Development and Application of a Flow Stressed Ranking Procedure

FINAL REPORT

„ Final B

„ 11 October 2005

Development and Application of a Flow Stressed Ranking Procedure

FINAL REPORT

„ Final B

„ 11 October 2005

Sinclair Knight Merz ABN 37 001 024 095 590 Orrong Road, Armadale 3143 PO Box 2500 Malvern VIC 3144 Australia Tel: +61 3 9248 3100 Fax: +61 3 9500 1182 Web: www.skmconsulting.com

COPYRIGHT: The concepts and information contained in this document are the property of Sinclair Knight Merz Pty Ltd. Use or copying of this document in whole or in part without the written permission of Sinclair Knight Merz constitutes an infringement of copyright.

Flow Stressed Ranking Project

Executive Summary

This report describes the development and application of an objective ranking of flow stress in all river systems across Victoria. The ranking establishes a relative indication of threat to river health based on the level of water extractions by rural, urban, and industry users. The ranking makes no assumptions about the environmental value of a river, but rather characterises the degree of hydrologic stress under current management conditions relative to “unimpacted” flow conditions, that is the flow regime that would occur if all anthropogenic extractions, water harvesting, and impoundments were removed.

In order to develop and test the formulation of a suitable index, unimpacted and current daily streamflow sites were derived for a total of fifty sites across Victoria. The selected sites were representative of the range of climate, topography, and degree of stream regulation found across Victoria. While daily streamflow data were used in the development of the flow stress ranking procedure, the greater availability of monthly streamflows across the State requires that the final indices be obtained using monthly data.

A review of previous studies was undertaken and a range of daily indices were formulated with input from a scientific panel of aquatic ecologists and hydrologists. A total of ten indices based on the analysis of daily data were developed. These indices were selected to be representative of flow components that are linked to ecologically important processes. Alternative versions of the indices were formulated using monthly data, and the degree of hydrologic stress captured by these monthly indices was compared to that contained in the daily data. It was found that the information contained in one flow component partially explained the variation in another component, and accordingly efforts were made to identify a small number of indices based on monthly data that yielded a similar indication of hydrologic stress as obtained using daily streamflows.

On the basis of the investigations undertaken, it was found that only five (largely independent) indices were required to characterise the degree of hydrologic stress in a river. The five monthly indices involve consideration of:

„ low flow index - the lowest and second lowest monthly flows in a year;

„ high flow index - the highest and second highest monthly flows in a year;

„ zero flow index - the proportion of time that the stream is dry (or nearly so);

„ variability index - the variability in monthly streamflows; and,

„ seasonality and periodicity index - the seasonal timing of when low and high flows occur.

Importantly, the indices are formulated to allow direct comparison between different catchments, seasons, and flow components. That is, for a particular index (or flow component) a stress rating of, say, 6, represents the same level of stress regardless of where the catchment is located, or which

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flow component is being considered. Of course, it should be emphasised that this parity in rating refers only to the degree of hydrologic stress, and in reality any inference regarding the relative importance of different indicators need to take into consideration the ecological sensitivity of the system of interest.

The indices can be calculated using all months of the year, or else (excluding the seasonality index) they can be calculated for specific seasons. A minimum of 15 years of monthly data is required to derive reliable estimates of flow stress.

Once a suitable form of the indices was developed, it was necessary to identify all the sites across Victoria for which the level of flow stress was to be to assessed. To this end sites were selected on all major streams, where sites were located to highlight any major changes in the level of flow stress, and to capture the full range of flow and catchment conditions found across Victoria. Site selection involved balancing the competing demands of data availability against those of hydrologic representativeness: on the one hand it was desirable to reduce the number of sites to be commensurate with the availability of relevant streamflow information, and on the other there was the motivation to increase the number of sites to capture all possible changes in flow regime. This balancing act was resolved by first proposing a set of sites on the basis of data availability and hydrological judgement, and then iterating with agency stakeholders (the Department of Sustainability and Environment, Catchment Management Authorities, and Rural Water Authorities) to ensure that the selected sites were suited to their management needs. Following several iterations, a final list of 551 sites was compiled.

Monthly time series of unimpacted and current streamflows were derived for all 551 sites. The derivation of the time series took into consideration the impacts of all rural and urban demands (at the current level of development), private diverters, and farm dams. Though not the prime focus of this study, this assemblage of data represents the most comprehensive collection of unimpacted and current streamflows available across the State, and the data set has many potential uses outside the scope of this study.

Calculation of the individual stress indices for each of the 551 sites reveals that 88% of the sites are most stressed in summer, and that in 75% of cases the low flow index is the most stressed component. This is perhaps to be expected, and reflects the high level of demand for summer flows, which are generally in short supply across the majority of the state. In the minority of catchments that are stressed in winter it is evident that high flows (captured by large dams) are the most affected.

In a catchment- or region-specific study the individual flow stress indices can be selected and evaluated in a manner best suited to the study objectives. For example, if considering a range of options for relieving summer stress in a catchment, it may be appropriate to quantify the relative

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environmental benefits using just the summer low flow index. However, as one of the main objectives of this project is to provide an overall rating of hydrologic stress for all streams across the State, a general characterisation of hydrologic stress is required that does not bias the assessment towards any particular flow component. To this end, a “seasonality-weighted” score of flow stress is devised using all five of the individual stress indices. The score is based on a uniform weighting of the individual indices, except flow seasonality is given twice the weight of the other individual indices. The justification for adoption of the seasonality-weighted score is essentially that it combines the flow stress attributes of five ecologically important flow components that have been shown to be highly correlated with a wide range of flow characteristics. The additional weighting given to the seasonal index merely ensures that highly impacted regulated rivers – that is those rivers that exhibit marked seasonal flow reversal but which still experience high flows associated with irrigation releases – are appropriately ranked.

The overall stress score is comprised of a three character alphanumeric, in which the first character is the seasonality-weighted score multiplied by ten, standardised, and then rounded to the nearest integer. The second character denotes whether winter or summer is the most stressed season, and the third denotes the index that is most stressed. The seasonality-weighted is standardised to represent the percentage ranking of the catchment. That is, a score of 7 indicates that 70% of Victorian catchments are more stressed than the catchment under consideration, and a score of 5 indicates a “typical”, or median (50%) level of stress. Thus, for example, a score of 2SL indicates that the seasonally-weighted score for the catchment is 2 (ie 80% of Victorian catchments are less stressed), summer flows are more stressed than in winter, and low flows are the most impacted component of the flow regime.

The efficacy of the seasonality-weighted score is illustrated using two heuristic investigations. Firstly, the scores are used to discriminate the differences in flow stress between catchments that are impacted by the presence of large (greater than 1000 ML) storages. Secondly, the scores are used to describe the flow stress in a number of catchments that vary in the degree and nature of their impacts.

It is found that catchments without significant upstream impoundments have higher scores than those with. The seasonality-weighted scores clearly differentiates between those catchments that are expected to have high flow stress (by virtue of the high volume of upstream impoundments) from those that are expected to have a lower flow stress. In general, the score for a catchment unaffected by large storages is around 3 units higher than one without, and the differences arising from the volume of major upstream storages are generally between 0.5 and 1.5 units.

Analysis of a range of individual sites confirmed that the flow stress score provide a stress ranking consistent with expectations. While analysis of the individual flow stress indices provides useful information concerning the nature of flow stress in a catchment, the seasonally-weighted score

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provides a parsimonious means of characterising flow stress for a range of ecologically important flow components.

Although the original intent of the index was to provide an objective ranking of threat to river health based on flow stress, it can also be used for evaluating the environmental trade-offs that may arise when considering options for relieving stress. Because the index is essentially a measure of the hydrologic difference between two flow series, it can be used to compare unimpacted flows with both current and proposed future flows, thereby allowing a direct comparison of current and proposed future hydrologic conditions. In this way, it is possible to compare the current index scores for a particular site with the index scores for any number of catchment management scenarios. For example, it would be possible to compare the hydrologic condition of a waterway before and after the construction of a large storage, determine the hydrologic condition of a waterway as a result of future changes in forestry or farming practices, investigate how environmental water reserves can best be utilised to provide the greatest improvement in hydrologic condition, and determine the hydrologic impact of conversion of summer irrigation licences to winterfill licences.

It should be stressed that this index is not an environmental assessment, and should not be used as a surrogate for the overall environmental condition of a waterway. The only data utilised in calculating the indices are the current and unimpacted monthly flows. If a stream has a poor score, it does not necessarily mean that the waterway environment is in poor condition, rather it suggests that the flow regime has changed, which has the potential to affect the waterway environment. Conversely, a stream which has a good score may have a severely degraded waterway environment, caused by factors wholly unrelated to streamflow.

The full set of index results will be made available on the Department of Sustainability and Environment website (http://www.dse.vic.gov.au). The data will be mapped, allowing the user to extract index results from individual sites. Other data available on the same map will include Sustainable Diversion Limits, stream gauge locations, extent of flooding, and a variety of other useful hydrographic and topographic data.

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Acknowledgments

The sponsorship of the Water Resources Policy Division of the Department of Sustainability and Environment (DSE) is acknowledged for all activities which have resulted in the preparation of this document. The leadership of Campbell Fitzpatrick is recognised for leading DSE’s implementation of environmentally sustainable water reform.

Project funding was provided jointly by the National Action Plan and DSE (River Health Program). The Flow Stress Ranking project was formulated and managed on behalf of DSE by Bernie O’Kane. Mr O’Kane was also responsible for convening and coordinating the Steering Panel for the project.

The Steering Panel comprised the following DSE officers: Paul Wilson, Phil Mitchell, and Steve Nicol. In addition, industry stakeholders were represented by Graham Hawke and Cameron Welsh (Southern Rural Water), Greg Holland (Goulburn-Murray Water), Jamie Ewart (Melbourne Water), Peter Swanson (Glenelg-Hopkins Catchment Management Authority) and Wayne Tennent (Goulburn-Broken Catchment Management Authority).

Environment Victoria, the Victorian Farmers Federation and Land Stewardship and Biodiversity were kept informed regarding project progress and outcomes.

Review of the appropriateness of the formulation for characterising flow stress was provided by the Index of Stream Condition Reference Panel, whose members comprise: Dr Jane Doolan (DSE), Prof. Barry Hart (Monash University), Dr Tony Ladson (Monash University), Prof. Sam Lake (Monash University), Prof. Tom McMahon (University of Melbourne), Leon Metzeling (EPA), Prof Ian Rutherford (University of Melbourne), Dr John Tilleard (private consultant).

The formulation of the flow stress index was based on the deliberations of a panel of specialists with knowledge of stream ecology, water quality, hydrology, and catchment management. The overall direction, process, and outcomes of the project are the result of their careful thought and judgement, and each contributed directly to the final form of the procedure. These individuals, and their field of specialisation and affiliation are listed in alphabetical order as follows: Tim Doeg (aquatic ecologist, private consultant), Dr Terry Hillman (aquatic ecologist, private consultant), Dr Tony Ladson (environmental hydrologist, Monash University), and Dr Mike Stewardson (environmental hydrologist, University of Melbourne).

Dr Rory Nathan was the overall Project Manager for delivery of the project outcomes to DSE. Technical support for the analyses undertaken during the course of the project was provided by Robert Morden, Avril Horne, Kate Austin, Lisa Lowe, Shaan Pawley, Anna Quayle, Georgina Race, Katherine Williams, and all of Sinclair Knight Merz.

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Contents

1. Introduction 1 1.1 Background 1 1.2 Objectives 2 1.3 Report Outline 2 2. Data Collection and Preparation 4 2.1 Introduction 4 2.2 Streamflow Data 5 2.2.1 Gauged streamflow data 5 2.2.2 REALM Model Data 6 2.2.3 Other studies/ projects 6 2.2.4 Daily flow data for testing FSR index 7 2.3 Demand Data 7 2.3.1 Farm dams 7 2.3.2 Private diverters 8 2.3.3 Urban demands 11 2.3.4 Other demands 13 2.3.5 Demands not included 13 2.4 Catchment Characteristics 14 2.5 Other Spatial Data 15 3. Identification of FSR Sites 16 3.1 Overall Approach 16 3.2 Selection Criteria 16 3.2.1 General Considerations 16 3.2.2 Specific Considerations 17 3.3 Adopted Locations 20 4. Estimation of Current and Unimpacted Flows 23 4.1 General 23 4.2 Assessment of Calculation Method 24 4.3 Transposition of Streamflows 25 4.3.1 Nature of the Problem 25 4.3.2 Mean Annual Flow Transposition 26 4.3.3 Beta Distribution Transposition 28 4.3.4 Adopted Method of Transposition 29 4.4 Private Diverter Impacts 30 4.5 Calculating Urban Impacts 32

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4.6 Farm Dam Impacts 32 5. Formulation of Stress Indices 34 5.1 Choice of Time Step 34 5.2 Overview of Approach 34 5.3 Selection of Test Sites 36 5.4 Review of Environmental Water Requirements 37 5.5 Review of Hydrological Indicators 40 5.6 Conceptual Basis of the Ranking Procedure 42 5.7 Preliminary Set of Indices Considered 45 5.8 Selection of Indices 47 5.9 Sensitivity Analysis 48 5.10 Seasonal Sub-Indices 49 6. Formulation and Assessment of Flow Stress Scores 50 6.1 Introduction 50 6.2 Nature of Flow Stress 50 6.3 Combination of Indices 52 6.4 Score Nomenclature 55 6.5 Discrimination of Sites Impacted by Large Storages 57 6.6 Results for Selected Sites 59 6.6.1 Werribee River upstream of Werribee Weir 61 6.6.2 Mitchell River at Bairnsdale 61 6.6.3 Thomson River d/s Thomson Reservoir 63 6.6.4 Yarra River at Yering offtake 64 6.6.5 Moorabool R @ Batesford 65 6.6.6 Glenelg River downstream of Rocklands Reservoir 66 6.6.7 Mitta Mitta River downstream of Dartmouth Reservoir 67 6.6.8 Goulburn River downstream of Eildon Dam 69 6.6.9 Wimmera River 70 6.7 Comparison with AAPFD 72 6.8 Overall Distribution of Indices and Scores 73 6.9 Potential applications 76 6.10 Availability of Index Results 79 7. Conclusions 80

8. References 82

9. Abbreviations 85

Appendix A Derivation of Daily Streamflow Test Data Set 86

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A.1 Derivation of Daily Flows for Testing FSR Formulation in Regulated Catchments 86 A.2 Derivation of Daily Flows for Testing FSR Formulation in Unregulated Catchments 88 A.2.1 Introduction 88 A.2.2 Current Flow Series 88 A.2.3 Unimpacted Flow Series 89 A.3 Hydrologic Characteristics of Selected Catchments 91 Appendix B Evaluation of Transposition Functions 93 B.1 The Beta Distribution 93 B.2 Transforming the beta distribution to represent flow duration curves93 B.3 Regional Prediction Equations for Beta Distribution Variables 95 B.3.1 Methodology for Developing and Testing Prediction Equations 95

B.3.2 Prediction of Q10 98

B.3.3 Prediction of Q90 99 B.3.4 Prediction of CTF 100 B.4 Using the Beta Distribution Method to Transpose Flows 101 B.5 Comparison of Transposition Methods 102 Appendix C Preliminary Flow Indices Considered 105 C.1 Mean Annual Flow Index 105 C.1.1 Ecological Significance 105 C.1.2 Description 105 C.1.3 Discussion and conclusions 107 C.2 Seasonal Amplitude Index 107 C.2.1 Ecological Significance 107 C.2.2 Description 108 C.2.3 Discussion and conclusions 109 C.3 Seasonal Period Index 109 C.3.1 Ecological Significance 109 C.3.2 Description 110 C.3.3 Discussion and conclusions 112 C.4 Low Flow Index 113 C.4.1 Ecological Significance 113 C.4.2 Description 113 C.4.3 Discussion and conclusions 114 C.5 High Flow Index 117 C.5.1 Ecological Significance 117 C.5.2 Description 117 C.5.3 Discussion and conclusions 118 C.6 High Flow Spells Index 120

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C.6.1 Ecological Significance 120 C.6.2 Description of Daily Index 121 C.6.3 Description of Monthly Index 123 C.6.4 Discussion and conclusions 124 C.7 Low Flow Spells Index 126 C.7.1 Ecological Significance 126 C.7.2 Description 126 C.7.3 Discussion and conclusions 127 C.8 Proportion of Zero Flow Index 128 C.8.1 Ecological Significance 128 C.8.2 Description 128 C.8.3 Discussion and conclusions 130 C.9 Flow Duration Index 131 C.9.1 Ecological Significance 131 C.9.2 Description 131 C.9.3 Discussion and conclusions 132 C.10 Variation Index 135 C.10.1 Ecological Significance 135 C.10.2 Description 135 C.10.3 Discussion and conclusions 135 C.11 Summary of Results 136 Appendix D Selection of Final Indices 138 D.1 Introduction 138 D.2 Correlation Between Indices 138 D.3 Selection of Monthly Indices 140 D.4 Adequacy of Selected Indices 144 D.5 Sensitivity Analysis 147 D.5.1 Implications of the Timing of Record Used 147 D.5.2 Implications of Time Period Adopted 148

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Document history and status

Revision Date issued Reviewed by Approved by Date approved Revision type A 19 April 2005 Rory Nathan Peter Hill 14 April 2005 Issue for client review B 11 Oct 2005 Peter Hill Peter Hill 11 Oct 2005 Final version incorporating client comments

Distribution of copies

Revision Copy no Quantity Issued to A Pdf 2 Bernie O'Kane, Paul Wilson, DSE B Pdf 2 Bernie O'Kane, Paul Wilson, DSE

Printed: 11 October 2005

Last saved: 11 October 2005 08:44 AM

File name: D:\Jobs\GSC\FSR\Reports\FSR_Report_FinalB.doc

Author: Rory Nathan, Robert Morden, Lisa Lowe, Kate Austin

Project manager: Rory Nathan

Name of organisation: Department of Sustainability and Environment

Name of project: Priority Ranking for Improved Stream Management

Name of document: Final Report

Document version: Final B

Project number: WC02795

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1. Introduction

1.1 Background The Victorian Government is committed to a substantial investment in sustainable water management over the long-term. One of the major initiatives arising from this commitment is the Victorian River Health Strategy (VRHS), which is the policy framework for the integrated management of rivers, floodplains, wetlands and estuaries in Victoria. In order to prioritise the investment in the protection and restoration of the State’s water bodies, the VRHS sets out a priority ranking system based on the consideration of a number of environmental, flow-related, and other factors. An important component of this prioritisation is the ranking of Victoria’s rivers by their level of flow stress. The ranking of Victoria’s rivers by their level of flow stress will be an important component of the VRHS framework, and will enable the initiatives outlined in the Government’s White Paper “Our Water Our Future” to be tackled in an efficient and accountable manner.

There are many competing demands for water, and thus it is imperative that any investment in sustainable water allocation be assessed in a manner that is transparent and defensible, objective and repeatable. The development of an indicator of flow stress that is linked to ecologically important flow components provides a means to objectively assess the relative environmental benefits of alternative investment strategies. Accordingly, the potential degree of environmental threat can be used in conjunction with an assessment of environmental values to help weigh up the pros and cons of any strategy that involves changes to the flow regime.

The traditional approach to assessment of flow stress involves the comparison of the differences in daily streamflow behaviour under current and “unimpacted” flow conditions. (The latter state is sometimes referred to as “natural” or “pre-development” conditions and it represents the streamflows that would occur if all anthropogenic extractions and diversions ceased, but under the current land-use cover – see Section 2.3.5). The derivation of such streamflow sets present some challenging technical problems. Ideally we could assume that historical gauged streamflows represent current conditions, and that unimpacted flows can be derived by simply adding back in the extractions associated with development. Unfortunately there are a number of issues that confound all aspects of this procedure; the derivation of streamflow series representative of current and unimpacted conditions involves the careful treatment of many sparse data sets related to system operation, extractions for urban and rural demands, farm dams, and streamflow gauging. While from an ecological perspective it is desirable to characterise flow stress based on consideration of daily behaviour, there are severe practical impediments to the derivation of daily streamflows. Indeed it could be argued that the nature of the information available for all of the different types of water extractions is so limited, particularly at the regional scales of interest, that a derived daily time series is less accurate (and potentially quite misleading) compared to a monthly time series.

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Accordingly, one of the major practical constraints – and hence technical challenges – involved in the characterisation of flow stress is that the approach needs to be based on monthly streamflow data, yet be developed in such a way that it captures the ecological impacts at a daily level.

The development of an objective flow stress procedure for application to all river systems across Victoria thus requires the solution of some significant theoretical and practical challenges. This report describes the development and application of a flow stress procedure that was formulated specifically to address the policy and planning initiatives contained in the Government’s White Paper “Our Water Our Future”.

1.2 Objectives The objective of this study is to develop a procedure using monthly streamflow data that provides a relative indication of threat to river health based on the level of water extractions by rural, urban, and industry users. This Flow Stress Ranking (FSR) procedure will assist the management of water resources and river health by:

„ Providing an improved hydrological sub-index for the Index of Stream Condition (ISC; CEAH/IDA, 1997);

„ Providing a quantitative measure of (seasonal) flow related threat that can be used to assess the relative merits of different management options;

„ Enabling selection of unregulated river systems, where flow related threat is high, for preparation of statutory stream flow management plans (SFMP);

„ Enabling selection of unregulated river systems where flow related threat is low or medium, for establishing non statutory management rules; and,

„ Providing input into the Regional River Health Strategies (RRHS) that will assist the detailed management and investment priorities for river health improvement.

The FSR will enable investment in both stream flow management planning and environmental flow improvement to be focussed and optimally assigned to catchments that are most in need, and in a manner that minimises environmental impacts of future development initiatives.

1.3 Report Outline The Flow Stress Ranking (FSR) project was divided into a number of tasks, a simplified summary of which is provided in Figure 1-1. This report is structured to broadly represent the project tasks as shown in this figure. Accordingly:

„ Chapter 2 summarises the data collation and preparation activities;

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„ Chapter 3 describes the process used to identify the locations at which flow stress ranking scores were derived;

„ The manner in which the streamflow data sets were derived for all required locations is described in Chapter 4;

„ The formulation of the flow stress indices is presented in Chapter 5; and,

„ The development and assessment of the flow stress scores is summarised in Chapter 6.

Additional details on selected components of the study are reported in the Appendices.

Task 1 Task 2 Task 4 Task 5 Task 6 Data Collection Identification of Develop trans- Derivation of Assessment and preparation FSR Reaches position function streamflow data sets of FSR scores

Task 3 Formulation of FSR

„ Figure 1-1 – Summary of project outline

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2. Data Collection and Preparation

2.1 Introduction A broad range of data was required to undertake this study, including existing flow data, streamflow models, demand data, climate data, and physiographic catchment characteristics. Table 2-1 lists the data obtained, and their predominant sources.

„ Table 2-1 - Summary of data collected

Data type Description Data source Streamflow Data Gauge streamflows Thiess / data warehouse Realm models SKM / DSE Other studies/ projects SKM Daily flow data for testing FSR index Various Topographic data / SDL1 project / Demand Data Farm dams previous studies Private diverters SDL1 project / SKM Water authorities, SKM / DSE Urban demands REALM models Other demands (REALM) SKM / DSE Hydro, non consumptive, WWTP No data obtained outflows Bureau of Meteorology gridded Catchment Characteristics Climate rainfall and evapotranspiration data Soils McKenzie et al (2000) Elevation Gridded elevation data Stream density / frequency 1:50k topographic data Vegetation Land Victoria Other Spatial Data Streams data 1:50k topographic data ISC reaches DSE Gauges Thiess SDL1 boundaries SDL1 project (1) Note: The SDL Project refers to the Sustainable Diversion Limit project (DSE; 2003)

The collation and preparation of these data sets is described in the following sections.

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2.2 Streamflow Data Streamflow data was required to determine current and unimpacted flows for each FSR site. The data was derived from gauged records, REALM models developed for previous studies, or rainfall runoff models developed from previous studies. Where no gauged data or REALM model data was available for a particular site, it was transposed from another site for which information was available.

Streamflow data was selected to:

„ be in monthly format;

„ have a minimum of 15 years record length (as discussed in Section 5.9); and

„ be comparatively recent, such that the end of the record is between 1995 and 2004, ensuring that the data is representative of current conditions.

Further details on the sources of the streamflow data are provided below, and the process used to determine the most appropriate data to use for each site is described in Section 4.

2.2.1 Gauged streamflow data Daily streamflow data for 165 gauges across Victoria was available from the SDL project (DSE, 2003). This data set represents catchments that are reasonably unimpacted by diversions, and the streamflow data had been fully infilled and extended as part of the SDL project. For this study these data were simply aggregated to a monthly time step and adopted directly.

Daily streamflow data for 181 additional gauges across Victoria were available from the SDL project, but these had not been infilled and extended. This data set was retrieved for use in this study, though it was also necessary to obtain additional streamflow records from Thiess. Some supplementary data was obtained directly from Melbourne Water, and also from the data warehouse (http://www.vicwaterdata.net). Data from these sources was processed prior to use, including infilling and extending, as indicated below.

Streamflow records were infilled, in the first instance, using linear interpolation for periods of up to seven days. The data was then aggregated to a monthly time step. Where large periods of missing data occurred near the beginning or end of a streamflow record then the data set was truncated to remove the missing period. Other missing periods of data were infilled by developing multiple regression relationships with other streamflow sites. Both logarithmic and power transformations were commonly used in developing the regressions to ensure a normalised distribution of residuals.

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2.2.2 REALM Model Data The majority of Victorian REALM models and their documentation were previously made available by DSE for the SDL project. Other models not included in the SDL project were obtained from DSE during the course of this project. In addition, an updated version of the Otway system model (version 2002) was obtained from South West Water.

Streamflows representing current conditions were obtained as a flow timeseries for particular model arcs. Streamflows representing unimpacted conditions were determined by summing the relevant model inputs, and allowance was made for any in-stream losses where such information was available. However, in most cases the unimpacted flows derived from REALM models only accounted for demands which were included in the models, and in many cases it was necessary to consider additional extractions (mostly private diverters) which were not incorporated in the original model. In other words it was only possible to derive “partial unimpacted” flows from the REALM models, and estimates of the total unimpacted flows required additional analysis. In addition, most REALM models have not previously considered the impact of farm dams, and thus these were also handled separately (Section 2.3.1).

Where the REALM models used weekly or daily timesteps, this data was aggregated to monthly.

2.2.3 Other studies/ projects Several studies have been undertaken by SKM which have involved estimating current and unimpacted flows for particular catchments and waterways. For each study, the methodology was examined to ensure the flows were calculated in a manner that is consistent with the current project. Flows were aggregated to a monthly time-step as required.

The data obtained from previous studies are listed in Table 2-2 below.

„ Table 2-2 - Streamflow data obtained from previous studies Study Data obtained Upper Barwon Current and Natural Flows (SKM, Current and partial unimpacted flows 2003a) Woori Yallock Farm Dam Impact Assessment Current flows and farm dam impacts (SKM, 2000b) Pauls Steels Dixons Ck REALM model (SKM, Current and partial unimpacted flows 2003b) Sustainable Rivers Audit (SKM, 2003c) Current and unimpacted flows for sites in the Ovens basin Lerderderg River Current and Natural Flows Current and partial unimpacted flows (SKM, 2003d)

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2.2.4 Daily flow data for testing FSR index A separate set of streamflow data was collected for the specific purpose of developing the flow stress ranking procedure. This data was required to be based on a daily time step, with preferably more than 20-25 years record length; also, the data needed to be representative of the range of climate, topography, and degree of regulation that is found across the State. Such data is difficult to obtain, and after appreciable effort (as summarised in Appendix A) data was derived for 50 sites.

2.3 Demand Data

2.3.1 Farm dams Farm dam demand was calculated for each site using the Tool for Estimating Dam Impacts (TEDI), a model developed by SKM specifically for the purpose of determining the impact of farm dams on streamflows (SKM, 2000a; Nathan et al, 2005). The model calculates a water balance each month for a set of farm dams by applying rainfall and evaporation, and determining dam spills and runoff. The input data required by TEDI is listed below, together with the source of that data.

„ Table 2-3 - TEDI input data and data sources TEDI input data Data source Monthly rainfall and evaporation 12 monthly average rainfall and evaporation values, taken from Bureau of Meteorology gridded data averaged over the upstream catchment area Catchment area GIS data Sub-catchment areas for a typical 5ML dam and Typical values obtained from many past farm dam 100ML dam studies (0.5 km2 and 1.3 km2) (SKM, 2001) Threshold volume between a small stock and 5 ML (SKM, 2004b) domestic dam and a large irrigation dam Annual demand factors for stock and domestic dams 0.5 and 0.84 (respectively) (SKM, 2004c) and irrigation dams A monthly pattern of demand for irrigation dams Taken from monthly average of PRIDE regional demand patterns (see section 2.3.2) A monthly pattern of demand for stock and domestic Uniform distribution adopted (SKM 2004d) dams The number and volume of farm dams, and the SDL project (SKM 2004a,d) distribution of dam sizes within the catchment A timeseries of streamflows See section 4

Information on the distribution of farm dam numbers and sizes was obtained from the SDL project (SKM, 2004a). Estimates of farm dam numbers and surface areas were originally obtained from a combination of:

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„ Aerial photography - each farm dam is digitised using GIS tools and the surface area of each dam is calculated;

„ Topographic data - the waterbodies layer from 1:25 000 GIS data is used to identify topographic features which are likely to be farm dams; this estimate of volume was then increased to account for the low accuracy of the topographic data with respect to small features such as farm dams.

Where aerial photography or previous studies were available, this data was used in preference to topographic data. Information on the surface areas of dams was converted to a volume estimate using a relationship based on the analysis of 110 dams in Victoria (SKM, 2004b).

The areal extent of this project extended beyond the boundaries used in the SDL project, and hence information on additional areas was obtained from the 1:25 000 GIS data and then factored as required (as described above). Information on the number, volume, and distribution of farm dams was then determined for each FSR catchment.

2.3.2 Private diverters Information on private diverters was obtained from data collated for the SDL project (SKM, 2004e). Raw information on surface water licences was originally obtained from the responsible rural water authority. The licences were spatially referenced using coordinate data where available, though a sizeable minority of the licences were allocated using a nested sequence of rules that took into consideration Crown Allotment, Parish, and Section details. There were a number of licences that were located outside the extent of the SDL region, and accordingly it was necessary to apply the same process as used in the SDL project to allocate licences to the remaining FSR catchments.

Data originally obtained from Wimmera Mallee Water only included those licences within SDL areas, and thus it was necessary to obtain additional information relevant to the remaining FSR catchments. Further information was supplied to cover the licences downstream of Glenorchy in the Wimmera basin, and downstream of Coonooer Bridge on the Avoca basin.

In this way, a complete list of all private diverter licences for each FSR catchment was obtained. The list was then divided into categories based on licence type, so that each type of licence could be accounted for using a different demand pattern.

Direct Irrigation Demands Using PRIDE As indicated above, the database of private diverter licences was divided into categories based on licence type. One type of private diverter licence is the summer direct irrigation licence, and this particular licence type required additional information to be collected to allow its impacts to be estimated accurately.

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For each FSR site, both a demand pattern and a peak annual usage volume had to be estimated. These could then be combined to produce the final demand time series for direct irrigation for each FSR site. Information on the variation of irrigation demands with climate was derived using the PRIDE model (Erlanger et al, 1992). This model estimates irrigation demand based on soil moisture, crop areas, and crop evapotranspiration information. It uses rainfall and evaporation data to estimate a time series of demand for irrigation water, usually on a weekly or monthly basis.

PRIDE model estimates of irrigation demands are available for many Victorian catchments as they were prepared by SKM as input to streamflow management plan studies (SFMP). The SFMP studies from which data was obtained and their corresponding basins are summarised in Table 2-4.

„ Table 2-4 - PRIDE demands obtained from previous studies and their corresponding basin

SFMP Study Catchments Basin Kiewa River 402 (Kiewa) King Parrot Creek, Seven Creek, and Delatite River 405 (Goulburn) Nariel Creek 401 (Upper Murray) Mitchell River 224 (Mitchell) Avon River 225 (Thomson) Morwell River 226 (La Trobe) Upper Latrobe River 226 (La Trobe) Stringybark, Steels, Dixons, Pauls, Olinda, Hoddles Cks; Little Yarra & Don R. 229 (Yarra) Upper Maribyrnong 230 (Maribyrnong) Lower Barwon 233 (Barwon) Glenelg River 238 (Glenelg)

In order to derive a time series of irrigation demands for a particular area, it was necessary to estimate the total volume of irrigation licences, the peak annual usage volume, and the within-year pattern of average demands. These estimates were derived as summarised below:

Total volume of irrigation licences. For each catchment the total volume was obtained by summing the volume of licences determined from the Rural Water Authority’s records. Where a detailed estimate of total licences was available from previous PRIDE modelling, this was used in preference to the volume determined from the Rural Water Authority’s surface water licence database.

Peak annual usage volumes were estimated as follows:

„ Where detailed PRIDE model results were available for a specific catchment the maximum annual volume of usage was obtained directly from the PRIDE time series of supplied demands.

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„ To cover areas in the same river basin that did not have PRIDE models, a ratio was calculated between the PRIDE current level of development demand and the licensed volume in the modelled area. For example, if there were 100 ML of direct irrigation licences in a catchment, but the PRIDE model showed that only 75 ML was used in a peak season, then the ratio was 75%. This ratio was then applied to all un-modelled areas in the basin to determine actual usage in a peak season. The ratios calculated for each model are given in Table 2-5 below.

„ Where no PRIDE model existed for a basin, the relevant Rural Water Authority was contacted to provide an indication of proportion of licensed volume currently in use.

„ In some cases, the impact of direct irrigation was already included in a REALM model, and therefore did not need to be accounted for separately.

„ Table 2-5 - PRIDE adjustment factors for each basin

Total Current Licence Basin REALM model PRIDE reach Demand1 Factor Volume (ML) (ML) 402 Mongans Bridge to Kiewa SFMP model 1552 7232 21% (Kiewa) Boyds Bridge 403 Ovens Harrietville to Ovens 573 2293 250% (Ovens) Bright 224 Mitchell River SFMP Wonnangatta River and 112 880 13% (Mitchell) model tributaries 225 Thomson/Macalister Eastern Irrigation Area 45320 23354 1941% (Thomson) 226 Upper Latrobe SFMP Upstream of 226205 190 76 249% (La Trobe) model 226 Morwell River SFMP Morwell River between 405 385 105% (La Trobe) model 226407 and 226210 228 Tarago River between Tarago/Bunyip 4164 4164 100% (Bunyip) Iona and Koo Wee Rup 230 Between Gisborne and Maribyrnong 311 156 199% (Maribyrnong) Sunbury 1 Taken to be the maximum demand from the PRIDE current level of demand run output 2 Demands corresponding to the current level of development taken to be 15% of the reach to compare to the FSR licence volume for catchment 40211 3 Actual licence volume for catchment 40323 4 Actual licence volume for catchment 22517

Demand patterns were estimated as follows:

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„ Where PRIDE models corresponding to the current level of development were available for a specific catchment, the pattern in the PRIDE demand series was adopted directly.

„ Where no appropriate PRIDE models were available, a pattern was adopted either from a nearby PRIDE model, or from one of 12 regional PRIDE patterns developed specifically for this project. These regional patterns were derived using typical crop types for that basin, and climate data from representative locations in the area. As shown in Figure 2-1 there is little variation in average monthly patterns of demands between these 12 regions.

In general where PRIDE modelling had been carried out for an SFMP REALM model or a TEDI model, all direct irrigation licences in the modelled area are included. However, in most large Bulk Entitlement (BE) or system REALM models direct irrigation licences are either ignored or only included for the regulated parts of the system (i.e. ignored for tributaries and upstream of storages). In these cases, only those licences not covered by REALM models were included in this analysis.

0.30 of

on Individual regions i t

or 0.25 State Average op r 0.20 l as p a t o nd t a

al 0.15 u y dem ann

hl 0.10 nt o 0.05 age m ver

A 0.00 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

„ Figure 2-1 – Within-year pattern of average monthly irrigation demands for 12 individual regions and overall State average.

2.3.3 Urban demands Initially, the location of each urban impact was determined so that each impact could be assigned to the appropriate FSR site. However, many towns could be excluded from the analysis for one of the following reasons:

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„ the urban impact was outside the FSR study area (eg. water sourced from the Murray River);

„ the system obtains its water from groundwater only; or,

„ the system obtains its water from a reservoir or channel which is covered by a REALM model.

For each town, an annual demand volume and a demand pattern were required.

Data was collected from all Non-Metropolitan Urban Water Authorities (NMUs) to obtain current level of development data for each town. This data, in conjunction with urban supply REALM models, was used to estimate annual demand volumes for each town as follows:

„ For towns with BE REALM models, the demand series was factored to correspond to current level of development;

„ For towns without REALM models, the data provided by the NMU was used to represent current level of development;

„ Where NMUs were unable to provide current demand data the magnitude of demand quoted in AWWA (1998) book was used.

„ Some data provided by the NMUs was not consistent with current understanding of system use and so volumes as reported in AWWA (1998) were substituted in these cases.

Demand patterns for each town were determined as follows:

„ Where BE REALM models existed, the pattern of impact for each case was taken as that obtained from the model run representative of the current level of development;

„ Where BE REALM models did not exist, data provided by the relevant NMU was used to generate a fixed monthly within-year pattern of urban demand for each basin.

If a town was supplied from both surface and groundwater, the total demand volume was adjusted to account for only the impact on surface flows. It was assumed that if a town was included in a BE REALM model, then conjunctive use would have been taken into account. A check was made of all towns without BE REALM models listed in AWWA (1998) as being sourced from both surface and groundwater. The share of resources as specified by AWWA (e.g. 60:40 groundwater to surface water usage) was applied.

Where more than one urban demand exists within a basin or FSR catchment and a basin wide model does not exist, the impact of the urban demands needed to be combined. This is quite straightforward where systems are located on parallel streams, but where systems are located on the one stream it was necessary to check, and then adjust as required, to ensure that upstream impacts were correctly accounted for in the downstream models.

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2.3.4 Other demands Where large REALM models were available, it was possible to extract current and "partial unimpacted" flows for individual FSR sites, as described in Section 2.2.2. The difference between the current and partial unimpacted flows includes the impact of all demands present within the REALM model. This often included urban demands and private diverters, and even farm dams in a few cases. Most importantly, the current and partial unimpacted stream flows included the impact of large storages and offtakes, such as Eildon Reservoir in the Goulburn basin or Mount Zero channel in the Wimmera basin.

Because many of the demands were included in REALM models, other demand data including urban impacts, farm dams, and private diverters were fully checked and excluded where necessary to avoid double counting.

Other demand data obtained for this project which did not form part of a REALM model included:

„ Impact of Snowy Hydro system at Jindabyne - a timeseries of estimated unimpacted streamflows for the Snowy River at Jindabyne was provided by the Department of Sustainability and the Environment (based on modelling undertaken by Snowy Hydro), as well as actual flows in the Mowambra River aqueduct. This data was compared with the recorded streamflow at gauge 222006 (Snowy River @ Dalgety) to obtain an estimate of the total impact at Jindabyne.

„ Impact of Dartmouth Reservoir - A timeseries of the impacts of Dartmouth Dam were obtained from the MDBC's BigMod Murray simulation model.

2.3.5 Demands not included While the majority of demands were included in this study, there are some which, due to lack of data or the relatively small magnitude of the impact, were not included. These demands are listed below:

„ Releases from hydro-electric power stations were not included except where they were specifically represented in a REALM model (eg. Eildon Dam power station, and the Snowy storages upstream of Jindabyne). For all other hydro-electric releases, the relevant data is usually either not available or difficult to obtain. As a result, the estimated difference between current and unimpacted streamflows are slightly underestimated, and so consequently will be the indices of flow stress. However, the effects of hydro-electric releases are estimated to be relatively small, and are unlikely to have a discernible effect compared to the other uses that the dams in which they are installed are put.

„ Wastewater treatment plant (WWTP) outflows were not included except where they were specifically included in a REALM model. Across the state, the availability of WWTP outflow

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data is generally poor. However, outflows from most plants are expected to be relatively small, which again will have the consequence that the indices of flow stress will be underestimated..

„ Non consumptive demands (eg fish farm, small power generation) have been ignored, except where they are directly represented in a REALM model. This approach was taken as most non- consumptive uses are unlikely to effect estimates of current and unimpacted streamflows on a monthly basis.

„ The impact of groundwater usage on streamflows was not considered except in the Moorabool and Ovens REALM models where it is explicitly represented. In some particular waterways groundwater usage may have a significant effect on streamflows, though the general paucity of relevant data means that any estimates would be largely based on speculation. Overall it was considered that the effort required to derive reliable estimates (outside the Moorabool and Ovens basins) would be disproportionate to the likely magnitude of the impacts.

„ The impacts of land use change were not considered at any site for two major reasons, namely (i) there is no basis for specifying a reference condition (ie should pre-European land-cover be used, and if so, how can it be characterised?), and (ii) there is no means at present of quantifying the within-year impacts of land-use at the required scales of interest. It is for these reasons that the reference regime used to compare current conditions is termed “unimpacted”, that is, streamflows under current land-use conditions but with all anthropogenic extractions reinstated.

2.4 Catchment Characteristics Catchment characteristics were obtained for all FSR catchments using a variety of Geographical Information Systems (GIS) layers and tools. The range of characteristics obtained and their sources are summarised in Table 2-6. Where appropriate, characteristics were calculated as an average over the catchment area.

„ Table 2-6 - Source of Catchment Characteristics

Characteristics Source Catchment Area Derived from FSR catchment boundaries Catchment Centroid Derived from FSR catchment boundaries Elevation Range Analysis of 1:25,000 topographic data Vegetation Cover Integrated Vegetation Cover 2003, Commonwealth of Australia (Bureau of Rural Sciences, 2004). Average monthly rainfall Bureau of Meteorology gridded rainfall data Average monthly areal actual and Bureau of Meteorology gridded actual evapotranspiration data potential evapotranspiration Stream density / frequency Analysis of 1:50,000 hydrographic topography data (Land Victoria) Soil Characteristics Soil Hydrological Properties of Australia (McKenzie et al, 2000)

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2.5 Other Spatial Data Several other spatial datasets were obtained as part of the project, and are listed in Table 2-7. These datasets were used for display purposes only, and any attached data tables were not used in any subsequent calculations.

„ Table 2-7 - Source of other spatial data

Characteristics Source Rivers, streams, and waterbodies 1:50,000 hydrographic topography (Land Victoria) ISC reaches DSE Streamflow gauges Thiess Environmental Services SDL boundaries SDL project (DSE 2003)

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3. Identification of FSR Sites

3.1 Overall Approach This chapter describes the process used to specify the location of sites used for the assessment of flow stress.

The overall intention was to locate sites on all major streams across the State. Sites were located to highlight any major changes in the level of flow stress, and to capture the full range of flow and catchment conditions across Victoria. Site selection involved balancing the competing demands of data availability against those of hydrologic representativeness: on the one hand it was desirable to reduce the number of sites to be commensurate with the availability of relevant streamflow information, and on the other there was the motivation to increase the number of sites to capture all possible changes in flow regime. This balancing act was resolved by first proposing a set of sites on the basis of data availability and hydrological judgement, and then iterating with agency stakeholders to ensure that the selected sites were suited to their management needs. Following several iterations, a final list of sites was compiled.

The following sections summarise the different elements of this process.

3.2 Selection Criteria

3.2.1 General Considerations The prime selection criteria adopted was to select those streams for which an environmental condition had been previously established in 1999 using the Index of Stream Condition (ISC, as summarised in http://www.vicwaterdata.net/vicwaterdata). The ISC provides an overall indication of changes in river condition based on consideration of five sub-indices, namely the current river flow regime, water quality, condition of the channel and riparian zone and the invertebrate communities living in the stream. In many cases an ISC assessment was undertaken at more than one reach along an individual stream, and in total for Victoria 950 river reaches representing 18 000 km of Victoria’s major rivers and their tributaries were surveyed.

Three of the five ISC sub-indices (streamside zone, physical form, and aquatic life) are reach- specific, that is they largely reflect conditions found at a specific location of the river. By comparison, the flow (and to a lesser extent water quality) sub-indices integrate the cumulative impacts of all upstream anthropogenic influences, and accordingly it is more defensible to select flow sites at points of major changes (such as dams) or changes in regime (below a stream confluence). Thus, it was expected that a fewer number of flow sites could be selected without loss of information compared to the number of sites at which physical investigations were required.

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Also, there are many ungauged catchments in Victoria, and many catchments where gauge data is unreliable, or the period of record is too short, or not representative of current flow conditions. Having a large number of sites in such catchments where meaningful data is in short supply is not helpful, thus fewer flow sites could be selected without any loss of information.

Accordingly, the salient selection criterion was to ensure that there was at least one flow site selected on every stream for which a condition assessment had been undertaken in 1999. Additional sites were included to capture the changes in flow regime associated with major confluences, diversion weirs, or impoundments.

The process of selecting flow sites was undertaken in consultation with each of the Catchment Management Authorities. This provided a means of confirming that each identified flow site was appropriate, and allowed for the strategically important sites in each catchment to be identified based on the experience of people with appropriate local knowledge.

3.2.2 Specific Considerations The decision on where exactly to locate sites along a given reach was constrained by the need to consider a number of other practical factors. A number of these factors are listed in Table 3-1, though in general it is seen that these considerations reflect dependencies on the nature of the data used to derive the relevant catchment and streamflow information. While these factors provide a guide, it was not possible to precisely codify the process as the ways in which the different factors influence site location are too variable and site-specific.

„ Table 3-1 - Factors considered to influence site selection.

Factor Influence Location of ISC reaches Used to ensure that required streams are considered. Location of SDL catchment boundary Where possible, sites are located at SDL boundaries to take advantage of information previously compiled on catchment characteristics, farm dam development, and private diverter licences. Ranking of ISC reaches If the reach is long and there is a change in ISC ranking, a second FSR site is considered for inclusion. Location of REALM sub-catchments Sites are located to correspond to REALM sub-catchments for which information is available on extractions and losses. Regulated and unregulated areas Reaches affected by regulation are separated from those influenced by more opportunistic extractions. Location of urban off-takes Used to demarcate reaches subject to volume of extractions and size of upstream area. Location of stream gauges No split at a gauge location, unless it was necessary to sub- divide a very long reach, in which case the split was aligned with a gauge where possible.

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As discussed above the first priority was given to reaches where information on the Index of Stream Condition (ISC) had been previously been determined, or else was identified for future assessment. In those cases where a stream was represented by more than one ISC site, the stream was represented by only a single FSR site at the downstream end of the reach unless there was a significant change in flow regime (i.e. a major diversion or confluence).

Division of streams into regulated and unregulated reaches is a straightforward exercise when considering mainstream flows upstream and downstream of a dam. Difficulties arise however when considering the (often numerous) small unregulated tributaries that join a mainstream downstream of a point of regulation. To avoid ending up with a large number of sub-catchments only the largest of inflow tributaries were considered.

Urban off-takes or major supply channels can have a large impact on the flow regime, and thus where practical an FSR site was located immediately upstream of an off-take. However in some cases with multiple off-take points, a single downstream site was selected to represent upstream conditions. The necessity of including an additional upstream site was decided on the basis of catchment size, the magnitude of the diversions, other catchment activity and available data.

In general ISC reaches with grey flags (i.e. those reaches that were located but not assessed in the 1999 review) were excluded. Each reach with a grey flag and how it is dealt with is listed in Table 3-2, excluding those reaches where a coloured flag was present elsewhere on the same stream. The exceptions to this rule were Cumberland River in the Otway Basin, Bossy River in the Tambo Basin, and an unnamed river in the Werribee Basin which were considered by DSE to be a significant streams.

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„ Table 3-2 – Treatment of unassessed ISC reaches (ie those with grey flags).

Basin No Basin Reach Mainstream Comment 1 Upper Spring Ck Victoria R Included in mainstream Murray Cobungra Ck Victoria R Included in mainstream Gibbo R Morass Ck, Mitta Mitta R Included in mainstream Lightning Ck Snowy Ck Included in mainstream Omeo Ck Murray River Excluded 5 Goulburn Kurkurac Ck Sugarloaf Ck Included in mainstream 22 Snowy Sarding Ck Brodribb R Included in mainstream Suggan Buggan R Snowy R Included in mainstream Bendoc R NSW border Excluded Yalmy R Snowy R Included in mainstream 23 Tambo Bossy R n/a Included unnamed ck Tambo R Included in mainstream 24 Mitchell unnamed ck Wonnagatta R Included in mainstream 25 Thomson Jordan R Thomson R Included in mainstream Ben Crauchan Ck Avon R Included in mainstream 26 Latrobe Eaglehawk Ck Latrobe R Included in mainstream Loch R Latrobe R Included in mainstream 31 Werribee unnamed creek n/a Included 35 Otway Cumberland R n/a Included

The location of REALM sub-catchments were not used to dictate the location of an FSR site, but where possible sites were located to coincide with REALM sub-catchments for which information was available on extractions and losses. There are REALM models representing covering the mainstream reaches of a good proportion of the study area, and the associated information on flows and extractions at these sites incorporate the outcomes of numerous detailed studies.

Large urbanised areas were excluded from consideration because the impacts arising from stormwater discharge and channelisation are both difficult to quantify and are of a nature unrelated to water extraction, and generally require data collection and analyses that lie outside the scope of this study.

Figure 3-1 illustrates how the above considerations were implemented. Site 1 is located just upstream of an urban off-take. There is a large catchment area upstream of the urban off-take and therefore Site 1 provides useful information on the impacts of upstream farm dams and private diversions on streamflow. Site 2 then represents the whole upstream reach (including the area upstream of Site 1) and highlights changes in streamflow caused by urban demands, farm dams and private diversions. Site 3 is located just upstream of a storage and separates the unregulated system from the regulated system. Site 4 represents the impacts of both regulation and all upstream

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diversions. The small creek (joining the mainstream upstream of the confluence at Site 2) is not considered as there is no ISC site on the creek and there is little associated development; any changes in flow regime in the creek are thus reflected in the FSR of Site 3.

Once it was decided what aspects of development each FSR site was to represent, the location of the sites were slightly adjusted (where necessary) to coincide with other sources of information. For example, sites were located to coincide with Sustainable Diversion Limit (SDL) catchment boundaries, REALM model nodes, and streamflow gauge locations. SDL boundaries are of particular interest as they indicate points at which information on farm dams, private diversions, physiographic characteristics, and hydrologic similarity criteria are already available. The location of FSR sites in this manner ensures that the information used to evaluate the stress ranking is both consistent with analyses undertaken for other studies, and is based on our best understanding of catchment behaviour.

3 2 4

FSR Site 1

Private Diversion

Urban Demands

Farm Dam

ISC site

„ Figure 3-1 - Example placement of FSR sites.

3.3 Adopted Locations Application of the above site selection criteria yields a total of 551 sites for the whole State, and a map illustrating the overall extent of FSR coverage and the spatial distribution of the adopted sites is shown in Figure 3-2.

The distribution of sites by basin are summarised in Table 3-3, along with the average catchment area covered by an individual FSR site. It is seen that on average across the State each FSR site represents an upstream area of 363 km2, though there is considerable variation between basins: for example in the Werribee Basin the average area is only 91 km2 (a coverage four times more dense

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than the State average) whereas in the Snowy and Avoca Basins the average is around 1100 km2 (which is three times lower than the State average). This large difference in the density of site coverage is a combination of both drainage density, intensity of development, and the management requirements of the responsible authorities.

„ Figure 3-2 - Overall extent of FSR coverage and distribution of adopted sites.

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„ Table 3-3 - Summary of FSR sites in each Basin

Number of Average Ratio of Basin Area Basin Sites coverage average coverage (km2) (km2) to State average 221 East Gippsland Basin 5,890 16 368 1.01 222 Snowy Basin 15,613 14 1115 3.07 223 Tambo Basin 4,183 13 322 0.89 224 Mitchell Basin 5,178 14 370 1.02 225 Thomson Basin 6,053 19 319 0.88 226 Latrobe Basin 5,470 19 288 0.79 227 South Gippsland Basin 6,321 24 263 0.72 228 Bunyip Basin 4,095 17 241 0.66 229 Yarra Basin 4,095 23 178 0.49 230 Maribyrnong Basin 1,423 13 109 0.30 231 Werribee Basin 2,008 22 91 0.25 232 Moorabool Basin 2,257 15 150 0.41 233 Barwon Basin 3,739 25 150 0.41 234 Corangamite Basin 4,247 12 354 0.97 235 Otway Basin 4,100 36 114 0.31 236 Hopkins Basin 9,105 23 396 1.09 237 Portland Basin 3,934 10 393 1.08 238 Glenelg Basin 13,028 25 521 1.43 401 Upper Murray Basin 10,107 25 404 1.11 402 Kiewa Basin 1,948 14 139 0.38 403 Ovens Basin 7,785 23 338 0.93 404 Broken Basin 6,977 19 367 1.01 405 Goulburn Basin 17,222 39 442 1.22 406 Campaspe Basin 3,942 17 232 0.64 407 Loddon Basin 15,240 29 526 1.45 408 Avoca Basin 12,162 11 1106 3.04 415 Wimmera Basin 24,101 34 709 1.95 Total for Victoria 200,223 551 363 1.00

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4. Estimation of Current and Unimpacted Flows

4.1 General In order to calculate the FSR index, estimates of current and unimpacted flows are required for each of the 551 FSR sites. The difference between current and unimpacted flow is caused by extractions and impoundments within the catchment. Therefore, any two of the following are required to determine hydrologic stress:

„ a current streamflow series;

„ an unimpacted streamflow series; and/or,

„ a timeseries of catchment water demands.

The nature of the most readily available data varies between sites. At some sites it is straightforward to obtain data on current streamflows, farm dams, and private diverters, whereas at others it is easier to obtain data on unimpacted flows derived from REALM models. Given the large number of sites involved, and the need for consistency dictated by the objectives of a comparative assessment of hydrologic stress, a robust and consistent approach was developed for the derivation of the required timeseries. To this end, each FSR site was assigned to one of the following four generic cases:

„ Case 1 - Gauged flows are available: it is assumed that gauged flows (over recent decades) is representative of “current” conditions, and unimpacted flows are estimated by the inclusion of all known extractions: Unimpacted flow = Gauged flow + private diverters + urban demands + farm dam impacts

„ Case 2 - Unimpacted flows are available: if estimates of unimpacted flows are available from previous detailed studies, then estimates of current flow are obtained by the subtraction of all known extractions: Current flow = Unimpacted flow – farm dam impacts – urban demands – private diverters

„ Case 3 – Unimpacted flows are transposed from other data. if estimates of unimpacted flows are available at a nearby site (i.e. from a Case 1 estimate), then they are transposed by a consistent approach (Section 4.3) and current flows are estimated as per Case 2: Current flow = Transposed(unimpacted flows) – farm dam impacts – urban demands – private diverters

„ Case 4 - REALM flows are available. If a REALM model is available for the catchment, then estimates of current flows are obtained directly from the REALM model at the current level of

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development and estimates of unimpacted flows are obtained by the consideration of all known extractions: Current flow = Flows extracted from the REALM model under current levels of demand Unimpacted flow = 'REALM partial unimpacted' + private diverters + urban demands + farm dam impacts where ‘REALM partial unimpacted’ flows are obtained by the summation of all upstream inflows to the REALM model and the application of specified loss functions. The reason that additional extractions need to be considered above those included in the REALM model is that often minor private diverters located on tributaries are not represented, and the impacts of farm dams have almost always been ignored.

It should be noted that the above summations and subtractions are undertaken in the specific order listed. This is done as the calculations – primarily those associated with the estimation of farm dam impacts – are not commutative. That is, the calculated demand from farm dams and some private diverters depends on the exact order in which the calculations are performed. Farm dams usually extract water from a catchment before it reaches a waterway, therefore farm dam demands must be either subtracted first, or summed last – changing this order would actually change the magnitude of assessed impact of farm dams.

The logic associated with each of the above four cases was codified into a computer program. As the streamflow estimates at many sites are dependent upon estimates of upstream sites, and there may be many different upstream tributaries combining in a cascade arrangement to downstream flows, the program applied an iterative approach that proceeded in a downstream direction to ensure that all upstream demands and flow data were correctly incorporated.

The following sections provide details of how each component of the calculations were undertaken, and the key issues and assumptions involved.

4.2 Assessment of Calculation Method Each site was assessed to determine the most appropriate source of streamflow data, and which of the four cases listed in Section 4.1 was the most appropriate to apply. The assessment took into account the following considerations:

„ If the site was at the same location as a streamflow gauge, then, provided that the gauge data was of high enough quality to allow infilling and extending as described in Section 2.2, the flows were estimated according to Case 1;

„ If streamflows representative of unimpacted conditions were available from a previous detailed study (e.g. a streamflow management plan) then the estimates of current flows were obtained according to Case 2.

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„ If the site was within a large REALM model and the model could provide current and (partial) unimpacted flows at that particular location, then the REALM model flows were adopted and the flows estimated according to Case 4;

„ If the above cases did not apply, then unimpacted streamflow data was sourced from a REALM inflow input, or from a nearby gauge after application of the Case 1 method, then transposed either upstream or downstream along a stream reach, or from an adjacent catchment. (Case 3). In basins where REALM model data was used, extra care was required when determining demands. If an upstream site used current and partial unimpacted streamflows from a REALM model, but a downstream site used streamflows from a different source, the downstream demands had to be computed in a way which took account of the upstream impacts that were included the REALM model.

4.3 Transposition of Streamflows

4.3.1 Nature of the Problem An important component of the methods discussed in Section 4.1 is the transposition of streamflows from a site with information to one without. The transposition of streamflows can only be made using unimpacted flow series as anthropogenic extractions do not vary in a predictable manner with catchment scale.

Many approaches have been developed for the transposition of streamflows. A simple approach to transposing streamflow data is to select the closest gauge and adjust the streamflow data based on the ratio of the gauged and ungauged catchment areas. If there is a gauge within close proximity, this approach will provide a streamflow data set that is responding to the same climatic events experienced in the ungauged catchment. However, the approach assumes that the two catchments will have the same runoff response to the climatic conditions and that they have the same yield per unit area, making factoring by catchment area appropriate. These assumptions are not always appropriate. Consider an ungauged catchment in a relatively flat catchment with impermeable soils. Clearly it is inappropriate to use the closest gauge if it is located in a mountainous area and receives sub-surface inflows. The ungauged catchment will flow intermittently and respond directly to rainfall events while the gauged catchment will flow continuously due to the sub-surface flows. A factor for transposition based solely on catchment area would also be inappropriate because the gauged catchment will have a higher yield due to increased rainfall in the mountainous area.

For this project two alternative approaches were considered. The first is based on the use of mean annual flow estimates, and the second is based on application of the Beta distribution. The concept

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of hydrologic similarity is considered in both these approaches, and a brief description of these methods is provided in the following sections.

4.3.2 Mean Annual Flow Transposition Full details of this approach are provided in Lowe and Nathan (2005), but in brief, the approach consists of the following steps:

1) Assess the hydrologic similarity of the nearest available time series of unimpacted flows by computing the Euclidean distance metric based on the linear combination of catchment characteristics: PC1 = 0.42KS + 0.39TREE – 0.15SFREQ + 0.639RAIN (4.1a) PC2 = -0.15KS – 0.06TREE + 1.01SFREQ + 0.05RAIN (4.1b) where KS denotes soil permeability, TREE is vegetation cover, SFREQ is stream frequency and RAIN is annual rainfall; the values of these hydrologic similarity indices for many locations is provided as a GIS layer in DSE (2003). 2) Determine the distance between the catchment centroids as a measure of geographical proximity. 3) Identify the site with the highest overall degree of catchment similarity on the basis of both geographical proximity and hydrological similarity using: Overall similarity = 0.15*(Hydrological Similarity) + 0.85*(Geographical Proximity) (4.2) 4) Estimate the mean annual flow (MAF) at the source and target sites using values developed for the SDL project (DSE, 2003); these estimates of MAF are based on a combination of gauged information and regional prediction, and smoothing was introduced to ensure consistency of estimates in nested catchments. The regional prediction equation involves catchment area (AREA), mean annual rainfall (RAIN) and average actual evaporation (AEVAP): ln(MAF) = -4.73 + 0.91ln(AREA) + 0.002RAIN + 0.003AEVAP (4.3) 5) Scale the time series of unimpacted flows from the site with the greatest hydrologic similarity by the ratio of the mean annual flow estimates.

The selection of the source site on the basis of both geographical proximity and hydrologic similarity helps ensure that the most suitable gauge for transposition is the one that is subject to the same hydroclimatic conditions and is also hydrologically similar.

The potential limitations of this approach are illustrated by the two sets of results provided in Figure 4-1. This figure shows the results for two different cases in which flows from a source catchment are transposed to a target site by the ratio of mean annual flow: the top panel shows the results for two catchments which are hydrologically similar, and the lower panel uses two sites that

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are quite dissimilar. While in both cases the mean annual flows are preserved, only the results for the hydrologically similar catchments are acceptable; the results in the lower panel clearly show that the simple factoring does not reproduce the ephemeral conditions of the target catchment.

100000

10000 ) h t

n 1000 o Transposed flows m

L/ Target flows M

( Source flows

ow 100 l F

10

1 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Proportion of time flow exceeded

100000

10000 ) h t

n 1000 o m L/

M Source flows (

ow 100 l F Transposed flows

10 Target flows

1 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Proportion of time flow exceeded „ Figure 4-1 – Comparison of flow duration curve resulting from the transposition of streamflows based on mean annual flow for (a) two similar catchments (top panel) and (b) two dissimilar catchments (lower panel).

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It is thus seen that this approach, based as it is on the application of a single adjustment factor, is heavily dependent on selecting a (source) site that is hydrologically similar to the (target) site of interest. It could be expected that use of multiple adjustment factors (that is factors that reflect changes in a range of flows rather than just the mean annual flow) would reduce dependency of the method on hydrologic similarity. Accordingly, an alternative approach based on ordinates of a flow duration curve was also trialled, as described in the following section.

4.3.3 Beta Distribution Transposition In order to incorporate differences in the overall flow regime – and thus hopefully lessen the explicit dependence on assessing hydrologic similarity from catchment characteristics – an approach was trialed in which the Beta distribution was used to characterise the shape of the flow duration curve. The intention with this approach was to describe in a parsimonious fashion the shape of the flow duration curve, and then transpose streamflows using a wide range of flow factors rather than use of a single factor based on mean annual flow.

Two example shapes of cumulative Beta probability distribution are shown in Figure 4-2. The shapes of these curves are controlled by two parameters which can be selected to reproduce a wide range of flow conditions, from highly ephemeral catchments to persistently perennial. The attraction of using the Beta distribution is that it provides a very parsimonious means of describing complex shapes. Full details of this approach are provided in Appendix B, though it is sufficient to state here that the Beta distribution function can be used to estimate a flow duration curve for any site using estimates of:

„ Q10 - the flow exceeded 10% of non-zero flow months in the target catchment;

„ Q90 - the flow exceeded 90% of non-zero flow months in the target catchment; and,

„ Cease to Flow (CTF) - the proportion of non zero monthly flows in the target catchment

Transposition of streamflows is undertaken by the following steps: 1) Select a source catchment on the basis of both geographical proximity and hydrologic similarity (using Steps 1 to 3 as listed in Section 4.3.2);

2) Estimate Q10, Q90 and CTF for both the source and target catchments from regional prediction equations and construct flow duration curves; and, 3) Convert the time series of flows at the source site to flow percentiles, and then derive a time series of flows at the target site by reference to the proportion of time that each flow is exceeded.

It is thus seen that this approach requires the estimation of three hydrologic parameters (Q10, Q90 and CTF) compared to the single estimate of mean annual flow described earlier. While it is very attractive that the use of the Beta distribution allows the full flow duration curve to be defined on

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the basis of only three parameters, the weakness of the method is that the final transposition is dependent on the accuracy of their prediction.

120 Fitted beta distribution 100 Observed flows

80

ow 60 Fl 40

20

0 0 20406080100 Proportion of time flow exceeded (%)

120 Fitted beta distribution 100 Observed flows

80

ow 60 Fl 40

20

0 0 20406080100 Proportion of time flow exceeded (%)

„ Figure 4-2 - Example applications of how the beta distribution may be used to parsimoniously characterise flow duration curves.

4.3.4 Adopted Method of Transposition An evaluation of the foregoing two transposition approaches was undertaken using data obtained from 36 streamflow gauges selected to reflect the range of hydroclimatic conditions found across Victoria. The location of these test sites and full details of the comparison are provided in Appendix B.

Streamflows were transposed to each of the 36 test sites using both the mean annual flow and beta distribution methods. These estimated flows were then compared to the known gauged flow at the site, and the performance of each was compared. It was found that if an even weight is given to

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both low and high flows then the method based on the Beta distribution performs slightly better than the mean annual flow approach. However, if more weight is given to high flows (ie as would be required if the focus was on estimating average yields) then the mean annual flow approach performs significantly better than the approach based on the beta distribution. The greatest source of uncertainty with the Beta distribution approach is associated with estimation of Q90 and CTF. These two parameters – particularly CTF – could only be predicted poorly, and their uncertainty is propagated through to the final flow calculations. If in the future it becomes possible to estimate these quantities with more precision, then it is possible that use of the Beta distribution would provide better results as the method ensures that both the low and high regime are appropriately represented. However, for this study the simpler approach based on estimation of the mean annual flow was adopted as providing the simplest and most accurate means of transposition.

4.4 Private Diverter Impacts The impacts of private diverters were estimated from separate analyses of the different licence types present in each individual catchment. As described in Section 2.3.2, a complete list of private diverter licences, categorised by licence type, was obtained from surface water licence databases maintained by the relevant Rural Water Authorities. The impact of each type of licence was calculated based on an annual demand volume and a demand pattern, as listed in Table 4.1.

„ Table 4-1 - Method of calculation for each private diverter licence type

Licence Type Demand Volume Demand Pattern Direct irrigation Estimated by the PRIDE Time series of demands as estimated by model (see Section 2.3.2) the PRIDE model (see Section 2.3.2) On-stream winterfill Full licence volume used Demand occurs only in May to October, each year (where possible) but with monthly demand varying proportionally with the monthly streamflow Off-stream winterfill Full licence volume used Demand assumed constant from May to each year (where possible) October Domestic and stock Full licence volume used Demand assumed constant throughout the Commercial and industrial each year (where possible) year Non consumptive None Not included in analysis, except where already included in a REALM model

Any other licence types were individually assessed to determine their likely demand pattern, and were then assigned to the licence type from the above list which most closely represented it.

The method of calculating the impacts of the above licence types is illustrated for a hypothetical catchment in Figure 4-3. This figure shows a typical year of monthly streamflows for a typical waterway, with calculated demands for 100 ML of each licence type. The 100 ML of constant demand licences, including commercial/industrial and stock/domestic, are assumed to be extracted

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uniformly throughout the year. The demand is 8.3ML per month, which gives a total of 100 ML for the year.

100 10,000

90 100 ML/yr On Stream Winterfill 9,000 100 ML/yr Off Stream Winterfill 80 8,000 100 ML/yr Constant Demand 70 Streamflow 7,000

60 6,000 month) month) 50 5,000 ow (ML/ nd (ML/ 40 4,000 ma amfl e e D

30 3,000 Str

20 2,000

10 1,000

0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1984 1984 1984 1984 1984 1984 1984 1984 1984 1984 1984 1984

„ Figure 4-3 - Example calculation of different private diverter licence types

The 100 ML of off-stream winterfill licences are assumed to be extracted uniformly over the May to October period, which equates to 16.6 ML per month over 6 months. This pattern represents constant pumping of water to an off-stream storage, and given that the individual licences are aggregated across the catchment, it can be assumed that extractions occur at a uniform rate over the winter season.

The 100 ML of on stream winterfill licences are assumed to occur only from May to October, but the magnitude of the demand varies with the magnitude of the streamflow. In this case, approximately 2% of the May to October streamflow occurs in May, so only 2% of the demand occurs in this month. Because the pattern of streamflows is different each year, the pattern of on stream winterfill demand also changes each year.

Direct summer irrigation is calculated based on PRIDE demand time series, as described in Section 2.3.2. As a result, the pattern of demands is different each year, and also varies between sites.

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4.5 Calculating Urban Impacts The impacts of urban demands were estimated from an analysis of the individual demands supplied from each catchment. As described in Section 2.3.3, urban demand data was collated from either:

„ a demand timeseries based on REALM modelling; or,

„ an annual demand volume and a repeating annual pattern of monthly demands based on historical usage data obtained from non-metropolitan urban water authorities.

The urban demand data was then compiled for each individual FSR catchment. Where more than one urban demand was present in a catchment, the time series or annual demand volumes were summed, as appropriate. A single demand time series and a single annual demand volume were developed for each catchment, allowing impacts to accumulate as calculations proceeded downstream.

Some problems arose when demands were aggregated up to the catchment level. The majority of smaller REALM models were developed to only consider the demands of one particular urban system, and they do not consider the impacts of other upstream systems; however, some models do take upstream demands into account through an adjustment of their upstream inflows. Accordingly, it was necessary to check all minor REALM models for how upstream demands were treated, and steps were undertaken to ensure that urban demands were not double counted.

As described in Section 2.3.3, only surface water impacts resulting from urban demands were considered, with any groundwater / channel supply contributions to urban supply systems being removed from calculations. Where no exact contribution data was available, the demands were factored down based on information in the urban system directory (AWWA, 1998).

4.6 Farm Dam Impacts As described in Section 2.3.1, farm dam demands were estimated using TEDI, which estimates the impact of farm dams based on climate data, catchment data, farm dam demand pattern information, and a timeseries of unimpacted streamflows.

In applying the TEDI model a choice needed to be made whether to estimate the impacts of farm dams for either the whole catchment upstream of an FSR site, or else for the intermediate catchment areas between nested FSR sites. With the latter approach, the impacts between FSR sites are progressively added together as calculations proceed downstream. This has the advantage that the impacts are calculated in relatively small catchment groups, and differences in climate and farm dam distribution between catchments are taken into account. However there are two difficulties with this approach. Firstly, the impacts of farm dams are calculated using monthly streamflows that are derived from the differences between streamflows estimated at two sites; for small increments in catchment areas, or else for catchments with a high density of farm dams, it is likely that the

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streamflows (being impacted by farm dams) would include periods of zero flows, a set of conditions under which the TEDI model is unable to provide estimates of unimpacted flows. Secondly, the stochastic scheme used to generate farm dam numbers in the TEDI model is not well suited to small catchment areas which may contain a small proportion of large dams.

The alternative approach is to calculate the farm dam impacts for the entire catchment upstream of each FSR site. This avoids the two problems mentioned above associated with zero impacted flows and catchments with a small proportion of large dams. However, the limitation of this approach is that regional variations in streamflow and farm dam distributions are averaged over the whole catchment, which may bias the estimation of unimpacted flows near the downstream and upstream ends of larger basins.

On balance, it was considered that the simulation of the entire catchment upstream of each FSR site was likely to provide more accurate and progressively consistent estimates of farm dam impacts than the approach based on intermediate areas. Accordingly, farm dam impacts were estimated by applying TEDI models to the entire catchment upstream of each FSR site.

Estimates of farm dam impacts were available from previous studies for a small number of catchments. For each of these studies, detailed demand, climate, and physical catchment data had been collected. However, these site-specific estimates were was not used for the following reasons:

„ The studies were undertaken over a number of years and are based on varying assumptions related to dam volume and demand characteristics – adoption of the results would thus introduce inconsistencies into the regional analysis of farm dam impacts; and,

„ These studies only apply to a handful of FSR sites (less than 5% of the total), and adopting this data, after identifying and correcting for inconsistencies in the inputs, would have required a disproportionate level of effort compared to the improvement in the resulting demand estimates.

Farm dam impacts were already included in streamflows extracted from the Moorabool and Ovens REALM models. Given the complexity of these models, the fact that they were developed within the last two years, and that in these catchments farm dams represent only a minor proportion of total extractions, the estimates of the farm dam impacts as represented in these two REALM models were considered to be consistent with the current approach and were they were thus retained.

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5. Formulation of Stress Indices

5.1 Choice of Time Step From an ecological perspective it is desirable to characterise flow stress based on consideration of daily behaviour. However, there are two practical considerations which make characterising flow stress across Victoria using daily data impractical.

Firstly, the information available for all the different types of water extractions is very limited, particularly at the regional scales of interest, and a derived daily time series is less accurate compared to a monthly time series. Apart from the hydrologic difficulty of characterising daily flow response to rainfall, there is considerable uncertainty surrounding the disaggregation of monthly demands down to a daily pattern at a catchment scale. Consequently an assessment of flow stress based on daily data may lead to quite misleading inferences regarding the true level of flow stress. Secondly, index scores need to be developed for 551 sites across Victoria. If daily flows were adopted for all of these sites, the amount of data processing required to estimate current and natural flows for each of these sites would be considerable. The effort required to ensure that the required assumptions were adequately dealt with at a daily time step, particularly when dealing with calculations for nested sites, is significantly greater than that needed to process monthly data. In short, the additional effort required to process data at a daily time step could not be justified, specially given that such additional effort would likely result in less accurate time series.

Accordingly, one of the major practical constraints – and hence technical challenges – involved in the characterisation of flow stress is that the approach needs to be based on monthly streamflow data, yet be developed in such a way that it captures the ecological impacts at a daily level.

The discussion below outlines the method used to derive a range of hydrologic indices which could be applied to monthly data, yet still capture the same information as contained in the corresponding daily data.

5.2 Overview of Approach The FSR index was formulated with input from a scientific panel of aquatic ecologists and hydrologists. A number of steps were required in developing a set of indices that represented ecologically important flow components, and these are briefly discussed below. Additional detail on each of these steps is provided in Sections 5.3 to 5.10, and in Appendices A and C.

Review of previous studies and indices.

Hydrological indices have already been developed and used to characterise flow stress in a number of previous studies, both in Australia and overseas. Relevant published studies were thus reviewed to assist in the selection and development of indices for the FSR project. In addition, a number of

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studies in which environmental flow recommendations were made using the FLOWS approach (NRE, 2003) were reviewed to gain information on thresholds that may be applicable to the definition of ecologically important flow components.

Selection of Test Sites

In order to develop and test the formulation of the FSR index, both unimpacted and current daily streamflow series were obtained for 50 sites across the State. The sites were selected to be representative of the different types of climate, topography, and stream regulation found in Victorian waterways, where attention was focused on those catchments where the necessary streamflow data could be derived with reasonable confidence.

Development of preliminary indices

Based on the literature review and input from the scientific panel a wide range of daily indices were developed based on daily streamflow data. Some indices were based on those developed recently for the Sustainable Rivers Audit (MDBC, 2004), however the approach used here included incorporation of a standardisation factor based on the range of variation in the unimpacted flow regime (see Section 5.3).

Monthly indices were calculated using a similar approach, though some computational differences were introduced to maximise the correlation between daily and monthly results.

Selection of Indices

The preliminary set of investigations and indices derived in Step 2 were discussed by the scientific panel, and further refinement and investigations were agreed to. The additional work was then undertaken, and after some iteration a final set of indices was developed.

The final set of (five) indices were selected to characterise flow stress on a range of ecologically important flow components. They were formulated to be applied at a monthly time step, over an annual or seasonal period, but in a manner designed to (i) reflect a similar degree of flow stress as found using daily data, and (ii) minimise the degree of cross-correlation (ie “information overlap”). Details of this development are provided in Section 5.8.

Sensitivity Analysis

It is desirable to ensure that any variation in the indices between sites is due to differences in the degree of flow stress in the river and not to differences in the timing or length of streamflow record used to calculate the index. Accordingly the sensitivity of the indices to timing and length of record was assessed (see Section 5.9) and recommendations made concerning the minimum allowable length of record.

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Combining Indices into a single FSR Score Various approaches were considered for the manner in which the five indices could be combined to provide an overall “score” reflecting the degree of annual or seasonal flow stress, and the final approach adopted is described in Section 6.

5.3 Selection of Test Sites In order to develop and test the formulation of the FSR index, both unimpacted and current daily streamflow series were required for a range of sites across the state. The sites were selected to be representative of the different types of climate, topography, and stream regulation found in Victorian waterways. A total of 50 sites were selected using the available data sources, and their spatial distribution is shown in Figure 5-1. The methodology used to select and process the streamflow data is described in Appendix A.

In order to assess the range of characteristics represented by the catchments selected for formulation of the FSR, similarity criteria based on physiographic and climatic characteristics were obtained from the SDL project (DSE, 2003). It was found that the hydrologic similarities of the selected sites are broadly representative of the range of conditions found across the whole state.

Regulated Sites Unregulated Sites

„ Figure 5-1 - Location of selected test sites

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5.4 Review of Environmental Water Requirements Environmental flow studies have been undertaken for many waterways across Victoria under the Bulk Entitlement process, as part of a Streamflow Management Plan, or as part of a Stressed River Program. These studies have been undertaken using a range of approaches, but since development of the FLOWS method (NRE, 2003) a more consistent approach has been used to determine environmental water requirements across the State.

A review of these environmental flow studies was undertaken to determine whether there was any consistency in the hydrological indices used to formulate the flow recommendations.

This review is based on the following Environmental Flow studies:

„ Goulburn River „ Upper Avoca River

„ Loddon River „ Mt William Creek

„ Mitchell River „ Steels, Pauls and Dixons Creek

„ Lerderderg River „ Macalister River

„ Avoca River „ Thomson River

The FLOWS method is based on the philosophy that the recommended flow regime must incorporate the key components of the unimpacted flow regime that are necessary for the biological, geomorphological and physiochemical processes. These key flow components are considered in terms of magnitude, frequency, timing and duration. A summary of the key features of these flow components is included in Table 5-1, and includes indicative periods of timing, frequency and duration (NRE, 2003).

The FLOWS method defines the process to be used in defining environmental flow recommendations, however it employs a number of other technical methods to determine the specific recommendations. A number of studies have used the Flow Events Method (FEM) (Stewardson and Cottingham 2002) to determine the frequency, magnitude and duration of events. Other studies have used a combination of hydraulic modelling to determine the area of inundation and an analysis of flow spells to compare the frequency, timing and duration of freshes and cease to flow events under unimpacted and current conditions. Although there are different technical components to each study depending on the site-specific requirements, the FLOWS method ensures that flow recommendations are based on ecological requirements. As such, there is no “rule” as to what size or frequency event constitutes a low flow or a fresh – this will vary depending on the habitat and values in each waterway.

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„ Table 5-1 Summary of key features of flow components

Flow Channel Flow Timing Frequency Duration Key Functions Component Characteristic Cease to No surface flow Summer Annual Varies Ecological Flow from days disturbance to months Dries habitats and substrates Facilitates organic matter and carbon processing Low Flow Minimum flow in Summer Annual Weeks to Connect instream channel months habitats Continuous flow in System maintenance some part of the channel Freshes Flow greater than Summer Can be Generally Biological triggers median flow for that Spring several in days Inputs to habitats period each period Physico-chemical changes High flow Connect most in Autumn May be Weeks to Inundation of channel habitats Winter several months instream habitats annually Less than bankfull Spring Physico-chemical May include flow in changes minor floodplain channels Bankfull High flow within Winter Generally at Days to Channel and habitat channel capacity Spring least annual weeks forming Flow in other Sediment transport channels (anabranches etc.) Over bank Flow extends to Winter Can be Days Floodplain floodplain surface Spring annual or connectivity flows less frequent Organic matter inputs

In order to determine whether there are any consistencies in the chosen magnitude, frequency and duration for key flow events then it is desirable to compare recommendations from the different studies in a non-dimensional manner. One way of achieving this is to relate flow magnitude to the proportion of time it is exceeded (ie using a flow duration curve). Thus, while the flow recommendations were not developed with regard to maintaining specific flow percentiles, the flows can easily be standardised in this fashion to facilitate comparison. It is possible, if not arguably preferable, that converting flow magnitudes to a frequency-based estimate (using a partial series analysis) would reveal greater consistency, but the necessary information is not available in the reports and would have had to be re-derived.

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There was a large variation in results – both between different studies and between different reaches within each study – and it should be noted that the following summary is a broad generalisation of the report outcomes.

The definition of seasons differs for different environmental flow studies, and is generally determined based on the mean monthly flows. Some studies report a more detailed assessment based on the frequency of unimpacted flows in different flow bands in each month. In the majority of studies, the summer season is defined as the season with low flows and the winter season as that with high flow months. In some cases transitional months are included. In general (although there is much variation), summer often includes December to May (inclusive) and winter often includes July to October. June and November are often transitional months.

Cease to Flow (CTF) events are only specified if they occur under unimpacted conditions. The CTF duration is then either set as a maximum or minimum duration depending on the current flow regime (ie. if the river currently has a higher than unimpacted flow or an extended CTF period). There are two main approaches:

„ If CTF occurs under unimpacted conditions, CTF is set as the duration at which 80% of CTF events are shorter in duration (eg. 80% of events are less than 180 days in duration, thus the CTF = 180 days)

„ Minimum flow recommendation is set as minimum or unimpacted to allow for CTF. This is referred to as a “cease to divert” rule in some cases.

There are a number of variations on how a minimum flow is defined. Some reports define a “cease to divert” flow, some a minimum flow and others a maximum summer flow. Most commonly, if the flow is defined as a minimum flow or a cease to divert flow, the recommendation corresponds to flow with an exceedance value of 90% or more, although there are a number of site specific recommendations with values less than this.

The FLOWS method defines the summer fresh as a flow that exceeds the median flow for a set period (ie. the summer season). This is implemented in some flow studies, however there is a wide variety of flow values chosen (ranging from flows exceeded 8% to 80% of the time). In most cases the duration is the median unimpacted duration or the duration that 75% of events would have exceeded under unimpacted conditions. The frequency is the unimpacted occurrence.

There is little correlation between values chosen for the winter low flow. In most cases, the winter low flow is based on the inundation of pools and determined using a hydraulic model such as HECRAS. The magnitude of flow required to inundate pools ranges from the 30% to 90% exceedance flows.

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Many reports recommend a winter fresh that corresponds to a flow exceeded between 25% and 30% of the time. There are also a number of reports that use the median winter flow. Again there is a wide range of values that have been chosen for the winter fresh recommendation. The frequency and duration of events is similar to that occurring under unimpacted conditions.

Both the high flow (bank full) and overbank flow are generally based on the frequency and duration of naturally occurring events. The high flow event tends to correspond to between 2% and 5% exceedance flow, while the over bank flow tends to correspond to flows exceeded less than 2% of the time.

In summary, the brief review of the environmental flow studies indicate that:

„ Seasonality is defined using the distribution of mean monthly flows; although there is much variation summer is defined as the low flow period spanning December to May (inclusive), and winter includes July to October; June and November are considered transitional months.

„ Minimum flow commonly corresponds to the flow exceeded 90% of the time in the season of interest.

„ There is little consistency in the flow exceedance values corresponding to the magnitude of summer freshes, though in most cases the duration is the duration that is exceeded 50% to 75% of the time under unimpacted conditions.

„ There is little consistency in the flow exceedance values corresponding to winter low flows

„ Winter freshes commonly correspond to flows exceeded between 25% and 30% of the time, though there is much variation in recommendations.

„ High flow (and overbank flow) events tend to correspond to flows exceeded between 2% and 5% of the time.

It should be emphasised that the flow recommendations described here represent the results of detailed studies of site specific environmental flow objectives and local river form conditions. The summary should not be used to set environmental flows in any particular system, but can be used as surrogates to assess changes in flow between unimpacted and current conditions.

5.5 Review of Hydrological Indicators Hydrological indices have already been developed and used to characterise a streamflow regime and to assess the health of rivers in a number of previous studies, both in Australia and overseas. Relevant published studies have been reviewed to assist in the selection and development of indices for the FSR project. It is noteworthy that there is little consistency in the methods between the studies. The type of information available and the specific purpose of the study have a large influence on the indices adopted.

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Olden & Poff (2003) looks at 171 published hydrologic indices. The purpose of their investigation was to look at the number of indices that are needed to adequately characterise a streamflow regime and which indices are most ‘important’. The study concluded that there was generally a high level of correlation between indices and the selected hydrologic indices for a given study should reflect the specific hydroclimatic characteristics of the study region.

Richter et al (1996) developed a method to assess the health of a river by comparing the current and unimpacted flow time series. The paper provides a strong emphasis on the link between hydrologic indicators and their ecological impact. The central tendency and the spread are assessed for 32 indices (making it essentially 64 indices). Each index is calculated for both the unimpacted and the current flow regime which are then compared using a percentage deviation of one to the other.

The study most relevant to this project is the Sustainable Rivers Audit (SRA). The purpose of the hydrology theme within the SRA was to assess the suitability of indices that measure the "changes in flow that have occurred between ‘natural’ [ie unimpacted] and ‘current’ conditions" (MDBC, 2003). The SRA considered the use of variance corrected indices. The premise of these indices is that an effect of the same magnitude in two streams will have a larger impact on river health for the stream that is less variable. The SRA did not adopt the use of variance corrected indices because (as discussed in Section 5.6) there were some limitations of the specific indices proposed. The indices considered by the SRA, and relevant to this study, are discussed throughout this report as a starting point for the selection of indices for the FSR project.

A study by Ladson (2003) discusses hydrologic indicators that are relevant to ephemeral streams. The key hydrologic features were identified as the frequency of cease to flow, duration of cease to flow and the level of water extraction during low flow and cease to flow periods.

One commonality between the studies is that the indices are generally based on streamflows at a daily time step. There is little in the way of hydrologic indices developed especially for monthly data, although many of the concepts can be applied at a monthly time step. The transferability of indices from a daily to monthly time step is explored in later sections of this report.

Although the specific indices used in each of the studies discussed vary considerably, they all include indices in the following categories:

„ magnitude (low, average and high);

„ frequency (low and high);

„ duration (low and high);

„ timing; and,

„ rate of change.

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Indices within the first four categories are considered in this current study. However to adequately assess the rate of change flow data is required at a daily time step and hence is not suitable for this study. Attention is also given to indices specific to ephemeral streams as discussed in Ladson (2003).

An alternative approach has been developed by Tim Doeg (pers. comm.), which is referred to as the “Cascading Seasonal Flow” method. This approach has been applied to streams within the Yarra basin, and its computational elements of depart significantly from other approaches. The method involves four major steps: 1) Determine four seasons (high, low and 2 transitional) based on the unimpacted flow regime; 2) For each season determine a minimum flow distribution (for example the distribution of flows in the high flow season should not be less than the distribution of flows in the highest flow month of the transitional periods); 3) A measure of stress is then made for each season by comparing the median flow of the unimpacted and current against the median flow of the minimum flow distribution. 4) Adjustments are made so that the ranking falls between 0 and 2.

The attractions of the method are that it is based on monthly data and it is “holistic” in the sense that no other indices need be considered to determine a ranking. The limitations of the method are: (i) that the comparisons are based on the difference between seasonal medians (thus variability of the distributions are ignored); (ii) the flow regime is assumed to be dominated by within-year seasonality rather than year-to-year variability, (iii) and it appears difficult to establish a transparent link between ecological requirements and shifts in seasonal median distributions. While further investigations may indicate that this approach is a viable alternative, it was considered that it did not fit easily within the adopted framework.

5.6 Conceptual Basis of the Ranking Procedure The underlying assumption of the index is that a measure of the threat to ecological health of a river can be characterised by the differences in flow regime between unimpacted and current conditions. The existing Index of Stream Condition (ISC) hydrology index attempts to do this by evaluating the seasonal shifts in average monthly flows. However, since the ISC was developed, the science has advanced towards identifying a range of flow components that are linked to salient ecological functions. The recent indices developed for the Sustainable Rivers Audit (SRA; MDBC, 2004) represent a more complete set of flow components, however they were developed for a wider range of flow (and biota) conditions than found across Victoria, and to some extent their development was somewhat constrained by the need to satisfy the (at times differing) individual requirements of the different States involved.

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One aspect that required further attention was the need to account more explicitly for the comparison of impacts on systems of differing variability. Thus, for example, with the SRA sub- indices a 20% reduction in mean annual flow in one system is deemed equivalent to a 20% flow reduction in another. While this approach removes differences in scale between two different systems – ie a small catchment is directly comparable to a large catchment – it does not cater for differences in variability between two systems. If, for instance, a particular biota is adapted to a system in which flows vary naturally over a range that is equal to the magnitude of the mean, then a 20% reduction in average flow conditions would not be very noticeable; that is, it is probable that flows in the new flow regime will fall within the range of flows that occurred naturally.

Conversely, a 20% reduction in average flow conditions would be much more significant to a biota which has adapted to conditions in which the natural range of conditions is only half the magnitude of the mean; in this case the probability of flows in the new regime falling within the natural flow regime are much reduced. The difference in the nature of these impacts is schematically illustrated in Figure 5-2, from which it is seen that while the impact has the same absolute magnitude in both cases, it represents half the range of unimpacted variability in one system and the whole range in the other.

(a) Flows of high variability (b) Flows of low variability

Average value Average value (natural regime) (natural regime) Average value Average value (current regime) (current regime)

Difference between natural Difference between natural and current regime is outside and current regime is well within range of natural variation range of natural variation

Range of natural values Range of natural values „ Figure 5-2 - Illustration of difference in impact of a fixed reduction in average flows on two systems of differing variability (the average flows under unimpacted and current conditions is the same for both cases, but the significance of the impacts is greater for biota adapted to a system of low variability).

While some efforts were made to investigate and resolve these issues in the SRA, the different requirements of this project allows the development of indicators better suited to the refinement of the ISC. In the SRA project the concept of “variance-correction” was trialled in which the degree of difference between two flow regimes was standardised by the sample variance of the unimpacted flows. The manner in which the sample variance was incorporated into the calculation of the variance-corrected indices was found to introduce either an unnecessary level of complication or else undesirable constraints on the characterisation of flow stress; these difficulties could not be resolved within the timeframe of the SRA project and accordingly the variance-corrected approach was not adopted.

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For this study, the variance-correction approach was re-visited and modified to rectify the deficiencies noted during trials undertaken in the SRA project. A non-parametric approach was adopted in which the degree of stress is standardised by reference to the cumulative exceedance distribution of the unimpacted flow regime. While this approach has direct parallels with the variance-correction approach, the use of the non-parametric exceedance distribution avoids problems associated with the highly non-normal and often zero-bounded distributions of interest. To distinguish between these two approaches and in recognition of the non-parametric basis of its formulation, the modified approach is referred to as providing “range-standardised” indices.

In simple terms, the incorporation of the range-standardisation concept ensures that a given index score infers the same degree of stress regardless of the flow component or the catchment being considered. Thus, an index value of “6” derived for the winter flow period in a catchment located in the Otways infers the same degree of flow stress as the same score calculated for a catchment located in the Wimmera region.

Figure 5-3 provides an example of the calculation of the range-standardised index for mean annual flow on the Goulburn River at Murchison. The thick black line in Figure 5-3 is the cumulative exceedance distribution of the unimpacted annual flows (given that annual flows are used this plot thus represents a traditional “flow duration” curve). The unimpacted mean annual flow is 2,700,000 ML/year and is displayed as a red square in Figure 5-3. Its position on the flow duration curve indicates that it is exceeded in approximately 53% of years. Note that in this catchment the average mean annual flow is less than the median annual flow which, by definition, is exceeded in 50% of years.

A blue circle shows the mean annual flow that is experienced under current conditions (Figure 5-3). It’s location on the unimpacted flow duration curve shows that the current mean annual flow is exceeded in approximately 96% of years under unimpacted conditions.

The range-standardised index is calculated using the difference between the proportion of time that the unimpacted and current mean annual flows are exceeded under unimpacted conditions:

A = 1− 2x prop(Qu )− prop(Qc ) where: A = Range-standardised mean annual flow index

Qc = Average current annual flow (ML/year)

Qn = Average unimpacted annual flow (ML/year)

prop(Qc) = Proportion of time that the average annual flow is exceeded under current conditions prop(Qu) = Proportion of time that the average annual flow is exceeded under unimpacted conditions

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5,000,000

Natural Mean Annual Flow 4,500,000 Current Mean Annual Flow

4,000,000

3,500,000 ) 3,000,000 year

L/ 2,500,000 M

ow ( 2,000,000 Fl

1,500,000

1,000,000

Difference = 43% 500,000

0 0 102030405060708090100 % years flow exceeded „ Figure 5-3 - Example of the calculation of the range-standardised mean annual flow index for the Goulburn River at Murchison

5.7 Preliminary Set of Indices Considered In order to evaluate the efficacy of different indices to characterise hydrologic stress, a total of ten indices were developed and evaluated. These indices were selected to be representative of flow components that are linked to ecologically important processes. Details of the indices selected and their ecological justification are provided in Appendix C, and the investigation between the daily and monthly versions of these indices is described in Appendix D. In brief the indices investigated included:

„ Mean Annual Flow (A): The change in mean annual flow between unimpacted and current conditions indicates the overall change in the volume of water carried by a river or creek over a year. The mean annual flow index is based around the difference between the percentage of time that the unimpacted and current mean annual flows are exceeded under unimpacted conditions.

„ Seasonal Amplitude (SA): The seasonal amplitude index compares the difference in magnitude between the high and low flows within each year under current and unimpacted conditions. The index reflects changes to seasonal variability in in-stream hydraulics and depth of flooding. The index is calculated using the difference between the percentage of years that the unimpacted and current seasonal amplitudes are exceeded under unimpacted conditions.

„ Seasonal Period (SP): The timing of periods of flooding and low flows has an important influence on how floodplain and riverine ecosystems respond, and this index provides a measure of the shift in the timing of the maximum flow month and the minimum flow month

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under both unimpacted and current conditions. The index is based on frequency distributions that reflect the percentage of years that peak and minimum annual flows fall within each given month under current and unimpacted conditions.

„ Low Flow Magnitude (LF): Altering the magnitude of low flows changes the availability of in- stream habitat, which can lead to a long term reduction in the viability of populations of flora and fauna. The index measures the change in low flow magnitude under current and unimpacted conditions. Review of previous studies showed that low flow requirements often correspond to the daily 90% exceedance flow, though as a monthly time step is used the index is calculated using two flow thresholds: one based on the flow exceeded 91.7% of the time (ie 11 months out of 12) and the other based on the flow exceeded 83.3% of the time (10 months out of 12).

„ High Flow Magnitude (HF): High flows act as a natural disturbance in river systems, removing vegetation and organic matter and resetting successional processes. This index measures the change in high flows under current and unimpacted conditions. The approach adopted to calculate the high flow index is similar to that used to calculate the low flow index. The monthly high flow index is calculated based on the 8.3% and 16.7% exceedance flows. Two intervals were used to cover a range of high flows rather than basing the index on a single value.

„ Low Flow Spells (LFS): The low flow index mentioned above is based solely on flow magnitude and does not consider the variations in duration that a stream may spend below a given threshold. Information on the frequency and duration of low flows provides a direct indication of the availability of aquatic habitat during low flow periods, which can impact on the ability of river systems to sustain plant and animal populations. The index is calculated from a partial series frequency analysis of the duration of spells above two thresholds corresponding to flows exceeded 83.3% and 91.7% of the time (these percentiles correspond to the rank of the lowest two months in a calendar year).

„ High Flow Spells (HFS): In a similar fashion to low flow spells, the high flow spells index is based on analysing differences in the frequency and duration of high flow spells above selected thresholds. The duration of the spell events for flows exceeded 8.3% and 16.7% of the time (which correspond to the first and second highest monthly flows in each year) are determined for both current and unimpacted conditions, and a partial series analysis is used to characterise differences in the duration and frequency of the events.

„ Proportion of Zero Flows (PZ): Periods of zero flow are a natural feature of ephemeral rivers and creeks, however increases in the natural duration of cease to flow periods are regarded as

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harmful to aquatic ecosystems. The proportion of zero flow index simply reflects the differences in the proportion of zero flow occurring under unimpacted and current conditions.

„ Flow Duration Curve (FD): The flow duration curve provides an efficient summary of the overall nature of the flow regime. It does not characterise any particular component of the flow regime, nor does it include any description of flow sequencing, and it is therefore difficult to identify any specific ecological effects. The flow duration index compares changes in the shape of the non-zero part of the flow duration curve under unimpacted and current conditions.

„ Flow Variability (CV): This index is similar to the seasonal amplitude index in that it reflects variability over a year. The key difference is that the variation index measures variability across all months rather than simply the difference between minimum and maximum monthly flows. The index simply compares the coefficient of variation of monthly flows between current and unimpacted conditions.

The indices were applied in an identical manner to both daily and monthly data, though in the case of the low and high flow spells the monthly indices were derived in a slightly different manner to the daily so as to better capture differences in spell behaviour.

5.8 Selection of Indices A difficulty in developing a single hydrological index of river health is that there are a number of flow components of interest, and moreover these flow components may be highly correlated. Thus, the information contained in one flow component may partially explain the variation in another component deemed to be of importance.

To ensure that this correlation does not bias the overall measure of ecological health, it was necessary to make a selection of uncorrelated indices. As described in Appendix D, each daily index was assessed against all monthly indices to determine which were the most highly correlated. A daily index was not always most highly correlated with its corresponding monthly index, and some other daily indices could be reasonably estimated based on a combination of other monthly indices. The selection of the following five monthly indices was based on these correlations:

„ Low Flow Index (LF);

„ High Flow Index (HF);

„ Proportion of Zero Flow Index (PZ);

„ Monthly Variation Index (CV); and,

„ Seasonal Period Index (SP).

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These five indices capture the flow stress characteristics represented by the five sub-indices adopted in the SRA study, namely: low and zero flows, high flows, variability, seasonality, and flow volume. Furthermore there is no significant correlation between the selected five monthly indices. A more detailed description of the basis for selection of the above five indices is provided in Appendix D-2.

The adequacy of the five selected monthly indices to represent the information contained in a larger set of daily indices was assessed (Appendix D-3). Two multivariate statistical techniques were employed: cluster analysis and principal component analysis. Cluster analysis is a statistical method that identifies groups within a sample that are similar, and it was used to create five groupings of the 50 catchments based on all ten daily and the five selected monthly indices. The groupings based on the daily and monthly indices were found to be very similar. Principal Component Analysis is a tool that reduces a set of correlated variables (ie the hydrologic indices) to a smaller number of completely uncorrelated factors. Catchments that were determined to be similar based on the ten daily indices were also determined to be similar based on the five monthly indices. This shows that the similarity of catchment stress when evaluated using all ten of the daily indices is very similar to an assessment based on the adopted five monthly indices.

5.9 Sensitivity Analysis It is desirable to ensure that any variation in the indices between sites is due to differences in the degree of hydrologic stress and not to differences in the timing or length of streamflow record used to calculate the index. The length of streamflow record varies between sites, and accordingly an investigation was undertaken to determine the extent to which the hydrologic scores are dependent on the length, and timing, of the record (Appendix D-5).

A bootstrap technique was employed to characterise the errors involve in estimating each of the indices. A range of values for each index at each site were computed by generating one hundred samples in which one year of data was randomly selected for exclusion. The sensitivity to each index was measured by its standard error. In order to assess the implication of the time periods adopted, the standard error for the five chosen indices was calculated for 5, 10, 15, 20 and 25 year flow records.

As expected, as the length of record increases, the standard error decreased. The figures generally show (see Figure D-10) that there is a dramatic reduction in standard error once the length of record reaches 15 years. Accordingly, it is recommended that the indices are only calculated using streamflow records greater than 15 years in length.

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5.10 Seasonal Sub-Indices The foregoing discussion has assumed that the indices are calculated using all months of the year. However, all but the seasonality index can be computed on a specified range of months. For example, if it is desired to investigate different options for transferring summer licences to the winterfill season via the construction of off-stream storages, then it would be appropriate to explicitly trade-off the (possibly large) decrease in summer flow stress against the (possibly slight) increase in winter stress: the relative movement in the relevant seasonal flow stress indices would provide a quantitative basis for assessing the environmental benefits of such a trade. Similarly, if management options are being considered for improving habitat conditions during the low flow months, then it would make sense to rank the options by indices calculated on the summer months rather than the whole year.

Calculation of the seasonal indices is undertaken in an identical manner to that described in Appendix C, the only difference being in the way that the samples used to derive the distributions are constructed. To clearly differentiate the response, winter indices are derived using the months of July to October inclusive (which is the same winterfill season used in defining the SDL), and summer indices are calculated using streamflows occurring over the months of December to March. The seasonal version of the high flow index (Appendix C.5) is determined using the single highest month in the season (rather than being based on the two highest months), and the seasonal low flow index (Appendix C.4) is based on the single lowest monthly flow.

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6. Formulation and Assessment of Flow Stress Scores

6.1 Introduction The foregoing section described the development of five indices that can be used to represent the degree of stress on a range of ecologically important flow components. The five indices are based on a comparison of monthly streamflows under unimpacted and current conditions, and they can be derived using flows from either all months of the year, or else just those from a particular season.

In a catchment- or region-specific study the individual indices can be selected and evaluated in a manner best suited to the study objectives. For example, if considering a range of options for relieving summer stress in a catchment, it may be appropriate to quantify the relative environmental benefits using just the summer low flow index. However, as one of the main objectives of this project is to provide an overall rating of hydrologic stress for all streams across the State, a general characterisation of hydrologic stress is required that does not bias the assessment towards any particular flow component.

This chapter describes the formulation and assessment of an overall score of hydrologic stress that can be used as a general indicator of flow stress conditions. To begin with the general nature of flow stress of all (FSR) catchments across Victoria is described (Section 6.2), and this is followed by a discussion that leads to the formulation of a stress score based on consideration of individual flow indices (Section 6.3). The efficacy of this score is evaluated in a heuristic manner using two types of investigations. Firstly, it is used to discriminate the differences in flow stress between catchments that are impacted by the presence of large (greater than 1000 ML) storages (Section 6.5). Secondly, the scores are used to describe the flow stress in a number of catchments subject to varying degrees – and types – of flow impacts (Section 6.6). The chapter concludes with an overall description of flow stress across the State.

6.2 Nature of Flow Stress Individual flow stress indices were calculated for all 551 FSR sites using the monthly streamflow series corresponding to unimpacted and current conditions. Results were obtained based on all months of the year for the five selected indices (as discussed in Section 5.8), namely:

„ Low Flow Index (LF);

„ High Flow Index (HF);

„ Proportion of Zero Flow Index (PZ);

„ Monthly Variation Index (CV); and,

„ Seasonal Period Index (SP).

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In addition, results were derived for the first four indices (that is for all indices excluding seasonal period) for the winterfill and summer months. The winterfill season was selected to be the same as that used to define the SDL, that is July to October inclusive. The summer season was defined to include the four months of December through March.

Table 6-1 summarises which indices (calculated on all months of the year) and seasons are the most stressed. It shows that 88% of all sites across the state are most stressed in summer, and that in 75% of cases the low flow index is the most stressed component. This is perhaps to be expected, and reflects the high level of demand for summer flows, which are generally in short supply across the majority of the state. In the minority of catchments that are stressed in winter it is evident that high flows (captured by large impoundments) are the most affected.

„ Table 6-1 Most affected annual index and season

Most Stressed Season Most Stressed Index Summer Winter High Flow Index (HF) 1% 6% Low Flow Index (LF) 74% 3% Seasonality Index (SP) 5% 0.4% Variability Index (CV) 1% 0.2% Zero Flow Index (PZ) 5% 0.5% Total 87% 10% Effectively unimpacted (score of "1.0") 3% (14 sites)

The table also shows that 5% of sites are most affected by a change in seasonality, and these sites are all affected in summer more than in winter. The most stressed site in this category is the Goulburn River downstream of Eildon, illustrating the effect of large irrigation storages harvesting water in winter and releasing water in late spring and summer. Downstream of the storage, this decreases the current winter flows and increases the current summer flows beyond the unimpacted range, effectively reversing the unimpacted "high spring/low autumn" regime which typically occurs throughout Victoria. For several other sites in this category, direct pumping from waterways to supply summer irrigation demands has had a similar effect, by reducing the peak spring flows, effectively delaying the peak flow season from spring to late winter.

Around 10% of catchments in Victoria are more stressed in winter than in summer. Many of these sites are located downstream of large reservoirs, such as the Barwon River downstream of East Barwon Reservoir, Werribee River downstream of reservoirs and off-takes, and the Campaspe River downstream of Eppalock. In these cases, there are some passing flows and spills from the reservoirs and off-takes all year round. However, the large winter flows are harvested, so the

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releases over this period are much less than under unimpacted conditions. In summer, the passing flows and releases are close to the range experienced under unimpacted conditions, and thus the winter flows appear more stressed. Also, in some (generally ephemeral) catchments the winter season is the most stressed simply because there is little opportunity to harvest flows in summer.

6.3 Combination of Indices In order to facilitate the comparative analysis across different systems an overall score of hydrologic stress may be derived by combining the individual scores for each index. There are many ways that this could be done. For example, weightings could be assigned to the different indices according to the relative ecological importance of the specific flow components, or else a hierarchical approach could be taken where different indices are introduced according to the attributes of the system being considered. The Sustainable Rivers Audit (MDBC, 2004) adopted a fuzzy logic approach to take account of the complex (and correlated) nature of the individual indices used in that study.

Of course, this decision about how to combine the individual indices is only relevant when the score is used for comparative condition assessments. For catchment-specific studies the manner in which the indices are combined can be tailored to suit the performance criteria of most interest. For example, if a range of different management scenarios were being assessed for their suitability in maintaining wetland habitats, then it will be appropriate to give the high-flow indices more weight. There will also be situations in which it is desirable to focus on particular seasons rather than the whole year.

However, for the purpose of characterising flow stress across the diversity of catchments found across Victoria, and for this purpose a general characterisation of hydrologic stress is required that does not bias the assessment towards any particular flow component. Of course, the weightings for each of the indices could be changed for a particular study if a site specific assessment justified it.

The most obvious “unbiased” choice for selection of an overall rating is to adopt a uniform weighting of all indices calculated on an annual basis. This approach takes into account all flow components and months of the year, and the non-dimensional manner in which the indices are derived ensures that the results are not influenced by differences in flow magnitude. Other obvious choices include adoption of a summer- or winter-weighted score in which only the seasonal version of the indices are used (except of course for the seasonal periodicity index which needs to be determined using all months of the year). However, adoption of a seasonal-weighting would imply that flow conditions in one season are more important than in another, and there are no defensible grounds for making such an assumption. Likewise, there is no basis for excluding one particular index, and thus for general condition assessment purposes all indices need to be considered.

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In short, the indices were identified, formulated, and selected to represent a range of ecologically important flow attributes, and there is no justification – at a statewide level – for excluding indices or censoring the seasonal period over which they should be derived. However, when assessing the efficacy of adopting a uniform weighting of all annual indices it became apparent that in some cases the inherent variability of monthly streamflows yielded a ranking that was not consistent with ecological inference. This apparent inconsistency was most evident when comparing regulated rivers downstream of major impoundments with those heavily impacted by a range of extractions.

By way of extreme example, Figure 6-1 compares the flow stress at two sites: the top panel of this figure shows the flow stress in the Goulburn River immediately downstream of Eildon Dam, and the lower panel shows the flow stress at the downstream end of the (heavily impacted) Wimmera River system. The uniform weighting of annual indices suggests that the site on the Wimmera River is more stressed than that below Eildon Dam (a score of 4.6 versus 5.7). At the Eildon site, the indices related to high flows, variability, and the proportion of zero flows are not greatly affected by the dam, whereas those related to low flows and seasonality are heavily impacted. At the Wimmera site, all indices except seasonality are heavily impacted. Application of uniform weights in this extreme example does not give sufficient weight to the flow stresses associated with the marked shift in flow seasonality.

800000 Goulburn River d/s Eildon Dam ) h

nt 600000 o m L/ M

( 400000 w o l f am

e 200000 r t S

3000000 Wimmera River at Dimboola Unimpacted ) h Current nt o

m 200000 L/ M ( w o l f 100000 am e r t S

0 Jan-80 Jan-81 Jan-82 Jan-83 Jan-84 Jan-85 Jan-86 Jan-87 Jan-88 Jan-89 Jan-90 „ Figure 6-1 – Comparison of flow stress at two sites, one a catchment downstream of a major storage (top panel), and the other heavily impacted by a range of extractions (lower panel).

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10

e 9 or

c 8 S

d 7 e 6 ight

e 5 w - 4 lly 3 ona

s 2 a e

S 1 0 0246810 Uniform Weighted Score

„ Figure 6-2 – Comparison of uniform and seasonally-weighted scores.

The solution to this problem is to increase the weighting assigned to the seasonality index. The choice of how much to increase this weighting is conditioned by the desire to keep the weighting as near to uniform as possible to ensure that the impact on other flow indices was not under- represented. Generally, the impact of major storages is reflected in other flow indices and thus it is not necessary to apply a high weighting. After some trials it was found that assigning a weight of two to the seasonality index and unity to the others provides a reasonable ranking. This additional weighting to the seasonality index has little effect on the results compared to adoption of uniform weighting (see Figure 6-2), though it is sufficient to ameliorate the few anomalies for those (regulated) rivers downstream of large storages. Accordingly, this “seasonality-weighted” score is adopted for use as a single numeric descriptor of flow stress. The raw seasonally-weighted score (RSWS) is calculated simply as:

RSWS = (LF+HF+CV+PZ+2.SP)/6 Eqn 6-1

where the terms on the right hand side of the equation are the individual stress indices as summarised in Section 6.2.

The justification for adoption of the seasonality-weighted score is essentially that it combines the flow stress attributes of five ecologically important flow components that have been shown to be highly correlated with a wide range of flow characteristics. The additional weighting given to the seasonal index merely ensures that highly impacted regulated rivers – that is those rivers that exhibit marked seasonal flow reversal but which still experience high flows associated with irrigation releases – are appropriately ranked.

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To continue with the example discussed above (Figure 6-1), adoption of a seasonally-weighted score yields a score of 5.0 for both sites; that is, the score for the Goulburn site reduces from 5.7 to 5.0, and that for the Wimmera increases from 4.6 to 5.0. While the sites have the same overall score when seasonal-weighting is used, inspection of the individual indices (as discussed in Section 6.6) reveals that different flow components are impacted to different degrees.

The efficacy of the seasonality-weighted score is illustrated using two heuristic investigations. Firstly, the scores are used to discriminate the differences in flow stress between catchments that are impacted by the presence of large (greater than 1000 ML) storages (Section 6.5). Secondly, the scores are used to describe the flow stress in a number of catchments that vary in the degree and nature of their impacts (Section 6.5). However, before these examples are provided, it is necessary to define the nomenclature used to summarise the degree of flow stress, and also to consider the manner in which the above Raw Seasonally-Weighted Score can be transformed to provide a standardised assessment for reporting purposes; these two aspects are addressed in the following section.

6.4 Score Nomenclature In the same way that the mean or the median may only provide a crude description of a sample’s properties, the use of a single numeric descriptor does not reveal any of the additional information available on the relative nature of the flow impacts. In order to provide a more complete but parsimonious assessment of flow stress, a three-character alphanumeric can be used to summarise the condition of a stream.

The attributes of the adopted alphanumeric descriptor are summarised in the following table:

„ Table 6-2 Most affected annual index and season

Stress Score Most stressed Season Most Stressed Index Value between 0 and 10, Either Summer, or Low flows derived as the seasonality- Winter High flows weighted stress score (Eqn Variability 6-1) multiplied by ten, then standardised, and rounded Proportion of Zero flows, or to the nearest integer Seasonality

The seasonally-weighted score referred to in the first column of the above table is standardised to represent the percentage ranking of the catchment. That is, a score of 7 indicates that 70% of Victorian catchments are more stressed than the catchment under consideration, and a score of 5 indicates a “typical”, or median (50%) level of stress. Thus, for example, a score of 2SL indicates that the seasonally-weighted score for the catchment is 2 (ie 80% of Victorian catchments are less

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stressed), summer flows are more stressed than in winter, and low flows are the most impacted component of the flow regime.

The main argument in favour of standardisation is that makes the scoring system more intuitive to use, and it is for this reason that the scores are standardised. However, it should be noted that the standardisation is only based on a sample of 551 catchments, and if over time additional catchments are considered or flow conditions change, then the link between the score value and the ranking may progressively lessen. While it is recognised that in the future the overall score may not accurately reflect the percentage rank of flow stress, it is considered that for the foreseeable future the score (especially as it is rounded to the nearest integer) provides an adequate – and intuitively meaningful – indication of flow stress.

The conversion of the raw seasonality weighted score (RSWS) to the standardised seasonality weighted score (SSWS) is obtained using the relationship displayed in Figure 6-4. This conversion yields a set of scores (for the 551 catchments considered) that varies uniformly between 0 and 10. Henceforth in the report, the term “seasonality-weighted score” refers to this standardised version.

Lastly, it is perhaps worth commenting on how the flow stress scores are communicated. Throughout this document the term “flow stress” is used to denote the degree of departure from unimpacted flow conditions and use of this term is consistent with the study objectives. However when reporting it may be preferable to refer to the score as being an index of “flow condition” rather than of “flow stress” as the former is perhaps more intuitively associated with a scheme in which a score of “10” denotes unimpacted conditions and a score of “0” denotes full impacted. Regardless of which term is used it is essential that any assessment of flow conditions be supported by a clear description of the nomenclature adopted.

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10 r

o 9 c S

ed 8 t h g i 7 e W

y 6 l l a n 5

easo 4 S d e

s 3 i d r

a 2 d an t 1 S 0 012345678910 Raw Seasonally Weighted Score

„ Figure 6-3 – Conversion of raw to standardised seasonally-adjusted scores.

6.5 Discrimination of Sites Impacted by Large Storages In general it can be expected that a large storage on a stream impacts adversely on the downstream flow regime. Depending on the nature of the release policy, the storage may capture all dry weather flows thus reducing summer flows to zero. Peak flows will be reduced, and if the storage is not full then flood flows over the winterfill season may be prevented from reaching downstream. Other ecologically important events, such as flushing flows and seasonal peaks that trigger spawning behaviour are also likely to be disrupted. Thus, it can be generally expected that sites affected by large upstream storages should have lower stress scores than those without.

In order to assess how well the seasonally-weighted score discriminates between those sites located downstream of large storages, a “degree of upstream impoundment” was estimated for each site. This measure was used to reflect the likely degree of streamflow impact due to the presence of large on-stream storages. The degree of impoundment (DI) was simply assessed as a dimensionless ratio based on the total volume of upstream impoundments (VUI) divided by the mean annual flow (MAF): VUI DI = MAF Eqn 6-2 The total volume of upstream impoundments is based on the summation of those (large) storages that individually impound more than around 1000 ML. This ratio is only a crude measure of the likely degree of flow impact as it doesn’t include the distributed effects of farm dams and upstream

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diversions. Nor does it reflect inter-basin transfers and whether or not the downstream waterway is used to supply demands (as is often the case for irrigation storages). However crude, the ratio is an unambiguous and readily derived measure that can be used to objectively differentiate catchments on the basis of the likely degree of flow stress.

Estimates of the degree of impoundment were used to divide the data set into three categories: (i) those without major upstream storages (that is without individual storages greater than around 1000 ML), (ii) those where the total volume of major storages is less than the mean annual flow, and (iii) those where the total volume of storage is greater than the mean annual flow. These three categories represent increasing levels of stress, and these differences should be reflected in the distribution of stress scores.

The results of this analysis are presented in Figure 6-4. It is seen that catchments without significant upstream impoundments have higher scores than those with. The seasonally-weighted scores clearly differentiate between those catchments that are expected to have high flow stress (by virtue of the high volume of upstream impoundments) from those that are expected to have a lower flow stress. It is seen that if a catchment has an upstream storage that is greater than around 1000 ML, then it is likely to be significantly more stressed than one without.

In general, the score for a catchment unaffected by large storages is around 3 units higher than one without, and the differences arising from the volume of major upstream storages are generally between 0.5 and 1.5 units.

10 Annual score for all sites Sites with no upstream storages e 9 r

o Volume storages < mean annual flow c

S 8 Volume storages > mean annual flow d e t 7 gh i e W

- 6 y l l a

n 5 o as

e 4 S d e

s 3 di r 2 nda a t

S 1

0 0 102030405060708090100 Proportion of catchments with score greater than given value (%) „ Figure 6-4 - Distribution of flow stress scores for different degrees of upstream impoundment.

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6.6 Results for Selected Sites Table 6-3 below gives scores for a selected number of locations around the state. Some of the locations given are not adopted FSR sites, but are included for the purposes of illustrating the behaviour of the stress indices. The “non-FSR” sites are not assigned a site number in Table 6-3, and the rank provided in the 3rd column of the table is indicative only as it is based on the distribution of 551 FSR sites. For example, there is no FSR site immediately downstream of Eildon Reservoir, but if this site were included then it would be notionally ranked 528 out of 551 in the State.

The fourth column of Table 6-3 shows the seasonally-weighted score (as discussed in Section 6.4), and the subsequent two columns provide the summer and winter scores. The next five columns present the five individual flow indices (CV, HF, LF, PZ, SP) calculated using all months of the year, as described in Section 5.8.

„ Table 6-3 - Scores for various sites around Victoria

Seasonally FSR Site Summer Winter Site Name Rank Weighted CV HF LF PZ SP Number score score Score Werribee R u/s Werribee Weir 23101 155 7SL 8.8 10.0 9.7 9.9 7.8 10.0 9.9 Mitchell R at Bairnsdale 22412 309 4SL 6.7 9.8 9.4 9.7 3.1 9.2 9.2 Thomson R d/s Thomson Res - (551) 0WL 7.1 0.9 5.2 0.7 0.2 5.0 1.1 Thomson R at Cowarr 22509 479 1WH 7.6 4.9 9.4 1.3 7.1 9.8 5.3 Yarra R at Yering offtake 22915 526 0SL 1.8 5.1 7.3 3.6 0.6 2.5 8.2 Moorabool R at Batesford 23214 538 0SL 2.3 4.6 6.1 5.1 0.0 1.2 7.1 Glenelg R d/s Rocklands Res - (551) 0WZ 2.1 1.2 3.6 0.5 2.2 0.0 3.5 Glenelg R at Nelson 23824 423 2SL 3.8 8.2 8.6 7.9 0.0 7.6 9.2 Mitta Mitta R d/s Dartmouth Res - (545) 0SL 2.1 2.2 5.8 6.9 0.9 1.8 3.8 Mitta Mitta R at Tallangatta 40106 381 3SL 5.2 6.2 8.8 9.1 6.1 8.9 6.4 Goulburn R d/s Eildon Res - (528) 0SL 3.2 3.6 8.6 7.6 0.9 9.9 1.5 Goulburn R at Yea 40528 531 0SS 3.4 6.1 5.8 6.2 3.3 9.9 2.0 Wimmera R d/s Mt Cole Ck 41528 373 3SL 4.0 9.7 9.4 9.7 0.9 6.7 9.6 Wimmera R at Dimboola 41504 522 1SL 3.6 5.0 6.3 4.3 0.0 6.6 6.8

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10 Standardised Seasonally Weighted Score - All Sites 9 e

r Standardised Seasonally Weighted Score - Selected Sites o

c 8 S

d Werribee R u/s Werribee Weir e t

h 7 ig e 6 W Mitchell R at Bairnsdale lly a

n 5

o Wimmera R d/s Mt Cole Ck s

a Mitta R at Tallangatta e 4 S

ed Wimmera R at Dimboola Glenelg R at Nelson

is 3 Yarra R at Yering offtake d

r Goulburn R d/s Eildon Res a Goulburn R at Yea d 2 n Moorabool R at Batesford Thomson R at Cowarr a t Mitta Mitta R d/s Dartmouth Res S 1 Thomson R d/s Thomson Res Glenelg R d/s Rocklands Res 0 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Percent of Victorian sites with higher score

„ Figure 6-5 – Standardised seasonally weighted scores for selected sites around Victoria

In order to show how the flow stress of the selected sites compare to the rest of the State, the seasonally-weighted scores for the selected sites are shown on the cumulative exceedance chart in Figure 6-5. It is seen, for example, that Glenelg River immediately downstream of Rocklands Dam is one of the most stressed sites in the State, with almost all Victorian sites exhibiting a higher score (ie are less stressed).

The performance of the index is illustrated by reference to the time series of streamflows representative of unimpacted and current conditions. Plots of these timeseries and a brief description of the hydrologic stress are provided below for each of the sites listed in Table 6-3.

The relative seasonal scores for each site are also summarised: the seasonal Winter and Summer scores are shown as a pie chart, and the annual indices are displayed as a bar chart; the standardised seasonally-weighted stress ranking for the site (as defined in Section 6.4) is shown in the lower left hand corner of the graphic.

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6.6.1 Werribee River upstream of Werribee Weir

There are no major impoundments upstream of 7SL this site, few direct irrigation private diversions, 10 Seas Zero and a number of farm dams. The differences 8.8 10.0 Low between unimpacted and current streamflows High are small and only evident at low flows. The 0 Cv Su Wi 0 10 impact of the upstream extractions are not easily distinguishable in a time series plot, but are most easily evident in a comparison of flow duration curves when plotted on log-arithmetic scales, as shown in Figure 6-6. The only flow component affected by upstream extractions is the low flow index, with a score of 7.8; all other flow indices are 9.7 or above. This is clearly a stream that is subject to only a low degree of flow stress and this is reflected in the standardised seasonally weighted score of 7.2. Around 28% of (FSR) streams in Victoria experience a lower degree of flow stress than this site.

100000

10000 h)

ont 1000 m L/ M

( 100 ow l f 10 eam r t S 1 Unimpacted flow Current flow

0.1

0 102030405060708090100 Proportion of time flow exceeded (%) „ Figure 6-6 – Flow duration curves of unimpacted and current conditions for the Werribee River upstream of Werribee Weir.

6.6.2 Mitchell River at Bairnsdale 4SL There are no major impoundments upstream of this 10 Seas site; the main sources of extractions are direct Zero 9.8 Low irrigation private diversions and some urban 6.7 High demands, which impact most upon river flows in 0 Cv Su Wi summer. Figure 6-7 shows both current and 0 10 unimpacted streamflows in the Mitchell River at Bairnsdale, over a selected period from 1980 to 1988. Even over this short period of time it is evident that current and unimpacted flows are very similar during high flow months, but current flows are marginally less during the low flow months,

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suggesting that the river is slightly more stressed during summer low flow periods. This is confirmed by the seasonal scores obtained for this site of 9.8 in winter but only 6.7 in summer. The impact of upstream extractions are more easily evident from inspection of the flow duration curves (Figure 6-8), from which it is seen that the low flows are more heavily impacted than the Werribee River site discussed above. The indices show that low flows are the most stressed, but that high flows and seasonality are relatively unimpacted. The overall standardised seasonally weighted score is 4.4, in other words around 56% of Victorian sites are less stressed than this catchment.

400000

Unimpacted h) Current 300000 ont m L/ M 200000 ow ( l f

eam 100000 r St

0 Jan-80 Jan-81 Jan-82 Jan-83 Jan-84 Jan-85 Jan-86 Jan-87 Jan-88 „ Figure 6-7 – Time series of unimpacted and current conditions for the Mitchell River at Bairnsdale.

1000000

h) 100000 t n o m L/ M

( 10000 w o l f am e r t

S 1000 Unimpacted flow Current flow

100

0 102030405060708090100 Proportion of time flow exceeded (%) „ Figure 6-8 – Flow duration curves of unimpacted and current conditions for the Mitchell River at Bairnsdale.

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6.6.3 Thomson River d/s Thomson Reservoir

At dam site At Cowarr 0WL 1WH 10 Seas 10 Seas Zero Zero Low Low 7.1 7.6 High 4.9 High 0.9 0 Cv 0 Cv Su Wi 0 10 Su Wi 0 10

The timeseries plots shown in Figure 6-9 shows streamflows for the Thomson River at two sites; one site is located immediately downstream of the dam wall and is shown in the lower panel of the figure, and the other is located further downstream at Cowarr and is shown in the top panel. At both sites, the most significant impact is due to Thomson Reservoir, with very few private diverters or farm dams upstream of either site. A number of major (relatively unimpacted) tributaries join the Thomson River between Thomson Dam and Cowarr.

The lower panel clearly shows the impact of Thomson Reservoir on streamflows. The average current flow is significantly lower than natural, and the seasonality has been almost fully reversed with high flows typically occurring during January February, and low flows during the winter months. Further downstream at Cowarr, the influent tributary streams help to partly restore the natural seasonality. However, the overall reduction in flow due to harvesting at the reservoir is still evident.

This pattern is reflected in the scores for each site, with a seasonally weighted score of 0.01 at the reservoir, improving to 1.3 at Cowarr. Also, the seasonality index increases from 1.1 at the dam to 5.3 at Cowarr. While low flows, high flows and seasonality are equally impacted at the dam site, further downstream at Cowarr only the high flows are still heavily impacted.

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100000 Thomson R at Cowarr Unimpacted Current h) 80000 ont m

L/ 60000 M ( ow

l 40000 f eam r

t 20000 S

60000 Thomson R d/s Thomson Res h) ont

m 40000 L/ M ow ( l f 20000 eam r t S

0 Jan-80 Jan-81 Jan-82 Jan-83 Jan-84 Jan-85 Jan-86 Jan-87 Jan-88 „ Figure 6-9 – Time series of unimpacted and current conditions for two sites on the Thomson River below Thomson Dam.

6.6.4 Yarra River at Yering offtake

This site is located just downstream of the Yering 0SL 10 Seas off-take pumps, and is impacted by the presence Zero of the Upper Yarra Dam and other more minor Low 5.1 High storages. In addition, the site is affected by 1.8 0 Cv appreciable extractions for irrigation and Su Wi 0 10 winterfill demand, and parts of the catchment have some of the highest levels of farm dam development in the state. Similar to the Thomson system described above, very little of the water held by the Yarra storages is released to the river, which results in a net loss of water to the stream. This net loss of streamflow volume is clearly evident in the comparison of streamflows, shown in Figure 6-10.

Low flows are the most heavily impacted, though high and zero flows are also appreciably affected. The flows that do reach the main stem of the Yarra are largely unregulated and thus exhibit a natural seasonal variation. Summer is significantly more impacted than winter, with an

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overall summer score of just 1.8 compared to a winter score of 5.1. The standardised seasonally weighted score for this site is 0.5, which indicates that 95% of streams in the State are less stressed.

240000

Unimpacted

h) 200000 Current ont

m 160000 L/ M

( 120000 ow l f 80000 eam r 40000 St

0 Jan-80 Jan-81 Jan-82 Jan-83 Jan-84 Jan-85 Jan-86 Jan-87 Jan-88 „ Figure 6-10 – Time series of unimpacted and current conditions for the Yarra River at Yering offtake.

6.6.5 Moorabool R @ Batesford

The Moorabool River contains almost all possible 0SL Seas types of demand, including several large storages, 10 Zero large urban systems, intensive farm dams Low

4.6 High development, direct summer irrigation, and 2.3 0 Cv winterfill storage and pumping. In addition, the Su Wi 0 10 estimate of unimpacted streamflows takes into account the substantial amount of extractions from groundwater upstream of Lal Lal Reservoir.

The time series of unimpacted and current streamflows is shown in Figure 6-11. It is seen that the Moorabool River is heavily stressed, with a standardised seasonally weighted score of only 0.25 (that is 97% of streams in the state are less stressed than this site). Both low and zero flows are heavily impacted, with subindices scoring zero and 1.2 respectively. The flows that do reach the main stem of the river follow a reasonably natural seasonal variation.

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50000

Unimpacted h) 40000 Current ont m

L/ 30000 M ( ow

l 20000 f eam r 10000 St

0 Jan-80 Jan-81 Jan-82 Jan-83 Jan-84 Jan-85 Jan-86 Jan-87 Jan-88 „ Figure 6-11 – Time series of unimpacted and current conditions for the Moorabool River at Batesford.

6.6.6 Glenelg River downstream of Rocklands Reservoir

At dam site At Nelson 2SL 0WZ Seas 10 Seas 10 Zero Zero Low Low 8.2 High 3.8 High 2.1 1.2 0 Cv 0 Cv Su Wi Su Wi 0 10 0 10

Figure 6-12 shows the time series of current and unimpacted streamflows for two sites on the Glenelg River: one is located immediately downstream of Rocklands Dam, and the other is located near the catchment outlet at Nelson. At Rocklands Reservoir, the current flows are so low that they cannot be discerned on the plot. However at Nelson, streamflows have been substantially replenished by the high yielding Wannon River catchment, and current streamflows are similar in timing and magnitude to those under unimpacted conditions.

At Rocklands Reservoir, streamflows are only a fraction of their unimpacted magnitude, and predictably index scores for all flow components are low. Indeed the standardised seasonally weighted score for this site is 0.01, thus showing that 99.9% of all streams in the State are less impacted.

The relative magnitude of the winter and summer scores at Nelson (8.2 and 3.8, respectively) suggests that the majority of stress in the Glenelg basin is occurring in summer. This is reinforced by the low flow index score of zero, which indicates that summer extractions reduce low flows to a

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point where they are now completely outside (ie lower than) their unimpacted range. The standardised seasonally weighted score is 2.3.

600000 Glenelg R at Nelson Unimpacted Current h) ont

m 400000 L/ M low ( f 200000 eam r t S

600000 Glenelg R d/s Rocklands Res h) ont

m 40000 L/ M ( w lo f 20000 eam r t S

0 Jan-80 Jan-81 Jan-82 Jan-83 Jan-84 Jan-85 Jan-86 Jan-87 Jan-88 „ Figure 6-12 – Time series of unimpacted and current conditions for two sites on the Glenelg River below Rocklands Dam.

6.6.7 Mitta Mitta River downstream of Dartmouth Reservoir

At dam site At Tallangatta 0SL 3SL 10 Seas 10 Seas Zero Zero Low Low High 5.2 6.2 High 2.1 2.2 0 Cv 0 Cv Su Wi 0 10 Su Wi 0 10

The main impact on flows in this reach is caused by Dartmouth Dam retaining winter flows and releasing in summer. There are a small number of private diverters and farm dams, plus two small urban systems, but these are insignificant compared to the impact of the dam.

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500000 Mitta R at Tallangatta Unimpacted Current h) 400000 ont m

L/ 300000 M ( ow

l 200000 f eam r

t 100000 S

400000 Mitta R d/s Dartmouth Res h) 300000 ont m L/ M 200000 ow ( l f

eam 100000 r t S

0 Jan-80 Jan-81 Jan-82 Jan-83 Jan-84 Jan-85 Jan-86 Jan-87 Jan-88 „ Figure 6-13 – Time series of unimpacted and current conditions for two sites on the Mitta River downstream of Dartmouth Dam.

The impact of flow impoundment by Dartmouth Dam is shown in Figure 6-13. The lower panel illustrates the flows immediately downstream of the reservoir, where the combination of (low) winter passing flows and large summer releases is easily identified. The top panel shows flows further downstream nearer to Hume Dam, where unregulated tributaries contribute to the partial re- instatement of a more natural seasonal pattern of flows; however, the large summer releases are still visible. This trend is reflected in the seasonally weighted scores of 0.1 just downstream of the dam, rising to 3.1 further downstream. Just downstream of the reservoir the low flows are generally lower than experienced under unimpacted conditions, and accordingly the low flow index score is heavily impacted. High flows still occur within the unimpacted range as the river is used to transfer water down to Hume Dam, and although there is a shift in seasonality the high flow index is not markedly affected.

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6.6.8 Goulburn River downstream of Eildon Dam

At dam site At Yea 0SL 0SS 10 Seas 10 Seas Zero Zero Low Low High 6.1 High 3.2 3.6 3.4 0 Cv 0 Cv Su Wi 0 10 Su Wi 0 10

Figure 6-14 shows how current and unimpacted flows in the Goulburn River change between Eildon Reservoir and Yea. At Eildon Reservoir, the reversed seasonality is the most striking feature of the streamflow series, but this effect is less evident at Yea as a number of unregulated tributaries flow into the Goulburn River between the two sites. Just downstream of the dam low flows and seasonality are equally impacted, but the proportion of zero flows index is unaffected as zero flows never occurred under unimpacted conditions and there is a passing flow requirement under current conditions.

1200000 Goulburn R at Yea Unimpacted Current h) ont

m 800000 L/ M ( ow l f 400000 eam r t S

800000 Goulburn R d/s Eildon Res h) 600000 ont m L/ M 400000 ow ( l f

eam 200000 r t S

0 Jan-80 Jan-81 Jan-82 Jan-83 Jan-84 Jan-85 Jan-86 Jan-87 Jan-88 „ Figure 6-14 – Time series of unimpacted and current conditions for two sites on the Goulburn River downstream of Eildon Dam.

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The site just downstream of the dam has the lowest seasonality score for any FSR site in Victoria, with the next lowest scores being 3.4 for the Campaspe River downstream of Eppalock Dam, and 3.6 for the Broken River downstream of Casey’s Weir. The summer and winter seasons are assigned almost equivalent stress scores at the dam site: in summer the flows are too high, and in winter the flows are too low. Given that Eildon Dam, the second largest storage in the state, harvests during winter and releases large volumes for irrigation during late spring and summer, this result is hardly surprising. The seasonally-weighted score at both sites is around 0.4, indicating that this reach of the Goulburn is within the most stressed 5% of streams in the State. The influent tributaries upstream of Yea only slightly ameliorate the impact on low flows and seasonality, but overall their contribution is small compared to effects of the dam.

6.6.9 Wimmera River

D/s Mt Cole Creek confluence At Dimboola 3SL 1SL 10 Seas 10 Seas Zero Zero

9.7 Low Low 5.0 4.0 High 3.6 High 0 Cv 0 Cv Su Wi 0 10 Su Wi 0 10

The Wimmera River just downstream of the Mount Cole Creek confluence is impacted by farm dams, private diversions, and some small urban off-takes, though there are no large impoundments upstream. Accordingly, low and zero flows are heavily stressed in summer but are largely unaffected in the winter months; the seasonal indices indicate that overall flows in summer are over twice as stressed as those in winter. The seasonally-weighted score for this site (3.2) indicates that around 68% of sites in the State are less stressed. While the flow impacts are not easily discerned from the scale of the plot presented in Figure 6-15, the progressively larger impact at low flows are easily evident in the flow duration curve comparison shown in Figure 6-16.

Much further downstream at Dimboola, the Wimmera River is subjected to significant diversions for irrigation, domestic and urban uses. The impacts of these diversions are now easily discerned in Figure 6-15, and the reduction over whole range of flows is clearly evident in the flow duration curves (Figure 6-16).

The seasonally-weighted scores for these sites reflect the increasing level of flow stress, with the scores decreasing from 3.2 at the Mt Cole Creek confluence to only 0.5 at Dimboola. While low flows are only impacted in summer upstream of Mt Cole Ck confluence, at Dimboola low flows are heavily impacted in both winter and summer.

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300000 Wimmera R at Dimboola Unimpacted Current h) ont

m 200000 L/ M ( ow l f 100000 eam r t S

60000 Wimmera R d/s Mt Cole Ck h) ont

m 40000 L/ M ow ( l f 20000 eam r t S

0 Jan-80 Jan-81 Jan-82 Jan-83 Jan-84 Jan-85 Jan-86 Jan-87 Jan-88 „ Figure 6-15 – Time series of unimpacted and current conditions for two sites on the Wimmera River.

D/s Mt Cole Creek confluence At Dimboola 100000 1000000

10000 100000 h) h) t n o ont 1000 10000 m m L/ L/ M M ( ( 100 1000 ow ow l l f f 10 100 eam eam r r t t S S 1 Unimpacted flow 10 Unimpacted flow Current flow Current flow

0.1 1

0 102030405060708090100 0 102030405060708090100 Proportion of time flow exceeded (%) Proportion of time flow exceeded (%)

„ Figure 6-16 – Flow duration curves of unimpacted and current conditions for two sites on the Wimmera River.

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6.7 Comparison with AAPFD It is of interest to see how the seasonally-weighted scores compare with that obtained using the existing ISC method. ISC scores are largely based on calculation of the Amended Annual Proportion of Flow Deviation (AAPFD), an index that reflects the deviation in monthly means between unimpacted and current conditions.

A comparison of the scores based on the 600 annual FSR indices with the ISC results ed t h

g are illustrated in Figure 6-17. Note that i 500 e W

the comparison provided here is with the y 400

all AAPFD directly, and that no manual n k o n 300 adjustment has been made as required in Ra eas

S 200 the rigorous application of the original

sed ISC method. This figure compares the i d

r 100

a relative ranking of each site as determined ad t 0

S by the two indices. Each site is assigned a 0 200 400 600 ranking from 1 to 551, where 1 AAPFD Rank represented the most pristine site. In

general, catchments given a high rank „ Figure 6-17 - Comparison of site rank using AAPFD index and FSR index (that is low indication of stress) in the ISC index are also given a low rank in the FSR index. To a lesser extent, many catchments with a high rank, do so with both the FSR and the ISC scores.

The nature of the differences between the two scores may be illustrated by reference to flows at the Lerderderg River at Lerderderg Weir (Figure 6-18), a site that is impacted by extractions during periods of high flow. Under the FSR approach this site is given a moderately impacted score for the high flow index (0.62); the score is not very low because the high flow events experienced under current conditions are still well within the unimpacted range of high flow events. Due to the decrease in higher flows the variance index is also reduced (0.86). However the low flow index, proportion of zero flow index and the seasonal periodicity index are all close to 1.0 because these aspects of the flow regime have not been altered. The standardised seasonally-weighted score for this site is 0.56, and the ISC index score of 0.23.

Clearly, inspection of the individual index results provides the best means of assessing the degree and nature of the flow stress, however there is a need to provide a single score that reflects the overall degree of hydrologic stress. Given the fundamentally different nature of the different scores the degree of correlation illustrated in Figure 6-17 is perhaps not surprising; however, it should be

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recognised that adoption of a seasonally-weighted score will infer a different degree of stress to that made using the original (1999) formulation of the ISC.

40000 ) Unimpacted h Current ont

m 30000 L/ M (

w 20000 o l f am e

r 10000 t S

0 Jan-90 Jan-91 Jan-92 Jan-93 Jan-94 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00

„ Figure 6-18 - Time series of flow in the Lerderderg River at Lerderderg Weir.

6.8 Overall Distribution of Indices and Scores The distributions of individual indices based on all months of the year are illustrated in Figure 6-19. This plot shows the distribution of scores as a cumulative exceedance plot, where for ease of interpretation the indices have been multiplied by ten so that the results vary between 0 and 10. It is seen that all indices are non-uniformly distributed, and that the low flow index is the most impacted. The proportion of zero flows is the next most impacted, and flow variability is the least impacted flow component.

The distributions of the seasonally-weighted and seasonal scores are also represented as cumulative exceedance plots, and these are shown in Figure 6-20. The cumulative exceedance plot of the standardised seasonally-weighted scores is simply a straight line between 0 and 10, ie the scores are uniformly distributed, which simply reflects the outcome of the standardisation step. Accordingly, the score is directly linked to the percentage rank of the flow stress, where for example 50% of catchments score less than 5, 70% score less than 7, etc.

If the 50% exceedance value (the median) is interpreted as “typical”, it is seen that flow stress scores in the winterfill season are typically around 9.7 compared to 7.0 in summer. While scores over the summer months are distributed reasonably uniformly between 0 and 10, it is clear that catchments are much less stressed over the winterfill period. It is evident that the level of stress exceeded by 40% of catchments in winterfill season is exceeded by 75% of catchments in summer.

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10

9

8

7

6 e r

o 5 Sc

4

3

2 Flow variability index High flow index Low flow index 1 Proportion of zero flow index Seasonality index 0 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Percent of sites where score is exceeded

„ Figure 6-19 - Distribution of flow stress scores for different seasons

10

9

8

7

6 e r

o 5 Sc

4

3

2 Winter score

Summer score 1 Standardised seasonality weighted score 0 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Percent of sites where score is exceeded

„ Figure 6-20 - Distribution of flow stress scores for different seasons

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The results for the individual indices and overall annual and seasonal scores can be viewed from within a Geographical Information System. Thus, the spatial distribution of flow stress can be viewed at different scales and in different ways. Examples of the spatial distribution of summer flow stress for the whole of Victoria is provided in the lower panel of Figure 6-21, and an alternative view of the differences in flow stress indices in a particular river basin is provided in the upper panel.

„ Figure 6-21– Example spatial views of stress scores.

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6.9 Potential applications The two main types of applications of the stress scores are condition assessment and the evaluation of environmental benefits of different management regimes.

Use of the seasonally-weighted score for condition assessment satisfied a primary objective of this study, namely it provides a relative indication of threat to river health based on the level of water extractions by rural, urban, and industry users. The ranking makes no assumptions about the environmental value of a river, but rather characterises the degree of hydrologic stress under current management conditions relative to the flow regime that would occur if all anthropogenic extractions, water harvesting, and impoundments were removed. Use of the nomenclature discussed in Section 6.4 yields a score that reflects the percentage rank of the catchment compared to all FSR sites considered. In addition, the score indicates which season is most stressed, and which flow component is most impacted by extractions. It is likely that in the future the direct link between stress score and percentage rank will weaken due either to the inclusion of additional sites, or else to the changing nature of stress condition. Strictly speaking, the inferred percentage ranking associated with the standardised seasonally-weighted score is only relevant to 551 sites as assessed in 2005. However, given that the scores are rounded to the nearest integer, and that a very wide range of regimes and stresses have been sampled, it is expected that the scores as currently reported will be meaningful for many decades.

The undertaking of a future assessment in 10 another ten years affords the opportunity to 8 compare the overall condition of Victorian e r

o 6

c catchments at two points in time. Figure 6- S s s 22 shows a comparison of the current (2005) tre 4 S assessment with a hypothetical set of scores 2 2005 assessment in ten years time. The comparison shows the 2015 assessment 0 degree to which the efforts spent on 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Proportion of time flow exceeded (%) relieving flow stress have been successful: „ Figure 6-22 – Comparison of a future this hypothetical scenario shows that the hypothetical state wide condition assessment with current. situation immediately downstream of large dams (the most heavily impacted streams in the State) has not altered, but that improvements are evident in the moderate to highly stressed catchments.

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Possibly a more common use of the A stress indices will be for options Flow stress

ss Cost e r assessment at either the catchment or B n t o i regional scale. With this application D gat ow s i l t the change in scores associated with i in f E m of se

different management initiatives t ea r C F

would indicate the relative “return-on- Cos effort” for the options considered. A Dec hypothetical example is shown in Figure 6-23, where it can be seen that Option Considered option D yields one of the highest „ Figure 6-23 – Comparison of a future reductions in flow stress, but at a hypothetical state wide condition assessment with current. relatively low cost. If there is a particular set of flow objectives – for example relief of summer low flow stress – then selected individual indices could be considered rather than the overall seasonally-weighted score.

The impacts of different scenarios can also be assessed in a spatial manner. For example, the difference in flow stress associated with a future land-use scenario is illustrated in Figure 6-24. This example is taken from a study of land-use impacts for the Water and Land use Steering Committee (SKM, 2005), where it was found that the higher differences in flow stress are associated with the development of (predominantly) blue-gum plantations.

Another pertinent example is to explore options for reducing summer flow stress via the transfer of summer diversion licences to winter. Even if the Sustainable Diversion Limit is already exceeded, calculation of seasonal scores may indicate that the decrease in summer flow stress has no appreciable impact on winter flow stress. It may be that the cost of subsidising the cost of the construction of off-stream storages yields a similar reduction in flow stress with no economic penalty than does, say, the cost of buying back the summer licence to increase the share allocated to the environment.

Other uses that evaluation of the indices could provide include:

„ Assessment of stream conditions for various SFMP scenarios, such as different environmental releases from storages, or changes to farm dam / private diverter harvesting rules;

„ Assessment of applications for farm dam / private diverter licences, where the impact of each new licence on the FSR indices can be used to justify the licence issue conditions. For example, if low flows in summer are under stress then:

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– the RWA could require all new farm dams to be winterfill only dams with summer bypass; or – if an existing summer irrigation licence is to be increased, the RWA could require that the licence is converted to an off-stream winterfill licence prior to any additional volume being approved.

„ Investigation of optimum location of agro-forestry to minimise environmental impacts;

„ Investigation of environmental trade-offs associated with inter-basin transfers;

„ Assessment of the relative environmental impact of climate change compared to the establishment of plantations; and.

„ Assessment of different proposals for use of recycled water.

N

# Ararat

Beaufort #

Casterton Ballarat # #

# Skipton # Hamilton Dunkeld # # Lake Bolac

#Meredith

# Heywood Geelong # #

# Portland Camperdown Warrnambool # # Colac

annual_base -0.6 - -0.4

-0.4 - -0.2

-0.2 - -0.05

-0.05 - 0.05

0.05 - 0.2 Yellow shaded sub-catchments have no data 30 0 30 60 Kilometers

„ Figure 6-24 – Difference in summer stress scores between current water regime and that with base case land use change scenario (SKM, 2005).

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6.10 Availability of Index Results The full set of all index results for each of the 551 sites around Victoria will shortly be made available on the Department of Sustainability and Environment website (http://www.dse.vic.gov.au). The data will be mapped, allowing the user to extract index results from individual sites. Other data available on the same map will include Sustainable Diversion Limits, stream gauge locations, extent of flooding, and a variety of other useful hydrographic and topographic data.

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7. Conclusions

The purpose of the FSR score is to establish a relative indication of threat to river health based on the level of water extractions by rural, urban, and industry users. By aligning the FSR indices with specific ecological flow components and then accounting for virtually all water demands in the state, the FSR score provides a meaningful measure of streamflow stress. Comparison of results between sites can be achieved easily using GIS tools, or by simple inspection of the score results.

The flow stress indices provide a measure of the stress in a waterway by comparing the current and unimpacted streamflow timeseries, and measuring the departure of each flow component as a proportion of the range experienced under unimpacted conditions. The final seasonally-weighted score is based on the consideration of five indices which cover a broad range of ecologically- important flow components. The flow indices include consideration of the flow stress on low flows, high flows, flow variability, the proportion of zero flows, and the seasonal timing of low and high flow periods.

On the basis of the investigations undertaken it can be concluded that:

„ Hydrological indices based on a comparison of streamflows under unimpacted and current conditions can be used to represent the degree of stress on a range of ecologically important flow components;

„ Indices based on departure from the unimpacted range (using the “range-standardised” approach) provides a robust means of characterising hydrologic stress, and in particular strengthens the degree of correlation between calculations based on daily and monthly data;

„ The analysis of monthly data provides a similar indication of hydrologic stress as determined using daily data;

„ The degree of stress evaluated using ten indices of (largely) daily behaviour can be adequately represented by differences in monthly flows related to the following five attributes: – the coefficient of variation; – the variation of the two lowest monthly flows in each water year; – the variation of the highest monthly flow in each water year; – the proportion of time that monthly flows are near zero; and, – the timing of the highest and lowest monthly flows in each calendar year.

„ The flow stress of a river is best characterised by inspection of individual indices computed for the abovementioned five attributes, but an overall score reflecting the degree of flow stress can be summarised by a single “seasonally-weighted” score that ensures that highly impacted

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regulated rivers – that is those rivers that exhibit marked seasonal flow reversal but which still experience high flows associated with irrigation releases – are appropriately ranked;

„ A three character code can be used to summarise the degree of flow stress in a river, where the first character denotes the standardised seasonally-weighted stress score, the second denotes the most stressed season, and the third denotes the most stressed flow component; the score is standardised to facilitate intuitive interpretation of the degree of stress whereby its numerical value indicates the proportion of streams across Victoria that are more stressed; and,

„ At least 15 years of data are required to adequately define indices of hydrologic stress;

The FSR index has been applied to 551 sites across Victoria, covering approximately 400 streams. Of the 551 sites, it was found that 88% are more stressed in summer than winter, and 78% are most stressed due to impacts on low flows.

The FSR scores (and individual indices) can be used for either general condition assessment, or else for evaluation of the environmental “return-on-effort” associated with a range of different management options. While any options assessment needs to take into account a range of factors, the provision of numerical scores that reflect the threat to river health enables options to be considered in a similar manner to other quantifiable aspects, such as economic indicators.

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8. References

Australian Water and Wastewater Assoc (AWWA; 1998): Victorian Water Systems Directory 1998, prepared by J. Macklin and T. Mackay.

Centre for Environmental Applied Hydrology and ID&A Pty Ltd (CEAH/ID&A; 1997): An index of stream conditions: reference manual. Report prepared for the Waterway and Floodplain Unit of the Department of Natural Resources.

Department of Sustainability and Environment (DSE; 2003): Estimation of Sustainable Diversion Limit Parameters over Winterfill Periods in Victorian Catchments.

Erlanger, P., Weinmann, P and Poulton, D. (1992): Development and Application of an Irrigation Demand Model Based on Crop Factors. Proc. Engineering in Agriculture Conference, Institution of Engineers, Australia, October 1992.

Ladson., T, 2003. Assessing the Health of Ephemeral Rivers – Review of Geomorphic and Hydrologic Indicators. Cooperative Research Centre for Catchment Hydrology and Monash University.

Lowe, L., Nathan , R. (2005): A robust procedure for transposing gauged streamflows to ungauged catchments. Proc. 29th Hydrol. & Water Resour. Symp., 20-23 Feb 2005, Canberra (ISBN 085 825 8439).

McKenzie, N.J., Jacquier, D.W., Ashton, L.J. and Cresswell, H.P. (2000): Estimation of Soil Properties Using the Atlas of Australian Soils. Technical Report 11/00, Feb 2000, CSIRO Land and Water, Canberra ACT.

Murray Darling Basin Commission (2004): Sustainable Rivers Audit, Pilot Audit. Hydrology Theme Technical Report. Draft Report.

Nash, J.E., and J.V. Sutcliffe, (1970): River flow forecasting through conceptual models: Part I - A discussion of principles. J. Hydrol., 10, 282-290.

Nathan, R., Jordan, J. and Morden, R. (2005): Assessing the Impact of Farm Dams on Streamflows, Part I: Development of Simulation Tools (submitted to Aus J Water Resources).

Nathan, RJ and Weinmann, PE (1993): Low Flow Atlas of Victoria, Dept. of Conservation and Natural Resources, Victoria.

Natural Resources and Environment (2003). Flows – a method for determining environmental water requirements in Victoria.

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Olden., J.D, and Poff, N.L, 2003. Redundancy and the Choice of Hydrologic Indices for Characterizing Streamflow Regimes. River Research and Applications 19: 101 – 121 (2003).

Richter BD, Baumgartner JV, Powell J, Braun DP. 1996. A method for assessing hydrologic alteration within ecosystems. Conservation Biology 10: 1163 – 1174.

Stewardson M., 2001. The Flow Events Method for Developing Environmental Flow Regimes. In Rutherfurd., I., Sheldon, F., Brierley, G. and Kenyon, C. (Eds). Third Australian Stream Management Conference, Brisbane, 2001.

Sinclair Knight Merz (2000a): Tool for Estimating Dam Impacts User Manual.

Sinclair Knight Merz (2000b): Farm Dam Impacts Study Stage One - TEDI Modelling - Impact of Farm Dams in Five Catchments. Report prepared for DSE, Melbourne Water, and Environment Australia.

Sinclair Knight Merz (2001): The development of regional tools for the prediction of farm dam impacts in Victorian Catchments. Report prepared for the Dept. of Natural Resources and Environment.

Sinclair Knight Merz (2003a): Lower Barwon REALM Model, report prepared for Southern Rural Water.

Sinclair Knight Merz (2003b): Estimation of Streamflow and Demand Data and Development of a REALM Model of Steels, Dixons and Pauls Creeks. Report prepared for Melbourne Water.

Sinclair Knight Merz (2003c): Sustainable River Audit Hydrology Theme – Ovens River Basin Hydrology Report. Report prepared for Dept. Sustainability and Environment.

Sinclair Knight Merz (2003d): Estimation of Daily Flows for the Lerderderg River and Goodman Creek. Report prepared for Dept. Sustainability and Environment.

Sinclair Knight Merz (2004a): Estimating Available Water Using Sustainable Diversion Limits: The estimation of farm dam volume and number for the State of Victoria. Report prepared for the Dept. Sustainability and Environment.

Sinclair Knight Merz (2004b): Estimating Available Water Using Sustainable Diversion Limits: Farm dam surface area and volume relationship. Report prepared for the Dept. Sustainability and Environment.

Sinclair Knight Merz (2004c): Estimating Available Water Using Sustainable Diversion Limits: Farm dam demand factor. Report prepared for the Dept. Sustainability and Environment.

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Sinclair Knight Merz (2004d): Estimating Available Water Using Sustainable Diversion Limits: The estimation of farm dam impacts on selected streamflow indices in SDL catchments. Report prepared for the Dept. Sustainability and Environment.

Sinclair Knight Merz (2004e): Estimating Available Water Using Sustainable Diversion Limits: Assessment of water available for development. Report prepared for the Dept. Sustainability and Environment.

Sinclair Knight Merz (2005): Water and Land Use Change Study Land use and hydrologic change in south-west Victoria, Consulting report prepared for Water and Land use Change Steering Committee, Glenelg Hopkins Catchment Management Authority.

Thackway, R., Donohue, R., & R, Smart. (2004). Integrated Regional Vegetation Information - A compilation of vegetation types for National Action Plan and Natural Heritage Trust regions. Bureau of Rural Sciences, Canberra.

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9. Abbreviations

The following abbreviations are referred to throughout the report:

CV Flow variability stress index

DSE Department of Sustainability and Environment (Victoria)

GIS Geographic Information System

HF High flow stress index

FSR Flow Stress Ranking

ISC Index of Stream Condition

HF Low flow stress index

PRIDE PRogram for Irrigation Demand Estimation (computer model)

PZ Proportion of zero flow stress index

REALM REsource Allocation Model (computer model)

RRHS Regional River Health Strategy

RWA Rural Water Authority

SDL Sustainable Diversion Limit

SFMP Streamflow Management Plan

SKM Sinclair Knight Merz

SP Seasonal periodicity stress index

VRHS Victorian River Health Strategy

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Appendix A Derivation of Daily Streamflow Test Data Set

A.1 Derivation of Daily Flows for Testing FSR Formulation in Regulated Catchments

A set of high quality daily current and unimpacted flows are required in order to test the formulation of the FSR index in regulated catchments. Current and unimpacted daily flows were derived from three sources, namely: 1) Daily REALM models; and, 2) Previous projects undertaken primarily for streamflow management plans. In all, data from a total of 28 sites were derived from these sources.

Daily Flows from REALM Models The daily REALM models available and their suitability for use are shown in Table A1.

Daily REALM models developed for Melbourne Water represent unregulated catchments and include the impact of farm dams and private diverters only. Data was not used from these models as it was considered that the impacts of farm dams can now be better characterised by the use of the CHEAT model (see Section A.2). The Stawell bulk entitlement model yields an impact on very small minor streams that are not well gauged, and so it was also excluded.

The lower Broken Ck model encompasses Broken Creek downstream of the East Goulburn Main Channel Outfall, and concentrates on the manipulation of these flows and weir pool behaviour. Due to the high degree of modification over a long period, the calculation of reliable unimpacted flows at a daily time step for these reaches would be very difficult. A similar situation exists for the Kerang Lakes (Torrumbarry) system. These two daily models were not adopted for use in the FSR project.

On this basis, current and unimpacted daily flows from July 1974 to June 2000 at key locations in the Goulburn, Upper Broken, Campaspe, Upper Loddon and Wandella Ck catchments were derived.

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„ Table A1 - Summary of Daily REALM Models

Model Client Suitability Goulburn Broken Campaspe DSE, MDBC, G-MW Suitable for use downstream of storages Loddon (GBCL) Wandella Ck DSE, MDBC, G-MW Suitable for use downstream of storages Kerang Lakes DSE, MDBC, G-MW Too difficult to calculate reliable unimpacted flows at a daily time-step Broken Creek lower (Fortran) G-MW Too difficult to calculate reliable unimpacted flows at a daily time-step Stawell DSE, Grampians Water Not considered suitable for use Steels Pauls and Dixons Ck Melbourne Water Suitable for use Stringybark Ck Melbourne Water Suitable for use Olinda Ck Melbourne Water Suitable for use Little Yarra River Melbourne Water Suitable for use Don River Melbourne Water Suitable for use

Daily flows from Previous Studies SKM obtained current and unimpacted daily flows series from a number of past projects. Most information was obtained from projects undertaken by SKM, though a number of data sets were provided by Dr Michael Stewardson (University of Melbourne). A summary of the data sources are summarised in Table A2, along with their suitability for use.

Daily flows were calculated at multiple locations in the Thomson-Macalister, Wimmera and Glenelg basins. However, examination of methods showed that all of the unimpacted flows except one were derived using method of fragments. Flow derived using this method are unsuitable for this purpose as they result in flows series which may not be temporally coincident.

The current and unimpacted flow series calculated for the Wannon River was considered suitable for use as it was derived using manipulated gauged data.

Current and unimpacted flows were calculated at a number of locations on Lerderderg River and Goodman Creek. The calculations involved the manipulation of gauged data and are considered suitable for use for this project

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„ Table A2 - Previously derived current and unimpacted daily flows

Project Client Suitability Thomson Macalister DSE Not suitable as unimpacted flows derived by method of fragments Lerderderg River and Goodman Creek DSE Suitable for use Wimmera and Glenelg basins DSE, GHCMA Only Wannon River data used as all others were derived using method of fragments. Sustainable Rivers Audit pilot data for the MDBC Two sites suitable for use, other sites Ovens basin derived using method of fragments Environmental flow requirement studies for DSE Suitable for use. the Goulburn, Broken, and Loddon Rivers undertaken by Dr Michael Stewardson (University of Melbourne)

A.2 Derivation of Daily Flows for Testing FSR Formulation in Unregulated Catchments

A.2.1 Introduction Little work has been done to date on the derivation of daily unimpacted streamflows using data influenced by farm dams and private diverters. While the TEDI model can be used to derive a time series of monthly unimpacted streamflows, there are no established procedures that are applicable at a daily time step.

The CHEAT model was developed during the SDL project to estimate the impact of farm dams on monthly streamflows. The CHEAT model is essentially a research tool and when operated at its most complex configuration it requires detailed information that is best derived from a digital elevation model. The information and resources required to undertake the required detailed topographic analysis is beyond the scope of this task, and accordingly a modified approach was adopted. The derivation of time series representative of current conditions is discussed in the next section, and this is followed by a description of the manner in which the CHEAT model is used to estimate a plausible time series of daily data under unimpacted conditions.

A.2.2 Current Flow Series Current flow series for unregulated sites were obtained from gauged daily data. A number of gauges were selected to have:

„ a reasonable length of streamflow record (>20 years);

„ a continuous streamflow record with minimal infilling required; 2 „ a catchment area less than approximately 200 km ;

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„ a density of farm dams greater than the median for the whole State;

„ a broad range of hydrologic similarity indicators; and,

„ a broad geographical distribution across the state.

Also, it was important that the selected gauges were currently open to ensure that available estimates of water use were applicable to the flow data. This also minimised the effect of trends in water use over each period of record.

Twenty catchments across the state were selected, as shown in Figure 2-3.

„ Figure A1 - Selected unregulated catchments.

A.2.3 Unimpacted Flow Series For unregulated catchments, a daily series of unimpacted flows was derived by adding the impact of farm dams and private diverters to the historical time series.

Impacts of Farm Dams The impact of farm dams was calculated using the CHEAT model. This model allows the simulation of each farm dam within a catchment and its effect on streamflow at the catchment outlet. This is achieved by explicitly describing each dam within the stream network in a

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catchment, and then applying rainfall, evaporation, and water demand to that network. Before applying the model to this project the model was first modified to run at a daily time-step.

Although the total level of farm dam development is known for each catchment, the details of each individual farm dam would require considerable effort to determine. Therefore, stream network information was obtained from existing CHEAT models, and the farm dam volume and sub- catchment information in these models was adjusted to match the new catchments. Additional modifications were incorporated in the CHEAT model to allow the transposition of a farm dam network from one catchment to another, whilst still preserving the required distribution of farm dam volumes.

To date, CHEAT models have only been developed for three catchments in detail, as given below in Table A3. It should be noted that the Lenswood Creek catchment is part of the Onkaparinga River basin in South Australia. Both Lenswood Creek and Woollen Creek have very high levels of farm dam development, near the upper limit of development levels typically found in Victoria.

„ Table A3 - Characteristics of CHEAT models developed for specific catchments. Avoca River at Woollen Creek at Lenswood Creek Feature Amphitheatre Lal Lal at Peacock Road Catchment area (km2) 77 11 28 Mean annual rainfall (mm) 580 930 1030 Mean annual flow (ML/km2) 70 66 230 Baseflow index 0.29 0.41 0.34 Number of dams 215 119 221 Density of dams (no. of dams per km2) 2.8 10.8 7.9 Volumetric density of dams (ML/km2) 6.7 40.1 36.8

Each gauge flow series was assigned CHEAT network information from one of the above catchments, based on farm dam density (number of dams per km2). The farm dam volumes and catchment areas contained in the network information were then scaled, ensuring that the distribution of farm dam volumes and the total catchment area corresponded to the new catchment.

A direct output of the CHEAT model for each gauge was an estimate of the unimpacted flow series, including the impact of farm dams.

Impact of Private Diverters The impact of private diverters was calculated using estimates of total annual licences for each catchment. These total annual volumes were converted to monthly volumes using average monthly demand patterns found in nearby Bulk Entitlement REALM models. The monthly volumes were then uniformly disaggregated to obtain daily demand volumes.

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Although uniform disaggregation from monthly to daily volumes may not provide a true estimate of patterns of demand, the actual volumes are generally very small in comparison to the streamflow. Therefore, the error introduced by this method is not likely to be significant.

Calculating Unimpacted Flow As a final step, the unimpacted flow series for each gauge was then calculated by adding the impact of farm dams and private diverters to the current flow series. In this manner daily time series for twenty sites representative of farm dam (and private diverter) impacts were derived at a daily time- step.

A.3 Hydrologic Characteristics of Selected Catchments

In order to assess the range of characteristics represented by the catchments selected for formulation of the FSR, similarity criteria based on physiographic and climatic characteristics were obtained. The similarity criteria used were developed in the SDL project using Principal Component Analysis. With this approach, a set of six correlated variables (that are linked to the hydrologic regime of the catchment) are reduced to just two factors that are completely uncorrelated. The hydrologic similarity of the catchment can thus be assessed as the linear distance between points on a scatter plot of the two factors: each point represents a single catchment, and two catchments that plot close to one another are hydrologically similar, and those distant from each other are hydrologically dissimilar.

A plot of the hydrologic similarity of the regulated sites and the unregulated sites is shown in Figure A2. It shows that the principal similarity factors for the selected sites are broadly representative of the range of similarity factors for the whole state. This suggests that the selected sites represent a reasonable coverage of the range of hydrologic conditions typically encountered across Victoria. The graph is divided into four regions, or principal component groups. Catchments within each group can be considered to be hydrologically similar, and the spatial distribution of these groupings is shown in Figure A3. It is seen that the while the representativeness of catchments in Groups 3 and 4 is not as comprehensive as other groups, the spatial distribution of these catchments is limited.

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5 Group 3 All Catchments Group 4 Regulated 4 Unregulated

3 t en pon 2 Com l a p i

c 1 n nd Pri

2 0

-1

Group 1 Group 2 -2 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 1st Principal Component „ Figure A2 - Similarity of selected catchments compared to all SDL catchments.

Legend PC value groups 1 2 3 4

„ Figure A3 - Spatial distribution of hydrologically similar groupings.

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Appendix B Evaluation of Transposition Functions

B.1 The Beta Distribution

The beta distribution is a probability function commonly used in statistics. The shape of the cumulative beta probability distribution is shown below.

1.0 x ability of ve prob i lat u Cum

0.0 0.0 1.0 Value x

„ Figure B 9-1- The cumulative beta probability distribution

This shows a typical shape of the distribution. However, the shape is affected by two variables α and β, which can make the distribution favour either high or low values of x, and can flatten or steepen the curve.

As can be seen above, the cumulative distribution resembles a typical flow duration curve. This similarity is the basis of the beta flow transposition method. This method of transposing flows from one catchment to another is based on the assumption that the target catchment flow duration curve can be approximated by a factored version of the beta probability distribution, and the temporal pattern of flows is then borrowed from the source catchment.

B.2 Transforming the beta distribution to represent flow duration curves

In order to use the beta distribution, a consistent method must be found to transform the curve to represent actual flows. As the examples in Figure 4-2 show, the distribution can be parameterised to resemble a flow duration curve that has been first factored and transformed into the logarithmic

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domain. Streamflows are first transformed into a standardised logarithmic domain in which the flow percentiles are constrained to lie between 0 and 100:

BETA(α, β,φ) = A⋅ LOG10 (B ⋅Q(φ)) Eqn B.1

Where BETA(αβ,φ) = Beta cumulative probability distribution for distribution parameters α and β, and cumulative exceedance φ Q(φ) = flow corresponding to percentile φ A and B are fitted parameters

In order to fit the two parameters (A and B), two specific points on the standardised flow duration curve need to be defined, as shown in Figure B-2.

100

ow Q10

Fl J sed i d r

a Q90 d

n K a t S

0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Proportion of time flow exceeded

„ Figure B 2- Schematic of parameters that define the Beta distribution representation of a flow duration curve.

To define the curve over a wide range of flows, the two defined points should be as far apart as

possible. However, estimating a flow that is exceeded less than 10% of the time (Q10) or more than

90% of the time (Q90) becomes increasingly dependent on the length of the available record and hence these two points were adopted to characterise the low and high flow ranges of the series.

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In order to achieve a standardised range of flow percentiles that is independent of catchments scale,

the Q10 and Q90 flow percentiles are arbitrarily mapped to a domain in which the flow exceeded 10% of the time is assigned a value of 60, and the flow exceeded 90% of the time is assigned a value of 40. These values were chosen to ensure that the flow range is constrained within a range notionally between 0 and 100. If these percentiles are substituted into Equation B-1, and re- arranged, the parameters A and B can be stated as:

40 ⎛ Q 2 ⎞ A = and B = ⎜ 10 ⎟ Eqn B-2 ⎛ Q 2 ⎞ ⎜ Q 3 ⎟ LOG ⎜ 10 ⎟ ⎝ 90 ⎠ 10 ⎜ 2 ⎟ ⎝ Q90 ⎠

As described in Section B.1, the shape of the Beta distribution is described using the variables α and β. However, given that J and K have been assigned values of 60 and 40, in order to achieve the simplicity of Equation B-2 it is necessary to set both α and β to 0.108.

The above approach is adequate to define the flow duration of a stream in which flows are non-zero for 100% of the time. However to be applicable to ephemeral streams it is necessary to include an additional factor that defines the proportion of time that the flows are non-zero. Equation B-2 can be applied to ephemeral streams if Q10 and Q90 represent the proportion of time that the flows are non-zero, and the maximum ordinate of the abscissa scale is set to (1-CTF), where CTF denotes the proportion of time that the stream ceases to flow.

B.3 Regional Prediction Equations for Beta Distribution Variables B.3.1 Methodology for Developing and Testing Prediction Equations

It was shown in the preceding section that the flow duration curve for any catchment can be

estimated given estimates of non-zero Q10, non-zero Q90, and cease to flow (CTF). In order for this transposition method to be widely applicable, these three variables need to be estimated for all catchments across the state.

Estimation of Q10, Q90 and CTF is undertaken by:

„ determining the variables for a range of sites across Victoria (based on monthly flows); and

„ using these known values of Q10, Q90 and CTF to develop prediction equations based on catchment characteristics.

These regression equations can then be used to predict Q10, Q90, and CTF for any catchment within the state.

The catchment characteristics used to develop the prediction equations are given in the Table B1.

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„ Table B 1 - Catchment characteristics used to develop prediction equations

Name Description MAF Mean annual flow (ML/d) Area Catchment area (km2) Perimeter Catchment perimeter (km) ShapeFactor Dimensionless function of area and perimeter ElMax Maximum elevation within catchment (mAHD) ElMin Minimum elevation within catchment (mAHD) ElRange Maximum elevation range within catchment (mAHD) ElMean Average elevation within catchment (mAHD) Veg % tree cover SlMax Maximum slope within catchment SlMin Minimum slope within catchment SlRange Range of slope within catchment SlMean Average slope within catchment RainAnn Mean annual rainfall (mm) RainSum Mean summer rainfall Dec-Feb (mm) RainWin Mean winter rainfall Jun-Sep (mm) ActAnn Mean annual actual evapotranspiration (mm) ActSum Mean summer actual evap Dec-Feb (mm) ActWin Mean winter actual evap Jun-Sep (mm) ArlAnn Mean annual areal potential evapotranspiration (mm) ArlSum Mean summer areal potential evap Dec-Feb (mm) ArlWin Mean winter areal potential evap Jun-Sep (mm) PtAnn Mean annual point potential evapotranspiration (mm) PtSum Mean summer point potential evap Dec-Feb (mm) PtWin Mean winter point potential evap Jun-Sep (mm) DTWT gauge Depth to water table at the gauge location (m) DTWT av Average depth to water table across the catchment (m) SD Stream density (length of topographic stream across catchment) (km/km2) SF Stream frequency (number of topographic stream junctions) (no./km2) SOLDEPTH Average soil depth across catchment (m) SOLPAWHC Average plant available water holding capacity across catchment (mm) A_THICK Top soil layer thickness (m) A_KSAT Top soil layer saturated hydraulic conductivity (mm/hr) A_SAT Top soil layer saturated volumetric water content (m) A_FCP Top soil layer nominal field water capacity (m) A_WP Top soil layer nominal wilting point water capacity (m) B_THICK Lower soil layer thickness (m) B_KSAT Lower soil layer saturated hydraulic conductivity (mm/hr) B_SAT Lower soil layer saturated volumetric water content (m) B_FCP Lower soil layer nominal field water capacity (m) B_WP Lower soil layer nominal wilting point water capacity (m)

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The map below shows the 165 selected streamflow gauges across the state where Q10, Q90, and CTF were calculated. These gauge sites were selected as the level of development upstream of each is comparatively low, and thus the recorded data is likely to be representative of unimpacted conditions.

„ Figure B 9-2 - Location of 165 gauges used to develop prediction equations

The 165 gauges were split up into two groups, so that 129 gauges were used to develop the prediction equations, and the remaining 36 gauges were then used to test the equations. This split sample was used to ensure statistical independence of the testing process.

The method of selecting the group of 36 gauges ensured that the transposition function would be tested in the same way that it would be applied in practice. When using the transposition function, the source catchment is selected using an overall index of similarity that incorporates both hydrological similarity and geographical proximity (see Section 4.3.2). Use of this similarity measure helps ensure that the flows are transposed from the most similar nearby catchment.

To simulate this, pairs of gauges were selected if their similarity indices showed they were hydrologically similar to each other, and if they were within the same river basin. In this way each pair of gauges could be transposed to each other, allowing 36 independent tests of the transposition approach.

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The 129 gauges which were not selected for the testing procedure were used to develop the prediction equations.

B.3.2 Prediction of Q10

Values of Q10, calculated based on monthly flows, were determined for each of the 129 gauges.

Using linear regression with various catchment characteristics, it was found that Q10 could best be predicted using the following equation:

Q 0.2 = 2.386(MAF )0.2 − 0.00277(VEG)− 0.00222(RAINSUM ) 10 Eqn B-3 +0.00357()ACTWIN + 0.0292(SF )− 0.153

R2 = 0.977, s.e.e. = 0.212

Where MAF = Mean annual flow (ML/day) VEG = Vegetation cover (%) RAINSUM = Average summer rainfall (mm, Dec-Feb) ACTWIN = Average (mm, Jun-Sep) SF = Stream frequency (stream junctions per km2)

Values of MAF are available for all FSR catchments from a GIS layer developed for the SDL project (DSE, 2003). These estimates of MAF are based on a combination of gauged information, prediction equations, and regional smoothing introduced to ensure consistency. Figure B-3 shows how the estimated values of Q10 compare to the observed values for the 36 test gauges. The results

indicate that Q10 can be accurately predicted for the majority of cases.

90,000

80,000

70,000 h t n

o 60,000 m / 50,000 (ML d e t a 40,000 m i

30,000 0 est Q1 20,000

10,000

0 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 Q10 actual (ML/month)

„ Figure B 9-3 - Actual and estimated values of Q10 for 36 test catchments

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B.3.3 Prediction of Q90

Values of Q90, calculated based on monthly flows were determined for each of the 129 gauges.

Using linear regression with various catchment characteristics, it was found that Q90 could best be predicted using the following equation:

Q 0.2 = 1.282(MAF )0.2 + 0.00491(VEG)+ 0.00429(RAINSUM ) 90 Eqn B-4 −0.180()SD + 0.00181(A _ KSAT )−1.013

(r2 = 0.787, s.e.e. = 0.537)

Where MAF = Mean annual flow (ML/day) VEG = Vegetation cover (%) RAINSUM = Average summer rainfall (mm, Dec-Feb) SD = Stream frequency (km streams per km2) A_KSAT = Saturated hydraulic conductivity of top layer of soil (mm/hr)

Figure B-4 shows how the estimated values of Q90 compare to the observed values for the 36 test gauges. This graph shows that Q90 can be predicted with a moderate level of accuracy, but clearly

not as accurately as for Q10 (the standard error of prediction is over twice that achieved for prediction of Q10).

5,000

4,500

4,000

3,500

3,000 (ML/month) 2,500 ed at 2,000

1,500 Q90 estim 1,000

500

0 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000 Q90 actual (ML/month)

„ Figure B 9-4 - Actual and estimated values of Q90 for 36 test catchments

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B.3.4 Prediction of CTF Values of CTF, calculated based on monthly flows, were determined for each of the 129 gauges. Typically, CTF is near to 100% for the majority of catchments. However, a small number of gauges were on ephemeral waterways where the CTF varies significantly. Therefore, the steps used to develop CTF prediction equations were as follows: 1) Logistic regression was used to predict whether catchment was perennial or ephemeral; a stream was assumed to be ephemeral if CTF was less than 98%; and, 2) Linear regression was used to predict the CTF for only those gauges found to be ephemeral.

Logistic regression showed that ephemeral streams could best be identified using the following equation:

e z Ephemeral = 1− e z where z = −0.0215()MAF 0.2 − 0.0216(VEG)− 0.0211(RAINWIN ) Eqn B-5 +0.0268()ACTANN −10.561

(log likelihood = -27.3, McFaddens ρ2 = 0.457)

where MAF = Mean annual flow (ML/day) VEG = Vegetation cover (%) RAINWIN = Average winter rainfall (mm, Jun-Sep) ACTANN = Average annual actual evapotranspiration (mm)

If Ephemeral = 0, the stream is perennial, and CTF is set to a flow that is exceeded 100% of the time. If Ephemeral = 1, the stream is ephemeral and CTF predicted using the the following equation:

CTF = 0.0264(AREA)+ 0.0311(ELRANGE)− 0.350(VEG)(+ 0.377 RAINSUM )

−0.530()SOLPAWHC + 0.187(A _ KSAT )+ 55.373 Eqn B-6 (r2 = 0.945, s.e.e. = 5.82) where AREA = Total catchment area (km2) ELRANGE = Maximum elevation range within catchment (m) VEG = Vegetation cover (%) RAINSUM = Average summer rainfall (mm, Dec-Feb) SOLPAWHC = Plant available water holding capacity of soil (mm) A_KSAT = Saturated hydraulic conductivity of top layer of soil (mm/hr)

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The graph below shows how the estimated values of CTF compare to the observed values for the 36 test gauges. This graph shows that CTF can be predicted with only a low level of accuracy.

100

90

80

70

60 ted (%) 50 tima

F es 40 T C 30

20

10

0 0 102030405060708090100 CTF actual (%)

„ Figure B 9-5 - Actual and estimated values of CTF for 36 test catchments

B.4 Using the Beta Distribution Method to Transpose Flows

The steps required to use the Beta distribution to transpose streamflows can be summarised as follows:

Step 1 – Estimate Q10, Q90, and CTF: derive estimates of Q10, Q90, and CTF using equations B3 to B6, and then determine parameters A and B using equation B2.

Step 2 - Convert source flows to percentiles: Each monthly value of the time series to be transposed is converted to a percentage of time that the flow is exceeded (this is most easily done by interpolation of the flow duration curve).

Step 3 – Convert flow percentiles to volumetric flows at the target site: Using the beta distribution function and parameters A and B, the target flows can be easily calculated using the inverse of Equation B1, namely:

10BETA(α ,β ,φ ) / A Q = φ > CTF Eqn B7 n B

= 0.0 φ ≤ CTF

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One problem with this approach – indeed with either of the transposition approaches considered – is that when the source flow is zero, then transposed flow will also be zero. Accordingly, this limits the proportion of zero flows in the target catchment to be the greater of the CTF percentile in either the source or target catchments.

B.5 Comparison of Transposition Methods

An evaluation of the two transposition approaches was undertaken using data from the 36 gauged sites discussed in B.3.1. Streamflows were transposed to each of the 36 test sites using both the mean annual flow (MAF) and Beta distribution methods. These estimated flows were then compared to the known gauged flow at the site, and the performance of each was compared.

The basis of the evaluation was to compare the Coefficient of Efficiency of the transposed monthly flows with the observed obtained using both methods. The Coefficient of Efficiency has been widely used to evaluate the performance of hydrologic models and is defined as (Nash and Sutcliffe) as follows:

2 2 ()x − x − (x − x ) ∑ obs obs ∑ obs est Eqn B8 CE = 2 ∑ ()xobs − xobs

CE varies between minus infinity (poor model) to 1.0 (perfect model). This coefficient represents an improvement over the Coefficient of Determination (R2) for model evaluation purposes as it is sensitive to differences in the observed and model-simulated means and variances. Due to the squared difference terms, however, CE is sensitive to extreme values (as is R2), and thus to focus on the performance of the model at low flows it is necessary to transform the data into the logarithmic or square root domains.

A comparison of the two methods is shown in Figure B6. Results are presented for streamflows in the arithmetic domain (that is where performance is skewed towards higher flow events) and the logarithmic domain (where a uniform focus is given to the whole flow range).

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1

0

) -5 -4 -3 -2 -1 0 1 F A M

( -1

cy (a) Arithmetic domain en ci i f f -2 E of

t n e ci

i -3 f ef o C -4

-5 Coefficient of Efficiency (Beta)

1

0

) -5 -4 -3 -2 -1 0 1 F A M

( -1 cy (b) Logarithmic domain en ci i f f

E -2

f o t n e ci

i -3 f ef o C -4

-5 Coefficient of Efficiency (Beta)

„ Figure B 9-6 – Comparison of Coefficient of Efficiency results for transposition based on Mean Annual Flow (MAF) and Beta Distribution methods, where results are assessed in both (a) arithmetic and (b) logarithmic domains.

It is seen that the transposition method based on mean annual flows generally outperforms the Beta distribution when high flows are given more weight (Figure B5a), though when a more equal focus is given to both low and high flows (Figure B5b) the results based on the Beta distribution are slightly better.

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The greatest source of uncertainty with the Beta distribution approach is associated with estimation

of Q90 and CTF. These two parameters – particularly CTF – can only be predicted poorly, and their uncertainty is propagated through to the final flow calculations. If in the future it becomes possible to estimate these quantities with more precision, then it is possible that use of the Beta distribution would provide better results as the method ensures that both the low and high regime are appropriately represented.

One advantage of the Beta distribution approach is that it is able to deal with a reduction in CTF between source and target catchment, which is something the method based on mean annual flows cannot. This is a minor advantage, and is balanced with the disadvantage of more complex calculations.

Overall, given the relative performance of the two methods, the method based on mean annual flows was adopted in preference to the beta distribution for: (1) it estimates the distribution of medium to high flows more accurately; and, (2) it is simpler to apply.

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Appendix C Preliminary Flow Indices Considered

C.1 Mean Annual Flow Index C.1.1 Ecological Significance The change in mean annual flow between unimpacted and current conditions indicates the overall change in the volume of water carried by a river or creek over a year. Aquatic, riparian and floodplain ecosystems would be impacted by any significant changes mean annual flow, however, it is difficult to link this index to any specific ecosystem impact. This index would be unable to reflect changes in the timing frequency or variability of the flow regime, all of which can have considerable environmental implications.

C.1.2 Description The mean annual flow index is based around the difference between the percentage of time that the unimpacted and current mean annual flows are exceeded under unimpacted conditions. The advantage of using a range-standardised index is that it takes into account whether the current conditions fall within the unimpacted flow regime. This method of calculating the range- standardised index also has the advantage of using the actual distribution of the unimpacted annual flows, rather than assuming that the mean annual flows are normally distributed. The formulation of a range-standardised index for mean annual flow is given by

Am = 1− 2x Pile (Qu )− Pile (Q c )

where: Am = Range-standardised mean annual flow index

Qc = Average current annual flow (ML/year)

Qu = Average unimpacted annual flow (ML/year)

Pile(Qc) = Proportion of time that the average current annual flow is exceeded under unimpacted conditions

Pile(Qu) = Proportion of time that the average unimpacted annual flow is exceeded under unimpacted conditions

In order to make the index more ecologically significant, the above equation was applied to five flow values, ranging from 80% to 120% of the mean. The mean annual flow index is calculated as the average of the range-standardised indices for the five flow intervals:

N ∑ Au A = n=1 N

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where: A = Range-standardised mean annual flow index

An = Range-standardised mean annual flow index for a given flow interval N = Number of flow intervals

It should be noted that there is no loss of information when using monthly rather than daily data, and a volumetric index is easily evaluated on an annual or seasonal basis.

Figure C1 provides an example of the calculation of the variance corrected index for mean annual flow on the Goulburn River at Murchison. The figure shows the flow duration curve of the unimpacted and current annual flows. The unimpacted mean annual flow is 2,700,000 ML/year and marked on the flow duration curve indicating that it is exceeded in approximately 52% of years. Values plus and minus 20% of the unimpacted mean are also marked.

Similarly, the mean annual flow under current conditions, along with flows plus or minus 20% of the mean are also marked. Under current conditions, the mean annual flow is 1,153,805 ML/yr. The location on the unimpacted flow duration curve shows that the current mean annual flow is exceeded in approximately 96% of years under unimpacted conditions. The arrows on Figure 5-3 show the difference in time exceeded for the mean annual flow and values plus or minus 20% of the mean annual flow. These values were used to calculate a series of indices for the five flow intervals, which were then averaged to determine the overall mean annual flow index.

5000000

4500000

4000000

3500000

Difference = 64% 3000000 Difference =53%

L/yr) Difference =41% 2500000 Natural Mean Difference =33%

Flow (M Difference =24% 2000000

1500000

Current Mean 1000000

500000

0 0 102030405060708090100 % years flow exceeded

„ Figure C1 Example of the calculation of the variance corrected mean annual flow index for the Goulburn River at Murchison

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C.1.3 Discussion and conclusions Figure C2 shows the mean annual flow index calculated for six Victorian catchments. The plot shows that the Goulburn River below Murchison is most highly impacted and that Three Mile Creek in the Ovens catchment is closest to pristine.

1

0.9

0.8

s 0.7 re o c s

x 0.6 inde 0.5 l Flow

0.4 Annua n a

Me 0.3

0.2

0.1

0 Bass River Little Yarra River Woolen Creek Three Mile Creek Goulburn River Richardson River

„ Figure C2 Mean annual flow index score for a sample of six Victorian catchments.

It may also be appropriate to use the median annual flow rather than the mean annual flow. The benefit of using the median annual flow is that the median unimpacted flow will always be exceeded 50% of the time under unimpacted conditions and the variance corrected index would never fall outside the bounds of zero to one. It was found that the mean and median indices are well correlated and either could be adopted. The mean annual flow index was adopted because it provides a volumetric volume that is easier to understand.

C.2 Seasonal Amplitude Index C.2.1 Ecological Significance The seasonal amplitude index compares the difference in magnitude between the high and low flows within each year under current and unimpacted conditions. The index reflects changes to seasonal variability in in-stream hydraulics and depth of flooding. Water level changes are a key driver of community composition, structure and zonation patterns of aquatic and riparian vegetation. Due to the relatively predictable nature of seasonal variability, it is an important hydrological driver of aquatic ecosystems. Rises in water levels are known to provide important

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life-history cues for many plant and animal species. It should be noted, however, that these cues have a seasonal timing component which is not measured by the index (ie. a reversed flow regime can have the same score as a unimpacted flow regime). A reduction in amplitude may also be indicative of reduced longitudinal and lateral connectivity (water level) and reduced habitat complexity (hydraulics).

C.2.2 Description The seasonal amplitude index (SA) is a measure of the change in the difference between the maximum monthly flow and the minimum monthly flow. The concept of seasonal amplitude is illustrated in Figure C3.

45,000

40,000

35,000

30,000 Natural Amplitude 25,000

20,000

15,000 Average Flow (ML/month) 10,000 Current Amplitude 5,000

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Current Natural „ Figure C3 Average monthly flows under unimpacted and current flow regimes.

The range-standardised seasonal amplitude index adopts the same approach as that used for the mean annual flow index (see Section C1). The range-standardised index is calculated using the difference between the percentage of years that the unimpacted and current seasonal amplitudes are exceeded under unimpacted conditions:

SA = 1− 2x Pile (SAu )− Pile (SAc )

Where: SA = range-standardised seasonal amplitude index

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SAc = average current seasonal amplitude (ML/month)

SAu = average unimpacted seasonal amplitude (ML/month)

Pile(SAc) = Proportion of time that the average current seasonal amplitude is exceeded under unimpacted conditions

Pile(SAu) = Proportion of time that the average unimpacted seasonal amplitude is exceeded under unimpacted conditions

And the average seasonal amplitude is computed as the arithmetic average of the time series of the difference between minimum and maximum monthly flows in each calendar year.

C.2.3 Discussion and conclusions Figure C4 shows the seasonal amplitude index calculated for six Victorian catchments using both the original and modified variance corrected index. The plot shows that the Goulburn River below Murchison is most highly impacted and that Three Mile Creek is closest to pristine.

1

0.9

0.8

0.7

0.6

ude index scores 0.5

0.4

0.3 Seasonal Amplit

0.2

0.1

0 Bass River Little Yarra River Woolen Creek Three Mile Creek Goulburn River Richardson River

„ Figure C4 Seasonal amplitude index score for a sample of six Victorian catchments.

C.3 Seasonal Period Index C.3.1 Ecological Significance The seasonal period index (SP) is a measure of the shift in the maximum flow month and the minimum flow month between unimpacted and current conditions. Floods stimulate biological productivity in aquatic ecosystems, while low flows are a time of reduced biological productivity. The timing of periods of flooding and low flow has an important influence on how floodplain and riverine ecosystems respond. In temperate Australia plants and animals are generally adapted to the

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natural occurrence of floods in winter/spring and low flows in summer/autumn. Changes to these flow patterns, such has occurred though regulation, are thought to have caused significant changes in some communities.

C.3.2 Description The index compares the unimpacted and current frequency distribution of maximum and minimum monthly flows. The first step in calculating the index is to create frequency distributions that show the percentage of years that peak and minimum annual flows fall within each given month under current and unimpacted conditions. The index is then calculated by summing the minimum proportions (from unimpacted or current) within each month. In MDBC (2003) the index is presented in terms of the number of years the peak or minimum flow falls within each given month. In this report the percentage of years the peak or minimum flow falls within each given month has been used. The equation used is presented below.

1 ⎧ ⎫ SP _ fd = ⎨∑∑[]MIN()PHCi ; PHUi + []MIN()PLCi ; PLUi ⎬ 200 ⎩ ii==11⎭

Where: SP_fd = Comparison of frequency distribution seasonal period index th PHCi = The percentage of years the i month has the peak annual flow under current conditions (%). th PHUi = The percentage of years the i month has the peak annual flow under unimpacted conditions (%). th PLCi = The percentage of years the i month has the minimum annual flow under current conditions (%). th PLRi = The percentage of years the i month has the minimum annual flow under reference conditions (%).

The index is best understood by considering three examples. In the first example (Figure C5) the frequency distribution of minimum annual flows is exactly the same between current and unimpacted conditions. Hence when the minimum (between current and unimpacted) percentage of each month is summed the value equals one. In Figure C5 the minimum value is illustrated with small black triangles.

In the second example (Figure C6) the current frequency distribution of minimum annual flow has been shifted by one month so that the majority of minimum annual flows fall within April, not March. In this example the sum of minimum monthly percentages is equal to 0.5.

In the third example (Figure C7) the current frequency distribution of minimum annual flow is completely different under current conditions. The minimum annual flow never falls within the

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range of months that it did under unimpacted conditions. In this example the sum of minimum monthly percentages is equal to zero.

60%

50%

40%

30%

20%

10% % of years the minimum event falls within month 0% Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Natural Current Intercept „ Figure C5 Example of the comparison of frequency distribution seasonal period index when there is no difference between unimpacted and current conditions (ie SP_fd = 1).

60%

50%

40% falls within the month

30%

20%

10% % of years the minimum event 0% Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Natural Current Intercept „ Figure C6 Example of the comparison of frequency distribution seasonal period index when there is a difference between unimpacted and current conditions (ie SP_fd = 0.5).

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60%

50%

40% t falls within the month

30%

20%

10% % of years the minimum even 0% Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Natural Current Intercept „ Figure C7 Example of the comparison of frequency distribution seasonal period index when there is a complete difference between unimpacted and current conditions (ie SP_fd = 0).

C.3.3 Discussion and conclusions Figure C8 shows the seasonal period index calculated for six Victorian catchments. The plot shows that again the Goulburn River below Murchison is most highly impacted, but the Richardson River is closest to pristine.

1

0.9

0.8

0.7 s e

0.6 ndex scor

od i 0.5 i Per 0.4

Seasonal 0.3

0.2

0.1

0 Bass River Little Yarra River Woolen Creek Three Mile Creek Goulburn River Richardson River

„ Figure C8 Seasonal period index scores for a sample of six Victorian catchments.

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C.4 Low Flow Index C.4.1 Ecological Significance The low flow index is a measure of the change in low flow magnitude under current and unimpacted conditions. Low flow periods are a natural feature of Australian river systems but are generally regarded as a period of high stress for aquatic biota. Increasing the magnitude of low flows reduces the availability of in-stream habitat, which can lead to a long term reduction in the viability of populations of flora and fauna.

C.4.2 Description Review of previous studies (as discussed in Section 5.4) showed that low flow requirements often correspond to the daily 90% exceedance flow. The constraint of using a monthly time step is that only coarse intervals of exceedance flow can be used. For this reason, the monthly low flow index has been calculated based on the 91.7% exceedance flow (11 months out of 12) and the 83.3% exceedance flow (10 months out of 12). Two intervals were used to cover a range of low flows rather than basing the index on a single value.

The approach adopted to calculate the low flow index is similar to that used for the mean annual flow index (see Section C1). The index is calculated using the difference between the percentage of years that the unimpacted and current 91.7% exceedance flows (evaluated over the whole period of record) are exceeded by the annual 91.7 percentile flow (evaluated on a year-by-year basis) under unimpacted conditions. The same process is then followed for the 83.3% exceedance flows:

LF91.7 = 1− 2x Pile (Q91.7u )− Pile (Q91.7c )

Where: LF91.7 = Range-standardised low flow index based on the 91.7% exceedance flow

Q91. 7c = Current 91.7% exceedance flow (ML)

Q91.7u = Unimpacted 91.7% exceedance flow (ML) th Pile(Q91.7c) = Proportion of years that the current 91.7 percentile flow is exceeded by the annual 91.7th percentile unimpacted flow th Pile(Q91.7u) = Proportion of years that the unimpacted 91.7 percentile flow is exceeded by the annual 91.7th percentile unimpacted flow

The low flow index is calculated as the average of the variance corrected low flow index based on the 91.7% exceedance flow and the variance corrected low flow index based on the 83.3% exceedance flow: LF + LF LF = 91.7 83.3 2

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Where: LF = Range-standardised low flow index

LF91.7 = Range-standardised low flow index based on the 91.7% exceedance flow

LF83.3 = Range-standardised low flow index based on the 83.3% exceedance flow

C.4.3 Discussion and conclusions The range-standardised index was calculated on both a daily and a monthly time step for the 50 study sites in Victoria. Figure C9 shows a good correlation (R2 =0.84) between the daily and monthly low flow indices demonstrating that the monthly variance corrected low flow index is a reasonably unbiased estimate of the corresponding daily flow characteristic. In Figure C9 the size of each ‘bubble’ represents the size of the catchment for which the index has been calculated. It is seen that the larger catchments (as indicated by the larger ‘bubbles’) are associated with lower indices; this weak correlation between catchment size and low-flow stress merely reflects the fact that larger catchments are more likely to be regulated by large impoundments than smaller catchments.

Figure C10 shows the low flow index calculated for six Victorian catchments. The plot shows that again the Goulburn River below Murchison is most highly impacted, and the Richardson River is closest to pristine.

The variance corrected low flow index compares flows currently exceeded 91.7% and 83.3% of the time with the range of flows experienced under unimpacted conditions. Figure C11 to Figure C13 illustrate examples of the time series of flows exceeded 91.7% of the time in each year with that calculated for the entire length of record. The example only includes the 91.7% exceedance flow for simplicity, however the example would be similar if discussing the flows exceeded 83.3% of the time. As discussed below, these examples highlight the need for range-normalisation.

Both the Don and Coliban Rivers have a similar range of variation in annual low flows but while the current 91.7% exceedance flow lies well within the range of unimpacted flows in Don River, current low flows in the Coliban River lie at the lower end of the unimpacted range. This difference is reflected in the low flow index score as the former score is 0.85 and the latter is 0.21.

It should be noted that if an index was used based on a simple ratio of Q90 values then the Don and Coliban indices would be the same – it is only when range-normalistion is considered that the differences compared to unimpacted variability are evident.

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1

0.8 x e d

n 0.6 I ow l F Low

y hl t

n 0.4 o M

0.2

0

0 0.2 0.4 0.6 0.8 1 Daily Low Flow Index „ Figure C9 Comparison of daily and monthly low flow indices where the size of the bubbles reflect the relative sizes of the catchments.

1

0.9

0.8

0.7 s re

o 0.6 c s x

0.5 inde

Flow 0.4 Low

0.3

0.2

0.1

0 Bass River Little Yarra River Woolen Creek Three Mile Creek Goulburn River Richardson River

„ Figure C10 Low flow index scores for a sample of six Victorian catchments.

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Olinda Creek has a much larger absolute variation in flows. Interestingly in this case low flows under current flow conditions are higher than under unimpacted conditions, though are within the unimpacted range of variability. The low flow index score for this site is 0.21; it is noteworthy that the distribution of low flows is not symmetric, and thus while the index scores for Olinda Creek and Coliban River are the same, the degree to which current conditions lie within the unimpacted range is slightly different.

400 Q91.7 time series Natural Monthly 97.1% exceedence flow 350 Current Monthly 97.1 exceedence flow

300

250

L/month) 200 M ( w o l F 150

100

50

0

1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 „ Figure C11: Time series of Q91.7 flows for Don River and unimpacted and current 97.1% exceedance flows (97.1% Index = 0.85).

350 Q91.7 time series Natural Monthly 97.1% exceedence flow Current Monthly 97.1 exceedence flow 300

250

nth) 200 o L/m M (

ow 150 l F

100

50

0 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 „ Figure C12Time series of Q91.7 flows for Coliban River and unimpacted and current 97.1% exceedance flows (91.7% Index = 0.21).

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1200

1000

800 onth

L/m 600 ow (M l F

400

200 Q91.7 time series Natural Monthly 97.1% exceedence flow Current Monthly 97.1 exceedence flow 0

65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 „ Figure C13 Time series of Q91.7 flows for Olinda Creek and unimpacted and current 97.1% exceedance flows (91.7% Index = 0.21).

C.5 High Flow Index C.5.1 Ecological Significance Flood flows determine the maximum depths, velocities and shear stresses that occur in a river system. High flows drive geomorphic process in rivers through transporting and depositing sediment and altering channel form. High flows act as a natural disturbance in river systems, removing vegetation and organic matter and resetting successional processes. A reduction in the magnitude of flood flows is likely to correspond with a reduction in overbank flows, important in providing connectivity between rivers and their floodplains.

C.5.2 Description The high flow index is a measure of the change in high flow magnitude from unimpacted to current conditions. The approach adopted to calculate the high flow index is similar to that used to calculate the low flow index (see Section C4). The monthly high flow index is calculated based on the 8.3% and 16.7% exceedance flows. Two intervals were used to cover a range of high flows rather than basing the index on a single value.

The index is calculated using the difference between the percentage of years that the unimpacted and current 8.3% exceedance flows (evaluated over the whole period of record) are exceeded by the annual 8.3 percentile flow (evaluated on a year-by-year basis) under unimpacted conditions. The same process is then followed for the 16.7% exceedance flows:

HF8.3 = 1− 2x Pile (Q8.3u )− Pile (Q8.3c )

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Where: HF8.3 = Range-standardised low flow index based on the 8.3% exceedance flow

Q8. 3c = Current 8.3% exceedance flow (ML)

Q8.3n = Unimpacted 8.3% exceedance flow (ML) rd Pile(Q8.3c) = Proportion of years that the current 8.3 percentile flow is exceeded by the annual 8.3rd percentile unimpacted flow rd Pile(Q8.3u) = Proportion of years that the unimpacted 8.3 percentile flow is exceeded by the annual 8.3rd percentile unimpacted flow

The high flow index is calculated as the average of the variance corrected high flow index based on the 8.3% exceedance flow and the variance corrected high flow index based on the 16.7% exceedance flow:

HF + HF HF = 8.3 16.7 2

Where: HF = Range-standardised high flow index

HF8.3 = Range-standardised high flow index based on the 8.3% exceedance flow

HF16.7 = Range-standardised high flow index based on the 16.7% exceedance flow

C.5.3 Discussion and conclusions The range-standardised high flow index was calculated on both a daily and a monthly time step for the 50 study sites in Victoria. Figure C14 shows a good correlation (R2 =0.84) between the daily and monthly high flow indices, where (similar the low flow index) it is seen that lower indices tend to be associated with the larger catchments. There is some indication of systematic over-estimation of the index for the larger and most heavily stressed catchments (the “outliers” in the lower left of this figure are associated with sites on the Goulburn and Loddon Rivers below major impoundments). Overall it is considered that the monthly variance corrected low flow index is a reasonable indication of the index calculated using daily flow data.

Figure C14 also shows that the lowest monthly high flow index is around 0.4, which is appreciably higher than the lowest low flow index of around zero (Figures C9). This comparison reflects the situation that higher flows are less likely to be impacted by diversions than the lower flow conditions. Large flow events usually occur outside the irrigation season and it is likely that for many of the rivers, infrastructure restrictions would stop the capture of the largest events.

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Figure C16 shows the high flow index calculated for six Victorian catchments. The plot shows the Goulburn River at Murchison is most highly impacted, and Three Mile Creek, Bass River and Little Yarra River are all close to pristine.

1

0.8 x nde

I 0.6

ow l F h g i H

hly 0.4 ont M

0.2

0

0 0.2 0.4 0.6 0.8 1 Daily High Flow Index „ Figure C14 Comparison of daily and monthly high flow indices (where the size of the bubbles reflect the relative sizes of the catchments).

1

0.9

0.8

0.7

0.6

0.5 index scores

0.4 High Flow

0.3

0.2

0.1

0 Bass River Little Yarra River Woolen Creek Three Mile Creek Goulburn River Richardson River „ Figure C15 High flow index scores for a sample of six Victorian catchments.

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Some consideration was given to determining how representative the adopted high flow index may be to flow conditions associated with stream sediment load. Experience has indicated (Michael Stewardson, pers. comm.) that the mean daily flow raised to the power of 1.4 is a threshold above which appreciable sediment transport occurs, and accordingly the flow percentile corresponding to this flow magnitude was determined for all 50 test sites. It was found that for the majority of catchments the corresponding flow percentile was less than 10% (Figure C16), and thus it is concluded that this characteristic is captured by the high flow index.

15

14

13

12

11

10 es l p 9

8 50 sam n 7 cy i n e

u 6 eq r

F 5

4

3

2

1

0 0 2 4 6 8 101214161820 Probability of exceedance for mean (daily flow ^1.4)

„ Figure C16 Histogram showing the probability of exceedance for the mean of the daily flow to the power 1.4.

C.6 High Flow Spells Index C.6.1 Ecological Significance The high flow index previously discussed (Section C.5) is based solely on flow magnitude but does not consider the behaviour of high flows, that is, on the characteristics of high flow periods. For example under unimpacted conditions a catchment may exhibit a flashy response to rainfall in which high flows tend to be frequent but of short duration, but under current conditions the high flow spells may occur less frequently but be of longer duration. The duration and frequency of high flow spell behaviour has important implications for geomorphological processes including sediment transport and channel form, which in turn strongly influence aquatic ecosystems. If high flows create overbank floods then they can provide important connectivity between rivers and their

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floodplain, allowing movement of nutrients, organic matter and biota between the two. High flows also act as a unimpacted disturbance and enhance biodiversity through resetting successional processes, and the duration of high flow spells is linked to the potential for the inundation of wetlands and other riparian zones.

C.6.2 Description of Daily Index The high flow spell index characterise the frequency and duration of high flow spells occurring within the period of record under both current and unimpacted conditions.

High flow spells were considered for two thresholds corresponding to flows exceeded 8.3% and 16.7% of the time (these percentiles correspond to the rank of the highest two months in a calendar year). The duration of the spell events for each unimpacted threshold were determined for both current and unimpacted flow series, and a partial series analysis was undertaken to derive a relationship between spell duration and average recurrence interval (ARI). Figure C17 shows the relationship between spell event duration and ARI for events above the 8.3% exceedance flow for Goulburn River at Murchison. Similar relationships were determined for events above the 16.7% exceedance flow.

For each threshold, the relationship between ARI and duration for unimpacted conditions was used to determine the event duration that corresponded to the 1, 2 and 5 year ARI events under unimpacted conditions. In the case of Goulburn River at Murchison for spells above the 8.3% exceedance threshold, the duration of events corresponding to 1, 2 and 5 year ARI are 13, 22 and 30 days respectively. These event durations were then used to determine, based on the relationship between ARI and duration for current conditions, the ARI that corresponds to each duration under current conditions. For Goulburn River at Murchison, under current conditions an event of 13 days duration has a 4.8 year ARI, an event of 22 days duration has a 9.8 year ARI and an event of 30 days a 21 year ARI. These shifts in the frequency of spell events is illustrated in Figure 6-16.

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100

tion) 10 Dura

5 year ARI 21 year ARI Natural Duration Current Duration 2 year ARI 9.8 year ARI Poly. (Current Duration) Linear (Natural Duration) 1 year ARI 4.8 year ARI

1 0.0000 0.5000 1.0000 1.5000 2.0000 2.5000 3.0000 3.5000 4.0000 ln(ARI) „ Figure C17 Change in frequency of selected daily spell durations above a threshold flow exceeded 8.3% of the time (21,573 ML/d) under unimpacted and current conditions for the Goulburn River at Murchison.

The ratio of the unimpacted ARI and the current ARI was calculated for each threshold for the unimpacted 1, 2 and 5 year events and these values averaged to determine the daily high flow spells index (HFS). Table C1 shows the calculation for the Goulburn River at Murchison. The reduction in ARI from unimpacted to current conditions shows that long duration, high flow events are occurring less frequently for the Goulburn River at Murchison. This is reflected in low index value of 0.21.

„ Table C1 Determining the high flow spells index for the Goulburn River at Murchison

Unimpacted Unimpacted Current Ratio Threshold Current ARI ARI Duration (d) Duration (d) ARIn/ARIc 16.6 percentile 1 22 22 3.34 0.30 unimpacted 2 41 41 9.30 0.22 flow (12,701 ML/d) 5 66 66 64.76 0.08 8.3 percentile 1 11 11 4.75 0.21 unimpacted 2 19 19 9.83 0.20 flow (21,573 ML/d) 5 30 30 21.60 0.23 Average Ratio 0.21

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C.6.3 Description of Monthly Index The approach to determining a monthly high flow spells index is similar to that used in determining the daily index. However in the case of the monthly index, it was found that adoption of a single high flow threshold corresponding to the flow exceeded 8.3% of the time provided the best correlation with the indices derived using daily data.

Figure C18 shows the relationship between spell duration and ARI based on monthly data for events above the 8.3% exceedance flow for Goulburn River at Murchison. The figure shows that it is more difficult to determine a relationship between ARI and event duration based on monthly data as the event durations are more staggered and have a smaller range. As a consequence, an iterative approach was used in the fitting of the partial series in which an increasingly linear model was fitted to the data to ensure that the quantile estimates increased monotonically with ARI. Initially the partial series is fitted with a 3rd order polynomial, but if this yielded lower spell quantiles at higher average recurrence intervals then a 2nd order polynomial was fitted; if this also yielded inconsistent quantiles then a linear function was adopted.

10 on 1 Durati

Natural Duration

Current Duration

Linear (Natural Duration)

Linear (Current Duration)

0.1 0.000 1.000 2.000 3.000 4.000 5.000 6.000 ln(ARI)

„ Figure C18 Change in frequency of selected monthly spell durations above a threshold flow exceeded 8.3% of the time (307,198 ML/m) under unimpacted and current conditions for the Goulburn River at Murchison.

Similarly to the daily index method, the ratio of the unimpacted ARI and the current ARI was calculated for the 1, 2 and 5 year events and these values are averaged to determine the monthly high flow spells index. Table C2 shows the calculation for the Goulburn River at Murchison.

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„ Table C2 Determining the high flow spells index for the Goulburn River at Murchison

Unimpacted Unimpacted Current Ratio Threshold Current ARI ARI Duration (m) Duration (m) ARIn/ARIc 8.3 percentile 1 2 2 8.33 0.12 unimpacted flow 2 3 3 60.25 0.03 (307,198 ML/m) 5 4 4 221.93 0.02 Average Ratio 0.17

C.6.4 Discussion and conclusions The high flow spells index was calculated on both a daily and a monthly time step for the 50 study sites in Victoria. Figure C19 shows the correlation between the daily and monthly high flow indices. The correlation between the daily and monthly indices is reasonable: if the results for four sites (differentiated in red) are excluded the correlation between daily and monthly indices is quite high (R2=0.84) and unbiased. The four “outliers” are two sites on Goodmans Creek (in the Werribee basin) and two sites on the Goulburn (Gin Gin and Northwood); while these are highly impacted sites, there are others (such as the Goulburn River at Murchison) which are adequately represented by the monthly index.

Figure C20 shows the high flow spells index calculated for six Victorian catchments. The plot shows that the Goulburn River at Murchison is most highly impacted, while Bass River and Three Mile Creek are close to pristine.

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1

0.8 x e d n I s 0.6 ell p S w o l F

h

Hig 0.4 hly nt o M

0.2

0

0 0.2 0.4 0.6 0.8 1 Daily High Flow Spells Index „ Figure C19 Comparison of daily and monthly high flow spells indices (where the size of the bubbles reflects the relative sizes of the catchments).

1

0.9

0.8

s 0.7 re o c s

x 0.6 inde

lls 0.5 e p S 0.4

High Flow 0.3

0.2

0.1

0 Bass River Little Yarra River Woolen Creek Three Mile Creek Goulburn River Richardson River

„ Figure C20 High flow spells index scores for a sample of six Victorian catchments.

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C.7 Low Flow Spells Index C.7.1 Ecological Significance The low flow index previously discussed (Section C4) is based solely on flow magnitude and does not consider the behaviour of the stream during periods of low flow. Knowledge of changes in the magnitude of low flows alone does not reveal whether or not low flow periods tend to be intermittent or sustained, that is once low flow conditions occur whether the stream sluggishly remains at low flows for a long period of time or else whether it fluctuates more rapidly in response to earlier recharge events. The behaviour of the stream at low flows can be characterised by an analysis of the frequency and duration of low flow spells. Such information provides a direct indication of the availability of aquatic habitat during low flow periods, which can impact on the ability of river systems to sustain plant and animal populations. Extending the duration of low flows may also result in poor water quality, with higher than natural water temperatures and decreased dissolved oxygen. The impact of changes to the frequency of low flow periods will depend on their length and the length of intervening periods of higher flow. The environmental effects of low flows tend to increase with duration, where high flows can ameliorate the deterioration in water quality that can occur over low flow periods.

C.7.2 Description The low flow spell index characterises the frequency and length of low flow spells occurring within the period of record under both current and unimpacted conditions.

Low flow spells were considered for two thresholds corresponding to flows exceeded 83.3% and 91.7% of the time (these percentiles correspond to the rank of the lowest two months in a calendar year). The duration of the spell events for each unimpacted threshold were determined for both current and unimpacted flow series, and in the same manner as adopted for high flow spells (refer to Section C6) a partial series analysis was undertaken to derive a relationship between spell duration and average recurrence interval (ARI). The same two flow thresholds were adopted for the analysis of both daily and monthly data.

For each threshold, the relationship between ARI and duration for unimpacted conditions was used to determine the event duration that corresponded to the 1, 2 and 5 year ARI under unimpacted conditions. These event durations were then used to determine, based on the ARI and duration for current conditions, the ARI that corresponds to each duration under current conditions. The ratio of the unimpacted ARI and the current ARI was calculated for each threshold for the unimpacted 1, 2 and 5 year events and these values averaged to determine the daily low flow spells index (LFS).

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C.7.3 Discussion and conclusions The low flow spells index was calculated on both a daily and a monthly time step for the 50 study sites in Victoria. Figure C21 shows the correlation between the daily and monthly low flow indices which demonstrates that the monthly low flow spells index provides a reasonably unbiased estimate of spells behaviour can be used to represent daily flow characteristics. Whilst the correlation between the two sets of indices are reasonably unbiased, the degree of correlation is not as strong as with the other indices.

There are a small number of sites where the monthly index is zero. In these cases, either there were no spell events under current conditions, or there was little or no variation in the duration of spell events so that no relationship could be found between the duration of spell events and ARI. Figure C21 also indicates that the sites with a monthly index of zero vary in catchment size.

1

0.8 ndex I

ls 0.6 el p S w o l w F o L y

l 0.4 h nt o M

0.2

0

0 0.2 0.4 0.6 0.8 1 Daily Low Flow Spells Index „ Figure C21 Comparison of daily and monthly low flow spells indices (where the size of the bubbles reflects the relative sizes of the catchments).

Figure C22 shows the low flow spells index calculated for six Victorian catchments. The plot shows that Woollen Creek upstream of Lal Lal is most highly impacted, and the Richardson River is closest to pristine.

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1

0.9

0.8

0.7 s re o c

s 0.6 x inde

lls 0.5 e p S 0.4 Flow

Low 0.3

0.2

0.1

0 Bass River Little Yarra River Woolen Creek Three Mile Creek Goulburn River Richardson River

„ Figure C22 Low flow spells index scores for a sample of six Victorian catchments.

C.8 Proportion of Zero Flow Index C.8.1 Ecological Significance Periods of zero flow are a natural feature of ephemeral rivers and creeks, however increases in the natural duration of cease to flow periods are regarded as harmful to aquatic ecosystems. In many ways they can be regarded as extreme low flow periods when habitat availability is restricted and water quality prone to deterioration. Extended cease to flow periods can result in partial or complete drying of channel. This can lead to loss of connectivity between pools and even complete loss of aquatic habitat. Under natural conditions aquatic biota are able to recolonise dried sections of creek channels once flow returns.

C.8.2 Description The proportion of zero flow index compares the proportion of zero flow occurring under unimpacted and current conditions. The value of the index varies from zero to one, and similar to other indices, the direction of change is not evident form the value of the index. If the number of cease to flow spells is unchanged between unimpacted and current conditions, then the value of the index is 1.

PZD =1− 2×[]max(PZu, PZc )− min(PZu , PZc )

Where: PZ = Proportion of zero flow index

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PZu = Proportion of zero flow over the whole record under unimpacted conditions

PZc = Proportion of zero flow over the whole record under unimpacted conditions

This index can be determined from either a daily or monthly streamflow record. This index is conceptually identical to that adopted in the SRA, but here zero flow is defined as the non-zero flow exceeded 99.5% of the time. This definition is a scale-independent means of defining zero flows that is less sensitive to the accuracy of gauging at low flows. The adopted index also differs from that adopted in the SRA as it is based on double the difference between unimpacted and current conditions. This factoring was introduced simply to ensure that the most impacted sites in Victoria (here represented by the downstream reaches of the Goulburn and Loddon Rivers) have suitably low scores.

To further illustrate the basis of this index, consider the unimpacted and current flow duration curves shown below. The unimpacted curve approaches zero at approximately 85%, indicating a zero flow proportion of 15% under unimpacted conditions. Similarly for the current curve, the zero flow proportion is 25%. Applying the above equation yields:

PZ = 1 – 2 x (0.25 - 0.15) = 0.8

10

Unimpacted

Current ow Fl

1 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Percent of time flow exceeded „ Figure C23 Example calculation of proportion of zero flow index

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C.8.3 Discussion and conclusions Analysis of the correlation between indices calculated on a daily and monthly basis is shown for the 50 test sites in Figure C24. It is seen that the correlation between the monthly and daily indices is reasonable (R2=0.73) and unbiased, and it is considered that the monthly indices are an adequate indicator of daily behaviour. Figure C24 also shows that the catchments with the lowest index score include some of the larger catchments, though clearly there are some large catchments that do not experience zero flow conditions.

Figure C25 shows the zero flow index calculated for six Victorian catchments. The plot shows that the Goulburn River at Murchison is most highly impacted, while Richardson River is the closest to pristine.

1

0.8 x nde I w o l 0.6 o F r Ze f o n o ti r o op

r 0.4 P hly ont M

0.2

0

0 0.2 0.4 0.6 0.8 1 Daily Proportion of Zero Flow Index „ Figure C24 Correlation between zero flow indices calculated using daily and monthly data, where the size of the bubbles reflects the relative sizes of the catchments).

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1

0.9

0.8

0.7 s re

o 0.6 c s x

0.5

0.4 ro Flow inde Ze

0.3

0.2

0.1

0 Bass River Little Yarra River Woolen Creek Three Mile Creek Goulburn River Richardson River

„ Figure C25 Zero flow indices calculated for six test catchments.

C.9 Flow Duration Index C.9.1 Ecological Significance The flow duration index is reflects overall changes to the flow regime. It does not identify any particular component of the flow regime and it is therefore difficult to identify any specific ecological effects. Broadly speaking a reduction in flows (shift to the left of the flow duration curve) may be indicative of reduced frequency and duration of high flows and increased frequency and duration of low flows and zero flows. All of these changes would be detrimental to aquatic, riparian and floodplain ecosystems.

C.9.2 Description The flow duration index compares changes in the shape of the non-zero part of the flow duration curve under unimpacted and current conditions. The value of the index varies from zero to one, and similar to other indices, the direction of change is not evident form the value of the index. If the flow duration curve is unchanged between unimpacted and current conditions, then the value of the index is 1.

⎛ min{}P (Q ) , P (Q ) ⎞ FD = mean⎜ ile u ile c ⎟ ⎜ ⎟ ⎝ max{}(Pile (Qu ) , Pile (Qc ) ⎠

Where: FD = Flow duration index

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Qn = flow under unimpacted conditions (at equal log intervals)

Pile(Qu) = the proportion of time that the flow Qn is exceeded under unimpacted conditions

Qc = the flow under current conditions that has an exceedance percentile

equal to Pile(Qu)

Pile(Qc) = the proportion of time that the flow Qc is exceeded under unimpacted conditions

This index can be determined from either a daily or monthly streamflow record. The basis of the index is very similar to that used to calculate the mean annual flow index (refer to Section C1).

The index is calculated using equal log intervals of unimpacted flow. To calculate the equal log intervals, a representative range of flows is determined. This is done by assuming the minimum representative flow is at least 1 ML, and the maximum representative flow is 80% of the peak recorded flow. This ensures that unusual flow events at either end of the curve do not affect the results. The resulting flow range is then broken into 10 equal logarithmic intervals.

To illustrate this, consider the unimpacted and current flow duration curves shown below. For each of the 10 flow intervals, the exceedance percentile is determined. The equivalent flow, for the same exceedance percentile, is determined under current conditions. The proportion of time that the current flow is exceeded under unimpacted conditions is then calculated. Applying the above equation, the ratio of each pair of unimpacted exceedance percentiles (based on the unimpacted and current flows) are then calculated, making sure that the ratio is always less than or equal to 1. This procedure gives a set of 10 ratios, all of them being less than or equal to 1. These values are then averaged to determine the final flow duration curve index.

C.9.3 Discussion and conclusions Figure C27 compares the daily and monthly indices for all 50 test sites. This comparison shows that there is strong correlation between daily and monthly results, indicating that the calculating the monthly index will provide a reasonable estimate of the daily index. It is also clear that the larger catchments are associated with lower scores.

Figure C28 compares values of the index for six selected catchments. The figure shows that there is significant change in the shape of the flow duration curve for several catchments where there are high levels of water demand, namely the Goulburn River at Murchison and Woollen Creek.

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10

Unimpacted

Current w o Fl

1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Percent of time flow exceeded a) Flow range is broken into 10 equal logarithmic intervals. nth) o L/m M ( ow Fl

Prob (Qu )Prob (Qc )

Probability of exceedance b) For each interval, calculate the probability of unimpacted and current flow being exceeded under unimpacted conditions.

„ Figure C26: Example calculation of flow duration index

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1

0.8 dex n I e v r

u 0.6 C n o i at r u D

w o l 0.4 F hly nt o M

0.2

0

0 0.2 0.4 0.6 0.8 1 Daily Flow Duration Curve Index „ Figure C27 Comparison of daily and monthly flow duration curve index, where the size of the bubbles reflects the relative sizes of the catchments.

1

0.9

0.8 s

e 0.7 r

ex sco 0.6 d n ve i r 0.5 cu n o

ati 0.4 r u d w o l 0.3 F

0.2

0.1

0 Bass River Little Yarra River Woolen Creek Three Mile Creek Goulburn River Richardson River

„ Figure C28 Flow duration curve indices calculated for six test catchments.

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C.10 Variation Index C.10.1 Ecological Significance This index is similar to the seasonal amplitude index in that it reflects variability over a year. The key difference is that the variation index measures variability across all months rather than simply the difference between minimum and maximum monthly flows. Water level changes are a key driver of community composition, structure and zonation patterns of aquatic and riparian vegetation. Seasonal variation in flow is relatively predictable and acts as an important hydrological driver of aquatic ecosystems. Rises in water levels are known to provide important life-history cues for many plant and animal species. It should be noted, however, that these cues have a seasonal timing component which is not measured by the index (ie. a reversed flow regime can have the same score as a unimpacted flow regime). A reduction in annual variation may also be indicative of reduced longitudinal and lateral connectivity (water level) and reduced habitat complexity (hydraulics).

C.10.2 Description The variation index compares the coefficient of variation of monthly flows between current and unimpacted conditions. This index is the same as that used in the SRA. The index is calculated as the ratio of the monthly flows under unimpacted and current conditions, where the coefficient of variation is defined as the standard deviation divided by the mean.

CV CV = u CVc

Where: CV = Index of monthly variability

CVc = Current monthly coefficient of variation

CVu = Unimpacted monthly coefficient of variation

C.10.3 Discussion and conclusions Figure C29 shows the low flow spells index calculated for six Victorian catchments. The plot shows that the six catchments have relatively high variation index scores, suggesting that they are in good condition. Woollen Creek and Goulburn River and Murchison are the most impacted.

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1

0.9

0.8

0.7 s e r 0.6 ex sco d

n 0.5 i n o ati i 0.4 Var

0.3

0.2

0.1

0 Bass River Little Yarra River Woolen Creek Three Mile Creek Goulburn River Richardson River

„ Figure C29 Variation index for six catchments in Victoria.

C.11 Summary of Results

An overall summary of the spread of results obtained for the 50 test sites is illustrated in Figure C30. Results are provided for indices calculated using both daily and monthly data, some indices (namely the mean annual flow index, the monthly variability index, and the two seasonal indices) are defined using annual and monthly data, and hence are shown on both plots for completeness.

It is seen that in both sets of results the scores span most of the range between 0 and 1, which is appropriate given that the test data sets are considered to be representative of the full range of hydrological stress found across Victoria. All of the indices developed can also be calculated on a seasonal basis, and this is further discussed in Section 5.10.

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1.0

0.9

0.8

0.7

0.6

0.5

Overall Score 0.4

max 0.3 25%

0.2 Median

75% 0.1 min

0.0 A AV ac CCVVm SSPPm SASA mc QLF9d0 d c Q1 HF0dd c SLPLFSAdd SPHHFSAdd PZdD d FFDDdd Index

1.0

0.9

0.8

0.7

0.6

0.5

Overall Score 0.4

max 0.3 25%

0.2 Median

75% 0.1 min

0.0 AVA ac CCVVm S SPPm SAmc Q9LF 0 m c Q H1F0 mc S LFPLSA m S HPHFSAm PZPZD m FFDD m Index

„ Figure C30 Overall distribution of indices based on daily data (top panel) and monthly data (lower panel); note that the first four indices are common to both sets of results.

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Appendix D Selection of Final Indices

D.1 Introduction

This Appendix provides details of the nature of the correlation between the initial list of individual indices selected (Section D.2), and the manner in which the final set of monthly indices was selected (Section D.3). The efficacy of the monthly indices to capture the same degree of hydrologic stress as their daily counterpart is described in Section D.4, and the minimum length of record required to compute the indices reliably is presented in Section D.5.

D.2 Correlation Between Indices

Some measures are quite independent of each other, and others (as expected) are quite correlated. Figure D-1 and Figure D-2 provide an indication of the degree of redundancy in the indices when computed using daily and monthly data. In these plots each panel is a scatter plot of the values of two indices for the 50 test sites. The line surrounding the points provides an indication of the degree of correlation between the two indices. For example, in Figure D-2 the scatter plot between the mean annual flow index (A) and the high flow index (HF) shows a high degree of correlation and so the line surrounding the points is narrow and falls along the 1:1 line. However, the correlation between the mean annual flow index (A) and the low flow index (HF) shows a low degree of correlation and the line surrounding the data points is wide.

Both the daily and monthly versions of the mean annual flow index (A) and the high flow index (HF) are correlated. At most sites the mean annual flow is influenced by large flow events and therefore is exceeded in less than 50% of years. On average, the mean annual flow is only exceeded in 22% of years for the 50 test catchments. The mean annual flow index (A) is based on the mean annual flow and the flows that are ±20% of the mean. Therefore the flow magnitudes measured in the mean annual flow index overlap with the flow magnitudes measured by HF, which includes the flow exceeded 16.6% of the time.

The mean annual flow index (A) and the high flow index (HF) are also highly correlated with the high flow spells index (HFS) and the seasonal amplitude index (SA). The high flow spells index is an indication of the changes in the duration of high flow events and hence its correlation with the high flow index (and with the mean annual flow index for reasons stated above) is obvious.

The seasonal amplitude index (SA) signifies changes in the difference between the high flows and the low flows. It follows that if the high flows have been significantly reduced, the seasonal amplitude will also be reduced, and the associated indices will be highly correlated.

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MV V C

a

_r

LF F L

a _r

HF F H

e r

_ S S LF F L

e r

_ S S F HF H

PZ Z P

FD D F

d

f

SP_ P S

a

r

SA_ A S

AV_ra MV LF_ra HF_ra LFS_re HFS_re PZ FD SP_fd SA_ra A CV LF HF LFS HFS PZ FD SP SA

„ Figure D-1 Cross-correlation between selected indices based largely on daily data. a r A_ A

MV V C

a r

_ F LF L

a r

_ HF HF

e

r _ S S F LF L

e r S S_ F HF H

Z PZ P

FD FD

d

f

SP_ P S

a

r

A SA_ S

A_ra MV LF_ra HF_ra LFS_re HFS_re PZ FD SP_fd SA_ra A CV LF HF LFS HFS PZ FD SP SA

„ Figure D-2 Cross-correlation between selected indices based largely on monthly data.

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Not surprisingly the flow duration curve index (FD) is reasonably correlated with all of the indices that relate to a flow magnitude (eg. mean annual flow index, high flow index etc). The flow duration curve index is most highly correlated with the monthly variation index (CV). The slope of the flow duration curve is determined by the variability of the flow regime. Hence if the variability of the stream is altered it will be shown in both the flow duration index (FD) and the monthly variation index (CV).

D.3 Selection of Monthly Indices

Given the high correlation between indices not all of the monthly indices were selected to measure the degree of change in the flow regime between historic and ‘natural’. Each daily index was assessed against all monthly indices to determine which was the most highly correlated (see Table D-1). The selection of monthly indices was based on these correlations.

The daily high and low flow indices were most highly correlated with their monthly counterparts. The monthly high and low flow indices were also the most highly correlated with the corresponding daily high and low flow spells indices. Interestingly, Figure D-3 shows that the daily low flow spell indices can be better predicted using the low flow indices than by their corresponding monthly spell equivalent; this is also the case for the high flow index. Hence the monthly high and low flow indices were included in the final selection and the monthly spell indices were not.

All of the other daily indices were most highly correlated with their monthly index. However, other daily indices could be reasonably estimated based on a combination of other monthly indices.

1 1 y = 1.369x - 0.4168 y = 0.7676x + 0.2124 2 (a) R2 = 0.7539 (b) R = 0.7716 0.9 0.9

0.8 0.8

0.7 0.7

ells Index 0.6 0.6 ells Index p p

0.5 0.5

0.4 0.4 gh Flow S i ily Low Flow S ily H 0.3 0.3 a a D D 0.2 0.2

0.1 0.1

0 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.10.20.30.40.50.60.70.80.9 1 Monthly High Flow Index Monthly Low Flow Index „ Figure D-3 Correlation between (a) the daily high flow spells index and the monthly high flow index and (b) the daily low flow spells index and the monthly low flow index.

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2 „ Table D-1 Proportion of variance explained (R ) between daily and monthly indices

Daily Monthly Indices Indices A MV LF HF LFS HFS PZ FD SP SA A 1.00 0.14 0.02 0.72 0.01 0.59 0.00 0.44 0.00 0.56 CV 0.14 1.00 0.34 0.31 0.24 0.22 0.00 0.66 0.30 0.38 LF 0.01 0.27 0.84 0.04 0.29 0.02 0.03 0.38 0.33 0.07 HF 0.64 0.37 0.06 0.85 0.07 0.67 0.02 0.49 0.13 0.61 LFS 0.03 0.34 0.77 0.14 0.65 0.07 0.01 0.52 0.46 0.14 HFS 0.49 0.32 0.01 0.75 0.05 0.61 0.02 0.31 0.15 0.66 PZ 0.00 0.00 0.02 0.01 0.04 0.02 0.73 0.01 0.10 0.00 FD 0.40 0.57 0.59 0.39 0.25 0.30 0.00 0.96 0.20 0.38 SP 0.00 0.30 0.35 0.17 0.42 0.22 0.00 0.28 1.00 0.17 SA 0.56 0.38 0.09 0.77 0.17 0.48 0.01 0.49 0.17 1.00 Note: shaded cells denote the monthly index that is most highly correlated with the given daily index.

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The mean annual flow index was found to be highly correlated with a number of other indices. A reasonable estimate the mean annual flow index can be obtained from a prediction equation with the monthly high and low flow indices (LF and HF) and the seasonal periodicity index (SP). The prediction equation has a R² of 0.83 and a standard error of 0.09. A plot of the calculated and estimated index is provided in Figure D-4(a).

The daily flow duration index is highly correlated with all indices that are related to flow percentiles and the monthly variation index. A reasonable estimate the daily flow duration index can be obtained from a prediction equation with the monthly high and low flow indices (LF and HF), the monthly variation index (CV) and the seasonal periodicity index (SP). The prediction equation has a R² of 0.85 and a standard error of 0.06. A plot of the calculated and estimated index is provided in Figure D-4(b).

Similarly the seasonal amplitude index could be predicted equation using the monthly variation index (CV), the monthly high flow index (HF) and the monthly proportion of zero flow index (PZ). The prediction equation has a R² of 0.82 and a standard error of 0.10. A plot of the calculated and estimated index is provided in Figure D-4(c).

(a) 1 (b) 1 0.9 0.9

0.8 0.8

Indes 0.7 0.7 ration Index

0.6 u 0.6 D nnual Flow 0.5 ow 0.5 ily Fl

0.4 a 0.4

0.3 0.3

0.2 0.2 Estimated Mean A Estimated D

0.1 0.1

0 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Calculated Mean Annual Flow Index Calculated Daily Flow Duration Index (c) 1 0.9 x 0.8

0.7

plitude Inde 0.6 m

0.5

0.4

0.3

0.2 Estimated Seasonal A

0.1

0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Calculated Seasonal Amplitude Index „ Figure 9-7 Calculated versus estimated (a) mean annual flow index, (b) daily flow duration index and (c) seasonal amplitude index.

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The proportion of zero flow index (PZ), the seasonal period index (SP) and the monthly variation index (CV) could not be reasonably estimated by any other index and so they were included in the final index.

The foregoing analysis suggests that the following five indices should be selected:

„ Low Flow Index (LF) – as it is highly correlated with both the corresponding daily index and with the low flow spells index, and is a statistically significant predictor of the mean annual flow index;

„ High Flow Index (HF) – as it is highly correlated both with the corresponding daily index and with the high flow spells index; and is a statistically significant predictor of the mean annual flow index;

„ Proportion of Zero Flow Index (PZ) – as it is the only index that correlates well with the corresponding index based on daily data;

„ Monthly Variation Index (CV) – as it is a statistically significant predictor of the daily flow duration index and the seasonal amplitude index; and,

„ Seasonal Period Index (SP) – as it cannot be explained by other indices and it is a statistically significant predictor of the daily flow duration index.

These five indices capture the flow stress characteristics represented by the five sub-indices adopted in the SRA study, namely: low and zero flows, high flows, variability, seasonality, and flow volume. Figure D-5 shows that there is no significant correlation between the selected five monthly indices. The efficacy of these selected indices is further explored in the following section. CV LF HF PZ SP

CV LF HF PZ SP „ Figure D-5 Cross-correlation between selected monthly indices.

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D.4 Adequacy of Selected Indices

The adequacy of the five selected monthly indices to represent the information contained in the ten daily indices was assessed. Two multivariate statistical techniques were employed: cluster analysis and principal component analysis.

Cluster analysis is a statistical method that identifies groups within a sample that are similar. Cluster analysis was used to create five groupings of the 50 catchments. The number of groupings selected was reasonably arbitrary and was selected to provide a range of categories for which the variation in hydrologic indices could be assessed. The grouping of catchments was firstly based on all ten of the “daily” indices (that is those listed in the first column of Table D-1), and secondly on the five selected monthly indices. The groupings based on the daily and monthly indices were very similar. Of the 50 catchments, 43 fell into the same categories. This shows that the similarity of catchment stress when evaluated using the daily indices is very similar to an assessment based on the adopted monthly indices.

Group A is the smallest group, containing only 4 catchments. Group B contains 6 large regulated catchments, including all of the sites on the Goulburn River. All of the sites in Group C are also regulated, apart from Woollen Creek in the Moorabool Basin. Group D contains a mixture of smaller regulated catchments and unregulated catchments. Sites on the Lerderderg River and in the Yarra Basin generally fall within this group. The largest group is Group E. Of the 23 sites in this group, only two are regulated. These are the Loddon River downstream of Laanecoorie and the Broken River at Swanpool.

Principal Component Analysis (PCA) is a tool used in this study to determine how similar one catchment is to another with respect to a set of defined characteristics. PCA reduces a set of correlated variables (ie the hydrologic indices) to a smaller number of completely uncorrelated factors. PCA was firstly used to assess the similarity of catchments with regards to the 10 daily hydrologic indices. The first and second principal components were derived using the equation below:

PC1 = 0.684 A + 0.826 CV + 0.605 LF + 0.838 HF + 0.709 LFS + 0.756 HFS + 0.228 PZ + 0.875 FD + 0.648 SP + 0.830 SA

PC2 = -0.558 AV + 0.106 CV + 0.687 LF - 0.436 HF + 0.576 LFS - 0.514 HFS + 0.573 PZ + 0.145 FD + 0.445 SP - 0.386 SA

The two principal components explained 75% of the variance exhibited by the ten hydrological indices. The 1st principal component predominantly reflects the monthly variance index (CV), the high flow index (HF), the flow duration index (FD) and the seasonal amplitude index (SA). The 2nd

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principal component predominantly reflects the low flow index (LF), low flow spells index (LFS) and the percentage of zero flow index (PZ).

A plot of the principal components for the daily indices is provided in Figure D-6. The closer the points are the more similar the catchments, and the more distant the more dissimilar. Catchments that plot close to one another on this plot should be given a similar overall rating of hydrological stress. The five groupings determined using cluster analysis are displayed on this plot using different symbols, and it is seen that catchments within the same group plot close to one another in the PCA plot.

Principal component analysis was also used to define the similarity between catchments according to the selected five monthly hydrological indices. Two principal components were derived using the equations below:

PC1 = 0.837 CV + 0.798 LF + 0.633 HF + 0.426 PZ + 0.828 SP

PC2 = -0.241 CV + 0.220 LF - 0.595 HF + 0.782 PZ + 0.085 SP

The two principal components explained 74% of the variance exhibited by the five monthly hydrological indices. The 1st principal component predominantly reflects the monthly variance index (CV), the seasonal periodicity index (SP) and the low flow index (LF). The 2nd principal component predominantly reflects the high flow index (HF) and the percentage of zero flow index (PZ).

A plot of the principal components for the daily indices is provided in Figure D-6. Catchments that plot close to one another on this plot will have a similar overall rating. It follows that if the monthly indices provide a good representation of daily conditions that two catchment plotted near each other on this plot should also plot close together in Figure D-7. The five groupings determined using cluster analysis based on the daily indices are also located within distinct groups in Figure D-7. The similarity between the two plots shows that that the five monthly indices are a useful surrogate for the daily indices.

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2.5 Group A Group B Group C 2 Group D Group E

1.5

1

0.5

PC2 based on daily indices 0

-0.5

-1 012345678 PC1 based on daily indices

„ Figure D-6 Principal Component Analysis of all daily indices

0.7 Group A Group B Group C 0.6 Group D Group E

0.5

0.4

0.3

0.2 PC2 based on monthly indices

0.1

0 00.511.522.533.54 PC1 based on monthly indices

„ Figure D-7 Principal Component Analysis of selected monthly indices

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D.5 Sensitivity Analysis

It is desirable to ensure that any variation in the indices between sites is due to differences in the degree of hydrologic stress and not to differences in the timing or length of streamflow record used to calculate the index. The simplest and best way of ensuring this is to calculate the indices for each site over a concurrent period, however, this would severely limit the data that can be used to generate historical and ‘natural’ time series at the FSR sites. This section investigates the sensitivity of the indices to timing and length of record.

D.5.1 Implications of the Timing of Record Used Hydrologic indices can be influenced by the timing of the streamflow record used to calculate the indices. Consider for a moment the time series in Figure D-8, which represents unimpacted monthly flow in the Richardson River from 1972 to 2000. The flow exceeded in 10% of months was calculated over the 20-year period from 1972 to 1992 as 1,850 ML/month. However, the value calculated over the 20-year period from 1980 to 2000 is only 1,450 ML/month, a reduction of over 20%. The high flow value calculated for the later period is lower because it does not include the high flow months that occurred between 1973 and 1976.

9000

8000

7000

6000

5000

4000 Flow (ML/month) 3000

2000

1000

0

1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 „ Figure D-8 Natural monthly flow time series in the Richardson River (gauge 415226)

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A bootstrap technique was employed to characterise the errors involve in estimating each of the indices for all 50 sites. A range of values for each index at each sites were computed by generating one hundred samples in which one year of data was randomly selected for exclusion. The sensitivity to each index was measured by its standard error.

The range of standard errors for the 50 sites for each index is displayed in box plots in Figure D-9. The low flow index (LF) was found to be the most sensitive, followed by the high flow index (HF). These two indices are based on less frequent events and hence are more sensitive to extreme events occurring in individual years. Even so, the standard error rarely exceeds 0.05 for any index, indicating that the indices chosen are relatively robust. Therefore it is appropriate to calculate the indices on non-concurrent periods of streamflow data across the state.

0.06

10%

20% 0.05 Median

80%

0.04 90% ror r

0.03 dard E an t S

0.02

0.01

0.00 CV HF LF SP PZ FSR Index „ Figure D-9 The standard error for each FSR index based on the 50 test sites

D.5.2 Implications of Time Period Adopted The 50 sites chosen all have various lengths of record. Due to data limitations, the time period adopted will vary across all FSR sites. In order to assess the implication of the time periods adopted, the standard area for the five chosen indices was calculated for 5, 10, 15, 20 and 25 year flow records. The assessment was based on five sites, one site from each of the five groups from the cluster analysis shown in Appendix D-4. The five sites were:

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„ Broken River @ Swanpool (Group A);

„ Loddon River downstream of Loddon Weir (Group B);

„ Campaspe River @ Rochester (Group C);

„ Lerderderg-Werribee confluence (Group D); and,

„ Jim Crow Creek @ Yadoit (Group E).

Figure D-10 to D-14 show that for each of the five indices the change in standard error as a result of the length of flow record. As expected, as the length of record increases, the standard error decreases. The figures generally show that there is a dramatic reduction in standard error once the length of record reaches 15 years. Accordingly, it is recommended that the indices are only calculated using streamflow records greater than 15 years in length.

0.1

0.09 Jim Crow Ck Broken @ Swanpol Loddon 0.08 Lerderderg Campaspe 0.07

0.06

rd Error 0.05 da n

Sta 0.04

0.03

0.02

0.01

0 0 5 10 15 20 25 30 35 40 45 50 No. years of record

I \ \P j \WC0279 \T k F l FSR\ i i i l i \l h h f d l [C ] „ Figure D-10 The effect of length of record on the standard error of the variation index

SINCLAIR KNIGHT MERZ

D:\Jobs\GSC\FSR\Reports\FSR_Report_FinalB.doc PAGE 149 Flow Stressed Ranking Project

0.35 Jim Crow Ck Broken @ Swanpol Loddon 0.3 Lerderderg Campaspe

0.25

0.2 rd Error nda

a 0.15 St

0.1

0.05

0 0 5 10 15 20 25 30 35 40 45 50 No. years of record „ Figure D-11 The effect of length of record on the standard error of the low flow index

0.3 Jim Crow Ck Broken @ Swanpol Loddon Lerderderg 0.25 Campaspe

0.2 rror

rd E 0.15 nda a t S

0.1

0.05

0 0 5 10 15 20 25 30 35 40 45 50 No. years of record „ Figure D-12 The effect of length of record on the standard error of the high flow index

SINCLAIR KNIGHT MERZ

D:\Jobs\GSC\FSR\Reports\FSR_Report_FinalB.doc PAGE 150 Flow Stressed Ranking Project

0.12

0.1

0.08

Jim Crow Ck rd Error 0.06 Broken @ Swanpol nda

a Loddon St Lerderderg 0.04 Campaspe

0.02

0 0 5 10 15 20 25 30 35 40 45 50 No. years of record „ Figure D-13 The effect of length of record on the standard error of the seasonal periodicity index

0.08

0.07

0.06

0.05 Jim Crow Ck Broken @ Swanpol Loddon

rd Error 0.04 Lerderderg Campaspe nda Sta 0.03

0.02

0.01

0 0 5 10 15 20 25 30 35 40 45 50 No. years of record „ Figure D-14 The effect of length of record on the standard error of the zero flow index

SINCLAIR KNIGHT MERZ

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