use of a bayesian network decision tool to mgt environ flow in river

8/12/2019 Use of a Bayesian Network Decision Tool to Mgt Environ Flow in River

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Report No 4

LWA/MDBC Project UMO43: Delivering Sustainability

through Risk Management

Use of a Bayesian Network Decision Tool toManage Environmental Flows in the

Wimmera River, Victoria


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Executive Summary

This report - Use of a Bayesian Network Decision Tool to Manage Environmental Flows in

the Wimmera River, Victoria- is the forth in a series of five produced by LWA/MDBC

project UMO43 Developing Risk-based Approaches for Managing Contaminants in

Catchments.

Decision making in ecosystem management is a process of balancing multiple objectives,

constraints, trade-offs and uncertainties against a complex backdrop of socio-economic,cultural and political considerations and limited ecological understanding. A central

challenge in providing credible, effective and defensible decision support therefore, is to

provide and apply frameworks and methods that will allow informed choices by providing

opportunities for genuine, substantive participation in decision making supported by best

available scientific knowledge that also incorporates uncertainty in an honest, rigorous and

consistent manner.

Using Ecological Risk Assessment (ERA) as an organizing framework, stakeholder

engagement, risk-based modelling and decision-analytic techniques were developed and

applied to a case study of environmental flow management in a degraded, semi-arid lowland

river in the Wimmera Catchment in Victoria. The case study focussed on the management of

the summer environmental flow regime to maintain adequate instream habitat and water

quality for aquatic biota in the Lower Wimmera River.

Formal management and expert stakeholder workshops were used to obtain stakeholder


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Table of Contents

Executive summary

1. Background ... 12. Ecological risk assessment .. 33. Wimmera catchment ....3.1.General description3.2.Environmental values

5

5

7

4. Problem formulation ...4.1.Ecological management issues in the Wimmera River4.2.Managing environmental flows in the Lower Wimmera under uncertainty4.3.

Planning, problem formulation and stakeholder involvement

4.4.Stakeholder workshops4.5.Workshop outcomes

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10

12

1517

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5. Development of the Bayesian network model for Freshwater Catfish in theLower Wimmera River

5.1.Bayesian network modelling5 2 O i f th d l t d t ti f th F h t C tfi h BN

30

30

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Figure 8: Predicted probability distributions for variables of management interest. The

worst case Dry scenario is represented with stippled bars and the SKMflow strategy is shown as solid grey bars 55

Figure 9: Variation in expected utility for a fixed volume of 240 ML allocated over 1-

4 freshes for median MDF values of 0, 10 and 30 ML/day 57


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Risk-based Approaches for Managing Contaminants in Catchments Report 4 1

1. Background

Natural resource managers currently have few quantitative tools to assist them in identifying

which of their environmental assets are at greatest risk from ecological degradation and then

to decide upon the best options for managing these risks.

Land & Water Australia (LWA) and the Murray Darling Basin Commission (MDBC) have

funded project UMO43Developing Risk-based Approaches for Managing Contaminants in

Catchments todevelop quantitative risk-based assessment guidelines that will offer a new

framework and guidance to assist in improving the management of diffuse contaminants in

catchments. These guidelines have been built around qualitative and quantitative models (the

backbone of risk assessment) that link catchment contaminant reduction targets (end-of-valley

targets for nutrients, salinity, SPM and pesticides) with the ecological benefits in receiving

waterbodies.

The project was undertaken through a collaborative partnership between Monash University,

the University of Melbourne and CSIRO.

The project objectives were:

To develop guidelines for risk-based approaches that can be used to prioritise ecologicalrisks from multiple contaminants in a catchment context (i.e. multiple-stressors resulting

in multiple issues),

To develop quantitative models of ecosystem components and relationships to addressk i ( ) f


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These reports are all available at www.sci.monash.edu.au/wsc.

This document is Report 4 of the series and covers the development of a Bayesian Network

decision tool to assist in the management of environmental flows in the lower Wimmera

River.


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2. Ecological Risk Assessment

Ecological risk assessments evaluate the ecological effects caused by natural events and

human activities and typically are defined as systematic, formal processes of estimating the

likelihood of occurrence of adverse events/circumstances and the magnitudes of

consequences to ecological values that result from these events (Burgman 2005). ERA is

carried out within a broader framework that includes problem formulation, hazard

identification and assessment, conceptual modelling, endpoint selection, risk analysis,

sensitivity analysis, communication of results, decision-making, monitoring, review andupdating (Figure 1). As shown in Figure 1, this is an iterative and cyclic process. Conceived

in this perspective as a megatool, ERA can facilitate the integration of social, scientific and

policy dimensions and serve as a comprehensive organizing framework for environmental

problem-solving in complex settings.

Learning is an elemental feature of the process and completion of the cycle ensures that (a)

the appropriate questions are being investigated; (b) a wide range of potential hazards, or

threats to what stakeholders value and want to protect are canvassed; (c) conceptual modelsof relationships between important interacting variables in the system under study are

explicated and explicitly communicated; (d) assumptions are probed; (e) risks are quantified

as rigorously as possible; (f) results are communicated; (g) feedback is collected; (h)

predictions are validated and (i) knowledge is updated (Burgman, 2005).

P bl F l tiP bl F l ti


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coincide with the publicly-meaningful attributes that are of concern to decision makers

(Reckhow, 1999a).

In tailoring the traditional ERA framework for use in complex natural resource management

problems on large spatial, temporal and ecological scales, the most important innovations are

an increased emphasis on: communication with stakeholders; stakeholder participation in the

tasks of problem formulation, hazard identification, endpoint selection and conceptual

modeling and iteration between the various stages involved in conducting an ERA (see e.g.

McDaniels et al., 1999; Borsuk et al., 2001; USEPA, 2001, 2002; Serveiss, 2002). On the

technical side, a great deal of effort is also being invested in the development of modelling

tools to address the problems of trying to estimate risks from diverse multiple stressors. This

paradigm recognizes that scientific knowledge is critical for ensuring that the risk assessment

addresses all important ecological concerns and characterizes uncertainty adequately but at

the same time acknowledges the validity of socio-cultural perceptions of risks and provides

procedural mechanisms for incorporating these in the risk assessment process.


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3. The Wimmera Catchment

3.1 General description

The Wimmera region is located in north-west Victoria, Australia and covers almost 30,000

km2, or about 13% of Victorias total land area. The dominant land use is agriculture, mainly

dryland cropping of cereals, grain, pulses and oilseeds (Figure 2).

The climate in the Wimmera region varies from temperate in the Grampians and Pyrenees

Ranges in the south-east to semi-arid in north (LCC, 1985). Rainfall is highest over the

ranges in the south and south-east of the catchment, being over 1,000 mm and 700 mm per

annum in the Grampians and the Pyrenees respectively. In the plains regions of the

Wimmera, mean annual precipitation steadily decreases from south to north, from 576 mm at

Stawell, to 449 mm at Horsham, to 384 mm at Jeparit and 368 mm at Rainbow (Bureau of

Meteorology, 2002). Most of the rainfall occurs over winter and spring. Annual areal

potential evapo-transpiration in the Wimmera region is high and greatly exceeds annual

precipitation in all areas except in the south and south-eastern ranges. It varies from about1,100 mm in the south to about 1,200 mm in the north (Wang et al., 2001).


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Flows in the Wimmera River are highly seasonal with most flow occurring in the winter-

spring period. Low or zero flows typically occur from December to May. Periods of lowflow (consecutive days with less than or equal to 5 ML/day) have lasted for up to 965 days at

Horsham.

The major aquifers of the Wimmera region are the unconfined Parilla Sand aquifer, the

Murray Group Limestone aquifer and the basal Renmark Group aquifer. Finer grained units

such as Ettrick Marl, Geera Clay, Winnambol Formation, Bookpurnong Beds and

Blanchetown Clay behave as aquitards thoughout the region (Macumber, 1990). The Parilla

Sand and Renmark Group aquifers extend throughout most of the Wimmera, but the Murray

Group aquifer is restricted to the west of the Wimmera River (Weaver et al., 2002). Within

the Wimmera region, water quality in the Murray Group Limestone is relatively fresh and

consistent with a total dissolved solids (TDS) content of approximately 1,500 mg/L. In

contrast, water quality varies greatly over a small spatial extent within the Parilla Sand (TDS

650-115,000 mg/L) and Renmark (TDS 600-16,000 mg/L) aquifers (Weaver et al., 2002;

Swane, 2004).

3.2 Environmental values

Since European settlement, about 86% of the total catchment area has been denuded of

native vegetation (WCCG, 1991). Most of the native vegetative cover that remains is in

National Parks, State Parks, Flora and Fauna Reserves and Crown land water frontage

(Fi 3)


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3.3 Development of Water Resources: The Wimmera-Mallee System

European settlement of the Wimmera region began in the 1850s and water regulation works

commenced soon after, in order to ensure a more reliable supply of water for domestic, stock

and irrigation purposes, in this region of highly variable streamflow. The resultant

Wimmera-Mallee system (previously known as the Wimmera-Mallee Stock and Domestic

System, WMSDS) harvests and distributes water in a complex system of impoundments,

weirs, diversions, on and off-stream storages and distribution channels (Figure 4). TheWimmera-Mallee system is administered by the rural water authority, Grampians Wimmera

Mallee Water (GWMW) (formerly Wimmera-Mallee Water, WMW).

The Wimmera-Mallee system services a total area covering about 30,000 km 2and supplies

15,760 properties and an estimated 4,500 farming enterprises through a total length of

approximately 14,000 km of open channels and 32,000 km of rural pipelines. The system

provides water supply services to approximately 52,000 people in 74 towns throughout the

region and domestic and stock water supplies for approximately 7,000 people throughout the

region.

The water supplied to the Wimmera-Mallee area is provided principally from the Glenelg

River and Wimmera River and its tributaries. The headworks system consists of 12 storages

in and around the Grampians, together with their connecting natural streams and man-made

channels (Figure 4). The system interacts directly with the Wimmera River at Glenorchy and

H ddl W i O di i f h Wi Ri f H h


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4. Problem Formulation

Under the new Victorian Water Allocation Framework, Catchment Management Authorities

(CMAs) have been invested with the task of managing environmental flows (DSE, 2004).

This case study of an ERA for environmental flow management in the Lower Wimmera

River was undertaken in partnership with the Wimmera Catchment Management Authority

(WCMA), the Environment Protection Authority Victoria (EPA) and a Land and Water

Australia/Murray Darling Basin Commission (LWA/MDBC) project on Developing Risk-

based Approaches for Managing Contaminants in Catchments (Contaminants RiskAssessment) (LWA/MDBC Project UMO43, 2005). The project represents a collaborative

attempt by the scientific, regulatory and natural resource management community to develop

a system for enhancing the quality of environmental decision making by engaging

stakeholders and integrating substantive stakeholder concerns into scientific analyses as part

of a holistic problem solving approach.

4.1 Ecological management issues in the Wimmera River

Since European settlement, the Wimmera River and its surrounding catchment has been

heavily modified by extensive vegetation clearance for agriculture and urbanization and

intensive river regulation. While the development of land and water resources for

agricultural, urban and industrial purposes has brought much socio-economic benefit to the

region and the state, it has exacted a high environmental cost.


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The primary water quality issue in the Wimmera River is high salinity levels. Salinity has

historically been high upstream of Glenorchy, decreasing below Glenorchy Weir due to thediluting effect of water entering from the weir pool from the Wimmera-Mallee system and

then increasing downstream towards the terminal lakes (EPA, 1984). In addition to high

surface water salinity, the lower reaches are subject to saline groundwater intrusions from

the Parilla Sand aquifer, leading to localized occurrences of extremely high bottom water

salinity (eg. 30,000-56,000 EC) which in turn leads to salinity-induced density stratification

of the water column (Anderson and Morison, 1988a,b).

In the Lower Wimmera River, the major in-stream problem from an environmental point of

view is not salinity per se, but rather salinity-induced density stratification of the water

column, which results in restricted vertical mixing in the water column during low flow

periods and subsequent severe and persistent deoxygenation (with levels of dissolved oxygen

(DO) frequently


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4.2 Managing environmental flows in the Lower Wimmera under uncertainty

As caretakers of river health, the Wimmera CMAs primary responsibilities in terms of

operational management of the Environmental Water Reserve (EWR) to address the problem

of altered flow regimes and water quality issues in the Wimmera River are as follows

(WCMA, 2003a):

Implement Stressed Rivers Project: Environmental Flows Study recommendations bySinclair Knight Merz (SKM, 2002a),

Continue to investigate the environmental flow requirements of tributaries anddistributaries, in light of new understanding and environmental water requirement

priorities,

Monitor and review the delivery and effectiveness of environmental flows.Studies of environmental flow requirements for the Wimmera River as part of the BulkEntitlements Conversion process have resulted in detailed environmental flow

recommendations for the Lower Wimmera River (SKM, 2002a,b). Essentially, a modular

approach was adopted for characterizing the complete flow regime, thus allowing different

flow types/events to be represented as separate flow components. The full suite of

recommendations, couched in terms of flow components, and the underlying rationale and

specific management objectives each recommendation addresses are summarized in Table 1

( f SKM 2002 )


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past, but no direct evidence was gathered to enable assessment of causality (Anderson and

Morison, 1988b; M. Burns, Fisheries Victoria, pers. comm., 2004).


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Table 1: Environmental flow recommendations for the Lower Wimmera River (between McKenzie River and Lake Hindmarsh)

(adapted from SKM, 2002a)

Season Flow Component Magnitude Frequency Duration Rationale & Target Management Objective

Summer Cease to Flow

(CTF)

0 ML/day annual 5-24 days Disturbance mechanism to maintain benthic

community diversity

Summer

Autumn

Minimum Median

Flow

5 ML/day annual Dec-May except

during periods of

CTF and Fresh

Minimum flow to maintain habitat for Murray

Cod, Macquarie Perch and Freshwater Catfish.

Summer Summer Fresh* >20 ML/day 4 per summer 7-15 days Maintain or improve water quality in pools via

mixing and/or destratification. Unlikely to mix

saline pools in Lower Wimmera River, but may

prevent significant increases in salinity that

occur at flows of less than 10-20 ML/day.

Spring Minimum Median

Flow

34 ML/day annual July-Nov except

during periods of

Fresh

Minimum flow to maintain habitat for Murray

Cod, Macquarie Perch and Freshwater Catfish.

Spring Spring Fresh* >334 ML/day 5 per annum Minimum 14 days May stimulate migration and/or spawning of

native fish species that inhabit the region.

Makes available instream habitats such as bars,

benches and undercuts. May mobilize and

redistribute fine sediment which might

otherwise smother habitats.

Promote recruitment of Murray Cod and

Macquarie Perch.

Annual Bankfull Flow > 3,000 ML/day annual Minimum 2 days Maintain or improve water quality in pools via

mixing and/or destratification.


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Annual Overbank Flow 6,000 ML/day annual 3-5 days Provide lateral connectivity for exchange of

energy and nutrients between in channel and

floodplain habitats. Disturbance mechanism

and fluvial power to shape and maintain

diversity in channel geomorphological

characteristics.

Maintain and promote recruitment of Yarra

Gum communities.

*A fresh is a small peak flow event that exceeds the median flow for a given period and lasts several days.


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Environmental flows released after a prolonged dry spell in the river may result in a sudden

influx of nutrients and organic material with high biochemical oxygen demand. The organic

matter may cause rapid depletion of dissolved oxygen causing hypoxia or anoxia and

nutrient influxes may fuel harmful algal blooms or encourage proliferation of aquatic

vegetation. Environmental flows released from storages with high levels of dissolved salts or

suspended sediments could increase salinity or turbidity and lead to adverse impacts on in-

stream biota. Large overbank flows recommended for the maintenance of lateral connectivity

and channel geomorphological processes (Table 1) also carry with them the risk of causing

accelerated streambank erosion in highly degraded river sections.

The quantities of water available for environmental flows are typically highly variable and it

may not be possible for the complete suite of flow recommendations (making up the full

flow regime) to be implemented. What then are the benefits (if any) of piecemeal

implementation of the environmental flow recommendations? How should river managers

allocate the fixed available volumes of water for environmental flows among the competing

targets? For instance, should environmental flows be allocated to freshes in summer rather

than freshes in autumn, or for a single high/bankfull flow event in spring rather than a fewfresh events in spring. Will implementation of certain flow components produce greater

relative benefit than others? Or to reframe the perspective, can the implementation of certain

flow components reduce the relative risks to particular values?

The application of an ERA approach to addressing these management issues is described in

the remainder of this report. Scientifically credible management of environmental flows

within an ERA framework rests on operationalizing sensitive endpoints that encapsulate


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prioritize key hazards and values requiring more detailed risk analysis and develop a setof candidate assessment endpoints for the risk analysis phase.

4.4 Stakeholder workshops

There were a total of 23 attendees at the first workshop held on the 20 July 2004 in

Horsham, the major service and commercial centre for the Wimmera region. This included

the 14 participants mentioned in the previous section and the nine members of the project

team, seven of whom doubled up as expert stakeholders for the workshop. The workshop

was mainly facilitated by Yung En Chee, with assistance from Anne-Maree Westbury, Jan

Carey and Mark Burgman. The participants included seven natural resource managers from

the local and regional CMAs, two officers from Fisheries Victoria, one regional officer from

the EPA, and 11 scientists with a range of expertise in hydrology, freshwater ecology, water

resource and quality management, ecotoxicology, population ecology, conservation biology,

forestry, risk assessment and decision analysis. Details of the list of participants at this

workshop are given in Appendix 1.

At the commencement of the workshop, an overview was given of the project context, aims

and objectives. Participants were invited to briefly introduce themselves, their credentials,

professional affiliations and responsibilities within their organizations. This was followed by

a presentation on the ERA process by Jan Carey to familiarize participants with the

underlying concepts, rationale and motivation for adopting the ERA approach. The


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stakeholders. Hazard identification and assessment in the workshop proceeded through

brain-storming, group discussion, hazard matrices and conceptual modelling.

The identification of hazards and their assessment depends on an understanding of how a

system is believed to work. The diverse pool of participants encompassed a range of

different experiences and perspectives on the system and different mental conceptions of

cause-effect relationships within the system. Group discussion and the development of

conceptual models helped to draw out and visually represent these mental models so that

they could be mapped, shared, documented and scrutinized.

Hazard matrices were used to depict the interactions between values and hazards arising

from natural events or human activities and helped in identifying hazards that could have

multiple effects. They also helped to reduce the probability of overlooking interactions. In

this way, a more comprehensive list of hazards could be generated. The hazard matrices

constituted a visual summary of interactions between values and activities and provided a

rudimentary but useful foundation for the hazard assessment phase.

Detailed notes and outcomes of the first workshop were organized and summarized. They

were then disseminated to all workshop participants for corrections, additions and

clarification.

Further deliberation took place with a second workshop in Horsham held on the 12 August

2004. There were a total of 11 attendees at the workshop. They included four participants

from WCMA, two participants from Fisheries Victoria, four participants from the EPA and


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in the second workshop and their relative merits deliberated. In selecting assessment

endpoints, social relevance was evaluated by the local natural resource managers whilescientists provided technical input on ecological relevance, susceptibility to hazards/stressors

and operational feasibility of measurement.

4.5 Workshop outcomes

Workshop participants tended to emphasize broad-scale, aggregate values such as a natural

flow regime, biodiversity, native endemic plant and animal species and plant communities,

diverse biofilm, phytoplankton, benthic and macrophyte communities. Values nominated by

individual participants tended to reflect their area of expertise and/or professional

responsibility. In particular, WCMA participants strongly identified with their role as

managers for broad big picture goals such as river health and biodiversity and were

reluctant to parse these concepts at a finer level of detail. Even as other workshop

participants contributed values of a more specific nature, such as particular fish and

vertebrate species, concerns were expressed throughout over reductionism and excessive

narrowing of objectives.

A broad range of hazards arising from natural events such as prolonged drought, fire and

algal blooms and human activities such as river regulation, environmental flow management,

agricultural practices, urbanization and industrial development were identified in the

workshop. Hazards were considered mainly in terms of their potential effects on broad


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as reduced habitat availability, low DO and salt toxicity. The latter two were also deemed to

be the key hazards to water quality for sustaining biota.

Physicochemical parameters of water quality are the most commonly measured metrics in

river management and conventionally, it has been assumed that if target levels of water

quality parameters are attained, the aquatic environment could be considered to be healthy

(Norris and Thoms, 1999). However, this premise is problematic for the following reasons:

appropriate parameters might not be measured (eg. measurement only of surface waterDO when the problem may lie with bottom water DO),

the approach does not take into account possible antagonism or synergism betweenstressors which may affect aquatic biota (eg. high temperatures and low DO increase the

toxicity of ammonia to fish (Alabaster and Lloyd, 1980),

spot measurements of parameters which frequently exhibit high natural variability areunlikely to reflect water quality over timescales relevant to aquatic biota and

furthermore, may miss intermittent inputs and flood events (Norris and Thoms, 1999;ANZECC/ARMCANZ, 2000), and

merely monitoring physicochemical water quality parameters provides no informationon other factors that may affect biotic distribution (e.g. quantity of hydraulic habitat,

flow velocity, availability of food resources and competitive interactions between biota

and/or exotic species).

Water quality is essential and if socially valued as an end in itself is appropriate as an


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Table 2: Examples from the workshop of some of the hazards identified and the assessment of their potential effects

Event/Activity Hazard/Threat/Stressor Potential primary effects Potential secondary effects

Natural Event

Fire in riparian zone Increased input of sediment

and particulate matter

Loss of riparian habitat

Increased turbidity levels and reduced

light penetration in water column

Transport into waterway of contaminants

bound to particulate matter

Smothering of benthic biota

Reduced feeding efficiency of filter-

feeding and visual feeding biota

Burial of structural habitat features such

as gravel and coarse debris

Reduced riparian habitat

Reduced connectivity between aquatic

and terrestrial ecosystems

Reduced primary productivity in deeper

waters

Acute toxic effects/indeterminate

sublethal effects on aquatic biota

Reduced abundance and/or diversity of

benthic community

Loss of benthic feeding, refuge and

spawning habitat

Prolonged drought Extended period of low/zero

flow

Loss of longitudinal

hydrologic connectivity

Lack of overbank flows ->

loss of lateral hydrologicconnectivity, loss of channel-

floodplain exchange

mechanism

Reduced quantity and quality of physical

instream hydraulic habitat

Reduced quantity and quality of

floodplain wetland habitat

Reduced productivity of riverine-

floodplain ecosystem


aquatic biota

Reduced abundance and/or diversity ofriverine and floodplain biota

Saline groundwater Increased salinity Acute toxic effects/indeterminate



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intrusion

Salinity induced density

stratification

sublethal on aquatic biota

Lack of vertical mixing of water column

leading to development of hypoxia/anoxia

in water column and bottom sediments

Lack of vertical mixing resulting in

increased residence time of algae in

euphotic zone

aquatic biota


instream hydraulic habitatBuild up of toxic levels of ammonia and

hydrogen sulphide due to activity of

anaerobic bacteria

Increased rates of nitrogen and

phosphorus mineralization which may

fuel algal blooms

Algal blooms

High temperatures insummer Temperature induced densitystratification Lack of vertical mixing of water columnleading to development of hypoxia/anoxia

in water column and bottom sediments

Lack of vertical mixing resulting in

increased residence time of algae in

euphotic zone

Reduced quantity and quality of physicalinstream hydraulic habitat

Build up of toxic levels of ammonia and

hydrogen sulphide due to activity of

anaerobic bacteria

Increased rates of nitrogen and

phosphorus mineralization which may

fuel algal blooms

Algal blooms

Occurrence of algal

bloom

Reduced light penetration

Algal bloom is harmful

Reduced primary productivity in deeper

waters

Illness/death of biota that graze on algae

that produce toxins

Bioaccumulation of toxins in

taxa/organisms at higher trophic levels

Die-off of al al bloom ma roduce DO


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Injury/death of fish via gill and tissue

damage caused by algal species that have

physical structures such as spines (eg.

some diatoms, dinoflagellates and

raphidophytes.)

sag.

River Regulation

Activities

Abstraction, diversion

and distribution

Reduced flow magnitude in

all seasons

Reduced frequency and

duration of cease-to-flow

events -> loss of disturbance

mechanism

Excessive baseflow stability -

> loss of flow variability



Reduced quantity and quality of

seasonally inundated low-lying backwater

and anabranch habitat

Reduced diversity of benthic community

Proliferation of monospecifc stands of

aquatic vegetation (eg.Azollaspp.,

Phragmites australiaand Typha spp.)

Impacts on life-history strategies of native

aquatic biota (eg. loss of spawning cues)


aquatic biota

Reduced diversity of macrophyte

community

Reduced diversity of instream structural

habitat

Respiratory requirements of large

standing crop can create hypoxic

conditions before sunrise and during days

of overcast weather.


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May favour establishment and

proliferation of exotic species

Die-off of large standing crop of aquatic

vegetation may produce DO sag.

Impaired survival, competitive ability and

reproduction of affected biota.


native biota

Leakage from

Wimmera-Mallee

distribution system

Raised water table -> reduced

frequency and duration of

cease-to-flow events -> loss

of disturbance mechanism

Excessive baseflow stability -

> loss of flow variability

Reduced diversity of benthic community

Proliferation of monospecifc stands of

aquatic vegetation (eg.Azollaspp., P.australiaand Typha spp.)

Impacts on life-history strategies of native

aquatic biota (eg. loss of spawning cues)

May favour establishment and

proliferation of exotic species

Reduced diversity of macrophyte

community

Reduced diversity of instream structural

habitat

Respiratory requirements of large

standing crop can create hypoxic

conditions before sunrise and during days

of overcast weather.

Die-off of large standing crop of aquatic

vegetation may produce DO sag.

Impaired survival, competitive ability and

reproduction of affected biota.


native biota


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Environmental Flow

Management

Fresh magnitude too

large

Sudden purging of saline

pools containing highlysaline, bottom waters

containing low DO and/or

low pH and/or high levels of

ammonia/hydrogen sulphide

Acute toxic effects/indeterminate


Flow release allocated

from storage with high

levels of salinity

Input of water with high

levels of salinity

At best, no improvement in water quality,

at worst, acute toxic effects/indeterminate


Flow release allocated

from distant storage

within the Wimmera-

Mallee system

Flow magnitude diminished

through seepage and

evaporative losses ->

extended period of low/zero

flow






aquatic biota

Lack of water for

environmental flow

release

Extended period of low/zero

flow






aquatic biota

Flow delivery impeded

by council weir pool

Extended period of low/zero

flow




aquatic biota


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management (eg. to

maintain full weir pools

for recreation)



Flow delivery absorbedby water users with

private entitlements

Extended period of low/zeroflow



Reduced quantity and quality of physicalinstream hydraulic habitat

Reduced abundance and/or diversity ofaquatic biota


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Table 3: Evaluation of the selected endpoints sustainable populations of Freshwater Catfish and macroinvertebrate community

diversity with respect to congruence with required attributes of assessment endpoints

Endpoint Attribute Sustainable populations of Freshwater Catfish Macroinvertebrate Community Diversity

Social importanceand relevance

Highly-valued native species; listed under the Flora andFauna Guarantee Act 1988; classified as Endangered

in Advisory List of Threatened Vertebrate Fauna in

Victoria (DSE 2003a).

Although non-endemic in the region, it has established

self-sustaining populations in the Lower Wimmera

River which now constitutes a stronghold for the species

in Victoria.

Recreationally important.

Macroinvertebrate community diversity is relatively wellappreciated by the community due to national awareness

programs such as Waterwatch.

Macroinvertebrate communities have been judged to have

sufficient credibility to be used in federal and state

environmental monitoring frameworks for reference

condition monitoring (eg. in National River Health

monitoring and the Victorian Index of Stream Condition).

Indices of macroinvertebrate community are a required

component of ecological objectives in the StateEnvironmental Protection Policy(Waters of Victoria)

(SEPP WoV).

Ecological

importance and

relevance

Species is of high conservation value.

Occupies bottom waters prone to water quality problems

which managers hope to address through environmental

flow management.

Macroinvertebrate community represents a critical

pathway for the transport and utilization of energy and

aquatic matter in aquatic ecosystems (Carmargo 2004)

Susceptible to

hazards/stressors

Species has declined in abundance and distribution

throughout its natural range in Victoria, but specific

stressors which might account for this have not been

established (Clunie and Koehn 2001). Monitoring is

required to establish response to flow regime and water

quality changes effected by environmental flow

strategies.

There is considerable evidence that macroinvertebrate

assemblages can be useful indicators of high nutrient

input or pollutants and contaminants (Mebane 2001;

Chessman 2003). However, there is little information on

how invertebrates might respond to changing flow

regimes (SKM 2003a). Monitoring is required to establish

response to flow regime and water quality changes


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effected by environmental flow strategies.

Operational

definition

Can be framed clearly and unambiguously. Can be framed clearly and unambiguously.

Measurementfeasibility

Sampling of Freshwater Catfish to estimatedemographic parameters for quantifying viability may

be time-consuming and costly. Monitoring of fish

communities has not occurred in any regular program,

but programs are under development (DNRE 2002b).

Some quantitative survey data exists from previous

studies. Angler reports and annual results from fishing

competitions held along the Lower Wimmera River are

a potential source of useful data.

Ubiquitous and relatively easy to collect. Standardizedsampling and processing protocols are well-developed

and taxonomic keys are available to identify most

macroinvertebrates.

Component of routine river health monitoring programs

in Victoria.

Good, baseline data exists from monitoring carried out by

the EPA.


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Selection of assessment endpoints was a difficult exercise because the Lower Wimmera

River is highly degraded and relatively depauperate in terms of socially important aquatic

and riverine biota. Uncertainty regarding the susceptibility of each assessment endpoint torelevant hazards and stressors is an important limitation and arises from incomplete scientific

understanding. Susceptibility is a function of exposure to the hazard/stressor and sensitivity

to direct as well as indirect adverse effects of the hazard/stressor and consideration of

susceptibility itself requires a qualitative or simple quantitative hazard assessment (Suter,

1995).

An anticipated difficulty in interpreting measurement of the endpoints is that the natural

variability of physical and biological components of semi-arid, intermittent rivers is very

high (Boulton, 1999). This variability has been documented for the macroinvertebrate

community in the Wimmera River (Metzeling, 2002). The amount of variability could make

it difficult to discern what effects, if any, environmental flows might have.

The effect of multiple stressors interacting in complex and often non-linear ways over

different spatial and temporal timescales makes it difficult to untangle and definitively

establish causal relationships for observed impacts. In addition to these uncertainties,environmental flow management in the context of a complex distribution system like the

Wimmera Mallee system needs to take into account logistical constraints. Consequently, it

would be advantageous if we could develop a knowledge base of how equivalent

environmental outcomes might be achieved (within limits of acceptable risk) for different

patterns of flow deployment. This would provide valuable flexibility in the operational

management of the EWR.


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5. Development of the Bayesian network model for Freshwater Catfish

in the Lower Wimmera River

5.1 Bayesian network modelling

Bayesian networks (BNs) (also known as Bayes nets, Bayesian belief networks, causal

probability networks and probability networks) are well suited to the task of modelling a

situation in which causality plays a role, but where our understanding is incomplete, so we

need to describe events probabilistically (Charniak, 1991). Bayesian methods provide a

formalism for reasoning about beliefs under conditions of uncertainty. In this formalism,propositions are given numerical parameters signifying the degrees of belief accorded them

under some body of knowledge and the parameters are combined and manipulated according

to the rules of probability (Pearl, 1991). Good introductions to BNs are available in

Neapolitan (1990), Pearl (1991), Jensen (1996) and Pearl (2000).

The use of BN models is well-developed in the fields of knowledge engineering, software

engineering and artificial intelligence (Neilet al

., 2000; Fenton and Neil, 2001). Recentapplications in ecological management include modelling a restoration strategy for a

temperate lake in Finland, modelling salmon fisheries management in the Baltic Sea (Varis

and Kuikka, 1999), evaluating fish and wildlife population viability under land management

alternatives (Marcot et al., 2001), evaluating land management alternatives on salmonid

populations and habitat in the Columbia River Basin (Rieman et al., 2001) and modelling

estuary eutrophication in the Neuse River in North Carolina, USA (Borsuk et al., 2004).


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presence of an arc (Pearl, 2000; Borsuk et al., 2004). Submodels may be derived from any

combination of process knowledge, statistical correlation or expert judgement depending onthe type and scale of information available (Borsuk 2004; Borsuk et al. 2004). Similarly,

CPTs can be constructed using empirical data, output from process models, theoretical

insight, probabilistic or deterministic functions, ancillary data from empiric studies

independent of the constructed system, and expert judgements (Reckhow, 1999b; Lee, 2000;

Borsuk et al., 2001; Cain, 2001. Generation of CPTs from functional relationships uses

Monte Carlo simulation to draw representative samples from a nodes predecessors, to build

up a discrete conditional distribution of the node based on the functional relationship.

With the model fully specified and validated, the BN model can be used to produce

probability distributions (or risk profiles) for model endpoints and any other variables of

management interest for both inference and prediction. This is basically performed by

altering the states of some nodes in accordance with observations/findings or proposed

interventions while observing the effect this has on model endpoints. The impact of changing

any variable is transmitted throughout the network in accordance with the relationships

encoded in the CPTs and the joint probability distribution of the entire network conditioned

on these observations is inferred or calculated for other variables using Bayes Theorem

(Pearl, 1991).

BN modelling is useful because the process of formal identification of system variables and

interactions forces model builders to articulate, structure and thereby clarify their

understanding, not only of the relevant influences on management objectives, but of the


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5.2 Overview of the development and construction of the Freshwater Catfish BN

The two main tasks in BN modelling are construction of a graphical structure and

construction of CPTs for each node.

Developing the graphical structure involves formal, systematic identification of system

variables and interactions and well-founded decisions for what variables and values to

include and omit from the network (Batchelor and Cain, 1999; Coup et al., 2000). For the

Freshwater Catfish BN key variables and the nature of their dependencies were identified

and refined through:

the workshop process of hazard/threat identification, the workshop process of conceptual model construction, a review of the environmental flow recommendations documented in the Stressed

Rivers Project: Environmental Flows Study (SKM, 2002a),

a comprehensive survey of the relevant literature, and consultations with fisheries managers and experts in hydrology, freshwater ecology and

Freshwater Catfish biology.

The sources above were used along with practical BN modelling guidelines provided in Cain

(2001) to develop a model linking Freshwater Catfish population characteristics with the


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BN modelling was carried out using the software Netica (Norsys Software Corp. 1997-

2003). Netica uses junction tree algorithms to perform probabilistic inference (Norsys,1997). Details on computation and algorithms used in Netica are available in Neapolitan

(1990) and Spiegelhalter et al. (1993).

5.3 Stakeholder considerations in BN model development

The objective of constructing the BN model was to capture understanding of how flow

conditions are believed to affect key in-stream physicochemical and habitat variables

governing Freshwater Catfish population structure and viability. The temporal focus of the

model is the summer period when environmental conditions are expected to be most stressful

for in-stream aquatic biota and when catchment managers are typically required to make

decisions about the deployment of environmental flows.

In the course of the risk assessment workshops, during which a large number of hazards

were considered, it became clear that the factors that stakeholders believed were mostimportant with respect to maintaining self-sustaining populations of fish were the quantity

and quality of in-stream habitat as determined by the magnitude, timing and duration of

flows. With respect to water quality, the importance of turbidity, pH and concentrations of

nitrogen and phosphorus were acknowledged, but it was believed that ultimately the two

most important aspects were dissolved oxygen (DO) and salinity.

There was an expectation that providing (minimum) flows over summer would increase the


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collated and reviewed. In general, Freshwater Catfish appear to be relatively long-lived,

hardy and extremely tolerant of poor physicochemical conditions. They favour slow,sluggish waters and spawn and develop in carefully tended nests constructed in shallow

waters. Detailed results from the review are summarized in Chee (2005).

The phenomenon of density stratification, bottom water deoxygenation and conclusions from

previous work done on salinity and thermal stratification in the Lower Wimmera was also

reviewed. The detailed review is available in Chee (2005). A summary of the review

follows.

In the Lower Wimmera River, both thermal and salinity stratification occur. Thermal

stratification is extensive and affects a much greater proportion of the river than salinity

stratification (Anderson and Morison, 1988b). It is persistent during summer, but also occurs

for shorter periods in autumn (Western and Stewardson, 1999). Salinity stratification

develops most often as a consequence of saline groundwater seeping directly into river

pools, and less frequently as a consequence of saline inflows to a relatively fresher pool from

an upstream source (Anderson and Morison, 1988b; Western, 1994). Salinity stratification istherefore largely dependant on the presence of sites of saline groundwater intrusion, which

may be very localized. Sites of groundwater intrusion characteristically have well-developed,

persistent stratification of the water column and where significant groundwater intrusion

occurs, salinity stratification will persist most of the year, particularly under low flow

conditions (Anderson and Morison,, 1988b; Western, 1994; Western et al., 1996; SKM,

1997) but also under moderate flow events (Ryan et al., 1999).


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5.5 Graphical structure of Freshwater Catfish BN

The spatial boundary of the model encompasses an approximately 62 km reach of the Lower

Wimmera River, from the junction of the Wimmera and McKenzie Rivers to Big Bend. This

reach was chosen because it is known to be inhabited by Freshwater Catfish, it contains

known sites of saline pools and cross-section profile elevation data are available for

developing flow, depth and cross-section area relationships. The model is focussed on

environmental conditions during summer and the model endpoint is the viability of

Freshwater Catfish populations within the specified river reach. The full graphical Bayesian

probability network model linking summer flow regime to Freshwater Catfish populations in

the Lower Wimmera River is shown in Figure 5.

The graphical BN model was structured according to the main conceptual ideas as follows:

The viability of Freshwater Catfish populations in the focal reach (Viability of CatfishPopn) is determined by the abundance of the breeding population (Abundance

Breeding Popn) and the recruitment of juvenile and larval catfish (Recruitment

Juvenile Catfishand Recruitment Larval Catfish). These population characteristicsare in turn dependent on the quantity and quality of physical hydraulic habitat for living

(Hydraulic Habitat) and spawning (Spawning Area Availability) and on stochastic

elimination via adverse slug events (Severity of Slug Event), and in the case of

larval catfish, via high flow velocities (Peak XS Velocity) as well. This is depicted by

the graphical structure in Box A in Figure 5.

The quantity and quality of physical hydraulic habitat (Hydraulic Habitat) is


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Table 4: Summary of model variable definitions, states and sources of information and/or data used to discretize variables and construct

probability models

Model

Variable

Model

Variable

Definition

Variable States: Names &

Descriptions

Node Parents Purpose/Usage of Variable in the Model Info source for

discretizing

variable and/or

specifying

probability

model

Median

Mean

Daily

Flow

(MDF)

Median mean

daily flow over

summer

MDF0: 0 ML/day

MDF5: 5 MlL/day

MDF10: 10 ML/day

MDF20: 20 ML/day

MDF30: 30 ML/day

None Characterizes and summarizes the

minimum flow conditions experienced

in the nominated reach over summer.

Determines stream depth distribution

over the reach. Also used to assess the

maximum interval between mix events.

Time series flow

data measured at

Horsham gauge

415200 (1945-

2001 inclusive):

(10)

Depth Distribution of

stream depths

over the reach

VShal: < 1.0 m

Shal: 1.0m 2.0 m

Mod: 2.0 m 2.5 m

Deep: >2.5 m

Median Mean

Daily Flow

(MDF)

Depth is used as a variable for

predicting the status of bottom DO and

the availability of spawning area for

Freshwater Catfish over the reach.

Data, hydraulic

model, empirical

probability

model: (9)

XS Area Distribution of

flow cross-

sectional area

over the reach

A_0to20: 0-20 m2

A_20to40: 20-40 m2

A_40to80: 40-80 m2

Median Mean

Daily Flow

(MDF)

Cross-sectional area is used as index of

the quantity of physical hydraulic

habitat. It is also used to calculate flow

velocities for the peak cross-sectional

velocit variable and to determine

Data, hydraulic

model, empirical

probability

model: (9)


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A_80to120: 80-120 m2

A_120to160: 120-160 m2

A_160to200: 160-200 m2

A_mt200: >200 m2

whether or not mixing occurs due to

median MDF and freshes.

Extent of

Groundwa

ter

Intrusion

Extent of

saline

groundwater

intrusion

Low: 0-5% of river section

affected

Mod: 5-10% of river section

affected

High: > 10% of river section

affected

None Estimates the extent of groundwater

intrusion as a precursor to assessing

bottom water salinity.

Data: (2), (8)

Obs Range

BottomSalinity

Observed

range ofbottom salinity

(as measured

by electrical

conductivity

below the

halocline)

Good: !3,000 EC

Okay: 3,000-12,000 EC

Poor: 12,000-20,000 EC

Very Poor: > 20,000 EC

None Characterizes the observed range of

bottom salinity values within aparticular river section. Used in

conjunction with the extent of

groundwater intrusion to assess bottom

salinity.

Data: (1), (3),

(4), 5), (8)

Bottom

Salinity

Salinity as

measured by

electricalconductivity of

water below

Good: !3,000 EC

Okay: 3,000-12,000 EC

Poor: 12,000-20,000 EC

Very Poor: > 20,000 EC

Extent of

Groundwater

Intrusion

Obs Range

Bottom

Characterizes bottom salinity conditions

over summer within the reach of

interest. Bottom salinity is an importantfactor in the assessment of bottom water

quality.

Analyst

judgement


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the halocline Salinity

Bottom

DO

Bottom

dissolved

oxygen

concentration

(% saturation)

Stressful: !10% sat.

Okay: > 10% sat.

Depth Characterizes bottom DO conditions

over summer within the reach of

interest. Bottom DO is an important

factor in the assessment of bottom water

quality.

Data, empirical

relationship: (1),

(3), (5)

Fresh

Magnitude

Magnitude of

summer

freshes

F20: 20 ML/day

F40: 40 ML/day

F80: 80 ML/day

F115: 115 ML/day

F150: 150 ML/day

F190: 190 ML/day

F240: 240 ML/day

None Allows exploration of the effects of

employing freshes of different

magnitudes. Needs to be used in

conjunction with Fresh Freq node.

User-defined

Fresh Freq Frequency of

occurrence of

freshes over

summer

NoFr: 0 freshes

Fr_x1: 1 fresh

Fr_x2: 2 freshes

Fr_x3: 3 freshes

Fr_x4: 4 freshes

None Allows exploration of the effects of

deploying freshes at different

frequencies. Needs to be used in

conjunction with Fresh Magnitude

node.

User-defined

Mixing

due to

Median

Occurrence of

mixing as a

result of

Yes

No

Median Mean

Daily Flow

(MDF)

Mixing events are expected to have an

ameliorating effect on bottom water

ualit over summer. Mixin of

Data, functional

relationship,

em irical


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MDF median MDF

for any given

cross-sectional

area

(MDF)

XSArea

thermally stratified waters occurs due to

flow-related turbulence when flow

velocities are sufficiently high. This

variable characterizes the probability

that mixing occurs due to median MDFover summer. The flow velocity criteria

used to determine the probability of

mixing are discussed in greater detail in

Chee (2005).

relationship: (6),

(11)

Mixing

due to

Freshes

Occurrence of

mixing as a

result of

freshes for

any given

cross-sectional

area

Yes

No

Fresh

Magnitude

XSArea

Distribution



quality over summer. Mixing of

thermally stratified waters occurs due to

flow-related turbulence when flow

velocities are sufficiently high. This

variable characterizes the probability

that mixing occurs due to freshes over

summer. The flow velocity criteria used

to determine the probability of mixing

are discussed in greater detail in Chee

(2005).

Data, functional

relationship,

empirical

relationship: (6),

(11)

Max

Interval

Betw MixEvts

Maximum

interval

betweenmixing events

for an iven

Int_0to21: 0-21 days



Mixing due to

Median MDF

Fresh Freq

Mixing due to



quality over summer. The effectivenessof this depends upon the length of time

between mix events and is characterized

Functional

relationship


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cross-sectional

areaInt_mt35: > 35 days Freshes by the maximum interval between

events.

BottomWater

Quality

Descriptiveindicator of

bottom water

quality

Poor: habitable by adultFreshwater Catfish but low in

terms of food productivity and

likely to be harmful to eggs

and larval catfish

Okay: habitable by Freshwater

Catfish (& other aerobic

biota), moderate in terms of

food productivity and not

detrimental to eggs and larvalcatfish

Good: habitable by Freshwater

Catfish (& other aerobic

biota), good in terms of food

productivity and provides

favourable physicochemical

conditions for eggs and larval

catfish

BottomSalinity

Bottom DO

Max Interval

Betw Mix Evts

Synthetic variable that characterizesbottom water (in the terms defined), on

the basis of bottom salinity, bottom DO

and the maximum interval between mix

events. Used in conjunction with cross-

sectional area to rate hydraulic habitat.

Processknowledge,

analyst

judgement

HydraulicHabitat

Descriptiveindicator of

hydraulic

LowMod

XSAreaBottom Water

Quality

Synthetic variable that characterizeshydraulic habitat on the basis of

quantity (as indicated by cross-sectional

Processknowledge,

analyst


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habitat High area) and quality (as indicated by

bottom water quality).

judgement

Flush

Magnitude

Magnitude of

the peak flow

event over

summer

F0to300: 0-300 ML/day

F300to600: 300-600 ML/dayF600to1200: 600-1,200

ML/day

F1200to3000: 1,200-3,000

ML/day

F3000to6000: 3,000-6,000

ML/day

Fmt6000: > 6,000 ML/day

None Characterizes the distribution of annual

summer peak flow events. In other

words, the probability of occurrence of

peak flow events of varying magnitudes

each summer (ie. on an annual basis).

Time series flow

data measured at

Horsham gauge

415200 (1945-

2001 inclusive)

and empirical

probability

model: (10)

Peak XSVelocity

Peak cross-sectional flow

velocity

experienced

over summer

Low: < 0.05 m/s

Mod: 0.05-0.08 m/s

High:> 0.08 m/s

FreshMagnitude

Flush

Magnitude

XSArea

Characterizes the peak cross-sectionalflow velocity experienced over summer,

whether as a result of freshes or a

flush event. Used in conjunction with

bottom water quality to assess the

severity of slug events. Also an

important factor in assessment of the

recruitment success of larval catfish.

Functionalrelationship,

expert

judgement: (13)

Severity of

SlugEvent

Descriptive

indicator of theseverity of an

adverse slug

Low

Mod

High

Bottom Water

QualityPeak XS

Velocity

Synthetic variable that characterizes the

severity of a slug event on the basis ofbottom water quality status and mean

cross-sectional peak velocity.

Analyst

judgement


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event

Abundanc

e Breeding

Popn

Descriptive

indicator of

abundance of

Freshwater

Catfish

breeding

population

within the

reach at the

end of summer

Low

Mod

High

Hydraulic

Habitat

Severity ofSlug Event


abundance of the Freshwater Catfish

breeding population within the reach on

the basis of hydraulic habitat and

mortality impact of slug events. Is a

key factor in assessing the viability of

Freshwater Catfish within the reach.

Expert

judgement: (13)

Recruitme

nt Juvenile

Catfish

Descriptive

indicator of

recruitmentsuccess of

juvenile

catfish within

the reach at the

end of summer

Poor

Mod

Good

Hydraulic

Habitat

Severity of

Slug Event


recruitment success of juvenile catfish

within the reach on the basis ofhydraulic habitat and mortality impact

of slug events. Is an important factor

in assessing the viability of Freshwater

Catfish within the reach.

Analyst

judgement

Spawning

Area

Availabilit

y

Descriptive

indicator of the

availability of

spawning areaover the reach

Low

Mod

High

Depth Characterizes availability of spawning

area within the reach over summer. Is

an important factor in the assessment of

the recruitment success of larval catfish.

Data, expert

judgement: (7),

(12)


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Recruitme

nt Larval

Catfish

Descriptive

indicator of

recruitment

success of

larval catfishwithin the

reach at the

end of summer

Low

Mod

High

Spawning

Area

Availability

Hydraulic

Habitat

Severity of

Slug Event

Peak XS

Velocity


recruitment success of larval catfish

within the reach on the basis of

spawning area availability, hydraulic

habitat and mortality impacts of slugevents and high flow velocity

conditions. Is an important factor in

assessing the viability of Freshwater

Catfish within the reach.

Analyst

judgement

Viability

of Catfish

Popn

Descriptive

indicator of the

viability of

FreshwaterCatfish

populations

within the

reach at the

end of summer

Poor

Good

Abundanace

Breeding Popn

Recruitment

JuvenileCatfish

Recruitment

Larval Catfish

Model endpoint. Synthetic variable that

characterizes the viability of Freshwater

Catfish populations within the reach.

Analyst

judgement

(1) Anderson and Morison (1988b); (2) Western (1994); (3) Metzeling (1995); (4) SKM (1997); (5) Ryan et al . (1999); (6) Western and

Stewardson (1999); (7) Clunie and Koehn (2001); (8) SKM (2003b); (9) Cooperative Research Centre for Catchment Hydrology; (10) Grampians

Wimmera Mallee Water (formerly Wimmera-Mallee Water; (11) A.Western, unpublished data; (12) P.Clunie, pers.comm., 2005; (13) T.Ryan,

pers.comm., 2005.


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5.6 Reviewing BN model structure and node relationships

The graphical and probability structure of the BN was checked and reviewed using methods

described in Cain (2001). With respect to overall graphical structure, checks were performed

to ensure that all parentless nodes represented either controlling factors or interventions and

that the childless node described the stakeholder management objective. Expert review (by

P. Clunie, Arthur Rylah Institute for Environmental Research, ARI, and T. Ryan, formerly of

ARI) was used to ensure that all important variables had been included and that any

omissions were justifiable/defensible. Node names and states were checked in consultation

with experts to ensure that they were adequately and appropriately defined in terms of spatial

and temporal scale and specific problem context.

Node connections, relationships and probability structure were reviewed in consultation with

experts and by altering node states to observe how the model behaved. Node relationships

were systematically reviewed according to Cains (2001) methodology. Full details of the

review process and outcomes are available in Chee (2005).

5.7 Sensitivity to findings

Sensitivity to findings analysis was used to identify network variables which have the

greatest influence on the variables of management interest. These variables, denoted as

query nodes, include: Bottom Water Quality, Hydraulic Habitat, Severity of Slug

Event, Abundance Breeding Popn, Recruitment Juvenile Catfish, Recruitment Larval

Catfishand Viability of Catfish Popn.


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where qis a state of the query variable, Q,fis a state of the findings variable, F, and the

summations refer to the sum of all states qof fof variables Qor F(Pearl, 1991).The entropy reduction or mutual measures can be used to rank variables according to the

capacity for entered evidence at these variables to change the posterior probability of the

query node. As entropy reduction describes the reduction in uncertainty in a query node

when information is available for a findings node, it can be used to help identify areas of

research priority by indicating which variables to target to enable optimal learning.


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6. Management Implications

6.1 General

The pattern of allocation of available water over the suite of flow components, comprising

cease-to-flow periods, minimum flows and freshes constitutes the environmental flow

release strategy over summer. Exploration of flow scenarios or strategies using the BN

model was carried out by entering findings at the appropriate nodes and examining the

resultant changes in probability distributions of the variables of management interest. This

allows a model-user to assess the sensitivity of outcomes to different managementinterventions. Two basic scenarios were used to provide baselines for comparative

evaluation of environmental flow strategies:

a worst case scenario (called Dry) in which the focal reach receives no flow at allthroughout summer, and

the SKM (2002a) environmental flow recommendations (Table 5) which consisted of acease-to flow period of 21 days; median MDF=5 ML/day and four evenly distributedfreshes over summer of 20 ML each. The implementation of this strategy in the full

BN model is shown in Figure 6.

Under conditions of low median MDF, the most likely state of cross-sectional area

distribution (XSArea) is 0-20 m2with a probability of 0.83 and the most likely states of

Bottom Water Quality are Okay and Good with probabilities of 0.44 and 0.35

respectively (Figure 6). These conditions in turn result in Hydraulic Habitat being most


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Table 6: Results of sensitivity to findings analysis for the seven variables of management interest. The rank of each network

variable (findings node) with respect to the query variable is given by the value in each cell (with a rank of 1 indicating the

network variable with the greatest influence on the query variable). The entropy reduction value associated with each

findings node, I, is given (to four decimal places) in parentheses within each cell.

Model Variables Bott Water

Quality

Hydraulic

Habitat

Severity of

Slug Event

Abundance

Breeding

Popn

Recruitmt

Juvenile

Catfish

Recruitmt

Larval

Catfish

Viability

Catfish Popn

Median MDF 15

(0.0041)

17

(0.0024)

17

(0.0027)

16

(0.0004)

16

(0.0010)

17

(0.0003)

16

(0.0001)

Depth 10

(0.0287)

10

(0.0131)

10

(0.0127)

10

(0.0027)

10

(0.0060)

8

(0.0186)

10

(0.0008)

XSArea 16

(0.0037)

11

(0.0088)

12

(0.0074)

11

(0.0024)

11

(0.0030)

12

(0.0086)

11

(0.0005)

Extent Grdwater

Intrusion

19

(0.0014)

19

(0.0006)

20

(0.0006)

19

(0.0001)

19

(0.0003)

19

(0.0001)

19

(0.0000)

Obs Range Bott

Salinity

6

(0.1005)

8

(0.0348)

9

(0.0135)

9

(0.0088)

9

(0.0110)

13

(0.0060)

9

(0.0011)

Bottom Salinity 3

(0.2762)

6

(0.0932)

6

(0.0402)

7

(0.0215)

6

(0.0300)

9

(0.0159)

8

(0.0029)

Bottom DO 5

(0.1340)

7

(0.0592)

5

(0.0603)

8

(0.0119)

8

(0.0260)

7

(0.0237)

7

(0.0030)

Fresh Magnitude 20 20 21 21 21 20 21


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(0.0001) (0.0001) (0.0002) (0.0000) (0.0000) (0.0000) (0.0000)

Fresh Freq 13

(0.0069)

14

(0.0030)

16

(0.0029)

15

(0.0005)

15

(0.0010)

16

(0.0005)

15

(0.0001)

Mixing due to MDF 11

(0.0156)

16

(0.0025)

14

(0.0032)

18

(0.0002)

17

(0.0009)

21

(0.0000)

17

(0.0000)

Mixing due to

Freshes

17

(0.0031)

18

(0.0010)

19

(0.0006)

17

(0.0003)

18

(0.0003)

14

(0.0015)

18

(0.0000)

Max Int Mix Evts 9

(0.0301)

12

(0.0059)

13

(0.0066)

13

(0.0007)

12

(0.0020)

18

(0.0002)

14

(0.0002)

Bott Water Quality 1

(0.4386)

1

(0.3344)

3

(0.0781)

2

(0.1500)

2

(0.0827)

4

(0.0150)

Hydraulic Habitat 1

(0.4386)

2

(0.1693)

1

(0.1839)

1

(0.3250)

1

(0.1584)

2

(0.0275)

Flush Magnitude 21

(0.0000)

21

(0.0000)

18

(0.0014)

20

(0.0000)

20

(0.0000)

15

(0.0005)

20

(0.0000)

Peak XS Velocity 18

(0.0020)

13

(0.0036)

8

(0.0246)

12

(0.0014)

13

(0.0020)

10

(0.0130)

12

(0.0004)

Severity of Slug

Event

2

(0.3344)

4

(0.1693)

5

(0.0383)

3

(0.0980)

3

(0.0769)

5

(0.0120)


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6.3Analysis/evaluation of environmental flow scenarios and strategies

The predicted probability distributions for all seven variables of management interest show

the relative likelihood of alternative states under the worst case scenario and the SKM flow

strategy (Figure 8). If flows cease completely over summer and there is no management

intervention, Bottom Water Quality is likely to be in a Poor or Okay state with a

probability of 0.38 and 0.37 respectively. Similarly, Hydraulic Habitat is likely to be a

Poor or Average state with almost equal probabilities of about 0.42. The probability that

the severity of a slug event is High is 0.26.

The most likely state for Abundance Breeding Popnis moderate with a probability of 0.53and the most likely state for Recruitment Juvenile Catfishand Recruitment Larval

Catfish is Low with a probability of 0.51 and 0.68 respectively. With respect to catfish

viability (Viability of Catfish Popn), the most likely state is Good with a probability of

0.85. Bottom Water Quality, Hydraulic Habitat, Severity of Slug Event and

Recruitment Juvenile Catfishare all predicted to be substantially better under the SKM

flow strategy, which provides a low level of minimum flows as well as four small, regularly

spaced freshes. This is evident in the decrease in probability of the least desirable state foreach variable (particularly bottom water quality and hydraulic habitat) under the SKM

strategy (Figure 8). Smaller improvements were observed in the abundance of catfish

breeding population and recruitment of larval catfish and as expected there was little change

in catfish population viability (Figure 8).

Examination of the decision function for Median Mean Daily Flow (MDF)showed thatfor

the utility function defined above, expected utility will be greatest when the median MDF is


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0

0.1

0.2

0.3

0.4

0.5

Poor Okay Good

B o t t o m W a t e r Q u a l i t y

Dry

SKM

0

0.1

0.2

0.3

0.4

0.5

0.6

Poor Ave Good

H y d r a u l i c H a b i t a t

Dry

SKM

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Low High

S e v e r i t y o f S l u g E v e n t

Dry

SKM

0

0.1

0.2

0.3

0.4

0.5

0.6

Low Mod High

A b u n d a n c e B r e e d i n g P o p n

Dry

SKM

0.3

0.4

0.5

0.6

Dry

SKM

0.4

0.5

0.6

0.7

0.8

Dry

SKM

0

0.1

0.2

0.3

0.4

0.5

Poor Okay Good

B o t t o m W a t e r Q u a l i t y

Dry

SKM

0

0.1

0.2

0.3

0.4

0.5

0.6

Poor Ave Good

H y d r a u l i c H a b i t a t

Dry

SKM

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Low High

S e v e r i t y o f S l u g E v e n t

Dry

SKM

0

0.1

0.2

0.3

0.4

0.5

0.6

Low Mod High

A b u n d a n c e B r e e d i n g P o p n

Dry

SKM

0.3

0.4

0.5

0.6

Dry

SKM

0.4

0.5

0.6

0.7

0.8

Dry

SKM


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mixing events occur tends be high because the majority of pools have small cross-sectional

areas see Figures 6 and 7), and the lower magnitude of such freshes reduces the risk of aslug event of high severity.

40

42

44

46

48

50

52

54

56

1 (240) 2 (120) 3 (80) 4 (60)

No. of 'freshes' ('Fresh' volume in ML)

E

xpected

UtilityValue

MDF=0 ML/day

MDF=10 ML/day

MDF=30 ML/day


67/84


environmental outcomes can be achieved for quite a wide range of environmental flow

volumes as long as the maximum number of four freshes are provided over summer.

Risk based Approaches for Managing Contaminants in Catchments Report 2 60


68/84


Table 7: Summary of the main characteristics and expected utility value of various flow strategies evaluated using the BN decision

network. The two shaded rows highlight the base cases of a worst case Dry scenario and the SKM flow strategy that serve as

benchmarks for evaluation.

Scenario/Flow Strategy (ID,

Description)

Median MDF

(ML/day)

Fresh

Magnitude (ML)

Fresh

Frequency

Total Volume

Required (ML)

Utility Value

1 Worst case Dry: no flows at all oversummer

0 0 0 0 42.5

2 SKM environmental flow

recommendations

5 20 4 765 52.4

3 High sustaining flow strategy: high

median MDF & no freshes

30 0 0 2070 52.6

4 Moderate sustaining flow strategy:

low-mod median MDF & no freshes

10 0 0 690 49.5

5 Low sustaining flow strategy: lowmedian MDF & no freshes

5 0 0 345 46.0

6a

b

c

d

e

f

g

Freshes only strategy:

No MDF & 4 freshes

0

0

0

0

0

0

0

use of a bayesian network decision tool to mgt environ flow in river

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