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

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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
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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 (Pyšek 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). 

 

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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. 

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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. 

 

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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. 

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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. 

 

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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. 

 

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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. 

 

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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. 

 

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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. 

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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. 

 

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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. 

 

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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. 

 

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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. 

 

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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 

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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. 

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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. 

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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. 

 


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 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. 

 

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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. 

 

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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. 


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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. 

 

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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. 

 

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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. 

 

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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. 

 

 



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