2030 indicate a range of 30 % decrease to a 30 % increase, relative to ~1990 (most models were calibrated to the period 1975-2005, hence 1990 can be considered as a rough average level of catchment development for this period), in the northern half of the SEACI region; and a 30 % decrease to 10 % increase in the southern half of the SEACI region. In Victoria, mean annual runoff projections range from a 50 % decrease to no change for 2030, relative to ~1990. Averaged over the entire SEACI region, projected estimates of mean annual runoff range from a 20 % decrease to a 6 % increase.

The projected decrease in mean annual runoff in the south of the SEACI region is higher than in the north because the projected decrease in rainfall is slightly higher in the south, and most of the projected rainfall decrease is in winter when most of the runoff in the south occurs.


2.1.6 FUTURE CLIMATE AND RUNOFF PROJECTIONS (~2030) FOR NEW SOUTH WALES AND AUSTRALIAN CAPITAL TERRITORY

In Vaze et al. (2008), estimates of climate change impacts by the 2030s on rainfall, areal potential evapotransipration and runoff are presented on a 0.05 degree resolution (approximately 5 by 5km) for the entirety of NSW and ACT. The scenarios were commissioned by the New South Wales Office of Water (NOW) and are extensively used for climate change impact studies across New South Wales (Vaze and Teng, 2011).

The future climate scenarios are derived from 15 IPCC AR4 GCMs following the IPCC SRES AIB emission scenario, with regional estimates being derived by daily scaling of historical data from 1895-2006; thus results from this exercise are comparable with results of the MDBSY-project and the SEACI. Methodology and results are also described in some detail in the peer-review literature (Vaze and Teng, 2011).

The exercise revealed large uncertainty about future rainfall and runoff impacts in this region as represented by the variability within the 15 member ensemble of future scenarios. Nevertheless, a number of observations could be made with regard to the ensemble results (see Vaze et al. (2008) for full details); for example, 9 of 15 scenarios indicate a decrease in mean annual rainfall, 11 of 15 show decreases in winter rainfall and 5 indicate decreases in summer rainfall. In terms of runoff, the median scenario indicate a relative (2030 relative to 1990) 5% decrease over the entire region. Looking at geographical patterns in runoff impacts, median estimates show relative reductions by 20% in the southern parts, no change or small reduction in the eastern parts and an increase by 20% in the northern parts.

2.1.7 CLIMATEQ

The Queensland State Government, as part of the Climate Smart Strategy, released a document entitled Climate Q (DERM, 2009) outlining key investments and policies relating to climate change mitigation and adaptation within the energy and transport sectors, planning and building, business and community sectors, as well as for primary industries and ecosystems. As part of this work, Climate Q assessed climate parameters for sub-regions throughout Queensland. Water availability yields were not assessed in this report, however the State’s Regional Water Supply Strategies, e.g. the Far North Queensland Regional Water Supply Strategy (DERM, 2010a), investigated future water demands and availability under various climate scenarios for certain sub-regions along the most densely populated coastal catchments and important inland catchments often associated with mining.

The Strategy reports and associated technical reports were released to the public and the intention is to eventually cover all of Queensland. Additionally, Water Resource Planning by DERM looks at environmental flows, including potential impacts of climate change on a 10 year time scale. Water Resource Plans and their associated Resource Operation Plans are subordinate legislation to the ‘Water Act 2000 (QLD)’ and direct the management of water entitlements. The Fitzroy Basin Water Resource Plan review includes climate risk assessments in its environmental assessment stage 2 report (DERM, 2010b) including implications to environmental flows and incorporating a precautionary approach to potential decreases in flows and impacts on ecological assets (DERM, 2010b). Risk assessments done for Regional Water Supply Strategies will also be included into Water Resource Plans and Resource Operations Plans, as these are updated in order to increase security of water entitlements.

Climate Q: Towards a Greener Queensland (DERM, 2009) was developed by DERM as part of a Climate Smart Strategy. This report includes a region by region Regional Climate Change Summaries (DERM, 2009 APP3) which divides QLD into the following regions (Figure 4):

Cape York

Central Queensland

Central West


Eastern Downs

Far North

Gulf

Maranoa

North West

South East

Townsville-Thuringowa

Whitsunday, Hinterland and Mackay Region

Wide Bay-Burnett

Figure 4 A map of the sub-regions of Queensland identified in the report Climate Q (DERM, 2009) for Regional Summaries.

For each of these regions, projected average temperature, rainfall and evaporation for 2030, 2050 and 2070 were given for low, medium and high greenhouse gas emissions scenarios (low emissions correspond to B1 and high emissions to A1FI), compared to historical climate. The Regional Summaries (DERM, 2009, APP3) also look at extreme events including cyclone intensity and frequency, sea level rise, and hot days (over 35 degrees Celsius).

A trend of rising temperatures has been observed for much of Queensland over recent decades and is expected to increase by about 1 degree by 2030 relative to historical (1900-2007). Queensland rainfall varies significantly from the wet tropics in the north with some areas experiencing up to 4000 mm in a year, to semi-desert in the south west with only 150 mm in a year. Coastal Queensland has experienced a general decline in rainfall over the recent period, with the exception of summer rainfall in the far west and north of Queensland increasing only slightly. There is significant uncertainty and variability associated with rainfall projections for Queensland with projections ranging from close to zero change in the far north, to as much as 10 % decline in the south by 2050. Evaporation is increasing over most the state, with important


implications for drought in recent times. The 2050 projections range from 2-5 % for low emissions to 4-8 % for high emissions scenarios. The 2030 values are not reported (DERM, 2009).

Climate-water assessments are prepared through the Regional Water Supply Strategies. These strategies look at the 50 yr water security in various regions. Currently only South East Queensland (SEQ), Far North and Central Queensland strategies are released, whose climate analyses were limited in nature but are expected to be updated at their respective 10 year reviews. The North Queensland, Wide-Bay Burnett, Mackay-Whitsunday Strategies are still under development11 however technical reports assess the potential impacts of climate change on water resources for each region.

The SEQ strategy was reviewed in 2010 in light of the recent drought and research was initiated to assess the impacts of climate change on inflows to the catchment areas in the western parts of SEQ, including Wivenhoe and Somerset dams. Following the A1F1 IPCC SRES scenario, mean temperatures in the western parts of SEQ were projected to increase by 0.8°C and 1.2°C, evaporation by 2 % to 8 %, and annual rainfall could reduce by 5 % or increase by 20 % indicating marked uncertainty. Projections of annual stream flow for the Brisbane River downstream of Mt Crosby Weir ranged from a reduction by 28 % in a dry scenario to an increase of 14 % in a wet scenario (State of Queensland, 2010).

The Wide Bay-Burnett, Mackay Whitsunday and North Queensland Strategy development included reports prepared by the Queensland Climate Change Centre of Excellence on the potential effects of climate change on water resources for each region (DERM, 2008a,b,c). Each of these reports looked at historical and future climate in each region and formed the basic methodology used subsequently for the release of Climate Q: Towards a Greener Queensland (DERM, 2009). In addition to climate projections, the reports look at datasets for hydrological modelling and the selection process of CGMs including the resulting assumptions and uncertainties. Finally, an expert workshop process was used to identify changes in water demand for each region as potential % changes in demand and the proportion of total demand affected. The work presented in these reports is subsequently used for Level of Service modelling using catchment models like Integrated Quantity and Quality hydraulic Models (IQQM12) to assess the future reliability of water supply.

Table 2 Estimated impact of climate change on urban water demand in 2056 (DERM 2008a,b,c).

SECTOR REGION POTENTIAL % CHANGE IN DEMAND PROPORTION OF TOTAL DEMAND AFFECTED

OVERALL % CHANGE IN DEMAND

Residential Mackay Whitsunday

+ 5% to 10% (no tariff)

+15% to 25% (user tariff)

60% to 80%

40% to 60%

+3.0% to 8.0%

+6.0% to 15.0%

North Queensland

+ 5% to 10% (no tariff)

+15% to 25% (user tariff)

60% to 80%

40% to 60%

+3.0% to 8.0%

+6.0% to 15.0%

Wide Bay Burnett

+15% to +25% 35% +5.3% to +8.8%

Commercial Mackay Whitsunday

+15% to 25% 25% +3.8% to 6.3%

North Queensland

+15% to 25% 10% +1.5% to 2.5%

Wide Bay Burnett

+15% to +25% 15% +2.3% to +3.8%

11 http://www.derm.qld.gov.au/water/regionalsupply/index.html accessed 23/11/2011 12 http://www.derm.qld.gov.au/services_resources/item_list.php?series_id=34000 accessed 23/11/2011

http://www.derm.qld.gov.au/water/regionalsupply/index.html

http://www.derm.qld.gov.au/services_resources/item_list.php?series_id=34000


SECTOR REGION POTENTIAL % CHANGE IN DEMAND PROPORTION OF TOTAL DEMAND AFFECTED

OVERALL % CHANGE IN DEMAND

Industrial Mackay Whitsunday

Uncertain Uncertain Uncertain

North Queensland

Uncertain Uncertain Uncertain

Wide Bay Burnett

+15% to +25% 15% +2.3% to +3.8%

Table 3 Estimated impact of climate change on rural water demand in 2056 (DERM 2008a,b,c).

SECTOR CROP % CHANGE IN DEMAND

NORTH QUEENSLAND MACKAY WHITSUNDAY WIDE BAY- BURNETT

Agricultural Sugarcane

- with efficiency gains

- no efficiency gains

- 5% per ha

+15% per ha

+10% per ha

+20% to 25% per ha

-10% per ha

+10% per ha

Vegetables + 5% per ha Negligible +5% per ha

Grains +10% per ha +10% per ha +7% per ha

Tree crops + 5% per ha Negligible Negligible

The estimated potential impacts of climate change on surface water flows in the Burrum River catchment are as follows (DERM, 2008a):

An average change of -15% for inflows into Lenthalls Dam (ranging from -39% to +17%)

An average change of -17% for end of system flows of the Burrum River (ranging from -43% to +18%).

The estimated potential impacts of climate change on surface water flows in the Pioneer River catchment are as follows (DERM, 2008b):

An average change of -8% for end of system flows of the Pioneer River (ranging from -30% to +2%).

An average change of -9% for inflows into Teemburra Dam (ranging from -30% to +19%).

The uncertainty associated with both climatic events and potential changes in their seasonality and longer-term patterns, means that the delivery of an acceptable ‘level of service’ from major storages may become more challenging. This leads to possible future scenarios where the level of service provided includes uncertainty in water balances, and thus uncertainty in the security of water entitlements available to the various sectors.

To improve water planning, DERM, in conjunction with the QCCCE, have developed a methodology for undertaking systematic hydrological modelling which includes the effects of climate change on regional water supplies. The IQQM system will be used to model hydrological responses based on a stochastic ‘probability’ approach using ‘low’, ‘medium’ and ‘high’ climate change scenarios, and demand will be assessed against performance indicators for both current and future demand scenarios (DERM, 2008b,c).


3 Key challenges for accessing water scenario data

Constructing hydrometrics (e.g. flow) for future water scenarios is both data and computing intensive and requires knowledge of scientific methods and practices. Although much can be learned about climate change science and its methods in the TGICA reports referred to in section 1, the ability to derive water scenarios is outside the scope for most public practitioners within the water and freshwater ecosystem fields. Instead, most practitioners would seek to use water scenarios prepared by trusted sources, e.g. data from Commonwealth and State departments, or University based research groups. However, whilst top-down impact work is carried out within a range of institutions the resulting climate or hydrological data is generally not readily accessible to the wider public. Only some of the data presented in this report is readily downloadable from websites, most require the user to either make contact with the organisation in question, some involve a fee and for some impact response data, legal and licensing issues are still being resolved before data can be released to a wider audience. In the following sections we summarise the key issues that were raised by the data providers with regard to accessing the data presented in this report.

3.1 Factors influencing data accessibility in relation to the data provider

Factors that impact on data accessibility tend to fall into two categories; one that represents the technical aspect of providing data, and the other representing concerns around the use of the data when released to a wider audience. The technical aspects of providing data to a wide user group involve format issues, meta-data support and the support of a virtual location for hosting the data. Concerns around data use include user understanding of the nature of the data, which is particularly important when dealing with climate change data. In some instances there may also be concerns around accountability issues.

In terms of formats, most data sets associated with model outputs are very large and require specific formats that can efficiently handle the size and complexity of the data structures (e.g. climate model data output files have a spatial, temporal and vertical extent as well as comprising information for several variables in one single file). The most commonly used format for these types of data sets is the network Common Data Form (netCDF13), a set of software libraries and self-describing, machine-independent data formats. To provide all project outputs in a consistent netCDF format requires significant computing effort and technical skills. The lack of technical staff able to conduct format conversions was given as one reason for either delays, or even inability to make data available to a wider audience. Licensing complexities were also mentioned as obstacles to releasing data, particularly in relation to impact model results as these often involve data inputs that are subject to licensing agreements with private or public stakeholders. Essentially two reasons were given for imposing a fee on the user, either the fee reflected the amount of work an individual staff member had to perform to extract the data or it reflected the amount of work involved in generating the data itself (thus a means to recoup some of the costs of generating the data set).

The second category of factors hampering the accessibility of data is the providers concern of how data will be used once downloaded. This is particularly true when providing scenario data for possible futures. Because the model data is provided for real time periods, real locations and in identical units as observed data there is concern that it will be analysed and presented as forecast data. However, as shown in Figure 1, climate and climate impact data (such as hydrological metrics in water scenarios) are associated with

13 http://www.unidata.ucar.edu/software/netcdf/docs/faq.html#whatisit


substantial uncertainties and require knowledge of how to most thoroughly and truthfully represent those uncertainties. The most common solutions data providers use to overcome this concern is to either provide information in conjunction with downloading the data or by not providing download links to data, which forces the user to contact the data provider who then can ensure that the user understands the limitations of the data that they require. Below is an example of what such a download disclosure might entail, in this case provided by the Climate Futures for Tasmania project (Michael Grose, pers. comm., 11th April 2011):

‘Climate models are our best tools for understanding and projecting likely changes to the climate system in the future with a warming climate. However, there are some important aspects of using model outputs that differ from using observed datasets. Model projections are not a forecast, but rather an indication of the likely climate response under an assumed emissions scenario. The day-to-day weather, the inter-annual changes, even the decadal variability are not tied to observed climate, and are not expected to match the observations. Climate models should be used to examine the long-term shifts on periods >30 years. Also, the response is shown for an assumed scenario, and if actual emissions are very different form this scenario, then the projections will also differ. This project used two emissions scenarios from the IPCC special report on emissions scenarios (SRES A2 and B1). Each model simulation gives one iteration or picture of the likely climate response to that emissions scenario, more simulations can help define the range of likely responses. This project used six model simulations for each of the emissions scenarios. Taking the average of the models, known as the central estimate or ensemble mean, may be appropriate for some applications, but other analyses must use each individual simulation. Models can save perfect spatial and temporal resolution with every variable of interest, so they produce large complex outputs. This means that researchers should work out their specific question and data requirement before interrogating the datasets. The Climate Futures for Tasmania project suggest starting by reading the published technical reports, then viewing layers on the GIS webtool TheLIST, and carefully considering their research question before obtaining model data directly. Researchers should get in touch with the TPAC data managers and/or someone from the project if there are any questions about how and when the data should be used.’

3.2 Factors influencing data accessibility in relation to the data user

The first hurdle facing a developer/user of water scenarios is finding out availability of data for the region of interest. As shown in this report, not all of Australia is covered by the Sustainable Yields projects or Government initiatives that provide water scenario data on a regional scale. In fact, with the exceptions of North America and Europe for which there are multiple observational data sets as well as several high resolution climate change experiments, much of the rest of the globe has limited coverage of not only climate change data but also observed climate and hydrological model data (Muller, 2007). From a climate change point of view, efforts are made to improve the spatial coverage of high resolution experiments, such as the those initiated by the World Climate Research Programme (WCRP) strategic framework: CORDEX: A Coordinated Regional climate Downscaling Experiment14, in which the Australian continent is included as a focus domain. In reality, however, many regions even within Australia may need to focus on less data intensive approaches to address climate change adaptation for the foreseeable future.

Assuming that climate/climate model/hydrological model outputs are available for the region of interest, the user is faced with challenges associated with the data itself. As mentioned in the above section, much scenario data is provided in netCDF format which is a formidable format if the user has some degree of programming or intermediate computing skills. For users without such skills, handling the data outputs can prove an obstacle in terms of extracting the required data. Other factors involve the sheer size of many data sets. Hence, the technical expertise required by the user may also be an obstacle to multi-model and multi-scenario analysis.

14 http://copes.ipsl.jussieu.fr/RCD_CORDEX.html


Part II Aquatic Ecosystems


4 Linkages between climate change, flow and aquatic ecosystem response

Responsible stewardship of Australian ecosystems requires effective strategies and actions for adapting to climate change. This need is motivated by a widespread appreciation of the value of Australia’s biodiversity, including heritage, ethical, intrinsic and utilitarian values, combined with the growing recognition of their vulnerability to climate change (Lindenmayer et al., 2010, Steffen et al., 2009). Given the anticipated impacts of climate change on precipitation, evaporation, hydrology and water availability across the nation, aquatic ecosystems warrant particular attention. A high-level review of the state of freshwater ecosystems in Australia recommended four key issues that stand to benefit from more effective integration of ecosystem science into freshwater ecosystem management (Likens et al., 2009): (1) limited capacity of aquatic ecosystems to cope with change and meet human demands for water (and other ecosystem services); (2) cumulative impacts of land and water use over long time periods (and slow recovery from such impacts); (3) the challenge of protecting and restoring systems in the face of multiple interacting pressures; and (4) nonlinearities, thresholds and cross-scale interactions.

The focus of this section of the report is to identify implications of anticipated hydrological futures for climate change adaptation approaches in the stewardship of Australian aquatic ecosystems. It is not a comprehensive review, but rather picks up on key pieces of work and identifies implications and fruitful directions for climate change adaptation efforts. Changes to flow provide one of the most readily available means to link climate change and hydrological scenarios to impact on aquatic ecosystems. There are many non-flow aspects of climate and hydrological change that will impact on aquatic ecosystems, but flow-mediated impacts are the primary emphasis in this section of the report

4.1 Linkages between climate variables and aquatic ecosystems

Anticipated impacts of climate change on Australian aquatic ecosystems have been reviewed thoroughly elsewhere (Sheldon et al., 2010). Key climate drivers were identified as temperature, sea level, precipitation, evaporation and ultra-violet-B (UVB) radiation, with impacts of these drivers cascading through to ecosystem responses in multiple ways, mediated by changes in flow regime, stratification, biogeochemistry, geomorphology, primary production and foodweb interactions (Figure 5). This conceptual framework is useful for communicating the multiple ways in which climate change triggers change in aquatic ecosystems. Such a diagram is not to imply that the explicit mechanisms and fine details underpinning each arrow are known or easily characterised. Even the relationship between the average air temperature and water temperature – a fundamental determinant of water quality and river ecology – is not trivial, for example; it is a result of non-linear interactions involving many factors including radiative fluxes, bed friction, convection, conduction, evaporation, groundwater fluxes, channel morphology and riparian vegetation (Webb et al., 2008).


Figure 5: Conceptual model of the cascading impacts of changes in the climate drivers on ecological responses in aquatic ecosystems, adapted from Sheldon et al. (2010).

Impacts on a variety of organisms were reviewed and include: fish, macroinvertebrates, zoo- and phytoplankton, macrophytes, amphibians, reptiles, algae and molluscs (Sheldon et al., 2010). For example, the impacts of temperature, precipitation or runoff changes on freshwater ecosystems focus on the biotic consequences (and changes) to some of the following:

Available habitat

Available thermal habitat

Bacterial activity (including contamination)

Biaccumulation of toxins

Biodiversity

Colonisation by macrophytes

Community structure

Species distribution (in stream)

Species distribution (latitudinal and altitudinal)

Food resources

Food web structure

Invasion potential

Likelihood of fire

Loss of breeding habitat

Loss of non-breeding habitat

Metabolism and growth

Mortality

Phenology

Population connectance

Population dynamics

Predator-prey interaction

Productivity

Recruitment

Sex ratios

Sexual/asexual reproduction

Spawning

Species coupling

Species richness (species loss and extinction)

Species loss and extinction

Water stress

Many responses are site specific, and the ability to transfer knowledge and data to other contexts is difficult, but highly desirable. To this end, interrelationships and correlations are studied in search of comprehensible patterns to inform our understanding of potential system responses to climate change. The patterns need not be quantified; qualitative conceptual models play a key role in understanding and communicating the complexity of the interactions between humans and ecosystems, particularly in interdisciplinary contexts (Heemskerk et al., 2003, Davies and Jackson, 2006).

Climate Change Drivers for Aquatic

systems

Hydrological Response

Physical and Chemical changes in aquatic systems

Ecological Responses

↑ Sea Levels ↑ Temperature

∆ Precipitation ↑ Evaporation ↑ UV-B

∆ Flow Regime

∆ Stratification

∆ Geomorphology deposition) ∆ Water Chemistry ∆ 1° & 2° Production

∆ species distribution/abundance, ∆ food web dynamics ∆ ecosystem processes

Habitat requirements of biota Environmental tolerances of biota


Species’ responses to climate change fall into three broad categories: move to follow shifting climate conditions; stay in place and adapt to new conditions created by climate change either through genetic evolution or phenotypic plasticity (Chevin et al., 2010); or become extinct. The second of these responses is most common (Vescovi et al., 2009). The complexities of aquatic ecosystems make the specifics of species responses and interactions under a changing climate, as well as other pressures, inherently unpredictable. For this reason, a particular challenge is to find useful indicators and relationships that guide useful decisions in the face of such unpredictability. Work focussed on linking ecological indicators to flow regime is helpful, for example, as flow measurements are the most commonly monitored, historical records exist and future flow scenarios are also becoming more available (as outlined in Part I).

Clearly, ecosystem response to climate change cannot be characterised only with respect to flow regime, but maintaining key components of flow regimes is a vital requirement for protecting and maintaining freshwater biodiversity, ecological processes and societal benefits provided by aquatic ecosystems. Strong calls from freshwater ecologists have provided particular clarity around the importance of environmental flows. The Brisbane Declaration (2007) identifies agreed reasons for protecting environmental flows and the actions required (Arthington et al., 2010). In this declaration environmental flows are defined as ‘the quantity, timing, and quality of water flows required to sustain freshwater and estuarine ecosystems and the human livelihoods and well-being that depend on these ecosystems’. The intensity and interest in work on environmental flows is reflected in an exponential increase in literature citations in this area (Figure 6).

Figure 6 Number of citations each year for papers with the topic “environmental flows” (Web of Knowledge search, March 2012, search term “environmental flows”, returning 582 papers with a total of 4523 citations).

The following sections look at hydrological-ecological relations (Section 4.2) and implications for adaptation strategies (Section 4.3).

4.2 Ecosystem response to hydrological change

4.2.1 ECOLOGICALLY RELEVANT FLOW COMPONENTS

A thorough understanding of hydrological flow regimes underpins the management of water quantity (e.g. storage, diversion, allocation). The attributes of the flow regime measured and modelled for these purposes are not necessarily the same attributes required by ecologists when characterising ecosystem responses to changes in flow and advising on environmental flow requirements (Arthington et al., 2006). Figure 7 illustrates some of the flow components that are useful to identify when inferring relationships between flow and ecological response. Extremes, rates of change, predictability, variability, timing, frequency and duration of events are all significant (Pusey and Kennard, 2009).


Figure 7: Different components of the natural flow regime are ecologically important over a range of temporal scales. Image from Pusey and Kennard (2009).

Multiple physical and ecological aspects of aquatic ecosystems are affected by these flow attributes (Poff et al., 1997). The strengths of observed correlations between hydrological and ecological variables are highly context-dependent, complicated by multiple (often unknown) mechanisms interacting across spatial and temporal scales; fully comprehensive knowledge is not possible in such systems. Despite these barriers, significant syntheses of freshwater biological research in Australia have distilled useful generic principles: principles that describe and organise ecosystem dependencies on flow (Bunn and Arthington, 2002) (Figure 8); and principles for informing environmental flow guidelines (Arthington et al., 2006).

Figure 8 Overview of synthesising principles: “Firstly, flow is a major determinant of physical habitat in streams, which in turn is a major determinant of biotic composition; Secondly, aquatic species have evolved life history strategies primarily in direct response to the natural flow regimes; Thirdly, maintenance of natural patterns of longitudinal and lateral connectivity is essential to the viability of populations of many riverine species; Finally, the invasion and success of exotic and introduced species in rivers is facilitated by the alteration of flow regimes.” (Bunn and Arthington, 2002). Image from Figure 1 in Bunn and Arthington, (2002).

In deriving these generic principles, the authors stressed the value of more sophisticated characterisations of flow time series and frequency distributions; averages and cumulative totals are impoverished


indicators. In particular, the behaviour at the extremes of frequency distributions, and the high temporal and spatial variability inherent in Australian systems all have significant functional implications for ecological assemblages.

4.2.2 ECOLOGICAL RESPONSE TO ALTERED FLOW REGIME

Inferring responses to climate change and altered flow regime presents yet more challenges. Even ignoring species interactions, there is insufficient knowledge to derive comprehensive, accurate estimates of which species will acclimatise and cope with new conditions, which species will migrate to new locations and which species will go extinct (Poff and Zimmerman, 2010). With changing species assemblages and shifting phenology, interactions between species (e.g. predator-prey interactions and competition) become altered in unpredictable ways (Walther et al., 2002). The reality of ‘novel and disappearing climates’ (Williams et al., 2007) and associated novel ecosystems (Lindenmayer et al., 2010) further complicates this picture. Furthermore, while field studies may be able to inform short-term (e.g. decadal) responses to rapid change, there is rarely the evidence base to support reliable estimates of longer-term responses (e.g. 100-year time scales). The existence of alternative ecosystem states is well acknowledged (Scheffer et al., 2001), but understanding the dynamics of transitions between such states requires detailed system-specific knowledge, particularly if the aim is to anticipate critical thresholds and timing of transitions.

There are general principles to draw on. For example, species with high genetic variance allow populations to track ‘phenotypic optima’ under environmental change, and promising conceptual frameworks have been established for characterising species persistence as a function of phenotypic plasticity and critical rate of environmental change (defined as ‘maximum rate of sustained environmental change that allows long-term persistence of a population’) (Chevin et al., 2010). Summaries classifying common ecological responses to flow alteration have been made from synthesising results in published literature (e.g. Table 4, (Poff and Zimmerman, 2010)). Integrated frameworks for assessing species vulnerability under climate change (Williams et al., 2008) and assessing environmental flow needs (Poff et al., 2010) represent constructive outcomes derived from existing knowledge. Growing efforts on effective collation, maintenance and availability of ecological datasets and experimental results are providing vital sources of data from which principles can be inferred, tested and updated.

Table 4: Modified summary table from Poff and Zimmerman (2010) looking at the common ecological responses of aquatic and riparian organisms in relation to flow parameter alteration.

FLOW COMPONENT PRIMARY FLOW ALTERATION COMMON ECOLOGICAL RESPONSES

Magnitude Stabilisation (loss of extreme high and/or low flow)

Loss of sensitive species

Lower species richness, abundance and diversity

Altered assemblages and dominant taxa

Increase in non-natives

Vegetation encroachment/Terrestrialisation of flora

Altered recruitment, failure of seedling establishment

Increased riparian cover

Greater Magnitude of extreme high and/or low flows

Life cycle disruption

Reduced species richness and loss of sensitive species

Altered assemblages and relative abundance of taxa

Frequency Decreased frequency of peak flows

Reduced or aseasonal reproduction

Decreased abundance or extirpation of native fishes

Decreased richness of endemic and sensitive species

Reduced habitat for young fishes

Shift in community composition

Increased riparian wood production


FLOW COMPONENT PRIMARY FLOW ALTERATION COMMON ECOLOGICAL RESPONSES

Duration Decreased duration of floodplain inundation

Decreased abundance of young fish

Change in juvenile fish assemblage

Loss of floodplain specialists in mollusc assemblage

Reduced growth rate or mortality

Terrestrialisation or desertification of species composition

Reduced area of riparian plant or forest cover,

Increase in abundance of non-natives

Timing Shifts in seasonality of peak flows

Disruption of spawning cues

Decreased reproduction and recruitment

Change in assemblage structure

Increased predictability Change in diversity and assemblages structure

Disruption of spawning cues

Decreased reproduction and recruitment

Loss of seasonal flow peaks

Reduced riparian plant recruitment

Invasion of exotic riparian plant species

Reduced plant growth and increased mortality

Reduction in species richness and plant cover

Rate of change Reduced variability Increase in crayfish abundance

Increased variability Decreased germination survival and growth of plants

Decreased abundance and change in waterbird assemblage

Not specified River regulations; type unspecified

Decrease in species richness

Increased abundance of some macroinvertebrate taxa

The accumulated knowledge about flow regimes and ecological response has important implications for interpreting ecological response from hydrological model output. Hydrological models calibrated and validated to match water volumes and average or cumulative quantities may perform very well by these metrics and yet provide very poor information on other flow attributes such as low flows, extremes or variability. Similarly, model output may not be sufficient to infer other biophysical consequences (e.g. morphological changes to the river bed and bank (and associated fluvial changes); changes to overbank flows and floodplain inundation; and altered residence times).

4.3 Implications for adaptation strategies

4.3.1 CHALLENGES FOR DEVELOPING ADAPTATION STRATEGIES

As discussed in Section 1.2.5, a widely acknowledged hindrance to developing workable adaptation strategies is the ‘cascade of uncertainty’: uncertain climate projections; uncertain hydrological and ecological impacts and responses; uncertain societal impacts and responses that in turn feedback and affect climate, hydrological and ecological futures; and uncertainty around specific objectives and long-term goals. This cascade of uncertainty has been described as an ever-increasing number of permutations of possible futures (Wilby and Dessai, 2010).

In addition to the uncertainty, there is a strong sense of urgency and a need for strategies that can be applied at a regional scale: “the pace and intensity of flow alteration in the world’s rivers greatly exceeds the ability of scientists to assess the effects on a river-by-river basis … a key challenge in securing freshwater ecosystem sustainability is synthesising the knowledge and experience gained from individual case studies into a scientific framework that supports and guides the development of environmental flow standards at the regional scale” (Poff et al., 2010).

A review of biodiversity management recommendations related to climate change found that open-ended general principles were recommended more frequently than ‘actionable’ strategies; an assessment of 524 recommendations from 113 papers found 70% of recommendations were general principles (Heller and


Zavaleta, 2009). General principles have the advantage of being able to hold across multiple contingencies and have considerable robustness to many sources of uncertainty and regional heterogeneity, however they are “remote from the sharp end of site-scale conservation” (Wilby and Dessai 2010). Ways of bringing general principles into tangible on-ground practice are needed.

Shared, workable understandings of desired outcomes are a useful entry point for relating high-level principles to actionable strategies. For example, the prospect of novel climates and ecosystems as a result of climate, land-use and other changes means that any assumption that the intended outcome is to restore prior natural flow regimes (however they are defined) is under increasing scrutiny. Distinctions between goals to restore, rehabilitate, protect or provide services valued by society warrant clarification. Objectives like “healthy”, “viable”, “sustainable”, “functional” and “resilient” have different realisations in different settings and systems, and require negotiation and shared understanding to implement in any tangible way.

Particular mention is warranted on the matter of invasive, exotic and introduced species. The appropriate way to handle these is a contentious issue in the published literature at present, with recent assertions that “the practical value of the native-versus-alien species dichotomy in conservation is declining, and even becoming counterproductive” (Davis et al., 2011) accompanied by recommendations such as:

“Today’s management approaches must recognize that the natural systems of the past are changing forever thanks to drivers such as climate change, nitrogen eutrophication, increased urbanization and other land-use changes. It is time for scientists, land managers and policy-makers to ditch this preoccupation with the native–alien dichotomy and embrace more dynamic and pragmatic approaches to the conservation and management of species — approaches better suited to our fast-changing planet.” (Davis et al., 2011)

Those with responsibility for stewardship of freshwater ecosystems don’t need to agree with this perspective, but a willingness to be open to it naturally opens up more management options for consideration.

Furthermore, in crafting specific adaptation measures, another observation in the literature is that “adaptation measures will have greatest acceptance when they deliver multiple benefits, including, but not limited to, the amelioration of climate impacts” (Wilby et al 2010). The fact that climate change has not only direct impacts but significant interactions with other causes of environmental change, is well acknowledged in high-level assessments of climate change in Australia:

“The added stressors from climate change would exacerbate existing environmental problems, such as widespread loss of native vegetation, overharvesting of water and reduction of water quality, isolation of habitats and ecosystems, and the influence of introduced plant and animal pests” Garnaut (2008).

These interactions are not limited to biophysical impacts, and instruments such as water sharing arrangements are receiving increasing attention as a key to addressing interrelated social dimensions:

“It is expected that while the impacts of climate change are unclear, the impacts of climate change would be exacerbated under current water sharing arrangements.” Human Rights and Equal Opportunity Commission (2009).

Frameworks that emphasise ‘ecosystem services’ reflect this requirement to be clear about end goals, and to do so in a way that integrates multiple pressures, benefits and interacting social and biophysical dimensions (Millennium Ecosystem Assessment, 2005). Identifying specific characteristics that enable high-priority ecosystem services provides a pragmatic set of management criteria (Arthington et al 2010). For example, Integrated Basin Flow Assessments (IBFA) in Africa and South-East Asia have demonstrated that making these links between flow characteristics and valued outcomes (e.g. household income) do enable effective implementation of recommendations from flow assessments (King and Brown, 2010).


4.3.2 DEVELOPING THE EVIDENCE BASE

Workable adaptation strategies are built upon the existing knowledge base, and require ongoing monitoring of key indicators and metrics. A common reason for measuring and monitoring such indicators and characterising flow alteration-ecological response relationships is to provide a solid evidence base for deriving environmental flow rules, and in particular do so in a way that enables applications at regional and national scales (Arthington et al., 2006). An example of a structured process for doing so is shown in Figure 9, which demonstrates the potential to develop flow guidelines that readily accommodate variability that is so characteristic of Australian systems. Using hydrological characteristics and geography related to flow-regime in order to group rivers gives the opportunity to make comparisons between systems, derive relationships that are grounded in evidence which can also inform operating guidelines in rivers for which data are scarce.

Figure 9 Structured approach to characterise ecologically significant changes to flow regime (a) classifying rivers and streams by hydrological characteristics; (b) for each class, identify frequency distributions for important flow variables; (c) compare flow-modified streams with reference streams within each class; (d) derive flow-response relationships to characterise health indicators as a function of departure from reference flow condition (Arthington et al., 2006). Image from Arthington et al. (2006) courtesy of Ecological Society of America.

Frameworks of this kind require a defined reference condition. There are several acknowledged reasons for exercising caution in using such references (Hughes et al., 2005, Stoddard et al., 2006, Dufour and Piegay,


2009), and aiming for reference conditions has been described as ‘the equivalent of hitting a moving target in a constantly changing world’ (Nilsson et al., 2007). Irreversible catchment land use changes, climate change and inevitability of novel assemblages of species (including alien species) are particularly salient complications when attempting to define achievable reference conditions. Studies aimed at understanding the processes of the formation and viability of novel population assemblages require timescales that are often incompatible with field research programs, yet such understanding would help inform adaptation strategies. Given these limitations and the growing focus on adaptation, a shift towards managing for resilience and adaptive capacity of valued ecosystems is understandable.

These complications are not reasons to overlook what is possible here and now. The first nation-wide classification of hydrological regimes for Australia has been published (Kennard et al., 2010). The assessment is based on 120 metrics of ecologically-relevant flow-regime characteristics, and the results enabled the derivation a flow-regime classification scheme that partitions Australian rivers into twelve distinctive classes. The assessment provides a strong foundation for building nation-wide assessments of flow-ecology relationships, which are widely recognised to being an urgent requirement for good stewardship of Australia’s freshwater ecosystems (Arthington et al., 2010). Poff et al (2010) outlined the challenging nature of this endeavour, and reviewed the scope to consider when identifying indicators of ecological response to flow regime (Table 5). The scope is broad and includes indicators that are not necessarily measurable (e.g. social values).

Table 5 Categories of useful indicators for flow alteration–ecological response relationships (adapted from Poff et al (2010)).

Mode of response:

The ecological response to flow alteration can be either direct (e.g. impact on spawning or migration) or indirect (e.g. impact on habitat).

Habitat responses linked to biological changes

Changing the flow conditions of a river system triggers a range of habitat changes: changes in physical (hydraulic) habitat (width–depth ratio, wetted perimeter, pool volume, bed substrate); changes in flow-mediated water quality (sediment transport, dissolved oxygen, temperature); and changes in in-stream cover (e.g. bank undercuts, root masses, woody debris, fallen timber, overhanging vegetation).

Rate of response

Fast versus slow

Fast: appropriate for small, rapidly reproducing, or highly mobile organisms

Slow: long-life span

Transient versus equilibrial

Transient: establishment of tree seedlings, return of long-lived adult fish to potential spawning habitat

Equilibrial: reflect and end-point of ‘recovery’ to some ‘equilibrium’ state

Taxonomic groupings

Categorising by taxonomic groupings can be a useful way to organise and interpret responses to change. Example groupings include: aquatic vegetation, riparian vegetation, macroinvertebrates, amphibians, fishes, terrestrial species (arthropods, birds, water-dependent mammals, etc.). Closely linked to taxonomic groups are composite measures such as: species diversity, or Index of Biotic Integrity.

Functional attributes


Organising by functional attributes is also useful, and such groupings include: production; trophic guilds; Morphological, behavioural, life-history adaptations (e.g. short-lived versus long-lived, reproductive guilds); habitat requirements and guilds; and functional diversity and complementarity.

Biological level of response (process)

Yet another way of categorising or organising responses is by biological processes operating at different scales, for example:

Genetic

Individual (energy budget, growth rates, behaviour, traits)

Population (biomass, recruitment success, mortality rate, abundance, age-class distribution)

Community (composition; dominance; indicator species; species richness, assemblage structure)

Ecosystem function (production, respiration, trophic complexity)

Social value

Ultimately, management decisions relate to human values and our understanding of how ecosystems enrich our wellbeing. Thus, linkages to human social values are also an important way of categorising and assessing ecological responses to flow alteration, and include:

Fisheries production, clean water and other ecosystem services or economic values

Endangered species

Availability of culturally valued plants and animals or habitats

Recreational opportunities (e.g. rafting, swimming, scenic amenity)

Indigenous cultural values

A recently published framework for developing regional environmental flow standards is entitled the “Ecological Limits of Hydrologic Alteration” (ELOHA)15 (Poff et al., 2010). The aforementioned work on nation-wide classification of flow regimes (Kennard et al., 2010) and the assessment of qualitative and quantitative relationships between flow alteration and ecological response (Poff and Zimmerman, 2010) are intended to contribute to the ELOHA framework. An important finding from that work is that syntheses of the global published literature, although rich with valuable information about ecological response to flow alteration, are insufficient to meet the requirements for the ELOHA framework (Poff and Zimmerman, 2010). Specified recommendations for more systematic field programs, data repositories and statistical inference methods have been made to fill this gap (Poff and Zimmerman, 2010, Arthington et al., 2010).

In Australia, initiatives like the National Plan for Environmental Information (NPEI)16, with accompanying evolution of the Bureau of Meteorology to provide a broader suite of ‘environmental intelligence’ (BOM, 2010), are intended to improve the availability, accessibility and application of knowledge of Australian ecosystems. Coordinating such facilities is a significant and difficult task, and needs to be done in coordination with other facilities. The Atlas of Living Australia (ALA)17 and the Terrestrial Ecosystem Research Network (TERN)18 are funded by the National Collaborative Research Infrastructure Strategy (NCRIS)19, for example. Other relevant assessments, knowledge repositories and modelling platforms

15 http://conserveonline.org/workspaces/eloha 16 http://www.environment.gov.au/npei/index.html 17 http://www.ala.org.au/ 18 http://www.tern.org.au/ 19 http://www.innovation.gov.au/Science/ResearchInfrastructure/Pages/NCRIS.aspx


include the eWater Toolkit20 (which includes a library of ecological response models for some systems), Australian River Assessment System (AUSRIVAS)21, the Australian Soil Resource Information System (ASRIS)22, and the State of the Environment reporting23. There are also legacy data and assessments no longer maintained, such as the National Land and Water Resources Audit24 and associated Australian Natural Resources Atlas25. Looking to the future, it is expected that there will be more ecogenomic data and assessments available, which offer exciting opportunities for novel advances in ecosystem assessment (Chariton et al., 2010, Hardy et al., 2010, Hardy et al., 2011).

4.3.3 INTEGRATING FRAMEWORKS

The ELOHA framework is an example of an integrating framework. Like many other integrating frameworks encompasses both a scientific and a social process: scientific knowledge about flow classification, flow alteration and ecological response relationships are compiled in a way to contribute to balanced judgments in a social process which integrates societal values and goals (Poff et al., 2010). The emphasis of the ELOHA framework is on the detailed hydrological and ecological processes, and the authors recommend its implementation should be nested within an adaptive management governance structure: “Ideally, the ELOHA framework should be used to set initial flow standards that can be updated as more information is collected in an adaptive cycle that continuously engages water managers, scientists and stakeholders to ‘fine tune’ regional environmental flow standards” (Poff et al., 2010).

Synthesising, prioritising and integrating knowledge into these broader frameworks, and bridging the gap between knowledge and action, are key challenges that are addressed in Part III of this report. These challenges were explored in a cross-sectoral workshop; the results are discussed in Section 5 and related implications for future research and modelling are discussed in Section 6. The report concludes with challenges, and recommendations for climate change adaptation in the water resources and freshwater biodiversity sector.

20 http://www.ewater.com.au/products/ewater-toolkit/ 21 http://ausrivas.canberra.edu.au/ 22 http://www.asris.csiro.au/ 23 http://www.environment.gov.au/soe/index.html 24 http://www.environment.gov.au/land/nlwra/index.html 25 http://www.anra.gov.au/


Part III Building Knowledge


5 Identifying cross-sectoral information needs for climate-water adaptation

The prospect of climate change poses a real challenge to current freshwater management practices with implications across all sectors of society. Cross-sectoral collaboration is crucial to avoid maladaptive responses and to better understand the links and feedbacks that occur as society responds to changes in climate. Water is a unifying thread relevant to many sectors, which makes it particularly important in climate change adaptation work (PMSEIC, 2010). In the context of adaptation planning of water resources there is also large potential to manage water in ways that deliver diverse co-benefits to many sectors, e.g. communities and ecosystems.

During a two-day meeting, 25 participants representing state government/ policy development, federal government/policy development, environment/ ecosystem, water service provision/water resources (management), primary industries, energy and mining, science/research, community stakeholders met to identify links between climate change impacts in their sector and freshwater ecosystem services. The workshop’s premise was that good stewardship of freshwater ecosystems both directly or indirectly underpins the work in participants’ sectors, and is key to climate change adaptation.

During the course of the workshop, the participants were asked to respond to the following questions:

1. Which climate and hydrological changes concern your area most?

a. What services are provided by the catchment to your sector/system/etc? b. What climate change impacts would compromise functioning or outputs? c. What value does maintaining a viable catchment have for you?

2. What would these conditions mean for your sector as it stands today?

3. What changes might your sector see under a future scenario?

a. What could trigger action in your sector? b. How much of your sector is vulnerable to climate change and indirect impacts?

4. What adaptation pathways would be required to deal with these changes?

a. Which water resource and freshwater characteristics and information are most relevant for adaptation?

The questions were addressed in facilitated discussion sessions, based on the following topics:

Catchment Values

Vulnerabilities and action triggers

Required Ecosystem characteristics

Adaptation Options

Cross-sectoral information needs

Having identified important catchment values relevant to their sector, the participants of the workshop worked to identify what values of the catchment were vulnerable to climate change, and therefore what knowledge might be required to better characterise potential impacts, understand cross-linkages, or initiate effective actions that would protect the desired ecosystem values. Identifying these characteristics illustrates the importance of maintaining overall functionality of the entire catchment system, and puts the responsibility of stewardship into the hands of each sector deriving value from it (WSA, 2007).


The questions were addressed in small groups (4-6 participants) and each group submitted answers into online facilitation software, iMeet26. The software collated information, and enabled other functions such as voting, ranking and comments. Additionally, when voting or prioritising within the workshop discussions, each participant identified which sector their vote was relevant to. For example, a scientist might vote on behalf of the environment, or on behalf of researchers when prioritising certain adaptation options. The raw information captured by iMeet was then collated and reviewed to identify patterns and common threads arising from the discussions. In the following sections, outputs from iMeet were interpreted in the context of each discussion topic.

5.1 Catchment Values

Prior to the workshop, participants were asked to complete a survey on identifying key catchment/water values. During the workshop, participants were asked to identify and prioritise catchment values/services upon which people are heavily reliant. A summary of this activity is provided in Table 6, along with a categorisation used in the Millennium Ecosystem Assessment (Millennium Ecosystem Assessment, 2005): listed values and services were classed as either supporting, provisioning, regulating or cultural values/services. The list is by no means exhaustive, but reflects attributes of catchments that participants considered important for underpinning values such as human wellbeing, or products and services in the economy. In an assessment of the ecological and economic benefits of environmental water in the Murray-Darling Basin, the CSIRO used a similar categorisation for classifying ecosystem services; however, the category ‘supporting’ was subsumed into other categories to avoid a possible risk of double-counting when used in the context of valuation of services (CSIRO, 2012).

Table 6: Catchment values as identified by workshop participants (Apx Table B.1). Following the workshop, values were loosely categorised into three areas (riparian zones, water, and water bodies) and crosses were added to denote whether related services are supporting, provisioning, regulating and/or cultural, a classification according to the Millennium Ecosystem Assessment (Millennium Ecosystem Assessment, 2005).

CATCHMENT VALUES SUPPORTING PROVISIONING REGULATING CULTURAL

Catchment wide: "I’m downstream and benefit from good management"

x x x x

RIPARIAN ZONES

Overall x x x x

Agricultural land buffer x x

Bed and Bank Stability x x

Sediment trapping x x

Nutrient cycling x x

Temperature control x

Habitat for terrestrial biodiversity x x x

Living haystack x

Aesthetic values x

26 www.imeet.com.au

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CATCHMENT VALUES SUPPORTING PROVISIONING REGULATING CULTURAL

WATER

Potable use x

Supply for food and fibre production x

Supply for irrigation x

Stock watering x

Fire fighting x

Quantity of water available x

Water of a particular quality x

Supply for industry/commercial use x

Energy – hydroelectricity, thermal cooling x

Access to water for livelihood x x

Climate moderation x

Beneficial flooding for ecosystem functioning (spawning, nutrient flows, etc.)

x x x

Connectivity (longitudinal along-river and lateral with floodplains, wetlands and groundwater)

x x

WATER BODIES (INCLUDING GROUNDWATER, FLOODPLAINS, EPHEMERAL WATER BODIES)

Overall environmental/ecosystem function x x x x

Habitat for aquatic biodiversity x x

Cultural and intellectual relationship with water x

Cultural, indigenous, spiritual connection x

Property values x

Recreation and tourism x

Catchment connectivity (longitudinal along-river and lateral with floodplains, wetlands and groundwater)

x x

Food and fibre production (in-stream/riparian zone/lake)

x

Nutrient cycling x

Water treatment (i.e. improving water quality) x

Transport and storage of water for consumptive uses x

Storage and transport of water that provides flood mitigation

x


Based on the pre-workshop survey and inputs populating Table 6, participants created a working list of agreed key catchment values and services that reflect commonalities across the sectors (listed here in alphabetical order):

Connectivity of habitat (along-river or laterally with floodplains and wetlands)

Cultural and intellectual relationships with water

Energy - hydroelectric, thermal cooling,

Flooding for ecosystem functioning (spawning, nutrient flows, floodplain recharge, etc)

Food and fibre production

Habitat for biodiversity (terrestrial and freshwater)

Intact riparian zones for multiple benefits

I’m downstream and benefit from good management

Potable use

Water quality

Water supply for industry/commercial use

5.2 Vulnerabilities and action triggers

Participants were asked to identify what kind of change to climate and hydrology would compromise the functioning or outputs in their sector, and what the affected ecosystem processes were. Participants were encouraged to be explicit about primary, direct impacts (e.g. crop failure due to reduced rainfall) as well as secondary or more distant, indirect impacts. For example, reduced rainfall reduces water allocations, with follow on consequences for irrigation and crop failure; or reductions in fish populations due to altered flow regime combined with fishing practices, could affect recreational fishing yields, so reduce tourism and trigger lower regional employment, leading to reduced social resilience in rural communities.

This discussion started by identifying impacts that would trigger action in businesses, sectors or communities. The participants listed potential climatic or hydrological conditions that might affect or compromise these critical components. These were noted as ‘potential climate/hydrological vulnerabilities’ within and across sectors. Links between climate change and hydrological change to catchment values, functions and services as perceived by the workshop participants are illustrated in Figure 10. The left-hand list gives examples of direct and indirect climate and hydrological impacts likely to trigger action, and the right-hand list details the anticipated impacts on the catchment itself.


Figure 10: Climate and hydrology changes identified in workshop discussion (left) that are anticipated to trigger changes to valued aspects of catchment (right). These lists are not comprehensive, but indicative of the range of changes and impacts discussed. (See Apx Table B.2.)

This workshop discussion attempted to elucidate ‘what drives people to action’, or what climate change and water information is relevant for different issues, needs and sectors. Participants expressed difficulties, when trying to articulate specifics about ‘what triggers action’ due to the complexity involved in fully anticipating, comprehending and interpreting climate change, hydrological change, and associated implications, impacts and timing of impacts. Further, certain combinations of circumstances could trigger action, while other combinations wouldn’t.

Key conclusions drawn from this exercise include:

Hydrological impacts are central to all sectors.

Climate and water impacts that are likely to trigger action, or are valued by sectors, are highly interdependent.

Anticipated impacts

Altered water supply (and increased

uncertainty in supply) for all purposes

including:

- industry/commercial use

- potable water supply

- agriculture, food and fibre

- energy and thermal

cooling

- recreational use

- cultural values

- human health

Changed water quality

Change in supporting functions: eg.

primary production; soil properties;

biogeochemistry (e.g. nutrient cycling,

stoichiometry and nutritional quality

underpinning foodwebs, increased

blackwater events and anoxia)

Changed habitat: aquatic and terrestrial

biodiversity

Changed species assemblages, breeding

patterns, phenology, spatial distributions

Impacts on groundwater-dependent

ecosystems

Altered distribution of weeds, invasive

vegetation species

Movement of insects, pest species and

disease vectors

Changing fire regime

Increased exposure to risk in sectors

dependent on water supply

Changes to government policy

Responses to government policy (e.g.

plantation timber, coal seam gas)

Change in demographic and geographic

distribution of human populations

Transformational shift in primary

industries/production (e.g. viticulture

shifting to Tasmania)

Human health impacts

Climate and hydrology change

Increased carbon dioxide

Increased temperatures

Change in climate extremes (e.g.

intensity, duration and frequency of

drought)

Heat waves

Sea level rise

Saltwater intrusion

Rainfall changes: increases,

decreases, variability, intensity,

frequency of extremes

Increased evapotranspiration

Changes in wetting/drying cycles

Changes in seasonality

Stream flow changes: mean,

variability, extremes

Changes in erosion and dissolution

Changed surface/groundwater

interactions, groundwater recharge

Altered water volume in storages

Increased uncertainty in water

availability

Changes in stream connectivity (both

longitudinal along-river connectivity

and terrestrial/river interactions)


Interdependencies are rich with feedbacks and causal links operating on different time scales.

Adaptation actions are susceptible to unanticipated (and potentially unwanted) consequences due to feedbacks, time delays, nonlinearities and other system attributes.

Scientific analyses are associated with large (and often irreducible) uncertainties. Emphasis is better placed on understanding the nature of uncertainties, rather than seeking to remove or ignore them.

Social processes, not easily understood by working solely with biophysical datasets and models, mediate many of the system links and feedbacks.

The decision-making context is characterised by uncertainty, change and subjectivity. Thus, it is important to be clear about valued aspects of the system. Participants recognised that providing clarity on how actions link to values is one of the largest challenges. The discussion highlighted that there are links between climate change impacts on water and freshwater biodiversity and many basic requirements for healthy activity across all sectors; and thus identifies water as a unifying thread in making these sectors vulnerable to climate change.

5.3 Required Ecosystem characteristics

Fundamental requirements underpinning desired outcomes need to be clear and transparent in decision-making. In order to identify such requirements, participants were asked to describe ecosystem characteristics that they believed to be required in order to maintain fundamental processes/values. Simple questions were asked: ‘what do we need’ and ‘why’. A complete table of responses is provided in Appendix B (Apx Table B.4), and a condensed summary is provided in Table 7.

The aim of the discussion was not to provide a comprehensive list of all the ecosystem characteristics required to maintain essential water values needed for each sector. Furthermore, characteristics like ‘healthy riparian zones’ are over-arching in nature and entail many specific assumptions and characteristics in themselves. By scoping out some ecosystem characteristics, however, participants were able to find some shared intent among their messages. The entries became focussed on commonalities, and responses to ‘what we need?’ included not only physical entities (e.g. water supply systems) but non-physical requirements such as knowledge, governance and institutional structures.


Table 7: Summarised responses to discussion on required characteristics for desired outcomes. Summary is derived from full table listed in Appendix B (Apx Table B.4).

WHAT IS NEEDED WHY

Water availability, transmission, supply, quality, regulatory infrastructure, reduced losses from storages.

Food & fibre production, energy (hydroelectricity and thermal cooling), water supply for industry/commercial use, rural community livelihoods, viable agricultural enterprises.

Maintaining groundwater recharge, and ensuring groundwater-surface water interactions

Water supply, groundwater-dependent ecosystems, connectivity, water quality (especially due to salinity impacts of rising water tables).

Environmental flows with dynamic flow regime Ecosystem requirements for specific component of flow regime (peaks, timing, magnitude, duration, frequency), connectivity and fish passage, allow floodplain connectivity for floodplain ecosystems, wetland ecosystem requirements for water, water quality improvement

Geomorphological processes that maintain channel morphology, river form and function

Ecosystem requirements for habitat and flow regime, prevention of scouring, bed stability, reduction of downstream sediment and nutrient loads, maintenance of hyporheic (stream bed) exchanges and flood buffering.

Healthy, intact, continuous riparian zone: shade, snags and debris, nutrient cycling

Water quality, sediment trapping, habitat, temperature regulation, shading, connectivity, and bank stability.

Habitat maintenance, conservation action (e.g. enabling species movement across landscape), management of threatening processes

To support ecosystem biodiversity and resilience, and to prevent species extinctions.

Increase the national investment in our scientific understanding and knowledge of current unknowns: ecosystem thresholds, social system thresholds, actions that can buffer expected changes, extreme events (floods, fires, droughts and storms), and human behavioural change.

Required for better management of ecosystems, food and fibre production, water supply, culturally significant sites and more.

Communicate the value of a functioning ecosystem to all stakeholders e.g. farmers and urban dwellers, not just scientists and policy makers

Required for better management of ecosystems, food and fibre production, water supply, culturally significant sites and more.

Diverse economy, diverse ecosystems, space to move, and removing regulatory restrictions.

Required for adaptation and flexible response to change (in both human and ecological communities).

Strategic relocation of industry to match suitable climate/water availability and other constraints (e.g. pests, disease, transport).

Food and fibre production, industry.

Political courage, resources, process for prioritising species/ communities/ industries for action

Required for structural adjustment, assisted migration and other strategic adaptive responses.

5.4 Adaptation Options

Participants were asked to identify adaptation options that would build resilience in their sector to potential impacts and uncertainties associated with climate change. These included adaptation options in response to both direct and indirect impacts of climate change.

A summarised list of options offered by participants is given in Table 8. The question prompted a diversity of responses, and it’s helpful to classify them approximately into three categories: direct on-ground actions, knowledge-seeking activities or changes to governance arrangements or conceptual framework.


Table 8 List of adaptation options identified by workshop participants (more details in Appendix B, Apx Table B.3), classified approximately according to three categories of response: knowledge acquisition/research; on-ground action; governance or conceptual framework.

ADAPTATION OPTION KNOWLEDGE ACQUISITION OR RESEARCH

ON-GROUND ACTION

GOVERNANCE, CONCEPTUAL FRAMEWORK

Continue investment in long-term monitoring data sets so that trends and change over time can be analysed and implications for the future can inform adaptation response.

x

Well functioning insurance/ risk assessment/ risk planning/ shared intelligence/ leading to reducing risk/ responding to risk incentives/ disincentives.

x

Assess vulnerability of different industry sectors to changes in water availability.

x

Understanding impact of government policy on primary production - eg water pricing policy.

x

Movement of insects and pests: knowledge to inform adaptation which could be changes to infrastructure (e.g. tank or storage design), changes to disease control, research and monitoring required to develop an appropriate adaptation response.

x

Changes in operation of existing storages for flood mitigation and energy in response to change in variability and seasonality.

x

Managed aquifer recharge to maintain coastal wetlands (grey water recycling).

x

Process water recovery, storm water capture, integrated water cycle management.

x

Forest thinning. x

Hypolimnetic oxygenation of waterbodies. x

Fence out all cattle from riparian zones to improve water quality and downstream impacts.

x

Improve energy use efficiency in agricultural enterprises. x

Improving water use efficiency with agricultural crops/diary/winery/horticulture/ irrigated crops.

x

Incremental adjustments to agricultural system - plant variety, breeding, genetics, planting timing, species selection.

x

Transformational adaption of agricultural industry. x x

Change in water entitlements. x

Targeted community redundancy - water buy back from targeted communities with low long term viability.

x

Reduce water extractions - ensuring that future development is done within the current envelope of water availability LESS climate change factor.

x x

Flexible water sharing/trading/offsets, and regulatory environment.

x x


ADAPTATION OPTION KNOWLEDGE ACQUISITION OR RESEARCH

ON-GROUND ACTION

GOVERNANCE, CONCEPTUAL FRAMEWORK

De-emphasise water use engineering efficiency approaches to water scarcity in the absence of addressing water entitlement change.

x

Avoiding maladaptive short-term adaptations. Focus steps along pathways of transformational adaptation changes, e.g. moving production systems vs. sinking capital in keeping them there.

x

Re-assessing definitions of what is an 'exceptional circumstance' e.g. droughts, floods etc in regard to government financial support.

x x

Regulations/policy to not build/exist on floodplains, or where sea level will rise, or where bushfires will rage.

x

Use sustainability as the framework for climate change adaptation - nothing wrong with triple bottom line, can accommodate resilience, thresholds, adaptive capacity, trade-offs.

x

Culturally appropriate decision making/engagement processes to determine water allocations (may be for spiritual, community or industry).

x

Local level collaborative planning to identify adaptation options including structural adjustment.

x x

Explore options for transformation of rural/regional communities (population change, infrastructure etc).

x x

Stakeholder endorsed standards and certification as drivers for update of best management practices.

x

Do nothing, deal with the crisis when it arrives, take advantage of crises to achieve rapid social and economic change, be prepared to put forward alternative opportunities.

x

Change the political system so we move from short term management decision frameworks to longer-term visionary approaches.

x

For practical purposes, the participants were asked to rank (and where possible merge) adaptation options to create a manageable working list for further discussion. Participant also provided indications of feasibility, relative cost and time horizon for different adaptation options (see Appendix B, Apx Table B.5).

Top priority adaptation options to emerge from participants’ deliberations included:

Explore options for transformation of rural/regional communities (population change, infrastructure etc.).

Stakeholder endorsed standards and certification as drivers for uptake of best management practices.

Avoiding maladaptive short-term adaptation - focus steps on transformational adaptation changes: e.g. moving production systems vs. sinking capital in keeping them there.

Regulations/policy to not build/exist on floodplains, or where sea level will rise, or where bushfires will rage.

Protect riparian zones: e.g. fence out all cattle to improve water quality and downstream impacts.

Well functioning insurance/ risk assessment, planning and knowledge-sharing to reducing and respond to risk.

Flexible water sharing/trading/offsets, and regulatory environment.


Continue investment in long-term monitoring so that trends and change over time can be analysed and implications for the future can inform adaptation response.

Culturally appropriate decision making/engagement processes to determine water allocations (may be for spiritual, community or industry).

Local level collaborative planning to identify adaptation options including structural adjustment (later combined with the previous option).

Improved water use efficiency within a whole-system framework.

Changes in operation of existing storages for flood mitigation and energy in response to change in variability and seasonality.

Ranking was used to extract priority areas for climate change adaptation planning and future research across the participants’ sectors in the context of water resources and freshwater biodiversity. However, there are many challenges associated with prioritising using simple ranking. The acknowledged interrelatedness of multiple catchment changes and impacts meant there was an appreciation that adaptation options can interact. To prioritise only a reduced set of adaptation options is to limit opportunities for identifying co-benefits or unanticipated impacts across sectors (discussed further in Section 5.6).

For example, the adaptation option: ‘water use efficiency’ came up early and was considered a high priority. Another suggested adaptation option was to ‘de-emphasise water use efficiency’. These two adaptation options appear to be in conflict, but the second option reflects an important recognition that focusing on water use efficiency alone without awareness of well-known systemic issues is problematic (Gawne et al., 2010). In a simple ranking process to identify priorities, the adaptation option “de-emphasise water use engineering efficiency” risked being overlooked entirely.

More generally, the complete list of options in Table 8 reveals that asked to find adaptation options that are relevant to multiple sectors, participants identified general principles, conceptual frameworks and governance requirements as well as more tangible on-ground actions. This is consistent with published findings that general principles are more readily identified and communicated than specific actionable recommendations (Heller and Zavaleta, 2009). It suggests two implications:

1. when working with a cross-sectoral group, the potential for identifying shared options is improved if there is a willingness to include general principles, conceptual frameworks and social processes (e.g. governance arrangements).

2. given the ready ability to identify shared generic, framing options, the challenge in climate change adaptation is to build experience of applying those principles and frameworks in different on-ground contexts and sharing those experiences as working examples.

The high-priority adaptation options identified by participants will be discussed in more detail in the following sections. Some options that were initially discussed separately are combined for the purposes of this summary. In these discussions, participants sought to identify fundamental requirements and who would be responsible for these adaptation actions. The following descriptions are summarised from all the comments of the workshop participants.

5.4.1 PROTECT RIPARIAN ZONES

Multiple benefits are derived from protected riparian zones and participants identified it as one of the most feasible and effective adaptation measures. Participants pointed to the ideal knowledge requirements to prioritise river reaches for protection and re-vegetation efforts. These included: riparian condition, channel condition, geomorphology, hydrology, threats, conservation priorities, indigenous landholder values, ecosystem composition and land tenure.

Participants suggested that there is good knowledge on how to protect riparian zones (the on-ground methods), but there’s less knowledge on what is needed to develop better incentive and regulatory structures to enable the on-ground work to happen. Participants recommended identifying existing programs and practices and building on what is working in those programs.


Funding limits mean that programs risk being small catchment-scale projects that don’t allow a more systemic view and set of restoration priorities. One-to-one interactions with individual landowners are needed for effective outcomes, but benefits are greatest when small-scale actions are embedded in a whole-of-system strategy (e.g. integrated in an Australia-wide water stewardship accreditation system).

5.4.2 IMPROVED WATER USE EFFICIENCY WITHIN A WHOLE-OF-SYSTEM FRAMEWORK

Adaptation options focussed on improving efficiency raise important issues that warrant broader understanding and awareness among stakeholders and decision-makers alike.

Improving water use efficiency is widely regarded as an obvious adaptation strategy, particularly when the criterion is to identify on-ground actions that are highly feasible with broad support. Agricultural, industry and environment sectors alike have demonstrated a willingness to direct water to uses with the highest returns (whether delivering economic or environmental benefits). Improvements in water accounting and flexible trading mechanisms enable well-informed actions to improve water use efficiency, all within a market structure aimed at ensuring water efficiency measures are paid for by those who receive the most benefit. Water stewardship accreditation and certification schemes would further strengthen the capacity for improving water use efficiency.

There is an important condition attached to this adaptation option: it needs to be within a whole-of-system framework. Without this extra condition, efficiency gains can be counterproductive for several (often unappreciated) reasons:

1. Water currently ‘lost’ through inefficiency can be a gain to a downstream ecosystem, so it is incorrect to assume that efficiency measures deliver only good outcomes across the system.

2. A system-wide increase in efficiency can decrease long-term resilience to future droughts as it reduces the option to invoke water savings in future times of scarcity (it removes a buffer that can be drawn upon during extreme events).

3. The ‘rebound’ effect is a well-documented effect in many systems: efficiency gains deliver little or no system-wide benefit if unappreciated feedbacks see the savings triggering unwanted impacts elsewhere in the system (Hertwich, 2005).

These considerations were raised in light of workshop discussions on system resilience. In particular, one of the adaptation options identified by participants was to “de-emphasise water use engineering efficiency approaches to water scarcity in the absence of addressing water entitlement change”. The issues surrounding a naive focus on water use efficiency in Australia are discussed in more detail in (Gawne et al., 2010).

5.4.3 REGULATIONS AND POLICY TO NOT LIVE OR BUILD ON FLOODPLAINS, OR WHERE SEA LEVEL WILL RISE, OR WHERE BUSHFIRES WILL RAGE.

Identifying hazard prone areas based on climate change scenarios and impact assessments provides a knowledge basis for local government to explore a wider range of policy and regulation options. Changes in regulation that entail relocating people are likely to be politically unpalatable, so need to be supported by transparent risk analysis and engagement about the trade-offs between social, environmental and economic outcomes. Such efforts will pay particular dividends in situations where maintaining the status quo can be likened to subsidising people to stay in hazard prone areas (i.e. regulations remain in place despite evidence of increased risk, and in the event of natural disaster costs are borne by all).

5.4.4 FLEXIBLE WATER SHARING/TRADING AND REGULATORY ENVIRONMENT

Flexible water sharing and trading arrangements already exist; discussion on this adaptation option was to stress its importance in a climate change adaptation context. In particular, participants want to see the water market cover all major users, including inflow interception activities, carbon farming, mining, coal


seam gas, energy users, groundwater etc. National policy commitments to do so (via the NWI) have yet to be fully implemented. Continued efforts at ensuring good availability of water accounting and trading data are required, including paying attention to surface/groundwater interactions, ‘losses’ from the system and environmental flow requirements. Responsibility for this option lies with the national policy framework (NWI) and Federal institutions (EPBC, Water Act, CEWH etc), and with states and regional institutions ensuring workable on-ground regulations and environmental protection practices are implemented.

5.4.5 AVOIDING MALADAPTIVE SHORT-TERM ADAPTATION

Participants pointed to the importance of the decision-making frameworks and institutions that are designed to ensure decisions contribute to achieving long-term (potentially transformational) objectives shared by multiple sectors. This could include providing legal mandates for decision makers to consider impacts on other sectors. Such efforts would involve horizontal integration of knowledge about impacts (e.g. water impacts of carbon policies and tree planting), and cross scale interactions (e.g. between individual actions and policy at local, regional and national levels).

Independent reviews of current policy and institutional arrangements would be useful for informing and designing required changes in institutional arrangements. Participants also identified integrated multi-sectoral leadership as more effective than leadership and decisions occurring in independent ‘silos’. Maladaptive solutions were described as expensive to fix, so making these costs explicit would allow better exploration of the cost-effectiveness of preventing short-term maladaptive actions. As a specific example, it is preferable to provide subsidies that encourage moving or adapting production systems rather than having subsidies sink capital into trying to fix productions systems in current locations and modes of operating.

5.4.6 EXPLORE OPTIONS FOR TRANSFORMATION OF RURAL/REGIONAL COMMUNITIES

Anticipating there will be expansion and contraction of regional populations in response to multiple influences (including shifting agricultural production, mining and coal seam gas activities), participants discussed opportunities for such changes to build, rather than erode, the health of rural and regional communities. There will be changes, and for change to be welcomed it needs to be: (a) underpinned by community aspirations and values; and (b) feasible given community adaptive capacity or constraints. With appropriate support from local industry and all levels of government, communities can shape significant change and transformation, especially if they have access to diverse knowledge sources (e.g. knowledge of changes in climate, hydrology, natural resources, technology, economics, ecosystems, agricultural and industry developments).

Given common resistance to change, it was suggested that natural disasters (e.g. cyclone, flood, fire) are times of uncertainty and upheaval that provide opportunities for community leadership in transformational change.

A particularly challenging situation is when, in the interests of meeting water supply priorities, an irrigation-dependent community is considered ‘redundant’ and is compensated through water buy backs it so that it either ceases to exist or transforms so that it is no longer reliant on irrigation water. Again, for such processes to yield good outcomes it requires effective, integrated knowledge-sharing and decision-making across all levels of government, community and local businesses and industries. The difficulty of this kind of option means that where possible anticipatory, pro-active, community-led initiatives to transform are to be favoured over reactive changes enforced on a community.


5.4.7 WELL FUNCTIONING INSURANCE AND RISK ASSESSMENTS, RISK PLANNING, AND SHARED INTELLIGENCE LEADING TO REDUCING RISK IN RESPONSE TO RISK-BASED INCENTIVES AND DISINCENTIVES

Flood risk attracted the most attention in discussions of this adaptation option. Underpinning information requirements include rainfall and stream flow forecasting, flood mapping and well-designed engineering and insurance standards. Such assessments are technically highly feasible, but need the funding support and commitment to act on such assessments from government (all levels), industry and individuals alike.

Participants had also identified ‘vulnerability assessments’ as a requirement for successful adaptation, recognising that options cannot be assessed appropriately in the absence of knowledge of vulnerabilities.

5.4.8 CONTINUE INVESTMENT IN LONG-TERM MONITORING DATA SETS SO THAT TRENDS AND CHANGE OVER TIME CAN BE ANALYSED AND IMPLICATIONS FOR THE FUTURE CAN INFORM ADAPTATION RESPONSE

This adaptation action recognises the long-term nature of climate-water-ecosystem dynamics and the benefits of good mechanisms for continually updating knowledge and reviewing decisions in light of new knowledge. Workshop participants pointed to past projects rich with data, model runs, literature reviews and lessons learned, yet that knowledge is often inaccessible due to lack of infrastructure and mechanisms to house, maintain and make available such archival material. The Bureau of Meteorology provides such a service for weather, climate and water data and model results, and the strategic plan for the Bureau extends this capability to a broader set of environmental information (BOM, 2010). Other initiatives that contribute to this need were listed in Section 4.3.2.

Three different, but related needs were identified:

1. Well-designed long-term monitoring complete with good standards for data quality control and metadata requirements. This would benefit from the support and knowledge contributions from many, including industry (e.g. water utilities data, data on agricultural yields), community groups (e.g. catchment groups), non-government organisations, local councils and government agencies.

2. Long-term housing, interpretation (e.g. modelling) and knowledge sharing. Participants acknowledged the challenges of integrating data sets collected by different agencies and groups with different protocols (and different purposes). Addressing these challenges was seen as the responsibility of an established national body like the Bureau of Meteorology or Australian Bureau of Statistics.

3. A retrospective inventory of data (including applications of the data, any updating or maintenance needs, identification of gaps).

Such knowledge-sharing facilities were seen as vital for informing future scenarios. Very different kinds of data requirements were mentioned. These included: hydrological (surface and groundwater) data, climate data, ecological response data, water quality data, socio-economic data, remote sensing data, indicator species, and a capacity to relate cause and effect (e.g. via models).

The key points repeatedly emphasised by participants were the need for a stable and reliable organisational framework that is immune to 3-year funding cycle, for good mechanisms to ensure that data quality and provenance information are provided, and for widespread accessibility to raw data through to model outputs and high-level interpretation of information.

5.4.9 STAKEHOLDER ENDORSED STANDARDS AND CERTIFICATION AS DRIVERS FOR UPDATE OF BEST MANAGEMENT PRACTICES

This option could be considered a tool to enable some of the other adaptation recommendations mentioned in this section; however it is also a standalone action. Participants identified a role for stakeholders, informed by scientists, ecologists, producer groups, conservationists and natural resource


managers. It is a market based and market driven process, relying on measurable outcomes monitored by third party accreditation bodies (for more info see Water Stewardship Australia website27).

5.4.10 REGIONAL GOVERNANCE TO ENABLE LOCAL, COLLABORATIVE DECISION MAKING

Two adaptation options identified by participants related to regional governance:

1. Local level and culturally appropriate decision making/engagement processes to determine water allocations (e.g. for industrial, agricultural, community, spiritual or ecosystem uses)

2. Local level collaborative planning to identify adaptation options including structural adjustment

The emphasis was on partnerships that ensure that adaptation decisions and actions have local relevance and support. ‘Non-token’ consultation was identified as important for reaching agreement on shared purposes, decisions and actions across different sectors and interests. To go beyond token efforts and foster culturally appropriate local engagement requires commitment, time and money. Approaches for more effective engagement are discussed further in Section 6.

5.5 Cross-sectoral information needs

Participants were asked to identify links between adaptation options and the information requirements that would aid decision making to enable these options. Information needs of various kinds were mentioned at many points throughout the workshop; the list in Table 9 illustrates the range of information needs identified.

Table 9 List of information needs identified during the workshop, organised here into three categories: physical, social/economic and system-level information.

INFORMATION NEEDS BIOPHYSICAL SOCIAL OR ECONOMIC

SYSTEM-LEVEL

Temperature: mean, days of temperature exceedance, temperature return frequency, water temperature and flood temperature.

x

Species presence and abundance, phenology. x

Water allocation and storage/availability. x

Flow components (mean, annual discharge, seasonal flow distribution, return frequencies, spatial distribution of inflows, reliability of flows).

x

Event (flood/drought) analyses: duration curves, frequency analysis, timing, magnitude, and duration.

x

Downscaled projections of extreme rainfall events. x

Groundwater: recharge to groundwater, underground water connectivity, underground water reserves, effect of saline water bought to surface, appropriate accounting for surface and groundwater (no double counting).

x x

Vegetation characteristics: evapotranspiration, normalized difference vegetation index (NDVI), crop wilting indicators, and relationship between CO2 and plant growth.

x

Water quality indicators: nutrients (inorganic and organic forms of C, N, P etc), biochemical oxygen demand (BOD), dissolved oxygen (DO), salinity, turbidity, secchi depth, sedimentation, algal counts, chlorophyll a etc.

x

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INFORMATION NEEDS BIOPHYSICAL SOCIAL OR ECONOMIC

SYSTEM-LEVEL

Tide gauge data, marine intrusion, and coastal recession. x

Land use information

Plant variety, breeding, genetics, planting timing, and species selection data. x

Food and fibre production data. x

Bushfire indices: change in flows due to post-fire regrowth, impacts on sediment movement, and nutrient dynamics.

x

Water use and storage data: water take vs. water allocation and loss, water use vs. financial return, storage, availability etc.

x x

Likely spread/change distribution of weeds e.g. leaucena x

Cultural practice requirements. x

Human population modelling. x

Social capital measures. x

Health statistics and vulnerability indices. x

Increased knowledge of drivers of drought, impact of climate change on ENSO and other drivers of Australian climate variability.

x

Need to better understand resilience of ecosystems within catchments.

Governance, e.g. institutional measures x

Interface between agriculture and ecosystems with regard to minimising conflict.

x

Economic tools for assessing water risk, e.g. price of secure water in response to climate variability, percentile distribution of water availability.

x

Vulnerability assessments, including anticipating unintended consequences (e.g. of floods) and economic/social impacts.

x x x

Characterisation of trade-offs, e.g. between ecosystems, forestry, water quality issues; between water-intensive and energy-intensive activities; resilience trade-offs between competing uses of water; between competing interests for water.

x x x

Stakeholder interests (via meaningful stakeholder consultation). x

Identified win-win situations, co-benefits. x

Full life-cycle analyses. x x x

A conclusion from this session, and the workshop more generally, is that improved knowledge or data alone isn’t sufficient, especially when taken out of context. The knowledge needed to understand the impacts of decisions on the water system are more likely to be appreciated and acted upon when diverse teams of people with specialist knowledge bring their insights together for a shared (applied) purpose.

Furthermore, a good proportion of the identified information needs involve sophisticated system-level analysis and interpretation.


5.6 Ranking and sorting

Early attempts to interpret information, both during the workshop and in interpreting the iMeet material afterwards, was to rank and sort the adaptation options according to various methods (including votes entered into iMeet). This is a very familiar way to attempt to prioritise and reduce the scope to allow more considered focus. However, ranking and sorting to identify the ‘top’ issue/priority increases the likelihood of overlooking apparently lower-priority aspects that can in fact contribute to either co-benefits or unwanted unanticipated outcomes (due to unappreciated interconnections). The workshop discussion on water use efficiency is an example thereof. The ranking and sorting process was useful for identifying commonalities to help collaboration and discovery of shared objectives. However discussions returned to the disadvantages of prioritising interdependent actions using simple ranking and ordering tools without considering the integrated picture. Furthermore, the process of ranking is subjective and different groups of people and problem contexts will see different rankings emerge.

The tables presented in this report reflect the impacts, sectors, ecosystem services and adaptation that emerged as a priority for this particular group of participants. The lessons learned from this exercise are not the specifics of identified impacts through to adaptation options, but rather the valuable insights into what is needed for such cross-sectoral groups to exchange knowledge in a way that builds shared objectives and adaptation strategies. These lessons are explored further in the next section with a particular focus on implications for researchers involved in modelling to inform climate change adaptation.


6 Modelling Opportunities

6.1 Modelling requirements and opportunities unique to adaptation

The role of mathematical models is often perceived to be aimed at providing predictions about the future. The purpose of this section is to reflect on the messages that emerged from the workshop (Section 5) and to point to a broad suite of ways in which models contribute useful knowledge and approaches to the challenges raised in the workshop. This section was further informed by a subsequent smaller meeting (to be referred to as the ‘modelling meeting’) of modellers experienced with hydrology, climate and ecosystem modelling. The purpose of the meeting was to review issues raised in the workshop and link them to modelling implications and opportunities in climate change adaptation work. The emphasis on modelling is not to suggest that models provide the only way to address climate change adaptation challenges, but rather to foster an appreciation for the range of opportunities modelling enables and point to fruitful future directions for model and research applications in climate change adaptation.

The word ‘model’ holds different meanings for different people. At its most general, a model is a metaphor: a representation of a situation or system. It can be as simple as a diagram, such as a conceptual model of the interlinked influences affecting river health. It can be as complicated as a high resolution, numerical weather simulation used to make daily weather forecasts. Mathematical models are a subset of models that use mathematics in their formulation, communication and analysis, and it is primarily this subset of models that are referred to in this report. Mathematical models can be partitioned into three categories: analytical models, numerical models and observational models (Gershenfeld, 1999). Within biophysical and ecological modelling there is a strong emphasis on using models to ‘confront theory with data’ (Hilborn and Mangel, 1997), and much effort is spent on model confirmation, characterising and quantifying the level of agreement between prediction and observation (Oreskes et al., 1994).

The past two decades have seen a growing body of research in a range of sectors into climate change impacts. There is no reason to assume that models developed to study climate change impacts and mitigation options are equally suited to climate adaptation applications: the objectives and drivers of climate change adaptation work are diverse, and different models are needed to match those. For example, a model that demonstrates species distribution shifts in response to climate change scenarios provides valuable knowledge: it can be a source of evidence on species impacts that triggers a shared acknowledgement among stakeholders of the existence of a problem (e.g. a valued ecosystem is at risk) and a common agreement to do something about it. The very same model may not be well suited to informing what that action should be, or how it should be carried out. Opportunities are lost by launching directly into designing and implementing actions without first clarifying purpose and goals, and those goals may require very different information. The species distribution modelling results, for example, may simply reflect the most visible, measurable part of a whole set of less visible changes that could be instrumental in informing adaptation options.

A recommendation that emerged from the modelling meeting was to recognise the different stages in any deliberate ‘change process’ and be clear about which stage of change is the focus:

Stage 1. Building evidence and knowledge to foster a shared understanding that change is needed.

Stage 2. Exploring and setting desired objectives of that change.

Stage 3. Designing and implementing actions that make change possible.

This recommendation is echoed in other analyses concluding that freshwater ecology in Australia has typically focussed on description and problem identification (stage 1) more than on building solutions and adaptation strategies (Gawne et al., 2010).


Modelling work contributes to all three stages, and each can benefit from a different array of modelling approaches. The following sections consider how modelling contributes to Stages 2 and 3, drawing on the needs that were identified by workshop participants when considering climate change adaptation options for the water resources and freshwater biodiversity sectors. The assumption is that the need to adapt has already been established and the main focus is on establishing the objectives for adaptation and means to meeting those objectives.

The options for climate change adaptation raised in the workshops (Section 5.4) can be approximately partitioned into three broad (and overlapping) areas:

1. Strategic knowledge acquisition and research (e.g. acquisition and provision of long-term data-sets, vulnerability and risk assessments, research aimed at improving understanding of key processes and cause-effect relationships). Stage 2 and Stage 3 both require this kind of knowledge and analysis.

2. Changing governance structures or problem framing (e.g. regulations, trading mechanisms and markets, stakeholder engagement processes, water stewardship frameworks). These changes are examples of actions in Stage 3.

3. On-ground action (e.g. fencing riparian zones, changing storage operations, water recovery and re-use, buying water entitlements). These changes are examples of actions in Stage 3.

Section 6.2 refers to issues relating to these categories of options and points to ways in which models can be used to respond to the issues. Some over-arching climate change adaptation challenges identified by workshop recipients include:

1. Linking scale and sectors. There is a clear need to be able to better account for links between sectors and between different spatial and temporal scales e.g. linking actions now to consequences at distant future times, linking paddock-scale actions to cumulative consequences at a larger catchment scale or anticipating cascading impacts across different sectors.

2. Handling uncertainty. Any rigorous characterisation and propagation of uncertainty can become unmanageable or uninformative given the long chains or networks of cause and effect when linking global climate, regional hydrology, ecosystem responses and human decision-making. Non-linear system effects and feedbacks are a particularly challenging aspect of uncertainty.

3. Governance arrangements, knowledge and action that mutually support one another. Adaptation requires constructive interplay between knowledge and action. Governance arrangements that include monitoring and learning processes make it possible for action and knowledge to inform each other iteratively. Effective engagement and communication practices are key part of such governance structures and learning processes.

Section 6.3 concludes with a summary of roles of modelling roles identified in response to adaptation issues

raised in the workshop, and suggested future directions for work in this area.

6.2 Issues in climate change adaptation: implications for models

This section discusses specific issues that were raised by workshop participants. Generic knowledge issues identified in the workshop are discussed first, followed by issues associated with interpreting and acting on that knowledge (e.g. in informing targets, standards and governance structures, and in communicating and engaging with stakeholders). Linking models to stakeholder needs is a central theme, where stakeholders are defined broadly as anyone with a stake in the system: people who contribute to or make decisions, people who are held accountable for outcomes of decisions, and people who are affected by impacts of decisions made (now and in the future).


6.2.1 KNOWLEDGE NEEDS AND UNCERTAINTY

Physical metrics

The usefulness of a comprehensive set of physical metrics derived from established modelling approaches is well appreciated (Part I). The emphasis is on ensuring open, widely available access to a growing set of temperature, rainfall and flow metrics covering a diverse range of future climate scenarios, provided at ever-improving spatial and temporal resolution for Australia. Other physical metrics sought by workshop participants include measures of groundwater responses, connectivity (e.g. connectivity between groundwater and surface water, connectivity between floodplains and rivers), flood extent mapping and seawater intrusion. A natural extension is to include measures of constituents carried in water (e.g. sediment, carbon, nutrients, contaminants, and salt).

Spatial and temporal resolution matters, particularly given the benefits of being able to capture cross-scale relationships. Examples of cross-scale questions are:

What does a local site contribute to cumulative catchment-scale effects?

Can current events be attributed to consequences in the distant future?

Can local extreme events (such as a heat waves or floods) be attributed to over-arching long-term drivers like climate change?

The ability to deal with information at multiple scales, and in particular infer relationships across scales, is a unique advantage of quantitative physical models such as the hydrological models used to assess impacts on runoff. These models are founded on water balance equations (so ensuring conservation of mass). In the lumped conceptual models used in most large scale hydrological assessments in Australia, there are a set of tuneable parameters, and these are chosen by calibrating models against available data and providing statistical analyses of uncertainty (Beven, 2012, Chiew, 2010). These characteristics are needed for any robust basis to attribution studies (attributing effects to causes), and enable relationships between catchment-scale and local effects to be calculated and well-grounded in evidence.

Closely related to this is the ability to be able to infer robust relationships – useful ‘rules of thumb’ – that are derived from multiple model runs across multiple scenarios. For example, at 10% reduction in rainfall elicits a larger reduction in runoff due to the nonlinear relationship between rainfall and runoff generation. Rainfall-runoff modelling is a sophisticated means to infer this relationship for a specific catchment. More generally, however, some ‘rules of thumb’ are useful to give estimates of the expected change in runoff given changes in rainfall and evapotranspiration across catchments for which detailed models or data might not be available. Meta-analyses of historical data and rainfall-runoff models for over 200 catchments in Australia provided such estimates (Chiew, 2006, Jones et al., 2006). The benefit of such analyses is that they ensure estimates are robust to sources of uncertainty and variability: results were derived from a diverse range of catchments, and estimates were calculated in multiple ways from a data-driven nonparametric method and from two different rainfall-runoff models. Such rules of thumb are useful ways to synthesise knowledge from observations and model results in a manner that is broadly applicable (Figure 11). The climate-runoff relationship may change in the future, particularly with climate change, but these rules of thumb are still very useful for broad scale assessments or initial assessments prior to a more detailed modelling study.


Figure 11 Rainfall elasticity of runoff in Australia (Chiew, 2006) Image courtesy of eWater CRC.

Workshop participants identified many metrics that they would find helpful in exploring, selecting and implementing adaptation options (Table 9). The use of such metrics requires an understanding of their uncertainty, and participants wanted to be able to understand and communicate assumptions and uncertainties before accepting model outputs at face value. Among modellers there is a concern that model results generated for one purpose will be picked up and used out of context to make inappropriate inferences in a subsequent application. Hence there is a mutual interest in ensuring good characterisation and communication of uncertainty.

Uncertainty can be expressed and interpreted in several different ways, particularly when interpreting and using the output from models. At its very simplest, it is useful to consider two extremes: ‘embrace’ or ‘ignore’ uncertainty. An example of ignoring uncertainty is to frame knowledge requirements so that quantities need to be exact values instead of probability distributions or ranges. As soon as knowledge requirements are restricted in this way, important information about the uncertainty is ignored and any decisions based on the information do not take uncertainty into consideration. The alternative, ‘embracing uncertainty’, is to require probability distributions and ranges instead of point quantities, and ensure that any interpretation or use of the information has accounted for that uncertainty and is robust to it. Given the level of irreducible uncertainty inherent in linking climate change to impacts and adaptation options, embracing rather than ignoring uncertainty is clearly preferable as a general rule; however such a view is not standard practice and there is much more that can be done do develop such approaches.

Associated with the aim to embrace uncertainty is the need for useful approaches to handle situations where uncertainty is overwhelming, e.g. when even the direction of change is uncertain, let alone the magnitude. The challenge here is to interpret highly uncertain information usefully, despite the inclination to regard such results as completely uninformative. For example, a comparison of 15 global climate models showed that rainfall projections for Australia are highly uncertain, and the direction of change was not consistent across all models (Chiew et al., 2011). This uncertainty in rainfall projections – the fact that different global climate models produce different rainfall projections – is one of the largest sources of uncertainty in future runoff predictions (Figure 12). A reaction from a funding body could be: ‘After all that, your models tell me it could be wetter or drier?’ An important lesson from this work is that even uncertainty in the direction of change does not prevent useful outcomes and insights. For example, it makes it particularly apparent that planning cannot be limited to one particular scenario. To quote from the authors of this work, ‘the drought has clearly shown that current water management approaches are inadequate to deal with the high variability in water availability, whatever the root cause’ (Chiew et al., 2011), and the spread in future hydroclimate projections reinforce the message that plans will need to


accommodate such variability. Strategies that prioritise helpful outcomes regardless of future hydroclimate offer a distinct advantage over strategies that are developed by choosing only one future scenario and optimising outcomes based on that single scenario. In particular, the authors suggest that decisions need not be predicated on worst-case scenarios, but it is prudent to have management plans in place should such scenarios eventuate.

Figure 12 Percentage change in annual runoff modelled using 15 different global climate models for a 0.9°C increase in global average surface air temperature. Image from Figure 8 in Chiew et al. (2011).

Note that the trend in modelling futures is to develop multiple scenarios that span a range of sources of variability and uncertainty, including using different models, data-based versus mechanistic-model derived analyses, and alternative assumptions within a particular modelling framework (e.g. parameter choices, model resolution, handling of model inputs). Thus the organisation, publication and availability of these kinds of analysis become extremely important.


Ecological and social metrics

Many of the desired metrics identified by workshop participants were not just physical measures, but measures or indicators of ecological and social dimensions. The knowledge requirements for ecological and social factors are of a fundamentally different nature and introduce new uncertainties of their own (e.g. issues of context dependence and sensitivity to diverse and conflicting values). For example, the eWater Cooperative Research Centre developed a useful and accessible library of ecological models, Eco Modeller28, and it placed a high priority on provision of comprehensive meta-data, so that the underlying assumptions and the applicability of any specific model to other situations are made clear (Marsh et al., 2007).

Even though future water scenarios and associated projections are about physical, measurable quantities, much of the uncertainty associated with those futures scenarios comes from a need to anticipate future human responses, such as changes in land use, urbanisation, cropping and irrigation decisions which all affect future water availability. Furthermore, workshop participants not only wanted to be able infer links across time and space (e.g. relating local and catchment phenomena), but draw links across sectors and between systems of very different kinds. Among workshop participants, and more generally (e.g. in government agencies, funding bodies, published literature), there’s growing interest in integrated assessments that provide useful syntheses of ecological, social and economic dimensions. Such analyses are particularly helpful if they can provide greater clarity and quantification of externalities and potential impacts in other sectors of the economy. They are useful if assessments are able to characterise trade-offs between different options, and also trade-offs between local benefits, private interests and the public good. Workshop participants referred to the Millennium Ecosystem Assessment conceptual model, which emphasises the links between ecosystem services and human wellbeing (Millennium Ecosystem Assessment, 2005).

Finally, closely related to the calls for integrated assessments, is the desire for whole-of-system analyses that pay particular attention to dynamic properties of systems such as feedbacks and time lags that lead to unintended consequences, resilience to shocks, tipping points or thresholds and system rebound effects. These are among the most difficult and challenging knowledge requirements. Standard approaches for multi-criteria deliberative analysis, triple-bottom-line (social, economic and environmental) assessment and risk assessments do not usually account for such nonlinear system properties. Section 5 suggested that in highly connected, contingent systems, methods based on an ordered ranking of measures of interest are not as helpful as seeking truly integrating insights.

6.2.2 EXPLORING OPTIONS

The knowledge needs referred to in the previous section were identified to inform alternative options for action. As noted previously, the knowledge about climate change impacts provided by modelling and other analyses is not necessarily the most suited to exploring options for action. The hallmarks of organisations adapting to climate change are many (Wilby and Vaughan, 2011), but three particularly relevant hallmarks are: flexible structures and processes for organisational learning; undertaking of low-regret anticipatory steps that are robust to uncertainty about future risks; and involvement in multi-partner networks for sharing information and taking action towards complementary goals. These hallmarks are consistent with themes that emerged from workshop discussions.

A strong emphasis on learning and innovation, along with a capacity to explore possibilities, is needed for these hallmarks to exist in organisations. A specialist drive to measure, understand and model details of how a particular part of the system works (e.g. role of vegetation in affecting rainfall-runoff relationships, or how a particular species responds to changes in flow regime) is a part of such learning, but in the broader context it cannot be the only part. In linking specialist knowledge to action there is a need to be able to (a) integrate that knowledge into a bigger picture in which many different forms of knowledge are

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active, including other specialist knowledge, individual experience, local community knowledge, organisational or political knowledge (Brown et al., 2010) and (b) distinguish impacts that are outside our control from those aspects of the situation that can be influenced.

Many of the impacts of interest to workshop participants were not biophysical impacts of climate change, but rather impacts of anticipated societal responses elsewhere in the system – e.g. altered energy demands, changed planning regulations, changed cropping and land-use decisions, political instability, and changing mechanisms for trading, allocating and transporting water. Having some capacity to anticipate or influence these networks of impacts is highly desirable, particularly when it comes to identifying potential consequences or co-benefits that help elucidate common objectives across sectors. Common objectives then become the key to cooperative action while avoiding maladaptation or perverse outcomes.

It is possible to build ‘end-to-end’ system models that can be used to explore, anticipate and highlight both cross-scale and cross-sectoral interconnections (Fulton, 2010). These are expensive and time consuming to build, yet in their absence there is still much that can be done with less complicated models – Fulton suggests that developing a single, all-purpose model is rarely as useful as developing ‘a range of approaches to capture the feedbacks and delays and multi-scale interactions’ (Fulton, 2010).

Modellers themselves learn a lot about their system simply through the rigour and clarity that is needed to build and implement models. A model is a very explicit representation of data, theory and assumptions, and the discipline of putting these together in a coherent, self-consistent way, along with the experience of implementing the model and learning from it as it is being developed, provides valuable insights to modellers. Finding ways to ensure modellers’ insights can foster such learning in others, such as clients and stakeholders, is more difficult. The most common vehicles for doing this are presentations and reports rich with descriptions, graphs, tables, maps and computer animations. Rapid changes are occurring, however, and there’s an increasing emphasis on tools that allow stakeholders to interact with data, models and future scenarios in more curiosity-driven, exploratory ways, bringing a more tangible and engaging approach to learning. Even simple changes, such as providing data or model results as interactive Google EarthTM maps, makes them more accessible to a broader range of stakeholders.

Commonly, modellers are commissioned to address a well-specified question and deliver a set of answers, solutions or recommendations according to an agreed time frame. An approach more suited to exploring options for adaptation is to embark on an iterative set of interactions among a network of stakeholders, where it is readily acknowledged that the scope of the research is not fixed a priori, and there is a strong emphasis on ongoing learning. The aim of a researcher in this setting may not be to deliver model results that provide solutions and recommendations according to pre-set criteria, but rather to have engendered a healthy working network of stakeholders who are well experienced at using modelling tools and interactive activities together in a way that tangibly helps them learn and explore options (Fulton et al., 2011). Recent modelling work in the Ningaloo-Exmouth region of Western Australia is an example of such an approach in a marine ecosystem. The work involved many kinds of models, each with a different role to play in stakeholder engagement on complex social-environmental issues (Fulton et al., 2011). The model categories referred to in this work include the following (illustrated in Figure 13):

Conceptual model: a diagram of key drivers and interactions representing understanding of how the system works. Conceptual models are useful for developing a shared understanding of the scope of interconnections and drivers relevant to the system, and for identifying important system structures such as feedback loops and network properties.

Toy model: a simplified model that captures key interactions useful for training and engagement. Such models can be used to highlight counter-intuitive effects and unanticipated consequences in a highly accessible and transparent way (e.g. for highlighting the impacts of unappreciated system feedbacks, the effects of time-delays or nonlinear system responses).

Single-system model: a detailed model of one component of the system, isolated from broader system interactions. This is where detailed specialist knowledge can be captured and represented in a high level of detail (e.g. detailed model of fish species response to altered flow regime).


Shuttle model: a model of intermediate complexity that includes the minimum processes to capture workings of the full system. It is called a ‘shuttle’ model because it is a medium for shuttling information between conceptual, toy, single-system and full-system models.

Full system model: a complete representation of all processes, including physical, biochemical, ecological, social and economic, to provide realistic whole-of-system simulations and scenarios.

Figure 13 Example of the range of model types and purposes used in a multi-model approach to stakeholder engagement (Fulton et al., 2011).

The multi-model approach may appear complicated and messy, however it offers significant benefits to all parties. It provides diverse and adaptable approaches to dialogue, negotiation and collaboration so facilitating useful ways to explore options. It involves stakeholders more fully in the model-building process, using simpler models that can be adjusted in real-time in response to stakeholder input (as opposed to full system models that have considerable computational requirements and run times). Modellers are fully engaged with stakeholders from the beginning, and so the modellers learn more about the system through active engagement with people, rather than simply through measurements, stakeholder surveys and other system data.

The emphasis is on learning, and this learning orientation is what enables exploration of diverse options needed for adaptation. The multi-model approach described above is an example of participatory modelling and action research. These methods are becoming more widespread, particularly in implementing adaptive management principles, and will be discussed further in Section 6.2.5.

6.2.3 TARGETS, STANDARDS AND GOALS

Section 6.1 referred to three stages in any change process, with the second stage being the setting of objectives and goals. Targets, standards and goals provide fundamental underpinnings to decision-making, and help ensure transparency, accountability and ongoing learning (by enabling progress reviews against objectives). A set of issues around targets, standards and goals emerged in the workshop, and this section points to the role of models in addressing these issues.

Workshop discussions on catchment targets, water stewardship frameworks, payment for ecosystem services, water trading and infrastructure water efficiency improvements all pointed towards a common need for good practices for verification, traceability and compliance. In the case of water trading the quantity being measured – water – is at least uncontroversial, albeit still confounded by measurement and


accounting issues. Even so, the infrastructure requirements are high, both for the physical measurements and interoperable systems for sharing data and integrating into modelling and decision support systems (e.g. Australian Water Resources Information System (AWRIS) and the Water Information Research and Development Alliance (WIRADA) between CSIRO and the Bureau of Meteorology). Many other desired metrics referred to in targets, standards and goals are derived from models. For example, catchment nutrient and sediment delivery to downstream recipients is calculated from concentration and flow measurements, but relies on mathematical estimation methods (simple models). Available loads calculation tools (e.g. eWater Water Quality Analyser tool29, National Action Plan Water Quality Online loads tool30) make it possible to test the sensitivity of such load calculations to underlying assumptions. Comprehensive sensitivity and uncertainty analyses are necessary as no single ‘best’ method for making such calculations exists (Coats et al., 2002, Marsh and Waters, 2009, Gronewold and Borsuk, 2009).

In circumstances that involve payment for ecosystem services or accreditation standards for on-farm water management practices, it is necessary to have means to relate individual local actions (e.g. protecting habitat) to catchment-level goals, and to do so in a way that is rigorous and accountable enough to enable payments, permit-trading and similar transactions. The challenge in this area is to balance practical usability (accessibility and comprehensibility) with scientific credibility and rigour.

Finally, the workshop prompted rich discussions on the framing and assumptions underlying goals, objectives and targets. What is the desired state of the ecosystem and why? Is the intention to protect an ecosystem ‘for its own sake’ or for the purpose of providing ecosystem services of benefit to humans? When protecting an ecosystem, what is the baseline against which options and developments are compared? Is the intention that the system remain static in its current state, or returns to an earlier (e.g. pre-development) condition, or should it have some adaptive capacity to change to a novel, but viable state, in response to anticipated future human or other impacts?

Models can work within any of these frameworks and assumptions. The important point is that answers to the above questions are value judgments that can be made only by people – we cannot look to models for the ‘right answer’ to such questions. Furthermore, the better clarified values and objectives can be, the better prospect there is for relevant and useful modelling. For example, if the focus is on restoration to an earlier condition, much of the science and modelling needs to be concerned with establishing the attributes of that earlier system, and exploring options for reaching that state from the current state. If instead the focus is on delivering human benefits from ecosystem services, the science and modelling is more typically structured around informing a cost-benefit analysis that quantifies trade-offs between ecosystem attributes and human costs or benefits.

Questions of system resilience received particular attention at the workshop. The international Resilience Alliance31 defines resilience as ‘the capacity of a system to absorb disturbance, undergo change and still retain essentially the same function, structure, identity, and feedbacks’. It was made clear in the workshop that this is the appropriate definition for resilience, as opposed to definitions of resilience that focus on the time required for a system to return to equilibrium, which is commonly referred to as ‘engineering resilience’ (Holling, 1996). A general recommendation expressed in the workshop was not to make ‘resilience’ an all-encompassing goal, but rather be more precise: what do we want to be resilient to what perturbations, changes or shocks? Note that resilience can be undesirable – resilient carp populations in Australian rivers, for example – hence the need for clarity if referring to resilience as a goal.

Workshop discussions also raised an important contrast: the difference between optimising for the ‘best’ outcome versus scoping out the space of acceptable and unacceptable options. It is common practice to use models to seek to find an optimal system configuration. An example of an optimising question is: what combination of riparian restoration sites will deliver the highest sediment load reductions at the least cost? Another example discussed in the workshop was the ‘optimal stopping’ problem, which can be explained

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most simply as follows. In the face of a known risk of future catastrophe, available choices can be between ‘wait and see’ (gather more information and continue to reap immediate benefits of business-as-usual decisions) and ‘act and change’ (choose to incur immediate costs and forego immediate benefits to avoid or make preparations for future catastrophe). When is it optimal to stop and make the change? Real life rarely reflects even the most well-posed optimisation problem, particularly when searching for wise adaptation options across many sectors.

6.2.4 DECISION MAKING AND GOVERNANCE

Given the diversity of experience in the workshop, the issues that resonated most clearly across all interests tended to be associated with governance structures and implications. Envisaging and realising change is a social process, and is achieved more readily when there are governance processes that support that. Governance refers to processes and institutions through which decision-making is implemented and made accountable. Specific governance issues raised are discussed below in light of the way models can inform or contribute.

Section 6.2.3 made the distinction between modelling approaches that seek to optimise versus explore. It is useful to make a similar distinction when clarifying the purpose or goals in governance:

Organisations face a fundamental tension between exploration captured by terms such as ‘search, variation, risk taking, experimentation, play, flexibility, discovery, innovation’ and exploitation, that is ‘refinement, choice, production, efficiency, selection, implementation, execution.’ … Adaptive capacity of a governance system can be understood as a function of the trade-off between exploration and exploitation. … The trade-off between exploration and exploitation in governance systems is rooted in a much more fundamental tension between the dual needs for institutional stability and change. (Duit and Galaz, 2008)

These distinctions were expressed in several workshop discussions, and participants had experienced the tension between ‘the imperative to act now’ and ‘why don’t we wait and adapt as needed’. Driving a focus on exploration and innovation (particularly exploring potential co-benefits) offers more opportunities for strategic adaptation, rather than reactive adaptation with its high costs and maladaptation risks.

‘Multi-stakeholder governance’ was a term that was referred to by workshop participants: governance structures that foster participation from multiple stakeholders and enable collective decision-making that addresses both local and whole-of-system needs (especially when it comes to common pool resource allocation issues). A representative from a Federal Government department at our workshop pointed out that centralised decision-making for adaptation is likely to be less effective than decision-making that comes from multi-stakeholder interactions. Further, there was an expressed desire for governance instruments that ensure that responsibility for adaptation does not lie with government alone.

There are (as least) three roles for models in addressing questions of multi-stakeholder governance:

In a research capacity there is much to be learned from social network modelling approaches, such as the analysis of management interactions for the Swan River, Western Australia (Robins et al., 2011).

Models themselves can become important facilitation tools for enabling wider participation and giving stakeholders the opportunity to explore innovative ways to navigate problems of sharing a scarce common resource. Listening to workshop participants, it was apparent that drivers for decision making emerge from a complex interplay between physical attributes (like resource scarcity) and concepts that are rarely represented in models (such as equity). When used as tools to facilitate multi-stakeholder dialogue, models allow human dimensions such as equity, self interest and social dynamics to interact with a biophysical modelling framework. This will be discussed further in Section 6.2.5.

Models are a useful vehicle for studying governance mechanisms themselves. For example, recent analyses in the Murray Darling system yield useful insights into how governance instruments like water trading mechanisms have significantly enhanced the adaptive capacity of drought-affected agricultural sectors.


Models are typically used within a decision-making or management framework. This could be an adaptive management framework (Holling, 1978, Pahl-Wostl, 2007), a risk management framework (Jones, 2001), or a management strategy evaluation framework (Sainsbury et al., 2000, Smith et al., 1999). Adaptive management and management strategy evaluation frameworks emphasise placing decisions within a learning loop: in particular, impacts of the decisions are observed, and new observations and knowledge are used to adapt goals and decisions. In these frameworks, decisions are regarded as experiments from which valuable lessons can be learned. Experiments are not always welcome in real-world settings: “stakeholders in resource systems seldom want to ‘experiment’ with their livelihoods” (Anderies, 2002). Such situations warrant a stronger emphasis on the learning part of the adaptive management cycle, and this is where modelling can play a particularly important role as it allows experiments and learning to take place in virtual settings.

There is a vast literature on adaptive management practices, and it is beyond the scope of this report to give a comprehensive overview, however it is worth noting that recent developments in this field point to the importance of ‘double loop’ and ‘triple loop’ learning in natural resource management (Figure 14), particularly if significant system transformation is required (Pahl-Wostl, 2009).

Figure 14 The importance of a ‘learning loop’ in natural resource governance is well appreciated. In dealing with requirements to transform a system significantly, it is useful to recognise double- and triple-loop learning processes that facilitate substantial change (Pahl-Wostl, 2009). Figure adapted from Pahl-Wostl (2009).

The distinctions are defined by as follows:

Single-loop learning refers to a refinement of actions to improve performance without changing guiding assumptions and calling into question established routines. Incremental changes in established practice and action aim at improving the achievement of goals.

...

Double-loop learning refers to a change in the frame of reference and the calling into question of guiding assumptions. Reframing implies a reflection on goals and problem framing (priorities, include new aspects, change boundaries of system analysis) and assumptions how goals can be achieved. Social learning processes are essential.

...

Triple-loop learning refers to a transformation of the structural context and factors that determine the frame of reference. This kind of societal learning refers to transitions of the whole regime. (e.g. change in regulatory frameworks, practices in risk management, dominant value structure).

(Pahl-Wostl, 2009)

Learning cycle: reframing

Learning cycle: transforming

Goal setting

Policy formulation

Monitoring and evaluation

Policy implementation

Assessment

Policy cycle: improving


The emphasis on double- and triple-loop learning acknowledges that current frameworks can be too inhibitive (e.g. reward structures, silo-like institutional arrangements, difficulties in making legislative amendments and other barriers), and significant reform requires mechanisms for reflecting on barriers resulting from the ways situations are framed and decision-making is structured.

6.2.5 COMMUNICATION AND ENGAGEMENT

Previous sections have highlighted different communication and engagement needs identified in the workshop, and alluded to ways in which modelling can better contribute to these. The processes of sharing knowledge, identifying objectives, comparing options, characterising trade-offs, exploring new possibilities and making decisions are the focus of a growing range of modelling approaches that emphasise stakeholder participation. There is wide recognition that in many real world settings ‘facts are uncertain, values in dispute, stakes high and decisions urgent’ (Funtowicz and Ravetz, 1993). In these settings, models can be more valuable as facilitation tools than as a source of analytical, objective ‘answers’.

For some inconsequential decisions in daily life it is routine practice to roll dice or draw straws to make a choice: it is well understood that the dice roll doesn’t have any insightful answers or particular information about the system. Rather, the dice or straws bring a transparent, random process to the table that in many settings is a useful device for ensuring an unbiased decision. This trivial example shows that it is not always truth and an objective ‘right answer’ that is needed to make an effective decision accepted by all. Sometimes it is not information or data, but rather a clear process that addresses needs for transparency and fairness.

Workshop participants expressed the need to enable perspective taking among very different stakeholders. For example, in developing water stewardship frameworks and practices it is useful to give stakeholders in a catchment the opportunity to comprehend the perspective of others who are geographically and temporally distant. Such opportunities can be particularly valuable when there is potential for significant conflict. A model set up as a role-playing game can allow stakeholders to experiment with roles in the system in a way that can never happen in real life. For example, downstream recipients of upstream land-use decisions can swap with upstream decision-makers in a safe, virtual setting and gain more appreciation for the pressures leading to deleterious decisions by upstream water-users.

Another way in which models can facilitate decision-making is by distilling out key system features in simple, useable ways so as to provide tighter focus and shared awareness when seeking effective problem resolution. For example, in the case of sharing a common resource among competing users, the very simplest of conceptual models such as ‘prisoner’s dilemma’ and ‘tragedy of the commons’ have proved to be immensely useful. Again, such models do not attempt to provide faithful representation of real-world details, but rather draw attention to unappreciated system properties.

There are examples of complex system models being used to better understand the human psychological processes that are triggered in decision-making situations rather than the dynamics of a specific system (Dorner, 1990). These examples relate to models and games that are conceptual, abstract or fictitious representations to highlight unexpected aspects relevant to real world decision making. The fact they are ‘toy’ models or games does not underplay their importance, and rather recognises that play is a fundamental way in which humans learn: ‘Playing games in dead earnest? Anyone who thinks that play is nothing but play, and dead earnest is nothing but dead earnest hasn’t understood either one’ (Dorner, 1990). There is a growing body of work on cultural values, meaning and mental preparation in water resource management and climate change (Kuruppu and Liverman, Kuruppu, 2009)

Such a philosophy can be brought to more system-realistic models of social-ecological systems, and this is exemplified in the growing field of multi-agent system modelling (Bousquet et al., 2002, Bousquet and Le Page, 2004, Queste et al., 2011). It is important to understand that from a mathematical perspective, there is rarely the observational data and process knowledge to validate such models with the same transparency and rigour as physical process models. Furthermore, the kinds of uncertainty analyses described in Section 6.2.1 are unfeasible for highly detailed simulation models. In these situations, stakeholder participation is important not only for the reasons listed above, but because it is one of the only available means to have


some measures of model reliability: if diverse stakeholders find the model a useful tool and are willing to work with it, while being fully aware of the underlying assumptions and uncertainties, then that is a useful outcome even if model uncertainties are high.

An approach to ensuring stakeholders find a model useful is to involve them in the beginning of model development and ensure that (a) the modelling purpose is defined in a way that is relevant (b) aspects of the system that matter to them are captured appropriately and usefully in the simulation model. Where there are disagreements between stakeholders such a model can make explicit representations of the differences and provide a tool for exploring the consequences. Another benefit of participatory model building and application is the emphasis on iterative stakeholder interactions. When stakeholders are exploring possibilities, developing options and devising appropriate processes for decision-making, these are more likely to emerge in iterative interactions rather than via one way purchaser-provider interactions. This is particularly relevant when there are important links to sectors that are not usually represented in hydrological models but hold potential co-benefits. Workshop participants were keen to ensure adaptation options are not limited to supply-side responses such as dams, pipelines, desalination plants, pricing and water allocation mechanisms. Demand-side responses require engaged water users who have skills in working together in a coordinated and cooperative manner, and participatory approaches can further the development of these skills.

In communication and engagement activities there can be inevitable trade-offs between scientific rigour and communication appeal. At the workshop, detailed satellite images of city inundation from sea level rise were presented and described as ideal for making the risks of sea level rise real and tangible for citizens. Yet modellers would be wary of making detailed, location-specific decisions on the strength of such maps without ensuring the decisions are robust to uncertainty that is not represented in the maps. Thus an important distinction needs to be made between model results aimed at communication or raising awareness, and model outputs used in the actual running of a system, such as day-to-day water storage operations.

6.3 Summary

6.3.1 ROLES FOR MODELLING

In light of the above issues raised by workshop participants and the implications for modelling, four categories of roles for models are apparent:

1. Make explicit the links between climate change and its impacts.

2. Enable transparent exploration of potential responses to climate change and their impacts.

3. Facilitate communication and engagement around issues, objectives and decisions.

4. Provide underpinning framework and metrics to organise and synthesis knowledge and to structure ongoing monitoring and learning.

Most modelling work in the climate change area falls into the first of these categories, with an emphasis on biophysical impacts, and the resulting repositories of model runs and results provide a necessary evidence base to trigger action on climate change. It is immensely valuable when such repositories of knowledge, data and model runs are well maintained, available and accessible to all. This is particularly valuable if there are people with the skills to interpret such modelling results and apply them to a local context.

The second category, involving the exploration of responses to climate change, is necessarily context-dependent. In other words, even the most comprehensive sets of high resolution model runs linking climate change to impacts will not be sufficient to guide exploration of potential local adaptation responses. Knowledge and information that is sufficient to trigger the need for action is rarely sufficient to inform what those actions should be. Useful modelling to inform local adaptation is generally custom-built and requires active collaboration between modellers, affected stakeholders and decision-makers. In this


context, model purpose is directed towards exploring unanticipated consequences of decisions, with an emphasis on finding heuristic insights rather than providing predictive certainty.

The third category, communication, is vital for enabling constructive dialogue and building shared understanding among stakeholders. There is less appreciation of the role that models can play in facilitating community dialogue and there is potential for increased use of such methods in climate change adaption work. Participatory modelling methods have been extremely effective in situations where there are differing worldviews and perspectives, or where conflict between incompatible interests prevents shared agreement on actions (or questions the need for action). Simple models offered as interactive games are also useful communication devices, especially as they are accessible to a broader range of stakeholders (e.g. the Catchment Detox game32). This category is arguably the most challenging, requiring investment and fostering of constructive communication skills and methods, combined with ways to incorporate physical constraints and societal values when exploring adaptation options.

The fourth category is a more routine approach to model application. Having developed adaptation options, agreed on actions and put implementation plans in place, there are many routine monitoring and compliance requirements that involve models. A vital part of adaptive management is to ensure on-going reflection and review. Frameworks for designing, organising and interpreting on-going monitoring data usually involve a strong modelling component (e.g. if local point measurements are used to evaluate progress towards catchment-scale targets).

6.3.2 FUTURE DIRECTIONS

Workshop participants emphasised that water and freshwater biodiversity issues are embedded in social-ecological systems – both social and ecosystem dynamics are strongly interlinked and working with either in isolation from the other is problematic. This has implications for the way in which modelling work is commissioned, conducted and interpreted in the field of climate change adaptation.

A primary implication to emerge from the workshop is the imperative to adopt strategies that create an integrated, adaptive decision-making environment. A management regime that is about integration and adaptation is very different to a management regime founded on prediction and control (Pahl-Wostl, 2007). Future research, modelling and management projects addressing adaptation needs will be more effective if these differences are acknowledged and integrative, adaptive practices are made a priority.

There is no one prescription for implementing such practices. The categorisation in Table 10 was developed in an international context. It provides a useful set of points against which a particular project or management situation can be compared. Developing an Australian-specific table of attributes would be a useful exercise.

32 http://www.catchmentdetox.net.au/


Table 10 Table from (Pahl-Wostl, 2007) comparing a prediction and control regime versus an integrated, adaptive regime.

PREDICTION AND CONTROL REGIME INTEGRATED, ADAPTIVE REGIME

Management paradigm

Prediction and control based on a mechanistic system’s approach

Learning and self-organisation based on complex systems approach

Governance Centralised, hierarchical, narrow stakeholder participation

Polycentric, horizontal, broad stakeholder participation

Sectoral integration Sectors separately analysed resulting in policy conflicts and emerging chronic

problems

Cross-sectoral analysis identifies emergent problems and integrates policy

implementation

Scale of analysis and operation

Transboundary problems emerge when river sub-basins are the exclusive scale of

analysis and management

Transboundary issues addressed by multiple scales of analysis and

management

Information management

Understanding fragmented by gaps and lack of integration of information sources

that are propriety

Comprehensive understanding achieved by open, shared information sources that

fill gaps and facilitate integration

Infrastructure Massive, centralised infrastructure, single sources of design, power delivery

Appropriate scale, decentralised, diverse sources of design, power delivery

Finances and risk Financial resources concentrated in structural protection (sunk costs)

Financial resources diversified using a broad set of private and public financial

instruments

Environmental factors Quantifiable variables such as BOD or nitrate concentrations that can be

measured easily

Qualitative and quantitative indicators of whole ecosystem states and ecosystem

services

A series of international meetings lead to a decision in 2010 to establish the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES)33, a new assessment body akin to the IPCC. A report in the journal Science outlined the implications for the science required (Perrings et al., 2011), including the following:

... assessments will need integrated models of social and environmental change ... This requires a step change in our capacity to model interactions between the socioeconomic system and the biophysical environment. Without an understanding of the feedbacks between the social and biophysical systems, it is not possible to assess the outcome of actions designed to alter the likelihood of environmental change (mitigation) relative to those designed only to alter its cost (adaptation) or to reduce stress on the uncontrolled parts of the system (stabilization) (Perrings et al., 2011)

In conclusion, the evolving research agenda at both National and International levels places a clear emphasis on social-biophysical interactions. The modelling capabilities required to serve this need are in very early stages relative to physical modelling. Developing such capability is a wise investment in that it offers the potential to better address both local issues (as highlighted in this report), and to contribute more effectively to coordinated international efforts in this area.

33 http://www.ipbes.net/


7 Conclusions

The focus of this report has been on anticipated future water scenarios in Australia and their implication for climate adaptation in the water resources and freshwater biodiversity sectors. Part I and Part II reviewed the available science, while Part III linked the science to implications for adaptation, as explored in a cross-sectoral workshop. Conclusions from each part of the report are summarised as follows.

Part I conclusions

The knowledge of climate change impacts on hydrology in Australia is built on a set of models that are clear in their assumptions and methods. Issues of downscaling, regional climate modelling, epistemic and stochastic uncertainty, variability and the challenges of defining baselines and scenarios are all important, as are uncertainties in end-user requirements and objectives. These issues do not need to be fully resolved for model results to be useful. The models provide unique ways to identify and communicate interdependencies of relevant processes, and knowledge of these interdependencies is crucial in informing decisions about adaptation.

Whilst models provide useful decision-support, they cannot be expected to provide comprehensive solutions. Knowing the implications of assumptions and uncertainties is useful for identifying actions and making decisions that are robust to a range of possibilities. A decision-making environment that enables skilful interpretation and application of uncertain information is therefore critical.

There is a growing range of water assessments available, as reviewed in this report. Each assessment is useful in itself, but comparisons across different assessments in different regions are also valuable. For example, the Sustainable Yields assessments of the Murray Darling Basin and Tasmania show strong responses to future water use or development decisions, the South West Western Australian assessment highlighted particular vulnerability to climate change and implications of a high dependence on groundwater, while the Northern Australia Sustainable Yields project offered the first comprehensive assessment of water quantity, availability and sustainable rates of extraction in this highly variable, high rainfall (yet water-limited) region of Australia.

There are unresolved challenges in striking a balance between (a) ensuring model outputs are freely available and readily accessible for use by others, and (b) ensuring where model outputs are taken up and used by others, these uses are appropriate and well-informed by an appreciation of the limitations of the models (and differences in appropriate uses compared with observational data).

Part II conclusions

Currently, assessments of aquatic ecosystem responses to climate change focus on changes to flow regime. It is well recognised that there are many other ways in which climate change impacts aquatic ecosystems. A focus on flow has been useful as changes in flow regime are also a first order impact from many other catchment changes (e.g. land use change, water resource development planning).

The flow metrics provided by hydrological models are usually aimed at informing flow quantity, and so much recent effort has been invested in prescribing more sophisticated measures of ecologically-important components of flow regime and assessing Australian riverine systems according to these criteria.

More can be done to calibrate hydrological models so that their outputs deliver better quality information on these ecologically relevant flow components.

The links from identified components of flow regime to ecological response are well established in general terms (i.e. holistic principles derived from meta-analyses of multiple field studies), and more


specific, local assessments are based on statistical analysis of correlations between changes in flow components and ecological response.

Syntheses of current literature are not sufficient to characterise ecological needs in response to flow – on-going fieldwork aimed at contributing to a synthesising framework is needed, particularly given the spatial heterogeneity, climate variability, rates of change and diversity of unique ecosystems in Australia.

Environmental intelligence efforts at a national level are significant, particularly developments in the Bureau of Meteorology, the National Plan for Environmental Information (NPEI) and investments via the National Collaborative Research Infrastructure Strategy (NCRIS). There is a clear need for such national-level knowledge infrastructure.

There are on-going challenges associated with managing aquatic ecosystems for prescribed reference conditions (e.g. a natural flow regime). Identifying benefits from ecosystem goods and services and seeking to ensure resilience of those benefits to climate change and other changes is a growing response to that challenge.

Part III conclusions

WORKSHOP CONCLUSIONS

Bringing stakeholders together in activities such as the workshop in this project is only a first stage in fostering shared awareness of exposures and sensitivities of water resources management and freshwater ecosystems to climate change. The reasons being that: a) it is a complex task involving many aspects of social and physical sciences and to get an objective overview is difficult without sufficient knowledge representation in the group; b) there is need for reflection and data collection, and this requires participants (or facilitators) to build on ideas derived at the workshop by gathering evidence that support the ideas that are raised in the workshop (e.g. surveys/case study/interviews); c) having established what the exposures are, then it may well be relevant to put these in context of what climate change scenario data tells us about plausible futures – this is an exercise that can be performed by researchers in liaison with stakeholders.

The ecosystem services framework was a useful one for bringing together the diverse perspectives represented by the participants in the cross-sectoral workshop. It acknowledges the links between ecosystem services and human benefits such as livelihoods, health, security, social cohesion, freedom of opportunity etc. Better understanding those links to human benefits is perhaps the most vexed and challenging aspect of crafting adaptation options, but there was a strong willingness to face that challenge among all workshop participants. One of the workshop’s limitations, however, lies in the sectors that were not represented amongst the participants, e.g. the health sector.

A common challenge was that information and data are developed in one context and yet usually applied in very different circumstances, often accompanied by political, social and economic influences. Bringing people together, rather than just information, enables more effective integration of knowledge into adaptation planning for this reason. The workshop reinforced benefits of cross-sectoral communication and collaboration: such exchanges were identified as the key to understanding, identifying, communicating and responding to the complex feedbacks between climate change and society within natural and built systems.

The crucial role of governance was clearly acknowledged in the workshop. Discussions included the role of leadership at many scales, the scale of decision drivers such as political cycles, and the risks of partial analyses or prioritisation activities without integrated knowledge of related system responses to these decisions.

Workshop experiences point to missed opportunities if prioritisation approaches are not complemented with some level of system integration that highlights interconnections and the potential for either a) co-benefits; or b) feedback loops that bring unanticipated consequences or render actions ineffective. There are dangers in seeking out the ‘low hanging’ fruit and the ‘top ranked’ priorities unless important interconnections have also been identified. The workshop discussions highlighted the importance of taking a system view. There is a temptation in any activity to narrow to notions of what’s ‘most


important’, ‘feasible’ and cost-effective ‘now’, or relevant to ‘the people here’. If decision making is based on prioritising only what is feasible and high priority without being mindful of system reactions and dependencies, problems may simply be shifted elsewhere (to sectors not visible in the decision-making frame) or persist despite dedicated effort (due to system feedbacks that reinforce the status quo). Herein lies a useful role for models that are unique in their capacity to make such interconnections explicit, transparent and accessible to a wider audience than narrow groups of modelling specialists.

Adaptive planning is hampered by inability to comprehend all the interconnections of the freshwater biodiversity and water resources sectors. There are no panaceas for working with that complexity, but any success in bridging disciplines, sectors and knowledge bases will help. It is the people doing the experiments, fieldwork and modelling who learn the most about their particular specialties, and that learning is rarely captured adequately in the resulting data sets and model outputs. This further reinforces the need for active cross-disciplinary collaboration when integrating diverse sources of knowledge.

Where projects have finished and people have moved on, good practices in archiving data, model code and model runs so that they are accessible and self-describing becomes paramount. Workshop participants cited past projects rich with data sets, literature reviews, model runs and lessons learned, now inaccessible due to the lack of infrastructure and mechanisms to house, maintain and make archives of such material readily available. This point again reinforces the benefits of national infrastructure to support the housing and sharing of knowledge (including metadata such as context and underpinning assumptions).

The water assessments conducted for Australia make clear that future scenarios involve climate change in interaction with future water resource development scenarios (which are the result of human decisions rather than biophysical drivers). The importance of non-climate factors, or even non-physical factors was clearly expressed by workshop participants. In stating their information needs, participants identified a range of metrics that capture social or economic attributes of importance, and more integrated measures of system-level responses to change.

An important shift in emphasis is that of stewardship rather than catchment management. Where catchment management involves anticipating and responding to pressures that come from outside the direct control of catchment managers (e.g. market demands for water, goods and services; population demographics; industry trends), stewardship frameworks emphasise shared responsibility with end recipients of catchment goods and services (e.g. via certification and accreditation requirements).

MODELLING IMPLICATIONS AND FUTURE RESEARCH DIRECTIONS

Uncertainty need not prevent decision-making, and there are examples of how to apply knowledge so that decision-making benefits from extra information about the uncertainty in that knowledge, and so that decisions are robust to that uncertainty.

Model-derived metrics and estimates underlie many management targets and standards. A continued emphasis on verification, traceability and compliance, combined with a wise balance between practical usability and scientific rigour, is needed as many of the decision-making and governance structures increasingly rely on these systems for timely and equitable management operations.

The development of adaptation options is more about exploration than optimisation, and benefits from modelling approaches that facilitate exploration. Governance structures that make learning and exploration a priority can draw on a range of modelling methods that support these priorities.

Participatory modelling and related approaches offer particular opportunities for stakeholder engagement, and engagement in a way that allows the dynamics of climate-water-society interactions to be explored and better understood.

Clarity about model purpose is paramount, and this is dependent on the management or decision-making framework any modelling work is contributing to (e.g. managing for prediction and control, versus managing for integration and adaptation). In general, a more diverse range of modelling purposes can be realised. Demonstrated roles for models include:

– Make explicit the links between climate change and its impacts.


– Enable transparent exploration of potential responses to climate change and their impacts. – Facilitate communication and engagement around issues, objectives and decisions. – Provide underpinning framework and metrics to organise and synthesis knowledge and to structure

ongoing monitoring and learning.

The evolving research agenda at both National and International levels places a clear emphasis on social-biophysical interactions. The modelling capabilities required to serve this need are in very early stages relative to physical modelling. Developing such capability is a wise investment in that it offers the potential to better address both local issues (as highlighted in this report), and to contribute more effectively to coordinated international efforts in this area.


Appendix A Sustainable Yields Projects

The main outputs from the various hydrological modelling projects are reproduced in this Appendix for direct access and comparison by readers of this report. Please note that due to differences in baseline periods and model choices, results are not directly comparable between individual Sustainable Yields projects. The modelling methods, caveats and assumptions, and more detailed results can be found in the reports reference here.

A.1 Murray-Darling Basin Sustainable Yields project

Apx Table A.1 Average annual surface water balance for the Murray-Darling basin (CSIRO, 2008, p. 30)

WITHOUT DEVELOPMENT, HISTORICAL CLIMATE

CURRENT DEVELOPMENT,

HISTORICAL CLIMATE

CURRENT DEVELOPMENT, MEDIAN 2030

CLIMATE

FUTURE DEVELOPMENT, MEDIAN 2030

CLIMATE

GL/y

Inflows

Inflows 28,630 28,711 25,846 25,602

Transfers into basin 1,010 1,068 1,041 1,041

Irrigation and urban returns 0 163 155 154

Sub-total 29,640 29,942 27,041 26,797

Surface water use

Surface water diversions 0 10,075 9,673 9,575

Channel and pipe loss 0 1,233 1,183 1,181

Net streamflow loss induced by groundwater use

0 181 229 352

Evaporation from reservoirs and lakes 4,448 3,851 3,473 3,428

Losses 12,959 9,868 8,908 8,779

Sub-total 17,407 25,209 23,467 23,315

Outflows

Outflows 12,233 4,733 3,575 3,482

Efficiency

Efficiency (outflow/net inflow) 41% 16% 13% 13%


Apx Table A.2 Effect of climate change by 2030 on water availability (GL/year) for each region and the Murray-Darling Basin as a whole (CSIRO, 2008, p. 35).

2030 CLIMATE

HISTORICAL CLIMATE

WET EXTREME MEDIAN DRY EXTREME

GL/y

Paroo 445 626 432 372

Warrego 420 619 393 292

Condamine-Balonne 1,363 1,616 1,249 1,004

Moonie 98 122 87 70

Border Rivers 1,208 1,427 1,092 891

Gwydir 782 1,049 703 554

Namoi 965 1,336 915 677

Macquarie-Castlereagh 1,567 1,967 1,450 1,180

Barwon-Darling* 41 61 40 32

Lachlan 1,139 1,212 1,012 792

Murrumbidgee 4,270 4,816 3,881 3,087

Murray* 5,211 5,391 4,614 3,358

Ovens 1,776 1,802 1,542 974

Goulburn-Broken 3,233 3,146 2,792 1,788

Campaspe 275 263 230 148

Loddon-Avoca 285 270 234 146

Wimmera 219 207 173 102

Eastern Mount Lofty Ranges 120 117 99 58

Total 23,417 26,047 20,936 15,524

MDB integrated to Wentworth 14,493 15,450 12,811 9,155

* For the Barwon-Darling and Murray regions only the fraction of the water availability generated within the region is shown


Apx Table A.3 Total surface water use (GL/year) by region under different climates (CSIRO, 2008, p. 39).

2030 CLIMATE

HISTORICAL CLIMATE

WET EXTREME MEDIAN DRY EXTREME

Paroo 0 0 0 0

Warrego 52 58 50 45

Condamine-Balonne 724 783 693 606

Moonie 34 37 32 29

Border Rivers 411 443 403 343

Gwydir 317 378 290 236

Namoi 359 368 358 327

Macquarie-Castlereagh 371 414 356 310

Barwon-Darling 230 237 234 219

Lachlan 321 331 296 254

Murrumbidgee 2,257 2,363 2,202 1,902

Murray 4,338 4,371 4,157 3,349

Ovens 25 25 25 26

Goulburn-Broken 1,071 1,062 1,011 765

Campaspe 342 343 325 245

Loddon-Acoca 350 346 330 234

Wimmera 121 120 108 66

Eastern Mount Lofty Ranges 6 6 6 6

Total 11,327 11,686 10,876 8,962

Murray at Wentworth 8,095 8,223 7,726 6,296


A.2 South-west Western Australia Sustainable Yields project

Apx Table A.4 Summary of gaps between yield and demand for the entire project area. Positive numbers are surplus of water; negative numbers are deficit (Table 8.2 in CSIRO, 2009a).

SURFACE WATER AREA CURRENT AVAILABLE YIELD

HISTORICAL CLIMATE (MM)

(SCENARIO A)

RECENT CLIMATE (SCENARIO B)

WET EXTREME FUTURE CLIMATE (SCENARIO CWET)

MEDIAN FUTURE CLIMATE (SCENARIO CMID)

DRY EXTREME FUTURE CLIMATE

(SCENARIO CDRY)

Current demand (GL/y) 824.7 875.1 817.8 847.8 676.9 451.2

Low demand 2030 (GL/y) 709.2 759.6 702.3 732.3 561.4 335.7

Med demand 2030 (GL/y) 399.9 450.4 393.0 423.0 252.1 26.4

High demand 2030 (GL/y) 125.7 176.1 118.8 148.8 -22.1 -247.8

Apx Table A.5 Average annual rainfall, runoff, runoff coefficients, and streamflow volumes under Scenario A (historical) and changes in these for each surface water region in the project area and the area as a whole under scenarios B, Cwet, Cmid and Cdry. (Table 2-4 in CSIRO, 2009a)

SCENARIO MEAN ANNUAL RAINFALL MEAN ANNUAL RUNOFF MEAN ANNUAL RAINFALL MINUS RUNOFF

RUNOFF COEFFICIENT

STREAMFLOW VOLUME

mm change from Scenario A



percent GL change from Scenario A

SURFACE WATER MODELLING AREA*

A (historical) 837 98 740 12% 3411

B (recent) 818 -2% 91 -7% 728 -2% 11% 3172 -239

Cwet 819 -2% 88 -10% 731 -1% 11% 3068 -343

Cmid 769 -8% 74 -25% 695 -6% 10% 2575 -837

Cdry 717 -14% 57 -42% 660 -11% 8% 1986 -1426


SCENARIO MEAN ANNUAL RAINFALL MEAN ANNUAL RUNOFF MEAN ANNUAL RAINFALL MINUS RUNOFF

RUNOFF COEFFICIENT

STREAMFLOW VOLUME




percent GL change from Scenario A

NORTHERN (GINGIN TO MURRAY) REGION

A (historical) 766 46 720 6% 457

B (recent) 738 -4% 40 -13% 698 -3% 5% 396 -62

Cwet 754 -1% 43 -8% 711 -1% 6% 422 -36

Cmid 692 -10% 33 -30% 659 -8% 5% 320 -137

Cdry 643 -16% 22 -53% 621 -14% 3% 217 -241

CENTRAL (HARVEY TO PRESTON) REGION

A (historical) 818 121 697 15% 742

B (recent) 783 -4% 108 -11% 675 -3% 14% 663 -79

Cwet 803 -2% 112 -7% 691 -1% 14% 689 -53

Cmid 742 -9% 93 -23% 649 -7% 12% 569 -174

Cdry 694 -15% 72 -40% 622 -11% 10% 443 -299

SOUTHERN (BUSSELTON COAST TO DENMARK) REGION

A (historical) 881 117 764 13% 2212

B (recent) 871 -1% 111 -4% 760 -1% 13% 2113 -99

Cwet 858 -3% 103 -12% 755 -1% 12% 1957 -255

Cmid 818 -7% 89 -24% 729 -5% 11% 1686 -526

Cdry 763 -13% 70 -40% 693 -9% 9% 1326 -886

* Calculated as area weighted mean of the three regions


Apx Table A.6 Current surface water yields by surface water management area under Scenarios A, B and C (Table 7-2 in CSIRO, 2009a)

SURFACE WATER MANAGEMENT AREA

CURRENT A- HISTORICAL B - RECENT CWET CMID CDRY

GL/Y % CHANGE GL/Y % CHANGE GL/Y % CHANGE GL/Y % CHANGE GL/Y % CHANGE

Gingin Brook & Tributaries 5.0 5.0 0 4.7 -6 4.2 -16 2.2 -56 0.7 -86

Swan River & Tributaries 2.6 2.6 0 2.6 0 2.3 -12 1.9 -27 1.1 -58

Helena 9.6 13.2 38 9.6 0 11.8 23 7.9 -18 3.9 -59

Canning River 25.4 28.8 13 25.4 0 26.6 5 21.1 -17 14.8 -42

Cockburn-Kwinana Coastal 0.4 0.4 0 0.4 0 0.4 0 0.3 -25 0.2 -50

Serpentine River Catchment

20.7 25.8 25 20.7 0 22.5 9 15.6 -25 9.1 -56

Dandalup River System 21.9 24.7 13 22.1 1 22.0 0 15.8 -28 9.6 -56

Kwinana-Peel Coastal 1.7 1.7 0 2.0 18 1.5 -12 1.0 -41 0.5 -71

Murray River & Tributaries 2.5 2.5 0 3.0 20 2.2 -12 1.6 -36 0.8 -68

Harvey 88.0 97.2 10 91.3 4 90.3 3 75.0 -15 58.7 -33

Collie 93.6 100.2 7 97.9 5 91.5 -2 72.9 -22 53.8 -43

Preston 8.1 8.1 0 10.8 33 7.0 -14 5.6 -31 4.2 -48

Capel River 10.6 10.6 0 15.1 42 9.3 -12 7.7 -27 4.2 -60

Busselton Coast 40.8 40.9 0 40.2 -1 36.0 -12 29.0 -29 16.3 -60

Lower Blackwood 40.4 40.4 0 40.9 1 35.1 -13 28.9 -28 18.1 -55

Donnelly River & Tributaries

7.9 7.9 0 7.9 0 6.3 -20 4.8 -39 2.7 -66


SURFACE WATER MANAGEMENT AREA

CURRENT A- HISTORICAL B - RECENT CWET CMID CDRY


Warren River & Tributaries 24.7 25.0 1 25.5 3 20.6 -17 15.9 -36 9.4 -62

Shannon-Gardner 7.4 7.4 0 9.5 28 6.5 -12 5.3 -28 3.1 -58

Muir-Unicup 0.0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0

Kent 5.8 5.8 0 6.2 7 5.2 -10 4.3 -26 3.1 -47

Denmark 8.3 8.4 1 8.6 4 7.6 -8 5.8 -30 3.1 -63

Total 425.2 456.5 7 444.4 5 408.7 -4 322.6 -24 217.6 -49

Apx Table A.7 Current groundwater yields by groundwater management area under scenarios A, B and C (Table 7-4 in CSIRO, 2009a)

GROUNDWATER MANAGEMENT AREA

CURRENT A B CWET CMID CDRY


Casuarina 15.7 15.7 0 15.7 0 15.7 0 15.7 0 15.7 0

Arrowsmith 186.9 186.9 0 186.9 0 186.9 0 186.9 0 186.9 0

Jurien 91.7 91.7 0 91.7 0 91.7 0 91.7 0 91.7 0

Gingin 326.2 326.2 0 321.3 -2 344.0 5 323.7 -1 299.1 -8

Gnangara 54.3 54.3 0 43.1 -21 58.5 8 50.9 -6 34.3 -37

Yanchep 11.6 11.6 0 11.6 0 11.8 2 11.6 0 11.4 -2

Wanneroo 38.8 38.8 0 38.6 -1 38.8 0 38.6 -1 32.0 -18

Swan 33.5 33.5 0 33.5 0 33.7 1 33.5 0 32.0 -4

Mirrabooka 45.4 45.4 0 45.4 0 45.4 0 45.4 0 43.7 -4


GROUNDWATER MANAGEMENT AREA

CURRENT A B CWET CMID CDRY


Gwelup 26.7 26.7 0 26.7 0 26.7 0 26.7 0 26.5 -1

Perth 168.8 168.8 0 168.8 0 168.7 0 168.4 0 162.1 -4

Jandakot 53.8 53.8 0 53.8 0 53.8 0 53.7 0 53.7 0

Cockburn 51.0 51.0 0 51.0 0 50.9 0 50.6 -1 50.5 -1

Serpentine 49.0 49.0 0 49.0 0 49.0 0 49.0 0 48.9 0

Rockingham 29.3 29.3 0 29.3 0 29.3 0 29.2 0 29.2 0

Murray 70.0 70.0 0 70.3 0 69.9 0 69.6 -1 69.0 -1

South West Coastal (Preston) 57.2 57.2 0 57.9 1 57.2 0 55.8 -2 53.8 -6

South West Coastal (Peel) 20.8 20.8 0 21.1 1 20.8 0 20.4 -2 19.9 -4

Bunbury 46.7 46.7 0 45.8 -2 50.1 7 46.4 -1 44.2 -5

Busselton-Capel 86.4 86.4 0 86.1 0 91.2 6 84.2 -3 81.7 -5

Blackwood 31.1 31.1 0 29.3 -6 33.4 7 27.7 -11 18.6 -40

Collie 55.5 55.5 0 55.5 0 52.7 -5 47.0 -15 40.9 -26

Albany 5.1 5.1 0 4.6 -10 4.1 -20 3.3 -35 2.6 -49

Total 1555.7 1555.7 0 1537.1 -1 1584.6 2 1530.2 -2 1448.8 -7


Apx Table A.8 Threshold flow rates associated with the identified ecological functions, flow frequency under Scenario A and the change under scenarios B, Cwet, Cmid, and Cdry relative to Scenario A. (CSIRO, 2009a, p. 222).

FLOW FREQUENCY

ECOLOGICAL FUNCTION FLOW THRESHOLD

A- HISTORICAL B- RECENT CWET CMID CDRY

ML/DAY PERCENT PERCENT CHANGE FROM SCENARIO A

Maintain pool habitat in summer 1 100.0% 0.0% 0.0% 0.1% 0.7%

Minimum flow to maintain pool quality 6 93.0% -0.9% 2.2% 5.5% 10.1%

Upstream migration of small native fish 10 82.7% -1.6% 3.3% 6.8% 12.3%

Summer habitat for invertebrates 16 67.1% -1.1% 2.1% 4.7% 8.10%

Winter habitat for invertebrates 60 43.8% 2.4% 1.4% 3.4% 5.9%

Inundate trailing vegetation 120 34.3% 1.3% 2.1% 4.4% 7.9%

Inundate active channel 120 34.3% 1.3% 2.1% 4.4% 7.9%

Inundate low elevation benches 320 16.6% -0.3% 2.4% 4.7% 8.1%

Inundate high elevation benches 1080 0.4% 0.3% 0.1% 0.3% 0.3%


Apx Table A.9 Threshold flow rates associated with the identified ecological functions, flow frequency under Scenario A and the change under scenarios B, Cwet, Cmid, and Cdry relative to Scenario A (CSIRO, 2009a, p.224).

FLOW FREQUENCY

ECOLOGICAL FUNCTION FLOW THRESHOLD

A- HISTORICAL B- RECENT CWET CMID CDRY

ML/DAY PERCENT PERCENT CHANGE FROM SCENARIO A

Maintain pool habitat in summer 1 98.1% 1.9% 0.9% 2.8% 7.5%

Minimum flow to maintain pool quality 6 67.6% 1.1% 2.0% 5.2% 9.3%

Summer habitat for invertebrates 6 67.6% 1.1% 2.0% 5.2% 9.3%

Upstream migration of small native fish 6 67.6% 1.1% 2.0% 5.2% 9.3%

Winter habitat for invertebrates 9 61.5% 1.0% 1.3% 4.6% 7.8%

Inundate trailing vegetation 9 61.5% 1.0% 1.3% 4.6% 7.8%

Inundate low elevation benches 9 61.5% 1.0% 1.3% 4.6% 7.8%

Riparian vegetation 9 61.5% 1.0% 1.3% 4.6% 7.8%

Upstream migration of large native fish 38 41.7% 1.3% 1.1% 3.6% 6.7%

Inundate medium elevation benches 100 29.7% 1.4% 1.5% 3.9% 8.0%

Inundate active channel 185 20.6% 0.5% 1.5% 3.9% 8.0%

Inundate floodplain 460 7.5% 0.8% 0.9% 2.3% 4.4%


A.3 Northern Australia Sustainable Yields project

Apx Table A.10 Summary table of climate changes for each region within each drainage division and all-of-project area (Table I in CSIRO, 2009c).

REGIONS OF THE TIMOR SEA DRAINAGE DIVISION REGIONS OF THE GULF OF CARPENTARIA DRAINAGE DIVISION DRAINAGE DIVISIONS ALL-OF-

PROJECT

AREA FITZROY

(WA)

KIMBERLEY ORD-

BONAPARTE

DALY VAN

DIEMEN

ARAFURA ROPER SOUTH-

WEST

GULF

FLINDERS-

LEICHHARDT

SOUTH-

EAST GULF

MITCHELL WESTERN

CAPE

TIMOR

SEA

GULF OF

CARPENTARIA

NTH

NORTH-

EAST

COAST†

Area (km2) 131,606 109,761 164,529 54,423 67,586 45,499 128,518 111,890 145,223 122,094 72,229 66,766 573,400 627,000 46,551 1,246,951

Relief (m) 980 906 919 478 568 464 441 431 1078 1068 1355 814 980 1355 1377 1377

Data availability

sparse very sparse

sparse (locally

reasonable)

sparse (locally

reasonable)

locally dense

sparse Sparse very sparse

locally reasonable

locally reasonable

locally reasonable

locally reasonable

sparse sparse sparse sparse

CLIMATE CLIMATE

Rainfall inter-annual variability

high high high moderate moderate moderate high high very high high moderate moderate high high moderate high

Rainfall coefficient of variation

0.39 0.3 0.32 0.25 0.21 0.23 0.3 0.39 0.42 0.38 0.29 0.22 0.3 0.35 0.27 0.33

Mean annual rainfall (mm)

577 950 730 1019 1390 1186 843 670 493 750 965 1417 868 779 1338 850

Mean annual volume of rain (TL)

76 104 120 55 94 54 108 75 72 92 70 95 504 511 62 1077



PROJECT

AREA FITZROY

(WA)

KIMBERLEY ORD-

BONAPARTE

DALY VAN

DIEMEN


WEST

GULF

FLINDERS-

LEICHHARDT

SOUTH-

EAST GULF

MITCHELL WESTERN

CAPE

TIMOR

SEA

GULF OF

CARPENTARIA

NTH

NORTH-

EAST

COAST†

10th percentile rainfall (mm/y)

963 1223 1486 1493 1695 1383 1357 1168 812 1078 1615 1803 1688 1806 3640 3640

90th percentile rainfall (mm/y)

383 628 441 667 1155 920 592 405 331 490 714 1054 383 334 917 331

Annual APET

high high high high high high high high high high high high high high high high

Mean annual APET (mm)

2023 1994 1988 1942 1936 1898 1928 1961 1939 1980 1905 1874 1979 1939 1853 1954

Mean annual rainfall deficit (mm)

-1446 -1044 -1258 -923 -546 -712 -1085 -1291 -1446 -1230 -940 -457 -1111 -1160 -515 -1104

Seasonality of rainfall

strong strong strong strong strong strong strong strong strong strong strong strong strong strong strong Strong

Mean wet season rainfall (mm)

534 898 689 975 1327 1140 805 631 437 710 917 1370 822 735 1233 802

Median wet season rainfall (mm)

515 876 682 954 1308 1136 812 549 396 675 913 1403 822 716 1252 785

Percent wet season rainfall

93% 95% 94% 96% 95% 96% 95% 94% 89% 95% 95% 97% 95% 94% 92% 94%



PROJECT

AREA FITZROY

(WA)

KIMBERLEY ORD-

BONAPARTE

DALY VAN

DIEMEN


WEST

GULF

FLINDERS-

LEICHHARDT

SOUTH-

EAST GULF

MITCHELL WESTERN

CAPE

TIMOR

SEA

GULF OF

CARPENTARIA

NTH

NORTH-

EAST

COAST†

Daily rainfall intensity

high high high high high high high high high high high high high high high high

Daily rainfall intensity trend

In-creasing

In-creasing

Increasing increasing increasing increasing increasing increasing increasing increasing increasing increasing increasing increasing increasing increasing

Wettest year

2000 2000 1974 1974 2000 2001 2001 2001 1974 1974 1974 1999 2000 1974 1974 1974

Driest year 1953 1936 1952 1952 1952 1952 1952 1952 1952 1952 1952 1961 1952 1952 1961 1952

Rainfall gradient

mod-erate

moderate moderate moderate moderate moderate moderate moderate weak weak weak moderate weak weak very steep

weak

Rainfall gradient (mm/km)

1.8 1.4 1.8 1.9 3 1.6 1.4 1.4 1 1.1 0.7 2.1 1.3 1.3 6.2 0.2

Recent rainfall relative to historical

wetter wetter wetter wetter wetter wetter wetter wetter similar similar similar similar wetter similar similar wetter

Recent rainfall percent difference

37% 27% 35% 25% 19% 22% 30% 37% 12% 10% 10% 11% 30% 19% 9% 24%

Future rainfall relative to historical

same same same same same same same same same same same same same same same same



PROJECT

AREA FITZROY

(WA)

KIMBERLEY ORD-

BONAPARTE

DALY VAN

DIEMEN


WEST

GULF

FLINDERS-

LEICHHARDT

SOUTH-

EAST GULF

MITCHELL WESTERN

CAPE

TIMOR

SEA

GULF OF

CARPENTARIA

NTH

NORTH-

EAST

COAST†

Future rainfall percent difference

0% 1% 2% 1% 0% 1% 0% 0% 0% 0% 1% 1% 1% 0% 1% 0%

Future rainfall relative to recent

drier drier drier drier drier drier drier drier same same same drier drier same drier drier

† Metrics for the Northern Coral region are the same as for the Northern North-East Coast Drainage Division.

Apx Table A.11 Summary table of changes to surface water for each region within each drainage division and all-of-project area (Table I in CSIRO, 2009c)


PROJECT

AREA FITZROY

(WA)

KIMBERLEY ORD-

BONAPARTE

DALY VAN

DIEMEN


WEST

GULF

FLINDERS-

LEICHHARDT

SOUTH-

EAST GULF

MITCHELL WESTERN

CAPE

TIMOR

SEA

GULF OF

CARPENTARIA

NTH

NORTH-

EAST

COAST†

Area (km2) 131,606 109,761 164,529 54,423 67,586 45,499 128,518 111,890 145,223 122,094 72,229 66,766 573,400 627,000 46,551 1,246,951

Relief (m) 980 906 919 478 568 464 441 431 1078 1068 1355 814 980 1355 1377 1377

Data availability sparse very sparse

sparse (locally

reasonable)

sparse (locally

reasonable)

locally dense

sparse Sparse very sparse

locally reasonable

locally reasonable

locally reasonable

locally reasonable

sparse sparse sparse sparse

SURFACE WATER SURFACE WATER

Runoff inter-annual variability

high high moderate moderate low low moderate high very high very high moderate low moderate high low moderate

Mean annual runoff (mm)

76 153 112 159 375 240 112 89 44 110 198 479 157 144 373 159



PROJECT

AREA FITZROY

(WA)

KIMBERLEY ORD-

BONAPARTE

DALY VAN

DIEMEN


WEST

GULF

FLINDERS-

LEICHHARDT

SOUTH-

EAST GULF

MITCHELL WESTERN

CAPE

TIMOR

SEA

GULF OF

CARPENTARIA

NTH

NORTH-

EAST

COAST†

Mean percent of rainfall

13% 16% 15% 16% 27% 20% 13% 13% 9% 15% 21% 34% 18% 19% 28% 19%

Runoff coefficient range

3-25% 10-30% 5-30% 3-35% 15-40%

10-40% 4-35% 4-20% 3-25% 4-16% 15-60% 15-50% 3-40% 3-60% 10-50%

3-60%

Annual coefficient of variation

0.93 0.78 0.67 0.69 0.49 0.48 0.65 1 1.51 1.49 0.75 0.43 nm nm 0.49 nm

Wet season mean runoff (mm)

73 148 110 149 361 217 103 87 43 109 194 458 149 140 333 nm

Wet season median runoff (mm)

45 129 93 127 336 195 94 57 22 67 172 454 nm nm 317 nm

Volume of streamflow (TL/y)

10 17 18 9 25 11 14 10 6 13 14 32 90 90 17 197

Percent runoff during wet season

96% 97% 98% 94% 96% 90% 92% 98% 98% 99% 98% 96% nm nm 89% nm

Groundwater dependence for dry season flow?

yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes

Modelled availability (GL/y)

nm nm 4257 8184 nm nm nm nm 3391 3724 6786 nm na na nm na

Estimated surface water use (GL/y)

NM NM 348* minimal NM NM NM NM 218 29 81 NM >348 >328 NM >676

Current level of use NR NR 8% <1% NR NR NR NR 6% 8% 1% NR na na na na

Major perennial rivers sub-flow

yes artificial yes yes yes yes yes no no no yes yes yes yes yes

Monitoring of surface water use?

limited no yes some yes no no no yes no no no some limited no locally



PROJECT

AREA FITZROY

(WA)

KIMBERLEY ORD-

BONAPARTE

DALY VAN

DIEMEN


WEST

GULF

FLINDERS-

LEICHHARDT

SOUTH-

EAST GULF

MITCHELL WESTERN

CAPE

TIMOR

SEA

GULF OF

CARPENTARIA

NTH

NORTH-

EAST

COAST†

Recent runoff percent difference

51% 71% 56% 66% 44% 38% 54% 78% 9% -13% 16% 27% 56% 30% 19% 41%

Future (Cmid) runoff difference

-3% -1% 0% 1% 1% 1% -2% -3% 2% -1% -1% 1% -1% -1% 1% -1%

† Metrics for the Northern Coral region are the same as for the Northern North-East Coast Drainage Division.

* Does not include water release for hydropower generation (up to 2500 GL/yr)

na is not applicable; NR is not reported; nm is not modelled; NM is not measured.

Hydrological regime metrics for either low flows or high flows are reported only where confidence levels are 1, 2 or 3. If confidence levels in the low flows or high flows are ranked 4 or 5, results are not reported and are labelled NR (not reported).


A.4 Tasmania Sustainable Yields Project

Apx Table A.12 Summary of key findings for the project area and each of the five project regions. Results are presented as absolute number for the historical climate whilst recent climate results are displayed as relative values to the historical climate. Note that results for recent climate are for an 11-year drought and are not directly comparable with the results for future climate, which are based on and 84-year average. Results for future development are presented relative to the future climate where indicated (CSIRO, 2009e, p. 4)

WHOLE PROJECT AREA

ARTHUR-INGLIS-CAM MERSEY-FORTH PIPERS-RINGAROOMA SOUTH ESK DERWENT-SOUTH EAST

HISTORICAL CLIMATE (1924 TO 2007)

Mean annual rainfall 1,046 mm 1,257 mm 1,351 mm 939 mm 801 mm 997 mm

Mean annual runoff 440 mm 539 mm 669 mm 315 mm 240 mm 456 mm

Mean annual surface water extraction 636 GL 93 GL 81 GL 79 GL 158 GL 225 GL

Mean annual non-extracted surface water 21,179 GL 4,696 GL 3,800 GL 2,185 GL 2,456 GL 8,042 GL

Mean annual groundwater extraction 38 GL 16 GL 17 GL 1 GL 1 GL 3 GL

Percent of groundwater recharge extracted 3% 4% 4% <1% 1% 5%

Percent of subcatchments potentially impacted 1% 1% 0% <1% 4% <1%

Key ecological sites potentially impacted * 13 (150) 1 (16) 0 (36) 0 (31) 6 (23) 6 (44)

RECENT CLIMATE (1997-2007)

Change in mean annual rainfall (relative to historical) –8% –9% –7% –12% –8% –6%

Change in mean annual runoff (relative to historical) –12% –14% –10% –24% –15% –8%

Change in mean annual surface water extraction –4% –2% –5% –5% –5% –3%

Change in mean annual non-extracted surface water –15% –14% –14% –24% –20% –13%

Percent of groundwater recharge extracted 12% 12% 19% 2% 6% 24%

Percent of subcatchments potentially impacted 21% 13% 25% 25% 24% 20%

Key ecological sites potentially impacted 71 8 10 16 21 16

FUTURE CLIMATE (~2030)

Change in mean annual rainfall (relative to historical) † –3% (1 to –7%)

–3% (–2 to –6%) –3% (–2 to –7%)

–4% (–1 to –8%) –3% (2 to –7%)

–1% (3 to –6%) Change in mean annual runoff (relative to historical) † –5% (1 to –

10%) –5% (–2 to –11%) –6% (–3 to –

10%) –8% (–3 to –14%) –6% (4 to –

11%) –3% (5 to –

8%) Change in mean annual surface water extraction (relative to historical climate) †

–2% (0 to –2%)

–1% (0 to –1%) –3% (0 to –3%)

–2% (–1 to –5%) –2% (0 to –3%)

–1% (0 to –1%)


WHOLE PROJECT AREA

ARTHUR-INGLIS-CAM MERSEY-FORTH PIPERS-RINGAROOMA SOUTH ESK DERWENT-SOUTH EAST

HISTORICAL CLIMATE (1924 TO 2007)

Change in mean annual non-extracted surface water (relative to historical climate) †

–5% (1 to –10%)

–5% (–2 to –11%) –6% (–1 to –10%)

–8% (–3 to –14%) –6% (4 to –11%)

–3% (3 to –8%) Percent of groundwater recharge extracted 3% 4% 4% <1% 1% 5%

Percent of subcatchments potentially impacted † 2% (1 to 2%) 1% (1 to 2%) 0% (0 to 3%) 1% (1 to 2%) 6% (4 to 7%)

1% (1 to 1%)

Key ecological sites potentially impacted † 10 (6 to 15) 2 (0 to 3) 0 (0 to 4) 0 (0 to 0) 6 (6 to 6) 2 (0 to 2)

FUTURE CLIMATE WITH FUTURE DEVELOPMENT (~2030)

Percent increase in commercial forest plantation area 5% 2% 16% 6% 6% 3%

Change in runoff due to forestry (relative to future climate) <–1% ‡ <–1% ‡ –2% –1% –1% <–1% ‡

Change in currently licensed surface water extractions (relative to future climate)

–3% –1% –7% –4% –3% 0%

New surface water extractions from proposed irrigation 120 GL 0 GL 11 GL 56 GL 53 GL 0 GL

Change in mean annual non-extracted surface water (relative to future climate)

–1% <–1% ‡ –3% –4% –5% <1%

Percent of subcatchments potentially impacted † 2% (2 to 4%) 2% (1 to 4%) 3% (3 to 6%) 2% (1 to 5%) 8% (4 to 9%)

1% (1 to 1%)

Key ecological sites potentially impacted † 14 (12 to 15) 2 (2 to 3) 4 (4 to 4) 0 (0 to 0) 6 (6 to 6) 2 (0 to 2) * The number in brackets in the total number of key ecological sites in the region

† The numbers in brackets show the range of future climate projections.

‡ <-1% means that the value is between zero and minus one.


Appendix B Workshop iMeet downloads

The following tables are the raw data from the iMeet input provided by workshop participants.

Apx Table B.1 Discussion 1 and 2: Service/Function value

VOTES (IMPORTANCE)

SERVICE VERY HIGH HIGH MED LOW VERY LOW SECTOR PERSPECTIVE

water supply for industry/commercial use 7 7 2 0 0 Stakeholder/Community Member

transport and storage of water for consumptive uses 5 5 2 0 0 PI: Agriculture

habitat for aquatic biodiversity 12 2 2 0 0 Environment/Ecosystem

Prioritising the uses 2 0 0 2 9 StateGovPolicyDev

habitat for terrestrial biodiversity 13 3 1 0 0 Environment/Ecosystem

Bed and Bank Stability 4 5 6 1 0 StateGovPolicyDev

environmental/ecosystems functions (habitat, nutrient cycling etc) 9 1 2 0 0 Stakeholder/Community Member

Recreation and tourism 0 7 3 1 1 StateGovPolicyDev

Habitat for biodiversity 12 2 0 0 0 StateGovPolicyDev

nutrient cycling 7 6 2 0 1 StateGovPolicyDev

Energy - hydroelectric, thermal cooling, 10 4 3 0 0 Energy and Mining

Food and fibre production 10 3 4 0 0 Primary industries

Cultural, indigenous, spiritual connection 3 8 4 0 1 Stakeholder/Community Member


VOTES (IMPORTANCE)

SERVICE VERY HIGH HIGH MED LOW VERY LOW SECTOR PERSPECTIVE

Recreation 1 6 7 1 1 StateGovPolicyDev

Stock watering 0 3 11 2 1 StateGovPolicyDev

Potable Use 14 1 0 0 1 StateGovPolicyDev

Irrigation 9 4 2 2 0 StateGovPolicyDev

i'm downstream and benefit from good managment 6 3 4 3 1 Water Service Provider/Water Resources

Property values 2 2 2 6 5 StateGovPolicyDev

Intact riparian zones for sediment trapping, habitat, agricultural land buffering, living haystack etc.. and it makes the riverbank a much nicer place to be!

8 5 3 0 0 Environment/Ecosystem

flooding for ecosystem functioning (spawning, nutient flows, floodplain recharge, etc) 9 5 3 1 0 Water Service Provider/Water Resources

cultural and intellectural rleationship with water (driven by climate variability - ENSO droughts and flooding rains)

0 5 3 4 4 Stakeholder/Community Member

accesss to water for livlihood for poor 1 3 6 4 2 Stakeholder/Community Member

Connectivity (longitudinal and lateral) 8 3 2 2 1 Environment/Ecosystem

water as a climate moderator 0 7 4 5 0 Stakeholder/Community Member

Water quality 11 3 0 0 0 StateGovPolicyDev

Water quantity 1 0 0 0 0 StateGovPolicyDev


Apx Table B.2 Discussion 3: Climate/Hydrology Impacts

VOTES: IMPORTANT ENOUGH TO TRIGGER ACTION?

CLIMATE CHANGE IMPACT IMPACTED ECOSYSTEM SERVICE/FUNCTION

IMPACTED FUNCTION/SERVICE

SECTOR BUSINESS PROCESS COMPROMISED

NO UNSURE YES ASSOCIATED METRICS

higher temperature fish breeding habitat- terrestrial and aquatic spp

fisheries 1 4 5 temperature, species presence and abundance, phenology

Co2 fertilisation Vegetation thickening on floodplains - habitat provision

habitat- terrestrial and aquatic spp

grazing breeding 2 4 2 flooding temperature?

increased greenhouse gases - primarily CO2

increased vegetative response (+) may lead to increased bushfire risk (-), changed carbon to nitrogen ratios effecting nutritional quality


habitat, species diversity, landscape connectivity

2 0 6 relationship between CO2 and plant growth

Change in mean flows potable supply water quality water quantity habitat energy water supply food and fibre production

all 0 0 8 Qmean

reduced streamflow water quantity and quality, biodiversity

all 0 0 8 mean annual discharge, flooding/ drought frequency, magnitude duration

Change in wetting and drying cycles

connectivity ecosystem function habitat

environmental processes

1 4 3 event analyses duration curves frequency analysis timing

response to government policy eg plantation timber, coal seam gas

underground water supply/connectivity

water supply for industry/commercial use

Energy/primary production eg carbon farming

0 1 7 underground water connectivity, underground water reserves, effect of saline water bought to







surface

Change in seasonality all water storage environmental processes operating rules water allocation

0 3 5 seasonal flows mean flows (seasonal) time of year of flow

groundwater recharge habitat connectivity ecosystems potable water food and fibre


environment economics

0 0 8 recharge to groundwater and associated processes

likely increase in rainfall intensity

change in historical streamflow, impact on biodiversity, impact on soil structure, erosion,

intact riparian zones for sediment trapping, habitat, ag land buffer, living haystack etc

agriculture, tourisim, 1 1 6 downscaled projections of extreme rainfall events

surface water/groundwater interaction

connectivity habitat food and fibre

connectivity (long. and lat.)

environment 1 1 6 appropriate accounting for surface and groundwater (no double counting)

Heat waves Agriculture, biodiversity, public health


All 2 3 3 Days of temperature excedence, ET, NVDI's, crop wilting

-rainfall increase -rainfall reduction -rainfall variability -rainfall intensity -extreme events frequency -drought frequency and duration -increased ET and temp

fragmented populations with reduced diversity and recruitment, reduced resilience and resistance, black water events and anoxia


fisheries biodiversity invertebrate biodiversity water fowl migration

0 1 8 low and high flow seasonal flow distribution annual runoff temperature return frequency water quality energy and nutrient (C, N, P) BOD and DO annual ET and temp

Increased uncertainty in less water for longer water supply for industry/commercial

prooduction, business planning and

0 0 8 -spatial distribution of inflows timing and reliability







water availability periods of use investment -land use -water take vs water allocation and loss -water use vs $$ return

change in extremes connectivity habitat energy food and fibre flooding potable supply water quality industry

environment industry energy production agricultural production

0 0 8 Q90, Q10, duration curves, event analysis, (+ equivalent for temperature)

-mean annual rainfall -mean annual Q -water storage - dams and GW -temperature (driving energy demand)

cooling energy production energy- hydro and thermal cooling

revenue operational safety energy efficiency

0 0 8 -Water allocation and storage/availability

water unavailable for ceremonial/social purposes/ recreation

cultural, recreation cultural and intellectual relationship with water

society 1 2 6 dependent on cultural practice requirements

Sea level rise Saltwater intrusion, flooding, water supply, coastal recession

Biodiversity, water supply, irrigation

0 0 8 tide gauges, marine intrusion, coastal recession

change in intensity and frequency of drought

provisioning services such as clean water for biomass prod, food, fuel and fibre, recreational uses


agricultural, tourisim, spiritual

0 0 9 increased knowledge of drivers of drought, impact of climate change on ENSO/drivers of Aust climate variability, need to better understand resilience of ecosystem wothin catchments

Changing fire regime all all 0 0 8 bushfire indices change in flows due to regrowth sediment movement







nutrients

-rainfall increase -rainfall reduction -rainfall variability -rainfall intensity -extreme events frequency -soil temperature -erosion and dissolution

-primary production -decomposition (anoxia) -biodiversity -light penetration -potable water use -recreation -fisheries

water quality -biodiversity -water treatment -agriculture -habitat -recreation

0 0 8 -salinity -turbidity -inorganic and organic C, N and P -secchi depth -sedimentation -DO -temperature -algal count -chla

Moving from impact assessment to action

All Diminishing returns in context of understanding the problem, towards shifting the effort to managing the impacts

0 5 3 Governance, institutional measures

transformational shift in primary production eg viticulture shifting to Tasmania

biodiversity, change in existing water use/extraction from creek, rivers, underground


agriculture, tourism, 1 3 4 interface between ag and ecosystems with regard to minimising conflict

change in demographic/geographic

potable water food production

infrastructure industry support services social infrastructure

1 5 4 population modelling population density social capital

Increased risk to water consumers

food and fibre production (irrigation)

increased cost of capital and operations variable returns loss of credbility of regulatory framewoks vairability in comodity

1 4 3 -economic tools for assessing water risk - eg price of secure water in response to climate variability -percentile distribution of water availability







prices

movement of insects and pests

food production human health ecosystems

health services pest management ecosystem diversity

0 1 6 health stats vulnerability indices

mean annual rainfall -mean annual Q -water storage - dams and GW -drought

-mineral or paper or food processing -agricultural production -manufacturing


-revenue -operational safety -energy efficiency -community employment

0 0 7 -Water allocation and storage/availability

change in distribution of weeds/pests

biodiversity, impact on ecosystems


everything 0 0 3 research into likely spread/change distribution of weeds eg leaucena

Apx Table B.3 Discussion 4: Adaptation Actions

VOTES TOP RANK

ADAPTATION OPTION TOP- 10?

TOP- 5?

TIME HORIZON COST RELEVANT ECOSYSTEM FUNCTION

COMMENTS AND DETAILS

assess vulnerability of different industry sectors to changes in water availability

0 1 01-05 years 1 Low water supply for industry/commercial use

impact focused but understanding of vulnerability is needed before adaptation options can be assessed - ag focussed

movement of insects and pests: knowledge to inform adaptation which could be changes to infrastructure (eg: tank or storage design), changes to disease control etc.. research and monitoring required to develop an appropriate adaptation response

0 0 05-10 years 2 Med Food and fibre production (irrigation)

human health also very important

Use sustainability as the framework for climate change adaptation - nothing wrong with triple bottom line, can

0 0 10-25 years 1 Low All


VOTES TOP RANK


TOP- 5?



accommodate resilience, thresholds, adaptive capacity, trade-offs

system change (incremental) 0 0 05-10 years 2 Med Food and fibre production (irrigation)

minor adjustments to production - plant variety, breeding, genetics, planting timing, species selection

Forest thinning 2 0 01-05 years 1 Low Potable Use Controversial, trade off between ecosystems, forestry, water quality issues

Changes in operation of existing storages for flood mitigation and energy in response to change in variability and seasonality

3 0 01-05 years 1 Low Energy - hydroelectric, thermal cooling,

also food and fibre

transformational adaption of ag industry 0 0 10-25 years 3 High Connectivity (longitudinal and lateral)

whole enterprise shift to different location eg wine prod to Tasmania, peanut prod to northern Aust. May also impact water use/extration.

change in water entitlements 0 0 01-05 years 2 Med Habitat for biodiversity (terrestrial and freshwater)

environmental flows and allocation of water for specific biodiversity outcomes

Reduce water extractions - ensuring that future develomnet is done within the current envelope of water availability LESS climate change factor.

2 0 10-25 years 3 High Beneficial in the long term to all those secotrs, increased resilience,

Flexible water sharing/trading/offsets, and regulatory environment

2 2 05-10 years 1 Low Connectivity (longitudinal and lateral)

unintended effects of flooding on communities and economic/social consequences

culturally appropriate decision making/engagement processes to determine water allocations (may be for spiritual, community or industry)

2 2 01-05 years 3 High cultural and intellectural relationship with water

we tend to be repeating the mistakes of the past by failing to engage in meaningful stakeholder consultation


VOTES TOP RANK


TOP- 5?



local level collaborative planning to identify adapation options including structural adjustment

2 1 05-10 years 2 Med water supply for industry/commercial use

yeah

Well functioning insurance/ risk assessment/ risk planning/ shared intelligence/ leading to reducing risk/ responding to risk incentives/ disincentives

4 1 05-10 years 3 High All

understanding impact of gov policy on primary prod - eg water pricing policy


need to understand competing interests for water - eg MDB water plan that values environment as well as other users

Managed aquifer recharge to maintain coastal wetlands (grey water recycling)

1 0 01-05 years 2 Med Habitat for biodiversity (terrestrial and freshwater)

Requires agreement with multiple levels of government, researcher input to test feasibility, water utility to monitor the quality.

Process water recovery, storm water capture, integrated water cycle management


Use/adoption driven by the price and availability of water

de-emphasise water use engineering efficiency approaches to water scarcity in the absence of addressing water entitlement change

1 0 01-05 years 1 Low Habitat for biodiversity (terrestrial and freshwater)

considering the broader system rather than focusing on individual farms - water savings may in fact be illusory

re-assessing definitions of what is an 'exceptional circumstance' eg droughts, floods etc in regard to government financial support

1 1 01-05 years 1 Low Food and fibre production (irrigation)

over-use of EC payments in past discourages appropriate use of risk management plans

avoiding maladaptive short-term adaptations ... focus steps along pathways transformational adaptation changes: eg moving production systems vs sinking capital in keeping them there;

3 2 greater than 50 years

2 Med Food and fibre production (irrigation)

eg moving rice, or changing crops. maybe energy vs water intensive too? coal seam gas/water unfriendly.


VOTES TOP RANK


TOP- 5?



Hypolimnetic oxygenation of waterbodies 0 0 01-05 years 3 High Water quality Life support for highly degraded systems. Should be used in conjunction with source interception/reduction

regulations/policy to not build/exist on floodplains, or where sea level will rise, or where bushfires will rage!

3 2 05-10 years 3 High cultural and intellectural relationship with water

potentially high political cost because unpalatable to community, could also be very expensive in infrastructure to enable people to stay where they are (levee banks, concrete bunkers etc..)

explore options for transformation of rural/regional communities (population change, infrastructure etc..)

4 4 05-10 years 2 Med cultural and intellectural relationship with water

this is more about lifestyles and livelihoods

Targetted community redundancy - water buy back from targetted communities with low long term viability.

2 1 10-25 years 2 Med Food and fibre production (irrigation)

Resilience tradeoff between competing water users - there will be winners and loosers. Politically unpalatable??

continue investment in long-term monitoring data sets so that trends and change over time can be analysed and implications for the future can inform adaptation response

2 2 05-10 years 2 Med Habitat for biodiversity (terrestrial and freshwater)

also water quality, and just about everything else

improving water use efficiency with ag crops/diary/winery/horti/ irrigated crops

2 1 01-05 years 2 Med i'm downstream and benefit from good managment

means more for everyone else/biodiversity. engineering response - t-tape etc. less use of harmful chemicals - more use of biodegradable products - lower salt output.

Do nothing, Deal with the crisis when it arrives, take advantage of crises to achieve rapid social and economic change, be prepared to put forward alternative opportunities

1 0 25-50 years 3 High All

Fence out all cattle from riparian zones to improve water 3 2 01-05 years 2 Med Water quality this will require regulatory framework and will


VOTES TOP RANK


TOP- 5?



quality and downstream impacts be politically unpalatable

improve energy use efficiency in ag enterprises 0 0 01-05 years 1 Low win/win scenario - lower input costs = more profits = lower environmental footprint.

change the political system so we move from short term management decision frameworks to longer-term visionary approaches........

0 0 greater than 50 years

3 High Cultural and intellectural relationship with water

unfortunately we think we might be dreaming......... but lets not give up

Stakeholder endorsed standards and certification as drivers for update of best management practices

4 2 05-10 years 1 Low Food and fibre production (irrigation)

Apx Table B.4 Discussion 5: A functioning ecosystem

CHARACTERISTICS OF ECOSYSTEM: PREREQUISITE FUNCTION OR PARALLEL REQUIREMENT

FUNCTION OR SERVICE OTHER FUNCTION OR SERVICE ASSOCIATED COMMENTS/DISCUSSION

Slower flowing water to reduce sedimentation and erosion

water quality (-) could be come stagnant Good for breeding habitat reduces erosion

important for habitats avoided later treatment costs prevents silting up of wetlands AND ADAPTATION OPTION 1. we identified as important etc.

intact riparian zone water quality sediment trapping, habitat, temperature regulation, shading,

management actions could include: fencing out of cattle crossing points restoring structural and floristic diversity restoring/maintaining flow regime for connectivity

relocate industry based on most suitable climate/water availability and other constraints eg pest disease transport

food and fibre production (irrigation) may be a negative impact in location/ecosystem

potential issue with reliability of interannual water supply eg citrus at Burke




Intact, continuous riparian zone water quality Primary production - Dissolved, particulate nutrient, and carbon delivery Shading

Riparian replanting, fencing, revegetation, riparian buffers, control of grazing, off-stream watering for stock,

groundwater-surface water interactions connectivity (long. and lat.) water for food/fibre production potable water water supply water quality (salinity - rising groundwater tables) groundwater dependent ecosystems

Vertical connectivity insufficient knowledge of this system

thresholds for ecosystems to shift into different states (eg. oligotrophic to eutrophic)

habitat- terrestrial and aquatic spp underpins all other functions including culturally significant sites, flooding and ecosystems

dynamic flow regime connectivity (long. and lat.) passage of biota between habitat patches access to refugia watering of floodplain wetlands

Providing a managed flow regime which accounts for critical links to ecosystem requirements (timing, magnitude, duration, frequency, etc.) Providing water infrastructure which facilitates passage of biota (fish ways etc.) and delivers water quality improvement opportunities (multi-level offtakes, etc.)

flood pulse flooding for ecosystem functioning environmental flow releases

thresholds for social systems food and fibre production (irrigation) cultural drivers (eg. Menindee Lakes, trout in Central Highlands in Tasmania)

Critical infrastructure taken from a community can results in demise Can be positive - eg. recreational duck hunters restoring wetlands

Water availability, transmission, quality, regulatory infrastructure

Rural community livelihoods, viable agricultural enterprises




supply of water of suitable quality food and fibre production (irrigation) Allocation against entitlement (how much the producer gets versus what is available) Availability of tradeable water Operation and control of the water - management of the resource in a time critical way (how much, when and where)

Engineering advances - supply end, water delivery system, on farm use, water market operation,

Habitat maintenance, conservation action, management of threatening processes

Species, ecological communities not going extinct

deepen water storages to reduce loss through evaporation

water supply for industry/commercial use may impact on riparian zone, may (+ or -) impact on fish habitat/biodiversity/turbidity

may reduce demand from natural water sources. potential for more design features (steps, reed beds etc) to be included in water storages.

water cycle (provisioning) and potential climate change impacts

energy- hydro and thermal cooling accuracy of prediction at appropriate spatial scales

flow and channel morphology habitat- terrestrial and aquatic spp water quality scouring diversity in-stream and riparian habitat

salinity flushing food and fibre production (irrigation) ecosystem functioning water quality appropriate for use for food /fibre production and for ecosystems

water temperature habitat- terrestrial and aquatic spp ways of using storages to dilute/regulate temperature overhanging vegetation for shading - habitat elements thermal pulsing

more knowledge to be able to do this well

Diverse economy, diverse ecosystems, space to move, removing regulatory restrictions

Being able to adapt or respond to change (human and ecological communities)

Risk and opportunity management




healthy riparian zones habitat- terrestrial and aquatic spp connectivity, water quality, bank stability, water flow, increased primary production, pest regulation

implement major program to encourage development of healthy riparian zones with land managers/owners - reward based, including farm forestry and drop heads into creeks, increased tenure of land leases

functional river forming process and geomorphology

habitat- terrestrial and aquatic spp flow regime - high flow souring and bed mobilisation, bank slumping, low flow siltation and deposition runoff (overland flow) sediment and nutrient loads deposition and movement of woody debris hyporheic exchange

Functional riparian corridors River restoration to return meanders and natural river geomorphology Better operation of dams and reservoirs, drains Catchment management to reduce sediment and nutrient export Replacement of large woody debris Constructed wetlands as interceptions, improve water quality, restoration

flow regime that allows floodplains to function and a floodplain ecosystem that can respond to that flow

flooding for ecosystem functioning water quality Floods maximise ecological benefits Need more understanding of how floodplains function in terms of connectivity between riparian zones and terrestrial landscape

healthy riparian habitat - shade, snags and debris, stable banks, nutrient provision and capture

water quality aesthetic, habitat for biodiverity, recreation

extreme drought, flood, fires, storm surges water supply for industry/commercial use habitat, water quality, impact on human society

need more understanding (including under climate change) to be able to develop adaptation strategies

Political courage, resources, process for prioritising species/ communities/ industries for action

Structural adjustment, assisted migration




maintaining groundwater recharge downstream beneficiary from good management

maintaining goundwaters to ensure continued dry season flows and aquifer sustainabilty and Groundwater Dependent Ecosystems

About the groundwater surface water interactions - managing vegetation and land use throuhgh to in stream water management (different sectionsof the river)

increase the national investment in our scientific understanding of water in the Australian environment recognize/celebrate our diverse cultural relationship with water in the environment

cultural and intellectual relationship with water

all of them education and research are the basis of being able to better manage/live with our freshwater ecosystems

Morphological diversity downstream beneficiary from good management

habitat, food web, flood buffering, water quality related to sedimentation processes, speed of water, aesthetic/amenity

human emotive responses to ecosystems/events that motivate action and positive behavioural change


we need to understand what people feel and know about their environment and how this can contribute to our knowledge. integrate social and biophysical knowledge.

enabling movement of species up and down river systems and across the flood plain to other 'waters' etc wetlands

connectivity (long. and lat.) ecosystem functioning, biodiverity Management options including:working fishways, removing wiers, mangement of flows (water extraction and addition), timing and volume of environmental flow, flood plain inundation

communicate the value of a functioning ecosystem to all stakeholders e.g. farmers urban dwellers, not just scientists and policy makers


all reinforcing practices many people undertake daily

Environmental water connectivity (long. and lat.)


Apx Table B.5 Discussion 6: Resilience and Adaptation

ADAPTATION OPTION WHO IS RESPONSIBLE? WHAT SERVICE/FUNCTION IS PRESERVED

INFORMATION REQUIRED FEASIBILITY COMMENTS

protect riparian zones if land belongs to council, council is responsible. Action can be taken by communities too


optimal reaches for revegetation land ownership

1 Highly Feasible

Finding sources of funding would be required. Public volunteer initiatives need to be driven, or organisational-based sponsorship... eg. via Water stewardship Australia: points for subsidising riparian protection?

Stakeholder endorsed standards and certification as drivers for update of best management practices

STAKEHOLDER groups as informed by scientists, ecologists, producer groups, conservationists, NRM

all (underpinning requirement)

quantitative benchmarks off which to base future decisions and measurable outcomes

2 Feasible market based driven and 3rd party monitored

avoiding maladaptive short-term adaptations

Community/industry/scientific leaders to provide a framework for individuals, industries, regions to consider the future and avoid short-term maladaptive responses. Leadership underpinned by knowledge and placed within contexts that are meaningful for people at individual and group levels. Form of leadership that is multi-sectoral and is integrated rather than perpetuating the current silo approach.


This operates at multiple scales (individual, local, regional, industry, ecosystem), there is a need to consider long-term integrated impacts/goals across sectors. Feasible and cost effective as maladaptive solutions are expensive to fix.

2 Feasible




Well functioning insurance/ risk assessment/ risk planning/ shared intelligence

Government (local, state and federal) and private insurance industry, individuals. Discussion related to flooding. Responsibility should rest at the appropriate level of government.

1 Flood mapping, rainfall/ streamflow modelling, applied research (problem centred), standards - engineering and insurance

1 Highly Feasible

Technically feasible, social issues related to equity/ affordability

continue investment in long-term monitoring data sets

government - all levels, industry contribution of background knowledge of yields, resource use etc interested persons/NGO's/other organisations


identifying gaps in existing knowledge sets, framework to share data sets,

3 Difficult but possible

requires commitment for long term financial support - not 3 year funding cycle; current government fiscal situation makes it difficult - requires collaborative approach between interested parties.


Federal, State and Local government responsibility but storage and management to go to an objective third party with bipartisan support/resources for long term quality control, modelling frameworks, data collection methodologies, coordination, interpretation and sharing of knowledge.


what data is out there, have we interrogated it, does it need updating, where are the gaps, is it relevant, etc.. this is ongoing and is vital for modelling future scenarios

2 Feasible While government would seem to be the likely candidate, the vagaries of three year cycles means it is an unstable and unreliable organisational framework to house long-term data. Could be open to an organisation best placed to retain a longer term commitment to maintaining data and sharing would be a better option.


BOM State Governments Water Utilities NRM/Catchment Groups ABS


Detailed hydrological (surface and groundwater) data Detailed climate data Ecological response data Water quality data Socio-economic data


Challenges integrating data sets collected by different agencies/groups Agreement on minimum data and collection standards Commitment to long term data collection Common repository for extracting data Data accessible to all




avoiding maladaptive short-term adaptations

so its the responsibility of those who design the instituions.

any land/water use change has positive and negative impacts, need understanding of impacts on multiple outcome areas to make an informed decion on the trade-offs. System that enables decisions to be reviewed and be responsive to new information esp about perverse impacts.

2 Feasible while everyone makes decisions, the institutions within which decisions are made need to be designed to ensure the longer term potentially transformational decisions are what decision makers work towards, EXAMPLES providing the legal mandate for decision makers to consider the impacts in other sectors horizontal integration - water impacts of carbon policies (tree planting) vertical integration - local, state, national independent review (eg NSW NRC, ACT Commisioner for Env) of policy with authority and expertise First steps along this path are feasible and eazy

improving water use efficiency

Largely private benefit so they should pay, public contribution should relate to the amount of public benefit.


Environment and agricultural benefits

1 Highly Feasible

Efficiency is highly variable and significant gains are still to be made. Public support that involves significant changes can be difficult. Productivity is an issue across the whole economy, not just water.

local level and culturally appropriate decision framework for adaptation

Partnership approach at locally relevant levels to implement standards and best practice frameworks designed to enable adaptation to occur.


transformational change relies on knowledge and guiding frameworks to enable a sense of purpose to be developed across different sectors/groups at local levels.

2 Feasible Government/ industry/ community groups at different levels will need to partner together to meet this objective. This will require relationships of trust and long time frames (ie. beyond 2 years)




explore options for transformation of rural/regional communities

All levels of government Community and industry representation


Risk and opportunity - future scenarios Economics, technological, Long term hydrological, climate forecasts to underpin scenario development community perceptions and aspiration, values recognising community adaptive capacity/constraints

4 Very difficult to implement

Conversation easier to have immediately following a major natural disaster (flood, fire, drought, etc.) Significant time to have these discussions and transition communities Takes leadership Inherent community resistance to change Cultural sensitivities


residents/communities of affected areas government - all levels


what does the community want? what other natural resources are available for use eg climate/water/soils


can be expansion/contraction of pop driven be different causes eg wine grapes shift to Tas, tropical fruit expansion in Nth Qld, mining, coal seam gas in Qld.


Expensive and valuable for everyone, therefore strong case for Australian government investment. Communication and access to outputs very important


Relates to several services. In relation to habitats we need information on indicator species. Need capacity to relate cause and effect.


Has proven to be difficult, no good track record of long term monitoring except for weather and to a lesser extent streamflow. Remote sensing provides some opportunities that are more cost effective and technically feasible. Community/ ranger monitoring/ offers good opportunities

Flexible water sharing/trading/offsets, and regulatory environment

National policy framework (NWI) & federal institutions (EPBC, Water Act, CEWH etc) States for much water regulation amnd environment In some cases regional institutions


Water accounting and trading data, inc. surface-groundwater, "losses" Environmental flow requirements

1 Highly Feasible

Extend water market to cover all major users, inc. inflow interception activities, carbon farming, mining, coal sean gas, energy users, groundwater etc. National policy committment (NWI) yet to be implemented Precedents elsewhere Transparent process




improving water use efficiency

Water utilities industry individuals planners developers irrigators


Water usage profiles for each sector Rate of water use per unit outcome/output Best practice benchmarking Technological advances - crop selection/genetic enhancement

2 Feasible Recognising the challenge of bringing all the information together Driver, incentive to effect change based on this information Certification systems Mix of carrot and stick

regulations/policy to not build/exist on floodplains, or where sea level will rise, or where bushfires will rage!

Local government within a broader policy framework that is informed by likely scenarios and impacts

2 This is about making decisions in light of possible future climate change scenarios and trading off social/environmental and economic outcomes. We should not be having people living in hazard prone areas or they do so at their own risk. At the moment we are subsidising people to avoid political angsst.

2 Feasible feasible but politically unpalatable and costly to move people in the short term but long term makes sense.


Local communities can active self transformation, Federal government resources can target structural adjustment, State government can implement, needs support of local communities. National may offer more equitable outcomes.


Broader than food and fibre, community preservation, social cohesion.

2 Feasible Normally very expensive, can involve buying back land and water.




Targetted community redundancy - water buy back

All levels of government Community and industry representation


Risk and opportunity - future scenarios Economics, technological, Long term hydrological, climate forecasts to underpin scenario development community perceptions and aspiration, values recognising community adaptive capacity/constraints

4 Very difficult to implement

Conversation easier to have immediately following a major natural disaster (flood, fire, drought, etc.) Significant time to have these discussions and transition communities Takes leadership Inherent community resistance to change Cultural sensitivities

local level and culturally appropriate decision framework for adaptation

local government and stakeholder groups ensuring non-token consultation. Need sign off that consultation has been satisfactory


appropriate /sensitive consultation method for sector/ industry e.g. aboriginal/farmer/industry/govt/community

1 Highly Feasible

requires commitment and money and time

protect riparian zones Landholders Regional NRM bodies State agencies National policy


Riparian condition and threat Conservation priorities Indigenous ecosystem composition Land tenure & current uses Legal status Develop prioritisation for interventions

1 Highly Feasible

Restoring riparian vegetation is one of the most important CC adaptation measures that can be undertaken Many successful smaller projects at catchment scale but systemic restoration lacking Linked to reoperation of water infrastructure and E-flows




protect riparian zones Coordinated government regulations combined with incentive programs to enable people to fence our stock, put in pumps, revegetate, flood fencing etc...


we've got the knowledge about how to protect riparian zones, but we need to learn how to better develop incentive and regulatory frameworks that can be rolled out (some really good examples like Project Catalyst in Qld)

1 Highly Feasible

feasible but politically unpalatable without incentives, but when well designed and time is spent working with individual landholders it is eminently achievable. One to one communication only way to go.....

protect riparian zones NRM/catchment groups land owners state government water utilities


Where to target riparian planting/intervention Condition of riparian zones Condition of channel, geomorphology, hydrological flow paths

2 Feasible Further enhancement/extension of existing programs and practices

Targetted community redundancy - water buy back

government and regional bodies.


Science/economics regarding feasibility of different 'irrigation channels' and/or catchments,

1 Highly Feasible

Good communication and government desire to achieve outcome


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