Research Report · A. S. Halls, J. M. King, C. V. Minte-Vera, R. E. Tharme and R. L. Welcomme...
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Water Requirements of Floodplain Rivers and Fisheries: Existing Decision Support Tools and Pathways for Development A. H. Arthington, E. Baran, C. A. Brown, P. Dugan, A. S. Halls, J. M. King, C. V. Minte-Vera, R. E. Tharme and R. L. Welcomme Research Report 17 Figure to be inserted
Transcript of Research Report · A. S. Halls, J. M. King, C. V. Minte-Vera, R. E. Tharme and R. L. Welcomme...
Water Requirements of Floodplain Rivers and Fisheries:Existing Decision Support Tools and Pathways for Development
A. H. Arthington, E. Baran, C. A. Brown, P. Dugan, A. S. Halls, J. M. King, C. V. Minte-Vera, R. E. Tharme and R. L. Welcomme
Research Report 17
Figure to be inserted
The Comprehensive Assessment of Water Management in Agriculture takesstock of the costs, benefits and impacts of the past 50 years of water developmentfor agriculture, the water management challenges communities are facing today,and solutions people have developed. The results of the Assessment will enablefarming communities, governments and donors to make better-quality investmentand management decisions to meet food and environmental security objectives inthe near future and over the next 25 years.
The Research Report Series captures results of collaborative research conductedunder the Assessment. It also includes reports contributed by individual scientistsand organizations that significantly advance knowledge on key Assessmentquestions. Each report undergoes a rigorous peer-review process. The researchpresented in the series feeds into the Assessment’s primary output—a “State ofthe World” report and set of options backed by hundreds of leading water anddevelopment professionals and water users.
Reports in this series may be copied freely and cited with dueacknowledgement. Electronic copies of reports can be downloaded from theAssessment website (www.iwmi.org/assessment).
If you are interested in submitting a report for inclusion in the series, pleasesee the submission guidelines available on the Assessment website or send awritten request to: Sepali Goonaratne, P.O. Box 2075, Colombo, Sri Lanka.
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Comprehensive Assessment of Water Management inAgriculture Research Report 17
Water Requirements of Floodplain Riversand Fisheries: Existing Decision SupportTools and Pathways for Development
A. H. Arthington, E. Baran, C. A. Brown, P. Dugan,A. S. Halls, J. M. King, C. V. Minte-Vera, R. E. Tharme andR. L. Welcomme
International Water Management InstituteP O Box 2075, Colombo, Sri Lanka
The authors: A. H. Arthington, Australian Rivers Institute and eWater CRC, Faculty ofEnvironmental Sciences, Griffith University, Nathan, Queensland 4111, Australia; E. Baran,WorldFish Center (formerly ICLARM), P. O. Box 500 GPO, 10670 Penang, Malaysia; C.A. Brown, Southern Waters Ecological Research and Consulting cc, 50 Campground Road,Rondebosch, Cape Town, South Africa; P. Dugan, WorldFish Center, P. O. Box 1261Maadi, Cairo, Egypt; A. S. Halls, Marine Resources Assessment Group Ltd, 47 PrincesGate, London SW7 2QA, UK; J. M. King, Freshwater Ecology Unit, Zoology Department,University of Cape Town, Rondebosch, Cape Town, South Africa; C. V. Minte-Vera,Research Nucleus in Limnology, Ichthyology and Aquaculture (NUPELIA), Department forBiology, State University of Maringa, Brazil; R. E. Tharme, International Water ManagementInstitute (IWMI), P. O. Box 2075, Colombo, Sri Lanka; R. L. Welcomme, RenewableResources Assessment Group, Faculty of Life Sciences, Department of EnvironmentalScience and Technology, Imperial College of Science Technology and Medicine, PrinceConsort Road, London SW7 2BP, UK.
Acknowledgements: We acknowledge the leadership and funding provided by Dr. PatrickDugan, WorldFish Center, the support provided to all authors by their host organisations,and the Editor of this report, Mahen Chandrasoma, IWMI, for his careful work on the finalmanuscript. This report has been reviewed prior to publication.
Arthington, A. H.; Baran, E.; Brown, C. A.; Dugan, P.; Halls, A. S.; King, J. M.; Minte-Vera, C. V.; Tharme, R. E.; Welcomme, R. L. 2007. Water requirements of floodplainrivers and fisheries: Existing decision support tools and pathways for development.Colombo, Sri Lanka: International Water Management Institute. 74 pp. (ComprehensiveAssessment of Water Management in Agriculture Research Report 17)
/ environmental impact assessment / flood plains / fisheries / water requirements / decisionsupport tools / ecology / models / rivers / methodology /
ISSN 1391-9407ISBN 978-92-9090-656-8
Copyright © 2007, by International Water Management Institute. All rights reserved.
Cover photograph: Sampling fish by cast net during an environmental flow study in theGeum River, South Korea. Photo Credit: Angela H. Arthington.
Please send inquiries and comments to: [email protected]
The Comprehensive Assessment (www.iwmi.cgiar.org/assessment) is organized through the CGIAR’sSystemwide Initiative on Water Management (SWIM), which is convened by the International WaterManagement Institute. The Assessment is carried out with inputs from over 100 national and internationaldevelopment and research organizations—including CGIAR Centers and FAO. Financial support forthe Assessment comes from a range of donors, including core support from the Governments of theNetherlands, Switzerland and the World Bank in support of Systemwide Programs. Project-specificsupport comes from the Governments of Austria, Japan, Sweden (through the Swedish Water House)and Taiwan; Challenge Program on Water and Food (CPWF); EU support to the ISIIMM Project; FAO;the OPEC Fund and the Rockefeller Foundation; Oxfam Novib and CGIAR Gender and DiversityProgram. Cosponsors of the Assessment are the: Consultative Group on International AgriculturalResearch (CGIAR), Convention on Biological Diversity (CBD), Food and Agriculture Organization(FAO) and the Ramsar Convention.
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Contents
Summary ............................................................................................................ v
Introduction ........................................................................................................ 1
Review of Environmental Flow Methodologies ................................................. 3
Holistic Methodologies and the Relevance of Hydrology-Ecology Models ...... 9
The DRIFT Methodology Further Examined..................................................... 23
River Fisheries Models ...................................................................................... 40
Dealing with Uncertainty .................................................................................... 51
Conclusions ........................................................................................................ 56
Literature Cited................................................................................................... 59
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Summary
Fisheries are some of the most valuable naturalresources that depend upon natural regimes ofriver flow for their productivity and full developmentbenefits. Managing rivers to sustain these benefitsrequires that environmental flow requirements ofriver fisheries be understood and conveyedeffectively into decision-making processes atmultiple levels within the river basin. The presentresearch report reviews existing environmental flowmethodologies and fisheries production models todetermine which combination of existingapproaches will provide most potential fordevelopment of such decision support tools.
This is the first comprehensive review of theuse of environmental flow methodologies formanaging large rivers and floodplains for fisheriesproduction. Previous reviews have focusedexclusively on the fisheries models themselvesand have not explored how these models can becombined with other approaches to understandand predict the impact of changes in river flowregimes on fisheries production. In view of theimportance of river fisheries in the livelihoods ofthe rural poor across much of the tropics, more
effective decision-support tools that improve theecological basis of water management in riverbasins can play an important part in increasingfisheries productivity and the health andlivelihoods of rural populations at the basin scale.
The review concludes that the methodologyDRIFT (Downstream Response to Imposed FlowTransformation) combined with use of Bayesiannetworks and age-structured fisheries models willprovide the most promising direction for futureresearch. There will however be cases where thisapproach will not work and flexibility in the use ofother environmental flow methodologies and toolswill be required.
The target audience for this review includesriver and fisheries scientists concerned withimproving water management for the poor of thedeveloping world, and river scientists andmanagers in general. The results of the researchcan be best applied through the development ofimproved environmental flow and fisheriesmodeling approaches, including studies in thefocal basins of the CGIAR Challenge Program onWater and Food.
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Water Requirements of Floodplain Rivers andFisheries: Existing Decision Support Tools andPathways for Development
A. H. Arthington, E. Baran, C. A. Brown, P. Dugan, A. S. Halls, J. M. King,C. V. Minte-Vera, R. E. Tharme and R. L. Welcomme
Introduction
As demand for freshwater continues to rise andways are sought to improve water productivity,decision-making bodies at local, basin andnational levels require accurate information on therole of river flows in sustaining a wide range ofenvironmental benefits. River and floodplainfisheries are one of these benefits, yielding anestimated 8 million tons of fish and other aquaticnatural resources annually, and providingemployment, income and an essential supply ofanimal protein for hundreds of millions of people(FAO statistics). In addition, respect for thediversity of living aquatic flora and fauna isinherent in international conservation instrumentsincluding the Conventions on Biological Diversityand Wetlands, which emphasize the duty ofsignatory nations to preserve the wealth of theirliving aquatic resources and the aquaticecosystems that support them. However, as useof water for agriculture, hydropower generationand supplies for domestic and industrial use fromriver systems has increased, progressivedegradation of ecosystem structure andfunctioning has occurred, with associateddeclines in fish catches and the disappearance ofindividual species (Dudgeon et al. 2006;Welcomme et al. 2006). There is now widespreadinternational recognition that future water allocationand flow management in river basins must takeaccount of the needs of river ecosystems, fishand fisheries and the human communities thatdepend upon them (Bunn and Arthington 2002;Naiman et al. 2002; Postel and Richter 2003;Revenga et al. 2000; Rosenberg et al. 2000).
The World Water Vision, the WorldCommission on Dams (WCD), the GlobalDialogue on Water for Food and Environment, andthe Convention on Biological Diversity (CBD),have all responded to these growing pressuresupon river ecosystems, calling for a newapproach to fisheries and river management asan integral part of efforts to improve thesustainable benefits that people obtain fromwater. For example, Guidelines 15 and 16 of theWCD call for “Environmental Flow Assessments”and “Maintaining Productive Fisheries” andspecify inter alia the importance of assessmentsof the water requirements of fish populations andthe mitigation of fish losses on the downstreamfloodplain through flow releases. Similarly, at the8th Meeting of the Conference of the ContractingParties to the Ramsar Convention on Wetlands(online: http://www.ramsar.org/index_cop8.htm),several pertinent resolutions were passed, notablyResolutions VIII.1 (guidelines for the allocationand management of water for maintaining theecological functions of wetlands), VIII.2 (dealingwith the outcomes of the WCD process), andVIII.34 (management of agriculture, wetlands andwater resources). These resolutions have recentlybeen superseded by Ramsar Resolution IX.4 (theRamsar Convention and conservation, productionand sustainable use of fisheries resources, i.e.,fish, crustaceans, mollusks and algae) passed atthe 9th Meeting of the Conference of the Partiesto the Convention on Wetlands held in Uganda in2005 (online: http://www.ramsar.org/res/key_res_ix_index_e.htm). Contracting Parties and
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relevant organizations are urged to use thehabitat and species conservation provisions ofthe Convention to support the introduction and/orcontinuance of management measures thatmitigate the environmental impacts of fishing,including the use of spatial managementapproaches as appropriate, and the RamsarSecretariat is requested to work with otherconventions, instruments and organizationsconcerned with the conservation of biodiversityand the management of natural resources,including the Food and Agriculture Organization ofthe United Nations (FAO) at an international andregional level, in order to promote the synergyand alignment of planning and managementapproaches that benefit the conservation andsustainable management of fisheries resourcesand recognition of the contribution this makestowards meeting CBD targets and MillenniumDevelopment Goals (MDGs).
While this growing international recognition ofthe importance of riverine resources provides thebasis for policy frameworks, efforts to build uponthese initiatives and improve water managementin individual rivers have been constrained by thelack of specific information on the value ofindividual fisheries, and the impact of changes inwater flow on these resources and the peoplewho depend upon them. At the same time, it isdifficult to establish the water managementregime required to sustain fisheries and theirbenefits in the face of increasing water demand(Welcomme et al. 2006). As a result the recenthistory of river fisheries in the tropics, inparticular, has continued to be one of decline andloss of benefits to people and ecosystems(Revenga et al. 2000; Allan et al. 2005).
If the increased awareness and emergingpolicy framework provided through theseinternational initiatives are to lead to sustainedbenefits for poor fishing communities, they need tobe followed now by the generation of policyrelevant information and development of practicaltools that can support water managementdecisions at local, basin and national level. Thegrowing demand for water to service human needsmeans that increasing quantities will be withdrawnfrom natural freshwater ecosystems. It is inevitable
that this trend to take increasing control offreshwater resources (surface and ground water)on all continents will expand, at least in part,through hard engineering solutions such as dams,channelization and inter-basin water transferschemes (Naiman et al. 2002). The withdrawal andregulation of water alters the absolute amountavailable in any one system, the ratio of high tolow water volumes and the timing, duration,frequency and rate of change of floods and lowflows. The organisms living in aquatic ecosystemshave become adapted over long periods of time tothe type of flow regime occurring in the riverconcerned (Arthington et al. 1992; Poff et al. 1997;Richter et al. 1997, among others). Alterations inflow quantity and temporal pattern impact on thespecies richness, distribution and relativeabundance of the organisms present, usually byreducing diversity and densities, as well as forcingshifts in communities towards organisms that areless favored for food and commerce.
These and related issues were reviewed inearly 2002 during a workshop on “Managing RiverFlows to Sustain Tropical Fisheries” held duringthe International Working Conference onEnvironmental Flows for River Systems and theFourth International Ecohydraulics Symposium(Cape Town, South Africa, March 4-8, 2002). Thisworkshop provided participants with an opportunityto examine these issues, and work together toidentify practical research priorities andopportunities. Following the workshop, aninternational collaborative research project wasestablished to review and develop decision-supporttools for water allocations that will sustain riverfisheries and so improve water productivity atbasin level. As the first phase of this project,funded by the Comprehensive Assessment ofWater Management in Agriculture (CA) and theWorldFish Center, the present study wasundertaken to effect state of the art reviews of thetools currently being used to assess waterrequirements for river ecosystems and fish/fisheries, and assess their suitability for applicationin large floodplain rivers, by identifying theirstrengths and weaknesses, and providing guidanceon the further development and testing of improvedtools in selected river basins.
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Decisions on the allocation of wateramong users should be made on the basis ofthe best available information on the sector-by-sector consequences of changinghydrological regimes. The following reviewexamines the various tools that are availableto provide information on the impact on riverecosystems and their fisheries (and othernatural aquatic resources) of changes inhydrological regime and of differing land-usepractices on the river-floodplain system.Some of the methods described directly
address the response of fish and fishers to suchchanges. Others place fisheries in the widercontext of other production and biodiversityrelated issues within the river basin. Themethodologies and models described form acomplementary suite of tools that is intended toprovide planners and policymakers withinformation as to the consequences for fisheriesof various possible changes in hydrologicalregime resulting from water abstractions andother types of alteration to natural river flowregimes.
Review of Environmental Flow Methodologies
The Conceptual Basis ofEnvironmental Flow Assessment
In many parts of the world there is growingawareness of the pivotal role of the flowregime (hydrology) as a key ‘driver’ of theecology of rivers and their associatedfloodplain wetlands (see Richter et al. 1997;Poff et al. 1997; Bunn and Arthington 2002;Naiman et al. 2002 for reviews). In large part,this recognition has stemmed from anincreasing body of scientific knowledgedescribing the range of negative impacts toriverine ecosystems, and consequent socialand economic effects, that are clearlyattributable, either directly or indirectly, toalterations of natural flow regimes (as outlinedin Rosenberg et al. 2000).
Every river system has a flow regime withparticular characteristics relating to flowquantity, temporal attributes such as seasonalpattern of flows, timing, frequency,predictability and duration of extreme events(floods, droughts), rates of change and otheraspects of flow variability (Poff et al. 1997;Olden and Poff 2003). Each of thesehydrological characteristics has individual (aswell as interactive) influences on the physical
nature of river channels and habitat structure,biological diversity, recruitment patterns and otherkey ecological processes sustaining the aquaticecosystem (Poff et al. 1997; Baron et al. 2002;Bunn and Arthington 2002; Naiman et al. 2002).These processes in turn govern the ecosystemgoods and services that rivers provide to humans(e.g., flood attenuation, water purification, fish andfibres, medicines).
Recognition of the escalating hydrologicalalteration of rivers on a global scale and resultantenvironmental degradation has led to the gradualestablishment of a field of scientific researchtermed environmental flow assessment (EFA). Insimple terms, such an assessment addresseshow much of the original discharge of a rivershould continue to flow down it and onto itsfloodplains, and when/how often such flowsshould occur in order to maintain specified,valued features of the ecosystem (Arthington andPusey 1993; Tharme and King 1998; King et al.2003; Tharme 2003). An EFA produces one ormore descriptions of possible modifiedhydrological regimes for the river, termed theenvironmental flow requirement(s) (EFRs) orenvironmental water allocation(s) (EWAs), eachlinked to a predetermined objective in terms ofthe ecosystem’s future ecological condition. In
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recent literature environmental flows andenvironmental water allocations refer to the waterregime of a river, wetland or coastal zonenecessary to maintain the biophysicalcomponents, ecological processes and health ofaquatic ecosystems, and associated ecologicalgoods and services (Arthington et al. 2006).
Environmental flow assessments are directedat two main types of management response tothe impacts of altered flow regimes: either tomaintain the hydrological regimes of undevelopedrivers in near to natural condition or at least tooffer some degree of protection of river flows andecosystem characteristics (i.e., riverconservation), or, in developed rivers withmodified flow regimes, to restore key componentsand patterns of the original flow regime in theexpectation that flow-related ecosystemcharacteristics will also be restored.
The level of resolution of environmental flowrecommendations ranges widely, from a singleannual volumetric allocation through to, morecommonly nowadays, a comprehensive, modifiedflow regime where the overall volume of waterallocated for environmental purposes is acombination of different monthly and/or event-based flow quantities (Tharme 2003; Arthington etal. 2006). The scale at which assessments areundertaken may also vary widely, for instance,from an entire river basin that includes aregulated mainstream and unregulated tributaries,to a flow restoration project for a single flow-impacted river reach (King et al. 1999). Differentmethodologies are appropriate over such scalesof space and resolution, and in relation to typicalproject constraints including the time frame forassessment, the availability of data, technicalcapacity and finances (Tharme 1996; Arthingtonet al. 2004a, 2004b). As described further below,methods range from rapid, reconnaissance-levelapproaches for regional, national or basin-widewater resources planning, to resource intensivemethodologies for highly exploited river reachessubject to multiple uses of water.
Evolution and Description of the MainTypes of Environmental Flow Methods
The origins of environmental flow assessment
Tharme (1996) traced the evolution ofenvironmental flow methods worldwide, observingthat historically, the United States of Americawas at the forefront of research, with the firstad hoc methods and a series of more formallydocumented techniques appearing in the late1940s and 1970s, respectively. In most otherparts of the world, the EFA process becameestablished far later, with approaches todetermine environmental water allocations onlybeginning to appear in the literature in the 1980s.Early on, and still today in some countries, thefocus of environmental flow assessment wasentirely on the maintenance of economicallyimportant freshwater fish species and fisheries inregulated rivers, where a minimum acceptableflow was recommended based almost entirely onpredictions of in-stream habitat availabilitymatched against the habitat preferences of one ora few target species. It was assumed that theflows intended to protect target fish populations,habitats and activities would ensure maintenanceof other riverine species and communities. Overthe past decade or so, a broader, ecosystemapproach to EFAs has been adopted in boththeory and practice.
Description of methods
Four relatively discrete types of environmentalflow methods have become established overtime, namely (1) hydrological, (2) hydraulic rating,(3) habitat simulation, and (4) holistic (ecosystem)methods (Table 1).
Hydrological methods
These represent the simplest set of techniqueswhere, at a desktop level, hydrological data, asnaturalized, historical monthly or average daily
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flow records, are analyzed to derive standard flowindices as recommended flows. Commonly, theEFR is represented by a proportion of flow (oftentermed the ‘minimum flow’, e.g., Q95, the flowequalled or exceeded 95% of the time). This lowflow quantity is intended to maintain river health,fisheries or other highlighted ecological featuresat some acceptable level, usually on an annual,seasonal or monthly basis. In a few instances,secondary information in the form of catchmentvariables, hydraulic, biological and/orgeomorphological criteria are also incorporatedinto the EFR. As a result of their rapidity andlack of ecological refinement, these low resolutionhydrological methods are only appropriate at theplanning level of water resource development, andin situations where bulk water entitlements mustbe estimated, or where limited tradeoff of waterallocations is required. These volumetric EFRsshould be regarded as preliminary flow targets(Tharme 1996; Arthington and Zalucki 1998;Dunbar et al. 1998; Table 1) to be refined at alater stage of water resource planning.
Hydraulic rating methods
These use changes in simple hydraulic variables,such as wetted perimeter or maximum depth,usually measured across single, flow-limited rivercross-sections (e.g., riffles), as a surrogate forhabitat factors known or assumed to be limitingto target biota. Environmental flows aredetermined from a plot of the hydraulic variableagainst discharge, commonly by identifying curvebreakpoints where significant percentagereductions in habitat quality occur with decreasesin discharge. It is assumed that ensuring somethreshold value of the selected hydraulicparameter at altered flows will maintain the in-stream biota and thus, ecosystem integrity.These relatively low resolution techniques are stillin use, but have been largely superseded bymore advanced habitat modeling tools orassimilated into holistic methodologies (Tharme1996; Arthington and Zalucki 1998; Table 1).
Habitat simulation or microhabitat modelingmethods
These also make use of hydraulic habitat-discharge relationships, but provide more detailed,model-based analyses of both the quantity andsuitability of in-stream physical habitat availableto target biota under different discharges, on thebasis of integrated hydrological, hydraulic andbiological data. Flow-related changes in physicalmicrohabitat are modeled in various hydraulicprograms, typically using data on depth, velocity,substratum composition and cover and, morerecently, complex hydraulic indices (e.g., benthicshear stress) collected at multiple cross-sectionswithin each study river reach. Simulated availablehabitat is linked with seasonal information on therange of habitat conditions used by target fish orinvertebrate species (or life stages, assemblagesand/or activities), commonly using habitatsuitability index curves. The resultant outputs, inthe form of habitat-discharge curves for specificbiota, or extended as habitat time andexceedance series, are used to derive optimumenvironmental flows. The relative strengths andlimitations of such methods are described inTharme (1996) and Arthington and Zalucki (1998),and they are compared with the other types ofapproach in Table 1.
Holistic (ecosystem) methodologies
As the above three types of methods haveevolved in recent times, so has understanding ofthe broader ecological requirements of fish andother aquatic biota such as invertebrates andaquatic vegetation, and of the interactionsbetween these groups and their physical habitats,as a consequence of altered flow regimes. Riverecologists in South Africa and Australia, inparticular, have argued for a broader approach toenvironmental flows and the conservation of riverecosystems (Arthington and Pusey 1993; Kingand Tharme 1994; Arthington et al. 1998). Aconceptual ‘holistic’ approach to environmentalwater allocation was first proposed in 1991, based
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TA
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on the natural flow regime as a guide to theenvironmental conditions that have maintainedriver ecosystems in their characteristic states.This approach argues that “if the essentialfeatures of the natural hydrological regime can beidentified and adequately incorporated into amodified flow regime, then, all other things beingequal, the extant biota and functional integrity ofthe ecosystem should be maintained” (Arthingtonet al. 1992). Likewise, Sparks (1992, 1995)suggested that “rather than optimizing waterregimes for one or a few species, a betterapproach is to try to approximate the natural flowregime that maintained the entire panoply ofspecies.”
Importantly, therefore, all holisticmethodologies aim to address the waterrequirements of the entire riverine ecosystem(Arthington et al. 1992) rather than the needs ofjust a few taxa (e.g., fish species of significancefor fishing or conservation, riparian and wetlandvegetation, or water birds). They are nowadaysunderpinned by the “natural flows paradigm” (Poffet al. 1997) and basic principles of river corridorrestoration (Ward et al. 2001), and share acommon objective - to maintain or restore thosecharacteristics of the natural (or modeledunregulated) flow regime and its variability thatare required to maintain or restore the biophysicalcomponents and ecological processes of in-stream and groundwater systems, floodplains anddownstream receiving waters (e.g., terminal lakesand wetlands, or estuaries).
In any holistic methodology, key flow eventsare identified in terms of selected criteria definingflow variability, for some or all major components/attributes of the riverine ecosystem(geomorphology, water quality, vegetation,invertebrates, fish and other vertebratesassociated with the aquatic and riparianecosystem). This analysis is undertaken in eithera prescriptive ‘bottom-up’ or, more recently in afew cases, an interactive ‘top-down’ process(Arthington et al. 1998) involving structured,workshop-based multi-disciplinary input from awide range of experts (Tharme and King 1998;King et al. 2000a, 2000b; Arthington et al. 2004a,2004b). In most approaches to date, a modified
flow regime is built from the bottom up by addingflow components to a baseline of zero flow on aflow event or month-by-month timescale, andelement-by-element. Each element represents adefined feature of the flow regime intended toachieve particular ecological or other objectives(King and Tharme 1994; Arthington et al. 2004b).In the few ‘top-down’, scenario-based approachesthat exist, environmental flow needs are definedthrough various levels of change from the naturalor other reference flow regime, rendering themless susceptible to any omission of critical flowsthan bottom-up approaches (Bunn 1998). Top-down approaches address the question - howmuch can we modify a river’s flow regime beforethe aquatic ecosystem begins to noticeablychange or becomes seriously degraded? Top-down approaches appear to be relatively rare, thetwo prominent ones being Downstream Responseto Imposed Flow Transformation or DRIFT (Kinget al. 2003) and the Benchmarking Methodology(Brizga et al. 2002; Arthington et al. 2004b): fordiscussion of similarities and differences, seeArthington et al. (2004b, 2006).
The most advanced holistic methodologiesroutinely utilize several of the tools forhydrological, hydraulic and physical habitatanalysis featured in the other methods discussedabove. Moreover, they may also rely on flow-ecology models to be sufficiently predictive intheir outputs (Tharme and King 1998; Bunn andArthington 2002). Further discussion of holisticmethodologies can be found in section: HolisticMethodologies and the Relevance of Hydrology-Ecology Models, and a summary comparison ofthis approach with hydrological and habitatmethods is provided in Table 1.
Other approaches
In addition to the four main categories ofmethodology, Tharme (1996, 2003), Dunbar et al.(1998) and Arthington and Pusey (2003) recognizean array of approaches that incorporatecharacteristics of more than one of the fourtypes, including partially holistic environmentalflow methods (EFMs), referred to as ‘combination’approaches (Tharme 2003). There are also a
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number of techniques not designed withenvironmental flow assessment in mind, butwhich have been variously adapted for thispurpose (termed ‘other’ approaches). Together,these approaches contribute to the diversity andtotal number of environmental flow methods andtools available globally (see section: An Overviewof Global Trends in Method Development andApplication).
An Overview of Global Trends inMethod Development and Application
Several comprehensive international reviews ofenvironmental flow methods have been publishedover the past decade (inter alia, Arthington andPusey 1993; Growns and Kotlash 1994; Tharme1996; Jowett 1997; Stewardson and Gippel 1997;Dunbar et al. 1998; Arthington 1998; Arthington etal. 1998; Arthington and Zalucki 1998; King et al.1999; Tharme 2003; Arthington et al. 2004b).Further information on new world developments inenvironmental flow methodologies can be found inpapers of the unpublished proceedings of the jointSouth African Environmental Flows for RiverSystems Working Conference and FourthInternational Ecohydraulics Symposium (held inCape Town, March 2002). Some papers from thisconference are available on CD, while othershave been published in River Research andApplications (Vol. 19, 2003).
Building on these and other sources, ananalysis by Tharme (2003) of the numbers ofenvironmental flow methods developed for rivers(and their associated floodplain wetlands) withinthe categories described above, revealed some207 discrete environmental flow techniquesapplied in 44 countries (within North America,Central and South America, Europe and theMiddle East, Africa, Australasia, and the rest ofAsia). A further seven countries demonstratedsome evidence of early environmental flowinitiatives with further advances as yetunreported.
Hydrological methods are the most frequentlyused approach for environmental flowassessment, at 30 percent of the global total(Tharme 2003). At least 61 different hydrologicalindices or techniques have been applied, mostcommonly the Tennant or Montana Method(Tennant 1976) and, even more simplistically,various percentages of average annual flow.Habitat simulation methods are second only tohydrological EFMs worldwide, representing 28percent of all methods. Within the approximately58 approaches documented, a subset ofmicrohabitat modeling approaches of similarcharacter and data requirements represents thecurrent state-of-the-art. Of the latter group, theInstream Flow Incremental Methodology, IFIM(including the Physical Habitat Simulation Model,PHABSIM; Bovee 1982; Milhous et al. 1989;Stalnaker et al. 1994), is currently the mostcommonly used environmental flow methodglobally, with applications in over 20 countries(Tharme 2003). Although it has been variouslyadapted over the years, it remains focusedprimarily on the in-stream flow requirements oftarget fish and, to a lesser extent, invertebrates.Applications, strengths and limitations of IFIM/PHABSIM are well documented (e.g., Arthingtonand Pusey 1993; King and Tharme 1994; Tharme1996; Pusey 1998 and the critiques cited therein).
Of 23 hydraulic rating methods reported inuse (11% of the global total), only the genericWetted Perimeter Method remains in wide usetoday (Gippel and Stewardson 1998). A fairly highnumber of combination type methodologies (17%overall) exist, the most common being theManaged Flood Release Approach of Acreman etal. (2000) or similar methods based onexperimental flow releases. The former approachhas been employed in river floodplain restorationprojects in Africa, for fisheries production andsustaining dependent livelihoods, but is onlypartially holistic in nature. Holistic methodologiescurrently represent around 8 percent of the globaltotal (Tharme 2003), and the individualmethodologies comprising this total are described
9
at length in sections: Holistic Methodologies andthe Relevance of Hydrology-Ecology Models andThe DRIFT Methodology Further Examined below.Finally, the smallest global proportion ofmethodologies (6.8%) represents alternative(‘other’) approaches, many of which are based onmultivariate regression analysis.
Applications of environmental flow methodsare addressed within a hierarchical framework inmany countries (King et al. 1999), at two or morestages: (1) reconnaissance-level assessment,primarily using hydrological methods; and (2)comprehensive assessment, using either habitatsimulation or holistic methodologies; examplesare provided in Arthington et al. (2003, 2004b),King et al. (2003) and Tharme (2003). Indeveloped countries of the Northern Hemisphereparticularly, habitat simulation methods remain themost commonly applied tools at levels ofassessment beyond planning estimates, withrecent efforts concentrated on advances in multi-dimensional habitat modeling. Holisticmethodologies, predominantly used in SouthAfrica and Australia, have begun to attractgrowing international interest in both developedand developing regions of the world, with strong
expressions of interest by some 12 countries inEurope, Latin America, Asia and Africa (Tharme2003). King et al. (1999) and Tharme (2003)consider such methodologies to be especiallyappropriate in developing countries, due to theneed for resource protection at an ecosystemscale and the direct dependence of local peopleon the goods and services provided by aquaticecosystems for food and broader livelihoodsecurity.
Importantly, although the analysis of globaltrends conducted by Tharme (2003) showed arecent, marked increase in the number ofcountries undertaking environmental flowassessment, there remain a number of countriesthat have not yet embodied these concepts inwater resources policy and legislation, and wherethe role of environmental flows in the long-termmaintenance and sustainability of aquaticresources is yet to be fully recognized.Significantly, while just over half of the countriesrepresenting the developed world are reported tobe routinely involved in environmental flowinitiatives, at various degrees of advancement,only 11 percent of developing countries are activein any such research to date (Tharme 2003).
Holistic Methodologies and the Relevance of Hydrology-EcologyModels
Overview
At least 16 methodologies based on the holisticprinciples described in section: Review ofEnvironmental Flow Methodologies have beendeveloped over the last ten years (Tharme 2003).The collaboration among Australian and SouthAfrican researchers that resulted in theestablishment of the first conceptual holisticframework for environmental flow assessment(see section: Description of Methods) providedthe direction and momentum for both countries todevelop, in parallel and often in collaboration,
most available methodologies of this type(Tharme 1996).
In South Africa, the Building BlockMethodology (BBM)(King and Tharme 1994;Tharme and King 1998; King and Louw 1998;King et al. 2000a), has advanced throughnumerous applications linked to water resourceprojects to become the most commonly appliedholistic methodology worldwide (Tharme 2003). Itis used especially in the modified forms legallyrequired for intermediate and comprehensivedeterminations of the Ecological Reserve (andrecently incorporating the Flow Stress-Response
10
Method, O’Keeffe et al. 2002). Other holisticmethodologies applied locally include DownstreamResponse to Imposed Flow Transformation(DRIFT, King et al. 2003), detailed in section: TheDRIFT Methodology Further Examined and afocal tool in the present overview, and theEnvironmental Flow Management Plan Method(Muller 1997; DWAF 1999). Tharme (1996, 2003tracks the establishment of holistic methodologieswithin the broader context of environmental flowassessment in South Africa.
Australia’s progression from hydrological‘rules-of-thumb’ to the present-day emphasis onthe water requirements of river ecosystems hasbeen outlined in several review papers (Arthingtonand Pusey 1993, 2003; Cottingham et al. 2002;Arthington et al. 2004b, 2006). Other recentdevelopments are presented in a special issue ofthe Australian Journal of Water Resources (Volume5, No. 1, 2002). Australian holistic methodologiesare numerous and diverse in character, and includethe original conceptual Holistic Approach(Arthington et al. 1992; Davies et al. 1996;Arthington 1998; Pettit et al. 2001), Expert PanelAssessment Method (EPAM; Swales and Harris1995), Scientific Panel Assessment Method(SPAM; Thoms et al. 1996), Habitat AnalysisMethod (HAM, Walter et al. 1994; Burgess andVanderbyl 1996), Flow Restoration Methodology(Arthington et al. 2000), BenchmarkingMethodology (Brizga 2000; Brizga et al. 2002),Flow Events Method (Stewardson 2001), andFLOWS (a method used routinely in Victoria).
Additional holistic methodologies developedand applied elsewhere include the River BabingleyMethod (Petts et al. 1999) developed in England,and the Adapted BBM-DRIFT Methodologydeveloped in Zimbabwe (Steward et al. 2002).
Brief notes on several of these approachesfollow in section: Comparative Description ofMethodologies (see also Arthington et al. 2004b).
Comparative Description OfMethodologies
For comparative purposes, selected holisticmethodologies from the broader group aresummarized in Table 2, in terms of their origins,key features, strengths, limitations, and presentstatus (adapted from Tharme 2003). Furtherdetails of these various methodologies areavailable in the source references provided inTable 2, as well as in the review papers listedpreviously.
Essential features of holisticmethodologies
All holistic methodologies must address twoessential questions. First, which flows areimportant, and linked to this question, whatquantities and temporal patterns of flow areneeded to sustain river biota and ecosystems?Second, what are the potential impacts of alteredflows on the aquatic ecosystem? Both of theserelated questions require understanding, andpreferably, quantification of the variousrelationships between hydrology, geomorphology,water quality and ecological systems.
Which flows are important?
Six major categories of flow characteristics havebeen identified as being of particulargeomorphological and ecological relevance, aswell as being sensitive to the changes producedin flow regimes by impoundment, diversions,groundwater exploitation, use of water forhydropower generation and catchment land-usechanges (Arthington et al. 1998; Richter et al.1997). Important flow characteristics are (Poff etal. 1997):
11
Met
hodo
logy
Ori
gins
Mai
n fe
atur
es,
stre
ngth
s an
d lim
itatio
nsS
tatu
s(K
ey r
efer
ence
s)B
ench
mar
kin
gD
evel
oped
in
Com
preh
ensi
ve,
scen
ario
-bas
ed,
top-
dow
n ap
proa
ch f
or a
pplic
atio
n at
bas
in s
cale
, us
ing
field
and
des
ktop
Sol
e ho
listic
EF
MM
eth
od
olo
gy
Que
ensl
and,
data
for
mul
tiple
riv
er s
ites;
EF
M h
as 4
mai
n st
ages
– (
1) e
stab
lishm
ent:
form
atio
n of
mul
tidis
cipl
inar
y ex
pert
for
basi
n-sc
ale
(DN
R 1
998b
,A
ustr
alia
, by
pane
l (T
AP
) an
d de
velo
pmen
t of
hyd
rolo
gica
l m
odel
, (2
) ec
olog
ical
con
ditio
n an
d tr
end
asse
ssm
ent:
asse
ssm
ent
and
cite
d in
loca
l re
sear
cher
sde
velo
pmen
t of
spa
tial
refe
renc
e fr
amew
ork,
ass
essm
ent
of e
colo
gica
l co
nditi
on f
or s
uite
of
ecos
yste
mas
sess
ing
risk
Art
hing
ton
1998
;an
d D
epar
tmen
t of
com
pone
nts
(usi
ng 5
-poi
nt r
atin
g of
deg
ree
of c
hang
e fr
om r
efer
ence
con
ditio
n an
d ap
prop
riate
met
hods
of e
nviro
nmen
tal
Briz
ga 2
000;
Nat
ural
Res
ourc
esfo
r ea
ch c
ompo
nent
), d
evel
opm
ent
of g
ener
ic m
odel
s (c
once
ptua
l, em
piric
al)
defin
ing
links
bet
wee
n flo
wim
pact
s du
e to
Briz
ga e
t al
.(D
NR
), t
o pr
ovid
ere
gim
e co
mpo
nent
s an
d ec
olog
ical
pro
cess
es,
sele
ctio
n of
key
flo
w i
ndic
ator
s/st
atis
tics
with
rel
evan
cew
ater
res
ourc
e20
02)
a fr
amew
ork
for
to t
hese
rel
atio
nshi
ps,
mod
elin
g-ba
sed
asse
ssm
ent
of h
ydro
logi
cal
impa
cts,
(3)
dev
elop
men
t of
ris
kde
velo
pmen
t;as
sess
ing
risk
ofas
sess
men
t of
pot
entia
l fr
amew
ork
to g
uide
eva
luat
ion
impa
cts
of f
utur
e w
ater
res
ourc
e de
velo
pmen
t/ad
opte
d fo
r ro
utin
een
viro
nmen
tal
man
agem
ent
scen
ario
s: b
ench
mar
k m
odel
s ar
e de
velo
ped
for
all/s
ome
key
flow
ind
icat
ors
show
ing
leve
lsap
plic
atio
n in
impa
cts
due
toof
ris
k of
eco
logi
cal/g
eom
orph
olog
ical
im
pact
s as
soci
ated
with
diff
eren
t de
gree
s of
flo
w r
egim
e ch
ange
,Q
ueen
slan
d w
ithw
ater
res
ourc
eris
k le
vels
are
def
ined
by
asso
ciat
ion
with
ben
chm
ark
site
s w
hich
hav
e un
derg
one
diffe
rent
deg
rees
of
appl
icat
ions
in
deve
lopm
ent,
flow
-rel
ated
cha
nge
in c
ondi
tion,
lin
k m
odel
s ar
e us
ed t
o sh
ow h
ow t
he m
odel
ed f
low
ind
icat
ors
affe
ct8
basi
ns;
unde
rat
bas
in s
cale
ecol
ogic
al c
ondi
tion,
(4)
eva
luat
ion
of f
utur
e W
RD
sce
nario
s, u
sing
ris
k as
sess
men
t an
d lin
k m
odel
s,co
nsid
erat
ion
for
ecol
ogic
al i
mpl
icat
ions
of
scen
ario
s an
d as
soci
ated
lev
els
of r
isk
read
ily e
xpre
ssed
in
grap
hica
l fo
rm;
use
in W
este
rnE
FM
is
part
icul
arly
sui
ted
to d
ata
poor
situ
atio
ns;
pote
ntia
l fo
r us
e in
dev
elop
ing
coun
trie
s co
ntex
t an
d fo
rA
ustr
alia
; on
lyap
plic
atio
n to
oth
er a
quat
ic e
cosy
stem
s; u
tiliz
es a
wid
e ra
nge
of s
peci
alis
t ex
pert
ise;
pre
sent
s a
appl
ied
inco
mpr
ehen
sive
ben
chm
arki
ng p
roce
ss;
prov
ides
sev
eral
way
s of
dev
elop
ing
risk
asse
ssm
ent
mod
els,
Aus
tral
ia t
o da
tegu
idan
ce o
n ke
y cr
iteria
for
ass
essi
ng c
ondi
tion,
and
key
hyd
rolo
gica
l an
d pe
rfor
man
ce i
ndic
ator
s; r
ecen
tap
proa
ch b
uilt
on s
ever
al p
rece
ding
EFA
ini
tiativ
es;
no e
xplic
it co
nsid
erat
ion
of s
ocia
l co
mpo
nent
, bu
t w
ithsc
ope
for
incl
usio
n (n
ote
that
soc
io-e
cono
mic
iss
ues
are
eval
uate
d se
para
tely
by
DN
R a
nd c
onsi
dere
d w
hen
the
final
EF
rec
omm
enda
tions
are
bei
ng d
ecid
ed);
req
uire
s ev
alua
tion
of s
ever
al a
spec
ts (
e.g.
, ap
plic
abili
ty/
sens
itivi
ty o
f ke
y flo
w s
tatis
tics,
deg
ree
to w
hich
ben
chm
arks
fro
m o
ther
bas
ins/
site
s ar
e va
lid c
onsi
derin
gdi
ffere
nces
in
river
hyd
rolo
gy a
nd b
iota
); r
ecom
men
ds a
mon
itorin
g pr
ogra
mm
e an
d ad
ditio
nal
rese
arch
on
impo
rtan
t is
sues
, as
cru
cial
com
pone
nts
of E
F i
mpl
emen
tatio
n.H
oli
stic
Dev
elop
ed i
nLo
osel
y st
ruct
ured
set
of
met
hods
for
bot
tom
-up
cons
truc
tion
of E
F r
egim
e, w
ith n
o ex
plic
it ou
tput
for
mat
;R
epre
sent
sA
pp
roac
hA
ustr
alia
to
addr
ess
prin
cipa
lly r
epre
sent
s a
flexi
ble
conc
eptu
al f
ram
ewor
k, e
lem
ents
of
whi
ch h
ave
been
ada
pted
in
a va
riety
of
conc
eptu
al(A
rthi
ngto
n et
al.
EF
Rs
of e
ntire
way
s in
to s
ever
al A
ustr
alia
n ho
listic
met
hodo
logi
es a
nd f
or i
ndiv
idua
l st
udie
s; l
ack
of s
truc
ture
d se
t of
basi
s of
mos
t19
92;
rive
rine
eco
syst
em;
proc
edur
es a
nd c
lear
ide
ntity
for
EF
M h
inde
rs r
igor
ous
rout
ine
appl
icat
ion;
bas
ic t
enet
s an
d as
sum
ptio
ns a
sot
her
holis
ticD
avie
s et
al.
shar
ed c
once
ptua
lpe
r B
BM
, w
hich
was
der
ived
fro
m i
t; sy
stem
atic
con
stru
ctio
n of
a m
odifi
ed f
low
reg
ime,
on
a m
onth
-by-
mon
thE
FM
s; a
pplie
d19
96;
basi
s w
ith B
BM
,an
d flo
w e
lem
ent-
by-e
lem
ent
basi
s, t
o ac
hiev
e pr
edet
erm
ined
obj
ectiv
es f
or f
utur
e riv
er c
ondi
tion;
inc
orpo
rate
sin
var
ious
Art
hing
ton
and
the
theo
retic
alm
ore
deta
iled
asse
ssm
ent
of f
low
var
iabi
lity
than
ear
ly B
BM
stu
dies
; in
clud
es m
etho
d fo
r ge
nera
ting
trad
eoff
form
s in
1998
)an
d co
ncep
tual
curv
es f
or e
xam
inin
g al
tern
ativ
e w
ater
use
sce
nario
s; s
ome
risk
of i
nadv
erte
nt o
mis
sion
of
criti
cal
flow
eve
nts;
Aus
tral
iaba
sis
of t
here
pres
ents
the
the
oret
ical
bas
is f
or m
ost
othe
r ho
listic
EF
Ms;
rec
omm
ends
a m
onito
ring
prog
ram
me
as a
Ben
chm
arki
ngcr
ucia
l co
mpo
nent
of
holis
tic f
low
ass
essm
ents
.M
etho
dolo
gy
TA
BLE
2.
Sum
mar
y of
a r
ange
of h
olis
tic e
nviro
nmen
tal f
low
met
hodo
logi
es, p
rese
nted
in n
o pa
rtic
ular
ord
er, h
ighl
ight
ing
salie
nt fe
atur
es, s
tren
gths
and
lim
itatio
ns, a
s w
ell a
s th
eir
curr
ent
stat
us in
ter
ms
of d
evel
opm
ent
and
appl
icat
ion.
(Con
tinue
d)
12
Met
hodo
logy
Ori
gins
Mai
n fe
atur
es,
stre
ngth
s an
d lim
itatio
nsS
tatu
s(K
ey r
efer
ence
s)B
uil
din
g B
lock
Dev
elop
ed i
nR
igor
ous
and
exte
nsiv
ely
docu
men
ted
(man
ual
avai
labl
e);
pres
crip
tive
botto
m-u
p ap
proa
ch w
ith i
nter
activ
eM
ost
Met
ho
do
log
yS
outh
Afr
ica
bysc
enar
io d
evel
opm
ent;
mod
erat
e to
hig
hly
reso
urce
int
ensi
ve;
deve
lope
d to
diff
erin
g ex
tent
s fo
r bo
thfr
eque
ntly
(BB
M)
loca
l re
sear
cher
sin
term
edia
te-le
vel
(2 m
onth
s) o
r co
mpr
ehen
sive
(1-
2 ye
ars)
EFA
s, w
ithin
Sou
th A
fric
a’s
Res
erve
fra
mew
ork;
used
hol
istic
(Kin
g an
d Lo
uwan
d D
WA
F,ba
sed
on a
num
ber
of s
ites
with
in r
epre
sent
ativ
e/cr
itica
l riv
er r
each
es;
incl
udes
a w
ell
esta
blis
hed
soci
alE
FM
glo
bally
,19
98;
Kin
g et
al.
thro
ugh
appl
icat
ion
com
pone
nt (
depe
nden
t liv
elih
oods
); f
unct
ions
in
data
poo
r/ric
h si
tuat
ions
; co
mpr
ises
3-p
hase
app
roac
h -
appl
ied
in 3
2000
)in
num
erou
s(1
) pr
epar
atio
n fo
r w
orks
hop,
inc
ludi
ng s
take
hold
er c
onsu
ltatio
n, d
eskt
op a
nd f
ield
stu
dies
for
site
sel
ectio
n,co
untr
ies;
wat
er r
esou
rce
geom
orph
olog
ical
rea
ch a
naly
sis,
riv
er h
abita
t in
tegr
ity a
nd s
ocia
l su
rvey
s, o
bjec
tives
set
ting
for
futu
re r
iver
adop
ted
as t
hede
velo
pmen
t pr
ojec
tsco
nditi
on,
asse
ssm
ent
of r
iver
im
port
ance
and
eco
logi
cal
cond
ition
, hy
drol
ogic
al a
nd h
ydra
ulic
ana
lyse
s,st
anda
rd E
FM
to a
ddre
ss E
FR
s(2
) m
ultid
isci
plin
ary
wor
ksho
p-ba
sed
cons
truc
tion
of m
odifi
ed f
low
reg
ime
thro
ugh
iden
tific
atio
n of
eco
logi
cally
for
Sou
th A
fric
anfo
r en
tire
river
ine
esse
ntia
l flo
w f
eatu
res
on a
mon
th-b
y-m
onth
(or
sho
rter
tim
e sc
ale)
, flo
w e
lem
ent-
by-f
low
ele
men
t ba
sis,
Re
serv
eec
osys
tem
s un
der
for
mai
nten
ance
and
dro
ught
yea
rs,
base
d on
bes
t av
aila
ble
scie
ntifi
c da
ta,
(3)
linki
ng o
f E
FR
with
wat
erde
term
inat
ions
cond
ition
s of
var
iabl
ere
sour
ce d
evel
opm
ent
engi
neer
ing
phas
e, t
hrou
gh s
cena
rio m
odel
ing
and
hydr
olog
ical
yie
ld a
naly
sis;
EF
Mre
sour
ces;
ada
pted
exhi
bits
lim
ited
pote
ntia
l fo
r ex
amin
atio
n of
alte
rnat
ive
scen
ario
s re
lativ
e to
DR
IFT,
as
BB
M E
F r
egim
e is
for
inte
rmed
iate
desi
gned
to
achi
eve
a sp
ecifi
c pr
edef
ined
riv
er c
ondi
tion;
inc
orpo
rate
s a
mon
itorin
g pr
ogra
mm
e an
d ad
ditio
nal
and
com
preh
ensi
vere
sear
ch o
n im
port
ant
issu
es,
inad
vert
ent
omis
sion
of
criti
cal
flow
eve
nts;
hig
h po
tent
ial
for
appl
icat
ion
to o
ther
dete
rmin
atio
ns o
faq
uatic
eco
syst
ems;
lin
ks t
o ex
tern
al s
take
hold
er/p
ublic
par
ticip
atio
n pr
oces
ses;
fle
xibl
e an
d am
enab
le t
oth
e ec
olog
ical
sim
plifi
catio
n fo
r m
ore
rapi
d as
sess
men
ts;
less
tim
e, c
ost
and
reso
urce
int
ensi
ve t
han
DR
IFT;
sha
red
conc
eptu
alR
eser
ve u
nder
basi
s w
ith H
olis
tic A
ppro
ach;
app
licab
le t
o re
gula
ted/
unre
gula
ted
river
s, a
nd i
n flo
w r
esto
ratio
n co
ntex
t; F
low
the
new
SA
Wat
erS
tres
sor-
Res
pons
e M
etho
d fa
cilit
ates
a t
op-d
own,
sce
nario
-bas
ed a
sses
smen
t of
alte
rnat
ive
flow
reg
imes
, ea
chLa
ww
ith e
xpre
ssio
n of
the
pot
entia
l ris
k of
cha
nge
in r
iver
eco
logi
cal
cond
ition
.
TA
BLE
2.
(Con
tinue
d)S
umm
ary
of a
ran
ge o
f hol
istic
env
ironm
enta
l flo
w m
etho
dolo
gies
, pre
sent
ed in
no
part
icul
ar o
rder
, hig
hlig
htin
g sa
lient
feat
ures
, str
engt
hs a
nd li
mita
tions
, as
wel
l as
thei
rcu
rren
t st
atus
in t
erm
s of
dev
elop
men
t an
d ap
plic
atio
n.
(Con
tinue
d)
13
Met
hodo
logy
Ori
gins
Mai
n fe
atur
es,
stre
ngth
s an
d lim
itatio
nsS
tatu
s(K
ey r
efer
ence
s)D
ow
nst
ream
Dev
elop
ed i
nA
ppro
pria
te f
or c
ompr
ehen
sive
EFA
s (1
-3 y
ears
) ba
sed
on s
ever
al s
ites
with
in r
epre
sent
ativ
e/cr
itica
l riv
erE
FM
with
Res
po
nse
to
sout
hern
Afr
ica
reac
hes;
fle
xibl
e, i
nter
activ
e, t
op-d
own,
sce
nario
-bas
ed p
roce
ss c
ompr
ised
of
4 m
odul
es -
(1)
bio
phys
ical
mos
t de
velo
ped
Imp
ose
d F
low
by S
outh
ern
mod
ule:
use
d to
des
crib
e pr
esen
t ec
osys
tem
con
ditio
n, t
o pr
edic
t ho
w i
t w
ill c
hang
e un
der
a ra
nge
of d
iffer
ent
capa
bilit
ies
for
Tra
nsf
orm
atio
nW
ater
s an
d M
etsi
flow
alte
ratio
ns f
or s
ynth
esis
in
a da
taba
se,
uses
gen
eric
lis
ts o
f lin
ks t
o flo
w a
nd r
elev
ance
for
eac
h sp
ecia
list
scen
ario
ana
lysi
s(D
RIF
T)
Con
sulta
nts
com
pone
nt,
dire
ctio
n sc
enar
io a
nd s
ever
ity o
f ch
ange
are
rec
orde
d to
qua
ntify
eac
h flo
w-r
elat
ed i
mpa
ct,
and
expl
icit
Pro
cess
(with
inp
uts
from
(2)
soci
olog
ical
mod
ule:
use
d to
ide
ntify
sub
sist
ence
use
rs a
t ris
k fr
om f
low
alte
ratio
ns a
nd t
o qu
antif
y th
eir
cons
ider
atio
n of
(Kin
g et
al.
Aus
tral
ian
and
links
with
the
riv
er i
n te
rms
of n
atur
al r
esou
rce
use
and
heal
th p
rofil
es,
(3)
scen
ario
dev
elop
men
t m
odul
e:so
cial
and
eco
nom
ic20
03)
sout
hern
Afr
ican
links
firs
t 2
mod
ules
thr
ough
que
ryin
g of
dat
abas
e, t
o ex
trac
t pr
edic
ted
cons
eque
nces
of
alte
red
flow
sef
fect
s of
cha
ngin
gre
sear
cher
s) t
o(w
ith p
oten
tial
for
pres
enta
tion
at s
ever
al l
evel
s of
res
olut
ion)
use
d to
cre
ate
flow
sce
nario
s (t
ypic
ally
4 o
r 5)
,riv
er c
ondi
tion
onad
dres
s ne
ed f
or(4
) ec
onom
ic m
odul
e: g
ener
ates
des
crip
tion
of c
osts
of
miti
gatio
n an
d co
mpe
nsat
ion
for
each
sce
nario
; E
FM
subs
iste
nce
user
s;in
tera
ctiv
em
odul
es r
equi
re r
efin
emen
t; w
ell
deve
lope
d ab
ility
to
addr
ess
soci
o-ec
onom
ic l
inks
to
ecos
yste
m;
cons
ider
able
limite
d ap
plic
atio
nsc
enar
io-b
ased
scop
e fo
r co
mpa
rativ
e ev
alua
tion
of a
ltern
ativ
e m
odifi
ed f
low
reg
imes
; re
sour
ce i
nten
sive
, hi
gh p
oten
tial
for
to d
ate,
with
inho
listic
EF
Map
plic
atio
n to
oth
er a
quat
ic e
cosy
stem
s; a
men
able
to
sim
plifi
catio
n fo
r m
ore
rapi
d as
sess
men
ts;
wel
l do
cum
ente
d;so
uthe
rn A
fric
aw
ith e
xplic
itus
es m
any
succ
essf
ul f
eatu
res
of o
ther
hol
istic
EF
Ms;
sam
e co
ncep
tual
bas
is a
s B
BM
and
Hol
istic
App
roac
h;so
cio-
econ
omic
exhi
bits
par
alle
ls w
ith b
ench
mar
king
app
roac
hes;
out
put
is m
ore
suita
ble
for
nego
tiatio
n of
tra
deof
fs t
han
inco
mpo
nent
BB
M/o
ther
bot
tom
-up
appr
oach
es,
as i
mpl
icat
ions
of
not
mee
ting
the
EF
R a
re r
eadi
ly a
cces
sibl
e; l
inks
to
exte
rnal
publ
ic p
artic
ipat
ion
proc
ess
and
mac
ro-e
cono
mic
ass
essm
ent;
gene
ric l
ists
pro
vide
cle
ar p
aram
eter
s fo
r in
clus
ion
in a
mon
itorin
g pr
ogra
mm
e; a
ppro
ach
prov
ides
lim
ited
cons
ider
atio
n of
syn
ergi
stic
int
erac
tions
am
ong
diffe
rent
flow
eve
nts;
lim
ited
incl
usio
n of
flo
w i
ndic
es d
escr
ibin
g sy
stem
var
iabi
lity;
app
licab
le t
o re
gula
ted/
unre
gula
ted
river
s an
d fo
r flo
w r
esto
ratio
n; r
ecom
men
ds a
mon
itorin
g pr
ogra
mm
e an
d ad
ditio
nal
rese
arch
on
impo
rtan
t is
sues
,as
cru
cial
com
pone
nts
of E
F i
mpl
emen
tatio
n.
TA
BLE
2.
(Con
tinue
d)S
umm
ary
of a
ran
ge o
f hol
istic
env
ironm
enta
l flo
w m
etho
dolo
gies
, pre
sent
ed in
no
part
icul
ar o
rder
, hig
hlig
htin
g sa
lient
feat
ures
, str
engt
hs a
nd li
mita
tions
, as
wel
l as
thei
rcu
rren
t st
atus
in t
erm
s of
dev
elop
men
t an
d ap
plic
atio
n.
(Con
tinue
d)
14
Met
hodo
logy
Ori
gins
Mai
n fe
atur
es,
stre
ngth
s an
d lim
itatio
nsS
tatu
s(K
ey r
efer
ence
s)A
dap
ted
BB
M-
Dev
elop
ed i
nS
impl
ified
top
-dow
n, m
ultid
isci
plin
ary
team
app
roac
h, f
or u
se i
n hi
ghly
res
ourc
e-lim
ited
(incl
udin
g da
ta)
Und
er e
arly
DR
IFT
Zim
babw
e by
situ
atio
ns a
nd w
ith d
irect
dep
ende
ncie
s by
rur
al p
eopl
e on
riv
erin
e ec
osys
tem
s; c
ombi
nes
pre-
wor
ksho
pde
velo
pmen
t,M
eth
od
olo
gy
Mot
t M
acD
onal
dda
ta c
olle
ctio
n ph
ase
of B
BM
with
DR
IFT
’s s
cena
rio-b
ased
wor
ksho
p pr
oces
s; c
ompr
ises
3 p
hase
s -
sing
le(S
tew
ard
et a
l.Lt
d. i
n co
llabo
ratio
n(1
) pr
epar
atio
n fo
r w
orks
hop
as p
er B
BM
/DR
IFT,
but
exc
ludi
ng c
erta
in c
ompo
nent
s (e
.g.,
habi
tat
inte
grity
docu
men
ted
2002
)w
ith Z
imba
bwe
and
geom
orph
olog
ical
rea
ch a
naly
ses)
and
with
lim
ited
field
dat
a co
llect
ion,
(2)
wor
ksho
p, w
ith s
impl
ified
appl
icat
ion
Nat
iona
l W
ater
DR
IFT
pro
cess
lin
king
the
mai
n ge
omor
phol
ogic
al,
ecol
ogic
al a
nd s
ocia
l im
pact
s w
ith e
lem
ents
of
the
flow
to d
ate
Aut
horit
y (w
ith i
nput
regi
me
(bas
ed o
n as
sess
men
ts o
f im
pact
and
sev
erity
for
com
pone
nt-s
peci
fic g
ener
ic l
ists
), u
sed
tofr
om S
outh
Afr
ica)
cons
truc
t a
mat
rix,
(3)
use
of m
atrix
in
eval
uatin
g de
velo
pmen
t op
tions
, w
here
the
mat
rix i
ndic
ates
thro
ugh
adap
tatio
nec
osys
tem
asp
ects
tha
t ar
e es
peci
ally
vul
nera
ble/
impo
rtan
t to
rur
al l
ivel
ihoo
ds,
soci
ally
and
eco
logi
cally
of k
ey e
lem
ents
of
criti
cal
elem
ents
of
the
flow
reg
ime
and
EF
rec
omm
enda
tions
for
miti
gatio
n; E
FM
inc
orpo
rate
s m
ore
BB
M a
nd D
RIF
T,lim
ited
ecol
ogic
al a
nd g
eom
orph
olog
ical
ass
essm
ents
tha
n B
BM
/DR
IFT;
lim
ited
cove
rage
of
key
spec
ialis
tin
res
pons
e to
disc
iplin
es;
no l
ink
to s
yste
m f
or d
efin
ing
targ
et r
iver
con
ditio
n; l
imite
d ca
pabi
lity
for
scen
ario
dev
elop
men
t;re
quire
men
ts i
n ne
wes
peci
ally
app
ropr
iate
in
deve
lopi
ng c
ount
ries
cont
ext;
requ
ires
furt
her
deve
lopm
ent
and
valid
atio
n;W
ater
Act
for
EFA
sw
ould
ben
efit
from
inc
lusi
on o
f ec
onom
ic d
ata.
Flo
w E
ven
tsD
evel
oped
by
Top-
dow
n m
etho
d fo
r re
gula
ted
river
s; c
onsi
ders
the
max
imum
cha
nge
in r
iver
hyd
rolo
gy f
rom
Rec
ent
appr
oach
Met
ho
d (
FE
M)
Aus
tral
ian
natu
ral
for
key
ecol
ogic
ally
rel
evan
t flo
w e
vent
s, b
ased
on
empi
rical
dat
a or
exp
ert
judg
emen
t;w
ith f
ew(S
tew
ards
on 2
001)
Coo
pera
tive
cons
ider
ed a
met
hod
of i
nteg
ratin
g ex
istin
g an
alyt
ical
tec
hniq
ues
and
expe
rt o
pini
on t
o id
entif
yap
plic
atio
nsR
ese
arc
him
port
ant
aspe
cts
of t
he f
low
reg
ime;
EF
M c
ompr
ises
4 s
teps
– (
1) i
dent
ifica
tion
of e
colo
gica
lin
Aus
tral
iaC
entr
e fo
rpr
oces
ses
(hyd
raul
ic,
geom
orph
ic a
nd e
colo
gica
l) af
fect
ed b
y flo
w v
aria
tions
at
rang
e of
spa
tial
to d
ate;
ofte
nC
atch
men
tan
d te
mpo
ral
scal
es,
(2)
char
acte
rizat
ion
of f
low
eve
nts
(e.g
., du
ratio
n, m
agni
tude
) us
ing
linke
d to
exp
ert-
Hyd
rolo
gy t
ohy
drau
lic a
nd h
ydro
logi
cal
anal
yses
, (3
) de
scrip
tion
of t
he s
eque
nce
of f
low
eve
nts
for
pane
l ap
proa
ches
prov
ide
stat
epa
rtic
ular
pro
cess
es,
usin
g a
freq
uenc
y an
alys
is t
o de
rive
even
t re
curr
ence
int
erva
lsag
enci
es w
ithfo
r a
rang
e of
eve
nt m
agni
tude
s, (
4) s
ettin
g of
EF
tar
gets
, by
min
imiz
ing
chan
ges
in e
vent
a st
anda
rdre
curr
ence
int
erva
ls f
rom
nat
ural
/ref
eren
ce o
r to
sat
isfy
som
e co
nstr
aint
(e.
g.,
max
imum
appr
oach
for
% p
erm
issi
ble
chan
ge i
n re
curr
ence
int
erva
l fo
r an
y gi
ven
even
t m
agni
tude
); E
FM
’s s
ingu
lar
EFA
sde
velo
pmen
t ap
pear
s to
be
anal
ysis
of
chan
ges
in e
vent
rec
urre
nce
inte
rval
s w
ith a
ltere
d flo
wre
gim
es;
draw
s gr
eatly
on
esta
blis
hed
proc
edur
es o
f ot
her
com
plex
EF
Ms
(e.g
., B
BM
, F
LOW
RE
SM
,an
d D
RIF
T);
may
be
used
to
(1)
asse
ss t
he e
colo
gica
l im
pact
of
chan
ges
in f
low
reg
imes
,(2
) sp
ecify
EF
man
agem
ent
rule
s/ta
rget
s, (
3) o
ptim
ize
flow
man
agem
ent
rule
s to
max
imiz
eec
olog
ical
ben
efits
with
in c
onst
rain
ts o
f ex
istin
g W
RD
sch
emes
; po
ssib
ly p
lace
s un
due
emph
asis
on f
requ
ency
com
pare
d w
ith o
ther
eve
nt c
hara
cter
istic
s; n
o so
cial
com
pone
nt;
requ
ires
addi
tiona
lva
lidat
ion;
inc
orpo
rate
d w
ithin
var
ious
exp
ert-
pane
l/oth
er a
sses
smen
t fr
amew
orks
; vi
ewed
by
seve
ral
rese
arch
ers
as a
too
l fo
r us
e in
var
ious
hol
istic
EF
Ms
rath
er t
han
as a
n E
FM
in
its o
wn
right
.
TA
BLE
2.
(Con
tinue
d)S
umm
ary
of a
ran
ge o
f hol
istic
env
ironm
enta
l flo
w m
etho
dolo
gies
, pre
sent
ed in
no
part
icul
ar o
rder
, hig
hlig
htin
g sa
lient
feat
ures
, str
engt
hs a
nd li
mita
tions
, as
wel
l as
thei
rcu
rren
t st
atus
in t
erm
s of
dev
elop
men
t an
d ap
plic
atio
n.
(Con
tinue
d)
15
Met
hodo
logy
Ori
gins
Mai
n fe
atur
es,
stre
ngth
s an
d lim
itatio
nsS
tatu
s(K
ey r
efer
ence
s)F
low
Res
tora
tio
nD
evel
oped
in
aP
rimar
ily b
otto
m-u
p, f
ield
and
des
ktop
app
roac
h ap
prop
riate
for
com
preh
ensi
ve (
or i
nter
med
iate
)M
ost
Met
ho
do
log
yst
udy
of t
heE
FAs;
des
igne
d fo
r us
e in
int
ensi
vely
reg
ulat
ed r
iver
s w
ith e
mph
asis
on
iden
tific
atio
n of
the
ess
entia
lco
mpr
ehen
sive
(FL
OW
RE
SM
)B
risba
ne R
iver
,th
at n
eed
to b
e bu
ilt b
ack
into
the
hyd
rolo
gica
l re
gim
e to
shi
ft th
e re
gula
ted
river
sys
tem
tow
ards
the
EF
M f
or f
low
-(A
rthi
ngto
nQ
ueen
slan
d,pr
e-re
gula
tion
stat
e; E
FM
use
s an
11-
step
pro
cess
in
2 st
ages
, in
whi
ch t
he f
ollo
win
g ar
e ac
hiev
ed -
rela
ted
river
et a
l. 20
00)
Aus
tral
ia,
for
(1)
revi
ew o
f ch
ange
s to
the
riv
er h
ydro
logi
cal
regi
me
(foc
usin
g on
unr
egul
ated
, pr
esen
t da
y an
dre
stor
atio
n;sp
ecifi
cally
futu
re d
eman
d sc
enar
ios)
, (2
) se
ries
of 8
ste
ps w
ithin
sce
nario
-bas
ed w
orks
hop,
usi
ng e
xten
sive
sing
le a
pplic
atio
nad
dres
sing
mul
tidis
cipl
inar
y sp
ecia
list
inpu
t: de
term
inat
ion
of f
low
-rel
ated
env
ironm
enta
l ef
fect
s fo
r lo
w a
nd h
igh
in A
ustr
alia
EF
Rs
in r
iver
flow
mon
ths,
rat
iona
le a
nd p
oten
tial
for
rest
orat
ion
of v
ario
us f
low
com
pone
nts
so a
s to
res
tore
to d
ate;
how
ever
,sy
stem
s ex
hibi
ting
ecol
ogic
al f
unct
ions
, an
d es
tabl
ishm
ent
of E
FR
s ba
sed
on i
dent
ifica
tion
of c
ritic
al f
low
thr
esho
lds/
EF
M r
epor
ta
long
his
tory
of
flow
ban
ds t
hat
mee
t sp
ecifi
ed e
colo
gica
l/oth
er o
bjec
tives
, (3
) de
velo
p se
ries
of E
F s
cena
rios
and
is b
eing
use
d as
aflo
w r
egul
atio
nas
sess
im
plic
atio
ns o
f m
ultip
le s
cena
rios
for
syst
em y
ield
, (4
) ou
tline
rem
edia
l ac
tions
not
rel
ated
proc
edur
al g
uide
and
requ
iring
to f
low
reg
ulat
ion
- al
tern
ativ
es t
o flo
w r
esto
ratio
n (e
.g.,
phys
ical
hab
itat
rest
orat
ion)
are
eva
luat
edin
oth
er r
ecen
tflo
w r
esto
ratio
nw
hen
som
e el
emen
ts o
f pr
e-re
gula
tion
flow
reg
ime
cann
ot b
e re
stor
ed f
ully
for
pra
ctic
al/le
gal
reas
ons,
EF
app
licat
ions
(5)
outli
ne m
onito
ring
stra
tegy
to
asse
ss b
enef
its o
f E
FR
s; E
FM
rep
rese
nts
hybr
id o
f H
olis
tic A
ppro
ach
(e.g
., O
rd R
iver
and
BB
M;
part
icul
ar r
elev
ance
to
river
s re
gula
ted
by l
arge
dam
s, b
ut a
pplic
able
to
any
river
sys
tem
stud
y, W
este
rnre
gula
ted
by i
nfra
stru
ctur
e or
sur
face
/gro
undw
ater
abs
trac
tion;
inc
lude
s w
ell
deve
lope
d hy
drol
ogic
al a
ndA
ustr
alia
)ec
olog
ical
mod
elin
g to
ols;
mor
e rig
orou
s th
an e
xper
t-pa
nel
met
hods
; so
me
risk
of i
nadv
erte
nt o
mis
sion
of c
ritic
al f
low
eve
nts;
inc
lude
s fle
xibl
e to
p-do
wn
proc
ess
for
asse
ssin
g ec
olog
ical
im
plic
atio
ns o
f al
tern
ativ
em
odifi
ed f
low
reg
imes
; po
tent
ial
for
adop
tion
of f
ull
benc
hmar
king
pro
cess
to
rank
out
com
es o
f no
t re
stor
ing
criti
cal
flow
s; r
equi
res
docu
men
tatio
n of
gen
eric
pro
cedu
re f
or w
ider
app
licat
ion.
Riv
er B
abin
gle
yF
irst
deve
lope
dB
otto
m-u
p fie
ld a
nd d
eskt
op a
ppro
ach;
EA
FR
(E
F r
egim
e) d
efin
ed i
n 4
stag
es -
(1)
eco
logi
cal
asse
ssm
ent
Rel
ativ
ely
limite
d(W
isse
y)fo
r ap
plic
atio
nof
riv
er a
nd s
peci
ficat
ion
of a
n ec
olog
ical
obj
ectiv
e co
mpr
isin
g sp
ecifi
c ta
rget
s (f
or r
iver
com
pone
nts
appl
icat
ion
to d
ate;
Me
tho
din
gro
undw
ater
-an
d bi
ota)
, (2
) de
term
inat
ion
of 4
gen
eral
and
2 f
lood
ben
chm
ark
flow
s to
mee
t th
e sp
ecifi
ed t
arge
ts,
gene
ral
appr
oach
(Pet
ts e
t al
. 19
99)
dom
inat
ed r
iver
s,(3
) us
e of
flo
ws
to c
onst
ruct
‘ec
olog
ical
ly a
ccep
tabl
e hy
drog
raph
s’,
whi
ch m
ay i
nclu
de p
rovi
sion
for
wet
appe
ars
to h
ave
Ang
lian
Reg
ion
year
s an
d dr
ough
t co
nditi
ons,
(4)
ass
ignm
ent
of a
ccep
tabl
e flo
w f
requ
enci
es a
nd d
urat
ions
to
the
been
ext
ende
dof
Eng
land
hydr
ogra
phs,
and
the
ir sy
nthe
sis
into
a f
low
dur
atio
n cu
rve,
the
EA
FR
; E
FM
use
s hy
dro-
ecol
ogic
al m
odel
s,to
oth
er E
FAha
bita
t an
d hy
drol
ogic
al s
imul
atio
n to
ols
to a
ssis
t in
ide
ntifi
catio
n of
ben
chm
ark
flow
s an
d ov
eral
l E
AF
R;
stud
ies
in t
heal
low
s fo
r fle
xibl
e ex
amin
atio
n of
alte
rnat
ive
EF
sce
nario
s; l
oose
ly s
truc
ture
d ap
proa
ch,
with
lim
ited
Uni
ted
Kin
gdom
expl
anat
ion
of p
roce
dure
s fo
r in
tegr
atio
n of
mul
tidis
cipl
inar
y in
put;
risk
of o
mis
sion
of
criti
cal
flow
eve
nts
from
EA
FR
; sp
ecifi
c to
bas
eflo
w-d
omin
ated
riv
ers
and
requ
ires
furt
her
rese
arch
for
use
in
flash
y ca
tchm
ents
.
(Con
tinue
d)
TA
BLE
2.
(Con
tinue
d)S
umm
ary
of a
ran
ge o
f hol
istic
env
ironm
enta
l flo
w m
etho
dolo
gies
, pre
sent
ed in
no
part
icul
ar o
rder
, hig
hlig
htin
g sa
lient
feat
ures
, str
engt
hs a
nd li
mita
tions
, as
wel
l as
thei
rcu
rren
t st
atus
in t
erm
s of
dev
elop
men
t an
d ap
plic
atio
n.
16
Met
hodo
logy
Ori
gins
Mai
n fe
atur
es,
stre
ngth
s an
d lim
itatio
nsS
tatu
s(K
ey r
efer
ence
s)H
abit
at A
nal
ysis
Dev
elop
ed b
y fo
rmer
Rel
ativ
ely
rapi
d, i
nexp
ensi
ve,
basi
n-w
ide
reco
nnai
ssan
ce m
etho
d fo
r de
term
inin
g pr
elim
inar
y E
FR
s at
mul
tiple
Pre
curs
or o
fM
eth
od
Que
ensl
and
poin
ts i
n ca
tchm
ent
(rat
her
than
at
a fe
w c
ritic
al s
ites)
; su
perio
r to
sim
ple
hydr
olog
ical
EF
Ms,
but
ina
dequ
ate
Ben
chm
arki
ng(W
alte
r et
al.
Dep
artm
ent
offo
r co
mpr
ehen
sive
EFA
s; f
ield
dat
a lim
ited/
abse
nt;
botto
m-u
p pr
oces
s of
4 s
tage
s us
ing
TAP
- (
1) i
dent
ifica
tion
Met
hodo
logy
1994
; B
urge
ssP
rim
ary
Indu
stri
es,
of g
ener
ic a
quat
ic h
abita
t ty
pes
exis
ting
with
in t
he c
atch
men
t, (2
) de
term
inat
ion
of f
low
-rel
ated
eco
logi
cal
with
in W
AM
Pan
d V
ande
rbyl
Wat
er R
esou
rces
requ
irem
ents
of
each
hab
itat
(as
surr
ogat
e fo
r E
FR
s fo
r aq
uatic
bio
ta),
usi
ng s
mal
l gr
oup
of k
ey f
low
sta
tistic
s,in
itiat
ives
; se
vera
l19
96;
Art
hing
ton
(now
DN
R),
plus
sel
ect
‘bio
logi
cal
trig
ger’
flow
s an
d flo
ods
for
mai
nten
ance
of
ecol
ogic
al/g
eom
orph
olog
ical
pro
cess
es,
appl
icat
ions
1998
)A
ustr
alia
, as
par
t(3
) de
velo
pmen
t of
byp
ass
flow
str
ateg
ies
to m
eet
EF
Rs,
(4)
dev
elop
men
t of
EF
R m
onito
ring
stra
tegy
;w
ithin
Aus
tral
iaof
wat
er a
lloca
tion
EF
M r
epre
sent
s an
ext
ensi
on o
f ex
pert
pan
el a
ppro
ache
s (E
PA
M,
SPA
M),
with
con
cept
ual
basi
s an
dan
d m
anag
emen
tas
sum
ptio
ns a
dapt
ed f
rom
Hol
istic
App
roac
h; l
ittle
con
side
ratio
n of
spe
cific
flo
w n
eeds
of
indi
vidu
al e
colo
gica
lpl
anni
ng i
nitia
tive
com
pone
nts;
req
uire
s st
anda
rdiz
atio
n of
pro
cess
, re
finem
ent
of f
low
ban
ds l
inke
d to
hab
itats
and
add
ition
of
flow
eve
nts
rela
ted
to n
eeds
of
biot
a; r
epre
sent
s a
sim
plifi
ed v
ersi
on o
f th
e H
olis
tic A
ppro
ach;
lar
gely
supe
rsed
ed b
y B
ench
mar
king
Met
hodo
logy
.E
nvi
ron
men
tal
Dev
elop
ed i
nS
impl
ified
bot
tom
-up
appr
oach
, ap
plic
able
in
high
ly r
egul
ated
and
man
aged
sys
tem
s w
ith c
onsi
dera
ble
oper
atio
nal
Lim
ited
to 3
Flo
w M
anag
emen
tS
outh
Afr
ica
bylim
itatio
ns;
cons
ider
ed f
or u
se w
ithin
Sou
th A
fric
a R
eser
ve d
eter
min
atio
n pr
oces
s on
ly w
here
BB
M o
r eq
uiva
lent
appl
icat
ions
;P
lan
Met
ho
dth
e In
stitu
te f
orap
proa
ch c
anno
t be
fol
low
ed;
wor
ksho
p-ba
sed,
mul
tidis
cipl
inar
y as
sess
men
t in
clud
ing
ecol
ogis
ts a
nd s
yste
mon
ly u
sed
in(F
MP
)W
ater
Res
earc
h,op
erat
ors;
3-s
tep
proc
ess
- (1
) de
finiti
on o
f op
erab
le r
each
es f
or s
tudy
riv
er a
nd s
ite s
elec
tion,
est
ablis
hmen
tS
outh
Afr
ica
to d
ate
(Mul
ler
1997
;fo
r us
e fo
rof
cur
rent
ope
ratin
g ru
les,
(2)
det
erm
inat
ion
of c
urre
nt e
colo
gica
l st
atus
and
des
ired
futu
re s
tate
, (3
) id
entif
icat
ion
unce
rtai
n st
atus
DW
AF
199
9)in
tens
ivel
yof
EF
Rs
usin
g si
mila
r pr
oced
ures
to
BB
M;
EF
M h
as l
imite
d sc
ope
for
appl
icat
ion;
str
uctu
re a
nd p
roce
dure
s fo
rw
ithin
the
nat
iona
lre
gula
ted
river
appl
icat
ion
are
not
form
aliz
ed o
r w
ell
docu
men
ted;
poo
rly e
stab
lishe
d po
st-w
orks
hop
scen
ario
pha
se;
noR
ese
rve
syst
em
sev
alua
tion
unde
rtak
en;
cons
ider
ably
mor
e lim
ited
appr
oach
tha
n F
LOW
RE
SM
.fr
amew
ork
Exp
ert
Pan
elF
irst
Bot
tom
-up,
rec
onna
issa
nce-
leve
l ap
proa
ch f
or i
nitia
l as
sess
men
t of
pro
pose
d W
RD
s; r
apid
and
ine
xpen
sive
,A
pplie
d on
lyA
sses
smen
tm
ultid
isci
plin
ary
with
lim
ited
field
dat
a co
llect
ion;
site
-spe
cific
foc
us;
appl
icab
le p
rimar
ily f
or s
ites
whe
re d
am r
elea
ses
are
in A
ustr
alia
; M
eth
od
pane
l ba
sed
poss
ible
; re
lies
on f
ield
-bas
ed e
colo
gica
l in
terp
reta
tion,
by
a pa
nel
of e
xper
ts,
of d
iffer
ent
mul
tiple
tria
l flo
wse
vera
l(E
PA
M)
EF
M u
sed
rele
ases
(ra
nked
in
term
s of
sco
red
ecol
ogic
al s
uita
bilit
y) f
rom
dam
s, a
t on
e/fe
w s
ites,
to
dete
rmin
e E
FR
appl
icat
ions
,(S
wal
es a
ndin
Aus
tral
ia,
(typ
ical
ly a
s flo
w p
erce
ntile
s);
low
res
ourc
e in
tens
ity;
limite
d re
solu
tion
of E
F o
utpu
t; ai
ms
to a
ddre
ssbo
th i
nH
arri
s 19
95)
deve
lope
driv
er e
cosy
stem
hea
lth (
usin
g fis
h co
mm
uniti
es a
s in
dica
tors
), r
athe
r th
an t
o as
sess
mul
tiple
eco
syst
emor
igin
al a
ndjo
intly
by
the
com
pone
nts;
bas
ed o
n sa
me
conc
epts
as
Hol
istic
App
roac
h an
d B
BM
; st
rong
ly r
elia
nt o
n pr
ofes
sion
alva
riou
sly
New
Sou
thju
dgem
ent;
limite
d su
bset
of
expe
rtis
e re
pres
ente
d by
pan
el (
e.g.
, fis
h, i
nver
tebr
ates
, ge
omor
phol
ogy)
;m
odifi
ed f
orm
sW
ales
sim
plis
tic i
n te
rms
of t
he r
ange
of
ecol
ogic
al c
riter
ia a
nd c
ompo
nent
s as
sess
ed (
but
scop
e fo
r in
clus
ion
depa
rtm
ents
of a
dditi
onal
one
s) a
nd t
he f
ocus
on
fish;
no
expl
icit
guid
elin
es f
or a
pplic
atio
n; p
oor
cong
ruen
ce i
n op
inio
nof
Fis
herie
sof
diff
eren
t pa
nel
mem
bers
(e.
g.,
due
to s
ubje
ctiv
e sc
orin
g ap
proa
ch,
indi
vidu
al b
ias)
; re
quire
s fu
rthe
ran
d W
ater
valid
atio
n; l
ed t
o de
velo
pmen
t of
mor
e ad
vanc
ed,
but
sim
ilar
SP
AM
, S
now
y In
quiry
Met
hodo
logy
and
oth
erR
eso
urc
es
expe
rt p
anel
app
roac
hes.
(Con
tinue
d)
TA
BLE
2.
(Con
tinue
d)S
umm
ary
of a
ran
ge o
f hol
istic
env
ironm
enta
l flo
w m
etho
dolo
gies
, pre
sent
ed in
no
part
icul
ar o
rder
, hig
hlig
htin
g sa
lient
feat
ures
, str
engt
hs a
nd li
mita
tions
, as
wel
l as
thei
rcu
rren
t st
atus
in t
erm
s of
dev
elop
men
t an
d ap
plic
atio
n.
17
Met
hodo
logy
Ori
gins
Mai
n fe
atur
es,
stre
ngth
s an
d lim
itatio
nsS
tatu
s(K
ey r
efer
ence
s)S
cien
tifi
cD
evel
oped
Bot
tom
-up
field
(m
ultip
le s
ites)
and
des
ktop
app
roac
h ap
prop
riate
for
pro
visi
on o
f in
terim
or
inte
rmed
iate
App
ears
lim
ited
Pan
eldu
ring
an E
FAle
vel
EFA
s; e
volv
ed f
rom
EPA
M a
s m
ore
soph
istic
ated
and
tra
nspa
rent
exp
ert-
pane
l ap
proa
ch;
aim
sto
a s
ingl
eA
sses
smen
tfo
r th
e B
arw
on-
to d
eter
min
e a
mod
ified
flo
w r
egim
e th
at w
ill m
aint
ain
ecos
yste
m h
ealth
; di
ffers
fro
m E
PA
M i
n th
at k
eyap
plic
atio
n in
Me
tho
dD
arlin
g R
iver
feat
ures
of
the
ecos
yste
m a
nd h
ydro
logi
cal
regi
me
and
thei
r in
tera
ctio
ns a
t m
ultip
le s
ites
are
used
as
Aus
tral
ia i
n its
(SP
AM
)S
yste
m,
basi
s fo
r E
FA;
EF
R p
roce
ss i
nclu
des
- (1
) id
entif
icat
ion
of m
anag
emen
t pe
rfor
man
ce c
riter
ia b
y pa
nel
orig
inal
for
m;
(Tho
ms
et a
l.A
ustr
alia
of e
xper
ts f
or 5
mai
n ec
osys
tem
com
pone
nts:
fis
h, t
rees
, m
acro
phyt
es,
inve
rteb
rate
s an
d ge
omor
phol
ogy,
gene
ral
appr
oach
1996
;(2
) ap
plic
atio
n of
the
crit
eria
for
thr
ee e
lem
ents
(an
d as
soci
ated
des
crip
tors
) id
entif
ied
as e
xert
ing
anva
rious
ly m
odifi
edC
ottin
gham
influ
ence
on
the
ecos
yste
m c
ompo
nent
s (v
iz.
flow
reg
ime,
hyd
rogr
aph
and
phys
ical
str
uctu
re a
t 3
for
othe
r ex
pert
-et
al.
2002
)sp
atia
l sc
ales
), (
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TA
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2.
(Con
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umm
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of a
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ge o
f hol
istic
env
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w m
etho
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, pre
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no
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wel
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s of
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d ap
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Sou
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adap
ted
fro
m T
harm
e 20
03
Not
es:
EF
- en
viro
nmen
tal f
low
EFA
- e
nviro
nmen
tal
flow
ass
essm
ent
EFR
- e
nviro
nmen
tal
flow
req
uire
men
t
EFM
- e
nviro
nmen
tal
flow
met
hod
olog
y
Furt
her
info
rmat
ion
on t
he s
tren
gth
s an
d d
efic
ienc
ies
of i
ndiv
idua
l ho
listic
met
hod
olog
ies
is p
rovi
ded
in
Thar
me
(199
6) a
nd A
rthi
ngto
n et
al.
(200
4b).
18
• the magnitude of river flows at any giventime,
• the timing of occurrence of particular flowconditions,
• the frequency of occurrence of particularflows such as flood flows,
• the duration of time over which specificflow conditions extend,
• the rate of change in flow conditions suchas rise and fall of flood waters, and
• the seasonality, variability andpredictability of the overall flow regime
As expressed recently (Olden and Poff 2003),these features of river flow regimes must be“retained or restored to maintain aquaticbiodiversity, ecosystem processes and the long-term evolutionary potential of freshwaterecosystems.” It is also well recognized that theseflows also sustain the ecological goods andservices provided by rivers to society (King et al.2003; Naiman et al. 2002).
Holistic environmental flow methods tend toapply relatively comprehensive sets of flowstatistics or ‘indicators’ that address many ofthese discharge characteristics. For example,Australian benchmarking studies typically usestatistical descriptors of total annual flow, inter-annual variability, seasonal variability, zero flow,low to medium flow, various categories of highflows/floods, and rates of hydrograph rise andfall (Brizga et al. 2002). Other environmentalflow studies have specified flows in relation to arange of ‘functional’ flow levels or ‘bands’relevant to particular geomorphological andecological attributes and processes (e.g.,Arthington et al. 2000), usually includinggroundwater, baseflow, low flow, mid flow, highflow, bankful flow, overbank flows andcatastrophic flows (e.g., SKM 2001). The DRIFT
Methodology considers different categories offlow (wet and dry season low flows; small,medium and large floods) that maintain differentparts of the river ecosystem (see Arthington etal. 2003; King et al. 2003).
Increasingly, holistic environmental flowstudies have incorporated qualitative andquantitative models to define the relationshipsbetween river hydrology, geomorphology, waterquality and ecological structure/functioning.Qualitative model types are outlined in section:Qualitative Hydrology-Ecology Models. Thesemodels are not predictive but can be used todevelop testable hypotheses linking flow and flow-related variables to biological or ecologicalresponse variables over time. Quantitative modelsare reviewed in section: River Fisheries Models.
Qualitative hydrology-ecology models
Six groups of qualitative models can berecognized in the literature and reports describingenvironmental flow studies: pictorial explanatorymodels, link models, gradient models, flow chartmodels, time series models and conceptualecosystem models.
Pictorial explanatory models
Pictorial explanatory models have been appliedwidely in Australia to depict the impacts ofvarious degrading processes including flowregulation on aquatic ecosystems, and are alsoused as a communication tool to explain theresults of river condition or ‘health’ assessments.Pictorial models are used in the BenchmarkingMethodology to integrate information relating tothe influence of flow on various river ecosystemcomponents (Brizga et al. 2002). Information canbe presented in a range of pictorial formats, suchas whole of river system, longitudinal profile, orriver cross-sections. These models summarizethe current state of knowledge of flow-ecosystemrelationships and are supported by references tothe literature or personal observations.
19
Link models
Link models show how environmental factors(such as components of the flow regime) andvarious attributes of aquatic ecosystems relate toone another and interact. These models are usedto show the relevance and function of flowindicators, and interrelationships between variousecosystem components. Link models do notexplain the processes involved in theinterrelationships but they may provideinformation on the direction of change. Forexample, a classic link model in thegeomorphological literature is the set ofrelationships published by Schumm (1969)between changes in flow and bedload (increase ordecrease in either or both of these parameters),and measures of channel morphology (includingwidth, depth, width/depth ratio, meanderwavelength and sinuosity).
Link models showing primary linkages of fivegroups of key flow indicators to non-tidal riversand estuaries have become part of theenvironmental flow assessment framework usedin the Benchmarking Methodology (Brizga 2000;Brizga et al. 2002).
Gradient models
Gradient models show the effect of gradients ofvariables on an ecosystem component. Locationsalong environmental gradients can be used todetermine likely ecological responses to aparticular environmental variable, such asdischarge. Vegetation frequently exhibits apredictable zonation pattern about stream banksin terms of structure (life form representation,height and stratification) and floristics (speciespresence/absence and relative abundance). Asimple gradient model can be developed todemonstrate the interaction between vegetationstructure in terms of occurrence of vegetationtypes/predominant functional guilds, changes inmoisture availability and flood disturbance withinthe landscape. The BBM, Flow Restoration,
DRIFT and Benchmarking methodologies all usethe relationships between river discharge,moisture levels and gradients of riparianvegetation to establish environmental flowrecommendations.
Flow chart models
Flow chart models show the processes underlyingrelationships between flow regime change andgeomorphological and ecological responses. Flowchart models expand on specific links developedin link models. They have been applied inbenchmarking studies to underpin assessmentsof the condition of plant and fish assemblages inrelation to flow regime change, for example.
Time series models
Time series models show the response ofgeomorphological and/or ecological variables toflow regime change through time. Models mayrefer to a range of timescales: e.g., seasonal,annual, long-term. Their utility lies in presentingprocesses with a temporal component in a simplediagrammatic format.
Conceptual ecosystem models
There are a number of ecological modelsdescribing ecosystem processes in relation toenvironmental factors, including the RiverContinuum Concept (Vannote et al. 1980), theFlood-Pulse Concept (Junk et al. 1989) and theRiverine Productivity Model (Thorp and DeLong1994). Each model differs considerably in itsemphasis on upstream and local in-stream versusfloodplain sources of organic matter. The SerialDiscontinuity Concept (Ward and Stanford 1983)describes how rivers might be expected torespond to the construction of a large dam atvarious points along the river continuum. It isbased upon the River Continuum Concept and assuch does not take the alternative models ofecosystem structure into account.
20
Implications of the Various HolisticMethodologies
Of the global suite of environmental flowmethods currently available, the majority, in theform of hydrology-based approaches, have beenapplied at a desktop planning level, with little, ifany, ecological input. For the reasons outlinedin Table 3, such methods are totally inadequatefor the detailed, flexible kinds of assessment ofenvironmental flows required for large riverswith highly diverse biological assemblages andmajor fisheries. At best they can provide onlyapproximate volumes of water to be allocatedto the aquatic ecosystem, or single minimumflows, thereby precluding any scenario-basedtradeoff analyses of basin water use. Althoughthe most recent and sophisticated hydrologicalmethodologies, such as the Range of VariabilityApproach (RVA) of Richter et al. (1996; 1997)more adequately address the full range of flowvariability in rivers, and are purported to bemore ecologically relevant, they still do notaddress the flow needs of the whole riverineecosystem or specific biotic components. RVAis perhaps most useful for providing interimenvironmental flow recommendations whilstmore detailed analyses are in progress (Richteret al. 1996, 1997).
Of all habitat simulation methods, the state-of-the-art tools such as IFIM provide for morecomprehensive environmental flow assessments,but are largely restricted in focus to the flow-related habitat requirements of specific in-streamtarget species, for various biological activitiesand times of the year. Outputs in the form offlow-habitat time series and exceedance plotsenable the environmental implications ofalternative hydrological scenarios to be explored,and can incorporate many aspects of flowvariability. Moreover, as most of these methodsare designed to focus on flow-related changes inthe suitability of physical habitat for freshwaterfish species, they are well suited to detailedmodeling of the requirements of indicator fishspecies or reproductive guilds. However, themodels tend to represent flow-habitatrelationships in a relatively simplistic way (inrelation to few hydraulic variables), they do notpredict biotic responses (e.g., populationbiomass, or increase/decline) to flow regulation,and do not take into consideration therequirements of other components of theecosystem and their interactions, including thosewith fish (Pusey 1998). Consequently, they areconsidered more appropriate as one of a widerset of tools for addressing the flow requirementsof entire river systems and their fisheries.
TABLE 3.Flow categories that are reduced, or increased, in magnitude or number, to produce described consequences, andthe five ecosystem components for which consequences are routinely predicted in DRIFT.
Flow categories Consequences described for: Ecosystem component
1. Dry-season low flow (range of low flows) 4 levels of increase 1. Fluvial geomorphology
2. Wet-season low flow (range of low flows) or decrease 2. Water quality
3. Intra-annual floods: Class 1 4 changes in the 3. Plants
4. Intra-annual floods: Class 2 number per annum 4. Aquatic invertebrates
5. Intra-annual floods: Class 3 5. Fish
6. Intra-annual floods: Class 4 The hydraulics of the
7. 1:2 year flood Presence river channel are
8. 1:5 year flood or absence also computed.
9. 1:10 year flood
10. 1:20 year flood
Source: King et al. 2003
21
Although a relatively small proportion of theglobal pool of methods, holistic methodologies bytheir very nature as whole-ecosystem approachesprovide the most appropriate means of assessingenvironmental flows for fisheries in complex riversystems. Earlier techniques of this type, includingthe expert panel assessment methods (e.g.,EPAM and SPAM), tend to provide outputs oflower resolution and more limited scope in termsof the ecosystem attributes considered andflexibility, than the most well-developed groupcomprising the Flow Restoration Methodology,BBM, DRIFT and the Benchmarking Methodology.This latter group essentially representsframeworks for the structured collation andinterpretation of complex, multi-disciplinaryinformation drawn from a large range of sources,including hydrological, environmental, and in afew instances (notably in the BBM, DRIFT andAdapted BBM-DRIFT) also social data pertainingto the dependencies of local communities onnatural riverine resources (including but notexclusive to fisheries).
The outputs generated from such tools, in theform of hydrological scenarios with attendantgeomorphological, ecological, and socialimplications, are sufficiently predictive andflexible that they can be used alongside otherwater and land resource development scenarios,to examine and evaluate tradeoffs in basin waterallocation between other water use sectors,notably agriculture, biodiversity conservation andfisheries. Such approaches are thereforeespecially valuable in floodplain rivers where theseasonally inundated floodplain is used for anumber of livelihood strategies in addition tofisheries.
It is noteworthy that although the ManagedFlood Release Approach of Acreman et al. (2000)(see section: Review of Environmental FlowMethodologies), based on experimental floodreleases from dams, has been used in riverfloodplain restoration projects in Africa to sustainfisheries production and dependent livelihoods, itis only partially holistic in nature. It does notappear to deal with the requirements of the
ecosystem for a range of hydrological conditionsother than large flood events, and does notprovide an explicit set of methods and tools forcalculating the ecological flow needs of the riversystem of concern. EPAM, also based onobservation of the ecological consequences ofexperimental flow releases from dams (Swalesand Harris 1995), is likewise insufficientlycomprehensive to address the needs of floodplainriver ecosystems.
As is the case with habitat simulationmethods, most applications of holistic approacheshave been undertaken primarily in upland,temperate rivers with limited floodplaindevelopment, and in relatively small basins. Ofthe holistic methodologies available, only theBenchmarking Methodology has addressed theenvironmental water requirements of large,tropical floodplain systems, including the flowsrequired to sustain the floodplain, estuarineecosystem and nearshore fisheries (e.g., theBurdekin River, North Queensland, Australia).This methodology has thus far only been used todefine the limits to new water resourcedevelopment, but has excellent potential to defineopportunities for flow restoration in exploitedrivers.
The Flow Restoration Methodology in turn isspecifically designed to provide detailedassessments of the impacts of past and extantflow regulation, to identify opportunities for flowrestoration and to quantify the hydrologicalcomponents that should be restored to achieveparticular geomorphological and ecologicalobjectives. The Flow Restoration Methodologycan be applied within the framework of a broaderbenchmarking exercise, such that together thesemethods provide guidance on limits to flowmodification as well as targets for flow andecological restoration in particular parts of aregulated catchment. Benchmarking cannot beused in catchments with very little water resourcedevelopment as it is these developments thatprovide the benchmarks for assessing the risk ofadverse ecological changes following flowregulation in that or a similar catchment. Adopting
22
benchmarks from other catchments, even verysimilar ones, is a possibility but is considered arisky procedure until the commonalities ofecological responses to flow regulation are betterunderstood (see Arthington et al. 2006, for furthercomment).
Many of the technical tools within the FlowRestoration Methodology have been incorporatedinto DRIFT (e.g., most of the field and analyticalmethods involved in the fish component ofDRIFT). Additional features of the FlowRestoration Methodology are the capacity toinclude consideration of non-flow related impactson the aquatic ecosystem, and to give advice onhow the river ecosystem as a whole could berestored to some degree by various measures,of which flow restoration is only one option andnot always one that can be achieved to the fullin a regulated river system for technical,economic, social and legal reasons. Suchconsiderations could also be built into the DRIFTframework, as discussed also in section RiverFisheries Models in relation to the range ofissues now being considered in the assessmentof impacts on floodplain fisheries.
Benchmarking and DRIFT appear to be theonly holistic methodologies incorporatingmethods to predict the implications (or assessthe risk) of flow regulation for river/nearshorefisheries, and DRIFT takes this further byassessing the implications for all types of use ofriver resources by people (e.g., food and fibrederived from aquatic and riparian vegetation).Unlike DRIFT, Australian holistic methodologiesdo not involve assessment of the social andeconomic implications of water resourcedevelopment and flow regulation, but they doprovide explicit inputs to related decision–makingprocesses involving appraisal of all of theenvironmental, social and economic costs andbenefits of water use. In other words, DRIFT hasexisting capacity to develop evaluations of
ecosystem goods and services and to predicthow water resource development, or flow regimerestoration, may impact on goods and servicesand the people dependent upon them.
Few of the methodologies described abovehave been subjected to rigorous validationprocedures or tests of the reproducibility of theiroutputs, although efforts to do so are in progressfor the BBM, Benchmarking Methodology andDRIFT. Studies incorporating comparisons of theoutputs from two methodologies are very rare.Two are in progress in Australia (BBM versusBenchmarking, Flow Restoration versusBenchmarking) and one has been conducted inSouth Africa (BBM versus DRIFT).
Examination of the strengths anddeficiencies of the most comprehensive group ofholistic methodologies available (Table 2)suggests that DRIFT is the methodology that iscurrently most amenable to further modificationfor consideration of flow requirements forcomplex river fisheries. However, to apply DRIFTin large floodplain river systems where thelivelihoods of local communities depend directlyand principally upon rivers for water resourcesand major floodplain fisheries composed of manyspecies, will require further development,refinement and field trials to accommodate theadded spatial complexity and higher biologicaldiversity of these ecosystems. In addition, forDRIFT to be able to reflect the particularimportance of river fisheries in these larger riversystems, it will need to incorporate recentdevelopments in fisheries production modeling.Finally, much work remains to be done on thephases of environmental flow assessmentdealing with scenario development and tradeoffanalysis, to enhance the role of holisticmethodologies in the allocation of water in riverbasins. These issues of tool adaptation andintegration are discussed further in the followingsections.
23
The DRIFT Methodology
Introduction
DRIFT (Downstream Response to Imposed FlowTransformation) is an interactive, “holistic”(Arthington et al. 1992; Tharme 1996) approach toadvising on environmental flows for rivers. It wasdeveloped from earlier holistic methodologies suchas the Building Block Methodology (BBM)(Kingand Louw 1998), through several applications insouthern Africa (e.g., King et al. 1999; Brown et al.2000a, 2000b, 2006; Brown and King 2002). It isdescribed in detail in King et al. (2000b, 2003),Arthington et al. (2003), Brown and Joubert (2003.)and Brown et al. (2005, 2006).
The central rationale of DRIFT is that differentcategories of flow (wet and dry season low flows;small, medium and large floods) maintain differentparts of the river ecosystem. Thus, manipulationof one or more of these categories of flow willaffect the ecosystem differently than manipulationof some other combination (King et al. 2000b,2003). Furthermore:
• these categories of flow can be identifiedand isolated from the historicalhydrological record,
• the probable biophysical consequences ofmanipulation of a single flow categorycan be described,
• once these consequences have beendescribed for all flow categories, flowscan be re-combined in various ways andthe overall impact on river condition ofeach new flow regime derived,
• the change in river condition can beindicated as a change in rivermanagement class,
• the implications of the change in rivercondition for common-property users ofthe river’s resources can be described.
The main features of the DRIFT process are:
• taking the present-day flow regime as astarting point, ecosystem changes linkedto a range of flow manipulations arepredicted,
• a database of these predictions iscompiled, which is queried to producebiophysical scenarios of the ecosystemchanges linked to any contemplated flowmanipulation,
• the biophysical scenarios are takenfurther to describe social impacts forcommon-property subsistence users ofthe river (the Population at Risk or PAR).
Outline of DRIFT
DRIFT Modules
DRIFT consists of four modules (biophysical,subsistence use, biophysical and social scenariodevelopment and social compensationeconomics) (King et al. 2000b). In the first, orbiophysical module, the river ecosystem isdescribed and predictive capacity developed onhow it would change with flow changes. In thesecond, or subsistence module, links aredescribed between riparian people who arecommon-property subsistence users of riverresources, the resources they use, and theirhealth. The objective is to develop predictivecapacity of how river changes would impact theirlives. In the third module, scenarios are built ofpotential future flows and of the predicted impactsof these on the river and the riparian people. Thefourth, or compensation-economics, module listscompensation and mitigation costs (King et al.2000b). Although all four modules have beenapplied (e.g., King et al. 1999; Sabet et al. 2002),the first and third modules can be applied alone(e.g., King et al. 1999; Brown et al. 2000a,2000b) and are the most developed.
The DRIFT Methodology Further Examined
24
Imparting structure to the DRIFT process
DRIFT has several characteristics that impartstructure to specialist deliberations on theconsequences of flow changes (King et al. 2000b):
Environmental Flow Sites
Data collection and subsequent deliberations arecentered on river sites, each of which isrepresentative of a river reach.
Daily hydrology
The present-day long-term daily flow data foreach site are separated into ten flow categoriesand specialists predict the consequences of up tofour levels of change from present condition ineach flow category for different components ofthe river ecosystem.
Multi-disciplinary team
The specialists routinely included are in thefollowing disciplines: sedimentology/fluvialgeomorphology, water quality, plants, aquatic
invertebrates and fish. Depending on the riverunder study additional components, such asmammals, birds and herpetofauna, can be added.The specialists build up a picture of predictedchange to any presented flow manipulation,starting with channel changes, then water qualityand temperature, then vegetation, invertebratesand fish. Other disciplines included whererelevant are inserted into this sequence asappropriate.
Generic lists
When recording the consequences of eachconsidered flow change, the specialists considerany number of sub-components that may berelevant to their ecosystem components. Theseare contained in Generic Lists for eachcomponent.
For each considered flow change at eachstudy site, the effect on each selected item onthe Generic List is described. Each specialist’slist may consist of any number of items, butusually four to less than twenty (Table 5). Foreach flow reduction at each study site, the effect
TABLE 4.Examples of entries in generic lists for an environmental flow study using DRIFT, based on the Lesotho HighlandsWater Project.
Discipline Generic list Description of the links to flow (upper entry) and social relevance
entry (lower entry)
Geomorphology Deposition of Minimum velocity for maintenance of movement of colloidal material
colloidal in main channel = 0.05 m s-1.
material Muddy areas are linked to loss of cobble habitat, increased algal
growth, bogging of livestock, and gastric illnesses.
Water quality Nutrient Nutrient levels in pools increase under low flow conditions. Water in
levels pools flushed by > Class II floods.
High nutrients encourage algal growth, which is linked to increased
incidence of diarrhea in people and livestock, and loss of cobble habitat.
Vegetation Chenopodium Found mostly in the wetbank vegetation zone, the width of which is reduced
album by a reduction in the volume and variability of low flows and in the number of
Class I floods. Abundance is affected by narrowing the zone.
Important source of firewood. Also used as a medicine.
Fish Maluti Inhabits quiet, shallow waters in rocky reaches with high water quality
Minnow IUCN Red Data Book rare species. Restricted to the Highlands of Lesotho.
Threatened with extinction.
Invertebrates Simulium Filter feeder in slow, eutrophic water.
nigritarse Blood-sucking pest of poultry.
Source: King et al. 2002b
25
on each item on each list is described. The listitems may include anything that increases ordecreases in abundance or size, such as speciesor habitats, and are chosen based on their knownsusceptibility to flow changes, their role as keyspecies or features, or their relevance tosubsistence users. In the Lesotho Study (King etal. 2000b), about 130 items were included in thevarious lists and, with all eight study sites andflow-change levels considered, more than 20,000consequences were recorded. These were enteredinto a custom-built database.
Severity ratings
Each consequence is accompanied by aSeverity Rating (Table 5), which indicates (1) ifthe sub-component is expected to increase ordecrease in abundance, magnitude or size; and(2) the severity of that increase/decrease, on ascale of 0 (no measurable change) to 5 (verylarge change). The scale accommodates someuncertainty, as each rating encompasses arange in percentage gain or loss. Greateruncertainty can be expressed through providinga range of severity ratings (i.e., a range ofranges) for any one predicted change (after Kinget al. 2000b).
Integrity ratings
To assist with the eventual placement of flowscenarios within a classification of overall rivercondition, the Severity Ratings are taken furtherto indicate whether the change would be a shift
toward or away from the natural condition. TheSeverity Ratings hold their original numericalvalue of between 0 and 5, but are given anadditional negative or positive sign, to transformthem from Severity Ratings (of changes inabundance or extent) to Integrity Ratings (of shiftto/away from naturalness), where:
• toward natural is represented by apositive Integrity Rating; and
• away from natural is represented by anegative Integrity Rating.
The DRIFT Biophysical Database
Data contained in the database
The output of the DRIFT specialist sessions is amatrix of consequences, completed by thespecialists, for a range of possible reductions (oradditions) in the ten flow categories. These data areentered into the DRIFT database (Figure 1),together with information on the data sources used.
In summary, each entry within the databaseconsists of (Table 6):
• A site name
• A flow reduction from (or addition to) thepresent-day status of one of the low orhigh flow categories (e.g., presently anaverage of four Class 2 floods perannum: reduce to two per annum);
TABLE 5.Severity Ratings for each prediction of flow-related change in DRIFT.
Severity Severity of Equivalent loss Equivalent gain
rating change (abundance/concentration) (abundance/concentration)
0 None no change No change
1 Negligible 80-100% retained 1-25% gain
2 Low 60-79% retained 26-67% gain
3 Moderate 40-59% retained 68-250% gain
4 Severe 20-39% retained 251-500% gain
5 Critically severe 0-19% retained; includes local extinction 501% gain to ∞: up to pest proportions
Source: King et al. 2003
26
TABLE 6.Example of a consequence entry in the DRIFT database for one ecosystem sub-component.
Type of information Information
Site 2
Flow reduction level Reduction level 4 of dry-season low flows
Component Invertebrates
Sub-component Simulium nigritarse
Direction of change in abundance Increase
Severity rating 5: critically severe
Integrity rating –5: away from natural
Ecological significance Filter feeder in slow, eutrophic water
Social significance Blood-sucking pest of poultry
Volume of water 12 m3 x 106 per annum
FIGURE 1.Framework for consequences of reductions (or additions) in low or high flows for ecosystem sub-components of DRIFT.
• The consequences of this for a range ofecosystem components (e.g., plants) andtheir sub-components (e.g., algae),expressed as:
• The direction of predicted change(increase or decrease in abundance);
• The extent of change (Severity Rating);
• The expected impact on rivercondition, relative to natural (IntegrityRating);
• Descriptions of the ecological andsocial significance of the predictedchange;
• The volume of water required todeliver this flow, expressed as m3 x106 per annum for each of the tenflow classes, per season and perannum.
27
Structure of the database
The DRIFT database comprises six Excelworksheets (Figure 2) that can loosely be dividedinto two groups: data storage, and scenario creationand evaluation (Brown and Joubert 2003). In the
second group, integer linear programming (Winston1994) is used to re-combine a selected changelevel for each flow category into a modified flowregime and describe its consequences, using thesoftware DRIFTSOLVER and DRIFT CATEGORY.
FIGURE 2.The Excel worksheets within the DRIFT database.
Source: from Brown et al. 2005
28
In DRIFTSOLVER, the integer linear programoptimizes the distribution of a given total volumeof water among different change levels of flowcategories in a way that results in the lowestaggregate impact on the riverine ecosystemaccording to the Integrity Ratings. It does this bysumming the Integrity Ratings of all the sub-components, taking into account all the negativeor positive signs, to produce combinations of highand low flows that return the highest possibleoverall Integrity Score for that volume.
DRIFT CATEGORY provides a summary ofmany scenarios, showing the relationship betweenoverall river condition and volume of waterremaining in the river. The shape of the plot isspecific for the river site under its present flowand management conditions, and is based on the‘least-damaging’ mix of high and low flows. Theplot can be used to examine the relationshipbetween volume of water and ecosystem integrity,and to identify features such as inflection pointswhere a small change in flow is linked to a largechange in integrity status. AlthoughDRIFTSOLVER automatically searches for theoptimal distribution of low and high flows for anyvolume of water, DRIFT CATEGORY can be usedto indicate the implications for river condition ofnon-optimal distributions of flows, such as mayhappen where large floods (e.g., greater thanClass 2 floods) cannot be released through anupstream dam.
The DRIFT CATEGORY plots are intended foruse in decision-making. The level of detail theyprovide is sufficient to inform the broad-leveltradeoffs considered by decision makers whenbalancing potentially conflicting uses such asenvironmental protection versus agriculturaldevelopment. The summary plots are, however,backed by the detailed consequence dataprovided by the specialists.
Detailed biophysical descriptions
Compilation of the detailed description of changefor any flow scenario is not yet automated. Whena flow scenario, with its descriptions, is compiled(whether manually or automated), this requires
interpretation by one or more experienced riverscientists as a quality-control measure. Examplesfrom a detailed description are given in Table 5.
Links to the subsistence and economicmodules
The predicted changes in river condition couldaffect the lives and livelihoods of common-property subsistence users of the river (thePeople at Risk - PAR). Thus, the biophysicalscenarios created by DRIFTSOLVER are used asthe template for developing social scenarios andtheir economic implications. For each scenario,site and resource, the impacts are ranked asNone, Low, Moderate and Severe, using expertopinion and considering the following issues(Sabet et al. 2002).
• The level of predicted change of aresource
• Importance of the resource for thelivelihoods of the affected populations
• Number of households harvesting theresource
• Frequency of usage of the resource
• Availability of alternative resources
The categories of social impact are broadlydefined as follows:
None: No appreciable change expected.
Low: The resource is not important or, ifimportant, its quantity is predicted tochange by less than 20 percent.
Moderate: The resource is important, and itsquantity is predicted to change by 20-50 percent.
Severe: The resource is considered essentialfor the livelihoods of the PAR; and itis used by more than 20 percent ofthe PAR households; and thepredicted biophysical change isgreater than 50 percent.
29
The predicted impacts on human health ofeach of the scenarios takes into consideration:
• the wide range of factors influencinghealth in the PAR, some of which willhave no links to the river;
• the extent of river use by members of thePAR; and
• any predicted biophysical changes thatcould influence people’s health.
Table 7 provides an example of theconsiderations that were used to create the linkbetween biophysical changes in the river and alisted PAR health concern. The column‘Ecological link’ lists some parts of theecosystem that could change with a flow change,and an explanation of how this could affectpeople and their livestock. The weighting
indicates the importance of this to the healthissue under discussion. The ‘Onset’ entryindicates the time span over which the impactsdescribed in column 1 may become apparent.The likelihood of the ecological conditionsdescribed in column 2 developing is given in thebiophysical scenarios developed inDRIFTSOLVER.
The fish component of DRIFT
DRIFT offers a structured process for predictingthe biophysical, social and economicconsequences of altering a river’s flow regime.The fish component of DRIFT is a fullycompatible, 10-step protocol designed to makesuch predictions using field data on a river’s fishfauna linked to information on flow-relatedaspects of fish biology drawn from the literatureand the knowledge base and professionalexperience of fish ecologists.
TABLE 7.Examples of health-related ways a river ecosystem could change, and the relevance of this for diarrhea and eye andskin diseases among the People at Risk (PAR) in the DRIFT methodology. Columns 1 and 2 should be consideredtogether.
Ecological link Weighting Onset (years)
Colloids High 2-10
An increase in colloidal material allows diarrheal, disease-causing
organisms such as Giardia to remain in the river for longer, thus increasing
the chances of people becoming infected either through contact
(skin and eye infections) or consumption (diarrheal disease).
Total dissolved solids High 2-10
Drinking turbid water with high TSS levels does not
necessarily have direct health effects, but such effects can occur when
infectious disease agents adsorb onto the particulate matter.
Algal blooms High 1-2
High summer flows flush algae from the river, but low winter flows allow the
plants to accumulate in quiet areas. Loss of flushing flows will increase the risk
of algal blooms, with resulting adverse health effects through swallowing
algae-contaminated water.
Blackflies Low 1-2
Increases in the numbers of blackflies can result in an increase in the level of
irritation caused by their bites. Blackflies can also carry disease from faeces to
food or utensils used by humans (faecal – oral route).
Source: Sabet et al. 2002
Note: Columns 1 and 2 should be considered together
30
The basic steps in the fish component ofDRIFT are the following:
Step 1: Review of literature to produce acompilation of published flow-relatedand other information about each fishspecies recorded in the river underinvestigation.
Step 2: Selection of study sites (incollaboration with the full team ofDRIFT specialists) to characterize riverreaches likely to be affected byexisting and future water resourcedevelopments.
Step 3: Seasonal field surveys at each site todetermine fish species composition,abundance, habitat use, life history,diet, etc., in relation to existing flowconditions.
Step 4: Analysis of field data to generatehabitat preference curves for each fishspecies.
Step 5: Tabulation of field data and informationfrom literature review to produce asummary of flow-related data abouteach fish species.
Step 6: Development of scenarios of flowregime change for evaluation usingDRIFT.
Step 7: Development of protocols to documentthe consequences of flow regimechange for each fish species at eachstudy site.
Step 8: Prediction of the ecological and socialconsequences of flow regime change foreach fish species at each study site.
Step 9: Preparation of a monitoring strategy toassess the outcomes of environmentalflow provisions.
Step 10: Implementation of monitoring program,evaluation of ecological outcomes ofany environmental flow provisions, andadjustment of those provisions in the
light of new knowledge generated bymonitoring (and research).
Steps 1-8 are described in Metsi Consultants(2000a, 2000b) and in a worked example(Arthington et al. 2003). Steps 9 and 10, themonitoring strategy and its implementation, arealso described in Metsi Consultants (2000b).
The fish component of DRIFT is presentlydesigned to predict the consequences of fourtypes of flow reduction: loss (or gain) of low flowsduring normally dry months; loss (or gain) of lowflows during normally wet months; loss (or gain) ofintra-annual floods; and presence or absence ofinter-annual floods with return periods of 1:2, 1:5,1:10 and 1:20 years (see Table 3 above). Eachchange in a flow characteristic is evaluatedseparately in terms of the likely ecologicalconsequences for each fish species, and theattendant social consequences are also evaluated.
Two generic lists present the flow relatedissues affecting fish. These are: (1) theecological requirements of fish likely to beaffected by reduction in low flows, and (2) theecological requirements of fish likely to beaffected by reduction (or loss) of intra- and inter-annual floods. The possible effects of flow regimechange on fish may be expressed for individuals,populations or species interactions (Table 8). Theultimate ecological consequence of flow regimechange was considered to be the loss of one ormore species of fish from a study site and theriver zone it represented. All generic issues wereidentified from the literature and researcher’sexperience of river ecology and environmental flowstudies conducted in Australia and South Africa.
Ecological consequence summaries for fishare entered in the DRIFT database as shown inTable 4 above, and the social implications ofthese ecological changes are also rated for eachfish species present at a site and likewise, areentered into the DRIFT database.
The level of confidence in each fishassessments made using DRIFT is ratedaccording to the information sources availableand their scientific quality, thus providing thewater manager/client with an explicit means toundertake his/her own assessment of the risks
31
associated with management actions based onlimited or low quality information. The ratingscheme applied in the fish component of DRIFTclosely resembles that recommended byCottingham et al. (2002) and Downes et al. (2002)as “levels of evidence that support environmentalflow assessments.”
The developers of the fish component ofDRIFT (Arthington et al. 2003) recommend thatthe predictions related to the various biophysicalcomponents could be strengthened byembedding qualitative and quantitative methodsand models linking the biological requirements offish to the hydrological and biophysicalcharacteristics of rivers. For example, thegeneric lists presenting the ecologicalrequirements of fish likely to be affected byreduction in low flows, and those likely to beaffected by reduction (or loss) of intra- and inter-annual floods, could be represented asqualitative models using various diagrammaticformats. Since these models represent thecurrent state of knowledge of such relationships,drawn from the literature and personalexperience, they represent both a qualitativeknowledge summary (explicit references topublished literature, or to personal experiencemust be suitably annotated) and an education/communication tool for users of DRIFT. Indeed,
qualitative models should provide thefoundations for the development of Bayesianmodels, since the essence of Bayesiannetworks consists in defining the system studiedas a network of variables linked by probabilisticinteractions (Jensen 1996). In large floodplainrivers, data exist to facilitate quantitativemodeling of floodplain-fish relationships (e.g.,Arthington et al. 2005; Balcombe et al. 2006)and studies discussed below.
These are other recommendations for furtherinnovation and research to further developDRIFT which are outlined in Arthington et al.(2003) and below.
Verification of results achieved using DRIFT
To date, DRIFT has been used in a predictivecapacity, and insufficient time has passed for theaccuracy of its predictions to be verified throughmonitoring. However, monitoring programmesaimed at verification of DRIFT biophysical andsocial predictions have been approved for therivers downstream of Lesotho Highlands WaterProject Phase 1 structures (LHDA 2003), and aprogramme for the verification of DRIFTbiophysical results has been proposed for thePalmiet River, in the Western Cape, South Africa(Brown et al. 2000a, 2000b).
TABLE 8.Effects of flow regime change on fish individuals, populations and species interactions (from the fish component ofthe DRIFT methodology).
Effect code Effect on fish
A Change in fish health (due to effects of disease, parasites, toxic algae, etc.)
B Change in fish body condition (size, weight)
C Change in abundances of adult fish
D Change in abundance of juvenile fish
E Change in abundance of larval fish
F Change in spawning and hatching success
G Change in level of passage and dispersal for: (i) Adults (ii) Juveniles (iii) Larvae
H Change in predation level
I Change in competitive pressure
J Change in mortality levels
K Loss of species from site
Source: Arthington et al. 2003
32
Additional support for the rationality of theresults obtained using DRIFT was alsoforthcoming from a dual application of the BBMand DRIFT methodologies at three sites on theBreede River, Western Cape, South Africa,where the same team of scientists used thesame datasets to provide environmental flowscenarios (Brown and King 2002). The resultsobtained for the two methods were very similarin terms of: the percentage Mean Annual Runoff(MAR) required to facilitate a desired rivercondition and the temporal distribution of theannual volume of water allocated to theenvironmental flow.
A detailed analysis of the dual application, andthe relative similarities, difference, advantages anddisadvantages of the BBM and DRIFT that itillustrated is currently being compiled (King et al.2003). Other recent developments of DRIFT arepresented in Brown and Joubert (2003.) and Brownet al. (2005, 2006).
Research needs
The following aspects and concerns have beenraised as possible areas for future developmentand research to refine DRIFT.
User manual
Write a manual for DRIFT (funds granted by theSouth African Water Research Commission, for2003/2004).
Generic lists
Expand and refine the drop-down lists ofcomponents, sub-components, elements and sub-elements. These lists exist in draft form forupland rivers, funded by the South African WaterResearch Commission. Generic lists needformulating for lowland rivers, floodplains andestuaries.
Hydrology
Explore additional hydrological parameters andstatistics that express variability. Develop newhydrological parameters for generic lists.
Large rivers – longitudinal and lateralinteractions
Develop a process for the inclusion andconsideration of longitudinal biological andhydrological interactions. Issues to include: theneed of a species to migrate, the extent androute of that migration, which life stages migrate,large-scale processes such as invertebrate drift,and lateral channel-floodplain exchanges ofmaterials, energy and biota, and provision of fishpasses to facilitate migration of fish affected byhydraulic structures.
Consideration of water quality
Most discussion of environmental flows iscurrently confined to quantity of water. However,increased attention needs to be given to includingconsideration of water quality. DRIFT shouldaddress this.
Interactions between biophysical components
Develop a process to assist biologists ininterpreting information from hydrologists,geomorphologists and aquatic chemists about theconsequences of flow change, to determine theimplications of these changes for their disciplines.
Variability
Build in natural variability and natural changes inabundance of biota. This may be possible using‘gates’ in the system (e.g., and/or and if/thenlogical statements). Explore the possibility ofusing a Bayesian approach.
Weightings
Assign weights that reflect the contributions ofdifferent sub-components to overall rivercondition.
Validation
Calibrate DRIFT CATEGORY using monitoringresults. There is also a need to check howadditive and/or synergistic effects of flowmodifications would be dealt with in the model.
33
Automation
Explore semi-automation of the detailed results,possibly through a Bayesian approach. Developand semi-automate the social component.Incorporate subsistence use data intoDRIFTSOLVER.
Floodplain rivers and fisheries
Develop new hydrological parameters anddetermine appropriate time steps for floodplainrivers. For instance, there is some indication thatit may not be necessary to quantify the spatialarray of habitats in large rivers: multi-speciesapproaches may be sufficient for environmentalflow concerns related to fisheries.
Anthropogenic effects
Explore possible inclusion of feedback loops thatwill affect ecosystem functioning, such as:
• effects of exploitation of riverineresources;
• losses that affect social groupsdifferently; and
• effects of land-use on hydrologicalpatterns.
Other models
Explore the scope for inclusion of conceptualmodels depicting/representing biophysicalresponses to natural flow components and to flowmodifications (e.g., benchmarking models fromAustralian studies in large floodplain rivers).These models could be used to illustrate theissues presented in generic lists, thus aidingusers of DRIFT in their summation andapplication of local knowledge.
Models such as PHABSIM (Bovee 1982;Stalnaker et al. 1994) could be applied to predictfish responses to flow modification in some riverchannels. Univariate and multivariate statisticalmodels of the environmental factors and flowattributes influencing fish populations andassemblage structure are also proving useful in
the design of environmental flow regimes for fishin Australian rivers (e.g., Kennard 2000; Kennardet al. 2006a, 2006b; Pusey et al. 2000; Arthingtonet al. 2005; Balcombe et al. 2006). Other types ofmodeling approaches are discussed in section:River Fisheries Models below.
Incorporating Bayesian Networks intoDRIFT
Bayesian networks have recently become quitepopular among holistic models dealing withecosystem issues. The principles and currentapplications of these networks of variablesprobabilistically interconnected are detailed insection: Bayesian Networks. The need for DRIFTto handle a large amount of information ofecological nature, involving multiple variables,interactions and feedback loops between thesevariables (whether species, hydraulic orphysicochemical), has called for an integrativetool able to deal with networks. The paucity ofquantitative data about these interactions andtheir probabilistic nature has led to the idea ofintegrating Bayesian networks into DRIFT. Wedetail below the differences between thesemethods, as well as the constraints and addedvalue of their integration.
Assessment of the merits of the twoapproaches
DRIFT and Bayesian networks differ both in theway they manage data, and in the informationthey convey. Bayesian networks are conceptualin nature, and do not deal with limited variations,i.e., a 100 percent chance of 20 percent change(DRIFT) is different from a 20 percent chance ofchange (Bayesian). Thus, the manner in whichdata are recorded and analyzed for flow regimeoptimization in DRIFT offers some advantagesover that in Bayesian networks. The extrainformation in DRIFT is that the direction andextent of possible change are provided, alongwith confidence limits, whereas Bayesian theoryprovides a direction and a probability of anunknown level of change.
34
The relative strengths and weaknesses of thetwo approaches are:
1. The hydrological manipulations andoptimization in DRIFTSOLVER are noteasily handled using the Bayesianapproach, and it seems neither practicalnor advantageous to replace the processused in DRIFTSOLVER with a Bayesianapproach.
2. One of the key activities in DRIFT ispopulation of the database withspecialists’ inputs on flow-consequencerelationships. The more structured andstandardized this activity, the morerigorous and repeatable will be theprocess. Several approaches to structurethis have been implemented (e.g., Kinget al. 2003) but this does seem an areawhere Bayesian networks could beuseful. Bayesian networks could guidethe deliberations of specialists by forcingthem to deal with all possible outcomesof interactions between variables. Notethat the conceptual models used in theBenchmarking Methodology areessentially the same in concept toBayesian networks (but without theassignment of probabilities) and shouldinform network development (seesection: Verification of Results Achievedusing DRIFT). The Bayesian networkssoftware is also highly visual anddynamic, making complex informationeasier to interpret. The relevant variablesmust still be identified and the valuelevels determined, and the DRIFT genericlists of sub-components and conceptualmodels would be critical in ensuring allrelevant variables are identified.
3. Bayesian networks become complexquickly as new variables are added, andparameterization of the probability tables(by the specialists) can becomeextremely complex. Thus, there is a need
to explore ways of incorporating Bayesiannetworks as small modular units (eachrepresenting a flow-fish conceptual model,for example), which could beparameterized individually.
4. DRIFT severity and integrity scores havebeen applied in several studies and workwell. Furthermore, they are integral to theDRIFTSOLVER optimization process.Thus, to avoid requiring specialists toscore impacts in two different ways, it isdesirable to retain basic DRIFT typescoring for the driving variables of thesystem. There is therefore a need toexplore ways of ensuring that theBayesian networks developed arecompatible with DRIFT scoring.
5. In Bayesian networks, the weighting isimplicit. The person who parameterizeseach of the probability tables may weighdifferent components in their mind butthe weights are not recorded, nor arethere any guarantees that they areapplied consistently. In DRIFT, explicitweighting systems are used andrecorded at all levels. Thus, if the twomethods are to be integrated, proceduresfor explicit weighting and recording willneed to be incorporated into theBayesian networks in order to retain theconsistency and transparency of theDRIFT process.
Proposed procedure to aid prediction of theconsequences of flow change
The possibility exists to use Bayesian networksto assist with determination of the consequencesof flow changes for ecosystem sub-componentsin DRIFT. As a first step, separate networkscould be developed to assist biologists ininterpreting information from geomorphologistsand aquatic chemists about the consequences offlow change, to determine the implications ofthese changes for their disciplines.
35
In line with a modular approach, a series ofconceptual models and small Bayesian networksfor each flow class, and each ecosystem sub-component of concern, could be constructed. Theinputs to the Bayesian module could be scores ofDRIFT consequences (i.e., severity and directionof change) as this would (1) ensure compatibilitywith the DRIFT database, and (2) simplify thelinkages between modules. The output of theBayesian module would be probabilities of acombination of consequences of flow changeresulting in optimal or sub-optimal conditions forthe sub-component of concern. Theseprobabilities could then be used to guide thepredictions of the consequences of flow changefor the sub-component.
For instance, changes in wet season lowflows (WSLF) may have implications for aminnow population in a river via its influences ona number of variables affecting different life-stages of the minnow (see Arthington et al.
2003). These variables could include: salinity,temperature, oxygen levels, riffle area, depth andvelocity, and supply of food. Using Bayesiannetworks, and the DRIFT scoring system(increase or decrease of severity 1-5) it ispossible for the minnow specialist to see how theconsequences of change in a range of variablescould affect the minnow. The relevant boxes forsalinity and riffle area are shown in Level 1 ofFigure 3.
For each individual variable, e.g., salinity andriffle area in Figure 3, a variable-specific nodecould be generated for each ecosystem sub-component of interest that considers whether thepredicted changes would result in conditions thatwere optimal, sub-optimal or lethal for that sub-component, e.g., minnows in Figure 3 (Level 2).These nodes are comprised of probability tables(Table 9) that should be parameterized by thespecialist using information that they havegathered on the tolerance of minnow to the
FIGURE 3.Hypothetical Bayesian network constructed for DRIFT consequences of changes in wet season low flows for minnows,using two ‘driving’ variables, salinity and riffle area.
A level three change in WSLF
would increase salinity concentrationsby Severity 2 to 3
which would produce optimal to sub-optimal conditions for
minnows
A level three change in WSLF
would decrease riffle ares by Severity 1 to 2
which would produce optimal to sub-optimal conditions for
minnows
the combined consequences of the salinity and riffle area changes
would provide conditions that were 72% 'good'for minnows
this information could then be used to predict the change in minnow numbers under
a level three change in WSLF, i.e., a decrease of 0 to 2.
Salinity concentrationZeroOneTwoThreeFourFive
025.050.025.0
0 0
Salinity direction
IncreaseDecrease
100 0
Riffle direction
IncreaseDecrease
0 100
SALINITY for minnow
OptimalSuboptimalDeadly
60.040.0
0
RIFFLES for minnow
OptimalSuboptimalDeadly
70.030.0
0
WSLF
PDChange1Change2Change3Change4
0 0 0 0 0
Riffle area
ZeroOneTwoThreeFourFive
050.050.0
0 0 0
Consequences for minno ...
GoodBad
72.028.0
Minnow abundance
ZeroOneTwoThreeFourFive
25.050.025.0
00
0
Minnow direction
IncreaseDecrease
0 100
explanation continued on right of figure
level 1
level 2
level 3
36
TABLE 9.An example of the possible probabilities in the variable node (Level 2 in Figure 3), based on ahypothetical Bayesian network constructed to show the consequences of changes in wet seasonlow flows for minnows, using two ‘driving’ variables, salinity and riffle area.
Magnitude Direction of change For sub-component (e.g., minnow)
Optimal Sub-optimal Lethal
Zero Increase 100 0 0
One Increase 100 0 0
Two Increase 100 0 0
Three Increase 0 100 0
Four Increase 0 0 100
Five Increase 0 0 100
Zero Decrease 100 0 0
One Decrease 100 0 0
Two Decrease 100 0 0
Three Decrease 0 100 0
Four Decrease 0 100 0
Five Decrease 0 0 100
variable and must take account of both thedirection (increase or decrease) of change andthe magnitude of that change (severity 1-5).Variable-specific nodes can be added for all theflow-related variables likely to dictate theresponse of the minnow.
To assess the overall consequences for theminnow of the changes in all the variablesconsidered another node would need to begenerated (Level 3, Figure 3), which provides anindication of whether the combination of predictedchanges is likely to result in conditions that arebeneficial or detrimental to the minnow. Thisinformation can then be used to assist thespecialists in deciding on the severity andintegrity ratings for DRIFT.
Ways by which the allocation of probabilitiesat Level 3 can be automated or semi-automatedwill need to be explored because there will bemany combinations to consider, making allocationof probabilities difficult.
Similarly, ways of automating the translationof the probability data (i.e., 72% good and 28%bad) into a DRIFT severity score will need to beexplored.
Variables and their states relevant toEnvironmental Flow Assessments
Bayesian networks allow the use of variousstates, e.g., good and bad, or fast, slow, veryslow to classify variables of interest. Although itis possible to include any number of states, it isadvisable to limit these to two or three as theaddition of each new state results in a geometricincrease in complication of the resultantprobability tables. We found that, whenconsidering a species, the best results wereachieved if we used the states Optimal, Sub-optimal and Lethal (e.g., as used in Figure 2).This allows the specialist to assess the suitabilityof any particular variable using tolerance data forthe species of interest.
Threshold values
We incorporated threshold (or lethal) values intothe Bayesian states to account for situationswhere change in one variable is such that thedegree of change in other variables is irrelevant.For instance, in Figure 3, if salinities increasebeyond a certain point then the minnow would not
37
be able to survive, whether or not riffle area wasoptimal. This is already done by the specialists inDRIFT but can be parameterized explicitly inBayesian networks.
The effect of adding variables, and the need forstandardization
In Figure 3, two variables were used and theresultant consequence for minnows was 72percent good. In Figure 4, however, threevariables were used (the first two with theidentical values to those in Figure 3, and the thirdone being neutral) and the resultant consequencefor minnows was 82 percent good. This dilution ofa bad outcome as variables are added to theanalysis is inherent in the calculation ofprobabilities and will be exacerbated by theaddition of variables that are robust to changes inflow. Thus, the variables used would need to bechosen with care.
It may be necessary to set up a standard,limited set of variables applicable to a particularecosystem sub-component that must be
considered by the specialist rather than leavingthe selection up to each individual specialist.Such a standard, limited set could be compiledfrom the scientific literature.
Furthermore, specialists will have toparameterize a tolerance box for every sub-component they choose to work with. Thus, itmay make sense to set a minimum andmaximum number of sub-components for eachecosystem component. It should make littledifference to the overall structure of the Bayesianmodule whether the specialist chooses to modelthe species as a whole or to model the life-history stages of that species separately.
Building an information base
The variable-specific nodes generated for eachecosystem sub-component are not necessarilyriver specific and therefore could be transferred toother systems. Ultimately it should be possible tostore variable-specific nodes created for one riveron a website for adaptation and use in riverswhere the same or similar sub-component occurs.
FIGURE 4.Hypothetical Bayesian network constructed for DRIFT consequences of changes in wet season low flows for minnows,using three ‘driving’ variables, salinity, riffle area and temperature.
Salinity rank
ZeroOneTwoThreeFourFive
025.050.025.0
0 0
Salinity concentration
IncreaseDecrease
100 0
Riffle area
IncreaseDecrease
0 100
SALINITY for m innow
OptimalSuboptimalDeadly
60.040.0
0
RIFFLES for m innow
OptimalSuboptimalDeadly
70.030.0
0
Tem perature for m innow
OptimalSuboptimalDeadly
100 0 0
Consequences for minno ...
GoodBad
81.718.3
WSLF
PDChange1Change2Change3Change4
0 0 0 0 0
Riffles rank
ZeroOneTwoThreeFourFive
050.050.0
0 0 0
Tem perature rank
ZeroOneTwoThreeFourFive
100 0 0 0 0 0
Tem perature
IncreaseDecrease
50.050.0
38
Conceptual relationship between DRIFT andBayesian networks
Networks similar to those described above willneed to be constructed for every species, guildand flow component of interest. However, thisneed not be done for all ecosystem componentsat once. It is possible to build the relationships ina modular fashion and to use a hybrid between aDRIFT and a Bayesian approach, concentratingon important components of the ecosystem, forinstance fish. More and more Bayesian modulescan gradually be added as they are developed.
Furthermore, retaining the originalDRIFTSOLVER framework would make it possibleto choose between a Bayesian network and othermodels (e.g., age-structured models) to assist indetermining DRIFT severities, should they beavailable.
Eventually, a generic system of Bayesianmodules could be constructed that includes allthe variables and interlinkages for a particulartype of river. The user would then be able toselect the ones to be used, and have otherstaken out of consideration. A procedure (Netica)allows for ‘node absorption’, which means that anode can be removed from the Bayesian networkwithout having to re-parameterize the probabilitytables.
Incorporating consideration of longitudinalinteractions/basin level concerns
Bayesian networks will allow for inclusion oflongitudinal biological interactions. Longitudinalissues relevant to each sub-component, such aswhether or not a species needs to migrate, towhere, how, at what life stages and time of year,etc., and the potential threats to these could beidentified and included in the set of variablesconsidered for that sub-component. These couldthen be dealt with according to the optimal, sub-optimal and lethal statements described above.
Recommendations
• Retain the DRIFT optimization frameworkfor dealing with consequence data inDRIFT and DRIFTSOLVER.
• Retain existing scoring procedures forconsequences of flow change.
• Explore the use of conceptual modelsand Bayesian networks in a modularfashion to assist with determination ofraw consequences for key flow andecosystem sub-components.
• Use the DRIFT lists of sub-componentsto guide development of conceptualmodels and the Bayesian framework.
• Explore ways to ensure that allweightings are explicit and can berecorded at all levels.
Adapting DRIFT for Large Rivers
DRIFT was originally designed for application inthe rivers of southern Africa, which, with a fewnoticeable exceptions (e.g., Zambezi River,Mozambique), do not display the vast floodplainscharacteristic of some other areas of the world.Thus, there are several areas where DRIFT willrequire adjustment before it can be usedsuccessfully in floodplain rivers. These include, butare not necessarily limited to, terminology,hydrological classification, hydrological time stepsand the content of the generic lists. These aredealt with in more detail below.
Linking DRIFT to a Bayesian network ispossible and potentially beneficial. There willhowever be considerable variation between thedetails of how this is done for upland, mid andfloodplain rivers.
We envisage that a DRIFT-Bayesian frameworkwill be developed for each different river reach. Theexisting DRIFT (King et al. 2003; plus Bayesiannetworks) could provide the basis for upland rivers,and new frameworks, with time and spatial scalesmore appropriate for mid- and lower rivers, could bedeveloped as required (Figure 5). For instance, thereis some indication that it may not be necessary tolook spatially at habitat in large rivers for fisheriesalone although this will still be needed forassessment of fish biodiversity issues.
39
FIGURE 5.Basic differences between upland streams and large floodplain rivers.
Proposed procedure for dividing a river basininto Environmental Flow Assessment zones
Changing flow classes, generic lists and timesteps are seen as calibration details and shouldnot necessitate changes to any overall DRIFT/Bayesian framework. However, it will be importantfor any environmental flow study that the flowclasses, time steps, and components and sub-components are compatible with those selectedfor other reaches of the study river.
The following procedure for dividing the riverinto reaches for Environmental Flow assessmentis proposed.
For a study river:
1. Start with the hydrology and analyzevariability of the flow regime with distancedownstream of the headwaters, and theinfluence of major tributaries.
2. Choose hydrological zones representativeof each level of variability.
3. Describe the ecological characteristics ofthe hydrological zones.
4. Determine a reach-specific division of thehydrological regime into flow classes basedon the resolution needed for both meaningfulecological assessment and the sorts ofmanagement questions that will be asked.
FLOODPLAINRIVERS
Simple smoothed hydrologyOne large flood
Species-rich communitiesComplex ecological networks
Bank type
HYDROLOGY
Flood zone land cover
ENVIR. for Black fishes
OPPORTUNISTS PRODUCTION
Delta Refuges
ENVIR. for White fishes
Refuges
Flooded area
Delta Bank Type Delta flood zone land coverDelta Turbidity
Delta flodded area
Delta hydrological factors
WHITE FISH PRODUCTION
Flood height
Flood duration
Flood regularity
Flood timing
Delta flood height
Delta flood duration
Delta flooding consistency
Delta flood timing
Delta Catch_(MF)
Delta Catch_(NMF)
Delta Envir (MF)
Delta Envir (NMF)
BLACK FISH PRODUCTION
UPLANDRIVERS
Complex jagged hydrologySeveral small floods
Communities with few speciesSimple ecological networks
Flood height
Bank type
Flooded area
HYDROLOGY
Flood duration
Flooding regularity
Flood timing
40
5. Use the identified management questionsto determine the level of changes ofinterest for each flow class, and the typeof change (e.g., changes in frequency,variability, duration, and/or magnitude).
6. Back-check to ensure that the divisionsmake hydrological sense in terms of flowrouting through the basin and in terms ofdata availability.
7. Choose a representative site(s) in each ofthe hydrological reaches of concern forthe implementation of DRIFT.
This sequence is virtually identical to theprocess used in setting up a river basin forapplication of the Benchmarking Methodology(Brizga et al. 2002).
River Fisheries Models
Decisions by fishery managers on how to meetthe various ecological, social and economic goalsof a fishery need to be based on information. Inthe past such information was derived fromsimple scientific studies and took into accounttradition, customs and taboos, as well as localand national political needs. More recentapproaches to inland fisheries management needto deal with interactions with other users of thewaters as well as purely fishery issues. As aresult the need for scientific advice hasintensified and now involves the use of one ormore formal quantitative models of the fisheryand the wider environment. Such models can bebroadly categorized as empirical, populationdynamics, or holistic.
Empirical Models
Empirical models are statistical representations ofrelationships between variables that do notgenerally refer to underlying processes. Empiricalmodels may be fitted to observations fromdifferent river systems, made either at the sametime or different times, or fitted to observationsfrom the same river system but from differenttimes. These models are typically fitted usinglinear (least-squares) or non-linear regressionmethods after appropriate data transformations tomeet normality assumptions.
Empirical models have been used to describethe response of fish yield to one or moreexplanatory variables including measures of rivermorphology such as drainage basin or floodplainarea (Lae 1992a, 1992b; Welcomme 1985),morpho-edaphic indices (Bayley 1988; Pusey etal. 1995, 1998, 2000), flow variables (Pusey et al.2004) and fishing intensity (Welcomme 1985;Bayley 1988; Halls et al. n.d.) (Figure 6). Theyhave also been developed to describe theresponse of fish yield to the quantity offreshwater discharged into estuaries (Loneraganand Bunn 1999).
A number of linear regression models of thegeneral form of equation (1) have been used bynumerous workers, including Welcomme (1975),Muncy (1978) and Van Zalinge (2003), to describethe response of fish yield in year y to somehydrological index (HI) in the same year orseveral previous years depending upon the agestructure of the catch.
0=
n
iibYield (Yy ) = a + (HI y – i ) + εi (1)
The hydrological indices (HI) are chosen tocapture key features of the hydrological regimethat are believed to have a significant effect onyield, including maximum flooded area, depth,discharge rate, or functions of summed weeklywater depths or areas during the flood and dryseasons (Figure 7).
41
FIGURE 6(a).A simple linear model describing the relationship between log-transformed floodplain area and catch; Figure 6(b).A modified Fox (Fox 1970) surplus production model of the form In Yield = i 0.5 exp (a+bi 0.5) + c describing therelationship between log
e transformed estimates of catch per unit area (CPUA) and square-root transformed fisher
density estimates (i) for river systems in Africa (•); Asia ( ); and Latin America ( ) where a, b, and c are fittedparameters.
FIGURE 7.Features of the hydrological regime commonly employed to construct hydrological indexes. Maximum flooded area,depth or discharge rate (1). Integrals of flooded area, depth or discharge rate based upon the flood (2) and dry season(3) periods.
Source: Modified from Welcomme 1985 Source: Halls et al. 2006
1
2
3
4
5
6
7
8
9
0 5 10 15 20 25 30 35 40 45 50 55
Week
Dep
th, f
lood
are
a or
dis
char
ge
rate
1
2
3
42
Empirical models are easy to construct andare often robust. However, they rely on crudeindices to describe complex hydrologicalconditions. In some cases, two distinctlydissimilar hydrographs can have the same floodindex. Existing models fail to simultaneouslyaccount for variation in both hydrologicalconditions and exploitation intensity, despite thefact that both factors have been reported assignificant in determining fish yield (Bayley 1988;Welcomme 1985, 2001). Moreover, because ofthe scale over which observations are made(typically the entire floodplain or drainage basinarea of a river system), models of this type areoften prone to significant measurement error.Consequently, model predictions have wideconfidence intervals that significantly diminishtheir utility for decision-making. Adding to this isthe unrealistic assumption that fish populationsreach an equilibrium (stabilized) state within themodel observation interval, usually one year, inresponse to any changes in hydrology and/orexploitation intensity. As a result, empiricalmodels have mainly explored generalrelationships in river fisheries and can only beused for decision-making at the most generalizedlevel. The statistical weakness of such modelssignificantly limits their utility for supportingdecision-making on the allocation of water,particularly for systems that exhibit significantvariation in hydrological conditions andexploitation intensity, when decisions may haveto be regularly revised or updated on an annual ormulti-annual basis.
Population Dynamics Models
Fish population dynamics models attempt todescribe the response of fish populations toexploitation and environmental variation basedupon established theories of population regulationand recent advances in understanding offloodplain-river fisheries ecology and biology.These powerful age-structured or biomassdynamics models are capable of providingdetailed insights into the way fish populations
respond to simultaneous changes in bothhydrology and exploitation through time andpotentially, space. All the models assume thatpopulation biomass responds in somecompensatory manner to changes in thehydrological conditions and to exploitation. Inother words, under favorable hydrologicalconditions, exploitable biomass increases inresponse to improved opportunities for growth,survival and reproduction, but will decrease underless favorable hydrological conditions. The maindifferences among the proposed models lie in theway the underlying processes regulatingpopulations are assumed to operate, in the waythey are modeled, and the techniques employedto estimate model parameters.
Biomass dynamics models
Biomass dynamics models (Hilborn and Walters1992) treat the whole fish stock as a pool ofbiomass subject to production, that is, the netresult of growth, reproduction and naturalmortality, and harvesting.
The processes of growth, mortality, andrecruitment determining exploiting biomass in theextended Fox biomass dynamics model, aredescribed by a single parameter r, the intrinsicrate of population growth. However, in thisextended form, hydrological variation (measuredin terms of flooded area) is assumed to affectboth the environmental carrying capacity of thehabitat, K, and the catchability coefficient, q(through variation in the density of thepopulation). These flooded area effects aremodeled as power functions of water area, aexpressed as a proportion of an arbitrarily definedreference area, A (e.g., the mean dry seasonflooded area). The resulting model is:
(2)
(3)
43
where Bt is the biomass at time t, Ct is catch, at
is the water area at time t relative to a referencearea A, p is the scaling factor of ecologicalcarrying capacity with area, and c is the scalingfactor of catchability with area. The model hasbeen successfully fitted to daily time series ofcatch, effort and water area data for aBangladesh beel fishery (Figure 8). Although it isfitted to a single species here it could equallywell be fitted to a complete species assemblage.
Minte-Vera (2003) describes a version of thediscrete lagged-recruitment, survival and growth(LRSG) model (Hilborn and Mangel 1997) wherethe biomass at time t+1 is expressed as:
Bt+1 = Bt s + Rt – Ct (4)
where Bt is the biomass at time t, s is the rate ofpopulation biomass growth arising from somaticgrowth and survival, Rt is the recruitment (juvenilebiomass) in time t and Ct is the catch during timet. Recruitment is described by an extended formof the Beverton and Holt stock-recruitmentrelationship (SRR) with a lag of two yearsbetween recruitment and spawning biomass andan extra term, c to describe the effect of relative
flood strength on recruitment success (equation(5)). Here dt-2 is the number of days flooding
above some arbitrary height in year t-2, d is theaverage number of days flooding above thisarbitrary height and α and β are the parameters ofthe basic SRR.
FIGURE 8.Observed and Predicted CPUE (catch per unit effort) for the spotfin swamp barb Puntius sophore in Gotokbari beel,Bangladesh.
(5)
Catches of Prochilodus from the Parana RiverBasin in Brazil have been successfully predictedusing this delay-difference model (Figure 9).Minte-Vera notes, however, that the model iscurrently not capturing the effect of the variationin flood strength on recruitment. This shepostulates may reflect the inadequacy of adeterministic recruitment model to describe theintrinsically variable (stochastic) nature ofrecruitment in this species.
Both the Lorenzen and Minte-Vera modelsneed fairly extensive catch-effort datasets toprovide satisfactory answers.
Source: Lorenzen et al. 2002
44
Age-structured models
Age-structured or dynamic pool models attempt toaccount explicitly for the processes of growth,mortality and reproduction to predict changes innumber and biomass in response to exploitationand environmental variation (Figure 10). Whilstmore complex, these models are more realistic
than biomass dynamics models since they cantake better account of the various time-delaysand changes in population structure associatedwith growth and recruitment and, therefore, arelikely to provide more precise predictions underconditions of rapid population change (Hilborn andWalters 1992) typically exhibited by floodplain fish
FIGURE 9.Observed and predicted Prochilodus CPUE (catch per unit effort) from the Parana River Basin in Brazil.
FIGURE 10.Schematic representation of the population model illustrating the processes by which the biomass in week w becomesthe biomass in the following week, w+1. The weekly process is repeated for the 52 weeks of the year, after whichrecruitment, determined by the surviving spawning stock biomass, is added at the end of week 52. Solid lines indicatedirect influences or operations and broken lines indirect influences or occasional operations.
Source: Minte-Vera 2003
Source: adapted from Welcomme and Hagborg 1977
45
populations. Their transparent nature alsofacilitates improved understanding of theresponse of populations to external factors andthe exploration of a greater range of potentiallyeffective management interventions.
Age-structured models were first applied tofloodplain river fisheries in 1977 to explore thedynamics of African fish populations underdifferent regimes of flooding and exploitation(Welcomme and Hagborg 1977). They wereapplied later to the lakes of Madagascar (Moreau1980), and the Central Delta of the Niger (Morandand Bousquet 1994).
The age-structured model described by Hallset al. (2001) builds on this earlier work,incorporating more conventional sub-modelsdescribing the (density-dependent) processes ofgrowth, mortality and recruitment that exploit newinsights into the dynamics of floodplain fishpopulations gained during the last two decades.Growth, via the asymptotic length, is modeled todecline linearly with increasing biomass densityreflecting increased competition for food resources.Recruitment is described by an extended Rickerstock-recruitment relationship with an extra term todescribe the effect of changes in system fertilitywith flood strength on recruitment success.Mortality is modeled to increase linearly with
increasing numerical density reflecting numericaland functional responses by predators anddeclining water quality, and competition for shelterfrom predators. Catches are also modeled to bedensity-dependent, through the effect of biomassdensity of gear catchability.
The model is general and flexible and can beeasily modified to include a range of otherspecies with or without interaction. Annual yieldor exploitable biomass can be predicted with themodel allowing for weekly variations in age-dependent growth and mortality rates and inter-annual variations in recruitment strength driven bychanges in population density in response toexploitation and/or dynamic hydrologicalconditions (Figure 11). The model algorithms aregiven in Halls et al. (2001) and Halls andWelcomme (2004).
Once established the model does not needextended datasets as estimates for many of themodel parameters can be drawn from theliterature or FishBase. Those that cannot be soderived can be estimated from empiricalrelationships derived from spatial comparisons orexperimentation within a single year (e.g., Halls etal. 2000) or from among-population comparisons(e.g., Lorenzen and Enberg 2002). Using keymodel parameters derived from experimentation
FIGURE 11.Observed and predicted catches for the spotfin swamp barb Puntius sophore in Gotokbari beel, Bangladesh.
Catc
h (k
g/m
onth
)
10,000
1,000
100
10
1
0.1
1992
1994 1996
1998
Year
Predicted
Observed
2000
46
during a single year, the model has successfullypredicted the same time series of catches fromthe Bangladesh Puntius fishery described aboveon the basis of weekly estimates of flooded areaand fishing effort (Figure 11).
These population dynamics models provide apowerful means to aid decision-making withrespect to the integrated management of water,fisheries and other water-dependent resourcessuch as agriculture. For example, the age-structured dynamic pool model described by Hallset al. (2001) has been used to quantify theimpacts of modified hydrological regimes on fishproduction inside flood control compartments inBangladesh and to identify mitigating measuresincluding the retention of more water during thedry and closed seasons (see Halls et al. 1998,1999). Shankar et al. (2002, 2003) use the modelto explore economic tradeoffs between dryseason crop irrigation and fisheries inBangladeshi floodplains (Figure 12a). Similarwater allocation decisions can be guided withoutputs generated by biomass dynamics models(Figure 12b).
These types of models can also provide interand intra-annual predictions of the effects ofmodifications to the natural hydrological regime ofrivers on exploitable fish biomass. Halls andWelcomme (2004) illustrate how the model can beused to generate guidelines for managing andmanipulating hydrological conditions in riversystems including the release of artificial floodsdownstream of dams and the manipulation ofwater levels within impounded floodplains, eitherto minimize the risk of species extinction (stockcollapse) taking account of existing and plannedpatterns of exploitation, or to maximizeexploitable biomass (Figure 13).
Holistic Fisheries Models
In the field of environmental flows assessment theterm “holistic methods” originates from the work ofArthington et al. (1992, 1998, 2004b) and King andLouw (1998), through several developments andapplications in Australia and South Africa. In thefield of inland and floodplain fisheries, the term
FIGURE 12a.The effect of dry season irrigation for boro rice on fish production in Bangladeshi floodplains measured in terms ofcatch per unit area (CPUA). Figure 12b. Predicted yield of P. sophore as a function of dry season area relative to floodseason area.
Source: Lorenzen et al. 2002Source: Shanker et al. 2003
47
).
FIGURE 13.Examples of the types of outputs that can be readily generated with age-structured models illustrating (a) exploitablebiomass as a function of flood and dry season channel depth for a 25 week flood season duration (figures on lines = totalexploitable biomass (kg)); and (b) exploitable biomass as a function of drawdown rate for flood season durations rangingfrom 5 to 40 weeks (lines refer to flood duration in weeks:
applies to models that address fish production oryield in the broader context of environmentalmanagement, and therefore integrate a diversity ofvariables of hydrological, environmental or socialnature (Lorenzen et al. 2007). The earliest exampleof a fully holistic fisheries model is that of thefisheries in the Central Delta of the Niger River(Bousquet 1994b), that calculates fish catch whileintegrating the strategies and requirements ofindividual fish species, as well as river hydrology,floodplain morphology, fishing effort and adaptivebehavior of fishers.
Holistic models can be broadly classified intoecological models, multi-agent models andBayesian networks.
Ecological models
Ecological modeling aims at modeling the wholeecological network in a given ecosystem, fromprimary production to top predators, includingfishers. The dominant modeling framework isEcopath with Ecosim, with which 44 site-specificmodels have been developed so far infreshwater systems, including 25 in Africa (see
www.ecopath.org). The principle of Ecopath(Christensen and Pauly 1992, 1993; Walters etal. 1997) is to create a mass-balanced model ofthe ecosystem, represented by trophically linkedbiomass units that consist of single species,ecological guilds or life history stages. Themodel is based on two core equations:
(i) an equation describing the productionterm for each unit:
Production = catch + predation + netmigration + biomass accumulation + othermortality
(ii) an equation of conservation of matterwithin a unit:
Consumption = production + respiration +unassimilated food
In general, an Ecopath model requires threeof the following parameters for each unit:biomass, production/biomass ratio (or totalmortality), consumption/biomass ratio, andecotrophic efficiency.
= 5; x = 10; + = 15; = 20; = 25; = 30; = 35; = 40
Source: Halls and Welcomme 2004
Minimum dry season depth (m) Drawdown rate (m week-1
)
Ann
ual a
vera
ge b
iom
ass
(kg)
Max
imum
floo
d se
ason
dep
th (m
)
8
7
6
5
90,000
80,000
70,000
60,000
50,000
40,000
30,0000 1 2 3 4 5 0.0 0.1 0.2 0.3
(a) (b)
48
Although popular for enclosed or well-studiedlarge aquatic ecosystems, this approach is dataand knowledge hungry and requires quantitativeinformation on biomass of certain groups such asphytoplankton, zooplankton, benthos, and onrelated trophic flows. Such data are simplynonexistent for most tropical rivers andfloodplains, hence the absence of these systemsamong the applications of Ecopath with Ecosim.
Multi-agent models
In a fishery the fishers are subject to the spatial,temporal and biological variability of the resource,and respond by adopting a range of differentexploitation strategies. However, the variableexploitation strategies and more generally theinteractions between the human actors and theresource are rarely taken into consideration byfisheries modeling approaches. For instance, asnoted by Weisbuch and Duchateau-Nguyen (1998),“although fisheries have been modeled byeconomists from an integrated perspective sincethe 30s, the influence of cultural constraints onresource exploitation is seldom taken into account”.
It is only recently that a field of modeling hasstarted to address social processes and theirdynamic links with the natural environment. Thisapproach involves a coherent conceptual basis(e.g., Ostrom et al. 1994; Holling 2001; Janssen2002) and relies, for modeling, on the tools ofDistributed Artificial Intelligence (Ferber 1999).Among the underlying principles are: (a) thatvariability is an inherent component of natural butalso social systems; (b) that addressing issues ofscale and scale transfer is essential; (c) thatthere are as many viewpoints on the dynamics ofa system as there are stakeholders; (d) that adiversity of individual behaviors often results inan emerging process that is different from thesum of individual behaviors.
The modeling of human-environmentinteractions has been based mainly on multi-agent systems. Agents are programmable entitiesable, in a limited way, to sense and react to their
context, to communicate with one another, toaccept and set themselves goals and to maintainand update individual belief sets (after Doran1997). A summary of this modeling approach isgiven in Bousquet et al. (1999).
Although this school of modeling is rapidlyexpanding, agent-based modeling has addressedfloodplain fisheries in one instance only(Bousquet 1994a, 1994b). This model of thefisheries of the Central Delta of the Niger Riverhas several components:
• a hydro-ecological one, with four spatialsub-components: river mainstream,channels, floodplain and ponds and theirattributes (flood threshold, water level,food provided, connected biota);
• a fish component, made of major speciesand their attributes (growth, diet,fecundity, migrations, natural mortality,biotope used, etc.); and
• a social component and its attributes(access to each water body, gears, ethnicgroup, asset, etc.).
These components are connected accordingto descriptions given by a multi-disciplinary teamof experts, including hydrologists, biologists,sociologists and anthropologists. Fishing behavior,for instance, depends on the ethnic group of thefisher: “if fisher belongs to ethnic group X, thenaccess to biotope Y is forbidden in season Z”;therefore the total proportion of fishers of ethnicgroup X will influence the mortality of targetedspecies S1, S2 and S3 that use biotope Y inseason Z. In this model the agents have acapacity of learning: for instance if in year n theaverage catch of fishers using gear G1 is higherthat that of fishers using gear G2, then in yearn+1 the proportion of fishers using gear G1 will behigher (which results in turn in higher mortalityrates on species targeted by gear G1). Theapproach is detailed in Bousquet et al. (1993) andCambier et al. (1993).
49
Multi-agent systems are the most holisticmodeling approach being developed for themanagement of human influenced naturalsystems (e.g., FIRMA 20001; see also http://cormas.cirad.fr/indexeng.htm). They are alsounderpinned by a strong theoretical backgroundthat reflects the complexity of the real worldmuch more precisely than equation-basedmodels. However, they require a strongbackground in computer science and, althoughthey are becoming increasingly popular, theircomplexity ultimately requires that the modelingteam is fully trusted by the decision-makers if themodel outputs are to be converted intomanagement decisions.
Bayesian networks
The need to make decisions in uncertainconditions led to the use of probability theory inthe development of modeling tools. In the field offisheries this has resulted in a large number ofapplications (see Punt and Hilborn 2001 for areview), but until very recently (Minte-Vera 2003)models of inland fisheries have not beendeveloped that incorporate probabilisticcomponents. Besides this refined approach toclassical fisheries modeling, a new developmentconsists in the combination of probability theory(in particular the Bayes’ theorem for probabilisticinference, Bayes 1763) with the theory of graphs(Jordan 1999), to result in a graphic modelingapproach called Bayesian networks. Although thistechnique is applied here primarily to fisheries, itcan be extended to include all other componentsof the river-floodplain system and is thus aholistic method.
The essence of Bayesian networks consistsin defining the system studied as a network ofvariables linked by probabilistic interactions(Jensen 1996). Bayesian networks (also calledBayes nets or Bayesian belief networks) weredeveloped in the mid-1990s as Decision SupportSystems for medical diagnostic and financial riskassessment. A detailed but accessible description
of these methods can be found in Charniak(1991), and for more specific ecological andmanagement applications, in Ellison (1996), Cain(2001) and Reckhow (2002).
• Bayesian networks are based onvariables representing the modeledenvironment. Variables can bequantitative (e.g., ”Flood level”) orqualitative (e.g., “Fishing strategy”). Foreach variable a small number of classesis defined (e.g., “Flood level” can becharacterized by a few numerical classessuch as 0 1m, 1-2m, 2-3m, or “Fishproduction” by qualitative classes suchas Low, Medium and High).
• In the network, variables are connectedby links expressed in terms ofprobabilities. This step is called elicitationof prior probabilities. “Prior probabilities”are those entered during the buildingphase of the model, as opposed to thosecalculated by the model and called“posterior probabilities”. If data isavailable, then the quantified relationshipbetween variables A and B is convertedinto probabilities. For instance studies ofthe relationship between flood level A andfloodplain fish production B, expressed asB = f(A), can be used to define thechance of having a high fish production(Low, Medium, High) for a given floodlevel (0-1m, 1-2m, 2-3m). New ways todevelop such relationships are outlined inArthington et al. (2006). If data is notavailable, Bayesian networks also allowexpert knowledge to be used tocharacterize the known relationshipbetween two variables, which is againexpressed in terms of probabilities. Thispossible integration of expert knowledge(i.e., knowledge from scientists, fieldpractitioners or persons with specificexperience of the topic addressed) into a
1FIRMA 2000 Freshwater Integrated Resource Management with Agents. A project supported by European Union (“Framework 5Programme for Research and Development”) and by the European Commission (“Key Action on Sustainable Management and Qualityof Water programme”) [see http://firma.cfpm.org]
50
modeling framework contributedsignificantly to the success of thisapproach, and explains the term“Bayesian belief networks, or BBN”.
• Ultimately the model calculates, basedupon the Bayes’ formula, the overall trendresulting from the interaction ofprobabilities within the system. ABayesian network is usually built in sucha way that a diversity of influencingvariables (e.g., water level, floodplainaccessibility) converge towards thevariable one is specifically interested in(e.g., fish recruitment).
An example of variables in a Bayesiannetwork is shown in Figure 14.
In the field of environment, Bayesiannetworks were originally developed for integratednatural resources management (Varis et al. 1990;review of early works in Marcot et al. 2001) andbecame popular for integrated water management(e.g., Varis 1998; Batchelor and Cain 1999; Varisand Lahtela 2002; Borsuk et al. 2002b).
Bayesian networks have been used forinland fisheries management to assess risks ofpopulation extinction (Shepard et al. 1997; Leeand Rieman 1997), consequences of land useoptions (Rieman et al. 2001; Marcot et al.2001), and environmental drivers of fish
FIGURE 14.Three variables connected by probabilistic links in a Bayesian network (example not related to a particular casestudy). Networks can be made of several dozens of variables.
Prior probabilities (elicited):
Floodplain accessibility to fishesin 20% of cases it is goodin 80% of cases it is badbecause of embankments, dikes, etc.
Floodplain accessibility
GoodBad
2080
Prior probabilities (elicited):
>1.5m6040
Posterior probabilities (calculated by the model)
In the system considered, given the probabilities defined for "Water level"
and "Floodplain accessibility", the model computes that there is an 18.4%
chance that the recruitment of floodplain fishes is good and a 81.6%
chance that it is bad.
-
-
Recruitment
GoodBad
18.481.6
Chances ofrecruitment
Status ofvariables
-
-
-
-
there is a 60% chance that the water level is <1.5mthere is a 40% chance that the water level is >1.5m
1.5 meters being a local thershold-
-
<1.5m
-Table of probabilitiesit is very unlikely (10% chance) to have a good recruitment when the water level is below 1.5m,even if the accessibility to the floodplain type isgood
- it is possible (30% chance) to have a good recruitment when the water level is above 1.5m,even if the floodplain accessibility is bad etc. -
GoodAccessibilityWater level Bad
>1.5m 30Bad 70
>1.5m 95Good 5
0Bad<1.5m 100
10Good<1.5m 90
River water level
-
-
51
production (Baran and Cain 2001; Borsuk et al.2002a; Baran et al. 2003). This latter paper isthe only one dedicated to tropical floodplains(Figure 15). Fisheries management has alsobeen addressed by Peterson and Evans (2003)and Kuikka et al. (1999).
The software used for these models is eithercommercial or developed by the researchersthemselves. Comprehensive reviews of softwareare available on several web sites, including:
• http://www.ia.uned.es/~fjdiez/bayes/software.html
Bayesian networks offer a solution to thecommon problem of data scarcity by the possibleuse of expert knowledge; they are easy tocompute, are intuitive and visually explicit, andthus are good tools for determining global trendsand communicating summaries of complexinformation beyond the reach of individual expertsor decision-makers.
The definition (or elicitation) of priorprobabilities is the most delicate part of themodeling process, and its quality determines thatof the whole model (e.g., Chen et al. 2000. The
elicitation of probabilities in a Bayesian beliefnetwork ideally requires the contribution of amultidisciplinary group of specialists (Cain 2001;Borsuk et al. 2001), but defining modalities of thisconsultation in view of minimizing biases andmaximizing representativity of the model is still atheme for research.
Another interesting feature of Bayesiannetworks is associated with the consultationprocess: the possible involvement of stakeholdersin the building of the model. It allows theintegration of various viewpoints on the systemstudied (those of the scientists, but also ofmanagers, of farmers or fishers, etc.) andfacilitates the appropriation of the model and theuse of its outcomes by managers and decision-makers. Most authors have outlined theimportance of the consultation process (e.g.,Moss et al. 2000; Borsuk et al. 2001; Soncini-Sessa et al. 2002; Peterson and Evans 2003)and its role in bridging the common gap betweenthe provision of scientific advice and its use indecision-making. In this field of applicationprobably lies the strongest potential of Bayesiannetworks for inland fisheries management.
Dealing with Uncertainty
This assessment has so far described fisheriesand environmental models and methods thatprovide advice for water management decision-making, including some approaches that useBayesian methods. Uncertainty is inherent in allthese models particularly with respect to how wellthey represent real systems and how preciselythey provide parameter estimates. Thisuncertainty results from scarce or imprecise dataand information, the existence of severalhypotheses about how the system works and thesheer variability of natural ecosystems. Despitethese uncertainties decisions concerningenvironmental flows and fisheries still need to bemade. Management decisions must, therefore,
explicitly account for these uncertainties byquantifying the risk of a particular course ofaction. Bayesian methods provide a powerfulmeans to measure such risks. The theory behindthese techniques is outlined below.
A Bayesian risk assessment integrates amodel, such as the ones described above, withprior information and data collected for theecosystem in order to estimate the probability ofdifferent states of the populations and of differentvalues for the parameters (Hilborn and Mangel1997; Punt and Hilborn 1997; Wade 2000).Bayesian methods have been used successfullywith population dynamics models (see McAllisteret al. 1994; Meyer and Millar 1999).
52
FIGURE 15.Summary of the natural fish production model developed for the Mekong River Basin.
Source: after Baran et al. 2003
Flood height
Bank type
Flooded area
WHITE FISH PRODUCTION
ENVIR. for White fishes
Flood zone land cover
Refuges
ENVIR. for Black fishes
OPPORTUNISTS PRODUCTION
HYDROLOGY
Uppercatchment
Floodplainsystem
Delta
Pro
bab
ility
of a
go
od
fish
pro
du
ctio
nin
th
e w
ho
le M
eko
ng
bas
in
Flood duration
Flood timing
BLACK FISH PRODUCTION
Delta flooding consistency
Delta flood timing
Delta flood duration
Delta flood height Delta Bank Type
Delta flooded area
Delta catch_(MF)Delta catch_(NMF)
Delta Envir (MF) Delta Envir (NMF)
Delta Turbidity
Delta Refugees
Delta flood zone land cover
Delta hydrological factors
Flooding regularity
HYDROLOGYGoodNeutralPoor
42.619.737.7
FLOODED AREALargeSmall
61.838.2
Flooding regularityRegularIrregular
6040
Flood durationLongMediumShort
204040
Flood timingEarlyMidLate
206020
Flood heightHighLow
6040
Bank TypeNat PlainEmbankmentValley
801010
ENVIRONMENT White fishes
GoodPoor
60.439.6
ENVIRONMENT Black fishes
GoodPoor
48.851.2
RefugesAvailableNone
5050
Flood zone land coverGrass riceShrubForest
302644
BLACK FISH PRODUCTION ...
Good
Normal
Bad
33.1
25.8
41.1
WHITE FISH PRODUCTION
Good
Normal
Bad
29.6
32.5
37.9
OPPORTUNISTS PRODUCTION
Good
Poor
68.2
31.8
Floodplain system
HYDROLOGYGoodNeutralPoor
42.619.737.7
HYDROLOGY
Good
Neutral
Poor
42.6
19.7
37.7
FLOODED AREALargeSmall
61.838.2
FLOODED AREALargeSmall
61.838.2
Flooding regularityRegularIrregular
6040
Flood durationLongMediumShort
204040
Flood timingEarlyMidLate
206020
Flood heightHighLow
6040
Flooding regularityRegularIrregular
6040
Flood duration
LongMediumShort
204040
Flood timingEarlyMidLate
206020
Flood heightHighLow
6040
Bank TypeNat PlainEmbankment
801010
Bank TypeNat PlainEmbankmentValley
801010
ENVIRONMENT White fishes
GoodPoor
60.439.6
ENVIRONMENT White fishes
Good
Poor
60.4
39.6
ENVIRONMENT Black fishes
GoodPoor
48.851.2
ENVIRONMENT Black fishes
Good
Poor
48.8
51.2
RefugesAvailableNone
5050
RefugesAvailableNone
5050
Flood zone land coverGrass riceShrubForest
302644
Flood zone land coverGrass riceShrubForest
302644
BLACK FISH PRODUCTION ...
Good
Normal
Bad
33.1
25.8
41.1
BLACK FISH PRODUCTION ...
Good
Normal
Bad
33.1
25.8
41.1
WHITE FISH PRODUCTION
Good
Normal
Bad
29.6
32.5
37.9
WHITE FISH PRODUCTION
Good
Normal
Bad
29.6
32.5
37.9
OPPORTUNISTS PRODUCTION
Good
Poor
68.2
31.8
OPPORTUNISTS PRODUCTION
Good
Poor
68.2
31.8
Floodplainsystem
53
FIGURE 16.Prior and posterior probability function for the growth-survival parameter for the Curimba in Parana River (Minte-Vera, 2003). The prior was uninformative and the posterior was driven by the data. The data supported the hypothesesthat the parameter is between 0.6 and 0.8.
Model and their possible parameter valuessummarize the different hypotheses about anecosystem. It is important that we know what theevidence is for each hypothesis. In a Bayesiananalysis we would start by stating the evidencefor each hypothesis in terms of a prior probabilitydistribution. Then the data are used to update theprior distribution and obtain a posteriordistribution. For example, in Figure 16 noknowledge existed about the parameter, all thepossible values were equally likely. After the useof the data, a posterior probability was pickedindicating that the data supported the hypothesesthat the parameter is between 0.6 and 0.8.
The first step in a Bayesian analysis is tochoose prior distributions for all the parametersused in the model. For most ecological problemswe have prior information that is valuable andshould not be wasted. This information can bereflected in an informative prior distribution thatwill reduce the uncertainty surrounding parameterestimates. Knowledge may come from scientificresults (from studies in other systems, or onother species), expert opinions and traditionalknowledge. For example, when using an age-structured model the values of the density-dependence parameter obtained experimentally forPuntius by Halls et al. (2001) can be used to
derive a prior distribution when applying the samemodel to other species. A combined analysis ofseveral datasets such as the Lorenzen andEnberg (2002) study on density dependency canalso be used. Another way to derive a priordistribution is to use opinions from experts orusers of the resource, who may have a good ideaabout what values are plausible. Users such asfishers have traditional knowledge transmittedbetween generations or acquired by repeatedobservation of the system. Another promisingway to reduce uncertainty is to implementexperiments designed to estimate the values ofcertain parameters that can be used as priors inmodels (Arthington and Pusey 2003; Poff et al.2003).
The second step in a Bayesian analysis is toincorporate data for the system that is beingstudied. The degree to which the data support ahypothesis can be derived. For example, whenusing an age-structured model, the available datamay be catch-per-unit-of-effort, abundanceestimates and length-frequency data. These dataare related to the population dynamics model usingprobability distributions. If the data are informativeabout the population, this will be reflected by thestronger selection of particular values in theposterior distribution than in the prior.
54
The results of the Bayesian estimation(posterior probability distributions) can be used ina risk assessment to calculate the risk of takinga particular course of action. A risk assessmentis the evaluation of the consequences ofalternative management action under uncertainty.A risk analysis involves six steps (Punt andHilborn 1997; Punt and Hilborn 2001):
Step 1: Identifying the alternative hypothesesabout the system;
Step 2: Determining the relative weight ofevidence in support of each alternativehypothesis (expressed as a probability);
Step 3: Specifying the alternative managementactions;
Step 4: Specifying a set of performancestatistics to measure the consequencesof each management action;
Step 5: Calculating the values of eachperformance statistic for eachcombination of a hypothesis and amanagement action; and
Step 6: Presenting the results to the decisionmakers.
Steps 1 and 2 consist of integrating all priorinformation, data and models in a Bayesianestimation procedure as described above. Thealternative management actions (step 3) arediscussed with managers. For environmentalflow assessments, management actions couldinclude modifications in the flow regime,changes in harvest levels or changes in habitat.The performance statistics (step 4) should bechosen to quantify the management objectives.For example, population dynamics models canproduce several performance statistics such aspopulation size, the average body size of a fish,the average catch, the probability of fallingbelow some threshold level, etc. Step 5 isperformed by projecting the population into thefuture many times using values drawn from the
posterior distribution of the parameters asstarting points for each management scenario.The results of the risk analysis may bepresented to decision makers (step 6) in theform of a table or a set of curves showing theprobability of values of the performance statistic.For example, if the performance statistic ischosen to be the ratio between the populationsize in one year and the current populationsize, we could assess the probability ofpopulation increase or decrease given eachmanagement scenario. A conclusion such as“with decrease in 30 percent of theenvironmental flow, the population has 20percent chances of decreasing by 50 percent ormore” can be drawn from these outputs.
Minte-Vera (2003) applied the discrete lagged-recruitment, survival and growth (LRSG) model(Hilborn and Mangel 1997) described above in arisk assessment for the migratory CurimbaProchilodus lineatus. Figure 17 presents aschematic view of the risk assessment.
The management scenarios explored wheredifferent levels of allowed catches combined withdifferent flood regimes.
The output of the assessment waspresented as a decision table (Table 10). Thedecision table presents the probability of eachpossible outcome, in this case the currentpopulation size. For each of those populationsizes as a starting value, the population wasprojected 4 years into the future applying themanagement option. The current population sizeis most likely between 150 to 500 tonnes(probability = 0.686). The performance statisticwas the ratio between the population size in2005 to the current population size. A valueabove one will indicate increase in populationsize and a value below one will indicatedecrease. The decision table indicates thatfishing is only sustainable at a level of 50tonnes if the population size is above 700tonnes. The probability of the population beingabove 700 tonnes is very low, indicating that itis very likely that fisheries are not sustainable.The regulation flow as modeled is not likely toaffect the population as the fisheries are.
55
TABLE 10.Decision table for the Bayesian-based risk assessment for the Curimba in Parana river (Brazil) showing short-termeffects of the management options. The performance statistic is the ratio between the population size in 2005 to thepopulation size in 2001. A ratio above 1 indicates increase in the population size. The average of the performancestatistic is obtained by multiplying the ratio by the probability of each value of the population size in 2001.
Population size in 2001 (biomass in tons)
<150 150 - 300 - 500 - 700 - 1000 -
300 500 700 1000 3000
Probability of each population size
Management Options 0.123 0.314 0.372 0.073 0.025 0.093
Flood Fishing Average Performance statistic (biomass 2005/biomass 2001)
performance
Natural No 1.12 0.32 1.23 1.25 1.22 1.29 1.10
50 tons 0.82 0.12 0.80 0.95 1.01 1.17 1.05
100 tons 0.54 0.03 0.38 0.65 0.80 1.05 1.00
150 tons 0.32 0.01 0.11 0.36 0.59 0.93 0.95
3 dry years No 1.21 0.37 1.34 1.36 1.35 1.55 1.04
50 tons 0.91 0.17 0.90 1.06 1.14 1.42 0.99
100 tons 0.63 0.06 0.48 0.76 0.93 1.30 0.94
150 tons 0.40 0.04 0.19 0.47 0.72 1.18 0.89
Source: Minte-Vera 2003
FIGURE 17.Schematic view of the Bayesian-based risk assessment for the Curimba in Parana River, Brazil.
Source: Minte-Vera 2003
56
Bayesian risk assessments are a tool thatcan be used to add value to modeling because:
1. They are simple to explain, they provideprobability distributions that are easilyunderstandable by managers and users.
2. Their flexibility and generality can beused to deal with very complex problemsand support different models.
3. They use prior knowledge and informationin a transparent way: it becomes possibleto restrict the values of the parameters ofthe model to biologically plausible valuesand to take account of information fromother systems, therefore it is also goodfor data poor situations.
4. They account explicitly for uncertainty:
a. Uncertainty from important but unknownparameters is automatically included
b. Uncertainty in model choice can beformally incorporated into analysisresults by combining the results fromdifferent plausible models
c. Natural variability in the dynamics caneasily be incorporated into the models
5. The results can be used simply to projectthe population into the future underseveral management scenarios andcalculate risk.
6. They facilitate the integration of differentapproaches such as holistic models andpopulation dynamics models through theuse of probabilities as a common‘currency’.
Also, the method forces clear thinking on themanagement problem since the objectives ofmanagement need to be stated clearly andtranslated into performance measures. Once themanagers make a decision, the consequencesshould be monitored and new data points couldbe used to update the analysis in order todecrease uncertainty.
Bayesian techniques are not widely used atpresent, mainly because they are not taughtregularly in statistical, modeling or managementcourses. They are also extremely computationallyintensive to apply to complex models and theprocess of specifying prior distributions can bevery time consuming. Clearly, the advantages ofusing Bayesian methods overcome thesedisadvantages.
Conclusions
There is a definite need for models to predict theimpacts of changes to natural, or establishedmodified, flow regimes on river ecosystems andfisheries. Much of the earlier work onenvironmental flows has aimed at establishingminimum flow requirements for the survival of thefish stock. In most of the cases that are nowarising, however, this is not thought to be helpfuland that rather, a continuum of response tochanging hydrology is needed by planners andmanagers to set flows at levels that are optimal
among all uses (e.g. Arthington et al. 2006). Theenvironmental flow methodologies and fisheriesmodels assessed here are, therefore, orientedmore towards a series of scenario-basedpredictions rather than the production of a singlefigure such as a minimum flow.
Because most fisheries in larger rivers existwithin a framework of rural livelihoods that usethe plant and animal resources of the riverfloodplain system in a number of ways, includingagriculture, any generalized decision-support
57
FIGURE 18.Proposed integration of DRIFT, Bayesian networks and age-structured models.
framework must have the capacity to trace theinteractions of these various uses with thefisheries component (Lorenzen et al. 2007). Atthe same time more specialized models canexplore in more detail the reactions of the fishand fisheries to such changes. The reviewpresented above outlines models and knowledgemanagement systems that permit suchpredictions to be made.
A great range of models are available thatrelate flow to population, species or assemblageresponses in fish communities. Many of these dovery similar things to those discussed above butnone were identified that presented clearadvantages over the various systems presentedin this review. It is preferable, therefore, to workwith the tools to hand and not complicate orintroduce delays by search for a ‘perfect’ system.Rather, additional models can be integrated intothe overall framework to integrate other modelsand methods as opportunities arise.
Examination of the strengths and deficienciesof the most comprehensive group ofenvironmental flow methodologies available (i.e.,holistic methodologies) suggests DRIFT ascurrently the most amenable to furthermodification for consideration of flow requirementsfor complex river fisheries. DRIFT provides apowerful integrating platform covering a range ofoutputs including direction of change of allecosystem components including individual fishspecies and fisheries, and remains at the core ofany assessment for a particular river basin. TheDRIFT database and engine can be improved byincorporation of conceptual models, empiricalrelationships between flow and ecologicalresponse (e.g., applying the river classificationand benchmarking methods recently outlined inArthington et al. 2006), and certain Bayesianprotocols. The idea is to develop a singleintegrating model with ‘tune-outable’ components,models and parameters so specialists can select
Hydrologylow andhigh waterspecifiers
Hydrologyscenarios
Bayesian networks
Engine
DRIFT
AGE-STRUCTUREDMODELS
Directmanagementadvice
Data exchange between agestructured models and DRIFT
Incorporation of Bayesiannetworks in DRIFT
Scenariobasedprediction
Bayesian based risk assessment
Knowledgebase
58
TABLE 11.Characteristics of the various types of model considered in this report.
Feature DRIFT BAYFISH AGE-STRUCTURED
MODELS
Nature Empirical – can incorporate Empirical tool Analytical tool
analytical models
Focus Comprehensive Comprehensive Specific
Links social Addresses land Incorporates
components use fisheries information
Geographic scale Site specific, needs to be Basin-wide Floodplains
extrapolated to basin
Application Applied 5-6 studies in southern Applied to Mekong upper/ Applied 5-6 studies, mainly
Africa. No floodplains yet delta and Tonle Sap in Bangladesh
Testing Implemented but not Not tested, needs to be Sensitivity analyses
tested adapted before testing done
can be done
Hydrological data Developed using daily Uses discrete Developed using weekly
requirements hydrological data – but can be hydrological hydrological data – but can
adapted to other timescales data be adapted to other timescales
Nature of output Discrete consequences, but Discrete Continuous
produces a modified time-series (time series)
for implementation of each scenario
Data comprise outputs of other Data comprise expert Data comprise published
models, published information, opinion density-dependent population
expert opinion processes for fish
Complexity Complex Complexity increases Simple
rapidly
Computer MS Excel based Programmed Mainly MS Excel based
the appropriate components, models andparameters depending on where they are situatedin a river system, the ecological and fisheriesissues under analysis and on the available data.DRIFT is not only an integrating platform but alsoa communication/training/educational tool anddecision-support system to assist inimplementation and future management.
The characteristics of the various types ofmodel considered in this review are listed in Table11 and the way in which the various approachescould interact together is shown in Figure 18.Age-structured models can provide specificfisheries and flow advice on floodplain fisheries,either independently or as an input to the fishcomponents of DRIFT. However, there aresystems where age-structured models will notwork and there is a need for the DRIFT type of
approach based on various information inputs andexpert opinion.
Bayesian approaches need to be added to age-structured models in order to give some idea of riskand the possibility of error. Such approaches are alsoneeded for the overall assessment of risk associatedwith all the approaches examined in this paper.
In order to progress the development of thisintegrated modeling approach to assessing flowrequirements for complex river fisheries, it isrecommended that an integrated pilot modelcombining DRIFT, empirical flow-ecologicalrelationships, Bayesian networks, and age-structured models be developed. This should betested in pilot river systems and the lessonsgenerated used to both refine the model andidentify river systems where other approachesmay need to be developed.
59
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