Integrating Environmental Flows Into Regulated River Systems

Integrating environmental ﬂows into regulated river systems

Celine Steinfeld

A thesis in fulﬁlment of the requirements for the degree of Doctor of Philosophy

Australian Wetlands, Rivers and Landscapes Centre School of Biological, Earth and Environmental Science University of New South Wales, Australia

October 2013 ii Some hours later, and after the moon had risen, a murmuring sound like that of a distant waterfall, mingled with occasional cracks as of breaking timber, drew our attention as I hastened to the river bank... At length, the rushing sound of waters and loud cracking of timber, announced that the flood was in the next bend. It rushed into our sight, glittering in the moonbeams, a moving cataract, tossing before it ancient trees and snapping them against its banks. It was preceded by a point of meandering water, picking its way like a thing of life, through the deepest parts of the dark, dry and shady bed, of what thus became a flowing river. By my party, situated as we were at that time, beating about the country and impeded in our journey, solely by the almost total absence of water - suffering excessively from thirst and extreme heat - I am convinced the scene can never be forgotten.

- Diary extract of Sir T. L. Mitchell, 1839 on expedition through the Macquarie River

ORIGINALITY STATEMENT

‘I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at UNSW or any other educational institution, except where due acknowledgement is made in the thesis. Any contribution made to the research by others, with whom I have worked at UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project’s design and conception or in style, presentation and linguistic expression is acknowledged.’

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Date ...... 28th October 2013...... COPYRIGHT STATEMENT

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Date ...... 28th October 2013...... Abstract

The global decline in freshwater ecosystem health has prompted governments to return water to rivers to restore natural flow regimes. Adequate volume, temporal variability and spatial connectivity of flows are essential requirements for maintaining freshwater ecosystem integrity. However, a major challenge is integrating environmental flow requirements into regulated river systems historically managed for water supply and flood mitigation. In my thesis, I aimed to: (1) evaluate operational opportunities and challenges for the allocation, release and delivery of environmental flow in regulated rivers; and (2) support environmental flow management capacity building through decision support tools and techniques. I focused on two inland river systems in Australia’s Murray-Darling Basin, the Gwydir River and the Macquarie River, where unprecedented environmental water investment aims to rehabilitate degraded semi-arid floodplain wetlands of international significance: the Gwydir wetlands and the Macquarie Marshes. Chapter 1 provides an overview of the global importance, progress and challenges of integrating environmental flows into regulated rivers. Chapter 2 outlines the development and application of a long term daily decision support tool for explicit simulation of environmental water allocation and flexible specification of water management rules. Chapter 3 demonstrates the importance of management and biophysical factors in influencing long term availability of environmental water. Chapter 4 examines operational implications of different environmental water release strategies, illustrating the need to include operational opportunities and risks together with ecological outcomes in a strategic environmental watering planning framework. Chap- ter 5 examines techniques for efficiently and effectively identifying floodplain earthworks built for irrigation and flood mitigation which often fragment spatial flow connectivity and interrupt delivery of environmental flows to target floodplain assets. Results demonstrated that semi-automated Geo- graphic Information Systems techniques were an effective and efficient alternative for large scale earthwork detection compared to traditional visual interpretation. Chapter 6 synthesizes the research presented in my thesis and suggests directions for future research towards a transparent, systematic and adaptive approach to decision making in river systems progressing towards sustainable integrated water resource management. Acknowledgements

This PhD has been one of the most enjoyable, rewarding and challenging experiences in my life. I cannot express my gratitude enough to the amazing colleagues, family and friends who have helped make it possible.

I am deeply grateful for the guidance and support of my supervisor Richard Kingsford. Richard took me under his wing for my honours research and I have never looked back. He shared tremendous scientiﬁc expertise, gave me intellectual challenges and freedom, and provided valuable opportunities to extend my research into policy and management. Richard introduced me to Bill Johnson from the Murray-Darling Basin Authority and Harry Biggs at South Africa National Parks who brought to life the complexities of managing river systems. Their unforgettable stories, wisdom and humour from decades as river managers have been a great source of inspiration.

I would like to thank my co-supervisor Shawn Laﬀan for his valuable advice, technical support and friendship. I am also grateful to colleagues at the Australian Wetlands, Rivers and Landscapes Centre at the University of New South Wales (UNSW), particularly Evan Webster for teaching me programming, Shiquan Ren for valuable statistical advice, Sharon Ryall for project management, Rachael Thomas for providing datasets, and Jo Ocock for her friendship and delicious baked treats. Thanks to Jonathan Russell for fantastic administrative support and friendship. I am also grateful to Ashish Sharma, Raj Mehrotra, Seth Westra and Eytan Rocheta at the UNSW Water Research Centre for their hydrological modelling advice.

I sincerely thank the New South Wales Government for supporting my research. I am grateful to Peter Christmas, for helping me to unravel the layers of river management. Thanks to Debbie Love and Jane Humphries for their knowledge of environmental ﬂow management in the Gwydir and Macquarie Rivers. I am grateful to Tahir Hameed, Stephen Roberts and Marina Sivkova for sharing their hydrological models and data. Thanks to Simon Williams who convened the Peter Cullen Postgraduate Scholarship and provided opportunities to access government expertise and knowledge.

I appreciate the assistance of Craig Cahill, Ken Gee, Sri Sritharan and Dan Berry from State Water who helped me understand how the Gwydir and Macquarie Rivers are operated, and advised me on development and veriﬁcation of my models. In these discussions, I developed a strong appreciation of the challenges facing river operators in delivering environmental ﬂow which shaped my research directions and ideas.

I would like to thank Wentworth Group of Concerned Scientists for mentor- ing and guidance. I am particularly fortunate for the unique insights into science and public policy from Bruce Thom, Peter Cosier, Tim Stubbs and Caroline McFarlane.

I acknowledge the generous ﬁnancial support of the Peter Cullen Postgrad- uate Scholarship, the UNSW Research Excellence Scholarship and the Aus- tralian Postgraduate Award.

I cannot imagine going through this without my friends, who have been so enthusiastic about my research and given me a life beyond study. Thanks to Jenna, Emily, Natalie, Tisha, Jacqui, Tanya, Mike, Ben, Bobby, Seb, Jenny and all my friends for the laughs and friendship through the years. To the underwater rugby crew and the touch footy team, thanks for being a wonderful PhD distraction.

Finally, thanks a million times to Mum, Dad and Jess. I really appreciate your support in every shape and form, from entertaining Sunday dinners to lifts home in the rain, I couldn’t wish for any better a family. To my ﬁanc´ee Tymek, thanks for your extraordinary love, care, humour and patience, I would truly be lost without you! Preface

This thesis consists of six chapters. Chapters two to four are self contained in preparation for a journal article and some repetition occurs. Chapter ﬁve has been published and is identical to the published version. To prevent unnecessary duplication a single reference list is provided at the end of the thesis.

This thesis is a compilation of my own work, with guidance from my supervisors Richard Kingsford, Shawn Laﬀan and Bill Johnson. I conceptualised my research, conducted all data analysis and wrote and illustrated the manuscripts. I also generated all maps and photographs included in this thesis. My supervisors proof-read and edited the ﬁnal manuscript versions. Contributions of co-authors are detailed below:

Chapter 2: R. Kingsford gave conceptual advice and guidance as my supervisor. E. Webster assisted with programming the eWASH tool in Python code. A. Sharma provided advice in hydrological modelling.

Chapter 3: R. Kingsford contributed signiﬁcant intellectual input and general guidance as my supervisor. S. Ren provided statistical advice.

Chapter 4: R. Kingsford provided conceptual advice and intellectual input. A. Sharma gave me advice on stochastic hydrology and statistics. B. Johnson provided comments and feedback on the draft manuscript.

Chapter 5: Steinfeld CMM, Kingsford RT, Laffan SW. 2012. Semi-automated GIS techniques for detecting floodplain earthworks. Hydrological Processes. DOI: 10.1002/hyp.9244. S. Laffan gave me technical advice and contributed significant intellectual input and guidance as supervisor. R. Kingsford provided guidance as supervisor. Contents

1 Environmental flows in regulated rivers 1 1.1 Abstract ...... 1 1.2 Introduction ...... 1 1.3 Global status of environmental flows ...... 5 1.4 Water management in the Murray-Darling Basin ...... 7 1.5 Study area ...... 11 1.6 Challenges of integrating environmental flows in regulated rivers . . . . 15 1.6.1 Water Availability ...... 16 1.6.2 Variability ...... 17 1.6.3 Connectivity ...... 18 1.6.4 Scientific knowledge for best practice management ...... 18 1.7 Research objectives ...... 19 1.8 Research approach ...... 20

2 Simulating environmental flow availability 23 2.1 Abstract ...... 23 2.2 Introduction ...... 24 2.3 Methods ...... 27 2.3.1 Water management in the Murray-Darling Basin ...... 27 2.3.2 Gwydir and Macquarie Rivers ...... 28 2.3.3 Water management ...... 29 2.3.4 Modelling environmental flow availability ...... 39 2.3.5 Configuring eWASH for the Gwydir and Macquarie ...... 40 2.3.6 Model validation ...... 45

xiii CONTENTS

2.4 Results ...... 46 2.4.1 Demand sub-model development ...... 46 2.4.2 Validation ...... 48 2.5 Discussion ...... 53 2.6 Conclusion ...... 57 2.A Gwydir resource assessment ...... 58 2.B Macquarie resource assessment ...... 59 2.C IQQM Demand sub-model ...... 62

3 What drives environmental water availability? 67 3.1 Abstract ...... 67 3.2 Introduction ...... 68 3.3 Methods ...... 71 3.3.1 Study areas (biophysical drivers) ...... 71 3.3.2 Modelling climatic drivers ...... 73 3.3.3 Modelling management drivers ...... 74 3.3.4 Modelling water allocations ...... 78 3.3.5 Sensitivity analysis ...... 79 3.3.6 Mitigating impacts on environmental water allocations ...... 80 3.3.7 Assessing policies for environmental water allocations ...... 81 3.4 Results ...... 81 3.4.1 Sensitivity of variables ...... 82 3.4.2 High security allocations ...... 85 3.4.3 General security allocations ...... 87 3.4.4 Supplementary allocations ...... 88 3.4.5 Impacts on environmental allocations ...... 90 3.4.6 Performance of management rules for environmental ﬂow (general security) ...... 91 3.5 Discussion ...... 91 3.6 Conclusions ...... 96 3.A Regression coeﬃcients ...... 97

xiv CONTENTS

4 Managing environmental flows in regulated rivers 101 4.1 Abstract ...... 101 4.2 Introduction ...... 102 4.3 Methods ...... 104 4.3.1 River systems ...... 104 4.3.2 Environmental water reserve ...... 106 4.3.3 Reserve size ...... 107 4.3.4 Environmental watering strategies ...... 108 4.3.5 Scenario simulation ...... 110 4.3.6 Achieving environmental flow objectives in regulated rivers . . . 111 4.3.7 Assessing operational and socio-economic risks ...... 112 4.4 Results ...... 114 4.4.1 Environmental flow demand ...... 114 4.4.2 Reservoir releases ...... 116 4.4.3 Physical release constraints ...... 119 4.4.4 Allocations ...... 121 4.4.5 Reservoir spills ...... 124 4.4.6 Evaporation ...... 125 4.5 Discussion ...... 126 4.6 Conclusion ...... 133 4.A Monthly constraints ...... 135 4.B Monthly allocation ...... 136 4.C Monthly mean spill ...... 137 4.D Monthly mean evaporation ...... 138

5 Detecting ﬂoodplain earthworks 139 5.1 Abstract ...... 139 5.2 Introduction ...... 140 5.3 Methods ...... 142 5.3.1 Technique 1: Visual interpretation ...... 144 5.3.2 Technique 2: Image segmentation (semi-automated) ...... 145 5.3.3 Technique 3: Digital Elevation Model (DEM) analysis (semi- automated) ...... 146

xv CONTENTS

5.3.4 Technique 4: Integrated analysis (semi-automated) ...... 147 5.3.5 Thematic and spatial accuracy ...... 148 5.4 Results ...... 150 5.5 Discussion ...... 156 5.5.1 Semi-automated analyses ...... 157 5.5.2 Visual analyses ...... 158 5.5.3 Cost ...... 158 5.5.4 Management implications ...... 159 5.6 Conclusion ...... 161 5.A A guide to detecting floodplain earthworks ...... 162 5.B Image segmentation parameters ...... 166 5.C Effects of buffer size on accuracy ...... 167

6 Fertile grounds for environmental ﬂows 169 6.1 Abstract ...... 169 6.2 A paradigm shift ...... 170 6.2.1 Overcoming challenges ...... 171 6.2.2 Fragmentation of water management ...... 176 6.2.3 Implications for environmental ﬂow integration ...... 178 6.2.4 Consolidating water management ...... 179 6.3 Conclusion ...... 182

References 183

xvi 1

Integrating environmental ﬂows in regulated rivers: a global challenge

1.1 Abstract

Environmental flows are increasingly incorporated into high level policy to promote sustainable freshwater ecosystems. The Australian approach is among the world’s most progressive examples of transboundary environmental flow policy and yet there are major challenges in the practical integration of environmental flows in regulated rivers. I describe key challenges in restoring the volume, variability and connectivity of flows to Ramsar wetlands and flow-dependent ecosystems in the Gwydir and Macquarie River systems of the Murray-Darling Basin, drawing on the experiences of water managers and river operators. Finally, I explain the quantitative analyses and research structure I used to address these challenges.

1.2 Introduction

Twentieth century water management has beneﬁtted billions of people worldwide and fostered social growth and economic prosperity of entire nations (Gleick, 2003). Con- trol of river ﬂows using engineered infrastructure (e.g. dams, weirs, channels, aque- ducts) was predominantly geared towards meeting social and economic objectives in-

1 1. ENVIRONMENTAL FLOWS IN REGULATED RIVERS cluding water and energy supply, flood protection and transportation (Anderies et al., 2006). Today, flows in almost 60 % of the world’s large rivers are regulated by storages, capable of impounding more than 6 500 km3 of water or 15 % of the total annual global runoff (Nilsson et al., 2005). Humans exploit 65 % of accessible runoff globally (Vörösmarty et al., 2010). This development has come at a tremendous ecological cost. Water for the environment has been drastically depleted, with catastrophic and often unanticipated ramifications for freshwater ecosystems (Gleick, 2003; Lundqvist and Falkenmark, 2000). Anthropogenic activities have caused direct and visible ecological impacts such as fish kills, increasing salinity and algal blooms, and less visible impacts including species decline and wetland contraction, with effects on humans reliant on hydrologic and ecosystem services (Falkenmark, 2001; Hirji and Davis, 2009; Richter, 2010). Freshwater ecosystems are now among the most threatened ecosystems in the world (Millennium Ecosystem Assessment, 2005; Postel, 2000). A major paradigm shift is required to overcome the complex and seemingly intractable problems of the conventional water management regime. The new water management paradigm recognises the triple bottom line, sustaining social, economic and environmental values to support healthy and resilient socio-ecological systems (Walker and Salt, n.d.). There are considerable differences compared to the conventional regime: inherent complexity, feedbacks and variability are embraced, not dampened and sta- bilised (Holling, 1973; Walker et al., 2004); ‘soft’ options such as governance and reoperation are favoured over ‘hard’ engineering solutions (Gleick, 2003); and management is not linear and siloed, but strategic, adaptive and decentralised across interacting scales and sectors (Kingsford et al., 2011; Scholz and Stiftel, 2005). Importantly, the entire river system, the continuum of terrestrial to aquatic landscapes drained by a river and its tributaries and its complex processes, is managed holistically according to natural units rather than arbitrary administrative boundaries. One of the most important advances in water management is the emerging science and management of environmental flows. Environmental flows (natural flow regime) is the water that supports aquatic ecosystems (e.g. rivers, lakes, wetlands, estuaries, aquifers) and their services to humans (Brisbane Declaration, 2007). Environmental flows provide the availability, variability and connectivity required for the long term health of river systems (Poff et al., 1997). All freshwater was natural or unappro- priated flow before human intervention. Aspects of the environmental flow regime

2 1.2 Introduction can be conceptualised in three dimensions: availability is the long term quantum or volume; variability is the short term fluctuation in amplitude, frequency and timing; and connectivity is the spatial pattern of lateral flows across the channel and floodplain (Puckridge et al., 1998; Ward, 1989). These dimensions are interlinked; each dimension may vary as a function of others (Ward, 1989) such that reductions in flow availability can also restrict variability and connectivity. Together, they characterise the spatially and temporally dynamic environmental flow regime that shapes physical habitats, species abundance and distribution, and ecosystem function (Naiman et al., 2008). In regulated rivers, environmental flows are intentionally manipulated using engineered structures to achieve environmental objectives. Dams, regulators and engineered channels are used to actively control the location and timing of delivery of environmental flows. These examples of active ‘watering’ of aquatic ecosystems may be accompanied by restrictive approaches which secure instream flow requirements by reducing diversions (Shiau and Wu, 2010). The concept of environmental flows is well established in scientific literature, but has only recently entered water policy and management dialogue. Translating policy into action has severely lagged due to major barriers in practical implementation of environmental flows. Barriers were identified in a survey of 272 respondents mainly from academic, government and non-government sectors around the world: poor understanding and appreciation of socio-economic costs and benefits; lack of political will; and poorly integrated legal, institutional and monitoring arrangements (Moore, 2004). Many of these concerns were reinforced in a more recent assessment of 22 global case studies: institutional barriers and conflicts of interest, lack of popular support and political will for change that will deliver long term benefits at the expense of short term socio-economic costs, and insufficient resources and capacity to implement effective programs (Le Quesne et al., 2010). The traditional paradigm of managing rivers only for social and economic objectives still underpins these barriers, albeit as a legacy effect. Consequently, environmental flow implementation occurs in only a few rivers globally (Richter, 2010) but it remains largely ad hoc, incremental and opportunistic. Implementing environmental flows at a national or regional level is difficult because of the need for parochial knowledge and effort at a catchment scale. Many obstacles for environmental flow implementation are magnified in large transboundary river systems where comprehensive, holistic assessments are required and environmental and human

3 1. ENVIRONMENTAL FLOWS IN REGULATED RIVERS objectives may vary considerably (Mbaiwa, 2004; Raadgever et al., 2008). Central to the challenge is the lack of comprehensive and systematic assessments supporting integration of environmental flows within traditional management and operations. A pro- liferation of literature exists on environmental flow assessment methodologies, mainly by hydrologists and ecologists (Acreman and Dunbar, 2004; Tharme, 2003), but far fewer studies exist on mechanisms for implementation. Of the latter, most focus on challenges and remedies of isolated aspects of the flow regime: balancing environmental and extractive water availability (see Grafton et al., 2011; Suen and Eheart, 2006); restoring variability through dam operations (see Paredes-Arquiola et al., 2011; Watts et al., 2011); and reconnecting floodplains by removing flow barriers (see Galat et al., 1998; Opperman et al., 2009). My main goals were to examine key challenges and opportunities in implementing environmental flows in large regulated river basins, framed around availability, variability and connectivity, and to build management capacity and tools for environmental flow decisions. Underpinning this research was the need to understand complex interactions among the water rights framework, river regulation, climatic variability and environmental flows. I drew upon scientific knowledge and management practices in two regulated river systems, the Gwydir and Macquarie Rivers in the Murray-Darling Basin (the Basin) in Australia. The Basin is the focus of Australia’s most ambitious commit- ment to rehabilitate a river system (Figs. 1.1a). The Basin covers one seventh of the Australian continent and is governed by five states and a territory. It is often referred to as Australia’s ‘food basket’ providing 40 % of gross national agricultural production (CSIRO, 2008c). Major declines in ecosystem health prompted governments to restore the sustainability of the Basin, primarily through the recovery of environmental flows (MDBA, 2012a). Over 2 000 GL of consumptive water rights have been reallocated for environmental use by national and state governments, however implementation of environmental flows has been slower than expected (Hirji and Davis, 2009). It is critical to reflect on progress in the Basin because of the implications locally and for other rivers with transboundary policy settings.

4 1.3 Global status of environmental ﬂows

(a) (b) QLD

NSW SA ACT VIC

Menindee Lakes

0 500 1,000

Kilometers

Figure 1.1: Boundaries for water management in the Murray-Darling Basin: (a) the Murray-Darling Basin Authority (MDBA) under the Commonwealth government; (b) states and territory (Queensland (QLD), New South Wales (NSW), Australian Capital Territory (ACT), Victoria (VIC) and South Australia (SA)); (c) water resource planning areas (MDBA, 2009); and (d) regional river operators, including Menindee Lakes jointly operated by NSW and the MDBA, and the Murray River (thick line), operated by the MDBA and the states.

1.3 Global status of environmental ﬂows

Environmental flows are now incorporated into high level policy in Australia, South Africa and western United States, and discussed by governments in most industrialised nations including China, Tanzania, Mozambique and the Philippines (Hirji and Davis, 2009; Le Quesne et al., 2010). Broadly, there are two approaches to providing environmental flows which can operate together or in isolation (Shiau and Wu, 2010). Active approaches in regulated rivers occur where partial or entire flow regimes are gener-

5 1. ENVIRONMENTAL FLOWS IN REGULATED RIVERS ated by releasing flows from storage to mimic natural flow regimes. This is known in Australia as environmental watering, involving dams, weirs and floodplain infrastructure to deliver a targeted flow regime. Restrictive approaches are possible in regulated and unregulated rivers, by controlling extractions and protecting flows. For example, low flows are protected with cease-to-pump rules during dry periods (Hirji and Davis, 2009). Active and restrictive approaches are implemented using a variety of instruments such as statutory reform (e.g. Australia (Arthington and Pusey, 2003); South Africa (Hughes and Mallory, 2008; King and Brown, 2006)), judicial processes (e.g. United States (Hirji and Davis, 2009; Le Quesne et al., 2010)), amendments to water resource strategies and river operations (e.g. China, Tanzania, Mozambique, Philippines (Hirji and Davis, 2009)) and multilateral treaties (e.g. southern Africa (Juizo and Liden, 2010); governments of the Great Lakes in the United States (Le Quesne et al., 2010)).

There is enormous variation in the allocation, management and provision of environmental flows globally, because of differences in water rights frameworks and legal instruments governing water rights. In many systems, water rights are in their in- fancy: they are poorly defined or informal and few opportunities exist for managing for critical periods or trading water to meet demands. Specific water rights rarely exist for environmental flows in such systems. In systems with clearly defined water rights, rights to use and control water are usually vested in governments. Water rights may be withdrawn or altered by governments in Australia, Mexico and South Africa, usually with no guarantee of compensation, but in California, Colorado and Chile water rights are protected in perpetuity (Productivity Commission, 2003). Environmental flow in these systems may be a specified as a dedicated water right (i.e. reserve in South Africa) or a pre-existing water right for multiple uses (i.e. general security licences in Australia). Water available for environment and extractive use are often fixed volumes that do not vary from year to year and managed using restrictive approaches, however in Australia, environmental water varies on the basis of water availability due to high variability of rainfall. Such differences have important implications for management of environmental flows around the world.

6 1.4 Water management in the Murray-Darling Basin

1.4 Water management in the Murray-Darling Basin

The Basin is a continuous biophysical unit managed by multiple governments as a transboundary river system (Fig. 1.1). Responsibility for water management lies largely at the state level, though the Commonwealth has far greater capacity to influence water management than in the past (Kildea and Williams, 2010). Control can be traced to the end of the 19th Century when states depended heavily on rivers initially as trade routes and then for irrigation (WCIC, 1971). Reflecting their concerns, the Constitution (1901) was largely silent on water resources and power over rivers remained with the states (Kildea and Williams, 2010). In the following century, water management was highly insular within states, further fragmented by the autonomy granted to regional water managers (Connell, 2007). This led to localised rules and poorly documented water rights systems, and different terminology (Table 1.1; Anderies et al., 2006; Connell, 2007). Management became increasingly differentiated and compartmentalized across the Basin as managers often stayed long term and built strong relationships with local communities (Connell, 2007). These long-established practices became the foundations of water rights and present day local water resource plans (Table 1.1; Fig. 1.1c). Water rights in Australia were initially available to riparian properties with river frontages on a ‘first-come, first-serve’ basis. With expansion of irrigated agriculture and increasing conflict over water resources, statutory rules for water sharing were introduced by state governments (Productivity Commission, 2003). Under current rules, water rights for environmental or consumptive purposes are available under an entitlement (licence, share, reserve). Entitlements provide perpetual or ongoing right to exclusive access to a share of water from a specified resource pool as defined in the relevant water resource plan. Characteristics of entitlements including volume and reliability may vary among jurisdictions (Shi, 2006). The specific quantum of water allocated to water entitlements in a given period is known as an allocation (provision, dividend). Allocations vary from year to year according to water availability and management rules. They are determined by a process known as a resource assessment, undertaken by the water authority each month or as new inflows arrive (BWR, 2011). In this process, water is allocated among different types of uses in a regulated river depending on their volume and priority (Fig. 1.2). First, sufficient volumes are secured for dead storage,

7 1. ENVIRONMENTAL FLOWS IN REGULATED RIVERS

(a) (b) (c) 4 Supplementary Environmental water Delivery 3 Extractive losses Planned Adaptive General security 2 High security DECREASING PRIORITY 1 Basic Domestic Local water rights & stock utility Dead storage Essential supplies INCREASING WATER AVAILABILITY

Figure 1.2: Schematic showing the allocation of water (grey shading) among different types of water uses (boxes) in a regulated river under low (a), medium (b) and high (c) storage volume scenarios. Types of water use (not to scale) are arranged on the y axis in decreasing priority from one to four. Environmental water may be specified as adaptive or planned, depending on whether it has been acquired under entitlement (dotted line indicates variable volume) or specified by management rules, respectively. the minimum volume of water held in an operational storage. Next, water is allocated to basic rights, domestic and stock and local water utility which provide reliable flows for non-commercial or domestic uses. Water is then allocated to high security entitlements which provide reliable flows for commercial purposes, mainly horticulture. The largest proportion of water is allocated proportionally to general security entitlements, planned environmental water and associated delivery losses. These provide access to regulated flows for irrigated agriculture and the environment. Finally, supplementary water access may be announced during opportunistic, unregulated flow events (i.e. not controlled by dams) when all other demands have been met. Allocations are added to an account for each entitlement holder, and volumes are subtracted from the account when a water order is placed by the user. Orders are the volume of water to be delivered to a nominated location at a specific day for use, limited by availability of water in an account and long term extraction limits. Water may be held in accounts from year to year depending on rules in the water resource plans (BWR, 2011). State river operators manage dam releases to ensure water supply is sufficient to meet orders. Resource assessments and river operations are managed regionally, further increasing complexity and inconsistency of water management across the Basin (Fig. 1.1d). There were two occasions in the 20th century where major integration of water

8 1.4 Water management in the Murray-Darling Basin

Table 1.1: Key terminology in this study and equivalent state and territory vocabulary, accompanied by deﬁnitions in the table footnotes (NWC, 2011).

Regiona Terminology Water resource planb Entitlementc Allocationd QLD water resource plan water allocation announced allocation percentage NSW water sharing plan water access licence available water determination ACT water sharing plan water access water allocation entitlement VIC stream ﬂow water diversion seasonal management plan licence determination SA water allocation plan water holding licence water taking licence

a Jurisdictions include Queensland (QLD), New South Wales (NSW), Aus- tralian Capital Territory (ACT), Victoria (VIC) and South Australia (SA). b Defined as the catchment management rules that specify the sharing of water among users and the environment, and additional operating requirements. c Defined as a licence granting exclusive access to a nominal share of water from a specific source. d Defined as the volume of water available under an entitlement.

management occurred in the Basin through intergovernmental agreements. The River Murray Water Agreement (RMWA) was signed in 1914 by the Commonwealth and three rivalling states (NSW, VIC, SA) in the Lower Murray, establishing water sharing rules and coordinated development of storages, locks and weirs in the southern Basin. Although progressive for its time, it ignored the extensive northern Darling system that occasionally provided important and highly variable ﬂows to the Murray. It was incapable of resolving the Basin-wide resource and environmental crisis which emerged in the 1980s (Powell, 2002). The Murray-Darling Basin Agreement was initially an amendment to the Agreement (1987) but eventually superseded the RMWA (1992) to promote coordinated and sustainable management of Basin resources. Queensland became a signatory in 1996 and the Australian Capital Territory (ACT) signed a mem-

9 1. ENVIRONMENTAL FLOWS IN REGULATED RIVERS orandum of understanding in 1998, becoming a full member in 2006 (MDBC, 2007). While the Basin was managed as a whole for the first time, it continued to rely precari- ously on cooperation of all jurisdictions in absence of Commonwealth legislative powers over water. Major water reforms under the National Water Initiative (NWI) in 2004 improved coordination among states and increased unification of governance across Basin (Con- nell, 2011). Introduction of water markets were a key aspect of the reforms. Arguably the world’s most sophisticated, the Australian water market allows voluntary buying and selling of water among public or private stakeholders for consumptive or non- consumptive purposes. Water trading benefits users with seasonal water availability and boosts regional economies by reallocating water to higher value uses (Zaman et al., 2009). Importantly, it allowed governments to acquire water for the environment from willing sellers. Previously, water rights were bundled with land rights, making the acquisition of water for the environment a costly and inflexible exercise. Competitive grants under s96 of the Constitution further provided financial incentives for states to cooperate with the Commonwealth on water reform (Kildea and Williams, 2010). Three years later, in the midst of the Millennium Drought, the Commonwealth departed from this participatory approach and wrested partial control of Basin water management from the States. The Commonwealth relied on legislative powers under paragraphs 51(i), (v), (viii), (xi), (xv), (xx), (xxix) and (xxxix) of the Consti- tution, relating to a broad range of matters including trade, commerce and external affairs (i.e. relevant international agreements such as the Ramsar Convention, and migratory bird agreements CAMBA, JAMBA and ROKAMBA). The resulting Water Act 2007 (Cwlth) was a ‘rescue package’ aimed at addressing the Basin-wide threats of resource insecurity, environmental decline and climate change. It established the Murray-Darling Basin Authority (MDBA), an autonomous body charged with developing a statutory Basin Plan for sustainable planning and management of water resources (MDBA, 2012a). Recovery of water for the environment was central to the Basin Plan, through $3.1 billion to buy back water from consumptive users and $5.8 billion for improving efficiency of irrigation infrastructure which also produced an environmental water dividend. The Act established an independent statutory authority, the Commonwealth Environmental Water Holder, to acquire and manage environmental water entitlements (‘adaptive’ water). These entitlements are tradeable water access

10 1.5 Study area rights that were voluntarily acquired from willing sellers. Long term management of the adaptive environmental water portfolio may include further trading to optimise environmental outcomes. Despite the national water policy direction under the NWI and the proposed Basin Plan, water management remained under state control through legislation and policy (e.g. Queensland (QLD): Water Act 2000, New South Wales (NSW): Water Man- agement Act 2000 ; Victoria (VIC): Water Act 1989 ; South Australia (SA): Natural Resources Management Act 2004 ; Fig. 1.1b). States remain largely responsible for Basin Plan implementation, through water resource plans. These specify the sustainable levels of extraction, the distribution of water among users and the environment, and the volume of water set aside for the environment (‘planned’ water), while ensuring sufficient supply to cover losses to groundwater, evaporation and distributaries. Planned environmental water is committed for environmental purposes according to provisions in water resource plans. Unlike adaptive environmental water, it is not tradeable and cannot be used for consumptive purposes. Managed environmental flows in the Basin are predominantly planned (92.4 %), with small volumes of adaptive and other flows (7.6 %; SEWPAC, 2012b). New generations of water resource plans will need to comply with the Basin Plan, but considerable heterogeneity will remain because they will be prepared according to the relevant state legislation (Gardner et al., 2009). Further, states will remain largely responsible for operational management of water and interpretation of water resource plans.

1.5 Study area

The Gwydir and Macquarie Rivers in NSW are large (Gwydir: 24 947 km2; Macquarie: 73 453 km2) inland river systems in northern Murray-Darling Basin, Australia (Fig. 1.3). They are bound to the east by the Great Dividing Range and to the west by the Barwon-Darling region, separated by the Namoi region and surrounded by the Darling Riverine country. Topography varies from steep terrain to the east, to the central slopes and low relief alluvial plains to the west (Pietsch, 2005). There is a sharp annual precipitation gradient, varying from 850 mm in the mountains to 500 mm (Gwydir) and 350 mm (Macquarie) on the semi-arid plains (CSIRO, 2007, 2008a). Evaporation on the semi-arid plains exceeds precipitation (Powell, 2011). Average surface water

11 1. ENVIRONMENTAL FLOWS IN REGULATED RIVERS

AUSTRALIA Gwydir River System

Gwydir Wetlands Gwydir River

Myall Creek #

Horton River

Halls Creek Macquarie Marshes Copeton Dam

Macquarie-Cudgegong River System

Macquarie River

Talbragar River Cudgegong River Windamere Dam

# #

Little River

# Major storages Bell River Burrendong Dam Rivers & Creeks Wetlands Catchment 0 40 80 160 Murray-Darling Basin Kilometers ´

Figure 1.3: Location of the Gwydir and Macquarie Rivers (northwesterly flow) in the Murray-Darling Basin, Australia, showing major storages, rivers and creeks, major wetlands and catchment area within the Murray-Darling Basin. availability is 782 GL/y in the Gwydir and 1 567 GL/y in the Macquarie (CSIRO, 2007, 2008a). Synoptic weather patterns produce enormously variable flows (Grootemaat, 2008; Puckridge et al., 1998). Upon reaching the large, low relief alluvial fan-plains, main channels may form anabranches, distributaries, depressions and lagoons (Pietsch, 2005; Ralph and Hesse, 2010; Yonge and Hesse, 2009), with most of the flow terminating in floodplain wetlands in all but exceptionally wet years. Highly dynamic inundation patterns support a complex, extensive and dynamic mosaic of floodplain wetlands of international significance (Ramsar, 2012), providing important habitat and breeding grounds for colonial nesting waterbirds (Kingsford and Johnson, 1998) and other water dependent organisms (Brock, 1998; Keyte, 1994; Kingsford, 2000). Between flood

12 1.5 Study area events, flows recede and mobile aquatic organisms retreat into permanent channels and waterholes (Rayner et al., 2009). The Gwydir and Macquarie regions have long histories of human activity. Tradi- tionally, rivers and wetlands were an important resource base and spiritual place for Aboriginal people (DECCW, 2010, 2011). Gradual displacement of Aboriginal people followed European settlement, as pastoralists and small scale irrigators spread inland. Settlers cleared land and modified channels to redirect flows for agriculture (Lloyd, 1988). Construction of public water storages was an important aspect of nation-building agendas in the mid-1900s, paving the expansion of large scale irrigation of the semi- arid floodplains (Connell, 2007). Storages regulate 55 % of total flows in the Gwydir (Copeton: 1976; 1 362 GL) and 70 % of total flows in the Macquarie (Burrendong: 1967; 1 188 GL + flood mitigation storage (475 GL; and Windamere: 1984; 368 GL; Keyte, 1994; Kingsford and Auld, 2005). Unregulated storage spills and tributary flows are opportunistically available for extraction under water entitlements (supplementary). Private off-river storages and extensive irrigation channel networks were developed on floodplains (Steinfeld and Kingsford, 2013; Steinfeld et al., 2013). Today, irrigation (mainly cotton), dryland cropping and grazing are the dominant agricultural industries in the region. Water resource development and abstraction have severely affected the hydrology of the Gwydir and Macquarie Rivers with major impacts on flow-dependent ecosystems. Abstraction and storage operations have reduced annual flows to wetlands by 41 % in the Gwydir and 24 % in the Macquarie (CSIRO, 2007, 2008a). River regulation has increased intervals between large floods and shifted flow seasonality from winter to spring (CSIRO, 2007, 2008a). Anthropogenic flow modifications are likely to be exacerbated by climate change, predicted to reduce average runoff by 2030 (Gwydir: 9 %; Macquarie: 6 %; CSIRO, 2007, 2008a). Hydrological changes have caused severe wetland contraction (Keyte, 1994), decline of biota (Kingsford and Thomas, 1995), reduced flooding (Thomas et al., 2011) and reduced ecosystem health (Kingsford, 2000; Steinfeld and Kingsford, 2013). Following widespread ecological decline in the 1980s, the Gwydir and Macquarie were the focus of government efforts to formally recognise and rehabilitate ecosystem assets and values (e.g. DECCW, 2010, 2011; Keyte, 1994; Ramsar, 2012). A key strategy was recovery of ‘planned’ environmental flows through management rules. In

13 1. ENVIRONMENTAL FLOWS IN REGULATED RIVERS the Gwydir, the planned environmental component is known as the Environmental Contingency Allowance (ECA; 45 GL per unit share up to 90 GL). The ECA supplements natural inundation events and supports waterbirds, fish, threatened species, weed management, and aquatic ecosystem health (NSW Government, 2002). In the Macquarie, the planned environmental component is known as the Wildlife Allocation (WLA, 160 GL). The WLA supports desirable biota and ecological and hydrological processes (variability and connectivity of flooding; NSW Government, 2003). Further ‘adaptive’ volumes of environmental flows were returned to rivers through purchasing water entitlements historically used for extraction (Fig. 1.2). In March 2012, the Commonwealth and NSW governments held 510 GL and 632 GL of moderate reliability entitlements from the regulated supply in the Gwydir and Macquarie, respectively, in addition to smaller volumes of entitlements with higher and lower reliabilities (OEH, 2012b; SEWPAC, 2012a). Ecological objectives guiding the use of this environmental water are specified in the Framework for Determining Commonwealth Environmental Watering Actions (DEWHA, 2009), the MDBA environmental watering plan (MDBA, 2012a) and NSW RiverBank Water Use Plans (DECCW, 2007b). Planned and adaptive water are managed together by state government environmental water managers in consultation with catchment-based environmental water advisory groups (Gwydir: Gwydir Environmental Contingency Allowance Operations Advisory Committee; Macquarie: Environmental Flows Reference Group). Annual watering objectives are developed for different water availability scenarios, which aim to meet ecological outcomes. These focus on the survival and maintenance of species or communities using knowledge of their water requirements (Rogers and Ralph, 2010). These objectives lead to watering actions which must be delivered within operational constraints such as physical capacity of infrastructure, private properties at risk of flooding and water travel times (MDBA, 2012b). Monitoring, evaluation and reporting of ecological responses are undertaken by multiple government agencies, researchers and consultants, however the effectiveness of managed environmental watering compared to natural watering remains to be assessed (Wallace et al., 2011). Environmental flow management remains a promising approach to support the health and function of aquatic ecosystems in regulated rivers, yet it should not be carried out in isolation. While the focus of this research was largely on environmental flows, integrated approaches to natural

14 1.6 Challenges of integrating environmental ﬂows in regulated rivers resource management are essential to maximise the ecological beneﬁts. Complemen- tary strategies may include physical interventions such as removal of in-stream barriers and resnagging rivers, improved farming practices and invasive species control, and improved land use planning and controls. These practices involve diverse stakeholders and require a coherent governance frameworks promoting coordination and collabo- ration across institutions at multiple spatial and temporal scales (Scholz and Stiftel, 2005).

1.6 Challenges of integrating environmental ﬂows in regulated rivers

Management of environmental flows occurs within a social, political and economic context, such that decisions about environmental flows are not made in isolation but are linked with many interacting factors (Fig. 1.4). Key decisions about the management of environmental flows involve determining the location, size and reliability of the environmental reserve, then selecting an appropriate release pattern. These decisions may be influenced by the biophysical setting, climate, management rules, river operations and importantly, social and political will (Fig. 1.4). These decisions are likely to have profound effects on the flow regime, with likely implications for social and ecological systems (Fig. 1.4). Such risks were identified (e.g. MDBA, 2012b) and concerns were raised by communities during consultation on the proposed Basin Plan (MDBA, 2012d), potentially compromising the ability to implement the Plan and restore healthy ecosystems. Significant progress in implementing environmental flows in the Gwydir and Mac- quarie has already resulted in measurable ecosystem responses (Bednarek and Hart, 2005; King et al., 1998; Lind et al., 2007; Mawhinney, 2003; Rayner et al., 2009) but many challenges remain. The challenge to provide adequate water availability, variability and connectivity were of immediate concern because they threatened the ability to provide requisite flows to ailing ecosystems. A final challenge, incorporating scientific knowledge and management capacity, was a requirement for the development of the Basin Plan (SEWPAC, 2007). Other challenges beyond the scope of this work include governance and adaptive management (MDBA, 2012d). Many of the challenges are not unique to the Murray-Darling Basin or Australia; water managers around the world

15 1. ENVIRONMENTAL FLOWS IN REGULATED RIVERS

Key factors influencing decision Decision Effect on flow regime Possible social (S) & ecological (E) consequences Social & political will

Location of Biophysical setting environmental E: Change in species & • Catchment size flow reserve • Geomorphology Availability communities (e.g. extent, • Soils condition, diversity) • Land use • Interceptions Size of E: Effect on habitat, environmental geomorphology & soils flow reserve Climate Variability • Water availability E/S: Constraints to flow delivery • Climate change Reliability of environmental S: Flood risk to downstream Management rules flows properties (e.g. spills) • Water rights framework • Resource assessment Connectivity • Water accounting E/S: Change in allocation • Trade provisions Release pattern reliability (volume & timing)

River operations Broader ecosystem management Flood mitigation • • Habitat connectivity (e.g. fish passage) • Land use planning Infrastructure capacity • • Water quality, sediment & salinity management • Pest control

Figure 1.4: Diagram of the system under investigation, indicating the relationship between management decisions, key inﬂuential factors, consequences for ﬂow regimes, and possible social and ecological consequences. are struggling to provide adequate availability, variability and connectivity to rivers worldwide.

1.6.1 Water Availability

Water available to the environment dictates the range of managed flow regimes that may be achieved: the more environmental flow available, the greater the flexibility to deliver the required regime and adapt as circumstances change. Insufficient water availability is a widespread problem (Vörösmarty et al., 2010). Excess water availability is rare but may be caused by interbasin transfer or significant increases in runoff due to land use change (Davies et al., 1992; Richter, 2010). For example in Australia, flows from the Snowy River are transferred to the Murrumbidgee catchment, using the Tumut River which has experienced significantly augmented flow availability (Page et al., 2005). Water availability is highly variable in space and time and so a key challenge is planning environmental flow strategies given this uncertainty. Currently in Australia, plausible alternative environmental flow strategies are developed based

16 1.6 Challenges of integrating environmental flows in regulated rivers on availability scenarios, leaving specification of environmental watering strategy until sufficient flows are available (Arthington et al., 1999). Elsewhere in the world, flow releases or minimum flows may be scaled according to availability (Flannery et al., 2002; Hughes and Mallory, 2008; Richter, 2010). However, the former approach inefficiently uses resources as only one scenario eventuates, and the latter may not be optimal for available environmental flows. Understanding factors driving environmental water availability can help reduce uncertainty of environmental flow. Water availability is driven by spatially and temporally variable factors, including biophysical (Arthington, 1996; Herron et al., 2002), climatic (Brekke et al., 2009; Raje and Mujumdar, 2010; Vaze et al., 2011; Wurbs et al., 2005) and management (Hughes and Ziervogel, 1998; Judd and McKinney, 2006; Shi, 2006), but their relative influence on environmental flow availability is unknown. Furthermore, most studies focus on single catchments, or examine overall water availability in relation to biophysical variables, without assessing the management drivers that influence availability of environmental flows. Given these examples, there is a clear need to understand availability and drivers, and how they vary among river systems, to enable effective strategic planning and assessment. There may also be a legislative mandate, such as the Basin Plan, which stipulates the need to determine the expected quantities of environmental flow (resource availability) in order to determine Basin-wide annual watering priorities (MDBA, 2012a).

1.6.2 Variability

Environmental flow releases are usually more variable than flows released for extractive use (Naiman et al., 2008), but the potential to improve variability in regulated rivers is rarely quantified. Physical and management constraints are key factors affecting the variability of flows. Physical constraints include storage capacity, outlet capacity, channel geometry and flooding of riparian land (MDBA, 2012b). Management constraints include restrictions placed on the volume in storage and release limits. Constraints can be modified through physical adjustment or reoperation (Hughes and Mallory, 2008; Richter and Thomas, 2007; Yin et al., 2011), but this is usually expensive and bureau- cratically difficult. Further, socio-economic impacts of increasing the variability of flows in a regulated river should be assessed as this is an important but seldom studied aspect (Moore, 2004). Such impacts may include: water availability, which drive ecological and economic productivity; storage spills, which can be environmentally beneficial but

17 1. ENVIRONMENTAL FLOWS IN REGULATED RIVERS may ﬂood downstream public infrastructure (e.g. road, bridges), private property and cultural heritage; and evaporation losses which reduce regulated water availability for irrigation or other users.

1.6.3 Connectivity

Extensive networks of floodplain earthworks (levees, channels, off-river storages, tanks) are often used to convey and store water on floodplains. They can intercept environmental flows and other surface water flowing laterally across floodplains, spreading it across pasture, channelling it into storage or blocking it from reaching target ecosystems (Steinfeld and Kingsford, 2013). Earthworks affect the mortality of flow- dependent vegetation (Steinfeld and Kingsford, 2013) and also affect flood risk (Yin and Li, 2001), reduce temporary flood storage capacity and cause flow bottlenecks. Despite the prevalence of earthworks, their effects on floodplain hydrology at multiple spatial and temporal scales is poorly known, including causal mechanisms, lag effects, non-linearities and compounded disturbances in regulated and unregulated rivers. Knowledge of earthwork distribution for effective management of environmental flows is critical yet often ignored. However, there is a lack of information of the distribution of earthworks. A critical first step is to improve large scale assessment of earthwork distribution. Detecting earthworks is technically challenging because, unlike dams, they are small, ubiquitous structures, varied in morphology and distributed across large regions of public and private land. Few organizations hold comprehensive spatial datasets of earthworks due to the labour and financial investment required. Despite this, increasing cumulative impacts of earthworks can reduce water security, increase flood risk and degrade freshwater ecosystems, particularly on floodplains. There is a clear imperative for developing high quality, cost-effective techniques for generating accurate, inexpensive spatial datasets of earthworks.

1.6.4 Scientiﬁc knowledge for best practice management

Water managers require information about the risks to environmental flow delivery across multiple systems and through time. Scientific tools are an important source of quantitative data for systematic assessment and scenario-testing, allowing identification and prioritisation of risks at regional scales. However software for environmental

18 1.7 Research objectives

flow assessment does not incorporate management rules (e.g. Marsh and Pickett, 2009; Mathews and Richter, 2007). In the absence of decision support tools, environmental flow managers draw from expert advice (i.e. river operators and water managers) which is an important but time intensive approach. There is a need to build capacity for environmental flow managers to scope watering options, and assess and negotiate management challenges inhibiting delivery. This requires a quantitative user-friendly simulation tool which explicitly models environmental flows and allows flexible manipulation of environmental flow management, while capturing the dynamics and uncertainties characterising complex river systems, subject to highly variable climatic influences. This can support systematic, quantitative and transparent approach to environmental flow integration, efficient allocation of effort and resources at regional and national scales, and importantly, build capacity to overcome key challenges and achieve management objectives.

1.7 Research objectives

This thesis addresses four objectives of global signiﬁcance to help resolve these urgent and priority needs, structured around water availability, variability, connectivity and provision of evidence for decision making:

1. To identify tools which support complex environmental ﬂow management decisions within the current water management framework of the Murray-Darling Basin;

2. To identify the eﬀects of climate, landscape and management rules on the availability of environmental ﬂows;

3. To identify the best environmental ﬂow release options, and risks for the environment and third parties (e.g. irrigators); and

4. To determine the most cost effective physical structures affecting flow connectivity in floodplain ecosystems.

19 1. ENVIRONMENTAL FLOWS IN REGULATED RIVERS

1.8 Research approach

My research was designed to address these four objectives, and illustrate how evidenced- based decision making should underpin effective environmental flow management. The first major step was the development of the eWASH tool, including calibration and validation. From this model, quantitative outputs were generated as the basis of a sensitivity analysis of water availability and scenario-testing of environmental flow regimes. These hydrological analyses were complemented by a spatial analysis where techniques for detecting floodplain earthworks were compared. A final step was to draw these analyses together to articulate the major research contribution. This process was reflected in structure of the thesis, which has been divided into six chapters. Chapter 2 outlines the development of a decision support tool for environmental flow management in regulated rivers. The objective was to build a long term daily hydrological simulation model which allowed flexible specification of environmental flow availability and storage release strategies. The calibrated and validated tool showed accurate representation of hydrology and management in the Gwydir and Macquarie Rivers. Chapter 3 examines drivers of availability of environmental water. The objective was to rank the relative importance of landscape, climatic and management drivers on environmental water availability across multiple rivers. Results of sensitivity analyses suggested that landscape factors set a template governing environmental water availability, management drivers influenced availability beyond natural variability and climate played a relatively small role. This has important implications for environmental water management and trading of environmental water. Chapter 4 investigates the challenges implementing environmental flows in regulated rivers. The objectives were to assess constraints restricting the delivery of the required environmental flow regime, and evaluate impacts of implementation on stakeholders, particularly land and water holders. Physical and institutional constraints restricted environmental flow delivery, with variable impacts on flood risk to downstream land holders and storage loss but little effect on water availability for multiple users. Chapter 5 examines techniques to identify floodplain barriers affecting environmental flow delivery to vegetation in wetlands and floodplains. The objective was to compare the accuracy and effectiveness of semi-automated and traditional geographic

20 1.8 Research approach information systems techniques for locating and classifying floodplain barriers. The best technique used three-dimensional imagery and showed potential for accurate, rapid detection across large scales. Chapter 6 summarises opportunities for overcoming challenges in providing sufficient availability, variability and connectivity of flows. The objectives were to examine fragmented water management and explore the effects on environmental water implementation at a Basin scale. Five recommendations are proposed to build on existing strengths and consolidate water management, promoting a cohesive framework supporting environmental flow implementation at Basin scales.

21 1. ENVIRONMENTAL FLOWS IN REGULATED RIVERS

22 2

Simulating environmental ﬂow availability in regulated rivers: a decision support tool

2.1 Abstract

Environmental flows provide the vital frequency, magnitude and duration of flows to sustain aquatic ecosystems, requirements which differ vastly from conventional water demands for extractive use. With increasing recognition of environmental flows in plans and policies worldwide, governments require decision support tools to effectively implement and manage these flows in regulated river systems where complex interactions exist between ecological requirements, water availability, infrastructure and management rules. We developed the Environmental Water Allocation Simulator with Hydrology (eWASH), a fast, flexible and user-friendly scenario-based hydrological modelling tool supporting environmental flow management decisions for single- or multi-reservoir systems. Environmental water demands and management rules are easily specified via the graphical user interface, which includes a batch processing function for stochastic assessment. We calibrated eWASH for the regulated Gwydir and Macquarie Rivers of Australia’s Murray-Darling Basin. Modelled annual and monthly water allocations exhibited Nash-Sutcliffe efficiencies of 0.92 and 0.55 for the Gwydir and 0.89 and 0.72 for the Macquarie catchments respectively, when assessed in validation mode. We illustrate the role of eWASH in environmental water management applications to show

23 2. SIMULATING ENVIRONMENTAL FLOW AVAILABILITY how ﬂexible and user-friendly tools can promote strategic and systematic assessments of environmental ﬂow decision making in complex systems.

2.2 Introduction

Rivers are complex natural systems sometimes extending for thousands of kilometres where flow regimes are governed by complex catchment climate and land use processes. Humans have regulated most of the large rivers of the world (Nilsson et al., 2005) with dams which capture, store and release water for human uses (e.g. irrigation, hydroelectricity generation). This fundamentally alters natural flows patterns affecting dependent ecosystems. Much of this development has contributed to a global decline of aquatic biodiversity (Baron et al., 2002; Millennium Ecosystem Assessment, 2005). In recognition of these problems, governments worldwide have protected flows for freshwater ecosystem health through statutory reform (e.g. Australia (Arthington and Pusey, 2003); South Africa (Hughes and Mallory, 2008; King and Brown, 2006)), judicial processes (e.g. United States (Hirji and Davis, 2009; Le Quesne et al., 2010)), amendments to water resource strategies and river operations (e.g. China, Tanzania, Mozambique, Philippines (Hirji and Davis, 2009)) and multilateral treaties (e.g. southern Africa (Juizo and Liden, 2010); governments of the Great Lakes in the United States (Le Quesne et al., 2010)). Environmental flow refers to the quality and quantity of flows required to sustain aquatic ecosystems and their services to humans (Bris- bane Declaration, 2007; Ward, 1989). As environmental flows are often regulated by large storages, they require management to ensure releases provide the quantity and variability dimensions that mimic natural flow regimes. Determining the optimal release of water from storage is a complex and inherently risky decision that depends upon many factors, particularly the availability of environmental flow. Environmental flow availability refers to the volume of water in a river system that may be managed exclusively for ecological outcomes. Environmental flows may be a fixed volume each year, but in hydrologically variable systems such as Aus- tralia, volumes are uncertain and vary as a function of water availability. Water scarcity limits the range of possible release options to small discharges, while water abundance generally provides for a wider range of release options. Only when there is knowledge and predictability about the quantum of environmental flow can managers attempt to

24 2.2 Introduction determine appropriate environmental flow releases and predict ecological consequences. Current management involves establishing an environmental flow reserve which is delivered to meet environmental requirements, which are then monitored within an adaptive management framework (Dyson et al., 2003; Richter et al., 2006). Such environmental flow assessments link the required flow regime to ecosystem health and function but without knowledge of variability, options for delivery remain uncertain. Ultimately, environmental flow availability in a particular year is dependent on a range of factors including climate, dam operations and other users. These can vary at different spatial scales from the river to an entire basin, often required for reporting and management. Probabilistic knowledge of water availability across multiple river systems informs strategic management of environmental reserves at a basin scale. Managing the storage and release of environmental flow over multiple years also requires understanding of long term reliability and variability. For example, estimates of availability over a series of years can inform multi-year decision making of whether to release water each year or retain it to build the volume available for the following year. Knowl- edge of availability can also guide environmental monitoring, providing projections of environmental flows. There are two major challenges for management of regulated environmental flows: estimating availability and determining release strategies. Estimating availability of environmental flow involves first estimating total physical availability then estimating the environmental component of flows legally available. This is a major challenge given it is driven by complex spatially and temporally variable factors including climate (Brekke et al., 2009; CSIRO, 2008b; Raje and Mujumdar, 2010; Vaze et al., 2011; Wurbs et al., 2005), meteorology (Hughes and Mallory, 2008; Kaczmarek and Krasuski, 1991), land cover (Arthington, 1996; Herron et al., 2002) and management factors (Hirji and Davis, 2009). Variable effects of climate and meteorological factors are compounded by climate change which increases water resources stresses in regions where runoff decreases, but may cause increased runoff in other regions (Arnell, 2004). Studies examining the impacts of land use, land cover change and climate change on catchment water availability suggest that afforestation can reduce streamflow substantially (Zhang et al., 2003). Capacity of physical structures such as storage and infrastructure affect the volume of flow that can be captured and regulated for use. Management factors are intangible human interventions such as the water rights framework, water accounting

25 2. SIMULATING ENVIRONMENTAL FLOW AVAILABILITY rules and water allocation process which affect demand patterns and water availability for users (McMahon and Adeloye, 2005). Estimating availability is not simply about supply, release of environmental flows is also critically important as feedback loops exist in confined storage systems which may alter storage dynamics and affect future availability (Hughes and Ziervogel, 1998; Judd and McKinney, 2006; Shi, 2006). There are different approaches to this complexity. Scenario approaches predict environmental flow availability under different climates but generally ignore or significantly simplify release strategies (CSIRO, 2010; Hughes and Ziervogel, 1998). For example, release strategies were quantified by three simple abstraction scenarios in the River Wylye, United Kingdom, from no abstraction to full abstraction (Acreman and Dun- bar, 2004). Other approaches rely on first identifying availability which then determine release strategies availability (Hughes and Mallory, 2008). These approaches often overly simplify the uncertainty, potentially reducing the accuracy of information used to determine environmental flow availability. There is a need to develop tools that allow this complexity to be quantified and resolved so that environmental flow managers can explore different scenarios. Assessment tools such as Hydrologic Alteration Software (Mathews and Richter, 2007) and EcoPredictor (Marsh and Pickett, 2009) include some of this complexity but do not incorporate management rules or interactions between management of releases and availability (e.g. infrastructure). There is considerable hydrological modelling software available to support water management decisions in regulated rivers, including HEC-5 (USACE, 1998), MIKE SHE (Graham and Butts, 2005), WEAP21 (Juizo and Liden, 2010), WRAP (Wurbs, 2005), REALM (DSE, 2009), IQQM (Simons et al., 1996) and Source (Welsh et al., 2013). Such models with spatially distributed parameters require large amounts of data to calibrate. Furthermore, these models usually have fixed demands and lump environmental flows with extractive water, ignoring different release patterns. There are other water allocation models which link science and decision making (e.g. Water Allocation Decision Support System (Letcher, 2005), Economical Reallocating Water Model (Elmahdi et al., 2007) and SAHRA Integrated Modelling (Liu et al., 2008)), informing trade-offs in water planning, management and use (Kendy et al., 2012) but these do not include environmental flow. There is a need for an effective flexible decision support tool, allowing environmental flow managers to develop scenarios for environmental flows, accounting for availability and releases.

26 2.3 Methods

Effective management of environmental flows is critical to achieve catchment and basin-wide ecological objectives and justify investment (MDBA, 2012a; NSW Govern- ment, 2002, 2003; SEWPAC, 2012c). We describe the development of the Environmen- tal Water Allocation Simulator with Hydrology (eWASH), a scenario-based simulation tool to manage environmental water. eWASH supports strategic and transparent water management decisions by explicitly modelling the availability and release of environmental flows. We applied eWASH to two regulated rivers in the Gwydir and the Mac- quarie Rivers of Australia’s Murray-Darling Basin (the Basin), where unprecedented volumes of environmental flows are available to rehabilitate degraded Ramsar wetlands. The conceptual modelling framework is transferrable to regulated river system where environmental flows are held in storage and released to meet environmental outcomes, while precise management rules need to be tailored according to the region.

2.3 Methods

2.3.1 Water management in the Murray-Darling Basin

Australian rivers are among the most variable in the world (Puckridge et al., 1998). Consequently, storages hold water for critical dry periods, sometimes several years, to meet agricultural and human needs. Water and river infrastructure in the Basin is managed by governments, a legacy of the federation period (1901) when rivers were trade routes as well as a means of water supply. State agencies are primarily responsible for water legislation, planning and implementation; they create water plans, water licencing and trading frameworks to regulate water use. Environmental ﬂows intersect with all three aspects. Water plans (or water resource plans, water sharing plans) specify most rules and regulations for sharing and managing water resources in a river. Publicly owned corporations and private water utilities usually operate storages and supply the water according to these plans. Chronic water scarcity and declining ecosystem health became apparent in the 1990s, prompting Australian government involvement in management of the Basins water resources through multilateral agreements with states (Connell, 2007; Kildea and Williams, 2010). The Australian government seized further control over water resources through the Water Act 2007 (Cwlth). This legislation spawned the Basin Plan promoting recovery of environmental ﬂow from consumptive use, supported by a

27 2. SIMULATING ENVIRONMENTAL FLOW AVAILABILITY

10 billion dollar investment (MDBA, 2012a). States remain in control of river operations and environmental ﬂow delivery.

2.3.2 Gwydir and Macquarie Rivers

The Gwydir (catchment area: 26 090 km2) and the Macquarie (74 000 km2) Rivers are tributaries of the Darling River in the Murray-Darling Basin, sharing many biophysical similarities (Fig. 2.1). The rivers ﬂow from the Great Dividing Range in well

AUSTRALIA Gwydir River System

Gwydir Wetlands Gwydir River ! !

Myall !Creek ! ! ! ! # ! ! ! ! Horton River

Halls Creek Macquarie Marshes Copeton Dam !

Macquarie-Cudgegong River System

Macquarie River

! !

Talbragar River Cudgegong River ! Windamere Dam

! # ! # ! # Major storages Little River ! ! ! ! Rainfall stations ! Bell River Burrendong Dam ! Rivers & Creeks Wetlands ! ! ! Catchment 0 40 80 160 ! Murray-Darling Basin Kilometers ´

Figure 2.1: Locations of the Gwydir and Macquarie river systems in southeastern Aus- tralia in the Murray-Darling Basin, showing location of storages, main channels, downstream tributaries and wetlands, and gauges selected for generating rainfall and evaporation. deﬁned channels that spread out onto alluvial semi-arid ﬂoodplains, where potential evapotranspiration (Gwydir: 1 750 mm/y; Macquarie: 2 000 mm/y) exceeds average annual rainfall (Gwydir: 450 mm/y; Macquarie: 300 to 400 mm/y; ANRA, 2009). The

28 2.3 Methods

Table 2.1: Properties of major water storages on the Gwydir (Copeton) and Macquarie (Windamere, Burrendong) Rivers.

Property Built Storage Dead Spillway Outlet capacity storage capacity capacity (GL) (GL) (GL/d) (GL/d) Copeton 1976 1361.72 18.49 1280.0 10.85 Windamere 1984 368.12 1.13 430.0 2.333 Burrendong 1967 1188a 33.73 1199.0 8.2

a Full supply limit. Additional storage capacity (475 GL) is available for ﬂood mitigation (Flood Mitigation Zone).

rivers break down into anabranching floodplain wetlands providing feeding and breeding habitat for flow-dependent biota (Fig. 2.1). Highly variable flow regimes are now almost entirely regulated by major storages (Gwydir: Copeton (93 % of inflows); Mac- quarie: Windamere (94 %) and Burrendong (91 %)) and minor storages (Fig. 2.1 and Table 2.1; CSIRO, 2007, 2008a). Water resource development and extraction have caused wetland contraction and major declines in abundance and health of aquatic biota (Kingsford and Thomas, 1995; Mawhinney, 2003; Thomas et al., 2011). Storages mitigate floods (Macquarie only) and regulate water for towns and industries, including irrigated agriculture. Reaches of the Gwydir and Macquarie Rivers affected by river regulation extend 700 km and 850 km downstream of storages (DIPNR, 2004; Karanja et al., 2008), providing managers with full or partial control over the releases of flow, including environmental flow. Major unregulated tributaries provide flows downstream of storages (Fig. 2.1) including the Horton (gauging station 418015), Myall (418017) and Halls (418025) in the Gwydir and the Bell (421018) and Talbragar in the Macquarie (421042; Fig. 2.1; NOW, 2012b).

2.3.3 Water management

The Gwydir and Macquarie Rivers are managed by State government of NSW (policy and management by NSW Oﬃce of Water, and operation by State Water a public corporation). The NSW Water Management Act 2000 (WMA) and the Gwydir and

29 2. SIMULATING ENVIRONMENTAL FLOW AVAILABILITY

Macquarie-Cudgegong Water Sharing Plans specify water entitlements, environmental ﬂows and management of the river and storages (NSW Government, 2002, 2003).

2.3.3.1 Water rights

Water entitlements grant use of water from the river. The WMA speciﬁes the purpose and priority of water entitlements (Table 2.2; Fig. 2.2) and water plans specify their

Figure 2.2: Schematic showing the allocation of water (grey shading) among different types of water uses (boxes) in a regulated river under low (a), medium (b) and high (c) storage volume scenarios. Types of water use (not to scale) are arranged on the y axis in decreasing priority from one to four. Environmental water may be specified as adaptive or planned, depending on whether it has been acquired under entitlement (dotted line indicates variable volume) or specified by management rules, respectively. quantity and distribution in each system (Table 2.3). Broadly, they consist of basic rights for access to small volumes for specific, non-commercial purposes, and entitlements which provide shares of surface water for commercial use, including public water supply and irrigation. Basic rights cannot be traded (Table 2.2). Domestic and stock rights are a legacy of the English common law riparian rights doctrine (superseded) which granted water rights to properties on river frontages on a ‘first-come first-serve’ basis (Productivity Commission, 2003). Native title rights provide water for indigenous or cultural use. Harvestable use allows landholders to collect rainwater or runoff on their property for storage in farm dams. All basic rights have equal high priority (Table 2.2). Water entitlements allow access to a share of surface water, which is subject to availability, storage volume and annual limits set by the water plan. Entitlements are

30 2.3 Methods tradeable, analogous to shares in a company; they have a nominal value related to the number of megalitres held and the type of entitlement (purpose and priority). A few specific entitlements provide reliable access to water for intensive animal stocking (domestic and stock; priority 1, i.e. equal to basic rights) and town water supply (local water utility; priority 1). These entitlements provide water for essential human and animal needs and are critical to maintain during drought periods. Most other entitlements provide access to water for general purposes (Table 2.3). There are relatively few high security entitlements, with a guaranteed access to stored water in most years (priority 2). The bulk of water is tied up as general security entitlements, supplied from stored water and occasionally from unregulated tributary inflows (priority 3). These are most suited to irrigated agriculture or the environment, because annual availability is dependent on climate and cannot be guaranteed each year. Finally supplementary entitlements provide users with opportunistic access to unregulated flows (i.e. storage spill, downstream tributary flows; priority 4). In the Gwydir River, only 50 % of unregulated flows may be allocated (NSW Government, 2002). In the Macquarie, 100 % of unregulated flows may be allocated but supplementary water may only be announced when flows downstream at Warren gauge exceed 5 GL/d.

31 2. SIMULATING ENVIRONMENTAL FLOW AVAILABILITY New South Wales Town water supply Intensive commercial stocking (i.e. Purpose Domestic consumption (e.g. cooking, washing, gardens), stock Personal, domestic and non-commercial use Any purpose including stockdomestic and piggeries, feedlots, battery poultry) Irrigation, environment Environmental Evaporation, evapotranspiration, seepage, floodplain harvesting, unauthorized extraction channels and end of system flows n/a Rights to collect a fraction of runoff for storage Water for cultural and spiritual use by adjacent to rivers or overlying aquifers. in dams of limited capacity. indigenous Australians. spills, downstream tributary flows). Access to water forhealth. fundamental ecosystem storage outlet and extraction point. Water for environmental or operational benefit. Requirements for storage outlet, Definition and the Gwydir and Macquarie Water Sharing Plans (NSW Government, 2002, 2003). (1) Water for owners or occupiers of properties (1) High reliability access for specific use. (1) a a a (n/a) Water to meet the delivery losses between the (2) Highly reliable access for unspecified use. Horticulture, industry, environment (n/a) Water in the inactive storage zone. a y) (1) a (1) a a a Priority, definition and purpose of surface water rights and operational provisisons according to the ts (n/a) supply to be maintained through a repeat of the worst period of low inflows on historical record. 2.2: a Domestic and stock Native title Harvestable Domestic and stock Local water utility High security General security (3)Supplementary (4) Moderately reliable access for unspecified Opportunistic use. unregulated access (i.e. storage Irrigation, environment Planned environmental water (3) Delivery losses Replenishment, minimum flow Dead storage Table Water Management Act 2000 Essential Category (priorit Basic righ a Entitlements Planning and operational requirements

32 2.3 Methods

2.3.3.2 Environmental ﬂow

Environmental flow entitlements are called ‘adaptive’ environmental water to reflect volumes of water that can be managed (Table 2.4). In Australia, environmental flow does not include water required to ensure that the river has base flow and can deliver water to users, this is usually met by operational requirements. In addition to adaptive environmental water, water plans set aside planned environmental water (Gwydir: 90 GL of entitlements; Macquarie: 160 GL) with same priority as general security entitlements. Adaptive and planned environmental flow availability varies with water availability and river management. Given this uncertainty and the necessity to manage this environmental flow, the government seeks advice from stakeholders (Gwydir: Gwydir Environmental Contingency Allowance Operations Advisory Committee; Mac- quarie: Environmental Flows Reference Group) to manage environmental flows each year to meet environmental objectives (DECCW, 2007a,b; DEWHA, 2009; NSW Gov- ernment, 2002, 2003). This requires decisions to release and deliver environmental flow, governed by operational constraints such as physical capacity, potential flooding of private riparian properties and travel times (MDBA, 2012b). In the Macquarie River, three fifths of planned environmental water must be released immediately after inflow as if no storage was present (NSW Government, 2003). The environment also receives unregulated floods, water sometimes not used by irrigators (Chong and Ladson, 2003).

2.3.3.3 River operations

Planned environmental water needs to meet river operating rules (specified in Water Sharing Plans (Table 2.2; NSW Government, 2002), including delivery losses (e.g. evaporation, evapotranspiration, infiltration into groundwater systems). In addition, river operations compensate for water removed from the floodplain and unauthorized extraction. These delivery losses are 30 % of allocated water. There are other rules relating to fish passage or minimum flows required in the channel at various points (minimum flows: 21 GL (Gwydir), 53 GL (Macquarie)). Minimum flow requirements (Gwydir only) are the lesser of 0.5 GL/d at Yarraman or the sum of flows from the major tributaries and storage spills. Operators also account for water stored below the outlet of the dam (dead storage) that cannot be drained by gravity.

33 2. SIMULATING ENVIRONMENTAL FLOW AVAILABILITY

Table 2.3: Quantities and proportions of diﬀerent water rights and environmental ﬂow provisions in the Gwydir and Macquarie Rivers based on respective water resource plans (NSW Government, 2002, 2003). Quantity is the number of available units and proportion shows their relative distribution. Water rights include basic rights (domestic and stock) and entitlements (domestic and stock, local water utility, high security, general security and supplementary).

Category Quantitya Proportion (%) Gwydir Macquarie Gwydir Macquarie Domestic and stock (right)b 6 000 1 200 0.8 0.1 Domestic and stock (entitlement)b 4 245 14 265 0.6 1.6 Local water utilityb 3 836 22 681 0.5 2.5 High securityb 19 293 19 419 2.5 2.2 General security 509 500 (1.5) 632 428 66.5 70.3 Supplementaryc 178 000 50 000 23.2 5.6 Planned environmental water 45 000 (2) 160 000 d 5.9 17.8 Total 765 874 899 993 100 100

a Each unit is equivalent to one megalitre (ML) of water unless a multiple is speciﬁed (parentheses) b Considered as high priority ‘essential supplies’ in addition to planning and operational provisions (annual estimate is 111 000 ML (Gwydir) and 170 000 ML (Macquarie)) c Supplementary allocations are a proportion (Gwydir: 50 %, Macquarie: 100 %) of uncontrolled ﬂows from storage spill and downstream tributaries that exceed a threshold (Gwydir: 500 ML/d at Yarraman; Macquarie: 5 000 ML/d at Warren) above other orders and requirements, up to the entitlement limit. d Available at Burrendong Dam. An additional 10 000 ML is available at Windamere Dam.

Table 2.4: High security, general security and supplementary entitlements providing adaptive environmental water, held by Commonwealth (Cwlth) and New South Wales (NSW) governments in March 2012 (OEH, 2012b; SEWPAC, 2012a).

Entitlement Gwydir Macquarie Cwlth NSW Total Cwlth NSW Total High security 375 0 375 0 0 0 General security 89 525 17 092 106 617 90 253 48 419 138 672 Supplementary 19 100 441 19 541 1 888 1 452 3 340

34 2.3 Methods

There are also operating rules for mitigating floods and transferring water between storages in the Macquarie River (Table 2.5; NSW Government, 2003). The former involves releasing different amount of daily flow from the dam, allocated as supplementary water, when water levels exceed the flood mitigation level of Burrendong Dam (100%; Table 2.1) by controlled releases of 5 GL/d (100 - 120%), 12 GL/d (120 - 130%) and 18 GL/d (130 - 140%). At the flood mitigation storage level, full volumes are announced (i.e. 100 % of entitlements), and water accumulated in accounts from the previous year (carryover) is eliminated. Burrendong Dam is also filled when it is less than 160.38 GL and there is sufficient water in upstream Windamere dam (90 GL - 130 GL; Fig. 2.5). This transfer cannot exceed 13.3 GL over 14 days to ensure protection of ecologically sensitive platypus habitat of the Cudgegong River (Fig. 2.1).

35 2. SIMULATING ENVIRONMENTAL FLOW AVAILABILITY / d (100 - . < 160 . 38 GL. b Controlled releases of 5 GL Triggered when Windamere is between Macquarie 120 % of FSL), 12and GL / d 18 (120 GL / d - (130 130 - %) 140 %). 90 GL and 130 GLvolume and Burrendong Includes storage volume andanticipated minimum inflows based onrecords historical (credit). Dynamic: Optimises AWD overfuture a period within constraintsresource of pool the and futureUses requirements. variable monthly estimates future requirements. Account unlimited. AWD-limited (100 %) 100 % of entitlement value.penalties Forfeitting apply. . AWD a Gwydir Includes only storage volume (debit) No FMZ rules apply. No BWT rule applies Static: Simple spreadsheet differencing the resource poolfuture and requirements. Assumes fixed future requirements. Account-limited (general security: 150 %; others: 100 %) unlimited. 150 % of entitlement value.forfeitting No of carryover. Key differences in management rules between the Gwydir and Macquarie Rivers. 2.5: Table t of entitlement volume. Orders are also limited to 125 % in 1 year and 300 % in 3 years. operations Resource pool, defines thewater sources available of for allocation. Flood mitigation zone (FMZ) operations, establishing flow release rules when storage volumesupply exceeds level full (FSL). Bulk water transfer (BWT),transfer allowing of water todownstream replenish storage. a Balance sheet, specifies thecalculating process the for Available Water Determination (AWD). Volumetric limit, restricting availability to promote equitable use of storage capacity Carryover rules. Apply tosecurity general and planned environmental water only. Limit applies to AWDs for water years (1 July - 30 June). Limit may be exceeded during high flows, under a rule which credits full Percen Rule River a Resource assessment b accounts when Burrendong Dam enters FMZ more than once during a water year. Water accounting

36 2.3 Methods

2.3.3.4 Resource assessment

A resource assessments is the process used to determine resource availability and allocate water among users. Assessments occur at the beginning of July and whenever sufficient inflows occur. The first step is calculation of total volume in dams (resource pool) and future requirements (e.g. essential supplies (Table 2.2), unused water from previous accounting period) using a balance sheet. These are limited by long term extraction limits, specified in water plans (NSW Government, 2002, 2003) and based on average historical consumption. For example, 34 % (392 GL) and 27 % (391 GL) of the long term average annual inflow is available for extraction in the Gwydir and the Macquarie, respectively (ANRA, 2009; CSIRO, 2008a), with further reductions likely under the Basin Plan (MDBA, 2012a). Available water is distributed among uses, according to their priorities from one to four (Fig. 2.2; Table 2.2). Water is first allocated for essential human requirements and non-commercial needs, consisting of basic rights, domestic and stock entitlements and local water utility entitlements (priority 1; Fig. 2.2). Reliable volumes of high security entitlements (priority 2) for commercial use, mainly horticulture, are then allocated. High priority uses (priorities 1 and 2) are known as ‘essential supplies’ (a in Table 2.2; Fig. 2.2) because they are generally maintained through a repeat of the worst period of low inflows on historical record. After essential supplies are met, water is allocated to general security entitlements and planned environmental water (priority 3) ensuring sufficient operational requirements for delivery losses (Fig. 2.2). Supplementary access (priority 4) is available only when higher priority requirements are satisfied (Table 2.2). Priorities may also change during droughts (NSW Government, 2000). After resource availability is determined, the NSW government announces the volume of water available as a fraction of general security entitlements (known as Available Water Determination; AWD). The volume allocated to an individual user is known as an allocation, calculated by multiplying the AWD by the number of entitlements held. The environmental allocation determines the amount of adaptive environmental flow available, most of which is defined as general security entitlements. Water allocation methods differ between the Gwydir and Macquarie Rivers (see 2.A, 2.B; BWR, 2011), mainly due to differential interpretations by regional river operators. In the Gwydir River, there is a simple water balance spreadsheet (Static balance sheet; Table 2.5)

37 2. SIMULATING ENVIRONMENTAL FLOW AVAILABILITY which calculates available water as the difference between what is held in storage and future requirements over the year. Contrastingly, there is a dynamic balance sheet (Dynamic balance sheet; Table 2.5) used for determining resource availability in the Macquarie River which optimises future availability. Water managers include the timing of inflows and withdrawals during the year, ensuring dams do not empty over the assessment period. The second key difference relates to the volume of stored water: debit (Gwydir) and credit (Macquarie) approach. In the Gwydir River, this calculation is based on actual volume held in the dam (Debit resource pool; Table 2.5) while, in the Macquarie River, this includes actual volume plus minimum anticipated inflows based on historical inflow data (Credit resource pool; Table 2.5). The Gwydir resource pool is a more conservative estimate which only allocates water that has arrived into storage at the assessment date.

2.3.3.5 Water accounting

Entitlement holders comply with accounting rules generally designed to promote rea- sonable and equitable use of storage capacity and speciﬁed in water plans (NSW Gov- ernment, 2002, 2003). There are limits to how long licenced volumes of water can be retained in accounts (carryover). Carryover is not permitted for essential supplies (priority 1), with unused water returned for assessment of resource availability at the end of the water year. Limited carryover is permitted for general security and planned environmental water but these vary with the river: 150 % of entitlements (Gwydir) and (100 % of entitlements; Macquarie). General security account volume is limited in the Gwydir, and AWD limits apply in the Macquarie (Table 2.5). Carryover water is forfeited in the Macquarie, when Burrendong exceeds 100 % for all or part of the past month according to the formula:

CS · D FS = min(CS, P ) CS where S denotes storage volume, C denotes carryover, and D denotes total inﬂows (for Burrendong accounts) or spills (for Windamere accounts) in the past month when Burrendong exceeds its ﬂood mitigation level.

38 2.3 Methods

2.3.4 Modelling environmental ﬂow availability eWASH is structured by interlinked sub-models representing management, demand and storage behavior, allowing estimation of available environmental ﬂow each year (Fig. 2.3). Each sub-model aligned with the major sources of information required to iden-

Management Resource Water rights Water accounting River operations assessment Essential supplies Volumetric limits Delivery losses Resource pool Planned Duration limits Replenishment & environmental (carryover) minimum flows Macq. Gwydir Adaptive rules rules Dead storage environmental Available water General security Flood mitigation (extractive) Bulk water transfer Allocations Supplementary

Storage Demand Generate demand Rainfall Evaporation Apply extraction limits Inflows Releases Determine orders

daily Debit accounts Storage Calculate delivery losses monthly Apply constraints

Figure 2.3: Schematic showing management, demand and storage sub-models in eWASH, and daily and monthly functions. tify environmental flow availability, with functions operating at daily or monthly scale for the input time series. eWASH flexibly modelled different management scenarios and their effects on environmental flow availability, using real management differences between the Gwydir and Macquarie (i.e. quantity of entitlements (Table 2.3), management rules (Table 2.5)). eWASH also allowed assessment of outcomes from altering environmental flow use. The software was developed in the open-source, object-oriented Python programming language, compatible with most operating environments (.exe; <324 kilobytes). The graphical user interface (GUI, Fig 2.4) allows users to input hydrology data, load preset characteristics of the river system, select management rules, specify a warm-up period and run single or batch simulations (20 sec per 120 years), generating graphical and spreadsheet outputs. We configured eWASH for the Gwydir

39 2. SIMULATING ENVIRONMENTAL FLOW AVAILABILITY

Figure 2.4: Graphical user interface of the Environmental Water Allocation Simulator with Hydrology, showing management panel allowing flexible specification of rules. and Macquarie Rivers, validating the model by simulating environmental flow allocations (planned and adaptive) under current holdings (Table 2.4) over a 110 year period (1 July 1900 - 30 June 2010; water years).

2.3.5 Conﬁguring eWASH for the Gwydir and Macquarie

2.3.5.1 Management sub-model

The management sub-model included assessment of water availability, the licencing framework (i.e. different shares) and their accounts, and water used to operate the rivers (e.g. delivery losses, Fig. 2.3). The licencing framework was coded into separate classes, reflecting the different reliabilities of access and their separate management within the dam. We also separated extractive and environmental general security entitlements, allowing users to alter environmental flow use (Table 2.4). Water availability was assessed on the first day of each month. We assumed no permanent or temporary trading of entitlements. We also assumed all planned and adaptive environmental water could be used and was not limited by rules; this meant disabling environmental

40 2.3 Methods

ﬂow transparency rules to allow full manipulation of environmental ﬂow use.

2.3.5.2 Demand sub-model

We implemented two daily extractive demand models for general security and supplementary irrigation in eWASH, providing flexibility when data availability were poor and reducing extrapolation errors (Fig. 2.3). The IQQM Demand Model was based on the spatially distributed irrigation module of the NSW Government Integrated Qual- ity and Quantity Model (IQQM; O’Neill, undated). The model requires historical planted area data to estimate the fraction of farmed area used for dominant crops at the beginning of each sowing season. Daily irrigation demand is a function of crop water requirements and climate. For increased computing efficiency and reduced data requirements, our model aggregates individual nodes to represent demand from an irrigation district below a major storage, with a shared on-farm storage and average farmer behaviour (Table 2.3; Dudley, 1988). The IQQM sub-model and parameterization are fully described in Appendix 2.C. Generation of future scenarios was an important aspect of environmental flow modelling. The alternative Empirical Demand sub-model developed with State Water generates daily demands for each entitlement and dam behaviour, according to historical water orders. The amount of water extracted by general security entitlements (the demand) was a function of the current account balance and the annual distribution of historical orders for water (Table 2.6). Supplementary water allocations were demanded instantly. We assumed extractive demand relationships were constant through time, reflecting Water Sharing Plans (implemented 2002 (Gwydir) and 2003 (Macquarie)) which fixed the allocation of water among essential supply, general security and supplementary users. Individual extractive users were lumped by entitlement for simplicity (Table 2.3; Dudley, 1988). For both models (general and supplementary entitlements), the amount of water for essential supplies was based on a lookup table of daily use for each month for local water utility, domestic and stock and high security orders. Values were calibrated from an annual average based on State Water advice (Gwydir; records unavailable) or State Water recorded orders during a calibration period (Macquarie; 70 % of data; Table 2.6). Use of water was measured at the extraction point not the storage, so we adjusted use according to the travel time, estimated as the lag when storage releases and use had

41 2. SIMULATING ENVIRONMENTAL FLOW AVAILABILITY

Table 2.6: Calibration (70 % of data) and validation (30 %) periods used to develop and test key variables for eWASH sub-models. Periods spanned from the commencement of Water Sharing Plans until termination of hydrological records (Gwydir) or Plan suspension due to drought (Macquarie; 30/06/2007). Records for allocations (management sub-model), storage volume and releases (storage behaviour sub-model) were downloaded from the NSW government website (NOW, 2012b). Data for essential supply and general security orders (demand sub-model) were provided by State Water (unpublished data). Calibration was not necessary for storage volume or allocations as they were derived from calibrated variables.

Variable Calibration Validation Allocations Gwydir n/a 01/07/2004 - 01/11/2009 Macquarie n/a 01/07/2004 - 01/07/2007 Storage volume Copeton (418035) n/a 01/07/2004 - 31/01/2010 Windamere (421148) n/a 01/07/2004 - 01/07/2007 Burrendong (421078) n/a 01/07/2004 - 01/07/2007 Essential supplies orders Copeton n/a n/a Windamere 01/07/2004 - 04/08/2006 05/08/2006 - 30/06/2007 Burrendong 01/07/2004 - 04/08/2006 05/08/2006 - 30/06/2007 General security orders Copeton 28/05/2004 - 14/03/2011 01/06/2001 - 27/05/2004 Windamere 28/06/2004 - 11/10/2006 12/10/2006 - 06/01/2007 Burrendong 24/06/2004 - 23/07/2006 24/07/2006 - 30/06/2007 Releases Copeton (418026) 31/05/2004 - 14/03/2011 01/07/2001 - 30/05/2004 Windamere (421079) 07/08/2006 - 27/06/2011 28/06/2004 - 06/08/2006 Burrendong (421040) 01/07/2006 - 23/06/2011 24/06/2004 - 30/06/2006

42 2.3 Methods the highest correlation. Calculated lags at seven days (Burrendong) and three days (Windamere) were consistent with estimated lead time to core irrigation areas between Gin Gin and Warren weirs (State Water, 2012). Environmental flow models (planned and adaptive) were coded for flexible manipulation (GUI). They were simplified into four options for environmental flow use: (1) transparent demand where inflows passed as if no storage was present to mimic natural hydrology (Hamstead, 2007); (2) tributary activated demand which piggy-backed natural downstream tributary events (Harman and Stewardson, 2005); (3) annual demand generating spring events; and (4) boom and bust demand generating largest possible account-based events by accumulating water until the account reached its volumetric limit (Fig. 2.5). Demands for strategies (3) and (4) were generated on the 1st Oc- tober over a 90 day period with smooth rising and receding limbs to mimic natural spring-summer flows. All use options were converted to volumes, according to management rules (Fig. 2.3). Carryover was deducted before allocations. Orders exceeding long term extraction limits were flagged but not constrained. Rainfall rejections were simulated by releases (0.368 GL/d and ≤29 GL/y) when storage rainfall exceeds 1 mm. Minimum flow requirements and replenishment flows were assumed to be met without additional storage releases. We estimated the amount of water needed for different entitlements along the river using a transmission loss function with relevant outlet capacity constraints and flood mitigation operations. Outlet capacity constraints (Table 2.1) applied when storage was at or below the mitigation flood level and above this, there were spillway constraints, restricting releases to the next possible day. The transmission loss poly- nomial function predicted releases, based on recorded use, current season and major downstream tributary inflows. Releases were calibrated and validated using discharge data (variable 141) from the nearest gauge downstream of storage (Table 2.6).

2.3.5.3 Storage sub-model

The storage sub-model was a mass balance model (Eqn. 2.1), representing physical storage behavior and hydrological processes (Fig. 2.3). A daily function tracked volume of a specific storage (S) on a particular day (t), including adding surface rainfall (R), adding inflows (I ), subtracting evaporation (E) and making releases (R; Eqn. 2.1a). Inflows were a combination of an input time series and sometimes regulated

43 2. SIMULATING ENVIRONMENTAL FLOW AVAILABILITY

Table 2.7: Bureau of Meteorology station names and numbers used to derive rainfall, evaporation and inﬂows in the Gwydir and Macquarie (BOM, 2012).

Gwydir Macquarie Bingara Post Office 54004 Mumbil (Burrendong Dam) 62003 Bingara (Derra Derra) 54014 Gulgong Post Office 62013 Gravesend Post Office 54017 Mudgee (George Street) 62021 Barraba (Mount Lindsay) 54021 Rylstone (Ilford Rd) 62026 Warialda Post Office 54029 Windamere Dam 62093 Bingara (Keera) 54039 Blayney Post Office 63010 Copeton Dam 54128 Hill End Post Office 63035 Bundarra Post Office 56006 Oberon (Springbank) 63063 Guyra Post Office 56016 O’Connell (Stratford) 63064 Inverell Research Centre 56018 Orange (McLaughlin St) 63066 Uralla (Dumaresq St) 56034 Trunkey Creek 63083 Wandsworth (Strabanne) 56036 Wattle Flat General Store 63089 Mendooran Post Office 64015 Coolah (Binnia St) 64025 Manildra (Hazeldale) 65022 Molong (King St) 65023 Wellington (Agrowplow) 65034 water released from an upstream storage (e.g. Windamere), lagged by travel time. Evaporation (mm) and rainfall (mm) input time series were multiplied by surface area to yield volumes. Spills of the dam (Rspill) were generated when inputs increase storage above capacity (Smax) or the flood mitigation level (Eqn. 2.1b). Storage volume was set to zero if there was a water deficit, and an alert was generated.

St = St−1 + It + Pt − Et − Rt, where 0 ≤ St; (2.1a)

if St > Smax,Rspill = St − Smax and St = Smax (2.1b)

Rainfall for the period of simulation (1/1/1890 - 31/12/2010; 10 year warm up period) was derived from long term daily recorded rainfall (mm) at gauging stations (Table 2.7) from the Bureau of Meteorology (BOM, 2012). Missing and unreliable data (quality code S) were patched, and records extended where necessary using an inverse

44 2.3 Methods distance weighting algorithm which interpolated rainfall from gauges within a 100 km radius, according to distances calculated in ArcGIS (ESRI, 2008; Shi et al., 2007). Storage inflows and pan evaporation were derived from stochastic rainfall sequences. Inflows and downstream tributary flows were generated using a physically lumped parameter rainfall-runoff model within 12 (Gwydir) and 16 (Macquarie) sub-catchments (Burnash, 1995). Evaporation and rainfall inputs from multiple gauges were aggregated at a sub-catchment scale, using Thiessen weights. Each model was calibrated and validated by the NSW Office of Water (O’Neill et al., 2009) and processed using the Hydromad package in R (Andrews et al., 2011; R Development Core Team, 2009). Sacramento flow outputs provided storage inflows using the NSW government Integrated Quality and Quantity Model (IQQM) (Hameed and Sharma, 1996). Pan evaporation was generated using the 1-nearest neighbour algorithm, sampling from historically similar daily rainfall and evaporation data recorded from gauges at or near the storage (Fig. 2.1; Sivakumar and Berndtsson, 2010). Pan evaporation was adjusted by an 0.7 scaling factor to account for the extra heat absorbed by the pans (Grayson et al., 1996).

2.3.6 Model validation

Key variables within each sub-model were validated against observed data. We compared means, standard deviations, skew of observed and simulated, and examined the goodness-of-fit using the frequently employed Nash-Sutcliffe coefficient of efficiency (NSE) measure (Table 2.6; Krause et al., 2005). Key variables were AWD (management sub-model), orders (essential supply, general security) and releases (demand sub-model) and storage volume (storage sub-model). Observed data spanned from when Water Sharing Plans started to the end of available hydrological data (Gwydir) or suspension of the plan due to drought (Macquarie). Model validation coincided with the Millennium Drought (1995-2009), providing a unique opportunity to examine whether underlying processes influencing environmental flow availability are likely to vary during critical dry periods. Data for storage volume and storage inflows at Copeton (418035), Windamere (421148) and Burrendong (421078) were from WaterInfo (NOW, 2012b). We used a simple Empirical Demand model because recent water order data were available (Table 2.6) and planted area data required for the IQQM Demand model were unavailable after 2000. Daily orders from State Water (unpublished data) were

45 2. SIMULATING ENVIRONMENTAL FLOW AVAILABILITY available for general and high security (Gwydir) and stock and domestic, local water utility, general security, high security and supplementary entitlements (Macquarie). Releases were discharge from the nearest gauge downstream of Copeton (418026), Win- damere (421079) and Burrendong (421040). Validation was independent of calibration data and was not followed by model adjustment.

2.4 Results

We provide calibration results and then evaluate eWASH performance for key variables within sub-models (Fig. 2.3): AWD (management sub-model), orders and releases (demand sub-model) and storage volume (storage sub-model).

2.4.1 Demand sub-model development

Orders and releases in the demand sub-model were calibrated in discussion with river operators. The lookup table showed calibrated local water utility, domestic and stock, high security and general security orders (Table 2.8). Storage release (Rr,t) functions for river (r) and day (t) were calibrated to minimize the mean squared diﬀerence between actual and modelled allocations for Copeton, Windamere and Burrendong (Eqn. 2.2),

i Xmax Rr,t = co + αi · Xi (2.2) i=1 based on empirical relationships between total recorded daily orders (X1 = ot), tributary ﬂows (X2 = Qr,t1 and X3 = Qr,t2) and season (X4 = s; JF = 1, MAM = 2, JJA =

3, SON = 2, D = 1) and a constant (co). The constant, coefficients (αi) and number of terms (imax) varied (Table 2.9). Independent variables were significant (p<0.05) and not correlated (ρ<0.2). Coefficients of determination (R2) for release calibration were 0.70 (Copeton), 0.55 (Windamere) and 0.49 (Burrendong). Calibration was also required for the 24 month storage evaporation loss function used to estimate future storage losses (Lr,t) in the Gwydir resource assessment, and the ratings curve used to convert storage volume to surface area (Ar,t). Functions based on storage volume (S) for Copeton, Windamere and Burrendong were high order polynomials of the form: imax X i Lr,t,Ar,t = αiS (2.3) i=0

46 2.4 Results

Table 2.8: Lookup table for daily extractive demand for each month, in the Gwydir River downstream of Copeton (C) and the Macquarie River downstream of Windamere (W) and Burrendong (B). Essential supplies (local water utility (LWU), domestic and stock (DS), high security (HS)) were ﬁxed volumes and general security (GS) demand was a percent scaled according to account volume. Values were based on State Water advice (Gwydir; records unavailable) or recorded orders during a calibration period (Macquarie; Table 2.6; State Water, unpublished data).

Month LWU (ML) DS (ML) HS (ML) GS (%) CWBCWBCWBCWB Jan 5 7.1 36.3 28.1 0.5 2.8 96 9 29.1 29.3 20.1 24.5 Feb 5 6.3 41 28.1 0.3 1.7 96 7.1 32.7 21.6 16.9 15.4 Mar 5 6.8 38.3 28.1 0.6 0.7 96 3.9 32.1 4.5 12.8 3.2 Apr 5 7 21.9 28.1 0.1 0.3 0 2.8 13.8 3.5 8.3 1.4 May 5 3.8 24.7 28.1 0.1 1.1 0 0.8 23.2 2.8 5.3 3.9 Jun 5 3.2 21.8 28.1 0.1 4.4 0 0.7 25.7 1.3 1.3 2.4 Jul 5 1.9 13.2 28.1 0 0 0 0.3 5.1 0.6 0.2 0.2 Aug 5 2.3 16.9 28.1 0.3 0 0 1.5 11.4 3.6 1.2 3.7 Sep 5 3.1 24.3 28.1 0.3 3.3 0 3 30.6 5.2 2.6 12.4 Oct 5 3.8 33.7 28.1 1 1.9 0 6.4 29.9 4.5 7.8 8.7 Nov 5 4.9 24.9 28.1 0.6 0.3 96 5.9 18.5 4.7 10.5 5.4 Dec 5 7.8 39.1 28.1 0.5 2.6 96 5.6 29.5 18.6 13.1 18.9 Total 60 58.2 335.9 336.8 4.4 19.2 480 46.9 281.4 100 100 100

Table 2.9: Coeﬃcients (αi) of terms (Xi) and constant (co) in the storage release calibration equations for the Gwydir (Copeton) and

Macquarie (Windamere, Burrendong) Rivers, where X1 is orders (ot),

X2 is tributary inﬂows (Qr,t1 and Qr,t2), X3 is season (s) and X4 is a

constant (co).

Storage ot s Qr,t1 Qr,t2 co imax Copeton 1.11 -128.11 -0.162a -0.025b 390.77 4 Windamere 1.88 4.56 n/a n/a n/a 2 Burrendong 1.89 224.78 0.63c 0.153d 673.79 4

a r = Myall; b r = Horton; c r = Talbragar; d r = Bell

47 2. SIMULATING ENVIRONMENTAL FLOW AVAILABILITY

Table 2.10: Coeﬃcients (αi) of terms (i) in the storage loss (li) and surface area (ai) calibration equations for the Gwydir (Copeton) and Macquarie (Windamere, Burrendong) Rivers.

Storage l1 a1 l2 a2 l3 a3 l4 a4 l5 a5 Copeton 9.90 1.32 -5.87 -6.10 1.36 -1.16 -1.61 -1.18 0.13 89.61 Windamere 2.14 1.43 -4.17 -2.78 3.02 -2.01 -1.02 -6.78 0.22 146.61 Burrendong 7.96 5.31 -6.17 -4.12 1.78 -1.19 -2.42 -1.61 0.21 141.98 following State Water’s practice. Coefficients (Table 2.10) were statistically significant (p<0.05) with high coefficient of determination (R2>0.99).

2.4.2 Validation

2.4.2.1 Management sub-model

AWD is a key output variable that dictates environmental water availability. Simulated and observed annual AWD were well correlated for the Gwydir and Macquarie (NSE: 0.92 (Gwydir) and 0.89 (Macquarie); Table

48 2.4 Results Standard deviation Skew NSE Mean . 83. 42 192 552 . 14. 34 105 601 . 50 93 814 . 73 215 746 . 37 61 703 . 37 24 198 . 27 124 222 . 70 1.84 129 816 . 96 10 855 . 27 -0.03 0.37 0.39 0.59 0.5 0.09 -0.45 0.81 n/a n/a n/a n/a n/a n/a n/a 0.100.180.01 0.12 0.240.01 0.01 0.12 0.23 0.02 0.02 0.13 0.26 0.25 0.06 0.02 0.32 0.20 3.34 0.34 0.92 2.20 0.89 0.06 0.55 5.07 4.22 0.72 0.010.010.00 0.000.04 0.02 0.01 0.02 0.04 0.02 0.01 0.01 0.10 0.02 4.23 1.61 3.27 0.03 1.00 0.51 0.10 2.38 0.60 2.44 3.36 -10.27 2.53 0.96 Obs. Sim. Obs. Sim. Obs. Sim. a a a a quartile quartile er system quartile quartile nd nd st st 2 Macquarie 2 Copeton WindamereBurrendongCopetonWindamere 15.97Burrendong 84.58 629.85Windamere 32.38 9.81 455.68 56.49BurrendongCopeton 375.00Windamere 30.14 376.90 10.88 78.43 51.85 665.04Burrendong 1132.18 113 222 113 520 259 551 543.25 1355.44 71.27 8.65 4.13 14.74 443.32 0.83 0.78 2.88 312.66 748.19 -0.09 103.71 -0.46 1.97 0.98 5.46 -0.11 0.04 1666.46 0.48 0.36 0.37 1.82 68.36 1.00 0.51 6.92 2.61 -0.09 3.24 -3.04 -0.45 Macquarie Gwydir 1 1 Gwydir Riv calibration period when storage volume was in the quartile indicated. Comparisons of means, standard deviations and skew, and the Nash-Sutcliffe coefficient of efficiency (NSE) for observed AWD 2.11: Subset of Variable Annual Essential supplies orders (ML/d) General security orders (ML/d) Releases (ML/d) CopetonStorage volume (ML/d) a 959.72 712.73 1572.41 702.66 2.66 2.80 0.31 Monthly AWD (ML/entitlement) (ML/entitlement) Storage did not enter upper quartiles during the validation period. Table and simulated variables during the validation2007) period Rivers. for the Gwydir (July 2004 - November 2011) and Macquarie (July 2004 - July

49 2. SIMULATING ENVIRONMENTAL FLOW AVAILABILITY

2.11), where an NSE of one is a perfect fit, zero represents the constant model, and values below zero indicate the mean would have been a better predictor. Simulated annual mean and standard deviation were similar to observed but skew was lower than observed (Table 2.11). Monthly simulated and observed AWD were reasonably correlated (NSE: 0.55 (Gwydir) and 0.72 (Macquarie; Table 2.11). Simulated monthly mean and standard deviation were similar to observed, but skew was lower than observed (Table 2.11). Goodness-of- fit of monthly AWD was influenced by storage volume. In the Gwydir, correlation was higher when observed storage volume (V ) was between 25 % and 50 % of the FSL of Copeton Dam (NSE = 0.60), than below this level (NSE = 0.51; Table 2.11). Similarly for the Macquarie, correlation was higher when storage was between 25 % and 50 % of the FSL of Burrendong Dam (NSE = 0.96) than below this level (NSE = -10.27; Table 2.11). There were mostly well correlated peaks (A) and troughs (B) between observed and simulated monthly AWD time series (Fig. 2.5) for the Gwydir (a) and Macquarie (b)

15% (a) Gwydir AWD 40% (b) Macquarie AWD C A A 30% 10% A Observed 20% Simulated C C D C 5% C,D 10% C Monthly AWD Monthly B C B B B B 0% 0% Jul-2004 Jul-2005 Jul-2006 Jul-2007 Jul-2008 Jul-2009 Jul-2004 Jul-2005 Jul-2006 Jul-2007 120

60 X 200 40 * W * * 100 Y 20 * Z

Storage inflow (GL) inflow Storage * 0 0 d Jul-2004 Jul-2005 Jul-2006 Jul-2007 Jul-2008 Jul-2009 Jul-2004 Jul-2005 Jul-2006 Jul-2007

Figure 2.5: Simulated (grey dashed) and observed (solid black; gaps where data missing) data for annual (a and b; water year: 1 June - 30 July) and monthly (b and c) Avail- able Water Determination (AWD; ML/unit entitlement) for the Gwydir (a and c) and Macquarie (b and c) Rivers, during the validation period (Gwydir: July 2004 Nov 2011; Macquarie: July 2004 July 2007). but some peaks poorly correlated in volume (C) and timing (D). These latter peaks were generally associated with overestimations of observed. Three times in the Macquarie,

50 2.4 Results simulated AWDs were a month before observed AWDs (D) while one simulated AWD lagged observed by two months in the Gwydir (Fig. 2.5).

These errors in the AWD (C, D) occurred on monthly storage inflow time series for the Gwydir (Fig. 2.5a) and Macquarie (Fig. 2.5b). Simulated values either over- or under-predicted inflow and AWD time series (*, Fig. x), indicating error propagation from input data, usually within the same month (*, Figs. 2.5c and d). Simulated and observed values are similar for inflows but not for AWDs, indicating errors from simulated inflows lagged by several months (W, X) or errors arising from the eWASH model (Y, Z) (Fig. ).

2.4.2.2 Demand sub-model

We ﬁrst validated daily orders for essential supplies and general security, downstream of each storage. For essential supplies, the NSE was close to zero (Windamere: 0.04; Burrendong: -0.11; Table 2.11). Observed mean, standard deviation and skew were higher than simulated demand for Windamere and Burrendong Dams. Copeton data were unavailable. For general security orders, NSE was better than the constant model for Copeton Dam (0.36) and Burrendong Dam (0.51), but not Windamere Dam (- 0.09), despite relatively small diﬀerences in mean and standard deviation (Table 2.11). Observed mean and standard deviation were higher than simulated, particularly for Copeton and Burrendong (Table 2.11). Observed skew was higher than simulated for Copeton and Burrendong, but not Windamere.

The simulation of storage releases performed better than a constant model of mean observed values for Copeton (NSE = 0.3) but not for Windamere (NSE = -0.71) or Burrendong (NSE = -3.28; Table 2.11). There were no clear trends in metrics across storages (Table 2.11). The mean of simulated releases was lower for Copeton, similar for Windamere and higher for Burrendong compared to observed. Simulated standard deviation was lower for Copeton and Windamere, and higher for Burrendong. Simulated skew was higher than observed for Copeton, but lower for Windamere and Burrendong (Figs, table).

51 2. SIMULATING ENVIRONMENTAL FLOW AVAILABILITY

2.4.2.3 Storage sub-model

We examined the goodness-of-ﬁt of observed and simulated storage volume. Observed and simulated storage volumes were well correlated for Copeton (NSE = 0.5) and Burrendong (NSE = 0.81), but not for Windamere (NSE = -0.45; Table 2.11). The simulated and observed time series ﬂuctuated similarly for all storages (Fig. 2.6).

(a) Gwydir at Copeton Dam 800

600 400 200

Volume (GL) Volume 0 Jul-2004 Jul-2006 Jul-2008 Jul-2010

(b) Macquarie at Windamere Dam (c) Macquarie at Burrendong Dam

200 800 150 600 100 400 50 Volume (GL) Volume 200 0 0 Jul-2004 Jul-2005 Jul-2006 Jul-2007 Jul-2004 Jul-2005 Jul-2006 Jul-2007

Figure 2.6: Simulated (grey dashed) and observed (solid black; gaps where data missing) data for daily storage volume for (a) Copeton Dam on the Gwydir River, and (b) Windamere and (c) Burrendong on the Macquarie Rivers, during the validation period (Gwydir: July 2004 - Nov 2011; Macquarie: July 2004 - July 2007).

However, there was evidence of systematic bias in Windamere and Burrendong Dams with consistent underestimation of simulated storage volume (Fig. 2.6b and c). Met- rics reﬂected this bias, showing lower mean simulated storage volumes (Table 2.11). Standard deviation results suggested observed storage volume ﬂuctuated more in Bur- rendong Dam than simulated for smaller storages, Copeton and Windamere (Table 2.11) for the validation period. Observed and simulated volumes were similarly skewed in Burrendong Dam, but observed was more skewed than simulated for Copeton and Windamere Dams (Table 2.11).

52 2.5 Discussion

2.5 Discussion

Environmental flows in rivers are essential for maintenance of healthy and functional aquatic ecosystems (Karr, 1991; Poff et al., 1997; Richter et al., 1996). Often, they need to be managed as they can be stored within dams where there can be considerable variability and opportunity to vary release. This raises the challenge of integration between environmental flows and political, socio-economic and operational spheres (Dyson et al., 2003; Falkenmark, 2001; Postel, 2000; Richter and Thomas, 2007). Evidence-based decision making is critical to achieve ecological objectives in highly complex and uncertain systems (Bradshaw and Borchers, 2000; Poff et al., 2003; Webb et al., 2010; Yin et al., 2011). eWASH was an accurate and flexible platform for simulating environmental flow availability in regulated rivers. eWASH’s can produce scenarios and explicitly model environmental flow availability, promoting transparent, accountable and systematic decision making for integrating environmental flow management in regulated river systems. eWASH accurately simulated environmental flow availability, shown by the correlation between simulated and observed AWD for the Gwydir (NSE = 0.92 and 0.55 for annual and monthly, respectively) and Macquarie (NSE = 0.89 and 0.72; Table 2.11). Errors in the simulated AWD time series (C, D; Figs. 2.5a and b) were mainly attributed to errors in simulated inflow inputs propagated through the model. Actual storage inflow data for eWASH inputs are likely to improve accuracy over rainfall-runoff model data subject to model error (Sharma and O’Neill, 2002). Other errors occurred in the Macquarie because eWASH was more sensitive to inflows: increased inflows failed to trigger an observed allocation announcement but succeeded in the eWASH model (Y, Z; Figs. 2.5), probably leading to a small increase in mean simulated AWD (Table 2.11). Future eWASH versions allowing users to vary management rules over time could further improve AWD accuracy, because managers often deviate from management rules during periods of low flow, resulting in conservative AWD announcements. eWASH accuracy was further underestimated because NSE is less sensitive to periods of zero or low availability and oversensitive to high availability. The validation period occurred during a drought (Table 2.6; Krause et al., 2005), where average AWDs were 40 % (Gwydir) and 60 % (Macquarie) of long term simulated averages. For example, a simulated AWD spike of 0.15 (1 August 2005) in the Macquarie resulted in

53 2. SIMULATING ENVIRONMENTAL FLOW AVAILABILITY poor NSE (-10.27 when storage volume was less than 25 % of FSL), despite correctly predicted zero AWDs in over half the period (64 %). Small differences between observed and simulated means (<1 %; Table 2.11) supported the validity of our simulated results. Small non-systematic errors in observed data may have further lowered correlation results, due to modelling errors, input data errors, particularly model-derived inflows and the use of observed data which was not quality controlled. Further validation is required when storage volume is above 50 % FSL, particularly if the results deviate significantly from this analysis as this may reflect different underlying processes which manifest depending on water availability. Management rules and environmental flow release strategies were readily customized through the GUI (Fig. 2.4). We demonstrated the effects of switching management rules between the Gwydir and Macquarie Rivers (Fig. 2.5). Management rules affected the temporal availability of water (Fig. 2.7) but long term annual average availability was similar (Fig. 2.11). Interannual shifts in availability may affect planning for environmental watering, particularly in August and September at the end of winter, when natural environmental flow events are likely to occur. eWASH is a portable and versatile platform which could be relatively easily implemented for developing environmental flow scenarios in rivers with single or multiple multi-reservoirs and different types of regulated water. It explicitly tracks the availability of managed environmental water, extractive water and other components (e.g. system losses, unlicenced flows) which are all interrelated. The environments share can be manipulated to determine potential impacts of climate change, different types of releases as well as how manipulation of environmental flow shares might affect other uses. eWASH can also assess target various environmental watering objectives: in-channel ecological outcomes including altering baseflows, replenishing groundwater, transport- ing nutrients and creating habitat in addition to water provision. eWASH also includes an alert function tracking the magnitude of selected risks (storage crash, storage spills, release constraints, account depletion, account limits). Uncertainty is another key el- ement of decision making in complex systems, requiring tools which can process large stochastic datasets. Our model provides users with the option of using deterministic or stochastic data. Stochastic hydrological data is readily handled by the eWASH batch processing function, enabling assessment of uncertainty in input data. Rapid processing speeds (Gwydir: 45s; Macquarie: 1m 15s per simulation using an Intel dual

54 2.5 Discussion

Gwydir Gwydir rules Macquarie rules

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

Allocation (GL) ● ● ● ● ● ● ● ● 0 10

JFMAMJJASOND

Month Macquarie

● ● ● ● ● ● ● Allocation (GL) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 20 50 80

JFMAMJJASOND

Month

Figure 2.7: Effects of swapping Gwydir and Macquarie management rules on mean (black circles) and distribution (boxplots) of monthly environmental water allocations (planned and adaptive) during a 110 year simulation. core processor) enable large numbers of runs with multiple management combinations without prohibitive time and financial cost. eWASH can support environmental flow assessments by providing knowledge of water availability under current and alternative management rules. Swapping the management rules of the Gwydir and Macquarie River systems revealed long term effects on volume and timing of water availability. Mean allocations were (2 GL/y higher and the winter peak was one month later under the Gwydir rules compared to Macquarie rules in both systems. eWASH can also inform risk assessment and operational feasibility of the proposed watering regime, such as flood risk to downstream inhabitants and capacity constraints limiting volume (Chen et al., 2011). eWASH can be applied in scenario testing to examine potential opportunities, risks and uncertainties. For example, it can be used to assess environmental flow constraints and evaluate feasibility of environmental watering strategies, such as options for pulsed flows, and piggybacking releases

55 2. SIMULATING ENVIRONMENTAL FLOW AVAILABILITY with natural events (Harman and Stewardson, 2005). It can be used to investigate alternative dam reoperation and water management rules, such as those requiring ta- pered extractive flow releases to mimic natural rising and receding flood limbs (Harman and Stewardson, 2005; Richter and Thomas, 2007). To effectively allocate management resources and improve scientific models, eWASH outputs can be used to identify key drivers of water availability that should be prioritized for improvement. For example, a hydrological model of the Brugga catchment, Germany, was highly sensitive to initial and boundary conditions (Sieber and Uhlenbrook, 2005), so modelling effort and resources should be directed towards a more accurate representation of these variables. eWASH can also be used to inform investment in environmental water, by quantifying long term water availability for different river systems and types of entitlements. Finally, coupling eWASH with ecological response models allow users to manipulate management levers and predict effects on biotic variables including species abundance and diversity. eWASH may also be coupled with spatially explicit inundation models to investigate the effects of water management on flood extent and connectivity. Two datasets are required to configure the physical characteristics of a river system to run eWASH: (1) infrastructure specifications for a storage (see Table 2.1), indicating system design and constraints; and (2) storage surface area at corresponding volumes between empty and full capacity, to calibrate the ratings curve. Four input daily datasets are required for simulation: (1) storage inflows; (2) pan evaporation at storage; (3) observed water orders, to parameterize demand; and (4) observed storage releases, to model transmission losses. Where daily data are unavailable, monthly disaggregation techniques may be used with potential reduction in model accuracy. Additional user- generated management alternatives can be implemented by modifying the source code (Python programming language). There is potential to exchange these alternatives via plugins from a collaborative online exchange, to promote inter-basin exchange of best practice and close the gap between science and management (Andreu et al., 1996). At present, eWASH should not be applied in rivers where management rules differ markedly from that of the Gwydir and Macquarie in the Murray-Darling Basin due to cost of configuration. Instead, the principles of flexibility and explicit consideration of environmental water can be integrated into existing hydrological models (e.g. IQQM; Simons et al., 1996) as a step towards generic models which accommodate differences in water rights systems, allocation frameworks and biophysical contexts. eWASH is

56 2.6 Conclusion currently configured up to two storages in series, and future model development could allow parallel storage configurations. Improvements in model accuracy may be achieved by identifying and reducing sources of uncertainty within sub-models. The management sub-model reflected relatively deterministic management rules bound by legislation, plans and practice. Human error may have been introduced when interpreting management rules and implementation them in code. Furthermore, some real-time management practices were not captured in eWASH, such as consideration of rainfall forecasts and antecedent conditions in the decision to release flows from storage. The demand sub-model was stochastic, so uncertainties may be reduced by incorporating additional complex factors such as market drivers, antecedent conditions, spatial relationships and farming technologies. Further- more, disaggregating individual extractive behavior may improve accuracy, however data is costly and would require ongoing updating. Improvements may also be made in the storage sub-model (Table 2.11), through better representation of volume and variability. Improved inflow modelling may be necessary as rainfall-runoff models are well known to reduce inflow variability (Sharma and O’Neill, 2002). Despite this, the key variable for environmental water availability, AWD, remained sufficiently accurate to support environmental flow management decisions.

2.6 Conclusion eWASH is a scenario-based hydrological modelling tool developed to support environmental water management decision making. The strengths of eWASH are explicit simulation of environmental ﬂows and manipulation of environmental watering strategies and management rules through the GUI. We demonstrated the accuracy and illustrated the applicability of eWASH for evaluating water management options in regulated rivers. Such tool promotes a strategic and systematic approach to environmental ﬂow decision making in complex systems.

57 2. SIMULATING ENVIRONMENTAL FLOW AVAILABILITY

2.A Gwydir resource assessment

The Gwydir resource assessment uses a simple balance sheet (Static balance sheet; Ta- ble 2.5) to determine the AWD. The ﬁrst item is the volume of the resource pool, calculated as the total volume in storage at previous resource assessment (sum of Sj, PrevRA, j is the storage), known as debit (Table 2.5). Dead storage (Sj, D) and future anticipated storage losses from the previous resource assessment (LPrevRA) are deducted from the resource pool (Eqn. 2.4a). Losses are estimated from a function based on a 24 month evaporation estimate by State Water (68 GL) and the surface area of total storage volume. Surface area is based on volume using a ratings curve derived from survey data (DWE, 2006). Unused essential supplies (ESCurrRA), planned environmental water (PEWCurrRA), general security (GSCurrRA) and delivery losses (DLCurrRA) are deducted to yield the volume of available water for sharing (AW; Eqn. 2.4a). Un- used water is the account balance at previous assessment minus orders and delivery losses (Eqns. 2.4b-e). Delivery losses are the diﬀerence between storage releases (R) and total orders since previous assessment, apportioned between the essential supplies account (Eqn. 2.4f) and delivery loss accounts (for planned environmental water and general security; P EW GSDL; Eqn. 2.4g) based on relative volumes ordered since last assessment.

storage n storage n X X AW = Sj,P revRA − Sj,D − LP revRA− j=storage 1 j=storage 1 (2.4a)

ESCurrRA − PEWCurrRA − GSCurrRA − DLCurrRA

ESCurrRA = ESP revRA − ESOrd − ESDL (2.4b)

PEWCurrRA = PEWP revRA − PEWOrd (2.4c)

GSCurrRA = GSP revRA − GSOrd (2.4d)

DLCurrRA = DLP revRA − P EW GSDL (2.4e) PEWOrd + GSOrd ESDL = R − (ESOrd + PEWOrd + GSOrd) · ESOrd + PEWOrd + GSOrd (2.4f)

ESOrd P EW GSDL = R − (ESOrd + PEWOrd + GSOrd) · ESOrd + PEWOrd + GSOrd (2.4g)

58 2.B Macquarie resource assessment

Available Water Determinations are announced when available water exceeds storage losses (Lreq) and essential supplies requirements (ESreq; Eqn. 2.5).

AW2 = AW − Lreq − ESreq; AW2 > 0 (2.5)

Remaining available water (AW2) is subsequently distributed amongst planned environmental water (PEWAdd; Eqn. 2.6a), general security (GSAdd; Eqn. 2.6b) and delivery losses accounts (DLAdd; Eqn. 2.6c), in proportion to entitlements (PEWEnt, GSEnt), ensuring the delivery loss account (DLCurrRA + DLAdd) is 30 % of the new balances. When planned environmental water or general security account capacity is reached

(PEWmax, GSmax), accounts spill internally (i.e. accounts forfeit water to other accounts).

PEWEnt PEWAdd = AW2 · , given PEWCurrRA + PEWAdd ≤ PEWmax PEWEnt + GSEnt (2.6a)

GSEnt GSAdd = AW2 · , given GSCurrRA + GSAdd ≤ GSmax PEWEnt + GSEnt (2.6b)

DLCurrRA + DLAdd = 30% · (PEWCurrRA + PEWAdd + GSCurrRA + GSAdd) (2.6c)

Account capacity is the product of user-speciﬁed entitlement quantities and their share value (Eqns. 2.7a and b; Table 2.3).

PEWmax = PEWEnt · PEWSha (2.7a)

GSmax = GSEnt · GSSha (2.7b)

Excess available water is held for the following resource assessment. Available Water Determination is the general security allocation divided by the number of entitlements

(GSEnt; Eqn. 2.8). GS AW D = Add (2.8) GSEnt

2.B Macquarie resource assessment

The Macquarie resource assessment uses a dynamic balance sheet that optimizes the AWD within the constraints of water availability over a future projected period (Table

2.5). Projected volume for all storages (SF,m,AWD) is the previous months storage

59 2. SIMULATING ENVIRONMENTAL FLOW AVAILABILITY

volume (SF,j,m-1,AWD) plus monthly minimum inﬂows into storages (Imin,j,m; credit,

Table 2.5) less water orders (OF,m,AWD), essential supplies (ESF,m) and evaporation loss from storages (EF,j,m), given storage capacity constraints (Eqn. 2.9).

SF,m,AW D = SF,m−1,AW D + Imin,m − OF,m,AW D − ESF,m − EF,m; (2.9) where 0 ≤ SF,m,AW D ≤ Smax

Storage volumes, minimum inﬂows and evaporation losses are aggregated in multi- storage systems (Eqns. 2.10a-c, respectively).

storage n X SF,m,AW D = SF,j,m,AW D (2.10a) j=storage 1 storage n X Imin,m = Imin,j,m (2.10b) j=storage 1 storage n X EF,m = ESF,m (2.10c) j=storage 1

The storage volume vector (SF,AWD) consists of storage volume for consecutive months (m) of a projected future period (P months) given the AWD (Eqn. 2.11).

SF,AWD = {SF,1,AW D,SF,2,AW D, ..., SF,P −1,AW D,SF,P,AW D} (2.11)

The minimum anticipated inflow vector (Imin) consists of the lowest monthly storage inflows for consecutive months of the historic record, commencing in the month of resource assessment (Eqn. 2.12). For example, for a January resource assessment, the first three values in the sequence are 0.264 GL (Jan 1999), 0.398 GL (Jan - Feb 1929) and 0.519 GL (Jan - Mar 1961) based on the historical record at Burrendong Dam (1880 - 2010; NOW, 2012b).

( m+1 P −1 P ) X X X Imin = min Im, min Im, ..., min Im, min Im (2.12) m m m

The projected orders vector (OF,AWD; Eqn. 2.13a) consists of anticipated planned environmental water and general security availability (current balance plus optimized AWD; general security includes adaptive environmental water; Eqn. 2.13b - c), proportioned monthly (E%,m) according to historical pan evaporation (Epan,m; Eqn. 2.14).

A user-speciﬁed reserve volume (GSRV; Gwydir and Macquarie: 5 GL) is held in the

60 2.B Macquarie resource assessment general security account for the ﬁrst 12 months, then released over the following year (Eqn. 2.13b).

OF,AWD = {OF,1,AW D,OF,2,AW D, ..., OF,P −1,AW D,OF,P,AW D} (2.13a)

f(AW D) = OF,m,AW D = E%,m · (PEWCurrRA + GSCurrRA+ (2.13b) AW D · (PEWmax + GSmax) − GSRV )

where GSRV = 0 if m > 12 ∨ GSCurrRA < GSRV (2.13c) E E = pan,m (2.14) %,m P12 m=1 Epan,m The essential supply vector (ESF) consists of the monthly requirements for essential supplies, proportioned according to evaporation, plus an allowance for delivery losses (DL; Gwydir and Macquarie: 6 % of orders) resulting from planned environmental water and general security orders (Eqn. 2.15).

ESF = {ESF,1,ESF,2, ..., ESP −1,1,ESP,1)} (2.15a)

ESF,m = ESreq · E%,m + DL · OF,m,AW D (2.15b)

The projected evaporation loss vector (EF; Eqn. 2.16a) are the monthly losses based on projected surface area (SAF,m) and mean historical monthly pan evaporation at the storage multiplied by a pan factor (Kp; Eqn. 2.16b).

EF = {EF,1,EF,2, ..., EF,P −1,EF,P } (2.16a)

EF,m = SAF,m · Epan,m · Kp (2.16b)

Given equations 2.9 to 2.16, AWD is a positive integer, optimized so that the projected storage volume at any month during the future projected period does not fall below dead storage (SD; Eqn. 2.17).

arg maxAW D f(AW D); where any SF,m,AW D ≥ SD; and (2.17a) AW D > 0; and (2.17b) AW D ∈ (1%, 2%, 3%, ..., (n − 1)%, n%) (2.17c)

The future projection period ends when the sum of minimum anticipated monthly inﬂows equal or exceed essential supplies (ESreq; estimated Macquarie: 170 GL, Win- damere: 8.2 GL; Eqn. 2.18).

P X f(P ) = arg minP Im,min; where f(P ) ≥ ESreq (2.18) m=1

61 2. SIMULATING ENVIRONMENTAL FLOW AVAILABILITY

2.C IQQM Demand sub-model

Planted area in the IQQM Demand sub-model varies in response to past and present water availability and farmers’ uncertainty of future availability (Ribbons and Podger, 2000). Water availability (AW ) is the sum of high security and general security water allocations (VA), carryover from previous years (Vco) and on-farm storage volume (Vn) less volume extracted since the water year began (Vdiv; Eqn. 2.19).

AW = VA + Vco + Vn − Vdiv (2.19) On-farm storage at day t is the storage volume on the previous day plus replenishment from a river into the storage (ROFS), less surface evaporation on the storage

(EOFS) and irrigation withdrawals (WOFS; Eqn. 2.20).

Vn,t = Vn,t−1 + ROFS − EOFS − WOFS (2.20)

Given future water uncertainty, the area to cultivate on the summer and winter sowing dates (Table 2.12) also depends on the risk behaviour of farmers. This behaviour is represented by a linear risk function of the relationship between water availability (A) and planted area (AW ), where slope, a (ha/ML), and the intercept, b (ha), represent the farmer’s risk-taking behaviour (Eqn. 2.21a). Slope and intercept (Table 2.12) are derived from a plot of annual historical water availability against planted area.

Minimum planting area (Amin) is the minimum ﬁnancially viable planted area where farmers expect no further water availability. Maximum planted area (Amax), is the area planted with suﬃcient water availability when constrained by land area and farm development. We assumed all crops were harvested without fail.

A = a · AW + b; (2.21a)

where A = 0 when A < Amin (2.21b)

and A = Amax when A > Amax (2.21c)

Daily irrigation demand is the product of planted area and irrigation depth requirement. Depth is calculated from a soil moisture store which generates irrigation demand when soil moisture is below a target threshold:

Ireq = TWL − SW, if T W L > SW ; (2.22a)

Ireq = 0, if TWL ≤ SW (2.22b)

62 2.C IQQM Demand sub-model

Table 2.12: Parameter values used to conﬁgure the irrigation model for the Gwydir and Macquarie from IQQM, unless speciﬁed otherwise.

Parameter Gwydir Macquarie Summer planting date 1st Oct Winter planting date 1st Apr Slope (a) 0.1382a 0.0619a Intercept (b) 10108a 42099a b Min. planted area (Amin) 10 108 ha 42 099 ha b Max. planted area (Amax) 122 000 ha 76 000 ha

Soil water capacity (SWmax) 300 mm c Eﬀective rainfall (Re) Daily rainfall, less 2 mm

Seepage (SL) 2 mm/day

Pan factor (Kp) 0.7 (CSIRO, 2008a) Regulated river delivery period On-farm storage d Capacity (Vnmax) 472 GL 65 GL Depth 3 me Historical utilization Reserve Irrigation period Maximum ﬁll rate

a Derived from scatterplot of historical regulated water used by irrigated agriculture between 1989 and 1994 (Hope, 2003; Hope and Bennett, 2003) b Proportioned among storages according to the distribution of general security licences (Windamere: 5 %; Burrendong: 95 %; State Water). c Measured at nearest available gauge: Gravesend (54017; Gwydir) and Mendooran Post Oﬃce (64015; Macquarie; Fig. 2.1). d Assumed only below Burrendong Dam. e Actual depth varied between 1 and 5 m (Steinfeld and Kingsford, 2013).

63 2. SIMULATING ENVIRONMENTAL FLOW AVAILABILITY

where Ireq is irrigation demand in millimetres, SW is the soil water level and TWL is the target water level (Eqn. 2.22). The target water level for non-ponded crops is half the soil moisture capacity (SWmax, Eqn. 2.23). SW TWL = max (2.23) 2 Soil moisture varies daily based on the previous days’ soil moisture, eﬀective rainfall and irrigation water on crops, less seepage and evapotranspiration:

ETO.aKc SWt = SWt−1 + Re + Ic − SL − (2.24) aKe where SWc,t is soil moisture for daily timestep, t, Re is effective rainfall, Ic is the irrigation water on crops, SL is actual seepage from the soil moisture store, and evapotranspiration is a function of the reference evapotranspiration (ETO) adjusted by an aggregate crop factor (aKc) and aggregate crop watering efficiency (aKe; Eqn. 2.24). Effective rainfall is the portion of daily rainfall available to meet crop water requirements (Table 2.12; Brouwer and Heibloem, 1986). Seepage is the daily depletion from the soil moisture store (Table 2.12). Reference evapotranspiration is calculated by adjusting daily pan evaporation time series (EEpan) by an empirically derived pan factor

(Kp; Eqn. 2.25; Table 2.12; Brouwer and Heibloem, 1986):

ETO = Kp · Epan (2.25)

Evapotranspiration is estimated by adjusting the reference evapotranspiration by crop factors and crop watering efficiency, aggregated to represent the crop mix within the catchment. Crop mix based on historical data (Table 2.13) is fixed for the period of simulation. Crop factors vary monthly (Table 2.13), accounting for crop and climate characteristics affecting evapotranspiration, including plant morphology, crop development and seasonality. Summer and winter crops are specified using a zero crop factor during the off-season. Crop watering efficiency accounts for losses in delivering water from the extraction point to the plant roots, i.e. leakage and evaporation, and are specific to each crop type (Table 2.13). Crop factors and crop watering efficiency are aggregated by summing the areal weights of crop-specific factors, based on the proportion of planted area (Ap) of each crop, n, using equations 2.26 and 2.27, respectively:

64 2.C IQQM Demand sub-model in the Gwydir and ) c K ) and monthly crop factors e c K JFMAMJJASOND e K 0.8 0.8 0.6 0.4 0 0 0 0 0 0.5 0.2 0.5 0.75 3 0.63 0 0 0 0 0.07 0.28 0.58 0.74 0.7 0.7 0.34 0 11 0.75 0.66 0.7 0.7 0.6 0.6 0.52 0.45 0.5 0.41 0.53 0.56 0.76 < 1 0.8 0.95 0.9< 1 0.8 0.72 0.8 0 0.7 0.55 0 0.55 0.65 0 0.75 0.85 0.2 0.95 0.7 0.7 1 0.71 0.71 0.64 0.4 0 0 < 1 0.9< 1 0.7 0.9< 1 0.75 0.7 0.7< 1 1.15 0.61 0.61 0.9 1.09 0.42 0.38 0.9 0.28 0 0.28 0.9 0 0.28 0 0.83 0.28 0.66 0 0.28 0 0.52 0.65 0 0 0.7 0.66 0.68 0 0.7 0 0.69 0.7 0 0.7 0 0.7 0 0 0.57 0.69 0 0.84 0.43 0.9 0.87 Crop types, crop mix (M; %of total), crop watering eﬃciency ( K ype M 2.13: Crop t CottonLucerne Pasture 84 Summer cerealWinter cereal 1Wheat 0.63 0 0.75Other 0.7 0.7 0.7 0.7 0.6 0.6 0.3 0.6 0.2 0.5 0.45 0 0.4 0 0.45 0.55 0 0.65 0.3 0.7 0.4 0.7 0.52 0.65 CottonLucernePasture 41Summer cereal 14 0.7Winter 16 cereal 0.85 0.7 10Olives 0.7 0.82 19 0.6 0.76 0.8 0.56 0.65 0.59 0.6 0.6 0.56 0.59 0 0 0.6 0.53 0.58 0.53 0.56 0 0 0.34 0 0.54 0.52 0 0 0 0 0.46 0.73 0.5 0.73 0 0 0 0.54 0.73 0.58 0.71 0 0 0 0.59 0.71 0.15 0.6 0.64 0.32 0.6 0.72 0 0.57 0.85 0.47 0 0.77 0.88 0.55 0.6 0 0 Grapes Vegetables Orchard Table the Macquarie river systems (DWE, 2008 a , b ). Gwydir Macquarie

65 2. SIMULATING ENVIRONMENTAL FLOW AVAILABILITY

cropn X aKc = Kc,i · Ap,i (2.26) i=crop1 cropn X aKc = Ke,i · Ap,i (2.27) i=crop1 Irrigation water is withdrawn from on-farm storages in combination with extractions directly from the regulated supply, in proportion to historical utilization (Table 2.12). Maximum daily withdrawal from on-farm storages is limited by pump and channel capacity, modelled by dividing the on-farm storage volume by a user-specified irrigation period (Table 2.12). On-farm storages are replenished when supplementary water is announced, when water orders at the extraction point exceed irrigation requirements (usually due to rainfall after the order date) or when water remains in accounts in major storages near the end of the water year. Maximum daily replenishment is constrained by pump and channel capacity (Table 2.12). Withdrawals are stopped at the end of the water year. Delivery time is less than a day for on-farm storage orders, but regulated river orders are lagged by a number of user-specified days (Table 2.12). We assumed no return flows and empty water stores when t was zero.

66 3

What drives environmental water availability? Inﬂuence of landscape, climate and regulated river management

3.1 Abstract

Water availability underpins ecological and economic productivity in regulated river systems worldwide. Effective planning and management requires understanding of how environmental water availability and management of water in storage varies among river systems. Past attention predominantly focused on streamflows, neglecting the role of management drivers in determining allocations. There is also an increasing importance in the interaction between environmental flows and allocations of water resources. We examined the relative importance of biophysical, climatic and management drivers on three types of water allocations (high security, general security and supplementary, representing different reliabilities) using flexible hydrological simulation models and regression-based sensitivity analyses. We focused on the Gwydir and Macquarie Rivers of the Murray-Darling Basin, where the Australian and New South Wales governments have invested considerable funding to restore degraded wetlands of international significance. Results showed the importance of the biophysical template and management

67 3. WHAT DRIVES ENVIRONMENTAL WATER AVAILABILITY? rules governing allocations. Changes in management rules were likely to have a much greater effect on allocations than plausible climate change. Sensitivity varied according to the reliability of water entitlements. Considering the heterogeneity of drivers among river systems and entitlement classes, environmental water initiatives must incorporate knowledge of these factors to promote effective management of environmental flow allocations within availability constraints. This also places environmental water managers under pressure to ensure that factors affecting allocations, such as management rules and climate impacts, are clearly specified to environmental water managers.

3.2 Introduction

Environmental flows refer to the volume, timing and quality of flows required to sustain aquatic ecosystems and deliver ecosystem services to humans (Poff et al., 1997). Environmental flows restore ecologically important aspects of the natural flow regime in highly regulated and fragmented river systems around the world (Nilsson et al., 2005). Recovery of environmental flows is a major focus of water reform globally. For example, the Australian government has embarked on a $3.1 billion program to purchase water from irrigators and restore environmental flows in rivers (Productiv- ity Commission, 2010), alongside considerable state government investment. In the western United States, governments have purchased water to protect endangered fish (Hollinshead, 2005). Environmental water rights are also recognised in water resource legislation in South Africa (Hughes and Mallory, 2008), and in water policy in eastern Africa (Hirji and Davis, 2009). Environmental flow science and management has paralleled this water reform. Best practice approaches broadly involve defining ecological water requirements (Acreman and Dunbar, 2004; Poff et al., 2010; Tharme, 2003), delivering environmental flows (Harman and Stewardson, 2005; Hughes and Mallory, 2008), and monitoring ecological outcomes in an adaptive learning framework (Kingsford et al., 2011; Reid and Brooks, 2000). However, the ability to provide a desired flow regime and achieve ecological outcomes ultimately depends upon the volume of water available for the environment. This demands quantitative estimates of allocations to inform decision makers. Water availability is the cornerstone of productive river landscapes. The volume of water available for environmental, industrial, urban and human use is spatially and

68 3.2 Introduction temporarily variable. In regulated systems, water available for use is often captured by storages and released to meet demands. Releases are conditional on sufficient availability, system constraints such as channel capacity and operating rules, and the appropriate water licence (Twort et al., 2000). A quantitative understanding of the complex variables driving water available for the environment is critical for effectively delivering ecological outcomes in river systems. In market-based systems (Australia, western United States), this knowledge supports strategic environmental water investment decisions at regional and national scales. Investment decisions involve determining the number, location and reliability of water licences (entitlements, shares, reserve) through time, allowing for optimisa- tion of environmental outcomes across regional or even national scales. Information of allocation drivers can support strategic purchase decisions, by identifying the level of investment yielding optimal environmental allocations and improving the reliability of allocation predictions (Hollinshead, 2005). Knowledge of drivers can also help investors to hedge short and long term risks to allocations (i.e. climate change or shifting political agendas) which can compromise ecological outcomes or investment returns (Productivity Commission, 2010). Similarly, environmental flow managers need to understand drivers of allocations, as these affect their ability to deliver required flow regimes and dictate the ability to deal with future uncertainties (Naiman et al., 2002; Viscito, 2009). From an operational perspective, knowledge of allocation drivers can help to assess the performance of water supply systems, to maximise storage yield for economic and ecological productivity and ensure the economic viability of water supply systems that sell water (McMahon and Adeloye, 2005). Finally, a quantitative understanding of allocation drivers, particularly the influence of climate change, can determine the level of investment required to mitigate adverse impacts on allocations, and consequently, aquatic ecosystems. Strategic environmental water management requires knowledge of drivers of allocations across multiple systems. However there is little published literature examining allocations, with most of our understanding based on studies of streamflow where climatic and biophysical variables dominate. Climate uncertainty, reflecting the stochastic sequence of climatic variables (e.g. short term fluctuations in precipitation, evaporation and solar radiation), drives the streamflow hydrograph (Singh, 1997; Soltani et al.,

69 3. WHAT DRIVES ENVIRONMENTAL WATER AVAILABILITY?

2008). Further, climate change is another key driver, with long term changes in precipitation and evaporation likely to be amplified in runoff, although predictions are highly uncertain (Arnell, 2004; Chiew and McMahon, 2002). Biophysical variables, including catchment size, land cover and geomorphology, govern rainfall-runoff relationships (Li et al., 2012; Singh, 1997). Integrated assessments at local, regional and global scales have assessed the relative influences of climatic and biophysical impacts on streamflow or related indices, including water availability (Li et al., 2012; Williams et al., 2012), water stress (Alcamo et al., 2007), flow regimes (Arrigoni et al., 2010), water security and biodiversity. These studies generally agree that biophysical and climatic factors are dominant drivers of water availability over climate change. They do not examine other important factors influencing allocations, particularly neglecting the important role of management drivers which could also affect environmental flows and ecological outcomes. Management rules are directly controlled by humans (Jakeman et al., 2007) including: allocation rules, water trading, pricing mechanisms, planning instruments and access rules (e.g. commence-to-pump threshold, cease-to-pump threshold, water sharing arrangements, carry-over limits; Jakeman et al., 2007). Most studies examining management drivers are for specific river systems (Gandolfi et al., 1997; Schlüter et al., 2005; Tilmant et al., 2010). Few studies examine effects of management drivers at a regional or global scale because rules are highly heterogeneous and difficult to collate. Of the few studies examining trends across multiple systems, management drivers were examined separately to climatic and biophysical variables (Ringler et al., 2006; Wang and Huang, 2012; Wurbs and Carriere, 1993). Integrated regional assessments of multiple allocation drivers are critical for guiding environmental flow management decisions across multiple systems. Our aim was to: (1) examine the relative contribution of biophysical, climatic and management variables influencing allocations; (2) assess the level of investment required to mitigate adverse effects of drivers on allocations, using climate change as an example; and (3) investigate optimal management scenarios to yield maximum environmental water allocations in specific river systems. We used global regression-based sensitivity analyses to quantify the relative influence of variables on allocations based on daily hydrological simulation models (110 years). We focused on two semi-arid regulated river systems in Australia’s Murray-Darling Basin, the Gwydir and Macquarie

70 3.3 Methods

Table 3.1: Environmental water entitlements (as of March 2012) and total water entitlements for regulated (high security, general security) and unregulated (supplementary) entitlement classes speciﬁed in the Gwydir and Macquarie Water Sharing Plans (NSW Government, 2002, 2003). Environmental water entitlements are held by national and state governments of Australia (SEWPAC, 2012a).

Gwydir Macquarie Environmental Total Environmental Total High security 0 19 293 0 19 419 General security 106 617 509 500 138 672 632 428 Supplementary 19 541 178 000 3 340 50 000

Rivers. These systems support wetlands of international importance under the Ram- sar Convention (Ramsar, 2012). They have also received substantial environmental ﬂow investment from the Australian government Water for the Future initiative ($3.1 billion nationally) and the New South Wales (NSW) state government through the RiverBank, Rivers Environmental Restoration Program and NSW Wetland Recovery Program (over $300 million statewide). Environmental ﬂow entitlements (Table 3.1) aim to support ecosystem function and health in the Ramsar wetlands (Kingsford and Thomas, 1995; Thomas et al., 2011). Additional environmental water is set aside in statutory Water Sharing Plans (Gwydir Environmental Contingency Allowance: 90 GL; Macquarie Wildlife Allocation: 160 GL). Environmental water investments are managed at state and national scales, and environmental water allocations are managed at a catchment scale by water advisory groups (Gwydir: Gwydir Environmental Contin- gency Allowance Operations Advisory Committee; Macquarie: Environmental Flows Reference Group).

3.3 Methods

3.3.1 Study areas (biophysical drivers)

The Gwydir and Macquarie Rivers in Australia’s Murray-Darling Basin represented the biophysical drivers in our analyses. They are characteristic dryland river systems with important differences in catchment area and hydrology. Rivers flow northwest from the Great Dividing Range, travelling through well-defined channels until they reach

71 3. WHAT DRIVES ENVIRONMENTAL WATER AVAILABILITY? semi-arid ﬂoodplains (Fig. 3.1) where they form a maze of interconnected streams,

AUSTRALIA Gwydir River System

Gwydir Wetlands Gwydir River ! !

Myall !Creek ! ! ! ! # ! ! ! ! Horton River

Halls Creek Macquarie Marshes Copeton Dam !

Macquarie-Cudgegong River System

Macquarie River

! !

Talbragar River Cudgegong River ! Windamere Dam

! # ! # ! # Major storages Little River ! ! ! ! Rainfall stations ! Bell River Burrendong Dam ! Rivers & Creeks Wetlands ! ! ! Catchment 0 40 80 160 ! Murray-Darling Basin Kilometers ´

Figure 3.1: Location of the Gwydir and Macquarie Rivers (northwesterly flow) in the Murray-Darling Basin, Australia, showing major storages, rainfall stations used for modelling, rivers and creeks, wetlands, catchment area, Murray-Darling Basin. ephemeral lagoons, distributary creeks and anabranching channels of the Ramsar-listed Gwydir wetlands and Macquarie Marshes. Frequent and complex flood pulse disturbances and subsequent drying drive biological diversity and ecosystem processes of semi-permanent wetlands (Brock et al., 2006). Average surface water availability in the Gwydir (782 GL/y) is lower than the Macquarie (1 567 GL/y), mainly reflecting the smaller catchment area (26 090 km2 and 74 000 km2, respectively; CSIRO, 2007, 2008a). Flow regimes are highly variable, particularly in the Gwydir, where the head- waters are more exposed to tropical weather systems (Blandford et al., n.d.). Flow regimes are almost entirely regulated by major storages (Table 3.2; Fig. 3.1) and mi- nor storages for flood mitigation and water supply to towns and irrigated agriculture

72 3.3 Methods

Table 3.2: Characteristics of major storages in the Gwydir (Copeton Dam) and Macquarie (Windamere and Burrendong Dams) Rivers, including the construction date, capacity, average annual inflow, percent of inflow captured and unregulated tributary flows downstream of storages.

Gwydir Macquarie Copeton Windamere Burrendong Construction date 1976 1984 1967 Capacity (GL) 1 362 368 1 188 + flood mitigation capacity (∼475) Average annual inflow 396.8 56.7 1 005.7 (GL/y)a Inflow captured (%) 93 94 91 Tributary flow (GL/y)a,b 189 n/a 67

a (CSIRO, 2007, 2008a) b Simulated ﬂows July 1990 to June 2006

delivered by channel networks (Steinfeld and Kingsford, 2013; Steinfeld et al., 2013). Tributaries deliver unregulated ﬂows downstream of major storages, with considerably higher tributary contribution in the Gwydir (189 GL/y) than the Macquarie (67 GL/y; CSIRO, 2007, 2008a). The major land cover is dryland pasture for livestock grazing, mixed with irrigated and dryland cropping on the ﬂoodplains (CSIRO, 2007, 2008a).

3.3.2 Modelling climatic drivers

To represent climate uncertainty in our analysis, we randomly generated 100 proba- ble realisations (i.e. time series) of rainfall, evaporation and ﬂow. We sampled non- parametric distributions of observed daily rainfall occurrence and amounts (1900 - 2010; gap-ﬁlled and extended) using an inverse distance weighted interpolation (Mehrotra and Sharma, 2007a). We maintained spatial correlation among multiple rainfall stations (Gwydir: 12; Macquarie: 15; Fig. 3.1) and preserved high and low frequency rainfall variability (90, 365 and 1,000 days) (Mehrotra and Sharma, 2007a). From rainfall, we generated 100 daily realisations of evaporation (k-nearest neighbour interpolation),

73 3. WHAT DRIVES ENVIRONMENTAL WATER AVAILABILITY? then derived daily storage inflow and tributary flows (Gwydir: Halls at station 418025, Myall at 418017, Horton at 418015; Macquarie: Bell at 421018, Talbragar at 418042 (NOW, 2012b); Fig. 3.1) using Sacramento rainfall-runoff sub-basin models (Burnash, 1995) linked to a calibrated Integrated Quality and Quantity Model (Simons et al., 1996). We used annual average storage inflow and tributary flow to represent climate uncertainty in our sensitivity analysis (100 values each) ensuring they were uncorrelated independent variables (Pearson’s correlation coefficient ρ = -0.42). To represent climate change within computational limitations, we reduced historical storage inflow and tributary flows using a Historical (0 %), Moderate (5 %) and Plausible (10 %) reduction in inflow. This range included the projected change in mean annual runoff for ∼2030 at current development (scenario C; Gwydir: 9 % (CSIRO, 2007) and Macquarie: 6 % (CSIRO, 2008a)). We selected three levels to check for non- linear effects which compromise extrapolation between and beyond levels (Frey and Patil, 2002).

3.3.3 Modelling management drivers

We examined the sensitivity of allocations to management rules associated with water allocation. The current water allocation processes in the Gwydir and Macquarie Rivers consist of a long term planning which balances different types of water use, a short term resource assessment for determining allocations, and fixed rules governing account management. Water resource plans (water sharing plans) are long term planning instruments defining tradable water entitlements for agricultural, environmental, urban, stock and domestic needs (NSW Government, 2002, 2003). They also specify unlicenced provisions for system losses, riparian landholders and the environment. Entitlements are classified by security: high security entitlements are regulated and guaranteed in all but record droughts; general security entitlements are also regulated and constitute the bulk of water supply with moderate reliability; and supplementary entitlements allow opportunistic unregulated water access when flows from tributaries and storage spills exceed requirements (Shi, 2006). Allocations for specific entitlements are announced by the government at the start of each water year (July - June) and when there is significant storage accumulation. Allocations are determined by the public water utility (State Water) based on a resource assessment. The resource assessment described fully in Chapter 2 uses a water balance sheet to calculate allocations, while

74 3.3 Methods ensuring sufficient supply for future needs (e.g. unused allocations, planning provisions and operational requirements). Regulated allocations are credited to individual accounts managed by the public water utility and delivered upon request, and unregulated allocations are available during a specified period. Many potential management variables influence water supply (Vogel et al., 2007; Wurbs, 2005). We focused on four water allocation variables that differed between the Gwydir and Macquarie Rivers. Two major differences in the resource assessment process, related to the resource pool, were identified following discussions with regional State Water operators. The resource pool describes the sources of water for allocation (Table 3.3). In the Gwydir, the Debit resource pool considers only the volume in storage, providing full assurance of supply. The more risky Credit approach in the Macquarie defines the resource pool as the volume in storage plus minimum anticipated inflows. Under this approach, water is effectively borrowed from the future to advance the timing of allocations, based on inflow probabilities.

75 3. WHAT DRIVES ENVIRONMENTAL WATER AVAILABILITY? , average annual 2 / y; / y , 1 567 GL 2 is allocated (Macquarie). b Stochastic realisations sampled from water availability 782 GL L: imposes an annualMacquarie); allocation limit (100%; Percent reduction of historicaltributary storage flow inflow and Gwydir); inflow (Gwydir); volume over a future period (Macquarie) non-parametric distributions of daily rainfall Macquarie (M) M: 74 000 km limited (L) Moderate (5%) Predicted (10%) Credit (C) C: StorageDynamic volume (Y) plus minimum anticipated Y: Balance sheet tracks the drawdown of storage 3 Historical (0%) 100 n/a Categorical 2 Allocation Categorical 2 DebitCategorical (D) 2 D: Static Storage (S) volume is allocated (implemented in S: Balance sheet applies to a single point in time Continuous, non- parametric Continuous, unknown distribution (range: 0-100%) a a Input variables selected to represent key drivers in sensitivity analyses. Levels indicate the number and intervals of c 3.3: er system Categorical 2 Gwydir (G) G: Catchment area 26 090 km & input variable Distribution Level Level name Description Riv Resource pool Balance Sheet General security limit Climate uncertainty (storage and tributary flow) Climate change (annual average flow reduction) Table levels selected within the variable distribution. Driver Biophysical Management Climatic

76 3.3 Methods i , F: Carryover subject toand forfeit spill due (Macquarie); to evaporation N : No carryover reduction (Gwydir) A: imposes an accountextraction volume limit limit (1.25 (150%) ML and year per and unit 3 share ML over per 1 unit share over 3 years; Gwydir) limited (F) No forfeit carryover (N) Account (A) Categorical 2 Forfeit carryover t process instituted by public water utility (State Water) c = month of resource assessment, and ending when total inflow equals or exceeds essential supply (Gwydir: 111 GL; Macquarie: 170 GL). i Carryover penalties Total inflow into storage (Gwydir: Copeton Dam; Macquarie: Burrendong Dam) during the driest period on record, commencing in any Resource assessmen Account management rule defined by Water Sharing Plan a b c where

77 3. WHAT DRIVES ENVIRONMENTAL WATER AVAILABILITY?

Another key difference between resource assessment in the Gwydir and Macquarie is the type of balance sheet (Table 3.3). The Static balance sheet, used in the Gwydir, subtracts commitments (e.g. existing water allocations) and future needs (e.g. drought reserve, losses) from available resources to yield allocations. It is independent of future conditions (assumes a constant evaporation and full use of allocations). The Dynamic balance sheet, used in the Macquarie, optimises allocations within the constraints of future projected storage volume. This accounts for seasonally varying evaporation and demand, and allows operators to more accurately estimate future needs. Two further differences in management rules relate to account management, and are described in water resource plans (NSW Government, 2002, 2003). The first relates to limits imposed on general security water. In the Gwydir, the account balance and annual withdrawals were limited (Account limited; Table 3.3), but annual allocations were not limited. In the Macquarie, the account balance and annual withdrawals were not limited, but annual allocations were limited (Allocation limited; Table 3.3). Ac- count limits provide an equitable rationing of storage space, but there are implications for water users who must forgo additional allocations if their account reaches capacity (Hughes and Goesch, 2009). Allocation limits allow more optimal use of storage space, are more favourable for water users who can accumulate allocations over multiple years, however a complication is the potentially inequitable use of storage space. In the Macquarie, this issue is addressed by imposing limits on the volume of allocations individuals may carry between water years (carryover). The second major difference in account management relates to penalties associated with carryover of general security allocations (Table 3.3). Carryover in the Macquarie is subject to evaporation penalties, and may be partially or completely forfeited if storage capacity exceeds full supply (Forfeit), providing incentive to reduce carryover. There are no carryover penalties in the Gwydir (No forfeit), so the costs of individual decisions to store water are absorbed by all water users. Carryover of high security and supplementary allocations between water years is prohibited in both systems.

3.3.4 Modelling water allocations

We used an Environmental Water Allocation Simulator with Hydrology (eWASH) to model allocations because it was rapid, included batch processing, and allowed ﬂexi- ble speciﬁcation of input variables (Table 3.3) without requiring programming of the

78 3.3 Methods underlying code (see Chapter 2 for description). We assumed stationary biophysical, climate and management drivers over the 110 year daily simulation (1900 - 2010; excluding 10 year warm up period). We generated high security, general security and supplementary allocations for every combination of variable levels (9 600 model runs; Table 3.3). We simulated allocations for all users, based on total entitlements, and environmental allocations, based on the environmental entitlements (Fig. 3.1). Individual extractive water users were lumped as a single ‘average’ user, according to an empirical relationship between season and mean monthly historical demand, assumed to be stationary. Our model was relevant to storage management so we assumed downstream ﬂow requirements were met by storage releases (NSW Government, 2002, 2003).

3.3.5 Sensitivity analysis

We used regression-based sensitivity analysis, routinely applied in hydrology (Christi- aens and Feyen, 2002; Sieber and Uhlenbrook, 2005) and aquatic ecology (Ginot et al., 2006), to measure and prioritise the influence of variables on allocations (Bahremand and De Smedt, 2008; Saltelli et al., 2008). Variable selection was informed by prior knowledge, because few variables contributed to most variability and including non- sensitive variables was inefficient (Bahremand and De Smedt, 2008). We focused on biophysical, climatic and management drivers influencing total allocations rather than demand drivers which influenced the distribution of allocations among users. Biophys- ical and management input variables were dichotomous (two categories represented the parameter space), climate change was ordinal (three categories) and climate uncertainty was continuous (100 values; Fig. 3.3). We compared categorical variables using orthogonal contrasts, defined differently for dichotomous and ordinal variables (Crawley, 2007). For dichotomous variables, the model mean treated the alphabeti- cally highest level as the baseline, and the coefficient represented the mean deviation from baseline due to the second level (i.e. for river system, Gwydir was baseline and the deviation was Macquarie). For ordinal variables, we measured the mean deviation from historical compared to other levels: for climate change, Historical climate was the baseline to which Moderate and Plausible levels were compared. Continuous input variables were included as covariates while the continuous response variables were annual average allocations for each class available under total entitlements (i.e. for extractive

79 3. WHAT DRIVES ENVIRONMENTAL WATER AVAILABILITY? and environmental use; Fig. 3.1). We used annual averages for continuous variables to align with management of extractive and environmental water decisions. We fitted separate least squares multiple regression relationships between input variables and each response variable. Regression coefficients represented average sensitivity of the corresponding input variable (Fig. 3.3), indicative of the value of linear influence of the variable on the model (Ekstrom, 2005). Regressions were fitted with all independent variables and second order interaction terms, then backward elimination of non-significant variables (p≥0.05) resulted in model parsimony. Family-wise error for three models was corrected using a Bonferroni adjustment (R Development Core Team, 2009). We examined scatterplots to ensure a monotonic relationship between inputs and output (i.e. the order of the output relative to the input was preserved; Ekstrom, 2005). Coefficients within 0.001 units of zero were removed. We checked for constant variance by plotting residuals against fitted values. Regression terms, including interactions, were assumed to be additive. To improve normality of residuals and model robustness, we refitted models using rank transformed response variables (Frey and Patil, 2002), with multiple regression analyses. We analysed rank and raw regression coefficients together (Iman and Conover, 1979). The former showed the order of importance of variables and nature of correlation (i.e. positive or negative) and the latter represented the magnitude of the sensitivity. Due to limitations in sampling the entire climate change parameter space, we assessed the potential to extrapolate effects on allocations. To test linearity, we fitted simple least squares regression models with the climate change (% reduction in historical flow) as the predictor variable and mean annual allocations for each class as the response variables. We specified the orthogonal contrasts so that regression coefficients represented equal climate change deviations (5 %). For example, the first coefficient

(β1) represented the mean deviation of a 5 % reduction in flow compared to a historical climate, and the second coefficient (β2) represented the mean deviation between a 5 % and 10 % reduction in flow. A ratio of climate change coefficients β1 approximating β2 one indicated a linear relationship within the tested range.

3.3.6 Mitigating impacts on environmental water allocations

Adverse impacts of drivers on environmental water allocations can be mitigated with investment in environmental water entitlements. We estimated the number and cost

80 3.4 Results of additional general security entitlements required to mitigate a moderate and plausible change in climate (5 % and 10 % reduction in historical ﬂows, respectively) in the Gwydir and Macquarie under current management rules. Using an iterative process, we increased the number of environmental entitlements in our simulation by 10 000 (equivalent to 10 GL) until median environmental water allocations under climate change scenarios equalled or exceeded median allocations under the historical climate scenario. We then calculated the ﬁnancial investment required to mitigate climate change, by multiplying the number of additional general security entitlements with average (2011 - 2012) water market prices (PSI Delta, 2012).

3.3.7 Assessing policies for environmental water allocations

We examined the quantities of environmental water allocations derived from the combined inﬂuence of management drivers. We focused on general security environmental water, the largest class of environmental water entitlement (Table 3.1), although the environment also received water from planning provisions and general system losses (Productivity Commission, 2010). Environmental water allocations were a component of allocations, based on the environmental fraction of total entitlements (Fig. 3.1), subject to unique demand behaviour (e.g. allocations were forgone in the Gwydir if the environmental account was 150 %). We used box plots to visualise environmental allocations under 16 unique combinations of four management arrangements (Fig. 3.3) in the Gwydir and Macquarie under a historical climate. Each box represented the distribution of average allocations due to climate uncertainty (100 levels). To identify optimal and sub-optimal management rules, we ranked scenarios in descending order of performance, based on median annual average environmental water allocation (GL/y). We also assessed the performance of current management rules for each system based on relative rank.

3.4 Results

Allocations (mean high security, general security, supplementary classes) were sensitive to biophysical and management variables, moderately sensitive to climate change, and robust to climate uncertainty (Fig. 3.2). Sensitivity varied according to classes: general security was most sensitive and high security least sensitive. Consequently, these

81 3. WHAT DRIVES ENVIRONMENTAL WATER AVAILABILITY?

100

Drivers 60 Biophysical x Climatic Climatic Management x Climatic Management 40 Management x Biophysical Biophysical Allocation sensitivity (%) Allocation

High General Supplementary security security

Figure 3.2: The relative contribution of drivers (biophysical, climatic, management) and their interactions to percentage sensitivity of high security, general security and supplementary allocations (means). Sensitive variables were statistically significant based on a rank regression (p<0.05). changes affected environmental water allocations, reflected in the classes of environmental entitlement, providing the opportunity to identify optimal and sub-optimal management rules for environmental water allocations (Fig. 3.3). Allocations were sensitive to climate change but we showed that reduced environmental allocations predicted under a drying climate could be mitigated by increasing environmental entitlements (Fig. 3.4).

3.4.1 Sensitivity of variables

We developed models with high goodness-of-ﬁt explaining sensitivity of high security, general security and supplementary allocations (R2 of 0.75, 0.97 and 0.96, respectively). Management and biophysical variables contributed to almost all signiﬁcant output sen-

82 3.4 Results

10 Mean environmental water allocation (GL/yr) water Mean environmental D−S−A−N ** D−S−A−F D−S−L−N D−S−L−F C−S−A−N C−S−A−F C−S−L−N C−S−L−F D−Y−A−F D−Y−A−N D−Y−L−F D−Y−L−N C−Y−A−F C−Y−A−N C−Y−L−F C−Y−L−N 0

(a) Gwydir

120

100

Mean environmental water allocation (GL/yr) water Mean environmental 20

0 D−S−A−F D−S−A−N D−S−L−F D−S−L−N C−S−A−F D−Y−A−F C−S−A−N C−Y−A−F D−Y−L−F D−Y−A−N D−Y−L−N C−Y−L−F ** C−Y−A−N C−Y−L−N C−S−L−N C−S−L−F

(b) Macquarie

Figure 3.3: Mean environmental water allocations (GL/y) from currently held general security entitlements under all combinations of four management variables for the (a) Gwydir and (b) Macquarie under historical climate. Management variables were the resource pool (Credit (C) and Debit (D)), balance sheet (Static (S) and Dynamic (D)), general security limit (Allocation limited (L) and Account limited (A)), and carryover penalties (Forfeit (F) and No forfeit (N)). Box (25th, median and 75th percentiles) and whisker (5th and 95th percentiles) plots represented uncertainty in allocations due to climate (100 values derived for each 110 year simulation). Current management arrangements in the Gwydir and Macquarie were indicated by asterisks (**) and a dashed horizontal line (median).

83 3. WHAT DRIVES ENVIRONMENTAL WATER AVAILABILITY?

Environmental entitlements Current Current+10GL Current+20GL

● 32 ● ● 30 Gwydir

28 ● ● ●

26 ● ●

24 ● ● 70 ● ● Macquarie

65 ● ● ● Mean annual allocations (GL/yr) Mean annual ● 60 ●

● 0% 5% 10% Climate change (reduction in historical flow)

Figure 3.4: Median of 100 mean annual environmental water allocations (GL/y) under climate change scenarios (0, 5 and 10 % reduction of historical flow) in the Gwydir and Macquarie Rivers, with three environmental entitlement levels (general security; current, current + 10 GL, current + 20 GL). sitivity (98 %) for high security allocations (Fig. 3.2). Management variables (resource pool, balance sheet, general security limit, carryover penalties) alone explained about half output variation (52 %), with their interaction with biophysical variables contribut- ing to further variation (29 %), while biophysical factors made up the final significant contribution (17 %). There was only a small influence of climate (1 %) which interacted with biophysical (1 %) and management (1 %) variables. For general security allocations, management and biophysical variables contributed to sensitivity (95 %; Fig. 3.2). Biophysical variables explained most of the variation (51 %), then management variables (23 %), followed by their interaction (21 %). Climate (1 %) and its interaction with biophysical variables (3 %) had a small influence. Biophysical variables affected most (93 %) of the sensitivity of supplementary allocations with a small influence of climate

84 3.4 Results

Table 3.4: Coeﬃcients of climate change variables (β1 and β2) and their ratio in three regression models. Coeﬃcients showed mean deviation in annual average allocations for

entitlement classes (GL/y) due to a 5% ﬂow reduction in inﬂows to the storage (β1: 0 -

5%; β2: 5 - 10%). A ratio approximating one suggested linearity between input (climate change) and output (average allocations) variables.

Entitlement class β β β1 1 1 β2 High security -0.069 -0.0641a 1.08 General security -12.28 -12.191a 1.01 Supplementary -1.85 -1.841a 1.01 ap<0.05 alone (3 %), and through interaction with biophysical (2 %) and management (2 %) variables (Fig. 3.2). The most dominant combination of input variables changed general security allocations the most (233.0 GL/y), followed by supplementary (20.4 GL/y) and then high security (6.3 GL/y). There was evidence of a linear relationship between climate change and allocations for all allocation classes (Table 3.4), as the ratio of coeﬃcients approximated one.

3.4.2 High security allocations

Mean high security allocations were significantly sensitive to 11 significant rank regression terms, reflecting variation in five variables (single or interacting): balance sheet, resource pool, river system, general security limit and climate change (10 % reduction; Fig. 3.5). Contrastingly, high security allocations were not sensitive to variations in the carryover penalties, small changes in climate (5 % reduction in historical flow) or climate uncertainty (storage inflow, tributary flow; Appendix 3.A). High security allocations were most sensitive to the interaction of two management variables: balance sheet and resource pool (Fig. 3.5). The raw regression model estimated a mean increase of 1.24 GL/y in mean high security allocations with a Dynamic balance sheet and Credit resource pool (Appendix 3.A). The second most important term was an interaction between the river system and balance sheet (Fig. 3.5), with decreased mean high security allocations (−1.16 GL/y) in the Macquarie with a Dynamic balance sheet (Appendix 3.A). Next, three single variables affected mean high security allocations: resource pool (Credit: −1.33 GL/y), river system (Macquarie: 0.80 GL/y)

85 3. WHAT DRIVES ENVIRONMENTAL WATER AVAILABILITY?

Rank Raw Balance sheet (Y) x Resource pool (C) Balance sheet (Y) x River system (M) Resource pool (C) River system (M) Balance sheet (Y) General Security limit (L) x River system (M) Resource pool (C) x River system (M) Balance sheet (Y) x General Security limit (L) Climate change (−10%) x River system (M) Balance sheet (Y) x Climate change (−10%) Climate change (−10%) Balance sheet (Y) x River system (G) Carryover penalties (F) x River system (M) Climate change (−10%) x Resource pool (C)

−10 0 10 20 −10 0 10 20 Volume (GL/yr)

Figure 3.5: Fourteen coefficients of the rank regression (11 significant) and raw regression (12 significant) and the corresponding regression term influencing mean high security allocations (p<0.05). The magnitude of regression coefficients (ranked, positive or negative) measured the average change in volume of mean high security allocations if the level of the variable shown in parentheses was satisfied. Variable levels (Dynamic (Y), Credit (C), Macquarie (M), Allocation limited (L) and Forfeit (F)) are described in Table 3.3. and balance sheet (negative effect of Dynamic balance sheet, not significant in raw model; Appendix 3.A). The next two significant terms were interactions between river system (Macquarie) and general security limit, and resource pool (Fig. 3.5). For the Macquarie, the effect reduced mean allocations when general security allocations were limited (−0.11 GL/y), and increased mean allocations when a Credit resource pool was used (1.07 GL/y; Appendix 3.A). The magnitude of the latter effect exceeded the effect of the more important term in the regression, indicating a discrepancy between the rank and raw results. Mean high security allocations were also sensitive to the interaction of balance sheet and general security limit (Fig. 3.5), increasing allocations under a Dynamic balance sheet with general security limited by Allocation (0.05 GL/y; Appendix 3.A). Climate change (10 % reduction of historical flow) and its interactions had a small but significant influence on high security allocations.

86 3.4 Results

3.4.3 General security allocations

Mean general security allocations were sensitive to changes in 14 signiﬁcant rank regression terms of six single or interacting variables: river system, resource pool, balance sheet, carryover penalties, climate change (10 % reduction of historical ﬂow) and general security limit (Fig. 3.6; Appendix 3.A). Mean general security allocations were not

Rank Raw River system (M) Resource pool (C) x River system (M) Balance sheet (Y) Resource pool (C) Balance sheet (Y) x Resource pool (C) Balance sheet (Y) x River system (M) Carryover penalties (F) x River system (M) Climate change (−10%) x River system (M) Climate change (−10%) Carryover penalties (F) x General Security limit (L) General Security limit (L) General Security limit (L) x River system (M) Carryover penalties (F) x River system (G) Balance sheet (Y) x Carryover penalties (F) Carryover penalties (F) General Security limit (L) x Resource pool (C) Carryover penalties (F) x Resource pool (C) Climate change (−10%) x Resource pool (C)

−20 0 20 −20 0 20 Volume (GL/yr)

Figure 3.6: Eighteen coefficients of the rank regression regression (14 significant) and raw regression (14 significant) and the corresponding regression term influencing mean general security allocations (p<0.05). The magnitude of regression coefficients (ranked, positive or negative) measured the average change in volume of mean high security allocations if the level of the variable (shown in parentheses) was satisfied. Variable levels (Macquarie (M), Credit (C), Dynamic (Y), Forfeit (F) and Allocation limited (L)) are described in Table 3.3. affected by climate uncertainty (storage inflow, tributary flow) and small changes in climate (5 % reduction of historical flow; Appendix 3.A). Mean general security allocations were most sensitive to biophysical variables (i.e. river system; Fig. 3.6). Estimated

87 3. WHAT DRIVES ENVIRONMENTAL WATER AVAILABILITY? mean annual allocations in the Macquarie were 34.47 GL/y higher than in the Gwydir (Appendix 3.A). The second most important term, the interaction between river system and resource pool, decreased general security allocations (−8.53 GL/y) in the Macquarie with a Credit resource pool (Fig. 3.6). The balance sheet variable was ranked third (Fig. 3.6), with the Dynamic balance sheet reducing mean allocations (−19.32 GL/y), compared to Debit (Appendix 3.A). Allocations were also sensitive to the resource pool, with the Credit resource pool reducing general security allocations (−9.40 GL/y), but its interaction with the Dynamic balance sheet positively influenced allocations (25.74 GL/y; Appendix 3.A). The next most sensitive variables were the interaction between river system and balance sheet, and river system and carryover penalty (Fig. 3.6). These reduced general security allocations in the Macquarie, with a Dynamic balance sheet (−31.17 GL/y) and with Forfeit carryover penalties (−35.96 GL/y; Ap- pendix 3.A). In the Gwydir, there was a smaller effect on allocations (ranked 13th), although the raw coefficient was not significant (Appendix 3.A). Allocations were also sensitive to the interaction of river system and climate change (Fig. 3.6). Mean general security allocations decreased in the Macquarie with a 10 % reduction in historical flow, but this term was not significant in the raw regression results. Similarly, the influence of climate change (10 % reduction) was significant in the ranked regression but not in the raw regression. The next three variables, associated with the general security limit, were significant in the raw regression but not the rank regression (Fig. 3.6). Combined with Forfeit carryover penalties, Allocation limited general security level decreased mean general security allocations (−9.17 GL/y), but alone produced the opposite effect (14.35 GL/y), and an interaction with river system enhanced allocations in the Macquarie (34.49 GL/y; Appendix 3.A). The variable with the smallest significant influence on mean general security allocations was the negative interaction between the balance sheet and carryover penalties (Fig. 3.6), although the corresponding term in the raw regression was not significant. Four terms in the raw regression were not significant in the rank regression and so were assumed not to affect allocations (Fig. 3.6).

3.4.4 Supplementary allocations

Mean supplementary allocations were sensitive to six rank regression terms, compris- ing four single or interacting variables: river system, climate change (5 % and 10 %

88 3.4 Results reduction of historical ﬂow), resource pool and balance sheet (Fig. 3.7; Appendix 3.A).

Rank Raw

River system (M)

Climate change (−10%) x River system (M)

Climate change (−10%)

Resource pool (C)

Climate change (−5%)

Balance sheet (Y) x Resource pool (D)

Resource pool (C) x River system (M)

Resource pool (C) x River system (G)

−25 −20 −15 −10 −5 0 −25 −20 −15 −10 −5 0 Volume (GL/yr)

Figure 3.7: Eight coefficients of the rank regression (6 significant) and raw regression (5 significant) and the corresponding regression term influencing mean supplementary allocations (p<0.05). The magnitude of regression coefficients (ranked, positive or negative) measured the average change in volume of mean high security allocations if the level of the variable (shown in parentheses) was satisfied. Variable levels (Macquarie (M), Credit (C), Dynamic (Y), Debit (D) and Gwydir (G) are described in Table 3.3.

Mean supplementary allocations were not affected by carryover penalties, general security limit and climate uncertainty (mean inflow, mean tributary flow). The size of the effect of the significant variables on supplementary allocations depended on the river system (Fig. 3.7), with reductions in mean allocations in the Macquarie (−17.99 GL/y; Appendix 3.A), compared to the Gwydir. Mean supplementary allocations were slightly sensitive to the interaction between river system and climate change (10 % reduction), increasing allocations in the Macquarie (1.12 GL/y). Climate change alone (10 % reduction of historical flow) reduced mean allocations in both systems (−0.51 GL/y). There was a small increase in mean supplementary allocations when a Credit resource pool was used, and with a small change in climate, however the magnitude of this increase was unclear because this term was not significant in the raw regression model (Fig. 3.7).

89 3. WHAT DRIVES ENVIRONMENTAL WATER AVAILABILITY?

Table 3.5: Number and cost (Australian dollars; AUD) of entitlements required to mitigate eﬀects of a moderate and plausible change in climate (5 % and 10 % reduction of historical ﬂow, respectively) on environmental water (general security) allocations in the Gwydir and Macquarie Rivers. Costs per entitlement were average market prices (2011 - 2012; PSI Delta, 2012).

River system Inﬂow Additional Cost per Total reduction entitlements entitlement cost (%) required (GL) ($/ML) ($’000) Gwydir 5 10 2 194 21 940 10 20 2 194 43 880 Macquarie 5 10 1 250 12 500 10 20 1 250 25 000

3.4.5 Impacts on environmental allocations

Reductions in flows significantly reduced average environmental water allocations from general security entitlements, but impacts could be mitigated (medians; Fig. 3.4). A 5 % reduction in historical flows reduced average allocations in the Gwydir from 28.09 GL/y (0 %) to 25.95 GL/y (5 %), a difference of 2.14 GL/y, and in the Macquarie from 63.06 GL/y (0 %) to 59.61 GL/y (5 %), a difference of 3.45 GL/y (Fig. 3.4). With a 10 % reduction in inflows, average allocations were further reduced compared to historical, by 4.36 GL/y in the Gwydir (to 23.73 GL/y) and 6.29 GL/y in the Macquarie (to 56.74 GL/y; Fig. 3.4). To mitigate the effects of climate change, allocations would need to equal or exceed historical (28.09 GL/y in the Gwydir and 63.03 GL/y in the Macquarie; Fig. 3.4; current entitlements and 0 % inflow reduction). A 10 GL increase in entitlements produced sufficient allocations to mitigate effects of a 5 % reduction in flows (Gwydir: 28.22 GL/y; Macquarie: 63.82 GL/y; Fig. 3.4; current + 10 GL entitlements and 5 % inflow reduction). However, it was insufficient to mitigate the effects of a 10 % reduction in flows (Gwydir: 25.94 GL/y; Macquarie: 60.62 GL/y; current + 10 GL entitlements and 10 % inflow reduction), but it was mitigated with a 20 GL increase in entitlements (Gwydir: 28.16 GL/y; Macquarie: 64.79 GL/y; current + 20GL entitlements and 10 % inflow reduction). These would require investment at current market prices for water (Table 3.5). Mitigating effects of a 5 % reduction in historical

90 3.5 Discussion

ﬂow would cost about $22 million in the Gwydir and $12.5 million in the Macquarie (Table 3.5) while a 10 % reduction would cost about $44 million in the Gwydir and $25 million in the Macquarie. Lower costs in the Macquarie reﬂected lower market prices (Table 3.5).

3.4.6 Performance of management rules for environmental ﬂow (general security)

Diﬀerent management scenarios altered mean general security environmental allocations (Fig. 3.3) but this varied with the river system. The worst scenario for the Gwydir was the Credit resource pool, with a Dynamic balance sheet, Allocation limited general security and No forfeit of carryover water (C-Y-L-N; 28.09 GL/y; Fig. 3.3a), while in the Macquarie it was the Credit resource pool, with a Static balance sheet, Al- location limited general security and Forfeit of carryover water (C-S-L-F; 61.50 GL/y; Fig. 3.3b). The best scenario for the Gwydir was the Debit resource pool, with a Static balance sheet, Account limited general security and No forfeit of carryover (D-S-A-N; 24.65 GL/y; Fig. 3.3a), while for the Macquarie, it was the Debit resource pool, with a Static balance sheet, Account limited general security and Forfeit of carryover (D-S- A-N; 86.70 GL/y; Fig. 3.3b). Switching from the worst to the best scenario increased environmental ﬂows by 3.45 GL/y of median average allocations in the Gwydir (Fig. 3.3a), and 25.20 GL/y in the Macquarie (Fig. 3.3b). For the Gwydir, the current scenario (D-S-A-N) was the best, maximising mean allocations (28.09 GL/y; Fig. 3.3a). However, the current scenario for the Macquarie (C-Y-L-F) ranked 12th of 16 scenarios (63.03 GL/y; Fig. 3.3b).

3.5 Discussion

Aquatic ecosystems are highly vulnerable to shifting flow regimes fundamentally altered by river regulation, including the building of dams and diversion of water (Bunn and Arthington, 2002; Magilligan and Nislow, 2005; Nilsson and Svedmark, 2002). Increasingly governments are dealing with degradation of rivers and their dependent ecosystems through the protection of environmental flows but this is often stored in dams in regulated rivers and so it needs to be managed. Understanding the drivers of environmental water availability, particularly anthropogenic influence beyond natural

91 3. WHAT DRIVES ENVIRONMENTAL WATER AVAILABILITY? variability, may help make informed decisions about management of aquatic ecosystems. Our regional synthesis showed climatic and management drivers influenced regulated water allocation classes (high security; general security). Changes in management rules were likely to have a much greater effect on allocations than plausible climate change (Fig. 3.2). Unlike regulated allocations, unregulated allocations for opportunistic environmental watering were relatively robust to management drivers, but sensitive to biophysical and climatic drivers (supplementary; Fig. 3.2). We examined general factors driving allocation sensitivity, but highlighted the heterogeneity among river systems and entitlement classes (biophysical; Fig. 3.2). This has important implications for environmental water management requiring knowledge of allocation availability, and investment requiring an optimal portfolio of water products at regional and national scales. With considerable variation in management rules among river systems, we identified the importance of clearly justified, evidenced-based management decisions to support optimal ecological benefits. We examined water allocated under entitlements (SEWPAC, 2012a), but similar effects are likely for variable water supply from environmental reserves (i.e. planned environmental water (Australia; Grafton, 2010) or ecological reserves (South Africa; Hughes and Mallory, 2008)). Biophysical differences were a fundamental driver of regulated and unregulated allocations, reflecting contextual differences between the two rivers examined. Regulated allocations were higher in the Macquarie (Figs. 3.5 and 3.6), where storage inflow is considerably higher than the Gwydir (Table 3.2). Also, major storages control relatively more flow in the Macquarie River compared to the Gwydir River. Consequently, unregulated allocations were higher in the Gwydir (Fig. 3.7), where unregulated tributaries delivered a major component of flows (Table 3.2). Flow variability was also predicted to affect allocations (McMahon and Adeloye, 2005), and probably explained the reduced regulated allocations in the Gwydir, a more variable system than the Macquarie (Figs. 3.5 and 3.6). These semi-arid river systems are highly variable, and further investigation of effects in more stable river systems would be warranted. While contextual differences in water availability and variability explained allocation sensitivity, regional differences in entitlements also likely contributed (Table 3.1). For example, reduced high security allocations in the Gwydir (Fig. 3.5) probably reflected fewer of these entitlements compared to the Macquarie River (Table 3.1). Similar trends occurred for

92 3.5 Discussion general security and supplementary classes (Figs. 3.6 and 3.7). Numbers of entitlements given in water resource plans (‘Total’ column in Table 3.1; NSW Government, 2002, 2003) probably reflected water availability, historic provision by government and irrigation demand. Climate change is predicted to affect water availability (Chiew et al., 2009), and consequently allocations but the impact was small albeit significant. This reflected the relative magnitude of the effect compared to anthropogenic modifications (Fig. 3.2), as in other systems (Arrigoni et al., 2010; Beck and Bernauer, 2011). Effects increased with severity of climate change, with unregulated allocations more susceptible to impacts than regulated allocations (Appendix 3.6). The linear relationship between climate (inflow reduction) and allocations (Table 3.4), provided confidence to interpolate effects within the range tested (i.e. 0 % - 10 % reduction in historical inflow) but further changes should be examined (e.g. >10 % reduction, or increases, compared to historical). While allocations were sensitive to climate change, they were robust to climate uncertainty (Figs. 3.5, 3.6 and 3.7). This is important for water resource management, given the variability of rainfall and flows. We did not specifically examine changes to the distribution (i.e. variability) of climate variables likely under a changing climate, such as increased frequency of extreme floods and droughts (Easterling et al., 2000) but this would be possible using our models. The most important management variables affecting regulated allocations were the resource assessment process (resource pool and balance sheet) of the public water utility. The least influential variables (carryover penalties and general security limit) were account management rules, stipulated by water resource plans (Fig. 3.6). Contrast- ingly, the resource assessment process is not publicly documented. The influence of management drivers on allocations was complex, with multiple rules causing additive, synergistic and antagonistic interactions. For example, a Credit resource pool and Dy- namic balance sheet individually decreased high security (Fig. 3.5) and general security (Fig. 3.6) allocations but, in combination, they increased allocations. This is likely due to a complex feedback loop that increases demand and lowers storage volume, consequently reducing spills and evaporation losses. There was further spatial variation, reflected in interactions between river systems. The ability to generalise results depended on the assumptions underpinning the raw and rank regression. Quantitative results of the raw regression analysis were likely to

93 3. WHAT DRIVES ENVIRONMENTAL WATER AVAILABILITY? apply to rivers from the same statistical distribution, such as those in the northern (Darling) Basin (e.g. Namoi, Lachlan, Border Rivers) which share similar underlying process including geology and land use history (Saintilan and Overton 2010). Raw regression results are not likely to apply in rivers beyond the northern Murray-Darling Basin because of differences in the rainfall-runoff relationships affected by drainage rates, soil moisture storage capacities and flow interceptions (i.e. farm dams or plantations capturing runoff before reaching the river). For these systems, the qualitative conclusions drawn from the non-parametric rank regression were more robust and suitable, however further testing is required. Environmental water management must incorporate the effects of biophysical, climatic and management drivers on different allocation classes. Already, carryover provisions and trading limitations are considered in environmental water investment and management in Australia because they influence allocation volume and timing (DE- WHA, 2009). Clearly, others such as the resource pool, outside the environmental flow decision-making need to be considered, given our results (Figs. 3.5, 3.6 and 3.7). They warrant far more consideration than general security limit and carryover penalties for regulated allocations (Figs. 3.5, 3.6 and 3.7). It is also possible to use an integrated model to identify how to mitigate the potential adverse influence of flow reductions from climate change, including the level of investment required (Table 3.4). Climate extremes may require increased investment if environmental objectives are to be maintained (Easterling et al., 2000). Similarly, it would be possible to estimate costs of compensating the impacts of other significant drivers (Figs. 3.5, 3.6 and 3.7). Clear specification of management rules and their effects on environmental flow allocations is critical for developing effective environmental water flow regimes and for allowing investors to properly hedge across multiple systems and entitlements (Young and McColl, 2009). Failure to consider management variables may result in sub-optimal environmental flow allocations (Fig. 3.3), but other important considerations may produce sub-optimal ecological outcomes or inefficient environmental flow management. For example, the risk of temporarily losing access to environmental flow allocations is increased under the Credit resource pool, because flows are borrowed from the future and may not arrive (Pittock and Finlayson, 2011). This occurred when access to environmental flow allocations was suspended in rivers operating under the Credit resource pool (Macquarie, Murrumbidgee, Lachlan Rivers of the Murray-Darling Basin)

94 3.5 Discussion during the millennium drought, but not in rivers operating under a Debit resource pool (Gwydir, Namoi or Border Rivers; NOW, 2011). These risks must be weighed with the benefits of the Credit resource pool strategy, which advances the timing of water allocations to benefit water use planning decisions (Gong et al., 2010). Other risks are associated with the delivery of allocations, including physical constraints that may prevent water from reaching target wetlands (Steinfeld and Kingsford, 2013). The failure to consider management drivers of allocations may be due to poor specification of variables affecting allocations. There were important discretionary differences (e.g. carryover penalties, general security limit; Figs. 3.5, 3.6 and 3.7) in water resource plans, developed by state government water managers with local stakeholders. These differences affected allocations, but no public documentation exists for the process used to develop or justify the rules, nor does any specification exist for the resource assessment process, again significantly affecting environmental flow allocations. Further, other poorly specified drivers (e.g. unmetered water use, plantations, evaporation loss) may also undermine the reliability of allocations (Young and McColl, 2009). Consequently, the effects of management rules and other drivers on allocations remain unclear, with important ramifications for environmental water management. Water management decisions must be transparent, scientifically defensible and clearly justified (Welsh et al., 2013). Evidence-based techniques, such as sensitivity analysis, can support and consolidate environmental flow and more generally water management decisions at regional and national scales while recognising local heterogeneity. We demonstrated its application in assessing current and potential performance of management rules at multiple scales. Despite being governed by the same state and national policies, there was considerable variation in management between the Gwydir and Macquarie, with significant effects on environmental flow allocations. Encouragingly, the current management rules in the Gwydir performed optimally, yielding the highest environmental water allocations among 16 alternative scenarios but the management of the Macquarie was sub-optimal for the environment (ranked 12th; Fig. 3.3). The best management scenario for environmental flow management in both systems was the Debit resource pool, Static balance sheet and general security account limits (Fig. 3.3). Implementation of the carryover penalty was context dependent. There was considerable opportunity to improve or degrade environmental flow allocations by adjusting management. This showed the best potential for immediately and

95 3. WHAT DRIVES ENVIRONMENTAL WATER AVAILABILITY? effectively increasing effectiveness of environmental flows because, unlike biophysical and climatic drivers, management is relatively centralised and directly influenced by humans. There may be further potential to increase allocations by incorporating other rules, mathematically optimising management (Grafton et al., 2011) or through decentralised approaches including regulating land use and demand management. However, the scenario yielding highest allocations may not necessarily be the best for managing for multiple outcomes (Vogel et al., 2007). Integrated assessment of management rules is encouraged to keep sight of broader environment, economic and social objectives (Letcher et al., 2004).

3.6 Conclusions

Our understanding of factors driving allocations has lagged because complex and heterogeneous management rules are difficult to generalise across multiple river systems. Our regional assessment of the effects of biophysical, climatic and management drivers on allocations showed clear trends across rivers, while recognising important contextual interactions. Such information is appropriate for environmental water management delivered at regional or national scales but implemented discretionarily among catchments. Environmental water managers have recognised the value of considering allocation drivers in decisions but collating and modelling heterogeneous and sometimes poorly specified water management rules is difficult. Sensitivity analysis may overcome this challenge by providing evidence for systematic and transparent water management decisions, affected by many different drivers including biophysical, climatic and management. Further studies can build on this work by optimising management scenarios for environmental water allocations, and examining sensitivity of allocations to plantations, farm dams, water trading and socio-economic factors. Such knowledge of water allocations is fundamental to inform environmental water management, and encourages transparent and sustainable management of freshwater systems.

96 3.A Regression coeﬃcients

3.A Regression coeﬃcients

97 3. WHAT DRIVES ENVIRONMENTAL WATER AVAILABILITY? 0.17** < 0.001* -2.05** -4.81** < 0.001** < 0.001** < 0.001** < 0.001** -0.08* 0.24** -2.48** -19.32** -0.34** -9.17** < 0.001** 0.21** < 0.001** < 0.001** < 0.001** Security General security Supplementary < 0.05). Coefficients represented the contribution of each term High 0.80** 0.05** -3.68** -0.51** Rank Raw Rank Raw Rank Raw 19.77** 1.24** 1.65** 25.74** -17.22** -1.16** -1.45** -31.17** (Y) All rank and raw regression terms assessed for their influence on high security, general security and supplementary water 3.6: Table allocations, with corresponding coefficientsto for the significant response terms variable, (p averagesatisfied. annual Single asterisks allocations (*) (GL/y) indicated signifiance ifterms (p) and the at terms 0.05, level with and of double small asterisks the coefficientsGeneral (**) ( < 0.001) variable indicated Security did significance (shown (GS), not at in 0.01. Dynamic affect parentheses; allocation Non-significant Storage (Y), see sensitivity inflow Allocation and Table (S) were (L), 3.3) and excluded Credit Tributary was from inflow (C), models. (T). Debit Levels (D), include Gwydir (G)) and Macquarie (M). Variables include Regression term Balance sheet Balance sheet (Y) xBalance Carryover sheet penalties (Y) (F) xBalance Climate sheet change (Y) (-10%) xBalance Climate sheet change (Y) (-5%) xBalance Climate sheet uncertainty (Y) (I) x Climate uncertainty (T) Balance sheet (Y) xBalance General sheet Security (Y) limit x (L) Balance Resource sheet pool (Y) (C) xBalance Resource sheet pool (Y) (D) xBalance River sheet system (Y) (G) xCarryover River penalties system (F) (M) Carryover penalties (F) xCarryover Climate penalties change (F) (-10%) xCarryover Climate penalties change (F) (-5%) xCarryover Climate penalties uncertainty (F) (I) xCarryover Climate penalties uncertainty (F) (T) xCarryover General penalties Security (F) limit x (L) Resource pool (C) Carryover penalties (F) x River system (G)

98 3.A Regression coeﬃcients -0.60** -0.21** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** 0.18* 0.68** 1.12** -0.64** -0.51** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** 2.18** -1.41** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** 0.29** 14.35** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** -0.11** 0.98** -35.96** -0.03** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** 1.54** 1.07** -3.51** -8.53** 0.72** 0.08** -0.88** -0.39** -0.11** -0.38** 12.13** 0.80** 15.25** 34.47** -27.69** -17.99** < 0.001* -12.24** -1.33** -1.92** -9.40** 0.34** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** < 0.001** ver penalties (F) x River system (M) Carryo Climate change (-10%) xClimate River change system (-5%) (M) Climate change (-5%) xClimate Climate change uncertainty (-5%) (I) xClimate Climate change uncertainty (-5%) (T) xClimate General change Security (-5%) limit x (L) Climate Resource change pool (-5%) (C) xClimate River uncertainty system (I) (M) Climate uncertainty (I) x Climate uncertainty (T) Climate uncertainty (I) x General Security limit (L) Climate change (-10%) Climate change (-10%) x Climate uncertainty (I) Climate change (-10%) x Climate uncertainty (T) Climate change (-10%) xClimate General change Security (-10%) limit x (L) Resource pool (C) Climate uncertainty (I) xClimate Resource uncertainty pool (I) (C) x River system (M) Climate uncertainty (T) Climate uncertainty (T) x General Security limit (L) River system (M) Climate uncertainty (T) xClimate Resource uncertainty pool (T) (C) x River system (M) General Security limit (L) General Security limit (L)General x Security Resource limit pool (L) (C) Resource x pool River system (C) (M)Resource pool (C) xResource River pool system (C) (G) x River system (M) -1.65** -0.11** -0.29** 34.49**

99 3. WHAT DRIVES ENVIRONMENTAL WATER AVAILABILITY?

100 4

Managing environmental ﬂows in regulated rivers: Constraints and risks to delivery

4.1 Abstract

Environmental flows for freshwater ecosystem health and function are recognized at a policy level in many countries, but implementation in regulated rivers remains a global challenge. Physical infrastructure, management rules and socio-economic impacts constrain the delivery of environmental flows, potentially compromising management outcomes for flow-dependent ecosystems. Quantitative assessment of multiple risks is a critical first step for effective environmental flow delivery. We manipulated the volume and variability of environmental flows in long term stochastic simulations of reservoir dynamics and assessed impacts on four key risk factors: physical release constraints, allocations, spills and evaporation at annual and monthly time scales. We focused on two regulated river systems, the Gwydir and the Macquarie Rivers in the Murray-Darling Basin, Australia, where major public investment in environmental flows supports internationally significant wetlands. Environmental flow releases from storage increased variability of the flow regime immediately downstream of storage, but environmental reserve size, infrastructure capacity and management rules constrained the release of flows. Importantly, there were no significant effects on allocations at present environmental reserve size, however effects on spills and evaporation losses varied. Risks

101 4. MANAGING ENVIRONMENTAL FLOWS IN REGULATED RIVERS compromising the delivery of the desired environmental flow regime must be prioritized and managed to ensure environmental flow objectives can be met effectively and safely. We recommend systematic and quantitative scenario-based risk assessment to support environmental water management decisions in regulated rivers worldwide.

4.2 Introduction

Through most of the 20th century, water resource development and management was focused on meeting a narrow range of socio-economic needs. By the end of the century, clear scientific evidence and public awareness of the global decline of freshwater ecosystems placed mounting pressure on governments to restore degraded river systems. Environmental flows are a central aspect of freshwater ecosystem protection and are incorporated in high level policy by governments worldwide (Puckridge et al., 1998; Richter et al., 1996). Governments in Australia and western United States protect a variable portion of surface flows for the environment through water access entitlements and planning provisions (Hollinshead, 2005; SEWPAC, 2012a). South African water law stipulates an environmental water reserve (DWAF, 1995). Environmental flow requirements are incorporated into water planning in New Zealand and parts of Europe and Africa (Hirji and Davis, 2009). Despite recognition of environmental flows at a policy level, delivering environmental flows remains a major challenge in regulated systems due to physical constraints and potential impacts on river communities (Le Quesne et al., 2010). These risks may prevent delivery of the desired flow regime and compromise the ability to achieve hydrological or ecological objectives. Regulated rivers efficiently attenuate natural flows, allowing for predictable and seasonal volumes of water for power generation, flood mitigation, crop irrigation and domestic use. As the focus shifts from a supply perspective to a more holistic perspective incorporating environmental, social and economic objectives (Dyson et al., 2003), managers of regulated river systems must also provide the increasing volume and variability of flows required to support aquatic ecosystems. Delivery of environmental flows is challenging due to physical infrastructure constraints, management rules and limited water supply (Watts et al., 2011; Yin et al., 2011), which may ultimately compromise the ability to achieve ecological outcomes. For example, major constraints affecting environmental flow provision in the Murray-Darling Basin in Australia were physical

102 4.2 Introduction structures such as outlet capacity and bridges, and reservoir release rules (MDBA, 2012b). Operational risks compromise or inhibit the storage, release or delivery of environmental flows in regulated river systems. They occur at different time scales: physical barriers are immediate constraints whereas the effects of some management rules manifest over longer time periods. For example, rules requiring users to forfeit a portion of allocations may compromise access to water held in storage over multiple years, a strategy that allows for large environmental releases and drought relief for stressed biota. Assessing, prioritizing and managing short and long term operational risks are essential for effective delivery of environmental flows, but surprisingly few studies address these risks (Hughes and Mallory, 2008). Even fewer studies evaluate the socio-economic risks of environmental flows. Reg- ulated rivers are complex systems characterized by complex feedbacks between many interconnected elements (Chen et al., 2011). Varying releases of a portion of flows may trigger complex feedbacks within a shared reservoir, with potential ramifications for water users or landholders downstream of reservoirs. Altered reservoir dynamics may affect: water allocations, which drive ecological and economic productivity; reservoir spills, which can be environmentally beneficial but may threaten downstream public infrastructure, private property including stock and crops, and cultural values; and evaporation losses which reduce regulated water availability. In addition to direct socio- economic risks, governments may be financially or legally liable if environmental flow management actions reduce water security or cause flood damage. Quantitative data is required to assess socio-economic risks and negotiate acceptable levels with water managers, water users and downstream landholders. Environmental flow managers require quantitative data from hydrological models to systematically assess and manage risks, but a major modelling challenge is the ability to vary environmental water demand and represent complex reservoir dynamics. Environmental flow demand is a critical but largely overlooked component in regulated river modelling (Beck and Bernauer, 2011; Li et al., 2010). Most water availability and reliability studies model the influence of variables, such as climate change and management rules, but assume stationary or fixed demand (see Brekke et al., 2009; Burn and Simonovic, 1996; Li et al., 2010; Mallory et al., 2008; Oliveira and Loucks, 1997; Wurbs, 2005). However if the model is highly sensitive to demand and there is high uncertainty or variability in actual demand (Vasiliadis and Karamouz, 1994;

103 4. MANAGING ENVIRONMENTAL FLOWS IN REGULATED RIVERS

Zahraie and Hosseini, 2009; Zaman et al., 2006), model predictions may not be reliable (Bar Massada and Carmel, 2008). Demand may be manipulated (Beck and Bernauer, 2011; Higgins et al., 2008; Raje and Mujumdar, 2010) but usually this concerns its magnitude, not its variability which is likely to change as the amount of environmental water increases. Our aim was to examine the operational and socio-economic risks of increased environmental flow volume and variability in complex regulated river systems. We utilized stochastic simulation models, widely used in hydrological modelling, which allow decision makers to investigate responses of different inputs under uncertainty (Yeh, 1985). We manipulated the volume and variability of environmental flow released from reservoirs with the aim of achieving a simple management objective, natural flow variability of reservoir releases. We first examined whether natural variability was satisfied, then assessed four risk factors: physical constraints, allocations, spills and evaporation losses. We compared environmental flow scenarios to a historical scenario which reflected extractive demand prior to introduction of managed environmental flows. We applied this approach in the Gwydir and Macquarie Rivers, two regulated and fully appropri- ated rivers of Australia’s Murray-Darling Basin where reduced volume and variability of flows have caused major decline in health of internationally significant wetlands (DECCW, 2010, 2011; Kingsford and Thomas, 1995; OEH, 2012a). Environmental flow rules were established in the 1980s (Macquarie) and 1990s (Gwydir), and the environmental water reserves have since doubled with the acquisition of water entitlements by New South Wales (NSW) and Commonwealth governments. Assessment of the operational and socio-economic risks is critical for effective management of environmental flows.

4.3 Methods

4.3.1 River systems

The Gwydir and Macquarie (Fig. 4.1) are large (26 090 km2 and 74 000 km2, respectively) inland river systems of Australia’s Murray-Darling Basin. Synoptic weather patterns in the Great Dividing Range (Grootemaat, 2008) produce highly variable ﬂow pulses that travel along an east-west climatic gradient, decreasing in moisture from

104 4.3 Methods

AUSTRALIA Gwydir River System

Gwydir Wetlands Gwydir River ! !

Myall !Creek ! ! ! ! # ! ! ! ! Horton River

Halls Creek Macquarie Marshes Copeton Dam !

Macquarie-Cudgegong River System

Macquarie River

! !

Talbragar River Cudgegong River ! Windamere Dam

! # ! # ! # Major storages Little River ! ! ! ! Rainfall stations ! Bell River Burrendong Dam ! Rivers & Creeks Wetlands ! ! ! Catchment 0 40 80 160 ! Murray-Darling Basin Kilometers ´

Figure 4.1: Location of the Gwydir and Macquarie Rivers (northwesterly flow) in the Murray-Darling Basin, Australia, showing major storages, rainfall stations, rivers and creeks, wetlands, catchment area, Murray-Darling Basin. the mountains to the semi-arid floodplain. Upon reaching the large, low relief alluvial fan-plains, channels may transform into anabranches, distributaries, depressions and lagoons, with most of the flow terminating in floodplain wetlands (Pietsch, 2005; Ralph and Hesse, 2010; Yonge and Hesse, 2009). Highly dynamic inundation patterns support a complex mosaic of floodplain wetlands of international significance (Ramsar, 2012), providing important habitat and breeding grounds for colonial nesting waterbirds (Kingsford and Johnson, 1998) and other water dependent organisms (Brock, 1998; Keyte, 1994; Rayner et al., 2009). Irrigated agriculture (mainly cotton), towns, stock and urban industry receive water from reservoirs (Gwydir: Copeton (1 362 GL); Macquarie: Windamere (368 GL) and Burrendong (1 188 GL)), weirs and unregulated tributaries including Halls, Myall and Horton (Gwydir) and Bell and Talbragar (Mac-

105 4. MANAGING ENVIRONMENTAL FLOWS IN REGULATED RIVERS

Table 4.1: Planned and adaptive environmental water available in the Gwydir and Mac- quarie Rivers. Planned environmental water is prescribed by New South Wales (NSW) government Water Sharing Plans (NSW Government, 2002, 2003). Adaptive environmental water includes high security, general security and supplementary entitlements held by Commonwealth (Cwth) and NSW governments in March 2012 (OEH, 2012b; SEWPAC, 2012a). Entitlements vary according to reliability: high security provides guaranteed water supply in all but drought years, general security provides access to moderately reliable ﬂow and supplementary ﬂow is available during unregulated events. Units are equivalent to 1 megalitre.

Environmental water Gwydir Macquarie Cwth NSW Total Cwth NSW Total Planned n/a 90 000 90 000 n/a 160 000 160 000 Adaptive High security 375 0 375 0 0 0 General security 89 525 17 092 106 617 90 253 48 419 138 672 Supplementary 19 100 441 19 541 1 888 1 452 3 340 quarie; Fig. 4.1). Water resource development and abstraction has caused major wetland contraction (Keyte, 1994), decline of biota (Kingsford and Thomas, 1995) and reduced ecosystem health (Kingsford, 2000; Steinfeld and Kingsford, 2013).

4.3.2 Environmental water reserve

The overallocation of rivers for irrigation, and subsequent ecological decline, prompted governments to redress the balance between environmental and extractive use. NSW and Commonwealth Governments established environmental flow reserves in the Gwydir and Macquarie (Table 4.1). Two types of reserves exist: planned and adaptive. Planned environmental water is committed through management rules (Water Sharing Plans), exclusively for fundamental ecosystem health or other specified environmental purposes (NWC, 2010). In the Gwydir, this includes the Environmental Contingency Allowance (ECA; 45 GL per unit share up to 90 GL) which supplements natural inundation events and supports colonial nesting waterbirds, fish, threatened species, weed management, and aquatic ecosystem health (NSW Government, 2002). In the Macquarie, it includes the Wildlife Allocation (WLA, 160 GL), supporting desirable hydrological char-

106 4.3 Methods acteristics (variability, connectivity) and processes (habitat formation, river channel formation), and may also be used to achieve Water Sharing Plan objectives and practice adaptive management (NSW Government, 2003). Adaptive (or entitlement-based, entitlement-based) environmental water is recovered through purchase or acquisition of water access entitlements, historically for extractive use, particularly irrigation. Adap- tive environmental water is reallocated from extractive to environmental use without expanding overall water demand. By contrast, the planned reserve is supplied above existing uses, expanding overall water demand. Entitlements vary according to reliability (high security, general security and supplementary access; Table 4.1). Most environmental water is available under moderately reliable general security entitlements with smaller volumes of high security (Gwydir only) guaranteed annually and supplementary ﬂow during unregulated events.

4.3.3 Reserve size

Environmental flow reserve size is an important management decision, governing the share of water for the environment. In Australia, the reserve size reflects the volume of planned and adaptive environmental water, so it is specified through a combination of rules and water entitlements. Actual volumes available under these water rights (allocations) vary from year to year according to reserve size, inflows and management rules. Reserve sizes differ among river systems and may change according to policy or management objectives. To examine the influence of reserve size on reservoir dynamics, we modelled five scenarios of reserve size: Historical, Planned, Present, Half and Maximum (Table 4.2). No environmental water was available under the Historical reserve: all water was for extractive use with existing regulatory structures and management rules. The Planned reserve sets aside environmental water according to Water Sharing Plan rules (specified in 2004). Under this scenario, all entitlements were held by extractive users. The Present, Half and Maximum scenarios included planned environmental water and varying amounts of adaptive environmental water: the Present reserve reflected current investment in general security entitlements; the Half reserve balanced extractive and environmental entitlements equally; and the Maximum reserve assigned all general security entitlements to the environment. These two latter scenarios were used to test sensitivity across the range of reserve sizes. In all scenarios, high

107 4. MANAGING ENVIRONMENTAL FLOWS IN REGULATED RIVERS

Table 4.2: Five environmental water reserve sizes (ML) modelled for the Gwydir and Mac- quarie Rivers. The reserve included diﬀerent amounts of planned (rules-based) and adaptive (entitlements) environmental water. The reserve size represented maximum annual water availability, but the actual volume allocated varied according to inﬂow, management rules, operational losses and future needs.

Scenario Gwydir Macquarie Planned Adaptive Total Planned Adaptive Total Historical 0 0 0 0 0 0 Planned 90 000 0 90 000 160 000 0 160 000 Present 90 000 106 617 196 617 160 000 138 672 298 672 Half 90 000 254 750 344 750 160 000 316 214 476 214 Maximum 90 000 509 500 599 500 160 000 632 428 792 428 security and supplementary water was available only for towns, urban industry, high value cropping and stock.

4.3.4 Environmental watering strategies

Environmental watering strategies determine the volume of environmental flow demanded over time. Strategies vary from simple reservoir release prescriptions to complex and adaptive flow regimes based on flow-ecology relationships (Acreman and Dunbar, 2004; Tharme, 2003). Strategies are developed by an environmental water manager, consulting an Environmental Flows Reference Group of government, non- government, industry and community representatives (Fazey et al., 2006). We tested effects of four hypothetical environmental watering strategies based on simple release rules: Boom and Bust, Annual, Transparent and Tributary Augmented. These heuris- tic planned and adaptive environmental water demand strategies ranged in variability and were elastic to water availability. The most variable strategy, Boom and Bust (alternatively known as bank and threshold, fill and spill), generated largest possible managed floods of varying frequencies. The basic idea of this strategy was to accumulate allocations until the reserve size is full, then generate demand over a specified period. The daily environmental demand function (De) was the ratio of the current day demand to total demand, where current account volume (v) and daily demand (f(d)) are a function of the number of days (d)

108 4.3 Methods

in the period (dmax; Eqn. 4.1). This ensured all existing and new allocations were released by the end of the speciﬁed period.

v · f(d) De = X (4.1) f(d)

Rising, peaking and receding demand, f (x), was modelled using a quadratic function (Eqn. 4.2) f(d) = ad2 + bd + c (4.2) where a = −1.0, b = dmax and c = 0. We require these to be selected so f(d) > 0 over the interval 0 < d < dmax. Demand was generated for a spring-summer event st commencing 1 October for 90 days (dmax) to coincide with spring flooding. The Annual strategy generated flow of varying magnitudes by demanding the entire account volume each year. This occurred during a specified period (spring-summer event over 90 days, commencing 1st October). Demand rose, peaked and receded and was modelled by Equations 4.1 and 4.2. The Transparent strategy passed reservoir inflows downstream as though no reservoir was present, conditional on environmental water allocations. This maintained natural variability when reservoir releases would be minimal (NOW, 2012a) and preserved the timing of high-flow events (Yin et al., 2011). Daily environmental demand was generated according to the equation:

De = Qs (4.3) where De is the environmental water demand (ML/d), and Qs is the reservoir inflow rate (ML/d). The Tributary Augmented strategy (also known as piggybacking; Harman and Stewardson, 2005) triggers environmental water demand when major downstream tributaries (Fig. 4.1) exceed a combined threshold (500 ML/d; NSW Government, 2002), providing regulated environmental flows to enhance unregulated flow events downstream of the reservoir. This strategy is beneficial in systems where tributaries provide substantial contribution to flows, such as the Horton River in the Gwydir (Keyte, 1994).

Demand (De) on a particular day was:

De = Qt − T (4.4)

109 4. MANAGING ENVIRONMENTAL FLOWS IN REGULATED RIVERS

where Qt is the combined flow rate of downstream tributaries (ML/d), and T is the demand threshold. We used exceedance probability curves to examine differences in the volume (area under the curve), variability (shape of the curve) and uncertainty (shaded band around the curve) of environmental water demands for each scenario. These represented the full distribution of annual environmental water demand within the 5th and 95th percentiles. They ranked environmental water demand from lowest to highest against percentage of time this demand was equalled or exceeded. Exceedance probability curves were related to the coefficient of variation, a single statistic which summarised all observations. As flow variability increased, the curve became steeper and the coefficient of variation increased. Conversely, as flow variability decreased, the curve tended towards the diagonal and the coefficient of variation decreased. Curves showed variance (shaded) as an indication of uncertainty, however differences between curves were not statistically comparable. The coefficient of variation allowed for statistical comparison, although information about uncertainty was lost.

4.3.5 Scenario simulation

We simulated 40 unique scenarios of two river systems (Gwydir and Macquarie), five reserve sizes (Historical, Planned, Present, Half and Maximum) and four environmental watering strategies (Boom and Bust, Annual, Transparent and Tributary Aug- mented). To quantify meteorological uncertainty and provide replication, we simulated 100 stochastic realizations of rainfall and pan evaporation for each scenario over the period of record (BOM, 2012). This preserved important characteristics of the natural sequence (mean and standard deviation), but randomly varied the temporal sequence (i.e. order of wet and dry days). To derive stochastic daily rainfall realizations, we used the daily observed rainfall time series (BOM, 2012) for all the locations of interest as the basis for generating multiple realizations of the data using the Modified Markov Model (Mehrotra and Sharma, 2007a). These stochastic rainfall realizations (100 per gauge) were generated assuming Markov order 1 persistence in both rainfall occurrence and amounts, along with persistence with selected low-frequency variability indicators that were formulated as summations of the rainfall for a specified number of preced- ing days. The best performing aggregate indicators of low frequency variability were specified as the summation over 90 days, 365 days and 1000 days, based on criteria

110 4.3 Methods of mean daily, monthly and annual rainfall, log-odds ratios and cross correlations of monthly and annual wet days. Additionally, a threshold of 1 mm per day was used to distinguish rainfall occurrence from amount, the realizations mimicking the distribu- tional characteristics associated with the observed time series at each station. Readers are referred to Mehrotra and Sharma (2007a & b) for details of the procedure adopted, and to Sharma and Mehrotra (2010) for an overview of daily rainfall generation and the need for representing low-frequency variability, of considerable importance in water allocation problems for systems affected by long droughts, as is the case for the catchments under consideration. We derived stochastic daily pan evaporation realizations from stochastic rainfall realizations using the 1-nearest neighbour algorithm based on daily rainfall and pan evaporation records. We transformed rainfall and pan evaporation to reservoir inflows using Sacramento rainfall-runoff models (Zambrano-Bigiarini, 2012) coupled with a NSW government In- tegrated Quality and Quantity Model (IQQM) representing hydrology (Simons et al., 1996). Sacramento models for each sub-catchment (Gwydir: 12; Macquarie: 16) were based on Thiessen weighted rainfall. We simulated daily reservoir behavior and management for the period of record (110 years) using an Environmental Water Alloca- tion Simulator with Hydrology (eWASH; Chapter 2). eWASH allowed manipulation of reserve size and environmental watering strategies. eWASH had feedback loops in demand (environmental and extractive) and reservoir dynamics, critical for evaluating the influence of environmental flows in confined reservoirs (Kaczmarek and Krasuski, 1991). eWASH was configured for the Gwydir and Macquarie Rivers, and tracked daily reservoir dynamics and monthly allocations (Chapter 2). Extractive general security demand consisted of seasonal, predictable, regulated demand exclusively for irrigation, based on actual orders (Gwydir: Copeton 28/05/2004 - 14/03/2011; Macquarie: Windamere 28/06/2004 - 11/10/2006, Burrendong 24/06/2004 - 23/07/2006). Envi- ronmental and extractive demands were converted to releases, conditional upon release constraints and extraction limits.

4.3.6 Achieving environmental ﬂow objectives in regulated rivers

Environmental ﬂows are usually managed according to objectives established by stakeholders (DEWHA, 2009; Hirji and Davis, 2009; Kingsford et al., 2011; Poﬀ et al., 2010).

111 4. MANAGING ENVIRONMENTAL FLOWS IN REGULATED RIVERS

We chose natural inflow variability of reservoir releases as a simple management objective, but any hydrological or ecological objectives may be selected in practice (see Poff et al., 2010; Tharme, 2003). We used coefficient of variation because it is a widely- accepted indicator of flow variability (Richter et al., 1996; Vogel et al., 1999). Reservoir releases are a major contributor of hydrological variability in regulated river systems. To assess whether environmental flow scenarios recovered natural variability, we compared coefficients of variation of reservoir releases to unregulated inflows at annual and monthly time steps. Inflows were simulated by IQQM and validated against streamflow records from the nearest upstream gauge (NOW, 2012b).

4.3.7 Assessing operational and socio-economic risks

We examined risks at Copeton Dam (Gwydir) and Burrendong Dam (Macquarie), the major dams regulating environmental and extractive water. We first determined whether environmental flow objectives had been achieved under each environmental flow release scenario, by comparing storage release variability with target variability. We then assessed impacts on physical release constraints, allocations, reservoir spills and evaporation (Table 4.3), statistically comparing different watering scenarios. The assessment consisted of generating stochastic simulations for different flow release scenarios, distilling the scenarios to a statistical distribution, then comparing distributions using a non-parametric test to determine whether effects were significant. For each of the forty scenarios, we generated 100 daily realizations (replicates) for the 110 year period. These were aggregated to annual and monthly time steps of interest to environmental flow managers (water year: July - June; Table 4.3). For each scenario, we applied the appropriate metric to each of the 100 realizations (Table 4.3) to yield a probability distribution consisting of 100 replicates. We compared differences between distributions using a non-parametric two-sample Kolmogorov-Smirnov test for continuous data and a post-hoc Bonferroni correction for multiple comparisons (p<0.05 significance). We visualized differences between river systems and trends in reserve size using box and whisker plots, with boxes representing 25th and 75th percentiles, the horizontal line across boxes representing the median, whiskers representing the 5th and 95th percentiles, and black circles representing outliers.

112 4.3 Methods

Table 4.3: Variables potentially affected by environmental flow strategies and associated risks, time step and metrics. Operational risks (O) were physical and management constraints restricting the passage of flows to target ecosystems. Socio-economic (S-E) risks affect water and land holders, and river managers may be hesitant to deliver environmental flows when these risks are likely.

Variable Risk Timea Metric Physical O & S-E: Prevents or delays water delivery A(M) Frequency, release from storage, affecting extractive use (e.g. magnitude constraints inability to irrigate, causing crop loss) and per eventb environmental use (e.g. failure to complete an environmental event such as bird breeding, leading to mortality of wetland biota). Allocation O & S-E: Low allocations result in low A(M) Mean, (general extractive and environmental productivity. coefficient of security) High allocation variability is difficult for variation long term planning. Storage spill S-E: Unregulated flow from storage A(M) Mean potentially threatening properties and infrastructure. It also reduces the volume of managed water in storage. There also important ecological benefits of storage spill and potentially increased supplementary water allocations for environmental and extractive users. Evaporation S-E: Reduces water availability in storage. A(M) Mean

a A = annual (water year: July - June); M = monthly; Brackets indicate plots included in Appen- dices b Calculated per event, by excluding time steps when the variable was zero.

113 4. MANAGING ENVIRONMENTAL FLOWS IN REGULATED RIVERS

4.4 Results

Environmental flow scenarios ranged in volume and variability, driven by differences in river systems, environmental water reserve sizes and environmental watering strategies. Environmental flow scenarios increased the overall variability of reservoir releases compared to the Historic scenario where no environmental water was reserved. However not all environmental demand was satisfied due to larger and more frequent infrastructure capacity constraints under environmental flow scenarios. Importantly, environmental flow scenarios did not affect water allocation volume or variability for Planned and Present reserve sizes, but small effects were detected for Half and Maximum reserve sizes. The effects on spills and evaporation were significant and highly dependent on the environmental watering strategy. There were differences between environmental flow demand scenarios and impacts at annual and monthly temporal scales.

4.4.1 Environmental ﬂow demand

Exceedance probability curves (Fig. 4.2) showed differences in environmental flow volume, variability and uncertainty before release constraints and extraction limits were applied. Differences reflected the river system, reserve size and environmental watering strategy. Annual demand was higher in the Macquarie compared to the Gwydir, reflecting higher water availability (Fig. 4.2a). Demand volume increased with increasing reserve size (Fig. 4.2a). There was a small reduction in environmental water demand for the Boom and Bust strategy in both river systems and the Tributary Augmented strategy in the Macquarie, relative to other environmental watering strategies. Variability of annual environmental demand was moderately affected by reserve size, shown by increasingly protracted tails of the curves (Fig. 4.2a). Annual demand was more variable (steeper curves) in the Gwydir, a more hydrologically variable system than the Macquarie. This effect was most pronounced for the Annual and Transpar- ent strategies. The most pronounced effect on annual variability was due to environmental watering strategy. The extremely variable Boom and Bust strategy produced a steep, L-shaped curve, characterized by high magnitude demands occurring infre- quently (Gwydir: <20% of years; Macquarie: <60% of years) shown by the relatively high tail of the curve. Other watering strategies (Annual, Transparent and Tributary Augmented in the Gwydir; Tributary augmented in the Macquarie) produced high

114 4.4 Results

Watering Strategy Boom and Bust Annual Transparent Tributary Augmented

Historical Historical Planned Planned PresentPresent Half Half Maximum Maximum 100 ●

80 ● ● 2.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

60 Gwydir 1.5 40 Gwydir

1.0 20 ● ● ● ● ● ● ● ● ● ● ● ● ● xceeded ● ● ● ● 0 0.5 100

0.0 ● ● ● ● ● ● % time equalled or e 60 Macquarie 1.0 40

0.8 20 ● Coefficient of variation Coefficient of variation

0.6 0 Macquarie 0 500 1,000 1,500 0 500 1,000 1,500 0 500 1,000 1,500 0 500 1,000 1,500 0 500 1,000 1,500

● 0.4 ● ● Ann●ual Demand (GL) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● (a) Annual demand● ● ● ● ● Historical Planned Present Half ●Maximum ● 0.0 ● ● ● ● ● ● ● ● 100 ● ● ● ● ● ● ● ●

60 Gwydir

20 xceeded

100

80 % time equalled or e 60 Macquarie

0 0 200 400 600 0 200 400 600 0 200 400 600 0 200 400 600 0 200 400 600

Monthly Demand (GL)

(b) Monthly demand

Figure 4.2: Exceedance probability curves of (a) annual and (b) monthly environmental water demand for the Gwydir and Macquarie River systems (rows), ﬁve reserve sizes (columns) and four watering strategies (colours). The solid line represents the median of stochastic realisations for each environmental water strategy and the shaded region represents data within the 5th and 95th percentile conﬁdence interval. For annual demand, each curve represents 100 stochastic realisations, but for monthly demand each curve represents 20 randomly selected realisations due computer graphics limitations.

115 4. MANAGING ENVIRONMENTAL FLOWS IN REGULATED RIVERS variability, shown by concave curves. They generated demands in most years, resulting in many small, some moderate and few large events. Moderately variable scenarios (Annual and Transparent in the Macquarie) produced curves that were closer to diagonal. They generated small, moderate and large events of similar frequencies, in all but exceptionally dry years. We observed small differences in uncertainty (shaded region) of annual demand under different scenarios, reflecting stochasticity of inflows (Fig. 4.2a). Uncertainty increased with reserve size and there was considerably more uncertainty in annual demand in the Macquarie compared to the Gwydir. There were no obvious differences between watering strategies, with the exception of the Tributary Augmented strategy in the Macquarie which displayed a narrower region of uncertainty compared to other watering strategies. Volume and variability of monthly environmental water demand were affected by river system, reserve size and watering strategy, but uncertainty remained relatively unaffected (Fig. 4.2b). Monthly environmental demand volume increased with reserve size and was higher in the Macquarie than the Gwydir. It was slightly lower for the Tributary Augmented strategy in the Macquarie, and the Boom and Bust strategy in both river systems. Variability of monthly demand was much higher than annual demand, indicated by steeper curves. Monthly variability was similarly affected by river system, reserve size and environmental watering strategy. It was more variable in the Gwydir compared to the Macquarie, was moderately affected by reserve size and was most variable under the Boom and Bust strategy. Monthly demand uncertainty was similar in the Gwydir and Macquarie, with a small increase in uncertainty with reserve size, but no obvious differences among watering strategies.

4.4.2 Reservoir releases

Reservoir release variability (coefficient of variation) was clearly impacted by river regulation, shown by the difference between natural (dashed line) and Historical (median) variability (Fig. 4.3). Natural and Historical annual variability for the Gwydir and Macquarie differed by 0.34 and 0.12, respectively (Fig. 4.3a). This represented a reduction of 37 % (Gwydir) and 17 % (Macquarie) of natural annual variability. Simulated natural variability underestimated observed natural variability (Table 4.4), although

116 4.4 Results

Watering Strategy Boom and Bust Annual Transparent Tributary Augmented

Historical Historical Planned Planned PresentPresent Half Half Maximum Maximum

●

● 1.5 ● ● ● 2.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

1.0 Gwydir ● ● ● 1.5 ● ● ● ● Gwydir 0.5 1.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.5 ● ●

0.0 1.0 ● ● ● ● ● ● ● ● ● Coefficient of variation Coefficient of variation ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● Macquarie ● 1.0 ● ● ● ● ● ● ● ● ● ● 0.5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.8 ● Coefficient of variation Coefficient of variation

0.6 Macquarie

0.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.4 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● (a) Annual storage release● variability ● ● ● ● Historical Planned Present Half ●Maximum ● 0.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● 3 ● ● ● ●

● 2 ● ●

● ● Gwydir ● ● ● ● ●

● 1 ●

0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

●

1.5 ●

● ● ●

Coefficient of variation Coefficient of variation ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1.0 ● ● ● ● ● Macquarie ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

0.5

0.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

(b) Monthly storage release variability

Figure 4.3: Coefficients of variation of annual (a) and monthly (b) storage releases (GL) for the Gwydir and Macquarie Rivers (rows), five reserve sizes (columns) and four watering strategies (colours). Box and whisker plots represent 100 stochastic values derived for each 110 year realisation. Connector lines below boxes indicate pairs of environmental watering strategies which were statistically different (p<0.05; non-parametric Kolmogorov-Smirnov test with Bonferroni adjustment). The dashed line shows the coefficient of variation of natural (unregulated) flows at the nearest upstream gauge (Table 4.4).

117 4. MANAGING ENVIRONMENTAL FLOWS IN REGULATED RIVERS

Table 4.4: Monthly and annual coefficient of variation for simulated inflows at Copeton Dam (Gwydir) and Burrendong Dam (Macquarie) and observed unregulated flows, upstream of the storages (Gwydir at Bruinbun (421025), 1/09/1947 - 1/04/2012; Macquarie at Bundarra (418008), 1/01/1937 - 1/01/2012; NOW, 2012b).

Gwydir Macquarie Monthly Annual Monthly Annual Simulated 1.96 0.93 1.48 0.71 Observed 2.36 1.09 1.78 1.00 observed natural data were measured upstream of the reservoir due to the absence of long term reservoir inflow records. Environmental watering strategies recovered different levels of variability among river systems, reserve sizes and strategies, but natural variability could not be recovered under all watering strategies. Four environmental flow scenarios exceeded natural annual variability (i.e. lower 25th percentile above blue dotted line), four scenarios recovered full natural variability (i.e. 25th to 75th percentiles overlapped blue dotted line), 24 scenarios recovered partial variability (i.e. upper 75th percentile below blue dotted line), and the remaining eight Historical scenarios represented variability under traditional extractive operation. The four scenarios which recovered full natural annual variability all occurred in the Macquarie (Boom and Bust with Present reserve size, An- nual with Half and Maximum reserve size, and Tributary Augmented with Maximum reserve size). With increased impacts of river regulation on annual variability in the Gwydir, it was necessary to reinstate more variability to achieve natural variability compared to the Macquarie. Annual variability of reservoir releases increased with reserve size in both rivers. There were also considerable differences between environmental watering strategies. The Boom and Bust watering strategy was the only strategy where releases exceeded natural annual variability (four scenarios: Half and Maximum reserve size in the Gwydir and Macquarie). The second most variable strategy in the Gwydir was the Annual strategy, followed by Tributary Augmented and Transparent strategies with similar variability. In the Macquarie, the Boom and Bust strategy was the most variable, followed by Annual and Tributary Augmented strategies, then the Transparent strategy. The relative increase in variability under the Tributary Augmented strategy in the Macquarie suggested an interaction effect between river system and watering

118 4.4 Results strategy. Differences between watering strategies were significant at the upper reserve sizes but not always significant at the lower reserve sizes (p<0.05; connector lines under box plots). Monthly reservoir release variability was more impacted by river regulation than annual release variability (Fig. 4.3b). The difference between natural and median His- torical monthly variability was 0.74 and 0.45 in the Gwydir and Macquarie, respectively (Fig. 4.3b). This represented a reduction of 38 % (Gwydir) and 30 % (Macquarie) in natural monthly variability. Reservoir releases under the environmental flow scenarios were more variable than the Historical scenario, and variability increased with increasing reserve size. There were significant differences (p<0.05) between watering strategies, with the most variable releases produced under the Boom and Bust strategy, followed by Annual, Tributary Augmented and Transparent strategies. One scenario exceeded natural variability (Boom and Bust with Maximum reserve size in the Gwydir); two scenarios recovered full natural monthly variability (Boom and Bust with Half reserve size, Annual with Maximum reserve size in the Gwydir); 29 environmental water scenarios partially recovered natural variability; and the Historical represented extractive conditions. Unlike annual variability, scenarios that fully recovered variability occurred in the Gwydir even though monthly variability was more impacted by river regulation compared to the Macquarie.

4.4.3 Physical release constraints

Reservoir outlet capacity constraints prevented or delayed release of water, affecting environmental and extractive water demands. The introduction of environmental water increased the frequency and magnitude of existing constraints (annual: Fig. 4.4; monthly: Fig. 4.A). In the Gwydir, constraints did not affect releases in the Historical scenario, but they became problematic with the introduction of environmental water. In the Macquarie, constraints affected releases in the Historical scenario (median of means: 85 GL/y, Fig. 4.4), and effects were exacerbated under the environmental flow scenarios. Differences between constraints for the environmental flow scenarios reflected differences in river system, reserve size and watering strategy. Annual (Fig. 4.4a) and monthly (Fig. 4.Aa) constraint frequency were similarly affected. Annual and monthly constraints occurred more frequently in the Macquarie, compared to the Gwydir. The frequency of constraints for environmental flow scenarios

119 4. MANAGING ENVIRONMENTAL FLOWS IN REGULATED RIVERS

Watering Strategy Boom and Bust Annual Transparent Tributary Augmented

Historical Planned Present Half Maximum Historical 80% Planned Present Half Maximum ●

60% ● ● ● 2.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

40% Gwydir 1.5 ● Gwydir ● ● ● ● 20% ● ●●● ● ● ● ●●● ● 1.0 ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●●● ●●●●●●●●●●●●●● 0% ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.5 ● ● ● ● ● ●

100% ● ● ● ● ●● ● ● ● ● 0.0 ● ● ● ● ● ● 80% ● ● ●● ● ● ● ● ● Frequency of occurrence ● ● ● ● ● 60% ●●●

● Macquarie ● ● 1.0 ● ● ● ● ● ● ● 40% ● ● ● ● ● ● ● ● 0.8 ●

Coefficient of variation Coefficient of variation 20%

0.6 Macquarie 0% ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −20% ● 0.4 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● (a) Annual constraint● frequency ● ● ● ● Historical Planned Present Half ●Maximum ● 0.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● 20,000 ● ●

● ●

15,000 Gwydir

10,000 ●

5,000 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●●● ● ● 0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

100,000

80,000 ● Magnitude per event (GL) Magnitude per event ● ● ●

60,000 Macquarie

40,000 ●

20,000 ●

● ● ● ● ● ● ● ● ● ● ● ● ● 0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −20,000

(b) Annual constraint magnitude

Figure 4.4: The frequency (a) and magnitude per event (b) of the annual volume constrained (GL) for the Gwydir and Macquarie Rivers (rows), ﬁve reserve sizes (columns) and four watering strategies (colours). Box and whisker plots represent 100 stochastic values derived for each 110 year realisation. Connector lines below boxes indicate pairs of environmental watering strategies which were statistically diﬀerent (p<0.05; non-parametric Kolmogorov-Smirnov test with Bonferroni adjustment).

120 4.4 Results were higher than Historical, and increased with reserve size. The frequency of release constraints was highest for the Tributary Augmented strategy (Gwydir) and Transpar- ent strategy (Macquarie), and lowest for the Annual strategy (Gwydir; Macquarie with Planned and Present reserve size) and Boom and Bust strategy (Macquarie with Half and Maximum reserve size). The magnitude of annual (Fig. 4.4b) and monthly (Fig. 4.Ab) constraints was considerably greater in the Macquarie compared to the Gwydir, and increased with reserve size. Constraint magnitude was signiﬁcantly larger for the Boom and Bust and Annual strategies. Constraint magnitudes under the Transparent and Tributary Augmented strategies were signiﬁcantly smaller and more similar to the Historical scenario.

4.4.4 Allocations

Allocations reflected water available under an entitlement (percentage of general security entitlements) for all users. Box plots showed that river system, reserve size and watering strategy influenced mean allocations (Fig. 4.5a) and allocation variability (Fig. 4.5b). Trends at the annual scale were similar to the monthly scale (Fig. 4.B). Water allocations were higher in the Macquarie (median of means: 54%/y), compared to the Gwydir (median of means: 26%/y; Historical scenario; Fig. 4.5a). There was an initial reduction in mean allocation between the Historical and Planned reserve size, associated with the expansion of environmental water demand through management rules. With increasing reserve size but no further expansion of environmental water demand, mean allocations were relatively stable in the Gwydir and increased slightly in the Macquarie. The environmental watering strategy did not significantly affect mean allocation for the Planned, Present (both systems) and Half (Macquarie only) reserve size, however differences were detected at upper reserve sizes (p<0.05). With Maximum reserve size, Annual and Transparent strategies yielded significantly higher mean allocations in the Gwydir, and the Boom and Bust and Tributary Augmented strategies yielded higher mean allocations in the Macquarie, benefitting environmental and extractive water entitlement holders. River system and reserve size affected annual allocation variability, but effects of watering strategy depended on reserve size (Figs. 4.5b). Historical annual water allocations were more variable in the Gwydir (median coefficient of variation: 1.32) compared

121 4. MANAGING ENVIRONMENTAL FLOWS IN REGULATED RIVERS

Watering Strategy Boom and Bust Annual Transparent Tributary Augmented

Historical Historical Planned Planned PresentPresent Half Half Maximum Maximum ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 40% ● ● ● ● ● ● ● ● ● ● ● ● ● 2.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 30%● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Gwydir 1.5 20% Gwydir

1.0 10% ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

0% ● ● ● ● ● ● 0.5 ● ●

● ● ● Mean 100% ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.0 ● ● ● ● ● ● 80% ● ● ● ●

60% 1.0 Macquarie

40%

0.8 ●

Coefficient of variation Coefficient of variation 20%

0.6 Macquarie 0% ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −20% ● 0.4 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● (a) Mean annual allocation● ● ● ● ● Historical Planned Present Half ●Maximum ● 0.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 2.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

1.5 Gwydir

1.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.5

0.0 ● ● ● ● ● ●

1.0

0.8 ● Coefficient of variation Coefficient of variation

0.6 Macquarie

● 0.4 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

(b) Annual allocation variability

Figure 4.5: Mean (a) and coefficient of variation (b) of allocation (percent) for the Gwydir and Macquarie Rivers (rows), five reserve sizes (columns) and four watering strategies (colours). Box and whisker plots represent 100 stochastic values derived for each 110 year realisation. Connector lines below boxes indicate pairs of environmental watering strategies which were statistically different (p<0.05; non-parametric Kolmogorov-Smirnov test with Bonferroni adjustment).

122 4.4 Results to the Macquarie (median coefficient of variation: 0.68). For annual allocations, environmental watering strategies did not affect variability for the Planned, Present (both systems) and Half (Macquarie only) reserve size, however significant differences were detected at upper reserve sizes (p<0.05; Figs. 4.5b and 4.Bb). At Maximum reserve size in the Gwydir, allocations under the Boom and Bust strategy were significantly more variable than the Annual, Transparent and Tributary Augmented strategies (Figs. 4.5b). At Maximum reserve size in the Macquarie, the Annual strategy produced most variable allocations, followed by Transparent, Boom and Bust and Tributary Aug- mented strategies (Fig. 4.5b). Monthly allocation variability was affected by river system, reserve size and watering strategy (Fig. 4.Bb). Historical monthly water allocation variability was similar in the Gwydir and Macquarie. Environmental watering strategies did not affect monthly allocation variability for the Planned, Present and Half reserve size in the Gwydir, but there were significant differences between some environmental watering scenarios in the Macquarie (p<0.05; Fig. 4.Bb). At Maximum reserve size in the Gwydir, allocations under the Boom and Bust strategy were most variable, followed by the Tributary Augmented strategy, then Annual and Transparent strategies with similar variability (Fig. 4.Bb). At Maximum reserve size in the Macquarie, the Transparent strategy produced highest allocation variability, followed by Annual, Boom and Bust and Tributary Augmented strategies (Fig. 4.Bb). Allocations were distributed to environmental accounts based on the reserve size (Fig. 4.6). There was no environmental allocation in the Historical or Planned scenario because all general security entitlements were held by extractive users. For the remaining levels, the volume increased in proportion to the reserve size. There was an important difference between environmental watering strategies. At Present and Half reserve sizes in the Gwydir, the Boom and Bust watering strategy was allocated significantly lower volumes compared to other strategies (Fig. 4.6). This difference was not observed in the corresponding scenario for allocation percentage (Fig. 4.5a). For other reserve sizes in both rivers, mean annual volume allocated to the environmental account (Fig. 4.6) reflected allocation percentage (Fig. 4.5a).

123 4. MANAGING ENVIRONMENTAL FLOWS IN REGULATED RIVERS

Adaptive environmental water allocation | Watering Strategy Boom and Bust Annual Transparent Tributary Augmented

Historical Planned Present Half Maximum

● ● ● ● ● ● ● ● 200 ● ● ● ● ● ● ● ● ● 150 Gwydir ● ● ● ● ● ● ● 100 ● ●

● ● ● ● ● 50 ● ● ● ● ●

0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● 600 ● ● ● Mean (GL) ● ●

400 Macquarie

● ● ● ● ● ● ● ● 200

● ● ● ● ●

0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

Figure 4.6: Mean annual environmental water allocation (GL) for the Gwydir and Mac- quarie Rivers (rows), ﬁve reserve sizes (columns) and four watering strategies (colours). Box and whisker plots represent 100 stochastic values derived for each 110 year realisation. Connector lines below boxes indicate pairs of environmental watering strategies which were statistically diﬀerent (p<0.05; non-parametric Kolmogorov-Smirnov test with Bonferroni adjustment).

4.4.5 Reservoir spills

Reservoir spills, including controlled flood mitigation zone releases in the Macquarie, occurred under the Historical scenario. The introduction of environmental water affected mean reservoir spill compared to Historical, but the influence varied depending on multiple factors including river system, reserve size and watering strategy (Fig. 4.7). Similar effects were observed for monthly mean spills (Fig. 4.C). There was considerably greater mean spill in the Macquarie (median of means: 119 GL/y), compared to the Gwydir (median of means: 0.5 GL/y; Historical scenario) (Fig. 4.7). We observed an interaction between reserve size and watering strategy. Mean spill was positively correlated with reserve size for the Boom and Bust and Trib- utary Augmented strategy, but negatively correlated with reserve size for the Annual and Transparent strategies. There was also an interaction between watering strategy and river system. The highest mean reservoir spill occurred under the Boom and Bust

124 4.4 Results

Spill | Watering Strategy Boom and Bust Annual Transparent Tributary Augmented

Historical Planned Present Half Maximum

●

60 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 40 ● ●

● Gwydir ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● Mean (GL) 1,000

● Macquarie ● ● ● ● ● ● ● ● ● ● ● 500 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

Figure 4.7: Mean annual spill (GL) for the Gwydir and Macquarie Rivers (rows), ﬁve reserve sizes (columns) and four watering strategies (colours). Box and whisker plots represent 100 stochastic values derived for each 110 year realisation. Connector lines below boxes indicate pairs of environmental watering strategies which were statistically diﬀerent (p<0.05; non-parametric Kolmogorov-Smirnov test with Bonferroni adjustment). in the Gwydir and the Tributary Augmented in the Macquarie. Lowest mean spill occurred under the Transparent strategy in both river systems. There was evidence of potential non-linearities with increasing reserve size, particularly for the Tributary Augmented strategy in the Gwydir.

4.4.6 Evaporation

Evaporation from the reservoir was affected by river system, reserve size and watering strategy. The introduction of environmental water caused mean annual evaporation to change compared to Historical, but the influence varied depending on the watering strategy (Fig. 4.8). We examined the influence of the river system, reserve size and watering strategy on mean annual evaporation. Trends were the same for monthly mean evaporation results (Fig. 4.D). The Macquarie experienced higher mean evaporation (median of means: 59 GL/y) compared to the Gwydir (median of means: 16 GL/y; Historical scenario; Fig. 4.8). The

125 4. MANAGING ENVIRONMENTAL FLOWS IN REGULATED RIVERS

Evaporation | Watering Strategy Boom and Bust Annual Transparent Tributary Augmented

Historical Planned Present Half Maximum

● ● ● ● Gwydir 20

●

0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

100 ●

Mean (GL) ●

80 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 60 ● Macquarie ● ●

0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −20

Figure 4.8: Mean annual evaporation (GL) for the Gwydir and Macquarie Rivers (rows), ﬁve reserve sizes (columns) and four watering strategies (colours). Box and whisker plots represent 100 stochastic values derived for each 110 year realisation. Connector lines below boxes indicate pairs of environmental watering strategies which were statistically diﬀerent (p<0.05; non-parametric Kolmogorov-Smirnov test with Bonferroni adjustment).

Boom and Bust strategy produced highest mean evaporation in the Gwydir, and Trib- utary Augmented strategy produced highest evaporation in the Macquarie (p<0.05). The Transparent strategy produced lowest evaporation in both systems. There was a clear interaction between reserve size and watering strategy. Mean evaporation was positively correlated with reserve size for the Boom and Bust and Tributary Aug- mented strategy, and produced higher evaporation than Historical scenario. However the Annual and Transparent strategies were negatively correlated with reserve size and produced lower evaporation than the Historical scenario.

4.5 Discussion

Achieving environmental flow objectives in regulated river systems usually requires reinstating aspects of flow variability (Poff et al., 1997; Rosenberg et al., 2000) but this is difficult where river regulation dominates the flow regime. We tested for the

126 4.5 Discussion multiple operational risks of managing environmental flows to achieve natural unregulated flow variability and impacts on water and land holders. We focused on the Gwydir and Macquarie Rivers in the Murray-Darling Basin, where considerable investment in environmental flows aims to rehabilitate internationally significant wetlands affected by regulation, extraction and climate change. Using a modelling framework which included reservoir behavior, management rules and environmental flow demand, we demonstrated that environmental water management is complicated by operational and socio-economic constraints. Physical constraints, reservoir spills and evaporation losses were affected by environmental watering strategies, with implications for communities and the environment. These must be addressed to ensure environmental watering objectives can be effectively and safely achieved. Importantly, allocation reliability and variability remained unaffected by environmental watering strategies at the Present reserve size. Our modelling framework could be implemented in any regulated river around the world, providing scenario-based data of the risks of environmental flow management in regulated rivers. The dominant impact of river regulation should not be underestimated, but changing the environmental water reserve size and watering strategy improved reservoir release variability (coefficient of variation) in our modelling. River regulation reduced variability by up to 38 % (Historical scenario; Fig. 4.3). Few environmental flow scenarios recovered or exceeded natural variability (Fig. 4.3). Reserve size was a critical factor. Natural variability was more likely to be recovered under scenarios with large reserve sizes (Half, Maximum) compared to scenarios with small reserve sizes (Planned, Present) where only a small proportion of reservoir releases were managed for the environment (Fig. 4.3). Variability would change downstream of the reservoir because extractions occur, so full recovery of natural variability longitudinally may require a reserve size equivalent to total system flow (Maximum scenario). Reserve size may be increased by purchasing adaptive entitlements, expanding the planned water reserve, and by converting water efficiency savings to entitlements. Reducing interception of water upstream may increase allocations (e.g. by farm dams, forestry plantations, urban water supply; Herron et al., 2002; Neal et al., 2001), however this is also likely to increase overall extractions and may not improve release variability. Environmental watering strategies also affected the ability to achieve natural reservoir release variability. The highly variable Boom and Bust strategy most closely

127 4. MANAGING ENVIRONMENTAL FLOWS IN REGULATED RIVERS approximated natural variability at low (Planned, Present) reserve size but it was not always the most appropriate. At high reserve size (Half, Maximum), the Boom and Bust strategy exceeded natural variability whereas other strategies (Annual, Trans- parent) better approximated natural variability at these reserve sizes (Fig. 4.3). Sce- narios exceeding natural variability may be modified back to natural variability. All other scenarios partially recovered natural variability. We only simulated four watering strategies and five reserve sizes but other combinations may better approximate natural variability in different rivers. Physical capacity constraints affect environmental flow delivery in rivers worldwide (MDBA, 2012b; Richter and Thomas, 2007; Yin et al., 2011). These include limited capacity to release large flows because of limited reservoir valves, channel volumes, and flood risk to riparian properties and infrastructure (e.g. bridges, roads), potentially compromising environmental watering strategies requiring large flows. De- creasing reservoir valve size increasingly altered natural flow regime, particularly when water supply reliability was low and more water was available for the environment (Yin et al., 2011). The most severe physical constraints in our analysis occurred when environmental watering strategies required large releases over short periods (Boom and Bust, Annual; Fig. 4.4). Small volumes were frequently constrained when environmental watering strategies required flows throughout the year (Transparent, Tributary Augmented; Fig. 4.4). Constraints were reservoir outlet capacities (Gwydir: originally 10.85 GL/d and Macquarie: 8.2 GL/d at full capacity, decreasing with head pressure as volume falls) and limitations on spillway volume (Gwydir: 1 280 GL/d and Macquarie: 1 199 GL/d; spillway activated during flood operations). Other constraints including partial closure of the Copeton spillway (buckled under pressure in 1976 floods), downstream infrastructure, property and channel capacity (Gwydir: 5 - 10 GL/d downstream of the “raft” and Macquarie: 4 GL/d at Marebone Weir) were not modelled but would further compromise environmental water delivery (Wilson and Berney, 2009). For example, environmental watering was delayed until the harvest of floodplain wheat in the Gwydir in 2012 due to the risk of inundation (Albertson, 2012). The challenge of managing environmental flow constraints becomes increasingly complex given the varying nature of constraints, uncertainty in inflows and environmental needs. Scenario modelling can identify and prioritize constraints and provide

128 4.5 Discussion assessment of hydrological, ecological and economic benefits of removal or modifica- tion. Soft options exist such as releasing water via the spillway, implementing flood mitigation strategies and purchasing easements (Pittock and Hartmann, 2009; Poff and Hart, 2002). These are preferable to options that require physical adjustment of infrastructure due to reliance by extractive industries (Pittock and Hartmann, 2009). For example, capacity constraints were a key factor limiting delivery of environmental water in the Murray-Darling Basin Plan (MDBA, 2012b), driving a recommendation for less water to the environment. Possibilities of removing or modifying constraints were not investigated as part of the Plan. Subsequent modelling showed ‘relaxing’ of constraints could meet four additional ecological targets and provide increased flow peaks and flood duration (MDBA, 2012c). There remain considerable challenges for managing constraints when multiple users require water at the same time. This is particularly problematic in the Gwydir and Macquarie Rivers because spring-summer environmental water releases coincide with peak irrigation demand. In many regulated rivers, rights for channel capacity are poorly defined (Beare et al., 1998), generally favouring extractive use because of traditional operations. In the absence of policy for managing competition over limited capacity, Gwydir and Macquarie river operators negotiate between competing users to prioritize access rights, but future access remains uncertain. Management arrangements to clar- ify access rights may include market-based trading of peak demand delivery capacity (Hughes, 2010; Young and McColl, 2005), capacity rights linked to water rights (Beare et al., 1998), or compensation if orders cannot be delivered (Sankarasubramanian et al., 2009). Management rules developed for extractive users may also constrain the delivery of environmental flows. Account limits constrained highly variable environmental water releases, but extractive users were not likely to be affected. In the Gwydir, general security account limits (150 % of entitlements) restricted the magnitude of regulated flood events. There were only two ways to achieve a flood larger than the account limit: new allocations could be announced and released during the event, or regulated releases could be combined with unregulated flows from tributaries or reservoir spills. However, new allocations are not necessarily announced and no discretion exists over timing of unregulated events. The account limit also penalized the most variable environmental

129 4. MANAGING ENVIRONMENTAL FLOWS IN REGULATED RIVERS watering strategy (Boom and Bust) by significantly lowering environmental water allocations (Present and Half reserve sizes in the Gwydir; Fig. 4.5). This occurred because the account more frequently reached capacity, whereby allocations were rejected and returned to the consumptive pool for redistribution. Extractive users were not likely to be affected because they required small volumes of water and their accounts remained low with their more frequent demand. By comparison of river systems, this was not a problem in the Macquarie River because accounts were unlimited, allowing flexibility in storing and releasing environmental flows. River regulation imposes structure and a management imperative on all stored water in a river, requiring integration of all water management (Tilmant et al., 2010). This demands assessment of how different environmental watering strategies affect other water users, particularly allocations. The primary objective should be a fair and equitable allocation system, accommodating varying and uncertain demand patterns without compromising allocations (Hashimoto et al., 1982; Ribbons, 2009; Vasiliadis and Karamouz, 1994). There is relatively poor understanding of the impact of environmental flow management on other users because like most river management, understanding centers on historical patterns of extractive users (Wurbs, 2005). Environmental flow demand is usually more variable, potentially reducing allocations for other water users. This is the first study to test the impacts of different environmental watering strategies on water allocations. Despite considerable variation in simulated environmental watering strategies, there was no significant impact on average annual allocations (Fig. 4.5a) or annual allocation variability (Fig. 4.5b) at Planned and Present reserve sizes. We detected some sensitivity at Half and Maximum reserve sizes when operational losses were more variable (Fig. 4.5a). These reserve sizes were probably unrealistic (Half and Maximum sizes were more than double and quadruple current adaptive environmental water investment, respectively; Table 4.2), but revealed a potential threshold where environmental watering strategies could affect allocations. Effects may be greater if new water rights are established, causing overall water demand to expand (i.e. increasing the planned water reserve; Planned scenario), rather than if existing water rights are reallocated from extractive to environmental use (i.e. purchasing adaptive entitlements; Present, Half and Maximum scenarios). Expanding water demand may affect water supply reliability by an order of magnitude, reducing the value of entitlements for all users (Palmer and Snyder, 1985).

130 4.5 Discussion

Reservoir spills provide unregulated flows to rivers and wetlands, and potentially increase the attractiveness of supplementary water entitlements for environmental water investment. However, river operators are reluctant to release environmental flows when land or water holders may be affected (Richter, 2010). Spills also reduce the managed reservoir yield and correspondingly increase unregulated yield, affecting water users and the environment. Spills increased under environmental watering strategies which raised reservoir levels (Boom and Bust, Tributary Augmented; Fig. 4.7), but were reduced under strategies which lowered reservoir levels (Annual, Transparent). Impacts may be greater in the Gwydir which is less prone to spills (only four since reservoir construction in 1976), and no flood mitigation zone exists. Operational losses (i.e. losses of water to atmosphere, groundwater, distributaries) reduce yield for water entitlement holders and profit for river operators (Mart`ınez- Granados et al., 2011), but provide important environmental benefits including evapotranspiration and aquifer recharge. Losses are the focus of global attention particularly in semi-arid systems (Barnes, 2008; Brown, 1988; Mart`ınez-Granados et al., 2011). Ef- forts to manage losses cannot ignore the environmental watering strategies due to their influence on evaporation (Fig. 4.8 and Appendix 4.D). Moderately variable strategies (Annual, Transparent) produced low evaporation losses, even compared to Historical scenario. The variable Boom and Bust and Tributary Augmented strategies produced the highest evaporation losses. Reservoir volume played an important role. Elevated reservoir levels in the Boom and Bust were because water accrued in reservoir before release, while in the Tributary Augmented strategy, underutilized water remained in reservoir. The framework used to account for losses in reservoir yield has implications for multi-year management of environmental flows. Storing environmental water for the future can be an ecologically important strategy, allowing flows to accumulate for large releases and providing a drought reserve for stressed biota. Yet this may be risky if allocations are discounted due to future evaporation or reservoir spills that reduce reservoir yield. Our two rivers had different approaches for accounting for losses. In the Macquarie, allocations carried over multiple years may be discounted based on their estimated contribution to evaporation losses and spills. This penalized environmental watering strategies with high evaporation losses or spills (Boom and Bust, Tribu- tary Augmented; Figs. 4.7 and 4.8). Strategies that release flows annually (Annual,

131 4. MANAGING ENVIRONMENTAL FLOWS IN REGULATED RIVERS

Transparent) generate fewer evaporation losses and spills, and are less vulnerable to discounting. However, discounting rules did not apply in the Gwydir, and no penalties occured for storing environmental water over multiple years. Multi-year watering strategies are also vulnerable to water access restrictions during drought. During severe water shortages, NSW government policy requires all supplies, including environmental water, to be reallocated for domestic and town water needs (NSW Government, 2000). Restrictions may become more frequent under environmental watering strategies with high evaporation losses (Boom and Bust, Tributary Augmented; Fig. 4.8 and 4.12). Environmental water is particularly vulnerable to access restrictions as it is held in reservoir over long periods. Environmental water access restrictions occurred in 2006/07 during the Millennium drought, when access to general security carry over water was suspended by the NSW Government (Hughes and Goesch, 2009). Water shortages are less likely under Annual and Transparent strategies with reduced evaporation losses (Figs. 4.8 and 4.12). Improved estimates of evaporation losses, including losses due to environmental watering, can help river operators ensure sufficient operational reserves, lowering the risk of water shortages. Ultimately, environmental water management requires sophisticated models that allow for development of scenarios testing the feasibility of different watering strategies. Delivery of environmental water in a regulated river is overwhelmingly governed by water management, operational risks and constraints (Beare et al., 1998; MDBA, 2012b), considerations rarely incorporated into environmental water assessments. Prevailing approaches to environmental flow management and assessment include identification of hydrological or ecological requirements (Gippel, 2001; Poff et al., 2010; Richter et al., 1996; Smakhtin and Eriyagama, 2008) and measuring ecological outcomes (Bednarek and Hart, 2005; King et al., 1998; Lind et al., 2007; Rayner et al., 2009). To progress, more sophisticated models integrated into operational water management are essential because outcomes of environmental water strategies will have significant impacts on ecological outcomes (e.g. colonial waterbird breeding and flooding; Kingsford and Auld, 2005). A Banking and Threshold scenario (Kingsford and Auld, 2005), similar to our most variable scenario (Boom and Bust), resulted in largest simulated waterbird breeding events and increased extent of large floods. This may be particularly important in dryland rivers with high variability (Kingsford et al., 1999; Puckridge et al., 1998). Ecological and operational principles guiding environmental watering actions in

132 4.6 Conclusion

Australia (DEWHA, 2009) can be integrated into scenario modelling to explore optimal outcomes for environmental watering. Scenarios can identify the best management option for environmental water and reveal existing challenges to decision makers (Brekke et al., 2009; Chen et al., 2011; Liu et al., 2008). Optimization modelling can further develop environmental watering strategies, more precisely targeting natural variability while minimizing negative effects (Suen and Eheart, 2006; Tilmant et al., 2010). We used coefficient of variation as a simple surrogate for hydrological variability at monthly and annual scales, but many other metrics characterizing hydrological variability could be simulated (see Puckridge et al., 1998). We focused on variability of reservoir releases, but this needs to also be modelled at other locations along the river, given variability of reservoir releases will change before reaching target ecosystems. The Tributary Augmented strategy could reinstate variability at a downstream tributary confluence when regulated and unregulated flows are superimposed, however unregulated flows may be used to meet extractive demands for water. Another option is to simulate variability at a desired location using spatially- explicit hydrological models, accounting for upstream reservoir releases and extracted flows. Future models could better represent extractive and environmental demand by disaggregating individual water users, introducing stochasticity, and including complex drivers such as markets and technology. Most importantly, effective implementation of environmental watering strategies should be underpinned by a transparent, systematic and adaptive decision process with ongoing involvement with stakeholders and best available science (Liu et al., 2008).

4.6 Conclusion

Environmental flows sustain freshwater ecosystems around the world, yet there are major challenges providing desired environmental flow regimes in regulated rivers. Reg- ulated rivers conventionally operate as delivery systems for extraction, rather than socio-ecological systems where flows are actively managed for multiple objectives. We developed complex models that explored opportunities and risks of introducing and managing environmental flows in two regulated rivers in Australia. The models clearly identified the management and physical constraints that may be modified to improve variability and achieve ecosystem objectives. They also identified risks associated with

133 4. MANAGING ENVIRONMENTAL FLOWS IN REGULATED RIVERS storing environmental flows over multiple years. It is incumbent on governments to assess risks because of considerable public investment in environmental flows (e.g. $3.1 billion in the Murray-Darling Basin). There needs to be increased focus on adapt- ing river management from the traditional delivery system for extraction to one that reflects ecological sustainability. Scenario modelling of environmental watering strategies at the reservoir and at their ecosystem target, coupled with testing of ecosystem responses, provides considerable promise.

134 4.A Monthly constraints

4.A Monthly constraints

Watering Strategy Boom and Bust Annual Transparent Tributary Augmented

Historical Planned Present Half Maximum Historical Planned Present Half ● Maximum

● ● 15% ● ● ● ● ● ● ● 2.0 ● ● ● ● ●10% ● ● ● Gwydir ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● 5% ●●● ● ● ● 1.5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Gwydir ● ● ●●● ●●●●●●●●●●●●●● 0% ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

Frequency of occurrence 40% 0.5 ● ● ● ● ● ● ●

● Macquarie ● ● ● ● ● ● ● ● ● ● ● ● 20% ● ● ● ● ● ● ● 0.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

0% ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1.0 ● ●

0.8 ●

Coefficient of variation Coefficient of variation (a) Monthly constraint frequency

0.6 Historical Planned Present Half Maximum Macquarie

5,000 ● ● ● 0.4 4,000 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 3,000 ● ● ● ●

● Gwydir ● ● ● ● ● ● ● ● ● ● ● 0.2 ● ● ● ● ● ● ● ● ● ● 2,000 ● ● ● ● ● ● ● ● ● ● ● ● 1,000 ● ● ● ● ● ● 0.0 ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●●● ● ● ● ● ● 0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −1,000 20,000

15,000 ● ● Magnitude per event (GL) Magnitude per event Macquarie 10,000

● ● 5,000 ●

● ● ● ● ● ● ● ● ● ● ● 0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

(b) Monthly constraint magnitude

Figure 4.9: The frequency (a) and magnitude per constraint (b) of the monthly volume constrained (GL) for the Gwydir and Macquarie Rivers (rows), ﬁve reserve sizes (columns) and four watering strategies (colours). Box and whisker plots represent 100 stochastic values derived for each 110 year realisation. Connector lines below boxes indicate pairs of environmental watering strategies which were statistically diﬀerent (p<0.05; non-parametric Kolmogorov-Smirnov test with Bonferroni adjustment).

135 4. MANAGING ENVIRONMENTAL FLOWS IN REGULATED RIVERS

4.B Monthly allocation

Watering Strategy Boom and Bust Annual Transparent Tributary Augmented

Historical Planned Present Half Maximum

Historical ● Planned● ● ● Present Half ● Maximum ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 3% ● ● ● ● ● ● ● 2.0 ● ●

● Gwydir ●2% ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

1.5 1% Gwydir

0% ● ● ● ● ● ● 1.0 ● ● ● ● ● ● ● Mean ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 8% ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.5 6% Macquarie

0.0 ● ● 2% ● ● ● ●

0% ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1.0 ● ● ● ●

0.8 ●

Coefficient of variation Coefficient of variation (a) Mean annual allocation

0.6 Historical Planned Present Half Maximum Macquarie

●

5 ● ● ● ● ● 0.4 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 4 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 3 ● ● Gwydir ● ● ● ● ● ● ● 0.2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● 2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1 ● ● ● ● 0.0 ● ● ● ● ● ● ● ● ● ● ● ● 0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● −1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 3 ● ● ● ● ● Coefficient of variation Coefficient of variation

● ● ● Macquarie 2 ● ● ● ● ● ● ● ●

0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

(b) Monthly allocation variability

Figure 4.10: Mean (a) and coefficient of variation (b) of allocation (percent) for the Gwydir and Macquarie Rivers (rows), five reserve sizes (columns) and four watering strategies (colours). Box and whisker plots represent 100 stochastic values derived for each 110 year realisation. Connector lines below boxes indicate pairs of environmental watering strategies which were statistically different (p<0.05; non-parametric Kolmogorov-Smirnov test with Bonferroni adjustment).

136 4.C Monthly mean spill

4.C Monthly mean spill

Spill | Watering Strategy Boom and Bust Annual Transparent Tributary Augmented

Historical Planned Present Half Maximum

●

5 ● ● ● ● ● ● ● ● ● ● ● 4 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● Gwydir 3 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −1 100 ● ● Mean (GL)

●

60 Macquarie ● ● ● ● ● ● ● ● ● ● ● ● ● 40 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ●

0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −20

Figure 4.11: Mean monthly spill (GL) for the Gwydir and Macquarie Rivers (rows), ﬁve reserve sizes (columns) and four watering strategies (colours). Box and whisker plots represent 100 stochastic values derived for each 110 year realisation. Connector lines below boxes indicate pairs of environmental watering strategies which were statistically diﬀerent (p<0.05; non-parametric Kolmogorov-Smirnov test with Bonferroni adjustment).

137 4. MANAGING ENVIRONMENTAL FLOWS IN REGULATED RIVERS

4.D Monthly mean evaporation

Evaporation | Watering Strategy Boom and Bust Annual Transparent Tributary Augmented

Historical Planned Present Half Maximum

● ● 2 ● Gwydir

● 1

●

0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

8 ● Mean (GL)

● ● ● ● ● ● ● ● ● ● ● ● ● ● 6 ● ● ● ● ● ●

● Macquarie ● ● 4

0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

Figure 4.12: Mean monthly evaporation (GL) for the Gwydir and Macquarie Rivers (rows), ﬁve reserve sizes (columns) and four watering strategies (colours). Box and whisker plots represent 100 stochastic values derived for each 110 year realisation. Connector lines below boxes indicate pairs of environmental watering strategies which were statistically diﬀerent (p<0.05; non-parametric Kolmogorov-Smirnov test with Bonferroni adjustment).

138 5

Semi-automated GIS techniques for detecting ﬂoodplain earthworks

5.1 Abstract

Levees, channels and water storages built on the world’s floodplain wetlands control flows for irrigation, flood mitigation and erosion management. Assessing their distribution and hydrological impacts through time and across broad extents is limited by significant costs and technical challenges. We tested the effectiveness of three new semi- automated Geographic Information Systems (GIS) and traditional visual interpretation techniques for detecting earthworks. We used commercially or freely available two- and three- dimensional spatial imagery within 19 quadrats in an agricultural floodplain of the Murray-Darling Basin, southeastern Australia. Semi-automated Digital Elevation Model (DEM) analysis performed best for spatial accuracy (78 % of earthworks correctly predicted within 25 m), overall classification accuracy (97.7 %) and kappa (0.64 ), compared with traditional visual interpretation techniques using Landsat TM (52 %, 96.3 %, 0.39), SPOT (53 %, 95.8 %, 0.27) and aerial photography (72 %, 97.2 %, 0.31). DEM analysis also outperformed semi-automated image segmentation (16 %, 93 %, 0.29) and integrated analysis (75 %, 96.0 %, 0.43) that used spectral information. Semi-automated techniques were slow (DEM analysis: 27 418 s/km2; integrated analysis: 27 737 s/km2; and image segmentation: 1 439 s/km2) compared to visual interpretation (Landsat TM:

139 5. DETECTING FLOODPLAIN EARTHWORKS

109 s/km2; SPOT: 166 s/km2; and aerial photography: 276 s/km2), however processing speed of semi-automated techniques can be further increased without compromising accuracy. Semi-automated techniques also offered operational autonomy following model calibration. High quality, cost-effective earthwork mapping techniques, particularly the semi-automated techniques in this study, are critical for understanding and managing ecosystem health, flood risk and water security in developed floodplains worldwide and should be implemented by governing institutions.

5.2 Introduction

Water resource development threatens biodiversity in the world’s rivers and wetlands (Nilsson et al., 2005; Sparks, 1995; Vörösmarty et al., 2010), altering physical processes such as hydrological dynamics and sediment transport upon which aquatic ecosystems depend (Tockner and Stanford, 2002). Floodplains, key geomorphologic features of large river systems, are among the world’s most modified landscapes (Bayley, 1995; Sparks, 1995; Tockner and Stanford, 2002). Earthworks on the world’s floodplains facilitate flood mitigation, erosion control, river transport and provision of extractive water. Levees, channels, off-river storages and tanks are the most common earthworks throughout most human settled or agricultural floodplains in the United States (∼40 000 km; FEMA, 1992), Europe (Carluer and Marsily, 2004), Middle East (Hritz and Wilkinson, 2006; Jones et al., 2008), Asia (Tsujimoto et al., 2006; Xu, 1993) and Australia (Callow and Smettem, 2009; Kingsford and Thomas, 2002). Earthworks block or confine lateral flow pathways, changing flood frequency and often disconnecting the main river channel from its floodplain (Gergel, 2002). En- croaching floodplain development increased flow stage (Criss and Shock, 2001; Heine and Pinter, 2012) and flood risk (Yin and Li, 2001). Some temperate and semi-arid floodplains have been disconnected almost entirely from their channel, mainly by earthworks: 80 % of the Middle Elbe River floodplain (Leyer, 2004); 90 % of the Lower Mis- sissippi floodplain (Kesel, 2003); 73 % of the Macquarie Floodplain ; and 59 % of the Lower Murrumbidgee floodplain (Kingsford and Thomas, 2002). This disconnection alters inundation patterns, affecting ecosystem productivity and function (Opperman et al., 2009; Thoms, 2003; Tockner and Stanford, 2002) including compromised growth, recruitment and survival of flood-dependent vegetation (Slavich et al., 1999), reduced

140 5.2 Introduction aquatic habitat availability (Larsen et al., 2006), impaired delivery of inorganic and organic particulates (Thoms, 2003) and reduced microbial activity (Kang and Stanley, 2005). Little is known of the non-linearities (Werner and McNamara, 2007), lag effects (Heine and Pinter, 2012) and compounded disturbances including dams and climate change (Gergel, 2002) associated with earthworks in regulated and unregulated rivers. Earthworks may opportunistically harvest lateral flows, compromising water security for downstream users and ecosystems, a considerable concern with increasing competition for limited water supplies, investment in environmental water and presence of water markets (Vörösmarty et al., 2010). Knowledge of earthwork distribution at multiple scales is critical for effective floodplain management (Stein et al., 2002; Thoms et al., 2005), particularly for managing flood risk, licencing, regulating access to water resources and assessing ecological impacts. However detecting earthworks is technically challenging because, unlike dams, they are small, ubiquitous structures, varied in morphology and distributed across large regions of public and private land. Past scholarly investigations manually identified and digitised earthworks using remote sensing imagery and/or field observations to assess hydrological impacts (Carluer and Marsily, 2004; Colvin and Moffitt, 2009), ecological impacts (Kingsford and Thomas, 2004) and trace ancient earthworks(Adams, 1981; Crutchley, 2006; Erickson, 2000). Few organisations currently hold comprehensive spatial datasets of earthworks due to the labour and financial investment required in manually acquiring location, orientation, height, classification, construction date and licence status for each structure. Despite this, increasing cumulative impacts of earthworks continue to reduce water security, increase flood risk and degrade freshwater ecosystems. There is a clear imperative for developing high quality, cost-effective techniques to generate accurate, inexpensive spatial datasets of earthworks at multiple scales. Highly functional and versatile geographic information systems (GIS) can anal- yse and compile spatial information at large spatial scales. Cost-effective and reliable semi-automated GIS techniques are used to detect linear features such as road networks (Baltsavias and Zhang, 2005; Wang et al., 1992). These techniques require human guidance or correction leaving the precise, time-consuming or repetitive tasks to software (Doucette et al., 2001; Gruen and Li, 1997). Early investigations into semi-automated extraction of drainage networks proved challenging in low relief floodplain environments

141 5. DETECTING FLOODPLAIN EARTHWORKS

(O’Callaghan and Mark, 1984) but this was soon overcome (Jenson and Domingue, 1988). Semi-automated techniques for detecting natural drainage networks have been extensively developed (Tribe, 1992) but application to artificial structures has only recently occurred (Bailly et al., 2008). We aimed to develop new semi-automated GIS techniques for locating and classifying floodplain earthworks, comparing their efficiency and efficacy with traditional visual techniques.

5.3 Methods

The regulated Gwydir River forms a characteristic dryland ﬂoodplain within the Murray- Darling Basin, eastern Australia (Fig.

Figure 5.1: Twenty-four quadrats (calibration and validation) were randomly selected across the Gwydir ﬂoodplain of the Murray-Darling Basin, southeastern Australia.

5.1), with low rainfall (500 mm/y), flat relief (average elevation decreases 63 cm/km across 150 km) and large area (>1 million km2). Episodic flood pulses inundate the floodplain, channels, pools and swamps. The floodplain supports aquatic fauna and flora, floodplain grazing, dryland agriculture and irrigated agriculture predominantly for cotton. Four classes of earthworks exist on the Gwydir floodplain (Fig.

142 5.3 Methods

Figure 5.2: Four classes of earthworks on the Gwydir floodplain. (a) Levees were earthen barriers up to 5 m in height. (b) Channels distributed water and were usually bound by levees. (c) Off-river storages (≥0.02 km2) stored water for irrigation. (d) Tanks (≤0.02 km2) stored stock and domestic water, sometimes rainfall fed by channels, flanked by small levees, called bore drains.

5.2). Levees (embankments or dykes/dikes) are long, straight or curvilinear, occasionally vegetated, earthen ridges (>0.1 m) which act as flow barriers for agriculture, flood mitigation and erosion control (Fig. 5.2a). Channels (ditches, canals or trenches) are elongated, straight or curvilinear open cut depressions for water transportation. They are usually flanked by levees (Fig. 5.2b). Off-river storages are impermeable sections of the floodplain (≥ 0.02 km2) usually enclosed by levees (>1 m high) for water retention (Fig. 5.2c). Tanks (farm dams, ring dams or turkey nests) are depressions (<0.02 km2) for storing stock and domestic water, usually surrounded by levees and sometimes fed by drains (Fig. 5.2d). Other features on the Gwydir floodplain, not defined as earthworks, included roads, dirt tracks, built-up land, natural watercourses and cleared agricultural land. We developed three semi-automated earthwork detection techniques and then investigated their efficacy and efficiency compared to visual interpretation techniques on the Gwydir floodplain. For model training, five independent calibration quadrats (1 km2, Fig. 5.1) were randomly selected within the LiDAR coverage (Fugro, 2009). For model validation, 23 independent quadrats were initially selected within LiDAR coverage of the Gwydir floodplain (north 6774700; south 6736900; east 695400; west 775800, zone

143 5. DETECTING FLOODPLAIN EARTHWORKS

55), but four were removed due discrepancies in development across imagery sources leading to temporal error, leaving 19 validation quadrats (1 km2, Fig. 5.1). Each quadrat was buffered by 1 km to prevent truncated earthworks causing edge effects during accuracy assessment, but this was excluded from assessment. Quadrats were randomly stratified across four earthwork density classes (channel length per km2): 0; <0.5; 0.5 - 1; and >1, calculated from a national channel (canal) inventory (GA, 2006) using a kernel density analysis with a 2 km bandwidth. All quadrats were randomly spaced at least 2 km from the nearest quadrat to minimize spatial autocorrelation. Earthwork detection and classification time were recorded per quadrat, for each technique. Calibration time for semi-automated analyses was not recorded because of high variability due to feature complexity and user ability. Performance was evaluated using standard map accuracy metrics in comparison with a reference.

5.3.1 Technique 1: Visual interpretation

Visual interpreters independently digitised and classified operational and remnant earthworks in 19 validation quadrats. For this study, 30 participants (age 20 - 43) were randomly allocated to one of three equal sized groups for analysis of commercial or free imagery of differing resolution: digital aerial photography (0.3 m), SPOT (2.5 m) and Landsat TM (30 m). Groups were gender-balanced by spreading twelve males and 18 females evenly among imagery sources. Interpreters experienced with ArcGIS software or familiar with floodplain earthworks were randomly distributed among groups (Land- sat TM: 4; SPOT: 5; aerial photography: 2). Digital aerial photography was captured using spectrum medium format digital cameras (Feb - Sept 2008). Raw SPOT imagery, acquired in 2004, was pan-sharpened and displayed in pseudo natural color. Two Land- sat TM scenes (March 2008, February 2009) were the nearest available cloud-free image selected from online archives, displayed in natural colour (USGS, 2009). All imagery was georectified into the Map Grid of Australia Zone 55 coordinate system (Geocen- tric Datum of Australia 1994). Participants initially viewed a Powerpoint presentation (Appendix 5.A) which familiarised users with different earthworks, tested their ability to recognize earthworks, and outlined instructions for digitising using ArcGIS version 9.2 (ESRI, 2008). Participants zoomed to each quadrat, started the stopwatch, visually examined the quadrat, manually digitised and classified earthworks, saved their

144 5.3 Methods data, then stopped the stopwatch and recorded time taken. Visual interpreters did not estimate earthwork height as stereo pair images for height derivation were unavailable. Post-processing eliminated overshooting lines and ensured topological and attribute consistency. Linear earthworks were assigned an areal extent which represented a pro- cedural buffer of the minimum mappable distance at the scale users were mapping (4 m wide). A preliminary practice quadrat was digitised by each interpreter but excluded from the main study due to learning effects (Christensen, 1991). The sequence of 19 validation quadrats was randomized for each participant to balance carryover effects (Griffin et al., 2006) such as learning, fatigue and sensitisation that may bias results. Learning effects were quantified by comparing the metrics for first and last quadrats, this last quadrat was an exact copy of the first but rotated 90◦ counterclockwise to ensure it was unrecognisable.

5.3.2 Technique 2: Image segmentation (semi-automated)

Image segmentation is a semi-automated technique for earthwork delineation and classification, driven by expert knowledge. For image segmentation, we aggregated pixels within a raster image into objects, then classified objects by shape, spectra and texture. We used georectified, non-compressed SPOT imagery (bands 1, 2, 3), resampled to 1 m using a nearest neighbour filter (ESRI, 2008). A multi-resolution segmentation algorithm in Definiens Developer 8 software (Definiens, 2009) aggregated adjacent pixels until object homogeneity approached a specified threshold. Eight thresholds were assessed (300, 250, 200, 150, 100, 50, 25 and 10) with 25 best matching earthwork scale. Homogeneity was defined by object shape, color, compactness and smoothness. We weighted these properties to best preserve elongated earthworks (shape: 0.9, color: 0.1, smoothness/compactness: 0). Classification rules were developed for calibration earthwork classes using spectral, geometric, topological, contextual and functional properties of the earthworks (Ap- pendix 5.B; Definiens, 2009). Rules were developed using the k-nearest neighbour classifier, a lazy learning algorithm (Aha, 1997) which stores raw data until the closest calibration object is classified (k = 1) in multidimensional feature space. Subsumptive categories created for channels (wet and dry) and levees (irrigation and bore drain) improved classification by partitioning within-class object diversity. Three additional

145 5. DETECTING FLOODPLAIN EARTHWORKS unclassiﬁed classes (cultivated, uncultivated, road) segregated background from earthworks.

5.3.3 Technique 3: Digital Elevation Model (DEM) analysis (semi- automated)

Earthworks were narrow regions of abrupt relief intruding into and protruding from the floodplain. We were unable to identify earthworks by elevation thresholds because the floodplain was gradually sloped, so we developed a semi-automated technique to extract earthworks from a DEM regardless of slope. The DEM was based on LiDAR (acquired Feb - Sept 2008). LiDAR point clouds integrated airborne sensor trajectory and airborne laser scanner range measurements, using the Leica ALS50-IIM ALS and Leica ADS40 digital sensor (0.13 m horizontal resolution; 0.06 m vertical resolution) (Fugro, 2009). Ground returns were extracted, corrected for sensor displacement and orientation offset, and interpolated as a 1 m DEM. We excluded natural channels based on the Geoscience Australia Geodata Topo 250K Series 3 (GA, 2006). Earthworks were located where DEM elevation differed from an earthwork-free DEM, then removed from the DEM by assigning the median height of absolute DEM pixels within an annulus-shaped filter (inner radius 25 m; outer radius 30 m) to the central cell. After two iterations, local earthwork relief (<25 m) was eliminated but natural landscape undulations e.g. hills (>30 m) were retained. Adja- cent pixels exceeding ±0.1 m elevation difference were aggregated into polygons, the vertical accuracy of LiDAR. Single, isolated pixels were discarded. Polygon height was based on the intersecting DEM difference contour line (0.1 m interval) with maximum absolute value. Area, perimeter, compactness index (perimeter2/area de Smith et al., 2006)and vertices were calculated for each polygon. A regression model was calibrated for each earthwork class based on shape and height variables from classified objects (2 %) within the five independent calibration quadrats (1 km2). We used multinomial logistic regression to predict odds of an earthwork class relative to a ‘non-earthwork’, with continuous independent variables and dichotomous dependent variables: Earthwork: Odds = eAr+Bc+Cv+D where r = maximum height above smoothed DEM, c = compactness index, v = number of polygon vertices and A, B, C, D were statistically significant coefficients derived from the calibration set. Four calibrated regression models for levees, channels, off-river storages

146 5.3 Methods and tanks respectively were produced, with corresponding coeﬃcients p:

3.228r+0.008c−0.003v−6.322 Levee: Odds = e pA < 0.01, pB < 0.01, pC < 0.01 −8.393r+0.005c−6.670 Channel: Odds = e pA < 0.01, pB < 0.01 −5.83r−0.012c+0.005v−6.010 Oﬀ-river storage: Odds = e pA < 0.01, pB = 0.04, pC = 0.03 −9.564r−6.498 Tank: Odds = e pA < 0.01

Odds The model yielding the highest probability ( 1+Odds ) was used to classify each object if probability exceeded 95 %, otherwise classiﬁcation was non-earthwork.

5.3.4 Technique 4: Integrated analysis (semi-automated)

Integrated analysis combined two- and three-dimensional imagery sources (SPOT, Land- sat TM and LiDAR DEM), using semi-automated image segmentation. The difference DEM (see Technique 3) was segmented using a multi-resolution algorithm (Definiens, 2009) with a homogeneity index threshold of 50, which best preserved linear earthworks compared to other thresholds tested (300, 250, 200, 150, 100, 25 and 10). Additional segmentation criteria promoted elongated objects by weighting object shape (0.9) over color (0.1) and smoothness/compactness (0). We developed simple, transferrable classification rules for calibration objects based on six of 59 spectral, geometric, topological and contextual properties (Table 5.1). Object density and object border contrast distinguished elongated levees and channels. Object density measured the distribution of object pixels in space; the most dense shape is a square and the least dense is threadlike (Definiens, 2009). Border contrast was the ratio of mean relative height, based on the difference DEM (see Technique 3), of outer and inner pixels along the object border. Water bodies were identified using Landsat TM (object mean band 1: 0.45 - 0.52 µm) and SPOT imagery (ratio of object mean band 1 (0.50 - 0.59 µm) to object mean band 3 (0.78 - 0.89 µm)). Water bodies were classified as an off-river storage using area and border index (jaggedness), or a tank using area and object density. Object properties were parameterized using fuzzy set theory to describe membership strength and incorporate uncertainty. Parameters were based on existing knowledge and visual experimentation of selected properties, defined iteratively for each property

147 5. DETECTING FLOODPLAIN EARTHWORKS

Table 5.1: Objects were assigned a fuzzy class membership probability based on the parameterization of diﬀerent properties. Maximum and minimum property values determined the direction and width of the curve.

Class Property Minimum Maximum Curve Levee Border contrast <0.05 >0.15 Gaussian DEM Density <0.5 >1 Gaussian Channel Border contrast >-0.16 <-0.2 Gaussian DEM Density >1 <0.5 Gaussian Waterbody Mean Landsat <50 >70 Gaussian TM (Band 3) Ratio <0.4 >0.5 Gaussian SPOT (Band1) SPOT (Band3) Oﬀ-river Area <0.02, 0.02 - square storagea >1.1025 1.1025 Border index >2 <1.5 Gaussian Tanka Area <0.0001, 0.0001-0.02 square >0.02 Density <1.5 >2.5 linear

a Membership assignment applied after water bodies were merged.

by setting maximum and minimum membership values and specifying a Gaussian or linear set boundary. Oﬀ-river storages and tanks were assigned precise values as membership rules were known (≥ 0.02 km2 and < 0.02 km2 respectively ). Earthworks were classiﬁed only if membership exceeded 0.1.

5.3.5 Thematic and spatial accuracy

We quantiﬁed thematic and spatial accuracy (Table 5.2) using a spatial reference dataset (5 m pixel size), compiled manually by an experienced image analyst, utilizing all available georectiﬁed imagery: LiDAR DEM, digital aerial photography, Landsat TM (7 bands), SPOT (3 bands) and the national channel layer (GA, 2006). Chan-

148 5.3 Methods

Table 5.2: Thematic (A:D; Cohen, 1960; Story and Congalton, 1986) and spatial (E; Federal Geographic Data Committee, 1998) accuracy metrics used for earthwork map comparison and their deﬁnition.

Accuracy metric Definition A. Overall accuracy Proportion of correctly classified pixels B. Kappa Proportion agreement after removing chance C. User’s accuracy Number of correctly classified pixels in a class divided by (commission) the total number of pixels classified in the same class D. Producer’s Number of classified pixels in a class divided by the total accuracy (omission) number of pixels in the corresponding reference class E. Positional Maximum displacement of 95 % of features in a projected accuracy datum, where displacement is root mean square error v u n u 1 X 2 (RMSE) per earthwork: t n di and d = Euclidean i=1 displacement (m) between every 1 m2 cell in the validation earthwork and the nearest reference earthwork within the same quadrat, n = number of pixels per earthwork and i = index of summation. nels, levees, off-river storages and tanks were manually digitised as polygons in ArcGIS (ESRI, 2008) and classified according to height, visual properties and contextual information. Visual interpretation error matrices were averaged within image source and standard error reflected variation among and within interpreters (Allan, 1999). Clas- sification error results were reported with a 25 m reference polygon buffer which best tolerated positional displacement compared with 0 m and 12.5 m buffers. Non-buffered error matrix values were used for unclassified predictions (top row of error matrix), as buffering falsely lowered producer’s accuracy when pixels predicted as unclassified overlayed reference buffer zones. The resulting matrix accounted for positional offset of predicted earthwork pixels without distorting producer’s accuracy. Carryover effects for visual interpretation were quantified by differencing accuracy metrics (overall accuracy and kappa), between initial and final quadrats for each individual, then averaged across each imagery group. We quantified spatial accuracy at a 1 m scale (16 % and 17 % of median levee and channel width respectively). To partition positional accuracy

149 5. DETECTING FLOODPLAIN EARTHWORKS from classiﬁcation accuracy, results were grouped according to displacement character: no displacement (0 m); boundary irregularities due to image misregistration or human error (0 - 12.5 m), displacement (12.5 - 100 m) and incorrectly classiﬁed rather than inaccurately positioned (>100 m; Foody, 2006).

5.4 Results

There were differences among earthwork maps produced from visual interpretation and semi-automated techniques (Fig. 5.3). Image segmentation and integrated analysis overestimated total classified extent of earthworks, while DEM analysis and visual interpretation underestimated earthworks (Table 5.3). Total classified extent was influenced by earthwork abundance and volume (Fig. 5.3). All techniques detected four earthwork classes, except for DEM analysis (no off-river storages). Integrated analysis was the only technique where ranked percentage of earthwork class corresponded to the reference. Classification accuracy (Fig. 5.4), derived from error matrices (Table 5.4), showed variation in techniques due to omission and commission. Overall accuracy was above 90 % for all techniques (Fig. 5.4a), with DEM analysis yielding highest overall accuracy (97.71 %), followed by visual interpretation (aerial photography: 97.23 %). Image segmentation and visual interpretation of SPOT imagery produced lowest accuracies

Table 5.3: The relative effects of different techniques on identifying total classified extent of earthworks (m2) and extent of earthworks classes (%) across 19 quadrats (1 km2), compared to reference maps.

Earthwork Visual interpretation Image DEM Integrated Reference class Landsat SPOT Aerial segment analysis analysis Total (m2) 470 951 393 731 144 537 1 153 045 467 455 708 906 565 613 Total (%) 83.26 69.61 25.55 203.86 82.65 125.33 Levee (%) 2.4 4.09 14.71 37.41 44.77 23.01 32.49 Channel (%) 11.3 38.89 52.25 32.93 48.72 7.7 23.19 Oﬀ-river 77.97 50.68 26.94 7.02 0 63.53 42.02 storage (%) Tank (%) 8.33 6.34 6.11 22.65 6.51 5.76 2.3

150 5.4 Results

Figure 5.3: Earthwork digitisation for two quadrats (1 km2) by visual interpretation (Landsat TM, SPOT and aerial photography), image segmentation, DEM analysis and integrated analysis with corresponding aerial photograph and reference. Quadrat 1 was a laser-levelled cotton irrigation property showing a partially-ﬁlled and empty oﬀ-river storage surrounded by levees and channels. Quadrat 2 contained mixed grazing and cropping with tanks surrounded by levees and fed by bore drains.

151 5. DETECTING FLOODPLAIN EARTHWORKS

Figure 5.4: Classiﬁcation accuracy for visual interpretation (Landsat TM, SPOT, aerial photography), image segmentation, DEM analysis and integrated analysis. Mean (SE) estimates were given for each group of 10 visual interpreters. Accuracy metrics were (a) overall accuracy and kappa, (b) user’s accuracy across earthwork classes, and (c) producer’s accuracy across earthwork classes.

152 5.4 Results

(93.31 %; 95.83 %). Kappa scores were lower, more varied (0.27 - 0.64) and techniques diﬀered in relative rank, due to exclusion of large number of correctly predicted un- classiﬁed cells compared to overall accuracy. DEM analysis retained the highest Kappa (0.64), followed by integrated analysis (0.43). Visual interpretation of SPOT imagery and image segmentation produced lowest kappa (0.27; 0.29).

Table 5.4: Error matrices for image analysis techniques, with reference data shown in columns and validation data in rows based on 76 000 pixels (5 m resolution) across 19 quadrats (km2).

Technique None Levee Channel ORSa Tank Total Landsatb None 726010 6810 4957 2969 297 741043 Levee 286 110 42 0 0 438 Channel 1056 253 923 23 4 2260 ORS 7421 185 292 6760 36 14693 Tank 1144 56 46 0 320 1566 Total 735916 7414 6261 9753 656 760000

SPOTb None 726080 6745 4489 6762 229 744304 Levee 459 110 70 0 3 643 Channel 3844 135 2078 11 22 6089 ORS 5350 242 153 2192 30 7968 Tank 41 48 10 563 335 997 Total 735775 7279 6800 9528 618 760000

Air None 734476 6836 3977 8053 287 753629 photob Levee 454 562 140 0 0 1156 Channel 937 138 2228 2 9 3314 ORS 0 14 8 1466 57 1545 Tank 115 23 24 0 193 355 Total 735982 7573 6377 9521 547 760000

DEM None 728503 2778 1568 6786 58 739693 analysis Levee 247 7980 197 26 0 8450 Channel 1990 1039 5368 2225 8 10629 ORS 0 0 0 0 0 0 Tank 53 238 160 21 756 1228 Total 730793 12035 7293 9058 822 760000

153 5. DETECTING FLOODPLAIN EARTHWORKS

Image None 699046 5114 4073 6302 315 714850 segment Levee 10984 4632 1206 0 34 16856 Channel 12022 319 2316 197 0 14854 ORS 267 1 11 2911 0 3189 Tank 9221 227 490 94 219 10251 Total 731540 10293 8096 9504 568 760000

Integrated None 718347 4015 2762 6464 268 731856 analysis Levee 545 5223 626 0 0 6394 Channel 2 0 2170 0 0 2172 ORS 13677 500 658 3114 0 17948 Tank 807 231 145 0 447 1630 Total 733378 9969 6361 9578 715 760000 aORS: Oﬀ-river storage bVisual interpretation, averaged across users

Mapping performance varied for earthworks classes and no technique performed best for all classes (Fig. 5.4). Consideration of class-based accuracy metrics is there- fore critical for interpreting results. For levees, DEM analysis had highest user’s and producer’s accuracy. For channels, integrated analysis yielded near-perfect results for user’s accuracy but image segmentation performed poorly (Fig. 5.4). For visual interpretation of channels, user’s and producer’s accuracy increased with image resolution. DEM analysis performed best for producer’s accuracy, although some small channels containing dense vegetation remained unidentified. For off-river storages, image segmentation yielded highest user’s accuracy with only 2 911 of 3 189 pixels correctly classified (Table 5.4). Visual interpretation of Landsat TM was best for producer’s accuracy, with accuracy decreasing with increasing image resolution (Table 5.4). Thirty visual interpreters detected more filled off-river storages (16) than empty (11). No off-river storages were detected using DEM analysis. For tanks, visual interpretation of Landsat TM yielded highest user’s accuracy and DEM analysis yielded highest producer’s accuracy. Positional error acted non-uniformly across earthwork classes, causing producer’s accuracy for levees to be underestimated at zero displacement (0 m: 38.64 %; 12.5 m: 50.43 %; 25 m: 52.40 %) but not for off-river storages (0 m: 32.10 %; 12.5 m: 32.42 %; 25 m: 32.51 %) in the integrated analysis (Appendix 5.C). Positional accuracy results are shown in Fig. 5.5. Only visual interpreters digitised

154 5.4 Results

Figure 5.5: The distribution of RMSE (m) for earthworks, expressed as a percentage, across image analysis techniques, plotted in order of increasing RMSE. a small number of earthworks with zero positional error, and this improved with image resolution (Fig. 5.5). Visual interpretation of aerial photography had the highest percentage of earthworks within 0 m and 12.5 m thresholds (9.63 % and 66.97 % respectively). DEM analysis had the highest percentage of earthworks in the 25 m and 100 m thresholds (78.2 % and 100 % respectively). Image segmentation yielded the lowest positional accuracy for all thresholds (12.5 m and 25 m were 15.56 %; 100 m was 35.56 %). There were clear differences in efficiency among techniques (Fig. 5.6). Visual interpretation was more efficient than semi-automated techniques for 19 quadrats. Average digitisation time per quadrat (1 km2) increased with image resolution as interpreters investigated image detail (Landsat TM: 109 s; SPOT: 166 s; aerial photography: 276 s). DEM analysis (27 418 s) and integrated analysis (27 737 s) relied on the processing- intensive DEM differencing procedure. By comparison, image segmentation was significantly faster (1 439 s). There was no evidence for a trade-off between time and classification accuracy, or average positional accuracy (Figs. 5.6a, b & c). Our comparison of identical initial and final quadrat (rotated 90◦) mapped by visual interpreters suggested visual interpreters experienced carryover effects. Overall accuracy declined (mean difference: -0.028 ± 0.001 (SE); n = 22). Kappa also decreased (mean difference: -0.075 ± 0.021 (SE); n = 14) though there were some improvements (mean difference: 0.040 ± 0.010 (SE); n = 8). Positional accuracy improved for the

155 5. DETECTING FLOODPLAIN EARTHWORKS

Figure 5.6: Scatterplots showing (a) overall accuracy, (b) Kappa and (c) positional accuracy (average RMSE), relative to average time taken to digitise a 1 km2) quadrat. Error bars show standard error variation among ten visual interpreters (a and b) and among earthworks (c). majority (mean diﬀerence −36.30 m ± 12.77 m (SE); n = 11) but decreased for others (mean diﬀerence 38.65 m ± 8.78 m (SE); n = 5).

5.5 Discussion

Floodplains cannot be managed adequately without knowledge of earthwork distribution. It is critical to locate earthworks so their impacts on ecology, flood risk and water security can be managed (Criss and Shock, 2001; Konrad et al., 2008; Leyer, 2004; Op- perman et al., 2009). We demonstrated that GIS techniques can be used to determine the distribution of earthworks with different implications for accuracy, efficiency and cost. Semi-automated techniques for detecting and classifying floodplain structures

156 5.5 Discussion had clear advantages in performance over visual interpretation (Figs. 5.4 and 5.5). With repeatability, transferability and economy of scale, semi-automated techniques for producing spatial datasets of earthworks could be implemented across large scales and provide fundamental information for managing ﬂoodplains. Some techniques may be preferable depending on the context and application.

5.5.1 Semi-automated analyses

DEM analysis outperformed all techniques for kappa and overall accuracy in detection of most earthworks (Fig. 5.4). Spatial accuracy for DEM analysis was almost equivalent to the highest performing visual interpretation technique (aerial photography, Fig. 5.5). DEM analysis was not sufficient for detecting flat-based off-river storages because the technique relied on elevation differencing. This could be improved by defining off- river storages as the area bounded by levees (>4 m height), incorporating spectral information or integrating techniques with superior off-river storage detection (Fig. 5.4). Positional accuracy was low for DEM analysis because there were few misclassified earthworks, however errors arose from consistently wider boundary delineation than the reference.

Image segmentation relied on spectral reflectance, so water-filled channels and off- river storages within dry surroundings were well detected but tanks were often misclassified (poor user’s accuracy; Table 5.4) as their characteristics varied considerably: filled, not filled, vegetated and non-vegetated. Low user’s accuracy (Fig. 5.4) and high RMSE (64 % of earthworks >100 m, Fig. 5.5) occurred because bright, elongated streaks within furrowed fields were incorrectly classified as levees. This could be overcome using integrated analysis which segmented furrowed fields based on relief rather than spectral information, improving user’s accuracy (Figs. 5.4 and 5.5). However, integrated analysis with spectral and elevation data did not exceed overall accuracy of DEM analysis, suggesting spectral information adds little value where elevation data are available, except for off-river storages and water-filled features impenetrable by LiDAR.

157 5. DETECTING FLOODPLAIN EARTHWORKS

5.5.2 Visual analyses

Visual interpreters traced along earthwork centrelines, not boundaries, creating narrow earthworks contained within the reference with zero displacement (Fig. 5.5). Visual interpreters misclassified some levees as gravel roads, dirt tracks, fence lines, field boundaries and dry channels, resulting in low user’s accuracies (Fig. 5.4). Road maps and cadastral information would aid classification. Visual interpreters had trouble detecting vegetated levees and levees flanking channels (poor producer’s accuracy; Fig. 5.4). Full or partially filled off-river storages were 45 % more likely to be detected than empty storages; selecting images with filled off-river storages would improve accuracy. Para- doxically, off-river storages were more successfully detected using coarse rather than fine resolution imagery (Fig. 5.4c). Only five of 20 off-river storages were identified using high resolution aerial photography, but their bounding levees were mostly detected (17 of 20). Some dry, vegetated off-river storages were overlooked by interpreters not trained to recognize them. Improvements in user’s and producer’s accuracy with image resolution (Figs. 5.4b, c) were probably because irrigation pumps, pipes and erosion rills became more discernable.

5.5.3 Cost

There were clear cost implications, as digitisation time depended on technique (Fig. 5.6). Visual interpretation was efficient for small regions but not large floodplains, offer- ing no economy of scale. Visual interpreters would take approximately 151 hours (Land- sat TM), 230 hours (SPOT) and 383 hours (aerial photography) to detect earthworks in a 5 000 km2 floodplain. Digitisation speed may improve marginally with experience but substantial increases would probably compromise accuracy. For the same floodplain, pre-calibrated semi-automated techniques would require 38 524 hours (image segmentation), 38 081 hours (DEM analysis) and 1 999 hours (integrated analysis) with a dual core 2.53 GHz processor and 2 GB RAM. Time savings from parallel processing and/or distributed processing and increased computer power could increase processing speeds without affecting the quality of results, making semi-automated techniques an efficient alternative to visual interpretation. Importantly, semi-automated processing time is not affected by factors influencing visual processing time (e.g. expertise, image resolution, earthwork density and land cover heterogeneity). Furthermore, semi-automated

158 5.5 Discussion methods oﬀer complete operational autonomy in detecting earthworks and assigning their attributes. Labour costs are only required for initial investment in algorithm development and model set up.

5.5.4 Management implications

Earthwork detection techniques can be applied to floodplain wetlands worldwide for ecological impact assessment, hydrological modelling, flood risk management and water security protection (Table 5.5). End-user applications and accuracy assessment are critical considerations when determining the most appropriate technique for producing spatial datasets of earthworks, given variation in classification accuracy (Figs. 5.4b, c; Cihlar et al., 1998). We recommend integrating techniques to improve mapping accuracy for specific applications (Table 5.5). For example, image segmentation at a broad scale may be followed by visual interpretation (Landsat TM or aerial photography) or DEM analysis for examining hotspots. These techniques and accuracy metrics may be used to define quality standards for spatial datasets of earthworks. For the Gwydir floodplain, earthworks maps could be used to critique the effectiveness of floodplain management guidelines (WRC, 1978a) and to improve the delivery of environmental flows to Ramsar wetlands through scenario testing of earthwork configurations (Wil- son et al., 2009). On a global scale, such techniques could increase our understanding of the important, and often overlooked, role of earthworks in governing hydrology in floodplain wetlands globally, where biodiversity is among the most threatened of all ecosystems worldwide(Millennium Ecosystem Assessment, 2005). There is considerable value in widespread use of semi-automated techniques. They are systematic, providing consistent results regardless of the interpreter. They also captured small, isolated, vegetated earthworks like tanks that were sometimes overlooked by visual interpreters. The national inventory (GA, 2006) based on visual interpretation, contained only 119 km of channels, about half of the length of channels in our reference dataset (231 km). Semi-automated techniques may have systematic errors that are normally easier to overcome than random errors or other biases associated with subjective techniques (Benz et al., 2004). Reduced classification accuracy over time was due to carryover effects including fatigue, sensitisation and increasingly hurried digitisation. Increased RMSE over time was likely due to improved manual dexterity and better utilization of zoom functions. These results captured interpreters’

159 5. DETECTING FLOODPLAIN EARTHWORKS

Table 5.5: Recommended mapping techniques for earthworks for diﬀerent applications and their essential requirements.

Application Essential requirements Recommended technique(s) Hydrological and High spatial accuracy, DEM analysis ecological impact efficiency at a large scale, assessment height data Floodplain flow High spatial accuracy, Visual interpretation modelling (hydrological potentially height data using aerial photography modelling, flood risk, or DEM analysis for a scenario testing) large extent, with height data from DEM analysis Flow interception risk Efficient at floodplain Initial rapid assessment and constriction extents, high accuracy at with image segmentation. small scales Overlay spatial earthworks dataset with inundation layer to identify potential flow interception. Calculate off-river storage High user’s and Use visual interpretation capacity producer’s accuracy for (Landsat TM) when off-river storages storages hold water to estimate area. Use height derived from the difference DEM to estimate volume. Spatial information Consistency, rigor and DEM analysis or system for earthworks spatial applicability integrated analysis (inventory, licensing and compliance) Identification of High spatial accuracy, DEM analysis paleochannels and height data remnant earthworks (see Adams, 1981; Erickson, 2000; Hritz and Wilkinson, 2006)

160 5.6 Conclusion

first exposure to digitising earthworks but ongoing visual interpretation was not likely to produce such pronounced effects. Global portability of semi-automated techniques depends on the maintenance of spectral characteristics and spatial resolution. Image segmentation and integrated analysis tolerate minor spectral variation in input imagery due to cloud cover, moisture, time of day and seasonality, because they incorporate fuzzy membership functions to accommodate uncertainty in sensor measurements (Benz et al., 2004). Image pre-processing (e.g. enhancing or suppressing earthworks using thresholding or contrast stretching) improves performance of techniques (Gopi, 2008). For global applicability of techniques, we suggest rapid visual assessment of imagery to ensure absence of floodwaters, cloud, shadow or canopy which may obscure earthworks, and exclusion of floodplain lakes and aeolian formations not present on the Gwydir floodplain. Techniques developed for low relief floodplains are likely to be applicable in regions of diverse topography provided shadows do not obscure earthworks (image segmentation and integrated analysis only) and earthwork relief is sharper than natural floodplain undulations (30 m; DEM analysis only). We recommend a rapid, post-classification visual assessment to confirm large earthworks (>3 km) were detected as models were calibrated to objects fitting within an envelope of 3 x 3 km.

5.6 Conclusion

Knowledge of the spatial distribution of earthworks is seldom available or incomplete due to prohibitive cost of mapping earthworks across broad extents. Floodplain development continues without adequate assessment of consequences for humans and ecosystems. We showed semi-automated earthwork detection techniques outperformed visual interpretation in accuracy. We recommend semi-automated techniques for ecological impact assessment, hydrological modelling, ﬂood risk management and water security protection. Advances in earthwork mapping will improve the science and management of ﬂoodplain earthworks and their hydrological impacts in agriculturally and ecologically important regions worldwide.

161 5. DETECTING FLOODPLAIN EARTHWORKS

5.A A guide to detecting ﬂoodplain earthworks22/10/2012

What are earthworks? Detecting Earthworks are physical soil barriers that are constructed in floodplain wetlands to control water as it flows through the earthworks in landscape, particularly for irrigation and flood control. floodplains: Irrigation infrastructure Flood Control a guide for This channel transports water from the Earthworks intercept water flow to visual river to irrigated crops downstream. protect crops Celine M. M. Steinfeld Australian Wetlands and Rivers Centre interpreters University of New South Wales wetland Sydney, Australia [email protected]

Water flowing through floodplain

Why do we need to manage earthworks? What can we do to manage earthworks? Thousands of kilometers of earthworks exist in developed floodplains, yet we don’t know where they are located. We need to create Mississippi The Nile, Sudan maps of the floodplain, United location and type of States earthworks to assist floodplain managers and planners to make The purpose of sound decisions. this study is to Rio Ciguela, Spain Macquarie test how well floodplain, people can Australia identify and map earthworks on photos taken by a

© Google Images satellite called SPOT. Floodplain managers are concerned about earthworks because: • Earthworks can prevent floodwaters reaching ecologically significant wetlands • Earthworks can divert unlicensed water to private land • Earthworks can trap water and cause drowning of floodplain vegetation • Earthworks may increase flood risk to properties

1. Off-River Storages 2. Channels Channels contain water between two raised banks. They make up approximately 75% of earthworks in irrigated floodplains. Off-river storages are artificial bodies of water enclosed by a large banks. They are the easiest structures to locate.

They transport water from the river to storages and irrigated pastures.

adjacent dry Water is pumped into off-river storages and kept until it channels is required for irrigation.

wet channels

In SPOT imagery, channels usually have two parallel lines.

The largest storage in Australia holds enough water to fill Although they might half of Sydney Harbour. not be so obvious.

162 5.A A guide to detecting ﬂoodplain earthworks 22/10/2012

3. Levees 4. Tanks

Tanks, or farm dams, are smaller bodies of water for stock and Levees are single earth banks that protect domestic uses. They are less than 0.2 km sq, agricultural land and infrastructure from flooding. much smaller than off-river storages. Levees are less common than channels.

Sometimes small levee banks channel rainfall and surface water into the dam.

Levees range between 0.5 and 5m in height and can extend several kilometers. tank

5m Levee 0.5m Levees Levees capture rainfall for the tank

Practice Exercise – Identifying earthworks There is one channel in this image, can you guess where it is?

Take time to locate earthworks in the following images, before you click to find the answers.

Ensure Powerpoint presentation is in Slide Show mode.

trees

This channel transports water from the river to tanks for stock and domestic water supplies.

There are several off-river storages in this image, can you guess where they are? There are two off-river storages in this image, can you guess where they are?

Off-river storages may be full, partially filled or empty

Click to find out… There are also many channels that deliver the water around the floodplain.

163 5. DETECTING FLOODPLAIN EARTHWORKS 22/10/2012

There are four channels in this image, can you guess where they are? There are several channels in this image, can you guess where they are? (Be careful of roads)

Gravel road

Cotton irrigation crop - large fields of homogenous colour or texture, similar to a patchwork.

Grazing areas contain sparse trees, scattered vegetation, patchy grass and rough tracks.

There is one levee in this image, can you guess where it is? There are several tanks in this image, can you guess where they are? (watch out for the gravel roads) There are also 6 earthworks for directing capturing rainfall, can you find them?

This levee is constructed over a dry channel. It activates during floods, spreading water across the floodplain.

Now for a harder example. There are multiple features in this image, can you guess their location and classification? A road or an earthwork? Earthworks are very difficult to distinguish from roads, because they are similar in shape, colour and texture, some roads are even elevated like earthworks. road Roads tend to:

Be linked to buildings

Have smoothed corners and T- junctions

Be lightly coloured and not green – they are not covered with vegetation

Channels tend to have double edges, they might be filled with water and they may connect tanks or surround irrigation areas.

Sometimes you can see intersections between earthworks and roads

164 5.A A guide to detecting ﬂoodplain earthworks 22/10/2012

ArcGIS Instructions for Mapping Part 1 Now that you are familiar with the 4 types of earthworks, you will need to identify and map them within the red cells seen below. 1. Start timing using stopwatch Please familiarise yourself with the ArcGIS working environment. You are welcome to refer to these slides later. Once you are ready, take a look at your own ArcGIS screen… 2. Zoom to the Test site: double click on the grey box next to Number 0 in To save, select the “Attributes of Sites” table. Editor > Save Edits 3. Examine image for earthworks

Black Arrow HINT 1: Zoom out beyond the red cell to see the (edit a line) surrounding landscape and context to help you Pencil Tool classify features. To zoom in and out, use the mouse (add a line) scroll wheel or the Magnifying Glass tool. Layer in which to add line HINT 2: Look for off-river storages first, then channels, (ensure it displays tanks and levees. There are not necessarily Earthworks) earthworks at every site.

Attribute table containing If in doubt, leave it out information about each line If you are uncertain about the presence of an earthwork, leave it Sites out. If you are certain there is an earthwork, take a guess of its classification.

SPOT satellite imagery

Instructions Part 2 Instructions Part 3

4. Digitize an earthwork - Select the Pencil Tool - Ensure Create New Feature is selected as the Task, and Earthworks is selected 7. Repeat steps 2 – 5 for the remaining number of earthworks. as the Target - Click on one end of the levee, then continue to click to extend segments along a 8. When you think that all the earthworks have been digitized, stop levee or channel until you reach the end of the feature or the red boundary. the stopwatch and record the time to the nearest second on the - Accuracy is important – try to draw along the centreline of a feature or in the case sheet provided. of off-river storages and tanks, draw around the perimeter

- Double click to finish a line. Made a Mistake? Just right click on the line, Select Delete vertex or Delete sketch

5. Classify the earthwork in the “Attributes of Earthworks” table - Ensure the line is highlighted blue, by using the Black Arrow tool 9. Repeat the process from the beginning for site Number - Click on the highlighted column in the “Attributes of Earthworks” 1…2…3…4 etc table and insert one of the following classes in the Class column: Levee, Channel, Off-river storage, Tank.

6. Save edits regularly - Click on Editor > Save edits

Thanks very much for helping to improve our understanding of Australian wetlands.

Celine M. M. Steinfeld Australian Wetlands and Rivers Centre University of New South Wales Sydney, Australia [email protected]

165 5. DETECTING FLOODPLAIN EARTHWORKS

5.B Image segmentation parameters

Table 5.6: Spectral, geometric, topological, contextual and functional parameters used to calibrate rules to identify earthworks using image segmentation (Deﬁniens, 2009).

Parameters X co-ordinate of the inner pixel Length of main line (no cycles) Y co-ordinate of the inner pixel Length of main line (regarding cycles) Area (excluding inner polygons) Length/Width Area (including inner polygons) Length/Width (only main line) Asymmetry Maximum diﬀerence Average length of edges (polygon) Maximum branch length Average area represented by segments Mean Layer 1 Border index Mean Layer 2 Border length Mean Layer 3 Brightness Number of edges (polygon) Compactness Number of inner objects (polygon) Compactness (polygon) Number of segments Curvature/length (only main line) Perimeter (polygon) Degree of skeleton branching Polygon self-intersection Density Radius of largest enclosed ellipse Elliptic Fit Radius of largest enclosed ellipse Elliptic Fit Radius of smallest enclosing ellipse GLCM Ang. 2nd moment (all directions) Radius of smallest enclosing ellipse GLCM Contrast (all directions) Rectangular Fit GLCM Correlation (all directions) Rectangular Fit GLCM Dissimilarity (all directions) Roundness GLCM Entropy (all directions) Roundness GLCM Homogeneity (all directions) Shape index GLCM Mean (all directions) Standard deviation Layer 1 GLCM StdDev (all directions) Standard deviation Layer 2 GLDV Ang. 2nd moment (all directions) Standard deviation Layer 3 GLDV Contrast (all directions) Standard deviation of area represented by segments GLDV Entropy (all directions) Standard deviation of length of edges (polygon) GLDV Mean (all directions) Width Length Width (only main line) Length of longest edge (polygon)

166 5.C Eﬀects of buﬀer size on accuracy

5.C Eﬀects of buﬀer size on accuracy

Table 5.7: Overall accuracy, Kappa, producer’s and user’s accuracy depended on the reference polygon buffer size (0 m, 12.5 m and 25 m). The 25 m buffer best accounted for positional offset without distorting classification accuracy.

Earthwork & Technique Positional displacement tolerance (m) 0 12.5 25 0 12.5 25 All earthworks Overall Accuracy (%) Kappa Landsat TM 96 96 96 0.34 0.38 0.39 SPOT 96 96 96 0.19 0.26 0.27 Aerial photography 97 97 97 0.22 0.3 0.31 Image segmentation 93 93 93 0.16 0.25 0.29 DEM analysis 97 97 98 0.44 0.6 0.64 Integrated analysis 96 96 96 0.32 0.4 0.43 Levees Producer’s accuracy (%) User’s accuracy (%) Landsat TM 1 2 2 7 32 40 SPOT 0 1 2 7 18 21 Aerial photography 3 7 8 22 46 52 Image segmentation 20 38 45 9 21 27 DEM analysis 61 65 66 54 87 94 Integrated analysis 39 50 52 45 74 82 Channels Producer’s accuracy (%) User’s accuracy (%) Landsat TM 5 13 15 10 32 40 SPOT 14 30 31 18 49 53 Aerial photography 22 35 36 34 67 69 Image segmentation 11 24 29 4 12 16 DEM analysis 55 65 74 27 41 51 Integrated analysis 30 34 34 72 98 100 Oﬀ-river storages Producer’s accuracy (%) User’s accuracy (%) Landsat TM 69 69 70 57 61 61 SPOT 23 23 23 30 30 30 Aerial photography 16 16 16 52 52 52 Image segmentation 31 31 31 91 91 91 DEM analysis 0 0 0 0 0 0 Integrated analysis 32 32 33 17 17 17 Tanks Producer’s accuracy (%) User’s accuracy (%) Landsat TM 39 46 46 44 62 64 SPOT 47 52 53 40 57 57 Aerial photography 27 28 28 57 61 61 Image segmentation 35 38 39 2 2 2 DEM analysis 86 91 92 36 59 62

167 5. DETECTING FLOODPLAIN EARTHWORKS

Integrated analysis 48 56 63 15 21 27 aVisual interpretation, averaged across users

168 6

Fertile ground for environmental ﬂows: rethinking water management in the Murray-Darling Basin

6.1 Abstract

Major challenges were identified in integrating environmental flows in regulated rivers. Challenges were symptomatic of the underlying problem of fragmented water management arising from spatially isolated evolution of policy and management across large transboundary river basins. This fragmentation has compromised Basin scale environmental flow integration, because environmental flow integration is largely inappropri- ately scaled, highly asymmetric across catchments and poorly informed. Consolidation of water management should involve five factors: realignment of boundaries of governance, improved coherence of water management, encouragement of mutual benefits, integration of land and water, and development of versatile tools for decision making. This can promote a coordinated and cohesive water management framework, providing fertile ground for effective and efficient environmental flow integration in rivers worldwide.

169 6. FERTILE GROUNDS FOR ENVIRONMENTAL FLOWS

6.2 A paradigm shift

Water management in many regions has shifted its paradigm, from a narrow pursuit of economic and social objectives to managing for sustainability and resilience (Folke, 2003). This has been driven by the realisation that that the rigid water management structures and systems that evolved during the command-and-control era of the 20th century were counterproductive to managing for sustainability and resilience (Holling and Meffe, 1996). Command-and-control approaches were highly effective at stabilising and homogenising natural systems to achieve ‘optimal’ resource yields, but could not sustain the flow variability upon which natural systems depend. Systemic and struc- tural changes can promote environmental flow integration in a growing number of river systems around the world, but progress is hampered by short term challenges in restoring availability, variability and connectivity in regulated rivers. Sharing knowledge from experiences in different river systems is critical to ensure the long term success of environmental flow integration in rivers worldwide. This study examines the multi-dimensional challenges and remedies in implementing environmental flows in river systems of the Murray-Darling Basin (the Basin) in Australia. There are striking similarities between the challenges faced in two separately managed rivers, the Gwydir and Macquarie, and those encountered in other developed river systems of the world. The first part of my discussion summarises how challenges in availability, variability and connectivity can be overcome, based on my quantitative analyses in the Gwydir and Macquarie river systems, and international literature. Critically, there is no unified, ‘one-size-fits-all’ solution, but challenges can be resolved with a coordinated, systematic approach using scientific knowledge. Such a systematic approach to environmental flow implementation is problematic in the Murray-Darling Basin because water management and operation is fragmented at multiple scales. The current approach has been necessarily ad hoc, opportunistic, and has required enormous knowledge and action at a local scale. Rather than repeatedly negotiating challenges, it is necessary to treat the generic framework, rather than symptoms recurring in other systems. Consolidation of water management can provide a necessary foundation for more efficient and effective integration of environmental flows at a Basin scale, and better opportunities for sharing knowledge, resources and capacity among river systems. In the final part of this discussion, I propose five opportunities for consolidating water

170 6.2 A paradigm shift management to progress eﬀective integration of environmental ﬂows in meeting desired outcomes.

6.2.1 Overcoming challenges

The overarching goal of this thesis was to use a quantitative approach to address some of the challenges common to many river basins worldwide. This thesis makes several important contributions to environmental flow research and management, and the results should be of interest to policy-makers, water managers, and environmental water managers in the Murray-Darling Basin. Development of the eWASH software tool helped unravel complex interactions between the water management framework, river regulations, variable hydrologic regimes and water requirements of ecosystems. This should facilitate a better understanding of the implications of hydrological variability and management rules for the security of water entitlements and the provision of environmental flows in the Murray-Darling Basin. Application of the eWASH tool was demonstrated in the analyses presented in Chapters 2 to 4 of the thesis, in which alternative management rules were evaluated and the sensitivity of water entitlements and environmental flows to various drivers was examined. Chapter 5 of the thesis evaluated techniques for identifying the location of earthworks which disrupt spatial patterns of water movement. Knowledge of the location, frequency and severity of channel capacity constraints (Chapter 4) and physical barriers likely to impede environmental flow delivery (Chapter 5) allows for identification of hotspots, prioritisation of remediation works, and selection of suitable sites for flood easements. Such knowledge is fundamental for protecting environmental flows moving through fragmented landscapes of large river basins. More broadly, improved knowledge and management of environmental flows can complement other natural resource management strategies in regulated river systems. Better predictions of environmental water availability (Chapter 3) and likely environmental flow regimes (Chapter 4) provide necessary information for improving interactions relevant for other approaches such as pest management. Exotic species eradication programs harnessing environmental flows for weed suppression may be far more effective than those using traditional and costly approaches such as pesticides and weed removal (Mawhinney, 2003). Better estimates of inundation frequency and extent can lead to more appropriate land use classifications which accommodate flooding and reduce risks

171 6. FERTILE GROUNDS FOR ENVIRONMENTAL FLOWS of property damage (Richter and Thomas, 2007). Tools and techniques developed in Chapters 2 and 5 provide quantitative methods for coordinating and integrating among management strategies at multiple spatial and temporal scales.

6.2.1.1 Water Availability

Insufficient environmental flow protection is arguably one of the most intractable challenges in providing ecological sustainability, because of the inevitable socio-economic conflicts in developed river systems (Hirji and Davis, 2009). A crucial first step is to limit expansion of water resource development, including extractions, flow alteration and floodplain development, that further deplete or alter available water flows and thereby compromise prospects for providing or recovering environmental flows (Le Quesne et al., 2010). Surface water extractions were capped in the Murray-Darling Basin at 1993/94 development levels in 1995, with a moratorium on floodplain development later implemented in some catchments in Queensland (DERM, 2012; MDBC, 2004). A next step is to establish a balance between environmental and extractive use, potentially increasing environmental flows by limiting extractive use. In the Basin, this has been achieved by: (1) changing water management rules to protect environmental flow through a reserve or minimum flow; (2) purchasing water from extractive users through market mechanisms; and (3) upgrading and improving efficiencies of public and private water supply infrastructure and reallocating water efficiency savings to the environment. The first approach may erode the security of water entitlements, and governments of the Murray-Darling Basin implementing such policies affecting water reliability may be liable for compensation under the Water Act 2007 (Cwlth). Many irrigation communities resist the second approach because of the potential ‘knock-on’ effects on rural communities through loss of irrigation communities. The third approach favours inefficient irrigation industries rather than those who have already privately invested in water efficient infrastructure. Despite this, recovery of environmental flow reserves have been the most effective and progressive aspect of environmental flow integration. Water availability is governed by factors largely beyond direct human control (e.g. climate, geomorphology, soil), but there are important management levers which increase availability, particularly in regulated systems (Chapter 3). For example, the process for allocating water among users may affect environmental flow availability

172 6.2 A paradigm shift

(Chapter 3), and encouraging eﬃcient demand practices may reduce storage evaporation (Chapter 4). Catchment management practices also drive environmental water availability (Chapter 3), including the management of upstream diversions, dams and forestry plantations that may reduce runoﬀ (Herron et al., 2002).

6.2.1.2 Variability

Traditionally, regulated rivers were operated to attenuate flow variability, using storages to dampen high flows and elevate low flows. Many opportunities exist to integrate more variable environmental flows by working within existing constraints, or through dam reoperation or physical modifications (Chapter 4). Identifying constraints and their impacts is essential for restoring requisite flow variability. For example, physical constraints restricting high environmental flow releases at the storage (i.e. outlet capacity) can be resolved by enlarging the capacity of the infrastructure, or using spill- ways for environmental flow releases (Chapter 4). Constraints restricting the passage of environmental flows downstream (i.e. channel capacity, bridges, earthworks, flooding of land) can be physically readjusted (e.g. raised), and easements or compensation can be implemented on flooded land (Chapter 4). Some landholders may welcome flooding because it increases their ecosystem services, for example, flooding benefits graziers by providing nourishment for pasture. Channel capacity constraints may be exacerbated when multiple users, including environmental flow managers, order water simultane- ously, but this could be averted through coordinated water delivery. Management rules may directly limit storage release magnitude, or indirectly restrict release magnitude by limiting the volume or duration of water held by individuals in storage. These restrictions can be lifted to increase the maximum volume of water accumulated in storage, but mechanisms that promote equitable sharing of capacity should be implemented (Chapter 4). These may include forfeiting water from accounts when storage is spilling, or using market mechanisms to promote efficient use of storage capacity (Chapter 4; Hughes, 2010). Many dryland rivers have strong ‘boom’ and ‘bust’ cycles (Bunn et al., 2006; Kings- ford et al., 1999), during which floodplains are inundated then dry out. In semi-arid systems, delivery of high flows may be as important as complete or partial drying of wetlands. However, unseasonal flow releases or establishing year-round baseflows for the purpose of extraction or storage of flows results in artificially augmenting flows,

173 6. FERTILE GROUNDS FOR ENVIRONMENTAL FLOWS preventing ecologically important drying (Chong and Ladson, 2003; Kingsford et al., 2004). Some of this unseasonal or augmented flow is created to provide dependable stock and domestic water which could be piped to users and lower in-channel flow volumes; this could be economically feasible when transmission losses are high. Some of the undesirable impacts of unseasonable flow can be mitigated with block scheduling of dam releases, which can create windows for managed drying, and releases can be slowly ramped up and down to mimic naturally rising and receding flow limbs. Seasonal variability of low flows can also be improved through irrigation management and crop mixes (Watts et al., 2011). Ecologically desirable drying cycles may be prevented by storage release rules. For example, a management rule in the Macquarie River requires some environmental flows to be released instantaneously as if no storage was present (NSW Government, 2003), so managing for drying is difficult. The ability to achieve the required variability is contingent on environmental flow availability held in storage, but there are opportunities to circumvent this constraint. Managed releases can be supplemented with unregulated tributary flow events, downstream of storages, using real-time gauges to detect such events. Timing of simultaneous releases of environmental flows from storages may also increase peak flows downstream where needed at a flow-dependent habitat. Antecedent soil moisture should also be considered due to its effect on necessary inundation extent, depth and duration (Powell et al., 2008). Finally, dam removal can be considered as a means of returning river systems to their naturally variable state, and is an attractive option for unsafe dams or where dams provide diminishing socio-economic returns (Poff and Hart, 2002).

6.2.1.3 Connectivity

Dams severely affect longitudinal connectivity while lateral flow connectivity is restricted by physical structures intercepting flows on floodplains. Over 2 000 km of levees, channels, off-river storages and dams in the Macquarie floodplain may intercept or constrain the passage of environmental flows to wetlands (Fig. 6.1; Steinfeld and Kingsford, 2013). This included 88 km of levees, 338 km of channels, 8 off-river storages and 84 tanks encroaching on the floodway corridor where development was prohibited (Fig. 6.1). Reconnecting floodplains can be attained by removing these structures or setting them back from the river to allow passage of floodwaters, perhaps by using public funding assistance (Opperman et al., 2009; Steinfeld and Kingsford, 2013). In the

174 6.2 A paradigm shift

(a) (b) (c)

0 35 70 Km (d) (e) (f) (g)

Figure 6.1: Policy boundaries for the floodplain of the Macquarie Marshes and earthworks that fragment and constrain flows of the Macquarie river in the Murray-Darling Basin: (a) Macquarie Marshes Nature Reserve (established 1971, revised 2002), (b) floodplain guidelines 1978/82, showing floodways meant to be kept free of earthworks (WRC, 1982; superseded by (f)), (c) designated northern floodplain (Millington, 1985), (d) designated floodplain (1986 Macquarie Marshes Water Management Plan); superseded by (e) restricted zone (1996 Macquarie Marshes Water Management Plan), and (f) floodway guidelines under the Floodplain Management Plan (DECC, 2008). The distribution of floodplain earthworks (levees, channels, off-river storages and tanks) in 2004/5 (g) shows dense networks of earthworks in the irrigation districts to the south and sparsely located earthworks in the north, with the exception of two irrigation regions (Steinfeld and Kings- ford, 2011). The 1990 flood extent is shown in grey.

175 6. FERTILE GROUNDS FOR ENVIRONMENTAL FLOWS long term, preventative measures and adequate enforcement are necessary to minimise remediation costs and manage land with an established history of periodic inundation (Opperman et al., 2009; Steinfeld and Kingsford, 2013). Such measures, while restricting certain types of ﬂoodplain use, may continue to support livestock grazing, hunting and tourism which can be encouraged through purchasing easements, land acquisition, raising insurance premiums, subsidies for remediation and rezoning (Birkland et al., 2003). Impacts of earthworks vary, so locating and classifying earthworks and their interactions with ﬂows can help to prioritise severe hotspots for remediation.

6.2.2 Fragmentation of water management

Spatial fragmentation of water management and river operations in the Murray-Darling Basin have challenged environmental flow integration in three ways. Firstly, recovery of variability and connectivity considerably lagged behind the recovery of environmental flow availability in regulated catchments within the Basin. Secondly, institutional fragmentation occurred, one of the most common impediments to environmental flow implementation worldwide (Le Quesne et al., 2010; Loehman and Charney, 2011). Un- coordinated governance in Australia has already compromised national water reform, resulting in unnecessary complexity, inconsistencies and antagonism among sectors and jurisdictions (Connell, 2011; Productivity Commission, 2010). Thirdly, parochial interests and idiosyncrasy across jurisdictions have disrupted progress in environmental flow implementation from large to small scales. Each of the five states and one territory has different management rules and entitlement definitions. Jargon varies across the states with different terms for the same concept, and the same terms for different concepts (Table 1.1; Shi, 2006). This increases transaction costs, administrative errors and auditing efficiency, limiting the benefits for environmental flow holders (Shi, 2006). At the regional scale, river operators determine resource availability (Fig. 1.1), measurably affecting environmental flow availability in the Gwydir and Macquarie (Chapter 3). At a catchment scale, water management can be non-uniform, reflecting biophysical differences and local stakeholder preferences. For example, a management rule only in the Macquarie grants water users a full account each time the storage re-enters the flood mitigation zone (i.e. full supply level) in a water year. This increases water availability during wet periods, especially to users who can empty their account into downstream large private off-river storage before the

176 6.2 A paradigm shift next announcement. This behaviour lowers storage volume, increasing capture of flows that would have otherwise spilled to the environment, while the environmental manager cannot operate this way because of the lack of downstream re-regulating structures. There are also perverse outcomes for environmental water management at a sub- catchment scale arising from inconsistent floodplain management. Six different management zones were prescribed in the past 40 years on the Macquarie floodplain (Fig. 6.1). The Ramsar-listed Macquarie Marshes Nature Reserve protected three disconnected sections of the core wetland area (Fig. 6.1a). Development guidelines were later established in the south floodplain to retain narrow riparian corridors for conveying flood pulses, without earthwork development (Fig. 6.1b; WRC, 1978b, 1982), primarily aimed at assisting with agricultural development outside these floodways (Johnson, 2005). Ostensibly, the southern floodplain was defined for development while there was a policy decision to designate only the north as a protected floodplain, resulting in idiosyncratic protection from most development (Millington, 1985) through Macquarie Marshes Water Management Plans (Fig. 6.1d and 6.1e). This was despite similarities in the hydrology and ecology of the floodplains (Fig. 6.1c). Irrigation development guided by this policy resulted in development of extensive earthworks as irrigation established on the southern floodplain (Steinfeld and Kingsford 2011). This network constrained flows and fragmented the adjacent floodplain from the river, and even intercepted flow passage upstream of the core wetland area. Satellite images showed over 2 000 km of earthworks potentially affecting flows (Fig. 6.1g). Further, developments continued in the southern floodplain (101 km of levees, 368 km of channels, 16 off-river storages, 69 tanks) within designated 1982 floodways, largely ignoring the boundaries set by policy. Government responded by revising the draft floodway network (Fig. 6.1f) and incorporating these newly established earthworks. Floodplain management failed to adequately recognise the need for connectivity of the entire floodplain, or the potential loss of connectivity and its ecological impacts. Integrated policies for reconnecting floodplains are required to facilitate the integration of environmental flow. Menindee Lakes on the lower Darling is another example of sub-catchment fragmented management affecting environmental flows (Fig. 1.1d). It was a natural system of lakes which was turned into a re-regulating storage for Darling River floods. The lakes are operated by the NSW public water utility and the MDBA. The former

177 6. FERTILE GROUNDS FOR ENVIRONMENTAL FLOWS operates the lakes for local irrigation and town water supply until total volume exceeds 640 GL, when control transfers to the MDBA for distribution of flows to South Australia until volume falls below 480 GL (MDBA, 2012e). Modelling has shown that environmental flow availability can be increased in Menindee Lakes by up to 248 GL/y by lowering lake levels to minimise evaporation losses (Wall, 2011). However, lowering lake levels reduces time under MDBA control, potentially lowering long term flows to South Australia. Further, part of the lake system is within Kinchega National Park but the conservation authority, National Parks and Wildlife Service, only manages this part of the lake system when there is no water. Lake operation needs to be holistic and conducive to opportunities to improve environmental flow availability.

6.2.3 Implications for environmental ﬂow integration

Fragmented water management does not preclude successful integration of environmental flows, but it is rarely effective or efficient (Cumming et al., 2006). Currently, integration of environmental flows involves opportunistic management at a local scale, essentially treating symptoms rather than the causes of problems (Pahl-Wostl, 2008). This approach is inappropriate because it is repetitive, resource intensive and not conducive to knowledge transfer across the Basin. Further, progress is highly variable and uncertain given the idiosyncrasy of many management rules. Water management decisions reflecting individual preferences can undermine environmental flow integration (Cumming et al., 2006). Stakeholders in one catchment may accommodate environmental needs, while in another catchment, extraordinary negotiation and compromise may be required to overcome local resistance (Quirk, 2005). Local constraints, rather than environmental objectives, can also become a reason for limiting environmental flow availability. For example, the Murray-Darling Basin Plan recommended additional environmental flows of 2 750 GL with a further 450 GL to be available with the relaxation of physical, legal, administrative and policy constraints impairing environmental flow delivery (MDBA, 2012b). Finally, strategic decision making is limited because information is not readily accessible across the Basin. Publicly available information is not centrally organised with limited overview of management or allowance for transferability across the Basin. For example, water resource plans, river operating rules, water allocation announcements and flow data are available for nearly every river system in the Basin, however much of

178 6.2 A paradigm shift this information is either distributed across many government websites that are difficult and time consuming to navigate. The problem is exacerbated by inconsistent terminology and formatting. Furthermore, some critical information is not publicly available so environmental flow managers must make strategic decisions based on incomplete knowledge of management rules and their impacts on environmental flows (Chapter 3).

6.2.4 Consolidating water management

Environmental ﬂows must be coordinated within a coherent water management framework (Hussey and Dovers, 2007; Le Quesne et al., 2010). Clarity of water management is essential, incorporating current strengths including diversity, stakeholder engage- ment, and nested semi-autonomous governance. Five recommendations provide key opportunities for a coherent management framework in the Basin: realigning boundaries of governance, strengthening coherence of water management, encouraging mutual beneﬁts, integrating land and water management and building versatile tools.

6.2.4.1 Realign boundaries of governance

Institutional boundaries should align to functional landscape units of the biophysical system (Cumming et al., 2006; Folke et al., 2005). In the Murray-Darling Basin, appropriate scales are continental (national), basin, catchment and sub-catchment (i.e. hillslope, foothills and floodplain). Aggregation of planning and administrative units may be required as multiple catchments coalesce into single floodplains. At these scales, institutions can more readily learn and respond to the ecosystem dynamics and social needs (Folke et al., 2005). Realigning institutional scales can reduce the problem of leakage between locations arising from regulations in one region that shifts impacts to another, such as arbitrary floodplain development restrictions (Fig. 6.1; Ostrom, 2010). Nested institutions should not be hierarchical, but polycentric (i.e. many independent decision making centres) and semi-autonomous (Ostrom et al., 1961), to stimulate stewardship, adaptation, innovation and cooperation (Folke, 2003; Ostrom, 2010; Pahl-Wostl, 2008). Responsibilities should be appropriately divided among institutions at different levels, while retaining a degree of spatial and functional redundancy to improve resilience of the system (Walker and Salt, n.d.).

179 6. FERTILE GROUNDS FOR ENVIRONMENTAL FLOWS

6.2.4.2 Strengthen coherence of water management

Water management needs to be seamless within and across scales to facilitate the integration of environmental ﬂows. Consistency in legislation and administration (e.g. property rights, terminology, reporting) is imperative for bridging across institutions and jurisdictions at a Basin scale (Shi, 2006). However such consistency must be balanced with diversity so that management is sensitive to local context and resilient to shocks (Walker and Salt, n.d.). Inherent heterogeneity and individuality of river systems should be valued and promoted in water planning and river operation (Rood et al., 2005; Rotmans et al., 2001), but critically, must arise from a publicly documented deliberative process where decisions are supported by evidence. This process should be consistent among catchments to promote transparency and cross-fertilisation of knowledge.

6.2.4.3 Encourage mutual beneﬁts

Mounting pressure on water resources demands a new approach to its management, recognising synergistic rather than rival use of water (Loehman and Charney, 2011; Postel, 2000; van Koppen et al., 2006). Existing systems benefitting industry and the environment are floodplain grazing, wild fisheries, recreation and tourism, and potential commerical opportunities include water and biodiversity ‘banks’, and carbon markets (Zedler and Kercher, 2005). Encouraging mutual benefits may reduce antagonism among competing water users and the environment, leading to increased public support for environmental flows (Hirji and Davis, 2009; Postel, 2000). Mutual benefits are an attractive option in fully allocated systems, but potential is yet be realised in the Murray-Darling Basin where environmental flow management is focused mainly on water buyback and efficiency. Managers play a central role in encouraging the use of water for mutual benefit, such as increased ecosystem services, through incentives, rewards and by facilitating dialogue among resource users.

6.2.4.4 Integrate land and water management

Water management sometimes focuses only on managing river flows but many land use impacts affect flow outcomes. The blue-water green-water conceptual approach (Falkenmark and Rockström,2006; Vidal et al., 2010) conceptually reframes water

180 6.2 A paradigm shift management by recognising the role of the entire catchment landscape, including non- irrigated lands supported only with rainfall. Using this approach, available water resources are classiﬁed along a non-linear and dynamic continuum from blue water, the water in dams, rivers and aquifers, to green water, the evapo-transpiration in terrestrial ecosystems. They propose that conventional blue water management extend to green water, to account for two-thirds of available water that arrives on land through rainfall (Falkenmark and Rockstr¨om,2006). Through this lens, managers seek an appropriate balance between blue and green water, by managing land components (e.g. clearing, tillage, drainage, forestry plantations, agriculture, wetlands and rainfall harvesting systems) as well as conventional water components (e.g. dams, channels, pipelines and wells; Lundqvist and Falkenmark, 2000). This may promote coordinated land use planning, agricultural policy and water management.

6.2.4.5 Build versatile tools

A new generation of tools are needed to parallel the diverse and changing information needs of decision makers (Raadgever et al., 2008). Flexible, scalable and adaptable tools offer the best value to decision makers and facilitate integration across institutions. Flexible tools are applicable in a variety of contexts and can be easily manipulated to evaluate different management scenarios. For example, management scenarios are easily customised through a user-friendly interface using eWASH, allowing rapid and systematic assessment of their effects on environmental flow availability in the Gwydir and Macquarie Rivers (Chapter 4). Scalability is important for tools that are implemented across nested institutions, allowing economy of scales to be achieved without compromising rigour or accuracy. Semi-automated earthwork detection techniques allow rapid assessment over broad extents while maintaining high accuracy and resolution at a fine scale (Chapter 5). Adaptable tools are key to improving their value as scientific knowledge and management evolve (Welsh et al., 2013). This versatility should facilitate knowledge and capacity sharing across and within institutions, promoting mutual understanding and shared insights (Raadgever et al., 2008).

181 6. FERTILE GROUNDS FOR ENVIRONMENTAL FLOWS

6.3 Conclusion

Restoring environmental flows is arguably one of the most intractable challenges in the management of large river basins worldwide. It requires a fundamental shift in the physical, legal and institutional foundations that have been refined over the past century to optimise socio-economic outcomes. Despite high level policy direction promoting environmental flows, implementation remains slow and ad hoc in river systems due to considerable challenges in providing sufficient availability, variability and connectivity of flows. Many of these problems can be attributed to fragmented water management that disrupts effective and efficient integration of environmental flows in large river systems. Coherent water management is a vital foundation for strategic environmental water planning, allowing challenges to be tackled at the appropriate scale, reducing ne- gotiations of idiosyncratic management rules, and promoting comprehensive knowledge platforms across the Basin. Environmental flows other than those managed specifically from storage, the focus of this thesis, also need to be integrated. These include flows from storage spills, unregulated tributaries and operational losses. There is also interaction with water quality. There are many different dimensions to environmental flow management and these require integration within a transparent governance framework driven by clear long term objectives.

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