Australia’s NationalScience Agency Assessment of the potentialecological outcomes of waterresource development in theVictoriacatchment A technical report from the CSIROVictoriaRiverWater ResourceAssessmentfor theNational Wate Grid Danial Stratford1,Simon Linke1, Linda Merrin1,Rob Kenyon1, Rocio PonceReyes1, Rik Buckworth1,2, Roy Aijun Deng1,Justin Hughes1,Heather McGinness1,Jodie Pritchard1, Lynn Seo1, NathanWaltham3 1CSIRO,2Charles Darwin University,3James Cook University A black background with purple text Description automatically generated A group of logos with a sun and waves Description automatically generated ISBN 978-1-4863-2097-4 (print) ISBN 978-1-4863-2098-1 (online) Citation Stratford D, Linke S, Merrin L, Kenyon R, Ponce Reyes R, Buckworth R, Deng RA, Hughes J, McGinness H, Pritchard J, Seo L and Waltham N (2024) Assessment of the potential ecological outcomes of water resource development in the Victoria catchment. A technical report from the CSIRO Victoria River Water Resource Assessment for the National Water Grid. CSIRO, Australia. Copyright © Commonwealth Scientific and Industrial Research Organisation 2024. To the extent permitted by law, all rights are reserved and no part of this publication covered by copyright may be reproduced or copied in any form or by any means except with the written permission of CSIRO. Important disclaimer CSIRO advises that the information contained in this publication comprises general statements based on scientific research. The reader is advised and needs to be aware that such information may be incomplete or unable to be used in any specific situation. No reliance or actions must therefore be made on that information without seeking prior expert professional, scientific and technical advice. To the extent permitted by law, CSIRO (including its employees and consultants) excludes all liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material contained in it. CSIRO is committed to providing web accessible content wherever possible. If you are having difficulties with accessing this document please contact Email CSIRO Enquiries . CSIRO Victoria River Water Resource Assessment acknowledgements This report was funded through the National Water Grid’s Science Program, which sits within the Australian Government’s Department of Climate Change, Energy, the Environment and Water. Aspects of the Assessment have been undertaken in conjunction with the NT Government. The Assessment was guided by two committees: i. The Assessment’s Governance Committee: CRC for Northern Australia/James Cook University; CSIRO; National Water Grid (Department of Climate Change, Energy, the Environment and Water); Northern Land Council; NT Department of Environment, Parks and Water Security; NT Department of Industry, Tourism and Trade; Office of Northern Australia; Queensland Department of Agriculture and Fisheries; Queensland Department of Regional Development, Manufacturing and Water ii. The Assessment’s joint Roper and Victoria River catchments Steering Committee: Amateur Fishermen’s Association of the NT; Austrade; Centrefarm; CSIRO; National Water Grid (Department of Climate Change, Energy, the Environment and Water); Northern Land Council; NT Cattlemen’s Association; NT Department of Environment, Parks and Water Security; NT Department of Industry, Tourism and Trade; NT Farmers; NT Seafood Council; Office of Northern Australia; Parks Australia; Regional Development Australia; Roper Gulf Regional Council Shire; Watertrust Responsibility for the Assessment’s content lies with CSIRO. The Assessment’s committees did not have an opportunity to review the Assessment results or outputs prior to their release. The ecology team received great support from a large number of people in the Northern Territory Government and associated agencies. They provided access to files and reports, spatial and other data, species and habitat information and they also provided the team with their professional expertise and encouragement. For the Northern Territory, this included Simon Cruikshank, Jonathan Vea and Thor Sanders. People in private industry, universities, local government and other organisations also helped us with parts of this work and advice, including Keller Kopf, Lindsay Hutley, Clement Duvert, Erica Garcia and Osmar Luiz. We thank Auvergne Station and Kidman Springs for information and access. The work would not have been possible without the contributions and assistance from Cuan Petheram, Matt Gibbs, Caroline Bruce, Fazlul Karim, Shaun Kim, Steve Marvanek, Steve Gao, Jackie O’Sullivan, Carmel Pollino, Adam Liedloff, Jane Thomas and Darran King. This report was reviewed in full by Dr Carmel Pollino and partially by Dr Adam Liedloff (both CSIRO). Useful comments from Dr Jackie O’Sullivan, Dr Cuan Petheram (both CSIRO) and Mike Grundy were also incorporated. Acknowledgement of Country CSIRO acknowledges the Traditional Owners of the lands, seas and waters, of the area that we live and work on across Australia. We acknowledge their continuing connection to their culture and pay our respects to their elders past and present. Photo Riparian habitat of the Victoria River. Source: CSIRO Director’s foreword Sustainable development and regional economic prosperity are priorities for the Australian and Northern Territory (NT) governments. However, more comprehensive information on land and water resources across northern Australia is required to complement local information held by Indigenous Peoples and other landholders. Knowledge of the scale, nature, location and distribution of likely environmental, social, cultural and economic opportunities and the risks of any proposed developments is critical to sustainable development. Especially where resource use is contested, this knowledge informs the consultation and planning that underpin the resource security required to unlock investment, while at the same time protecting the environment and cultural values. In 2021, the Australian Government commissioned CSIRO to complete the Victoria River Water Resource Assessment. In response, CSIRO accessed expertise and collaborations from across Australia to generate data and provide insight to support consideration of the use of land and water resources in the Victoria catchment. The Assessment focuses mainly on the potential for agricultural development, and the opportunities and constraints that development could experience. It also considers climate change impacts and a range of future development pathways without being prescriptive of what they might be. The detailed information provided on land and water resources, their potential uses and the consequences of those uses are carefully designed to be relevant to a wide range of regional-scale planning considerations by Indigenous Peoples, landholders, citizens, investors, local government, and the Australian and NT governments. By fostering shared understanding of the opportunities and the risks among this wide array of stakeholders and decision makers, better informed conversations about future options will be possible. Importantly, the Assessment does not recommend one development over another, nor assume any particular development pathway, nor even assume that water resource development will occur. It provides a range of possibilities and the information required to interpret them (including risks that may attend any opportunities), consistent with regional values and aspirations. All data and reports produced by the Assessment will be publicly available. Chris Chilcott C:\Users\bru119\AppData\Local\Microsoft\Windows\Temporary Internet Files\Content.Word\C_Chilcott_high.jpg Project Director The Victoria River Water Resource Assessment Team Project Director Chris Chilcott Project Leaders Cuan Petheram, Ian Watson Project Support Caroline Bruce, Seonaid Philip Communications Emily Brown, Chanel Koeleman, Jo Ashley, Nathan Dyer Activities Agriculture and socio- economics Tony Webster, Caroline Bruce, Kaylene Camuti1, Matt Curnock, Jenny Hayward, Simon Irvin, Shokhrukh Jalilov, Diane Jarvis1, Adam Liedloff, Stephen McFallan, Yvette Oliver, Di Prestwidge2, Tiemen Rhebergen, Robert Speed3, Chris Stokes, Thomas Vanderbyl3, John Virtue4 Climate David McJannet, Lynn Seo Ecology Danial Stratford, Rik Buckworth, Pascal Castellazzi, Bayley Costin, Roy Aijun Deng, Ruan Gannon, Steve Gao, Sophie Gilbey, Rob Kenyon, Shelly Lachish, Simon Linke, Heather McGinness, Linda Merrin, Katie Motson5, Rocio Ponce Reyes, Nathan Waltham5 Groundwater hydrology Andrew R. Taylor, Karen Barry, Russell Crosbie, Geoff Hodgson, Anthony Knapton6, Shane Mule, Jodie Pritchard, Steven Tickell7, Axel Suckow Indigenous water values, rights, interests and development goals Marcus Barber/Kirsty Wissing, Peta Braedon, Kristina Fisher, Petina Pert Land suitability Ian Watson, Jenet Austin, Bart Edmeades7, Linda Gregory, Jason Hill7, Seonaid Philip, Ross Searle, Uta Stockmann, Mark Thomas, Francis Wait7, Peter L. Wilson, Peter R. Wilson, Peter Zund Surface water hydrology Justin Hughes, Matt Gibbs, Fazlul Karim, Steve Marvanek, Catherine Ticehurst, Biao Wang Surface water storage Cuan Petheram, Giulio Altamura8, Fred Baynes9, Kev Devlin4, Nick Hombsch8, Peter Hyde8, Lee Rogers, Ang Yang Note: Assessment team as at September, 2024. All contributors are affiliated with CSIRO unless indicated otherwise. Activity Leaders are underlined. For the Indigenous water values, rights, interests and development goals activity, Marcus Barber was Activity Leader for the project duration except August 2022 – July 2023 when Kirsty Wissing (a CSIRO employee at the time) undertook this role. 1James Cook University; 2DBP Consulting; 3Badu Advisory Pty Ltd; 4Independent contractor; 5 Centre for Tropical Water and Aquatic Ecosystem Research. James Cook University; 6CloudGMS; 7NT Department of Environment, Parks and Water Security; 8Rider Levett Bucknall; 9Baynes Geologic Shortened forms SHORT FORM FULL FORM EPBC Act Environment Protection and Biodiversity Conservation Act 1999 AEP annual exceedance probability EOS end-of-system IUCN International Union for Conservation of Nature Units UNIT DESCRIPTION ML megalitre d day y year t tonne GL gigalitre m metre km kilometre ha hectare ppt parts per thousand Preface Sustainable development and regional economic prosperity are priorities for the Australian and NT governments and science can play its role. Acknowledging the need for continued research, the NT Government (2023) announced a Territory Water Plan priority action to accelerate the existing water science program ‘to support best practice water resource management and sustainable development.’ Governments are actively seeking to diversify regional economies, considering a range of factors. For very remote areas like the Victoria catchment (Preface Figure 1-1), the land, water and other environmental resources or assets will be key in determining how sustainable regional development might occur. Primary questions in any consideration of sustainable regional development relate to the nature and the scale of opportunities, and their risks. Preface Figure 1-1 Map of Australia showing Assessment area (Victoria catchment and other recent CSIRO Assessments FGARA = Flinders and Gilbert Agricultural Resource Assessment; NAWRA = Northern Australia Water Resource Assessment. How people perceive those risks is critical, especially in the context of areas such as the Victoria catchment, where approximately 75% of the population is Indigenous (compared to 3.2% for Australia as a whole) and where many Indigenous Peoples still live on the same lands they have inhabited for tens of thousands of years. About 31% of the Victoria catchment is owned by Indigenous Peoples as inalienable freehold. For more information on this figure please contact CSIRO on enquiries@csiro.au Access to reliable information about resources enables informed discussion and good decision making. Such information includes the amount and type of a resource or asset, where it is found (including in relation to complementary resources), what commercial uses it might have, how the resource changes within a year and across years, the underlying socio-economic context and the possible impacts of development. Most of northern Australia’s land and water resources have not been mapped in sufficient detail to provide the level of information required for reliable resource allocation, to mitigate investment or environmental risks, or to build policy settings that can support good judgments. The Victoria River Water Resource Assessment aims to partly address this gap by providing data to better inform decisions on private investment and government expenditure, to account for intersections between existing and potential resource users, and to ensure that net development benefits are maximised. The Assessment differs somewhat from many resource assessments in that it considers a wide range of resources or assets, rather than being a single mapping exercise of, say, soils. It provides a lot of contextual information about the socio-economic profile of the catchment, and the economic possibilities and environmental impacts of development. Further, it considers many of the different resource and asset types in an integrated way, rather than separately.The Assessment has agricultural developments as its primary focus, but it also considers opportunities for and intersections between other types of water-dependent development. The Assessment was designed to inform consideration of development, not to enable any particular development to occur. The outcome of no change in land use or water resource development is also valid. As such, the Assessment informs – but does not seek to replace – existing planning, regulatory or approval processes. Importantly, the Assessment does not assume a given policy or regulatory environment. Policy and regulations can change, so this flexibility enables the results to be applied to the widest range of uses for the longest possible time frame. It was not the intention of – and nor was it possible for – the Assessment to generate new information on all topics related to water and irrigation development in northern Australia. Topics not directly examined in the Assessment are discussed with reference to and in the context of the existing literature. CSIRO has strong organisational commitments to reconciliation with Australia’s Indigenous Peoples and to conducting ethical research with the free, prior and informed consent of human participants. The Assessment consulted with Indigenous representative organisations and Traditional Owner groups from the catchment to aid their understanding and potential engagement with its fieldwork requirements. The Assessment conducted significant fieldwork in the catchment, including with Traditional Owners through the activity focused on Indigenous values, rights, interests and development goals. CSIRO created new scientific knowledge about the catchment through direct fieldwork, by synthesising new material from existing information, and by remotely sensed data and numerical modelling. Functionally, the Assessment adopted an activities-based approach (reflected in the content and structure of the outputs and products), comprising activity groups, each contributing its part to create a cohesive picture of regional development opportunities, costs and benefits, but also risks. Preface Figure 1-2 illustrates the high-level links between the activities and the general flow of information in the Assessment. Preface Figure 1-2 Schematic of the high-level linkages between the eight activity groups and the general flow of information in the Assessment Assessment reporting structure Development opportunities and their impacts are frequently highly interdependent and, consequently, so is the research undertaken through this Assessment. While each report may be read as a stand-alone document, the suite of reports for each Assessment most reliably informs discussion and decisions concerning regional development when read as a whole. The Assessment has produced a series of cascading reports and information products: • Technical reports present scientific work with sufficient detail for technical and scientific experts to reproduce the work. Each of the activities (Preface Figure 1-2) has one or more corresponding technical reports. • A catchment report, which synthesises key material from the technical reports, providing well- informed (but not necessarily scientifically trained) users with the information required to inform decisions about the opportunities, costs and benefits, but also risks associated with irrigated agriculture and other development options. • A summary report provides a shorter summary and narrative for a general public audience in plain English. • A summary fact sheet provides key findings for a general public audience in the shortest possible format. The Assessment has also developed online information products to enable users to better access information that is not readily available in print format. All of these reports, information tools and data products are available online at https://www.csiro.au/victoriariver. The webpages give users access to a communications suite including fact sheets, multimedia content, FAQs, reports and links to related sites, particularly about other research in northern Australia. For more information on this figure please contact CSIRO on enquiries@csiro.au Executive summary The freshwater, terrestrial and near-shore marine zones of the Victoria catchment contain important and diverse species, habitats, industries and ecosystem functions supported by the patterns and volumes of river flow. Although irrigated agriculture may occupy only a small percentage of the landscape, relatively small areas of irrigation can use large quantities of water and the resulting changes in the flow regime can have profound effects on flow-dependent flora and fauna and their habitats. Changes in flow may extend considerable distances onto the floodplain and downstream, including into the marine environment and can be exasperated by other changes including changes to connectivity, water quality and invasive species. The magnitude and spatial extent of ecological impacts arising from water resource development are highly dependent on the type of development, the location, the extraction volume and any mitigation measures implemented. Ecological risks, inferred here by calculating change in ecological flow dependency considered with habitat significance for a range of freshwater dependent ecological assets, increases with increasing scale of surface water development (i.e. large instream dams and greater volumes of water harvesting). At equivalent levels of water resource development (i.e. in terms of volume of water extracted) and without significant mitigation measures, instream dams have a larger mean impact to surface-flow-dependent ecology than water harvesting across the Victoria catchment. Large instream dams result in significantly larger local impact to the modelled flow dependencies in those reaches below the dam wall than water harvesting. Water harvesting developments extracting between 80 and 690 GL/year of water without any mitigation strategies resulted in negligible mean changes to ecology flow dependencies of freshwater species across the Victoria catchment. Local impacts below points of extraction, however, were moderate for some species, including those near-short marine assets only found at the river mouth. Mitigation strategies that protect low flows and first flows of a wet season are successful in reducing change to important flow dependencies for assets. These can be particularly effective if implemented for water harvesting based development. Impacts accumulate downstream so ecological assets only found near the bottom of the catchment experienced greatest mean catchment impact. Cryptic waders, threadfin, banana prawns and floodplain wetlands are among the ecological assets most affected by flow changes for water harvesting. At equivalent volumes of water extraction, imposing an end-of-system (EOS) annual flow requirement, where water harvesting can only commence after specified volumes of water have flowed past the end-of-system (EOS) and into the Joseph Bonaparte Gulf, is an effective mitigation measure for water harvesting. For EOS annual flow requirements greater than 200 GL additional mitigation measures (e.g. increasing pump start capacity or decreasing pump rate) have little additional modelled ecological benefit. Relative to catchments with large dry-season flows maintained by groundwater discharge from a regional-scale groundwater system (e.g. the Roper catchment), increasing pump start thresholds in the Victoria catchment above 200 ML/day only results in a marginal improvements in reducing ecological change to important freshwater flow dependencies for assets. For instream dams location matters, with potential for high change in flows with local impacts; reduced levels of change are associated with maintaining attributes of the natural flow regime. Potential dams located in small headwater catchments may result in an extreme change to the ecological flow dependencies immediately downstream of the dam, however, this reduces downstream with the accumulation of additional tributary flows so when averaged over the entire catchment or measured at the EOS, the change in important flow dependencies is moderate. Providing transparent flows (flows allowed to ‘pass through’ the dam for ecological purposes) improve flow regimes for ecology through reducing the mean yield of potential dams by 18%. Mean outcomes for fish assets are able to be improved from minor to negligible, and for waterbirds from moderate to minor at catchment scales. A dry future climate has the potential to have a larger mean impact on flow dependencies across the Victoria catchment than the largest physically plausible water resource development scenarios. However, the perturbations to flow arising from a combined drier future climate and water resource development result in greater impacts on ecology than either factor on their own. At catchment scales, the direct impacts of irrigation on the terrestrial environment are typically small. However, indirect impacts such as weeds, pests and landscape fragmentation, particularly to riparian zones, may be considerable. Loss of connectivity associated with new instream structures and changes in low flows may limit movement patterns of many species within the catchment and into important habitats. Changes in ecosystem productivity, including in marine environments, are often associated with a combination of floodplain inundation and the resulting discharge, which may change due to water resource development. Contents Director’s foreword .......................................................................................................................... i The Victoria River Water Resource Assessment Team ................................................................... ii Shortened forms .............................................................................................................................iii Units ............................................................................................................................... iv Preface ............................................................................................................................... v Executive summary ....................................................................................................................... viii 1 Introduction ........................................................................................................................ 1 1.1 Water resource development and flow ecology ................................................... 1 1.2 Ecology of the Victoria catchment ........................................................................ 2 2 Methods .............................................................................................................................. 5 2.1 Scenarios of water resource development and future climate ............................ 5 2.2 Ecological modelling and the analysis approach ................................................. 11 3 Catchment results and implications ................................................................................. 20 3.1 Water resource development and potential mitigation strategies .................... 20 4 Asset assessments ............................................................................................................ 33 4.1 Fish, sharks and rays ............................................................................................ 33 4.2 Waterbirds ........................................................................................................... 66 4.3 Turtles, prawns and other species ...................................................................... 88 4.4 Freshwater-dependent habitats ........................................................................ 103 5 Synthesis ......................................................................................................................... 129 References ........................................................................................................................... 133 Asset assessment nodes and their weightings .................................................. 151 Asset hydrometrics selected in the flow dependencies modelling .................. 159 Waterbird groups and their species .................................................................. 167 Asset metrics with the largest contribution to changes in asset flow dependencies by scenario ........................................................................................................... 175 Figures Preface Figure 1-1 Map of Australia showing Assessment area (Victoria catchment and other recent CSIRO Assessments .............................................................................................................. v Preface Figure 1-2 Schematic of the high-level linkages between the eight activity groups and the general flow of information in the Assessment ...................................................................... vii Figure 2-1 Map of the Victoria catchment and the marine region showing the locations of the river system modelling nodes at which flow–ecology relationships were assessed (numbered) and the locations of hypothetical developments ........................................................................... 9 Figure 2-2 Flow dependencies analysis conceptual models for linking flow–ecology relationships for different assets to important parts of the flow regime under hypothetical wet, medium and dry years ........................................................................................................................................ 13 Figure 2-3 Considering scenario change based upon the percentile change from the distribution of each metric under the historical flow at each node ................................................................. 14 Figure 2-4 Spatial weighting of the metrics for the freshwater-dependent ecological assets in the Victoria catchment ................................................................................................................. 14 Figure 2-5 Locations of the model domain used in the lateral connectivity analysis .................. 18 Figure 3-1 Spatial heatmap of change to asset flow dependencies across the Victoria catchment considering mean change across all assets in the locations which each asset is assessed .......... 22 Figure 3-2 Mean change to assets important flow dependencies across scenarios and nodes .. 23 Figure 3-3 Mean change to assets important flow dependencies across water harvesting increments of system target and pump start threshold with no EOS requirement and pump rate of 30 days ...................................................................................................................................... 25 Figure 3-4 Mean change to assets important flow dependencies across water harvesting increments of system target and pump start threshold for an EOS requirement of 500GL and pump rate of 30 days .................................................................................................................... 26 Figure 3-5 Mean change to assets important flow dependencies across water harvesting increments of system target and EOS requirement for a pump rate of 30 days ......................... 28 Figure 3-6 Mean change to assets important flow dependencies across water harvesting increments of system target and pump rate with no EOS requirement ...................................... 30 Figure 4-1 Spatial heatmap of change for barramundi, considering the weighted habitat across the catchment ............................................................................................................................... 35 Figure 4-2 Change in barramundi flow dependencies by scenario across the model nodes ....... 37 Figure 4-3 Change in barramundi flow dependencies by water harvest scenarios at sample nodes across the catchment showing change in response to system targets and pump start thresholds ..................................................................................................................................... 39 Figure 4-4 Spatial heatmap of change for catfish, considering the weighted habitat across the catchment ..................................................................................................................................... 42 Figure 4-5 Change in catfish flow dependencies by scenario across the model nodes ............... 44 Figure 4-6 Spatial heatmap of change for grunter, considering the weighted habitat across the catchment ..................................................................................................................................... 48 Figure 4-7 Change in grunter flow dependencies by scenario across the model nodes .............. 50 Figure 4-8 Change in mullet flow dependencies by scenario across the model nodes................ 53 Figure 4-9 Spatial heatmap of change for sawfish, considering the weighted habitat across the catchment ..................................................................................................................................... 57 Figure 4-10 Change in sawfish flow dependencies by scenario across the model nodes ............ 59 Figure 4-11 Change in sawfish flow dependencies by water harvest scenarios at sample nodes across the catchment showing change in response to system targets and pump start thresholds ....................................................................................................................................................... 61 Figure 4-12 Change in threadfin flow dependencies by scenario across the model nodes ......... 64 Figure 4-13 Spatial heatmap of change for colonial and semi-colonial wading waterbirds, considering the weighted habitat across the catchment ............................................................. 68 Figure 4-14 Change in colonial and semi-colonial wading waterbirds flow dependencies by scenario across the model nodes ................................................................................................. 70 Figure 4-15 Spatial heatmap of change for cryptic wading waterbirds, considering the weighted habitat across the catchment ....................................................................................................... 73 Figure 4-16 Change in cryptic wading waterbirds flow dependencies by scenario across the model nodes .................................................................................................................................. 75 Figure 4-17 Spatial heatmap of change for shorebirds, considering the weighted distribution across the catchment .................................................................................................................... 78 Figure 4-18 Change in shorebirds flow dependencies by scenario across the model nodes ....... 80 Figure 4-19 Change in shorebird flow dependencies by water harvest scenarios at sample nodes across the catchment showing change in response to system targets and pump start thresholds ....................................................................................................................................................... 82 Figure 4-20 Spatial heatmap of change for swimming, diving and grazing waterbirds, considering the weighted habitat across the catchment ............................................................. 84 Figure 4-21 Change in swimming, diving and grazing waterbirds flow dependencies by scenario across the model nodes ................................................................................................................ 86 Figure 4-22 Change in banana prawns flow dependencies by scenario across the model nodes 90 Figure 4-23 Spatial heatmap of change for freshwater turtles, considering the weighted habitat across the catchment .................................................................................................................... 94 Figure 4-24 Change in freshwater turtles flow dependencies by scenario across the model nodes ............................................................................................................................................. 96 Figure 4-25 Change in freshwater turtles’ flow dependencies by water harvest scenarios at sample nodes across the catchment showing change in response to system targets and pump start thresholds ............................................................................................................................. 98 Figure 4-26 Change in mud crabs flow dependencies by scenario across the model nodes ..... 101 Figure 4-27 Spatial heatmap of change for floodplain wetlands, considering the weighted distribution across the catchment .............................................................................................. 104 Figure 4-28 Change in floodplain wetlands flow dependencies by scenario across the model nodes ........................................................................................................................................... 106 Figure 4-29 Time series of the floodplain inundation for each scenario for the 2021 modelled flood event in the Victoria catchment ........................................................................................ 109 Figure 4-30 Time series of the floodplain inundation for each scenario for the 2023 modelled flood event in the Victoria catchment ........................................................................................ 109 Figure 4-31 Maximum floodplain inundation for each scenario for the 2021 modelled flood event in the Victoria catchment ................................................................................................. 110 Figure 4-32 Maximum floodplain inundation for each scenario for the 2021 modelled flood event in the Victoria catchment ................................................................................................. 111 Figure 4-33 Spatial heatmap of change for inchannel waterholes, considering the weighted distribution across the catchment .............................................................................................. 113 Figure 4-34 Change in inchannel waterholes flow dependencies by scenario across the model nodes ........................................................................................................................................... 115 Figure 4-35 Change in mangroves flow dependencies by scenario across the model nodes .... 118 Figure 4-36 Change in saltpans and salt flats flow dependencies by scenario across the model nodes ........................................................................................................................................... 121 Figure 4-37 Spatial heatmap of change for surface-water-dependent vegetation, considering the weighted distribution across the catchment ........................................................................ 125 Figure 4-38 Change in surface-water-dependent vegetation flow dependencies by scenario across the model nodes .............................................................................................................. 127 Tables Table 2-1 Water resource development and climate scenarios explored in this ecology analysis††......................................................................................................................................................... 6 Table 2-2 The ecological assets and their dominant ecological domains .................................... 12 Table 2-3 Reporting values for the flow dependencies modelling as percentile change of the hydrometrics, considering the change in mean metric value against the distribution observed under Scenario A ........................................................................................................................... 15 Table 2-4 Water resource development and climate scenarios explored in this ecology analysis ....................................................................................................................................................... 19 Table 3-1 Scenarios of different hypothetical instream dam locations showing end-of-system (EOS) flow and mean changes of ecology flows for groups of assets across each asset’s respective catchment assessment nodes ..................................................................................... 24 Table 3-2 Scenarios of different hypothetical instream dam locations showing end-of-system (EOS) flow and mean changes of ecology flows for groups of assets across each asset’s respective catchment assessment nodes ..................................................................................... 31 Table 4-1 Maximum floodplain inundation (in km2) and percentage change from Scenario A as the maximum flood extent for each scenario for a 2021 modelled flood event and a 2023 modelled flood event .................................................................................................................. 108 1 Introduction 1.1 Water resource development and flow ecology The ecology of a river is intricately linked to its flow regime, with species broadly adapted to the prevailing conditions under which they occur. Associations within freshwater systems are not limited to just the persistence or ephemerality of rivers. They are also linked with the volumes of river flows and patterns of floodplain inundation and discharges that support species, habitats and ecosystem functions. Flow-dependent flora, fauna and habitats are defined here as those sensitive to changes in flow and those sustained by either surface water or groundwater flows or a combination of these. In rivers and floodplains, the capture, storage, release, conveyancing and extraction of water alters the environmental template on which the river functions, and water regulation is frequently considered one of the biggest threats to aquatic ecosystems worldwide (Bunn and Arthington, 2002; Poff et al., 2007). Changes in flows due to water resource development can act on both wet and dry periods to change the magnitude, timing, duration and frequency of flows (Jardine et al., 2015; McMahon and Finlayson, 2003). Impacts on fauna, flora and habitats associated with flow regime change often extend considerable distances downstream from the source of impact and into near-shore coastal and marine areas as well as onto floodplains (Burford et al., 2011; Nielsen et al., 2020; Pollino et al., 2018). The environmental risks associated with water resource development are complex, and particularly so in northern Australia. This is in part because of the diversity of species and habitats distributed across and within the catchments and the near-shore marine zones, and because water resource development can produce a broad range of direct and indirect environmental impacts. These impacts can include changes to flow regime, loss of habitat, loss of function such as connectivity, changes to water quality, and the establishment of pest species. Instream dams create large bodies of standing water that inundate terrestrial habitat and result in the loss of the original stream and riverine conditions (Nilsson and Berggren, 2000; Schmutz and Sendzimir, 2018). Storages can capture flood pulses and reduce the volume and extent of water that transports important nutrients into estuaries and coastal waters via flood plumes (Burford et al., 2016; Burford and Faggotter, 2021; Tockner et al., 2010). Further, even minor instream barriers can disrupt migration and movement pathways, causing loss of essential habitat for species that need passage along the river and fragmentation of populations (Crook et al., 2015; Pelicice et al., 2015). With water resource development and irrigation comes increased human activity. This can add additional pressures, including biosecurity risks associated with invasive or pest species transferring into new habitats or increasing their advantage in modified habitats (PyÅ¡ek et al., 2020). This report analyses the risks resulting from changes in the flow regime change in the catchment of the Victoria River to flow-dependent freshwater, estuarine and near-shore marine assets and terrestrial systems. See the companion technical report on water storages Yang et al. (2024) for more details on the impacts of habitat loss within hypothetical dam impoundments and connectivity loss due to the development of new instream barriers. Refer to the companion technical report on ecological asset descriptions in the Victoria catchment by Stratford et al. (2024a), for a qualitative overview of groundwater-dependent ecosystems in the context of water resource development. This asset descriptions report (Stratford et al., 2024a) also qualitatively examines existing and potential threatening processes for freshwater-dependent ecological assets, including possible influences of synergistic impacts. 1.2 Ecology of the Victoria catchment The Victoria River is a large perennial river originating near Judbarra National Park. At over 500 km in length, it is one of the longest perennial rivers in the Northern Territory (NT). The catchment area of 82,400 km2 makes it one of the largest ocean-flowing catchments in the NT with flows that enter the south-eastern edge of the Joseph Bonaparte Gulf. The catchment and the surrounding marine environment contain a rich diversity of important ecological assets, including species, ecological communities, habitats, and ecological processes and functions. The ecology of the Victoria catchment is maintained by the river’s flow regime, shaped by the region’s complex geomorphology and topography, and driven by patterns of seasonal rainfall, evapotranspiration and groundwater discharge. Much of the natural environment of the Victoria catchment consists of rolling plans, mesas, escarpments and plateaux with savanna woodlands and various grasslands including spinifex (Kirby and Faulks, 2004). The wet-dry tropical climate results in highly seasonal river flow with 90% of rainfall occurring between November and March (Kirby and Faulks, 2004). As typical for much of northern Australia, the dynamic occurring between wet and dry seasons provides both challenges and opportunities for biota (Warfe et al., 2011). During the dry season, river flows are reduced with many of the streams in the catchment receding to isolated pools. However, some of the larger tributaries in the catchment are perennial, including sections of Wickham River (upstream of Humbert River junction) and the Angalarri River (Midgley, 1981). In the dry season, the streams and waterholes that persist provide critical refuge habitat for many species both aquatic and terrestrial. During the wet season, many low-lying parts of the catchment flood, inundating floodplains, connecting wetlands to the river channel and driving a productivity boom. While the extent of floodplain wetlands is comparatively moderate than many other tropical catchments, topography of the catchment makes flooding more evident around the junctions of the Victoria River with both the West Baines and Angalarri rivers. Annual flooding delivers extensive sediment-rich discharges into the southern Joseph Bonaparte Gulf, and sediment plumes can extend large distances into the marine waters of the gulf. Judbarra National Park is the second largest national park in the NT covering approximately 1,300,000 ha (Australian Government, 2022b). Once fully gazetted, the Keep River National Park including the proposed extension from the neighbouring Keep River catchment into the Victoria catchment will cover a total area of approximately 272,000 ha (Australian Government, 2022b; Department of Environment Parks and Water Security, 2023). The Wardaman Indigenous Protected Area extends across the northern Victoria catchment and beyond and covers a total area of approximately 225,000 ha (Australian Government, 2022b). The Joseph Bonaparte Gulf Marine Park is a Commonwealth marine park of 15 to 100 m depth and approximately 860,000 ha (Australian Government, 2022a). This marine park straddles the offshore portion of the Victoria catchment marine region, has tides up to 7 metres and is home to the Australian snubfin dolphin (Orcaella heinsohni) (Department of Agriculture Water and the Environment, 2021; Parks Australia, 2023). The Bradshaw Field Training Area Directory of Important Wetlands in Australia (DIWA) site lies north of the Victoria River near Timber Creek. It is bound by the Fitzmaurice River to the north and the Victoria River to the south. The site includes two wetland complexes covering a total of approximately 871,000 ha within the Victoria Bonaparte biogeographic region (Department of Agriculture Water and the Environment, 2023a). Large areas of the wetlands are inundated each wet season by floods from both the Fitzmaurice and Victoria rivers, with flooding enhanced during coincidence with high tides. Some areas of the site retain permanent water during the dry season (Department of Agriculture Water and the Environment, 2023a). The Legune Wetlands straddles the Keep and Victoria river catchments with inflows from surface water from local creeks and in wet years from major floods in the Keep River, providing some additional inflows (Department of Agriculture Water and the Environment, 2023b). The wetlands include areas identified as an Important Bird and Biodiversity Area by Birdlife International. The freshwater sections of the Victoria catchment include diverse habitats such as perennial and intermittent rivers, anabranches, wetlands, floodplains and groundwater-dependent ecosystems. The diversity and complexity of habitats, and the connections between habitats within a catchment, are vital for providing the range of habitats needed to support both aquatic and terrestrial biota (Schofield et al., 2018). Riparian habitats that fringe the rivers and streams of the Victoria catchment have been rated as having moderate to high cover and structural diversity for riparian vegetation (Kirby and Faulks, 2004). These riparian habitats include widespread Eucalyptus camaldulensis overstorey with Lophostemon grandiflorus, Terminalia platyphylla, Pandanus aquaticus and Ficus spp. The dominant overstory across many parts of the catchment includes Acacia holosericea and Eriachne festucacea (Kirby and Faulks, 2004). Further away from the creeks and rivers, the overstorey vegetation in the Victoria catchment becomes sparser, opening up into savanna woodlands and various grasslands. In the dry season, biodiversity is supported within perennial rivers, wetlands and the inchannel waterholes that persist in the landscape. Waterholes provide habitat for water-dependent species including fish, sawfishes and freshwater turtles, and also include a source of water for other species more broadly within the landscape (McJannet et al., 2014; Waltham et al., 2013a). Marine and estuarine habitats in northern Australia are highly productive and have high cultural value. They include some of the most important, extensive and intact habitats of their type in Australia, many of which are recognised as being of national significance. The mouth and estuary of the Victoria River up to 25 km wide and includes extensive mudflats and mangrove stands (Kirby and Faulks, 2004). The dominant mangrove species in the catchment is Avicennia marina, which is largely confined to the estuary (Kirby and Faulks, 2004). Marine habitats in northern Australia are vital for supporting important fisheries, including banana prawn, mud crab and barramundi, and for biodiversity more generally, including waterbirds, marine mammals and turtles. In addition, the natural waterways of the sparsely populated catchments support globally significant stronghold populations of endangered and endemic species (e.g. sharks and rays) that often use a combination of both marine and freshwater habitats. A number of aquatic and terrestrial species in the Victoria catchment are currently listed as Critically Endangered, Endangered or Vulnerable under the Commonwealth Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act) and by the NT Government’s wildlife classification system, which is based on the International Union for Conservation of Nature (IUCN) Red List categories and criteria. The Commonwealth’s Protected Matters Search Tool (Department of Agriculture Water and the Environment, 2021) lists 45 Threatened species for the Victoria catchment, four of which are listed as Critically Endangered (the nabarlek rock wallaby (Petrogale concinna concinna), Rosewood keeled snail (Ordtrachia septentrionalis), curlew sandpiper (Calidris ferruginea) and eastern curlew (Numenius madagascariensis)). Also listed are 49 migratory species. This report builds upon, and should be considered in conjunction with, the ecological asset descriptions work in Stratford et al. (2024a). It seeks to address the question what is the relative risk to ecology associated with potential water resource development in the Victoria catchment? To address this question, the report is structured as follows: • Section 2 provides details of the hypothetical scenarios and the quantitative methods used to understand the change in important ecological flow dependencies associated with water resource development in the Victoria catchment for selected ecological assets. • Section 3 provides a high-level overview of the scenarios showing aggregated results (mean of assets) and discusses specific differences in the spatial pattern and magnitude of change between scenarios. These differences include different potential water resource development options and their mitigation and management. • Section 4 provides an overview and discussion of the modelling results for the selected ecological assets across a subset of scenarios. Outcomes for particular ecological assets consider their water needs, distribution within the catchment and the changes in flow conditions occurring under each of the scenarios providing the context of the change in flows for each asset. The ecology of the Victoria catchment including the knowledge base for selected ecological assets and their flow–ecology is further detailed in the companion report Stratford et al. (2024a). 2 Methods This ecology analysis aims to assess the relative risks to species and habitats posed by potential water resource development in the Victoria catchment. The goal is to support long-term decision- making and planning processes for sustainable and responsible development in northern Australia. The development scenarios presented here are hypothetical and serve to explore a range of options and issues in the Victoria catchment. In the event of any future development occurring, additional studies would need to evaluate the environmental impacts associated with the specific development across a broad range of environmental considerations, including water quality. Note that this ecology analysis is broad in scale and includes significant uncertainty in results. This uncertainty is due to a range of factors, including incomplete knowledge, variability within and between catchments, and limitations in data and modelling processes. Furthermore, the modelling process may not have adequately captured unknown thresholds, temporal processes, issues of scale or local conditions, ecological interactions, synergistic effects and feedback responses in the ecology of the system. There is also uncertainty associated with possible future weather and climate conditions, such as rainfall patterns, and any additional synergistic threatening processes that may emerge. Northern Australia is vast and diverse, and the knowledge base of species occurrences is limited. More broadly, the understanding of freshwater ecology in northern Australia is still developing. 2.1 Scenarios of water resource development and future climate This ecology analysis used modelled hydrology to explore the potential changes in flow regimes associated with water resource development in the Victoria catchment through a series of hypothetical scenarios. It used a modified version of AWRA-R – for more detail, see the companion technical report on river system modelling in the Victoria catchment by Hughes et al. (2024a). The scenarios were designed to explore how different types and scales of water resource development might affect selected water-dependent ecosystems. The developments considered include instream infrastructure (i.e. large dams) and water harvesting (i.e. pumping river water into offstream farm-scale storages). Consult Section 1.2.2 of the Victoria catchment report (Watson et al., 2024) when evaluating the likelihood of a hypothetical development scenario occurring. Scenarios also explored the effects of dry future climate conditions may impact water-dependent ecosystems, as well as the interactions between water resource development and a potential dry climate future. The scenario terminology used in the Assessment is broadly described in Table 2-1. Hughes et al. (2024b) provides further details of the river system modelling and hypothetical scenarios. Table 2-1 Water resource development and climate scenarios explored in this ecology analysis††Descriptions of the river system modelling scenarios are provided in Hughes et al. (2024b). FSL = Full Supply Level. DCRF = Annual diversion commencement flow requirement. GCM = Global Climate Model. SCENARIO DESCRIPTION TRANSPARENT FLOW ANNUAL TARGET EXTRACTION VOLUME / YIELD (GL) DCFR (GL) PUMP-START THRESHOLD (ML/D) PUMP CAPACITY (D) Scenario A Historical climate and current levels of development A Historical climate and no development No 0 0 na na Scenario B Historical climate and hypothetical future development B-DLC Single dam on Leichhardt Creek No 60‡ na na na B-DVR Single dam on Victoria River No 500‡ na na na B-D2 Two hypothetical dams, LC, VR No 560‡ na na na B-DLCT Single dam on Leichhardt Creek Yes 60‡ na na na B-DVRT Single dam on Victoria River Yes 500‡ na na na B-D2T Two hypothetical dams, LC, VR Yes 560‡ na na na B-WV, EF, PT, CR Water harvesting with varying target extraction volume (V), DCR requirements (F), pump- start threshold (T), and/or pump rate (R) na V = 40, 80, …, 960, 1000‡ F = 0, 200, 500, 700, 1000 T = 200, 300, …, 900, 1000 R = 10, 20, 30, 40, 50 Scenario C Future climate and current level of development Cdry Dry GCM§ projection No 0 na na na Scenario D Future climate and hypothetical future development Ddry-D2 Two hypothetical dams (same as B-D2), for each Scenario C climate (clim = dry) No 591‡ na na na Ddry-D2T Two hypothetical dams (same as B-D2), for each Scenario C climate (clim = dry) with transparent flows Yes 591‡ na na na Ddry-W150,600 Water harvesting under Scenario C climate (clim = dry) na 680‡ 0 200 30 ‡Target extraction volume applies to water harvesting scenarios. Yield applies to hypothetical dam scenarios and is the amount of water that could be supplied by the dams reservoir in 85% of years ††The scenarios used in the hydrodynamic modelling used in the lateral connectivity analysis are described in Section 2.2.2. The hydrology generated with the Victoria AWRA-R model included processes for considering rainfall, evaporation and runoff, routing of water across subcatchments, losses, irrigation extraction and reservoir behaviour. These parameters were modelled across 41 nodes within the Victoria catchment (node and hypothetical development locations are shown in Figure 2-1). A long time series of daily flow from 1 September 1890 to 31 August 2022 was generated and used (except where otherwise stated). This period provided a wide range of environmental conditions that encompassed extended dry periods, including those that occurred in the first half of the 20th century and periods of variability, including both low-flow and high-flow conditions across scales of days, inter-decadal variability and different event sequencing. Scenario A, which represents the historical climate under current development, was calibrated using data from river gauges across the catchment (Hughes et al., 2024a). Changes in important ecological flow dependencies were assessed relative to Scenario A. The analysis considered cumulative ecological change from the historical natural conditions of the catchment, acknowledging that significant lag effects may exist for some ecological assets unless otherwise stated (i.e. potential existing changes in surface or groundwater may not yet have resulted in ecological change). Additional analysis used hydrodynamic model inputs, which provided estimates of flood extent for sample flood events of different magnitudes and durations. See the companion technical report on flood modelling, Karim et al. (2024), for more detail. 2.1.1 Key terminology used in this report Water harvesting – an operation where water is pumped or diverted from a river into an offstream storage, assuming no instream structures. Offstream storages – usually fully enclosed circular or rectangular earthfill embankment structures situated close to major watercourses or rivers so as to minimise the cost of pumping. Large engineered instream dams – usually constructed from earth, rock or concrete materials as a barrier across a river to store water in the reservoir created. In the Victoria catchment most hypothetical dams were assumed to be concrete gravity dams with a central spillway (see companion technical report on water storage (Yang et al., 2024). Annual diversion commencement flow requirement (DCFR) – also known as an end-of-system requirement, the cumulative flow that must pass the most downstream node (81100000) during a water year (1 September to 31 August) before pumping can commence. Usually implemented as a strategy to mitigate the ecological impact of water harvesting. Pump start threshold – a daily flow rate threshold above which pumping or diversion of water can commence. Usually implemented as a strategy to mitigate the ecological impact of water harvesting. Pump capacity – the capacity of the pumps expressed as the number of days it would take to pump the entire node irrigation target. Reach irrigation volumetric target – the maximum volume of water extracted in a river reach over a water year. Note, the end use is not necessarily limited to irrigation. Users could also be involved in aquaculture, mining, urban or industrial activities. System irrigation volumetric target – the maximum volume of water extracted across the entire study area over a water year. Note, the end use is not necessarily limited to irrigation. Users could also be involved in aquaculture, mining, urban or industrial activities. Transparent flow – a strategy to mitigate the ecological impacts of large instream dams by allowing all reservoir inflows below a flow threshold to pass ‘through’ the dam. 2.1.2 Scenario definitions The Assessment considered four scenarios with subsets, reflecting combinations of different levels of development and historical and future climates: • Scenario A – historical climate and current development • Scenario B – historical climate and future development • Scenario C – future climate and current development • Scenario D – future climate and future development. Figure 2-1 Map of the Victoria catchment and the marine region showing the locations of the river system modelling nodes at which flow–ecology relationships were assessed (numbered) and the locations of hypothetical developments The flow ecology of the environmental assets was assessed in subcatchments downstream of the river system nodes. The locations of assets across the catchment are documented in Stratford et al. (2024a), and the nodes used for assessment of each asset are provided within each asset’s section of this report and compiled in Appendix A. For more information on this figure please contact CSIRO on enquiries@csiro.au Scenario A – historical climate and current development Scenario A assumes a historical climate and no hypothetical development. The historical climate series is defined as the observed climate (rainfall, temperature and potential evaporation for the water years from 1 September 1890 to 31 August 2022). All results presented in this report are calculated over this period unless specified otherwise. Justification for use of this period is provided in the companion technical report on climate (McJannet et al., 2023). Scenario A assumes no surface water or groundwater development. Scenario A was used as the baseline against which assessments of relative change were made. This will give the most conservative results. Historical tidal data were used to specify downstream boundary conditions for the flood modelling. Scenario B – historical climate and hypothetical future development Scenario B is historical climate and future development. Scenario B uses the same historical climate series as Scenario A. River inflow, groundwater recharge and flow, and agricultural productivity were modified to reflect potential future development. Potential development options were devised to assess responses of hydrological, ecological and economic systems. Modifications ranged from small incremental increases in surface water through to extraction volumes representative of the likely physical limits of the Victoria catchment (i.e. considering the co-location of suitable soil and water). Scenario C – future climate and current level of development Scenario C is future climate and current levels of surface water and ground development assessed at approximately the year 2060. Future climate impacts on water resources were explored within a sensitivity analysis framework by applying percentage changes in rainfall and potential evaporation to modify the 132-year historical climate series (as in Scenario A). The percentage change values adopted were informed by projected changes in rainfall and potential evaporation under Shared Socioeconomic Pathways (SSPs) 2-4.5 and 5-8.5. SSP2-4.5 is considered broadly representative of a likely projection given current global commitments to reducing emissions and SSP5-8.5 is representative of an (unlikely) upper bound (IPCC, 2022). Scenario D – future climate and hypothetical future development Scenario D is future climate and future development. It used the same future climate series as Scenario C. River inflow, groundwater recharge and flow, and agricultural productivity were modified to reflect potential future development, as in Scenario B. Therefore, in this report, the climate data for scenarios A and B are the same (historical observations from 1 September 1890 to 31 August 2022) and the climate data for scenarios C and D are the same (the above historical data scaled to reflect a plausible range of future climates). The ecology analysis explores the interaction and combined changes associated with hypothetical water resource development and a drying climate. The different potential water resource development pathway resulted in different changes to flow regimes, considering rainfall and upstream catchment sizes, inflows, the attenuation of flow through the river system (including accumulating inflows with river confluences), and the many ways each water resource development could unfold and be implemented and managed. The scenarios in Table 2-1 explored some of these interactions between the location and the types and scale of development and their potential mitigation, and how these may influence ecological outcomes within and across the catchment. Many of the hypothetical scenarios listed in Table 2-1 do not provide minimum level of flows for the environment (for dams, transparent flows and for water harvesting actions such as pump-start thresholds and annual diversion commencement flow requirements). They were optimised for water yield reliability deliberately not considering policy settings or additional restrictions that may mitigate the impacts on water-dependent ecosystems. These scenarios are useful for assessing the level in change of ecologically important flows of different development options in the absence of mitigation measures or policy settings and are conservate as they effectively represent a situation where there was no regulatory compliance. By comparing these scenarios to those that incorporate different mitigation strategies including transparent flows or different annual diversion commencement flow requirements, it becomes possible to identify the relative benefits of various mitigation options to important asset flow dependencies. In a real-world setting, management and regulatory requirements would likely provide a range of safeguards for environmental outcomes, possibly establishing a combination of transparent flows, end-of-system requirements, extraction limits and minimum flow or pump-start thresholds (see Section 2.1.1). Each of these safeguards, if implemented, would likely improve environmental outcomes. Further, many of the scenarios explored, while being technically feasible, exceeded the level of development that would reasonably occur (see Watson et al. (2024)). These scenarios were included as a stress test of the system and can be useful for benchmarking or contrasting various levels of change. Additional scenarios using mitigation options are further explored and discussed in Section 3.1. 2.2 Ecological modelling and the analysis approach The ecology activity used an asset-based approach to analysis and built upon work presented in Pollino et al. (2018) and Stratford et al. (2024b). For the Victoria catchment, 19 ecological assets were selected for analysis. Consider the material in this ecology analysis report in conjunction with the ecological asset descriptions report (Stratford et al., 2024a), which describes the ecology and flow requirements of the assets. The ecological assets spanned freshwater, marine and terrestrial habitats that depend on river flows Table 2-2. Eighteen of the assets were modelled with regards to changes to surface water, and individual results and discussion are provided in Section 4, while ground water extraction is covered in the companion report on groundwater modelling (Knapton et al. 2024). Assets were included if they were distinctive, representative, describable and significant within the catchment. The assets’ flow ecology and locations were described in Stratford et al. (2024a) and provides distribution maps. Each asset had different needs from, and linkages to, the flow regime and occurred across different parts of the catchment or the near- shore marine zone. Understanding the flow relationships of assets was important for identifying potential impacts to ecologically important flows. The flow dependencies of assets may consider, for example, life-history needs, habitat suitability, ecosystem functions or behavioural triggers provided by environmental events. The outcome is that assets had different sensitivities to the different manifestations of development. This included whether the changes in flow occurred within the low, medium or high components of the flow regime, while also considering the annual timing of events and the location of the asset in the catchment relative to the change in flow. Together, the suite of assets covered a broad range of flow requirements with different sensitivities to change across the catchment and are indicators of ecology needs or habitat change (such as cease-to-flow). Table 2-2 The ecological assets and their dominant ecological domains Domains represent the main patterns of occurrence and assets may also occur across the other domains. For more information on this figure, table or equation please contact CSIRO on enquiries@csiro.au. 2.2.1 Flow dependencies (hydrometrics) modelling The flow dependencies (hydrometrics) assessment provides a consistent, quantitative approach to identify which assets are likely to be impacted by potential changes, based on their flow needs, distribution within the catchment and the type of flow changes resulting from development. For each asset, the modelling calculates an index of flow regime change across different scenarios using asset-specific hydrometrics (Figure 2-2). Stratford et al. (2024a) details each asset’s ecology and relationship to flow, including: • habitat dependencies (e.g. floodplain inundation to provide habitat, recharging of groundwater) • life cycle processes (e.g. flow to trigger spawning) • migration and movement pathways (e.g. high flows to enable migration into floodplain wetlands and along the river) • flow to support productivity and food resources (e.g. nutrient plumes into coastal areas). Figure 2-2 Flow dependencies analysis conceptual models for linking flow–ecology relationships for different assets to important parts of the flow regime under hypothetical wet, medium and dry years Biota icons: Integration and Application Network (2023). These flow–ecology relationships were quantified and linked to river hydrology using asset-specific hydrometrics (conceptualised in Figure 2-2 and listed in Appendix B for each asset). Hydrometrics, which are statistical measures of long-term flow regimes (including aspects such as flow magnitude, duration, timing, frequency, and rate of change; see Kennard et al. (2010)), have been broadly used in ecohydrology assessments in national and international contexts for a range of purposes, including water allocation planning, and in ecohydrology research and literature (Leigh and Sheldon, 2008; Marsh et al., 2012; Olden and Poff, 2003). For each asset, a set of hydrometrics that was considered important in supporting its ecology or habitat was selected (see Appendix B for hydrometrics selected for each asset and their definitions). In this analysis, the flow dependencies modelling considered reach and catchment- wide changes in each asset’s important flow dependencies across the subcatchments in which the assets occur, including the near-shore marine zone. Hydrometrics were calculated for each scenario to quantify relative changes in important parts of the flow regime. These changes were expressed as percentile change relative to the distribution of annual values of Scenario A, calculated over the Assessment period (i.e. 1 September 1890 to 31 August 2022; Figure 2-3). The index of change is calculated as: Percentile change=x − scenario medianscenario median × 100 (1) Where x is the median of metric i, for the hypothetical scenario, and all values are for individual nodes. Flow relationships analysis conceptual models for linking flow–ecology relationships for different assets to important parts of the flow regime under hypothetical wet, medium and dry years. For more information on this figure, please contact CSIRO on enquiries@csiro.au. Figure 2-3 Considering scenario change based upon the percentile change from the distribution of each metric under the historical flow at each node The quantitative reporting values and a description of the qualitative terms are provided in Table 2-3. The impact of a hypothetical development on water-dependent ecological assets is inferred and reported here in terms of a habitat-weighted percentile change in asset-specific important flow dependencies. This change is weighted by the habitat value downstream of each node where the asset occurs, and the change in flow dependency is then calculated (Figure 2-4). The weighted values at each node are aggregated to calculate the catchment-wide means of asset flow dependencies (Appendix A). Figure 2-4 Spatial weighting of the metrics for the freshwater-dependent ecological assets in the Victoria catchment Illustration of indicative suitable potential habitat for freshwater-dependent ecological assets along rivers in the Victoria catchment as predicted by species distribution models. High suitability habitat areas are shown in dark blue, while low suitability habitat areas are represented in yellow or light blue. The species distribution models were developed using a combination of Random Forests, Generalised Linear Models (GLMs), and Maxent algorithms (see Stratford et al. (2024a)). These models were applied to a 2.5 km buffer surrounding the rivers to quantify habitat suitability. The change in the flow dependencies was weighted by habitat suitability for each asset between the river system model nodes of each river reach. Flow relationships analysis conceptual models for considering scenario change based upon the percentile change from the distribution of each metric under the historical flow at each node. For more information on this figure, please contact CSIRO on enquiries@csiro.au. For more information on this figure please contact CSIRO on enquiries@csiro.au To quantify change, each metric was calculated annually for all water years in the period 1 September 1890 to 31 August 2022 under Scenario A. This created a distribution for each metric under ‘baseline’ conditions (Figure 2-3). Only metrics that could be calculated on an annual basis were included. Change was calculated as the percentile rank difference for each metric under the scenario of interest relative to the baseline scenario (Scenario A). This difference provided an index of change, allowing for an understanding of how each metric varied under the scenario compared to Scenario A, given the historical variability at the site. For each asset, a relative change value was calculated for each selected metrics, and these values were then averaged to obtain a scenario index at each node where the asset was modelled to occur. Conceptually, a scenario index of zero indicates that the mean conditions under the scenario are not different from the mean baseline conditions. A value of 25 indicates that the mean conditions under the scenario are equal to or outside the quartile ranges under Scenario A (using low flows as an example, the scenario’s mean for the entire period is equivalent to the lowest flow with a 75% annual exceedance probability under Scenario A). These index values are provided with associated descriptive terms in Table 2-3 (with cut-off values and qualitative descriptors defined by experts) and illustrated as a heat map for reporting (see Figure 2-3 for interpretation of percentile changes). The flow dependencies method enables understanding and quantifying the level of change in each of the important flows metrics for each asset but does not quantify the level of the expected outcomes (e.g. population abundance or condition changes in the asset), the level of sensitivity to the changes, or the level of dependence on flows compared to other environmental drivers such as local rainfall or localised runoff adjacent to the river. Table 2-3 Reporting values for the flow dependencies modelling as percentile change of the hydrometrics, considering the change in mean metric value against the distribution observed under Scenario A PERCENTILE VALUE RATING IMPLICATION >0–2 Negligible The mean for the asset’s metrics under the scenario has negligible change as considered against the modelled historical conditions and lies well within the normal conditions experienced at the model node. The assets’ hydrometrics are within two percentile of the historical Scenario A mean 2–5 Minor The change is minor with the mean for the asset’s metrics for the scenario between two and five percentile of Scenario A and the historical distribution of the hydrometrics 5–15 Moderate The change is moderate with the mean for the asset’s metrics under the scenario between five and 15 percentile of Scenario A and the historical distribution of the hydrometrics 15–30 Major The change is major with the mean for the asset’s metrics for the scenario between 15 and 30 percentile of Scenario A and the historical distribution of the hydrometrics >30 Extreme The change is extreme, with the mean for the asset’s metrics under the scenario being very different from the modelled historical conditions and metrics occurring well outside typical conditions at the modelled node. The scenario mean is more than 30 percentile from the historical Scenario A mean One advantage of this method is that multiple attributes of the flow regime (weighted by known importance) are specifically incorporated when considered important for the asset. For example, for an asset that depends on low flows for survival and high flows for breeding and movement, the method would consider both aspects. However, this method does not consider the comparative importance of these aspects nor any correlations between metrics. The method is generalisable across large spatial domains and highly differing flow regimes, and it is robust to different knowledge and data limitations across the broad range of assets. The model does not consider other sources of water, such as rainfall or local discharges of groundwater, where these may be important for supporting ecology. The method provides values of change for each node that can be aggregated (taking the weightings into account) to summarise the mean weighted change occurring across the catchment considering the relative importance of each subcatchment for individual assets. The analysis of flow dependencies does not consider non-linearity, thresholds of change or spatial variability in specific flow requirements (such as the flood magnitudes that inundate floodplains in a specific location) but is generalised across these. The method targets understanding of relative differences between scenarios using Scenario A as a baseline, rather than absolute values of change. A threshold level of change in the flow regime (generally equivalent to a one-percentile change of the historical distribution for each metric singularly) must be exceeded for at least one of the metrics before the method can detect change. While the method incorporates the range of conditions occurring over the modelled period, it does not explicitly consider event sequencing or predict endpoints such as condition or biomass. To simplify the presentation of the results, the changes in important flow-dependent metrics for each asset were averaged to produce a single (mean) change value for each location where assets were modelled to occur. However, this approach can confound the interpretation of flow dependency changes. To assist interpreting the mean values of asset–flow dependency changes arising from hypothetical development and projected climate change scenarios, these values were compared against an analogue and three benchmarks. The analogue and benchmark values are plotted alongside the hypothetical development and projected climate scenario values to provide context to the level of potential change. The Ord River serves as an analogue, offering a comparison to illustrate potential changes in asset–flow dependencies. Changes were calculated by comparing simulated current streamflow levels with pre-European development streamflow in the Ord River near the end-of-system, i.e., below the Ord River Irrigation Area, Lake Kununurra, and Lake Argyle. For comparison, assets in the lower reaches of the Nicholson and Leichhardt catchments are assumed to also occur in the lower Ord River. For the Victoria catchment, three historical low-flow periods are used as benchmarks to assess changes in asset–flow dependencies. These periods represent the lowest 30-year flow (1905– 1934), lowest 50-year flow (1890–1939), and lowest 70-year flow (1890–1959) in the historical climate. Importantly these comparisons — the Ord River analogue and the historical benchmarks— represent similar flow conditions but are not necessarily equivalent to the outcomes of change if development were to occur. Nonetheless, they do provide some context as to the extent to which asset flow dependencies have changed over long-timer periods in the historical record. 2.2.2 Lateral connectivity modelling Lateral connectivity is the connection of the floodplain to the river channel through inundation associated with a flood event. Lateral connectivity provides an important exchange of nutrients and organic carbon between the floodplain and the river channel, which is important for primary and secondary productivity (Junk et al., 1989; Nielsen et al., 2015). It also allows for the movement of biota and provides habitat for waterbirds, floodplain-dependent fish and other aquatic and riparian species (van Dam et al., 2008; Ward and Stanford, 1995). The purpose of the lateral connectivity analysis is to understand the potential impacts that each of the scenarios can have on the connectivity between the river and relevant ecological assets, namely floodplain wetlands, compared to what occurs under Scenario A. To determine the lateral connectivity of the river to the floodplain, floodplain hydraulics (e.g. depth, velocity) and inundation dynamics were modelled using MIKE 21 Flow Model FM. The model domain has an area of 16,730 km2, and includes the floodplains of the Angalarri, West Baines and Victoria rivers (Figure 2-5). The model was run with a 5 m resolution digital elevation model across most of the floodplain along the Angalarri and West Baines rivers, with 30 m resolution data used for the remaining modelling domain. Two individual flood events were modelled. A 2021 flood event representative of a 33.3% annual exceedance probability (AEP) event (i.e. a flood event that occurs, on average, 1 in 3 years), and a 2023 flood event representing a 5.6% AEP event (i.e. a flood event that occurs, on average, 1 in 18 years) (Karim et al., 2024). Each flood event was simulated for 30 days to ensure both the rising and falling limbs were included. The hydrodynamic scenarios modelled are detailed in Karim et al. (2024) and are specific to the lateral connectivity section. They include development scenarios (dam scenarios (B-D) and water harvesting scenarios (B-W)), future climate scenarios (Cdry and Cwet) and combinations of future climate and development scenarios (Ddry-D and Ddry-W). For the water harvesting scenarios (B-W), extraction occurred at six nodes located on the West Baines and Victoria rivers. An extraction limit of 680 GL per year with and a pump rate of 200 ML/day was used (Table 2-4) (see Karim et al. (2024)). For the dam scenarios (B-D), dams were located on the Leichhardt Creek, Victoria River and Gipsy Creek (see Karim et al. (2024)). At the start of each flood event, each dam was set to 50% capacity. The results for the lateral connectivity analysis are shown in Section 4.4.1. Figure 2-5 Locations of the model domain used in the lateral connectivity analysis The hydrodynamic model domain is described in Karim et al. (2024). For more information on this figure please contact CSIRO on enquiries@csiro.au Table 2-4 Water resource development and climate scenarios explored in this ecology analysis Karim et al. (2024) describes the hydrodynamic modelling and additional scenario details. SCENARIO DESCRIPTION A Historical climate and current development. B-D Historical climate and hypothetical future development – instream dams. Dams were located on the Leichhardt Creek, Victoria River and Gipsy Creek. At the start of each flood event, each dam was set to 50% capacity. B-W Historical climate and hypothetical future development – water harvest extraction. Six water extraction nodes were used in the model, with an extraction limit of 680 GL per year with and a pump rate of 200 ML/day. Cdry Future climate and current level of development – 10th percentile exceedance changes in rainfall and potential evaporation. Cwet Future climate and current level of development – 90th percentile exceedance changes in rainfall and potential evaporation. Ddry-D Future climate and hypothetical future development – the climate scenario used is Cdry with the hypothetical future development the same as B-D. Ddry-W Future climate and hypothetical future development – the climate scenario used is Cdry with the hypothetical future development the same as B-W. 3 Catchment results and implications This section provides an overview of how changes in flow regimes resulting from water resource development could affect environmental assets of the Victoria catchment and the near-shore marine zone. Hypothetical flow scenarios, including water harvesting and instream dams are used to represent different potential pathways of development (see Section 2.1.1 for terminology and Section 2.1.2 for scenario definitions). Changes in flow regimes can have impacts across a broad range of flow dependent ecological assets which can extend considerable distances downstream from the source of change and onto floodplains. The flow requirements (such as the magnitude, timing, duration and frequency of both low and high flows) of different species and habitats vary. Flow dependencies modelling considers the location of 18 ecological assets across 41 nodes in the Victoria catchment, including the end-of-system node for near-shore marine assets (Figure 2-1 and Appendix A). Modelling explores how different changes in flow associated with the type, location and management (including mitigation strategies) of water resource development could affect ecological assets relative to Scenario A. 3.1 Water resource development and potential mitigation strategies This section provides a high-level overview of the scenarios showing aggregated results (mean of assets) and discusses specific differences in the spatial pattern and magnitude of change driven by the scenarios. These scenarios enable the exploration of a range of outcomes across the modelled environmental assets with a focus on understanding broad changes in asset flow dependencies with different hypothetical development pathways and potential mitigation measures. Outcomes for specific assets vary depending upon flow-requirements and flow-ecology and are discussed with implications and interpretation of results for individual assets in Section 4. The values associated with the catchment means include, but do not show, the range in outcomes across assets, where change in important flows for individual assets or at specific locations can be considerably higher or lower than the mean. 3.1.1 Scenario trends and summaries for the catchment Water harvesting and dams resulted in different changes in flows, affecting outcomes for ecology by different magnitudes of change across different parts of the catchment, and in different ways (Figure 3-1 and Figure 3-2 and see Section 3.1.2 for changes associated with dams and Section 3.1.2 for water harvesting). For the highest mean change in flow dependencies for a water harvesting scenario, Scenario B-Wv800t200r30f0, (Figure 3-2), the largest catchment mean change in flow dependencies for assets was for saltflats, mangroves, floodplain wetlands and banana prawns, all with moderate mean change in flow dependencies across their respective nodes (Figure 3-2). The largest single site flow change under water harvesting scenarios were major for assets including floodplain and riparian vegetation, floodplain wetlands, shorebirds and colonial and semi-colonial wading waterbirds. Under Scenario B-D2 with two dams the largest catchment mean change in flow dependencies for assets were for threadfin, cryptic wading waterbirds, banana prawns and mangroves, each with moderate mean change in flow dependencies across all their assessment nodes. For scenarios with dams, the largest site-based changes in flow for assets were often directly downstream of hypothetical dams and resulted in node impacts with up to extreme change for assets including floodplain wetlands, colonial and semi-colonial wading waterbirds, grunter and sawfish at these impacted downstream nodes (e.g. Figure 3-2d). Under Scenario B-D2, 89 nodes were rated as moderate mean change across all the assets than 43 under Scenario B-Wv800t200r30f0. For nodes with extreme levels of change in flows, Scenario B-D2 resulted in 16 nodes across all assets, which was reduced to none under Scenario B-Wv800t200r30f0. Under drying climate (Scenario Cdry), flow regime change impacts on ecology occurred largely across the catchment (Figure 3-1e), and cumulative impacts of water resource development in combination with dry future climate often lead to the greatest catchment-level changes to flow ecology (Figure 3-1f showing Ddry-W160t200r30 and Figure 3-2). Figure 3-1 Spatial heatmap of change to asset flow dependencies across the Victoria catchment considering mean change across all assets in the locations which each asset is assessed Scenarios are: (a) B-Wv80t200r30f0, (b) B-Wv160t200r30f0, (c) B-W160t60r20f0, (d) B-DLC, (e) Cdry and (f) Ddry-W160t200r30. See Table 2-1 for interpretation of scenarios. River shading indicates the mean level of flow change of important metrics weighted by the habitat value of each asset for each reach. For more information on this figure please contact CSIRO on enquiries@csiro.au Figure 3-2 Mean change to assets important flow dependencies across scenarios and nodes Colour shading indicates the mean level of flow change of all assets’ important metrics weighted by the habitat value of each reach for each asset. Horizontal grey bars and number correspond to the mean change across all model node locations. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios (see Table 2-1) are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Only the 30 highest impact nodes are shown (x-axis). Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50- year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. For more information on this figure please contact CSIRO on enquiries@csiro.au Under the largest hypothetical development scenarios for water harvesting (e.g. B-Wv800t200r30f0) and instream dam (Scenario B-D2) developments, the impacts at the end-of-system node alone were greater under water harvest than dams for sawfish, shorebirds and saltflats, and inversely greater under dams for mullet, threadfin, barramundi and mangroves. The flow changes under scenarios with a single dam ranged from negligible to moderate at the end-of-system across assets (negligible for Scenario B-DLC and minor to moderate for Scenario B-DVR). While some assets have extreme change at some nodes downstream of dams, as unimpacted tributary inflows increasingly dominate streamflow patterns with distance downstream from the dam the impact is reduced. Sixteen assets had higher catchment wide levels of change under Scenario B-D2 than B-Wv800t200r30f0, while two had higher catchment wide change under Scenario B-Wv800t200r30f0 than Scenario B-D2 (Table 3-1). When considering only the end-of-system node (81100000) and assets that occur at this node, six assets had higher change in flow dependencies under Scenario B-D2 than B-Wv800t200r30f0 considering the EOS node alone, this contrasted to three with higher change for B-Wv800t200r30f0 than Scenario B-D2 for assets at the EOS. While Scenario B-D2 resulted in broader and larger changes in flow than Scenario B-Wv800t200r30f0, the magnitude of this was comparatively reduced at the end-of-system for Scenario B-D2 than B-Wv800t200r30f0 (12.5% than 50% with higher levels of change under Scenario B-Wv800t200r30f0 at the end-of-system). Table 3-1 Scenarios of different hypothetical instream dam locations showing end-of-system (EOS) flow and mean changes of ecology flows for groups of assets across each asset’s respective catchment assessment nodes CATCHMENT WIDE EOS ONLY Higher change under B-Wv800t200r30f0 than Scenario B-D2 2 3 Higher change under Scenario B-D2 than Wv800t200r30f0 16 6 3.1.2 Water harvesting with different mitigation strategies For water harvesting scenarios, measures to mitigate the impacts of extraction include limiting the system target thereby reducing extraction across the catchment, providing a pump start threshold by limiting pumping of water from the river during periods of low river flows, providing an end-of- system requirement for a volume of water to pass through the last node in the system before pumping is allowed, and limiting the pump rate that water can be extracted from the river (see Section 2.1.1 and Hughes et al. (2024b) for more details). Providing reduced limits on system targets improves outcomes for ecological flow dependencies than larger targets (Figure 3-3 and Figure 3-4 y axis); this applies broadly across all asset groups and throughout the range of explored irrigation targets. Larger extraction volumes resulted in increases in mean changes in flow dependencies across asset groups up to moderate change across the catchment’s ecological assets. Some assets, including flow dependent habitats, the ‘other’ species group and marine assets experienced higher changes in important flows at some system targets (Figure 3-3). While improvements are likely to occur in conjunction with providing either minimum flow thresholds or end-of-system requirements, greater extraction equates to a greater level of changes in asset flow dependencies. Figure 3-3 Mean change to assets important flow dependencies across water harvesting increments of system target and pump start threshold with no EOS requirement and pump rate of 30 days Colour intensity represents the mean level of change occurring in the assets’ important flow metrics with the scenarios given the habitat importance of each node for each asset. For more information on this figure please contact CSIRO on enquiries@csiro.au Figure 3-4 Mean change to assets important flow dependencies across water harvesting increments of system target and pump start threshold for an EOS requirement of 500GL and pump rate of 30 days Colour intensity represents the mean level of change occurring in the assets’ important flow metrics with the scenarios given the habitat importance of each node for each asset. For more information on this figure please contact CSIRO on enquiries@csiro.au Providing minimum flow pump start thresholds improved ecological flow dependencies across increasing pump start threshold levels (Figure 3-3 x axis). Modelled minimum flow thresholds varied incrementally from 200 to over 1000 ML/day and are provided by requiring that flow volume in the river exceeds required thresholds before pumping commences. Increasing pump start threshold to 1000 ML/day results in a significant reduction in modelled mean change in important flow dependencies than only 200 ML/day (Figure 3-3). Increasing the pump start threshold above 600 ML/day results in incremental improvements to ecological flows with reduced rate of relative improvement to levels of change in important flow dependencies above about 900 ML/day (Figure 3-3). The benefit of higher pump start thresholds was largest in scenarios with no or low EOS requirements, as the benefit of having higher pump start thresholds was reduced in combination with scenarios that had greater EOS requirements (Figure 3-4). This is likely because large flows have already passed the system to the EOS node before the pump start threshold is triggered. Providing an end-of-system (EOS) flow requirement of 500 GL reduced the level of changes in ecological flow dependencies broadly across asset groups in comparison to having no EOS requirement (compare between (Figure 3-4 and Figure 3-3) considering asset means across all their assessment nodes. Smaller EOS requirement volumes were also found to proportionally reduce changes in important flow dependencies and while larger volumes of EOS requirements provided additional benefit; however, for smaller irrigation targets, the largest gain was often achieved with the initial 100 GL requirement (Figure 3-5 x axis). End-of-system flow requirements provide for a specified volume of water to pass through the last node in the river system model before pumping for water harvesting can commence. The outcome associated with providing end-of-system flow requirements occurs by delaying the start of pumping to later in the wet season, thus retaining initial wet-season flows while also reducing the period of time available for water harvest (Hughes et al., 2024b). In this analysis, different end-of-system flow requirement volumes (ranging from 0 to 800 GL) were modelled. Figure 3-5 Mean change to assets important flow dependencies across water harvesting increments of system target and EOS requirement for a pump rate of 30 days Colour intensity represents the mean level of change occurring in the assets’ important flow metrics with the scenarios given the habitat importance of each node for each asset. For more information on this figure please contact CSIRO on enquiries@csiro.au Setting pump capacity limits on the rate that water can be extracted showed that changes in important flow dependencies associated with water harvest are reduced when pump rates are slower (Figure 3-6). As water can only be extracted when river flow exceeds the minimum pump threshold, this limits the volume of water that can be extracted during a wet season and reduces impact at commencement of pumping (i.e. on any day the extraction volume is limited but pumping may extend to later in the season). Additionally at larger extraction volumes, limiting the pump capacity often resulted in lower total volumes of water extracted (Hughes et al., 2024b) which would further limit the extent of change for ecology. Figure 3-6 Mean change to assets important flow dependencies across water harvesting increments of system target and pump rate with no EOS requirement Colour intensity represents the mean level of change occurring in the assets’ important flow metrics with the scenarios given the habitat importance of each node for each asset. For more information on this figure please contact CSIRO on enquiries@csiro.au 3.1.3 Instream dams with and without transparent flows Two locations for hypothetical instream dams were selected (Leichardt Creek and Victoria River) for modelling and analysis (Yang et al., 2024) and simulated following the hydrology modelling approach outlined in (Hughes et al., 2024b). Their locations are shown in Figure 2-1. The goal of this analysis is to test the effect of different dam locations and configurations on changes to streamflow to understand the effect on downstream ecology. These dams are modelled individually, as well as two together to better understand cumulative impacts and have variants with and without mitigation measures of providing transparent flows (see Section 2.1.1 for definitions). Instream dams create a range of impacts on streamflow associated with the capture and extraction of water, affecting the timing and magnitude of downstream flows. The changes to downstream flow associated with instream dams are explored here across broad asset groups, and results are shown as the mean of asset values. Impacts associated with loss of connectivity due to the dam wall and loss of habitat associated with the dam inundation extent are discussed in (Yang et al., 2024). The dam scenarios and the resulting flow-ecology relationships are discussed in more detail for each asset in Section 4. Assessment of the individual dams found varying levels of impact on ecology flow dependencies (Table 3-2). None resulted in mean changes greater than minor for all assets across the catchment, although local impacts were often considerably higher. The dams vary in size, inflows and capture volumes, and the location of the dam in the catchment influences outcomes. Impacts directly downstream of modelled dams can often be high and may cause extreme changes in ecology flow dependencies. Areas further downstream have contributions from unimpacted tributaries that help support natural flow regimes. Dams further up the catchment may however affect a larger proportion of streams and river reaches when considering flow regime change but may have lower impacts associated with connectivity. Impacts are not equivalent across assets, and large local impacts may lead to changes in ecology across other parts of the catchment due to the connected nature of ecological systems. Table 3-2 Scenarios of different hypothetical instream dam locations showing end-of-system (EOS) flow and mean changes of ecology flows for groups of assets across each asset’s respective catchment assessment nodes Higher values represent greater change in flows important to the assets of each group. Values are asset means across their respective catchment assessment nodes (see Appendix A). Some assets are considered in multiple groups, where the mean across the nodes is used. Asset means include values from all nodes that the asset is assessed in, including in reaches that may not be affected by flow regime change. EOS net reduction in flow includes changes resulting from evaporative losses from dams. SCENARIO DESCRIPTION EOS NET REDUCTION IN FLOW (GL/Y) ALL ASSET MEAN FISH WATERBIRDS OTHER SPECIES HABITATS FRESHWATER ASSETS MARINE ASSETS BDLC 1.1 1.0 1.0 0.9 1.2 1.2 0.9 BDLCT 0.6 0.4 0.6 0.6 0.8 0.5 0.7 B-DVR 3.6 3.2 3.1 4.3 4.0 2.5 4.5 B-DVRT 2.6 1.9 1.9 3.3 3.5 1.4 3.7 B-D2 4.5 4.1 4.1 4.9 5.1 3.7 5.2 B-D2T 2.7 2.0 2.2 3.1 3.9 1.8 3.6 The cumulative change in flow dependencies from multiple dams (Scenario B-D2) are greater than those of individual dams, considering both change flow volumes and ecology flow dependencies (Table 3-2). Cumulative change on flow ecology may be associated with a combination of a larger portion of the catchment being affected by changes in flows across larger parts of the catchment and residual flows being lower due to the overall greater level of abstraction (Table 3-2). Measures to mitigate the impacts of large instream dams, such as transparent flows (inflows let to pass the dam wall for environmental purposes; see Section 2.1.1 and Table 2-1), resulted in reduced ecological change in flows broadly across all assets than without these (Table 3-2). Particularly strong benefits from transparent flows were found for fish (Table 3-2). Instream dams capture inflows and change downstream flow regimes. Transparent flows are a type of environmental flow provided as releases from dams that maintain natural low-flows. Inflow thresholds used in the transparent flows analysis are conceptually similar to the commence-to- pump thresholds used in water harvesting, facilitating comparison. Transparent flows are provided across both dams under Scenario B-D2 (Hughes et al., 2024b). 4 Asset assessments This section provides an overview and discussion of the modelling results for the prioritised ecological assets across a subset of scenarios (described in Section 2.1). Asset outcomes consider their flow requirements, their distribution and habitat suitability within the catchment and the range of flow conditions occurring under each of the scenarios to provide a discussion on the ecological context of the change in flows for the asset. The scenarios used in the asset results are selected to reflect different hypothetical pathways of development. Many of the scenarios have minimal environmental flow provisions, so they can be viewed as providing a pessimistic estimation of risks on ecological assets and are provided to highlight the potential stress points associated with the development option and what may happen if there is no compliance. Section 3.1 provides an overview of the influence of providing mitigation strategies in association with the water resource development scenarios. 4.1 Fish, sharks and rays The fish, sharks and rays group comprises of six ecological assets including barramundi, catfish, grunters, mullet, sawfishes and threadfin. The members of this group are obligatory aquatic species that can inhabit freshwater, marine or a combination of both. Members of this group can have flow associations to support function and important life-history phases. With some members of this group, including barramundi, requiring movement between freshwater and marine habitats to support lifecycle processes, as well as connectivity between the river and floodplain habitats. Some members such as grunter species require specific flow and habitat conditions such as riffle habitat to support different life stages. Refuge habitats during the dry season can be important for some species within this group. 4.1.1 Barramundi Barramundi are large opportunistic, predatory fish that inhabit riverine, estuarine and marine waters in northern Australia, including those in the Victoria catchment. Adults mate and spawn in the lower estuary and coastal habitats near river mouths during the late dry season and early wet season. Small juveniles migrate upstream from the estuary to freshwater habitats where they grow and mature before emigrating downstream to estuarine habitats as adults where they reside and reproduce (Roberts et al., 2019). In the Victoria catchment, barramundi occupy relatively pristine habitats in both freshwater and estuarine reaches, as well as coastal marine waters. Their life history renders them critically dependent on river flows (Tanimoto et al., 2012) as new recruits move into supra-littoral estuarine and coastal salt flat habitats, and freshwater riverine reaches and wetland habitats occupied as juveniles (Crook et al., 2016; Russell and Garrett, 1983a; 1985). Barramundi are sensitive to changes in flow regime in Australia’s tropical rivers. Critical requirements affecting growth and survival include riverine–wetland connectivity, riverine– estuarine connectivity, passage to spawning habitat and volume of flood flows (Crook et al., 2016; Roberts et al., 2019). In years of natural low flows, or flows reduced by anthropogenic activity, the range of facultative habitat and ecosystem processes available to barramundi is reduced, reducing growth and survival (Blaber et al., 1989; Brewer et al., 1995; Milton et al., 2005). Barramundi is an ecologically important fish species capable of modifying the estuarine and riverine fish and crustacean communities throughout Australia’s wet-dry tropics (Blaber et al., 1989; Brewer et al., 1995; Milton et al., 2005). It is targeted by commercial, recreational and Indigenous fisheries. Barramundi is an important species for Indigenous Peoples in northern Australia, both culturally (Finn and Jackson, 2011) and as a food source (Naughton et al., 1986). The analysis considers change in flow regime and related habitat changes but does not consider the addition or loss of potential habitat associated with the creation of a dam impoundment or instream structures (see also Yang et al. (2024) for dam impoundment impacts). Flow dependencies analysis Barramundi were modelled across a total of 1918 km of assessment reaches in the Victoria catchment and in the marine region with contributing flows from a total of 41 model nodes. Some of the key river reaches for barramundi within the catchment were modelled downstream of nodes 81100070, 81100002 and at the end-of-system (81100000) based upon modelling of suitable potential habitat. The locations for modelling barramundi in the Victoria catchment were based upon species distribution models (Stratford et al., 2024a) with reach weightings shown in Appendix A. Hypothetical water resource development in the Victoria catchment resulted in varying levels of change in important flow dependencies for barramundi. For the mean change in flow dependencies across all 41 barramundi analysis reaches and nodes, the hypothetical dam scenarios ranged from negligible (0.5) to minor (3.2) for scenarios B-DLCT (with transparent flows) and B-D2 respectively. For water harvesting, the change was negligible, ranging from 0.1 to 1.0 for B-Wv80t200r30f500 and B-Wv800t200r30f0 respectively. Scenario Cdry resulted in moderate change in flow dependencies (5.1) for barramundi. The resulting spatial change associated with dam, water harvesting and climate scenarios varied as a result of the different spatial patterns, including the extent and magnitude of flow change across different parts of the catchment (Figure 4-1). Under scenario B-D2, the largest contributing change in important flow dependencies was for the metric high flood pulse count (1th percentile) at node 81100063. For scenario B-Wv800t600r30f500, the largest contribution of change was for the metric high flood pulse count (10th percentile) at node 81100001. See Appendix B for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Stratford et al. (2024a) for descriptions of flow-ecology relationships for barramundi. Figure 4-1 Spatial heatmap of change for barramundi, considering the weighted habitat across the catchment Scenarios are: (a) B-Wv80t200r30f0, (b) B-Wv160t200r30f0, (c) B-W160t60r20f0, (d) B-DLC, (e) Cdry and (f) D-dryw160t200r30. See Table 2-1 for interpretation of scenarios. River shading indicates the level of flow change of important metrics weighted by the habitat value of each reach for barramundi. For more information on this figure please contact CSIRO on enquiries@csiro.au Dams and changes in important flows for barramundi For the dam scenarios, Scenario B-DLC without transparent flows resulted in a negligible mean change in important flow dependencies (1.2) across the 41 barramundi assessment nodes. When transparent flows (B-DLCT) were provided to support environmental functions, the change to important flows for barramundi was reduced, but remained negligible (0.5). Scenario B-DVR resulted in larger change than Scenario B-DLC, with a minor (2.1) mean change across the assessment nodes. This was reduced to negligible (1.3) with the provision of transparent flows for Scenario B-DVRT. Under Scenario B-D2, which includes both the B-DLC and B-DVR dams, a minor (3.2) change in flow dependencies occurred across the catchment without transparent flows. This was reduced to negligible (1.8) with provision of transparent flows. Scenario B-D2 with multiple dams resulted in a larger mean change across the catchment, than either of the single-dam scenarios. This was due to the combined effects on flows downstream of the confluence of the two dams and the changes affecting a larger portion of the catchment from which flows would be impounded. Scenario B-D2T with transparent flows resulted in a smaller change to important flows than without transparent flows, indicating the importance of providing environmental flows to provide ecosystem functions downstream, where the scenarios with transparent flows demonstrated the significance of flows in reducing impacts for barramundi (Figure 4-2). During their freshwater juvenile and young-adult life phases, barramundi populations depend on habitat connectivity being maintained throughout the catchment; both upstream riverine and palustrine monsoon-season habitat. The physical barriers of instream dam infrastructure as well as reduced overbank flows due to impounded floodwaters limit both habitat extent and habitat connectivity. Figure 4-2 Change in barramundi flow dependencies by scenario across the model nodes Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for barramundi. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Only the 30 highest impact nodes are shown (x-axis). Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. For more information on this figure please contact CSIRO on enquiries@csiro.au Under Scenario B-D2T, habitat-weighted flow changes for barramundi were greatest at node 81100063 (Figure 4-2), with a major (20.6) change associated with flow dependencies at this single node. Nodes directly downstream of the dams in scenarios B-DLC and B-DVR resulted in extreme (39.9) and major (26.6) change in important flows respectively. These changes were reduced to major (18.8) and moderate (11.4) with provision of transparent flows. This pattern reflects the combined effect of flow changes directly downstream of the dams and the benefits of providing flows for both the environment and ecosystem functions for riverine resident species, and the importance of the habitat for barramundi at these two locations. Water harvesting and changes in important flows for barramundi The hypothetical water harvesting scenarios resulted in a mean negligible change across barramundi assessment nodes from 0.1 to 1.0 for B-Wv80t200r30f500 and B-Wv800t200r30f0 respectively. For the water harvest scenario resulting in the greatest change (B-Wv800t200r30f0), the single node with the highest change was 81100001 with moderate (11.8) change in important flow dependencies. The change in important flows for barramundi with water harvesting varies with the extraction targets, pump-start thresholds, pump rates and locations (Figure 4-2 and Figure 4-3). With a low extraction target of 80 GL under Scenario B-Wv80t200r30f0, the mean weighted change across the catchment was negligible (0.3), increasing to 1.0 with an extraction target of 800 GL under Scenario B-Wv800t200r30f0. Increasing the pump-start threshold from 200 to 600 ML per day (scenarios B-Wv160t200r30f0 and B-Wv160t600r30f0) with a target extraction volume of 160 GL reduced the negligible change across the assessment nodes (from 0.4 to 0.3, respectively) (Figure 4-2). Measures to protect important parts of the flow regime can support catchment ecology where reducing the extraction target puts limits on the volume of water extracted in any water year benefitting the barramundi population as also modelled in Plagányi et al. (2024). In addition, increasing the pump-start threshold protects the low flows that are important for barramundi ecology, such as habitat connectivity and waterhole refugia water quality, particularly at the end of the annual dry season (Arthington et al., 2005; Crook et al., 2022). Figure 4-3 Change in barramundi flow dependencies by water harvest scenarios at sample nodes across the catchment showing change in response to system targets and pump start thresholds Colour intensity represents the level of change occurring in the barramundi’s important flow metrics with the scenarios at the important nodes. Results incorporate the rank percentile change of each scenario relative to the distribution of Scenario A and the importance of the reach. For more information on this figure please contact CSIRO on enquiries@csiro.au Climate change and water resource development for important flows for barramundi Scenario Cdry resulted in mean moderate change (5.1) for barramundi flow dependencies across the 41 barramundi assessment nodes (Figure 4-2). This indicates that the dry climate scenario had on average across all catchment nodes larger changes than scenarios B-D2T (negligible; 1.8) and B-Wv160t200r30f0 (negligible; 0.4). However, it is important to note that local changes under some water resource development scenarios can be considerably higher. Scenarios Ddry-D2T and Ddry-Wv160t200r30 resulted in moderate change (7.2 and 5.2, respectively) when weighted across all barramundi assessment nodes. This shows that the combined changes associated with scenarios Ddry-D2T or Ddry-Wv160t200r30 were higher than of Scenario Cdry, or either of scenarios B-D2 and B-Wv160t200r30 alone. Barramundi populations depend on habitat connectivity being maintained throughout the catchment. Physical barriers of instream infrastructure (particularly scenarios B-DVR or B-D2) would limit access to some riverine habitats (see Yang et al. (2024)). Access to upstream habitats and estuarine supra-littoral habitats would be reduced if water harvesting or dam scenarios reduced the inundation level, frequency and duration of overbank flows. High river flows expand the extent of wetland and estuarine-margin habitats, increase connectivity, deliver nutrients from terrestrial landscapes, create hot spots of high primary productivity and food webs, increase prey productivity and availability, and increase migration within the river catchment (Burford et al., 2016; Burford and Faggotter, 2021; Leahy and Robins, 2021; Ndehedehe et al., 2020a; Ndehedehe et al., 2021). Reduced flow levels under a future drier climate would reduce wetland habitat connectivity and productivity. A wetter climate would likely increase wet-season flow levels and increase wetland–riverine–estuarine connectivity, and it could ameliorate the effects of possible anthropogenic flow reduction compared with current conditions. The mean catchment difference in flow effects between single dams (negligible or minor) and two dams (minor) are expected, because the single dam on Leichhardt Creek reduces flow a relatively minimal amount as it does not affect most of the subcatchments. A much larger area of the river catchment is located above the dam on the Victoria River (B-DVR). In both cases, the construction of dam infrastructure will reduce barramundi habitat by reducing both catchment connectivity and flows (see Yang et al. (2024) for changes associated with instream structures). However, many subcatchments would not be affected. Across the total catchment, water extraction between 80 and 800 GL (i.e. scenarios B-W v80t200r30f0 to B-W v800t200r30f0) results in negligible change to barramundi via flow reduction, including both wet-season high-level flows and low-level flows during September to March prior to the wet season. Barramundi growth and year-class strength are enhanced by large wet-season flows during the wet-season months of January to March (Crook et al., 2022; Leahy and Robins, 2021). Larger flows both preceding and following the wet-season peak flows also enhance barramundi growth and recruitment. Previous studies have shown that reduced high flows lowers growth rates of barramundi: a model of flow–growth estimates a 12% reduction in barramundi growth under an 18% reduction in natural flow regime (Leahy and Robins, 2021). Recent research on monsoon-driven habitat use by barramundi has shown that, during drier years with lower river flows, a large proportion of the juvenile barramundi immigrate upstream from estuarine spawning habitat to freshwater habitats, probably seeking out riverine and palustrine productive hot spots (Roberts et al., 2023). Hence, maintaining low-level flows would be critical. Water harvesting having negligible impacts on seasonal flow levels would help to maintain the natural seasonality of flow patterns and support barramundi populations within the Victoria River catchment. While two dams within the catchment are predicted to result in a minor change to important barramundi flows, mitigation scenarios such as transparent flows reduce this to negligible. The documented impacts on barramundi populations from modifying the level and seasonality of flows would be larger under a future dry climate and greatest with water resource development under a dry climate, as was modelled in other studies of tropical Australian catchments (Plagányi et al., 2024). 4.1.2 Catfish Catfish are a diverse group of fish that inhabit both inland and coastal waters globally. In northern Australia, some catfish species are freshwater, some are marine and some move between the river and the estuary (Pusey et al., 2020). Catfish in the Victoria catchment belong to two families: Ariidae (five species, including marine and freshwater) and Plotosidae (five species, mainly freshwater in the Victoria catchment). The larger-bodied ariid catfish like Neoarius graeffei (fork- tailed catfish), N. midgleyi and Sciades leptasis are mainly found in the main stems of the Victoria River and the larger tributaries like the Wickham River. The usually smaller-bodied Neosilurus species (in the Plotosidae) are mainly found in smaller tributaries. While not as important as barramundi or sooty grunters (Leiopotherapon unicolor), the fork-tailed catfish has considerable importance as a subsistence fish for Indigenous communities (Finn and Jackson, 2011). The key threats to the two most common Neosilurus species are associated with instream barriers causing changes in downstream flow and loss of habitat connectivity. Plotosidae need high flows to trigger spawning migration, and they require a barrier-free passage to spawning grounds in the headwater streams (also see Yang et al. (2024) for changes associated with instream structures). Flow dependencies analysis Catfish were modelled across a total of 1918 km of assessment reaches in the Victoria catchment with contributing flows from a total of 40 model nodes. Key river reaches for catfish within the catchment were modelled downstream of nodes 81101070, 81100140 and 81100060 based upon modelling of suitable potential habitat. The selection of these locations for modelling catfish in the Victoria catchment was based upon the species distribution models of the fork-tailed catfish (Stratford et al., 2024a) with reach weightings shown in Appendix A. Hypothetical water resource development in the Victoria catchment resulted in varying levels of change to the important flow components for catfish. When considering the mean change across all 40 catfish analysis reaches and nodes, the hypothetical dam scenarios ranged from negligible (0.2) to minor (3.7) for scenarios BDLCT and BD2 respectively. For water harvesting scenarios, the change in important flow dependencies remained negligible, ranging from 0.0 under B-Wv80t600t30f500 to 0.2 under B-Wv720t200r30f0. Scenario Cdry resulted in minor change (4.3) for catfish. The resulting change associated with dam, water harvesting and climate scenarios varied as a result of the different spatial patterns, including the extent and magnitude of flow impacts across different parts of the catchment (Figure 4-4). Under scenario B-D2, the largest contributing change in important flow dependencies was for the metric annual minima of 90-day means of daily discharge at node 811011135. For scenario B-Wv800t600r30f500, the largest contribution of change was for the metric high flood pulse count (10th percentile) at node 81100060. See Appendix B for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Stratford et al. (2024a) for descriptions of flow-ecology relationships for catfish. Figure 4-4 Spatial heatmap of change for catfish, considering the weighted habitat across the catchment Scenarios are: (a) B-Wv80t200r30f0, (b) B-Wv160t200r30f0, (c) B-W160t60r20f0, (d) B-DLC, (e) Cdry and (f) D-dryw160t200r30. See Table 2-1 for interpretation of scenarios. River shading indicates the level of flow change of important metrics weighted by the habitat value of each reach for catfish. For more information on this figure please contact CSIRO on enquiries@csiro.au Dams and changes in important flows for catfish For the dam scenarios, Scenario B-DLC without transparent flows resulted in a mean negligible change to important flow dependencies (1.0) across the 40 catfish assessment nodes. When transparent flows (B-DLCT) were provided to support environmental functions, the change to important flows for catfish was reduced to negligible (0.2). Scenario B-DVR resulted in a larger change than Scenario B-DLC, with a minor (2.8) mean change across the assessment nodes. This was reduced to negligible (0.3) with the provision of transparent flows for Scenario B-DVRT. Under Scenario B-D2, which includes both the B-DLC and B-DVR dams, a minor (3.7) change in flow dependencies occurred across the catchment without transparent flows. This was reduced to negligible (0.4) with provision of transparent flows. Scenario B-D2 with multiple dams resulted in a larger mean change across the catchment than either of the single-dam scenarios. This was due to the combined effects on flows downstream of the confluence of the two dams and the impacts to a larger portion of the catchment. Scenario B-D2T with transparent flows resulted in a smaller change to important flows than without transparent flows, indicating the importance of providing transparent flows for environmental outcomes for catfish (Figure 4-5). Figure 4-5 Change in catfish flow dependencies by scenario across the model nodes Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for catfish. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Only the 30 highest impact nodes are shown (x-axis). Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. For more information on this figure please contact CSIRO on enquiries@csiro.au Nodes directly downstream of the dams in scenarios B-DLC and B-DVR resulted in extreme change to important flow dependencies (35.2 and 40.2 respectively). These changes were reduced to moderate (6.8) and minor (2.8) with provision of transparent flows. This reflects a combination of the higher impacts on flow changes directly downstream of dams, the benefits associated with provision of flows for the environment and the habitat importance for catfish in these two locations. Additionally, instream infrastructure that blocks upstream movement and captures high-flow events disrupts spawning migrations, posing additional impacts to catfish (see Yang et al. (2024) for details on these impacts). Water harvesting and changes in important flows for catfish The hypothetical water harvesting scenarios resulted in a mean change in important flow dependencies across catfish assessment nodes with negligible values, ranging from 0.0 to 0.2 for B-Wv80t600t30f500 and B-Wv720t200r30f0 respectively. The change for catfish with water harvesting varies with the extraction targets, pump-start thresholds and pump rates (Figure 4-5). With a low extraction target of 80 GL under Scenario B-Wv80t200r30f0, the mean weighted change across the catchment was negligible (0.1), increasing but remaining negligible (0.2) with an extraction target of 800 GL under Scenario B-Wv800t200r30f0. Increasing the pump-start threshold from 200 to 600 ML per day (scenarios B-Wv160t200r30f0 and B-Wv160t600r30f0) with a target extraction volume of 160 GL reduced the change in important flows across the assessment nodes from negligible (0.2) to no detectable change (0.0) (Figure 4-5). Implementing measures to protect key aspects of the flow regime can significantly support ecological health. Reducing the extraction target limits the volume of water that can be taken in any given year, while raising the pump-start threshold helps preserve low flows critical to catfish ecology. However, dam infrastructure, water extraction, and river regulation can disrupt seasonal flow patterns, leading to longer cease-to-flow periods and reduced overbank flows. These flow modifications pose a major threat to catfish by limiting access to riverine habitats and decreasing the frequency of floodplain connections that are essential for juvenile recruitment (Allen, 1982; Bishop et al., 1990). Climate change and water resource development for important flows for catfish Scenario Cdry resulted in minor (4.3) mean change for catfish flow dependencies across the 40 catfish assessment nodes (Figure 4-5). This indicates that the dry climate scenario had on average across all catchment nodes larger changes than scenarios B-D2T (negligible; 0.4) and B-Wv160t200r30f0 (negligible; 0.2). However, it is important to note that local changes under some water resource development scenarios can be considerably higher. The scenarios Ddry-D2T and Ddry-Wv160t200r30 resulted in minor changes (4.5 and 4.3, respectively) when weighted across all catfish assessment nodes. This shows that the combined changes of scenarios Ddry-D2T or Ddry-Wv160t200r30 were higher than of Scenario Cdry or either of scenarios B-D2 and B-Wv160t200r30 alone. Four of the identified key threats for catfish can be found across the catchment. Flow modification can result from water harvesting, dam infrastructure and river regulation, and there is the added threat of climate change. All catfish species depend on connections to the floodplain, often for the purpose of juvenile recruitment. River regulation and water extraction can reduce overbank flows, leading to a decrease in connection frequency and therefore a loss in recruitment opportunities. Some Plotosidae species prefer flowing water in the main channel. The construction of instream infrastructure that inhibits upstream movement and captures high-flow events removes the pathways and stimulus for spawning migrations, providing additional impacts to catfish (see Yang et al. (2024) for changes associated with instream structures). In addition, seasonal flow patterns are affected by dam infrastructure or extraction, both of which may increase cease-to-flow periods and thereby limiting access to riverine habitats (Allen, 1982; Bishop et al., 1990). The combination of impacts on fish movement and the loss of spawning migration triggers from reduced flow, is highly likely to affect population sizes of Plotosidae, especially Neosilurus ater (Pusey, 2004). Thermal impacts on catfish habitat also may affect upstream populations. Despite limited data on tropical catfish, Pusey (2004) hypothesises that, in upland areas, winter thermal tolerances of Neoarius graeffei are close to their thermal limit. Cold-water releases from bottom water in a stratified dam may breach temperature tolerances of tropical catfish and cause mortality. 4.1.3 Grunters Grunters include a total of 37 species from 11 genera, of which the most species-rich genera are Hephaestus, Scortum, Syncomistes and Terapon. Grunters inhabit riverine, estuarine and marine waters in northern Australia. The sooty grunter (Hephaestus fuliginosus) is an important recreational species for which environmental flow is managed to maintain suitable habitat conditions in some modified river systems (Chan et al., 2012). Grunters are also important species for Indigenous Peoples in northern Australia, both culturally (Finn and Jackson, 2011) and as a food source (Naughton et al., 1986). The Victoria catchment hosts a unique mix of grunters than Gulf of Carpentaria catchments. While the widespread spangled grunter (Leiopotherapon unicolor) and barred grunter (Amniataba percoides) are present, the western sooty grunter (Hephaestus jenkinsi) replaces the eastern species H. fuliginosus. Less abundant species include the sharpnose grunter (Syncomistes butleri), Drysdale grunter (Syncomistes rastellus) and Neil’s grunter (Scortum neili). The western sooty grunter is particularly important for recreational and cultural activities (Chan et al., 2012). Grunters are likely widespread in the Victoria River whose headwaters serve as key spawning and nursery grounds. Grunters are sensitive to changes in flow regime – some critical requirements are flowing water and passage to spawning habitat, and grunters are also sensitive to cold-water pollution. The flow dependencies analysis considers change in flow regime and related habitat changes but does not consider the addition or loss of potential habitat associated with the creation of a dam impoundment or instream structures (see also Yang et al. (2024) for changes associated with instream structures). Flow dependencies analysis Grunters were modelled across a total of 1918 km of assessment reaches in the Victoria catchment with contributing flows from a total of 40 model nodes. Some of the key river reaches for grunter within the catchment were modelled downstream of nodes 81100140, 81102380 and 81100063 based upon modelling of suitable potential habitat. The locations for modelling grunter in the Victoria catchment were based upon the species distribution models of the sooty grunter (Stratford et al., 2024a) with reach weightings shown in Appendix A. Hypothetical water resource development in the Victoria catchment resulted in varying levels of changes to the important flow components for grunters. The mean change across all 40 grunter analysis reaches and nodes under the hypothetical dam scenarios ranged from negligible (0.0) in scenario BDLCT to minor (3.0) in BD2. For water harvesting, change in important flows was negligible, ranging from 0 in B-Wv320t600r30f500 to 0.4 in B-Wv800t200r30f0. Scenario Cdry resulted in minor change (3.2) to grunter flow dependencies. The spatial change associated with dam, water harvesting and climate scenarios varied due to the differing spatial patterns, including the extent and magnitude of flow change across different parts of the catchment (Figure 4-6). Under scenario B-D2, the largest contributing change in important flow dependencies was for the metric mean Autumn discharge at node 81100063. For scenario B-Wv800t600r30f500, the largest contribution of change was for the metric annual minima of 30-day means of daily discharge at node 81100002. See Appendix B for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Stratford et al. (2024a) for descriptions of flow-ecology relationships for grunters. Figure 4-6 Spatial heatmap of change for grunter, considering the weighted habitat across the catchment Scenarios are: (a) B-Wv80t200r30f0, (b) B-Wv160t200r30f0, (c) B-W160t60r20f0, (d) B-DLC, (e) Cdry and (f) D-dryw160t200r30. See Table 2-1 for interpretation of scenarios. River shading indicates the level of flow change of important metrics weighted by the habitat value of each reach for grunter. For more information on this figure please contact CSIRO on enquiries@csiro.au Dams and changes in important flows for grunter In the dam scenarios, Scenario B-DLC without transparent flows resulted in a negligible mean change (1.5) to grunter flow dependencies across the 40 grunter assessment nodes. Introducing transparent flows (B-DLCT) to support environmental functions further reduced the change in important flows to a level that change was undetected (0.0). Although Scenario B-DVR resulted in a larger change than to Scenario B-DLC, the mean change was still negligible (1.6) across the assessment nodes. This change in important flows was reduced to negligible (0.2) with the provision of transparent flows in Scenario B-DVRT. Under Scenario B-D2, which includes both the B- DLC and B-DVR dams, a minor (3.0) change occurred across the catchment without transparent flows, but this was reduced to negligible (0.1) with provision of transparent flows. Scenario B-D2, with multiple dams, resulted in a larger mean change across the catchment than either single-dam scenario due to the combined effects on downstream flows of the confluence of the two dams and the impacts to a larger portion of the catchment. Scenario B-D2T with transparent flows resulted in a smaller change than Scenario B-D2 (without transparent flows), indicating the importance of providing transparent flows for environmental outcomes – the scenarios with transparent flows demonstrated the significance of flows in reducing impacts for grunters (Figure 4-7). Figure 4-7 Change in grunter flow dependencies by scenario across the model nodes Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for grunter. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Only the 30 highest impact nodes are shown (x-axis). Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. Nodes directly downstream of the dams in scenarios B-DLC and B-DVR resulted in extreme (55.8) and major (22.0) change to important flows, respectively. These changes were reduced to negligible (0.5 and 1.4, respectively) with provision of transparent flows. This reflects a combination of the higher impacts to flow changes directly downstream of dams, the benefits associated with providing flows for the environment and the habitat importance for grunter in these two locations. According to a study by Gehrke (1997), the abundance of sooty grunter in river reaches regulated by a single dam was significantly reduced. This decline is attributed to For more information on this figure please contact CSIRO on enquiries@csiro.au barriers to fish mobility and changes in sediment composition that alter habitats, though these effects are likely confined to areas directly downstream of the dam. Water harvesting and changes in important flows for grunter The hypothetical water harvesting scenarios resulted in a mean negligible change in important flow dependencies across the grunter assessment nodes, ranging from 0 to 0.4 for B-Wv320t600r30f500 and B-Wv800t200r30f0, respectively. The change in important flows for grunters from water harvesting varies based on extraction targets, pump-start thresholds and pump rates (Figure 4-7). With a low extraction target of 80 GL under Scenario B-Wv80t200r30f0, the mean weighted change across the catchment was negligible (0.2), increasing slightly but still negligible (0.4) with an extraction target of 800 GL under Scenario B-Wv800t200r30f0. Increasing the pump-start threshold from 200 to 600 ML per day (i.e. from Scenario B-Wv160t200r30f0 to Scenario B-Wv160t600r30f0) with a target extraction volume of 160 GL reduced changes in important flows across the assessment nodes but maintained them at negligible (0.3 to 0.1) (Figure 4-7). Grunter relies on specific flow regimes for critical life processes such as spawning, feeding, and migration. Disruptions to these flows could have long-term ecological impacts. Measures to protect important parts of the flow regime can support ecology where reducing the extraction target puts limits on the volume of water extracted in any water year, while increasing the pump-start threshold protects the low flows that are important for grunter ecology. Climate change and water resource development for important flows for grunter Scenario Cdry resulted in mean minor (3.2) change to important flow dependencies for grunters across the 40 grunter assessment nodes (Figure 4-7). This indicates that the dry climate scenario had on average across all catchment nodes larger changes than scenarios B-D2T (negligible; 0.1) and B-Wv160t200r30f0 (negligible; 0.3). However, it is important to note that local changes under some water resource development scenarios can be considerably higher. The scenarios Ddry-D2T and Ddry-Wv160t200r30 resulted in minor (3.2) and (3.3) changes, respectively, when weighted across all grunter assessment nodes. Overall grunters face four of the key threats related to flow modification: water harvesting, dam infrastructure, river regulation and the added threat of climate change. For Amniataba percoides, changes in flow regimes that lead to faster-flowing environments – for example, a dam structure that first holds back water, then releases it at higher velocity – can lead to decreased population viability (Pusey et al., 2004). The key mechanisms for this are desynchronisation of thermal regimes and juvenile mortality caused by out-of-season high flows. In addition, dams impede access to spawning grounds (see Yang et al. (2024)). Species such as Leiopotherapon unicolor have habitat associations with riffle habitat (Keller et al., 2019), so any loss of this habitat by either reducing or increasing flows, or through inundation due to impoundment, would affect this species. 4.1.4 Mullet Mullet (a group including the genera Liza, Mugil and Moolgarda) are fish that use marine habitats as adults to spawn and freshwater habitats as juveniles (a life history known as ‘catadromous’). Their life histories entail ‘catchment to coast’ habitats (i.e. freshwater, estuarine and marine) (Marin et al., 2003; Whitfield et al., 2012). Adults spawn in coastal habitats near river mouths, and small juveniles migrate upstream to freshwater habitats where they forage and grow (De Silva, 1980; Grant and Spain, 1975; Kailola et al., 1993; Robins et al., 2005). After about four years, they leave freshwater habitats and move to lower estuaries and the ocean. Mullet grow fastest during the tropical wet season, in response to a seasonal increase in productivity of coastal waters (Grant and Spain, 1975; Whitfield et al., 2012). About 20 tropical mullet species occur in northern Australian waters from Townsville on the east coast to Broome in the west (Blaber et al., 2010). Records show Planiliza ordensis (river diamond mullet), Moolgarda buchanani (bluetail mullet) and Moolgarda seheli (bluespot mullet) as present in the estuarine and freshwater reaches of the Victoria River. Short-lived, fast growing and productive, mullet are important as a commercial, recreational and Indigenous fish resource. Mullet are of cultural significance for Indigenous communities throughout Australia and among the most numerous species in their catch (Henry and Lyle, 2003). In NT fisheries, they are a target for Aboriginal coastal fishing licences (Boyer, 2018; Wilton et al., 2018) and a target or bycatch in several fisheries (Northern Territory Government, 2022). The key threats to mullet are associated with the loss of riverine and overbank flood flows that reduce riverine–wetland connectivity and so reduce nutrient inputs and feeding opportunities. In addition, the loss of instream connectivity among deep-water pools due to reduced low-level flows would be a potential barrier to downstream movement of mullet to coastal waters. Flow dependencies analysis Mullet was modelled in the marine region with one model node at the end-of-system. Locations for modelling mullet in the Victoria catchment were based upon consideration of the habitat of key species (Stratford et al., 2024a) with weightings shown in Appendix A. Hypothetical water resource development in the Victoria catchment resulted in varying levels of change associated with important flow dependencies for mullet. When considering change, the hypothetical dam scenarios ranged from negligible (0.7) to moderate (5.4) for scenarios BDLCT and BD2 respectively. For water harvesting, the change ranged from negligible (0.6) to minor (4.7) for B-Wv80t200r30f500 and B-Wv800t200r30f0 respectively. Scenario Cdry resulted in moderate change to flow dependencies (10.9) for mullet. Under scenario B-D2, the largest contributing change in important flow dependencies was for the metric annual maxima of 90-day means of daily discharge at node 81100000. For scenario B-Wv800t600r30f500, the largest contribution of change was also for the metric Annual maxima of 90-day means of daily discharge at node 81100000. See Appendix B for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Stratford et al. (2024a) for descriptions of flow-ecology relationships for mullet. Dams and changes in important flows for mullet For the dam scenarios, transparent flows invariably produced a smaller change to important flow dependencies than no transparent flows. Scenario B-DLC without transparent flows resulted in a negligible change (0.8). When transparent flows (B-DLCT) were provided to support environmental functions, the change to important flows for mullet was reduced (negligible; 0.7). Scenario B-DVR resulted in a larger change than Scenario B-DLC, with a moderate (5.1 mean change across the assessment nodes). This was reduced to minor (4.1) with the provision of transparent flows for Scenario B-DVRT. Under Scenario B-D2, which includes both the B-DLC and B-DVR dams, a moderate (5.4) change occurred across the catchment without transparent flows. This was reduced to minor (4.0) with provision of transparent flows. Scenario B-D2 with multiple dams resulted in a larger change than either single-dam scenario, due to the combined effects on flows downstream of the confluence of the two dams. As was evident for single dams, Scenario B-D2T with transparent flows, resulted in a smaller change to important flows than Scenario B-D2 (without transparent flows), indicating the importance of providing flows for ecosystem function, and demonstrating the significance of flows in reducing impacts for mullet (Figure 4-8). Figure 4-8 Change in mullet flow dependencies by scenario across the model nodes Colour intensity represents the level of change occurring in important flow metrics for mullet, expressed as percentile change from the historical conditions. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change shown on the right axis. Horizontal grey bars and numbers correspond to the change. Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50- year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. For more information on this figure please contact CSIRO on enquiries@csiro.au Water harvesting and changes in important flows for mullet The hypothetical water harvesting scenarios resulted in change in important flow dependencies from negligible (0.6) to minor (4.7) for B-Wv80t600t30f500 and B-Wv800t200r30f0 respectively. With a low extraction target of 80 GL under Scenario B-Wv80t200r30f0, the change in important flows was negligible (1.4), increasing to minor (4.7) with an extraction target of 800 GL under Scenario B-Wv800t200r30f0. Increasing the pump-start threshold from 200 to 600 ML per day (i.e. from Scenario B-Wv160t200r30f0 and to Scenario B-Wv160t600r30f0) with a target extraction volume of 160 GL reduced the change in important flows across the assessment nodes from negligible (1.7) to negligible (1.5) (Figure 4-8). Measures to protect important components of the flow regime can support catchment ecology where reducing the extraction target puts limits on the volume of water extracted in any water year, hence maintaining higher flood-flow regimes, while increasing the pump-start threshold protects the low flows that are important for mullet ecology within riverine habitats. Climate change and water resource development for important flows for mullet Scenario Cdry resulted in moderate change (10.9) in important flow dependencies for mullet indicating that the dry climate scenario had on average across all catchment nodes larger changes than scenarios B-D2T (minor; 4.0) and B-Wv160t200r30f0 (negligible; 1.7). However, it is important to note that local changes under some water resource development scenarios can be considerably higher. The scenarios Ddry-D2T and Ddry-Wv160t200r30 resulted in moderate (14.3) and moderate (10.7) changes, respectively. This shows that the combined changes of scenarios Ddry-D2T or Ddry-Wv160t200r30 were higher than Scenario Cdry or either of scenarios B-D2 and B-Wv160t200r30 alone. The juvenile and early-adult phase mullet prefer fresh and brackish waters, including palustrine wetlands which support optimal growth and survival (Cardona, 2000; Whitfield et al., 2012). Wetland ‘perimeter to area ratio’ and wetland ‘number of patches’ have been found to be strongly related to mullet catch, suggesting the extent and connectivity of estuarine habitats, intertidal and supra-littoral areas, and creeks and channels are important to mullet production (Meynecke et al., 2008). The frequency and duration of high-flood events that support the inundation and availability of river floodplain and estuarine supra-littoral habitats used prolifically by juvenile mullet during the wet season are important for mullet (O'Mara et al., 2021). Flooded wetland habitats are hot spots for primary productivity (Burford et al., 2016; Ndehedehe et al., 2020a; Ndehedehe et al., 2020b) and refugia for fish during the subsequent dry season (O'Mara et al., 2021). Reduced river flow volume and modified seasonality and volume of flows under dam construction, water harvesting scenarios and Scenario Cdry affect mullet negatively by reducing the extent and connectivity of estuarine and freshwater habitats. The physical barrier of a dam blocks access to up-river habitats for juvenile mullet restricting ontogenetic habitat select that is crucial for the catadromous life history of mullet (Larson et al., 2013; Waltham et al., 2013a). Modified flows limit growth and survival via lower seasonal food accessibility and non-optimal environmental conditions (Faggotter et al., 2013; Jardine et al., 2013; O'Mara et al., 2021), and by disrupting cues for spawning movements. (also see Yang et al. (2024) for changes associated with instream structures). 4.1.5 Sawfishes Tropical Australian waters are one of the last strongholds for sawfishes (Phillips et al., 2011). The two largest species, largetooth or freshwater sawfish (Pristis pristis) and green sawfish (P. zijsron) are listed as Critically Endangered on the IUCN Red List of Threatened Species and Vulnerable under the Commonwealth EPBC Act. The dwarf sawfish (P. clavata) is listed as Critically Endangered (IUCN) and Vulnerable (EPBC Act), while the narrow sawfish (Anoxypristis cuspidata) is listed as Vulnerable (IUCN) and not listed under the EPBC Act. Only the freshwater sawfish is found in riverine reaches during the juvenile phase, after which it moves to coastal and marine habitats as adults. During research surveys, juvenile dwarf sawfish have been caught in upper estuary and lower riverine reaches in relatively pristine tropical Australian rivers, while adults are regularly caught as part of offshore fishing operations. The green sawfish is common in estuaries and on occasion also found in riverine habitats across northern Australia. Published data on sawfish distribution in the Victoria River is limited, with most records from nearby rivers like the Ord, Keep, and Daly. Only four records of freshwater sawfish were found in the Victoria River through the Ocean Biodiversity Information System (OBIS, 2022). However, surveys conducted by Dr Richard Pillans in 2018 and 2019 as part of the Ord River Offset program recorded both freshwater and dwarf sawfish in the river. Dr Pillans documented 28 freshwater sawfish up to 400 km upstream and 29 dwarf sawfish up to 120 km upstream. This highlights the limited biological inventory in remote tropical Australia. Additionally, four sawfish species have been caught as bycatch during prawn trawling in the Joseph Bonaparte Gulf, narrow sawfish being the most common. In northern Australia, all sawfish species pup in estuarine and inshore waters, and estuarine and riverine connectivity is critical for the survival (Dulvy et al., 2016; Morgan et al., 2017). Sawfish are important for Indigenous Peoples in northern Australia, both culturally (Ebner et al., 2016; Finn and Jackson, 2011) and as a food source (Naughton et al., 1986). In Australia, only Indigenous Australians are allowed to capture sawfishes. Freshwater sawfish, in particular, are affected by variability in the flow regime despite sustained riverine and estuarine connectivity during the wet season. Strong upstream recruitment of juveniles to riverine habitats only occurs during the highest flood flows (Lear et al., 2019). The higher the volume of flood flows, the greater the sustained body condition of sawfish during the subsequent dry season (Lear et al., 2021). The key threats to sawfishes are associated with the loss of high-level flood flows to support upstream recruitment and with any reduction in low-level dry-season flows that would reduce instream connectivity or create barriers among deep-water pools and reduce their persistence or water quality during the dry season. Flow dependencies analysis Sawfish were modelled across a total of 1918 km of assessment reaches in the Victoria catchment and in the marine region with contributing flows from a total of 41 model nodes. Some of the key river reaches for sawfish within the catchment were modelled downstream of nodes 81101131, 81100060 and at the end-of-system (81100000) based upon modelling of suitable potential habitat. The locations for modelling sawfish in the Victoria catchment were based upon the species distribution models of freshwater or largetooth sawfish (Stratford et al., 2024a) with reach weightings shown in Appendix A. Hypothetical water resource development in the Victoria catchment resulted in varying levels of changes in the important flow components for sawfish. Mean changes in important flows across all 41 sawfish analysis reaches and nodes, the hypothetical dam scenarios showed levels ranging from negligible (0.5) in scenario BDLCT to minor (3.7) in scenario BD2. For water harvesting scenarios, the change in important flows remained negligible, ranging from 0.1 in B-Wv80t200r30f500 and B-Wv800t200r30f0 respectively. Scenario Cdry resulted in moderate change (5.4) for sawfish. The resulting change associated with dam, water harvesting and climate scenarios varied as a result of the different spatial patterns, including the extent and magnitude of flow change across different parts of the catchment (Figure 4-9). Under scenario B-D2, the largest contributing change in important flow dependencies was for the metric mean July discharge at node 81100063. For scenario B-Wv800t600r30f500, the largest contribution of change was for the metric high flood pulse count (10th percentile) at node 81100001. See Appendix B for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Stratford et al. (2024a) for descriptions of flow-ecology relationships for sawfish. Figure 4-9 Spatial heatmap of change for sawfish, considering the weighted habitat across the catchment Scenarios are: (a) B-Wv80t200r30f0, (b) B-Wv160t200r30f0, (c) B-W160t60r20f0, (d) B-DLC, (e) Cdry and (f) D-dryw160t200r30. See Table 2-1 for interpretation of scenarios. River shading indicates the level of flow change of important metrics weighted by the habitat value of each reach for sawfish. For more information on this figure please contact CSIRO on enquiries@csiro.au Dams and changes in important flows for sawfish In the dam scenarios, Scenario B-DLC without transparent flows resulted in a negligible mean change (1.2) across the 41 sawfish assessment nodes. When transparent flows (B-DLCT) were provided to support environmental functions, the change to important flows for sawfish was further reduced to negligible (0.5. Scenario B-DVR resulted in a larger change compared than Scenario B-DLC, with a minor (2.5) mean change across the assessment nodes. This was reduced to negligible (1.0) with the provision of transparent flows for Scenario B-DVRT. Under Scenario B-D2, which includes both the B-DLC and B-DVR dams, a minor (3.7) change in flows occurred across the catchment without transparent flows, but this was reduced to negligible (1.4) when transparent flows were provided. Scenario B-D2, involving multiple dams, resulted in a larger mean changes across the catchment than the single-dam scenarios, due to the combined effects on flows downstream of the confluence of the two dams and the larger affected area. Scenario B-D2T (with transparent flows) resulted in a smaller change than Scenario B-D2 (without transparent flows), indicating the importance of providing transparent flows for environmental outcomes – the scenarios with transparent flows demonstrated the significance of flows in reducing impacts for sawfish (Figure 4-10). Figure 4-10 Change in sawfish flow dependencies by scenario across the model nodes Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for sawfish. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Only the 30 highest impact nodes are shown (x-axis). Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. For more information on this figure please contact CSIRO on enquiries@csiro.au In Scenario B-D2T, habitat-weighted changes in flow dependencies for sawfish were most pronounced at node 81100063 (Figure 4-10), with a major (17.8) change associated with important flows at this single node. Nodes directly downstream of the dams in scenarios B-DLC and B-DVR resulted in extreme changes with values of 43.4 and 39.5 respectively. These changes were reduced to major (16.4) and moderate (11.1) with provision of transparent flows. This reflects a combination of the higher impacts to flows directly downstream of dams, the environmental benefits associated with provision of flows and the habitat importance for sawfish in these two locations. The location of single dams further up in the catchment likely reduces the level of potential impact on sawfish; however, sawfish are likely to be affected by a combination of impacts associated with water resource development beyond changes in flow and loss of connectivity. Water harvesting and changes in important flows for sawfish The hypothetical water harvesting scenarios resulted in a mean change in important flow dependencies across sawfish assessment nodes, that remained negligible (0.1 in Scenario B-Wv80t200r30f500 to 0.8 in Scenario B-Wv800t200r30f0). For the highest impact water harvest scenario (B-Wv800t200r30f0), the single node with the highest change in flow dependencies was 81100001 with moderate (8.3) change. The change in flows for sawfish with water harvesting varies with the extraction targets, pump-start thresholds, pump rates and locations (Figure 4-10 and Figure 4-11). With a low extraction target of 80 GL under Scenario B-Wv80t200r30f0, the mean weighted change in important flows across the catchment was negligible (0.2), increasing to 0.8 with a higher extraction target of 800 GL under Scenario B-Wv800t200r30f0. Increasing the pump-start threshold from 200 to 600 ML per day (i.e. from Scenario B-Wv160t200r30f0 to Scenario B-Wv160t600r30f0) with a target extraction volume of 160 GL reduced the level of change across the assessment nodes from 0.3 to 0.2, but remained negligible (Figure 4-10). The scenarios involving developing a single dam on the Victoria River, or two dams in the catchment resulted in minor impacts on freshwater sawfish via flow modification. However, introducing transparent flows past the dams moderated the impact to negligible. Dam development would result in impacts resulting from both flow reduction and loss of connectivity due to the physical barrier of the instream dams. Flow modification due to the highest water extraction scenario (800 GL/year) had negligible effects on freshwater sawfish. Despite the minor and negligible impacts on flows due to water extraction and impoundment, both reduced high flows and reduced duration of the upper 25% of flows would affect floodplain inundation and wetland connectivity (modelled in Section 4.4.1). Furthermore, the maintenance of depth and persistence of important riverine pools during the dry season may be reduced by water impoundment or upstream extraction (see also Section 4.4.2 for refuge waterholes). Figure 4-11 Change in sawfish flow dependencies by water harvest scenarios at sample nodes across the catchment showing change in response to system targets and pump start thresholds Colour intensity represents the level of change occurring in the barramundi’s important flow metrics with the scenarios at the important nodes. Results incorporate the rank percentile change of each scenario relative to the distribution of Scenario A and the importance of the reach. For more information on this figure please contact CSIRO on enquiries@csiro.au Climate change and water resource development for important flows for sawfish Scenario Cdry resulted in moderate (5.4) mean change to important flow dependencies for sawfish across the 41 sawfish assessment nodes (Figure 4-10). This indicates that the dry climate scenario had on average across all catchment nodes larger changes than scenarios B-D2T (negligible; 1.4 and B-Wv160t200r30f0 (negligible; 0.3). However, it is important to note that local changes under some water resource development scenarios can be considerably higher. The scenarios Ddry-D2T and Ddry-Wv160t200r30 resulted in moderate (6.5) and moderate (5.4) changes to important flows, respectively, when weighted across all sawfish assessment nodes. This shows that the combined changes of scenarios Ddry-D2T or Ddry-Wv160t200r30 were higher than Scenario Cdry or either of scenarios B-D2 and B-Wv160t200r30 alone. Reduction of wet-season high flows due to multiple dams or water extraction would reduce the potential for sawfish neonate recruitment upstream and connectivity to wetland habitats. In addition, dams impede access to juvenile riverine habitats (see Yang et al. (2024)). Research has shown that recruitment and body condition, growth and survival of largetooth sawfish within riverine freshwater habitats are critically dependent on large flood flows (the 98th percentile of recorded flows) (Lear et al., 2019) and persistent, extensive riverine pools that act as critical refugia for sawfish during the following dry season (Lear et al., 2021). Modified flows can reduce sawfish neonate recruitment (Morgan et al., 2016), affect the potential growth of individuals (Hunt et al., 2012), reduce the abundance of sawfish prey species that use floodplain wetlands during their life cycle (Novak et al., 2017), and reduce sawfish abundance and survivorship (Close et al., 2014; Jellyman et al., 2016; Morgan et al., 2016). At similar latitudes among Australian tropical rivers, water resource development and a drying climate have been modelled to have significant negative impacts on sawfish populations (Plagányi et al., 2024). Measures to protect important parts of the flow regime can support ecology where reducing the extraction target puts limits on the volume of water extracted in any water year, while increasing the pump-start threshold protects the low flows that are important for sawfish ecology. Flow modifications, particularly the reduction of high flows and shortened duration of the peak water levels (25%), can affect species, such as the sawfish, that rely on floodplain inundation and wetland connectivity (modelled in Section 4.4.1). Furthermore, the maintenance of depth and persistence of important riverine pools during the dry season may be reduced by water impoundment or upstream extraction (see also Section 4.4.2 for refuge waterholes). 4.1.6 Threadfin King threadfin (Polydactylus macrochir, formerly P. sheridani) is a large (>1.5 m) marine carnivorous fish in the order Perciformes. Endemic to Australasia, king threadfin range from the Exmouth Gulf, WA, across northern Australia and southern Papua New Guinea to the Brisbane River in Queensland (Motomura et al., 2000). In the Victoria catchment, king threadfin occupy relatively pristine habitats in estuarine reaches, as well as coastal marine waters. They are not found in freshwater habitats (Blaber et al., 1995; Moore et al., 2012). King threadfin are long lived (22 years) and fast growing. They begin life as males but change to females as they age (protandrous hermaphrodites). They mate and spawn in the lower estuary during the dry season to early wet season. King threadfin use both visual and tactile cues as predators. They benefit from turbid waters during wet-season flows as they can successfully forage for prey while turbidity protects young threadfin from large predators (Welch et al., 2014) The key threats to threadfin are associated with the loss of estuarine overbank flood flows and consequent reduction of salt flat inundation and ephemeral habitat for foraging threadfin. Also, infrequent inundation would reduce nutrient inputs to estuaries, and thus affect habitat for populations threadfin prey that are subsequently available within the estuarine habitat. In addition, the loss of instream connectivity among deep-water pools due to reduced low-level flows would be potential barriers to downstream movement of threadfin's prey to coastal waters. Flow dependencies analysis Threadfin were modelled in the marine region with contributing flows from the end-of-system node. Hypothetical water resource development in the Victoria catchment resulted in varying levels of change in important flow dependencies for threadfin. When considering change in important flows, the hypothetical dam scenarios ranged from negligible (0.7) to moderate (5.5) for scenarios BDLCT and BD2 respectively. For water harvesting it ranged from negligible (0.7) to moderate (5.1) for B-Wv80t200r30f500 and B-Wv800t200r30f0, respectively. Scenario Cdry resulted in moderate change in important flows (11.2) for threadfin (Figure 4-12). Under scenario B-D2, the largest contributing change in important flow dependencies was for the metric annual maxima of 90-day means of daily discharge at node 81100000. For scenario B-Wv800t600r30f500, the largest contribution of change was also for the metric annual maxima of 90-day means of daily discharge at node 81100000. See Appendix B for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Stratford et al. (2024a) for descriptions of flow- ecology relationships for threadfin. Figure 4-12 Change in threadfin flow dependencies by scenario across the model nodes Colour intensity represents the level of change occurring in important flow metrics for threadfin, expressed as percentile change from the historical conditions. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change shown on the right axis. Horizontal grey bars and numbers correspond to the change. Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. For more information on this figure please contact CSIRO on enquiries@csiro.au Dams and changes in important flows for threadfin For the dam scenarios, Scenario B-DLC (without transparent flows) resulted in a negligible change to important flow dependencies (0.7). When transparent flows (B-DLCT) were provided to support environmental functions, the change to important flows for threadfin remained negligible (0.7). Scenario B-DVR resulted in a larger change than Scenario B-DLC, with a moderate (5.1) change to important flows. This change was reduced to minor (4.2) with the provision of transparent flows for Scenario B-DVRT. Under Scenario B-D2, which includes both the B-DLC and B-DVR dams, a moderate (5.5) mean change in important flows occurred across the catchment without transparent flows. This was reduced to minor (4.1) with provision of transparent flows. Scenario B-D2 (with multiple dams) resulted in a larger mean change across the catchment than either of the single-dam scenarios. This was due to the combined effects on flows downstream of the confluence of the two dams and the impact to a larger portion of the catchment. Scenario B-D2T (with transparent flows) resulted in a smaller change than Scenario B-D2 (without transparent flows), indicating the importance of providing flows for ecosystem function that supports the life history of threadfin. Scenarios with transparent flows demonstrated the significance of flows in reducing changes for threadfin in downstream estuarine habitats, particularly enhancing brackish ecotones which are supported by seasonal freshwater flows. Water harvesting and changes in important flows for threadfin The hypothetical water harvesting scenarios resulted in change in important flow dependencies from negligible (0.7) to moderate (5.1) for B-Wv80t200r30f500 and B-Wv800t200r30f0, respectively. The change in important flows for threadfin with water harvesting varies with the extraction targets, pump-start thresholds and pump rates (Figure 4-12). With a low extraction target of 80 GL under Scenario B-Wv80t200r30f0, the mean weighted change across the catchment was negligible (1.5). This change in important flows increased to moderate (5.1) with an extraction target of 800 GL under Scenario B-Wv800t200r30f0. Increasing the pump-start threshold from 200 to 600 ML per day (i.e. from Scenario B-Wv160t200r30f0 to Scenario B-Wv160t600r30f0) with a target extraction volume of 160 GL reduced the change across the assessment nodes from 1.8 to 1.6, remaining negligible (Figure 4-12). Measures to protect important parts of the catchment flow regime can support catchment- wide ecology where reducing the extraction target puts limits on the volume of water extracted in any water year maintaining annual freshwater inputs to the estuary, while increasing the pump- start threshold protects the early-season low flows that are important to invigorate preferred brackish threadfin estuarine habitats at the end of the dry season. Climate change and water resource development for important flows for threadfin Scenario Cdry resulted in moderate change to important flow dependencies (11.2) for threadfin. This indicates that the dry climate scenario had, on average across all catchment nodes, larger changes than scenarios B-D2T (minor; 4.1) and B-Wv160t200r30f0 (negligible; 1.8). Scenarios Ddry-D2T and Ddry-Wv160t200r30 resulted in moderate change (14.2 and 10.8, respectively). King threadfin do not use freshwater reaches of rivers as habitat. However, both recruitment and survival of juvenile king threadfin have been found to be positively related to the annual levels of freshwater flow during spring and summer in a large Queensland subtropical estuary (Halliday et al., 2008). Carbon and nutrients are exported to the estuarine and near-shore habitats where they support the food chain and the prey of king threadfin. In Australian tropical rivers, both commercial catch (as a measure of abundance) and year-class strength were positively related to monsoon rainfall (often year-lagged) in some rivers, but not for all aspects of river flow (Halliday et al., 2012; Welch et al., 2014). Dam construction has a moderate flow changes to king threadfin under the Victoria River dam scenario, similar to water harvesting which has moderate changes to the species important flow dependencies. Under the two dams and 800 GL range water harvesting scenario, moderate changes occurred in association with reduced natural flow volumes, particularly interrupted late- dry-season flows, and any changes in seasonality of flows would reduce the growth and abundance of king threadfin, as has been found for other large predatory fish estuaries as prime habitat (Leahy and Robins, 2021). The brackish estuarine ecotone is prime habitat for threadfin prey (Cardona, 2000; Russell and Garrett, 1983a; Vance et al., 1998), and the decrease in wet- season flow volumes would reduce the extent and persistence of the brackish ecotone and hence prey abundance. In addition, low-level flows in the spring and late dry season are used by threadfin larvae in marine habitats as cues to access estuarine habitats. Under a future dry climate, and particularly in combination with water resource development, moderate impacts on threadfin via river flow reduction would exacerbate the suite of impacts on threadfin via flow reduction. 4.2 Waterbirds The waterbird groups comprise colonial and semi-colonial nesting waders, shorebirds, cryptic waders, swimmers, grazers and divers groups. These groups are based on waterbird foraging behaviour and habitat dependencies, together with nesting behaviour and habitat dependencies. Both foraging and nesting dependencies need to be taken into account, because while some species both forage and nest in northern Australia, others migrate annually to take advantage of foraging opportunities and avoid the northern hemisphere winter. 4.2.1 Colonial and semi-colonial wading waterbirds The colonial and semi-colonial wading waterbirds (‘colonial waders’) group comprises 21 species from five families, including ibis, spoonbills, herons, egrets, avocets, stilts, storks and cranes (Appendix C). Changes in the depth, extent and duration of inundation in shallow wetland habitats used by colonial and semi-colonial nesting waders for nesting and foraging can have significant impacts on nesting, nest success, juvenile recruitment and adult survival. Because of the specific needs of colonial waders in terms of water regimes in suitable nesting habitats, colony sites in areas subject to changes in flood regimes due to water resource developments (e.g. river regulation through dams and weirs, water extraction from rivers, floodplain water harvesting) or climate change are at high risk of damage or loss, which has implications for population maintenance. Flow dependencies analysis Colonial and semi colonial waders were modelled across a total of 1918 km of assessment reaches in the Victoria catchment with contributing flows from a total of 40 model nodes, using the royal spoonbill as representative species for understanding patterns of distribution. Some of the key river reaches for colonial and semi colonial waders within the catchment were modelled downstream of nodes 81101100, 81100003 and 81100002 based upon modelling of suitable potential habitat. The locations for modelling colonial and semi colonial waders in the Victoria catchment were based upon species distribution models of the royal spoonbill (Stratford et al., 2024a) with reach weightings shown in Appendix A. Hypothetical water resource development in the Victoria catchment resulted in varying levels of changes in the important flow dependencies for colonial and semi colonial waders. When considering mean change in important flows across all 40 colonial and semi colonial waders analysis reaches and nodes, the hypothetical dam scenarios ranged from negligible (1.2) to minor (4.0) for scenarios BDLCT and BD2, respectively. For water harvesting, change in important flows was negligible, ranging from 0.3 to 1.7 for B-Wv80t600t30f500 and B-Wv800t200r30f0 respectively. Scenario Cdry resulted in moderate change (7.9) for colonial and semi colonial waders. The resulting spatial impacts associated with dam, water harvesting and climate scenarios varied as a result of the different spatial patterns, including the extent and magnitude of flow change across different parts of the catchment (Figure 4-13). Under scenario B-D2, the largest contributing change in important flow dependencies was for the metric high flow pulse duration (25th percentile) single spell at node 81100063. For scenario B-Wv800t600r30f500, the largest contribution of change was also for the metric high flow pulse duration (25th percentile) single spell at node 81100001. See Appendix B for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Stratford et al. (2024a) for descriptions of flow-ecology relationships for colonial and semi-colonial wading waterbirds. Figure 4-13 Spatial heatmap of change for colonial and semi-colonial wading waterbirds, considering the weighted habitat across the catchment Scenarios are: (a) B-Wv80t200r30f0, (b) B-Wv160t200r30f0, (c) B-W160t60r20f0, (d) B-DLC, (e) Cdry and (f) D-dryw160t200r30. See Table 2-1 for interpretation of scenarios. River shading indicates the level of flow change of important metrics weighted by the habitat value of each reach for semi-colonial wading waterbirds. For more information on this figure please contact CSIRO on enquiries@csiro.au Dams and changes in important flows for colonial waders For the dam scenarios, Scenario B-DLC (without transparent flows) resulted in a negligible mean change in important flow dependencies (1.9) across the 40 colonial and semi-colonial waders assessment nodes. When transparent flows (B-DLCT) were provided to support environmental functions, the change to important flows for colonial and semi-colonial waders was reduced (1.2). Scenario B-DVR resulted in a larger change than Scenario B-DLC, with a minor (2.1) mean change across the assessment nodes. This was reduced slightly (2.0) with the provision of transparent flows for Scenario B-DVRT. Under Scenario B-D2, which includes both the B-DLC and B-DVR dams, a minor (4.0) change in flow dependencies occurred across the catchment without transparent flows. This was reduced slightly (3.1) with provision of transparent flows. Scenario B-D2 (with multiple dams) resulted in a larger mean change across the catchment than either of the single- dam scenarios. This was due to the combined effects on flows downstream of the confluence of the two dams and the impact to a larger portion of the catchment. Scenario B-D2T (with transparent flows) resulted in a smaller change than Scenario B-D2 (without transparent flows), indicating the importance of providing transparent flows for environmental outcomes – the scenarios with transparent flows demonstrated the importance of maintaining environmental flows for supporting colonial and semi-colonial waders habitats (Figure 4-14). While transparent flows are beneficial in mitigating impacts in single-dam scenarios, their effectiveness seems limited when multiple dams are involved. Dams play a significant role in altering flood regimes, reducing the extent, frequency, and depth of floods essential for colonial waders’ breeding environments. Such alterations can lead to long-term abandonment of breeding sites due to increased nest failure and predation risks, as well as prolonged intervals between necessary inundation events, threatening population maintenance (Bino et al., 2014; Brandis et al., 2018; Brandis et al., 2011; Kingsford et al., 2011). Figure 4-14 Change in colonial and semi-colonial wading waterbirds flow dependencies by scenario across the model nodes Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for semi-colonial wading waterbirds. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Only the 30 highest impact nodes are shown (x-axis). Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. For more information on this figure please contact CSIRO on enquiries@csiro.au Nodes directly downstream of the dams in scenarios B-DLC and B-DVR resulted in extreme (64.6) and major (22.8) change respectively. These changes were reduced with provision of transparent flows (41.3 and 22.4 respectively). This reflects a combination of the higher impacts of flow changes directly downstream of dams, the benefits associated with provision of flows for the environment and the habitat importance for colonial and semi colonial waders in these two locations. Water harvesting and changes in important flows for colonial waders The hypothetical water harvesting scenarios resulted in a mean change in important flow dependencies across colonial and semi colonial waders assessment nodes of 0.3 to 1.7 for B-Wv80t600t30f500 and B-Wv800t200r30f0 respectively. The change in important flows for colonial and semi colonial waders with water harvesting varies with the extraction targets, pump-start thresholds and pump rates (Figure 4-14). With a low extraction target of 80 GL under Scenario B-Wv80t200r30f0, the mean weighted change across the catchment was negligible (0.4), increasing to 1.7 with an extraction target of 800 GL under Scenario B-Wv800t200r30f0. Increasing the pump-start threshold from 200 to 600 ML per day (i.e. from Scenario B-Wv160t200r30f0 to Scenario B-Wv160t600r30f0) with a target extraction volume of 160 GL reduced the change in important flow dependencies across the assessment nodes slightly from 0.5 to 0.4 (Figure 4-14). Increasing the pump-start threshold protects the low flows that are important for colonial and semi-colonial waders’ ecology. Climate change and water resource development for colonial waders Scenario Cdry resulted in moderate mean change in important flow dependencies (7.9) for colonial and semi-colonial waders across the 40 assessment nodes (Figure 4-14). This indicates that the dry climate scenario had, on average across all catchment nodes, larger changes than scenarios B-D2T (minor; 3.1) and B-Wv160t200r30f0 (negligible; 0.5). However, it is important to note that local changes under some water resource development scenarios can be considerably higher. Scenarios Ddry-D2T and Ddry-Wv160t200r30 resulted in moderate change to important flows (10.7 and 8.4, respectively) when weighted across all colonial and semi-colonial waders assessment nodes. This shows that the combined impacts of scenarios Ddry-D2T or Ddry-Wv160t200r30 were higher than for Scenario Cdry or either of scenarios B-D2 and B-Wv160t200r30 alone. Considering downstream flow regime change, colonial and semi-colonial waders are sensitive to changes in the depth, extent and duration of shallow wetland environments, particularly during nesting events (also see Section 4.4.1 for wetland habitats. Completion of a full nesting cycle can take several months. During this time, changes in water depth, water extent, water duration or food availability can force adults to abandon their nests or expose nests to predation, resulting in nest failure. In the long term, this can result in abandonment of regular breeding sites (Brandis, 2010; Brandis et al., 2011). Breeding sites in areas subject to changes in flood regimes are at high risk of damage or loss, with implications for population maintenance (Bino et al., 2014; Brandis et al., 2018; Brandis et al., 2011; Kingsford et al., 2011). Changes can occur when flood peaks are reduced by water extraction or dams (e.g. by reducing flood extent, frequency, duration or depth), when floodwater is captured on floodplains (e.g. by dams, levees or roads) or when the time between the inundation events that create these habitats is extended (Kingsford and Thomas, 2004). 4.2.2 Cryptic wading waterbirds The cryptic waders group comprises wading waterbird species that are relatively difficult to detect and have a high level of dependence on shallow temporary and permanent wetland habitats with relatively dense emergent aquatic vegetation (Marchant and Higgins, 1990). Their habitats (e.g. reeds, rushes, sedges, wet grasses) require regular or ongoing inundation to survive. In northern Australia, this group comprises 13 species from four families, including bitterns, crakes, rails and snipe (Appendix C). Cryptic waders are found throughout the Victoria catchment. The cryptic waders’ need for appropriate vegetation and shallow-water environments makes them sensitive to changes in both water regimes and vegetation throughout their life cycles. Thus, the primary pathway of potential water resource development impact on cryptic waders is habitat loss, fragmentation and change caused by changes in the timing, extent, depth and duration of inundation, which in turn changes vegetation. Flow dependencies analysis Cryptic waders were modelled across a total of 488.5 km of assessment reaches in the Victoria catchment, with contributing flows from a total of 8 model nodes, using black bittern as a representative species for understanding patterns of distribution. Some of the key river reaches for cryptic waders within the catchment were modelled downstream of nodes 81100070, 81100180 and 81101660 based upon modelling of suitable potential habitat. The locations for modelling cryptic waders in the Victoria catchment were based upon species distribution models of the Australian painted snipe (Rostratula australis) (Stratford et al., 2024a) with reach weightings shown in Appendix A. Hypothetical water resource development in the Victoria catchment resulted in varying levels of change in the important flow dependencies for cryptic waders. When considering mean change in important flows across all eight cryptic waders’ analysis reaches and nodes, the hypothetical dam scenarios ranged from negligible (0.3) to moderate (5.9) for scenarios B-DLCT and B-D2, respectively. For water harvesting, change in flows ranged from negligible (0.7) to minor (4.0) for B-Wv80t600t30f500 and B-Wv800t200r30f0 respectively. Scenario Cdry resulted in moderate change (10.4) for cryptic waders. The resulting spatial change associated with dam, water harvesting and climate scenarios varied as a result of the different spatial patterns, including the extent and magnitude of flow change across different parts of the catchment (Figure 4-15). Under scenario B-D2, the largest contributing change in important flow dependencies was for the metric median flows divided by catchment area at node 81100180. For scenario B-Wv800t600r30f500, the largest contribution of change was for the metric high flood pulse count (25th percentile) at node 81100001. See Appendix B for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Stratford et al. (2024a) for descriptions of flow-ecology relationships for cryptic wading waterbirds. Figure 4-15 Spatial heatmap of change for cryptic wading waterbirds, considering the weighted habitat across the catchment Scenarios are: (a) B-Wv80t200r30f0, (b) B-Wv160t200r30f0, (c) B-W160t60r20f0, (d) B-DLC, (e) Cdry and (f) D-dryw160t200r30. See Table 2-1 for interpretation of scenarios. River shading indicates the level of flow change of important metrics weighted by the habitat value of each reach for cryptic wading waterbirds. For more information on this figure please contact CSIRO on enquiries@csiro.au Dams and changes in important flows for cryptic wading waterbirds For the dam scenarios, Scenario B-DLC (without transparent flows) resulted in a mean negligible change in important flow dependencies (0.3) across the eight cryptic waders assessment nodes. When transparent flows (B-DLCT) were provided to support environmental functions, the change in important flows for cryptic waders was lowered and remained negligible (0.3). Scenario B-DVR resulted in a larger change than Scenario B-DLC, with a moderate (5.5) mean change across the assessment nodes. This was reduced to minor (3.3) with the provision of transparent flows for Scenario B-DVRT. Under Scenario B-D2, which includes both the B-DLC and B-DVR dams, a moderate (5.9) change in flows occurred across the catchment without transparent flows. This was reduced to minor (3.1) with provision of transparent flows. Scenario B-D2 (with multiple dams) resulted in a larger mean change across the catchment than either of the single-dam scenarios. This was due to the combined effects on flows downstream of the confluence of the two dams and the impact to a larger portion of the catchment. Scenario B-D2T (with transparent flows) resulted in a smaller change than Scenario B-D2 (without transparent flows), indicating the importance of providing transparent flows for environmental outcomes – the scenarios with transparent flows demonstrated the significance of environmental flows in reducing impacts for cryptic waders (Figure 4-16). Cryptic wading waterbirds, a group sensitive to changes in shallow wetland environments and the fringes of deeper water habitats, are particularly vulnerable under these scenarios. These species typically nest on the ground or in low vegetation, making them susceptible to fluctuations in water levels caused by dam operations. Such changes can alter foraging, nesting, and refuge habitats, degrade water quality, reduce food availability, and increase competition, predation, and disease (Kingsford and Norman, 2002b; Marchant and Higgins, 1990). The extreme changes observed at certain nodes highlight the potential for significant ecological disruption in areas directly affected by dam operations, especially if transparent flows are not implemented. Figure 4-16 Change in cryptic wading waterbirds flow dependencies by scenario across the model nodes Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for cryptic wading waterbirds. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. For more information on this figure please contact CSIRO on enquiries@csiro.au Water harvesting and changes in important flows for cryptic wading waterbirds The hypothetical water harvesting scenarios resulted in a mean change to important flow dependencies across cryptic waders assessment nodes ranging from negligible (0.7) to minor (4.0) for B-Wv80t600t30f500 and B-Wv800t200r30f0, respectively. The change to important flows for cryptic waders with water harvesting varies with the extraction targets, pump-start thresholds and pump rates (Figure 4-16). With a low extraction target of 80 GL under Scenario B-Wv80t200r30f0, the mean weighted change across the catchment was negligible (1.1), increasing to minor (4.0) with an extraction target of 800 GL under Scenario B-Wv800t200r30f0. Increasing the pump-start threshold from 200 to 600 ML per day (i.e. from Scenario B-Wv160t200r30f0 to Scenario B-Wv160t600r30f0) with a target extraction volume of 160 GL reduced the level of change across the assessment nodes slightly from 1.7 to 1.4 (Figure 4-16). Increasing the pump-start threshold protects the low flows that are important for cryptic wader ecology. Climate change and water resource development for important flows for cryptic wading waterbirds Scenario Cdry resulted in mean moderate (10.4) change to important flow dependencies for cryptic waders across the 8 assessment nodes (Figure 4-16). This indicates that the dry climate scenario had on average across all catchment nodes larger changes than scenarios B-D2T (minor; 3.1) and B-Wv160t200r30f0 (negligible; 1.7). However, it is important to note that local changes under some water resource development scenarios can be considerably higher. The scenarios Ddry-D2T and Ddry-Wv160t200r30 resulted in major (17.8) and moderate (11.8) changes, respectively, when weighted across all cryptic waders assessment nodes. This shows that the combined changes resulting from scenarios Ddry-D2T or Ddry-Wv160t200r30 were higher than Scenario Cdry or either of scenarios B-D2 and B-Wv160t200r30 alone. Considering the sensitivity of cryptic waders to changes in their habitat, particularly during nesting events, the observed changes in flow metrics under these scenarios underscore the importance of implementing measures to protect and manage wetland environments. Changes in water depth, extent, and duration, or disruptions caused by climate change and water resource development, can force these birds to abandon nests or lead to habitat loss, with long-term implications for their populations (Brandis et al., 2009; Kingsford and Norman, 2002a). Climate change and climate- change-driven extremes are likely to interact with changes induced by water resource development, including inundation of freshwater habitats by seawater and inundation of nests by extreme flood events or seawater intrusion. 4.2.3 Shorebirds The shorebirds group consists of waterbirds with a high level of dependence on end-of-system flows and large inland flood events that provide broad areas of shallow-water and mudflat environments (see Appendix C for species list). Shorebirds are largely migratory and mostly breed in the northern hemisphere (Piersma and Baker, 2000). They are in significant decline and are of international concern (Clemens et al., 2010; Clemens et al., 2016; Nebel et al., 2008). Shorebirds depend on specific shallow-water habitats in distinct geographic areas, including northern hemisphere breeding grounds, southern hemisphere non-breeding grounds, and stopover sites along migration routes such as the East Asian-Australasian Flyway (Bamford, 1992; Hansen et al., 2016). In northern Australia, this group comprises approximately 55 species from four families, including sandpipers, godwits, curlew, stints, plovers, dotterel, lapwings and pratincoles. Approximately 35 species are common regular visitors or residents. Several species in this group are endangered globally and nationally, including the bar-tailed godwit (Limosa lapponica), curlew sandpiper (Calidris ferruginea), eastern curlew, great knot (Calidris tenuirostris), lesser sand plover (Charadrius mongolus) and red knot (Calidris canutus). An example species from this group is the eastern curlew, which is listed as Critically Endangered and recognised through multiple international agreements as requiring habitat protection in Australia. Eastern curlews rely on food sources along shorelines, mudflats and rocky inlets and also need roosting vegetation (Driscoll and Ueta, 2002; Finn et al., 2007; Finn and Catterall, 2022). Developments and disturbances such as recreational, residential and industrial use of these habitats have restricted habitat and food availability for the eastern curlew, contributing to population declines. The intertidal mudflats and coastal flats (see also Section 4.4.4) provide important habitat for shorebirds, as do the large open shallow wetlands (Chatto, 2006). Shorebirds rely on the inundation of shallow flat areas such as mudflats and sandflats during seasonal high-level flows to provide invertebrates and other food sources. Without inundation events, these habitats cannot support high densities of shorebird species, and lack of food can increase mortality rates both on- site and during and after migrations (Barbaree et al., 2020; Canham et al., 2021; Durrell, 2000; Kozik et al., 2022; van der Pol, et al., 2024; West et al., 2005). Flow dependencies analysis Shorebirds were modelled across a total of 1918 km of assessment reaches in the Victoria catchment and in the marine region, with contributing flows from a total of 41 model nodes, using eastern curlew as a representative species for understanding distribution patterns. Some of the key river reaches for shorebirds within the catchment were modelled downstream of nodes 81100180, 81100140 and at the end-of-system (81100000) based upon modelling of suitable potential habitat. The locations for modelling shorebirds in the Victoria catchment were based upon the species distribution models of the eastern curlew (Numenius madagascariensis) (Stratford et al., 2024a) with reach weighting shown in Appendix A. Hypothetical water resource development in the Victoria catchment resulted in varying levels of change associated with the important flow dependencies for shorebirds. When considering mean change in important flows across all 41 shorebird analysis reaches and nodes, the hypothetical dam scenarios ranged from negligible (0.5) to minor (2.9) for scenarios B-DLCT and BD2, respectively. For water harvesting, change in flows was negligible, ranging from 0.2 to 1.5 for B-Wv80t600t30f500 and B-Wv800t200r30f0 respectively. Scenario Cdry resulted in minor change (4.3) for shorebirds. The resulting spatial change associated with dam, water harvesting and climate scenarios varied as a result of the different spatial patterns, including the extent and magnitude of flow change across different parts of the catchment (Figure 4-17). Under scenario B-D2, the largest contributing change in important flow dependencies was for the metric fall rate (mean rate of negative changes in flow from one day to the next) at node 81100063. For scenario B-Wv800t600r30f500, the largest contribution of change was for the metric high flow pulse duration (25th percentile) single spell at node 81100001. See Appendix B for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Stratford et al. (2024a) for descriptions of flow-ecology relationships for shorebirds. Figure 4-17 Spatial heatmap of change for shorebirds, considering the weighted distribution across the catchment Scenarios are: (a) B-Wv80t200r30f0, (b) B-Wv160t200r30f0, (c) B-W160t60r20f0, (d) B-DLC, (e) Cdry and (f) D-dryw160t200r30. See Table 2-1 for interpretation of scenarios. River shading indicates the level of flow change of important metrics weighted by the habitat value of each reach for shorebirds. For more information on this figure please contact CSIRO on enquiries@csiro.au Dams and changes in important flows for shorebirds For the dam scenarios, Scenario B-DLC (without transparent flows) resulted in a negligible mean change in important flow dependencies (0.9) across the 41 shorebirds assessment nodes. When transparent flows (B-DLCT) were provided to support environmental functions, the change to important flows for shorebirds was reduced (0.5). Scenario B-DVR resulted in a larger change than Scenario B-DLC, with a minor (2.1) mean change in important flows dependencies across the assessment nodes. This was reduced to negligible (1.4) with the provision of transparent flows for Scenario B-DVRT. Under Scenario B-D2, which includes both the B-DLC and B-DVR dams, a minor (2.9) change in important flows occurred across the catchment without transparent flows. This was reduced to negligible (1.6) with provision of transparent flows. Scenario B-D2 (with multiple dams) resulted in a larger mean change across the catchment than compared with either of the single- dam scenarios. This was due to the combined effects on flows downstream of the confluence of the two dams and the impact to a larger portion of the catchment. Scenario B-D2T (with transparent flows) resulted in a smaller change than Scenario B-D2 (without transparent flows), indicating the importance of providing transparent flows for environmental outcomes – the scenarios with transparent flows demonstrated the significance of environmental flows in reducing changes for shorebirds (Figure 4-18). Figure 4-18 Change in shorebirds flow dependencies by scenario across the model nodes Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for shorebirds. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Only the 30 highest impact nodes are shown (x-axis). Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. Under Scenario B-D2T, habitat-weighted flow changes for shorebirds were greatest at node 81100063 (Figure 4-18), with where a major (15.5) change in important flows occurs at this single node. Nodes directly downstream of the dams in scenarios B-DLC and B-DVR resulted in extreme (30.3) and moderate (14.3) levels of change in important flows, respectively. These changes were reduced to moderate (14.5 and 6.5, respectively) with provision of transparent flows. This reflects a combination of the changes to flow changes directly downstream of dams, the benefits For more information on this figure please contact CSIRO on enquiries@csiro.au associated with provision of flows for the environment and the habitat importance for shorebirds in these two locations. Given the shorebirds' sensitivity to changes in their preferred habitats, the extreme changes observed at certain nodes underscore the potential for significant ecological disruption under dam scenarios, particularly without transparent flow measures. Water harvesting and changes in important flows for shorebirds The hypothetical water harvesting scenarios resulted in a negligible mean change to important flow dependencies for shorebirds across shorebirds assessment nodes, ranging from 0.2 to 1.5 for B-Wv80t600t30f500 and B-Wv800t200r30f0, respectively. For the highest change to important flows was for water harvest scenario (B-Wv800t200r30f0), the single node with the highest change in important flow dependencies was 81100001, which had major (16.2) change in important flows. The change for shorebirds with water harvesting varies with the extraction targets, pump-start thresholds and location (Figure 4-18 and Figure 4-19). With a low extraction target of 80 GL under Scenario B-Wv80t200r30f0, the mean weighted change to important flows across the catchment was negligible (0.4), and remained negligible but increasing to 1.5 with an extraction target of 800 GL under Scenario B-Wv800t200r30f0. Increasing the pump-start threshold from 200 to 600 ML per day (i.e. from Scenario B-Wv160t200r30f0 to Scenario B-Wv160t600r30f0) with a target extraction volume of 160 GL reduced the change in flow dependencies across the assessment nodes from 0.6 to 0.4 (Figure 4-18). Measures to protect important parts of the flow regime can support ecology where reducing the extraction target puts limits on the volume of water extracted in any water year, while increasing the pump-start threshold protects the low flows that are important for shorebird ecology. Climate change and water resource development for important flows for shorebirds Scenario Cdry resulted in mean minor change in important flow dependencies (4.3) for shorebirds across the 41 shorebirds assessment nodes (Figure 4-18). This indicates that the dry climate scenario had on average across all catchment nodes larger changes than scenarios B-D2T (negligible; 1.6) and B-Wv160t200r30f0 (negligible; 0.6). However, it is important to note that local changes under some water resource development scenarios can be higher. Scenarios Ddry-D2T and Ddry-Wv160t200r30 resulted in moderate (6.4) and minor (4.6) changes, respectively, when weighted across all shorebirds assessment nodes. This shows that the combined changes of scenarios Ddry-D2T or Ddry-Wv160t200r30 were higher than the change of Scenario Cdry or either of scenarios B-D2 and B-Wv160t200r30 alone. Waterbird species in the shorebirds group are sensitive to changes in the depth, extent and duration of inundation of open very-shallow-water environments, including the edges of inland floodplains and lakes, and estuarine and coastal mudflats and sandflats (Albanese and Davis, 2015; Donnelly et al.; Fernandez and Lank, 2008; Ge et al., 2009; Jackson et al., 2019; Schaffer-Smith et al., 2017). Their preference for open flat areas and good visibility when foraging means that encroachment of dense vegetation or human activity can prevent their use of a site (Baudains and Lloyd, 2007; Ge et al., 2009; Tarr et al., 2010). Shorebirds rely on the inundation of shallow flat areas such as mudflats and sandflats to provide invertebrates and other food sources (Aharon- Rotman et al., 2017; Galbraith et al., 2002). Without inundation events, these habitats cannot support high densities of shorebird species, and lack of food can increase mortality rates both on- site and during and after migrations (Aharon-Rotman et al., 2017; Goss-Custard, 1977; Rushing et al., 2016). Climate change is affecting habitat availability and quality among other factors for shorebirds, including changing freshwater inflows and the availability of mudflats and similar environments (Bellisario et al., 2014; Iwamura et al., 2013). Figure 4-19 Change in shorebird flow dependencies by water harvest scenarios at sample nodes across the catchment showing change in response to system targets and pump start thresholds Colour intensity represents the level of change occurring in the barramundi’s important flow metrics with the scenarios at the important nodes. Results incorporate the rank percentile change of each scenario relative to the distribution of Scenario A and the importance of the reach. For more information on this figure please contact CSIRO on enquiries@csiro.au 4.2.4 Swimming, diving and grazing waterbirds The swimming, diving and grazing waterbirds group comprises species with a relatively high level of dependence on semi-open, open and deeper water environments, who commonly swim when foraging (including diving, filtering, dabbling, grazing) or when taking refuge (see Appendix C for species list). In northern Australia, this group comprises 49 species from 11 families, including ducks, geese, swans, grebes, pelicans, darters, cormorants, shags, swamphens, gulls, terns, noddies and jacanas. Reduced extent, depth and duration of inundation of waterhole and other deep-water environments are likely to reduce habitat availability and food availability for swimming, diving and grazing waterbirds. Reduced high-level flows increases competition, and predation also increases the risk of disease and parasite spread. Conversely, species in this group that nest at water level or just above, such as magpie geese, are particularly at risk of nests drowning when water depths increase unexpectedly. Flow dependencies analysis Swimming, diving and grazing were modelled across a total of 1918 km of assessment reaches in the Victoria catchment, with contributing flows from a total of 40 model nodes, using the magpie goose as a representative species to understand distribution patterns. Some of the key river reaches for swimming, diving and grazing within the catchment were modelled downstream of nodes 81100001, 81100180 and 81100003 based upon modelling of suitable potential habitat with reach weighting shown in Appendix A. Hypothetical water resource development in the Victoria catchment resulted in varying levels of change in important flows for swimmers, divers and grazers. When considering mean change across all 40 analysis reaches and nodes, the hypothetical dam scenarios ranged from negligible (0.3) to minor (3.7) for scenarios B-DLCT and B-D2 respectively, while water harvesting was negligible, ranging from 0.1 to 1.0 for B-Wv80t600t30f500 and B-Wv800t200r30f0 respectively. Scenario Cdry resulted in minor change (4.6) in flow dependencies for swimmers, divers and grazers. The resulting spatial change in flows associated with dam, water harvesting and climate scenarios varied as a result of the different spatial patterns, including the extent and magnitude of flow change across different parts of the catchment (Figure 4-20). Under scenario B-D2, the largest contributing change in important flow dependencies was for the metric high flow pulse duration (25th percentile) single spell at node 81101135. For scenario B-Wv800t600r30f500, the largest contribution of change was also for the metric high flow pulse duration (25th percentile) single spell at node 81100001. See Appendix B for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Stratford et al. (2024a) for descriptions of flow-ecology relationships for swimming, diving and grazing waterbirds. Figure 4-20 Spatial heatmap of change for swimming, diving and grazing waterbirds, considering the weighted habitat across the catchment Scenarios are: (a) B-Wv80t200r30f0, (b) B-Wv160t200r30f0, (c) B-W160t60r20f0, (d) B-DLC, (e) Cdry and (f) D-dryw160t200r30. See Table 2-1 for interpretation of scenarios. River shading indicates the level of flow change of important metrics weighted by the habitat value of each reach for swimming, diving and grazing waterbirds. For more information on this figure please contact CSIRO on enquiries@csiro.au Dams and changes in important flows for swimming, diving and grazing waterbirds For the dam scenarios, Scenario B-DLC (without transparent flows) resulted in a mean negligible change in important flow dependencies (0.9) across the 40 swimming, diving and grazing waterbirds assessment nodes. When transparent flows (B-DLCT) were provided to support environmental functions, the change to important flows for swimming, diving and grazing waterbirds was reduced (0.3). Scenario B-DVR resulted in a larger change than Scenario B-DLC, with a minor (2.8) mean change across the assessment nodes. This was reduced to negligible (1.0) with the provision of transparent flows for Scenario B-DVRT. Under Scenario B-D2, which includes both the B-DLC and B-DVR dams, a minor (3.7) change occurred across the catchment without transparent flows. This was reduced to negligible (1.1) with provision of transparent flows. Scenario B-D2 (with multiple dams) resulted in a larger mean change in flows across the catchment than either of the single-dam scenarios. This was due to the combined effects on flows downstream of the confluence of the two dams and the impacts to a larger portion of the catchment. Scenario B-D2T (with transparent flows) resulted in a smaller change in flow dependencies than Scenario B-D2 (without transparent flows), indicating the importance of providing transparent flows for environmental outcomes – the scenarios with transparent flows demonstrated the significance of environmental flows in reducing impacts for swimming, diving and grazing waterbirds (Figure 4-21). Figure 4-21 Change in swimming, diving and grazing waterbirds flow dependencies by scenario across the model nodes Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for swimming, diving and grazing waterbirds. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Only the 30 highest impact nodes are shown (x-axis). Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. For more information on this figure please contact CSIRO on enquiries@csiro.au Nodes directly downstream of the dams in scenarios B-DLC and B-DVR both had extreme change in important flow dependencies (30.8 and 36.3, respectively). These changes were reduced to moderate (9.6 and 10.0, respectively) with provision of transparent flows. This reflects a combination of the higher flow changes directly downstream of dams, the benefits associated with providing flows for the environment, and the habitat importance for swimming, diving and grazing waterbirds in these locations. Water harvesting and changes in important flows for swimming, diving and grazing waterbirds The hypothetical water harvesting scenarios resulted in a mean negligible change in important flow dependencies across swimming, diving and grazing waterbirds assessment nodes ranging from 0.1 to 1.0 for B-Wv80t600t30f500 and B-Wv800t200r30f0, respectively. The change for swimming, diving and grazing waterbird flow dependencies with water harvesting varies with the extraction targets, pump-start thresholds and pump rates (Figure 4-21). With a low extraction target of 80 GL under Scenario B-Wv80t200r30f0, the mean weighted change in important flows across the catchment was negligible (0.3), increasing to 1.0 with an extraction target of 800 GL under Scenario B-Wv800t200r30f0. Increasing the pump-start threshold from 200 to 600 ML per day (i.e. from Scenario B-Wv160t200r30f0 to Scenario B-Wv160t600r30f0) with a target extraction volume of 160 GL reduced the level of change in flows across the assessment nodes slightly from 0.4 to 0.3 (Figure 4-21). Measures to protect important parts of the flow regime can support ecology where reducing the extraction target puts limits on the volume of water extracted in any water year, while increasing the pump-start threshold protects the low flows that are important for swimming, diving and grazing waterbird ecology. Climate change and water resource development for important flows for swimming, diving and grazing waterbirds Scenario Cdry resulted in mean minor change (4.6) for swimming, diving and grazing waterbird flow metrics across the 40 assessment nodes (Figure 4-21). This indicates that the dry climate scenario had on average, across all catchment nodes, a larger change than scenarios B-D2T (negligible; 1.1) and B-Wv160t200r30f0 (negligible; 0.4). However, it is important to note that local changes under some water resource development scenarios can be considerably higher. Scenarios Ddry-D2T and Ddry-Wv160t200r30 resulted in moderate (6.3) and minor (5.0) mean change to important flows, respectively, when weighted across all swimming, diving and grazing waterbird assessment nodes. This shows that the combined changes under scenarios Ddry-D2T or Ddry-Wv160t200r30 were higher than the change of Scenario Cdry or either of scenarios B-D2 and B-Wv160t200r30 alone. Waterbird species in the swimming, grazing and diving waterbirds group are sensitive to changes in the depth, extent and duration of perennial semi-open and open deeper water environments such as waterholes (Section 4.4.2) and wetlands (Section 4.4.1) (Marchant and Higgins, 1990; McGinness, 2016). They can also be sensitive to changes in the type, density or extent of the fringing aquatic or semi-aquatic vegetation (Section 4.4.5) in and around these habitats. Such changes can occur when water is extracted directly from these habitats or when the time between connecting flows or rainfall events that fill these habitats is extended (Kingsford and Norman, 2002a). Climate change and extremes are likely to interact with changes induced by water resource development, including inundation of freshwater habitats by seawater and inundation of nests by extreme flood events or seawater intrusion (Nye et al., 2007; Poiani, 2006; Traill et al., 2009a; Traill et al., 2009b). Reduced extent, depth and duration of inundation of waterhole and other deep-water environments is likely to reduce habitat availability and food availability for this group, increasing competition and predation and also increasing risk of disease and parasite spread. Conversely, species in this group that nest at water level or just above, such as magpie geese, are particularly at risk of nests drowning when water depths increase unexpectedly (Douglas et al., 2005; Poiani, 2006; Traill et al., 2010; Traill et al., 2009a; Traill et al., 2009b). 4.3 Turtles, prawns and other species The members of this group and broad and distinct and include banana prawns, freshwater turtles and mud crabs. The members of this group include obligatory aquatic species as well as others that forage within the intertidal zone or can frequent the terrestrial habitats of riparian and floodplain habitats. The prawns and mud crabs inhibit marine and estuarine habitats, while the freshwater turtles occupy rivers, lakes and wetlands within the freshwater portions of the catchment. Members of this group can have flow associations to support function and important life-history phases and connectivity between habitats and supply of nutrients. 4.3.1 Banana prawns Banana prawns are large decapods that are a prized fishery target species throughout their geographic distribution. Within the Northern Prawn Fishery, banana prawn catch supports a ‘sub- fishery’ harvesting approximately 4942 t (recent 10-year mean) mostly caught in the Gulf of Carpentaria and valued at about $70 to $80 million annually (Laird, 2021b). In the Joseph Bonaparte Gulf, into which the Victoria River flows, redleg banana prawns (Penaeus indicus) are the dominant species. Their annual catch is highly variable but can reach greater than 600 t (Laird, 2021 a). Adult redleg banana prawns live and spawn offshore from the Victoria River in waters 60 to 80 m deep; the larvae and postlarvae drift inshore to settle in the mangrove forest and mudbanks of estuarine mangrove habitats (Crocos and Kerr, 1983; Kenyon et al., 2004; Staples, 1980; Vance et al., 1998). In the Victoria catchment, juvenile redleg banana prawns inhabit the full extent of the estuary including saline tributaries. Adult banana prawn populations depend on emigration cues from freshwater river flows that reduce salinity and, at high-level freshwater flows, also reduce juvenile prawn food resources within estuarine habitats. These cues initiate banana prawn emigration to offshore habitats where a large population survives (Plagányi et al., 2021; Vance and Rothlisberg, 2020). Once offshore, their growth and survival are enhanced (Gwyther, 1982), possibly due in part to nutrient deposition in the flood plume (Burford et al., 2016). The key threats to banana prawns are associated with the loss of high-level flood flows that cue emigration from estuarine juvenile habitats to coastal near-shore adult habitats. Threats also arise with any reduction or temporal shift in low-level late-dry-season flows that that support facultative, brackish estuaries for juvenile banana prawn populations during October (approximately) to December, prior to wet-season floods. Flow dependencies analysis Banana prawns were modelled in the marine region from the end-of-system node. Hypothetical water resource development in the Victoria catchment resulted in varying levels of change in important flow dependencies for banana prawns. When considering change in important flows, the hypothetical dam scenarios ranged from negligible (0.7) to moderate (6.2) for scenarios B-DLCT and BD2, respectively. For water harvesting it ranged from negligible (0.7) to moderate (5.9) for B-Wv80t200r30f500 and B-Wv800t200r30f0 respectively. Scenario Cdry resulted in moderate change (10.8) for important flows for banana prawns. Under scenario B-D2, the largest contributing change in important flow dependencies was for the metric median daily flow. For scenario B-Wv800t600r30f500, the largest contribution of change was for the metric high flood pulse count (10th percentile). See Appendix B for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Stratford et al. (2024a) for descriptions of flow-ecology relationships for banana prawns. Dams and changes in important flows for banana prawns For the dam scenarios, Scenario B-DLC without transparent flows resulted in a negligible (0.8) change to important flow dependencies at the one banana prawn assessment node. When transparent flows (B-DLCT) were provided to support environmental functions, the change to important flows for banana prawns remained at negligible (0.7). Scenario B-DVR resulted in a larger change than Scenario B-DLC, with a moderate (5.7) mean change across the assessment nodes. A dam on the Victoria River (B-DVR) impounds flows from a much larger catchment area than does a dam on the Leichhardt Creek (B-DLC), which is a tributary of the river. The change in important metrics associated with a dam on the Victoria River was reduced to minor (4.7) with the provision of transparent flows for Scenario B-DVRT. Under Scenario B-D2, which includes both the B-DLC and B-DVR dams, a moderate (6.2) change occurred across the catchment without transparent flows. This was reduced to minor (4.2) with provision of transparent flows. Scenario B-D2 (with multiple dams) resulted in a larger change to important flows than either of the single-dam scenarios. This was due to the combined effects on flows downstream of the confluence of the two dams and the changes to a larger portion of the catchment. Scenario B-D2T (with transparent flows) resulted in a smaller change than Scenario B-D2 (without transparent flows), indicating the importance of providing environmental flows to support estuarine habitats for banana prawns, particularly at the dry-season/wet-season interface when early-season flows create a brackish estuary more suitable as prawn habitat than hypersaline dry-season estuaries (Figure 4-22). Figure 4-22 Change in banana prawns flow dependencies by scenario across the model nodes Colour intensity represents the level of change occurring in important flow metrics for banana prawns, expressed as percentile change from the historical conditions. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change shown on the right axis. Horizontal grey bars and numbers correspond to the change. Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. Water harvesting and changes in important flows for banana prawns The hypothetical water harvesting scenarios resulted in a mean change in important flow dependencies across banana prawns’ assessment nodes ranging from negligible (0.7) to moderate (5.9) for B-Wv80t600t30f500 and B-Wv800t200r30f0, respectively. The change in important flows for banana prawns with water harvesting varies with the extraction targets, pump-start thresholds and pump rates (Figure 4-22). With a low extraction target of 80 GL under Scenario B-Wv80t200r30f0, the mean weighted change to flows across the catchment was negligible (1.7), increasing to moderate (5.9) with an extraction target of 800 GL under Scenario B-Wv800t200r30f0. Increasing the pump-start threshold from 200 to 600 ML per day (i.e. from Scenario B-Wv160t200r30f0 to Scenario B-Wv160t600r30f0) with a target extraction volume of 160 GL reduced the change across the assessment nodes from minor (2.1) to negligible (1.7) (Figure 4-22). Increasing the pump-start threshold protects the low flows that are important for banana prawn ecology, particularly low- For more information on this figure please contact CSIRO on enquiries@csiro.au level flows during September to December, outside the high-precipitation monsoon period from January to March. Implementing measures to protect important parts of the flow regime can support estuarine ecology. Reducing the extraction target limits the volume of water extracted annually, which has shown benefits for the banana prawn population as modelled in other northern Australia catchments (Plagányi et al., 2024). Climate change and water resource development for important flows for banana prawns Scenario Cdry resulted in moderate change to important flow dependencies (10.8) for banana prawns (Figure 4-22). This indicates that the dry climate scenario had larger changes than scenarios B-D2T (minor; 4.2) and B-Wv160t200r30f0 (minor; 2.1). Scenarios Ddry-D2T and Ddry-Wv160t200r30 resulted in moderate (14) and moderate (11.0) change, respectively. This shows that the combined changes of scenarios Ddry-D2T or Ddry-Wv160t200r30 were higher than that of Scenario Cdry or either of scenarios B-D2 and B-W160t200r30 alone. Banana prawns’ life-history strategy renders them critically dependent on the natural flow regime in the Australian wet-dry tropics. Adult prawns spawn offshore, and postlarvae use currents to move shoreward to settle within estuarine benthic habitats before the annual wet season (Vance and Rothlisberg, 2020). In estuarine habitats, a brackish ecotone within the estuary supports lower mortality and faster growth (Staples and Heales, 1991; Vance et al., 1998; Wang and Haywood, 1999) before freshwater-cued emigration causes them to move to offshore marine habitats (Plagányi et al., 2021; Vance et al., 1998). Hence, both high-level pulsed flood flows and low-level early-season wet flows are positive for the estuarine population of banana prawns. Water harvest resulted in a moderate change to flow dependencies important to banana prawns via flow modification, reducing wet-season flood flows and low flows. Estuaries can often be hypersaline during the annual recruitment window for juvenile prawns in the late dry season (Kenyon et al., 2004; Vance et al., 1990), and flows occurring during October to December can reduce environmental stress before the onset of the wet season. Scenario B-Wv800t200r30f0 reduces both late-dry-season low flows and wet-season flood flows to the detriment of estuarine banana prawn growth and emigration. Dam construction and water harvest resulted in negligible to moderate changes to important flows for banana prawns. Both dam construction and water harvest potentially change flows by reducing low flows, and particularly so during from August to November which is the recruitment window of juvenile banana prawns to estuaries before the wet season. During this period, juvenile banana prawns recruiting to a hypersaline estuary with no catchment inputs to ameliorate dry-season stressors would have potential strong detrimental effects on recruitment success. Supporting seasonal critical flows via providing transparent flows past dams, or via higher pump initiations thresholds before water extraction can occur, reduces the changes in important flows for redleg banana prawns from moderate to minor, or minor to negligible, as has been demonstrated via modelling of banana prawns elsewhere across tropical Australia (Plagányi et al., 2024). Predation on juvenile prawns by fish within the estuary can be high, and a significant proportion of the resident estuarine population is lost (Wang and Haywood, 1999). Wet-season flood flows cue juvenile banana prawns to emigrate to offshore habitats. The larger the flood, the greater the emigration event (Staples and Vance, 1986), and emigrants probably benefit from nutrient deposition within the flood plume (Burford et al., 2012; Burford and Faggotter, 2021). Abundant adult populations of banana prawns, as measured by commercial catch in coastal marine habitats, are associated with higher flood flows from adjacent estuaries (Broadley et al., 2020; Duggan et al., 2019; Plagányi et al., 2023). Water harvesting reduces high flows in January, February and March during the wet season. Reduced high flows lowers the emigration cues within the estuary (Plagányi et al., 2024), so fewer prawns move to offshore waters where mortality in productive marine habitats is lower (Gwyther, 1982). Supporting seasonal critical flows via the provision of transparent flows past dams, or via higher pump initiations thresholds before water extraction can occur, reduces the impacts on banana prawns, as has been modelled via mitigation measures for banana prawns within nearby tropical Australian estuaries (Plagányi et al., 2022; Plagányi et al., 2024). 4.3.2 Freshwater turtles In northern Australia, freshwater turtles occupy a range of aquatic habitats, including both river and floodplain wetland habitats such as main channels, waterholes, and oxbow lakes (Cann and Sadlier, 2017; Thomson, 2000). Turtles inhabit the freshwater reaches of the Victoria River and depend upon the seasonal wet-season flows to support habitat and movement needs. Many of the freshwater turtle species in northern Australia have developed adaptive traits to survive conditions in both the wet and dry seasons, such as timing the emergence of hatchlings with the wet-season onset (Cann and Sadlier, 2017). During the dry season, the movements of the freshwater turtles on and off the floodplain are limited, making them more vulnerable to changes in water quality, invasive species and habitat degradation (Cann and Sadlier, 2017; Doupe et al., 2009). Therefore, changes to hydrology (particularly riverine–wetland connectivity), habitat loss and climate change are some of the key threatening processes for freshwater turtles (Stanford et al., 2020). Three of the ten freshwater turtle species found in the NT have been collected in the Victoria catchment: the sandstone snake-necked turtle (Chelodina burrungandjii), the northern snake-neck turtle (Chelodina oblonga; formerly C. rugosa) and the northern snapping turtle (Elseya dentata). The remoteness of this region means that records are sparse than many other regions of Australia. The analysis considers change in flow regime and related habitat changes but does not consider the addition or loss of potential habitat associated with the creation of a dam impoundment or instream structures (see also Yang et al. (2024) for dam impoundments). Flow dependencies analysis Freshwater turtles were modelled across a total of 1918 km of assessment reaches in the Victoria catchment with contributing flows from a total of 40 model nodes. Some of the key river reaches for freshwater turtles within the catchment were modelled downstream of nodes 81101100, 81100040 and 81101010 based upon modelling of suitable potential habitat. The locations for modelling freshwater turtles in the Victoria catchment were based upon species distribution models of Chelodina oblonga (Stratford et al., 2024a) with reach weighting shown in Appendix A. Hypothetical water resource development in the Victoria catchment resulted in varying levels of change to important flow dependenceies for freshwater turtles. When considering mean change across all 40 freshwater turtles analysis reaches and nodes, the hypothetical dam scenarios ranged from negligible (0.5) in scenario BDLCT to minor (3.1) in scenario BD2. For water harvesting senarios, the change in important flows was also negligible, ranging from 0.1 in B-Wv80t600t30f500 to 0.9 in B-Wv800t200r30f0. Scenario Cdry resulted in minor (4.8) change for freshwater turtle flow dependencies. The resulting spatial change associated with dam, water harvesting and climate scenarios varied as a result of the different spatial patterns, including the extent and magnitude of flow change across different parts of the catchment (Figure 4-23). Under scenario B-D2, the largest contributing change in important flow dependencies was for the metric mean annual number of days having zero daily flow at node 81100063. For scenario B-Wv800t600r30f500, the largest contribution of change was for the metric mean January discharge at node 81100001. See Appendix B for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Stratford et al. (2024a) for descriptions of flow-ecology relationships for freshwater turtles. Figure 4-23 Spatial heatmap of change for freshwater turtles, considering the weighted habitat across the catchment Scenarios are: (a) B-Wv80t200r30f0, (b) B-Wv160t200r30f0, (c) B-W160t60r20f0, (d) B-DLC, (e) Cdry and (f) D-dryw160t200r30. See Table 2-1 for interpretation of scenarios. River shading indicates the level of flow change of important metrics weighted by the habitat value of each reach for freshwater turtles. For more information on this figure please contact CSIRO on enquiries@csiro.au Dams and changes in important flows for freshwater turtles For the dam scenarios, Scenario B-DLC (without transparent flows) resulted in a mean negligible (1.2) change to important flow dependencies across the 40 freshwater turtle assessment nodes. When transparent flows (B-DLCT) were provided to support environmental functions, the change to important flows for freshwater turtles was reduced (0.5). Scenario B-DVR resulted in a larger change than Scenario B-DLC, with a mean negligible (1.9) change across the assessment nodes. This was reduced (1.0) with the provision of transparent flows for Scenario B-DVRT. Under Scenario B-D2, which includes both the B-DLC and B-DVR dams, a minor (3.1) change in flows occurred across the catchment without transparent flows but was reduced to negligible (1.4) when transparent flows were provided. Scenario B-D2 (with multiple dams) resulted in a larger mean change across the catchment than either of the single-dam scenarios. This was due to the combined effects on downstream flows at the confluence of the two dams and the change to a larger portion of the catchment. Scenario B-D2T (with transparent flows) resulted in a smaller change than Scenario B-D2 (without transparent flows), indicating the importance of providing transparent flows for environmental outcomes – the scenarios with transparent flows demonstrated the significance of environmental flows in reducing changes to important flows for freshwater turtles (Figure 4-24). Dams in the Victoria catchment can significantly alter hydrological patterns through water extraction and flow barriers. These changes may impact the distribution, growth, and reproduction of freshwater species, including turtles, making them more vulnerable (Hunt et al., 2013). The resulting loss of connectivity, see Yang et al. (2024)—through fragmentation and habitat loss—can disrupt turtle nesting sites, refugia, and limit their movement among wetlands (Bodie and Semlitsch, 2000; Bowne et al., 2006). Figure 4-24 Change in freshwater turtles flow dependencies by scenario across the model nodes Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for freshwater turtles. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Only the 30 highest impact nodes are shown (x-axis). Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. For more information on this figure please contact CSIRO on enquiries@csiro.au Nodes directly downstream of the dams in scenarios B-DLC and B-DVR resulted in extreme (43.0) and major (22.0) change to important flows, respectively. With the provision of transparent flows, these changes were reduced to moderate levels (14.4 and 6.8, respectively). This reflects the higher change associated with flow changes immediately downstream of dams, the environmental benefits of providing flows, and the habitat importance for freshwater turtles in these two locations. Water harvesting and changes in important flows for freshwater turtles The hypothetical water harvesting scenarios resulted in a mean change in important flow dependencies across freshwater turtles assessment nodes, ranging from negligible (0.1; Scenario B-Wv80t600t30f500) to negligible (0.9; B-Wv800t200r30f0). The change in important flows for freshwater turtles associated with water harvesting varies depending on extraction targets, pump-start thresholds, pump rates and locations (Figure 4-24 and Figure 4-25). In scenario B-Wv80t200r30f0, with a low extraction target of 80 GL, the mean change across the catchment was negligible (0.3), increasing to negligible (0.9) with an extraction target of 800 GL under Scenario B-Wv800t200r30f0. Increasing the pump-start threshold from 200 to 600 ML per day (i.e. from Scenarios B-Wv160t200r30f0 to Scenario B-Wv160t600r30f0) with a target extraction volume of 160 GL reduced the change across the assessment nodes was negligible (from 0.5 to 0.3, respectively) (Figure 4-24). Measures to protect important parts of the flow regime can support ecology where reducing the extraction target puts limits on the volume of water extracted in any water year, while increasing the pump-start threshold protects the low flows that are important for freshwater turtle ecology. Figure 4-25 Change in freshwater turtles’ flow dependencies by water harvest scenarios at sample nodes across the catchment showing change in response to system targets and pump start thresholds Colour intensity represents the level of change occurring in the barramundi’s important flow metrics with the scenarios at the important nodes. Results incorporate the rank percentile change of each scenario relative to the distribution of Scenario A and the importance of the reach. Freshwater turtles are not assessed at node 81100000. For more information on this figure please contact CSIRO on enquiries@csiro.au Climate change and water resource development for important flows for freshwater turtles Scenario Cdry resulted in minor (4.8) mean change to important flow dependencies for freshwater turtles across the 40 assessment nodes (Figure 4-24). This indicates that the dry climate scenario had on average across all catchment nodes, larger changes than scenarios B-D2T (negligible; 1.4) and B-Wv160t200r30f0 (negligible; 0.5). However, it is important to note that local changes under some water resource development scenarios can be considerably higher. The scenarios Ddry-D2T and Ddry-Wv160t200r30 resulted in moderate (6.3) and minor (4.9) changes, respectively, when weighted across all freshwater turtles’ assessment nodes. This shows that the combined changes of scenarios Ddry-D2T or Ddry-Wv160t200r30 were higher than the Scenario Cdry or either of scenarios B-D2 and B-Wv160t200r30 alone. The development of dams in the Victoria catchment as part of the water resource development in northern Australia has the potential to change the catchment’s hydrological pattern through water extraction and the creation of barriers to flow. These changes can affect the distribution, population growth and reproduction of freshwater species (Hunt et al., 2013) and make freshwater turtles more vulnerable. The loss of connectivity (fragmentation and habitat loss – see Yang et al. (2024)) resulting from new infrastructure, can disrupt turtle nesting sites and refugia, and also restrict their emigration and dispersal among wetlands (Bodie and Semlitsch, 2000; Bowne et al., 2006). Scenario Ddry-Wv160t200r30, which includes both water harvesting and dry climate change, poses a further risk of reducing dry-season baseflows across a larger area of the catchment. This scenario leads to a decrease in available suitable habitats supported by flows, and there is potential for longer or more severe dry periods. Such reductions in baseflow could shift rivers from perennial to intermittent, diminishing the likelihood of turtles reaching freshwater refuges during the dry season (Hunt et al., 2013). Changes to the inundation and flow regime reduce freshwater turtles’ feeding and the suitability of habitats such as waterholes (Warfe et al., 2011), which increases the competition for resources (Chessman BC, 1988). 4.3.3 Mud crabs In the Victoria catchment, mud crabs (Scylla serrata and small numbers of S. olivacea) occupy the estuary of the river and shallow coastal habitats north and south of the river mouth. Mud crabs are an ecologically important crustacean capable of modifying the estuarine habitats throughout Australia’s wet-dry tropics (Pati et al., 2023; Robins et al., 2020). Within mangrove forests, adult mud crabs re-work mud substrates and play a significant trophic role in mangrove ecosystems. Mud crabs consume 650 kg biomass per hectare per year in the mangrove forest and 2100 kg biomass per hectare per year in mangrove fringe habitat (Alberts-Hubatsch et al., 2016). Mud crabs are targeted by commercial, recreational and Indigenous fisheries. Mud crabs are important species for Indigenous Peoples in northern Australia, both culturally (Finn and Jackson, 2011) and as a historical and current food source (Naughton et al., 1986). Brackish estuaries provide the optimal conditions for the growth and survival of juvenile mud crabs. Hence, the loss of low-level flows and flood flows would affect the mud crab population in the Victoria River. Flow dependencies analysis Mud crabs were modelled in the marine region with the end-of-system node (see Appendix A). The locations for modelling mud crabs in the Victoria catchment were based upon habitat maps and the location of important habitat types (Stratford et al., 2024a). Hypothetical water resource development in the Victoria catchment resulted in varying levels of change in important flow dependencies for mud crabs. When considering change in flows, the hypothetical dam scenarios ranged from negligible (0.7) to moderate (5.4) for scenarios BDLCT and BD2 respectively. For water harvesting, change in important flows for mud crabs ranged from negligible (0.7) to moderate (5.2) for B-Wv80t200r30f500 and B-Wv800t200r30f0 respectively. Scenario Cdry resulted in moderate change (10.4) for mud crabs. Under scenario B-D2, the largest contributing change in important flow dependencies was for the metric annual maxima of 90-day means of daily discharge. For scenario B-Wv800t600r30f500, the largest contribution of change was also for the metric annual maxima of 90- day means of daily discharge. See Appendix B for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Stratford et al. (2024a) for descriptions of flow-ecology relationships for mud crabs. Dams and changes in important flows for mud crabs For the dam scenarios, Scenario B-DLC without transparent flows resulted in a negligible (0.8) change to important flow dependencies. When transparent flows (B-DLCT) were provided to support environmental functions, the change in important flows for mud crabs was remained at negligible (0.7). Scenario B-DVR resulted in larger change than Scenario B-DLC, with a moderate (5.2) change. This was reduced to minor (4.2) with the provision of transparent flows for Scenario B-DVRT. Under Scenario B-D2, which includes both the B-DLC and B-DVR dams, a moderate (5.4) change occurred across the catchment without transparent flows. This was reduced to minor (3.8) change with provision of transparent flows. Scenario B-D2 with multiple dams resulted in a larger changes to important flow dependencies than either of the single-dam. This was due to the combined effects on flows downstream of the confluence of the two dams and changes to a larger portion of the catchment. Scenario B-D2T (with transparent flows) resulted in a smaller change than Scenario B-D2 (without transparent flows), indicating the importance of providing environmental flows for ecosystem function – the scenarios with transparent flows demonstrated the significance of environmental flows in reducing change in importatn flows for mud crabs (Figure 4-26). Rainfall and high levels of river flow have been shown to be positively related to mud crab catch, and seasonal freshwater inflows to downstream estuarine habitats support brackish ecotones which enhance the habitat of juvenile crabs during annual recruitment following offshore spawning (Robins et al., 2020). For more information on this figure please contact CSIRO on enquiries@csiro.au Figure 4-26 Change in mud crabs flow dependencies by scenario across the model nodes Colour intensity represents the level of change occurring in important flow metrics for mud crabs, expressed as percentile change from the historical conditions. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change shown on the right axis. Horizontal grey bars and numbers correspond to the change. Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. Water harvesting and changes in important flows for mud crabs The hypothetical water harvesting scenarios resulted in negligible (0.7) to moderate (5.2) change to important flow dependencies for B-Wv80t200r30f500 and B-Wv800t200r30f0, respectively. The change in flows for mud crabs with water harvesting varies with the extraction targets, pump-start thresholds and pump rates (Figure 4-26). With a low extraction target of 80 GL under Scenario B-Wv80t200r30f0, the change in flows was negligible (1.6), increasing to moderate (5.2) with an extraction target of 800 GL under Scenario B-Wv800t200r30f0. Increasing the pump-start threshold from 200 to 600 ML per day (i.e. from Scenario B-Wv160t200r30f0 to Scenario B-Wv160t600r30f0) with a target extraction volume of 160 GL reduced the change from negligible (2.0) to 1.7 (Figure 4-26). Measures to protect important parts of the flow regime can support estuarine ecology where reducing the extraction target puts limits on the volume of water extracted in any water year maintaining historical annual flow patterns to the benefit of mud crabs (Blamey et al., 2023), while increasing the pump-start threshold protects the low flows and early-season flows that are important for mud crab populations (Blamey et al., 2023). Climate change and water resource development for important flows for mud crabs Scenario Cdry resulted in moderate (10.4) change to important flow dependencies for mud crabs (Figure 4-26). This indicates that the dry climate scenario had on average across all catchment nodes, larger changes than scenarios B-D2T (minor; 3.8) and B-Wv160t200r30f0 (negligible; 2.0). However, it is important to note that local changes under some water resource development scenarios can be considerably higher. The scenarios Ddry-D2T and Ddry-Wv160t200r30 resulted in moderate change (12.9 and 10.0, respectively), when weighted across all mud crab assessment nodes. The life history of mud crabs would be significantly affected by any major interruptions to the natural flows of northern Australian rivers (Blamey et al., 2023). Juvenile and adult mud crabs can tolerate a wide range of salinities. They live in estuarine and littoral-coast habitats which are both supported by freshwater inflows (Alberts-Hubatsch et al., 2016). Juvenile mud crabs benefit from perennial baseflows and low-to-medium flood flows that create brackish conditions in an estuary (Alberts-Hubatsch et al., 2016; Welch et al., 2014) (optimal conditions −25 to 30 °C, salinity 10 to 30 parts per thousand (ppt); (Ruscoe et al., 2004)). Estuaries in the Australian tropics often are hypersaline before the wet season; hence, growth and survival of the crabs would be inhibited if river regulation or extraction significantly reduced first-season low flows. High-level wet-season flows and October to December low-level flows that occur as a result of early-season precipitation reduce environmental stress that persists in estuarine habitats during the extended months with negligible rainfall (approximately April to December). The loss of either of these characteristic flows due to water resource development, especially water harvesting and two dams in the catchment, would reduce flows in the Victoria River and have downstream negative impacts on estuarine mud crabs as modelled in other studies (Blamey et al., 2023). A drier future climate contributing to lower river flow levels would result in risks to mud crab populations, and the combination scenario of dam construction under a dry climate (Ddry-D2) would provide greater changes to mud crab population via reduced flows as modelled in other work (Blamey et al., 2023). 4.4 Freshwater-dependent habitats The members of this group include floodplain wetlands, inchannel waterholes, mangroves, saltpans and salt flats, and surface-water-dependent vegetation communities. These habitat groups span freshwater, marine or a combination of both. Members of this group can have flow associations to support ecological function and support a diverse range of species during different flow conditions or times of the year. 4.4.1 Floodplain wetlands For the purpose of this analysis, floodplain wetlands are defined as freshwater lakes, ponds, swamps and floodplains with water that can be permanent, seasonal or intermittent. Floodplain wetlands provide permanent, temporary or refugia habitat for a range of species, are important for driving both primary and secondary productivity, and provide a range of additional ecosystem functions (Junk et al., 1989; Mitsch et al., 2015; Nielsen et al., 2015; van Dam et al., 2008; Ward and Stanford, 1995). Floodplain wetlands are highly influenced by the timing, duration, extent and magnitude of floodplain inundation, which can have significant impact on the ecological values, including species diversity, productivity and habitat structure (Close et al., 2015; Tockner et al., 2010). The key threats to floodplain wetlands are associated with changes in flood regimes, that is, the timing, duration, extent and magnitude of floodplain inundation that affect species diversity, productivity and habitat structure of floodplain wetlands. Flow dependencies analysis Floodplain wetlands were modelled across a total of 367.1 km of assessment reaches in the Victoria catchment with contributing flows from a total of five model nodes. Some of the key river reaches for floodplain wetlands within the catchment were modelled downstream of nodes 81101660, 81100001 and 81100003 (Figure 2-1Figure 2-1). The locations for modelling floodplain wetlands in the Victoria catchment were based upon wetland and floodplain mapping (see Stratford et al. (2024a)) with reach weighting shown in Appendix A. Hypothetical water resource development in the Victoria catchment resulted in varying levels of change to important flow dependencies for floodplain wetlands. When considering mean change in flows across all five floodplain wetlands analysis reaches and nodes, the hypothetical dam scenarios ranged from negligible (0.5) to minor (3.3) change for scenarios BDLCT and BD2 respectively. For water harvesting it ranged from negligible (0.5) to moderate (5.5) for B-Wv80t600t30f500 and B-Wv800t200r30f0 respectively. Scenario Cdry resulted in moderate change (12.8) for floodplain wetlands. The resulting spatial change associated with dam, water harvesting and climate scenarios varied as a result of the different spatial patterns, including the extent and magnitude of flow change across different parts of the catchment (Figure 4-27). Under scenario B-D2, the largest contributing change in important flow dependencies was for the metric high flood pulse count (10th percentile) at node 81100002. For scenario B-Wv800t600r30f500, the largest contribution of change was also for the metric high flood pulse count (10th percentile) at node 81100001. See Appendix B for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Stratford et al. (2024a) for descriptions of flow-ecology relationships for floodplain wetlands. Figure 4-27 Spatial heatmap of change for floodplain wetlands, considering the weighted distribution across the catchment Scenarios are: (a) B-Wv80t200r30f0, (b) B-Wv160t200r30f0, (c) B-W160t60r20f0, (d) B-DLC, (e) Cdry and (f) D-dryw160t200r30. See Table 2-1 for interpretation of scenarios. River shading indicates the level of flow change of important metrics by the location of floodplain wetlands across the catchment. For more information on this figure please contact CSIRO on enquiries@csiro.au Dams and changes in important flows for floodplain wetlands For the dam scenarios, Scenario B-DLC without transparent flows resulted in a mean negligible change to important flow dependencies (1.2) across the five floodplain wetlands assessment nodes. When transparent flows (B-DLCT) were provided to support environmental functions, the change to important flows for floodplain wetlands was reduced to negligible (0.5). Scenario B-DVR resulted in larger change than Scenario B-DLC, with a mean minor (2.1) change to important flows across the assessment nodes. This was reduced to minor (2.0) with the provision of transparent flows for Scenario B-DVRT. Under Scenario B-D2, which includes both the B-DLC and B-DVR dams, a minor (3.3) change occurred across the catchment without transparent flows. This was reduced to minor (3.1) with provision of transparent flows. Scenario B-D2 with multiple dams resulted in a larger mean change across the catchment, than either of the single-dam scenarios. This was due to the combined effects on flows downstream of the confluence of the two dams and changes to a larger portion of the catchment. Scenario B-D2T (with transparent flows) resulted in a smaller change than Scenario B-D2 (without transparent flows), indicating the importance of providing environmental flows for environmental outcomes, where the variants with transparent flows demonstrated the significance of environmental flows in reducing changes to important flows for floodplain wetlands (Figure 4-28). Dams can have a significant impact on floodplain wetlands, as they capture runoff from rainfall events that would otherwise spill onto floodplains during larger events, facilitating the connection of the wetlands to the main river channel. The reduction in flood magnitude due to dams can change the connectivity between the river channel and the floodplain wetlands, significantly affecting the size of the inundated area. A loss of connectivity between the river channel and the floodplain wetland may also occur. This disconnection can alter the frequency and duration of wetland inundation, potentially leading to changes in the structure, function and biodiversity of these wetland habitats (Poff and Zimmerman, 2010; Richter et al., 1996). Figure 4-28 Change in floodplain wetlands flow dependencies by scenario across the model nodes Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for floodplain wetlands. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. For more information on this figure please contact CSIRO on enquiries@csiro.au The hypothetical water harvesting scenarios resulted in a mean change across floodplain wetlands assessment nodes ranging from negligible (0.5) to moderate (5.5) for BWv80t600t30f500 and BWv800t200r30f0 respectively. The change in flows for floodplain wetlands with water harvesting varies with the extraction targets, pump-start thresholds and pump rates (Figure 4-28). With a low extraction target of 80 GL under Scenario B-Wv80t200r30f0, the mean weighted change across the catchment was negligible (1.2), increasing to moderate (5.5) with an extraction target of 800 GL under Scenario B-Wv800t200r30f0. Increasing the pump-start threshold from 200 to 600 ML per day (i.e. from Scenario BWv160t200r30f0 to Scenario BWv160t600r30f0) with a target extraction volume of 160 GL reduced the negligible change across the assessment nodes from (1.7 to 1.3) (Figure 4-28). Measures to protect important parts of the flow regime can support ecology where reducing the extraction target puts limits on the volume of water extracted in any water year, while increasing the pump-start threshold protects the low flows. Climate change and water resource development for important flows for floodplain wetlands Scenario Cdry resulted in moderate mean change (12.8) to important flows for floodplain wetlands across the five floodplain wetlands assessment nodes (Figure 4-28). This indicates that the dry climate scenario had on average across all catchment nodes larger changes than scenarios B-D2T (minor; 3.1) and BWv160t200r30f0 (negligible; 1.7). However, it is important to note that local changes under some water resource development scenarios can be considerably higher. Scenarios Ddry-D2T and Ddry-Wv160t200r30 resulted in major (15.0) and moderate (13.5) change, respectively, when weighted across all floodplain wetlands assessment nodes. This shows that the combined change of scenarios Ddry-D2T or Ddry-Wv160t200r30 were higher than Scenario Cdry or either of scenarios B-D2 and B-Wv160t200r30 alone. A drying climate will lead to lower rainfall and runoff and higher potential evapotranspiration patterns (Grieger et al., 2020; Salimi et al., 2021) and would result in the biggest change to floodplain wetlands in the Victoria catchment of the assessed scenarios. Lateral connectivity analysis The lateral connectivity within the Victoria catchment was modelled using floodplain hydraulics (e.g. depth, velocity) and inundation dynamics for a 2021 modelled flood event with a 33.3% AEP, and a 2023 modelled flood event with a 5.6% AEP (see Section 2.2.2, and Karim et al. (2024)). For the 2023 modelled flood event, Scenario B-W had little impact on floodplain inundation than Scenario A (2.8% reduction). Scenario B-D however, had a minor reduction in the area inundated than Scenario A (15.5% reduction; Table 4-1 and Figure 4-29). For the 2021 modelled flood event, there was a negligible difference between Scenario B-D and Scenario B-W than Scenario A (9.8% and 9.5% reduction respectively; Table 4-1 and Figure 4-30). Table 4-1 Maximum floodplain inundation (in km2) and percentage change from Scenario A as the maximum flood extent for each scenario for a 2021 modelled flood event and a 2023 modelled flood event SCENARIO 2021 MODELLED FLOOD EVENT 2023 MODELLED FLOOD EVENT KM2 PERCENT CHANGE KM2 PERCENT CHANGE A 229.8 1354.5 B-D 207.2 -9.8% 1145.1 -15.5% B-W 207.9 -9.5% 1316.9 -2.8% Cdry 186.6 -18.8% 1002.4 -26.0% Cwet 504.2 119.5% 1814.6 34.0% Ddry-D 167.6 -27.1% 770.2 -43.1% Ddry-W 169.2 -26.3% 935.7 -30.9% The reduction in area inundated under Scenario B-W was proportionally greater under the smaller 2021 modelled flood event than the 2023 modelled flood event (9.5% and 2.8% respectively) as the same amount of water was extracted under both events, due to extraction limits. Therefore, water harvesting will have a greater impact on smaller flood events, assuming pump thresholds are met. Scenario Cdry had a greater reduction of area inundated in the larger flood event, than the smaller flood event (26% and 18.8% reduction than Scenario A respectively; Table 4-1). Whereas Scenario Cwet had a greater increase in area inundated for the smaller flood event than Scenario A (119.5%% and 34.0% increase respectively; Table 4-1, Figure 4-29 and Figure 4-30). The greatest impact on the area of floodplain wetland inundation were the scenarios for future climate and future development (Ddry-D and Ddry-W). The impact on the area inundated was greater for the larger 2023 modelled flood event than the 2021 modelled flood event (43.1% and 27.1% for Ddry-D and 30.9% and 26.3% for Ddry-W; Table 4-1). The spatial distribution of floodplain inundation under the different scenarios showed that the smaller, more upstream wetlands were less likely to inundate under a future drying climate and future development scenarios, with flows more likely to be restricted to the channel (Figure 4-31 and Figure 4-32). The main floodplain area within the model domain is at the confluence of the Angalarri and Victoria rivers, and the West Baines and Victoria rivers, which combine to form a large floodplain area under high flows. Scenario Ddry-D showed the biggest reduction in area than Scenario A, resulting in less habitat for floodplain species (Figure 4-32). Water harvesting reduces the flow within a river channel, reducing inundation onto the floodplain. Dams capture moderate to large flows, preventing flood pulses and reducing inundation onto the floodplain (Kingsford, 2000). Loss of floodplain wetland connectivity to the river channel will reduce the available habitat for species such as fish and birds. Floodplain vegetation species that require inundation may also be affected. As a results, there is a risk of these areas transitioning into more terrestrial environments (Kingsford, 2000; Pettit et al., 2017). Reduced floodplain wetland connectivity will affect the overall productivity of the system by reducing the exchange of nutrients and carbon between the floodplain wetlands and the river channel (Brodie and Mitchell, 2005; Hamilton, 2010). Figure 4-29 Time series of the floodplain inundation for each scenario for the 2021 modelled flood event in the Victoria catchment Figure 4-30 Time series of the floodplain inundation for each scenario for the 2023 modelled flood event in the Victoria catchment For more information on this figure please contact CSIRO on enquiries@csiro.au For more information on this figure please contact CSIRO on enquiries@csiro.au Figure 4-31 Maximum floodplain inundation for each scenario for the 2021 modelled flood event in the Victoria catchment Scenarios are: (a) A, (b) B-D, (c) B-W, (d) Cdry, (e) Cwet, (f) Ddry-D and (g) Ddry-W. Note: The maximum extent may occur at a different timestep between scenarios. For more information on this figure please contact CSIRO on enquiries@csiro.au Figure 4-32 Maximum floodplain inundation for each scenario for the 2021 modelled flood event in the Victoria catchment Scenarios are: (a) A, (b) B-D, (c) B-W, (d) Cdry, (e) Cwet, (f) Ddry-D and (g) Ddry-W. Note: The maximum extent may occur at a different timestep between scenarios. For more information on this figure please contact CSIRO on enquiries@csiro.au 4.4.2 Inchannel waterholes For the purpose of this analysis, inchannel waterholes are defined as locations within the river channel in which water persists during periods of dry conditions (for comparison, see Section 4.4.1 for floodplain wetlands). Waterholes are found broadly across the Victoria catchment. Many tributaries demonstrate the ephemeral flows that are seasonally characteristic of northern Australian rivers more broadly (Petheram et al., 2008). In these ephemeral reaches, waterholes that persist provide important habitat values. In the Victoria catchment, important biodiversity values of waterholes are highlighted by their providing habitat for species listed under the EPBC Act, including the freshwater sawfish (Vulnerable; Section 4.1.5). Waterholes are sensitive to changes in low-flow magnitudes, low-flow duration, periods of cease-to-flow and timing of first- wet-season inflows. In ephemeral river systems, waterholes that retain water for periods sufficient to outlast dry spells provide vital refuge habitat and resources for both flora and fauna (Sheldon, 2017). Flow dependencies analysis Inchannel waterholes were modelled across a total of 1676.9 km of assessment reaches in the Victoria catchment with contributing flows from a total of 37 model nodes. Some of the key river reaches for inchannel waterholes within the catchment were modelled downstream of nodes 81101135, 81100171 and 81100040 (Figure 2-1). The locations for modelling inchannel waterholes in the Victoria catchment were based upon remote sensing of persistent waterbodies (Sims et al., 2016; Stratford et al., 2024a) with reach weighting shown in Appendix A. Hypothetical water resource development in the Victoria catchment resulted in varying levels of change in important flow dependencies for inchannel waterholes. When considering mean change across all 37 inchannel waterholes analysis reaches and nodes, the hypothetical dam scenarios ranged from negligible (0.2) to minor (2.6) change in important flows for scenarios BDVRT and BD2 respectively. For water harvesting, the change was negligible, ranging from 0.0 to 0.1 for B-Wv80t200r30f500 and B-Wv800t600r30f500 respectively. Scenario Cdry resulted in moderate change (5.3) for inchannel waterholes. The resulting spatial change to important flows associated with dam, water harvesting and climate scenarios varied as a result of the different spatial patterns, including the extent and magnitude of flow change across different parts of the catchment (Figure 4-33). Under scenario B-D2, the largest contributing change in important flow dependencies was for the metric annual minima of 90-day means of daily discharge at node 81101135. For scenario B-Wv800t600r30f500, the largest contribution of change was for the metric high flood pulse count (1th percentile) at node 81100001. See Appendix B for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Stratford et al. (2024a) for descriptions of flow-ecology relationships for inchannel waterholes. Figure 4-33 Spatial heatmap of change for inchannel waterholes, considering the weighted distribution across the catchment Scenarios are: (a) B-Wv80t200r30f0, (b) B-Wv160t200r30f0, (c) B-W160t60r20f0, (d) B-DLC, (e) Cdry and (f) D-dryw160t200r30. See Table 2-1 for interpretation of scenarios. River shading indicates the level of flow change of important metrics by the location of inchannel waterholes across the catchment. For more information on this figure please contact CSIRO on enquiries@csiro.au Dams and changes in important flows for inchannel waterholes For the dam scenarios, Scenario B-DLC without transparent flows resulted in a mean negligible change to important flow dependencies (0.7) across the 37 inchannel waterholes assessment nodes. When transparent flows (B-DLCT) were provided to support environmental functions, the change to important flows for inchannel waterholes was reduced to negligible (0.2). Scenario B-DVR resulted in larger change than Scenario B-DLC, with a negligible (2.0) mean change across the assessment nodes. This was reduced to negligible (0.2) with the provision of transparent flows for Scenario B-DVRT. Under Scenario B-D2, which includes both the B-DLC and B-DVR dams, a minor (2.6) change occurred across the catchment without transparent flows. This was reduced to negligible (0.4) with provision of transparent flows. Scenario B-D2 with multiple dams resulted in a larger mean change across the catchment, than either of the single-dam scenarios. This was due to the combined effects on flows downstream of the confluence of the two dams and the change to a larger portion of the catchment. Scenario B-D2T (with transparent flows) resulted in a smaller change than Scenario B-D2 (without transparent flows), indicating the importance of providing transparent flows for environmental outcomes – the scenarios with transparent flows demonstrated the significance of environmental flows in reducing change for inchannel waterholes (Figure 4-34). Nodes directly downstream of the dams in scenarios B-DLC and B-DVR resulted in major (21.7) and extreme (42.1) change, respectively. These changes were reduced to moderate (7.1) and minor (2.5) with provision of transparent flows. This reflects a combination of the higher change to flow changes directly downstream of dams, the benefits associated with provision of flows for the environment and the habitat importance for inchannel waterholes in these two locations. Water harvesting and changes in important flows for inchannel waterholes The hypothetical water harvesting scenarios resulted in a mean negligible change to important flow dependencies across inchannel waterholes assessment nodes from 0.0 to 0.1 for B-Wv80t200r30f500 and B-Wv800t600r30f500, respectively. The change for inchannel waterholes with water harvesting varies with the extraction targets, pump-start thresholds and pump rates (Figure 4-34). With a low extraction target of 80 GL under Scenario B-Wv80t200r30f0, the mean change across the catchment resulted in no detectable change (0.0), increasing to negligible (0.1) with an extraction target of 800 GL under Scenario B-Wv800t200r30f0. Increasing the pump-start threshold from 200 to 600 ML per day (i.e. from Scenario B-Wv160t200r30f0 to Scenario B-Wv160t600r30f0) with a target extraction volume of 160 GL maintained the change at undetectable level of change across the assessment nodes (Figure 4-34). Increasing the pump-start threshold protects the low flows that are important for inchannel waterholes ecology. Figure 4-34 Change in inchannel waterholes flow dependencies by scenario across the model nodes Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for inchannel waterholes. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Only the 30 highest impact nodes are shown (x-axis). Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. Climate change and water resource development for important flows for inchannel waterholes Scenario Cdry resulted in moderate mean change to important flow dependencies (5.3) for inchannel waterholes across the 37 inchannel waterholes assessment nodes (Figure 4-34). This indicates that the dry climate scenario had on average across all catchment nodes larger changes than scenarios B-D2T (negligible; 0.4) and B-Wv160t200r30f0 (no detectable change; 0.0). However, it is important to note that local changes under some water resource development scenarios can be For more information on this figure please contact CSIRO on enquiries@csiro.au considerably higher. Scenarios Ddry-D2T and Ddry-Wv160t200r30 resulted in moderate change (5.7 and 5.3, respectively) when weighted across all inchannel waterholes assessment nodes. This shows that the combined changes of scenarios Ddry-D2T or Ddry-Wv160t200r30 were higher than the change of Scenario Cdry or either of scenarios B-D2 and B-Wv160t200r30 alone. In the context of water resource development in the Victoria catchment, the development of water resources, including dam construction and water harvesting, has the potential to reduce flows and influence the natural filling and drying cycles of waterholes (Arthington et al., 2010; McJannet et al., 2014; Waltham et al., 2013b). Waterholes persist because of the hydrological balance within the system, affected by the timing and duration of both filling events and drawdown (Close et al., 2012). Waterholes are likely to be particularly sensitive to changes in the duration and severity of dry periods and changes in the timing of first flushes and inflows. Lower dry-season flows resulting in longer periods of low flows due to water resource development threaten to reduce the habitat value of waterholes. This can occur due to loss of waterholes within the landscape and decreases in the condition of the waterholes that remain. This may result in a localised loss or degradation of habitat and of dependent biota (both aquatic and terrestrial) (McJannet et al., 2014) and affect community structure and food webs (Arthington et al., 2005). Where loss of waterholes occurs more frequently within the landscape, it has the potential to affect biodiversity from local to more regional scales across the catchment (James et al., 2013). The number, size and heterogeneity of waterholes in a catchment are considered important for sustaining biodiversity at larger spatial scales. Development that affects the low flows by reducing water volumes or extending the duration of low-flow periods threatens to affect the quality and persistence of waterholes within the landscape. These hydrological changes occurred predominantly under the water harvest scenarios, which were found to reduce early wet-season flows. Protecting early wet-season flows by providing end-of-system requirements or by protecting low flows would help alleviate some of the risks to waterholes. The changes in important flows associated with dam development were greatest in subcatchments directly downstream. While these changes were significant, they may also be pessimistic due to the model set-up of removing water at the dam wall rather than routing to downstream uses. These risks may also be mitigated to some extent by providing end-of-system requirements or transparent flows. 4.4.3 Mangroves Mangroves forests include species of shrubs and trees that occupy a highly specialised niche within the intertidal and near-supra-littoral zones along tidal creeks, estuaries and coastlines (Duke et al., 2019; Friess et al., 2020; Layman, 2007). Mangroves are an important and prolific habitat-forming species group in the Victoria River estuary and coastal littoral habitats. Mangrove forests provide a complex habitat that offers a home to many marine species, including molluscs (McClenachan et al., 2021), crustaceans (Guest et al., 2006; Thimdee et al., 2001), reptiles (Fukuda and Cuff, 2013), birds (Mohd-Azlan et al., 2012) and numerous fish species, when connected to coastal waters. During periods of inundation at high tide, species including crustaceans access mangrove forests as settlement substrates and shelter against predation. The mangroves’ trunks and prop roots are used as refugia during postlarval and benthic juvenile phases (Meynecke et al., 2010). Fishes and crustaceans also access mangroves and their epiphytes for food (Layman, 2007; Skilleter et al., 2005). Mangrove forests also provide a diverse array of ecosystem services, including shoreline stabilisation (Zhang et al., 2012). Via the natural loss of leaves, branches and roots, mangrove forests contribute detrital carbon to the food chain: approximately 44 to 1022 grams of carbon per square metre per year from leaves and 912 to 6870 grams of carbon per square metre per year from roots (Robertson, 1986; Robertson and Alongi, 2016). Despite occupying saline habitats, mangroves require freshwater inputs from precipitation, groundwater or overbank inundation to thrive (Duke et al., 2017), so reduced flood flows and an increased frequency and duration of no-flow periods or other impacts on hydro-connectivity are key threats to mangroves. Flow dependencies analysis Mangroves were modelled in the marine region with the end-of-system node (see Appendix A). The locations for modelling mangroves in the Victoria catchment were based upon habitat maps (see Stratford et al. (2024a)). Hypothetical water resource development in the Victoria catchment resulted in varying levels of changes to important flow dependencies for mangroves. The hypothetical dam scenarios ranged from negligible (0.9) to moderate (7.4) for scenarios BDLCT and BD2 respectively. For water harvesting, the change in important flows ranged from negligible (0.7) to moderate (5.7) for B-Wv80t600t30f500 and B-Wv800t200r30f0 respectively. Scenario Cdry resulted in moderate change (11.3) for mangroves. Under scenario B-D2, the largest contributing change in important flow dependencies was for the metric median flows divided by catchment area. For scenario B-Wv800t600r30f500, the largest contribution of change was for the metric mean flows divided by catchment area. See Appendix B for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Stratford et al. (2024a) for descriptions of flow- ecology relationships for mangroves. Dams and changes in important flows for mangroves For the dam scenarios, Scenario B-DLC (without transparent flows) resulted in negligible change (0.9). When transparent flows (B-DLCT) were provided to support environmental functions, the change to important flows for mangroves remained negligible (0.9). Scenario B-DVR resulted in a larger change than Scenario B-DLC, with a moderate (6.7) mean change across the assessment nodes. This remained moderate (6.1) with the provision of transparent flows for Scenario B-DVRT. Under Scenario B-D2, which includes both the B-DLC and B-DVR dams, a moderate (7.4) change occurred across the catchment without transparent flows. This was reduced but remained moderate (5.7) with the provision of transparent flows. Scenario B-D2 (with multiple dams) resulted in a larger mean change across the catchment than either of the single-dam scenarios. This increased change in important flow dependencies was due to the combined effects on flows downstream of the confluence of the two dams, impacting a larger portion of the catchment. Scenario B-D2T (with transparent flows) resulted in a smaller change than Scenario B-D2 (without transparent flows), indicating the importance of providing transparent flows for environmental outcomes – the scenarios with transparent flows, demonstrating the importance of providing environmental flows to support ecosystem functions for mangroves despite their estuarine habitats being located far downstream from the dams (Figure 4-35). Figure 4-35 Change in mangroves flow dependencies by scenario across the model nodes Colour intensity represents the level of change occurring in important flow metrics for mangroves, expressed as percentile change from the historical conditions. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change shown on the right axis. Horizontal grey bars and numbers correspond to the change. Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. Water harvesting and changes in important flows for mangroves The hypothetical water harvesting scenarios resulted in changes for mangrove flow dependencies from negligible (0.7) to moderate (5.7) for B-Wv80t600t30f500 and B-Wv800t200r30f0, respectively. The change in important flows for mangroves with water harvesting varies with the extraction targets, pump-start thresholds and pump rates (Figure 4-35). With a low extraction target of 80 GL under Scenario B-Wv80t200r30f0, the change across the catchment was negligible (1.2), increasing to moderate (5.7) with an extraction target of 800 GL under Scenario B-Wv800t200r30f0. Increasing the pump-start threshold from 200 to 600 ML per day (i.e. from Scenario B-Wv160t200r30f0 to Scenario B-Wv160t600r30f0) with a target extraction volume of 160 GL reduced the change across the assessment nodes, though both remained negligible (1.6 and 1.4, respectively) (Figure 4-35). Measures to protect important parts of the flow regime can support estuarine ecology where reducing the extraction target puts limits on the volume of water extracted in any water year as For more information on this figure please contact CSIRO on enquiries@csiro.au has been modelled to be beneficial to mangroves in catchments in the Gulf of Carpentaria (Plagányi et al., 2024). Climate change and water resource development for important flows for mangrove Scenario Cdry resulted in moderate change (11.3) to important flow dependencies for mangroves (Figure 4-35). This indicates that the dry climate scenario had larger changes than scenarios B-D2T (moderate; 5.7) and B-Wv160t200r30f0 (negligible; 1.6). The scenarios Ddry-D2T and Ddry-Wv160t200r30 resulted in major (22.4) and moderate (12.6) changes, respectively. This shows that the combined changes of scenarios Ddry-D2T or Ddry-Wv160t200r30 were higher than Scenario Cdry or either of scenarios B-D2 and B-Wv160t200r30 alone. The hydrological requirement for mangroves is complex: they are influenced by tidal inundation, rainfall, soil water content, groundwater seepage and evaporation. All of these factors influence soil salinity, which can have profound effects on mangrove growth and survival. Mangroves require access to fresh water via their roots, though many species occur at their upper salinity threshold (Robertson and Duke, 1990). Sediment delivered to the coast during flood flows helps to sustain mangrove forests, supports their expansion (Asbridge et al., 2016) and increases the accumulation of carbon in sediments (Owers et al., 2022). Altered freshwater flow that reduced the likelihood of rivers spreading across coastal floodplains could contribute to mangrove stress and potentially dieback similar to the events which have been recorded in the Gulf of Carpentaria (Duke et al., 2019). Each scenario, from scenarios B-DLC and B-DVR to Scenario B-D2, has negligible to moderate flow- modification impacts on mangroves in the catchment (Figure 4-35). Water harvesting had moderate negative effects on freshwater service provision to mangroves via flow modification during the year. One dam on a single upstream tributary had little effect on overall catchment flows. In contrast, a dam on the Victoria River itself reduced flow volumes compared to the natural flow regime. Transparent flows past the dams did not mitigate the impact of dam construction on mangroves to a great extent; the B-DVRT flows continued to have a moderate impact on the habitat-forming species group. Annual high flows are important to inundate the mangrove forests during the wet season and replenishing soil water that is critical later in the year during the dry season (Duke et al., 2019). Water harvesting with low pump-start thresholds would extract water during wet-season flows, thus reducing the magnitude of flood flows at the critical period of wet-season ecological replenishment in the wet-dry tropics. In addition, reduction of sediment loads and reduced coastal deposition that historically maintain estuarine soils for the benefit of the mangrove community would be greater under a modified high-flow scenario (Asbridge et al., 2016). Cumulative detriment to mangrove communities due to flow modification due to water extraction and dam construction has been modelled in similar, nearby tropical catchments in northern Australia (Plagányi et al., 2024). Mangroves species dominate many of the creeks and rivers in the intertidal zone of the Southern Gulf catchments (Palmer and Smit, 2019; Smyth and Turner, 2019) where freshwater provided by inundation is an important process for supporting mangrove species. Changes in flow regimes leading to a reduction in the area of mangrove habitat inundated due to a future drying climate and water resource development would lead to a reduction in mangroves, affecting the available habitat for many species, including birds, fish, prawns, mud crabs and reptiles for which mangroves provide both foraging and breeding habitat (see Stratford et al. (2024a)). Mangroves also provide a range of ecosystem services, which would be reduced under a future drying climate and water resource development. These include shoreline stabilisation, carbon capture and storage, storm surge protection and reducing nutrient loads and suspended sediments, which is important for water quality (Palmer and Smit, 2019). 4.4.4 Saltpans and salt flats Saltpans and salt flats are extensive intertidal areas devoid of marine plants and located between mangrove and saltmarsh meadows within the upper-most intertidal zone. Saltpans and salt flats occur across much of northern Australia. Despite their infrequent inundation, when they are covered by the tide saltpans and salt flats provide habitat for some estuarine fish species, such as barramundi (Russell and Garrett, 1983b), and other species such as metapenaeid shrimps (Bayliss et al., 2014). In addition, saltpans and salt flats provide important resting and feeding grounds for migratory shorebirds in the NT, with counts occurring in the tens of thousands (Palmer and Smit, 2019). During the wet-season, king tides, heavy rainfall and overbank inundation may create months-long ephemeral habitats for fishes and crustaceans (Russell and Garrett, 1983a; 1985) and stimulate primary production by the dry-season-senescent microphytobenthos that encrust the saltpan soils in the wet season (Burford et al., 2016). Saltpans and salt flats also provide habitat for a range of benthic infauna (Dias et al., 2014), which are an important food source for high-order consumers such as shorebird species that use these habitats as feeding areas during their migration, which can include long flights to Asia (Cotin et al., 2011; Lei et al., 2018; Rocha et al., 2017). In the Victoria catchment, saltpans and salt flats commonly occur adjacent to estuaries and coastal floodplains. Saltpans and salt flats form a spatially extensive habitat at the land–sea interface adjacent to estuaries and coastal littoral zones around river mouths (Short, 2020). Saltpans and salt flats support many of the species and groups reported as biota assets in this report (e.g. see sections 4.1.1 for barramundi and 4.2.3 for shorebirds), particularly during high-flow events that connect the saltpans and salt flats more frequently to the seascape. Despite occupying supra-tidal habitats, saltpans and salt flats require freshwater inputs from precipitation, groundwater or overbank inundation for cyanobacteria and marine plants (including saltmarsh species) to thrive (Duke et al., 2017). Hence, reduced flood flows and an increased frequency and duration of no-flow periods are key threats to assets that require these habitats. Flow dependencies analysis Salt flats were modelled in the marine region with contributing flows from the end-of-system node. The locations for modelling saltpans and salt flats in the Victoria catchment were based upon habitat mapping (Stratford et al., 2024a). Hypothetical water resource development in the Victoria catchment resulted in varying levels of change in important flow dependenies for saltpans and salt flats. When considering change in important flows, the hypothetical dam scenarios ranged from negligible (0.8) to moderate (6.9) for scenarios B-DLC and B-D2 respectively. For water harvesting, the change ranged from negligible (0.9) to moderate (8.5) for B-Wv80t600t30f500 and B-Wv800t200r30f0 respectively. Scenario Cdry resulted in moderate change (9.7) for salt flats (Figure 4-36). Under scenario B-D2, the largest contributing change in important flow dependencies was for the metric high flood pulse count (1th percentile). For scenario B-Wv800t600r30f500, the largest contribution of change was for the metric mean January discharge. See Appendix B for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Stratford et al. (2024a) for descriptions of flow-ecology relationships for saltpans and salt flats. Figure 4-36 Change in saltpans and salt flats flow dependencies by scenario across the model nodes Colour intensity represents the level of change occurring in important flow metrics for saltpans and salt flats, expressed as percentile change from the historical conditions. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change shown on the right axis. Horizontal grey bars and numbers correspond to the change. Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. Dams and changes in important flows for mangroves For the dam scenarios, Scenario B-DLC (without transparent flows) resulted in negligible change (0.8). When transparent flows (B-DLCT) were provided to support environmental functions, the change to important flows for saltpans and salt flats remained negligible (0.8). Scenario B-DVR resulted a in larger change than Scenario B-DLC, with a moderate (6.4) mean change across the assessment nodes. This was reduced to moderate (6.3) with the provision of transparent flows for Scenario B-DVRT. Under Scenario B-D2, which includes both the B-DLC and B-DVR dams, a moderate (6.9) change occurred across the catchment without transparent flows. This was reduced to For more information on this figure please contact CSIRO on enquiries@csiro.au moderate (6.0) with provision of transparent flows. Scenario B-D2 (with multiple dams) resulted in a larger mean change across the catchment than either of the single-dam scenarios. This was due to the combined effects on flows downstream of the confluence of the two dams. Scenario B-D2T (with transparent flows) resulted in a smaller change than Scenario B-D2 (without transparent flows), indicating the importance of providing transparent flows for environmental outcomes – the scenarios with transparent flows demonstrated the significance of environmental flows in reducing changes for saltpans and salt flats. Water harvesting and changes in important flows for mangroves The hypothetical water harvesting scenarios resulted in a change to salt flats flow dependencies from negligible (0.9) to moderate (8.5) for B-Wv80t600t30f500 and B-Wv800t200r30f0, respectively. The change for saltpans and salt flats with water harvesting varies with the extraction targets, pump- start thresholds and pump rates (Figure 4-36). With a low extraction target of 80 GL under Scenario B-Wv80t200r30f0, the change was minor (2.3), increasing to moderate (8.5) with an extraction target of 800 GL under Scenario B-Wv800t200r30f0. Increasing the pump-start threshold from 200 to 600 ML per day (i.e. from Scenario B-Wv160t200r30f0 to Scenario B-Wv160t600r30f0) with a target extraction volume of 160 GL reduced the minor change across the assessment nodes from 3.0 to 2.8, respectively (Figure 4-36). Climate change and water resource development for important flows for mangroves Scenario Cdry resulted in moderate change (9.7) for saltpans and salt flats flow dependencies (Figure 4-36). This indicates that the dry climate scenario had larger changes than scenarios B-D2T (moderate; 6.0) and B-Wv160t200r30f0 (minor; 3.0). Scenarios Ddry-D2T and Ddry-Wv160t200r30 resulted in moderate change (13.1 and 9.1, respectively). This shows that the combined change of scenarios Ddry-D2T or Ddry-Wv160t200r30 were higher than Scenario Cdry or either of scenarios B-D2 and B-Wv160t200r30 alone. The ecological impacts of dams and river regulation on saltpans and salt flats can be numerous. Principally, reduced flows can prevent water from flowing overbank onto low-lying habitats because large rainfall events can be captured, preventing flood pulses from moving down the catchment and reaching dynamic estuaries and low-lying coastal areas. Loss of connectivity to coastal floodplain areas, including low-lying saltpans and salt flats, would result in the reduction or loss of coastal wetland habitats (Lei et al., 2018; Velasquez, 1992). In addition, sediment replenishment delivered to the coast during flood flows sustains coastal habitats, preventing erosion and degradation of important estuarine and adjacent habitats (Asbridge et al., 2016) and allowing the accumulation of carbon in deposition sediments (Owers et al., 2022). 4.4.5 Surface-water-dependent vegetation Across much of the Victoria catchment, terrestrial vegetation survives on water derived from local rainfall that recharges soils during the wet season and can be accessed by the root systems within unsaturated soils throughout the year. Terrestrial vegetation that receives extra water (i.e. in addition to local rainfall) often provides a lush green and productive forest ecosystem (with high diversity and dense tree cover) within an otherwise drier or more sparsely vegetated savanna environment (e.g. Pettit et al., 2016). This water may be available to vegetation from recharge from flood waters or by accessing shallow groundwater. Terrestrial vegetation communities that receive and are supported by surface water in addition to incident rainfall are considered surface- water-dependent vegetation in this report. Habitats of surface-water-dependent vegetation often occur along rivers and floodplains, fringing wetlands and springs, and they may also have access to groundwater within reach of the root system. Surface-water-dependent vegetation can be highly sensitive to changes in flooding regime (inundation extent, depth, duration and frequency). There may be a lagged response in vegetation condition to reduced surface water availability because water stored in soil or local aquifers can provide a buffer for maintaining vegetation condition. However, if these sources are not regularly topped up by flood recharge, less water will be available to support floodplain vegetation during dry periods. Furthermore surface-water-dependent vegetation may need floodwater inundation to support growth, flowering and fruiting, germination and successful establishment of new saplings to maintain the diversity of ecosystem species and their functions and services. In northern Australia, surface-water-dependent vegetation provides food and habitat for high levels of biodiversity (e.g. for migratory waterbirds, honeyeaters, flying foxes and crocodiles), plays a role in nutrient cycling and provides buffering against erosion. The key threats to surface-water-dependent vegetation are associated with changes in flood regimes (inundation extent, depth, duration and frequency) that support vegetation survival and growth, flowering and fruiting, germination and establishment of new individuals. Flow dependencies analysis The flow dependencies modelling investigates flow parameters likely to affect surface-water- dependent vegetation. However, some of this vegetation may also be groundwater dependent, and the flow dependencies modelling does not explicitly investigate the potential impacts of captured recharge (reduction in recharge to local aquifers due to surface water regulation by changing river flows). Where surface water regulation influences the inundation extent, duration or frequency, it is also likely to alter the local aquifer recharge and therefore groundwater availability to vegetation during dry periods. While metrics of flow magnitude, duration and frequency are included in the flow dependencies modelling, extent of inundation is not explicitly modelled. Therefore the impacts of dams and water harvesting on captured recharge is not fully accounted for within this analysis alone. Nevertheless, the flow dependencies analysis is a useful preliminary investigation of potential impacts of dams, water harvesting and climate on important flow components of surface-water-dependent vegetation, with the caveat that not all recharge mechanisms are fully incorporated. Surface-water-dependent vegetation was modelled across a total of 1918 km of assessment reaches in the Victoria catchment with contributing flows from a total of 40 model nodes. Some of the key river reaches for surface-water-dependent vegetation within the catchment were modelled downstream of nodes 81101135, 81100171 and 81100040 near Yarralin, Daguragu and Bulla respectively (Figure 2-1). The locations for modelling surface-water-dependent vegetation in the Victoria catchment were based upon evaluation of available knowledge of the catchment (Stratford et al., 2024a) with reach weightings shown in Appendix A. Under scenario B-D2, the largest contributing change in important flow dependencies was for the metric high flow pulse duration (25th percentile) single spell at node 81100063. For scenario B-Wv800t600r30f500, the largest contribution of change was also for the metric high flow pulse duration (25th percentile) single spell at node 81100001. See Appendix B for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Stratford et al. (2024a) for descriptions of flow-ecology relationships for surface-water-dependent vegetation. Hypothetical water resource development in the Victoria catchment resulted in varying levels of change associated with the important flow components for surface-water-dependent vegetation. When considering mean change in flow dependencies across all 40 surface-water-dependent vegetation analysis reaches and nodes, the hypothetical dam scenarios ranged from negligible (1.5) to moderate (5.4) for scenarios BDLCT and BD2 respectively. For water harvesting, the change to important flow dependencies was negligible, ranging from 0.3 to 2.0 for BWv80t600t30f500 and BWv800t200r30f0, respectively. Scenario Cdry resulted in moderate flow change (11.6) for surface- water-dependent vegetation. The resulting spatial change in flows associated with dam, water harvesting and climate scenarios varied as a result of the different spatial patterns, including the extent and magnitude of flow change across different parts of the catchment (Figure 4-37). Figure 4-37 Spatial heatmap of change for surface-water-dependent vegetation, considering the weighted distribution across the catchment Scenarios are: (a) B-Wv80t200r30f0, (b) B-Wv160t200r30f0, (c) B-W160t60r20f0, (d) B-DLC, (e) Cdry and (f) D-dryw160t200r30. See Table 2-1 for interpretation of scenarios. River shading indicates the level of flow change of important metrics by the location of surface-water-dependent vegetation across the catchment. For more information on this figure please contact CSIRO on enquiries@csiro.au Dams and changes in important flows for surface-water-dependent vegetation For the dam scenarios, Scenario B-DLC (without transparent flows) resulted in a minor mean change (2.6) across the 40 surface-water-dependent vegetation assessment nodes. When transparent flows (B-DLCT) were provided to support environmental functions, the change to important flows for surface-water-dependent vegetation was reduced to negligible (1.5). Scenario B-DVR resulted in a larger change than Scenario B-DLC, with a minor (2.8) mean change across the assessment nodes. This remained minor (2.7) but reduced with the provision of transparent flows for Scenario B-DVRT. Under Scenario B-D2, which includes both the B-DLC and B-DVR dams, a moderate (5.4) change occurred across the catchment without transparent flows. This was reduced to minor (4.0) with provision of transparent flows. Scenario B-D2 (with multiple dams) resulted in a larger mean change across the catchment than either of the single-dam scenarios. This was due to the combined effects on flows downstream of the confluence of the two dams and the change to a larger portion of the catchment. Scenario B-D2T (with transparent flows) resulted in a smaller change in important flows than Scenario B-D2 (without transparent flows), indicating the importance of providing transparent flows for environmental outcomes – the scenarios with transparent flows demonstrated the significance of environmental flows in reducing changes for surface-water-dependent vegetation (Figure 4-38). Nodes directly downstream of the dams in scenarios B-DLC and B-DVR resulted in extreme changes to important flow dependencies (88.6 and 39.7, respectively). These changes were reduced but still extreme (54.6 and 35.8, respectively) with provision of transparent flows. This reflects a combination of the higher change to flow changes directly downstream of dams, the benefits associated with provision of flows for the environment and the habitat importance for surface- water-dependent vegetation in these two locations. Water harvesting and changes in important flows for surface-water-dependent vegetation The hypothetical water harvesting scenarios resulted in negligible mean change (0.3 to 2.0) across surface-water-dependent vegetation assessment nodes for B-Wv80t600t30f500 and B-Wv800t200r30f0, respectively. There is low variability in the mean change for surface-water-dependent vegetation across assessment nodes to variation in water harvesting the extraction targets, pump-start thresholds and pump rates (Figure 4-38. With a low extraction target of 80 GL under Scenario B-Wv80t200r30f0, the mean weighted change across the catchment was negligible (0.4 increasing to 2.0) with an extraction target of 800 GL under Scenario B-Wv800t200r30f0. Increasing the pump-start threshold from 200 to 600 ML per day (i.e. from Scenario B-Wv160t200r30f0 to Scenario B-Wv160t600r30f0) with a target extraction volume of 160 GL reduced the negligible change across the assessment nodes to 0.6 and 0.5, respectively (Figure 4-38). Reducing the pump-start threshold protects the low flows that are important for surface-water-dependent vegetation ecology. Figure 4-38 Change in surface-water-dependent vegetation flow dependencies by scenario across the model nodes Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for surface-water-dependent vegetation. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Only the 30 highest impact nodes are shown (x-axis). Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. For more information on this figure please contact CSIRO on enquiries@csiro.au Climate change and water resource development for surface-water-dependent vegetation Scenario Cdry resulted in moderate mean change (11.6) to important flow dependencies for surface-water-dependent vegetation across the 40 surface-water-dependent vegetation assessment nodes (Figure 4-38). This indicates that the dry climate scenario had, on average across all catchment nodes, larger changes than scenarios B-D2T (minor; 4.0) and B-Wv160t200r30f0 (negligible; 0.6). However, it is important to note that local change under some water resource development scenarios can be considerably higher. Scenarios Ddry-D2T and Ddry-Wv160t200r30 resulted in major (17.1) and moderate (12.2) change, respectively, when weighted across all surface-water- dependent vegetation assessment nodes. This shows that the combined changes of scenarios Ddry-D2T or Ddry-Wv160t200r30 were higher than Scenario Cdry or either of scenarios B-D2 and B-Wv160t200r30 alone. A key threat to surface water dependent vegetation in the Victoria catchment is change to the dynamics of water availability. The development of water resources, including dam construction, water harvesting and groundwater extraction in combination with climate change, have the potential to influence floodplain inundation extent, timing and duration, which affects recharge and discharge from floodplains and the availability of suitable quality water to floodplain vegetation at critical times. The specific surface water flow relationships of surface water dependent vegetation are highly dependent on local site conditions (climate, soils, topography, groundwater) and vegetation type. Some riparian vegetation is adapted to inundation for several months of each year (Department of Environment and Science Queensland, 2013), whereas some floodplain vegetation may only require inundation for two months every five years (Wen et al., 2009). Dam operations or water harvesting could potentially be managed to mimic the natural timing, frequency, duration and extent of surface water flows that naturally inundate surface water dependent vegetation habitats and recharge local aquifers that sustain some surface water dependent vegetation during dry times. However, this flow requirement analysis shows major changes in the important surface water flow components that support surface water dependent vegetation downstream of instream dams and moderate widespread changes in response to water harvesting scenarios. It also shows moderate to extreme changes catchment-wide to the important surface water flow components that support surface water dependent vegetation when paired with a drying climate. The demand for water for irrigated agriculture is likely to coincide with times when surface water dependent vegetation is most likely to experience water deficit. Furthermore, overall reduced inchannel flows may enhance drainage of alluvial aquifers, potentially reducing availability of both groundwater and surface water to surface water dependent vegetation at critical times. Vegetation that experiences water stress might first exhibit loss of ecosystem function (e.g. reduced flowering and seed dispersal) but could ultimately result in local dieback of higher-water- need vegetation and transition to vegetation resilient of drier conditions. This transition may take years depending on the magnitude and rate of change in water availability and the resilience of the vegetation to water stress (Mitchell et al., 2016; Van Mantgem et al., 2009). 5 Synthesis Context of the catchments The Victoria River is a large river originating to the south of the Judbarra National Park. At over 500 km in length, it is one of the longest perennial rivers in the NT. The catchment area of 82,400 km2 makes it one of the largest ocean-flowing catchments in the NT, with flows that enter the south-eastern edge of the Joseph Bonaparte Gulf. The protected areas located in the Victoria catchment include one gazetted national park (Judbarra), a proposed extension to an existing national park (Keep River), the Commonwealth Joseph Bonaparte Gulf Marine Park, two Indigenous Protected Areas and two Directory of Important Wetlands in Australia (DIWA) sites. The two DIWA sites are the Bradshaw Field Training Area Wetlands and the Legune Wetlands (Figure 2-1). The freshwater sections of the Victoria catchment include diverse habitats such as perennial and intermittent rivers, anabranches, wetlands, floodplains and GDEs. The diversity and complexity of the habitats, and the connections between the habitats within a catchment, are vital for providing the range of habitats needed to support both the aquatic and terrestrial biota (Schofield et al., 2018). The mouth and estuary of the Victoria River is up to 25 km wide and includes extensive mudflats and mangrove stands (Kirby and Faulks, 2004). Mangroves and mudflats are prominent along the coastal margins with approximately ten plant species recorded. The Legune (Joseph Bonaparte Bay) Important Bird and Biodiversity Area can support over 15,000 waterbirds across mudflats, salt flats, and seasonally inundated wetlands (BirdLife International, 2023). Marine habitats in northern Australia are vital for supporting important fisheries, including banana prawn (Fenneropenaeus merguiensis), mud crab and barramundi (Lates calcarifer), as well as for supporting biodiversity more generally, including waterbirds, marine mammals and turtles. In addition, the natural waterways of the sparsely populated catchments support globally significant stronghold populations of endangered and endemic species that often use a combination of both marine and freshwater habitats (e.g. sharks and rays). The flow regime in northern Australia is highly variable with large seasonal and inter-year variability. The natural flow regime is important for supporting species, habitats and a range of ecosystem functions. Species life-histories are often intricately linked to specific flow conditions considering the magnitude, timing and frequency of flow events. Flow regimes support habitats and ecological functions. The ecological assets considered in this report represent a range of flow dependencies and have different spatial patterns of occurrence across the catchments. For ecology: • High flows provide a range of important functions including providing connectivity for movement, increasing productivity and nutrient exchange, providing cues for spawning and migration, and wetting habitat and supporting vegetation growth and persistence. The magnitude, duration and timing of high flows is important in ecological systems. • Low flows are also an important component of the flow regime with many species adapted to these conditions. Persistent waterholes provide important refuge habitat from environmental conditions and the higher levels of predation that may occur in connected rivers. For many species refuge waterholes function as a source for recolonisation during the wet season. Persistent low flows during dry periods can help support suitable habitat conditions including thermal and water quality for species in connected rivers and in supporting riparian vegetation and movement and provide a source of water within the broader landscape. • The timing of flow events is important in supporting life-cycle processes including breeding and migration cues for aquatic species. The timing of flood events and the associated increase in productivity supports function in the river channel and connected marine environments. Understanding potential change This report should be read in conjunction with the Victoria River Water Resource Assessment Ecological Assets Description report by Stratford et al. (2024a) which also provides a qualitative discussion other threatening processes on assets that can occur as a result of, or in synergy with, water resource development. Stratford et al. (2024a) documents the asset ecology including flow relationships and dependencies and the distribution of each asset across the catchment. The impacts associated with loss of habitat and connectivity by the creation of instream dams is discussed in (Yang et al., 2024). • Different ecological assets have different flow dependencies. These were modelled using a suite of asset specific hydrometrics based upon the flow-ecology of each asset. Change in important flow dependencies were modelled as the percentile change of the scenario median hydrometrics relative to the historical distribution and are hence benchmarked against the variability occurring in the historical flow regime. • Ecological assets have different distributions across the catchments. Water resource development in different parts of the catchment will not affect assets equally as asset occurrence differs across the catchments. The importance of habitat across the catchments were quantified using a combination of species distribution modelling, observed distribution, habitat maps and expert knowledge. The asset flow dependencies were modelled in the important reaches for each asset downstream of river system model nodes to capture changes in flow affecting the assets’ important locations. • Different scenarios of water resource development resulted in different volumetric, temporal and spatial patterns of change in flow regimes. Ecological assets are adapted to, or occur as a result of, the flow regime. Changes to the flow regime will result in changes to the ecology of the system. While most changes from the current ecological function of the system are considered detrimental, there are some species that may benefit or new habitats conditions that arise because of changes in flow. Ecology is complex, and difficult to predict into novel conditions. • Ecological systems are more than the sum of their parts. Systems have complex interactions that occur across different temporal and special scales. Important habitats or functions may be nested within the landscape, for example, refuge waterholes are important for providing a source for recolonisation of the surrounding system following dry conditions and the loss of waterholes in a location may have larger impacts beyond that of the individual sites, while a river reach may be important for connecting vast riverscapes. Individuals of a species occur within a population, and populations within communities. • Aquatic systems do not occur in isolation. With water resource development comes land-use change and intensification. This can result in a suite of synergistic and co-occurring threatening processes including changes to water quality from increased nutrient loads and changes to runoff, increased threat from invasive species associated with higher likelihood of both introduction and establishment, changes to connectivity associated with roads, culverts, and instream structures, as well as changes to fire regimes. Climate change adds additional uncertainty and risk associated with drying conditions and changes in rainfall patterns. • Non-linear responses and trade-offs occur in natural systems. The outcomes of any potential water resource development do not affect all assets equally, nor ecological assets equally across the catchments. The approach to analysis is generalisable across the catchments, however thresholds are likely to occur in ecology. Wetlands may have a commence-to-fill, where once river flows exceeds this level the wetland is inundated. Surface-water-dependent vegetation along riparian corridors downstream of dams may benefit from persistent watering associated with releases from dams, however watering of vegetation higher on the floodplain may be reduced due to the capture of flood flows into the dam. Water resource development In the Victoria catchment, changes associated with water resource development demonstrated that: • Different ecological assets had variable sensitivity to flow change. This depended on each assets flow dependencies, location in the catchment, and the type and size of the development considered including the volumes of water extracted, the timing of water extraction and the volume of water that is passing through the river. Outcomes for ecology is more than about just the volume of water extracted. • Mitigation measures including providing an annual diversion commencement flow requirement, pump commencement thresholds, extraction targets and pump rates for water harvesting, and transparent flows for dams often provided effective in reducing the effects of flow regime change. • Interannual variability in the flow regime across the catchments was larger than the mean change associated with water resource development – but not consistently. Some scenarios resulted in a level of change greater than what was observed in historical flows. This was usually confined to sites directly downstream of dams. • While the effects of water resource development depend upon the scale, location and type of development, effects are typically reduced with further distance downstream of the last development. However, the effects of change can have consequences considerable distances downstream and into the near-shore and marine environments. Water harvesting • Impacts from water harvesting tend to accumulate downstream, so ecological assets found near the bottom of the catchment experienced the greatest average catchment impact. Cryptic waders, threadfin, banana prawns and floodplain wetlands are among the ecological assets most affected by flow changes for water harvesting. • For water harvesting, measures to mitigate the changes associated with extraction include limiting the system target thereby reducing extraction across the catchment, providing a pump start threshold by limiting pumping of water from the river during periods of low river flows, providing an annual diversion commencement flow requirement for a volume of water to pass through the last node in the system before pumping is allowed, and limiting the pump rate that water can be extracted from the river provide for better environmental outcomes than without these measures. Dams • Site impacts were often highest directly downstream of dams with often extreme changes in flow dependencies for assets. These ecological assets often-had flow dependencies for low flow requirements or periods of stable flows. Areas further downstream of hypothetical dams have contributions from unimpacted tributaries thereby reducing change of the flow regime. Freshwater assets typically had distributions that included many unimpacted parts of the catchment. • Construction of dams results in loss of connectivity along the river (longitudinal connectivity) due to instream barries and changes in flow, as well as loss of connectivity between the river and its surrounding floodplains (lateral connectivity) due to changes in flow. These changes limit species movement between habitats. Many species need movement between freshwater and marine environments or different habitats within the catchment. Dams in headwater catchments typically result in smaller changes to longitudinal connectivity than dams closer to the end-of-system. • Creation of dams inundate terrestrial and stream habitat resulting in the creation of new habitat conditions associated with the impoundment. In some cases, these new habitats may provide resources for some species such as waterbirds, but other habitats are degraded or lost impacting the species that depend upon them. Persistent flows from storages change downstream habitat and result in modification of ecological communities and potential loss of existing species. • Providing transparent flows- inflows let to pass the dam wall for environmental purposes- provided improvements for most assets than without these. Particularly strong improvements in flow dependencies for some assets occurred. Climate change and water resource development Climate change had larger potential mean change in flows across the catchments than many scenarios of water resource development. Under a drying climate, flow regime change resulted in a moderate change in important flow dependencies across all assets, a change greater than that of either of the feasible dam or water harvesting scenarios. The influence of a dry climate scenario has confounding effects when in combination with water resource development and provides additional pressure on ecological systems than either component alone. The combined cumulative effects of water resource development and the drying climate scenario led to the greatest catchment-level changes with moderate change to asset flow dependencies. References Aharon-Rotman Y, McEvoy J, Zheng ZJ, Yu H, Wang X, Si YL, Xu ZG, Yuan Z, Jeong W, Cao L and Fox AD (2017) Water level affects availability of optimal feeding habitats for threatened migratory waterbirds. Ecology and evolution 7(23), 10440-10450. DOI: 10.1002/ece3.3566. Albanese G and Davis CA (2015) Characteristics within and around stopover wetlands used by migratory shorebirds: Is the neighborhood important? Condor 117(3), 328-340. DOI: 10.1650/condor-14-166.1. Alberts-Hubatsch H, Lee SY, Meynecke J-O, Diele K, Nordhaus I and Wolff M (2016) Life-history, movement, and habitat use of Scylla serrata (Decapoda, Portunidae): current knowledge and future challenges. Hydrobiologia 763(1), 5-21. Allen GR (1982) A field guide to inland fishes of Western Australia. Western Australian Museum Perth. Arthington AH, Balcombe SR, Wilson GA, Thoms MC and Marshall J (2005) Spatial and temporal variation in fish-assemblage structure in isolated waterholes during the 2001 dry season of an arid-zone floodplain river, Cooper Creek, Australia. Marine and freshwater research 56(1), 25-35. Arthington AH, Olden JD, Balcombe SR and Thoms MC (2010) Multi-scale environmental factors explain fish losses and refuge quality in drying waterholes of Cooper Creek, an Australian arid-zone river. Marine and Freshwater Research 2010(61), 842-856. Asbridge E, Lucas R, Ticehurst C and Bunting P (2016) Mangrove response to environmental change in Australia's Gulf of Carpentaria. Ecology and evolution 6(11), 3523-3539. Australian Government (2022a) Collaborative Australian Protected Areas Database (CAPAD) 2022. Marine CAPAD 2022 Commonwealth summary. (https://www.dcceew.gov.au/environment/land/nrs/science/capad/2022). Australian Government (2022b) Collaborative Australian Protected Areas Database (CAPAD) 2022. Terrestrial CAPAD 2022 NT summary. (https://www.dcceew.gov.au/environment/land/nrs/science/capad/2022). Bamford M (1992) The impact of predation by humans upon waders in the Asian/Australasian Flyway: evidence from the recovery of bands. Stilt, 38-40. Barbaree BA, Reiter ME, Hickey CM, Strum KM, Isola JE, Jennings S, Tarjan ML, Strong, CM, Stenzel LE, Shuford DW (2020) Effects of drought on the abundance and distribution of non- breeding shorebirds in central California, USA. PLoS ONE 15(10): e0240931. https://doi.org/10.1371/journal.pone.0240931 Baudains TP and Lloyd P (2007) Habituation and habitat changes can moderate the impacts of human disturbance on shorebird breeding performance. Animal Conservation 10(3), 400- 407. DOI: 10.1111/j.1469-1795.2007.00126.x. Bayliss P, Buckworth R and Dichmont C (2014) Assessing the Water Needs of Fisheries and Ecological Values in the Gulf of Carpentaria: Final Report Prepared for the Queensland Department of Natural Resources and Mines (DNRM). Commonwealth Scientific and Industrial Research Organisation. Bellisario B, Cerfolli F and Nascetti G (2014) Climate effects on the distribution of wetland habitats and connectivity in networks of migratory waterbirds. Acta Oecologica-International Journal of Ecology 58, 5-11. DOI: 10.1016/j.actao.2014.04.002. Bino G, Steinfeld C and Kingsford RT (2014) Maximizing colonial waterbirds' breeding events using identified ecological thresholds. and environmental flow management. Ecological Applications 24(1), 142-157. DOI: 10.1890/13-0202.1. Bishop K, Allen S, Pollard D and Cook M (1990) Ecological studies on the freshwater fishes of the Alligator Rivers Region, Northern Territory. Blaber S, Brewer D and Salini J (1989) Species composition and biomasses of fishes in different habitats of a tropical northern Australian estuary: their occurrence in the adjoining sea and estuarine dependence. Estuarine, Coastal and Shelf Science 29(6), 509-531. Blaber S, Brewer D and Salini J (1995) Fish communities and the nursery role of the shallow inshore waters of a tropical bay in the Gulf of Carpentaria, Australia. Estuarine, Coastal and Shelf Science 40(2), 177-193. Blaber S, Griffiths S and Pillans R (2010) Changes in the fish fauna of a tropical Australian estuary since 1990 with reference to prawn predators and environmental change. Estuarine, Coastal and Shelf Science 86(4), 692-696. Blamey LK, Plagányi ÉE, Robins JB, Kenyon R, Deng RA, Hughes J and Kim S (2023) Altering river flow impacts estuarine species and catches: lessons from giant mud crabs. ICES Journal of Marine Science. Bodie JR and Semlitsch RD (2000) Spatial and temporal use of floodplain habitats by lentic and lotic species of aquatic turtles. Oecologia 122(1), 138-146. DOI: 10.1007/PL00008830. Bowne DR, Bowers MA and Hines JE (2006) Connectivity in an agricultural landscape as reflected by interpond movements of a freshwater turtle. Conserv Biol 20(3), 780-791. DOI: 10.1111/j.1523-1739.2006.00355.x. Boyer A (2018) Fresh food and community pride are two of the benefits that come with the opportunity to expand fishing activities for the Northern Territory’s remote coastal Indigenous communities. In: Fisheries Research and Development Corporation (ed.), FISH magazine Brandis K, Nairn L, Porter J and Kingsford RT (2009) Preliminary assessment for the environmental water requirements of waterbird species in the Murray-Darling Basin. University of New South Wales, Sydney. Brandis KJ (2010) Colonial waterbird breeding in Australia: Wetlands, water requirements and environmental flows. University of New South Wales, Sydney. Brandis KJ, Bino G, Spencer JA, Ramp D and Kingsford RT (2018) Decline in colonial waterbird breeding highlights loss of Ramsar wetland function. Biological Conservation 225, 22-30. DOI: 10.1016/j.biocon.2018.06.022. Brandis KJ, Kingsford RT, Ren S and Ramp D (2011) Crisis water management and ibis breeding at Narran Lakes in arid Australia. Environ Manage 48(3), 489-498. DOI: 10.1007/s00267-011- 9705-5. Brewer D, Blaber S, Salini J and Farmer M (1995) Feeding ecology of predatory fishes from Groote Eylandt in the Gulf of Carpentaria, Australia, with special reference to predation on penaeid prawns. Estuarine, Coastal and Shelf Science 40(5), 577-600. Broadley A, Stewart-Koster B, Kenyon RA, Burford MA and Brown CJ (2020) Impact of water development on river flows and the catch of a commercial marine fishery. Ecosphere 11(7), e03194. DOI: ARTN e03194 10.1002/ecs2.3194. Brodie JE and Mitchell AW (2005) Nutrients in Australian tropical rivers: changes with agricultural development and implications for receiving environments. Marine and Freshwater Research 56(3), 279-302. Bunn SE and Arthington AH (2002) Basic principles and ecological consequences of altered flow regimes for aquatic biodiversity. Environmental Management 30(4), 492-507. Burford M, Valdez D, Curwen G, Faggotter S, Ward D and Brien KO (2016) Inundation of saline supratidal mudflats provides an important source of carbon and nutrients in an aquatic system. Marine Ecology Progress Series 545, 21-33. Burford M, Webster I, Revill A, Kenyon R, Whittle M and Curwen G (2012) Controls on phytoplankton productivity in a wet–dry tropical estuary. Estuarine, Coastal and Shelf Science 113, 141-151. Burford MA and Faggotter SJ (2021) Comparing the importance of freshwater flows driving primary production in three tropical estuaries. Marine Pollution Bulletin 169, 112565. Burford MA, Revill AT, Palmer DW, Clementson L, Robson BJ and Webster IT (2011) River regulation alters drivers of primary productivity along a tropical river-estuary system. Marine and Freshwater Research 62(2), 141-151. DOI: 10.1071/MF10224. Canham R, Flemming SA, Hope DD, Drever MC (2021) Sandpipers go with the flow: Correlations between estuarine conditions and shorebird abundance at an important stopover on the Pacific Flyway. Ecol Evol. 2021; 11: 2828–2841. https://doi.org/10.1002/ece3.7240 Cann J and Sadlier R (2017) Freshwater turtles of Australia. CSIRO, Melbourne. Cardona L (2000) Effects of salinity on the habitat selection and growth performance of Mediterranean flathead grey mullet Mugil cephalus (Osteichthyes, Mugilidae). Estuarine, Coastal and Shelf Science 50(5), 727-737. Chan TU, Hart BT, Kennard MJ, Pusey BJ, Shenton W, Douglas MM, Valentine E and Patel S (2012) Bayesian network models for environmental flow decision making in the Daly River, Northern Territory, Australia. River Research and Applications 28(3), 283-301. Chatto R (2006) The distribution and status of waterbirds around the coast and coastal wetlands of the Northern Territory. Darwin. Parks and Wildlife Commission of the Northern Territory. Technical Report. 76. Chessman BC (1988) Habitat Preferences of fresh-water turtles in the Murray Valley, Victoria and New-South-Wales. Wildlife Research 15(5), 485-491. Clemens RS, Weston MA, Haslem A, Silcocks A and Ferris J (2010) Identification of significant shorebird areas: thresholds and criteria. Diversity and Distributions, 16: 229-242. https://doi.org/10.1111/j.1472-4642.2009.00635.x Clemens R, Rogers DI, Hansen BD, Gosbell K, Minton CDT, Straw P, Bamford M, Woehler EJ, Milton DA, Weston MA, Venables B, Wellet D, Hassell C, Rutherford B, Onton K, Herrod A, Studds CE, Choi CY, Dhanjal-Adams KL, Murray NJ, Skilleter GA, Fuller, RA (2016) Continental-scale decreases in shorebird populations in Australia. Emu - Austral Ornithology, 116(2), 119–135. https://doi.org/10.1071/MU15056 Close P, Bartolo R, Pettit N, Ward D and Kennard M (2015) Vulnerability and risk assessment of northern Australian catchments and biodiversity. Climate Change Adaptation across Australia’s Monsoonal North–Northern Monsoon NRM Cluster, Griffith University, Nathan. Close P, Dobbs R, Tunbridge D, Speldewinde P, Warfe D, Toussaint S and Davies P (2014) Customary and recreational fishing pressure: large-bodied fish assemblages in a tropical, intermittent Australian river. Marine and Freshwater Research 65(4), 466-474. Close PG, Wallace J, Bayliss P, Bartolo R, Burrows D, Pusey BJ, Robinson CJ, McJannet D, Karim F, Byrne G, Marvanek S, Turnadge C, Harrington G, Petheram C, Dutra LXC, Dobbs R, Pettit N, Jankowski A, Wallington T, Kroon F, Schmidt D, Buttler B, Stock M, Veld A, Speldewinde P, Cook BA, Cook B, Douglas M, Setterfield S, Kennard M, Davies P, Hughes J, Cossart R, Conolly N and Townsend S (2012) Assessment of the likely impacts of development and climate change on aquatic ecological assets in Northern Australia. A report for the National Water Commission, Australia. Tropical Rivers and Coastal Knowledge (TRaCK) Commonwealth Environmental Research Facility, Charles Darwin University, Darwin. Cotin J, GarcÃa-Tarrasón M, Sanpera C, Jover L and Ruiz X (2011) Sea, freshwater or saltpans? Foraging ecology of terns to assess mercury inputs in a wetland landscape: The Ebro Delta. Estuarine, Coastal and Shelf Science 92(1), 188-194. Crocos PJ and Kerr J (1983) Maturation and spawning of the banana prawn Penaeus merguiensis de Man (Crustacea: Penaeidae) in the Gulf of Carpentaria, Australia. Journal of Experimental Marine Biology and Ecology 69(1), 37-59. Crook D, Buckle D, Allsop Q, Baldwin W, Saunders T, Kyne P, Woodhead J, Maas R, Roberts B and Douglas M (2016) Use of otolith chemistry and acoustic telemetry to elucidate migratory contingents in barramundi Lates calcarifer. Marine and Freshwater Research 68(8), 1554- 1566. Crook DA, Lowe WH, Allendorf FW, ErÅ‘s T, Finn DS, Gillanders BM, Hadweng WL, Harrod C, Hermoso V, Jennings S, Kilada RW, Nagelkerken I, Hansen MM, Page TJ, Riginos C, Fry B and Hughes JM (2015) Human effects on ecological connectivity in aquatic ecosystems: Integrating scientific approaches to support management and mitigation. Science in the Total Environment 534(2015), 52-64. Crook DA, Morrongiello JR, King AJ, Adair BJ, Grubert MA, Roberts BH, Douglas MM and Saunders TM (2022) Environmental drivers of recruitment in a tropical fishery: Monsoonal effects and vulnerability to water abstraction. Ecological Applications, e2563. De Silva S (1980) Biology of juvenile grey mullet: a short review. Aquaculture 19(1), 21-36. Department of Agriculture Water and the Environment (2021) EPBC Act Protected Matters Report: Victoria catchment. Department of Agriculture Water and the Environment (2023a) Directory of Important Wetlands in Australia - Information sheet. Bradshaw Field Training Area - NT033. Viewed 3/12/2023, <https://www.environment.gov.au/cgi-bin/wetlands/report.pl>. Department of Agriculture Water and the Environment (2023b) Directory of Important Wetlands in Australia - Information sheet. Legune Wetlands - NT030. Viewed 20/12/2023, <https://www.environment.gov.au/cgi-bin/wetlands/report.pl>. Department of Environment and Science Queensland (2013) Coastal and subcoastal floodplain tree swamp–Melaleuca spp. and Eucalyptus spp., WetlandInfo website. Viewed 1st August 2022, <https://wetlandinfo.des.qld.gov.au/wetlands/ecology/aquatic-ecosystems- natural/palustrine/floodplain-tree-swamp/>. Department of Environment Parks and Water Security (2023) NT Parks Masterplan 2023-53. Northern Territory Government. Available online: <https://depws.nt.gov.au/__data/assets/pdf_file/0017/1203434/nt-parks-masterplan-2023- 53.pdf>. Dias MP, Lecoq M, Moniz F and Rabaça JE (2014) Can human-made saltpans represent an alternative habitat for shorebirds? Implications for a predictable loss of estuarine sediment flats. Environmental Management 53(1), 163-171. Donnelly JP, King SL, Silverman NL, Collins DP, Carrera-Gonzalez EM, Lafon-Terrazas A and Moore JN (2020) Climate and human water use diminish wetland networks supporting continental waterbird migration. Global Change Biology. DOI: 10.1111/gcb.15010. Douglas MM, Bunn SE and Davies PM (2005) River and wetland food webs in Australia’s wet–dry tropics: general principles and implications for management. Marine and Freshwater Research 56(3), 329-342. Doupe RG, Schaffer J, Knott MJ and Dicky PW (2009) A description of freshwater turtle habitat destruction by feral pigs in tropical north eastern Australia. Herpetological Conservation and Biology 4, 331-339. Driscoll, PV and Ueta, M (2002), The migration route and behaviour of Eastern Curlews Numenius madagascariensis. Ibis, 144: E119-E130. https://doi.org/10.1046/j.1474-919X.2002.00081.x Duggan M, Bayliss P and Burford MA (2019) Predicting the impacts of freshwater-flow alterations on prawn (Penaeus merguiensis) catches. Fisheries Research 215, 27-37. Duke NC, Field C, Mackenzie JR, Meynecke J-O and Wood AL (2019) Rainfall and its possible hysteresis effect on the proportional cover of tropical tidal-wetland mangroves and saltmarsh–saltpans. Marine and Freshwater Research 70(8), 1047-1055. DOI: https://doi.org/10.1071/MF18321. Duke NC, Kovacs JM, Griffiths AD, Preece L, Hill DJ, Van Oosterzee P, Mackenzie J, Morning HS and Burrows D (2017) Large-scale dieback of mangroves in Australia’s Gulf of Carpentaria: a severe ecosystem response, coincidental with an unusually extreme weather event. Marine and Freshwater Research 68(10), 1816-1829. Dulvy NK, Davidson LN, Kyne PM, Simpfendorfer CA, Harrison LR, Carlson JK and Fordham SV (2016) Ghosts of the coast: global extinction risk and conservation of sawfishes. Aquatic Conservation: Marine and Freshwater Ecosystems 26(1), 134-153. Durrell, SE dit (2000) Individual feeding specialisation in shorebirds: population consequences and conservation implications. Biological Reviews, 75: 503-518. https://doi.org/10.1111/j.1469- 185X.2000.tb00053.x Ebner BC, Morgan DL, Kerezsy A, Hardie S, Beatty SJ, Seymour JE, Donaldson JA, Linke S, Peverell S and Roberts D (2016) Enhancing conservation of A ustralian freshwater ecosystems: identification of freshwater flagship fishes and relevant target audiences. Fish and Fisheries 17(4), 1134-1151. Faggotter S, Webster I and Burford M (2013) Factors controlling primary productivity in a wet–dry tropical river. Marine and Freshwater Research 64(7), 585-598. Fernandez G and Lank DB (2008) Effects of habitat loss on shorebirds during the non-breeding season: Current knowledge and suggestions for action. Ornitologia Neotropical 19, 633-640. Finn PG, Catterall CP, and Driscoll PV (2007) Determinants of preferred intertidal feeding habitat for Eastern Curlew: A study at two spatial scales. Austral Ecology 32(2), 131-144. Finn PG & Catterall CP (2022) Towards an efficient indicator of habitat quality for Eastern Curlews on their intertidal feeding areas. Australasian Journal of Environmental Management, 30(1), 26–47. https://doi.org/10.1080/14486563.2022.2084166 Finn M and Jackson S (2011) Protecting indigenous values in water management: a challenge to conventional environmental flow assessments. Ecosystems 14, 1232-1248. Friess DA, Yando ES, Abuchahla GM, Adams JB, Cannicci S, Canty SW, Cavanaugh KC, Connolly RM, Cormier N and Dahdouh-Guebas F (2020) Mangroves give cause for conservation optimism, for now. Current Biology 30(4), R153-R154. Fukuda Y and Cuff N (2013) Vegetation communities as nesting habitat for the saltwater crocodiles in the Northern Territory of Australia. Herpetological Conservation and Biology 8(3), 641- 651. Galbraith H, Jones R, Park R, Clough J, Herrod-Julius S, Harrington B and Page G (2002) Global Climate Change and Sea Level Rise: Potential Losses of Intertidal Habitat for Shorebirds Waterbirds 25(2), 173-183. Ge ZM, Zhou X, Wang TH, Wang KY, Pei E and Yuan X (2009) Effects of Vegetative Cover Changes on the Carrying Capacity of Migratory Shorebirds in a Newly Formed Wetland, Yangtze River Estuary, China. Zoological Studies 48(6), 769-779. Gehrke PC (1997) Differences in composition and structure of fish communities associated with flow regulation in New South Wales rivers. In: Harris JH and Gehrke PC (eds), Fish and rivers in stress: the New South Wales Rivers Survey. NSW Fisheries, Cronulla, NSW. Goss-Custard JD (1977) Density-related behaviour and the possible effects of a loss of feeding grounds on wading birds. Journal of Applied Ecology 14, 721-739. Grant C and Spain A (1975) Reproduction, growth and size allometry of Valamugil seheli (Forskal)(Pisces: Mugilidae) from north Queensland inshore waters. Australian Journal of Zoology 23(4), 463-474. Grieger R, Capon SJ, Hadwen WL and Mackey B (2020) Between a bog and a hard place: a global review of climate change effects on coastal freshwater wetlands. Climatic Change, 1-19. Guest MA, Connolly RM, Lee SY, Loneragan NR and Brietfuss MJ (2006) Mechanism for the small scale movement of carbon among estuarine habitats: organic matter transfer not crab movement. Oecologia. Gwyther D (1982) Yield estimates for the banana prawn (Penaeus merguiensis de Man) in the Gulf of Papua prawn fishery. ICES Journal of Marine Science 40(3), 245-258. Halliday IA, Robins JB, Mayer DG, Staunton-Smith J and Sellin MJ (2008) Effects of freshwater flow on the year-class strength of a non-diadromous estuarine finfish, king threadfin (Polydactylus macrochir), in a dry-tropical estuary. Marine and Freshwater Research 59(2), 157-164. Halliday IA, Saunders T, Sellin MJ, Allsop Q, Robins JB, McLennan MF and Kurnoth P (2012) Flow impacts on estuarine finfish fisheries of the Gulf of Carpentaria. FRDC Project No. 2007/002. Hamilton SK (2010) Biogeochemical implications of climate change for tropical rivers and floodplains. Hydrobiologia 657(1), 19-35. Hansen B, Fuller R, Watkins D, Rogers D, Clemens R, Newman M, Woehler E and Weller D (2016) Revision of the East Asian-Australasian Flyway Population Estimates for 37 listed Migratory Shorebird Species. Melbourne. Available online: <https://www.birdlife.org.au/documents/SB-Revision-Flyway-Sept-2016.pdf>. Henry GW and Lyle JM, (Eds.), (2003) The National Recreational and Indigenous Fishing Survey. Final Report, Fisheries Research and Development Corporation Project 99/158. ISBN: 0642539847. Hughes J, Yang A, Marvanek S, Wang B, Gibbs M and Petheram C (2024a) River model calibration for the Victoria catchment. A technical report from the CSIRO Victoria River Water Resource Assessment for the National Water Grid. CSIRO, Australia. Hughes J, Yang A, Wang B, Marvanek S, Gibbs M and Petheram C (2024b) River model scenario analysis for the Victoria catchment. A technical report from the CSIRO Victoria River Water Resource Assessment for the National Water Grid. CSIRO, Australia. Hunt R, Jardine T, Hamilton S and Bunn S (2012) Temporal and spatial variation in ecosystem metabolism and food web carbon transfer in a wet-dry tropical river. Freshwater Biology 57(3), 435-450. DOI: DOI: 10.1111/j.1365-2427.2011.02708.x. Hunt SD, Guzy JC, Price SJ, Halstead BJ, Eskew EA and Dorcas ME (2013) Responses of riparian reptile communities to damming and urbanization. Biological Conservation 157, 277-284. DOI: https://doi.org/10.1016/j.biocon.2012.08.035. Integration and Application Network (2023) Integration and Application Network (ian.umces.edu/media-library). Attribution-ShareAlike 4.0 International (CC BY-SA 4.0). Victoria catchment icons. Bos taurus, Penaeus monodon adult: Jane Hawkey; Corymbia spp, Melaleuca rhaphiophylla, Generic tree rainforest 3, Avicennia germinans, Macropus fuliginosus, Pluvialis fulva, trawler: Tracey Saxby; Pennisetum ciliare, Eucalyptus camaldulensis: Kim Kraeer and Lucy Van Essen-Fishman; Lates calcarifer, Carcharhinus leucus (bull shark) 2: Dieter Tracey; Haematopus longirostris (pied ooystercatcher): Jane Thomas. https://ian.umces.edu/media-library/symbols/#overview. IPCC (2022) Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. Cambridge, UK and New York, USA. Iwamura T, Possingham HP, Chadès I, Minton C, Murray NJ, Rogers DI, Treml EA and Fuller RA (2013) Migratory connectivity magnifies the consequences of habitat loss from sea-level rise for shorebird populations. Proceedings of the Royal Society B: Biological Sciences 280(1761), 20130325. Jackson MV, Carrasco LR, Choi CY, Li J, Ma ZJ, Melville DS, Mu T, Peng HB, Woodworth BK, Yang ZY, Zhang L and Fuller RA (2019) Multiple habitat use by declining migratory birds necessitates joined-up conservation. Ecology and evolution 9(5), 2505-2515. DOI: 10.1002/ece3.4895. Jackson S, Finn M, Woodward E and Featherston P (2011a) Indigenous socio-economic values and river flows. Darwin, Northern Territory: CSIRO Ecosystem Sciences, 127. Jackson S, Finn M, Woodward E and Featherston P (2011b) Indigenous socio-economic values and river flows. Darwin, NT: CSIRO Ecosystem Sciences. James C, VanDerWal J, Capon S, Hodgson L, Waltham N, Ward D, Anderson B and Pearson R (2013) Identifying climate refuges for freshwater biodiversity across Australia, National Climate Change Adaptation Research Facility, Gold Coast, 424 pp. Jardine TD, Bond NR, Burford MA, Kennard MJ, Ward DP, Bayliss P, Davies PM, Douglas MM, Hamilton SK, Melack JM, Naiman RJ, Pettit NE, Pusey BJ, Warfe DM and Bunn SE (2015) Does flood rhythm drive ecosystem responses in tropical riverscapes? Ecology 96(3), 684-692. Jardine TD, Hunt RJ, Faggotter SJ, Valdez D, Burford MA and Bunn SE (2013) Carbon from periphyton supports fish biomass in waterholes of a wet–dry tropical river. River Research and Applications 29(5), 560-573. Jellyman D, Gehrke PC and Harris JH (2016) Australasian freshwater fisheries. In: Craig JF (ed.), Freshwater Fisheries Ecology. John Wiley & Sons, Ltd., Oxford. Junk W, Bayley P and Sparks R (1989) The flood pulse concept in river-floodplain systems. In: Dodge D (ed.), Proceedings of the International Large River Symposium. Canadian special publication of Fisheries and Aquatic Sciences. Kailola P, Williams M, Stewart P, Reichelt R, McNee A and Grieve C (1993) Australian Fisheries Resources. Bureau of Resource Sciences and the Fisheries Research and Development Corporation Canberra, Australia. Imprint Limited: Brisbane. Karim F, Kim S, Ticehurst C, Hughes J, Marvanek S, Gibbs M, Yang A, Wang B and Petheram C (2024) Floodplain inundation mapping and modelling for the Victoria catchment. A technical report from the CSIRO Victoria River Water Resource Assessment for the National Water Grid. CSIRO, Australia. Keller K, Allsop Q, Brim Box J, Buckle D, Crook DA, Douglas MM, Jackson S, Kennard MJ, Luiz OJ, Pusey BJ, Townsend SA and King AJ (2019) Dry season habitat use of fishes in an Australian tropical river. Scientific Reports 9(5677), 1-14. Kennard MJ, Mackay SJ, Pusey BJ, Olden JD and Marsh N (2010) Quantifying uncertainty in estimation of hydrologic metrics for ecohydrological studies. River Res. Applic. 26(2010), 137-156. Kenyon R, Loneragan N, Manson F, Vance D and Venables W (2004) Allopatric distribution of juvenile red-legged banana prawns (Penaeus indicus H. Milne Edwards, 1837) and juvenile white banana prawns (Penaeus merguiensis De Man, 1888), and inferred extensive migration, in the Joseph Bonaparte Gulf, northwest Australia. Journal of Experimental Marine Biology and Ecology 309(1), 79-108. Kingsford R and Norman F (2002a) Australian waterbirds—products of the continent's ecology. Emu 102(1), 47-69. Kingsford RT (2000) Ecological impacts of dams, water diversions and river management on floodplain wetlands in Australia. Austral Ecology 25(2), 109-127. Kingsford RT and Norman FI (2002b) Australian waterbirds - products of the continent's ecology. EMU 102(1), 47-69. DOI: Unsp 0158-4197/02/01004710.1071/Mu01030. Kingsford RT and Thomas RF (2004) Destruction of wetlands and waterbird populations by dams and irrigation on the Murrumbidgee River in arid Australia. Environmental Management 34(3), 383-396. Kingsford RT, Walker KF, Lester RE, Young WJ, Fairweather PG, Sammut J and Geddes MC (2011) A Ramsar wetland in crisis - the Coorong, Lower Lakes and Murray Mouth, Australia. Marine and Freshwater Research 62(3), 255-265. DOI: 10.1071/mf09315. Kirby SL and Faulks JJ (2004) Victoria River Catchment. An assessment of the physical and ecological condition of the Victoria River and its major tributaries. Northern Territory Government. Department of Infrastructure, Planning and Environment. Knapton A, Taylor AR and Crosbie RS (2024) Estimated effects of climate change and groundwater development scenarios on the Cambrian Limestone Aquifer in the eastern Victoria catchment. A technical report from the CSIRO Victoria River Water Resource Assessment for the National Water Grid. CSIRO, Australia. Kozik R, Meissner W, Listewnik B, Nowicki J, Lasecki R (2022) Differences in foraging behaviour of a migrating shorebird at stopover sites on regulated and unregulated sections of a large European lowland river. J Ornithol 163, 791–802. https://doi.org/10.1007/s10336-022- 01984-3 Larson HK, Williams RS and Hammer MP (2013) An annotated checklist of the fishes of the Northern Territory, Australia. Zootaxa 3696(1), 1–293-291–293. Layman CA (2007) What can stable isotope ratios reveal about mangroves as fish habitat? Bulletin of Marine Science 80, 513-527. Leahy SM and Robins JB (2021) River flows affect the growth of a tropical finfish in the wet-dry rivers of northern Australia, with implications for water resource development. Hydrobiologia 848(18), 4311-4333. Lear KO, Gleiss AC, Whitty JM, Fazeldean T, Albert J, Green N, Ebner BC, Thorburn DC, Beatty SJ and Morgan DL (2019) Recruitment of a critically endangered sawfish into a riverine nursery depends on natural flow regimes. Scientific Reports 9(1), 17071. Lear KO, Morgan DL, Whitty JM, Beatty SJ and Gleiss AC (2021) Wet season flood magnitude drives resilience to dry season drought of a euryhaline elasmobranch in a dry-land river. Science of The Total Environment 750, 142234. Lei W, Masero JA, Piersma T, Zhu B, Yang H-Y and Zhang Z (2018) Alternative habitat: the importance of the Nanpu Saltpans for migratory waterbirds in the Chinese Yellow Sea. Bird Conservation International 28(4), 549-566. Leigh C and Sheldon F (2008) Hydrological changes and ecological impacts associated with water resource development in large floodplain rivers in the Australian tropics. River Res. Applic. 24(2008), 1251-1270. Marchant S and Higgins P (1990) Handbook of Australian, New Zealand and Antarctic Birds. Volume 1, Ratites to ducks. Oxford University Press, Melbourne. Marin B, Baumar J, Quintero Antonio, Bussiere Dany and Dodson Julian J (2003) Reproduction and recruitment of white mullet (Mugil curema) to a tropical lagoon (Margarita Island, Venezuela) as revealed by otolith microstructure. Marsh N, Sheldon F, Wettin P, Taylor C and Barma D (2012) Guidance on ecological responses and hydrological modelling for low-flow water planning, March 2012. The National Water Commission, Canberra. McClenachan G, Witt M and Walters LJ (2021) Replacement of oyster reefs by mangroves: Unexpected climateâ€driven ecosystem shifts. Global Change Biology 27(6), 1226-1238. McGinness HM (2016) Waterbird responses to flooding, stressors and threats. A report prepared for the Murray-Darling Freshwater Research Centre as part of the Environmental Water Knowledge and Research Project. CSIRO, Canberra, Australia. McJannet D, Marvanek S, Kinsey-Henderson A, Petheram C and Wallace J (2014) Persistence of in- stream waterholes in ephemeral rivers of tropical northern Australia and potential impacts of climate change. Marine and Freshwater Research 65(12), 1131-1144. DOI: 10.1071/MF14035. McMahon TA and Finlayson BL (2003) Droughts and anti-droughts: the low flow hydrology of Australian rivers. Freshwater Biology 48(2003), 1147–1160. Meynecke J-O, Lee S and Duke N (2008) Linking spatial metrics and fish catch reveals the importance of coastal wetland connectivity to inshore fisheries in Queensland, Australia. Biological Conservation 141(4), 981-996. Meynecke J, Lee S, Grubert M, Brown I, Montgomery S, Gribble N, Johnston D and Gillson J (2010) Evaluating the environmental drivers of mud crab (Scylla serrata) catches in Australia. Final Report 2002/012. The Fisheries Research and Development Corporation and Griffith University. Midgley S (1981) A biological resource study of fresh waters conducted during August-September, 1981. Report for the Fisheries Division Department of Primary Production, NT. Milton D, Yarrao M, Fry G and Tenakanai C (2005) Response of barramundi, Lates calcarifer, populations in the Fly River, Papua New Guinea to mining, fishing and climate-related perturbation. Marine and Freshwater Research 56(7), 969-981. Mitchell PJ, O'Grady AP, Pinkard EA, Brodribb TJ, Arndt SK, Blackman CJ, Duursma RA, Fensham RJ, Hilbert DW and Nitschke CR (2016) An ecoclimatic framework for evaluating the resilience of vegetation to water deficit. Global Change Biology 22(5), 1677-1689. Mitsch WJ, Bernal B and Hernandez ME (2015) Ecosystem services of wetlands. Taylor & Francis. Mohd-Azlan J, Noske RA and Lawes MJ (2012) Avian species-assemblage structure and indicator bird species of mangroves in the Australian monsoon tropics Emu - Austral Ornithology Taylor & Francis MELBOURNE Moore BR, Simpfendorfer CA, Newman SJ, Stapley JM, Allsop Q, Sellin MJ and Welch DJ (2012) Spatial variation in life history reveals insight into connectivity and geographic population structure of a tropical estuarine teleost: king threadfin, Polydactylus macrochir. Fisheries Research 125, 214-224. Morgan D, Whitty J, Allen M, Ebner B, Keleher J, Gleiss A and Beatty S (2016) Wheatstone Environmental Offsets-Barriers to sawfish migrations. A report for Chevron Australia and the Western Austalian Marine Science Institution. Freshwater Fish Group & Fish Health Unit, Centre for Fish & Fisheries Research, Murdoch University. Morgan DL, Somaweera R, Gleiss AC, Beatty SJ and Whitty JM (2017) An upstream migration fought with danger: freshwater sawfish fending off sharks and crocodiles. Ecology 98(5), 1465-1467. Motomura H, Iwatsuki Y, Kimura S and Yoshino T (2000) Redescription of Polydactylus macrochir (günther, 1867), a senior synonym of P. sheridani (macleay, 1884)(perciformes: Polynemidae). Ichthyological research 47(3), 327-333. Naughton JM, O'Dea K and Sinclair AJ (1986) Animal foods in traditional Australian aboriginal diets: polyunsaturated and low in fat. Lipids 21(11), 684-690. Ndehedehe CE, Burford MA, Stewart-Koster B and Bunn SE (2020a) Satellite-derived changes in floodplain productivity and freshwater habitats in northern Australia (1991–2019). Ecological indicators 114, 106320. Ndehedehe CE, Onojeghuo AO, Stewart-Koster B, Bunn SE and Ferreira VG (2021) Upstream flows drive the productivity of floodplain ecosystems in tropical Queensland. Ecological indicators 125, 107546. Ndehedehe CE, Stewart-Koster B, Burford MA and Bunn SE (2020b) Predicting hot spots of aquatic plant biomass in a large floodplain river catchment in the Australian wet-dry tropics. Ecological indicators 117, 106616. Nebel S, Porter JL and Kingsford RT (2008) Long-term trends of shorebird populations in eastern Australia and impacts of freshwater extraction. Biological Conservation 141(4), 971-980. Nielsen D, Merrin L, Pollino C, Karim F, Stratford D and O’Sullivan J (2020) Climate change and dam development: Effects on wetland connectivity and ecological habitat in tropical wetlands. Ecohydrology 2020, 13pgs. DOI: 10.1002/eco.2228. Nielsen DL, Cook RA, Ning N, Gawne B and Petrie R (2015) Carbon and nutrient subsidies to a lowland river following floodplain inundation. Marine and Freshwater Research 67(9), 1302- 1312. Nilsson C and Berggren K (2000) Alterations of riparian ecosystems caused by river regulation. BioScience 50(9), 783-792. Northern Territory Government (2022) Fishery licences Viewed 28th March 2022, <https://nt.gov.au/marine/commercial-fishing/fishery-licenses>. Novak P, Bayliss P, Crook DA, Garcia EA, Pusey BJ and MM D (2017) Do upstream migrating, juvenile amphidromous shrimps, provide a marine subsidy to river ecosystems? Freshwater Biology 62(5), 880-893. DOI: DOI: 10.1111/fwb.12907. NT Government (2023) Territory Water Plan: A plan to deliver water security for all Territorians, now and into the future. Office of Water Security. Viewed 17 September 2024, https://watersecurity.nt.gov.au/territory-water- plan#:~:text=Priority%20actions%20in%20the%20plan,and%20productivity%20in%20water% 20use. Nye ER, Dickman CR and Kingsford RT (2007) A wild goose chase-temporal and spatial variation in the distribution of the Magpie Goose (Anseranas semipalmata) in Australia. EMU 107(1), 28- 37. DOI: 10.1071/MU05012. O'Mara K, Venarsky M, Stewart-Koster B, McGregor GB, Schulz C, Kainz M, Marshall J and Bunn SE (2021) Connectivity of fish communities in a tropical floodplain river system and predicted impacts of potential new dams. Science of The Total Environment 788, 147785. Olden JD and Poff L (2003) Redundancy and the choice of hydrologic indices for characterizing streamflow regimes. River Res. Applic. 19(2003), 101-121. Owers CJ, Woodroffe CD, Mazumder D and Rogers K (2022) Carbon storage in coastal wetlands is related to elevation and how it changes over time. Estuarine, Coastal and Shelf Science, 107775. Palmer C and Smit N (2019) Limmen Bight Marine Park: Marine and coastal biodiversity values. Department of Environment and Natural Resources, Northern Territory Government. Parks Australia (2023) Joseph Bonaparte Gulf Marine Park. Commonwealth of Australia. Viewed 19/12/2023, <https://parksaustralia.gov.au/marine/parks/north/joseph-bonaparte-gulf/>. Pati SG, Paital B, Panda F, Jena S and Sahoo DK (2023) Impacts of Habitat Quality on the Physiology, Ecology, and Economical Value of Mud Crab Scylla sp.: A Comprehensive Review. Water 15(11), 2029. Pelicice FM, Pompeu PS and Agostinho AA (2015) Large reservoirs as ecological barriers to downstream movements of Neotropical migratory fish. Fish and Fisheries 2015(16), 697-715. Petheram C, Philip S, Watson I, Bruce C and Chilcott C (eds) (2024) Water resource assessment for the Victoria catchment. A report from the CSIRO Victoria River Water Resource Assessment for the National Water Grid. CSIRO, Australia. Pettit N, Dowe J and Dixon I (2016) Riparian vegetation of tropical Northern Australia. In: Capon S, James C and M R (eds), Vegetation of Australian riverine landscapes: Biology, ecology and management (pp. 221-237). Pettit NE, Naiman RJ, Warfe DM, Jardine TD, Douglas MM, Bunn SE and Davies PM (2017) Productivity and Connectivity in Tropical Riverscapes of Northern Australia: Ecological Insights for Management. Ecosystems 2017(20), 492-514. Piersma T & Baker A J (2000) Life history characteristics and the conservation of migratory shorebirds. In L. M. Gosling, & W. J. Sutherland (Eds.), Behaviour and conservation (pp. 105- 124). (Conservation Biology Series; Vol. 2). Cambridge University Press. Plagányi É, Deng RA, Hutton T, Kenyon R, Lawrence E, Upston J, Miller M, Moeseneder C, Pascoe S and Blamey L (2021) From past to future: understanding and accounting for recruitment variability of Australia’s redleg banana prawn (Penaeus indicus) fishery. ICES Journal of Marine Science 78(2), 680-693. Plagányi É, Kenyon R, Blamey L, Burford M, Robins JB, Jarrett A, Laird A, Hughes J, Kim S and Hutton T (2022) Ecological modelling of the impacts of water development in the Gulf of Carpentaria with particular reference to impacts on the Northern Prawn Fishery. Plagányi É, Kenyon R, Blamey L, Robins J, Burford M, Pillans R, Hutton T, Hughes J, Kim S and Deng R (2024) Integrated assessment of river development on downstream marine fisheries and ecosystems. Nature Sustainability 7(1), 31-44. Plagányi ÉE, Blamey LK, Deng RA and Miller M (2023) Accounting for risk-catch-cost trade-offs in a harvest strategy for a small, highly variable fishery. Fisheries Research 258, 106518. Poff NL, Olden JD, Merritt DM and Pepin DM (2007) Homogenization of regional river dynamics by dams and global biodiversity implications. PNAS 104(4), 5732-5737. Poff NL and Zimmerman JK (2010) Ecological responses to altered flow regimes: a literature review to inform the science and management of environmental flows. Freshwater biology 55(1), 194-205. Poiani A (2006) Effects of floods on distribution and reproduction of aquatic birds. Advances in Ecological Research, Vol 39 39, 63-83. DOI: 10.1016/s0065-2504(06)39004-6. Pollino C, Barber E, Buckworth R, Cadiegues M, Deng R, Ebner B, Kenyon R, Liedloff A, Merrin L, Moeseneder C, Morgan D, Nielsen D, O'Sullivan J, Ponce Reyes R, Robson B, Stratford D, Stewart-Koster B and Turschwell M (2018) Synthesis of knowledge to support the assessment of impacts of water resource development to ecological assets in northern Australia: asset analysis. A technical report to the Australian Government from the CSIRO Northern Australia Water Resource Assessment, part of the National Water Infrastructure Development Fund: Water Resource Assessments. CSIRO, Canberra. Pusey BJ (2004) Freshwater fishes of north-eastern Australia. CSIRO Publishing. Pusey BJ, Jardine TD, Bunn SE and Douglas MM (2020) Sea catfishes (Ariidae) feeding on freshwater floodplains of northern Australia. Marine and Freshwater Research 71(12), 1628- 1639. DOI: https://doi.org/10.1071/MF20012. Pusey BJ, Kennard MJ and Arthington AH (2004) Freshwater Fishes of North-Eastern Australia. CSIRO Publishing, 684. PyÅ¡ek P, Hulme PE, Simberloff D, Bacher S, Blackburn TM, Carlton JT, Dawson W, Essl F, Foxcroft LC, Genovesi P, Jeschke JM, Kühn I, Liebhold AM, Mandrak NE, Meyerson LA, Pauchard A, Pergl J, Roy HE, Seebens H, van Kleunen M, Vilà M, Wingfield MJ and Richardson DM (2020) Scientists’ warning on invasive alien species. Biological Reviews 2020(95), 1511–1534. DOI: doi: 10.1111/brv.12627. Richter BD, Baumgartner JV, Powell J and Braun DP (1996) A method for assessing hydrologic alteration within ecosystems. Conservation biology 10(4), 1163-1174. Roberts BH, Morrongiello JR, King AJ, Morgan DL, Saunders TM, Woodhead J and Crook DA (2019) Migration to freshwater increases growth rates in a facultatively catadromous tropical fish. Oecologia 191(2), 253-260. Roberts BH, Morrongiello JR, Morgan DL, King AJ, Saunders TM, Banks SC and Crook DA (2023) Monsoonal wet season influences the migration tendency of a catadromous fish (barramundi Lates calcarifer). Journal of Animal Ecology. Robertson AI (1986) Leaf-burying crabs: Their influence on energy flow and export from mixed mangrove forests (Rhizophora spp.) in northeastern Australia. Journal of Experimental Marine Biology and Ecology 102(2-3), 237-248. Robertson AI and Alongi DM (2016) Massive turnover rates of fine root detrital carbon in tropical Australian mangroves. Oecologia 180(3), 841-851. Robertson AI and Duke NC (1990) Mangrove fish-communities in tropical Queensland, Australia: spatial and temporal patterns in densities, biomass and community structure. Marine Biology 104, 369-379. Robins JB, Halliday IA, Staunton-Smith J, Mayer DG and Sellin MJ (2005) Freshwater-flow requirements of estuarine fisheries in tropical Australia: a review of the state of knowledge and application of a suggested approach. Marine and Freshwater Research 56(3), 343-360. Robins JB, Northrop AR, Grubert M and Buckworth RC (2020) Understanding environmental and fisheries factors causing fluctuations in mud crab and blue swimmer crab fisheries in northern Australia to inform harvest strategies. Department of Agriculture and Fisheries. Brisbane. Rocha AR, Ramos JA, Paredes T and Masero JA (2017) Coastal saltpans as foraging grounds for migrating shorebirds: an experimentally drained fish pond in Portugal. Hydrobiologia 790(1), 141-155. Ruscoe IM, Shelley CC and Williams GR (2004) The combined effects of temperature and salinity on growth and survival of juvenile mud crabs (Scylla serrata ForskÃ¥l). Aquaculture 238(1-4), 239-247. Rushing CS, Marra PP and Dudash MR (2016) Winter habitat quality but not long-distance dispersal influences apparent reproductive success in a migratory bird. Ecology 97(5), 1218-1227. DOI: 10.1890/15-1259.1. Russell D and Garrett R (1983a) Use by juvenile barramundi, Lates calcarifer (Bloch), and other fishes of temporary supralittoral habitats in a tropical estuary in northern Australia. Marine and freshwater research 34(5), 805-811. Russell D and Garrett R (1985) Early life history of barramundi, Lates calcarifer (Bloch), in north- eastern Queensland. Marine and Freshwater Research 36(2), 191-201. Russell DJ and Garrett RN (1983b) Use by juvenile barramundi, Lates calcarifer (Bloch), and other fishes of temporary supralittoral habitats in a tropical estuary in northern Australia. Australian Journal of Marine and Freshwater Research 34(5), 805-811. Salimi S, Almuktar SA and Scholz M (2021) Impact of climate change on wetland ecosystems: A critical review of experimental wetlands. Journal of Environmental Management 286, 112160. Schaffer-Smith D, Swenson JJ, Barbaree B and Reiter ME (2017) Three decades of Landsat-derived spring surface water dynamics in an agricultural wetland mosaic; Implications for migratory shorebirds. Remote Sensing of Environment 193, 180-192. DOI: 10.1016/j.rse.2017.02.016. Schmutz S and Sendzimir J (eds) (2018) Riverine Ecosystem Management Science for Governing Towards a Sustainable Future. Springer Open. Schofield KA, Alexander LC, Ridley CE, Vanderhoof MK, Fritz KM, Autrey BC, DeMeester JE, Kepner WG, Lane CR, Leibowitz SG and Pollard AI (2018) Biota connect aquatic habitats throughout freshwater ecosystem mosaics. Journal of the American Water Resources Association 54(2), 372-399. Sheldon F (2017) Characterising the ecological effects of changes in the ‘low-flow hydrology’ of the Barwon-Darling River. Advice to the Commonwealth Environmental Water Holder Office. Short AD (2020) Australian coastal systems: beaches, barriers and sediment compartments. Springer. Sims N, Anstee J, Barron O, Botha E, Lehmann E, Li L, McVicar T, Paget M, Ticehurst C, Van Niel T and Warren G (2016) Earth observation remote sensing. A technical report from the CSIRO Northern Australia Water Resource Assessment to the Government of Australia. CSIRO, Australia. Skilleter GA, Olds A, Loneragan NR and Zharikov Y (2005) The value of patches of intertidal seagrass to prawns depends on their proximity to mangroves. Mar Bio 147, 353-365. Smyth C and Turner J (2019) Natural values and resource use in the Limmen Bight Marine Region. Australian Marine Conservation Society. Stanford CB, Iverson JB, Rhodin AGJ, Paul van Dijk P, Mittermeier RA, Kuchling G, Berry KH, Bertolero A, Bjorndal KA, Blanck TEG, Buhlmann KA, Burke RL, Congdon JD, Diagne T, Edwards T, Eisemberg CC, Ennen JR, Forero-Medina G, Frankel M, Fritz U, Gallego-GarcÃa N, Georges A, Gibbons JW, Gong S, Goode EV, Shi HT, Hoang H, Hofmeyr MD, Horne BD, Hudson R, Juvik JO, Kiester RA, Koval P, Le M, Lindeman PV, Lovich JE, Luiselli L, McCormack TEM, Meyer GA, Páez VP, Platt K, Platt SG, Pritchard PCH, Quinn HR, Roosenburg WM, Seminoff JA, Shaffer HB, Spencer R, Van Dyke JU, Vogt RC and Walde AD (2020) Turtles and Tortoises Are in Trouble. Current Biology 30(12), R721-R735. DOI: https://doi.org/10.1016/j.cub.2020.04.088. Staples D (1980) Ecology of juvenile and adolescent banana prawns, Penaeus merguiensis, in a mangrove estuary and adjacent off-shore area of the Gulf of Carpentaria. I. Immigration and settlement of postlarvae. Marine and Freshwater Research 31(5), 635-652. Staples D and Heales D (1991) Temperature and salinity optima for growth and survival of juvenile banana prawns Penaeus merguiensis. Journal of Experimental Marine Biology and Ecology 154(2), 251-274. Staples D and Vance D (1986) Emigration of juvenile banana prawns Penaeus merguiensis from a mangrove estuary and recruitment to offshore areas in the wet-dry tropics of the Gulf of Carpentaria, Australia. Marine Ecology Progress Series 27(239), 52. Stratford D, Kenyon R, Pritchard J, Merrin L, Linke S, Ponce Reyes R, Buckworth R, Castellazzi P, Costin B, Deng R, Gannon R, Gao S, Gilbey S, Lachish S, McGinness H and Waltham N (2024a) Ecological assets of the Victoria catchment to inform water resource assessments. A technical report from the CSIRO Victoria River Water Resource Assessment for the National Water Grid. CSIRO, Australia. Stratford D, Merrin L, Linke S, Kenyon R, Ponce Reyes R, Buckworth R, Deng RA, McGinness H, Pritchard J, Seo L and Waltham N (2024b) Assessment of the potential ecological outcomes of water resource development in the Roper catchment. A technical report from the CSIRO Roper River Water Resource Assessment for the National Water Grid. CSIRO, Australia. Tanimoto M, Robins JB, O’Neill MF, Halliday IA and Campbell AB (2012) Quantifying the effects of climate change and water abstraction on a population of barramundi (Lates calcarifer), a diadromous estuarine finfish. Marine and Freshwater Research 63(8), 715-726. Tarr NM, Simons TR and Pollock KH (2010) An Experimental Assessment of Vehicle Disturbance Effects on Migratory Shorebirds. Journal of Wildlife Management 74(8), 1776-1783. DOI: 10.2193/2009-105. Thimdee W, Deein G, Sangrungruang C and Matsunaga K (2001) Stable carbon and nitrogen isotopes of mangrove crabs and their food sources in a mangrove fringed estuary in Thailand. Benthos Research 56, 73-80. Thomson SA (2000) The identification of the holotype of Chelodina oblonga (Testudines: Chelidae) with a discussion of taxonomic implications. Chelonian Conservation Biology 3(4), 745–749. Tockner K, Pusch M, Borchardt D and Lorang MS (2010) Multiple stressors in coupled river– floodplain ecosystems. Freshwater Biology 55, 135-151. Traill LW, Bradshaw CJA, Delean S and Brook BW (2010) Wetland conservation and sustainable use under global change: a tropical Australian case study using magpie geese. Ecography 33(5), 818-825. DOI: 10.1111/j.1600-0587.2009.06205.x. Traill LW, Bradshaw CJA, Field HE and Brook BW (2009a) Climate Change Enhances the Potential Impact of Infectious Disease and Harvest on Tropical Waterfowl. Biotropica 41(4), 414-423. DOI: 10.1111/j.1744-7429.2009.00508.x. Traill LW, Whitehead PJ and Brook BW (2009b) How will climate change affect plant-herbivore interactions? A tropical waterbird case study. EMU 109(2), 126-134. DOI: 10.1071/MU09003. van Dam R, Bartolo R and Bayliss P (2008) Identification of ecological assets, pressures and threats. Environmental Research Institute of the Supervising Scientist: Darwin, Australia, 14-161. van de Pol, M, Bailey, LD, Frauendorf, M, Allen, AM, van der Sluijs, M, Hijner, N, Brouwer, L, de Kroon, H, Jongejans E, Ens, BJ. (2024) Sea-level rise causes shorebird population collapse before habitats drown. Nat. Clim. Chang. 14, 839–844. https://doi.org/10.1038/s41558-024- 02051-w van Mantgem PJ, Stephenson NL, Byrne JC, Daniels LD, Franklin JF, Fulé PZ, Harmon ME, Larson AJ, Smith JM and Taylor AH (2009) Widespread increase of tree mortality rates in the western United States. Science 323(5913), 521-524. Vance D, Haywood M, Heales D, Kenyon R and Loneragan N (1998) Seasonal and annual variation in abundance of postlarval and juvenile banana prawns Penaeus merguiensis and environmental variation in two estuaries in tropical northeastern Australia: A six-year study. Marine Ecology Progress Series 163, 21-36. Vance D, Haywood M and Staples D (1990) Use of a mangrove estuary as a nursery area by postlarval and juvenile banana prawns, Penaeus merguiensis de Man, in northern Australia. Estuarine, Coastal and Shelf Science 31(5), 689-701. Vance DJ and Rothlisberg PC (2020) The biology and ecology of the banana prawns: Penaeus merguiensis de Man and P. indicus H. Milne Edwards. Advances in Marine Biology. Elsevier. Velasquez C (1992) Managing artificial saltpans as a waterbird habitat: species' responses to water level manipulation. Colonial Waterbirds, 43-55. Waltham N, Burrows D, Butler B, Wallace J, Thomas C, James C and Brodie J (2013a) Waterhole ecology in the Flinders and Gilbert catchments. A technical report to the Australian Government from the CSIRO Flinders and Gilbert Agricultural Resource Assessment, part of the North Queensland Irrigated Agriculture Strategy, 460. Waltham N, Burrows D, Butler B, Wallace J, Thomas C, James C and Brodie J (2013b) Waterhole ecology in the Flinders and Gilbert catchments. A technical report to the Australian Government from the CSIRO Flinders and Gilbert Agricultural Resource Assessment, part of the North Queensland Irrigated Agriculture Strategy. CSIRO Water for a Healthy Country and Sustainable Agriculture flagships, Australia. Wang Y-G and Haywood M (1999) Size-dependent natural mortality of juvenile banana prawns Penaeus merguiensis in the Gulf of Carpentaria, Australia. Marine and Freshwater Research 50(4), 313-317. Ward JV and Stanford J (1995) Ecological connectivity in alluvial river ecosystems and its disruption by flow regulation. Regulated rivers: research & management 11(1), 105-119. Warfe DM, Pettit NE, Davies PM, Pusey BJ, Hamilton S, Kennard MJ, Townsend SA, Bayliss P, Ward D and Douglas M (2011) The ‘wet–dry’ in the wet–dry tropics drives river ecosystem structure and processes in northern Australia. Freshwater biology 56(11), 2169-2195. Welch D, Robins J, Saunders T and et al. (2014) Implications of climate change impacts on fisheries resources of northern Australia. Part 2: species profiles. Fisheries Research Development Corporation, Final Report project 2010/565., Jame Cook University, Townsville. Wen L, Ling J, Saintilan N and Rogers K (2009) An investigation of the hydrological requirements of River Red Gum (Eucalyptus camaldulensis) Forest, using Classification and Regression Tree modelling. Ecohydrology: Ecosystems, Land and Water Process Interactions, Ecohydrogeomorphology 2(2), 143-155. West AD, Goss-Custard JD, dit Durell SE and Stillman, RA (2005) Maintaining estuary quality for shorebirds: towards simple guidelines. Biological Conservation 123(2), 211-224. Whitfield A, Panfili J and Durand J-D (2012) A global review of the cosmopolitan flathead mullet Mugil cephalus Linnaeus 1758 (Teleostei: Mugilidae), with emphasis on the biology, genetics, ecology and fisheries aspects of this apparent species complex. Reviews in Fish Biology and Fisheries 22(3), 641-681. Wilton D, Olsen J, Stacey N, Bresson C and Van Wyk P (2018) Maningrida Aboriginal coastal fishing enterprise: lessons learned and future prospects. National Native Title Conference 2018. Yang A, Petheram C, Marvanek S, Baynes F, Rogers L, Ponce Reyes R, Zund P, Seo L, Hughes J, Gibbs M, Wilson PR, Philip S and Barber M (2024) Assessment of surface water storage options in the Victoria and Southern Gulf catchments. A technical report from the CSIRO Victoria River and Southern Gulf Water Resource Assessments for the National Water Grid. CSIRO Australia Zhang K, Liu H, Li Y, Xu H, Shen J, Rhome J and Smith III TJ (2012) The role of mangroves in attenuating storm surges. Estuarine, Coastal and Shelf Science 102, 11-23. Asset assessment nodes and their weightings Flow regime change for each asset is assessed within the downstream subcatchments from the river system model nodes considering the significance and presence of assets within each subcatchment. Further information on the distribution of species and habitats and the rational for node selection and weighting for each asset is provided in Stratford et al. (2024). Apx Table A-1 River system model nodes used for each of the ecological assets Node 90300000 is the designated end-of-system node unless otherwise stated. NODE ID BARRAMUNDI CATFISH GRUNTER MULLET SAWFISH THREADFIN 81101135 56.5 50.3 24.8 0 67.3 0 81100171 34.5 64.3 53.6 0 45.9 0 81100040 61.4 72.5 77.6 0 67.7 0 81100060 81.3 84.1 73.3 0 93.4 0 81100120 95.6 71.8 78.5 0 66.6 0 81100160 20.9 52.6 38.5 0 48.3 0 81100730 56.2 53.2 64.8 0 69.6 0 81100740 5.0 61.4 29.1 0 48.6 0 81101010 78.3 83.7 75.0 0 86.0 0 81101070 64.6 100 70.9 0 59.7 0 81101100 65.4 31.1 78.0 0 26.1 0 81101130 67.8 55.2 37.5 0 84.4 0 81102320 47.8 71.4 55.4 0 70.4 0 81102380 66.1 63.1 92.6 0 68.6 0 81102510 34.1 53.8 27.2 0 43.21 0 81102530 47.97 69.71 62.4 0 52.17 0 81100070 100 76.09 64.71 0 81.87 0 81100180 80.55 77.42 57.08 0 77.29 0 81101660 44.9 80.1 51.24 0 73.05 0 81100170 35.41 60.05 40.75 0 85.26 0 81101670 39.38 59.05 33.73 0 67.87 0 81101700 40.64 59.47 39.86 0 71.46 0 81100750 35.44 59.7 30.79 0 51.76 0 81100001 96.65 67.77 66.71 0 85 0 81100003 95.42 65.65 58.65 0 82.38 0 NODE ID BARRAMUNDI CATFISH GRUNTER MULLET SAWFISH THREADFIN 81100002 98.37 71.57 54.98 0 77.35 0 81101131 40.42 69.71 44.93 0 100 0 81101132 33.35 61.56 34.06 0 56.23 0 81101133 37.22 66.23 35.42 0 59.13 0 81101134 35.51 61 41.62 0 84.44 0 81100181 92.14 67.26 46.8 0 77.04 0 81100182 92.16 79.3 59.42 0 70.55 0 81100183 79.19 68.82 60.67 0 74.79 0 81102321 42.65 59.96 48.61 0 62.15 0 81100172 37.42 54.6 24.84 0 68.68 0 81102322 54.44 78.98 67.43 0 55.69 0 81100061 63.95 76.59 76.27 0 83.21 0 81100062 62.97 78.82 80.04 0 84.62 0 81100063 70.48 80.06 89.41 0 81.75 0 81100140 85.56 90.22 100 0 53.41 0 81100000 100 0 0 100 100 100 Apx Table A-2 River system model nodes used for each of the ecological assets- waterbirds Node 90300000 is the designated end-of-system node unless otherwise stated. NODE ID COLONIAL AND SEMI COLONIAL WADERS CRYPTIC WADERS SHOREBIRDS SWIMMERS, DIVERS AND GRAZERS 81101135 58.38 0 25.7 68.42 81100171 54.46 0 45.24 25.14 81100040 55.19 0 37.97 87.85 81100060 74.36 0 38.6 87.34 81100120 61.64 0 80.77 83.47 81100160 59.1 0 39.34 17.26 81100730 31.16 0 24.85 39.4 81100740 42.92 0 12.56 21.61 81101010 76.17 0 35.59 76.93 81101070 72.61 0 32.97 77.99 81101100 100 0 15 33.39 81101130 46.49 0 29.67 79.01 81102320 67.05 0 22.36 64.48 81102380 78.57 0 87.9 89.41 81102510 20.6 0 14.58 31.51 81102530 83.58 0 34.14 28.26 81100070 89.9 100 93.08 83.46 81100180 80.97 100 100 97.57 81101660 61.64 100 24.95 59.37 81100170 40.48 0 20.39 46.86 81101670 62.39 0 25.7 19.13 81101700 34.52 0 13.68 9.69 81100750 41.41 0 12.92 20.54 81100001 84.31 100 90.14 100 81100003 96.84 100 78.25 91.54 81100002 92.62 100 67.36 90.94 81101131 86.26 100 21.01 88.04 81101132 42.27 100 10.39 36.85 81101133 41.46 0 9.41 36.48 81101134 39.58 0 20.1 48.09 81100181 85.96 0 68.16 85.36 81100182 77.65 0 78.39 81.5 81100183 68.39 0 71.81 83.88 81102321 67.34 0 25.65 51.38 81100172 42.23 0 39.98 18.08 NODE ID COLONIAL AND SEMI COLONIAL WADERS CRYPTIC WADERS SHOREBIRDS SWIMMERS, DIVERS AND GRAZERS 81102322 75.19 0 42.29 36.97 81100061 72.39 0 28.12 75.93 81100062 73.81 0 31.98 76.54 81100063 73.54 0 41.86 75.45 81100140 54.62 0 99.84 75.74 81100000 0 0 100 0 Apx Table A-3 River system model nodes used for each of the ecological assets- turtles, prawns and other species Node 90300000 is the designated end-of-system node unless otherwise stated. NODE ID BANANA PRAWNS FRESHWATER TURTLES MUD CRABS 81101135 0 32.8 0 81100171 0 23.58 0 81100040 0 99.88 0 81100060 0 83.67 0 81100120 0 87.89 0 81100160 0 24.72 0 81100730 0 30.77 0 81100740 0 21.21 0 81101010 0 89.44 0 81101070 0 77.04 0 81101100 0 100 0 81101130 0 50.19 0 81102320 0 68.79 0 81102380 0 58.77 0 81102510 0 43.13 0 81102530 0 69.49 0 81100070 0 61.45 0 81100180 0 59.84 0 81101660 0 43.27 0 81100170 0 35.04 0 81101670 0 26.49 0 81101700 0 24.11 0 81100750 0 23.68 0 81100001 0 77.27 0 81100003 0 70.99 0 81100002 0 77.5 0 81101131 0 35.32 0 81101132 0 39.68 0 81101133 0 38.45 0 81101134 0 35.18 0 81100181 0 68 0 81100182 0 74.68 0 81100183 0 72.77 0 81102321 0 54.28 0 81100172 0 26.48 0 81102322 0 61.13 0 NODE ID BANANA PRAWNS FRESHWATER TURTLES MUD CRABS 81100061 0 54.26 0 81100062 0 53.27 0 81100063 0 60.12 0 81100140 85.56 90.22 100 81100000 100 0 0 Apx Table A-4 River system model nodes used for each of the ecological assets- habitats Node 90300000 is the designated end-of-system node unless otherwise stated. NODE ID FLOODPLAIN WETLANDS INCHANNEL WATERHOLES MANGROVES SALTPANS AND SALT FLATS SURFACEWATER DEPENDENT VEGETATION 81101135 0 100 0 0 100 81100171 0 100 0 0 100 81100040 0 100 0 0 100 81100060 0 100 0 0 100 81100120 0 100 0 0 100 81100160 0 100 0 0 100 81100730 0 100 0 0 100 81100740 0 100 0 0 100 81101010 0 100 0 0 100 81101070 0 100 0 0 100 81101100 0 100 0 0 100 81101130 0 100 0 0 100 81102320 0 100 0 0 100 81102380 0 100 0 0 100 81102510 0 100 0 0 100 81102530 0 100 0 0 100 81100070 0 0 0 0 100 81100180 0 0 0 0 100 81101660 100 100 0 0 100 81100170 0 100 0 0 100 81101670 0 100 0 0 100 81101700 0 100 0 0 100 81100750 0 100 0 0 100 81100001 100 100 0 0 100 81100003 100 100 0 0 100 81100002 100 0 0 0 100 81101131 0 100 0 0 100 81101132 100 100 0 0 100 81101133 0 100 0 0 100 81101134 0 100 0 0 100 81100181 0 100 0 0 100 81100182 0 100 0 0 100 81100183 0 100 0 0 100 81102321 0 100 0 0 100 81100172 0 100 0 0 100 NODE ID FLOODPLAIN WETLANDS INCHANNEL WATERHOLES MANGROVES SALTPANS AND SALT FLATS SURFACEWATER DEPENDENT VEGETATION 81102322 0 100 0 0 100 81100061 0 100 0 0 100 81100062 0 100 0 0 100 81100063 0 100 0 0 100 81100140 0 100 0 0 100 81100000 0 0 100 100 0 Asset hydrometrics selected in the flow dependencies modelling Hydrometrics for each asset are selected to represent and consider aspects of habitat function, life-history and flow-ecology. An overview of flow dependencies and flow ecology for each asset is provided in Stratford et al. (2024). Metrics used in the analysis are provided in Apx Table B-5 with definitions. Apx Table B-1 The hydrometrics selected as important for each of the ecological assets METRIC BARRAMUNDI CATFISH GRUNTER MULLET SAWFISH THREADFIN meanAnMinMov30 1 1 1 1 1 1 meanQ10 1 1 1 1 1 lowQDuration90singleSpell 1 1 1 1 1 JDayAnMax 1 1 1 1 1 meanQ12 1 1 1 1 meanQ11 1 1 1 1 meanQ03 1 1 1 1 meanQ02 1 1 1 1 meanQ01 1 1 1 1 meanAnMinMov7 1 1 1 1 meanAnMinMov3 1 1 1 1 lowQDuration99singleSpell 1 1 1 1 lowQDuration75singleSpell 1 1 1 1 JDayAnMin 1 1 1 1 meanQ09 1 1 1 meanAnMaxMov7 1 1 1 meanAnMaxMov30 1 1 1 highQduration01singleSpell 1 1 1 exceedQ99. 1 1 1 exceedQ90. 1 1 1 medQ 1 1 meanQ08 1 1 meanAnZeroFlowDays 1 1 meanAnMinMov90 1 1 meanAnMaxMov90 1 1 meanAnMaxMov3 1 1 exceedQ75. 1 1 METRIC BARRAMUNDI CATFISH GRUNTER MULLET SAWFISH THREADFIN exceedQ10. 1 1 exceedQ1. 1 1 specificMeanQ 1 specificMeanAnMax 1 skewnessQ 1 seasonMeanQ4 1 seasonMeanQ3 1 seasonMeanQ1 1 meanQ07 1 meanQ06 1 meanQ05 1 meanQ04 1 meanQ 1 maxQrelativeToMeanDailyQ 1 meanAnMinMov30 1 1 1 1 1 1 meanQ10 1 1 1 1 1 lowQDuration90singleSpell 1 1 1 1 1 JDayAnMax 1 1 1 1 1 meanQ12 1 1 1 1 meanQ11 1 1 1 1 meanQ03 1 1 1 1 meanQ02 1 1 1 1 meanQ01 1 1 1 1 meanAnMinMov7 1 1 1 1 Apx Table B-2 The hydrometrics selected as important for each of the ecological assets METRICS COLONIAL AND SEMI COLONIAL WADERS CRYPTIC WADERS SHOREBIRDS SWIMMERS, DIVERS AND GRAZERS exceedQ1. 1 exceedQ10. 1 0.6 0.5 0.8 exceedQ25. 1 0.8 0.5 exceedQ75. 1 0.8 exceedQ90. 0.8 fallRate 1 0.6 0.9 1 highQduration01singleSpell 1 highQduration01Total 0.8 highQduration10singleSpell 1 0.5 highQduration10Total 0.8 0.6 highQduration25singleSpell 1 0.5 1 highQduration25Total 0.7 1 JDayAnMax 0.8 JDayAnMin 0.3 lowQDuration90singleSpell 0.5 lowQDuration90Total 1 lowQDuration99singleSpell 1 maxQ 0.5 0.6 meanAnMaxMov30 0.8 0.8 0.5 meanAnMaxMov7 0.8 0.5 0.2 meanAnMaxMov90 0.8 0.9 meanAnMinMov30 0.7 0.8 1 meanAnMinMov90 0.5 0.5 1 meanQ 0.5 0.3 meanQ01 0.5 meanQ10 0.5 meanQ11 0.5 meanQ12 0.5 medQ 0.7 1 minQ 0.7 riseRate 0.6 0.8 0.5 seasonMeanQ4 0.6 specificMeanAnMax 0.6 specificMeanAnMin 0.6 0.5 1 specificMeanQ 0.8 0.5 specificMedQ 0.8 0.5 1 Apx Table B-3 The hydrometrics selected as important for each of the ecological assets METRICS BANANA PRAWNS FRESHWATER TURTLES MUD CRABS exceedQ1. 1 exceedQ10. 1 exceedQ75. 1 exceedQ90. 1 exceedQ99. 1 fallRate 0.8 highQduration01singleSpell 1 highQduration10singleSpell 1 JDayAnMax 0.5 1 JDayAnMin 1 0.7 1 lowQDuration75singleSpell 1 1 lowQDuration90singleSpell 1 lowQDuration99singleSpell 1 meanAnMaxMov3 1 1 meanAnMaxMov30 1 0.5 meanAnMaxMov7 1 1 meanAnMaxMov90 1 meanAnMinMov30 1 1 1 meanAnMinMov90 1 1 1 meanAnZeroFlowDays 1 1 meanQ 1 0.6 meanQ01 1 0.9 1 meanQ02 1 0.8 1 meanQ03 1 1 meanQ09 1 meanQ10 1 0.7 1 meanQ11 1 0.8 1 meanQ12 1 0.9 1 medQ 1 1 riseRate 0.5 seasonMeanQ1 0.5 seasonMeanQ4 1 specificMeanAnMax 0.5 Apx Table B-4 The hydrometrics selected as important for each of the ecological assets NODE ID FLOODPLAIN WETLANDS INCHANNEL WATERHOLES MANGROVES SALTPANS AND SALT FLATS SURFACEWATER DEPENDENT VEGETATION exceedQ1. 1 1 1 1 1 exceedQ10. 1 1 exceedQ25. 1 exceedQ75. 1 exceedQ90. 1 exceedQ99. 0.5 fallRate 0.5 0.4 highQduration01singleSpell 0.8 1 1 highQduration10singleSpell 0.8 1 1 highQduration25singleSpell 1 JDayAnMax 0.4 1 0.2 JDayAnMin 0.8 lowQDuration75singleSpell 0.8 lowQDuration75Total 0.2 lowQDuration90singleSpell 0.9 lowQDuration90Total 0.5 lowQDuration99singleSpell 1 1 maxQrelativeToMeanDailyQ 0.5 meanAnMaxMov3 0.6 1 1 meanAnMaxMov30 0.6 1 1 meanAnMaxMov7 0.6 1 0.6 meanAnMaxMov90 0.4 meanAnMinMov3 0.1 1 meanAnMinMov30 1 meanAnMinMov7 0.3 meanAnMinMov90 1 meanAnZeroFlowDays 1 meanQ01 0.6 1 1 meanQ02 0.6 meanQ03 0.6 meanQ10 0.6 meanQ11 0.6 1 meanQ12 0.6 1 1 minQ 1 minQ05 0.2 minQ06 0.2 NODE ID FLOODPLAIN WETLANDS INCHANNEL WATERHOLES MANGROVES SALTPANS AND SALT FLATS SURFACEWATER DEPENDENT VEGETATION minQ07 0.2 minQ08 0.5 minQ09 0.8 minQ10 1 minQ11 1 minQ12 0.8 riseRate 0.8 seasonMeanQ4 1 specificMeanAnMax 1 specificMeanQ 1 0.6 specificMedQ 1 volHighQ1x 1 Metrics are those used for asset analysis drawn from a longer list of metrics. Metrics are selected based upon asset flow-ecology and consider flow-relationships and needs and those that can be calculated on an annual basis (i.e. not multi-year such as annual recurrence intervals). An overview of flow dependencies and flow ecology for each asset is provided in Stratford et al. (2024). Apx Table B-5 Hydrometric and their definitions as used in the ecological modelling HYDROMETRIC DEFINITION meanAnIndFloodDuration Mean annual independent flood pulse duration meanQ Mean daily flows medQ Median daily flow specificMeanQ Mean flows divided by catchment area specificMedQ Median flows divided by catchment area cvMeanQ Coefficient of variation in daily flow skewnessQ Skewness in daily flows meanQ01 Mean January discharge meanQ02 Mean February discharge meanQ03 Mean March discharge meanQ04 Mean April discharge meanQ05 Mean May discharge meanQ06 Mean June discharge meanQ07 Mean July discharge meanQ08 Mean August discharge meanQ09 Mean September discharge meanQ10 Mean October discharge meanQ11 Mean November discharge meanQ12 Mean December discharge seasonMeanQ1 Mean Spring discharge seasonMeanQ2 Mean Summer discharge seasonMeanQ3 Mean Autumn discharge seasonMeanQ4 Mean Winter discharge exceedQ75 Low flood pulse count (<75th percentile) exceedQ90 Low flood pulse count (<90th percentile) exceedQ99 Low flood pulse count (<99th percentile) specificMeanAnMin Mean annual minimum flows divided by catchment area exceedQ1 High flood pulse count 1 (1th percentile) exceedQ10 High flood pulse count 1 (10th percentile) exceedQ25 High flood pulse count 1 (25th percentile) specificMeanAnMax Mean annual maximum flows divided by catchment area meanAnIndOverbankFloodDuration Mean annual independent overbank flood pulse duration meanAnFloodDuration Mean annual flood flow pulse duration HYDROMETRIC DEFINITION bankfullQ Mean annual flood volume with respect to bankfull volume volHighQ1x Mean of the high flow volume (calculated as the area between the hydrograph and the upper threshold during the high flow event) volHighQ3x Mean of the high flow volume (calculated as the area between the hydrograph and the upper threshold during the high flow event) volHighQ7x Mean of the high flow volume (calculated as the area between the hydrograph and the upper threshold during the high flow event) meanAnMinMov1 Annual minima of 1-day means of daily discharge meanAnMinMov3 Annual minima of 3-day means of daily discharge meanAnMinMov7 Annual minima of 7-day means of daily discharge meanAnMinMov30 Annual minima of 30-day means of daily discharge meanAnMinMov90 Annual minima of 90-day means of daily discharge lowQDuration75 Low flow pulse duration (75th percentile) lowQDuration90 Low flow pulse duration (90th percentile) lowQDuration99 Low flow pulse duration (99th percentile) meanAnZeroFlowDays Mean annual number of days having zero daily flow meanAnMaxMov1 Annual maxima of 1-day means of daily discharge meanAnMaxMov3 Annual maxima of 3-day means of daily discharge meanAnMaxMov7 Annual maxima of 7-day means of daily discharge meanAnMaxMov30 Annual maxima of 30-day means of daily discharge meanAnMaxMov90 Annual maxima of 90-day means of daily discharge highQduration25 High flow pulse duration (25th percentile) highQduration10 High flow pulse duration (10th percentile) highQduration01 High flow pulse duration (1st percentile) seasonalityMeanQ Seasonality (M/P) of mean daily flow (month) perenniality Perreniality - % contribution to mean annual discharge by the six driest months of the year JDayAnMin Julian date of annual minimum seasonalityMinQ Seasonality (M/P) of minimum instantaneous flow (month) JDayAnMax Julian date of annual maximum seasonalityMaxQ Seasonality (M/P) of maximum instantaneous flow (month) riseRate Rise rate - Mean rate of positive changes in flow from one day to the next fallRate Fall rate - Mean rate of negative changes in flow from one day to the next revPerYear Number of reversals - Number of negative and positive changes in water conditions from one day to the next Waterbird groups and their species To provide a simple basis for understanding and communicating the associated risks and opportunities for waterbirds related to potential water resource development in northern Australia, waterbird species have been grouped into four high-level groups. These groups are based on foraging behaviour and habitat dependencies, together with nesting behaviour and habitat dependencies. Both foraging and nesting dependencies need to be taken into account, because while some species both forage and nest in northern Australia, others migrate annually to take advantage of foraging opportunities and avoid the northern hemisphere winter. The four waterbird groups are: 1. colonial and semi-colonial nesting waders 2. shorebirds 3. cryptic waders 4. swimmers, grazers and divers. Group 1: ‘Colonial and semi-colonial nesting waders’ (Apx Table C-1). Colonial and semi-colonial wading species have a high level of dependence on flood timing, extent, duration, depth, vegetation type and condition for breeding. They are also often dependent on specific important breeding sites in Australia. They are usually easily detectable when breeding and good datasets are available for most species. These species are typically nomadic or partially migratory. Group 2: ‘Cryptic waders’ (Apx Table C-2). Cryptic wading species have a high level of dependence on shallow temporary and permanent wetland habitats with relatively dense emergent aquatic vegetation that requires regular or ongoing inundation to survive (e.g. reeds, rushes, sedges, wet grasses and lignum). These species breed in Australia and usually nest as independent pairs though some may occasionally nest semi-colonially. They may be sedentary, nomadic, migratory or partially migratory. Few data are available; however, habitat requirements can be used as surrogates to assess vulnerability. Group 3: ‘Shorebirds’ (Apx Table C-3). Shorebirds have a high level of dependence on end-of- system flows and large inland flood events that provide broad areas of very shallow water and mudflat type environments. They occur across freshwater and marine habitats and are largely migratory or nomadic, mostly breed in the northern hemisphere rather than Australia, and are a group of international concern. Group 4: ‘Swimmers, grazers and divers’ (Apx Table C-4). These are species with a relatively high level of dependence on semi-open, open and deeper water environments, who commonly swim when foraging (including diving, filtering, dabbling, grazing) or when taking refuge. These species breed in Australia and may be sedentary, nomadic, migratory or partially migratory. Apx Table C-1 Species in the colonial and semi-colonial nesting wading waterbird group, and their national and international conservation status (LC = Least Concern). SPECIES NAME SPECIES SCIENTIFIC NAME FAMILY SCIENTIFIC NAME IUCN STATUS Australian white ibis Threskiornis moluccus Threskiornithidae LC Banded stilt Cladorhynchus leucocephalus Recurvirostridae LC Black-winged stilt (pied stilt) Himantopus himantopus (Himantopus leucocephalus) Recurvirostridae LC Cattle egret Bubulcus ibis (Ardea ibis) Ardeidae LC Eastern reef egret Egretta sacra Ardeidae LC Glossy ibis Plegadis falcinellus Threskiornithidae LC Great Egret (eastern great egret) Ardea alba (Ardea modesta, Ardea alba modesta) Ardeidae LC Great-billed heron Ardea sumatrana Ardeidae LC Intermediate egret Ardea intermedia Ardeidae LC Little egret Egretta garzetta Ardeidae LC Nankeen night-heron Nycticorax caledonicus Ardeidae LC Pied heron Egretta picata (Ardea picata) Ardeidae LC Red-necked avocet Recurvirostra novaehollandiae Recurvirostridae LC Royal spoonbill Platalea regia Threskiornithidae LC Sarus crane Grus Antigone Gruidae Vulnerable Straw-necked ibis Threskiornis spinicollis Threskiornithidae LC White-faced heron Egretta novaehollandiae Ardeidae LC White-necked heron Ardea pacifica Ardeidae LC Yellow-billed spoonbill Platalea flavipes Threskiornithidae LC Black-necked stork Ephippiorhynchus asiaticus Ciconiidae LC Brolga Antigone rubicunda Gruidae LC Apx Table C-2 Species in the cryptic wading waterbird group, and their national and international conservation status (LC = Least Concern). SPECIES NAME SPECIES SCIENTIFIC NAME FAMILY SCIENTIFIC NAME IUCN STATUS Australian little bittern Ixobrychus dubius (Ixobrychus minutus) Ardeidae LC Australian painted snipe Rostratula australis Rostratulidae Endangered Australian spotted crake Porzana fluminea Rallidae LC Baillon's crake Porzana pusilla (Zapornia pusilla) Rallidae LC Black bittern Ixobrychus flavicollis Ardeidae LC Buff-banded rail Hypotaenidia philippensis Rallidae LC Chestnut rail Eulabeornis castaneoventris (Gallirallus castaneoventris) Rallidae LC Latham's snipe Gallinago hardwickii Scolopacidae LC Lewin's rail Lewinia pectoralis Rallidae LC Red-necked crake Rallina tricolor Rallidae LC Spotless crake Zapornia tabuensis (Porzana tabuensis) Rallidae LC Striated heron Butorides striatus (Butorides striata) Ardeidae LC White-browed crake Amaurornis cinerea (Poliolimnas cinereus) Rallidae LC Apx Table C-3 Species in the shorebirds group, and their national and international conservation status LC = Least Concern). For more information on this figure, table or equation please contact CSIRO on enquiries@csiro.au. For more information on this figure, table or equation please contact CSIRO on enquiries@csiro.au. For more information on this figure, table or equation please contact CSIRO on enquiries@csiro.au. Apx Table C-4 Species in the swimmers, grazers and divers waterbird group, and their national and international conservation status (LC = Least Concern). For more information on this figure, table or equation please contact CSIRO on enquiries@csiro.au. For more information on this figure, table or equation please contact CSIRO on enquiries@csiro.au. Asset metrics with the largest contribution to changes in asset flow dependencies by scenario The following tables show the most altered location (node) and associated metrics for all the ecological assets under the different water management scenarios. The hydrometric values should be considered as an indicator of the level of hydrological change occurring within the key components of the hydrograph important for each asset. Considering where change occurs across the different flow components facilitates an understanding of where change is most significant in association with the different scenarios for each asset. Apx Table D-1 Most changed metric at the most altered location (node) in Scenario B-Wv80t200r30f0 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. Apx Table D-2 Most changed metric at the most altered location (node) in Scenario B-Wv80t200r30f500 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. Apx Table D-3 Most changed metric at the most altered location (node) in Scenario B-Wv80t600r30f0 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. Apx Table D-4 Most changed metric at the most altered location (node) in Scenario B-Wv80t600t30f500 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. Apx Table D-5 Most changed metric at the most altered location (node) in Scenario B-Wv160t200r30f0 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. Apx Table D-6 Most changed metric at the most altered location (node) in Scenario B-Wv160t200r30f500 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. A screenshot of a computer Description automatically generated Apx Table D-7 Most changed metric at the most altered location (node) in Scenario B-Wv160t600r30f0 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. A screenshot of a computer Description automatically generated Apx Table D-8 Most changed metric at the most altered location (node) in Scenario B-Wv160t600r30f500 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. A screenshot of a computer Description automatically generated Apx Table D-9 Most changed metric at the most altered location (node) in Scenario B-Wv320t200r30f0 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. Apx Table D-10 Most changed metric at the most altered location (node) in Scenario B-Wv320t200r30f500 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. Apx Table D-11 Most changed metric at the most altered location (node) in Scenario B-Wv320t600r30f0 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. A screenshot of a computer Description automatically generated Apx Table D-12 Most changed metric at the most altered location (node) in Scenario B-Wv320t600r30f500 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. Apx Table D-13 Most changed metric at the most altered location (node) in Scenario B-Wv720t200r30f0 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. Apx Table D-14 Most changed metric at the most altered location (node) in Scenario B-Wv720t200r30f500 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. A screenshot of a computer Description automatically generated Apx Table D-15 Most changed metric at the most altered location (node) in Scenario B-Wv720t200r30f500 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. A screenshot of a computer Description automatically generated Apx Table D-16 Most changed metric at the most altered location (node) in Scenario B-Wv720t600r30f500 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. A screenshot of a computer Description automatically generated Apx Table D-17 Most changed metric at the most altered location (node) in Scenario B-Wv800t200r30f0 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. Apx Table D-18 Most changed metric at the most altered location (node) in Scenario B-Wv800t200r30f500 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. A screenshot of a computer Description automatically generated Apx Table D-19 Most changed metric at the most altered location (node) in Scenario B-Wv800t600r30f0 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. Apx Table D-20 Most changed metric at the most altered location (node) in Scenario B-Wv800t600r30f500 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. A screenshot of a computer Description automatically generated Apx Table D-21 Most changed metric at the most altered location (node) in Scenario B-DLC for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. Apx Table D-22 Most changed metric at the most altered location (node) in Scenario B-DLCT for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. Apx Table D-23 Most changed metric at the most altered location (node) in Scenario B-DVR for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. Apx Table D-24 Most changed metric at the most altered location (node) in Scenario B-DVRT for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. Apx Table D-25 Most changed metric at the most altered location (node) in Scenario B-D2 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. Apx Table D-26 Most changed metric at the most altered location (node) in Scenario B-D2T for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. Apx Table D-27 Most changed metric at the most altered location (node) in Scenario B-Cdry for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. Apx Table D-28 Most changed metric at the most altered location (node) in Scenario B-Ddryw160t200r30 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. Apx Table D-29 Most changed metric at the most altered location (node) in Scenario B-DdryD2 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. Apx Table D-30 Most changed metric at the most altered location (node) in Scenario B-DdryD2T for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table B-5. As Australia’snational scienceagency and innovation catalyst, CSIRO is solving the greatestchallenges through innovativescience and technology. CSIRO. Unlocking a better futurefor everyone. Contact us 1300 363 400+61 3 9545 2176csiroenquiries@csiro.aucsiro.au For further informationEnvironment Dr Chris Chilcott+61 8 8944 8422chris.chilcott@csiro.au Environment Dr Cuan Petheram+61 3 6237 5669cuan.petheram@csiro.au Agricultureand Food Dr Ian Watson+61 7 4753 8606 Ian.watson@csiro.au